dataesr/scientific-paragraphs-categorization huggingface dataset:
Treatment outcome measures-ART outcomes during data abstraction were categorized as alive and retained on therapy, died, stopped treatment, transferred out, defaulted, loss-to-follow-up (LTFU), and other. LTFU was defined as patient absenteeism from a healthcare facility for >90 days after the last scheduled appointment with the healthcare provider or pharmacy, whilst defaulters were those who had not come back to the clinic for <90 days from the last scheduled appointment. The LTFU date was recorded as the date of the most recent visit or one day after ART initiation if patients only attended the initiation visit. In this analysis, our primary outcomes of interest were clinical ART outcomes and immunological failure. Clinical outcomes comprised all-cause mortality, LTFU, and attrition. Attrition referred to patients who were documented as having stopped ART, died, or who were LTFU.
Data variables, source of data, and data collection Data variables included demographic data such as sex and age at enrolment into HIV care. Clinical information abstracted included WHO stage, weight, co-trimoxazole use, and CD4 count prior to ART initiation. Follow-up visit information included weight, haemoglobin, CD4 count, clinical stage, co-trimoxazole prescription, ART regimen, and the final patient outcome. Selected patient files were retrieved by ART clinic staff who were responsible for retrieving selected patient files, whilst missing ones were replaced by the next eligible patient record on the randomly ordered list. Trained health workers were responsible for abstracting information from patient files into paper-based questionnaires, which were eventually entered into an MS Access database and scanned separately into a TeleForm database.
Statistical analysis Data management was done using SAS 9. 2 (SAS Institute Inc. , Cary, NC, USA), whilst all statistical analyses were performed using Stata/IC 12. 1 (StataCorp, 2011, Stata Statistical Software: Release 12. 1; StataCorp LP, College Station, TX, USA). Data were weighted and the analysis accounted for the complex design of the survey using the 'svy' prefix command in Stata. Domain analyses were performed using the 'subpop()' option. Missing data were assumed 'missing at random' (MAR)  19  and were multiple imputed by chained equations using the 'mi impute chained' procedure in Stata. Twenty imputed datasets were created and estimates were combined according to Rubin's rules  19  using the 'mi estimate' procedure. The imputation model included the Nelson-Aelen estimate of cumulative hazard,  20  baseline demographic and clinical variables, and the event indicator.
Time-to-event data were complete for all individuals. Twenty-five patients were LTFU after their first clinic visit and were assigned one day of person-time. Gender differences in baseline clinical and demographic characteristics were analysed using linear and logistic regression models. These analyses were limited to the first imputed dataset. Cox proportional hazards models were used to compare differences in ART outcomes (mortality, LTFU, and attrition) and immunological ART outcomes by gender, and also to identify gender-specific factors associated with attrition. Multivariable adjusted hazard ratios (HRs), their 95% confidence intervals (CI), and p-values were calculated whilst adjusting for the potential confounding effects of age, baseline weight, baseline CD4 count, baseline WHO staging, prior/current TB, anaemia, size and location of OI/ART clinic, and level of health facility.
Baseline demographic, clinical, and immunological characteristics Data were abstracted from 3919 medical charts of patients eligible for ART. The level of missing baseline patient data in these charts varied from <1% (n = 12) for sex, 5% (n = 186) for age, 16% (n = 630) for WHO stage, 27% (n = 1049) for body weight, 43% (n = 1688) for current active TB, and 53% (n = 2085) for CD4 cell count to 77% (n = 3031) for haemoglobin levels. Original and imputed datasets are reported in Table  1 , for overall and gender-specific baseline demographic and clinical characteristics. The results of the weighted imputed data are reported below. At enrolment into HIV treatment, men were older (39 (interquartile range (IQR) 34-48) vs. 36 (IQR 31-44) years; p < 0. 001), had higher median baseline weight (57 (IQR 47-60) vs.
Our study did not assess self-reported ART interruptions or adherence, which may be linked to ART attrition. In a Cameroonian study by Marcellin et al. ,  38  pharmacy stock shortages, binge drinking, and the number of selfreported slimming symptoms were associated with ART interruptions, and food supply programs were suggested as interventions to limit these ART interruptions. Other study limitations include missing data on haemoglobin measurements, and given that nearly three-quarters of those with documented haemoglobin measurements were anaemic, could suggest that these measurements were taken in those suspected of having anaemia. Data were also unavailable for patient height and thus we were unable to calculate body mass index (BMI). Whilst it may have been ideal to determine actual patient outcomes of all patients, we could not trace and establish true patient outcomes in those patients who were classified as LTFU and there is a possibility that some may have been alive or dead and thus misclassified.
Acknowledgments The authors would like to acknowledge those who implemented data abstraction, data cleaning, and data entry activities:  Gibson Chikono ,  Millicent Chilongo ,  Luke Chimhanda ,  Priscilla Chirisa ,  Yvonne Chitate ,  Somkie Dube ,  Janet Dzangare ,  Harusekwi Andrew Hukuimwe ,  Wendy Inouye ,  Nhamo Jaravaza ,  Lydia Madyira ,  Alpha Mukaronda ,  Cansio Makombe ,  D. Mambudzi ,  Judith Mbirimi ,  Richard Mashapa ,  Alice Masoha ,  Absolom Mbinda ,  Winter Mkwananzi ,  M. Moyo ,  Thomas Moyo ,  Christopher Muchira ,  Maclean Mukombachoto ,  Pardon Munharira ,  Rutendo Munharira ,  Mirirai Muyengwa ,  John Nyamayaro ,  Tendai Zimunya. Funding source: The project has been supported by the  President's Emergency Plan for AIDS Relief (PEPFAR)  through the  Centers for Disease Control and Prevention (CDC)  as part of country activities.
5  replicated the original experiments performed by Zhang et al. 1  but were not able to reproduce their findings. An examination of publicly available small RNA sequencing datasets on various tissues from mammals, chickens and insects could not confirm the presence of high levels of plantderived microRNAs in these species. 6 Several specific feeding studies found only small quantities of plant microRNAs in human, bee, mouse and non-human primate tissues. 7, 8 However, Snow et al. 7  were able to show that when MIR156a was expressed at higher levels in a food when compared with MIR159a and MIR169a, it was more easily detected in recipient organisms, albeit at extremely low levels. They therefore considered that an improvement in delivery might theoretically be possible through increased ingestion, but making such dietary alterations would be challenging under normal circumstances.
RT-qPCR The reverse transcription (RT) and quantitative PCR (qPCR) steps were performed according to the manufacturer's protocol. miR-30b were quantified using RT-qPCR with TaqMan Micro-RNA Expression Assays (Applied Biosystem, #4427975 Assay ID 000602). Briefly, 10 ng of total RNA for tissue and milk samples, and 100 ng for blood and stomach content samples, were reverse transcribed under the following conditions: 16 C for 30 min. , 42 C for 30 min. , 85 C for 5 min. PCR reactions were as follows: 95 C for 10 min. followed by 40 cycles of 95 C for 15 s and 60 C for 1 min. using the Mastercycler ep Realplex (Eppendorf). After normalization with the small RNA RNU6B (Applied Biosystems, #4427975 Assay ID 001973), used to calculate the relative abundance of microRNA, the data were analyzed using the Delta-Delta Ct method (2 ¡DDCt ).
Background: A rapidly growing amount of knowledge about signaling and gene regulatory networks is available in databases such as KEGG, Reactome, or RegulonDB. There is an increasing need to relate this knowledge to highthroughput data in order to (in)validate network topologies or to decide which interactions are present or inactive in a given cell type under a particular environmental condition. Interaction graphs provide a suitable representation of cellular networks with information flows and methods based on sign consistency approaches have been shown to be valuable tools to (i) predict qualitative responses, (ii) to test the consistency of network topologies and experimental data, and (iii) to apply repair operations to the network model suggesting missing or wrong interactions. Results: We present a framework to unify different notions of sign consistency and propose a refined method for data discretization that considers uncertainties in experimental profiles.
Background The advancements of measurement technologies and high-throughput methods in molecular biology have led to a tremendous increase in the availability of factual biological knowledge as well as of data capturing the response of biological systems to experimental conditions. Knowledge about metabolic, signaling, and gene regulatory interactions and networks is available in databases such as KEGG, Regulon DB, PID, or Reactome which can be used as a starting point to build causal models of biomolecular networks  [1]. Specifically, signaling and gene regulatory networks carrying signal and information flows can be represented as interaction (or influence) graphs  [2] [3] [4] [5] [6] , Bayesian networks  [7] , some form of logic (including Boolean or constrained fuzzy logic) modeling  [4, 8, 9] , or ordinary differential equations  [10] [11] [12].
The goal of this study is fourfold. First, we aim at unifying existing approaches into a general framework. We show that different notions of sign consistency mainly differ in the way zero changes are modeled. Then, we propose a refined method for data discretization allowing one to express uncertainties during the discretization step. In addition, we introduce a new constraint to filter undesired self-fulfilled explanations which result from positive feedback loops. Finally, we introduce an extended prediction method that allows not only strong (e. g. , "increase") but also weak predictions (e. g. , "increase or 0change"), enlarging the predictive power of the approach. We applied the extended framework to a realistic case study where we analyze high-throughput transcriptomic measurements of Escherichia coli in the context of a large-scale gene regulatory network model obtained from RegulonDB.
g. , which data might be questionable or which edges might be missing or inactive) but as a quantitative measure for the fitness of model and data. Also note that once consistency is re-established, network and data can again be used for predicting behaviors of unmeasured entities. In  [17] , four repair operations were introduced; two of them for single experiments (SCEN-FIT, Minimal Correction Sets (MCoS)) and two for multiple experiments (OPT-SUBGRAPH, OPT-GRAPH). The latter two are computationally more demanding as they seek to optimize the whole network structure based on many perturbation experiments. SCEN-FIT, as explained in detail in the Additional file 1, seeks to find a consistent node labeling that is closest to the given measurements and can thus help to identify inconsistencies between network and dataset.
Software The different consistency notions as well as the methods for consistency checking and quantification, prediction, and all data and network repair operations were implemented in an open source application iggy  [24]. iggy uses ASP  [23]  as logical modeling and constraint solving paradigm, it is part of the BioASP software collection and can easily be installed via the python package index (PyPI). ASP is used to model problems from NP and provides state-of-the-art solvers. In particular, we use the solver clasp  [25]  via the pyasp  [26]  package. On an AMD Opteron 6168 1. 9 GHz with 96 GB RAM, given a network with 1646 nodes and 4277 edges our software needs ≈ 20 Fig. 7  Repair by adding signed influences example (Minimal Correction Sets -MCoS). There exist three alternative repair sets: repair set a adds a positive influence to A and repair set b includes a negative influence on B, repair set c includes a positive influence on A and a negative influence on B.
Repair sets a and b are minimal containing only one repair, repair set c is not minimal having two repairs. Looking at the intersection of the labelings under minimal repairs, we can conclude that C is either responsible for an increase in A or a decrease in B. We can therefore exclude a labeling of C with 0, we can predict: pred(C) = ± min to compute the predictions under minimal repair (MCos) for the unmeasured species of 105 experiment data sets each containing 1392 measurements. For further information visit http://bioasp. github. io/iggy.
Results and discussion To investigate the suitability of the different consistency notions, we used the gene regulatory network of Escherichia coli and confronted it with Microarray data. The network was obtained from RegulonDB  [27] , version 8. 3 in october 2013, and we focused on its biggest weakly connected component which is composed of 1646 nodes and 4277 edges and covers 94 % of the nodes of the full RegulonDB network. Unsigned edges are treated as two parallel edges with opposite signs. The data refers to the microarray log ratio expression of 3607 genes measured under 240 different stress conditions in E. coli published in  [28]. We chose 105 of 240 experiments which can be interpreted as steady state shift experiments and 1392 of the 3607 genes which occur in the RegulonDB network.
Quality of regulatory network when confronted to the expression profiles As a first step, we assess the quality of network and data by comparing it to randomized data. We generated 100 randomized datasets for each real experiment by shuffling the observed signs among the observed nodes; but preserving the sign distribution for each dataset. We then computed for real and randomized data the inconsistency index which is defined as the quotient of the number of minimal corrections (MCoS) to restore consistency (under notion FSP) divided by the number of observations in the experiment. Then we computed the Wilcoxon signed-rank test to assess whether the population means of the two samples differ. The obtained p-value of 2. 0497e-11 indicates a highly significant difference of real and randomized data, suggesting that the real data are more (sign-) consistent with the network topology than random data.
Predictions under the different consistency notions To investigate the suitability of the different consistency notions to predict the behavior of unobserved entities in a regulatory network, we performed a cross-validation using the E. coli data. While other validation methods exist, we decided to use cross-validation as a model validation technique because it allows us to assess how the results of the approach will generalize to independent datasets. To set up cross-validation, we created for each experiment 100 samples each containing a random 10 % share of the measurements. We then confronted the E. coli network with these samples, determined the minimal corrections necessary to restore consistency, and computed the predictions that hold under all minimal correction sets. In Table  2  one can see the distribution of the +, -, 0 and weak predictions as well as how the precision of the different types of predictions varies among the different notions (WP is similar to FWP see Additional file 1).
For more information on how to compute the information gain we refer to the Additional file 1. To validate the quality of the predictions (obtained from 10 % of the data), we compared them with the validation data (the remaining 90 % of measurements). For the nodes where a prediction and validation data was available, we compared both. We obtained on average precisions that range from 73 % to 80 %. Overall, SP and FSP allow us to make predictions for a much bigger part of the network, resulting in a much higher information gain with only a slightly decreased precision, and for + andpredictions with a significant higher precision than FWP. In Section 5 of the Additional file 1 we plot the detailed recovery and precisions per experiment for notions FWP and FSP. To test the influence of the number of measurements on recovery rate and precision, we also created a dataset with 50 % and 75 % (see Additional file 1: Table  S3 ) of the measurements.
Compared to the results with 10 % the overall recovery rate increases up to 82 % (FSP). This is due to the fact that the increased amount of data helps to put more constraints on the systems behavior. For notion SP and FSP the number of weak predictions drops slightly because many of them become strong predictions. The precision of +,and weak predictions benefits from the richer datasets under notion SP and FSP, while the precision of 0-change decreases only slightly. Weak predictions easily have higher precisions, because they have a bigger chance to be true positives. To validate that the precisions obtained in our test case are indeed meaningful, we tested our approach on a randomized dataset. We could verify that the predictions from randomized data have less precision than the predictions obtained from the real data (see Additional file 1: Table  S3 ),especially for notions SP and FSP.
Gray fields denote either predictions that could not be verified because no validation data was available or nodes for which validation data was available but no predictions had been made that 53 % to 72 % of the network remains unaltered (0change). Understanding the differentially expressed network regions becomes more delicate, since the precision remains on average 54 % to 62 % which, however, is still significantly higher than for notion FWP. Nevertheless, 48 % of the experiments had a precision above 75 % for up-or down-regulation (strong) predictions when considering a dataset with 50 % of the measures. Note, that the notion of precision changes its conclusiveness when applied to incompletely determined predictions. Thus, we use confusion matrices as an alternative representation to illustrates the performance of our prediction method.
Conclusion We presented a unified framework to express different notions of sign consistency on interaction graphs. A refined methodology for data discretization into five values allows the consideration of uncertainties in experimental profiles. Within this framework we introduced a new constraint to filter undesired self-fulfilled regulations that result from positive feedback loops. Finally, our extended prediction method considers not only strong (unique value) but additionally weak (multiple admissible values) predictions, enlarging the predictive power of the approach. We evaluated our framework by confronting the full RegulonDB network with 105 experimental geneexpression profiles. Our cross-validation results obtained when choosing 10 % of the initial dataset show that the overall precision of the methods ranges from 72 % to 80 %.
The precision of the FSP notion has a much higher and significant p-value. With its increased precision and recovery, FSP appears to be the superior notion. We expect that the information gain is in general higher for datasets from (typically smaller) signaling networks (see e. g. [17] ). This might be due to the fact that in the stress experiments considered here the (perturbed) inputs of the gene regulatory network were unknown which poses less constraints than in signaling networks with normally well-defined signal inputs (given by the applied ligands, inhibitors etc. ). Our method requires a careful selection of discretization thresholds. Therefore, we performed a detailed sensitivity analysis on a wide range of the discretization thresholds (see Additional file 1: Section 4). The analysis shows that there is a relatively small sensitivity of the results (precision, information gain) w.
Data collection and procedures Data are derived from two cross-sectional surveys conducted at enrollment (baseline) and at endline, 18 months later. After completing the verbal informed consent process, participants at baseline assessment and endline were surveyed using a structured, interviewer-administered questionnaire that included socio-demographic characteristics, injecting behaviors, criminal history, social integration, and general and mental health topics. Data were collected by trained MdM interviewers at baseline and by trained JHU interviewers at endline. All questions were developed in English, translated into Dari, and back-translated by a certified translator. No participation incentive was provided. The study was approved by the Institutional Review Boards of the Afghan MoPH and Johns Hopkins Bloomberg School of Public Health.
Data analysis Secondary data analysis was conducted with data from baseline participants and endline participants. MdM collects participant's data anonymously, prohibiting identification of the participants who were later enrolled into  in a total of 57 clients who were surveyed at endline by JHU (Fig. 1 ). All participants were male and with a mean age of 32. 2 years (SD = 7. 8) at baseline. The mean daily methadone dose was of 131 mg/day to all participants (range = 30-270 mg/day). Of the 83 clients who were initially enrolled and surveyed by MdM, 38 clients were lost to follow-up (45. 7 %) and 45 clients were retained (54. 2) and at 18 months (Table  1 ). Reasons for loss to follow-up included voluntary withdrawal (n = 9, 20 %), imprisonment (n = 8, 17. 8 %), death (n = 5, 11. 1 %), migration (n = 3, 6.
This may limit our capacity to accurately understand the factors associated with retention and OST outcomes. The OSTPP assessment was carried out within an existing program, lacking a control group and randomization. Baseline and endline data analysis reflect group changes and not individual change. Another potential bias was social desirability bias that is inherent in all socio-behavioral studies, particularly those that address illegal behaviors. The mental health questions used as part of the evaluation have not been validated for use among PWID. Future research should include validated mental health assessments that can better characterize the mental health problems of PWID. Reasons for recidivism should be identified with the aim of improving the program's performance in maintaining heroin cessation.
Acknowledgements The authors acknowledged the clients of the OSTPP,  Medecins du Monde ,  Dr. Ajmal Sabawoon ,  Dr. Mobeen Bromand ,  Dr. Khalid Ikram ,  Dr. Bareq Sadiq ,  Jessica Kali ,  Dr. Chris Beyrer ,  Dr. Andrea Ruff ,  Dr. Carl Latkin ,  Dr. Isabel Rodriguez  and  Dr. Homayonn Farzadegan  for contributing in the study.
This work was performed in partnership with the SAS PIVERT within the framework of the  French Institute for the Energy Transition (Institut pour la Transition Energétique-ITE ), P. I. V. E. R. T. (www. institut-pivert. com),selected as an  Investment for the Future ("Investissements d'Avenir "). This work was supported as part of the Investments for the Future by the  French government  under reference  ANR-001-01.
Outcomes evaluated The main outcome of interest was the incidence of proven/probable IFI using the consensus criteria described by Ascioglu et al. [21]. Other outcomes were all-cause mortality, proven/probable IA, proven invasive candidiasis (IC), and administration of other licensed antifungal therapy (OLAT; defined as any systemic antifungal other than randomized study drug, including a temporary switch to an intravenous agent in patients not tolerating oral prophylaxis). The cumulative proportions of patients for each of these outcomes at 180 days post-transplant (or the closest available time point) were extracted from each RCT. Data extraction was performed in duplicate by 2 of the authors (LC, AJS) and the extracted data were independently reviewed by 2 additional authors (EJB, SS).