dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Data collection and determination of host's lineage. Viral nucleotide reference sequences were retrieved from the NCBI Nucleotide database on October 11, 2017. Besides the sequence itself, the files in XML format often contain information about viral lineage, natural hosts, nucleic acid type (DNA/RNA), number of strands, etc. In the process of metadata verification, the naming convention of hosts was verified and supplemented with Latin names in case of a common name usage. In certain cases, if a user submitted a common name of the host that appeared ambiguous (e. g. 'sugarcane') or was not present in the NCBI Taxonomy ('mulberry'), the proper Latin name was supplied manually. We have developed a script that utilizes the host's name to extract its full lineage from the NCBI Taxonomy database.
The features were extracted using our original script and standardized in order for each feature to have zero-mean and unit-variance. As a result of the previous procedures, we obtained 5275 feature vectors that could be easily split by lineage into two subsets: viruses infecting eukaryotes and those infecting prokaryotes and Archaea, comprising 4056 and 1219 viruses, respectively. Each vector consisted of 101 elements (4 mononucleotide frequencies, 16 dinucleotide frequencies, 16 relative dinucleotide frequencies, 64 trinucleotide frequencies and nucleic acid type).
Phages were sorted alphabetically along their lineages and then divided into 5 subsets. The sorting along viruses' lineages prevents redundancy between the subsets of the cross-validation set. The initial dataset contained larger number of Eukaryota-infecting viruses. Thus, in order to balance virus categories, a different strategy to select a virus to one of the cross-validation splits was employed. The viruses were arranged according to their lineages, and a random sample of viruses of a given lineage were selected to the cross-validation set. The probability of picking an i-th virus of the given lineage to the cross-validation set P(v i ) is determined by the equation = -. P v e ( ) i i 0 217. The number 0. 217 was earlier determined using the minimize function from scipy. optimize  59  to ensure that the number of Eukaryota-infecting viruses assigned to the cross-validation set is close to the number of phages assigned to this set.
In second approach, genetic algorithms (GA) with tournament selection implemented in the Python DEAP package  60  were used. Each feature set was represented as an ordered vector of 101 binary attributes, while each attribute corresponds to the presence/absence of a given feature in the feature set. The grid search strategy was adopted to run the feature selection on a small population of 100 individuals (binary vectors) to choose best GA settings. Following an analysis of initial run results, the grid of GA settings was extended in the most promising directions. Finally, the best settings to run GA on bigger population of 500 individuals was chosen. In each case, evolution was terminated after 200 generations or when all individuals were identical. Each analysis was repeated five times, and the individual that maximizes the 'fitness' measure was selected for further analysis.
GA setup is described in detail in Supplementary Notes. Feature selection: sVC. In the case of SVC, different feature selection techniques were applied: selecting k best features (SelectKBest from scikit-learn) -according to the highest values for the ANOVA F-value between a label and a feature with k ranging from 1 to 101, RFE from scikit-learn  61  , SCAD from penalizedSVM R-package  62  , and QuiPT from biogram R-package  40. All of analyses were performed using SVC with linear kernel and equal range of C parameter values, as in the case of logistic regression with Lasso regularization. In process of selecting features with biogram, only those with p-value < 0. 01 were considered. Evaluation of the final models. Following the selection of the best model hyperparameters and/or the best feature sets using cross-validation, the final model was trained with all data from the cross-validation set and evaluated on the test set.
The partitioning is described in detail in Supplementary Notes. The host prediction was transformed to three binary classification tasks, and we checked separately for virus' target group (seed plants, arthropods, vertebrates) or lack of it. Feature selection with Lasso regularization (logistic regression) and recursive feature elimination (linear SVC) was performed similarly as for distinction between eukaryotic viruses and phages with the use of AUC in place of 'fitness' measure. Viral Feature extractor and Host taxon predictor software. Scripts for downloading viral genomic sequences, extracting viral and host lineage, and transforming sequences into features, written in Python 2. 7, were merged together in one package named Viral Feature Extractor. For all of machine learning algorithms (LR, QDA, kNN, SVC), hyperparameters and feature sets exhibiting the highest 'fitness' on cross-validation data were adopted to train the final classifiers using all the feature vectors from cross-validation as well as test set.
Host taxon predictor evaluation on new viruses. A new collection of viral nucleotide reference sequences was downloaded on August 27, 2018 and hosts' lineages were determined as described in Data collection and determination of host's lineage sections. Host's lineages for 837 new viruses were determined, of which 756, 72, and 9 infects Eukaryota, Bacteria, and Archaea, respectively. The viral sequences were later analyzed by HTP to obtain class probabilities and compute AUC and MCC. Identifiers of the viral sequences are available at https://github. com/wojciech-galan/supplementary_materials/blob/master/datasets/new_viruses_ids. Host taxon predictor evaluation on sequence fragments. The newly downloaded sequence collection was used to evaluate HTP's performance on sequence fragments. The generation of sequence fragments datasets was attempted with WGSIM simulator (https://github.
com/lh3/wgsim) which crashed with fragment length > 1000 nt. The challenge was addressed by creation of authors' own script which is available at https:// github. com/wojciech-galan/supplementary_materials/blob/master/code/my_read_simulator. py. The script was applied to produce sequence fragments of lengths s and base substitution rate r, where s equals to 100, 250, 500, 1000, 3000 or 10000, and r equals to 0 or 0. 02. The sequence fragments were generated randomly. Probability of generating a subsequence from a sequence of length S was proportional to S-s + 1 for S-s ≥ 0, so a subsequence could be generated only if the subsequence is supposed to be no longer than the original sequence. Next, sequence fragments were analyzed with HTP as described above.
True Positive Rate (TPR) and False Positive Rate (FPR) values essential to compute the AUCs are available in a serialized form at https://github. com/ wojciech-galan/supplementary_materials/blob/master/datasets/ROC_test_set_phages_vs_rest. dump. Receiver operating characteristics curves calculated on the test dataset consisting of eukaryotic viruses when detecting viruses infecting seed plants, vertebrates and arthropods, respectively. TPR and FPR values essential to compute the AUCs are available in a serialized form at https://github. com/wojciech-galan/ supplementary_materials/blob/master/datasets/eukaryotic_viruses_fpr_tpr. dump. Comparison of the performance of the classifiers designed to distinguish between different groups of eukaryotic viruses.
Acknowledgements This work was supported by the grant  2012/07/N/NZ2/00108  from the  National Centre of Science (NCN) ,  Poland  and in part by  PLGrid Infrastructure (grant hostphylumpred). Faculty of  Biochemistry, Biophysics and Biotechnology of the Jagiellonian University  in Kraków is a partner of the  Leading National Research Center (KNOW)  supported by the  Polish Ministry of Science and Higher Education.
Data Availability Viral Feature Extractor and Host Taxon Predictor are available as git repositories at https://github. com/wojciech-galan/Viral_feature_extractor and https://github. com/wojciech-galan/viruses_classifier, respectively. To support the idea of transparency in science and allow everyone to reproduce our results we have released also our datasets as well as scripts that we used to obtain particular results. They are available on https://github. com/ wojciech-galan/supplementary_materials. Stable versions of these repositories are accessible on zenodo under https://doi. org/10. 5281/zenodo. 2531529, https://doi. org/10. 5281/zenodo. 2531545, and https://doi. org/10. 5281/ zenodo. 2531541, respectively.
Phase characterization Transmission electron microscopy (TEM). Bulk samples of Ag 3 LiRu 2 O 6 and Ag 3 LiIr 2 O 6 were ground and distributed onto a holey carbon/copper grid. TEM was performed with a Philips CM 30 ST microscope (300 kV, LaB6 cathode) equipped with a spinning star device enabling the use of precession electron diffraction (PED). 23 Simulations of the diffraction patterns and HRTEM micrographs (multi-slice formalism) were obtained with the JEMS 24 software package. Laboratory X-ray powder diffraction. For comparison the XRPD-patterns of the solid phases were collected at room temperature on a laboratory powder diffractometer in Debye-Scherrer geometry (Stadi P-Diffractometer (Stoe), Ag-K α1 radiation from primary Ge(111)-Johann-type monochromator, Mythen 1 K detector (Dectris)).
Synchrotron X-ray powder diffraction Synchrotron X-ray total scattering patterns of the samples were recorded in Debye-Scherrer mode at the European Synchrotron Radiation Facility (ESRF), Grenoble (France), at a wavelength of λ = 0. 18087 Å (≈ 68. 5 keV) on beamline ID31 in rapid acquisition mode. 25 Diffracted X-rays were detected using a single photon counting detector (Dectris, Pilatus3X CdTe 2 M with 172 × 172 μm 2 pixel size) mounted orthogonal to the beam path with a sample-to-detector distance of 273. 35 mm. The measured samples were sealed in kapton tubes of 1. 0 mm diameter. The collected Debye-Scherrer rings were subsequently azimuthally integrated with the pyFAI-software  26  to one-dimensional powder diffraction patterns in Q [nm -1 ] and 2θ [°] versus intensity. Parameters for intensity integration were determined from a NIST CeO 2 reference sample (SRM 674b).
Pair distribution function (PDF) analyses PDFGETX2  32  was used to correct and normalize the diffraction data and the Fourier transform in order to obtain the experimental pair distribution function (PDF), G(r). The Q range used in the Fourier transform was limited to 30 Å -1. Nickel was previously measured as a standard material to determine the Q-damp and Q-broad parameters which are the parameters that correct the PDF envelope function for instrumental resolution effects. 33, 34.
Hence, only semi-quantitative information can be extracted, i. e. the transition probabilities cannot be optimized directly. In addition, the layer constitution should not be optimized simultaneously due to the mentioned potential overextension of the parameter space. Recently, a routine was published for the TOPAS software, which enables recursive-like DIFFaX-like simulations and fitting of stacking faulted samples, by creating a number of stacking sequences through supercells and averaging them. 43 ombining this routine with python scripts enables the iterative variation of the applied transition probabilities, while executing the input file multiple times and thus performing a grid-search optimization in the parameter-space of the transition probabilities. 40 This can also be realized purely in TOPAS by using the run-number as a running index (ESI, Table  S4.
Acknowledgements Maxwell W. Terban  from the  Max Planck Institute of solid  state research is gratefully acknowledged for his valuable support in performing the synchrotron powder diffraction experiment and in the related data processing. The high energy powder diffraction patterns were collected at ID31 beamline at the  ESRF  in the frame of in-house research program. Open Access funding provided by the  Max Planck Society. Funding by  DFG  for the project " In search of structure " (grant  EG 137/9-1 ) is gratefully acknowledged.
Methodology Purpose To evaluate the risk factors for developing MDR-TB among the patients registered under TB-DOTS at Federal General Hospital (FGH) Islamabad. Methodology A case control study was carried out at FGH Islamabad from 15 November to 20 December 2016. A case was defined as "any TB patient visiting FGH Islamabad found resistant to anti-tuber-culous therapy (ATT) and confirmed by Gen Expert test from January 2014 to December 2016. " While a control was defined as "any TB patient who is not resistant to ATT and is negative on Gen expert test. " Three Age and sex matched controls were selected for each case. Data was collected using structured questionnaire. Frequencies were calculated, and odd ratios determined at 95% confidence interval and p value of <0. 05. Results Among total of 27 cases 14 (51%) were male.
Methodology We use the Center of Diseases Control (CDC) updated guideline for evaluating public health surveillance systems. In-depth interviews were conducted with six NTCP and 12 central and regional MDR-TB Centers' managers/staff. We used 5-Likert scale to assess performance by attributes (e. g. usefulness, simplicity, flexibility) according to the following scoring system: Poor (<60%), average (60-80%), and good (>80). NTCP-modified WHO case definitions for treatment outcome was used where treatment success defined as completing treatment according to protocol with at least five consecutive negative smears from samples collected at least 30 days apart in the final 12 months of treatment. Results MDR-TB Surveillance System achieved good performance in usefulness, acceptability and data quality compared to average score in flexibility and simplicity; and poor score in stability.
Methodology We conducted case-control study in intensive care unit of neonatal medical service in Rabat university hospital considered as the inter-regional hospital. We included 87 preterm births before 37 gestations weeks and 174 term-controls. Data about the women's obstetrical and gynecological history, pregnancy complications and behavior during pregnancy was obtained using a structured questionnaire and medical records. The data was analyzed using SPSS version 20. The logistic regression was employed to identify risk factors of preterm birth.
Conclusions Methodology A cross-sectional study was conducted in May 2014 targeting 986 male students of King Saud University. A questionnaire was used to obtain data on possessing a driving license, years of driving, driving hours, and collision or near collision in the six months preceding the study. Eight statements were used to assess the behavior and perceptions related to the use of mobile phones while driving. Data were analyzed using the chi-square statistic, odds ratio (OR) and the 95% confidence interval (95%CI).
Methodology The food poisoning outbreak was described by time, person, and place. A case was defined as any person with two or more of the following symptoms: diarrhea, vomiting, fever, and abdominal pain, who had been in the town of Hatem (Irbid) in the period 14-19 September 2017. We conducted a cohort study and collected data on demographics, water sources, and food sources. We conducted environmental survey for water sources and for food in local restaurants for fecal contamination, and tested water, food, and stool samples for microorganisms.
Conclusions This was an outbreak associated with the consumption of Briwates contaminated by two germs, which was the source of this episode. We recommended to strengthen hygiene measures and safe food handling, to raise awareness and educate the staff. Background Iraq is a cholera-endemic country with outbreaks occurring every 3-4 years. Since 29th, August 2017, Iraq CDC received reports on confirmed cholera patients in Baghdad/ Resafa (Eastern Baghdad with a population of 4. 8 million). Up to Nov 29th the total number of confirmed cholera patients was 257. Purpose To determine the epidemiological characteristics of the patients and identify potential risk factors. Methodology A team of FETP residents dispatched to investigate the outbreak and developed a form to compile demographic and epidemiological data from all the patients' investigation forms that were filled at the treating hospitals.
Conclusions Methodology A descriptive and analytical studies were conducted. Active search of cases and control was performed. WHO case definition of cholera during epidemic was adopted. Stool samples were taken and sent to the public health central lab. for confirmation. The data was entered and analyzed by Epi Info 7. 2 and Excel sheet were performed. Chi square and odds ratio were calculated. Results Out of 28 cases, 15 (54%) were females. The index case, a 65 years-old female, was identified on October 3rd. The peak of cases was on October 8th, 2016 (46%). The attack rate was 3/1000 population with no case fatality. Children < 5 (24%) and those 5-10 years (24%) were mostly affected. Out of 18 stool samples, 11 Vibrio cholera serotype 01 Eltor-ogawa was isolated (61%). The identified risk factors were: untreated water OR=10.
A control was defined as a person from the same house or neighborhood of a case who does not has diarrhea or vomiting. Data were collected from cases and control using semi-structured questionnaire including basic demographic information, potential food and water exposures, and hygienic practices. Seven stool samples were taken only from patients for laboratory confirmation of cholera by culture. Water and sewage samples were also taken. We performed bivariate analysis using Epi-Info version 7. 2. Results A total of 12 cases and 24 control that met the case definition were recruited. About 83% of cases were clustered in one house, 40% of cases aged 7 -14 years, and 58% were females. The overall attack rate in Haran area was 0. 1% and the case fatality among cases was 8. 3%. The only significant risk factor identified was poor sanitation [OR= 5.
Abstract Methodology At the end of June 2017, the reporting form, database and the stan-dard Operating Procedures (SOP's) of EBS were updated. EBS team received training for the system updates. Data from April to September was investigated for the source of information, signal filtration, verification, and timeliness of response. For the events that are routinely reported by IBS, early detection was checked for both systems. The results were compared three months before and after the training.
Conclusions Increase EBS capacity for early detection of potential public health threats. Marked improvement of EBS team performance. Raise community awareness, expand the sources of information and shift to electronic database are highly recommended. Background Adverse drug reactions (ADRs) are a major cause of drug related morbidity and mortality. Globally, about 5% of all hospital admissions are due to an ADR and 10%-20% of inpatients have at least one ADR during their hospitalization. Pharmacovigilance is the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other drug-related problem(s). Purpose To evaluate the knowledge, practice and attitudes (KAP) toward ADRs reporting and pharmacovigilance among hospital pharmacists in Mansoura, Egypt, 2016.
Methodology Cross sectional study con-ducted from 1st march to 1st September 2012 in three sentinel sites at Salahaldin governorate. Depending on Hepatitis B median endemic index of 10%, a total of 900 subjects of both genders attending these sentinel sites and screened by ELISA technique for HBs Ag presence, were re-screened by the ACON® HBs Ag rapid test device. The ACON HBsAg One Step Test is a qualitative, solid phase, two-site sandwich immunoassay for the detection of Hepatitis B surface Antigen (HBsAg) in serum or plasma. Results of both techniques were compared and validity of HBs Ag rapid test was calculated statistically by SPSS version 17. Results ACON HBs Ag rapid test showed an overall sensitivity, specificity, positive predictive value, and negative predictive value of 92. 8%, 99.
KSA MOH, developed a database of available published and unpublished data sources to generate the burden of disease for KSA. Key inputs into this database included systematic reviews of the lit- erature, analysis of household survey data, lesterol not exceeding 6. 2 mmol/L, but the antenatal clinic surveillance, reportable respondent reported taking medications for disease notifications, disease registries, hos- hypercholesterolemia. pital admissions data, outpatient visit data, Results Overall, 1,095,776 (8. 5%) Saudis population-based cancer registries, active reported being diagnosed with diabetes. screening data, and other administrative However, a total of 1,745,532 (13. 4%) Sau- data. Moreover, the MOH provided detailed dis aged 15 years or older had diabetes. Respondents were considered hypercho- lesterolemic if they met any of the following criteria: (1) measured cholesterol equal to or exceeding 6.
This paper summarizes recent efforts on Observing System Evaluation (OS-Eval) by the Ocean Data Assimilation and Prediction (ODAP) communities such as GODAE OceanView and CLIVAR-GSOP. It provides some examples of existing OS-Eval methodologies, and attempts to discuss the potential and limitation of the existing approaches. Observing System Experiment (OSE) studies illustrate the impacts of the severe decrease in the number of TAO buoys during 2012-2014 and TRITON buoys since 2013 on ODAP system performance. Multi-system evaluation of the impacts of assimilating satellite sea surface salinity data based on OSEs has been performed to demonstrate the need to continue and enhance satellite salinity missions. Impacts of underwater gliders have been assessed using Observing System Simulation Experiments (OSSEs) to provide guidance on the effective coordination of the western North Atlantic observing system elements.
OSSEs are also being performed under H2020 AtlantOS Fujii et al. Observing System Evaluation project with the goal to enhance and optimize the Atlantic in-situ networks. Potential of future satellite missions of wide-swath altimetry and surface ocean currents monitoring is explored through OSSEs and evaluation of Degrees of Freedom for Signal (DFS). Forecast Sensitivity Observation Impacts (FSOI) are routinely evaluated for monitoring the ocean observation impacts in the US Navy's ODAP system. Perspectives on the extension of OS-Eval to coastal regions, the deep ocean, polar regions, coupled data assimilation, and biogeochemical applications are also presented. Based on the examples above, we identify the limitations of OS-Eval, indicating that the most significant limitation is reduction of robustness and reliability of the results due to their system-dependency.
It is also preferable to change the model sufficiently from the Nature Run, either due to reduced resolution or differences in physical parameterizations. The impact of the synthetic data on forecast improvement is assessed from the error reduction in OSSEs when assimilating the new data. In this framework, the errors on the full model space can be directly estimated by taking difference of variables between the Nature Run and OSSE fields. An important aspect of OSSEs is that they can be used for evaluating both existing and future observation data types (e. g. ,  Halliwell et al. , 2017). To evaluate existing observations, Observing System Experiments (OSEs) may be performed. An OSE is a data assimilative run in which a certain observation-type is withheld from, or added to, the regularly assimilated data.