dataesr/scientific-paragraphs-categorization huggingface dataset:
PPI-network construction and clustering Protein-to-protein interaction (PPI) networks describe the physical contact of proteins within cells. PPI networks were constructed to better understand the changes in cell physiology represented by transcriptome analysis in HB. Lists of differentially expressed metabolic genes in each cell line  (Supplementary Table  S3 ) were used to construct the PPI networks using the STRING database. Highly interconnected areas (clusters) in each PPI-network were identified, scored, and ranked on size and density. The highest-scoring cluster of each cell line is shown in Figures  4A-F. The scores of the top clusters were 34 (HB-243), 24 (HB-279), 32 (HB-282), 29 (HB-  HECW2, HERC3, HERC4, LRSAM1, MKRN1, NEDD4, NEDD4L, PJA1, RCHY1, RNF115, RNF130, RNF14, RNF144B, RNF19A, RNF19B, RNF217, SH3RF1, SIAH1, SMURF1, STUB1, TRAF7, TRIM32, TRIP12, UBA1, UBA7, UBE2A, UBE2D4, UBE2E1, UBE2E2, UBE2E3, UBE2H, UBE2J1, UBE2J2, UBE2L6 UBE3C, UBR1, and UBR2.
Validation of findings with HB patient microarray dataset Genes present in all six of the highest-scoring clusters in the PPI-network were RNF130, UBE2E2, UBE2C, RNF14, HERC3, HERC4, STUB1, UBE2S, RNF144B, MYLIP, and UBE2D4. Of these, four genes-RNF130, UBE2C, HERC3, and RNF144B-were found to be significantly altered in the GSE131329 microarray dataset. This dataset includes 53 HB tissue samples and 14 noncancerous liver (NCL) tissue samples collected at the time of surgery from HB patients. RNF144B, RNF130, and HERC3 mRNA expression was downregulated compared to the normal liver samples (log 2 FC -0. 84, adj. p-value 1. 10*10 -5 ; log 2 FC -0. 6, adj. p-value 3. 0*10 -06 , and log 2 FC -0. 62, adj. p-value 0. 00186, respectively), whereas UBE2C mRNA expression was upregulated in HB samples (log 2 FC 1.
01), events (p-value <0. 05), and death (p-value <0. 01) (Table  2 ). Downregulation of HERC3 and RNF144B was associated with occurrence of events (p-values <0. 05) (Table  2 ). RNF130 expression did not show a statistically significant association with any of the studied variables (Table  2 ). Clinical information adapted from GSE131329 dataset is summarized in Table  3.
UBE2C associated protein interactions and gene co-expression analysis To analyze the functional enrichment of UBE2C specific protein interactions in general, STRING network analysis was carried out. UBE2C was used as the input protein. Thirty proteins were interacting with UBE2C when the cut-off was set to 0. 4 (Figure  7A ). Next, we assessed the level of differential RNA expression of these UBE2C interacting proteins in our RNA sequencing data of the six HB cell models. Of these 30 proteins, fourteen were found to be significantly differentially expressed on RNA level in all 6 HB models (Figure  7B ). Gene co-expression analysis was conducted for UBE2C using the online tool GeneFriends to further explore the relationship between UBE2C and related genes. Using UBE2C as the seed gene, top 10 coexpressed genes with the highest Pearson correlation values were CCNB2, TOP2A, NUSAP1, CKS2, NUF2, CDK1, NEK2, PTTG1, CKS1B, and lncRNA RP11-102C16.
Discussion Metabolic derangements have been associated with enhanced tumorigenesis and cancer progression in several tumor entities  (Faubert et al. , 2020) , and these tumor-specific changes can be exploited to develop targeted therapies  (Sullivan et al. , 2016). In our RNA sequencing analysis of HB cell models, ubiquitination emerged as the most significantly altered metabolic pathway. The expression of three ubiquitin ligases (HERC3, RNF130, and RNF144A) and one ubiquitin conjugating enzyme (UBE2C) was significantly dysregulated in all studied HB models. These four genes were assessed more thoroughly in a HB patient dataset to validate their significance in the clinical setting. We noticed a remarkable association between high UBE2C expression and aggressive disease in the particular HB patient cohort.
Patients MIRRA (GSK ID: 115921/NCT02020889) enrolled patients ≥18 years of age, with a diagnosis of relapsing or refractory EGPA ≥6 months prior to the study. EGPA was defined by a history or presence of asthma, blood eosinophil count >1000 cells/μL or >10% of leucocytes, and two further features of EGPA (histopathological evidence of eosinophilic vasculitis; neuropathy; pulmonary infiltrates; sino-nasal abnormality; cardiomyopathy; glomerulonephritis; alveolar haemorrhage; palpable purpura; ANCA positivity). Patients with granulomatosis with polyangiitis or microscopic polyangiitis at screening were excluded, as were those who had organ-threatening or life-threatening EGPA ≤3 months before screening Eligible patients were receiving a stable dose of prednisolone (≥7. 5 to ≤50 mg per day, with or without stable immunosuppressants) for ≥4 weeks before baseline.
Sample size and statistical analysis Sample size calculations for the primary and secondary outcomes of the MIRRA study have been described previously  [21]. In the current analysis, ordered categorical data were analysed using proportional-odds regression. Binary outcomes were analysed using logistic regression. Patients who discontinued mepolizumab or placebo were followed until the end of the study, when possible, and all efficacy data were included in the analysis. All data were analysed using SAS version 9 (SAS Institute).
GSK was involved in the study design, data acquisition, data analysis and interpretation, and manuscript preparation. The sponsor did not place any restrictions on access to data or statements made in the manuscript. Authors had full access to the study data and had final responsibility to submit the manuscript for publication. In assessing cumulative dose, observed data following treatment discontinuation were used where available for participants who withdrew from treatment. OCS, oral corticosteroid; SC, subcutaneous; SD, standard deviation. Figure Legends.
Acknowledgments Editorial support (in the form of writing assistance, including preparation of the draft manuscript under the direction and guidance of the authors, collating and incorporating authors' comments for each draft, assembling tables and figures, grammatical editing and referencing) was provided by  Alice Rees, PhD , at  Fishawack Indicia Ltd, UK , part of  Fishawack Health , and was funded by  GSK. We would like to thank all patients who took part in the original MIRRA trial.
Funding sources and role: This post hoc analysis and the parent study (GSK ID:  115921 ;  NCT02020889 ) were funded by  GSK ; and funded in part by the  Division of Intramural Research ,  NIAID/NIH ; and an investigator-initiated clinical-trial application ( U01AI097073 ) through  NIAID/NIH , through which five of eight clinical sites in the United States were.
Recent years have seen the emergence and application of artificial intelligence (AI) in diagnostic decision support systems. There are approximately 80 etiologies that can underly uveitis, some very rare, and AI may lend itself to their detection. This synthesis of the literature selected articles that focused on the use of AI in determining the diagnosis, classification, and underlying etiology of uveitis. The AI-based systems demonstrated relatively good performance, with a classification accuracy of 93-99% and a sensitivity of at least 80% for identifying the two most probable etiologies underlying uveitis. However, there were limitations to the evidence. Firstly, most data were collected retrospectively with missing data. Secondly, ophthalmic, demographic, clinical, and ancillary tests were not reliably integrated into the algorithms' dataset.
Search Strategy and Selection Criteria A systematic literature review was completed on 6 March 2023. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the protocol was registered in the International Prospective Register of Ongoing Systematic Reviews (PROSPERO) (ID: 409568)  [13]. The search strategy was conducted across four electronic databases (PubMed, Cochrane, MEDLINE, and EMBASE). To capture all relevant articles, the following combination of terms and/or MESH headings were used: "uveitis", "algorithms", "artificial intelligence", "decision support system, clinical", and "Bayes Theorem". An example search in PubMed was (("uveitis" [MeSH Terms]) AND ("algorithms" [MeSH Terms] OR "artificial intelligence" [MeSH Terms] OR "decision support system, clinical" [MeSH Terms] OR "Bayes Theorem" [MeSH Terms])) (Table  1 ).
Posterior Segment Choroidal vascularity was used by Agrawal et al. to quantify choroidal inflammation  [27]. They defined the choroidal vascularity index (CVI) as the proportion of the intraluminal subfoveal choroidal area to the total circumscribed subfoveal choroidal area in enhanced-depth imaging OCT. This was achieved by manually segmenting the choroid with custom software after processing it with ImageJ. They showed a 6. 2% decrease in CVI between the time of uveitis diagnosis and 3-month follow-up, which was significantly higher than the 0. 7% CVI decrease in control eyes. As such, CVI has potential as a novel tool for monitoring the clinical course of posterior uveitis, panuveitis, and choroidal disease. McKay et al. proposed to identify and quantify inflammatory choriocapillaris lesions from automated swept-source OCT  [28].
In 2016, Gonzalez-Lopez et al. reported the results of a Bayesian belief network algorithm designed to help diagnose the cause of anterior uveitis  [10]. The center node of the Bayesian network was the uveitis etiology (11 etiologies: idiopathic; ankylosing spondylitis; psoriatic arthritis; reactive arthritis; inflammatory bowel diseases; sarcoidosis; tuberculosis; Behçet disease [BD]; Posner-Schlossman syndrome; juvenile idiopathic arthritis; and Fuchs heterochromic cyclitis). Chance nodes included demographic data (gender), ophthalmic data (ocular symptoms and signs), extraocular data (systemic signs and symptoms), and laboratory findings, with a retrospective collection. In a dataset of 200 patients, the etiology (determined by the senior expert clinician) matched the first or second most likely diagnosis given by the algorithm in about 80.
The expert-guided diagnostic tree provided an area under the curve (AUC) of 0. 92 (95% CI, 0. 89-0. 96), but this study was focused only on a single etiology. Most recently, Jamilloux et al. used a Bayesian belief network on a dataset composed of 877 incident uveitis cases to identify the etiology (center node)  [11]. Variables included age, gender, ethnicity, and the anatomic and clinical characteristics of the uveitis. To assess the algorithm performance, internal (by Monte Carlo cross-validation) and external validations were carried out. Network performance was quantified in terms of the proportion of patients correctly classified. Performance indicators were estimated for the most and the two most probable diagnoses. When the algorithm's two most probable diagnoses were considered, they reported 80% agreement with the clinical diagnosis in the training dataset, confirmed at 85% in the independent validation set.
Limitations of this study were collection from just one tertiary care center, which may affect generalizability to other centers and settings, and the risk of bias from retrospective data collection. Two web-based diagnostic decision support systems (DDSSs) have been reported  [35, 40]. The first, Uveitis Doctor (Lara-Medina, Alcazar de San Juan, Spain), comprises more than 59 uveitis syndromes and an inference engine based on decision trees. The second, Uvemaster, contains a knowledge base of 88 uveitis syndromes acquired from the medical literature data, each comprising 76 clinical items. Each clinical sign was assigned a value of 0 to 100, depending on its prevalence for each specific uveitis syndrome. Then, the interference engine combines the filtering rules with the patient dataset in order to offer possible diagnoses with diagnostic performance indicators (sensitivity, specificity, or positive predictive value).
Performance was assessed as the percentage of cases for which a specific diagnosis was obtained using the app. In a series of 228 patients, the diagnostic accuracy of the algorithm was 96. 6%, and when the first diagnoses proposed by the app were considered, the sensitivity was 73. 9% and the positive predictive value was 29. 5%. Helpfully, it incorporates ophthalmic, demographic, and clinical data in the dataset, but there are relatively few cases reported relative to the number of potential causes. These apps may improve the clinical management of uveitis by offering potential diagnoses and reducing the number of cases labeled as idiopathic uveitis. However, the knowledge base includes diagnoses with very high specificity tests and ophthalmic diagnoses that can usually be easily recognized.
, 2019 [33] (multilayer rule design) Dataset: 61 cases (case severity, laterality, granulomatous) 53 probable etiology): 60% Sensitivity (four most report, all uveitis type) Treatment (response to therapy) probable etiologies): 100% Demographic (Age, sex, and Jamilloux et al. , 2021 [11] Bayesian belief networks Dataset: 877 cases (all uveitis type) ethnicity) Ophthalmic (Anatomy, laterality, chronicity, vasculitis, granulomatous, and ocular 8 Sensitivity (most probable etiology): 54% Sensitivity (two most probable etiologies): 85% hypertension).
DNA Extraction, Leptospira Detection, and Identification Total nucleic acids were extracted from kidney samples of each collected bat specimen using the Biorobot EZ1 and EZ1 Virus Mini Kit version 2. 0 (Qiagen, Hilden, Germany). Samples were spiked with MS2 phage RNA and used as an internal extraction control  [26]. A reverse transcription step was performed on total nucleic acids using ProtoScript ® II reverse transcriptase (New England Biolabs, Ipswich, MA, USA). Finally, generated cDNAs were used as templates for the detection of pathogenic Leptospira by qPCR following a previously described scheme targeting the rrs gene  [27]. Six negative controls were included on each qPCR assay run on a Stratagene MX3000P thermocycler. Analyses were conducted using the MxPro software, and Ct-values were obtained using default parameters.
Phylogenetic Analysis The consensus sequences of secY and sequence alignments were conducted under Geneious software version 9. 1. 7  [29]. In order to compare the phylogenetic relationships between Leptospira from Mozambique and the SWIO region, additional sequences from this region available under GenBank were included in the phylogenies, as well as two sequences from samples collected on Madagascar in the context of another research program (see Supplementary Table  S1 ). The best model of sequence evolution was determined using jModelTest2 v2. 1. 10 [30,31]  based on the Akaike Information Criterion (AIC). Phylogenetic constructions were carried out using Bayesian Inference analyses implemented in MrBayes 3. 2. 6  [32]. The phylogeny constructions consisted of two independent runs of four incrementally heated Metropolis Coupled Markov Chain Monte Carlo (MC3) starting from a random tree.
Positive Samples and Identification Fifty-nine animals tested positive for pathogenic Leptospira infection, representing a global detection rate of 18. 8% ± 4. 3% (Table  1 ). At least one specimen was positive for each bat family, with the exception of the Vespertilionidae represented by samples of Afronycteris nana and Scotophilus viridis. Sequences from a 473-bp-fragment of the secY gene were then obtained by conventional PCR from 25 out of the 59 positive specimens from Mozambique. Two additional secY sequences were obtained from Madagascar specimens and completed the dataset of this study. The absence of ambiguous peaks allowed the production of good-quality sequences that were deposited in GenBank under accession numbers MT037447-MT037473. Based on the secY sequences, a major part of obtained sequences was related to Leptospira borgpetersenii (n = 13), followed by lineages embedded in clades J (11 samples) and M (1 sample), both with no Leptospira assignment (Figure  1 ).
The analysis of molecular variance (AMOVA) performed on the secY sequences dataset of Leptospira in SWIO does not show any geographical structuration of Leptospira lineages (p = 0. 52) but strongly supports a structuration according to the bat host families (p < 0. 001) (Table  2 ). Lastly, the topology supports that most bats are infected with L. borgpetersenii (clades A-F), in keeping with previous studies  [22] , while Leptospira clustering in the remaining clades requires full MLST genotyping for an assignment of Leptospira at the species level.
Acknowledgments: We wish to thank  Marc Stalmans  ( Department of scientific services of Gorongosa National Park ) and  Pedro Muagura  ( Conservation of Gorongosa National Park ) for facilitating the field missions in Gorongosa Park,  Bartolomeu Soto  ( National Administration of Protected area of Mozambique ) for his advice during research permits application, and  Jen Guyton  ( Princeton University ),  Bonginkosi C. Gumbi  ( University of Eswatini ) and  Iara Martins Gomes  ( Centro de Biotecnologia da Universidade Eduardo Mondlane ) who participated in field missions.
• Finally, we performed extensive simulations to verify the effectiveness of this approach. Our evaluation was performed using the realistic traffic simulator. The proposed strategy was found to be more effective than the shorter path strategy in managing the charging of electric vehicles. The structure of this paper is as follows: In Section 2, we provide an overview of the relevant related literature and propose a description of the system and linear mathematical model of the global context of the system and the constraints. In Section 3, we propose an assignment algorithm. Then, in Section 4, we detail the data collection procedure with SUMO and analyze the obtained results. The Section 6 includes a conclusion and recommendations for further research.
Mobility Simulators The primary function of mobility simulators is to simulate the movement of vehicles. In this context, we specifically highlight SUMO, TSIS-CORSIM  [18] , and Quadstone Paramics as the most popular simulators  [19]. In the development of road traffic simulation, these software have advantages and disadvantages; however, combining at least two software enables optimal results. One of the most practical approaches for this development is the combination of SUMO and Matlab  [18]. In the same field, the authors of ref. [20]  proposed TraCi, which allows SUMO to be linked to other applications through a client-server architecture based on TCP.
Mobility Simulators The primary function of mobility simulators is to simulate the movement of vehicles. In this context, we specifically highlight SUMO, TSIS-CORSIM  [18] , and Quadstone Paramics as the most popular simulators  [19]. In the development of road traffic simulation, these software have advantages and disadvantages; however, combining at least two software enables optimal results. One of the most practical approaches for this development is the combination of SUMO and Matlab  [18]. In the same field, the authors of ref. [20]  proposed TraCi, which allows SUMO to be linked to other applications through a clientserver architecture based on TCP.
Problem Formulation The notation used in our work is presented in Table  1. As shown in Figure  2 , the system starts with the EV trip. If the SOC of the vehicle's battery is sufficient, then the driver can continue the trip. On the other hand, if the battery SOC reaches a certain limit, the driver will receive a low battery warning notification. In this case, a drop-off request is sent to a collaboration platform, which takes care of finding the CS that matches all of the received requests based on the information stored in its database or by communicating directly with the CS. Once the free CS is found, the platform will use other services to allocate the appropriate EV to the free CS, taking into account their characteristics (load state, path, power of each CS, etc. ). Among these services, the collaboration platform uses an optimization algorithm to compute the optimal allocation, taking into account the system characteristics as well as the information provided by other services (traffic information services, road conditions, etc.
). When the optimal allocation is found, the EV is informed of a CS corresponding to its criteria. After the charging process is completed, the used charging point is released and the DB platform database is updated. Then, the EV can continue its journey. As shown in Figure  2 , the system starts with the EV trip. If the SOC of the vehicle's battery is sufficient, then the driver can continue the trip. On the other hand, if the battery SOC reaches a certain limit, the driver will receive a low battery warning notification. In this case, a drop-off request is sent to a collaboration platform, which takes care of finding the CS that matches all of the received requests based on the information stored in its database or by communicating directly with the CS. Once the free CS is found, the platform will use other services to allocate the appropriate EV to the free CS, taking into account their characteristics (load state, path, power of each CS, etc.
). Among these services, the collaboration platform uses an optimization algorithm to compute the optimal allocation, taking into account the system characteristics as well as the information provided by other services (traffic information services, road conditions, etc. ). When the optimal allocation is found, the EV is informed of a CS corresponding to its criteria. After the charging process is completed, the used charging point is released and the DB platform database is updated. Then, the EV can continue its journey. The goal was to assign each electric vehicle to the appropriate station with minimal energy consumption. We proposed that:.
Preparation of the Simulation SUMO is a traffic simulation software designed to manage large vehicle networks. It is, above all, a micro simulation because it applies a micro model of vehicle mobility. One of its main features is that it includes vehicle traffic without collisions. It also includes many other features, such as a large number of vehicle types, multi-lane roads, lane change options, single and dynamic routing, different road hierarchies and types of junctions, and the implementation of all traffic rules at intersections. This allows SUMO to manage large simulation environments since it can accept various input network file formats, such as XML descriptors, etc. The SUMO traffic simulator was in charge of collecting traffic data. This section provides a full overview of the methods involved in gathering traffic statistics (distance, speed, density, and flow) from the extracted map.
In this study, we chose the city of Valenciennes, more precisely, the area that included the university campus because of the high traffic congestion in this area. The first step, as shown in Figure  3 , was to extract the map from the OpenStreeMap website, which gave us an. OSM file. To insert this map in SUMO, we converted it into a Net. xml file using the Netconvert command netconvert [-osm-files map. osm -o filename. net. xm]. After the file was converted, Python script was used to add Trip and Route to the network using RandomTrips. py. Using the command py [randomTrips. py -n Filename. net. xml -r File-name. rou. xml -e 1000 -l -e] created two automatic files with the extensions. rou and. rou. alt, which contained all of the information concerning the road and vehicles. Finally, to run the map on SUMO, a configuration file was created with a.
Insertion of EV and CS The electric vehicles were integrated into the map after importing the Valenciennes map and the flows were generated using SUMO. To generate the electric vehicles, the following lines of code were added to the map, as shown in Figure  5. Since Sumo does not allow station information to be edited, the charging stations were manually inserted on the map using Netedit, a graphical network editor included in SUMO. The charging stations were located on highways, which SUMO referred to as curbs, as shown in Figure  6.