dataesr/scientific-paragraphs-categorization huggingface dataset:
Application to a Curved Viral Membrane of Arbitrary Shape We performed a preliminary simulation for the membrane insertion of a dodecameric AlphaFold2 model of the MetY domain from the hepatitis E virus replication polyprotein, as described in  [47]. The construction of this dodecameric ring, which likely represents a membrane pore, was inspired by a general putative model of the membrane environment and in particular by a curved membrane shape that this protein might require for its function. The model, designed by Thibault Tubiana in the 3D modeling software Blender, is shown in Figure  13A. This is a hypothetical model of a cellular endomembrane, which is typically curved or tubular and can have a variety of shapes depending on the cell type in which it occurs. They can form complex structures such as vesicles or vacuoles.
Discussion Our implementation of the UNILIPID model is directly related to the original IMPALA method developed by Brasseur's group. We have shown that UNILIPID corrects for limitations such as the inhomogeneous reproduction of reference transfer energies. By providing extensions to the model, we have made it more accurate and tunable. The novelty lies in our ability to use interactive software with improved calibration to position membrane proteins in implicit membranes of arbitrary shape. Our approach encompasses dual and even more complex membrane shapes found in membrane fusion applications. The concept of implicit membranes is not new, but the ability to use interactive software with improved calibration for complex membrane shapes in real time is. This opens the possibility of providing real-time analysis feedback and interactive refinement, as we have demonstrated for the SNARE system with insertion plots commonly used in EPR studies.
We can use optimized methods such as the Neighbor Search Grid Burst (NSGB) package implemented in the UnityMol software by Xavier Martinez. We compared the UNILIPID implementation with reference simulations and validated the accuracy of the results. So far, we have performed a comprehensive comparison with one hydrophobicity scale and shown that our proposed method improves the parameterization by reproducing this scale exactly. In future work, an extended comparison of our results with actual partitioning data would be valuable. Although the present work focuses on improving the methodology by building on Brasseur's original approach and characterizing the extension by mesh representations to handle arbitrary shapes, we can now move on to determining the best parameterization scale. In other words, the next step and question is: what is the best available hydrophobicity scale that can be used for this purpose, and what is the best associated representation scale.
This is an investigation for a future paper. After agreeing on a preferred scale, after comparing many of the existing scales, we could compare our method with reference methods such as the OPM (Orientation of Proteins in Membranes) database in a proteome-wide study. In UNILIPID, we chose an energy-based approach to characterize the lipid-protein interaction for membrane insertion, in contrast to e. g. , a rule-based approach  [50]. This choice makes biological sense as these interactions are widely characterized experimentally  [51, 52]. Computational approaches are abundant for such characterization, as for instance discussed in  [53, 54]. When computational power is not a limitation, molecular dynamics type approaches in fully hydrated explicit membrane environments may represent the gold standard  [55].
Hence, there are many other methods of inserting proteins into membranes, including some approaches capable of treating curved membranes. One of the most widely used ones with this capacity may be the approach of positioning proteins in membranes (PPM)  [56]. Furthermore databases such as MemProtMD provide insertion information for much of the structurally characterized membrane proteome  [57]. These methods span several scales of representation. Our method emphasises computer interactivity to allow efficient interaction by a human operator and to provide intuitive feedback on the insertion properties of the membrane protein under study. To our knowledge, there is no other generally applicable interactive method for this purpose. In certain specific cases, such interactive simulations have been explored, e.
Acknowledgments: We thank  Alex Tek  for the preliminary work he did for his dissertation, which formed the basis for our study. We also thank  Xavier Martinez  for assistance with the UnityMol software. We especially thank  Stéphane Bressanelli  and  Thibault Tubiana  for providing their MetY dodecamer model and a membrane system for the initial experiments. We thank  Benoist Laurent  for assistance with data availability and sharing. We thank  Hubert Santuz  for assistance with software deployment and compilation.
This research was funded by the " Initiative d'Excellence" program  from the  French State , grants " DYNAMO ",  ANR-11-LABX-0011 , and " CACSICE ",  ANR-11-EQPX-0008. We thank  ANR for support of grant  " Mitofusion ",  ANR-19-CE11-0018. M. B. thanks  Sesame Ile-de-France  for co-funding the display wall used for data analysis.
Data Availability Statement: The software packages used for our implementation, UnityMol, MD-Driver, and BioSpring, are open source projects, generally available via the websites https://sourceforge. net/projects/unitymol/files/ (accessed on 7 February 2023) and https://sourceforge. net/project/ biospring/ (accessed on 7 February 2023). Our experiments were carried out with versions Unity-Mol_1. 1. 4_unilipid and BioSpring 1. 0. 1-5a92fad. The extensions to these software packages presented in this study are immediately available on request from the corresponding author and will become publicly available at the official sourcefourge sites after further testing by including them in the next software package release. To help experiment with our approach, we provide ready-to-use compiled versions for selected platforms along with specific input data.
Each cluster generated within the simulations possesses a unique "cluster index" value, i. e. , molecules from the same cluster will have the same index value. Background molecules are given the index value of "0". For each cluster scenario, 50 simulations were generated. The different parameter values used to generate the simulations in this work are summarised in the table below;  Fluorescence Blinking Cluster Simulations. The positions of molecules/fluorophores in the ground truth cluster scenarios were used as the basis for simulating data which has multiple blinking and detection precision inherent in SMLM. The simulateSTORM. r script from the RSMLM package (available at: https://github. com/JeremyPike/RSMLM) was used to generate the blinking simulations  25. Briefly, transition between the fluorescent on-and off-state were modelled using a geometric distribution  20, 25  with probability of transition to the dark state set to 0.
Therefore, for each simulation, 980 total combinations of epsilon vs minPts threshold were performed, and the resulting indexing from each combination retained for further analysis. ToMATo clustering and parameter scanning. Topological Mode Analysis Tool (ToMATo) algorithm was implemented in R using the clusterTomato function from the RSMLM library (available at: https://github. com/JeremyPike/RSMLM)  25. For ToMATo parameter scanning, the search radius (nm) and birth density threshold were varied. For the search radius, a minimum of 5 nm was used and stepped by 5 nm up to a maximum value of 100 nm (20 steps). For the birth density threshold, a minimum value of 2 was used and stepped by 1 up to a maximum value of 50 (49 steps). Therefore, for each simulation, 980 total combinations of search radius vs birth density threshold were performed, and the resulting indexing from each combination retained for further analysis.
For the kernel width values of a minimum of 50 nm was used and stepped by 50 nm up to a maximum value of 500 nm (10 steps). For the density threshold a minimum value of 0. 1e -07 was used and stepped by 0. 5e -07 up to a maximum value of 0. 1e -06 (10 steps). Therefore, for each simulation, 100 total combinations of kernel size vs density threshold were performed, and the resulting indexing from each combination retained for further analysis. Adjusted Rand Index Scoring. Cluster indexes results from the clustering and parameter scanning are used to compute the adjusted Rand Index (ARI). The ARI calculation was implemented in R using the function mclustcomp from the dbscan library (full ARI R script can be found here: https://github. com/DJ-Nieves/ARI-and-IoU-clusteranalysis-evaluation). Briefly, the Rand Index (RI) is a measure of the similarity between two sets of cluster indexes, and is calculated using;.
This is repeated for all parameter combinations to generate an ARI matrix. Intersection of Union Scoring. Intersection of union (IoU) scoring was implemented in custom written script in R (https://github. com/DJ-Nieves/ARI-and-IoU-cluster-analysisevaluation). A convex hull was used to identify the molecule coordinates at the edge of each ground truth cluster. A filled polygon for each cluster was then generated within a binary image matching the limits of the data (pixel area = 1 nm 2 ). All cluster images were then added together to generate a single image, and then the image was flattened to generate again a combined binary image. This process was performed for the ground truth clustering as well as each of the clustering results from the cluster parameter scanning. IoU is calculated as follows;.
Data Availability The simulation data used as the basis for this work is available for download at https://github. com/DJ-Nieves/ARI-and-IoU-cluster-analysis-evaluation without restriction. All other data are available upon request. R Code for calculating ARI and IoU for clustering results against a ground truth scenario is also available for download at https://github. com/DJ-Nieves/ARI-and-IoU-cluster-analysis-evaluation without restriction.
, 2020). Quantification of the relative CD9 and CD81 signals was performed using a protocol developed in Icy imaging software  (de Chaumont et al. , 2012)  (http://icy. bioimageanalysis. org). For differential labelling of the surface and intracellular pools of CD9P-1 and EWI-2, the cells were first labelled with primary and secondary antibodies in the absence of saponin, to label the surface pool. This procedure was performed again in the presence of 0. 1% saponin and with a secondary antibody coupled to a different fluorochrome to label the intracellular pool (and the surface pool again).
Mass range was set from 100 to 1700 m/z in MS and MS/MS and mobility range from 0. 6 to 1. 6 Vs/cm 2. Dynamic exclusion was activated for ions within 0. 015 m/z and 0. 015 V s/cm 2 and released after 0,4 min. Exclusion was reconsidered if precursor ion intensity was four times superior. Low abundance precursors below the target value of 20,000 a. u. and intensity of 2500 a. u. were selected several times for PASEF-MS/MS until the target value was reached. Parent ion selection was achieved by using a two-dimensional m/z and 1/K0 selection area filter allowing the exclusion of singly charged ions. Total cycle time was 1. 1 s with 10 PASEF cycles. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  (Perez-Riverol et al. , 2022)  partner repository with the dataset identifier PXD042940 at https://doi.
.  Quantitative data analysis for proteomics Bruker raw files were analysed using MaxQuant software v1. 6. 17. 0  (Tyanova et al. , 2016)  against the UniProtKB human proteome database (UP000005640, 74823 entries, release 2020_02). The following parameters were used for peptides and proteins identification: trypsin as enzyme specificity with a maximum of two missed cleavages; cysteine carbamidomethylation as a fixed modification and protein N-terminal acetylation and methionine oxidation as variable modifications; peptides minimum length of seven amino acids; precursor mass tolerance of 25 ppm (first search) and 10 ppm (main search); fragment mass tolerance of 25 ppm; 1% FDR. Label-free protein quantification (LFQ) was performed using 'unique+razor' peptides with the 'min ratio count' parameter set to 2.
.  Data analysis Each experiment included a WT sample as a reference and was analysed separately using Maxquant. The results were combined using Microsoft Access and transferred to Microsoft Excel. To analyse the protein composition of EVs released by WT cells, only the proteins identified and quantified in at least four out of six experiments were considered. To compare the LFQ values of WT samples between different experiments, the LFQ value obtained for each protein was divided by the mean LFQ value of all proteins identified in the same experiment. The median value was used to compare different proteins.
 of  FAN et al. We used 'linear models for microarray data' (limma,  (Ritchie et al. , 2015) ) to compare the expression levels, after log2 transformation, observed in WT and KO samples, taking into account the paired designs. Missing values were first imputed using the nearest-neighbour approach (knn. impute from R package impute). We computed adjusted P-values using the Benjamini-Hochberg approach controlling the FDR at level 5%. Funrich  (Pathan et al. , 2017)  v3. 1. 3 was used to compare the listing of identified proteins with proteins previously found in EVs secreted by breast cancer cells, notably MCF7 cells. To this end, the Vesiclepedia database  (Pathan et al. , 2019)  of vesicular proteins was downloaded (Version 4. 1, August 15 2018) and filtered to retain the data from studies of breast cells.
We also compared our results with those reported in two previous studies analysing the protein repertoire of EVs secreted by MCF7 cells  (Hurwitz et al. , 2016; Rontogianni et al. , 2019). Funrich and The Database for Annotation, Visualization, and Integrated Discovery (DAVID, v2021) were used for cellular compartment and functional (GOTERM_BP_DIRECT) analyses of the proteins identified, respectively.
5 (Figure  1b , Supplementary Table ). Most of them have been reported in the Vesiclepedia compendium of extracellular vesicle molecular data, and have been identified in previous mass-spectrometry analyses of EVs secreted by MCF7 cells isolated by ultracentrifugation  (Rontogianni et al. , 2019)  or a combination of polyethylene glycol precipitation and ultracentrifugation  (Hurwitz et al. , 2016)  (Figure  S1A ). Accordingly, cellular component analysis showed that ∼70% of the proteins identified in at least 4 experiments are associated with the term 'exosome' (note that this term is used by the GO database as homonymous to small EVs, and not to MVB-derived exosomes). Approximatively 70% of the identified proteins were cytoplasmic and ∼50% were associated with the term lysosome (Figure  S1B ).
falciparum and P. vivax. The villages were stratified by forest cover, with a target sample size of 2650 households. All individuals who had been residing in selected households for the past month were asked to participate. Finger prick blood sampling was performed to prepare blood smears for the detection of malaria parasites by microscopy, and whole blood spots were collected using finger-prick blood sampling and EDTA microtainers (BD). Samples were spun to separate plasma and red blood cells and stored at -20 °C. Participants also completed a questionnaire survey on individual and household information including demographic, health and socio-economic indicators. Location data was recorded for all households using a handheld GPS receiver (Garmin, USA).
Classification of exposure. All analysis was performed in R statistical software  57. To classify individuals as seropositive for recent exposure to P. falciparum, we used an ensemble approach for binary classification with the Super Learner algorithm  58. This algorithm estimates the performance of multiple machine learning models by cross validation, then creates an "ensemble" optimal weighted average of those models  59. The machine learning models included in the ensemble were support vector machine (SVM), random forest (RF), lasso, K-nearest neighbour (KNN) and boosted regression tree (BRT). Weights for base learners were calculated using the Nelder-Mead method to maximise the Area Under the ROC Curve (AUC). The algorithms were trained with datasets of antibody responses from individuals with a known exposure to, or concurrent infection with P.
falciparum, and negative controls from immunologically naïve individuals from the UK and USA (Supplementary Information Table  1 ). To avoid overfitting, we used a random 70% of the dataset to build the model, leaving the remaining data for independent validation. The full dataset with tenfold cross-validation was used to make predictions. The outcome was a prediction of probability of recent exposure to P. falciparum. Individuals with a probability of > 0. 5 were classed as positive to infection within the last 2 years, as training data included cases within this timeframe. This is hereby referred to as "recent exposure". To specify the combination of antigens included in the final classification model, the relative influence of each antigen in the prediction of positivity for recent exposure to P.
To classify exposure to P. vivax, training data for sero-positives included molecularly confirmed P. vivax infections 47 , confirmed P. vivax exposed individuals from endemic areas in Ethiopia and Brazil  56  , and positive P. vivax controls. Negative reference populations for both species consisted of samples from UK residents with no history of travel. Risk factor assessment. Individual-level demographic, health and socio-economic risk factors for recent exposure to P. falciparum, historic exposure to P. falciparum, and historic exposure to P. vivax were assessed. Additionally, remote-sensing derived environmental risk factors for recent exposure to P. falciparum over multiple spatial scales (100 m-5 km) were also investigated. All risk factors investigated are detailed in Fornace et al.
The spatial distribution of the household prevalence of recent exposure to P. falciparum was assessed and a prediction map was developed using Earth Observation-derived data. Potential environmental and spatial covariates including climatic variables, accessibility to hospitals and clinics, population density, distance to various types of land cover and forest, and topographic measures, were assembled (Supplementary Information Table  4. ) Demographic data was not available across all locations within this region, therefore questionnaire survey data was not included. Geostatistical models of household prevalence were fit in a Bayesian framework where p(x i ) denoted the seroprevalence at locations x i , i = 1. n , with m i individuals sampled per household location. The full model was specified as: With the linear predictor for the binomial model specified as: where β 0 represents the intercept, β 0 + d(x i ) ′ β + w i represents a vector of location-specific covariate effects, and w i represents the spatial effect.
Acknowledgements We acknowledge the  UK Medical Research Council, Natural Environment Research Council, Economic and Social Research Council and Biotechnology and Biosciences Research Council  for funding received through the  Environmental and Social Ecology of Human Infectious Diseases Initiatives  (Grant No. G1100796 ). KMF is supported by a  Sir Henry Dale fellowship  jointly funded by the  Wellcome Trust and Royal Society  (Grant No. 221963/Z/20/Z ). We would like to thank the  Director General of Health Malaysia  for the permission to publish this article.
1 ). We rst assessed the transcriptional similarity by comparing our snRNA-seq data with that from a previous study across seven common tissues  27. The Spearman correlation values between the two × pseudo-bulk single-cell transcriptomic pro les were high for all tissues, ranging from 0. 653 to 0. 825 (Supplementary Fig. 2 ), suggesting globally consistent transcriptional pro les of samples between these two single-cell RNA-seq studies at the bulk tissue level. The complete snRNA-seq dataset was grouped into 77 cell clusters and manually annotated as 67 major cell types based on the expression of canonical marker genes from the literature (Fig. 1b-c , Supplementary Fig. 3 , and Supplementary Table  1 ). All tissues and cell types showed su cient transcriptional abundance, with a median of 4,008 unique molecular identi ers (UMI) and 2,064 transcribed genes per nucleus, therefore displaying higher expression than the previously reported singlecell data in pigs  27.
3f  and Supplementary Fig. 10a-b ). The differentiation trajectory of these intestinal epithelial cells was highly similar among the four individual intestine segments (Supplementary Fig. 10c-f ). Functional annotation analyses based on the Gene Ontology (GO) database demonstrated that gene signatures of each cell subgroup in intestinal stem cells were mainly enriched in cell cycle-related biological processes as expected (Fig. 3g ). The highly expressed genes in BEST4 + enterocytes were over-represented in cell development and morphogenesis, which was highly different from the functions of immature and mature enterocytes. The gene sets restricted in enteroendocrine cells were signi cantly enriched in signal release and protein secretion (Fig. 3g ). The distinct transcriptional pro les and functions of these cell types can be attributed to their diverse gene regulatory programs (Supplementary Fig.
We identi ed a total of 45,491 immune cells prevailing in 17 tissues, including T cells, B cells, natural killer cells (NK), macrophages, and other tissue-resident immune cells (Fig. 4a  and Supplementary Fig. 14 ). Hierarchical clustering analysis revealed three main branches of immune cells: myeloid and lymphoid lineages, as well as microglia, which are brain-resident macrophages (Fig. 4b ). As expected, each tissue has its own immune compartments, with speci c immune cell compositions. For example, the four major parts of the brain exclusively contain microglia cells. A large population of B cells was evident in the spleen, whereas lymph nodes were enriched for multiple T cell types. We next subdivided and reanalyzed the immune dataset to explore further heterogeneity within macrophages and T cells, which were abundantly present across tissues.
Genetic mapping and functional implications of cell-typespeci c eQTL Bulk tissue samples often contain a high degree of cellular heterogeneity, which can mask genetic effects that are active only in speci c cell types within the sampled tissue. To address this, we explored ieQTL by performing the cell-type deconvolution analysis of 3,921 bulk RNA-seq samples in the PigGTEx project via this newly built cross-tissue cell atlas. First, we tested the cell estimation performance of the CIBERSORT algorithm  44  in pigs by deconvoluting pseudo-bulk samples generated from simulation studies using the SCDC software  45. By employing the gene signature matrix built from our liver snRNA-seq data, we observed that the estimated cell proportions from pseudo-bulk samples were highly correlated with the putative cell populations identi ed in the liver snRNA-seq, with the highest correlation in Hepatocyte_1 subtype (Pearson's r = 0.