dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, immunolabeling experiments with GFAP antibody showed that most of these cells are GFAP-, while larger cells reminiscent of astroglial cells are GFAP+ (Additional File 1: Fig. S2 ). Multiple loci of ependymal abrasions were also seen. Both lesions may be secondary to the high intracranial pressure. H&E staining of the eyes reveal no disorganization of the neural retina. Whole exome sequencing identified two compound heterozygous variations in the exon 8 of CRB2 (NM_173689. 6): c. 2325C > A; p. (Cys775*) and c. 2400C > A; p. (Asn800Lys). The stop variation was never reported in any database, neither in patients nor in healthy individuals and is expected to lead to the degradation of the mutated transcript by NMD. The missense variation involves the same nucleotide as in family 1, that is substituted to a different nucleotide (here C > A versus C > G in family 1) but leading to the same amino acid modification (Asn to Lys), and thus with the same predicted consequences on the protein stability (Fig.
Acknowledgements This work was supported by grants from the '' Agence Nationale de la Recherche'' (ANR)  to  S. T. ( ANR-17-CE16-0003-01 ). The  Imagine Institute  is supported by state funding from the  ANR  under the '' Investissements d' Avenir'' program  ( ANR-10-IAHU-01 ) and as part of the  second ''Investissements d' Avenir'' program  ( ANR-17-RHUS-0002 ). The authors are grateful to the families involved in this study and thank the  French Society of Fetal pathology (SOFFOET).
III. METHOD A. Study Selection The corpus of this article was gathered using Google Scholar, ACM-DL (Association for Computing Machinery-Digital Library) and PubMed databases. The articles were collected and read between October 2021 and August 2023 and the keywords used were "(virtual (reality OR environment) OR oculus OR quest OR vive) AND (vocational OR occupational) AND (rehab OR training OR adaptation OR readaptation OR readaptative) AND (cognitive OR cognition)". In addition, we considered adding one by one to the keywords either the targeted population or the cognitive domains. The population was selected among pathologies inducing neurodegenerative or acquired cognitive impairment and additional keywords were: "Alzheimer", "acquired" or "traumatic brain injury", "cancer", "leukemia", "encephalitis", "dementia", "schizophrenia", "multiple sclerosis", "stroke","head trauma", "brain lesion","mild cognitive impairment", "MCI", "anoxia", "hypoxia", "post concussion", "post concussive".
This selection process resulted in 452 screened articles among the databases, from which 290 were excluded when identified out of the scope of this article based on title and abstract. The remaining 162 articles were then reduced, according to our exclusion criteria, to 16 articles. The collected articles are the works of Jacoby et al. [31] , Man et al. [49] , Tsang et al. [50] , Yip et al. [32] , Canty et al. [33] , Faria et al. [34] ,  [51] , Sohn et al. [9] , Gamito et al. [52] , Aubin et al. [53] , Mrakic-Sposta et al. [35] , Liao et al. [54] , Park et al. [29] , Giachero et al. [38]  and Oliveira et al. [55] ,  [56].
B. Data Analysis In the collected studies, we extract the following information: 1) VR apparatus and 2) tasks, 3) level of immersion, 4) targeted population, 5) results, 6) adaptivity to the user and 7) transferability of acquired knowledge. Comparing the apparatus would only be possible on a global level, due to the lack of evaluation of the devices in studied articles. For the VR tasks, we analyze the inner proceedings of the tasks, the duration and frequency of sessions. As defined in section I, the immersion contributes to the sensation of being physically in a VE and not every type of interaction needs a high level of immersion. The components of immersion can be summarized as the field of view (FOV), the field of regard (FOR), the display size, the display resolution, the stereoscopy, the head-based rendering, the realism of lighting, the frame rate and the refresh rate  [57].
Liao et al. do not detail the tasks used for rehabilitation. Their study combines vocational simulations of supermarket employee or kitchen chef video games from Owlchemy Labs's "Job Simulator" software, with virtual rehabilitation activities like Tai Chi or football  [54]. It involves 36 sessions of 1 hour with five different activities: taking public transportation, cooking as a kitchen chef, working in a convenience store, training in Tai Chi or football. This choice of vocational context is justified in the studies by store employee being a common job for the targeted population in these three studies. For one study, a virtual office was used  [49]. With another approach, Man et al. provide an immersive office module, with tasks related to an office work (identification of office items and utilities in the office, handling files and printers, sending and receiving emails, managing inventory, etc)  [49].
| Generation of GES NGS data was interrogated to identify the optimal set of genes (GES) that best predicts the differences between the BD drug cocktail-treated and vehicle-treated control cells as we have previously described. 5 Given a set of 20 replicates per treatment, the predictive set of genes was limited to <8 to guarantee sufficient statistical estimation of joint predictors. A GES of <8 genes was | 3 BORTOLASCI et al. also preferred in view of its subsequent requirement-a manageable number of genes that can be measured for compound library screening. Briefly, we used diagonal linear discriminant analysis (DLDA) and the signal-to-noise ratio (SNR) statistic on genes with evidence of differential expression between the BD drug cocktail and vehicletreated cells (p < 0. 01, t-tests).
| Effects of trimetazidine in cortical networks Cells were seeded (100 K/well) into 24-well plates and treated with 0. 1, 1, 10 or 100 μM trimetazidine (Sigma) or vehicle control (0. 01% water). After 24 h, cells were harvested and NGS was performed as described previously. Genes with trends of up-or down-regulation across increased trimetazidine doses were identified using the R package IsoGene. 13 The likelihood ratio test for monotonic trend was calculated based on permutations (n = 100). p-values were adjusted for multiple testing using Benjamini-Hochberg correction. To find potential pathways regulated by trimetazidine, genes with significant dose-response analysis results (adjusted p-value<0. 05) were submitted to the functional annotation tool of the database for annotation, visualization and integrated discovery (DAVID).
| Cellular bioenergetics and mitochondrial function analysis NT2-N cells were seeded (80 K/well) into 24-well Seahorse V7 plates and treated with 0. 1 or 1 μM trimetazidine or vehicle control (n = 4) for 24 h. The oxygen consumption rate (OCR) was assessed using a Seahorse XFe Flux Analyser (Seahorse Bioscience) as previously described. 19 Data was normalised to total protein using the Pierce BCA Protein Assay (ThermoFisher). For respiration analyses from rat hippocampus, tissue was sampled using a 1 mM punch biopsy needle and placed in a Seahorse Islet capture plate in Kreb Henseleit Buffer (KHB) followed by capture screens before the plate was incubated in a non-CO 2 incubator for 10 min at 30°C. Respiration was measured for two 2 min periods, separated by a 2 min mix period, after injection of malate 5 mM, glutamate 1 mM and succinate 5 mM.
| RE SULTS The overall features of the NGS dataset obtained from the NT2-N cells treated with a combination of BD drugs are described elsewhere. 9 A GES was generated which best describes the overall biological effects of the drug cocktail in NT2-N cells (Figure  1A ). The GES consists of 3 genes: ANXA2 and FBN1, whose expression was decreased after drug treatment, and TPP3, which was increased. Together, the expression of these three genes predicted which treatment group the cells were in with a power of >99%. The differential expression of the GES genes was confirmed by RT-PCR (Figure  1B ). The effects of the drug cocktail on these genes were not dominated by one of the drugs but were representative of the overall effects of the 4 drugs in combination (Figure  1C ), representing a net therapeutic effect.
In total, 960 drugs were screened for their effects on the expression of the GES genes in NT2-N cells and this data was used to calculate a similarity score for each drug, with a lower similarity score indicating more similar effects to the BD drug cocktail. The screening results were filtered to exclude drugs that were not approved for use in humans, had been withdrawn or received a black box warning. The "hit" drugs from the screen are shown in Table  1 , along with their effects on the expression of the GES genes and overall GES similarity scores. Trimetazidine was identified as a drug of interest for further investigation because it has an excellent safety profile, is known to cross the blood-brain barrier and has not previously been used to treat neuropsychiatric disorders. Given that trimetazidine has been NGS was also used to investigate the transcriptional effects of trimetazidine in iPSC-derived cortical networks, a more biologically relevant human pre-clinical model.
The first objective of this paper is to evaluate the bias-corrected GCM data obtained with the cumulative distribution function (CDF-t) transformation method in Senegal and then quantify the sensitivity of the bias-corrected data for the study of climate impact on malaria. The second aim of this study is to understand the effects of future climate change on malaria transmission using, for the first time in West Africa, the VECtor-borne disease community model of ICTP, TRIeste (VECTRI), which has been developed by the International Centre for Theoretical Physics (ICTP). The model physics and associated parameters are taken from the literature for the Anopheles gambiae complex and the malaria parasite Plasmodium falciparum. This biological model is a mathematical model of malaria transmission.
Climate Data For the evaluation part of this study, we use different reference datasets, including re-analysis data (ERA5), estimated satellite data (ARC2 and CHIRPS), and CPC observations from meteorological station networks around the world, and on the other hand, data from different global climate models (GCM) of the CMIP5 project (Coupled Model Intercomparison Project Phase 5 (CMIP5) for the historical data and the projections. ERA5 data represent the terrestrial component of ERA5 reanalysis climate data from 1979 to the present  [30]. The data has a 0. 25 • × 0. 25 • (25 km × 25 km) resolution grid, and the temporal frequency of output is hourly  [31]. The Climate Hazards Group InfraRed Rainfall with Station data (CHIRPS) is a rainfall dataset covering more than 30 years. CHIRPS creates gridded rainfall time series using satellite imagery and in situ station data for resolutions of 0.
05 •  [32]. Africa Rainfall Climatology 2. 0 (ARC2) is a 29-year rainfall estimation dataset focused on Africa at a spatial resolution of 0. 1 •. ARC2 is a revision of the first version of ARC1 that uses data from two sources in accordance with version 2 of the operational precipitation estimation algorithm  [33]. The Climate Prediction Center (CPC) is a gridded dataset of precipitation and temperature with a resolution of 0. 5 •. These data have been available since 1979  [28]. Three objective techniques are applied to data from daily quality-controlled precipitation reports from approximately 16,000 land stations to obtain daily precipitation analyses  [34]. Table  1  summarizes the re-analysis and data satellite combined with observation data (model, definition, grid, and references). 1.
) are increasingly using these simulation outputs either to compute related impact metrics or to alter models. It is well known that GCM (general circulation model) simulations require a downscaling process to increase their spatial resolution before projecting onto the local domain. In this study, we extracted the dataset from the daily data produced by  [26]  using the CDF-t (cumulative distribution function transform) method developed by  [37]  to correct the statistical distribution of the key climatic parameters for impact simulations. This bias technique reduces the errors in the present-day simulations of the CMIP5 models compared to observed data according to  [12]  (figure not shown) and also reduces the spread between models for the different representative concentration pathways (RCPs).
5 and RCP8. 5 emission scenarios separately. The VECTRI model uses climate data at this native resolution. All the models used were interpolated to the resolution of the Senegal mask, which is 0. 0625 • longitude for 0. 0625 • latitude, to standardize the resolutions, improve intercomparison, and achieve the multi-model average. For validation, the time series and spatial patterns of the GCM models are compared to those of the reference datasets, considering the common period. The Taylor diagram described by  [61]  is used to evaluate the performance of bias-corrected CMIP5 models against reference datasets. It is used by meteorologists and atmospheric scientists. This diagram offers the advantage of representing at the same time 3 statistics: the centered root-mean-square error (RMSE), the standard deviation (STD) of the simulation compared to the observation, and the correlation coefficient between observation and simulation.
The correlation coefficient measures the degree of connection or dependence between two quantitative traits. It is between -1 and 1. The standard deviation is the most commonly used measure of data dispersion in statistics. The lower the standard deviation, the less the values are dispersed around their mean. The RMSE of the arithmetic mean of the square of the difference between the observed and simulated values. A new approach for the VECTRI model input parameters was used. The bias correction technique using the CDF-t (cumulative distribution function transform) method was applied to the climate simulations of the fifth phase of the Coupled Models Intercomparison Project (CMIP5)  [26]. This eliminates systematic biases in climate models that could alter impact predictions. The results were analyzed for two representative concentration pathway (RCP) scenarios (RCP4.
Validation of the Rainfall and Temperature Inputs For the annual cycle (Figure  2a ), the datasets show a peak (maximum precipitation) in August. On the other hand, CHIRPS data shows the highest rainfall amount approaching 200 mm in August, while ARC2 shows the lowest rainfall amount in August (~150 mm). A small variation between data sets is observed in the amplitudes. The CMIP5 climate simulations were compared to these datasets. The annual cycle is well reproduced by the models (Figure  2a ), with the maximum precipitation in August, but with some divergences in the amplitudes. Some models have a maximum value of around 200 mm (CMCC-CM and CMCC-CMS) approaching validation data (CHIRPS), others between 205 and 245 mm (ACCESS1-3, CanESM2, CSIRO, CNRM-CM5, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, inmcm4, ENSMEAN-GCM), and one of about 250 mm (IPSL-CM5B), which overestimate rainfall over the period 1983-200.
Greater variability prevails in the interannual cycle (Figure  2b ) of precipitation. In Figure  2b , all datasets show an increasing trend from 1983 to 1989 (CPC [400-700 mm], ARC2 [310-550 mm], CHIRPS [410-650 mm], ERA5 [400-650 mm], and ENSMEAN [399-640 mm]). Beyond this year, we see that precipitation decreased slightly between 1990 and 1998. The years 2001 and 2002 was marked by a sharp drop in precipitation, which was mainly manifested by the CPC. The CMIP5 data, for the year-to-year variation, shows that the IPSL-CM5B model strongly overestimates precipitation over almost the entire period compared to the validation datasets. With the exception of a few models such as CSIRO, inmcm4, and GFDL-ESM2M, which slightly overestimate rainfall for some years, all other models, such as ACCESS1-3, CanESM2, CMCC-CM, CMCC-CMS, CNRM-CM5, GFDL-CM3, GFDL-ESM2G, and ENSMEAN-GCM, are close to validation.
The spatial distribution of precipitation for the different datasets was represented (Figure  3 ) considering the high rainy season, namely July-September, for the validation period 1983-2005. We recall that there is only one rainy season in Senegal and more generally in the Sahel, between June and October, but that more than 85% of total rainfall falls between July and September (JAS). Indeed, the climate of Senegal presents a unimodal rainfall regime. The long dry season (seven months) extends from November to May, and the wet or rainy season (five months) from June to October. The maximum rainfall is usually recorded in August. In Figure  3a -e, a noticeable latitudinal gradient separates the northern and southern parts of Senegal. The wettest area of Senegal is clearly highlighted by the different datasets.
The regions of Ziguinchor, Sedhiou, Kolda, and Kedougou are located, respectively, in the southwest and southeast of the country. The spatial pattern of the models is shown in Figure  3f -q for average rainfall in July-August, as this is a period of highest rainfall, usually followed by an upsurge in malaria epidemics. Figure  3f -q illustrate the rainfall gradient from the south to the north of Senegal. The GCM precipitation patterns show a minimum in the central and northern parts, with maximums in the south and southeast, as obtained with the baseline data. The models show the strong signal obtained in the southwestern and southeastern parts as obtained with the validation data (CPC, ARC2, CHIRPS, ERA5, and the ensemble mean). The annual cycle indicates a bimodal temperature cycle with two peaks in May (31.
Spatio-Temporal Variability of the EIR for the Validation of the GCM The simulations of the VECTRI model, represented on Figure  6 , are based on the reference data and their overall average (CPC, ARC2, CHIRPS, ERA5, and ENSMEAN-OBS) and on the outputs of 11 models from the CMIP5 and their overall mean (ACCESS1-3, CanESM2, CSIRO, CMCC-CM, CMCC-CMS, CNRM-CM5, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, inmcm4, IPSL-CM5B, and ENSMEAN-MOD). The malaria season simulated by VECTRI through the variable EIR (entomological inoculation rate) in Senegal, in particular, runs from August to October, with a sharp peak in September (Figure  6 ). The highest EIR value is given by CHIRPS (105 ib/p/m), followed by CPC, ERA5, and ENSMEAN-OBS (105 ib/p/m), and finally, ARC2, which has the smallest value (85 lb/p/m). For the simulation of the CMIPS models, we find that the models reproduced the annual cycle well.
Apart from the IPSL-CM5B model, which gives a peak in August (maximum peak at 121 ib/p/m), all the others agree with the validation data over the period with a strong transmission in August, September, and October and a maximum peak in September. All models overestimate malaria in Senegal compared to baseline data. However, the ACCESS1 model greatly exceeds the other models (maximum peak at 125 ib/p/m), followed by the CanESM2, CSIRO, CMCC-CM and CMCC-CMS, CNRM-CM5, inmcm4, and ENSMEAN models (maximum peak at 118 ib/p/m), and finally the GFDL-CM3, GFDL-ESM2G, and GFDL-ESM2M models (maximum peak at 110 ib/p/m). We note, for the variation of the transmission of malaria during the year according to the latitudes, that the regions located between 13 • N and 15 • N have a strong tendency for the transmission of malaria during the month of September, where the maximum is observed for validation data (CPC, ARC2, CHIRPS, ERA5, and ENSMEAN-OBS).
In other words, the simulations overestimate the number of infectious mosquito bites per man per month over time. The period of strong transmission (between August, September, and Oc-tober) is reproduced by the models, as is the maximum peak (obtained in September). The ACCESS1-3 model and the ensemble mean of all CMIP5 models (ENSMEAN-MOD) appear to provide a fairly good representation of the variation in malaria over time (Figure  7f, q ). For the variation of malaria transmission from one year to another according to latitudes, we note that the periods  1986-1989, 1992-2000, and 2003-2005   Figure  8  shows the variation of the EIR as a function of latitudes for the four reference datasets and their overall mean (CPC, ARC2, CHIRPS, ERA5, and ENSMEAN-OBS). We see a decrease in EIR from the southern latitudes to the northern latitudes of Senegal.
25). The GFDL-ESM2M, GFDL-ESM2G, and GFDL-CM3 models are less dispersed (lower standard deviation values) with strong correlation values (greater than 0. 95). The GFDL-CM3 model, compared to the two other versions of the model, simulates the EIR variable less well, with high RMSE values, a weaker correlation, and a significant standard deviation. Most models overestimate the magnitude of the entomological inoculation rate. The results show that the CMCC-CM and CNRM-CM5 models perform better at representing the EIR variable. It should also be noted that the CMCC-CMS models present scores very close to those of the CMCC-CM. In summary, in Figure  10a ,b, we note that for all models, r is greater than 0. 99, RMSE is lower than 0. 2, and the STD is approximately around 1. 25. These three statistical values illustrate the performance of individual bias-corrected CMIP5 in reproducing precipitation observations (ERA5) and the ad value by using these data as input for VECTRI simulations.
However, according to our results, malaria will be more important in the RCP4. 5 scenario, which is less restrictive than the RCP8. 5 scenario. Relating climate change to malaria projections is not a simple matter. There are many uncertainties in the projection of malaria-related temperatures and rainfall. The uncertainties come from the imperfection of climate models, the bias correction methods, the choice of climate parameters, the choice of an observation-based reference dataset on bias correction, climate change scenarios, and the malaria model (we used VECTRI). The present study focuses on three sources (climate models, bias correction methods, and malaria models). In the chosen GCMs, depending only on simulation availability, we have not evaluated their projection skills in the Sahel area and were not based on their spatial resolution.
Conclusions In this study, the VECTRI malaria model is used to simulate the spatio-temporal variation of the malaria parameter (EIR) in Senegal. The simulations are based on ERA5, CPC, ARC2, and CHIRPS data to assess and CMIP5 global climate models, separating historical data and projection data. The two scenarios (RCP8. 5 and RCP4. 5) made it possible to know the presence and evolution of malaria in the future. This means that the model can reproduce the effect of climate change on malaria transmission. The study revealed that the maximum of the EIR (obtained in September) follows the rainfall regimes (obtained in August), with a strong period of transmission generally located between August and October for a major part of the models and July and November for certain models. Precipitation provides breeding grounds for mosquitoes, while temperature impacts larval and mosquito life cycles.
Data Availability Statement: The reanalysis data used in this work was obtained from the Climate Data Store (CDS) of the European Center for Medium-Range Weather Forecasts (ECMWF), i. e. , this link: https://cds. climate. copernicus. eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form. The data is downloaded using the CDS API (Python script). CHIRPS, ARC2, and CPC data were downloaded programmatically into a bash file.