dataesr/scientific-paragraphs-categorization huggingface dataset:
Three genes, SYT11, FGF20 and GCH1 were identified with a statistically significant burden of rare variants comparing Parkinson's Disease to controls. SYT11 is located close to the GBA locus, and it was reported that the GWAS signal from SYT11-GBA locus in Parkinson's Disease can be attributed to coding GBA variants  (Blauwendraat et al. , 2018). The SYT11 variant mainly responsible for the association in our data is in weak LD with two coding GBA variants, p. Glu326Lys (p. E326K) and p. Asn370Ser (p. N370S). After exclusion of carriers of these variants, and further exclusion of all other GBA variants, the association of SYT11 remained strong. These results suggest that SYT11 may independently be associated with risk of PD. The Protein encoded by SYT11, Synaptotagmin 11, belongs to the synaptotagmin family, known calcium sensors mediating calcium-dependent regulation of membrane trafficking in synaptic transmission  (Xie et al.
Replications in other cohorts will be required to determine whether this variant is associated with Parkinson's Disease. The associations described above were detected using different coverage thresholds for the detection of common and rare variants. For common variants, we used a threshold of coverage of ≥15X, which based on our previous studies  (Rudakou et al. , 2019; Krohn et al. , 2019b; Rudakou et al. , 2020; Krohn et al. , 2020)  is highly reliable for detection of known common variants. Here too, when comparing to available data from GWAS genotyping, we were able to confirm with 100% accuracy the presence of common variants in MAPT, TMEM175 and GPNMB in samples with available data. The different thresholds of ≥ 30X and ≥ 50X are being used to increase our confidence that the variants and the results are real.
Thus far, the causative genes within most Parkinson's Disease GWAS loci are unknown, with the exception of SNCA, GBA, LRRK2, and likely VPS13C, MAPT, TMEM175 and GCH1. The current study suggests that SYT11, FGF20, and potentially PM20D1, BST1 and GPNMB should be considered as possible Parkinson's Disease-related genes and targets for further research. Parkinson's Foundation, the National Institutes of Health (K02NS080915, and UL1 TR000040) and the Brookdale Foundation.
Acknowledgements We thank the participants for contributing to the study. GAR holds a  Canada Research Chair in Genetics of the Nervous System  and the Wilder Penfield Chair in Neurosciences. EAF is supported by a Foundation Grant from the  Canadian Institutes of Health Research  ( FDN grant   -154301 ). ZGO is supported by the  Fonds de recherche du Québec -Santé (FRQS) Chercheursboursiers award , in collaboration with  Parkinson Quebec , and by the  Young Investigator Award by Parkinson Canada. The access to part of the participants for this research has been made possible thanks to the  Quebec Parkinson's Network  (http://rpq-qpn. ca/en/). We thank  Daniel Rochefort ,  Helene Catoire ,  Sandra B. Laurent ,  Clotilde Degroot  and  Vessela Zaharieva  for their assistance.
The Rietveld renement parameters are listed in Table  S1  (ESI). † We have observed a low value of goodness of t (c 2 ) which suggests that the rening of the samples is effective and that the obtained samples are of good quality. From Fig. 3b , it is observed that the width at half maximum of (311) peak decreases with increase in annealing temperature. Diffraction peaks become narrower and sharper with increasing temperature, thus demonstrating that the average size of the crystallites increases accordingly  The lattice constant (a exp ) of Ni-Zn nanoparticles was determined from X-ray data analysis using the relation: 12 a exp ¼ l ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi h 2 þ k 2 þ l 2 p 2 sin q : where l is X-ray wavelength, q is the diffraction angle and h, k, and l are Muller indices.
0] [31]  Bestha et al. 32  reported that the intensities of (220), (422), and (400) planes are sensitive to cations on tetrahedral (A-) and octahedral (B-) site. In our studies, the distribution of cations was estimated from XRD data by using the Bertaut method. 32, 33 From the best-matched intensity ratio, the cation distribution was estimated and the values are presented in Table  S1. † On the basis of estimated cation distribution, the value of ionic radius per molecule at the tetrahedral (r A ) and octahedral (r B ), theoretical lattice constant (a th ), oxygen positional parameter (u), tetrahedral bond lengths (d AL ), octahedral bond lengths (d BL ), tetrahedral edge length (d AE ), shared (d BE ), unshared octahedral edge lengths (d BEU ), jump length of the tetrahedral (L A ) and octahedral (L B ) for various samples has been determined using the relation (S5-S13) (ESI).
61% to 4. 61%) and 6% (90% confidence interval -6. 32% to 18. 32%), respectively. There was no difference in renal and pancreas graft survival at 5 years. Thirty-four patients (68%) in the sirolimus group vs three (6%) in the tacrolimus group needed definitive withdrawal of the study drug. Despite noninferiority of sirolimus compared to tacrolimus for kidney and pancreas graft survival, the high rate of sirolimus discontinuation does not favor its use as cornerstone therapy after SPK transplantation (NCT00693446).
Protocol kidney graft biopsies at 1 year consistently showed a trend toward more features of humoral rejection and IFTA in the SRL group. We assume that a decreased level of immunosuppression in the SRL group may have elicited more rejections and more antidonor immunization, which may not have persisted over time because of the high rate of conversion to CNI. In kidney transplantation, few randomized studies evaluating conversion from CNI to mTOR inhibitors reported data on de novo DSA appearance, with mixed evidence. 19, 20 Retrospective studies showed, however, a higher incidence of de novo DSA after early conversion to an mTOR inhibitor. 22, 23 Our team has shown previously that de novo DSAs are an independent risk factor for pancreas and kidney graft failure in SPK recipients. 24 Only two recent clinical trials with conversion from CNI to Everolimus after kidney transplantation included protocol kidney biopsies, reporting either no difference in IFTA or higher IFTA score in CsA groups.
Conclusion In the present study, we assess the sanitary impacts attributable to BC exposure in terms of population exposures and disease burden (cardiovascular disease mortality, CVM) for the South Asia Indo-Gangetic Plain (IGP) using an integrated modeling framework. The assessment includes using a simulated spatial distribution of BC surface concentration obtained from a highresolution chemical transport model (CHIMERE) and comparing favorably with observations. The efficacy of CHIMERE to simulate the observed very large concentrations of BC aerosol during the wintertime with low values for the normalized mean error and root mean square error functions, is due to the implementation of an observationally-constrained BC emission dataset for the Indian region. Population exposure to BC is remarkably high for more than three hundred million persons living in the IGP including 94% of the megacity and 70% of semi-urban/urban inhabitants being exposed to an all-day mean BC concentration of magnitude twice larger than the defined theoretical threshold limit (TTL) based on available epidemiological information (5 µg m -3 ).
• × 1 •. Meteorological fields are extracted from WRF at the temporal resolution of one-hour and the horizontal resolution of 10 × 10 km 2 (same as that for CHIMERE simulation). The characteristics of the land surface such as soil type, vegetation index, albedo or surface topography are provided by the National Center for Atmospheric Research (NCAR) and available at http://dss. ucar. edu/datasets/ds083. 2/.
Acknowledgments This work was supported through a grant received for the project  National Carbonaceous Aerosol Programme-Carbonaceous Aerosol Emissions, Source Apportionment and Climate impacts (NCAP-COALESCE ) from the  Ministry of Environment, Forest, and Climate Change  ( 14/10/2014-CC (Vo. II) ), Govt. of India at the  Indian Institute of Technology Kharagpur.
Novel corona virus  is biggest challenge for whole world. World Health Organization (WHO) mentioned this is epidemic. Data has been collected from 209 different individual situation reports of World health organization for Covid-19 in India. First compared the forecasting models on the basis minimum AIC, MAPE, MAE and then best model have been used for forecasting on the India epidemiological data to anticipate the epidemiological pattern of the prevalence. New and total deaths and occurrence of Covid-19 ARIMA and SARIMA found suitable respectively and forecasted for 1 September, 2020. Also, for total cases of corona in India prophet model used for accuracy and forecasting. Predicated values checked with past observed values, so both values are very close to each other. From the using such kind of time series models we can predict for next 15-20 days and make planning accordingly.
Based on the SIR model, it is estimated that India will enter equilibrium by the end of May 2020  [14]. We aim to develop a best model using a prediction model for India. To forecast the daily total deaths and confirmed cases and helps to make planning to fight against this Covid-19. The rest of the paper is organized as follows: Section 4 explains the material and methods, including a Covid-19 dataset, the prediction algorithm, and model accuracy metrics. Section 5 describes our re-sults and discussion. Section 4 present conclusions and suggestions for future work.
Covid-19 dataset The dataset used in this study includes the India total & daily confirmed, deaths cases of Covid-19, collected from the official website Our World in data  [15]  in the period from January 30, 2020, to August 22, 2020. It comprises 206 time-series cases from which to build our model, which we compare to other predictive models. Descriptive statistics tells the nature of data. To know the information about the data tools to summary statistics used like mean, standard error, minimum and maximum, skewness and kurtosis.
Model accuracy metrics The modelling errors were used to compare the fitness and prediction performance of the ARIMA, SARIMA and Prophet models. These criteria include the Akaike information criterion (AIC), Schwarz criterion (SC), Root mean square error (RMSE), Mean absolute error (MAE), Mean absolute percentage error (MAPE) and Theil inequality coefficient (Theil's U). The study is about Covid-19 predictions of India. This virus has proven a potential threat to human life. To control this pandemic situation, this study based on future prediction on confirmed cases in the upcoming months. The forecasting has been done by using best models that are suitable to this framework. The dataset used in this study contain the number of confirmed cases, new cases, total deaths and daily deaths of India in the start pandemic days.
ARIMA and SARIMA models were applied to Covid-19 new cases and new deaths, total cases and total deaths of India. The Machine learning model Prophet has been used. Firstly, the dataset has been pre q) is: -i = + + + , i = 1. , = 1. t t i t j t j X c x j ε ϕ ϑ ε - ∑ ∑ The ARMA models are used when the time series data is stationary. This usually a strong and robust opportunity for these models. However, not all time series data are stationary. As Ratnadip and Agrawal, (N. d) puts it, the existence of trends and seasonal patterns makes time series data non-stationary and therefore, ARMA models are insufficient to address the modelling process. Therefore, ARIMA models in this case prove to be efficient and robust. The introduction of differencing provides leverage for ARIMA models to be generalized to non -stationary time series.
ARIMA (p, d, q). The (p, d, q) within the model for autoregressive, integrated, and moving average are integers whose expectations are greater than or equal to zero. The special case of ARIMA (p, 1, q) called the random walk model proscribes for the model to be written as: 1 -1 2 -2 -p 1 1 = + +. + + - -. t t t p t t t q t q X c a x a x a x x ε θ ε θ ε - - ∆ ∆ ∆ ∆ By inference, 1 = t t t X X X - ∆ -. The data for this paper considers descriptive analysis to allow easy and convenience forecasting and the ARI-MA (p, 1, q) procedure is considered effective because of the trend of the Covid-19 data. ARIMA models are the widely utilized method for time series forecasting, showing the autocorrelations in the data. The model is to decide if the data is stationary or not. In the event that non-stationarity exists, it tends to be demonstrated by differencing it to a suitable degree of distinction.
Results and Discussion In this section, Table  1  shows the summary statistics of Covid-19 dataset of India. We find that since 30 January, 2020, to 22 August, 2020, the new cases have increased during the period from (0) to (69878). Average daily new cases are (14445). Ex. Kurtosis value is (0. 63) indicates the data follows a Leptokurtic distribution which shows heavy tails on either side, which means there are outliers in the data. Followed by positive value of skewness (1. 41) which indicates there is some probability of increasing in the new cases. The new deaths have increased from (0) to (  2003    the series, which could provide substantial advantages for the identification and construction of the ARIMA and SARIMA models. Therefore, the Hodrick-Prescott (HP) method was implemented to generate filtered sample datasets.
For the series Total Deaths and the series New Deaths and the series Total Cases we found that SARI-MA Model is better than ARIMA Model, considering the seasonality in theses series. After assessment of each and every trend series, we forecast the series for coming Days. For purpose of forecasting ARIMA (p, d, q) Model and SARIMA (p,d,q)(P, D,Q)s Model, as discussed in material and methods section. Data for period 30 January, 2020, to 22 August, 2020 was used for model building. As model validation data used for period 15 May, 2020 to 22 August, 2020. Best models are utilized to predict the series for the coming days. Different series are seen as fitted with various SARIMA (8,2, ,0,0), ARIMA (6,2,10), SARIMA (7,2,3) (1,1,1) models individually. These models are seen as best fitted models for forecasting during the period 23 August, 2020 to 1 September, 2020.
Among the particular models, these are the best fitted models. We use HP Filter for the series (New cases) and (New deaths), (Total Deaths) and Exponential moving average Filter for the series (Total Cases). The Hodrick-Prescott (HP) method was implemented to generate filtered sample datasets to obtain a smoothed-curve representation of  Figure  5 , Figure  6  and Figure  7  shows the forecast graph for Covid-19 new cases, total cases, total death and new deaths. Figure  8  shows the prophet model forecast plot. Figure  9  shows the correlogram of forecasting models.
the survival outcomes of laparoscopic staging in comparison with laparotomic staging for patients presenting with an early stage EOC. Methods: data of patients with early stage EOC (FIGO I-IIA) who underwent primary surgery between 2000 and 2018 were extracted from the FRANCOGYN database. OS and RFS of these two groups, constituted according to the surgical route, were compared using Log rank test. Results: of the 144 patients included, 107 patients underwent laparotomy and 37 underwent laparoscopy for a staging purpose. The median follow-up was 36. 0 months (18. 0 to 58. 0). For the laparoscopy and the laparotomy group, the median follow-up period was 24 (11. 0 to 50. 0) and 42. 0 (24. 0 to 66. 0) months, respectively, (p < 0. 001). Tumor recurrence occurred in 33 (23%) patients: 2 (5. 4%) in the laparoscopy group and 31 (29%) in the laparotomy group (p = 0.
Thus, the role of MIS in the treatment of early stage EOC is still a matter of debate and there is currently no consensus concerning the surgical route for the staging of early stage EOC. Whereas many studies indicated that laparoscopic staging appeared to be feasible without compromising survival  [4, [8] [9] [10] [11] [12] , the Cochrane database review  [13]  published in 2016 has not provided high-quality evidence to recommend laparoscopy as routine clinical practice. Recently, in the 2019 ESMO-ESGO guidelines, the consensus reports that minimally invasive surgery can be carried out for restaging with a level of evidence IV and a strength of recommendation B. However, laparotomy remains the standard surgical approach to treat and stage patients with apparent early stage EOC with a level of evidence V and a strength of recommendation A  [14].
Study Population Data of all patients who received primary surgery between 2000 and 2018 were extracted from eleven institutions with maintained EOC database. The study was approved by the ethics committee of the National College of French Gynecologists and Obstetricians (CEROG 2020-GYN-0302) and patients were duly informed about the study as required by French law. Clinical and pathological variables included patient age, hormonal status, comorbidities (body mass index (BMI), prior abdominal/pelvic surgery, familial history of gynecological cancer), final international Federation of Gynecology and Obstetrics (FIGO) stage, final pathological analysis (histological type and grade) and adjuvant treatment. All patients included in this study were early stage EOC. Early stage EOC was defined by stage FIGO I-IIA according to FIGO 2014 classification  [15]  which means cancer was confined to the ovaries with possible cancer cells in the intra-abdominal fluid secretion or extended to the uterus/the fallopian tubes without cancer cells in the abdominal cavity.
We assessed the proportional hazard assumption using Schoenfeld residuals plots  [16]. To handle the missing values on histological grade, we used multiple imputation with a regression switching approach (chained equations with m = 10). Imputation procedure was performed under the missing at random assumption using all variables listed in Table  1  (including study group) with a predictive mean matching method for continuous variables and logistic regression model for categorical variables. We combined effect sizes from each imputed dataset using Rubin's rules  [17]. Statistical testing was done at the two-tailed α level of 0. 05. Data were analyzed using SAS software package, release 9. 4 (SAS Institute, Cary, NC, USA).
Overall and Recurrence-Free Survival The overall median follow-up period was 36. 0 months (18. 0 to 58. 0). For the laparoscopy and laparotomy group, the median follow-up period was 24. 0 (11. 0 to 50. 0) and 42. 0 (24. 0 to 66. 0) months, respectively (p < 0. 001). RFS and OS are presented in Figures  2  and 3. Thirty-one patients developed tumor recurrence in the laparotomy group and two patients in the laparoscopy group. In the laparotomy group, there were 16 peritoneal recurrences, 4 lymph node recurrences, 9 distant metastases and this data was missing for two patients. In the laparoscopy group, one patient developed peritoneal recurrence, and distant metastases were recorded in one patient. RFS was better in the laparoscopy group than in the laparotomy group, however, the difference did not reach the significance level (p = 0.
In our study, 4 tumor ruptures were observed in the laparotomy group and none in the laparoscopy group. Among them, there was no recurrence or death noticed for three patients with follow-up durations of 31, 44 and 55 months. The other one died after 92 months of follow-up. Unfortunately, we had no information on the size of the tumor, which may have influenced the chosen surgical route. It is difficult to establish a causal relationship between the occurrence of tumor rupture and its influence on survival data due to the small number of patients. Many studies have reported that the incidence of tumor rupture was similar between the laparoscopy and laparotomy group and ranges from 11. 4 to 30. 3%  [10]. Vergote et al. , in the largest retrospective, multicenter study involving >1500 patients, reported that rupture during surgery would appear to be associated with a poor prognosis for patients with stage I EOC  [22].
Laparoscopy, allowing better intraoperative and postoperative outcomes, could reduce the delay between surgery and chemotherapy and thus improve survival outcomes. The strengths of our study are the consistency and homogeneity of our patient population and the relatively long follow-up compared to other trials previously published (Table  3 ). Our study was limited because of its design. The retrospective nature of the database might be associated with a possible selection bias and under-recorded clinical data. Ovarian cancer is mainly diagnosed at an advanced stage; we therefore had to exclude 1116 patients for advanced stage (within the 1509 patients reported in the database). Thirty-three other patients had been excluded because they had a nonepithelial histological type. We also had to exclude a significant number of patients (n = 214), including patients with early stage EOC, in order to have the most precise and complete data.
Unfortunately, it was not possible for us to realize it in a precise way because of the data available in our database. We caution that the present findings were derived from an observational, nonrandomized design with limited sample size and despite any statistical models to address imbalanced data regarding study groups, we could not exclude confusion and selection bias as any observational study. Regarding the differences in follow-up between the two groups, we believe that there is no informative censoring reason which could strongly bias the difference between the two groups. Although laparoscopy has become essential in the management of gynecological cancers, the results of the LACC trial (Laparoscopic Approach to Cervical Cancer) bring us to go back forward to laparotomy for management of early stage cervical cancer  [32, 33].
Methods The ILCOR Research and Registries Working Group conducted three face-to-face meetings and five teleconferences between January 2016 and September 2017, and a consensus was reached for a strategy to collect data from participating registries. Participation in this project by registries was voluntary. We conducted two surveys of the participating national and regional registries (Table  1 ): the first survey aimed to describe which of the Utstein elements were collected by each registry and the second survey aimed to report summary data from each registry to describe characteristics of OHCAs in the nation or region. The first survey assessed which core elements of the latest Utstein style recommendation for OHCA in 2014 were collected by each registry,  [10, 11]  and identified any discrepancies in the data collection process.
Based on the availability of the data elements in each registry in the first survey, we chose the elements for the second survey and descriptively reported the 2015 summary data from each registry. If 2015 data were not available, the most recently available data were reported. The data from the Rescu Epistry in Toronto, Canada were extracted from a published paper. [28]  We included population-based registries which covered all EMS resuscitation attempted OHCAs in each area. We defined a national registry as one that collected data from the whole nation or J o u r n a l P r e -p r o o f 10 multiple regions within one nation designated to be representative of the whole nation; other registries were designated as regional registries. We calculated the estimated annual incidence of EMS-treated OHCA at each registry, using the annual number of EMS-treated OHCA as the numerator and the total population of covered area as the denominator.