dataesr/scientific-paragraphs-categorization huggingface dataset:
The number of variables for each tree (mtry) and the number of trees in the forest (ntree) are two user-defined parameters which need to be optimized. The number of trees (ntree) was 1000 in this study. Mtry was identified as those returning the lowest RMSE by iterating mtry values from 1 to 11 representing the number of predictors. The relative importance of variables was estimated from the mean decrease in predictive accuracy. The model performances for prediction of the production of casts on the test dataset were evaluated using coefficient of determination (R 2 ), root mean square error of prediction (RMSE) and residual predictive deviation (RPD). The RPD denotes the ratio of the standard deviation of the measured cast production to the RMSE calculated between the measured and predicted cast production.
The relationship between the number of casts collected in the three vegetation units after one week of production in February 2017 and the cumulative mass of casts produced for 2017 for each plot was calculated. The relationship was used to predict the mass of casts produced in 2017 at every grid sampling points using the number of active casts collected during the sampling of earthworm casts at catchment scale in February 2017. All statistical calculations and plots were carried out using R software using 'caret', 'car', 'agricolae' and 'ggplot2' packages.
Acknowledgments This project was financially supported by the  CNRS/INSU (VINAWORM) research program  under the framework of the  EC2CO program. Dong Cao catchment is part of Multiscale Tropical Catchments (M-Tropics ) project (https://mtropics. obs-mip. fr/)supported by the  French national research institute for sustainable development (IRD). We are very grateful to  Kim Yen Do  for her invaluable help in organizing the experiment. We also acknowledge the technical assistance of the farmers in Dong Cao village for their participation in the sampling of soil samples.
Pellets of uniform size and weight (5 mm, 11 ± 1 mg) were pressed for each type of fiber impregnated with Ag (four replicates per type of fiber). The wool was not included in the test because the pellets lost their shape and the fibers unwound on the agar plate. Hydrophilic cotton was used as a negative control. Pellets were placed on the agar plate, and after 24, 48, and 72 h at 30 °C, the average diameter of the inhibition zone was measured. The biocide effect is evaluated by the diameter of the inhibition halos, normalized with respect to the diameter of the pellet size (5 mm). The significance of differences between treatments was assessed using a general linear model followed by a post hoc Tukey test using the software SPSS version 19. 0. r Q m Ag out AgNPs = [ ] (2 FTIR Measurements.
■ ACKNOWLEDGMENTS The authors are grateful to  CNRS and UGA  for providing funding support. The  Labex SERENADE  ( ANR-11-LABX-0064 ) project funded the master (M1R) of M. D. G. at ISTerre. We thank  Christine Lancelon Pin  from  CERMAV (Grenoble, France ) for the preparation of the thin sections. Electron microscopy was performed at the  CMTC characterization platform of Grenoble INP  supported by the  Centre of Excellence of Multifunctional Architectured Materials ″CEMAM″  (no. AN-10-LABX-44-01 ) funded by the ″Investments for the  Future″ program. The ATR-FTIR instrument was funded by  Region Auvergne Rhone-Alpes CPER  funds awarded to the project " CEMBRO ". We thank the staff of the  Geochemistry and Mineralogy platform at ISTerre  for their technical support. ISTerre is a part of  Labex OSUG@2020  ( ANR10 LABX56 ).
The VDFs categorized in this paper may, however, be used as predictions for future magnetotail observations with higher temporal and angular resolutions than available now. storage maintained within the CSC -IT Center for Science. Data presented in this paper can be accessed by following the data policy on the Vlasiator website. The THEMIS and ARTEMIS data are available at http://themis. ssl. berkeley. edu/ (last access: 28 June 2021) and via the SPEDAS system (https://spedas. org/, last access: 28 June 2021).
Acknowledgements. The manuscript was outlined and drafted during the  Second International Vlasiator Science Hackathon held in Helsinki, Finland , from  20-24 August 2018. The Hackathon was funded by the  European Research Council  grant no. 682068-PRESTISSIMO. We acknowledge  Vassilis Angelopoulos  for the use of the data from THEMIS and ARTEMIS missions. We thank  Thiago Brito  for his help with the software and Vlasiator data analysis during the Hackathon. Financial support. The development of Vlasiator has been supported by the  European Research Council  (grant nos. 682068-PRESTISSIMO  and  200141-QuESpace ) and the  Academy of Finland ,  Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta  (grant nos. 312351 ,  309937 , and  328893 ). The run shown in this paper was carried out with a Tier-0  PRACE grant  (project number  2014112573 ) on  HazelHen/HLRS.
Code and data availability. Vlasiator (https://www. helsinki. fi/en/ researchgroups/vlasiator, Palmroth, 2021) is distributed under the GPL-2 open-source license at https://github. com/fmihpc/ vlasiator/ (Palmroth and the Vlasiator team, 2020). Vlasiator uses a data structure developed in-house (https://github. com/fmihpc/vlsv/,  Sandroos, 2019). The Analysator software (https://doi. org/10. 5281/zenodo. 4462515, Battarbee and the Vlasiator team, 2020) was used to produce the presented figures. The run described here takes several gigabytes of disk space and is kept in.
Background: We aim to present the development and the initial results of the Golestan Cancer Biobank (GoCB), in a low resource setting in northern Iran. Methods: The GoCB protocol and its standard operation procedures (SOP) were developed according to internationally accepted standards and protocols with some modifications considering the limited resources in our setting. The main biological samples collected by the GoCB include blood sample, urine sample, fresh endoscopy tissue sample, fresh surgical tissue sample and formalin fixed paraffin embedded (FFPE) tissue sample. The GoCB collects patients' demographic data, tumor characteristics as well as data on risk factors. We developed a specific GoCB software for management of patient data and biological sample information. The GoCB dataset is annually linked with the Golestan cancer registry dataset to add complementary data (e.
Results: The GoCB started collection of data and biological samples in December 2016. By November 2020, a total number of 1217 cancer patients participated in the GoCB. The majority of the GoCB participants (n = 942, 77%) were those with gastrointestinal and breast cancers. Data on risk factors were successfully collected in 684 (56. 2%) of the participants. Overall, 3563 samples were collected from the GoCB participants and 730 samples were used in 7 national and international research projects. Conclusion: We considered specific strategies to overcome major limitations, especially budget shortage, in the development and maintenance of a cancer-specific biological repositories in our setting. The GoCB may be considered as a model for the development of biobank in low-and middle-income countries (LMICs).
For this purpose, the Golestan Cancer Biobank (GoCB) was established in 2016 to provide biological samples and associated data collected from cancer patients by applying international standards and supporting high-quality cancer research. This article will present the process of designing and creating this biobank infrastructure (within an LMIC context), as well as the initial results of the GoCB, as the first and only cancer-specific biobank in the Golestan province, a high-risk area in northern Iran.
Collection of Data After obtaining informed consent, data collection is performed using two GoCB-specific data collection forms. The GoCB data collection form 1 (general questionnaire) consists of patients' demographic data, clinical data and tumor characteristics (site of tumor, morphology, behavior and tumor grade); the latter are collected based on the pathology report. The third edition of the International Classification of Diseases for Oncology (ICD-O-3) is used for coding tumor characteristics, including topography, morphology, behavior and grade. 18 he GoCB data collection form 2 (risk factor questionnaire) was developed using the Golestan Cohort Study questionnaire. 19 It covers a broad range of generic risk factors applicable to all cancer types as well as specific ones based on the epidemiological studies on common cancer types in our population, including anthropometric measures, level of education, occupation, smoking behavior, alcohol and opium consumption, diet, oral hygiene habits and medical history, cancer history in family, exposure to X-ray radiation, exposure to pesticides, physical activity and data on reproductive behaviors (only for women).
Quality of Collected Samples and Data Upon receiving the biological samples and questionnaires, the quality of data and biological samples are checked by GoCB secretariat staff to ensure adherence to the GoCB guidelines and protocols. An in-house developed quality control checklist allows to check for the completeness and accuracy of data in a consistent manner. If there is any deviation observed, e. g. , missing data, errors in filling out the questionnaire, unclear data, inadequate amount of specimen (volume or size) and errors in labeling, the GoCB secretariat staff provide feedback to the field staff and follow up the issue until resolved.
Processing and Preservation of Biological Samples GoCB uses high-quality cryotubes (of 0. 5, 1. 4 and 3 mL) according to the type of biological sample. The tubes are sterile and free from DNase, RNase, DNA, and endotoxins. Each tube has a unique ID, as a laser etched 2-D barcode on the base. A 2-D barcode reader is used to ensure entering the correct sample code into the GoCB software.
Processing and Preservation of FFPE Samples After receiving formalin fixed tissue samples, the tissue specimens are processed and embedded in paraffin in accordance with routine histological techniques for the preparation of FFPE blocks. 20 The FFPE samples are located in the GoCB-specific FFPE box and stored in our specific FFPE cabinet at room temperature. GoCB Software and Dataset An in-house software was developed for management of patient data and biological samples information (Figure  2 ). The GoCB software consists of two main interfaces: the sample registration interface and the patient registration interface. The two interfaces are linked using a unique GoCB registration number. The sample registration interface is a laboratory information management system, called Bio-Tracker, version 3.
3. 0, locally developed by "Batab Tajhiz Parsin" Company, Iran. It contains detailed information on the biological samples. The patient registration interface consists of detailed information on the patients including demographic data, clinical data as well as risk factor data, as collected by the standardized questionnaires. The patient registration interface of the GoCB software was developed using the CanReg5, an open-source tool developed by the IARC to input, store, check and analyze cancer registry data. 21 The CanReg5 tables, fields and variables were modified and aligned to the GoCB questionnaires and prepared a GoCB specific dataset in the CanReg5. Importantly, using the CanReg5 facilities (providing unique codes for patient, tumor and source) and considering patients' national identification numbers, all biological samples of a patient collected from different sources (field centers) or at different times and even from different tumor sites (in patients with multiple primary tumors) are linked to the patient's file using a unique GoCB registration number.
Ethical Considerations Participants must complete an informed consent form before starting sample and data collection. All information about the project including its benefits and risks as well as indications of using the samples, especially possible use of the samples in international research project are explained to the patient. All patients are well informed about their right to refuse or stop their participation at any stage of the project. The GoCB secretariat and field staff as well as clinicians are trained to ensure that participation in the GoCB does not interfere with appropriate patient diagnosis or care. The GoCB uses anonymized codes to ensure the protection of individuals' privacy. The GoCB secretariat and staff have limited access to the dataset according to their role and only a representative of the GoCB steering committee (GR) is authorized to have full access to the GoCB dataset.
In order to maximize the use of funds in each GoCB field center, the center personnel operating as GoCB field staff collaborate with the GoCB on a parttime basis, while maintaining their main jobs in the field center. In order to maintain the stakeholder engagement for the longer term, all GoCB collaborators, including physicians and field staff were provided official certificates according to their contributions during the different phases of the GoCB project. As part of the quality maintenance activities, the GoCB also considered continuous professional training and education programs, as well as annual appreciation ceremonies for the GoCB collaborators. In addition, providing regular feedback on the quality of samples and data to the GoCB field centers was another strategy to ensure maintenance of the GoCB strong communication channels.
GI and breast cancers were the most common malignancies recruited in the GoCB during its first four years. 4, 25  According to the latest GPCR report (unpublished data), cancers of the breast (female), stomach, colorectum, lung and esophagus were the most common malignancies in the Golestan population in 2017 (Table  S1 , Supplementary file 1). Access to high-quality population-based cancer registry data is a major strength of the GoCB. By linking the GoCB dataset with the GPCR dataset, we could add complementary data (e. g. vital status and survival data) to the GoCB dataset and this could result in improving the GoCB dataset and could consequently improve the quality of researches on GoCB samples. Collaboration of GoCB with international research projects and networks  22, 26  is another strength of the GoCB.
These collaborations may result in accessing shared resources (e. g. , expertise, protocols) and joining further collaborative works and these will finally improve the quality and promote the GoCB project. As GoCB was designed and funded as a research project, lack of continuous financial support in our setting is a major limitation and its maintenance may be affected once the project ends. Therefore, although research grants were available and accessible resources in development of the GoCB, stable and continuous supports are necessary for successful maintenance. This point should be taken into account in all low-resource settings including LMICs. In conclusion, we considered specific strategies to ensure the development and maintenance of the GoCB as a cancer-specific biological repository regarding limited resources in our high-risk population.
Acknowledgements The authors would like to thank  Professor Reza Malekzadeh  for his great support and supervision during all stages of the GoCB establishment including the design, implementation and maintenance. We also appreciate great contributions of  Dr Mehdi Alimadadi ,  Dr Abdollah Pooshani ,  Miss Nesa Shokouhifar  as well as all clinicians and healthcare workers (the  GoCB  field staff) in the GoCB field centers for their help and support in collection of data and biological samples. This project was supported by the  Golestan University of Medical Sciences  (grant number=  931128254 ) and (grant number  = 700-D-5548 ).
Proceedings All the medical and administrative data gathered on the potentially eligible patients were analyzed retrospectively. All patients known to have survived major trauma were then contacted by mail, and then by phone. Initially, all necessary documents were sent by mail, including various quality of life surveys, a written information form, an informed consent form and a stamped envelope for the main investigator, in order to collect all duly completed informed consent forms and surveys. In the second stage, once the initial documents were mailed to patients, a telephone call was made to gather additional information, and if the patients agreed, a complete interview was conducted to allow them to answer questions on the phone.
Acquired data Data were retrieved from the Orbis ® software (Agfa Healthcare, Bordeaux, France) which gathers all hospitalized patients' medical and administrative information at the University Hospital Center. The following data were collected: • Demographic data: age, gender, nature of the initial injury, severity scores (SAPS II (New Simplified Acute Physiology Score II) and ISS (Injury Severity Score)), data relating to treatment plans followed (transfusion, surgical care, embolization, mechanical ventilation), the main biological parameters and the patient outcome (length of stay in intensive care units and length of hospitalization). • Previous habits: professional career, driving, regular leisure activities. • Follow-up information after moderate to severe trauma: number of repeat surgical procedures, number of specialized consultations, evaluation of rehabilitation (length of care, quality of rehabilitation, patient's wish to be under the care of a trauma specialist such as an anesthesiologist).
Socio-economic and demographic, and health related data was collected using structured, interviewer administered, and pre tested questionnaire. The 24 hr dietary recall was used for three different days with seven days interval as per the recommendation. When dietary data is collected, often it was converted into nutrients, especially the calcium intake by using Ethiopian food composition table. Collected data was checked for completeness, and entered in to Epi Data version 3. 02 for data clearance and exported to SPSS version 20 for analysis. AOR with 95% C. I results of multivariate logistic regression was used to assess the statistical signi cance of associations between independent and dependent variables. The level of statistical signi cance was declared at p <0. 05. Result: The median calcium intake was 543mg per day.
Third pass:-Estimating portion sizes In order to accurately estimate the portion size of foods and drinks consumed, Calibrated locally available labeled cup, bowl, spoon, serving spoons, food weighing scale. Moreover, the data collectors carried different sizes of locally consumed fruits and vegetables such as bananas, avocado, shallot, tomato, potato, yum to facilitate the recall  [21]. 4. Pass four:-The interviewer reviewed the recall to ensure that all the items have been recorded correctly. Every respondent were probed for snacks, drinks and outdoor consumption of any kind of food after they complete recall. The interviews were conducted on all seven days of the week to capture changes in intakes across various days of the week. The dietary recall was repeated in randomly selected of the households by interviewer and in a different nonconsecutive day than the rst.
The standard Questionnaire Data was collected by using structured questionnaire which is adopted after reviewing literatures of similar studies based on the objective of this study. An interactive, multiple-pass 24hr dietary recall questionnaire adapted and validated for use in developing countries  [30]  was used to collect data on dietary intake. The knowledge and attitude of pregnant was categorized on two and ve-level which includes the responses for given negative and positive statement as correct and incorrect and strongly disagree, disagree, neither agree nor disagree ,strongly agree, agree and (1,2,3,4,5) of likert scale.
Data quality assurance Before data collection, the study questionnaire was checked using a pretest of 5% of invited pregnant women from households. Before estimating portion size we were standardizes measuring tools, and we were observed locally available measuring tools in study area then measure the common foods from 10(ten) household by digital food scale used to measure the gram amount of food consumed and of ingredients used in food preparation and nally took the average amount. Drinking cups glasses, spoons and ladles were labeled in to three categories based on their size (large, medium and small). The questionnaire was prepared initially in English and then translated into Amharic Language, then retranslated back to English by another person who has good command of English, Amharic language to check for its consistency.
The data collectors and supervisors were trained two days particularly in the proper lling of multiple pass 24 hour recalls. Information on 24-hour recall is collected using an openended format. Quantitative information on food intake, as described using portion size, allows for the calculation of calcium intakes of a 24-hour recall form. Estimation of portion size is facilitated by the use of measurement aids such as standard household measurement tools  [11]. To calculate calcium intakes, the estimated portion size or the amount of food intake was multiplied by the values of nutrient content in foods as found in the Ethiopian food composition tables  [22].
Data management and analysis plan Data was checked for completeness and consistency, edited manually, coded and then entered in to Epi Data version 3. 02 for data clearance and exported into SPSS version 20 for analysis. Descriptive statistics was used to calculate frequencies and percentages of study participants with regard to different variables. Variables with a p-value less than 0. 25 in the bi variable logistic regression analysis were candidate for multivariable analysis. AOR with 95% C. I. of p < 0. 05 in multivariable logistic regression was considered as statistically signi cant. Ethical clearance was obtained from Ethical Review Committee (ERC) of Wolaita Sodo University, department of Human Nutrition and support letter was to Loma Woreda Health o ce.
008, p = 0. 82). The most common translocation in the SMM data set was the t (11;14)  seen in 23% of cases (n = 19/82) followed by the t(4;14) (4. 9%, n = 4/82), t(6;14) (3. 7%, n = 3/82), and t(14;16) (2. 4%, n = 2/82). Two samples were identified with a MAFB translocation [one t (14;20) and one t (20;22)]. There were significantly fewer t(4;14) among the SMM patients (non-Yates adjusted χ 2 = 4. 4, p = 0. 03) and more t(11;14) (non-Yates adjusted χ 2 = 4. 6, p = 0. 03), suggesting that SMM carried fewer adverse risk cytogenetic groups [t (4;14), t(14;16), t (14;20)] than MM (9. 8% versus 20%, non-Yates adjusted χ 2 = 4. 5, p = 0. 03) (Fig. 1a ). The genomic partners of translocations and secondary rearrangements detected at the MYC locus were similar in the two datasets but differed in frequency.
Other key biallelic events included DIS3, RB1, FAM46C, and TRAF3. Interestingly, biallelic DIS3, which has been associated with an adverse outcome in MM  18  , was present in 5% of MM cases vs. only 2% of SMM cases, again consistent with it being associated with a more aggressive disease phenotype. These results are consistent with data previously generated at relapse after treatment and indicate that biallelic inactivation of tumor suppressor genes is an important mechanism of progression at all disease stages. Mutations in KRAS are associated with a shorter time to progression. In order to determine the molecular factors associated with time to progression to MM, we assessed outcome in 77 patients evaluable for progression. Thirteen percent of patients were identified as high-risk according to the GEP4 risk-score  15  , 23% were high-risk and 35% intermediate risk according to IMWG criteria.