dataesr/scientific-paragraphs-categorization huggingface dataset:
The program is able to perform calculations for any complex meal taking into account weight changes after thermal processing. Few studies have been performed to evaluate other computer/smartphone applications facilitating food calculations and dedicated for people with diabetes  [8] [9] [10]. To our best knowledge, the only software with available trial results that seems comparable to the ELKa system is Diabetics  [11]. Nevertheless, the requirement of Diabetics to manually enter all meal components may be considered too time-consuming for many patients. The ELKa innovative solution differs from other systems by using a real-time transmission of the ingredients' weight onto the computer. This feature allows users to calculate food exchanges without having to manually enter each weighing result.
Participants. The inclusion criteria were age less than 18 years, T1DM recognized at least 1 year before, HbA 1C level ≤ 10% (86 mmol/mol), undergoing intensive insulin therapy, the patient's kitchen arrangement providing enough space for the toolset, and the patient disposing of a computer meeting the ELKa system requirements. Patients who were excluded met at least one of the following criteria: nutritional disorders, celiac disease recognized less than 4 months before the enrolment, a preceding experience with the software, and expecting 21 or more consecutive days pausing in the system usage. Written, informed consent from patients aged 16 years or more and all parents/caregivers has been obtained. This study has been performed according to the Declaration of Helsinki and has been approved by the Bioethics Committee at the Medical University of Warsaw (KB17/2012).
2. 3. Procedure. At the trial entry, the eligible patients were randomly assigned to one of the two groups: the intervention group, given the ELKa toolset, or the control group. In every group, the patients and/or the caregivers were involved in everyday meal preparations, received a short, recall training about the rules of exchange calculation and the proper insulin dosage, and filled a questionnaire concerning computer skills and sociological data. Additionally, the intervention group was trained in the ELKa toolset usage. To minimize the risk of variability in teaching, only two persons performed the training interchangeably and according to the previously agreed program: the developer of the software and a skilled pediatrician. The patients in the control group were requested to continue their prandial insulin dose calculations based on their common habits.
Additionally to the baseline visit, there were two followup visits: one after the 13th week and one after the 26th week, scheduled simultaneously with routine appointments with the diabetologist in the Outpatient Clinic to minimize the risk of absence. During all 3 appointments, HbA 1C levels, body weight, and height were measured, and frequency of severe hypoglycemia events was assessed. AccuChek 360 software (Roche Diabetes Care, Indianapolis, IN, USA) and CareLink Pro Therapy Management Software (Medtronic, Minneapolis, MN, USA) were used to download data from insulin pumps. All data concerning insulin dosage and bolus types from 14 days preceding the doctor's appointment were analyzed. Blood glucose levels results derived from the self-monitoring of blood glucose and were used for the assessment of mean glycemic values and hypoglycemia rate.
Data, gathered during a 14-day time frame preceding the first or the follow-up visits with at least 4 entries per day, were considered. AccuChek 360 software was used for reading  = 51 sets of glucometers' data. CareLink software was used for reading  = 147 sets of data. Remaining glucose meters were read with the use of other dedicated software (Gluco Contro v. 1. 5. 2. 2650 (Bayer, Leverkusen, Germany) for the  = 7; FreeStyle Auto-Assist v2. 0 (Abbott Diabetes Care, Alameda, CA, USA) for  = 3; OneTouch5 Zoom5 Pro Diabetes Management Software v5. 1. 1, (LifeScan, Chesterbrook, PA, USA) for  = 15 datasets downloads) or hand copied if electronic data collection was not possible. The intervention group at follow-up visits was asked to determine the frequency of the toolset usage, the satisfaction, and the wish to continue.
Outcomes. A change in HbA 1C after 26 weeks of observation was the primary endpoint. Secondary endpoints were HbA 1C after 13 weeks, ELKa usage frequency, differences in the total daily insulin doses (TDD), the daily doses of basal insulin, the total daily doses of bolus insulin, basal as % of TDD, the number of different types of boluses, and the changes in Body Mass Index-Standard Deviation Score (BMI-SDS). Furthermore, other secondary endpoints were data concerning glycemic values: the daily mean, the diurnal mean (measures between 07:00 AM and 09:59 PM) and the nocturnal mean (10:00 PM-06. 59 AM) of blood glucose levels, and glycemic variability assessed by analyzing standard deviation (SD) of the given set of blood glucose levels results. HbA 1C was measured using a high-pressure liquid chromatography method with normal range 4.
1-6. 4% (21-46 mmol/mol) for patients without diabetes. Severe hypoglycemic events were defined as hypoglycemia with the presence of seizure and/or unconsciousness, requiring administration of glucagon or intravenous glucose infusion. The number of episodes was established on the basis of the patients' medical history and interview with researcher performed on each follow-up visit. Hypoglycemia, defined as glycemia below 70 mg/dL (<3. 9 mmol/L) and, separately, glycemia below 50 mg/dL (2. 5 mmol/L) were also assessed on the basis of the data from self-monitoring of blood glucose and were expressed as a number of episodes in a 2-week period per patient per 24 h. Compliance was assessed on an interview with caregivers supported by a questionnaire and described as the percentage of meals prepared with the help of the ELKa toolset.
The results were analyzed using the available case analysis. We performed a per protocol (PP) analysis on patients who received the allocated intervention with a declared ELKa system usage for minimum 50% of meals. Student's -test was applied for intergroup and intragroup comparisons of HbA 1C and secondary outcomes comparisons; alternatively the Mann-Whitney  test and Fisher's exact test for nonparametric and ordinal-scaled data were applied. Wilcoxon matched-pairs signed-rank test was also used for intragroup comparisons of changes in HbA 1C between visits. The blood glucose variability was assessed by analyzing SD of the set of glycemic measurements (separately daily, diurnal, and nocturnal). Results are shown as a mean with SD. Differences between the study groups were considered significant when  value was <0.
The regular use of the ELKa toolset during the trial period resulted in significant improvement in day-to-day stability of blood glucose levels. We interpret this observation as a consequence of an increased precision in insulin dosage. Blazik and Pańkowska report similar findings, regarding the mean glycemic values as well as SD, but state no differences in basal and total insulin doses  [13]. In our study, after the 6-month trial, 22/53 (41. 5%) patients allocated to the intervention group reported ELKa usage for more than 50% of meals. The vast majority of rejections were observed during the first 3 months of the trial. It may be explained by difficulty of the software itself, but, evenly, by the difficulties in changing the patients' diabetes management style. We know from clinical practice how difficult it is to motivate patients to calculate the food when weighing the products is necessary.
Conclusions The results of our trial prove that ELKa usage provides its users with a clinically important improvement in metabolic control as well as it helps to stabilize day-to-day insulin dosage. Our study concerned a broad group of pediatric patients with type 1 diabetes who were asked to use the software as frequently as they managed to, but without special forcing, in order to possibly imitate normal conditions. Therefore we assume that the general population of patients may benefit from using the toolset. Regarding the compliance and the PP analysis results, we believe that the ELKa system will be a useful tool for well-motivated patients who require and wish to improve on their food calculation. The tool may especially prove useful to patients who, despite being already well metabolically controlled and performing everyday calculations using the standard method, still struggle to reduce further their HbA 1C levels and achieve normoglycemia.
Molecular procedures For each isolate, DNA was extracted from 50 mg of fresh fungal material scraped from a fiveday culture grown on PDA medium in the dark at 18°C. DNA was extracted in an automated system (Biorobot 3000, Qiagen), with the appropriate DNeasy kit (Qiagen). All 794 isolates were genotyped for 11 single sequence repeat (SSR) markers  (St2, St3A, St3C, St4, St5, St6, St7, St9, St10, St11 and St13; Gautier et al. , 2014)  in two multiplex PCRs, ensuring that amplicons with similar magnitudes of sizes bore distinct fluorochromes. Automatic allele recognition and annotation, based on binning analysis, were performed, to ensure the homogeneous naming of the alleles at each locus (Beckmann Coulter CEQ 8000 software;  Gautier et al. , 2014). In total, 585 isolates were completely genotyped for the 11 SSRs.
The remaining isolates (27% of the total dataset) provided no amplicons for only one or two markers, even spread between the SSRs; they were treated as missing values in subsequent analysis. Because the proportion of identical haplotypes (clones) in the subpopulations was very low, genetic analyses were performed without clone correction (i. e. using only a single representative haplotype from the assessed isolates). Mating type was determined by PCR for 739 isolates  (Waalwijk et al. , 2002). 8% of the isolates were not genotyped for mating type due to technical failures or the contamination of PCR assays.
, 2005)  with STRUCTURE HARVESTER  (Earl & von Holdt, 2012; Belkhir et al. , 1996 Belkhir et al. , -2004)). Three STRUCTURE analyses were carried out independently, to investigate three factors thought likely to affect the structure of the dataset: 1) collection time, during one of three or four epidemic periods during the three cropping seasons, 2) isolate origin (plot with or without debris), and 3) spore type giving rise to the isolate (ascospore or pycnidiospore). A first set of analyses was carried out to assess, for each cropping season separately  (2009-2010, 2010-2011, 2011-2012) , the effect of epidemic period on the structure of the pycnidiospore subpopulations collected from the plot with debris (P02-P03-P04, P09-P10-P11-P12, and P17-P18-P19-P21, respectively). An analysis grouping together the three cropping season datasets plus the data for P29  (2012) (2013) , was also carried out to assess the effect of year.
A second set of analyses was carried out to assess, for each cropping season separately (and then overall), the effect of isolate origin (plot with or without debris) on the structure of pycnidiospore subpopulations (P02-P03-P04-P05-P06-P07, P09-P10-P11-P12-P13-P14-P15-P16, and P17-P18-P19-P21-P23-P24-P25-P27, respectively). Finally, we also analyzed the structuring effect of spore type giving rise to the isolate on the complete dataset (combining P01-P08-P28).
The rate of change in the log probability of data between successive K values (∆K) had a mode at K = 3 for the 2009-2010 season, at K = 2 for the 2010-2011 and 2011-2012 cropping seasons, and at K=7 for the overall dataset. Regardless of the epidemic period considered in each cropping season (year), it was difficult to assign individuals to any one of these K clusters (none of the individuals were assigned to one cluster with a posterior probability > 0. 5). Instead, the posterior probability was split equally between the seven clusters (0. 1426 < overall mean P < 0. 1431), suggesting that neither the epidemic period nor the cropping season had any major effect on the structure of pycnidiospore subpopulations. In the second set of analyses, pycnidiospore subpopulations from the D+ and D 0 plots were analyzed for each cropping season separately (Supplementary Table  1 ) and then for all cropping seasons together (Supplementary  Table 1;   was equally distributed between the two clusters (0.
498 < overall mean P < 0. 502). Only 0. 15 % of the individuals were assigned to one cluster with a posterior probability greater than 0. 6, whatever the epidemic period, again suggesting that isolate origin (D+ or D 0 plots) had little effect on the structure of the dataset. Finally, after the addition of ascospore subpopulations to the previous dataset in the third STRUCTURE analysis (Supplementary Table  1 ; Fig. 2C ), a mode at K = 3 and evenly distributed posterior probabilities (0. 332 < overall mean P < 0. 334) were again observed. We can therefore conclude that spore type, giving rise to the isolate, had little effect on structure. As no population structure was detected without prior assumptions, we explored the dataset with the a priori subpopulations defined in Fig. 1. The relationships between the 26 subpopulations are presented in the phylogenetic tree (Fig.
Acknowledgments This research received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 261752  " Plant and Food Biosecurity " (PLANTFOODSEC). The authors would like to thank  Angélique Gautier  ( INRA BIOGER ) and  Christophe Montagnier  ( INRA Experimental Unit, Thiverval-Grignon ) for technical assistance. The authors have no conflict of interest to declare.
Aims Although left ventricular assist devices (LVADs) are currently approved for coverage and reimbursement in France, no French cost-effectiveness (CE) data are available to support this decision. This study aimed at estimating the CE of LVAD compared with medical management in the French health system. Methods and results Individual patient data from the 'French hospital discharge database' (Medicalization of information systems program) were analysed using Kaplan-Meier method. Outcomes were time to death, time to heart transplantation (HTx), and time to death after HTx. A micro-costing method was used to calculate the monthly costs extracted from the Program for the Medicalization of Information Systems. A multistate Markov monthly cycle model was developed to assess CE. The analysis over a lifetime horizon was performed from the perspective of the French healthcare payer; discount rates were 4%.
Data sources The PMSI was used to derive the resources use and healthcare costs of LVAD recipients. The PMSI, the heart of the French financing system, is a nationwide database providing the main source of information on healthcare expenditure in all private and public hospitals. Patient level data are based on the diagnosis-related group classification. The PMSI-MCO, which includes all in-hospital outpatient and inpatient data from medical, surgical, and obstetrics wards, was used. All patients having received an LVAD were identified using the appropriate codes, and subsequently, all data (pre-LVAD and post-LVAD implantation) related to these patients were extracted from the database and analysed. The PMSI provides demographics (sex and age), primary diagnostic for each admission, dates of admission and discharge, implanted medical devices, associated diagnostics and medical procedures, and vital status at discharge.
Routine follow-ups and medications are not collected in this database, neither professional/physician fees. The strict indication of the LVAD used as 'DT' or 'BTT' was not available in the PMSI; hence, it was not possible to analyse separately these populations. Our analysis included both conditions without distinction, reflecting so the real-life management. A mandatory approval from Commission Nationale de l'Informatique et des Libertés (CNIL) (French data protection competent authority) was required to access PMSI database. CNIL approval was obtained prior to data extraction.
Survival estimates for the comparator group were grounded in published data  3, 4  and were provided by a clinical expert opinion. Setting a comparator group was very challenging; it was decided that the comparator group was the same patients assuming that they will not receive an LVAD (no LVAD group), but only medical management and die probably in the 3 months. The model was developed using TreeAge Pro 2016 software. Descriptive statistics and Kaplan-Meier analyses were performed is SAS 9. 4 (SAS Institute, Cary, NC).
Calculations of quality-adjusted life years Quality-adjusted life years is the effectiveness metric used in this analysis. This measure was not available in the PMSI; therefore, utility data were obtained from literature review. 8, 9 Each health state is associated with a utility value ranging from 0 to 1, where 0 represents 'death' and 1 represents 'ideal health'. Quality-adjusted life years were computed by aggregating the total time spent in each health state and applying the appropriate utility weight. The model outputs include mean life years gained (LYG), mean QALY, mean costs, mean incremental CE ratio (ICER) as €/LYG and as €/QALY.
Calculations of costs All costs for all patients having received LVAD in France since its reimbursement were derived from the real-life PMSI database. These costs include pre-LVAD and post-LVAD implantation. A micro-costing method was applied to calculate the monthly costs. Four main categories of costs were included: device costs, direct medical costs associated with the index hospitalization for LVAD implantation, direct costs for subsequent HTx if any, and direct and indirect costs for repeat inpatient and outpatient hospitalizations and follow-up inpatient/outpatient care, including related and unrelated costs as per the latest recommendations. 20 Routine follow-up costs were not available therefore were not included in the analysis. Also, professional or physician fees were not available, because the PMSI includes only patient hospital data.
Patients' characteristics at the index hospitalization All patients having received one LVAD were extracted from the PMSI. Patients excluded from the analysis were as follows: patients under 18 years old, patients having received more than one LVAD, and patients having received other than LVAD (n = 104). There were 508 patients reported in the PMSI database as having had an LVAD implanted between 2009 and 2014 in France. Among these, 363 patients received Thoratec HeartMate, 97 HeartWare, and 48 Jarvik 2000. Almost 50% of the devices were implanted from 2012 to 2013 (Figure  3 ). Patients' characteristics, at the index hospitalization for LVAD surgery, are summarized in Table  1. Patients were mostly male (83%) with a mean age of 57. The main primary Figure  3  The number of implanted left ventricular assist devices per year.
Discussion We used individual patient data from the PMSI to investigate the cost-effectiveness of LVAD in patients with end-stage HF. French patients treated with LVAD had a follow-up to 60 months; to our best knowledge, this is the longest LVAD follow-up described. In the last Interagency Registry for Mechanically Assisted Circulatory Support annual report, the patients' follow-up reached the 48th month. 25 ur findings suggest that in comparison with medical management (here no LVAD), patients with end-stage HF implanted with an LVAD had higher mean costs and higher survival benefit, delivering a probabilistic ICER of €125 580/QALY [95% confidence interval: 105 587 to 150 314] and a similar deterministic ICER of €123 109/QALY for a lifetime horizon. When the model was run for shorter time horizons (i.
e. 3, 5, and 10 years), the ICER increased significantly. This could be explained by the fact that most costs are attributable to the device implant and initial ICU and ward stay (at model entry). These costs decreased with time. The next biggest component of cost was the transplant procedure and post-transplant care. According to PMSI, the probability of receiving a donor heart beyond 3 years is close to zero; we therefore set 'p12' to be 0 after 36 months. Therefore, all costs related to the heart transplant are accounted for in the first 3 years of the model. Because the database does not provide information about medication, the impact of a new medical therapy could not be studied. However, other CE studies confirmed that the main cost drivers in this population are the devices, the procedure and the post-procedure complications costs.
(ii) Another topic to highlight is that all these studies were conducted to evaluate the LVAD in BTT settings, suggesting that patients were in a better health status than those who receive LVAD as a DT. In Clarke et al. ,  8  13. 2% of patients presented with minimal heart failure and slight limitation of physical activity (NYHA II) at the implantation. In our analysis, BTT and DT patients were pooled; no distinction in the database was possible. (iii) The third issue to be highlighted is the discount rates. The French discount rate recommended by the authorities is among the highest (4%), while it ranges between 1. 5% and 3. 5% in other countries. In the latest recommendations, 3% was considered as the most appropriate real discount rate for CE analyses. 20 owever, as our aim is to inform decision makers in France, we used the rate required by the French Health Authority.
However, in the absence of such data, it is appropriate to use individual patient data from the French PMSI database to derive transition probabilities and costs. To our best knowledge, this is the first CE evaluation of LVAD performed in France. The ICER exceeds the minimal WTP threshold adopted in France (€50 000/QALY), but is significantly lower than that adopted for some rare conditions or oncology drugs (€300 000/QALY). Our findings should be carefully considered in light of the disease burden, available funding and future supply of donor hearts.
Meat sheep farms and criteria of sustainability All the meat sheep farms that were sampled were located in the centre of France in an area where the main activity is meat sheep farming and experienced similar climatic conditions (average rainfall: 850-1000 mm/year, average mean temperature: 10. 9-11. 4°C). The studied farmers made their living mostly from meat sheep production and had been involved in this production for several years as their main (or exclusive) source of income. Data from a set of 34 farms, 9 organic (2 in plain and 7 in semi-mountain areas) or 25 conventional farms in plain (11) or semi-mountain (  14 ) areas were analysed for physical and financial performance of their flocks. These included the number of ewes, ewe and lamb mortalities, veterinary costs, and number of lambs produced and gross margin per ewe.
Further, an example has been provided to elucidate the efficacy of this comprehensive dataset to identify a co-expressed gene network exclusive to floral tissues  (bud, flower, stamen, pistil, sepal and petal). The targeted tissues are highly specialized and their development is tightly controlled and coordinated for effective fertilization and production of viable seeds. The candidate genes identified were associated to pollen fertility and seed setting, which have specific implications for the key agronomic trait of yield for their possible deployment in GAB in pigeonpea.