dataesr/scientific-paragraphs-categorization huggingface dataset:
Modeled natural history A great potential value of GenIDA is the collection of longitudinal data on KdVS and other rare NDDs. This is important not only for counseling but also for determining outcome measures for possible treatment and interventions. Clinical information on individual patients at different time points is still limited, but cross-sectional data from individuals diagnosed with KdVS of different ages allowed the modeling of the natural history of 4 important aspects of NDDs: speech, language, reading, and writing ability. The results regarding speech and language in KdVS were encouraging: children continued to develop verbal speech and language after the delayed onset of first words, with the majority of respondents noting that children could speak using well-formed sentences as teenagers.
Yet, the clarity of speech or "understandability" of individuals with KdVS (Figure  2 ) was significantly reduced, with only 48. 1% of the persons >6 years of age that could be understood by strangers. These findings are in line with detailed speech and language phenotyping studies of children with KdVS that used standardized assessments, which also show a high percentage of speech production disorders associated with poor speech intelligibility. 16, 25  Writing skills appear limited even at a later age and could be affected by the fine motor skills problems reported in the KdVS (42. 0%), a finding that is again in line with the previous phenotyped cohorts. 16, 25  Information on adverse effects of drugs For epilepsy, GenIDA confirms earlier data based on smaller cohorts with respect to the proportion of KdVS individuals with epilepsy.
Conclusion The analysis of GenIDA information on 237 individuals with KdVS shows that caregiver-driven data collection is an approach that is effective in terms of both global recruitment and centralization of clinical data in rare NDDs. The caregiver derived data are in line with previous KdVS cohort studies, and there was no significant genotype-phenotype relation (CNV deletion vs KANSL1 pathogenic variant). Comparison of the KdVS cohort with other NDD cohorts in the registry revealed clinical features that are overrepresented in the KdVS cohort, including tracheomalacia/ laryngomalacia. Modeling of natural history showed a positive development of speech and language over time and relatively good reading ability. In case of epilepsy, valproate and oxcarbazepine were reported as effective antiepileptic drugs, whereas levetiracetam may have a relative lack of efficacy.
Finally, childhood respiratory disorders, including recurrent pneumonia, is a group of novel clinical conditions that was not reported in KdVS before. In conclusion, GenIDA is a powerful registry to collect and harness valuable data on rare NDDs, which is essential for genetic counseling, clinical decision making, and determining clinical outcome measures. The registry may serve as a starting point for follow-up research into the clinically relevant observations, which might result in better management and personalized treatment strategies in NDDs.
Data Availability Data specific to a cohort of individuals diagnosed with an NDD in GenIDA are accessible in processed form via the visualization tab (https://genida. unistra. fr/visualizations/), item by item, on the website to any caregiver registered in GenIDA in the said cohort (https://genida. unistra. fr/register/), as soon as the threshold of 10 completed files for the said condition is reached (defined for statistical analysis). They are also available to any professional user upon simple registration in GenIDA. Each participating caregiver can download its relevant PDF file, sort of anonymous clinical records containing all questionnaire responses for a single individual diagnosed with an NDD. Registered investigators/professionals wishing to have access to these PDFs must justify their request (short statement explaining their project) to the GenIDA administration by email (genida@igbmc.
Acknowledgments The authors thank the  Institute for Advanced Studies of the University of   Strasbourg (USIAS) , the  University of Strasbourg Foundation  ( Roche Fund for Personalized Medicine) , and the  Fondation Jérôme Lejeune  and the  National Research Network "Groupement d'Intérêt Scientifique Autisme et Troubles du Neuro-D éveloppement" (GIS Autisme et TND)  for their financial support. The  GenIDA  project is supported by the  French RaDiCo ( Rare Disease Cohorts) research program  for national and European cohorts of patients with rare diseases. This work is generated within the European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies (ERN ITHACA). The authors also thank  Doulaye Dembele ,  Genomeast, IGBMC , for his help in the statistical analysis of the GenIDA data.
PDT involves the use of photosensitizing agents and light to activate them, leading to localized cancer cell death  [2]. However, PDT alone may not be sufficient to eradicate prostate cancer completely. Thus, the combination with other therapeutic options may pro-2 of 17 vide opportunities to draw on the strong points of each option and offset their weaknesses, leading to additive or synergistic therapeutic effects  [4]. This review article aims to explore the potential of PDT as a treatment for prostate cancer, including its mechanism of action, limitations, photosensitizers, and combination with other therapies. It also provides insights into the future directions of PDT in the context of prostate cancer treatment. To ensure the accuracy and comprehensiveness of our review, our team embarked on a rigorous data collection and search process from a wide range of reputable databases, including Google Scholar, Pubmed, Springer, Elsevier ScienceDirect, and Web of Science.
United States Drug: WST09 Phase 2 Completed 28 NCT00308919 Princess Margaret Hospital Toronto, Ontario, Canada No Results Posted Drug: WST09 Vascular Photodynamic therapy Phase 2 Phase 3 Terminated 16 NCT00312442 Abramson Cancer Center of The United States University of Pennsylvania Philadelphia, Pennsylvania, No Results Posted Centre Hospitalier Universitaire (CHU) Angers, France Hôpital Claude Huriez Lille, France Institut Mutualiste Montsouris (IMM) Drug: WST11 Phase 2 Completed 86 NCT00975429 Paris, France University College London No Results Posted Hospital (UCLH) Kings College Hospital (KCH) Frimley Park Hospital NHS Trust Catharina Ziekenhuis Abramson Cancer Center of The Drug: Motexafin lutetium Phase 1 Terminated 24 NCT00005067 University of Pennsylvania Philadelphia, Pennsylvania, No Results Posted United States Drug: TOOKAD ® Soluble 4 mg/kg Phase 2 Active 50 NCT03315754 Memorial Sloan-Kettering Cancer United States Center New York, New York, No Results Posted Focal therapies including PDT (PS undetermined) Recruiting 200 NCT03492424 Weill Cornell Medicine New York, New York, United States No Results Posted University Health Network-Princess Margaret Hospital Toronto, Canada Centre Hospitalier Universitaire Angers, France Drug: WST11 Phase 2 Completed 42 NCT00707356 Hôpital Bocage-CHU Dijon, France Hôpital Claude Huriez, Lille, France No Results Posted Institut Mutualiste Montsouris (IMM), Paris, France Frimley Park Hospital NHS Trust, Frimley, United Kingdom Drug: TOOKAD ® Soluble Phase 2 Completed 8 NCT00305929 The Prostate Centre Princess Margaret Hospital Toronto, Ontario, Canada No Results Posted Hospital General Tlahuac Mexico DF, Mexico Pan-American Medical Research Drug: TOOKAD ® Soluble Phase 3 Completed 81 NCT01875393 Institute (PAMRI) then moved to Consultario del Dr Rodriguez Panama No Results Posted city, Panama Hospital Nacional Cayetano Heredia San Martin de Porres, Peru.
To obtain accurate estimates for biodiversity and ecological studies, metabarcoding studies should be carefully designed to minimize both false positive (FP) and false negative (FN) occurrences. Internal controls (mock samples and negative controls), replicates, and overlapping markers allow controlling metabarcoding errors but current metabarcoding software packages do not explicitly integrate these additional experimental data to optimize filtering. We have developed the metabarcoding analysis software VTAM, which uses explicitly these elements of the experimental design to find optimal parameter settings that minimize FP and FN occurrences. VTAM showed similar sensitivity, but a higher precision compared to two other pipelines using three datasets and two different markers (COI, 16S). The stringent filtering procedure implemented in VTAM aims to produce robust metabarcoding data to obtain accurate ecological estimates and represents an important step towards a non-arbitrary and standardized validation of metabarcoding data for conducting ecological studies.
Implementation VTAM is a command-line application that runs on Linux, MacOS or Windows Subsystem for Linux (WSL) based on the method described in  [11]. VTAM is implemented in Python3, using a Conda environment to ensure repeatability and easy installation of VTAM and these third-party applications: WopMars (https://github. com/ aitgon/wopmars/), NCBI BLAST, Vsearch  [32] , Cutadapt  [27]. Data is stored in an SQLite database that ensures traceability. Detailed documentation is available at https://vtam. readthedocs. io/en/latest/. The algorithm is described in detail in Supplementary Information 1. First raw reads are merged, demultiplexed, and trimmed. Then amplicon sequence variants (ASVs; unique sequences characterized by the number of underlying reads) are computed and ASV read counts in different samples and replicates are stored in an SQLite database.
Benchmarking We have tested VTAM 0. 2. 0 on two HTS datasets of Cytochrome Oxydase subunit I (COI) obtained from fish and bat feces  [11, 23]  and a 16 S dataset from shark gut content  [19]. All three datasets included both negative and positive (mock samples) controls, as well as technical (PCR) replicates in their experimental design. Most samples of the fish dataset were from freshwater, but five samples contained feces from marine fishes. We then compared the results obtained from VTAM to those obtained using the DALU and OBIbaR pipelines. The DALU pipeline is based on the denoising algorithm of DADA2 1. 12. 1  [9] , followed by LULU 0. 1. 0 [22]. The OBIbaR pipeline starts with filters implemented by OBIT-ools3 3. 0. 1b19 [7] , followed by metabaR 1. 0. 0 R package  [44]. To ensure comparability as much as possible, DALU and OBIbaR pipelines were completed using steps equivalent to VTAM: i) replicates were merged using the equivalent to the MinReplicateNumber step of VTAM; ii) pseudogenes were eliminated by the equivalent of FilterCodonStop and FilterIndel for the COI datasets, and iii) chimeras were eliminated by the equivalent of FilterChimera.
Occurrences with a low number of reads are frequently eliminated at the end of analyses, using arbitrary thresholds  [25]. Hence, the DALU and OBIbaR pipelines were both completed using 0, 10, 40, and 60 as a cut-off of the minimum number of reads for all three datasets. These values were based on the suggested cut-offs optimized by VTAM (10 for fish, 40 for shark, 60 for bat). For VTAM, this filtering step is included by default and its parameter is established by the "optimize" command. Taxonomic assignments were done by VTAM's "taxassign" command for all three pipelines to ensure homogeneity. Detailed protocols are found in Supplementary Information 2 and the result of each filtering pipeline is available at https://osf. io/rtngk/. To compare the performance of pipelines we estimated their sensitivity (TP/(TP + FN)), and their precision (TP/(TP + FP)), where TP, FP and FN are the numbers of true positives, false positives, and false negatives, respectively  [21].
Precision and sensitivity Based on the control samples, the precision (TP/(TP+FP) and sensitivity (TP/(TP+FN)) were measured for the three pipelines (Fig. 2 ). Expectedly, the precision of the DALU and OBIbaR pipelines increased with the minimum read count cut-off. While the precision of the OBIbaR pipeline with the minimum read count cut-off 60 was comparable to the precision of VTAM for the fish and bat datasets, it decreased rapidly with the other cut-offs. Moreover, the precision of the DALU pipeline was lower than that of the VTAM even with the highest tested minimum read count cut-off 60. Sensitivity was high (0. 95-1)Agorize for all datasets and pipelines, and it was independent of the minimum read count cut-off for most comparisons. However, for the bat dataset with the DALU pipeline, sensitivity was the highest with the lowest minimum read count cut-off (0) but decreased with increasing minimum read count cut-off, suggesting a trade-off between sensitivity and precision.
Overall, the filtering parametrization based on control samples (including but not limited to the read count cut-off) implemented by VTAM allowed us to achieve high precision and sensitivity, outperforming the other pipelines. Furthermore, we reviewed the ASVs found in the five marine samples from the fish dataset. The ASVs validated by DALU and OBIbaR (using 10 as read count cut-off) contained false positives likely to originate from tag-jump (8 for OBIbaR 41 for DALU), since they were variants also found in freshwater samples and from taxa that cannot be encountered in marine environments. Based on these marine samples VTAM revealed far higher precision (1. 00) than OBIbaR (0. 66) or DALU (0. 19).
The effect of the filtering procedure on biodiversity estimates The ASV richness was significantly higher in DALU and OBIbaR compared to VTAM, in the bat dataset, for all read count cut-offs, in the fish dataset using 0 and 10 cut-offs and in the shark dataset using 0, 10, and 40 cut-offs (Fig. 3 , Supplementary Fig. S1 , Supplementary Table  S1 ). On the contrary, between-sample dissimilarities (related to β-diversity) were significantly lower when estimated from data obtained from DALU and OBIbaR compared to VTAM in all comparisons (Fig. 3  and Supplementary Fig. S1 ). This indicates that VTAM filters out more variants per sample and implies the ASV data obtained from VTAM has a more discriminating power than the data obtained from the other pipelines (Fig. 3 ).
VTAM addresses most technical pitfalls and determines nonarbitrary thresholds The biggest challenge in filtering metabarcoding data is to consider the trade-off between false positive (FP) and false negative (FN) occurrences. All filtering procedures are a compromise between eliminating spurious sequences and losing true signals  [16, 25, 37]. It is therefore critical to find optimal filtering parameters to achieve a balance between false positives and negatives. We have compared the performance of VTAM to two different pipelines using three different datasets and two different markers. Each pipeline is composed of a denoising step (DADA2 and OBITools) enhanced by a series of further steps to decrease false positives and thus increase precision. The precision of VTAM estimated both from control samples or a subset of marine environmental samples is the highest among the pipelines, and its sensitivity is also high and similar to the sensitivity of the other pipelines.
To achieve a similar sensitivity and precision to VTAM, the original denoising algorithms (DADA2, OBITools) had to be completed by further filtering steps including a series of arbitrary cut-offs of minimum read count to find the best performance. On the contrary, VTAM integrates several filters to control for known artifacts of metabarcoding dealing with false  positives (sequencing and PCR errors, tag-jump, chimeras, pseudogenes) and repeatability (PCR heterogeneity). Furthermore, the real strength of VTAM is to include the explicit and non-arbitrary optimization of parameter combinations, while the parametrization must be done separately and more subjectively for the other pipelines. The underlying hypothesis of the parameter optimization based on control samples is that the best parameter setting that produces clean control samples can also be applied to real samples and reduces FP and FN in the whole dataset.
Acknowledgments We thank  Diane Zarzoso-Lacoste  and  Samanta Ortuño-Miquel  for valuable comments on the operational use of VTAM,  Luc Giffon  and  Lionel Spinelli  for the development of Wopmars, and  Kurt Villsen  for English editing. The  Centre de Calcul Intensif d'Aix-Marseille  is acknowledged for granting access to its high-performance computing resources.
Of the 2,272 Gram-negative strains, 478 (21%) had complete T3SS (Fig. 1a ); similar proportions have T4SS (527) and T6SS (719), both of which can also deliver effectors into host cells but also have other functions (Extended Data Fig. 1  and Extended Data Table  1 )  14. Together 729 of the 2,272 Gram-negative strains, i. e. , 34%, have at least one host-directed secretion system. Because culturing can bias the relative proportions of taxa, we sought to confirm the presence of T3SS in commensal microbiota using metagenome datasets. From 16,179 Pseudomonadota MAG bins with high or intermediate genome quality  15, 16  , 770, i. e. , 5%, encoded complete T3SS (Fig. 1a  and Extended Data Table  1 ). Notably, we only identified T3SS in Gammaproteobacteria, whereas no secretion systems were found in the Beta-or Epsilonproteobacteria in the datasets, except for a few Helicobacter strains.
It is unclear if gut commensal strains in these orders lack T3SS, or if the systems differ from those of the better-characterized Gammaproteobacteria and they were missed by the algorithm. Across the analyses, T3SS were identified in strains of multiple genera and were especially common among Escherichia (Fig. 1b  and Extended Data Table  1 ). Notably, a recent in vivo profiling study of human digestive tracts using in situ sampling found Escherichia as the genus that was most significantly enriched in intestinal over stool samples  17. Of the T3SS-positive (T3SS + ) species, 24 matched representatives in two cohorts of a dataset provided by the Weizmann Institute of Science (WIS cohorts)  18. 59. 4% of individuals in the Israeli cohort and 47. 1% in the Dutch cohort had potentially T3SS + species in their gut microbiome, with relative abundances of 0.
A microbiome-host protein-protein meta-interactome map To investigate the functions of commensal effectors, we cloned effector ORFs for experimental studies from 18 bacterial strains with diverse effector complements (Fig. 1e  and Extended Data Fig. 1 ). We successfully PCR-cloned 786 ORFs for the 1,300 encoded effectors (60. 2%) and 173 of 186 effector ORFs from MAG bins (meta-effectors) following chemical synthesis (Fig. 2a ). Thus, 959 sequence-verified full-length effector ORFs were assembled as the human microbiome effector ORFeome (HuMEOme_v1) (Extended Data Table  2 ). With these, we conducted binary interactome (contactome) network mapping against the human ORFeome9. 1 collection encoding 18,000 human gene products using a stringent multi-assay mapping pipeline  25. In the main screen by yeast-2-hybrid (Y2H), we identified 1,071 interactions constituting the human-microbiome meta-interactome main dataset (HuMMIMAIN) (Fig.
2b, c ). To assess sampling sensitivity  26  , i. e. , saturation of the screen, we conducted three additional repeats of 290 randomly picked effectors and 1,440 human proteins, which yielded 39 verifiable interactions constituting the HuMMI repeat subset (HuMMIRPT). The saturation curve indicates that the single main screen has a sampling sensitivity of ~32% (Fig. 2d ). Last, to address how effector sequence similarity affects their interaction profiles we conducted a homolog screen. Effectors were grouped if they shared ≥ 30% sequence identity (Extended Data Table  2 ) and all effectors of one group were experimentally tested against the union of their human interactors. The resulting dataset (HuMMIHOM) contains 398 verified interactions, of which 179 were not found in the other screens.
2f , Extended Data Fig. 2 , and Extended Data Table  3 ). At thresholds where the control sets were well separated, the retest rate of 173 randomly selected HuMMI interactions was statistically indistinguishable from the positive control sets, and significantly different from those of the negative controls (Fig. 2f , Extended Data Fig. 2 , and Extended Data Table  3 ), indicating that the biophysical quality of our dataset is comparable to those of well-documented interactions in the curated literature. The degree distribution of HuMMIMAIN shows that numerous human proteins are targeted by multiple effectors (Fig. 2g  and Extended Data Table  3 ), often from different species. Indeed, sampling analysis demonstrates that commensal effectors significantly converge on fewer host proteins than expected from a random process (Fig.
Analyzing a large dataset with > 800 IBD patient-derived and > 300 healthy control-derived metagenomes  64  we found 64 effectors that were significantly more prevalent in the metagenomes of CD patients compared to healthy controls (Fig. 5f  and Extended Data Table  6 ). In metagenomes of UC patients only three effectors had a significantly different prevalence, and, intriguingly, these were less common compared to healthy controls (Extended Data Table  6 ). This trend was recapitulated when the prevalence distributions of all detected effectors 13 were analyzed. Whereas CD patients had a significantly higher load of effectors, the overall effector prevalence was lower in UC patients compared to healthy subjects (Fig. 5g  and Extended Data Table  6 ). These opposing findings were unexpected as an increased abundance of Pseudomonadota has been reported both for CD and UC patients  65.
Identification of T3SS+ strains in culture collections and MAGs To collect reference genomes for strains available from culture collections, three large culture collections were queried for all Pseudomonadota strains: DSMZ via BacDive  75  , ATCC (atcc. org) and BEI (beiresources. org). The strain numbers were looked up in GenBank (Release 229) from which 77 strains could be identified as perfect match. MAGs that were at least 50% complete and less than 5% contaminated (as estimated by CheckM 76 from two different meta-studies were selected. 92,143 MAGs of Almeida et al. 15  and 9,367 Pseudomonadota MAGs from Pasolli et al. 16  were used as input for T3SS prediction scaled via massive parallel computing. The computational predictions presented have been achieved in part using the Vienna Scientific Cluster (VSC).
Commensal vs pathogen effector similarity We gathered the sequences of 1,195 known pathogenic T3 effectors from the BastionHub database  79  (August 29 th , 2022). We assessed the similarity between commensal and pathogenic effector sequences using BLAST (stand-alone, version 2. 10 80 ). For each commensal effector, the pathogen effector with the highest sequence similarity was considered as best match. Subsequently, we computed the alignment coverage over the pathogenic effector sequence. Full data in Extended Data Table  2.
Cohort analyses Genomes of bacterial isolates from the human gut were gathered from multiple published datasets  [11] [12] [13]. The presence of T3SS was predicted for each of these genomes as described above. GTDB-Tk (v2. 1)  81  was used to assign the taxonomy to each of the genomes, and the concatenated bac120 marker proteins from this were used to generate a phylogenomic tree of the isolates, visualized in iTOL  82. FastANI was used to match the T3SS positive genomes to the WIS representative genomes of the human gut  18  based on ANI values > 95%  83. The relative abundance of the 10 matching representative genomes was then identified across 3,096 Israeli, and 1,528 Dutch patients  18.