dataesr/scientific-paragraphs-categorization huggingface dataset:
For both data sets, dReLU encode more efficiently the gap modes. One of the key aspect that explains the difference of performance between dReLU and Gaussian RBM is the ability of the former to better model 'outlier' sequences, with rare extended features such as Bikunin-AMBP (Weight 5 in Main Text, Fig. 2 ) or non-aromatic W28-substitution feature (Weight 3 in Main Text, Fig. 3 ). Indeed, thanks to the thresholding effect of the average activity, dReLU can account for outliers, without altering the distribution for the bulk of other sequences -unlike quadratic potentials. To illustrate this property, we compare in Appendix 1, Fig. 5  the likelihoods for all sequences of two RBMs trained with quadratic (resp. dReLU) potentials, = 100, 2 1 = 0. 25 on the Kunitz domain MSA. The color code measures the degree of anomaly of the sequence, which is obtained as follows: 1.
Figure2Ashows the MSA sequence logo and the secondary structure of the Kunitz domain. It is characterized by two helices and two strands; Cystein-Cystein disulfide bridges largely contribute to the thermodynamic stability of the domain, as frequently observed in small proteins. BPTI structure was the first one ever resolved Ascenzi et al. (2003), and is often used to benchmark folding predictions based on simulations Levitt and Warshel(1975)  and coevolutionary approaches Morcos et al. (2011); Hopf et al. (2012); Kamisetty et al. (2013); Cocco et al. (2013); Haldane et al. (2018). We train a RBM with = 100 dReLU on the MSA of PF00014, constituted by = 8062 sequences with = 53 consensus sites.
Library quality control was performed using the 2100 Bioanalyzer System with the Agilent High Sensitivity DNA Kit (Agilent, Santa Clara, CA, USA). The library was quantified via qPCR using a KAPA Library Quantification Kits for Illumina platforms (KAPA Biosystems, Wilmington, MA, USA) and sequenced in an MiSeq sequencing system (Illumina) with pairedend reads (2 × 75 bp) obtained using MiSeq Reagent Kits v3 (150-cycles). Samples are available at SRA repository under the accession numbers SRR5934425 and SRR5934426. All the experimental procedures using goats were in accordance with the principles and guidelines adopted by the Brazilian College of Animal Experimentation (COBEA) and approved by the Animal Care Ethics Committee/UNESP (CEEA).
Trypanosoma vivax bloodstream transcriptomes Transcriptome data from bloodstream forms of three T. vivax strains were analyzed. Transcriptome for the African T. vivax IL1392 strain was acquired from bloodstream forms maintained in vivo by continuous passages in mice  [11]. Trimmomatic v0. 32  [41]  was used for trimming the reads. All replicates in fastq format (SRA accession numbers: ERR236859, ERR236860, ERR236861, and ERR236862) were combined and aligned to the reference genome of T. vivax Y486, version 2013-01-16, downloaded from TriTrypDB  [42]  using TopHat2  [43]. Samtools v0. 1. 18 [44]  was used to filter reads with mapping quality ≥10. To assemble these reads into contigs, the PASA  [45]  pipeline [http://pasa. sourceforge. net/#A_ComprehensiveTranscriptome] combining de novo and genome-guided assemblies were applied.
For each assembly, the Trinity  [46]  package was used. In order to filter contaminants from the Mus musculus host genome present at de novo assembly, all contigs were blasted  [47]  against both the T. vivax Y486 and Mus musculus genomes, removing all sequences with best hit associated with mouse sequences. Finally, to determine highly probable proteincoding regions, Transdecoder [http://transdecoder. sourceforge. net/] was run with final assembled transcripts, requiring a minimum protein length of 30 codons. Replicates from bloodstream transcriptome for the South American T. vivax Lins strain were also combined and assembled using Trinity  [46]  and the Capra hircus (GCF_000317765) genome as reference for filtering contaminants. Reads from bloodstream transcriptome of T. vivax Venezuelan isolate (LIEM-176) were downloaded from the SRA database (SRR527163 and SRR527235), and used only for FPKM quantitative analysis.
Assembled transcripts were downloaded from http://bioinformatica. fcien. edu. uy/Tvivax [25]. Protein-coding regions were equally predicted with Transdecoder as above. To calculate FPKM values, reads were aligned to assembled transcripts with bowtie 2  [48]  and HTSeq v0. 5. 4p5 (http://www-huber. embl. de/users/anders/HTSeq/doc/over view. html) was used to count reads uniquely aligned to predicted protein-coding regions. Transcriptome assembly completeness was assessed with BUSCO  [49]  v1. 22, using 429 benchmarked universal single-copy genes from the Eukaryotes dataset and e-value cutoff of 1e-05.
Prediction of cell-surface and secreted proteins Annotated proteins from a fourth strain, T. vivax Y486, were downloaded from TriTrypDB  [42]  (Build 28) and analyzed together with predicted proteins from transcriptomic data, through the screening of both predicted cell-surface/secreted and non-cell-surface/secreted proteins, based on a procedure described by Staats et al. [50]. Briefly, an automatic PERL pipeline was used with the MySQL database. Initially, all coding sequences smaller than 30 codons were removed. To detect a signal peptide, we combined predictions by both SignalP v4. 1  [51]  (with default D-score cutoffs 0. 45 for SignalP-noTM networks and 0. 50 for SignalP-TM networks; http://www. cbs. dtu. dk/services/SignalP/ ) and TargetP v1. 1  [52]  (LOC = S; http://www. cbs. dtu.
In silico prediction of linear B-cell epitopes Complete protein sequences were screened for linear B-cell epitopes using BepiPred 1. 0  [56]  and LBtope  [57]. Usually about five amino acids in each epitope and paratope sequences are the key contributors to the binding energy, but about 15 amino acids are present in this interaction, therefore, a PERL script was developed to screen for 15mer peptide windows with BepiPred mean score ≥ 1. 3 and overlapping windows were combined into one. This combination may decrease the final mean somewhat below 1. 3 due to the summation of local minimums. Selected peptides were then screened with LBtope (Lbtop_Confirm as database). Complete protein sequences from predicted linear Bcell epitopes were further screened with Immune Epitopes Database Analysis Resource (IEDB) tools for the prediction and analysis of linear B-cell epitopes: Chou & Fasman Beta-Turn Prediction, Emini Surface Accessibility Prediction, Karplus & Schulz Flexibility Prediction, Kolaskar & Tongaonkar Antigenicity, and Parker Hydrophilicity Prediction  [58]  (http://tools.
infantum JPCM5, and L. major SD75. 1. BLAST parameters were adjusted for short peptide match (Word size = 2, SEG filter off, Composition-based Statistics, evalue = 20,000, and PAM30 score matrix). Linear B-cell epitopes containing alignments with ≥15mer and ≥70% identity were removed. Finally, BLASTp and tBLASTn searches using the complete protein sequences from each selected linear B-cell epitope against NCBI nr/nt databases (e-value ≤ 1e -03 ) were performed removing those with no significant hits found in the Trypanosoma genus, to eliminate the chance of including uncharacterized sequences from any sort of contamination. Clustering of peptide sequences was performed with local OrthoMCL  [60]  v2. 0. 9 with BLAST adjusted parameters for short sequences and OrthoMCL similarity cutoff (percentMatchCutoff) was set to 70%.
In the present study, we have combined several bioinformatics tools in a complete pipeline (Fig. 1A  and B ), from RNAseq assembly to epitope prediction, in order to characterize and allow the selection of the best linear B-cell epitope candidates for future experimental tests in the pursuit of efficient and species-specific serological tests for the diagnosis of T. vivax infection. Transcriptomic data from bloodstream forms were analyzed for one Western African strain, TvIL1392: T. vivax IL1392 from Nigeria  [9, 11]  (a clone derived from TvY486: T. vivax Y486) and two South American isolates: TvLins (T. vivax Lins, from São Paulo state, Brazil) and TvLIEM-176 (T. vivax LIEM-176, from Venezuela). The annotated genome of the Western African strain (TvY486, from Nigeria) was also included. Our study comprised the most representative dataset of T.
vivax strains compared to date. The transcriptome of TvIL1392 assembly yielded 9249 transcripts, which generated 21,697 putative protein sequences (13,829, or 63. 7%, are complete Open Reading Frames -ORFs; and 11,021, or 50. 8%, ≥100 residues). Similarly, the assembly for TvLins yielded 11,544  Completeness of transcriptome assemblies was assessed by measuring and comparing the number of detected genes from benchmarked universal single-copy orthologs, using the BUSCO  [49]  software. The assembled transcriptomes, with the exception of TvLIEM-176, presented results comparable or better than the completely annotated genomes. The genome of TvY486 presented 74. 4% of the expected eukaryote orthologs while the transcriptomes of TvIL1392, TvLins, and TvLIEM-176 presented 73. 9%, 77. 2% and 23. 8%, respectively (Additional File 1).
9%), 4411 (20. 6%), 10,206 (23. 8%), and 1278 (10. 8%) protein sequences from TvIL1392, TvLins, TvLIEM-176, and TvY486, respectively. All proteins were submitted to linear B-cell epitope predictions, combining three highly sensitive tools. Epitopes were first predicted by BepiPred 1. 0 Server, a method based on the hidden Markov model and propensity scale, whose predictions have already been experimentally validated for T. cruzi  [29, 38, 63]  and L. braziliensis  [66]. Selected linear B-cell epitopes were further analyzed by LBtope, which is a method based on the Support Vector Machine (SVM). Next, five Immune Epitope Database (IEDB) methods were used for characterization: Chou & Fasman Beta-Turn Prediction, Emini Surface Accessibility Prediction, Karplus & Schulz Flexibility Prediction, Parker Hydrophilicity Prediction, and Kolaskar & Tongaonkar Antigenicity  [58].
Linear B-cell epitope characterization TvY486 complete protein sequences have been clustered with T. b. brucei and T. congolense into ortholog families and organized in a comprehensive database of African trypanosome genes predicted to encode cell-surface proteins (Cell-surface Phylome -CSP)  [69]. A total of 368 TvY486 epitopes (7. 9%) were observed in proteins assigned to a CSP family, from which 279 (75. 8%) were from T. vivax-specific CSPs (from Fam23 to Fam45, Additional File 2). Although care was taken to remove those with significant similarity to other trypanosome species, some proteins assigned to other CSP families grouping T. vivax, T. brucei, and T. congolense were found. The reason behind this is that even though the whole proteins were classified as orthologs, regions corresponding to the linear B-cell epitope residues were highly variable (Additional File 7).
Acknowledgments This work was supported by the  LNCC/USP/Pasteur Project Tvivax DIAG-DI-2015-17  and the  Brazilian Agency CNPq  (PROFRICA, process  490735/2010-0 ). The authors are indebted to the  MAEDI, Chaire USP/ Pasteur ,  RLMG  and CMR received three months  Calmette & Yersin  fellowships from the  Institut Pasteur International Division. HAG and CMR are postdoctoral fellows of  FAPESP  and  CNPq , respectively. We are also grateful for the collaboration of  Andrew Jackson  ( Liverpool University, UK ).
Later on, a complete simulation tool based on the proposed methodology has been developed  [36]. The cited research and especially the simulation tool appears to be a real breakthrough in the field of coil-wound heat transfer modeling, which until recently was occupied solely by software named GENIUS, developed and tested by Linde AG  [37]. The same research team has decided to enhance their distributed-parameter model for LNG coil-wound heat exchanger design with capabilities for simulation of floating LNG CWHEs under rolling conditions  [38]. In a tilted CWHE there are liquid-rich and gas-rich regions on the shell side due to the downward flow of the liquid refrigerant along the gravity direction, as shown in Fig. 9. During a rolling period, the boundary between the gas and liquid-rich regions dynamically changes at different tilt angles, leading to dynamically changing liquid maldistribution on the shell side and then the non-uniform heat transfer for the floating CWHEs applied in floating LNG plants.
The model has been validated against the Aspen MUSE software, which is treated as an industry reference model. The discrepancy observed between the models is very small. The cited research and elaborated model appear to be a real breakthrough in PFHE simulation, which until recently was dominated by commercial design software (ASPEN HYSIS, Honeywell UniSim, etc. )  [50]. A particle swarm optimization (PSO) algorithm has been developed by Peng et al. for sizing plate-fin heat exchangers  [51]. The optimization algorithm aims to minimize the weight and annual cost of PFHEs. The objective function that gave an account to the weight of the heat exchanger provides the minimization of the size and the initial cost of the heat exchanger under the defined constraints, while the annual cost target yields the optimum pressure drops, considering the trade-off between energy consumption and heat exchanger weight.
To address this issue, Morgan et al. [76]  propose a multi-cell solution where individual cells of packed beds are arranged in parallel or series depending on the flow rate. This configuration is equivalent to a variable geometry of the packed bed, which can be adjusted according to the load. Another major benefit is that individual cells can be isolated from the rest, thereby limiting the self-discharging effect. Based on modelling and simulation with the Flowmaster software, the authors observe a 4. 8 % improvement of the efficiency over a single-cell packed bed, without accounting for the additional benefit of reduced self-discharge losses  [76]. Various other studies have investigated the heat transfer processes in packed bed regenerators. White et al. presented a numerical and theoretical analysis of thermal wave propagation in a packed bed thermal reservoir  [78].
Parts of the model are developed in EES and COMSOL, and the coupled simulations are performed through MATLAB. The simulations show a strong temperature gradient between the hot and cold end of the packed bed, but the stratification degenerates rapidly over the charge/discharge cycles. A steady temperature profile is established after 15-20 cycles  [9]. The degradation of the thermal front in the packed bed leads to 25 % increase of the air outlet temperature at the end of the discharge cycle, which in turn reduces the recovered cold by the same amount. Similarly, the degradation of the thermal front at the end of each charge cycle reduces the efficiency of the liquefaction process  [9]. Using liquids, PCMs or PCM slurries for hot or cold thermal energy storage is a contemporary trend when developing LAES systems and associated heat exchangers  [82, 83].
Consistent with this observation, the downregulation of genes involved in mitochondrial and synaptic function were also reported by using multiple genomics datasets like RNA-Seq and microarray studies previously published  [26]. Interestingly, dysfunction in synaptic pathways was also described in another neurodevelopmental disease, namely schizophrenia  [27] [28] [29] [30]. This pathology affecting approximately 1% of the population is characterized by personality disturbances, hallucinations, delusions, and/or disorganizing behavior. High-throughput transcriptomic analysis revealed multiple deregulated genes in schizophrenia  [29] [30] [31] [32]. Several of them are implicated in neurodevelopmental pathways, neuronal communication, energy metabolism, and synaptic function  [29, 30, 32]. Changes in DNA methylation related to the prenatal-postnatal life transition were also reported by comparing schizophrenia postmortem and unaffected control brain samples, strongly arguing for the implication of an epigenetic regulation in the disease's development  [33] [34] [35].
LncRNAs also play a role in neuronal differentiation and neurogenesis, as suggested by studies highlighting a differential expression of lncRNAs during differentiation from human pluripotent stem cells  [41, 42]. One example is the lncRNA rhabdomyosarcoma 2-associated transcript (RMST) which through its interaction with SOX2 regulates downstream genes implicated in neurogenesis  [43]. The dysregulation of miRNA or lncRNA expression was also observed in autism  [44] [45] [46] , schizophrenia  [47] , and intellectual disability  [48]. In this last case, lncRNAs were shown to be implicated in synaptic transmission, neurogenesis, or neurodevelopment. Across these different transcriptome studies, a variety of databases hosting microarray and/or RNA-Seq data are currently available (for a comprehensive review, see  [49] ).
Among them, we can cite the HB Atlas  [4, 9] , the BrainSpan Consortium  [14] , Brain Cloud  [5] , the Allen Brain map portal  [12] , the cortex single cells  [19] , or the single-cell portal  [18]. In addition, several consortia, sometimes covering topics beyond the brain tissue, are at the basis of the establishment of major databases. Among others, we can cite the "Genotype Tissue Expression (GTex)" regrouping gene expression data issued from different tissues covering more than 600 donors  [50]. Similarly, the "Encyclopedia of DNA Elements (ENCODE)" regroups large-scale datasets from various projects and combines multi-omics data from different species, variety of cell lines and tissues at different stages of development. A more specialized version of ENCODE, the "Psychiatric Encyclopedia of DNA Elements (PsychENCODE)," collects datasets concerning epigenetic modifications and non-coding RNA in healthy and disease-related human brains  [51].
In the context of the data issued from brain samples, Huisman et al. developed the web portal "Brainscope" providing an interactive visualization of Allen Atlas adult brain transcriptome and across different stages of development  [52]. Recently, a method to predict mRNA expression in whole brain using microarray data from Allen Brain Atlas with in-vivo positron emission tomography (PET) data was developed  [53]. Overall, the generation of these databases correspond to major efforts for the research community, providing centralized access to the large collections of data; thus, further efforts of data integration can be performed, for instance by the reconstruction of gene regulatory networks on the basis of previously generated transcriptomes.
Furthermore, they have focused on structural mutations that disrupt genes, and evaluated their functions with the help of computational solutions querying for gene enrichment in one or more functionally defined pathways (PANTHER and Ingenuity Pathway Analysis). This strategy per se aims at establishing gene relationships on the basis of their annotation to a given program (or pathway), even though in this case such relationships are inferred in-silico. Since then, further studies incorporated other types of data, like the use of RNA-Seq transcriptomic analysis to identify the differentially expressed genes between controls and individuals with schizophrenia, which are then associated to biological functions by Gene ontology analysis  [55] [56] [57]. Furthermore, the development of computational solutions for enhancing data integration has being performed like in the case of NETBAG, which allows to integrate multiple types of genetic variations like single nucleotide variants (SNVs), rare copy number variants (CNVs), and genome-wide association studies (GWAS), to identify highly connected gene clusters, potentially related to functional roles.
Although powerful for the identification of the target genes for a given factor, performing ChIP-Seq assays remains still challenging for covering a large number of TFs, epigenetic modifications, and/or chromatin remodelers which could appear associated to neurodevelopmental events. In fact, identifying strategies to prioritize the list of TFs to be immunoprecipitated remains a key step, which is currently handled by applying computational strategies. In this context, we have recently developed TETRAMER, a computational approach able to reconstruct gene regulatory networks from the integration of transcriptomes provided by the user and annotations retrieved in various databases concerning TF-Target gene relationships  [69]. Furthermore, TETRAMER simulates transcription regulation propagation over the reconstructed connectivity to identify master TFs, which could then be prioritized for experimental assays.
alzheimer's disease and other types of dementia are the top cause for disabilities in later life and various types of experiments have been performed to understand the underlying mechanisms of the disease with the aim of coming up with potential drug targets. these experiments have been carried out by scientists working in different domains such as proteomics, molecular biology, clinical diagnostics and genomics. the results of such experiments are stored in the databases designed for collecting data of similar types. However, in order to get a systematic view of the disease from these independent but complementary data sets, it is necessary to combine them. In this study we describe a heterogeneous network-based data set for alzheimer's disease (HENa). additionally, we demonstrate the application of state-of-the-art graph convolutional networks, i.
Background & Summary Alzheimer's disease (AD) is an age-related neurodegenerative disorder that progresses with age and eventually leads to death. Several approved drugs can be applied to reduce the symptoms of Alzheimer's disease, however, no current treatments can modify the underlying disease processes  1, 2. A number of experiments have been performed to understand the regulatory mechanisms of the disease  [3] [4] [5] [6]. The results obtained from such experiments are collected in various databases that were created for depositing and providing further access to similar types of experimental biological data such as ArrayExpress, IntAct, hu. MAP, and ADNI  [7] [8] [9] [10]. The studies that utilize the results of such experiments address the causes of brain ageing by researching the mechanisms involved in this process.
Due to this variety of the interactions' nature, the origin from various databases and experimental sources, there is a lack of unified node identifiers. To overcome this problem the identifiers of the proteins, SNPs, probes and gene names originating from various databases are mapped to one unique Ensembl name space  27. We represented each individual interaction data set in a form of the network (Fig. 3 ), where nodes stand for entities like proteins, genes, SNPs, etc. , and where edges depict biological relations between the nodes, e. g. protein-protein interactions, co-expression, epistatic interactions. Multiple individual networks were then combined into one network using the unifying feature. As a unifying feature we used genes onto which SNPs, transcripts and proteins can all be mapped.
We mapped identifiers from each individual data set such as SNP, protein and gene identifiers to a common name space. Due to the fact that genes or proteins coming from different databases might have a few alternative names, unique gene identifiers provided by Ensembl ver. 93 (ENSG ID) were used as a common name space. Each interaction A-B in the networks is described by the score, interaction type and the data set where the interaction can be found. A summary of edge attributes is presented in Table  1. Additional disease-related information about each node, e. g. GWAS association, positive selection, expression in the brain regions, is collected in the form of a table with the attributes described in Table  2. For each node a list of attributes is a vector of the length 237. In the sections below we describe how experimental and computational data sets were acquired, transformed and combined.