dataesr/scientific-paragraphs-categorization huggingface dataset:
The set has now been distributed to almost 200 research groups globally in the last two years, with the only stipulation that information from the screens is deposited in the public domain. This paper reports for the first time on 236 screens that have been carried out against the Malaria Box and compares these results with 55 assays that were previously published, in a format that allows a meta-analysis of the combined dataset. The combined biochemical and cellular assays presented here suggest mechanisms of action for 135 (34%) of the compounds active in killing multiple life-cycle stages of the malaria parasite, including asexual blood, liver, gametocyte, gametes and insect ookinete stages. In addition, many compounds demonstrated activity against other pathogens, showing hits in assays with 16 protozoa, 7 helminths, 9 bacterial and mycobacterial species, the dengue fever mosquito vector, and the NCI60 human cancer cell line panel of 60 human tumor cell lines.
Toxicological, pharmacokinetic and metabolic properties were collected on all the compounds, assisting in the selection of the most promising candidates for murine proof-of-concept experiments and medicinal chemistry programs. The data for all of these assays are presented and analyzed to show how outstanding leads for many indications can be selected. These results reveal the immense potential for translating the dispersed expertise in biological assays involving human pathogens into drug discovery starting points, by providing open access to new families of molecules, and emphasize how a small additional investment made to help acquire and distribute compounds, and sharing the data, can catalyze drug discovery for dozens of different indications. Another lesson is that when multiple screens from different groups are run on the same library, results can be integrated quickly to select the most valuable starting points for subsequent medicinal chemistry efforts.
In supplementary data S1 Table, note that columns with data presented for the first time in this paper, representing 236 assays, are colored pink on the top row; published /in press data columns, 55, are grey, with citations provided. Presenting the combined dataset provides insights into the hit rates in these various assays while allowing rapid access to the data by the wider scientific community. The Heat Map (S1 Table ) presents the Malaria Box chemicals grouped by chemical relatedness. Of the 400 compounds, over 100 are closely-related paired molecules so immediate structure-activity-relationships (SAR) can often be seen from hits with these pairs. The Heat Map identified obvious correlations in chemistry and biology between compounds (both Mechanism-of-Action and phenotypic activity).
Mechanism-of-action screening Data from one-hundred-nineteen MoA assays for compounds from the Malaria Box are included, identifying potential targets for 135 of them (S1 Table and S1 Methods and Results). The MoA assay data are presented in Column M of S1 Table , and further information about the screens and their results are given in S1 Methods and Results. These screens included biochemical screens for enzyme inhibition, protein-protein interactions, behavior by altered yeast or malaria organisms, and a variety of other screens. Some associations are strong and have been followed up with additional experimentation (e. g. MMV008138 and its target Pf-IspD  [13] [14] [15] ), but most target associations are still tentative. Indeed, some listed MoA activities occur only at higher concentrations than activity in cell-based screens and therefore are unlikely to explain that compound's activity against a pathogen or tumor cell.
An exception was MMV665979, an outstanding hit for Naegleria fowleri, with limited toxicity elsewhere in the dataset. With respect to screening Babesia and Theileria, ten novel anti-Babesia and anti-Theileria hits with nanomolar IC 50 s were identified: MMV666093, MMV396794, MMV006706, MMV665941, MMV085203, MMV396693, MMV006787, MMV073843, MMV007092 and MMV665875. The most interesting hits were MMV396693, MMV073843, MMV666093, MMV665875 and MMV006706 with mean SIs greater than 230 and IC 50 s ranging from 43 to 750 nM for both bovine Babesia and equine Babesia and Theileria parasites. Additionally, 64, 45 and 49 Malaria Box compounds exhibited IC 50 s lower than those of diminazene aceturate (the most widely used antibabesial drug) against the in vitro growth of B. bovis, B. bigemina and T.
Moreover, screening against pathogens for additional neglected diseases has been catalyzed and hits found. The sharing of data from safety screens flags compounds that probably work through a general toxicity mechanism, and those compounds can be down-prioritized at an early stage. This is key to prioritizing compounds for medicinal chemistry, since the paucity of good starting points against some parasites has encouraged groups to screen at what may be inappropriately high drug concentrations. Another advantage of having a standardized, publically available library and dataset is that this allows benchmarking assay sensitivity, setting compound concentrations for expanded screens and deciding on acceptable hit criteria  [41]. We saw some discrepancies in the values obtained for the same compounds in similar assays that were carried out by multiple groups, such as activity against asexual or gametocyte forms of P.
Only the free compound would likely be available for activity in biological assays. Some assays had extensive follow up, and if a compound was tested and activity confirmed with a dose-response, it is more likely to be a true positive than a compound flagged as positive from a single screening run. This complex dataset highlights the need to consider integrating more standardized criteria, such as similar (free) compound concentrations, assay media, or compound exposure duration, into future screening initiatives of this nature. This could potentially reduce inter-assay differences, and facilitate more direct data comparison across the different platforms. However it is clear in the case of gametocyte screens, that different assays that interrogate different biological processes do not necessarily achieve the same result for a given compound, even when the assay conditions have been standardized  [42].
We are aware of such new medicinal chemistry programs against pathogens such as Plasmodium  [43] [44] [45] , Babesia, Toxoplasma  [46] , Trypanosoma  [47] [48] [49] , Cryptosporidium  [31] , Schistosoma  [50] , filaria, Echinococcus, helminths, bacteria, cancer and other diseases  [30]. Ensuring that data becomes freely available is a challenge, and this paper represents the first such summary of over 290 screens against the compound collection, highlighting new activities and new MoAs. For the future, three goals are important. First, to track these compound series to ascertain whether any of these hits do become leads of drug development candidates. Second, data must be rapidly published, even with follow-up incomplete. Finally and most importantly, this model can be taken further. A second collection of 400 compounds, the Pathogen Box, (www.
The compounds were then screened in biochemical and biological screens as documented in detail in S1 Methods and Results. More detailed methods are provided for screens presented in this paper than for those whose results have already published. In addition, S1 Methods and Results provides data for both positive and negative controls obtained for each assay. In most assays, a single-concentration screen was run first and bioactives were identified. Some work was stopped after the primary screen, but most groups went on to perform confirmatory assays, and many provided hit concentrations that achieve 50% activity (S1 Table ). The assays included a variety of cell-based pathogen screens covering multiple taxonomic groups, including Plasmodium (multiple life-stages), other protozoa, bacteria, mycobacteria, HIV, and also multicellular-organism screens such as helminths and a mosquito (See Fig  1   ).
Acknowledgments We thank  Scynexis and Evotec  for plating and distribution, Syngene for synthesis of the  Malaria Box compounds ,  Essen Bioscience  (particularly  Del Trezise ,  Tim Dale , and  Srinivasan Kanumilli ) for determination of hERG inhibition,  WuXi App Tec  (particularly  Genfu Chen ,  Hongshi Yu ,  Yusong Zhu ,  Ying Huang , and  Wen Hua ) for the PK/DMPK determinations, and ChEMBL for data maintenance and particularly  Louisa Bellis  and  Anna Gaulton  of the  European Bioinformatics Institute, EBI. The purified HSP90 was a gift from  Prof. Juan Pizzaro. We acknowledge the technical assistance of  An Matheeussen ,  Jessica Richard  of  Louisiana State University, LSU , and  Koji Sagane, Ph. D. ,  Eisai Co. , Ltd. for the construction of transgenic yeast clones. We wish to thank  Suresh Solapure PhD  for discussions about PK determinations,  Dr.
DGE-1232825  (DDCL), the  South African Medical Research Council Strategic Health Innovation Partnerships  (grant  V6YBT51  to DM) and the  Council for Scientific and Industrial Research  (grant  V1YTB95 , to DM), and the  French ANR program Mammamia  ( ANR-12-BS07-0020-01 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Carcass parts including wings, drumsticks, thighs, skinless and boneless breasts [pectoralis (P. ) major muscles], and tenders (P. minor muscles) were separated and weighed individually. The cost of grower (21 to 42 d of age) feed for yielding 1 kg of BW or carcass weight was calculated for each experimental diet. This was accomplished by dividing the total price of grower feed that was consumed by the birds in each pen by the total BW or carcass weight of the birds in that particular pen. In addition, the ratio of grower feed cost to the value of each particular carcass part was calculated. Carcass part values were calculated based on "Georgia Dock" (online database, updated daily) [24] quoted poultry prices. Ratios of feed cost to carcass part value were calculated as follows: (feed cost × feed consumed in grower feed phase)/[(breast weight × breast price) + (tender weight × tender price) + (thigh weight × thigh price) + (drumstick weight × drumstick price) + (wing weight × wing price)].
Statistical Analysis A randomized complete block design was used to test for the effects of the different dietary Lys and Met regimens. Pen location was considered as the blocking factor, and pen was considered as the experimental unit. A 2 × 4 (2 Lys and 4 Met dietary levels) factorial arrangement was used in this trial. A two-way ANOVA was used to determine the significance of responses to Lys and Met supplementation and their interactions. Linear and quadratic regression analyses of Met level on each measured parameter at either low (100%) or high (120%) RL Lys levels were performed. All data were analyzed by the GLM procedure of SAS software. The Fisher's least significant difference option of SAS was used to separate treatment means  2 Mean values of 10 replicate pens of 12 birds each pen during grower phase.
Acknowledgments This is journal article number No. J-12548 from the  Mississippi Agricultural and Forestry Experiment Station  ( Mississippi State ), under MIS-322280 and financially supported by  Adisseo France. Use of trade names in this publication does not imply endorsement by the  Mississippi Agricultural and Forestry Experiment Station  of the products, nor similar ones not mentioned. The authors wish to express their gratitude to  Ms. Donna Morgan  and the  Poultry Research Center.
5 were tested: 3750 mg/kg; 5625 mg/kg; 8437. 5 mg/kg; and 12,656. 25 mg/kg. Groups of three 8-12 weeks old male and female NMRI mice were administered a single oral treatment of the extracts at each dosage; control mice received only the solvent. Animals were kept under observation for 48 h and any signs of toxicity were recorded. Body weight data were taken on day 0 (before extract administration) and day 14 posttreatment.
The effect of NA treatment on mosquito survivorship was analysed using Cox's proportional hazard mixed regression models ("coxme" function in the "coxme" package  [42] ). Because all the mosquitoes from the first survivorship experiment (in deprived sugar conditions, see above) died eventually, there was no censoring in this case. In contrast, since individuals were still alive when last seen on day 18 post-treatment, the analysis of the survival data from the second experiment (when mosquitoes were allowed to feed on a 5 % glucose solution) accounted for censoring. NA treatment was specified as a fixed effect and cage number as a random effect. Cox's mixed models were also used to analyse tsetste fly survival data with treatments specified as a fixed effect and replicate as a random effect.
Acknowledgements We would like to thank  Da Fulgence ,  Dabiré Baudoin  and  Tamboula Souleymane  for technical help with mosquito production,  Barry Issiaka  for tsetse release and re-capture and  Ulrich Zoungrana  for logistic help with calves. The authors also acknowledge  Trifolio-M GmbH, Lahnau, Germany  for providing NeemAzal ®. This study was funded by the  Laboratoire Mixte International LAMIVECT.
The presence of only one lepton in the final state for the signal removes all processes with Z bosons from the list of relevant backgrounds. Furthermore the presence of large missing transverse energy, the jet multiplicity, and specific angular distributions of the final-state particles can be exploited to distinguish the signal from the background. These specific features are the motivation behind the cuts that will now be discussed. We generate the inclusive signal and SM background samples by employing the ME+PS merging scheme with Pythia6  [68] , implemented in MadGraph5 aMC@NLO  [69]. The fast detector simulation is performed by the Delphes3 package  [70]  with the CMS-based detector setup. We employ MadAnalysis5  [71, 72]  for the analyses. The t t and single-t cross sections are normalised to 831 pb and 299 pb, respectively [73], while the W +jets sample is normalised to σ NLO (W ± j) of about 3 × 10 4 pb  [63].
Acknowledgements The authors would like to thank fruitful discussions with  P. Anastasopoulos ,  L. Calibbi ,  E. Dudas  and  B. Zaldivar. This work is supported in part by  Vrije Universiteit Brussel  through the  Strategic Research Program 'High-Energy Physics ', and in part by the  Belgian Federal Science Policy Office  through the  Inter-university Attraction Pole   P7/37. A. M. and P. T. are also supported in part by  FWO-Vlaanderen  through project  G011410N. A. M. aknowledges the Pegasus FWO postdoctoral Fellowship. K. M. is supported by the " Theory-LHC-France Initiative " of  CNRS  ( INP/IN2P3 ). S. M. is a Aspirant van het  Fonds Wetenschappelijk Onderzoek -Vlaanderen.
Statistical analysis In this study, only the first semen sample collected either in patients or fertile men was considered for descriptive and comparative statistical analyses. All statistics were performed with the BMDP statistical software (Statistical Solutions, Cork, Ireland). Quantitative values were expressed as mean ± standard deviation and/or median with interquartile (IQ) ranges. The percentage of normozoospermic men according to WHO reference values (≥39 × 10 6 per ejaculate and ≥32 % of progressive motility  [16] , excluding morphology) was calculated in order to appreciate the extent of men with an acceptable fertility potential for each pathology studied in comparison to fertile men. In addition, the similarity of the mean values for various semen characteristics between all groups of patients and healthy men studied was assessed by a one-way analysis of variance (BMDP 7D subroutine), taking unequal variances into account (Brown-Forsythe test) when necessary.
P values ≤0. 05 were considered significant for all analyses. Kaplan-Meier survival analysis techniques were used for analysis of time-to-event endpoints, and the 95% CI for the difference between arms was calculated using the normal approximation method. In addition, the differences in incidence between arms were compared using a chi-square test. All data processing, summaries, and analyses were performed using SAS â software version 9. 1. 3 or higher on UNIX.
Methods Molecular dynamics methods. The molecular assembly consisted of a methanol molecule placed near a lipid bilayer formed by 100 POPC molecules in equilibrium with 9,282 water molecules, corresponding to a cell dimension of about 59 × 59 × 119 Å 3. Owing to the periodic boundary conditions, the membrane spanned the xy-plane in a continuous manner. All the molecular dynamics simulations reported here were performed using the parallel, scalable program NAMD 2. 10 54 , using the CHARMM36 force field for lipids  55  , the CHARMM General Force Field (CGenFF)  56  for methanol, and the TIP3P water model  57. A momentum-conserving Lowe-Andersen thermostat  58  was utilized to maintain the temperature at 308 K with a rate of collision of 50 ps -1. The pressure was maintained at 1 atm applying the Langevin piston method independently to the z-axis and xyplane  59.
Acknowledgements This research is part of the Blue Waters sustained-petascale computing project, which is supported by the  National Science Foundation  (awards  OCI-0725070  and  ACI-1238993 ) and the state of  Illinois. Blue Waters  is a joint effort of the  University of Illinois at Urbana-Champaign  and its  National Center for Supercomputing Applications. Anton computer time (award PSCA14057P) was provided by the  National Resource for Biomedical Supercomputing (NRBSC) , the  Pittsburgh Supercomputing Center (PSC) , and the  BTRC for Multiscale Modeling of Biological Systems (MMBioS)  through Grant  P41GM103712-S1  from the  National Institutes of Health. The Anton machine at NRBSC/PSC was generously made available by D. E. Shaw Research. Additional computing for this project was performed on the  Beocat Research Cluster at Kansas State University , which is funded in part by  NSF  grants  CNS-1006860 ,  EPS-1006860 , and  EPS-0919443.
Enfin, la mortalité à 30 jours est plus importante dans l'EGB (10,2 % vs 4,4 % ; p < 0,001). Conclusions. -Les registres comme FAST-MI sont indispensables pour apprécier au fil du temps les changements épidémiologiques, mais aussi l'impact pronostic de la prise en charge moderne du STEMI. L'exploitation de datas issues d'une base de données généralistes apporte un complément d'information pertinent puisqu'incluant une population plus large non systématiquement admise en USIC. © 2016 Elsevier Masson SAS. Tous droits réservés.
These results are extremely encouraging for the acute management of STEMI. However, clinical studies, as well as other American and European  [6] [7] [8]  registries, report more subtle results  [9, 10]. This difference is possibly explained by the fact that the patients enrolled in FAST-MI are those hospitalized exclusively in cardiac intensive care units (CICUs). It would thus appear both useful and important to compare these selected data with a population obtained from the Échantillon généraliste des bénéficiaires (EGB; general sample of beneficiaries). Indeed, this database, the validity of which we have shown previously in the field of heart failure, allows for a more tailored approach to daily clinical practice  [11, 12]  (indexing of all managed STEMI patients even if not hospitalized in CICUs).
EGB database Data were extracted from the EGB database, which comprises a representative 1/97th random sample of the population covered by the French national health insurance system (approximately 80% of the French population)  [13, 14]. The EGB database currently includes more than 600,000 beneficiaries, and has been used widely for public health and pharmacoepidemiological purposes for more than 5 years  [15] [16] [17] [18] [19]. Since 2005, the EGB database has included basic demographic data and has prospectively collected all claims for visits to physicians and exhaustive claims for all reimbursed drugs dispensed in retail pharmacies (including dates of prescription and dispensing, and quantities delivered). Medications are identified by their Anatomical therapeutic chemical class codes, which are included in the EGB database.
The EGB database also contains data collected by the Programme de médicalisation des systèmes d'information (PMSI; programme for medicalization of information systems) in healthcare institutions (medical and surgical departments); thus, during the patient's stay, principal diagnoses and associated diagnoses are available, coded according to the International classification of diseases 10th edition (ICD-10). Associated diagnoses represent a proxy for comorbidity assessment and identification of triggering factors. All medical procedures performed during each stay are identified with their specific codes from the Common classification of medical procedures. The EGB database also includes registration of the date of death, recorded automatically from the Institut national de la statistique et des études économiques (INSEE; National institute for statistics and economic studies), independent of the use or not of health care resources.
Patient inclusion in the present study Only those patients treated for STEMI stemming from the initial publication of FAST-MI 2010  [5]  were included in the present study. Patients included from the EGB database were hospitalized patients, with a discharge date during the year 2010 and at least one ICD-10 code of STEMI as principal diagnosis. Patients with a non-STEMI in the EGB database or the FAST-MI 2010 registry, as well as those who died before hospital admission, were not included. Information regarding death was directly accessible, whether indicated in the INSEE or via the PMSI and recorded by the French public administration. Treatment data included drugs dispensed in pharmacies, within 60 days after hospitalization for STEMI, that had been the subject of a reimbursement claim.
Statistical analysis All analyses were performed using Stata software, version 12 (StataCorp, College Station, TX, USA). All analyses were done for a two-sided type I error of ␣ = 5%. Population data are described as frequencies and associated percentages for categorical variables and as means ± standard deviations for quantitative variables (age). Quantitative data were compared between groups (EGB 2010 and FAST-MI 2010) using Student's t-test, according to their assumptions (normality studied by the Shapiro-Wilk test and homoscedasticity by the Fisher-Snedecor test). Comparisons concerning categorical data were performed using the 2 test or, when appropriate, Fisher's exact test. Then, a univariate analysis of 30-day mortality was performed using logistic regression, followed by a multivariable logistic regression model to determine predictive factors of 30-day mortality, by backward and forward stepwise selection of factors considered significant in the univariate analysis and according to clinical relevance variables  [20, 21].
Baseline characteristics During the year 2010, 403 patients with a diagnosis of STEMI were listed in the EGB database. In the FAST-MI 2010 registry, 56% of patients (n = 1716/3079) were included with the same diagnosis. Patients in the EGB database were older (67. 2 ± 15. 3 vs 63. 3 ± 14. 5 years; P < 0. 001), were more often women (36. 0% vs 24. 7%; P < 0. 001) and were less frequently treated in university hospital centres (27. 1% vs 37. 0%; P = 0. 001) (Table  1 ). The majority of cardiovascular risk factors and comorbidities were similar. However, patients in the EGB database more often had diabetes (22. 1% vs 16. 5%; P = 0. 008), chronic renal failure (6. 4% vs 2. 1%; P < 0. 001), peripheral artery disease (9. 4% vs 4. 8%; P < 0. 001) and a history of coronary stent angioplasty (15. 6% vs 10.
Acute phase angiography and coronary angioplasty Compared with patients in the EGB database, patients in the FAST-MI 2010 registry more frequently underwent coronary angiography (96. 3% vs 75. 2%; P < 0. 001) and coronary stenting during hospitalization (86. 7% vs 58. 8%; P < 0. 001) (Fig. 1 ).