dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenomics of Vinavirales Using MCPs of the viruses of the order Vinavirales detected in human gut metagenomes as queries, we extracted related GenBank protein sequences; the previously detected PM2 group MCPs  [8]  were also added to the sequence set. The protein sequences were clustered at 80% identity, aligned, and used to construct a maximum likelihood phylogenetic tree (see Section 2). At coarse grain, the Vinavirales MCPs formed five strongly supported major clades (A to E). Clades B to E were largely consistent in terms of the taxonomic affiliation of the inferred hosts, whereas clade A was heterogeneous (Figure  2 ). The largest clade, A, is composed of gut metagenome sequences and contigs from various environments and includes no cultured phages. Viruses in this clade appear to be associated mostly with alphaproteobacterial hosts, along with some that are likely associated with Verrucomicrobia.
Thus, phages of Alphaproteobacteria encode a cell wall hydrolase of the SleB superfamily; phages of Gammaproteobacteria, despite belonging to different clades, all encode the PM2 P5 homolog of LytD superfamily, whereas phages predicted to infect Betaproteobacteria encode a T4lyz-like protein (Figure  5B ). Although the great majority of the members of the Vinavirales detected in human gut metagenomes appear to be prophages, only HGV-2 contigs encode a homolog of the DDE transposases of the IS630 family (Figure  3 ; Supplementary File S4), which likely mediate the prophage integration. The integration mechanisms for other Vinavirales prophages remain unclear. Various subsets of replication proteins, such as DNA polymerase B, non-homologous rolling circle replication initiation endonucleases of the Rep_trans (PF02486) and HUH superfamilies, DnaDlike winged HTH domain replication initiators, and others (Supplementary Files S3 and S5), were identified in nearly all members of Vinavirales.
These genes are apparently subject to frequent nonorthologous displacement during the evolution of this phage group, similarly to the replication proteins of crAss-like tailed phages  [20]. An example replication protein replacement is shown in Figure  6. The Paremcibacter congregatus strain ZYLT genome encodes two prophages that belong to clade A. Strikingly, these prophages are nearly identical for most of their length, with the notable exception of the replication genes (divergent Rep_1 family rolling circle replication endonucleases of the HUH superfamily; pfam01446) and two or three ORFs upstream of these genes. The replication proteins and the upstream ORFs lack detectble sequence similarity between the two prophages, and the upstream ORFs in each phage encode short (80-110 aa) proteins with no detectable homologs in current sequence databases.
An example replication protein replacement is shown in Figure6. The Paremcibacter congregatus strain ZYLT genome encodes two prophages that belong to clade A. Strikingly, these prophages are nearly identical for most of their length, with the notable exception of the replication genes (divergent Rep_1 family rolling circle replication endonucleases of the HUH superfamily; pfam01446) and two or three ORFs upstream of these genes. The replication proteins and the upstream ORFs lack detectble sequence similarity between the two prophages, and the upstream ORFs in each phage encode short (80-110 aa) proteins with no detectable homologs in current sequence databases.
Introduction The majority of real-life datasets resulting from complex processes are infrequently characterized and predicted adequately by standard fundamental distributions in practice. The choice of a flexible distribution for data analysis is crucial, as the perfection of results from statistical analysis greatly relies on the model that is adopted. In order to obtain statistical outputs of higher quality and greater accuracy, it is essential to identify more connected distributions. Several continuous distributions are readily accessible in the statistical literature for modelling real data. Among them, the skewed distributions have more strategic significance, and are frequently used in many areas of applied research. For this reason, numerous families of skewed distributions have been proposed.
Azzalini (1985)  (see  [1] ) introduced a method for introducing skewness to the normal (N) distribution. It should be noted that while the majority of skewed distributions have the positive real values set as the support, the selected based distribution has the full real line as the support. Subsequently, many authors have continued to develop this concept; for example, see  [2, 3]. In the same respect, we cite  Coles (2001)   [4] ,  Castillo et al. , (2005)  [5], and Ferrari and Pinheiro (2012)  [6] , all of whom emphasize the Gumbel distribution (defined on the real line) for analyzing real datasets. As a fair competitor,  [7]  developed the log-Dagum (LD) distribution, which is derived by transforming the Dagum distribution logarithmically. The LD distribution has the feature of having leptokurtic shapes.
The leading motivations behind the introduction of this new distribution are: (i) to develop a new generalization for the well-known N distribution, which continues to pique the interest of statisticians across the board; (ii) as mentioned, to investigate the characteristics of the HLN distribution's logarithmically transformed version, which has surprisingly not received much attention in the statistical literature. The practical viewpoint is considered as well; we aim to demonstrate how the HN distribution can be concretely used as a model for dealing with the analysis of important data, and show that it outperforms comparable well-known models. We examine an experimental dataset of glass fiber strength from the National Physical Laboratory in England as an application in this research. Because the HN distribution is an extended version of the well-known N distribution, which has numerous applications in physics, including various dynamical systems (see  [8, 9] ), it is quite conceivable to employ the HN distribution for further physical applications.
The remainder of the present article is divided into several sections, and is structured as follows. We introduce the HN distribution and discuss its special cases and moments in Section 2. The various functions and moments related to reliability measures are discussed in Section 3. In Section 4, the unknown parameters of the new model are estimated using efficient procedures, namely, the maximum likelihood (ML) and Bayesian estimation procedures. A simulation study is conducted in Section 5 to analyze the performance and flexibility of the ML estimates (MLEs). Additionally, Section 6 presents a parametric bootstrap simulation technique utilizing these estimates. Section 7 illustrates the application of the HN model based on a real-life dataset. The final concluding remarks are presented in Section 8.
( 27 ) By computationally solving the nonlinear system of equations U = (0, 0, 0) T , it is possible to derive the MLEs. In this study, we obtain the MLEs numerically using the fitdistrplus package in R software (see  [15] ). The following link provides more information about this package: (https://CRAN. R-project. org/package=fitdistrplus). We now depict the asymptotic confidence intervals (CIs) for the parameters. On taking the second partial derivatives of Equations (  25 )-(  27 ) at the MLEs, the observed Hessian matrix of the HN distribution can be obtained, and is specified by H =             ∂ 2 L n ∂α 2 ∂ 2 L n ∂α∂µ ∂ 2 L n ∂α∂σ ∂ 2 L n ∂µ∂α ∂ 2 L n ∂µ 2 ∂ 2 L n ∂µ∂σ ∂ 2 L n ∂σ∂α ∂ 2 L n ∂σ∂µ ∂ 2 L n ∂σ 2             (α,µ,σ)=( α, µ, σ). Based on the above, the observed Fisher's information matrix is J = -H, from which we can derive the estimated dispersion matrix as Ξ = J -1 =   Ξ 11 Ξ 12 Ξ 13 Ξ 21 Ξ 22 Ξ 23 Ξ 31 Ξ 32 Ξ 33   , and Ξ ij = Ξ ji for i = j = 1, 2, 3.
From Equation (28), there is obviously no analytical way to determine the MCMC Bayesian estimates. To perform numerical computations for MCMC Bayesian estimation, the LaplacesDemon package of the R software, which provides a comprehensive environment for Bayesian inference, is used. For more information and examples about this package, the following link is recommended: https://cran. r-project. org/package=LaplacesDemon (accessed on 14 September 2022).
Bootstrap CIs In this section, we approximate the distribution of the MLEs of the HN model parameters using the parametric bootstrap method. Then, we can CIs for each parameter for the fitted HN distribution using the bootstrap distribution. Using the dataset comprised of x 1 , x 2 ,. , x n , let the MLE of θ ∈ {α, µ, σ} be θ. In our setting, the bootstrap stands for a method for estimating the distribution of θ by drawing a random sample (θ * 1 , θ * 2 ,. , θ * B ) for θ based on B random samples that are drawn with replacement from x 1 , x 2 ,. , x n. The sample of bootstrapping, i. e. , (θ * 1 , θ * 2 ,. , θ * B ), can be used to construct bootstrap CIs for each HN distribution parameter. Thus, using the following formulas, we calculate the 100 × (1δ)% bootstrap CIs for the parameters as I α,boot = [ α ∓ z δ/2 se α,boot ], I µ,boot = µ ∓ z δ/2 se µ,boot , I σ,boot = [ σ ∓ z δ/2 se σ,boot ], where z δ represents the δth percentile of the bootstrap sample; for θ ∈ {α, µ, σ}, se θ,boot = 1 B B ∑ b=1 θ * b - 1 B B ∑ b=1 θ * b 2.
Applications and Empirical Study The purpose of this section is to illustrate the empirical significance of the HN distribution. We consider a real dataset from the area of subject physics. The data consist of a sample from an experimental dataset of the strength of glass fibers with a length 15 cm from the National Physical Laboratory in England. This dataset, usually called the glass fiber (GF) dataset, can be found in  [16]. The summary statistics of the data are provided in Table  5. We use the R software for numerical evaluations. We study the empirical hazard rate function of the dataset using the concept of total time on test (TTT) plot. The TTT plot is a graph that is primarily used to distinguish between various ageing types indicated by hazard rate shapes. For more details, see  [17].
In light of this, we can draw the conclusion that the suggested HN model provides a better fit than those from the compared distributions. The plots of the empirical cdfs, empirical pdfs, and the theoretical probabilities against empirical ones (P-P plot) for the real dataset (for the HN model only for the P-P plot) are displayed in Figure  5. This graphic displays attractive curves for the fitted and empirical functions. In light of this, we can draw the conclusion that the HN distribution is the most appropriate for this dataset among the alternative distributions. Now, the empirical Hessian matrix is obtained as follows: In order to complete the above analysis, Table  7  provides the 95% (asymptotic) CIs for the HN distribution parameters. On the basis of the GF dataset outlined above, we now concentrate on estimating the HN distribution parameters using the MCMC Bayesian technique.
The investigation was carried out using the MH algorithm of the MCMC method with respect to 1000 iterations in the perspective of Bayesian estimation. All three MCMC Bayesian estimates of the HN distribution parameters are included in Table  8  for comparison with the MLEs. Numerical computations for MCMC Bayesian estimation were performed using R software. We now focus on the 95% bootstrap CIs for the HN distribution parameters using the computed MLEs. To this end, we generate 1001 samples of the same size as the GF dataset using the HN distribution, with the true values of parameters chosen as the corresponding MLEs. For each obtained sample, we calculate the MLEs α * b , µ * b , and σ * b for b ∈ {1, 2,. , 1001}. Thus, for the parameters α, µ, and σ, Table  9  shows the median and 95% bootstrap CIs.
Goodness-of-fit tests are applied to a real dataset concerning the strength of glass fibers with a length of 15 cm from the National Physical Laboratory in England in order to demonstrate the new model's real-world applicability. In comparison to the other analyzed models, the new model consistently offers a superior fit. We expect that the proposed model can be used more frequently to represent actual datasets in a variety of fields, including physics, engineering, economics, hydrology, survival analysis, and others.
Social networks (SN) shape HIV risk behaviors and transmission. This study was performed to quantify research development, patterns, and trends in the use of SN in the field of HIV/AIDS, and used Global publications extracted from the Web of Science Core Collection database. Networks of countries, research disciplines, and most frequently used terms were visualized. The Latent Dirichlet Allocation method was used for topic modeling. A linear regression model was utilized to identify the trend of research development. During the period -, in a total of , publications, topics with the highest volume of publications consisted of ( ) mental disorders (. %); ( ) HIV/sexually transmitted infections prevalence in key populations (. %); and ( ) HIV-related stigma (. %). Discrepancies in the geographical distribution of publications were also observed.
Searching strategy and eligibility criteria In our study, the Web of Science (WoS) Core Collection database was chosen for the searching process because it includes more information for our analysis than other databases including publications' contents (e. g. , title, abstract, keyword, and research discipline) and metrics (e. g. , citations and download times)  (39, 40). The Web of Science database was chosen for our analysis because it is valuable for analyzing research fields, something that Scopus, PubMed, and other databases are unable to do. The WOS database also included superior scientific journals, whereas other databases only included articles of varying quality. In order to evaluate the research productivity in various subgroups, we were also able to conduct sophisticated searches and filter the results based on predetermined criteria thanks to the WOS.
Additionally, the WOS supports a number of analytic metrics and has a high citation report coverage, both of which help with bibliometric analysis of already published works. We developed the search query aiming to retrieve the articles about social networks application in HIV research. We divided the search process into two stages. First, we used the search terms "HIV, " "AIDS, " "Human Immunodeficiency Virus, " and "Acquired Immune Deficiency Syndrome" for the topic search. Then, after achieving the HIV/AIDS dataset, we searched the following terms in the title/abstract to filter the social network-related studies: "social network(s), " "social network analysis, " "network analysis, " "network analyses, " "friendship network(s), " "peer network(s), " "sociometric(s), " "sociogram(s), " "sociomap(s), " "egonetwork(s), " "respondent driven, " "respondent-driven, " "social support, " "interpersonal, " "cliques, " "community support, " and "social capital.
" We also searched for terms regarding popular software for social network analysis such as "UCINET, " "NetDraw, " and "Pajek. " Only articles were (1) original or review articles, (2) published in peer-reviewed journals in the English language, and (3) used or discussed the social networks in HIV-related topics were included. We excluded gray literature, book/book chapter, conference abstract/proceeding, or other types of documents that did not meet the inclusion criteria. Finally, a total of 5,698 articles were used for bibliometric analysis (Figure  1 ).
Study design and statistical analysis We applied the bibliometric approach to evaluate the selected articles. For bibliometric analysis, we used Stata version 15. 0 (Stata Corp. , Texas, United States) to perform basic descriptive statistics for publication year, the number of papers/per year, total citations up to 2019, mean citation rate per year, total usage in the last 6 months/5 years, and mean use rate in the last 6 months/5 years. For visualizing the co-occurrence of the most frequent terms, the VOSviewer (version 1. 6. 8, Center for Science and Technology, Leiden University, the Netherlands) software was used to illustrate the networks of 226 terms, which appeared at least 100 times. In addition, this software was used to present the research disciplines and countries of collaboration among the selected publications.
As a result, we could use LDA to annotate the topic of the papers to find hidden themes and gain a comprehensive understanding of the patterns of HIV/AIDS and social networks  (44). A total of 15 topics emerged based on consultation with HIV and social network experts  (45). Each topic was labeled by referring to The VOSviewer software: https://www. vosviewer. com/. the topic's 15 most frequent terms as well as titles/abstracts within the topic. We then computed the volume of publications per topic per year, as well as the share of publications across topics within each year. We performed the linear regression model, with the share of publication as the dependent variable and the number of years as the independent variable, to examine the research tendency in general and in different time intervals  (1991-2000, 2001-2010, and 2011-2019).
Results The general characteristics of selected articles are presented in Table  1. During the period 1991-2019, there were a total of 5,698 papers published in English peer-reviewed journals indexed in the WoS database. Overall, the number of publications increased rapidly from 7 articles in 1991 to 560 articles in 2019. The highest number of articles was also recorded in 2019. However, publications in 2012 had the highest number of citations (8,978 citations) and mean use rate in the last 5 years (3. 19 downloading times/paper/year). Articles published in 2001 had the highest mean cited rate per year (3. 84 cites/paper/year), while those published in 2015 had the highest total usage in the last 5 years (5,908 downloading times). There were 134 active countries, of which 90 countries had five publications or more.
Methods Participants' recruitment, data collection, and clinical and genetic investigation By exome sequencing (ES) of families affected by undiagnosed NDD, data mining of DNA sequences from families with rare disorders aggregated across several diagnostic and research laboratories, and using Gene-Matcher data sharing platform  11  six independent families reported here were identified. A uniform clinical proforma was distributed to collect clinico-demographic details. Facial photographs and brain magnetic resonance imaging (MRI) scans were obtained from the present cases and were retrieved from the previous reports  8, 9  for a review conducted by an experienced dysmorphologist and pediatric neuroradiologist (M. S. and F. A. ). Parents and legal guardians of all affected individuals gave their consent for the publication of clinical and genetic information according to the Declaration of Helsinki, and the study was approved by the respective local Ethics Committees.
Follow-up details on the parathyroid gland function and levels of global developmental delay/intellectual disability (GDD/ID) were obtained from the cases reported in Shao et al. 9  Trio ES or solo ES with subsequent Sanger segregation analysis was carried out in DNA extracted from blood-derived leukocytes in six families in four different centers following slightly different protocols (Table  1  for methods). ES data analysis, variant filtering, and prioritization were performed using in-house implemented pipelines of the local genetic centers (Table  1  for methods). In brief, the bioinformatics filtering strategy included screening for only exonic and donor/acceptor splicing variants. In accordance with the pedigree and phenotype, priority was given to rare variants (<0. 01% in public databases) that were fitting a recessive (homozygous or compound heterozygous) or a de novo model and/or variants in genes previously linked to NDD/ID and other neurological disorders.
Genetic findings We identified five new and one previously reported recurrent homozygous LOF EMC10 (NM_206538. 4) variants in six consanguineous families as follows: c. 543dup; p. Asn182GlnfsTer16 in family 1, c. 66delC; p. Ser23Valf-sTer82 in family 2, c. 259C>T; p. Gln87Ter in family 3, c. 289C>T; p. Arg97Ter in family 4; c. 287del; p. Gly96Alaf-sTer9 in family 5 and c. 188-2A>C in family 6 (Table  1 ). These variants reside within sizeable regions of homozygosity and are segregated with the disease in the families (Fig. 2A ). The location of the variants did not indicate any cluster sites in the gene nor in the protein (Fig. 2B-C ). Inspection of multiple public and private variant databases including gnomAD (v2. 1. 1 and v3. 1. 2), TopMED, Iranome, GME Variome, UK Biobank, and our in-house database of ~25,000 exomes (total number of alleles ~1,314,000) revealed that the variants are absent or with extremely rare allele frequency reported ranging from <0.
Acknowledgments We thank all patients and relatives for consent to be part of the study. Families 1 and 4 were collected as part of the SYNaPS Study Group collaboration funded by The  Wellcome Trust and strategic award (Synaptopathies)  funding ( WT093205   MA  and  WT104033AIA ) and research was conducted as part of the  Queen Square Genomics group at University College London , supported by the  National Institute for Health Research University College London Hospitals Biomedical Research Centre. We are also grateful to  Queen Square  genomics at the  Institute of Neurology University College London , supported by the  National Institute for Health Research University College London Hospitals Biomedical Research Centre , for the bioinformatics support. For the purpose of Open Access , the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.
Funding Information This research was funded in part, by the  Wellcome Trust  [ WT093205MA ,  WT104033AIA , and the  Synaptopathies Strategic Award ,  165908 ]. This study was funded by the  Medical Research Council (MRC)  ( MR/S01165X/1 ,  MR/S005021/1 ,  G0601943 ). D. D. S. was supported by the  National Institute of Neurological Disorders and Stroke (NINDS)   Neurology Resident Research Education Program  ( R25 NS070682 ). For the purpose of Open Access , the author has applied a CC BY public copyright.
259C>T p. Gln87Ter Ucuncu et al. 14 <0. 00001 Pathogenic (PVS1, PM2, PP3) Egyptian Family 3 S4 Stop gained c. 259C>T p. Gln87Ter <0. 00001 Pathogenic (PVS1, PM2, PP3) Egyptian Family 2 S2 S3 Frameshift c. 66delC c. 66delC p. Ser23ValfsTer82 p. Ser23ValfsTer82 Yaron et al. 13 <0. 000001 <0. 000001 Pathogenic Pathogenic (PVS1, PM2) (PVS1, PM2) Bukharan Bukharan Jewish Jewish Family 1 S1 Frameshift c. 543dup p. Asn182GlnfsTer16 Makrythanasis et al. 12 <0. 00001 Pathogenic (PVS1, PM2) Azerbaijani Patient Variant type Variant at the cDNA level (NM_206538. 4) Variant at the protein level Methods Maximum allele frequency in variant databases 1 ACMG classification Ethnic group Gender/current Variant annotation Variant characterisitcs Epidemiological data.
Efforts are being made to understand the similarities and differences between these two systems due to their architectures, dynamics, behavioral patterns, and representational structures  [2] [3] [4] [5]. At the same time, DNNs themselves are getting better and better on more human-like tasks. Recently, Radford et al. [1]  proposed a model that could simultaneously learn visual and linguistic information from a huge dataset using a constrastive loss function. Importantly, this multimodal model, known as CLIP, was found to possess neurons in its last layer that encoded specific concepts  [6]. These artificial neurons are reminiscent of 'concept cells' in the human medial temporal lobe (MTL)  [7, 8] , biological neurons that appear to represent the meaning of a given stimulus or concept in a manner that is invariant to how that stimulus is actually experienced by the observer.