dataesr/scientific-paragraphs-categorization huggingface dataset:
By moving the evaluation windows 1 year forward, the second scenario would be  1961-1980-2000. And so on given a certain mortality level, the same values of life expectancy (indicator evaluated in this article) are obtained regardless of whether the life table construction starts from m x , q x , l x or f x. In this article, the goal is achieved by using the life table methods implemented in the MortalityLaws R package (2018). until the last scenario  : 1977-1996-2016. In total, 18 scenarios have been defined, containing equal fitting and forecasting period lengths, making possible in this way the aggregation (by averaging) and comparison of the accuracy results over all scenarios in addition to the specific scenario results.
Reproducible research The presented model and algorithm is implemented using the R programming language (R Core Team 2018) and can be downloaded and installed in form of an R software package from authors GitHub repository (https://github. com/mpascariu/MortalityForecast). The results and figures for the countries presented in the article can be reproduced using the code and data saved in the MortalityForecast package -version 0. 8. 0.
ABSTRACT Introduction Worldwide, 2 million patients aged 18-50 years suffer a stroke each year, and this number is increasing. Knowledge about global distribution of risk factors and aetiologies, and information about prognosis and optimal secondary prevention in young stroke patients are limited. This limits evidence-based treatment and hampers the provision of appropriate information regarding the causes of stroke, risk factors and prognosis of young stroke patients. Methods and analysis The Global Outcome Assessment Life-long after stroke in young adults (GOAL) initiative aims to perform a global individual patient data meta-analysis with existing data from young stroke cohorts worldwide. All patients aged 18-50 years with ischaemic stroke or intracerebral haemorrhage will be included. Outcomes will be the distribution of stroke aetiology and (vascular) risk factors, functional outcome after stroke, risk of recurrent vascular events and death and finally the use of secondary prevention.
Subgroup analyses will be made based on age, gender, aetiology, ethnicity and climate of residence. Ethics and dissemination Ethical approval for the GOAL study has already been obtained from the Medical Review Ethics Committee region Arnhem-Nijmegen. Additionally and when necessary, approval will also be obtained from national or local institutional review boards in the participating centres. When needed, a standardised data transfer agreement will be provided for participating centres. We plan dissemination of our results.
Study design The GOAL initiative is an international multicenter consortium in which individual patient data will be collected from young stroke cohorts. The relevant cohort studies were identified through a systematic search of PubMed using the following Mesh Major Topics: 'Young Adult', 'Stroke', 'Risk Factors', 'Stroke/etiology', 'Prognosis' and 'Secondary Prevention' (online supplementary Open access appendix 1). References of relevant studies were examined to detect other potential cohorts of interest. Prospective and retrospective, as well as hospital-based and population-based cohorts were considered eligible for enrolment if the patients would meet our inclusion criteria. The principal investigators were contacted and informed about the study with the request to participate. Furthermore, when participants indicated they knew of other cohorts or collaborators who might be interested in participating, we contacted these centres.
A specific website for potential participants was developed to provide more information about the aims of the project ( www. goalinitiative. org). An overview of the participating centres is listed in table 1 and figure  1. Analyses and results on baseline variables will be conducted and before March 2020. Furthermore, the GOAL study strives to be as inclusive as possible. Therefore, we have set an ongoing open invitation for all researchers in the field who are interested in participating. We do this with the aim of setting up a worldwide young stroke consortium with an ongoing data collection and data analyses on a broad range of aspects of young stroke. Therefore, we cannot indicate a precise time period for conducting all GOAL-related studies. data collection and storage Participating centres will be requested to transfer their anonymised coded data electronically, according to current laws and legislation concerning research conduct at each participating research centre, to the GOAL-research team of the department of Neurology, Radboudumc, Nijmegen.
Co-authors who met the ICMJE criteria are listed as authors. Other contributors can be found in online supplementary appendix 2. Each participating centre will provide their data by using an encrypted excel sheet containing pre-specified variables of interest. The key linking anonymised data to individual patients will remain at the participating centres. All received data will be entered in a uniform database in IBM SPSS Statistics V. 22 and stored on secured servers at the co-ordinating study centre, and will only be accessible to designated researchers working under the supervision of the study co-ordinator. All data will be processed, stored and destroyed after end of the study according to European Union General Data Protection Regulation. The data will be verified, before entering the data in the uniform database, on data completeness and missing data.
Baseline variables Data for each individual patient is collected from the hospital at index stroke. Baseline data will include demographic characteristics, medical history including medication used on admission and data of diagnostic workup within 1 month after the index stroke (table  2 ). In addition, the season of the index event and climate of the country or region of origin will be registered. The climate will be classified according to the Köppen-Geiger climate classification system,  10  and the season of index stroke according to the date of admission (table  3 ). The severity of stroke is assessed with the National Institutes of Health Stroke Scale (NIHSS) score,  11  and the functional performance with modified Ranking Scale (mRS)  12  right after a stroke. The mRS will also be assessed during follow-up, preferably at 3 months and when available later during follow-up.
outcome during follow-up Follow-up evaluation of use of secondary prevention, recurrence of vascular events and death has been collected differently across the various studies: either in person, by telephone interviews with patients or relatives, by collecting hospital/general practitioner medical records or discharge diagnosis ICD codes. The following cardiovascular risk factors will be collected at follow-up, whenever available, according to the same definitions described at baseline and in table 2: hypertension, diabetes mellitus, dyslipidaemia, cigarette smoking, obesity, AF, heavy drinking and illicit drug use. Functional performance will be assessed with the mRS score and data on the use of secondary prevention medication will be collected from the medical records files. Outcomes will include recurrent vascular events and all-cause death.
Statistical analysis We aim to perform statistical analyses with IBM SPSS Statistics or R (most recent versions). A p value of<0. 05 with correction for multiple testing when appropriate will be considered statistically significant, and/or a 95% CI not containing 1. Subanalysis to identify differences in demographic characteristics including gender, age, ethnicity, pregnancy-related stroke, climate and season at time of admission will be performed. Differences between groups will be compared using ANOVA test, Student's t-test for continuous variables and χ 2 test for categorical variables. Univariate multivariable logistic regression analysis will be performed to determine risk factors and potential trigger factors. In case of missing data at baseline, and if considered necessary, multiple imputation will be used.
The cumulative risk of death and vascular events will be calculated with Kaplan-Meier survival analysis. Differences in survival between different subgroups will be assessed using Log-Rank tests. In the analysis for vascular events, patients who have died or were lost to follow-up will be censored from the last available follow-up. We will use Cox regression models to obtain HRs and their corresponding 95% CIs to calculate the risk of death and recurrent vascular events between different aetiologies and demographic characteristics while adjusting for confounders. Only patients for whom follow-up data is available will be included for these separate analyses.
Acknowledgements We would like to thank all patients who originally signed informed consent and thank all physicians of participating centres who helped during the original data collection (online supplementary appendix 2). Contributors All authors made a substantial contribution to the concept and design of the work; acquisition, analysis and interpretation of data. ME and FEdL designed the study protocol,  ME, MJ, MvD , and FEdL drafted the article. KA, AA, AA, MA, MB, MB, RFRB,  BC, EC, BD, SD, AD, CE, EE, SF-H, FF, AF, TG, GG, MH, TH, CJ, KJ, MK, YSK, TK, SK, TK, KK, JK, T-HL, DL, NM, NM-M, JPM, MM, VM, VM, MOO, VP, MP, BP-I, AP, JLR-S, BS, FS, RS, KST, DT, TT, VT, AT, MV-B, RV, TW, NY, UW-A, AP  and JP revised the article critically for important intellectual content and approved the version to be published.
Objective In this study, we use data measured at high-time resolution (i. e. , every minute) showing the power drawn from the grid at the charging station. Each charging station is associated with a single privately owned EV. With this data, the charging habits of each EV user are modeled in a probabilistic way. This model is described in Section 2. The charging habits model is then used for forecasting purposes: from short-term, one day ahead, to long term, in 2030. In Section 3, we generate forecasting scenarios of an EV's consumption profile for the next day. Although our model forecasts a single EV, we validate the scenarios at the aggregated level, i. e. , for a fleet of several EVs. We observe that scenarios result in accurate probabilistic forecasts of the fleet's aggregated consumption.
Data Description A set of 46 privately owned EVs located in Austin, Texas, was selected. Austinites are known to be very climate conscious and supportive of green policies  [16] , as exemplified by the Pecan Street project run by the University of Texas  [2]. The Pecan Street platform provided us with the electric consumption of each EV recorded every minute of the year in 2015. Insight into the households owning the EV was provided; houses were modern (built around 2007) and large (around 195 m 2 ), meaning that total household consumption was high  [17]. In our dataset, electric consumption related to EVs was responsible for approximately 15% of total household consumption.
The power of the time series fluctuated slightly around a nominal value, translating a noisy phenomenon. This fluctuation was negligible compared to the nominal power, as can be seen on Figure  2. Our hypothesis of perfect charging blocks simplified these two facts. Our observations indicate that nominal power was always the same for a particular EV as long as there was no technological replacement (i. e. , battery and charging station). Such replacements occurred for two of our 46 EVs in the Austin yearlong dataset (nominal power goes from 3. 5 to 6. 5 kW), requiring a minor adjustment in later modeling. On the other hand, the duration and start-up time were not fixed. Charging blocks almost never started at the exact same time each day, and did not have the same duration; start-up time and duration depend on the unknown users' habits.
We also note that the nominal powers used to charge vehicles matched the power outputs of levels 1 and 2, i. e. , slow private EV chargers described in the Nordic EV Outlook 2018  [3]. We logically observe no fast chargers (> 22 kW and ≤ 150 kW) in our dataset, since these are mostly public and almost negligible compared to slow private chargers [3], although numbers are growing  [19]. From the charging blocks detected, we detailed the number of charging cycles for each day and each EV. Data show that, on average for the 365 days of the year and one EV, there were 150 days with no cycle at all, 158 with only one cycle, and 57 with two or more blocks. Furthermore, considering only days with more than two charging blocks, the main block accounts for more than two thirds of the daily energy requirements.
This shows that the main blocks are of paramount importance. Visually, see Figure  4 , the characteristics of a main block and any residuals blocks (i. e. , second block, third block of the day, etc. ) are almost indiscernible. We ascertain this observation with a statistical test comparing the estimated density function in the 2D plan (duration × start-up time) for the main blocks and residual blocks separately, using package ks available on R software  [20]. For more than half of the EVs in the dataset, p-values of the non-parametric test looking for different distribution are below 0. 01  [21]. Considering these results and the fact that we observed fewer residual charging blocks, which hinders an accurate statistical model, we considered in the following that main and residual charging blocks came from the same distribution duration × start-up time.
Scenarios of a Single EV For a specific EV, charging habits detected from the time series data allow us to forecast daily profile scenarios. This forecasting process is done in three steps: 1. Forecast number of charging blocks for the next day; 2. Forecast possible patterns (normalized duration × start-up time) for each block; 3. Use of characteristics of the EV (maximal duration and nominal power) to obtain a consumption profile. For step 1, the forecasting model used is a probabilistic random forest, which provided a convenient way to draw random numbers of charging blocks according to forecast probabilities, using package ranger available on R software  [24]. The random forest algorithm has long been established  [25]  and we detail here the version implemented in the package. The algorithm trains multiple regression trees in parallel, and each tree is fitted on a different dataset.
Contrary to our bottom-up approach that forecast each individual charging before computing the aggregated load of the fleet, these benchmarks directly forecast the aggregated load, and do not consider the individual charges. First, a persistence model using the value of the aggregated consumption at the same minute on the previous day as a forecast point (no probabilistic framework is proposed with this model). Second, an advanced benchmark is a gradient tree boosting model (GTB), with package gbm available on R software  [27]. The gradient tree boosting successively combines weak classifiers to model a complex phenomenon. For regression purposes, the weak classifier is turned into a regression trees of moderate complexity. Therefore, while each tree was quick to compute, the combination of the trees was highly flexible and can model any phenomenon.
Hypotheses Our dataset described EV charging habits in Austin, Texas. We wanted to extend the study area to a larger region. Therefore, we focused on the South Central region of Texas. The main Texan distribution system operator, Electric Reliability Council Of Texas (ERCOT), defines this region as a weather zone covering 25 contiguous counties, comprising two major cities, Austin and most of San Antonio. According to the Texas Demographic Center, the total population of the 25 counties was 4. 8 million in 2017, meaning that there were about 3. 4 million vehicles. According to the 2017 National Household Travel Survey, the market share for EVs in Texas at time of writing was around 1. 9%, meaning that the current number of EVs-or hybrid EVs-was around 65 thousand in the South Central region.
A performance evaluation assesses that this method is as efficient as the advanced machine-learning method, but decomposes the aggregated load into single EV consumption profiles. Since the market share of EVs is expected to greatly increase in the next 15 years, we evaluate the impact of the additional load on the total electrical load in a region in Texas with a population of around 5 million. Based on the four types of charging habits identified on our reduced dataset, we simulate the future load expected in 2030 with and without the EV market share increase, and show that it seems to only moderately impact the shape of the load curve. However, the future of EVs is uncertain, especially concerning the battery capacity and deployment of fast chargers, which may lead to complications for the grid, requiring carefully coordinated charging planning for a large number of vehicles.
Background: Spoligotyping is a reproducible, reverse hybridization approach for genotyping of Mycobacterium tuberculosis complex (MTBC). Molecular typing of MTBC is helpful for understanding and controlling tuberculosis epidemics. Methods: Spoligotyping was performed on 166 clinical isolates of Mycobacterium tuberculosis (MTB) collected from 25 districts of Khyber Pakhtunkhwa, Pakistan. Results were compared to SITVIT2, an online database developed by the Institut Pasteur de la Guadeloupe, France. Results: Spoligotyping results showed that 145 strains (88%) displayed known patterns while 21 (12%) were new. Lineage 3/Central Asian strain (L3/CAS) was the predominant family (73%, χ 2 =19. 9, P=0. 001), followed by L2/Beijing (5. 4%) and L4 (4. 2%). L3/CAS1-Delhi was the major sublineage (82%) among the L3/CAS family (χ 2 =664, P=0.
Conclusion: The present study demonstrated that L3/CAS is the predominant lineage of MTB, widely distributed in different areas of the Khyber Pakhtunkhwa province of Pakistan. Spoligotyping patterns of some clinical isolates could not be matched to other reported patterns in an international database. Other tools, such as mycobacterial interspersed repetitive unit-variable number tandem repeat (MIRU-VNTR), will be helpful in future investigations into the epidemiological characteristics of clinical isolates in the Khyber Pakhtunkhwa province.
4  DNA was extracted from the culture using the cetyl-trimethyl-ammonium-bromide (CTAB) method. 4 poligotyping assay was performed on all samples using a Luminex 200 (Luminex Corp. , Austin, TX, USA) instrument. 8 Spoligotype patterns were entered in binary format into the SITVIT2, a proprietary database of the Institut Pasteur de la Guadeloupe, which is an updated version of the SpolDB4 and SITVIT-WEB databases. 10, 11 linical isolates were initially grouped in one of nine lineage and sublineages: L1/EAI, L2/Beijing, L3/CAS, L4. 1. 1/X, L4. 1. 2/Haarlem, L4. 3/LAM, L4. 4/S, L4/T-others, L5 and L6 (Mycobacterium africanum WA1 and WAS2). 11 The selected clinical isolates that matched with a published spoligo-international type (SIT) were assigned with the corresponding SIT label, while strains with an unknown pattern were declared "orphans".
Lineages were deduced based on the latest next-generation sequencing-based taxonomy when correspondence between whole-genome-based and spoligotyping-based taxonomy was possible. 12, 13 All strains whose pattern did not fall within the above-mentioned lineages were declared new patterns. Furthermore, the patterns of all strains originating from Pakistan were collected from the SITVIT2 database. Evolutionary relationships between the unclustered spoligotype patterns and strains reported from Pakistan were determined using MEGA7 software. 14 he genetic diversity was laid out as a maximum parsimony (MP) tree construct using default settings in MEGA7. The phylogenetic tree was constructed using the subtree-pruning-regrafting (SPR) algorithm, taking the 43 DR pattern as a sequence. 14.
Results In total, 200 isolates of MTB were analyzed by the spoligotyping method. Of these isolates, 166 produced valid and interpretable results. Patterns of 145 isolates were found in the international database (SITVIT2) and 21 unique patterns (13%) were declared new types (Table  1 ). The results showed that lineage 3/Central Asian strain (L3/CAS) was the predominant genotype lineage with 72. 8% (n=121), followed by L2/ Beijing with 5. 4% (n=9) and L4/T with 4. 2% (n=7), and 4. 8% (n=8) were other unknown types (Figure  1 ).
DovePress (SIT26) was the predominant genotype (n=78), followed by L3/SIT1401 (n=5) and L3/SIT794 (n=4). The pattern of one member of the L3/CAS family that did not find a match in the SITVIT2 database was declared as L3/CAS-Orphan. All strains belonging to the L2/Beijing family were grouped in two clusters, SIT1 (n=8) and SIT941 (n=1). Members of L4/ T-other spoligotypes were grouped in five different clusters (SIT51, SIT53, SIT273, SIT732 and SIT189) and one other T orphan. It was also found that EAI1-SOM, EAI5, EAI6-BGD1, H1, LAM9-ZWE, Manu, EAI3-IND and X1 were rarely distributed in this region. All 21 strains with unique spoligotyping patterns were grouped into 13 clusters based on the similarity index of the MP test. The new clusters were further analyzed using different methods to determine any potential evolutionary relationship to previously published genotypes.
The results indicated that the majority of the unique patterns were sublineages of the L3/CAS family, except for some orphans (KP1-KP3) that were more closely related to L4. The detailed phylogenetic tree is represented in Figure  2. These spoligotyping patterns were unique not only to Pakistan but also to the international database. Therefore, no SIT has been assigned so far (Table  1 ). 6] [17] [18] [19]  The new spoligotype patterns in this study were numbered Orphan KP1 to Orphan KP13.
Discussion Many techniques have been used for MTB genotyping, including restrictionfragment length polymorphism (RFLP), amplified fragment length polymorphism (AFLP), mycobacterial interspersed repetitive unit-variable number tandem  repeat (MIRU-VNTR) and spoligotyping. The spoligotyping technique has been used worldwide for genotyping and as a first-line method in epidemiological studies of MTB, in conjunction with VNTR analysis. It was originally developed by Kamerbeek et al, based on identical DRs interspersed by short spacers of variable length. 4 A public database, SITVIT2, is available for the analysis of spoligotyping patterns, which consists of 111,635 clinical isolates from 169 countries of patient origin. 20 The distribution of these spoligotypes was analyzed among the 20 studied subregions.
Overall, the distribution of genotypes in Khyber Pakhtunkhwa was in line with other spoligotyping analysis in Pakistan. 16, 19, 21 owever, we do not report any genotype of L4. 5 "NEW-1", an emerging genotype in Pakistan, since no SIT127 types were observed. 23 Spoligotyping, in some cases, is not able to identify L4. 5, which is better characterized by RD122, specific VNTR signatures and specific single-nucleotide polymorphisms. 13, 23 A rather important population of 12% of strains could not be matched to the internationally defined genotypes in the database and was declared "new". In previous investigations, the spoligotyping patterns of new genotypes were underestimated and left unstudied. 16, 19 In-depth analysis revealed clear identification of L3/CAS by the presence of the first three spacers followed by a set of four absent spacers (4-7) and two more spacers (33-34).
Acknowledgments This paper was published in Journal of Biological Regulators and Homeostatic Agents, 2018;32(2): 195-198  ( PMID 29685006 ; https://www. ncbi. nlm. nih. gov/pubmed/?term=S  P O L I G O T Y P I N G + A N A L Y S I S + O F +MYCOBACTERIUM+TUBERCULOSIS+WITH+TB-SPRINT+TECHNOLOGY ). The paper was withdrawn by authors for reanalysis of the results. We are thankful to  Christophe Sola ,  Harrison M Gomes ,  Erica Chimara , and the  Provincial TB Control Program Khyber Pakhtunkhwa ,  BSL-III laboratory facility  manager, for ethical support.