dataesr/scientific-paragraphs-categorization huggingface dataset:
PK modeling Data were pooled from two healthy volunteer studies, one single ascending dose (SAD) and the other multiple ascending dose (MAD). Observed concentration data were transformed into their natural logarithms and evaluated using nonlinear mixed-effects modeling in NONMEM v7. 4 (Icon Development Solution, Ellicott City, MD). Pirana v3. 0, Perl-speaks-NONMEM (PsN) v4. 8. 1, and Xpose version 4. 7. 1, were used for automation, model evaluation, and diagnostics during the model building process. Different structural distribution models were evaluated (1-, 2-, and 3-compartment models) as well as different absorption models (firstorder absorption and transit-compartment (n = 1-10) models). Relative bioavailability was fixed to unity for the population to evaluate interindividual variability and the influence of covariates in the same parameter.
Acknowledgements We thank  Medicines for Malaria Venture (MMV)  for providing the  Pathogen Box  and  GSK  for providing the  Kinase Inhibitors Box. We thank prof. Pietro Alano  for providing the P. falciparum strain 3D7 pULG8-GFP and  Françoise Benoit-Vical  for providing the strain F32-TEM. We are very grateful to  Jeremy Burrows  and  Didier Leroy  from  MMV  and to  Laurent Fraisse  from  Drugs of Neglected Diseases Initiative (DNDi)  for their critical review of the entire project. We thank  Theravance Biopharma Inc. as sponsor of the clinical trial described here and their former senior vice presidents  Brett Haumann  (sponsor signatory of the study) and  Philip Worboys  for collecting and analyzing the data of the trial and made them available for this paper, as well as the Executive Director  Wayne Yates  for his constant support.
Data availability All data associated with this paper are present in the main manuscript or in the Supplementary Materials. The results from the primary in vitro screen of the ReFrame library generated in this study have been deposited in the Reframe database (https://reframedb. org/assays/ A00279) under accession code A00279. The Phase I clinical study (ID: NCT02022306) described in this paper is deposited in the clinicaltrials. gov database (https://clinicaltrials. gov/ct2/results?cond=&term= NCT02022306&cntry=&state=&city=&dist=) under the accession code NCT02022306. Source data are provided with this paper.
gov (iden- tifier NCT02022306), it started on February the 4 th 2014 (first subject enrolled) and was concluded on August the 18 th 2014 (last subject concluded), in the Healthcare Discoveries (a subsidiary of ICON Development Solutions) in San Antonio, Texas, USA. Healthy, non- smoking subjects with no history of significant medical conditions, with a BMI included between 18 and 30 kg/m 2 , who weight at least 50 kg, between 18 and 60 years old, were selected for this study. Subjects were able and willing to give an informed consent. No bias was reported in the selection process. The protocol and all amend- ments for this study and all accompanying material that was provided to subjects (including advertisements, subject's information sheets, and descriptions of the study used to obtain informed consent) were submitted by the investigator to the centralized Institutional Review Board (IRB), namely the IntegReview, established in 1999 at Austin (Texas,.
)  [14]. Many risk factors for the emergence of resistant bacteria coexist in ICUs, making them places where antibiotic use should be as prudent as possible. In this context, antimicrobial stewardship programs (ASP) should be the forefront of efforts to control consumption in ICUs. Antimicrobial stewardship may be defined as "a coherent set of actions which promote using antimicrobials in ways that ensure sustainable access to effective therapy for all who need them"  [15]. It should be viewed as a strategy to optimize antimicrobial prescribing, its main goals being to improve patient outcomes, prevent adverse events, and reduce antimicrobial resistance. For the present review, we systematically searched the literature using the Medline database until December 1, 2022, and selected only English articles.
In a survey of 94 physicians, 88% of whom were ICU specialists with a median experience of 8 years, inter-observer agreement in identifying septic shock or sepsis was poor (Fleiss' kappa 0. 23)  [23]. In the emergency department, up to one-quarter of patients admitted with suspected sepsis have proven or possible non-infectious diagnosis  [24]. In another study of 2579 ICU patients, 43% of patients admitted for suspected sepsis had either no (13%) or possible (30%) infection  [25]. This is also true in patients suspected of septic shock; a monocenter study found that in 25% of patients in whom septic shock was suspected, this diagnosis was refuted  [26]. The last argument is that all studies that showed a relationship between time to antibiotic administration and mortality were retrospective studies on databases and included patients with sepsis and septic shock, not patients with suspected sepsis or septic shock.
When carbapenems are administered empirically, the use of chromogenic tests to detect ESBL may help to de-escalate sooner. Their positive and negative predictive values on urine and respiratory specimens approach 100%. If cultures retrieve Enterobacteriaceae with a negative ESBL-chromogenic test, physicians may safely discontinue carbapenems  [52]. A randomizedcontrolled trial is underway to evaluate the benefit of a rapid antibiotic de-escalation strategy based on this test (NCT03147807  [53] ). On the contrary, the performance of mPCR tests in the ICU is disappointing: the detection of the resistance mechanism of identified germs is not always reliable, and these tests do not yet allow correct guidance of empirical antibiotic therapy, particularly for Pseudomonas aeruginosa and ESBL-producing Enterobacteriaceae  [38, 39].
Although there is frequent resistance  [117]  to this class of antibiotics in ESBL-producing Enterobacteriaceae, it seems possible to use fluoroquinolones when there is no resistance to nalidixic acid and the MIC is < 0. 25 mg/L  [108, 118]. Higher MICs were associated with a higher risk of death  [119]. • Temocillin might be an interesting option, due to its activity on ESBL and AmpC beta-lactamase  [120] ; however, there are only few data available, and all are retrospective without control group  [108, 121]. A randomized-controlled trial, currently recruiting, will evaluate the usefulness of temocillin in ICU patients with ESBL infection (NCT05565222  [122] ). • The new beta-lactam-beta-lactamase inhibitors (ceftazidime-avibactam, ceftolozane-tazobactam) are not used as carbapenem-sparing agents, since their ecological impact, as compared to carbapenems impact, is currently not known.
The results of another similar trial, currently recruiting, will confirm or not these results (BALANCE trial, NCT03005145  [158] ). Short duration of treatment for bacteraemia is not always possible, since actual recommendations emphasize the need for a 14-day course of antimicrobial for uncomplicated Staphylococcus aureus bacteraemia. However, several observational studies suggested a shorter duration of treatment may be harmless in the absence of endocarditis, sustained bacteraemia or persistent fever, metastatic infection, or implanted prosthesis  [159, 160]. A randomized-controlled trial is currently recruiting to test the non-inferiority of a 7-day course for uncomplicated S. aureus bacteraemia, as compared to a 14-day course (NCT03514446  [161] ). Awaiting these results, the choice of shortening duration of treatment of uncomplicated S.
Study Design and Participants Chemoimmune (NCT03139851) was an open-label, single arm, multicenter, phase II study conducted according to a Simon's minimax two stage design to evaluate the safety (safety run-in) and clinical activity of pembrolizumab combined with metronomic cyclophosphamide in lymphopenic patients with HER2-negative MBC. Eligible patients were women, ≥18-year-old, with histologically confirmed HER2-negative MBC regardless of PD-L1 expression of the tumor for whom one to three prior regimens of chemotherapy have failed. Key other eligibility criteria included adequate performance status according to Eastern Cooperative Oncology Group (PS ECOG) of 0 to 2, documented lymphopenia (lymphocyte count<1. 5G/L within 15 days before treatment start), measurable disease as per RECIST V1.
Whole slides were imaged at a 40x magnification using the Vectra Polaris multispectral scanner (Akoya Biosciences) and digital images were visualized with the Phenochart viewer (Akoya Biosciences) and unmixed using the spectral library from the software. Data for 5 patients (2 patients with OR) and 3 patients in the PD group) with lymph node samples collected during the study are presented to illustrate the immune infiltrate. A representative hotspot area was chosen for each tumor sample, cells of each phenotype were manually counted and their density (number of cells by mm 2 ) was calculated by dividing the number of cells by the surface of tissue.
Whole Exome Sequencing Analysis Whole exome was captured from 200 ng of tumor from FFPE tumor and paired constitutional DNA and libraries were prepared using Agilent SureSelect XTHS2 All Exon V8 (35 Mb). Libraries were sequenced of subsequent libraries was performed using the NovaSeq6000 Illumina sequencer in 100 bp paired-end. Demultiplexing was made with Illumina's software bcl2fastq-v2-20-0. Raw sequence fastq files from normal et tumoral samples were aligned to human reference genome GRCh38. p13 with bwa-mem-v0. 7. 17. Duplicates were then marked with biobambam-v2. 0. 89. Coverage distributions were computed with mosdepth-v0. 2. 9. Somatic point mutations and small indels (<50bp) were called using Mutect2 from GATK-v4. 1. 2. 0. A panel of normal samples to remove systematic sequencing artifacts was used.
Cells were subsequently annotated with ensembl-vep-v98. 3 using its internal annotation cache. Tumor mutation burden was computed on CDS regions only (34,780,817 bp length) taking only HIGH and MODERATE impact variations from ensembl-vep annotations (and thus ignoring synonymous variations) with an allelic frequency greater than 0. 1. MSI status was computed using MSIsensor-v0. 6. This tool computes both length distributions of microsatellites from the tumor and its matched normal data and then compare these distributions to infer putative instability of each site. We used a list of 2932 curated sites and a tumor was considered as MSI if the fraction of unstable sites was > 20%.
The CBR-6M and ORR-6M were presented by percentages with 95% CIs. PFS and OS were analysed using the Kaplan-Meier method to estimate the median values with the corresponding 95% CIs. All analyses were performed using SAS version 9. 4 (SAS Institute, Cary, NC, USA). An Independent Data Monitoring Committee (IDMC) was established to review interim analyses as well to provide recommendations to the sponsor.
Funding This work was an investigator-initiated trial (no grant number is applicable). CLB was the legal sponsor. MSD provided the investigational agent and funding, but had no role in the study design, data collection, analysis, interpretation, writing of the report, or decision to publish this report. The database is held by  CLB , and  CLB  statisticians carried out the analysis. Research grants were perceived from " INCa " ( PRT-K INCA 2009-113  (BreastImmun),  Transla13-097  (Camel),  Transla13-061 (TNBT) ), " Lyrican" (LYRICAN INCa-DGOS-Inserm_12563), "Ruban Rose" for the blood immunomonitoring and "Région Rhône-Alpes " ( IRICE  project) for the development of multiplex immunofluorescence staining.
, 2014a). Delineating the boundaries of MUs and identifying those in need of protection is thus important to adapt management scales to preserve the genetic diversity of the species. It also serves as a reference dataset to determine the natal origin of turtles sampled outside nesting events  (Jensen et al. , 2013). Several populations usually mix on the foraging grounds where they are exposed to high threats  (Bowen et al. , 1992; Bjorndal and Bolten, 2008). Identifying the connectivity between MUs and foraging grounds is thus important to better assess the threats to green turtle populations in their different habitats and to improve the conservation and management of the species. In the Pacific Ocean, efforts to describe MUs and their relative contribution to mixed stocks on foraging grounds have intensified in recent years.
Study area and data collection Skin and muscle tissues were collected from Chelonia mydas specimens across 21 islands and atolls of French Polynesia between 2005 and 2021 (Figure  1 ). Tissues were sampled from nesting females and hatchlings (known maternal origin), as well as free-swimming turtles, and from individuals seized from poaching (origin to be determined). In total, 443 specimens including 136 adults, 103 hatchlings, 85 juveniles, and 119 of unknown maturity stage were used for this study. Animals were classified as "adults" when their curved carapace length was larger than 85cm  (Bjorndal et al. , 2013; Phillips et al. , 2021) , "hatchlings" when sampled from nests, "juveniles" otherwise, and "unknown" when no information about the size was available. Juvenile size ranged from 7.
8 to 78 cm, and adult size ranged from 85 to 115 cm. Samples collected and processed by  Boissin et al. (2019)  are included in this study (n=97). For structure and mixed stock analyses, the samples were divided into two datasets. The first included only individuals from known origin, i. e. nesting females and hatchlings (n=239). Nesting females were assumed to have originated from the island where they were sampled  (Bowen and Karl, 2007). This dataset is referred to as the baseline population. As offspring share the same mtDNA as their mother, and to avoid double counting, the DNA from only one hatchling per identified nest was considered and sequenced, unless the mother was identified (in which case only her DNA was sequenced). When hatchlings were from different nests with the same identified mother, only the mother was sequenced.
When two hatchlings were sampled on the same date and location with no nest information reported, they were considered potential siblings, and only one was kept in the dataset. We recognize that hatchlings from different dates may still be siblings, as turtles can nest several times in one season. Although, the probability of such an occurrence is low in our dataset due to the limited sampling size in most of the islands (Table  1 ). On Tetiaroa and Mopelia, islands with the largest sample, most often females were sampled. On these islands, nesting females were marked with flipper tags on the first encounter to avoid resampling within and across seasons. The second dataset included juveniles and individuals of unknown maturity stages (n=204). This dataset is referred to as the mixture. These individuals were occasionally found in the lagoons and barrier reefs around 21 islands across the Society and Tuamotu archipelagos and were sometimes injured or poached.
The calculations were performed on the baseline dataset using Arlequin v3. 5. 2. 2 with 1000 permutations. To decrease the genetic variance due to the difference in sampling size between Tetiaroa and the other islands, 3 random sub-samplings of Tetiaroa were created and F st and j st were also calculated between these, Mopelia, and Moorea. Bonferroni correction for multiple tests was applied on p_values.
F st was calculated between the source populations of French Polynesia and the MUs in the rest of the Pacific Ocean to ensure that the genetic structure is significant and to avoid FIGURE  2  Regional map of the green turtle management units in the Pacific Ocean. Grey areas represent the management units used for the baseline of the mixed-stock analysis. The main current flows are represented with grey arrows. Dashed lines represent the regional management units (RMU)  (Wallace et al. , 2010)  any bias in the MSA. The R package rubias  (Moran and Anderson, 2019)  was used to perform the MSA. First, simulations were run to evaluate the accuracy of mixing proportion estimates for the mixture. For each baseline scenario, several sets of 40 simulations with 200 individuals in the mixture were performed with the leave-one-out cross-validation method, which improves accuracy estimates  (Anderson et al.
, 2008). The method draws 200 individuals from the baseline to constitute a mixture in which the contribution of each MU is known. These contributions are then calculated with Conditional Bayesian MSA based on the haplotype frequency of each MU. Known contributions were plotted against calculated contributions to assess the accuracy of MSA calculations. Each set of simulations was run with a different prior parameter that assigned different weights to the MUs from which the 200 individuals were sampled according to i) MU size (i. e. estimated number of nesters), ii) MU distance from the mixture, and iii) both the size and the distance. The size prior was defined to be p s = s/s max , with s being the size of the MU and s max the size of the largest MU of the dataset. Population sizes were those defined in Balazs et al.
1 belongs to clade VI (Supplementary Figure  1 ), which is mostly restricted to the Central South Pacific  (Jensen et al. , 2019). Haplotypes CmP56. 1, CmP85. 1, and CmP65. 2 also belong to clade VI, which represented 59% of our total sample. CmP22. 1 was dominant in American Samoa, moderate in the Marshall Islands, and found at low frequency in Micronesia  (Jensen et al. , 2016). This haplotype belongs to clade III, which represented an additional 31% of our total sample. CmP97. 1 was an orphan haplotype (never linked with any rookery)  (Naro-Maciel et al. , 2014; Chaves et al. , 2017; A ́lvarez-Varas et al. , 2022). The other 20 haplotypes each accounted for less than 5% of the sequences in both the baseline and the mixture populations. Three haplotypes, CmP65. 3, CmP271. 1, and CmP272. 1 are described here for the first time (GenBank accession numbers: OQ683229-OQ683231).
Acknowledgments We would like to thank the volunteers of the  NGO Te mana o te moana  for providing Chelonia mydas samples from  Tetiaroa  and  Moorea  for this study. We also thank the volunteers of the  Bora Bora Turtle Center  for samples from the  Leeward Islands , as well as the  Direction of Environment  for coordinating the sampling across French Polynesia and providing them to the  CRIOBE  for the analyses. Thank you to  Jeanine Almany  for the English corrections.
Data collection Quantitative data from the school children were collected using structured questionnaires which were designed as part of a larger study conducted to understand the knowledge, attitudes, and practices on rabies, which has been published elsewhere  [23]. Qualitative data were collected using FDGs and IDIs. To ensure standardization, all the discussions and interviews were guided by the standard set of questions. Probes and clarifications were sought as deemed necessary. During the process of discussions and interviews, the participants were asked to express themselves in the language in which they were most comfortable. Therefore, the sessions were run in English, Dzongkha (the national language), and Sharchop language (the native language of eastern Bhutan) interchangeably. Each FDG lasted for 2-3 h and one key interview (KI) took approximately 30 min to 1 h.
Data analysis Descriptive analysis of the quantitative data collected from the school children was analyzed using R software  [25]. The qualitative data collected during the FGDs and IDIs were analyzed using thematic analysis techniques  [26]  with the support of the Weft QDA freeware (www. pressure. to/qda). For qualitative analysis, narratives of IDIs and FGDs were transcribed into English, followed by coding into different themes and categories. Development and placement of the coded data under the relevant themes were performed by considering each line, phrase, or paragraph of the transcript. The transcripts were read carefully and cross-checked by listening to the digital voice records to ensure the accuracy of the data. The codes were refined, updated, or removed depending on the significance of the data.
Funding Lungten (L. L),  Waraphon Phimpraphai  (W. P), and  Michel de Garine-Wichatitsky  (M. d. G-W) were supported by  CIRAD/GREASE network  (www. grease-network. org/). M. d. G. -W was also supported by the  French Agence Nationale de Recherche  ( ANR-19-ASIE-0002 ,  SEA-dog-SEA ). The study was also partially supported by the  graduate school of Kasetsart University, Thailand.
IIOs, which we show can recapitulate clinical outcomes, provide the opportunity for in-depth analysis of the underlying cellular and molecular events. We used scRNA-seq to interrogate the transcriptomic dynamics underlying TCB-dependent TRM activity at the onset (4h) and peak (48h) of epithelial cell targeting. Lymphocyte populations within the integrated dataset were annotated using differential gene expression together with previously published signatures and surface markers  (39) (40) (41) (42) 45) , revealing diverse T cell, macrophage and B cell populations (Fig. 3d-e , Extended Data Table  1  and 2 ). TCB treatment induced a time-dependent shift in proportions of both the T cell and B cell states relative to the non-targeting control (Fig. 3f ). This transition was driven by the emergence of several effector populations at both time points.
Sequencing of inflamed colonic biopsies of patients suffering from druginduced colitis demonstrated the presence of both cytotoxic (CTL) and IEL CD8+ T cell populations  (40). By cross referencing TCB-treated IlOs to this dataset, we found that IIO CD8+ T cells acquired gene signatures related to a cytotoxic and IEL state, mirroring those observed in clinical samples (Extended Data Fig. 4d-e ). The concurrent increase in lymphocyte-epithelium association and cytotoxicity may underlie the severe clinical adverse events triggered by these molecules. Aside from CD8+ CTLs, other populations displayed clinically relevant dynamics of early-vs. late-transcriptional hallmarks. Consistent with clinical reports  (56) , a Th1 population (c9) shifted from a cytokine-(TNF, IFNG and IL2)producing state to a cytotoxic, GZM-producing state (Fig.
Methods Isolation of tissue-resident memory cells and donormatched crypts for organoid generation. Human intestinal tissue samples and annotated data were obtained and experimental procedures performed within the framework of the non-profit foundation HTCR (Munich, Germany) including informed patient's consent. Tissue was first processed by removing the underlying muscularis, serosa and fat from the basal side of the tissue by pinching with forceps and trimming away with scissors. The remaining mucosal tissue was washed with PBS (supplemented with penicillin [1500U/ml], streptomycin [1500 pg/ml], gentamicin [500 pg/ml]) multiple times before using a scalpel to remove excess mucus from the luminal side and blood vessels from the basal side. Trimmed, cleaned tissue was then cut into square explants approximately 5 mm by 5 mm with a scalpel.
Cells were then counted, phenotyped by flow cytometry and frozen prior to use in downstream applications. Flow cytometry analysis of immune-organoids. Triplicate wells of immuno-organoid cocultures from each condition were harvested 5h, 24h and 48h post-treatment. 4h prior to culture harvest at each timepoint, wells were treated with Protein Transport Inhibitor Cocktail (Thermo Fisher) to facilitate intracellular accumulation of cytokine protein. Cocultures were washed with PBS and then digested to single cells using Accutase solution (STEMCELL Technologies) at room temperature for approximately 30 minutes. Cell suspensions were passed through a 70 um strainer and stained for surface proteins (Extended Data Table  3 ). Cells were then fixed and permeabilized using the Foxp3 Transcription Factor Staining Buffer Set (Thermo Fisher), and subsequently stained for intracellular and intranuclear proteins.