dataesr/scientific-paragraphs-categorization huggingface dataset:
The reported data include the age at which the retinopathy was diagnosed, the bestcorrected visual acuity measured before treatment, type of treatment against the neovascularization, and the result of the treatment observed in the DMD patients. Based on these case reports, it can be concluded that the onset of proliferative retinopathy in DMD patients, with severely impaired visual acuity, is associated with an advanced stage of the disease, since all patients except one  (Ober et al. , 2006)  were older than 20 years. Moreover, cardiopulmonary insufficiency may influence the onset of proliferative retinopathy, although in one clinical case the cardiac function was normal  (Park et al. , 2019). Overall, non-surgical treatment may be effective in some cases while surgical interventions may be necessary in advanced cases.
Financial support Research from our groups has been supported by  National Research, Development, and Innovation Fund of Hungary  to MTSB ( OTKA-PD  grant number  134799 );  Tempus Public Foundation (Hungary)  to MTSB (TKA grant number  167949 ); a short-term scientific mission from  COST Action  ( DARTER CA17103 ) to MTSB;  German Academic Exchange Service  to JK (DAAD grant number  57513929 ); AJ and JK were supported by  German Research Foundation  (DFG grant number  KR1317/ 17-1 ) to JK;  Centre National de la Recherche Scientifique (CNRS, France) ,  Université Paris-Sud (France) ,  Université Paris-Saclay ,  Agence Nationale de la Recherche  ( ANR ,  France , grant  ANR-14-CE13-0037-01 ), Association Monégasque contre les Myopathies (AMM, Monaco), and  Association Française contre les Myopathies (AFM-Téléthon, France ; grant number  17117 ) to CV; CNRS,  HFSP  (grant number  RGY0004/ 2003 ) to MJR;  Sao Paulo Research Foundation  ( FAPESP  grant numbers  2014-26818-2 ,  2016/04538-3  and  2022/00191-0 ) and  CNPq Productivity Fellow  (grant number  314630/2020-1 ) to DFV.
35, 36  More specifically, NODDI enables the voxel-wise estimation of three metrics: the neurite density index (NDI), which gauges the packing of neurites including both axons and dendrites, the orientation dispersion index (ODI), which quantifies the angular variability of the neurites, and the free-water fraction (FWF), which assesses the proportion of water molecules moving freely, not hindered in their course, along no preferential direction. As stated earlier, currently available data indicate that microstructural brain changes underlying motor sequence learning can be evidenced already in the short term, up to tens of minutes. 30, 31  There is also robust evidence that post-training sleep contributes to the consolidation of motor memories. 23  However, concrete evidence for sleep-dependent and offline structural modifications in learning-related networks in human is still lacking.
Behavioral data For motor learning at Day 1, the ANOVA computed on mean reaction time (RT) for sequential blocks performed with within-subject factor Block (1:25, 27:30) and between-subject factor Sleep (RS versus SD) disclosed a main Block effect (F For delayed motor memory consolidation at Day 5, the ANOVA compared mean performance on the 2 last blocks of the learning session (LS D1; blocks 29:30) and the 2 first blocks of the relearning session (RL D5; blocks 33:34) with within-subject factor Day (End LS D1 versus Begin RL D5) and between-subject factor Sleep (RS versus SD) to assess delayed offline gains in performance. The analysis revealed a significant decrease in mean RT between D1 and D5 (F 1, 59 = 13. 968, p < 0. 001, h p 2 = 0. 191). However, both groups exhibited a similar decrease over time with no significant Sleep (F 1, 59 = 0.
MRI data processing Anatomical processing Raw T1-weighted images were corrected for bias field signal using BiasFieldCorrection_sqrtT1wXT2w script from https://github. com/ Washington-University/HCPpipelines/tree/master, as described in the minimal preprocessing pipelines for the Human Connectome Project. 89  Segmentation was performed on the T1-weighted images using FastSurferCNN,  90  an advanced deep learning model trained to replicate Freesurfer DKT's segmentation. It segments the brain into 95 cortical and subcortical regions following the Desikan-Killiany-Tourville protocol. 91, 92  Cortical surface reconstruction was performed on T1-weighted images using FastSurfer  90  which is an extensively validated pipeline to efficiently mimic Freesurfer recon  [93] [94] [95]  by leveraging FastSurferCNN output.
ACKNOWLEDGMENTS W. S. is supported by the  Fonds de la Recherche Scientifique (FRS-FNRS. ,  Aspirant Research Fellowship ). The study and the postdoctoral fellows A. L. and M. G. were also supported by the  FNRS  and the  Fonds Wetenschappelijk Onderzoek -Vlaanderen (FWO)  under the  Excellence Of Science (EOS)  Project ( MEMODYN , No. 30446199  to P. P. and H. Z. ). The authors thank  Brecht Somers ,  Julie Galinier , and  Katja Skoda  for their help in data acquisition.
Thus, we speculate that the low levels of Gst in gcm 26 /+;Toll 10b /+ hemocytes (Figure  2D ) may increase their level of ROS, hence leading to a stronger response to Toll activation. Unexpectedly, the GO term enrichment analyses carried out on each dataset highlighted GO term related to synapse activity enriched in the two comparisons (Supplementary Figure  S2C, E ). These genes included several neurotransmitter receptors, suggesting an involvement of neurotransmitter mediated signaling in Toll activation and gcm 26 sensitization. A targeted analysis of neurotransmitter receptor expression in our dataset revealed the expression of 26 receptors in hemocytes and significant upregulations of the Nicotinic Acetylcholine receptor alpha 6 (nAchRalpha6), the serotonin receptor 5hydroxytryptamine receptor 1B (5-HT1B), the dopamine receptor Dopamine 1-like receptor 1 (Dop1R1) and the short neuropeptide F receptor (sNPF-R) by Toll    2H , Supplementary Table  S1 ).
Cholinergic signaling regulate hemocyte homeostasis We evaluated the impact of other neurotransmitter receptors and down-regulated 5-HT1B, Dop1R1, nAchRbeta3 or the glutamate receptor Eye-enriched kainate receptor (Ekar) (Figure  3B ). In all cases, the KD animals are completely viable (data not shown). In terms of hemocyte number, 5-HT1B-KD and Dop1R1-KD show no difference compared to control WL3. Ekar-KD displays less hemocytes (Figure  3B ), suggesting that glutamate signaling may also be involved in the regulation of hemocyte homeostasis. nAchRbeta3-KD shows a strong increase in the number of hemocytes (Figure  3C ), which further highlights the importance of cholinergic signaling in hemocytes' homeostasis. At last, to assess the impact of cholinergic transmission on hemocytes, we monitored the number of larval hemocytes after inhibiting the expression of the choline-acetyltransferase ChAT in neurons.
Acknowledgments We thank  K. Bruckner ,  D. Siekhaus ,  D. Hultmark ,  H. Tanimoto ,  S. Kondo ,  S. Russell ,  V. Honti ,  I. Ando  and  P. Soba  for providing fly stocks and antibodies. In addition, stocks obtained from the  Bloomington Drosophila Stock Center  ( NIH P40OD018537 ) and antibodies obtained from the  Developmental Studies Hybridoma Bank  created by the  NICHD  of the  NIH  and maintained at The  University of Iowa (Department of Biology, Iowa City , IA 52242) were used in this study. We thank  F. Stenger ,  M. Heinis ,  M. Moog ,  A. Kucan ,  A. Bhan ,  E. Naegelen ,  G. Zhang  for technical assistance. We thank the  Imaging Center of the IGBMC  for technical assistance. We thank  A. Maglott-Roth  from the  IGBMC screening facility  for the hemocyte analysis. The sequencing was performed by the GenomEast platform, a member of the " France Génomique " consortium ( ANR-10-INBS-0009 ).
This work was supported by  INSERM ,  CNRS ,  UDS ,  Ligue Régionale contre le Cancer, Hôpital de Strasbourg ,  ARC ,  CEFIPRA ,  USIAS ,  FRM  and  ANR  grants. P Cattenoz  was funded by the  ANR  and by the  ARSEP ,  W Bazzi  by the  USIAS  and by the  FRM  ( FDT20160435111 ). S Monticelli  was funded from  CEFIPRA  and  ANR  fellowships. This work of the  Interdisciplinary Thematic Institute IMCBio , as part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS  and  Inserm , was supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ), and by  SFRI-STRAT ' US  project ( ANR 20-SFRI-0012 ) and  EUR IMCBio  ( ANR-17-EURE-0023 ) under the framework of the  French Investments for the Future Program.
Results In this study, we aimed to characterise metabolic changes associated with cognitive decline and AD as well as to elucidate the role of the human gut microbiota in these changes. We rst generated 1D-NMR urine metabolome data from individuals on the clinical AD spectrum and from cognitively healthy agematched individuals from the Germany-based DELCODE study cohort. We identi ed a statistically signi cant decrease in formate and fumarate concentrations in people with AD dementia. To dissect potential microbial contributions, we then used WGS data generated from an independent cohort consisting of 24 participants with AD dementia and 24 cognitively unimpaired age-sex-BMI-matched controls to construct personalised host-microbiome models for each individual. We found that the formate microbial secretion was lowered in AD host-microbiome models compared to healthy control host-microbiome models.
Metabolomic analysis reveals decreased fumarate and formate concentrations in the urine of people with AD To characterise altered metabolism in AD and cognitive decline, we utilised 1D-NMR urine metabolome data generated at the baseline visit from n = 177 DELCODE participants  24  (Fig. 1A ). The DELCODE cohort has four principal study groups, from which a subsample was characterised using urine metabolomics: healthy controls (n = 49), individuals with subjective cognitive decline (n = 45), mild cognitive impairment (n = 49), and AD-Dementia (n = 34). A targeted metabolomics analysis resulted in the quanti cation of 49 creatinine-normalised urine metabolites (Table  S01 ). We screened all quanti ed metabolites with at least 50% non-zero measurements in association with the study group variable.
We generated pan-species microbial metabolic reconstructions consisting of the union of all metabolites and reactions present in any corresponding AGORA2 strains of the same species (Methods). These pan-species microbial reconstructions were then combined into one microbiome reconstruction for each sample. This AGORA2 mapping resulted in 815 different species present in at least one of the 48 samples with an average of 476 (SD = 74) microbes in each sample. After the cut-off of low abundant species (Methods), the samplespeci c microbiome models were composed of an average of 259 (SD = 29) pan-species models, accounting for an average of 73. 60% (SD = 11. 92%) of the total reads in the sequencing data (Fig. 2A ). The resulting 48 sample-speci c microbiome models consisted, on average, of 373,118 (SD = 40,819) non-unique metabolites and 337,340 (SD = 36,718) non-unique reactions (Fig.
While the COBRA modelling approach is a very valuable approach for investigating host-microbiome cometabolism involvement in AD, certain limitations should be noted. For instance, we used a relatively small cohort of subjects and controls. Hence, our results need to be validated in larger independent cohorts. It has to be noted that discarded microbes not accounted for by the AGORA2 resource could lead to loss of metabolic capacities in the correspondent microbiome models. Additionally, the models were built on microbial relative abundance data generated from the reads count data, and results were subject to the intrinsic compositional structure of the models. Genome-scale metabolic reconstructions are also continuously updated as new experimental data and biochemical knowledge become available  [50] [51] [52].
The incompleteness of the metabolic reconstructions is particularly true for gut microbes, for which only limited data are available. Computational reconstruction tools, such as DEMETER  53  , which has been used for the construction of the AGORA2 microbial reconstructions, permit the inclusion of experimental data, e. g. , from BacDive 54 , during the reconstruction process. Similarly, re ned genome annotations that correct missing and mis-annotations should be performed to minimise the errors in the reconstruction, and thus increase the delity of the predictions. Such reannotation has been done for most of the microbes in AGORA2. Moreover, an inherent limitation of the COBRA approach is that it assumes the biological system to be in a steady-state condition, thereby ignoring the dynamic nature of microbial communities.
H-NMR measurements The urine samples were handled and prepared as in  58. The urine samples from 185 individuals were measured at the University Medicine Greifswald on a Bruker AVANCE-II 600 NMR spectrometer operated by TOPSPIN 3. 2 software (both Bruker Biospin, Rheinstetten, Germany). The spectrometer was equipped with a 5-mm z-gradient probe and an automated tuning and matching (ATMA) unit (both Bruker Biospin, Rheinstetten, Germany). Specimens were automatically delivered to the spectrometer via SampleJet (Bruker Biospin, Rheinstetten, Germany) into standard 5 mm NMR tubes. The acquisition temperature was set to 300°K. A standard one-dimensional 1 H-NMR pulse sequence with suppression of the water peak (NOESYPREAST) was used  59. Urine samples of four individuals could not be processed at all due to too little biomaterial available for 1 H-NMR measurements.
WISCONSIN Faecal samples Our study utilised data from the Wisconsin Alzheimer's Disease Research Center (WADRC) and the Wisconsin Registry for Alzheimer's Prevention (WRAP) studies. Participants with AD-Dementia were recruited from the WADRC, while cognitively unimpaired participants were recruited from the WADRC and the WRAP studies. Data on age, sex, BMI, APOE genotype, diagnosis, and cognitive test results were collected  61, 62. Our study used a subset of the cohort comprising 48 individuals (24 AD-Dementia cases and 24 sex and age-matched healthy controls). Covariates were compared against the clinical diagnosis using Fisher's exact test for APOE4 and sex, and Welch's t-test for age and BMI.
Sequencing and processing of metagenomic data The faecal samples were sequenced and then processed through Qiita 31 using the default work ow for metagenomics data. In short, the raw les were loaded in multiple preparations to represent the multiple runs and processed with default parameters; then the raw sequencing data were demultiplexed and trimmed at 150 bases. Adapter removal was carried out using fastp 63 and human reads were ltered using minimap2 64. Genomic OTUs (gOTUs) were generated using the Woltka Toolkit 29 by aligning reads to the Web of Life (Release 1) reference genome database 30 using bowtie2 65.
Construction of personalised microbiome metabolic models First, we assigned taxonomy to gOTUs using the Web of Life database, and we summed together the gOTUs with matching taxonomical information. We considered all the reads assigned to the species accounted for by our database of genome-scale metabolic microbial reconstructions (AGORA2)  17. We normalised the considered reads so that the sum of all reads was one, thereby obtaining the relative abundances of each microbe in the sample. The abundances were then mapped to 1,742 AGORA2 panspecies reconstructions, the union of reactions and metabolites of each strain-speci c reconstruction of one species. Then, sample-speci c microbiome models were derived using the mgPipe module of the Microbiome Modelling Toolbox 18,19. Brie y, for each individual, all the pan-species reconstructions found in the corresponding sample were assembled into one microbiome reconstruction 18 , which was converted into a microbiome model by personalising the community biomass reaction based on the relative species abundance data in the sample.
Funding and acknowledgments This study was funded by the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  ( 757922 ) to IT, the  Science Foundation Ireland  under Grant number  12/RC/2273-P2. Funding for this project was also provided through  NIA  grant  U19AG063744  Funding for the  Wisconsin studies  includes a  Wisconsin Partnership Program  grant (BBB, FER),  National Institute on Aging  grant  R01AG070973  (BBB, FER),  National Institute on Aging  grant  P30AG062715  (SA),  National Institute on Aging  grant  RF1AG027161  (SCJ),  National Institute of General Medical Sciences  grant  T32GM081061  (MBH), and  National Institute of Neurological Disorders And Stroke  grant  F99NS130922  (MBH). Additional funding was provided by the  National Institute on Aging  ( RF1AG058942 ,  R01AG037639 ,  R01AG021155 ,  R01AG069901 ,  R01AG054047 ,  U01AG061359 ).
Materials and methods In order to thoroughly explore the literature on visual electrophysiological (retinal and cortical) indicators in psychiatric disorders, a search for relevant articles was conducted in the Pubmed and Google Scholar databases using the following keywords ("flash electroretinogram" OR "pattern electroretinogram" OR "retinal electrophysiological measurements" OR "electroencephalogram" OR "visual evoked potentials" OR "visual electrophysiology") AND ("psychiatric disorders" OR "mental disorders" OR "schizophrenia" OR "psychotic disorders" OR "mood disorders" OR "bipolar disorders" OR "depressive disorders" OR "Alzheimer's" OR "Parkinson's disease"). All results up to April 1st, 2023, were examined for the selection process. Relevant publications were chosen via individual independent selection of titles by the authors: IR, KT, TS and VL.
ACKNOWLEDGMENTS This work was supported by research grants  ANR-19-CE14-0045-002  (to M. C. ) and  ANR-14-IFEC-0006-01  (to S. S. and X. N. ),  ANR-14-CE016-0006-01  (to C. C. and L. M. ),  Inserm  and  UniversiteParis Cite-IDEX  (to M. C. , X. N. , and C. G. ). Image acquisition and image analysis were performed at the  Structure Federative de Recherche (SFR) Necker (Inserm   US24/CNRS UMS3633 ) and at  IMAG'IC core facility  ( Institut Cochin ) which is part of the  National Infrastructure France-BioImaging  supported by the  French research Agency  ( ANR-10-INBS-04 ). Image analysis of lighsheet acquisition were performed at the SFR Necker Image analysis platform with the help of  Nicolas Goudin. EC PhD  is supported by  Fondation pour la Recherche Medicale. The authors want to thank  Dr Christophe Helary  (Laboratory of chemistry of condensed matter-LCMCP,  Sorbonne-Universite, Paris)  for the generous gift of rat tail collagen I.
Standard protocol approvals, registrations, and participant consents Participants were screened at 99 outpatient specialty clinics or hospitals in 25 countries. Institutional review boards or ethics committees at each study site approved the protocol. The study is registered with ClinicalTrials. gov (NCT02200770). The study was conducted in accordance with the provisions of the International Council for Harmonisation Guidelines for Good Clinical Practice and the principles of the Declaration of Helsinki. All participants provided written informed consent.
Assessment of NMOSD attack Predefined attack criteria were used to diagnose each on-study attack (Supplementary Table  1 ). The 18 criteria covered optic neuritis, myelitis, and brain/brainstem domains. 1, 6 e attack assessment process has been described previously and is defined in Figure  1 (a). 1, 6 Briefly, potential attacks were evaluated by an on-site investigator, who also decided whether rescue therapy was needed independently of the adjudication status. In addition, the AC independently determined whether the event met the definition of an NMOSD attack in all cases. The AC comprised three physicians (two neurologists and one neuro-ophthalmologist) not involved in the study. For data analysis, cases in which the participant had reported a potential attack that was evaluated as a non-attack by the investigator and AC were labeled "participant-reported symptoms.
MRI assessment Participants received a full neuroaxis MRI at screening, at the end of the RCP, and at attack assessment visits. All MRI images were sent to the central imaging vendor (NeuroRx, Montreal, QC, Canada) and read by two independent neuroradiologists. In the event of a discrepancy between the readers, a consensus read was performed and reported. Of note, images of the optic nerve were obtained during acquisition of brain scans. During attack adjudication, the investigator and AC reviewed MRI data of only the relevant neuroanatomic region when the selected attack criteria required MRI review (criteria 9-11, 14-17). In addition, MRI review could be requested by AC members when clinical data were deemed inconclusive or potentially inconsistent. An independent request for MRI review by a majority of AC members resulted in MRI images of the relevant domain being provided to all AC members.
Attack incidence and MRI findings Overall, MRI lesion correlates were found in most adjudicated attacks (37/41, 90%) for which MRI data were available, including those adjudicated without MRI data (23/27, 85%). Of the four adjudicated attacks not found to have MRI lesion correlates, two were adjudicated as optic neuritis and two as myelitis (3/4 in the inebilizumab treatment group). For cases in which MRI lesion correlates were observed, gadolinium (Gd+) T1 lesions were detected more often than T2 lesions in AC-adjudicated events (Table  1 ). By contrast, domain-specific MRI findings were identified retrospectively in only 1/8 investigator-determined attacks not confirmed by the AC (a Gd + T1 lesion in the optic nerve from the affected eye in an inebilizumab-treated AQP4-seronegative participant).
Among participants with AC-adjudicated attacks, seven participants had missing sGFAP data and two had missing MRI data. sGFAP concentrations correlate well with ACadjudicated attacks. Among these, elevations in sGFAP were notably less common in the inebilizumab than in the placebo group. This is consistent with previous demonstration of a reduction in sGFAP with inebilizumab in participants without an adjudicated attack, and fewer inebilizumab-treated participants with raised sGFAP compared with participants receiving placebo. 7 Increased sGFAP (>2-FC from baseline) was also observed in 1/7 participants with investigator-determined attacks (non-AC-confirmed) and 4/13 participants with reported symptoms that were deemed not attacks by both investigators and AC. Given the previously documented favorable effects of inebilizumab on sGFAP levels and the significant reduction in risk of attack with inebilizumab versus placebo, it is possible that these observed cases in the inebilizumab group may be relatively mild.
Acknowledgements The authors thank all the participants of the N-MOmentum trial. This manuscript was submitted on behalf of the principal investigators of the  N-MOmentum study group , 6 who administered the clinical trial. Writing and editing assistance were provided by  Ester Baixauli PhD  and  Jenny Thorp of Oxford PharmaGenesis. journals. sagepub. com/home/msjgrants from the  National Institutes of Health ,  Novartis , and Mallinckrodt; and has a patent for Aquaporumab issued. H. J. K. has received a grant from the  National Research Foundation of Korea ; and consultancy/ speaker fees from  Alexion ,  Celltrion ,  Eisai ,  HanAll BioPharma ,  Merck Serono ,  Novartis ,  Sanofi Genzyme ,  Teva-Handok , and  Viela Bio ; serves on a steering committee for  MedImmune/Viela Bio ; and is a co-editor for the Multiple Sclerosis Journal and an associate editor for the Journal of Clinical Neurology.