dataesr/scientific-paragraphs-categorization huggingface dataset:
These studies provide a promising outlook for the clinical application of U2AF1 in the future. Through rigorous study design and appropriate controls, this treatment may bring new opportunities for both diagnosis and therapy. NCT04447651 aimed to explore the use of spliceosome mutational markers (PRISMMs) in the treatment of patients with metastatic solid tumours and haematological malignancies or lymphoma who may benefit from immunotherapy agents. NCT04278768 was a dose alteration trial of the monotherapy combination of CA-4948 and venetoclax or azacitidine in the treatment of AML or MDS patients with mutations in U2AF1, SRSF2, ZRSR2 and SF3B1. As a response to the increasing use of hypomethylating agents in MDS treatment, NCT02060409 investigated the prognostic impacts of U2AF1, SRSF, and ZRSR2 mutations after treatment with decitabine.
To reduce predictability of a random sequence, details of any planned restriction statistical methods to handle missing data (eg, multiple imputation) _____7______ ______25______ Methods: Participants, interventions, and outcomes (eg, blocking) should be provided in a separate document that is unavailable to those who enrol participants or assign interventions limit such access for investigators Methods: Monitoring Access to data 29 Statement of who will have access to the final trial dataset, and disclosure of contractual agreements that _____40______ Study setting Eligibility criteria Allocation concealment mechanism Data monitoring Ancillary and post-trial care Interventions Implementation Dissemination policy 31a 9 10 16b 21a 30 11a 11b 16c Blinding (masking) 17a 21b Harms 22 31b 17b 31c Description of study settings (eg, community clinic, academic hospital) and list of countries where data will be collected.
Acknowledgments It is a pleasure to thank  Andrea Dei ,  Gastón Giribet ,  Carmen Núñez ,  Osvaldo Santillán  and  David Turton  for discussions, and especially  Davide Bufalini  for a careful reading of the manuscript. The work of J. T. is supported by  CONICET. The work of N. K. is supported by the  ERC  Consolidator Grant  772408-Stringlandscape. Open Access. This article is distributed under the terms of the Creative Commons Attribution License ( CC-BY 4. 0 ), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited. SCOAP 3 supports the goals of the  International Year of Basic Sciences for Sustainable Development.
Conclusions Force production parameters, fatigue and push and pull force values were assessed with and without vibration on five subjects. Vibration does not seem to influence the fatigue phenomenon because of a neuromuscular rearrangement. These changes were recognized by ∆G values representing the push and pull balance during the gripping task. These findings show a clear relationship with vibration acceleration. Present data confirm the neuromuscular plasticity involved in adapting the force production in interfering conditions at the dispense of fine muscle control. The loss of fine muscle control should be better investigated to monitor muscular integrity.
Data collection In total, we conducted 40 interviews (28 formal and 12 informal) with biologists of the Biolab group from 2016 to 2017, at a moment when the group had 88 biologists. The formal interviews were semi-structured interviews conducted face-to-face and were audio-recorded and transcribed. We selected our interviewees by using an intensity sampling strategy by interviewing medical biologists representative of the phenomenon of interest but not highly unusual cases  (Patton, 2002). As a consequence, we interviewed medical biologists occupying various of positions within the Biolab group (site manager, sample analyst and board member), that worked in different geographical areas and made sure that our sample was diverse and representative of the population of medical biologists in the Biolab group terms of gender, age and tenure (for more details please refer to Table  1 ).
In total we analyzed 314 documents. This additional information helped us to understand better the environmental forces that influenced the Biolab group and why it changed its structure the way it did. By triangulating the interview data, the non-participation data, and the archival data, we were able to verify the consistency of our findings  (Patton, 1999). For our interviews, we developed an interview guide and used a semi-directive technique to allow for the widest possible scope for data collection  (Lincoln and Guba, 1985). We collected data until data saturation was reached. Theoretical saturation was reached after 40 interviews, 15 h of observation, and the examination of 300 archival records. We then decided to end our data collection, given that the additional data collected after this point did not provide new insights  (Bowen, 2008).
Monitoring tool The accurate predictions of the currents performed by both the GLM and autoencoder can be used to detect anomalies in the RPC detector current performance. The implemented tool follows the workflow presented in the flowchart in Fig. 4. Raw Figure  4 : Monitoring tool workflow data coming from the CMS non-physics event bus, referred to as online condition data, are written in the cms omds adg database copy. For each point in time for which data is available, the tool performs comparisons between the measured and predicted RPC currents. If differences higher than some predetermined threshold values are detected for a given HV channel, a flag is raised and the case of that particular channel is followed. There are two thresholds, the lower one inducing a warning and the higher one inducing an error.
Software implementation The monitoring tool is programmed in Python. Tensorflow  [5]  is used for the implementation of ML. The software is conceptualized and implemented with modularity in mind (Fig. 5 ). All modules communicate back-and-forth with a database. The. The "Estimator Module" loads the models and performs predictions, which are also stored into the database. Finally, the "Notifications Manager" searches for anomalies in the current values, as described in the previous section and provides notifications.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid and other centres for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the  CMS detector , and the supporting computing infrastructure provided by the following funding agencies:  FWO (Belgium) ;  CNPq ,  CAPES  and  FAPERJ (Brazil) ;  MES  and  BNSF (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  MINCIENCIAS (Colombia) ;  CEA  and  CNRS/IN2P3  (France);  SRNSFG (Georgia) ;  DAE  and  DST (India) ;  IPM (Iran) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  BUAP, CINVES-TAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  PAEC (Pakistan) ;  DOE  and  NSF (USA).
If not counterbalanced, hyperglycemia may result in insulin resistance, worsening of NAFLD and the development of T2DM and metabolic syndrome. Hence, understanding how metabolic risk factors such as (increased) glucose intake may affect the LACA is essential. Additionally, AA and glucagon levels during oral glucose tolerance test (OGTT) may hold predictive/diagnostic information on metabolically associated diseases. Besides providing an overview of the LACA, the aim of this systematic review is to summarize the relationship between glucose and the mediators of the LACA (i. e. , glucagon and AAs) under physiological conditions and in conditions of metabolical impairment. This review is structured to report data of the mediators`interactions with glucose and each other as well as their dynamics during experimental glucose administration including results from the pediatric population.
In the PPP-Bonita study, of the 98 individuals with incident diabetes, significantly fewer had increasing or stable glucagon 120min than subjects without diabetes (25 vs. 39%, P = 0. 002). Glucagon 120min in NGT was 15. 2 median (12. 6-18. 9 [IQR/95%CI]), while in IGT it was 13. 5 median (11. 5-18. 1 [IQR/95%CI]). The TULIP study assessed glucagon (fasting and 120 minutes during OGTT) before and nine months after a lifestyle intervention. Lifestyle intervention reduced fasting glucagon (19 vs. 17   (118) , that had registered lower early suppression of glucagon (minutes 0-30) and higher late suppression of glucagon (minutes 30-120) in patients with prediabetes and incident diabetes, compared to individuals with NGT  (118). The authors found comparable data on late glucagon suppression, however, the TUEF study did not show differences between prediabetes and NGT for shorter intervals (30/0, 60/0, and 90/0).
Also, glucagon levels were about two-fold higher than those of Faerch et al. (118). Wagner et al. stated that similar AUCs for glucagon levels and additional controlling for fasting glucagon suggest that fasting glucagon does explain the association between glucagon suppression and insulin sensitivity. Additionally, they found a strong inverse association of hepatic triglyceride content with non-suppressed glucagon 120min in their data  (51). Similarly,  Gar et   ], respectively. This applied to only 21% of cases and 8% of the control group. Delayed glucagon suppression was clearly associated with obesity and metabolic syndrome. One cluster of seven individuals with low fasting glucagon had rising glucagon levels during the OGTT. The women in this cluster were lean, insulin sensitive, and displayed low plasma glucose  (58).
The study with the smallest population was done by Kosuda et al. , who investigated glucagon response to an OGTT in 17 patients with idiopathic postprandial syndrome (IPS). They reported two types of glucagon dynamics. The first was characterized by lower fasting glucagon and further suppression during OGTT, and the second with fasting hyperglucagonemia and drastic decrease during OGTT. The authors concluded that glucagon suppression in patients with IPS is more substantial than in healthy individuals  (59). Two publications addressed glucagon levels during an OGTT in a pediatric collective, which are presented in the section on pediatric data below  (62, 71). Of the studies that included intravenous glucose administration, one study by Wang et al. was on rhesus monkeys. They performed i. v.
05) and tryptophan (r=0. 77, P<0. 05) in controls. In the group with obesity, changes in arginine and histidine were positively associated OGTT (plasma samples at minutes 0 and 120). In healthy controls, the levels of 4-hydroxy-L-proline, valine, asparagine, tyrosine decreased, and the concentrations of four amino acids (serine, taurine, cysteine and creatine) increased significantly after the OGTT. In HLP there were significant increases in leucine, isoleucine, serine, histidine, lysine, taurine, cysteine, and creatine, while reductions in six metabolites (methionine, dimethylglycine, aminobutyric acid, niacinamide, allantoin, and creatinine) were noted after the OGTT. Li et al. reported that their data positively associated the postprandial changes in isoleucine and HOMA-IR. The study only listed fasting values and used graphics for measurements at 120 minutes and their association to clinical parameters (e.
Boxplots and fasting glucose concentrations are indicated for each subpopulation  (62). The second study's objective was to determine how the hyperresponsiveness of the beta cell and the insulin resistance in youth compared to adults (both with IGT or T2D) was due to an increased glucagon release. Data was gathered from the RISE study (66 youth and 350 adults), and patients on antidiabetic drugs were excluded. The younger population was 10-19 years of age with a Tanner stage >1. Fasting glucagon and steady-state glucagon did not differ between younger and adult participants. While fasting glucose and glucagon were positively correlated in adults (r=0. 133, p=0. 012), they negatively correlated in the younger group (r= -0. 143, p=0. 251). At comparable fasting glucagon levels, the 10-19 years olds had higher C-peptide levels and a lower insulin sensitivity.
While a number of publications on fasting glucagon exist, there is very limited data on its dynamics after glucose administration. In general, glucagon levels are expected to decline when blood glucose levels rise. In adults, this response has been constantly observed after i. v. glucose administration, while responses to oral glucose vary. Of note, T2D is not only attributed to beta-cell dysfunction but also alpha-cell dysfunction. In keeping with this, hyperglucagonemia was repeatedly reported during OGTT in T2D and has been attributed to an incretin effect as well as to intestinal glucagon secretion  (7, 119). Additionally, endogenous glucose production is higher during OGTT than after i. v. glucose administration  (7). The lack of early glucagon suppression, especially within the first 30-60 minutes, may be an early and reliable hallmark of prediabetes and IGT in adults  (56, 60, 118) , although contradictory results question this  (53).
Thus, hyperglucagonemia might precede impairments in glucose regulation  (160). This finding is in line with the pediatric data from the RISE study  (71). Kahn et al. also reported an inverse relationship between glucagon and fasting glucose in their collective of 10-19 year old patients. In the RISE study there was no difference in glucagon suppression during OGTT between the age groups, however, arginine administration resulted in significantly lower glucagon levels in the pediatric population. In youth with IGT or newly diagnosed T2D, hypersecretion of beta cells and reduced insulin sensitivity have been described. At comparable glucagon levels as adults have, they also display higher C-peptide levels. Kahn et al. concluded that alpha cell dysfunction in youth does not explain beta cell dysfunction but suggests a greater glucagonsensitivity in the pediatric population  (71).
Data sources Detailed information on the methods including data sources, endpoint definitions and collection codes can be found in a previous study. 16 Briefly, five HFrEF RCTs and two HF registries were included in this study. BEAUTIFUL and SHIFT were phase III ivabradine trials (n = 15 732),  17, 18  FAIR-HF and CONFIRM were phase III and phase IV studies on intravenous iron supplementation (n = 763)  19, 20  and PAN-THEON was a phase II trial for neladenosone bialanate (n = 427). 21 For final analysis, aggregated data from both treatment and placebo arms of each RCT were pooled to represent one RCT population (n = 16 917). The Dutch CHECK-HF and SwedeHF registries enrol patients with clinician-judged HF and detailed information on the methods can be found elsewhere. 22, 23 For the current analysis, only HFrEF patients, defined as those enrolled with left ventricular ejection fraction (LVEF) <40%, were considered.
Eligibility criteria, study population, and outcomes The inclusion and exclusion criteria listed in the study protocols of the five RCTs were tabulated to identify common eligibility and ineligibility criteria (Figure  1 , online supplementary Table  S1 ). 16 These common criteria were applied to the SwedeHF and CHECK-HF dataset to identify subgroups of patients who would have been eligible for trial participation or not. Data were then presented by the following groups and additionally stratified by sex: RCT, RCT-eligible, and RCT-ineligible (Figure  1 ). The following clinical outcomes at 1 year were assessed: all-cause mortality, cardiovascular mortality, and first HF hospitalization.
Statistical analysis Continuous data are presented as mean with standard deviation, while categorical variables are reported in absolute and relative frequencies. Mean and proportion differences between each group were calculated and reported as significant based on their corresponding 99% confidence intervals (CI). Unadjusted outcomes were calculated with cumulative incidence curves for each of the six subgroups outline above. The competing event for cardiovascular mortality was death from other causes whereas for first HF hospitalization, it was all-cause deaths. To test whether the RCT group was more, less, or equally likely to die than the RCT-eligible group, standardized mortality ratios (SMRs) were calculated and stratified by sex. SMRs were calculated by dividing the observed mortality count in the RCT group by expected mortality count in the RCT group.
Study limitations The strength of this study lies in large sample sizes and access to individual patient data from both trial and observational datasets. There are also potential limitations of the study. The harmonized criteria selected to define the RCT-eligible population were chosen based on data availability and commonality between trials. Therefore, the percentage of patients eligible for trial inclusion is likely overestimated but still considered appropriate for mortality analysis to make fairer comparisons between the RCT population and the real world. The RCTs involved in this study were selected based on the availability of data from industry partners. However, a comparison of baseline characteristics with contemporary trials does not show meaningful differences (online supplementary Table  S4 ).
Funding This study is part of the  BigData @ Heart  project and is funded by the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement number  116074. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme  and the  European Federation of Pharmaceutical Industries and Associations. The funders had no role in the design, analysis, interpretation of data or writing of the manuscript.
Out of 46 914 patients from the registries, 14 584 were females (31. 1%). After applying the harmonized set of eligibility and ineligibility criteria, 8294 out of 14 584 (56. 9%) females and 17 818 out of 32 330 (55. 1%) males in the registries were considered eligible for RCT recruitment for a final RCT-eligible group of 26 104 (31. 8% females). Cancer was the most restricting criterion with 27. 4% of females and 28. 1% of males excluded. The exclusion criteria DATASETS 5 HFrEF RCTs SwedeHF registry Check-HF registry (2005 -2018) (2000 -2016) (2013 -2016) n = 16 917 n = 40 230 n = 6 684 Total registry populaƟon N = 46 914 (31% Females) Inclusion criteria applied: Age ≥ 18 NYHA class II-IV B-blocker-yes ACEI/ARB-yes Exclusion criteria applied: CreaƟnine >200 μmol/L Haemoglobin <11 g/dL (men) or <10g/dL (women) Cancer RCT-eligible RCT-ineligible RCT Females: 22% (3 663) Males: 78% (13 254) n = 26 104 (56% eligible) Females: 57% eligible (8 294 / 14 584) Males: 55% eligible n = 20 810 (44% ineligible) Females: 43% ineligible (6 290 / 14 584) Males: 45% ineligible (17 810 / 32 330) (14 520 / 32 330) POPULATIONS STUDIED.
ACKNOWLEDGMENTS We are grateful to  X. Y. Luo ,  J. L. Bohn , and  T. Karman  for valuable discussions, and  Bo Yang  for laboratory assistance. We thank  K. L. Wu  and  Y. M. Fung  for lending us a signal generator at the early stage of the project. The Hong Kong team was supported by  Hong Kong RGC General Research Fund  (Grants No. 14301818 , No. 14301119 , and No. 14302722 ) and  Collaborative Research Fund  (Grant No. C6009-20GF ). W. Z. acknowledges support from the  National Natural Science Foundation of China (NSFC)  under Grant No. 12274331. T. S. and S. Y. acknowledge support from the  NSFC  (Grants No. 12135018  and No. 11974363 ). Note added. -Recently, we became aware of similar work on microwave shielding with bosonic NaCs molecules [63]. We obtained hints regarding the calculation of N col from their results.
RESULTS: We present a standardized terminology to describe gene-disease relationships, and to support variant annotation. We demonstrate application of the terminology for classification of variation in the ACMG SF 2. 0 genes recommended for reporting of secondary findings. Consensus terms were agreed and formalized in both sequence ontology (SO) and human phenotype ontology (HPO) ontologies. GenCC member groups intend to use or map to these terms in their respective resources. CONCLUSION: The terminology standardization presented here will improve harmonization, facilitate the pooling of curation datasets across international curation efforts and, in turn, improve consistency in variant classification and genetic test interpretation.
Using an incorrectly classified variant for family cascade testing and the delivery of screening, treatment, or reproductive choices can have severe adverse consequences. Historically, disease genes were identified by linkage studies in large families  7  often using polymorphic markers in close proximity to the gene responsible for the disease. Subsequently candidate gene studies based on known or hypothesized disease mechanisms became commonplace  8. Until as recently as 10 years ago it was not fully appreciated that individually-rare genetic variants are collectively extremely common, and many studies did not adequately control for background genetic variation in the population. Inadequately small control cohorts, often just 100 chromosomes, were used for assessment of novel disease genes and variants, and consequently both the literature and disease databases were flooded with assertions of gene-disease relationships and variant pathogenicity that have not proven robust over time  [9] [10] [11].
Recent years have seen concerted efforts to correct this bias, employing large, publicly available population databases such as ExAC, and later gnomAD  12, 13  , as control cohorts, and applying standardized approaches to reinterpret evidence for gene-disease relationships  5  and variant pathogenicity  2  Many groups are invested in curation of disease-gene validity, including academic and healthcare centers, private companies, and consortia. The Gene Curation Coalition (GenCC) is a coalition aiming to harmonize approaches amongst these entities to ensure gene-level curated resources are comparable and interoperable, and to provide access to structured representations of consensus data. As a first undertaking the GenCC developed a consensus term set for grading gene-disease validity, and developed a unified database to display curated gene-disease validity assertions from its members (the Clinical Genome Resource (ClinGen), DECIPHER, Gene2Phenotype (G2P), Transforming Genetic Medicine Initiative (TGMI), MedlinePlus Genetics, Genomics England PanelApp (PanelApp), PanelApp Australia, Online Mendelian Inheritance in Man (OMIM), Orphanet, Ambry Genetic, Illumina, Invitae, Mass General Brigham Laboratory for Molecular Medicine (LMM), Myriad Women's Health, HUGO Gene Nomenclature Committee (HGNC), Franklin by Genoox, King Faisal Specialist Hospital and Research Center, and PharmGKB).
This database can be likened to a "ClinVar for genes" in that members can submit assertions of disease association but in this case for genes, not variants. The GenCC database provides a single route of access to comprehensive aggregated assertions (https://search. thegencc. org)  14  and currently contains over 16,911 gene-disease assertions on 4704 unique genes from 12 submitters. OMIM will connect its large dataset to the GenCC in real time via a submission API being launched soon. Resolution of genedisease validity discrepancy across GenCC submitters is ongoing using a manual review process. As the next step to facilitate harmonized gene-disease validity assessments and support variant classification within the ACMG/AMP framework, GenCC has focused on developing standardized terminology for the characterization of disease mode of inheritance, allelic requirement, and diseaseassociated variant consequences.