dataesr/scientific-paragraphs-categorization huggingface dataset:
The prediction accuracy of PRS depends mostly on the (co-)heritability of the analysed traits, the number of SNPs, and the size of the discovery sample. The size of the target sample only affects the reliability of R 2 and typically a few thousand of subjects in the target sample are sufficient to achieve a significant R 2 if the (co-)heritability of the trait(s) of interest and the sample size of the discovery sample used are sufficiently large. For an R script to perform power calculations for your own PRS analysis, we refer to the POLYGENE script on https:// sites. google. com/site/fdudbridge/software (Dudbridge, 2013). A convenient program to perform PRS analysis is PRSice (see http://prsice. info;  Euesden, Lewis, & O'Reilly, 2015). It takes care of clumping, p value thresholds, MDS components, and plots attractive graphs.
| CONCLUSION A basic understanding of the theory behind genetic analysis (e. g. , GWAS and PRS), the essential QC steps, and the use of appropriate software and methods, along with practical experience are imperative to be able to conduct a genetic study with reliable and reproducible results. This tutorial highlights important concepts to successfully conduct a GWAS and PRS analysis. We presented a tutorial based on commonly used, open-source, freely available software tools, that are accessibly for novice users. In addition, we made scripts and a simulated data set available to provide hands-on practice at https:// github. com/MareesAT/GWA_tutorial/. As a GWAS is usually undertaken to increase our understanding of the biological mechanisms that contribute to disease risk, a GWAS will usually be followed up by post-GWAS analyses.
Hands-on experience with the performance of QC Steps 1-7 can be obtained by following all steps outlined in our online tutorial at https://github. com/MareesAT/GWA_tutorial/ (1_QC_GWAS. zip + 2_Population_stratification. zip). It offers scripts for data QC and visualization of potential sources of bias. These scripts perform QC on the CEU group of the HapMap data but can be applied to other datasets with the exception of family-based datasets and datasets involving multiple distinct ethnic groups. Generally, if a sample includes multiple ethnic groups (e. g. , Africans, Asians, and Europeans), it is recommended to perform tests of association in each of the ethnic groups separately and to use appropriate methods, such as meta-analysis (Willer, Li, & Abecasis, 2010), to combine the results.
indicate evolutionary selection. equilibrium For binary traits we suggest to exclude: HWE p value <1e (HWE) -10 in cases and <1e-6 in controls. Less strict case threshold avoids discarding disease-associated SNPs under selection (see online tutorial at https://github. com/MareesAT/GWA_tutorial/). For quantitative traits, we recommend HWE p value <1e-6. 5: Heterozygosity For an example script Excludes individuals with high or low Deviations can indicate sample contamination, see https://github. heterozygosity rates inbreeding. com/MareesAT/ We suggest removing individuals who deviate ±3 SD from GWA_tutorial/ the samples' heterozygosity rate mean. 6: Relatedness --genome Calculates identity by descent (IBD) of all sample pairs. --genoExcludes SNPs that are missing in a large proportion of the subjects.
Conclusion: The study confirms the rapid onset and demonstrates the long-lasting efficacy of Vectra 3D against mosquitoes that may carry various pathogens such as heartworm. In real-life situations, compliance is a recurrent issue and pet owners may not follow the monthly administration schedule. This dataset provides the evidence of the high efficacy standard of the product and additional piece of mind for the prescribing veterinarian. https://doi. org/10. 1016/j. ijid. 2018. 04. 3671.
Studied systems, collective variables and trajectories Several systems were prepared using selected atoms from the first model of the NMR (Nuclear Magnetic Resonance) structure (PDB entry: 2NDF)  [38]  (Fig. 1 ). Each molecule or complex was solvated in a water box using the Amber 14's LEaP program  [46]  and the Amber ff03 force field  [47]  along with a specific parameter file to account for residue acK18 (called ALY)  [48]. The systems were then minimized, thermalized, and equilibrated using NAMD 2. 9b2  [49]. The following systems were studied: (i) the complexes between the protein YEATS and the peptides 12-24 (pt13), 15-21 (pt7) and 17-19 (pt3) from the N terminal tail in histone H3, (ii) the YEATS domain in the absence of the peptides and (iii) the isolated peptides pt13, pt7 and pt3.
The TAMD approach was implemented in NAMD using a tcl script  [25] [26] [27]. The friction coefficient, γ = 2 ps -1 , and the physical thermal energy, β -1 = 0. 6 kcal/mol, are the parameters of the conventional Langevin thermostat, allowing to obtain a simulation temperature of 300 K. The restraint force constant is set to κ = 100 kcal/(mol. Å 2 ). Along the re-TAMD trajectories, the artificial friction γ of the Langevin thermostat attached to the collective variables was set as a constant equal to 0. 02 ps -1 , whereas the artificial thermal energy β-1 was varied continuously depending on the smallest distance min(D) between the H3 peptide and the YEATS domain. β-1 = k min(D) + h (4) where h and k values are given in Table  2. In order to keep the peptide close to the YEATS domain, at each simulation step, the distances D new i measured between the new target values of the i-th CV and the YEATS domain were compared to the corresponding previous distances D i.
Analysis of trajectories The atomic interactions, hydrophobic and polar, between the peptides and the protein, were analyzed by calculating the number of proximities (distance smaller than 4 Å) between polar/hydrophobic groups present in the peptide and in the protein throughout the trajectory. This analysis was performed using a python script based on the MDAnalysis module  [52]. The number of inter-atomic contacts was rescaled between 0 and 1 for each trajectory. The number of contacts per residue was determined as the sum of atomic contacts involving each residue divided by the largest contact value. The partial charges used for this script were taken from the AMBER ff03 force field  [47] ; partial charges with absolute values smaller than 0. 2 were considered to correspond to hydrophobic groups.
The peptides pt13 and pt3 display contact with a subset of residues while p7 displays contact with a very wide range of residues. Therefore, the specificity of contact is greater for pt13 and pt3 than it is for pt7. Noticeably, the profiles of polar and hydrophobic contacts  The partial charges come from the AMBER ff03 force field  [47]. This analysis was done with a python script using the MDAnalysis module  [52]  are mostly superimposed, except for the residue I85 for which the hydrophobic profile dominates. The mapping of the atomic contacts on the YEATS domain's surface (Fig. 4 ) shows that the front surface, which contains the acK18 binding site, is much more sampled than the opposite surface in the case of pt13 and pt3. The peptide's preference for the side containing the acK18 site is certainly influenced by the peptide's initial position, but agrees with the specificity of the interaction between H3 peptide and YEATS.
Under the alignment, the first horizontal bar represents the secondary structure of the AF9 YEATS NMR structure (PDB 2NDF: α helices in red and β strands in orange). The following six bars represent the proportion of contact of each YEATS residue with the peptide, from blue (near 0% of maximum number of contacts) to red (over 80% maximum number of contacts). There is one bar for each trajectory: MDYpt13, MDYpt7, MDYpt3, TAMDYpt13, TAMDYpt7, TAMDYpt3. Contacts were computed with a python script using the MDAnalysis module  [52].
However, the use of FFT or of NMA does not permit to simulate the relative effect the dynamics of each interaction partner has on the other partner. By contrast, in re-TAMD simulations, the peptide as well as the protein are free to move in the force field, the peptide motions being accelerated. In that way, the peptide explores more protein surface, and one can expect that the effect of the peptide on the protein should also be enhanced. In the present work, 100 ns of re-TAMD trajectory was recorded for each analyzed peptide. This computational load is thus smaller than that of reconnaissance or funnel metadynamics  [13, 14] , but much larger than that of many docking approaches  [58, [63] [64] [65]. The re-TAMD approach can thus be considered to be efficient on small datasets of several dozens to a hundred ligands.
The Atmospheric Correction Inter-comparison eXercise (ACIX) is an international initiative with the aim to analyse the Surface Reflectance (SR) products of various state-of-the-art atmospheric correction (AC) processors. The Aerosol Optical Thickness (AOT) and Water Vapour (WV) are also examined in ACIX as additional outputs of AC processing. In this paper, the general ACIX framework is discussed; special mention is made of the motivation to initiate the experiment, the inter-comparison protocol, and the principal results. ACIX is free and open and every developer was welcome to participate. Eventually, 12 participants applied their approaches to various Landsat-8 and Sentinel-2 image datasets acquired over sites around the world. The current results diverge depending on the sensors, products, and sites, indicating their strengths and weaknesses.
The exercise aimed to contribute to a better understanding of the different uncertainty components and to the improvement of the AC processors' performance. In order to obtain an accurate SR product, ready to use for land or water applications, two main steps are required: first, the detection of cloud and cloud shadow and then, the correction for atmospheric effects. Although both parts of the process have equal importance, ACIX only concentrated on the atmospheric correction in this first experiment. This paper describes in detail the protocol defined for the implementation of the exercise, the results for both L-8 and S-2 datasets, and the experience gained through this study. In particular, details are given for the input data, sites, and metrics involved in the inter-comparison analysis and its outcomes are presented per sensor and product.
ACIX Protocol The ACIX protocol was designed to include some typical experimental cases over diverse land cover types and atmospheric conditions, which were considered suitable to fulfil the purposes of the exercise. In particular, the ACIX sites were selected based on the locations of the international Aerosol Robotic Network (AERONET). The network provides a reliable, globally representative and consistent dataset of atmospheric variables that allows for the validation of the performance of an AC processor using common metrics  [14, 15]. Since there were no other global networks mature enough or with similar global representation, the AERONET in-situ measurements were considered to be the ground truth in ACIX. The inter-comparison analyses were conducted separately for Aerosol Optical Thickness (AOT), Water Vapour (WV), and Surface Reflectance (SR) products.
The organizers, together with the participants, prepared the protocol after having discussed and agreed on all the major points, i. e. , sites, input data, results' specifications, etc. The protocol was drafted in the 1st ACIX workshop  (21) (22)  June 2016, USA) taking into account most of the recommendations that were feasible in this first implementation. ACIX was conceived as a free and open exercise in which any developer team of an AC algorithm could participate. The list of the processors, and the corresponding participants' names and affiliations are presented in Table  1. Some of the participants for various reasons, e. g. , time constraints, tuning of the processor, processor's limitations, etc. , implemented their AC algorithms on certain L-8 and/or S-2 imagery of the available dataset (Table  1 ).
ACIX Sites and Datasets The inter-comparison analysis was made over 19 AERONET sites around the world, as agreed unanimously by the ACIX organizers and participants (Table  2 ). The sites were used for L-8 and S-2 datasets and covered various climatological zones and land cover types. Although ACIX was only initiated to inter-compare the performance of AC processors over land, five coastal and inland water sites were included in the analysis, in order to examine the performance over diverse sites. The availability of AERONET measurements for the study time period was a critical parameter during the selection phase.
Comparison with AERONET Corrected Data The SR products from L-8 OLI and S-2 MSI were compared to a reference SR dataset computed by the 6S radiative transfer (RT) code  [22]  and AERONET measurements. AOT, aerosol model, and column water vapour were derived from the AERONET sunphotometer measurements and were used in the RT model. In this way, the SRs were retrieved based on the TOA reflectances acquired from Level-1C products  [11] [12] [13]. A constant aerosol model (size distribution and refractive indices) was derived for each site using all good quality almucantar inversions available and parameterized as a function of optical depth and angstrom exponent following an approach similar to the one of Dubovik et al. [23]. The choice of 6S as the RT model for the computation of the "reference" SR could constitute a moderate, but not negligible, advantage for the AC codes that use this same model in their RT simulations, i.
Landsat-8 In total, four AC processors, i. e. , ATCOR, FORCE, iCOR, and LaSRC, were applied to most of the L-8 datasets. GA-PABT was only implemented on the Australian site (Canberra), while ACOLITE and SeaDAS only for the coastal sites. Because of the small sample size of these cases, the interpretation of the inter-comparison results may be biased; therefore, the corresponding analyses are not presented in this paper. These results, however, can be found on the ACIX web site. The data involved in the AOT analysis were filtered from (i) cloud; (ii) cloud shadow; (iii) adjacent to cloud; (iv) cirrus cloud; (v) no data values; and (vi) interpolated values. All the quality masks provided by the participants were taken into consideration and a combined mask was used to exclude the unwanted pixels.
The greatest discrepancies though were detected for high aerosol values, where the AOT was mostly underestimated except for LaSRC, which achieved a good assessment. However, LaSRC did not manage to accurately estimate the AOT over coastal scenes; an expected fact since the processor was only suitable for AOT retrievals over land areas at the time of ACIX implementation. Similar results were observed for LaSRC over Davos, where the snow cover yielded overestimations. For the rest of the experimental cases, all the processors assessed the AOT quite accurately. The surface reflectance products obtained by the processors for each of the seven OLI bands were compared pixel-by-pixel with the corresponding reference dataset (Section 2. 2. 2). Only pixels that were characterized as land and clear, and were not labeled as snow, water, high aerosol, and shadow by the participants' quality masks, were considered in this comparison.
Sentinel-2 Eight processors, i. e. , CorA, FORCE, iCOR, LaSRC, MACCS, S2-AC2020, GFZ-AC, and Sen2Cor, provided results for Sentinel-2 datasets over most of the sites (Table  1 ). Similar to the L-8 case, GA-PABT was only implemented on the Australian site (Canberra), while ACOLITE only on the water/coastal sites, and their results are not included in paper. iCOR did not deliver any WV products, while LAC provided only SR products. It is worth noting at this point that overall, the AC codes involved in ACIX were in their early validation stage for S-2 data. In addition, the S-2A spectral response functions were inaccurately known at that time, mainly having an effect on bands B01 and B02.
Aerosol Optical Thickness Figure  3  shows the scatterplots of the AOT estimates based on the S-2 datasets, which were processed by the processor, versus the corresponding AERONET measurements. The dot dashed line refers to the exact agreement between the two sets of data, meaning that the further the points fall from the line, the greater the differences between the two datasets. FORCE managed to retrieve the AOT with a good accuracy for most of the sites, apart from a few dates/points of Banizoumbou and Sede_Boker. The arid sites were found to be challenging for all the AC processors, basically because of the absence of DDV pixels. iCOR set AOT values to zero in some of these cases, while other processors, i. e. , CorA, FORCE, S2-AC2020, and Sen2Cor, set default values. MACCS also encountered difficulties in dealing with the high SR values of arid sites and retrieving the AOT correctly.
Surface Reflectance Products The surface reflectance products for S-2 MSI bands were compared on a pixel basis with the reference SRs (Section 2. 2. 2). Similar to the analysis of L-8 OLCI SRs, the pixels involved were labeled as land and clear, and were filtered from snow, water, high aerosol, and shadow based on the participants' quality masks. As has already been mentioned, the APU analysis of all the SR values obtained by every processor and for every site is available on the ACIX web site. Band 9 (Water vapour) and Band 10 (SWIR-Cirrus) were excluded from this analysis because they are not intended for land applications. Figure  5  demonstrates a representative example of the APU outcomes for MSI Band 4 (Red, central wavelength 665 nm) for all the datasets processed by the processor. The overall analysis of the plots shows that FORCE, LaSRC, MACCS, and Sen2Cor managed to estimate the SRs quite well over all the sites.
Conclusions The ACIX is designed as an open and free initiative to compare AC codes applicable either to L-8 or S-2 imagery. Therefore, every developer of an AC algorithm was welcome to participate in the exercise. Indeed, in the first implementation of ACIX, several participants from different institutes, companies, and agencies around the world contributed by defining the inter-comparison protocol and processing a big volume of data. However, different factors, e. g. , time constraints, tuning of the processors, processor's limitations, etc. , prevented the application of some AC algorithms to the whole L-8 and/or S-2 dataset. Due to this variance of the submitted results, it was not feasible to draw common conclusions among all the algorithms, but fortunately, these cases were not the majority.
Considering S-2 datasets for instance, ACIX only started six months after the beginning of S-2 Level-1 data provision to all users. The research community was still inexperienced during that phase, and time and effort was needed to adapt the processors to the new data requirements. The discrepancies observed in ACIX inter-comparison results have assisted, in many cases, the developers to learn about the performance and identify the flaws in their algorithms. As a matter of fact, the participants have already modified and improved their processors and will have a chance to present the enhanced versions during the following ACIX implementation. The continuation of the exercise has already been discussed and agreed, suggesting some new implementation parameters. More datasets need to be exploited, in order to obtain more concrete conclusions, so at least a one-year period of complete time series from L-8, S-2A, and S-2B will be employed.
In addition, Sen2Cor managed to produce accurate results across all the visible bands, while higher discrepancies were observed for the infrared bands. As has already been mentioned, in this study, the reference SRs were computed by 6S RT code that is the same as the one used in some of the AC codes. This can provide a modest but non-negligible advantage to these AC codes, e. g. , LaSRC. The best uncertainty scores per band are underlined in Table  8.
Motivation: Due to the complexity and heterogeneity of multicellular biological systems, mathematical models that take into account cell signalling, cell population behaviour and the extracellular environment are particularly helpful. We present PhysiBoSS, an open source software which combines intracellular signalling using Boolean modelling (MaBoSS) and multicellular behaviour using agent-based modelling (PhysiCell). Results: PhysiBoSS provides a flexible and computationally efficient framework to explore the effect of environmental and genetic alterations of individual cells at the population level, bridging the critical gap from single-cell genotype to single-cell phenotype and emergent multicellular behaviour. PhysiBoSS thus becomes very useful when studying heterogeneous population response to treatment, mutation effects, different modes of invasion or isomorphic morphogenesis events.
, 2008; Schluter et al. , 2014)  and thus difficult to adapt to other biological questions. Moreover, CellSys to date has not been released as open source. Among other available tools, Chaste provides an environment to implement different kinds of modelling approaches  (agent-based, cellular automaton, vertex model, etc. )  within the same framework  (Mirams et al. , 2013; Pitt-Francis et al. , 2009)  as well as ODEs for intra-cellular modelling. Importantly, its implementation allows the direct comparison of outputs from the different modelling techniques and outlines their advantages and limits  (Osborne et al. , 2017). However, due to all these possibilities, it is a more complex environment than other agent-based tools. PhysiCell is an agent-based software with the particular advantages of being open source and having minimal dependencies.
Nevertheless, this software still lacked intra-cellular modelling. Recently, promising agent-based open source software, such as MecaGen  (Delile et al. , 2017)  and EmbryoMaker  (Marin-Riera et al. , 2016) , showed the power of combining mechanical behaviour and gene regulation to understand embryogenesis and could be used to study other developmental problems. However, the gene regulatory networks are modelled with ODEs, which might restrict it to relatively small signalling representations and raises the difficulty of parameter estimation. Moreover, due to its morphogenesis scope, MecaGen does not consider cell division dynamics (cell volume growth, death, etc. ), or issues such as clonal diversity. All of these software tools have been summarized and can be compared in Supplementary Table  S1.
We choose to start from an agent-based-dedicated open source software, PhysiCell, and, since we wish to link intra-and intercellular descriptions, we combine it with a tool for modelling Boolean networks, MaBoSS. Our ultimate goal is to develop an open source flexible modelling framework that combines the description of the signalling pathways inside individual cells and their interaction with the environment. To explore and integrate cell population dynamics, cellular signalling mechanisms, and the interplay between cells and their surrounding (i. e. other cells or the microenvironment), we propose to combine an agent-based approach with a Boolean representation of biochemical events taking place in each cell. For that purpose, we have developed a new software framework, PhysiBoSS, that combines and extends two well-established tools: a signalling pathway modelling tool, MaBoSS  (Stoll et al.