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C Description of the **data** repository

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The estimates of the logarithms of the probability of the **data** under the models and assumptions regarding independence of allele frequencies are shown in Table 1. Under the admixture model, the smallest probability is associated with a prior K of 1 and little of the posterior probability is associated with higher K values. The distribution of members of the sample to inferred clusters is consistent with this observation. The proportion of individuals assigned to each cluster is approximately the same with little variation between ethnic groups ( Table 2). This symmetry is strongly suggestive of the absence of population structure in the AADM study sample. This is so because real population structure is associated with individuals being strongly assigned to one inferred cluster or another with the proportions assigned to each ethnic group showing asymmetry. The posterior probability under the no-admixture model also favours a K of 1. Examination of the distribution of individuals sampled to inferred clusters also shows the same strong symmetry. These consistent displays of symmetry suggest that a K of 1 is the most parsimonious model. The same conclusion was reached by examining the membership coefficients (Q). Irrespective of the value of K between the range of 2 and 6, Q is similar across the whole sample as illustrated by the bar plots in Figure 2.

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If an individual relies entirely on instinct and its instinct is correct , then the probability that it will correctly classify all 32 input vectors in the testing phase is : (19) genotype d 1 … d 32 s 1 … s 32 , , , , , = bias direction d 1 … d 32 , , = bias strength s 1 … s 32 , , = d i 0 1 , { } ∈ 0 s i 1 ≤ ≤ s i s i d i 1 s i - α i guess g 1 … g 32 , , g = = g i 0 1 , { } ∈ P g i d i = d i α i ≠ ( ) s i = P g i α i = d i α i ≠ ( ) 1 s -i = P g i d i α i = = d i α i = ( ) 1 = s i 0 = α i s i 1 = d i α i ∀ ( ) s i 1 = [ ] i ∀ ( ) d i t i = [ ] 1 p - ( ) 32 i ∀ ( ) s i 1 = [ ] i ∀ ( ) d i t i = [ ] ∧ [ ] P i ∀ ( ) g i β i = [ ] ( ) 1 p - ( ) 32 = [ ] → If an individual relies entirely on learning , then the probability that it will correctly classify all 32 testing vectors is : (20) For convenience, we want our fitness score to range from 0 (low fitness) to 1 (high fitness). To make the problem challenging, we require the individual to correctly guess the class of all 32 testing examples. (This is analogous to Hinton and Nowlan's (1987) requirement that the individual close all 20 switches to get a fitness score above the minimum.) We assign a fitness score of 0 when the guess does not perfectly match the testing **data** and a score of when the match is perfect.

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The second component of the survey included an assessment of general capacity for implementation, measuring domains such as: general availability of resources and needed infrastructure, organisational climate and staff capacity. We used an adapted version of the validated Organizational Readiness for Change Assessment tool, 20 whereby the tool was shortened to minimised respondent fatigue. Questions under each domain were prioritised based on relevancy to the context of QI. Participants were asked to provide qualitative feedback regarding their readiness to implement the PC-QIs and to confirm their willingness to be contacted for a future interview. Survey development and **data** collection was supported via a web-based platform called 'Qualtrics'. 21 A copy of the survey is available (see online supplemental file 1).

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Theorem 2.2 ( [7,14]). For every solution (u,u) of (NLW), in the sense of Definition 2.1, with initial **data** (u 0 , u 1 ) ∈ H = V A × W P ≡ H 1 0 (Ω) × L 2 (Ω), only one of the following holds.

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Implementation details for iNaturalist 2018 For iNaturalist 2018, following most of the existing work, we use ResNet-50 [10] as backbone network. The **data** augmentation is similar to that used in long-tailed CIFAR datasets except that random cropping with size 224 × 224 is used. To fit two NVIDIA 2080Ti GPUs, we use a batch size of 100 for both SC and PSC based hybrid networks. The networks are trained for 100 epochs using SGD with momentum 0.9 and weight decay 1 × 10 −4 . The initial learning rate is 0.05, which is decayed by a factor of 10 at epoch 60 and epoch 80. Motivated by the fact that iNaturalist has a large number of classes which can make classifier learning more difficult, we assign higher weighting to the classifier learning branch by using a linearly decayed weighting factor α, i.e., α = 1 − T /T max . The temperature τ is set to be 0.1 for both SC and PSC loss functions. For SC loss function, the number of positive samples for each anchor is fixed to 2.

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Statistical **data** analysis in the natural sciences is most often founded on a probabilistic modelling of the underlying data-generating process, where p(x|θ) denotes the probability of experimentally observing **data** x given a set of theory parameters θ. Inference aims at assessing the theory space in light of the observed **data** in order to estimate points or intervals in this space that are compatible with the **data** as well as test hypotheses for data-driven decision-making. In frequentist statistics the main tools for these tasks are estimates based on the well-developed methodology of maximum-likelihood estimation, confidence intervals construction and test statistics. In a Bayesian context, most inference tasks derive their results from methods that aim to compute posterior densities of the form p(θ|x). A major problem for both approaches, however, are likelihood-free settings, i.e. experimental situations where samples x ∼ p(x|θ) are available but evaluating the likelihood p(x|θ) is computationally intractable. The field of likelihood-free inference thus aims to develop methods that allow us to still perform the desired inference tasks without requiring explicit evaluation of the model. High-Energy Physics **data** analysis is a prominent example of such a likelihood-free problem, which appears due to a rich, but unobservable evolution of the original particle collision through many latent intermediate states z i culminating into a high-dimensional measurement x. While the evolution probability itself is p(x, z|θ) is tractable, the model of the observable **data** p(x|θ) = dz p(x, z|θ) is not. Classical approaches to likelihood-free inference often use simulation and summary statistics f (x) to derive a low-dimensional approximate model p(f (x)|θ) to which the standard methodology can then be applied. More recently a new breed of methods are developed that aim to use machine learning to eschew an explicit approximation of the statistical model, in favor of directly targeting only the model-derived quantities required for inference. In this work we add to this program by presenting a method to learn a test statistic with best average power. For models which lie in the asymptotic regime, this is equivalent to the profile likelihood ratio test statistic, which is a key quantity in frequentist **data** analysis for models that incorporate systematic uncertainties through nuisance parameters.

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In addition to pursuing a high transmission rate, it is essential to provide reliable transmission in wireless communications systems. Therefore, the retransmission-based automatic repeat request (ARQ) schemes and in particular hybrid-ARQ (HARQ) schemes, which combines ARQ and FEC, has been widely adopted for error correction in practical systems. Generally, there are mainly three HARQ types according to the difference of **data** retransmitted [9]: type-I, type-II and type-III. Specifically, type-I HARQ is also denoted as chase combining HARQ (CC-HARQ), in which the same **data** packet is transmitted for all retransmissions. As such, its performance mainly depends on the error correction ability of FEC. Unlike CC-HARQ, type-II HARQ, also denoted as incremental redundancy HARQ (IR-HARQ), only transmits the redundant information whenever they are needed to bring higher throughput. On the other hand, Type-III HARQ is similar to IR-HARQ but each **data** packet is required to be self-decodable, which means that the information bits can be extracted independently of other transmitted **data** packets.

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The uninformative equilibrium denotes the case where all workers collude by always reports the same answer to all tasks . For traditional peer prediction mechanisms , under this equilibrium , all the workers still can get high payments because these mechanisms determines the payment by comparing the reports of two workers . However , the data requester only can get uninformative labels , and thus this equilibrium is undesired .

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In the current times, technology plays a crucial role in identification and detection of fake news. Most social media are turning to use this methodology to curb the fake news. The basic concepts used are **data** mining techniques with algorithms like feature selection, Natural language processing, Document-term-matrix construction.

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(http://tjj.sh.gov.cn/). is 0.04 t/m 2 , and is cited from the Handbook of Green Building Evaluation Standards (GB/T 50378-2019) enacted by the Ministry of Housing and Urban-Rural Development of China. The **data** of construction area and construction waste from 2010 to 2021 are presented in

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Mapping is a visual way to reflect the patterns of spatial and structural distribution of vegetation. The complexity of mapping of vegetation cover is due to its heterogeneity and complex structure, seasonal dynamics, variability under the influence of natural and anthropogenic factors. Thus, the most important problem of mapping the vegetation cover is operative to obtain reliable information about its spatial characteristics and condition. The use of remote sensing **data** is one way to quickly obtain **data** for mapping plants. Currently this method is actively used and developed.

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Both algorithms were trained, with highly acceptable results. Both algorithms had over 90% accuracy during training, with the KNN having 95.55% accuracy and the ANN scoring 96.79% over the training data. Figures 10 and 11 show the confusion matrices generated with the test data. While the KNN algorithm did have minor errors in classifying non-anomalous **data** and curves, the ANN had trouble classifying only curves. Thus, the number 0 represents healthy data; number 1 represents possible potholes, number 2 speed bumps, and number 3 harsh curves. Lastly, both algorithms were given 85% of the whole dataset to train and 15% to test. These tests consisted of accuracy for training, and F1-Score for test data, as well as a confusion Matrix, to determine the specific classifications in which they did not perform well enough.

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Direct single cancer cell to normal cell comparison . To validate and further explore this proposition , we performed single - cell RNA - sequencing ( scRNA - seq ) analysis ( 10x Genomics ) of diagnostic specimens from six infants with KMT2A - rearranged infant B - ALL , including a relapse presentation ( case 3 ) and additional day 8 specimens from responding ( case 1 ) and nonresponding ( case 2 ) patients . We compared these to four other leukemias : NUTM1 - rearranged infant B - ALL ( n = 1 ) , KMT2A - rearranged infant AML ( n = 1 ) , megakaryoblastic neonatal AML ( n = 1 ) and childhood ETV6 - RUNX1 B - ALL ( a common subtype of standard - risk childhood B - ALL ; n = 1 ) ( Supplementary Table 3 ) . From these 12 diagnostic leukemia samples , we obtained a total of 30,242 cells , including 23,286 cancer cells that we identified based on gene expression matching patient - specific diagnostic flow cytometric profiles ( Supplementary Table 4 and Extended Data Fig . 2 ) . Using a published cell - matching method based on logistic regression 12,16 , we directly compared leukemia transcriptomes with mRNA profiles of human fetal bone marrow cells to determine which normal cell type the cancer cells most closely matched . We found that KMT2A - rearranged infant B lymphoblasts overwhelmingly resembled ELP cells at diagnosis and relapse and in nonresponding disease ( Fig . 2a - c ) . By contrast , non - ELP cell signals predominated in other types of leukemia , precisely as predicted from the We assessed the differentiation state of KMT2A - rearranged infant ALL by measuring signals of human fetal bone marrow cell types across the entire spectrum of childhood leukemia in data derived from two different cohorts ( St Jude 's and TARGET ) . We then validated cell signals by single - cell mRNA sequencing for direct comparison of cancer and normal cells . b , Heatmap showing mean cell signals of human fetal bone marrow cells ( y axis ) in human leukemia bulk transcriptomes subdivided by genetic subtype ( see labels underneath , KMT2A rearrangements shown in red text ) , age ( gray circle , infant ; black circle , noninfant ) and source ( S , St Jude 's ; T , TARGET ) . Numbers next to labels refer to case load per subtype . Subtypes with only one case were excluded from analysis . baso , basophil ; CMP , common myeloid progenitor ; Eo , eosinophil ; LMPP , lymphoid - primed multipotent progenitor ; MEM progen . , ; MK , megakaryocyte ; mono . , monocyte ; MOP , monocyte progenitor ; MPP , multipotent progenitor ; Neut . , neutrophil ; NK , natural killer ; Promono . , promonocyte . c , Top : box and whisker plots showing proportional contribution of signals ( lymphomyeloid - primed progenitor , ELP and later B - cell stages combined ( i.e. , pre-/pro - B , pro - B , pre - B and naive B ) ) to the transcriptome of leukemias ( see x axis labels ) . Bottom : box and whisker plots summarizing the ratio of ELP to later B - cell stage signals . Center lines represent the median , box limits represent 25%/75 % quartiles and whiskers represent minimum/ maximum ( top ) and 1.5× interquartile range ( bottom ) . n is the number of biologically independent variables , as listed below each group of plots . Risk refers to the clinical cytogenetic risk as defined in the protocol of the current European ALL trial ' ALLTogether ' ( EudraCT 2018 - 001795 - 38 ) .

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The original **data** provides information on particles per million, which is converted to kilograms per hectare as discussed in Berkhout et al. [10] This **data** considers Sub-Saharan Africa as all land mass below a latitude of 28 degrees north. Hengl et al. [18] also provide estimates for phosphorus (P) and sulfur (S). As the fit of these estimates is very low (R 2 of 0.11 and 0.10 respectively) these elements are not included in this analysis. Altogether 10 variables are thus available for inclusion in (1). We use factor analysis to reduce the dimensionality of this **dataset**. If one or more variables co-vary strongly, the estimation of (1) is affected by multi-collinearity. In addition, factor analysis is likely to reduce the impact of measurement error underlying the variables in Table 2. Moreover, an understanding of which soil nutrients co-vary is insightful information in itself. In fact, strong communal variation between certain soil variables may stem from the fact that certain geological or soil formation processes affect soil nutrient densities similarly.

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But the most important issue to stress is that experimental datasets considered for nuclear **data** evaluation are often not statistically ideal. The process of UQ is usually not straightforward. Also, not all uncertainties (known or unknown) are reported. In addition u may vary over the years, between laboratories, or with respect to the used experimental methodologies. However, it has been demonstrated that in specially designed interlaboratory comparisons, often for single physical quantities, such conditions can be optimized and mastered [13][14][15][16][17].

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At the core of the system's sensorimotor coordination are concurrently-running interaction processes. An interaction process writes **data** derived from its execution to an interaction history, which is kept in shared memory to be read by related processes. During each cycle, an interaction process reads from various interaction histories, performs some processing, and writes to its own interaction history.

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10. Does the website work after rejecting all Cookies (iteworkafterrejectingcoookies) -whether the site works after all cookie purposes are rejected (it is binary **data** with some comments);

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Analysis of molecular variance (AMOVA) was done using **data** from all 372 loci as implemented in Arlequin 2000 [25]. AMOVA enables the partition of genetic variance at a locus or several loci into variation within populations and variation between populations. In addition, AMOVA can be used for a hierarchical analysis of three genetic-variance components -those due to genetic differences (i) between individuals within groups, (ii) between populations within groups, and (iii) between groups. We conducted AMOVA analyses on the study sample using two models (a) a model in that partitioned the genetic variance into that within each ethnic group and that between ethnic groups, (b) a hierarchical model with the country as the first level and the ethnic group within each country as the second level. Additional locus-by-locus AMOVA analysis was done (see Additional file 2). Significance of the AMOVA values was estimated by used of 10,000 permutations. F ST , the fixation index or coancestry coefficient [26], was also computed as a measure of the effect of population division. F ST ranges from 0 (no population subdi-vision, random mating occurrence, no genetic divergence within the population) to 1 (complete isolation or extreme division), and F ST values of up to 0.05 represents negligible genetic differentiation. Allele-sharing genetic distances [14] were also computed between each pair of ethnic groups.

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The two main types of **data** used in this study are spatial **data** and statistical yearbook data. Spatial Data. (1) Administrative boundary vector **data** of Three Gorges Reservoir area (SHP format). (2) Soil dataset provided by Harmonized World Soil Database (HWSD) and Cold Arid Regions, Available online: http://www.westdc.westgis.ac.cn (accessed on 17 April 2019), which contains the spatial coordinates and properties of the soil (GRID format).

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Jatamansi potentiated the effect of pentobarbital by shortening sleep latency and prolonging total sleeping time in swiss albino mice, though not as remarkable as that of diazepam. Earlier reports suggest that the increase and decrease of pentobarbitone-induced sleep time can be a useful tool for examining the stimulatory or inhibitory effects on CNS, especially for investigating influences on gamma-aminobutyric acid (GABA A ) ergic systems in CNS. [24] The previous report suggested that ethanolic extract of Jatamansi significantly altered locomotor activity. [25] Jatamansone exerted a tranquilizing effect in mice and monkeys [13] and a significant reduction in hyperactivity and improvement in restlessness and aggressiveness on hyperkinetic children similar to amphetamine. [8] Alcoholic extract of Jatamansi root increased the level of GABA on acute administration and increased the levels of most of the central biogenic amines and inhibitory neurotransmitters on chronic administration. [27] Thus, earlier studies indicate isolated compounds or extracts exert highly significant sedative activity while **data** of this study indicates Benzodiazepines have been used for the treatment of insomnia and other CNS disorders predominantly. These drugs potentiate the effects of the inhibitory neurotransmitter of GABA, by binding to a specific site on the GABA A receptors to produce allosteric enhancement of anion flux through this ligand-gated chloride channel. [28,29] Most sedative-hypnotics used in the treatment of insomnia target the GABA A receptor. As test drug increased the duration of sleep time induced by a sub hypnotic dose of pentobarbitone, it can be stated that the drug may interact with pentobarbitone on the CNS via GABA A -ergic mechanisms in CNS.

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Each test was repeated at least three times in the present work . The data were presented as the means ± standard deviations . Statistical analysis was performed using one - way analysis of variance .

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Assuming limitations of the deposited data , this work was undertaken to gain a comprehensive view about the influence of cold and heat treatment ( as well as cold and heat recovery ) on the cauliflower mitochondrial proteome in relation to leaf transpiration and respiration rate , stomatal conductance , rate of leaf photosynthesis , photorespiration as well as chlorophyll content and fluorescence . The current study extends our previous complexomic and functional data [ 41 ] .

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In view of device failure , it is important to consider why the like - species of oxygen vacancies and interstitials can cluster together as observed . We propose a schematic picture to enable a better qualitative understanding of the microphysics underlying the observed oxygen migration into rings , and the subsequent formation of clusters of oxygen vacancies and interstitials and the associated band diagram ( Figure 4 ) . Initially , incipient oxygen defects are uniformly spread throughout the as - grown TaOx film ( Figure 4a ) , which then laterally migrate due to thermallydriven forces upon formation of the conducting channel ( Figure 4b ) , as discussed above . Local changes in the electrical potential due to the clustering of negative interstitials and positive vacancies can cause significant bending of the tantalum oxide conduction and valence bands that can partially neutralize the charge on each species , [ 38 ] as indirectly evidenced by significant band - shifts in Figure 2c . This bandbending can decrease repulsions among likecharged species and also enable them to agglomerate , as the cohesion energy for oxygen vacancies can be quite low , and is likely to stabilize the ring [ 7,34,39,40 ] ( Figure 4c ) . We also calculated an approximate potential profile across the ring ( Figure S16 ) that look qualitatively similar to the one proposed in this cartoon . Subsequent lateral forces followed by clustering of vacancies and interstitials follows the initial bending of bands , as shown in Figures 4b-4c . Due to continued supply of energy through cycling and the large surface - area - to - volume ratio of the rings , they break apart to form clusters of oxygen interstitials and vacancies ( Figure 4d ) , associated with the fading of the ring observed in Figure 2b . Additionally , we observe that most of the bright regions in Figures 1b-1c are in proximity or contact with a dark region , and vice versa ( pointed out in Figure S3 ) . This suggests that there are significant vacancy - vacancy and interstitial - interstitial attractive forces or there are significant vacancy - interstitial barriers . The attractive forces likely originate from the strong clustering , as mentioned above . The barrier could originate from the oppositely charged defects in the bright and dark regions behaving as dopants , creating an electric field at the interface of these regions to prevent complete neutralization of the charged defects , much like oppositely charged dopants in a p - n junction . Direct observation of clustering of like - species is an important observation that shines light on a prominent failure mechanism of such devices . We specifically point to the fact that many real world devices are smaller than the ring features observed here and are operated at much lower power levels . Similar experiments utilizing low - power operations on identical devices have yielded strikingly different results , with the device endurance being much higher ( > 10 8 ) and no rings were observed . [ 17 ] As our measurements show , in - operando x - ray absorption spectromicroscopy is a powerful tool for studying chemical and electronic structure in oxide materials , including device evolution and failure with electrical cycling and inhomogeneous localized phenomena . With in - operando , high - voltage electrical cycling of tantalum oxide devices , we observed the development of submicrometer features with a ring of oxygen interstitials and an inner core of oxygen vacancies , which could be reproduced using thermally - driven lateral forces . A key observation here is that a significant amount of displaced oxygen moved radially outward from the conduction channel and was stored as interstitials , with a unique spectral signature , in the tantalum oxide film rather than in to the adjacent tantalum metal electrode . [ 41,42 ] These results provide experimental data that help in understanding previous models regarding oxygen ion migration , [ 3,[5][6][7][8]17,26,32 ] metastable cohesion of oxygen defects , [ 7,34,40 ] role and sign of thermophoresis , [ 12][13][14]43 ] the composition and structure of conduction channels that tend towards failure , [ 5,6,8,31 ] and the localization of resistance switching . [ 14,37,43 ] Most importantly , we directly observed a failure mechanism caused by clustering of like - species of oxygen .

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The standard approach for partitioning a **data** set is through clustering. The works of Ruan et al. [11] have proven that for some **data** sets, community detection in graphs provides more accurate partitions. In line with this, we use 4 different community detection algorithms to predict the grouping of genes based on the 5 functional groups. The first three algorithms use the concept of modularity while the last algorithm uses edge-betweenness. We'll discuss these two network metrics in the succeeding subsections. We subjected the largest component of each graph obtained by using value-based and rank-based construction with varying input parameters. Here, we will compare how the different community detection algorithms perform in predicting the 5 functional groups.

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This approach that taught p-o, m-o, p-m and their interrelationships was also effective in teaching students in grades 4 and above with specific learning disabilities, such as dyslexia with or without co-occurring dysgraphia (impaired handwriting) . Both behavioral and brain imaging **data** before and after instruction for children in grades 4 to 9 who met evidencebased criteria for dyslexia, characterized by spelling as well as reading disability, showed significant gains in spelling achievement and brain normalization during spelling tasks (Berninger & Richards, 2010). This occurred after receiving instruction in p, o, and m awareness and their interconnections (e.g., through word sorts, Bear, Ivernezzi, Templeton, & Johnston, 2015;see Berninger et al., 2008, Study 1) and/or orthographic patterns in word-specific spellings (see Berninger et al., 2008, Study 2). In both studies, instructional activities also facilitated transfer of ideas and word concepts through spelling to composing. Thus, pom instruction can improve idea expression in written language (Bahr et al., 2009;Nagy et al., 2014).

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More recently , contradictory data have been reported . Lemon et al . [ 52 ] investigated cancer development and longevity of cancer - prone Trp53 + /− mice exposed to a single 10 - mGy CT scan or gamma irradiation . CT - scanned mice lived longer than the control mice , and CT caused a significant increase in the latency of sarcoma and carcinoma . In another experiment from the same group , 4 Gy was administered first to the same mice and weekly CT scans were repeated 10 times [ 53 ] . The overall lifespan was about 8 % longer in mice exposed to multiple CT scans after 4 - Gy irradiation than the control mice receiving 4 Gy alone . Increased latency periods for lymphoma and sarcoma progression contributed to the overall lifespan increase . Thus , conflicting data exist regarding the oncogenicity of CT radiation exposure . However , it should be noted that the former study suggesting the bionegative effect used only 20 mice per group , whereas the latter two studies employed about 100 or 200 mice per group . than the control mice receiving 4 Gy alone . Increased latency periods for lymphoma and sarcoma progression contributed to the overall lifespan increase . Thus , conflicting data exist regarding the oncogenicity of CT radiation exposure . However , it should be noted that the former study suggesting the bionegative effect used only 20 mice per group , whereas the latter two studies employed about 100 or 200 mice per group .

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An interesting problem is the coding of palettized images. While the palettization process offers some compression (typically 3 : 1), this is usually significantly lower than what is attainable with other image coding schemes. Smoothness assumptions are typically invalid for color mapped "image" data. Therefore, normal coding schemes are inapplicable unless the images are remapped to full color images before coding. Recently, Wu [322] has suggested a new YIQ palette architecture that uses joint VQ of spatial and color information to obtain modest compression ratios. A more aggressive coding scheme for palettized images has been suggested in [323], where the colormap **data** is locally reorganized to obtain smooth blocks, and DCT coding is then utilized. Lossless entropy coding schemes have also been presented recently in [324] and [325].

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Finally, Ashby (2020) examined **data** from January 1, 2016 to May 10, 2020 for ten of the largest U.S. cities to determine if there were changes in service calls after the start of the pandemic. ARIMA models were used to forecast weekly total call count frequencies as well as weekly call counts broken down across 18 different call types. The frequency of total CFS was estimated to be significantly below what was forecasted for the model for multiple consecutive weeks after pandemic lockdowns occurred in six of ten cities. Three of the remaining four cities also saw non-significant decreases in total call counts. New Orleans saw an initial decrease, followed by a two-week spike in calls, then another decrease. In terms of specific call types, crime-related CFS did not deviate from expected frequencies. Domestic violence calls increased in three cities, decreased in one city, and were in the expected ranges in three cities. Disturbance calls increased in four cities but remained the same in six cities. Drug-related calls were lower than expected in two cities. Finally, traffic-related calls saw an initial decrease, but then slowly increased in later weeks of the study period (Ashby, 2020).

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Electrons generated by DM lose essentially all their energy via Inverse Compton scattering, e ± γ → e ± γ , on ambient light with average energy E γ ∼ eV. Such scatterings give rise to photons with larger energy E γ ∼ E γ (E e /m e ) 2 ∼ 10 GeV, which is in the energy range being probed by FERMI. As discussed below, this DM ICS γ flux is only marginally affected by astrophysical and DM distribution uncertainties. The reasons for this can be traced back to two observations: (i) Far away from the Galactic Center, the DM uncertainties are relatively mild. (ii) As we will see in Section 4, all DM models that fit the **data** predict roughly the same e ± spectrum, as it is now mostly fixed by the new measurements (given the new FERMI and HESS results). Thereby the DM ICS spectrum is well predicted. As already illustrated in fig. 1 it is not much below the first FERMI diffuse γ-ray data, released for energies ≤ 10 GeV in a specific angular region. Therefore, if the e ± excess is due to DM, FERMI is expected to observe an associated γ excess which is not sensitive to the specific DM model or DM density profile. Whether such an excess is seen or not, will decisively implicate on the DM (or any other mechanisms that produces e ± in a spherical region away from the galactic plane) interpretation of the measured excesses. Alternative scenarios involve e ± generated locally (e.g. by a powerful pulsar) or along the galactic plane (e.g. by supernovae).

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1 More precisely , one should write for the evidence p(d|model ) , in order to show explicitly that it is conditional on the assumption that the model is the true theory . From there one can further employ Bayes ' theorem to obtain the posterior probability for the model 's parameters given the observed data , namely p(model|d ) . This is the subject of Bayesian model comparison ( see e.g. [ 21 ] for an illustration ) . Here we do not employ the evidence for this purpose ( see instead [ 10,16 ] for applications to the CMSSM ) , and therefore drop the explicit conditioning on the model under study , although in the following one should always interpret p(d ) ≡ p(d|model ) .

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The experiment was laid out in a split plot design with 3 replications having unit plot size of 5m 2 (2.5m ×2.0 m). Varieties were: BRRI dhan39, BRRI dhan40, BRRI dhan41 accommodated in the main plot and split application of potassium fertilizer (muriate of potash) was placed in the sub plot. Total number of plot was 36. The layout was done on 20 July having 1 m and 75 cm spacing between replications and the unit plots, respectively. Land preparation involved ploughing, harrowing and leveling in the field to make it suitable for crop establishment by a four wheel tractor. Soil was flooded and irrigated once with sufficient water to bring the top soil saturation and create an overlying water layer. The water depth was 5 cm but 10 cm was maintained for about one week after transplanting. From 30 days before head formation and flowering to the start of maturity, soil was frequently covered with water to a depth of 8 or 10 cm. A continual flow of water was maintained. The field was drained completely 30 to 45 days before harvest to ensure that the field would be dry enough for harvest. The source of K was commercially produced Muriate of Potash. Urea, Triple super phosphate, gypsum and zinc sulphate was applied at 120,100, 60 and 10 kg ha -1 respectively during final land preparation. Thirty days old seedlings were transplanted from a nursery bed to the main field maintaining 3 seedlings hill -1 with a spacing of 20 cm×15 cm. Necessary intercultural operations such as weeding, irrigation, pest management etc were performed accordingly and whenever needed to ensure the growth of a successful crop. Ten hills plot -1 were randomly uprooted before harvesting in order to collect the following **data** : Total number of tillers hill -1, number of effective tillers hill -1 , panicle length, number of grain panicle -1, number of unfilled spikelets panicle -1, grain yield, straw yield, biological yield, harvest index ( HI). Data on grain and straw yield were recorded on a plot basis after drying in the sun maintaining 14% moisture, threshing, winnowing and finally converted to grain/straw weight per hectare.

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From the time series observations obtained between 2005 and 2019 , we have built eight different light curves . Additionally , we used data published in the literature by Jewitt & Sheppard ( 2002 ) ; Lellouch et al . ( 2002 ) and Hicks et al . ( 2005 ) , thus incorporating three additional light curves to our study from previous years ( figure 1 ) . All the light curves were corrected from light travel time . Because Varuna 's body is assumed to have an ellipsoidal shape ( e.g. , Jewitt & Sheppard 2002;Lellouch et al . 2002 ) , data from each light curve were fitted to a Fourier series m = Σ i [ a i sin(2iπφ ) + b i cos(2iπφ ) ] , where m is the theoretical value of the relative magnitude obtained from the fit , φ is the rotational phase ( calculated as the fractional part of ( JD − JD 0 ) /P , where JD is the Julian Date , JD 0 = 2451957.0 is the initial Julian Date , and P is the rotation period in days ) , and ( a i , b i ) are the coefficients of the Fourier function ( with i = 0 , 1 , 2 , ... ) . In our specific case , we used up to secondorder ( i = 2 ) or up to fourth - order ( i = 4 ) Fourier functions . The second order is the minimum order that allows a double - peaked fit ; however , higher orders take into account small deviations on inhomogeneous objects and can be used to fit light curves that are highly sampled . The 2001The , 2002A , 2011The , 2018 and 2019 light curves were fitted to a fourth - order function , while the remaining light curves were fitted to a second - order Fourier function , because the number of data points in those runs was not large enough to use a higher order . Data were folded using Varuna 's rotation period of 6.343572±0.000006 h , which is obtained using the Lomb periodogram analysis of all our data , in agreement with the previous one reported in Belskaya et al . ( 2006 ) . The peak - to - valley amplitudes ∆m ( amplitudes in the following ) of each light curve are given by the absolute maximum and minimum produced by the fits . Table 1 contains the results from the fit to each light curve , i.e. , the amplitude and the dispersion of the residuals of the Fourier function fit to the observational data . One of the evident results is that the amplitude has changed considerably along these 19 years , with an increase of ∼ 0.13 mag . Online table 2 presents all the relative photometry observations from 2005 to 2019 .

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We have demonstrated that hetero-regulators in the same signaling pathway tend to have a high HeRS score, and HeR modules map well to known pathways. Conversely, a high HeRS score can indicate a co-pathway relationship of the corresponding hetero-regulators. Here, we take TF Sok2 as an example to illustrate how to predict gene functions based on HeR modules. Sok2 forms a HeR module with known HOG TFs and HOG kinase ( Figure 4A, Table 1), and it binds to many genes in the HOG pathway ( Figure 4B). These **data** predicts Sok2 as a potential TF in the HOG pathway. Although no previous study has reported Sok2's function in HOG pathway, there is indirect evidence to support this claim.

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Table 1 1comparison of the demographic and neuropsychological **data** of the ciD and hc groups Notes: # chi-square tests; *head motions were evaluated according to the FD criteria described by Van Dijk et al. 31 Abbreviations: ciD, chronic insomnia disorder; hc, healthy controls; sD, standard deviation; M, male; F, female; y, years; sTai-s, state Trait anxiety inventory-state; sTai-t, state Trait anxiety inventory-trait; BDi, Beck Depression inventory; PsQi, Pittsburgh Sleep Quality Index; n/a, not available; FD, framewise displacement. Neuropsychiatric Disease and Treatment 2018:14 submit your manuscript | www.dovepress.comCharacteristic Patients with CID

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: Conceptualization, M.A.K. and M.A.; methodology, M.A.K.; software, M.I.; validation, M.A.K., A.A. (Abdullah Alshememry), A.A. (Aws Alshamsan) and M.I.; formal analysis, M.A.K.; investigation, M.I.; resources, M.A.K and M.A.; **data** curation, M.A.K. and M.I.; writingoriginal draft preparation, M.A.K.; writing-review and editing, A.A. (Aws Alshamsan) and A.A. (Abdullah Alshememry); visualization, M.A.K.; supervision, A.A. (Aws Alshamsan); project administration, M.A.K., A.A. (Aws Alshamsan) and M.A.; funding acquisition, M.A.K., M.A. and A.A. (Aws Alshamsan). All authors have read and agreed to the published version of the manuscript. Funding: This project was funded by the National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia, Award Number (2-17-03-001-0035).

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Both new visualization and Sigils visualizations can be mixed in dashboards. Once these visualizations and dashboards are created, they benefit from the **data** produced by the rest of GrimoireLab.

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Further information on experimental design is available in the Nature Research ' Life Sciences Reporting Summary ' linked to this article . Compartment - specific eQTLs show greater cell - type specificity and distal regulatory element enrichment . a , Diagram describing the integration of kidney eQTLs , GWAS , singlecell expression and regulatory region . b , Heatmap of cell - type - specific expression of identified CKD target genes . The blue / yellow color corresponds to the level of expression ( z - score ) . Endo , endothelial ; Podo , podocyte ; PT , proximal tubule ; LOH , loop of Henle ; DCT , distal convoluted tubule ; CD - PC , collecting duct principal cell ; CD - IC , collecting duct intercalated cell ; Fibro , fibroblast ; Macro , macrophage ; Neutro , neutrophil ; NK , natural killer cell . c , Density plots of best eVariants in tubule ( top ) and glomerulus ( bottom ) and the relationship to transcription start site ( TSS ) . d , Distance of top eVariants from TSS ( -log 10 ) by groups . e , Odds ratios of the top eVariants on kidney promoter by groups . The groups were compared to randomly selected variants matched by MAF and distance to TSS ( n = 5,000 randomly selected times ) . Center lines show the medians ; box limits indicate the 25 th and 75 th percentiles ; whiskers extend to the 5 th and 95 th percentiles , outliers are represented by dots ( d , e ) . f , Odds ratios ( y - axis ) of eGenes from each group enriched by kidney - specific cell type expression . P was calculated by two - sided Fisher 's exact test . RTEC : PT , LOH and Colocalization of CKD GWAS leading SNPs with kidney compartment eQTLs . a , GWAS variant and eQTL variant are not the same one , but in the same LD ( r 2 > 0.8 ) ; b , The regulatory direction of risk allele of each SNP on its target gene . ↑ , higher gene expression with risk allele ; ↓ , lower gene expression with risk allele ; N / A , this gene or SNP was excluded by data pre - processing .

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We used SAS ( version 9.1 ) for univariable data analysis and to generate a multivariable logistic regression model . We used Student 's t test , Wilcoxon rank sum test , or χ 2 test for comparing characteristics in the study population and pregnancy outcomes between women who did and did not develop pre - eclampsia . Stepwise logistic regression was used to determine independent risk factors for pre - eclampsia in both datasets . The order of variable selection was determined by the χ 2 statistic for each potential variable and the forward selection step could be followed by removal of variables in one or more backward elimination steps . We calculated receiver operating characteristics curves and determined screening test characteristics at a 25 % , 10 % , and 5 % false positive rate . For internal validation we evaluated the calibration and discrimination ( 10 - fold cross validation ) of the model using methods described by Altman et al . 30 Calibration was assessed by plotting the observed proportion of events against the predicted probabilities . For the cross validation , participants were stratified by region ( New Zealand , Australia , Ireland , and UK ) , pre - eclampsia status ( positive or negative ) , and gestation ( < 260 days or ≥260 days ) and randomly allocated to one of 10 groups . Tenfold cross validation was then performed , with 90 % of the data used to generate a model , and estimation of disease risk was performed in the 10 % remaining . These predicted values were then combined across the 10 runs and summarised by the C statistic ( AUC ) . This entire procedure was repeated 10 times .

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Several of the most abundant bacterial and archaeal taxa detected in the SCS are frequently associated with either sulfate-replete or methanogenic sediments and their abundances are thought to be largely driven by vertical gradients in electron acceptor availability. Among these are members of the Atribacteria, which are commonly associated with methanogenic sediments and are thought to ferment organic matter and provide substrate for methanogens (Carr et al., 2015). In the SCS, however, Atribacteria comprised greater than 15% of the community in U1431 and did not show a significant change in abundance between the sulfate reduction and methanogenic zones of sites U1432 and U1433 FIGURE 4 | Number of detected species at the OTU level (97% identity) across sites and sediment lithologies for sulfate reduction zone and methanogenic zone communities. Boxes indicate first and third quartiles of **data** ranges and whiskers extend to highest and lowest points within 1.5 times the interquartile range. Dots indicate outlying points.

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The first 11 cohorts of students selected using this new process, that enrolled from 1999 through 2009, were followed serially in relation to specific course outcomes at the end of 2009. The majority of those enrolled from 1999 through 2004 had graduated from the 6-year undergraduate course, the majority from 2005 were in their final year and the remainder of entrants were progressing through the course, with the majority of those who commenced in 2009 having completed their first year. Hence, the quantity of **data** for each academic year of the course varies from 1174 in Year 1 to 547 in Year 6 for Standard entrants. All entrants via this selection process were studied, including those who withdrew or were excluded for unsatisfactory progress. In cases where students had repeated a unit their first unit score was included in the analysis. International full fee paying students and indigenous students admitted via special entry criteria were not included in the study.

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Data are expressed as mean±SD of 3 independent experiments. Data analysis was done using the t test and one-way ANOVA. GraphPad prism 6 software was used for **data** analysis and mapping. Statistical significance was defined as p<0.05.

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Certain limitations in both the available expression **data** as well as NCA itself could be addressed to make this approach more powerful. Gene expression analyses obtained from whole blood leukocyte samples provide an integrated signal from different leukocyte populations which are difficult to deconvolute, and so using a single cell population would be advantageous, such as could be obtained using cell sorting or other methods. Additionally, the number of transcription factors which can be used in NCA is approximately the number of expression profiles in the **data** set, and so a greater number of expression profilesobtained at best shortly after the endotoxin administrationwould also have been useful. Finally, NCA's scaling property, which makes it difficult to predict the direction of transcription factor activity, as well as NCA's current inability to incorporate time course information from the **data** set are important limitations to the method. Some approaches that may overcome these challenges include recent studies in

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All statistical analyses were performed with SPSS for Macintosh, Version 16. After carefully inspecting **data** for out-of-range values and pulling 6 outliers in to within 3 SD of the mean (all within the CPT omissions variable), our initial step was to perform Pearson correlations (for continuous variables) or t-tests (for dichotomous PA variables) regarding the associations between baseline EF variables and follow-up criterion variables. Following a significant correlation or t-test, we used linear or (in the case of PA variables) binary logistic regression with EF measures as predictors and academic, social, and global impairment measures as criterion variables, first controlling for IQ and then controlling for group status (ADHD vs. comparison), both of which were assessed at baseline. On the basis of these initial regressions, we reasoned that because IQ deficits are inherent to ADHD, controlling for both IQ and group concurrently would constitute overcontrol (see Miller and Chapman 2001). Thus, we emphasize findings with the more stringent covariate of group status (ADHD vs. comparison). Finally, following a significant regression analysis, we added the interaction term of the EF predictor x group status as the last step in the equation, to test for moderation by ADHD status.

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In the case of the signal process , top quarks are produced more abundantly relative to their antiquark partners due to the charge asymmetry of the W boson radiated from the initial - state quark in pp collisions at the LHC . This leads to a higher relative background contamination in the l − final state arising from top antiquark decay compared to the l + final state from top quark decay , as shown in Fig . 7 . As a result , the measurement in the l − final state is more sensitive to the sources that significantly alter the background contributions along with the signal , compared to the ones that impact the signal contribution only . This is reflected in Table 3 where the uncertainties from the signal modeling are lower for the l − case ; whereas other sources , except for the ones listed under flavor - dependent JES , that alter the background contributions along with the signal have a larger impact on the total uncertainty . In the case of the flavor - dependent Table 3 : Summary of the m t uncertainties in GeV for each final - state lepton charge configuration . The statistical uncertainties are obtained by performing the fits again in each case while fixing the nuisance parameters to their estimated values from data . With the exception of the flavor - dependent JES sources , the total systematic uncertainty is obtained from the quadrature sum of the individual systematic sources . The amount of statistical fluctuations in the systematic shifts are quoted within parentheses whenever alternative simulated samples with systematic variations have been used . These are determined from 1000 pseudo - experiments in each case . Entries with < 0.01 denote that the magnitude of the systematic bias is less than 0.01 .

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In some cases it is convenient to schedule the retrieval as a collection of tasks that can run in parallel. This happens for example when we can benefit from several nodes analyzing different Git repositories in parallel, or when several nodes can consume a certain API quicker than a single one. In these cases we can add Arthur, which will schedule Perceval and Graal jobs taking into account aspects such as availability of tokens to access **data** sources, or refresh periods (how often **data** will be retrieved incrementally from repositories). Arthur uses a Redis database to manage jobs and batches of retrieved items (see Fig. 17).

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ing that GW170817 produced a successful jet ( Duffell et al . 2018 ) . Using these new data we can also derive robust constraints on the smoothness parameter ( s ) and therefore the sharpness of the light curve peak , something which has not been possible with previously - reported data . Together with the sharpness of the peak , the steep decline indicates that the jet is extremely narrow and that most of the outflow energy of GW170817 resides in the jet . Through simple analytical arguments we are able to place a constraint on the geometry , θ v 8θ j ( θ v 6θ j with semi - analytical modeling ; θ v is the viewing angle and θ j is the jet half - opening angle ) , and implies θ j 5 o if we further use the viewing angle constraint provided by the LIGO - Virgo Collaboration . Using Γ 4 close to the peak of the light curve ( estimated from the observed superluminal motion in GW170817 ) gives θ j 3 o and θ v 15 o .

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In summary, results presented here show a complex transcriptional regulation of the Smirnoff-Wheeler pathway in legumes under water-deficit conditions. Based on their expression patterns and correlation with the AsA levels and biosynthetic activity, our **data** suggest that GalLDH, together with VTC1, are the most likely candidates to play a role in the regulation of AsA biosynthesis in legume plants. Future work may provide answers to questions such as the role of post-transcriptional regulatory mechanisms in the drought-induced decline in GalLDH activity using targeted proteomic approaches (Wienkoop et al., 2008) or the individual contribution of the various paralogous genes. This knowledge will contribute to further understand the regulation of the pathway under water-deficit conditions to, ultimately, improve abiotic stress tolerance in legume crops.

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In this paper , we have applied data from various sources . A detailed list of the variables , their definitions and sources is provided in Table 1 . Our dataset , including daily price indices , daily trading volumes and the daily CBOE VIX index cover the period 1 July 2019 to 14 August 2020 . We have collected these capital market , resilience and macroeconomic data for 34 economies based on the data availability and comprehensiveness of the market . A description of our sample by country is presented in Table 2 . There are an equal number of observations for each country and thus we have a balanced panel .

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Besides the high throughput and performance that it can offer, it can be easily integrated with Entity Framework, and furthermore, the development environment facilitates the integration between the two. Automapper is used to facilitate the transformation on entity objects to **data** transfer objects.

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Fig. 1 1The potential effects of forests on June-August (JJA) cloud cover fraction and their attribution. The potential effect is defined as the differences in cloud cover fraction between forests and nearby non-forest (ΔCloud) from MODIS and MSG satellites that detect clouds. a Potential effects of forests on cloud cover fraction based on MODIS **data** from 2002 to 2018 (overpass at 13:30 local time) and b their latitudinal patterns with cloud enhancement and inhibition effects separated. c, d Potential effects of forests on cloud cover fraction based on hourly MSG **data** from 2004 to 2013 (overpass at 14:00 local time) and e the timing of the maximum effect during a day. The numbers in panels b and d show the percentage of cloud enhancement (red) and inhibition (blue). f Attribution of cloud effects of forests to tree cover and elevation based on MODIS and MSG data. The five attribution categories include tree cover induced cloud increase (Tree+) and decrease (Tree−), orography induced cloud increase (Orography+) and decrease (Orography−), and other unexplained effects. The percentage of each attribution category is calculated based on the MODIS results.

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Changes in Stroop task scores were assessed using repeated-measures analysis of variance (RM ANOVA) with the sit/walk condition as a fixed grouping variable, and simple post hoc analysis to determine differences from timepoint one, to timepoints two and three. EEG **data** were divided into time-bound epochs prior to each Stroop task, including: (1) Baseline, (2) Sit/Walk condition and (3) Lecture. Each mental state was analyzed separately, using repeated measures analysis of variance (RM ANOVA) with condition as a grouping variable. Repeated post hoc analysis was also included to determine differences between each timepoint. Finally, Pearson correlations and multiple analysis of variance (MANOVA) were utilized to elucidate the influence of survey instruments on mental states during the sit/walk condition. All **data** were analyzed in SPSS, with ANOVA being utilized for parsimonious comparison of epochs. While this overlooks dynamic changes within each time-bound epoch, it renders immediately interpretable results for comparison with similar research.

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The likelihood function of (H, σ 2 ) conditional on observations of averaged energies from levels j 1 , . . . , j 2 is L(H, σ 2 |y j 1 , . . . , y j 2 ) = j 2 i=j 1 g(y i ). We use beta distribution and non-informative prior 1/σ 2 as independent priors on H and σ 2 , respectively, π(H, σ 2 ) = H α−1 (1 − H) β−1 B(α, β) × 1 σ 2 . The hyperparameters in beta distribution, α and β are calibrated by considering the impact of effective sample size (ESS) and the mean of the beta distribution, α α+β , which is linked to the Hurst exponent of an input signal. The ESS for the beta(α, β) prior is approximated with α + β and is closely related to the performance of the Bayesian estimation. For example, when ESS is large, the posterior distribution is dominated by the prior [13]. Based on simulations, we selected the ESS to be approximately 50% the original **data** size, but the ESS can be calibrated based on the level of certainty about H. The larger the ESS is, the more confident we are about the mean of a prior, that is, about the "true" value of H.

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Remark 10. We have assumed that agent utility functions (and resulting true baseline consumption b k ) are deterministic. However, b k depends on (exogenous) random parameters such as temperature and occupancy. For example, A more realistic model would accommodate dependence on exogenous random processes such as temperature and occupancy. This might result, for example, in a baseline consumption of the form b k = b k +a k |θ−θ 0 |. Here, θ is the realized temperature during the DR event, and θ 0 is the predicted temperature. In this case, agents can be required to report their best-effort forecast b k of their baseline consumption along with the temperature sensitivity a k . Historical consumption **data** can be used to assist agents in making these reports. The SRBM mechanism can be easily extended to incorporate these more complex reporting scenarios. The most general scenarios with uncertain utility functions that explicitly depend on exogenous random processes θ is challenging and is an ongoing work.

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Qualitatively, one can see a clear increase in the frequency of such currents from -90 to -85 inV. Just counting events gives a nearly 10-fold increment over this range of potential. A further increment is seen between -85 and -80 mV, but the difference is not as large. When these **data** were quantified with either half amplitude threshold analysis or mean-variance histograms (shown for these **data** in Fig. 2 B), channel open probabilities and open times could be determined. Fig. 3 A shows open probability and open time for these wild-type channels. Consistent with the qualitative estimates described above, the Po curve shows a steep increase in currents between -90 and -85 mV, but then becomes nearly level at more positive potentials. Open times were ~0.1 ms over the whole voltage range, with a gradual increase (roughly e-fold/25 mV) in the range from -90 to -70 inV.

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Manganese is an essential trace element in the metabolism of all living organisms, including plants and bacteria, and is found in all tissues of man. But manganese is also a potential carcinogen. 1 Among the inorganic chemicals, manganese, chromium, and selenium and certain organometallic derivatives of these metals have been found to be carcinogenic under special conditions and are also considered potential inorganic genotoxic agents. 2 The hazards of manganese have been known for a long time. The accurate determination of manganese in environmental and biological materials is of considerable importance for both metabolic and toxicological studies in humans and animals because of its dose-dependent harmful and benecial roles. Until recently, analytical techniques of satisfactory sensitivity and accuracy have not been widely available and attempts by many researchers to use unsatisfactory procedures have led to the publication of much **data** and also to biochemical conclusions of dubious value. 3 Clearly, major problems which have given rise to this situation are the very low concentrations of manganese in biological samples, and a lack of awareness of the need to exercise control over the extraneous contamination and interfering of foreign ions during all the steps of an analytical procedure. The present method that is being recommended over the existing methods almost in every respect in their own terms-selectivity, range of determination, accuracy, sensitivity, simplicity and rapidity of the operation, stability of the uorescent system and acidity of wide variation, etc.

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Since there was a lack of necessary data, the research team collected field level **data** in the summer of 2017 from both cities to develop the drainage model. Data pertaining to the primary water level, water discharge, and existing drainage networks and their cross-sections were collected from the field. The primary **data** were used for both model development and calibration. Moreover, historical water level **data** (from 1938 to 2015) for the Surma River and rainfall **data** at Sylhet recorded by the Bangladesh Water Development Board (BWDB) (from 1957 to 2011) and the Bangladesh Meteorological Department (BMD) (from 1957 to 2015) were collected (Table 3).

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Supplementary **data** are available at Journal of Tropical Pediatrics online.

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Disulfiram (Antabuse), a drug used for almost 70 years to treat alcohol abuse, is an emerging candidate for repurposing in cancer therapy. Antitumor activity of disulfiram (DSF) is supported by numerous preclinical studies, case reports, and small clinical trials [1][2][3], yet clinical **data** from larger randomized trials are still lacking. Despite several promising case reports about durable remissions of advanced-stage cancer patients after DSF therapy [1,4,5], results from clinical trials are still limited and less favorable [6], a trend that is shared with other repurposed drugs [7]. The results from the few clinical trials available so far suggest that DSF´s anticancer effect may be limited to a subset of cancer patients [8,9], thereby raising a need for the identification of biomarkers that would help guide the patient selection in the future. A broader assessment of DSF in clinical oncology had been hindered mainly by the unknown identity of the active anticancer metabolite and its mechanism of action in cancer cells, including the key molecular target. Consequently, there is currently no reliable way to predict who among cancer patients is likely to benefit from the DSF treatment. In an effort to improve this situation, we have recently discovered that DSF is metabolized in the human body to bis-diethyldithiocarbamatecopper complex (CuET), that CuET represents the long-sought-after active compound that kills cancer cells, and that mechanistically, such toxicity to cancer cells reflects CuET-mediated impairment of NPL4, an essential cofactor of p97 segregase broadly involved in the degradation of cellular proteins [10,11]. We have also noticed that the CuET complex levels assessed after administration of the same dose of DSF vary significantly among patients [10]. We hypothesize that the observed variable clinical responses to DSF treatment might be attributable, at least in part, to the divergent extent of CuET formation, a process that is likely influenced by genetic and environmental factors, the latter including copper intake and the overall diet. Furthermore, the effectiveness of DSF treatment may be affected also by factors such as concomitant exposure to other drugs or pharmaceutically active compounds, a scenario particularly likely for advancedstage cancer patients. With the primary mechanism of anticancer activity of DSF known, the identification of such factors that impact cellular responses to DSF/ CuET is key to facilitate the repurposing of DSF in clinical oncology.

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Analysis of the RNA-seq **data** of Ti-treated plants revealed enriched GO categories related to defense responses to bacteria, fungus, and viruses, as well as programmed cell death (Figure 4). Therefore, we decided to explore in more detail the effect of Ti treatment on the transcript level of genes involved in SA biosynthesis and signaling routes. Our **data** showed that several SA-related molecular components were responsive to Ti treatment in both shoot and roots. We found that several NBS-LRR receptors are upregulated in Ti-treated roots but only a few in shoots ( Figure 8A). PAD4, which is part of the SA regulatory module is upregulated in roots and lightly in shoots. The positive regulators of SA biosynthesis WRKY54, WRKY70, and CBP60g, are upregulated in roots, and WRKY54 only in shoots. The negative regulators of SA biosynthesis CAMTA2, CAMTA3, and CAMTA5 are also upregulated in roots with only CAMTA3 faintly upregulated in shoots. Interestingly no SA biosynthesis genes were found upregulated either in roots or shoots, suggesting that Ti might preactivate positive regulators of SA biosynthesis but at the same time negative regulators to prevent an undesirable high level of SA that could affect plant growth. NPR1 and NPR3, which regulate the SA response pathway are upregulated in roots but only NPR3 is upregulated in shoots. Downstream of SA perception by NPR1, pathogen-related genes, and WRKYs transcription factors are activated directly by TGA2, TGA5, and TGA6, which ultimately activate the global plant immune response known as SAR. TGA2 and TGA6 are upregulated in shoots while PR1 and WES1 are upregulated in the shoot. The cell death positive regulator SBB1 is upregulated in both roots and shoots whereas the mRNA exporter DRH1 which is needed to initiate cell death, is not. We also found the receptors BAK1 and BKK1, negative regulators of SAdependent cell death, are down-regulated in roots but not in shoots ( Figure 8B). These results altogether suggest that Ti might induce a molecular priming-like response against pathogens.

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The research was undertaken in three phases ( Figure 1). Animals 2020, 10, x 3 of 20 collected **data** before milking (for a maximum of 2 h) and two people collected **data** during afternoon milking.

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We represented the thesaurus as two matrices where T syn is the synonym graph and T ant is the antonym graph. The signed graph can then be written in matrix form asŴ = γW + β ant T ant W +β syn T syn W , where computes Hadamard product (element-wise multiplication). The parameters γ, β syn , and β ant are tuned to the **data** target dataset using cross validation. The reader should note that σ and are not found using a target dataset, but instead using cross validation and grid search to minimize the number of negative edges within clusters and the number of disconnected components in the cluster.

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As is evident from Table 3, the extracts of C. icosandra (73% inhibition at 45.20 ± 8.25 μg/ml), R. damascena (81% inhibition at 68.20 ± 7.23 μg/ml) and C. scariosus (78.23% inhibition at 45.23 ± 0.37 μg/ml) also caused considerable scavenging of superoxide anion in comparison to the reference compound quercetin. The IC 50 values for the superoxide scavenging activities of extracts and the reference standard are shown in Table 3. As evident from results, C. scariosus (28.85 ± 0.23 μg/ml) was able to quench superoxide radicals more effectively than the reference compound quercetin (41.98 ± 0.95 μg/ml). Figure 1. Densitometric analysis confirmed the experimental **data** ( Figure 2).

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To address a potential diagnostic value of miR-574 - 5p , we measured its expression in serum samples from TAA patients and controls ( serum cohort ) . Interestingly , miR-574 - 5p was significantly up - regulated in the serum of patients with TAA compared to the control ( 3 - fold , p < 0.001 ; Figure 2D ) and this up - regulation was higher in patients with a large ( above 49 mm ) aneurysm ( Figure 2E ) . Of note , the cut - off of 49 mm corresponds to the median of the aortic diameter of the TAA group . Circulating levels of miR-574 - 5p are up - regulated in patients suffering TAA independently of the etiology of TAA , compared to the controls ( Figure S3 ) , and miR-574 - 5p levels were not significantly modulated by hyperlipidaemia or the smoking status , either in the control or in the TAA groups ( Figure S4 ) . The ROC ( receiver operating characteristic ) curve analysis revealed an association between miR-574 - 5p and the diagnostic of TAA with an area under the curve of 0.87 ( Figure 2F ) . MiR-574 - 5p discriminated the TAA patients from the controls with a specificity of 85 % and a sensitivity of 78.6 % . These data support a diagnostic potential of miR-574 - 5p for TAA .

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Author Contributions: Conceptualization, R.B.O., G.Z., K.I.S. and D.M.; methodology, G.Z., D.Z.-D. and R.G.; software, K.I.S. and A.I.U.; validation, G.Z., U.Ç., A.K. and S.D.; formal analysis, G.Z.; investigation, R.B.O., G.Z., K.I.S., D.M., D.Z.-D. and R.G.; resources, U.Ç.; **data** curation, R.B.O., G.Z. and S.D.; writing-original draft preparation, R.B.O., G.Z., K.I.S., D.M., D.Z.-D., R.G., S.J. and M.F.M.; writing-review and editing, G.Z., S.D. and M.F.M.; visualization, A.K.; supervision, G.Z.; project administration, G.Z.; funding acquisition, D.M. All authors have read and agreed to the published version of the manuscript.

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By introducing the concept of genetically encoded BRET - activated PDT , we propose a viable solution of the bottleneck problem of limited treatment depth of PDT , while avoiding problems of currently available chemically assembled BRET complexes . For the demonstration of the concept , we designed a genetically encoded NanoLuc - miniSOG platform , which can be delivered by a properly selected carrier to a tumor , and then triggered by the injection of furimazine substrate . To assess the efficiency of this pair , we expressed NanoLuc - miniSOG BRET - pair in breast ductal carcinoma BT-474 cancer cells and then examined its operation in vitro and in vivo . Spectroscopic characterization of BT-474 / NanoLuc - miniSOG cell line confirmed that BRET between NanoLuc and miniSOG c Tumor - growth curves for control group ( mice treated with PBS only ) , as well as for groups carrying NanoLuc or NanoLuc - miniSOG LVs particles and treated with furimazine . Data are presented as the mean ± SD ( n = 6 ) . d In vivo bioimaging of animals bearing HER2 - positive tumors and injected i.t . with LVs - NanoLuc - miniSOG or LVs - NanoLuc . Three mice from each group on the 13th day after LVs injection are presented indeed occurs with the BRET ratio reaching 0.74 ± 0.05 ( Fig . 1d ) . The recorded ratio is in agreement with our previous data 23 , as well as with data for other BRET - based genetically encoded sensor systems 33,34 , confirming a high efficiency of the pair . We demonstrated by MTT assays that NanoLuc - miniSOG efficiently kills the cancer cells in the presence of furimazine ( Fig . 1e ) , while other tests confirmed that the endogenous bioluminescence can serve as a light source to activate the miniSOG for ROS generation ( Fig . 1f ) . Using the model of mice bearing engineered BT-474 cells expressing NanoLuc - miniSOG , we recorded TGI exceeded 72 % after the injection of furimazine without external light irradiation ( Fig . 2 ) . It means that the inhibition of tumor growth occurs due to BRET - induced PDT , although cytotoxicity of oxidized product of furimazine could also partially contribute to the cell death ( Fig . 2b ) . It is also important that bioluminescence imaging can be used in our case to assess BRET efficiency in vivo . The differences of the average luminescence signal with or without Rf in animals evidenced an efficient process of energy transfer from the donor ( oxidized NanoLuc luciferase - substrate ) to the acceptor ( miniSOG ) in the case of tumor Rf saturation ( Fig . 2c , d ) . Finally , genetic nature of proposed NanoLuc - miniSOG BRET - pair allowed us to use targeted viral system for gene delivery precisely into tumor ( Fig . 4 ) . Using HER2 - specific LVs carrying NanoLuc - miniSOG gene we achieved significant regression ( TGI 67 % ) of HER2 - positive xerograph tumor in the animal model , confirming a high efficiency of the proposed concept . We foresee several major advantages of the proposed concept of genetically encoded BRET - activated PDT over currently present chemically assembled BRET - based systems for PDT . First , the genetic nature of functional PDT elements provides an ideal spatial architecture of the BRET construct , conditioning optimal distance between the internal triggering source and the PS , which is obviously very difficult or impossible in the case of chemical assembling . Second , the proposed approach does not require any sophisticated and costly chemical conjugation protocols to generate functional BRET pair for PDT . Third , as showed in our studies , despite certain local toxic effects in organs due to furimazine injection , genetic encoding nature of synthesized protein complexes makes them safe and easily excretable from the organism , which is not the case for many chemically assembled structures . Finally , as probably the main advantage , the used principle of genetic encoding opens up novel appealing prospective for future improvements of PDT systems profiting from on genetic engineering approaches . In particular , the expression of used NanoLuc - miniSOG pair can be controlled on the genetic level by properly designed promoters ( e.g. , telomerase promoters ) , which are specific to some tumors . In addition , by fusing with a well - known protein localization motif ( nuclear , membrane , mitochondrial ) or a whole protein , the genetically encoded pair can be easily directed to any cell compartment ( or even sub - compartment ) . Furthermore , the use of different targeting molecules in the composition of lentiviruses or other carriers renders possible an easy re - direction of a genetically encoded BRET - activated system on any tumor type , while the therapeutic action will address not only the primary tumor , but also its metastasis distributed in the organism . Thus , the employment of genetically encoded constructs provides novel opportunities to direct BRETinduced generation of active forms of oxygen to different cellular compartments or particular cell lines , which is hardly possible with chemically assembled BRET systems .

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Furthermore, in terms of dominant adopter category, Figure 2 shows that the late majority is the most prevalent category (36.6%) in the study region. This suggests that a number of smallholder farmers in Ghana, especially in the Ketu North District, are more sceptical about new technologies and may want to see pictures or videos of people using them before making adoption decisions or probably wait for about half of the population adopting them before they also decide to use them. Also, by comparison, the percentages obtained for each adopter category in the present study vary from the percentages of Roger adopter categories (see Figure 1). In the present study, we observed a trend of downwards reduction in innovativeness to an upwards increase in less innovativeness. For instance, innovators are 1.4% in the current study as against 2.5% in Rogers and Laggards are 17.9% presently as against 16% in Rogers. Therefore, the obtained result suggests that a result from a macro level study (used by Rogers) may not show the same result at the micro-level (present study). i.e., Rogers' work was developed for the market (macro-scale), whereas the current study was developed for a category of farmers (micro-scale). Table 5 presents the logistic regression analysis results that test the four hypotheses on the effect of the predictor variables on the response variable. The main variables adapted in the model to predict the innovativeness of the respondents were educational level, age, gender, and income. The likelihood of the model to predict the outcome correctly was 89.7%, which indicates that the **data** fit the model.

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We represent in fig.3 .12 ) ] . Hence the existing shift between each adjacent dashed and full line represents the small change in the sterile neutrino component , proportional to cos 2 α , resulting from introducing in the scheme aν X component up to its 95 % CL upper bound . This shows that the possible sterile neutrino flux is hardly sensitive to the presence of antineutrinos , a fact whose origin becomes clear on examination of the denominator in eq.(2.13 ): the multiplier of sin 2 α is very close to unity for any value of ψ owing to the fact thatr d ≃ 1 . From fig.3 it is also seen that in the absence ofν x ( x = e , sin 2 ψ = 1 ) the fraction of solar neutrinos oscillating to active ones is greater than 0.59 ( SNO II ) and 0.63 ( SNO I ) at 2σ of the non - ν e flux . Allowing for non - electron antineutrinos up to their 2σ upper bound this fraction becomes respectively 0.62 and 0.66 . This result is consistent with the result of ref . [ 15 ] where the authors also included KamLAND data in their analysis but were restricted to the case sin 2 ψ = 1 .

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(73%) tetranucleotide, 46 (12%) trinucleotide and 54 (15%) dinucleotide microsatellites. The markers and their characteristics are provided [see Additional file 1]. The raw genotype **data** can be obtained by contacting the authors ([email protected] or [email protected].)

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In this setting, we conduct multiple **data** generation runs in the SAPs R/3 on HANA ERP system together with ERPsim R11.2 with a group of 5 research participants. We let the group play the game twice, obtaining a run of exclusively normal operation (normal 1) as well as a run that has fraudulent activities incorporated next to normal business processes (fraud 1). To obtain differing company characteristics such as varying business strategies and user behavior, we additionally select a second group of participants to generate data. Our second group of participants generate one run of normal operation (normal 2) and two individual runs containing different fraud cases (fraud 2, 3), resulting in 3 datasets, each simulating one financial year.

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To discern the appropriate U for power curves, 10 min power **data** P norm are plotted as a function of (a) nacelle cup anemometer 80 m U, (b) nacelle cup anemometer 'true-flux' equivalent U, (c) SODAR 80 m U, and (d) SODAR 'true-flux' equivalent U for a typical summer day in figure 3. The uncertainty induced by a non-co-located SODAR wind speed in the power curves can be seen in figures 3(c) and (d). When compared with the manufacturer's power curve, the SODAR-based power curves have lower Pearson's coefficient (r) values (r = 0.88-0.89) than the nacelle-based (r = 0.94-0.95). Furthermore, a small improvement (in terms of a higher r value and lower standard deviation of residuals) is evident from using the nacelle-adjusted 'true-flux' equivalent wind speed instead of the nacelle hub-height U ( figure 3(b)). Though small, these differences suggest that the nacelle-adjusted 'true-flux' equivalent wind speed generates the most accurate power curves at this site.

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Based on the **data** in this study, we conclude that the Indonesian version of NoMoPhobia Questionnaire (NMPQ) meets psychometric aspects of measurement. NMPQ has a stable reliabilities and validities and can be used to measure the degree of connection between the mobile phone and human interactions.

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The effect of the push pulse can thus be summarised as projecting the 2 1 Agwavefunction into a manifold of spatially separated triplet-pairs by using an optical perturbation that selectively couples the triplet-pair amplitudes of the 2 1 Agwavefunction to the 1 (TT)* excited states, and their decay pathways towards nearly-free entangled triplet-pairs. Not only does this show how triplet-pairs can be harvested with low energy photons from non-luminescent polymers, it also provides a type of analysis of the total triplet character in the many-body 2 1 Agstate, as well as the possibility to study real-time, real-space triplet motion in an organic material. In fact, as ultrafast time-resolved microscopy is rapidly emerging as a viable experimental techniques 80 , it may even become possible in the near future to observe and manipulate the individual triplets in the nearly-free pairs. Push-induced dynamics of PDA with push pulse centred at 940 nm and tpush of 400 fs. The negative signals indicate that the effect of the push is to increase the 'hot' ground state and 2 1 Agpopulations. c. Push-induced response of the 2 1 Agstate when pushing at 400 fs. The 2 1 Aglifetime is increased to 15 ± 0.1 ps in this case. d. Comparison of decay (circles; solid line fit) for spectral region associated with the 2 1 Agstate in pumpprobe measurements, and 2 1 Agand 'hot' ground state in pump-push-probe experiments (tpush = 200 fs; mapped back to single scale where all pulses arrive at t = 0 fs). The PIA associated with 2 1 Agrapidly decays in pump-probe experiments; application of the push however enhances the lifetime of this PIA by at least one order of magnitude. e. Spectral cuts of pump-push-probe spectrum. The 'hot' ground state and 2 1 Ag -PIAs are present almost immediately after the push pulse with the latter being slightly delayed. The **data** is shown for tpush = 400 fs, with the times in the legends indicating the pump-probe delay. f. Magnification of the spectral region associated with the pushed 2 1 Ag -PIA; the spectra are normalised at 900 nm. Dashed overlay is a fit of two Gaussians to the profile (see SI, Figure S10 for further details) and highlights the narrowing of the spectrum at longer time delays following the push.

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All data and complete replication code for this aggregate - level study will be shared immediately following publication , with no end date , for public access and replication . Data are available indefinitely on the Harvard Dataverse at https://doi.org/10.7910/DVN/JTTNKO .

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The structure of this paper is as follows . In the next section we outline the sectors , input data , model and scenarios used for our modelling application . The results are then presented , discussed and conclusions drawn in the subsequent sections .

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This joint optimization problem is extended in [8] to complex-baseband quadrature amplitude modulation (QAM) of a WSS complex-valued **data** symbol sequence. Under the linear MMSE (LMMSE) optimality criterion and the average transmit power constraint, the jointly optimal transmit and receive waveforms are derived for use over an additive WSCS noise channel. It is well known that a WSCS noise model is better than a WSS model for the case in which data-like QAM interferences are present as well as an ambient Gaussian noise [1]. In contrast to the previous results only with an additive WSS noise, the optimal waveforms are shown in general to have nonzero spectral values on a frequency interval whose length is greater than that of the generalized Nyquist interval. This is because, unlike a WSS random process, a WSCS random process possesses non-zero correlation in the frequency domain among the components that are spaced integer multiples of the symbol rate apart [9]. To exploit such spectral correlation of the WSCS random process, a vectorized Fourier transform (VFT) technique is employed in [8]. This technique is motivated by the harmonic series representation [9] of a WSCS random process, and the use of that representation for joint Tx and Rx optimizations in cyclostationary interference and noise has been examined in [10] and [11].

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Prior to the actual **data** collection, the situations in the original studies were radically modified and adapted to suit the Iranian context. The modified form of the (DCT) is given to two professors for validation. Their suggestions and observations are well taken into consideration in designing the final form of the (DCT). For further validation, the (DCT) was piloted on six subjects, similar to the main group, not included in the sample to see if the language was comprehensible for EFL learners, and based on their opinion a few changes were made. After this stage, to assign students into two proficiency levels, in one session an English language proficiency test (PET, 2004) including questions in reading and writing was given to the participants. The participants, whose score were between 50 and 65, were considered as intermediate, those whose score were above 65 were considered as upper-intermediate and the students whose score were below 50 were sacked from the study. After a week interval, a DCT was administrated to the selected students in two different groups in their classes at Islamic Azad University, Tabriz branch. The **data** was collected by means of this questionnaire that was administered to about sixty EFL students. The important point that needs to be mentioned here is that while sixty students were asked to do the questionnaire, about fifty questionnaires were returned, and out of fifty, forty of them were analyzed as they included incomplete/ misunderstood responses. Selection of disagreement situations in DCT was based on social factor of relative power. The DCT consists of five scenarios, in which the subjects were expected to disagree with a higher status, three with peers and one with a lower status. The scenarios covered a variety of topics and types of situations to avoid intervening effects of topic selection. The participants were asked to produce appropriate disagreement utterances for a given context of situation in DCT. These contexts were selected as they will think to occur frequently. The students in both groups were given enough time to write their answer to each situation. As English was not the participants' native language, the wording of the questionnaire was kept rather simple to minimize any misunderstanding. In the case of intermediate learners, it was decided that the researcher would be available during the questionnaire administration to provide assistance.

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Fully annotated microarray **data** has been deposited in ArrayExpress (E-MTAB-162).

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, but also Lagrangian **data** (FTLE field). Thus, the new ROMs are Lagrangian data-driven ROMs.In Sec- tion 4.1, we construct the new Lagrangian ROMs. In Section 4.2, we briefly contrast traditional Eulerian ROMs (built from Eulerian data) and the new Lagrangian ROMs (which are built from both Eulerian and Lagrangian data).

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We further tested MixMir on a published set of adrenal cortex Dicer - KO experiments , performed by Krill et al . ( 25 ) . The authors found that while mouse embryos with Dicer - KO adrenal cortex cells developed normally up to E14.5 , at E18.5 they experienced total adrenal cortex failure . In all they found 16 miRNAs that were downregulated in the adrenal cortex of both E15.5 and E16.5 mice , including miR-34c , miR-21 , miR-10a and let-7d , which play a role in tumorigenesis among other functions ( 25 ) . They also presented lists of miRNAs specifically downregulated at each stage . We analyzed the mRNA microarray expression data ( see Methods ) from both E15.5 and E16.5 embryos using the linear model , miReduce , Sylamer , cWords and MixMir . When compared to the miRNAs that are downregulated at both E15.5 and E16.5 , we found that most methods were able to find either an exact or offset seed match to let-7d either as the first or second motif returned , with the exception of the linear model , which performed worse . Overall , MixMir ranked true miRNA seeds higher than the other methods in both E15.5 and E16.5 datasets ( Table 5 ) . Most notably , MixMir found both miR-34b and miR-34c in the top ranked motifs at E15.5 , which no other method was able to do . We also performed a separate analysis of motif ranks and miRNA matches for E15.5 and E16.5 separately , as some miRNAs were found to be significantly downregulated at one stage and not at another -- namely , there were more such miRNAs at E16.5 , as expected . We found sim - ilar results in this analysis , in particular that MixMir consistently found biologically significant miRNAs , with performance comparable to miReduce for both time points . cWords and the linear model were comparable for E16.5 only ( Supplementary Table S8 ) .

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To determine the values of the lower escarpment angles in (19) for the first time conducted ridges experimental studies composed of sediments with the average particle diameter of d=0.23 mm. Analyzing of experiments results involving materials of field studies and experimental **data** of other authors, he concluded that the angle of the ridges lower escarpment in changing their forms until antidune does not depend on the flow hydraulic characteristics and it is determined by the value of the relative density of noncohesive grounds: = 7.08 0.19 1 (1)

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it follows from the reconstruction formulae written in the form ( 4.45 ) that the reconstructed currents are anti - hermitian j † ± = −j ± , i.e. j ± ∈ su ( 2 ) . Now with the extra reality conditionθ 0 ∈ R/2πZ = S 1 on the extension of the algebro - geometric data , the reconstruction formula ( 4.24 ) is also easily checked to give an anti - hermitian current so that j ± ∈ su ( 2 ) . It therefore follows that as a result of imposing the reality conditions , the reconstructed field g in ( 4.27 ) becomes SU(2)-valued , and in particular , the original fields X 1 , . . . , X 4 describing the embedding of the string into the S 3 ⊂ R 4 part of the target space are real valued as required .

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To establish strong privacy guarantees , it is important to limit the student 's access to its teachers , so that the student 's exposure to teachers ' knowledge can be meaningfully quantified and bounded . Fortunately , there are many techniques for speeding up knowledge transfer that can reduce the rate of student / teacher consultation during learning . We describe several techniques in this paper , the most effective of which makes use of generative adversarial networks ( GANs ) ( Goodfellow et al . , 2014 ) applied to semi - supervised learning , using the implementation proposed by Salimans et al . ( 2016 ) . For clarity , we use the term PATE - G when our approach is combined with generative , semisupervised methods . Like all semi - supervised learning methods , PATE - G assumes the student has access to additional , unlabeled data , which , in this context , must be public or non - sensitive . This assumption should not greatly restrict our method 's applicability : even when learning on sensitive data , a non - overlapping , unlabeled set of data often exists , from which semi - supervised methods can extract distribution priors . For instance , public datasets exist for text and images , and for medical data .

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Data analysis was performed using CasaXPS version 2.3.25rev1.0Y. 7 Peaks were integrated using a U 2 Tougaard background according to ( ) = ∫ ( ′ − ) ( ′ ) ′ ∞ , where S(E') is the measured spectrum, ( ) = ( + 2 ) 2 , C is a user adjustable parameter, and B is automatically adjusted to make the background meet the **data** at the limits of the interval over which the background is computed. 8 These peak areas were converted to equivalent homogeneous composition 9 using relative sensitivity factors (RSFs) based on Scofield photoelectron crosssections with angular distribution correction for a source-analyser angle of 60°. Escape-depth correction was performed using the electron attenuation length according to Seah. 10

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they represent conservative Hamiltonian systems, while the surrogate models established from **data** are usually not. The lack of symplecticity can result in artificial damping or excitation in long-term transformations. The established surrogate models should not be used for the DA computation. However, while constructing data-driven chaos indicators, surrogate models were only used to characterize the sensitivity of transformations to their initial conditions. Even if the transformations are not perfectly symplectic, it does not affect such applications. Due to the lack of a real physics model, this chaos indicator cannot replace the diffusion rate obtained with the FMA, which measures the regularity of resonant motions of a nonlinear dynamical system. Using this indicator as the optimization objective might not be as competitive as the direct tracking-based optimization if not taking the computation cost into account. This method

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In order to identify the type and frequency of disagreement strategies made by Iranian EFL learners across two different proficiency levels, using taxonomy from Muntigl and Turnbull (1995), which recognizes five types of disagreement: irrelevancy claim (IC), challenge (CH), contradiction (CH), counterclaim (CC) and contradiction followed by counterclaim , the **data** were analyzed. In this taxonomy they rank the disagreement types from the most to the least face "aggravating". Irrelevancy claim (IC) is the most face-threatening disagreement in which a speaker questions the relevancy of previous claim to the discussion at hand. The second disagreement type in this taxonomy is challenge (CH) in which the speaker demands that addressee provide supporting evidence for his and her claim. Contradiction (C) is the next type of disagreement in which a speaker explicitly contradicts with the previous claim, but it is less face-threatening than IC and CH in that it does not decline the capability of other interlocutor. Another type of disagreement is counterclaim that is the least face-threatening act. In this case the speaker does not contradict directly. By bringing reason for disagreement and using positive markers, CC mitigates threat and damage to the others' positive face (Peter Muntigl 1995).

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All patients who had a D-dimer assay and CDUS had their **data** analysed. The Biopool Autodimer quantitative immunoturbidometric microparticle latex assay (Diagnostica Stago, UK) was used for D-dimer level estimation. A D-dimer result below 230 ng/mL was considered to be negative.

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From the other potential intermediates for which adsorption geometries and AFM images have been calculated we can with high confidence rule out structures 20 , 21 , 22 , 23 , and 25 which do not fit to the experimental AFM data . In the simulated AFM images of structures 20 - 23 , at the onset of repulsive interaction ( 4 th column ) , the entire outline of the molecule appears with dark contrast , in contrast to the experiment . In addition , the range of tip - sample distances to simulate AFM images from the onset of repulsive / bright contrast at the highest part to repulsion on the lower laying parts of the molecule is smaller than in the experiment . The simulated contrast of structure 25 is significantly different to any of the measured molecules .

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For every rational value of the reduced magnetization of the array, m = µ /µ < 1, (ignoring sign) there exist very numerous possible arrangements of up and down dots, one (or more) of which must have minimum dipolar interaction energy W m . We have calculated that energy for a range of excellent candidates for these ground states at various values of m, in particular those of the chessboard AFM state of m = 0 and the uniform FM state with m = 1. We suggest and argue that, for the sequence of optimal configurations, W m and dW m /dm increase monotonically with m, and this is supported by our **data** in Fig. 4. (dW m /dm is likely to be locally discontinuous). An analytic formula for W m (m) is derived on the assumption that for m → 1, the minority down dots are located near points on an equilateral triangular super-lattice. A similar formula is derived for m → 0 relating to the excess up dots. Remarkably, these formulae agree at m = 1/2 (though their gradients differ) and fit the **data** for specific states very well, especially near m = 0 and 1. A similar formula involving a square super-lattice instead of the triangular super-lattice gives results in precise agreement with the **data** for specific structures with m = 0, 0.6 and 0.8, these being those structures in which the minority dots do indeed lie on square super-lattices. It differs from the first asymptotic formula by little more than 1.11% over the appropriate range 0.5 ≤ m ≤ 1.

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Angiogenic growth factors are critical to the development of both maternal and fetal vasculature at the uteroplacental interface. Our **data** suggest that T3 affects the timely decidual secretion of three key angiogenic growth factors implicated in human placentation. In the first trimester, VEGF-A, which promotes maternal and fetoplacental angiogenesis (Wulff et al., 2003) as well as EVT motility (Lash et al., 1999), is downregulated by T3 in total decidual cells. This is appropriate as it is at a time prior to the peak of EVT invasion when invasion needs to be tightly controlled. In contrast, Ang-2, which destabilizes vasculature and mediates some of the initial stages of spiral artery remodelling , is appropriately up-regulated in stromal-depleted cells in preparation for the next phase of placentation. In the second trimester, the secretion of angiogenin (a potent inducer of angiogenesis whose placental expression increases with gestation (Rajashekhar et al., 2002)) by total decidual cells is suitably up-regulated by T3.

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Forests exhibit regionally varying effects on JJA cloud cover based on MODIS **data** (overpass at 13:30 local time, Fig. 1a). Most temperate and boreal forests in Eurasia and North America have higher cloud fractions than non-forest, indicating a cloud enhancement effect (positive ΔCloud) accounting for 63.21% of all grid samples with a global mean magnitude of +0.0133. In contrast, forests in South Amazon, Central Africa, and Southeast US have lower cloud fractions than nearby non-forest, signifying a cloud inhibition effect (negative ΔCloud) over the forest with a global mean magnitude of −0.0115. The strength of these contrasting cloud effects (i.e., cloud enhancement and inhibition) follows a latitudinal dependency with the largest magnitude in the tropical regions and diminished toward higher latitudes (Fig. 1b). This is likely due to preferential conditions for convection development at low latitudes, as indicated by their high convective available potential energy, which decreases at higher latitudes 27 . Our additional sensitivity tests indicate that the global pattern of ΔCloud holds when estimated using alternative window sizes ( Supplementary Fig. 2) and split time periods (2002-2007, 2008-2013, 2014-2018, Supplementary Fig. 3), suggesting the robustness of results to scale of a local window and interannual variability of cloud cover.

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As of 2016, sixteen of these designations have been adopted by the IMO, all proposed by a State or States in national waters, the most recent being Jomard Entrance in Papua New Guinea. Roberts et al. [62] discuss possible application in the high seas but as yet no proposal for Areas Beyond National Jurisdiction (ABNJ) has been forthcoming. Four further areas are currently under consideration within IMO as proposed respectively by the Philippines (Tubbataha Reefs), Malaysia (Pulau Kukup and Tanjung Piai), Indonesia (Lombok Strait including Gili Islands and Nusa Penida Islands) and Mauritania (Banc D'Arguin and adjacent sea area). The Banc d'Arguin proposal is of note in the context of this paper having drawn **data** from the CBD EBSA described for the area (CBD COP 12, 2014). Banc d'Arguin National Park and an adjacent zone of the Atlantic (Gulf d'Arguin) can be described as an ecologically inter-connected region of global significance situated at the junction of two biogeographic realms, hosting the largest concentration of wintering wading birds in the world (the area is a core component of the East Atlantic Flyway) and one of the most diversified communities of piscivorous birds. The National Park has been listed as a World Heritage Site since 1989 (UNESCO 13COM XV.A) and UNESCO's World Heritage Committee has taken a keen interest in 2014, requesting the State Party (Mauritania) to submit the request to designate Banc d'Arguin region as a PSSA (UNESCO 38COM 7B. 62).

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In a single reception and transmission, the **data** frame is only 8 bytes. See Table 3 for the format of the **data** frame. Specifically, the first two bytes are respectively the **data** head and the command code, and the last byte is the end-of-string identifier 0xaa. As for the sent **data** frame, the meaning of the first two bytes remain the same, while the third byte is different from that of the received **data** frame. Each reading and writing via SPI contains 16 bits: 1 bit for the select bit, 7 bits for the address and 8 bits for the data. The R/W select bit is used to identify the reading and writing operations. 1 stands for reading, and 0 stands for writing.

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We used BWA version 0.6.2 (Li & Durbin, 2009) to align the next generation sequencing **data** to the BTx623 sorghum reference genome sequence (version 2.1; Patterson, 2009) which was downloaded from Phytozome (Goodstein et al., 2012). Suffix array co-ordinates for each query sequence were generated using the BWA aln command with non-default parameters: "-t 8". Paired end alignments were generated using the BWA sampe command with non-default parameters: "-P -r "@RG\tID:SampleID\tSM:SampleName\tPL:Illumina" ". In the case of the single-end reads, sequence alignments were produced using the BWA samse command. Sequencing **data** and mapping statistics were obtained using the SAMtools flagstat command with default parameters. Genome coverage statistics were generated from the sequence alignment files using the combination of SAMtools and BEDTools (Quinlan & Hall, 2010). We used the SAMtools view command to uncompressed BAM files and sent the results to standard output. We then used the BEDTools genomeCoverageBed command to estimate the coverage depth from the uncompressed alignment BAM output. The SAMtools view command parameter options were "-u -q 0", while the BEDTools genomeCoverageBed command parameter options were "-ibam stdin -g ".

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Sessile plants are permanently confined to their germination place. Some plant species have adapted growth responses (morphological, physiological, biochemical, and molecular adaptations) to deal with the profuse and quick variations in environmental stress, such as drought, through diversity in the context of stress adaptation, higher plants develop sophisticated abiotic stress responses too, such as resistance to drought, to optimize growth under stress (Takahashi et al., 2020). ABA is Differential expression profile of SlCOPT and SlMT genes by Reverse transcription-quantitative PCR in the root and shoot tissues of S. lycopersicum L. subjected to the single and combined stress of Cu and drought inoculated with AGH786 compared with the control. Quantitative **data** represent the means ± standard deviation of three independent experiments and at least three technical replicates each.

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Johannes Tröger conceptualized this work; he drafted the manuscript and edited the final version. Ebru Baykara contributed to the overall interpretation of the work and drafting of the manuscript. Elisa Mallick, Simona Schäfer, Louisa Schwed, and Mario Mina implemented the biomarker, analyzed the speech, conducted the statistical work, as well as drafted the methods and results sections of this article. Daphne ter Huurne and Nina Possemis acquired parts of the data, contributed to the clinical interpretation of the results, and revised the document. Jian Zhao oversaw the design of the V3 framework validation pipeline from a regulatory standpoint and revised the document. Nicklas Linz contributed to the overall concept of this research and revised the manuscript. Inez Ramakers is responsible for the DeepSpA study and **data** acquisition, drafted DeepSpA relevant parts of the document, and revised the manuscript. Craig Ritchie is the principal investigator of SPeAk, responsible for the concept and **data** acquisition, drafted SPeAk relevant parts of the document, and revised the manuscript.

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Our goal is to synthesize a novel view I t , given target camera parameters K t , R t , T t , from a set of input images, I s i , i = 1, 2, ..., N . We assume there is sufficient overlap between the source views such that correspondences can be established. We estimate source view camera intrinsic and extrinsic by a well-established structure-from-motion (SfM) pipeline, e.g.COLMAP [26]. Fig. 2 illustrates the situation. Mathematically, we formulate this problem as: I t * = argmax I t p I t |I s 1 , I s 2 , ..., I s N ,(1) where p(·) is a probability function. Due to the expensive accessibility of 3D **data** (e.g., depths) and a limited number of input views, it is hard to compute accurate 3D geometry from input source views. Therefore, our intuition is to develop an end-to-end framework that combines geometry estimation and image synthesis, to eliminate the error propagation issue. We achieve this goal by estimating target-view depth and source-view visibility for target pixels directly under the target view.

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from experiments using Fura-2-loaded canine airway smooth muscle cells grown to confluence then serum deprived in insulin-supplemented medium for 7 days. Each tracing is the mean of 8-12 elongate cells identified in a single microscopic field. Cells were first stimulated with methacholine (MCh1, left panels) (10 -9 to 10 -5 M) and changes in intracellular Ca 2+ ([Ca 2+ ] i ) recorded. Thereafter, cells were incubated with either vehicle (top row) or latrunculin-A (1M, 37°C) (bottom row) for 1 hour, and were subsequently treated with methacholine (MCh2) at the same concentration used for MCh1. Changes in [Ca 2+ ] i in response to MCh2 were recorded for the same cells monitored after MCh1 treatment. (B)Concentration-response curves for MCh2 in control and latrunculin-A-treated cells plotted as peak [Ca 2+ ] i . Curves are derived using individual **data** points that are the mean ± s.e.m. of at least 30 cells in total (assayed in at least three different experiments). *P<0.05, **P<0.01, for control versus latrunculin A at a given MCh concentration. cells (Fig. 4D,E). These **data** indicate that -dystroglycan is essential for the association of caveolin-1 with lipid rafts and for supporting caveolae formation and/or stability.

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Supporting information S1 Table. Method of diet analysis of diets retrieved from the literature. The column labelled "Code" is used in S2 Table. (DOCX) S2 Table. Diet **data** as retrieved from the literature. Including information pertaining to the species studied, the study reference, the location in which the study took place, the number of individuals sampled, the study method (See S1 Table), and the diet data. See S2 Table. (CSV) S3 Table. Criterion used in categorizing guano particle size (a) and colour (b).

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We incorporate location - specific COVID-19 case and death counts into our analyses , as well as counts for the US as a whole . Such data represent immediate indicators of COVID-19 severity and are likely the factors driving Google searches for masks , hand sanitizer , and disinfectants . Although case fatality rates and the prevalence are epidemiologically better indicators of the severity and the transmissibility of the virus spreading in communities , at the time of the study , news outlets were presenting both the number of deaths and the confirmed cases separately , which were driving the public perception about the risk associated with COVID-19 . Therefore , for each state and city considered in the study , daily cases and death counts are obtained from New York Times data repository and used . 3 Fig . 4 shows the COVID-19 daily cases and deaths at the three locations of interest as a time series .

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