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Risk of Bias and reporting practices in studies comparing VO2max responses to sprint interval vs.continuous training:A systematic review and meta-analysis

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Journal of Sport and Health Science 2022年第5期11卷 552-566页

作者： Jacob T.Bonafiglia Hashim Islam Nicholas Preobrazenski Brendon J.Gurd School of Kinesiology and Health Studies Queen’s UniversityKingstonOntarioK7L 3N6Canada

Background:It remains unclear whether studies comparing _(max)imal oxygen uptake(VO_(2max))response to sprint interval training(SIT)vs.moderate-intensity continuous training(MICT)are associated with a high risk of Bias and poor reporting quality.The purpose of this study was to evaluate the risk of Bias and quality of reporting in studies comparing changes in VO_(2max) between SIT and MICT.Methods:We conducted a comprehensive literature search of 4 major databases:AMED,CINAHL,EMBASE,and MEDLINE.Studies were excluded if participants were not healthy adult humans or if training protocols were unsupervised,lasted less than 2 weeks,or utilized mixed exercise modalities.We used the Cochrane Collaboration tool and the CONSORT checklist for non-pharmacological trials to evaluate the risk of Bias and reporting quality,respectively.Results:Twenty-eight studies with 30 comparisons(3 studies included 2 SIT groups)were included in our meta-analysis(n=360 SIT participants:body mass index(BMI)=25.9±3.7 kg/m^(2),baseline VO_(2max)=37.9±8.0 mL/kg/min;n=359 MICT participants:BMI=25.5±3.8 kg/m^(2),baseline VO_(2max)=38.3±8.0 mL/kg/min;all mean±SD).All studies had an unclear risk of Bias and poor reporting quality.Conclusion:Although we observed a lack of superiority between SIT and MICT for improving VO_(2max)(weighted Hedge’s g=0.004,95%con-fidence interval(95%CI):-0.08 to 0.07),the overall unclear risk of Bias calls the validity of this conclusion into question.Future studies using robust study designs are needed to interrogate the possibility that SIT and MICT result in similar changes in VO_(2max).

关键词： Bias Cardiorespiratory fitness CONSORT Moderate-intensity continuous training Sprint interval training

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Bolstering integrity in environmental data science and machine learning requires understanding socioecological inequity

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FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING 2024年第5期18卷 65-65页

作者： Bozeman III, Joe F. Georgia Inst Technol Sch Civil & Environm Engn Atlanta GA 30332 USA Georgia Inst Technol Sch Publ Policy Atlanta GA 30332 USA

Socioecological inequity in environmental data science-such as inequities deriving from data-driven approaches and machine learning (ML)-are current issues subject to debate and evolution. There is growing consensus around embedding equity throughout all research and design domains-from inception to administration, while also addressing procedural, distributive, and recognitional factors. Yet, practically doing so may seem onerous or daunting to some. The current perspective helps to alleviate these types of concerns by providing substantiation for the connection between environmental data science and socioecological inequity, using the Systemic Equity Framework, and provides the foundation for a paradigmatic shift toward normalizing the use of equity-centered approaches in environmental data science and ML settings. Bolstering the integrity of environmental data science and ML is just beginning from an equity-centered tool development and rigorous application standpoint. To this end, this perspective also provides relevant future directions and challenges by overviewing some meaningful tools and strategies-such as applying the Wells-Du Bois Protocol, employing fairness metrics, and systematically addressing irreproducibility; emerging needs and proposals-such as addressing data-proxy Bias and supporting convergence research; and establishes a ten-step path forward. Afterall, the work that environmental scientists and engineers do ultimately affect the well-being of us all.

关键词： Equity Bias Machine Learning Data Science Justice Systemic Equity AIR-POLLUTION PUBLIC TRUST

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Why ecosystem characteristics predicted from remotely sensed data are unBiased and Biased at the same time-and how this affects applications

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Forest Ecosystems 2024年第1期11卷 24-31页

作者： Goran Ståhl Terje Gobakken Svetlana Saarela Henrik J.Persson Magnus Ekstrom Sean P.Healey Zhiqiang Yang Johan Holmgren Eva Lindberg Kenneth Nystrom Emanuele Papucci Patrik Ulvdal Hans OleØrka Erik Næsset Zhengyang Hou Håkan Olsson Ronald E.McRoberts Department of Forest Resource Management Swedish University of Agricultural Sciences90183UmeåSweden Faculty of Environmental Sciences and Natural Resource Management Norwegian University of Life SciencesÅsNorway USDA Forest Service Rocky Mountain Research StationOgdenUTUSA The Key Laboratory for Silviculture and Conservation of Ministry of Education Beijing Forestry UniversityBeijing100083China Department of Forest Resources University of MinnesotaSt.PaulMNUSA

Remotely sensed data are frequently used for predicting and mapping ecosystem characteristics,and spatially explicit wall-to-wall information is sometimes proposed as the best possible source of information for decisionmaking.However,wall-to-wall information typically relies on model-based prediction,and several features of model-based prediction should be understood before extensively relying on this type of information.One such feature is that model-based predictors can be considered both unBiased and Biased at the same time,which has important implications in several areas of application.In this discussion paper,we first describe the conventional model-unBiasedness paradigm that underpins most prediction techniques using remotely sensed(or other)auxiliary data.From this point of view,model-based predictors are typically unBiased.Secondly,we show that for specific domains,identified based on their true values,the same model-based predictors can be considered Biased,and sometimes severely so.We suggest distinguishing between conventional model-Bias,defined in the statistical literature as the difference between the expected value of a predictor and the expected value of the quantity being predicted,and design-Bias of model-based estimators,defined as the difference between the expected value of a model-based estimator and the true value of the quantity being predicted.We show that model-based estimators(or predictors)are typically design-Biased,and that there is a trend in the design-Bias from overestimating small true values to underestimating large true values.Further,we give examples of applications where this is important to acknowledge and to potentially make adjustments to correct for the design-Bias trend.We argue that relying entirely on conventional model-unBiasedness may lead to mistakes in several areas of application that use predictions from remotely sensed data.

关键词： Bias Model-based inference Design-based inference

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Effect of cognitive behavior therapy combined with exercise intervention on the cognitive Bias and coping styles of diarrhea-predominant irritable bowel syndrome patients

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World Journal of Clinical Cases 2019年第21期7卷 3446-3462页

作者： Shi-Rui Zhao Xiao-Mei Ni Xin-An Zhang Hong Tian College of Kinesiology Shenyang Sport UniversityShenyang 110102Liaoning ProvinceChina Department of Psychotherapy and Counseling Shenyang Mental Health CenterShenyang 110168Liaoning ProvinceChina Department of Digestion The 4th People’s Hospital of ShenyangShenyang 110031Liaoning ProvinceChina

BACKGROUND Irritable bowel syndrome(IBS)is a common digestive system disease with a high incidence rate and is common in women.The cause of IBS remains unclear.Some studies have shown that mental and psychological diseases are independent risk factors for IBS.At present,the treatment of IBS is mainly symptomatic treatment.Clinically,doctors also use cognitive behavioral therapy to improve patients'cognitive ability to diseases and clinical symptoms.In recent years,exercise therapy has attracted more and more attention from scholars.Improving the symptoms of IBS patients through psychosomatic treatment strategy may be a good treatment method.AIM To explore the effects of an intervention of cognitive behavioral therapy combined with exercise(CBT+E)on the cognitive Bias and coping styles of patients with diarrhea-predominant irritable bowel syndrome(IBS-D);and to provide a theoretical reference for the management of IBS.METHODS Sixty IBS-D patients and thirty healthy subjects were selected.The 60 IBS-D patients were randomly divided into experimental and control groups.The experimental group was treated with the CBT+E intervention,while the control group was treated with conventional drugs without any additional intervention.The cognitive Bias and coping styles of the participants were evaluated at baseline and after 6 wk,12 wk and 24 wk using the Automatic Thoughts Questionnaire(ATQ),Dysfunctional Attitudes Scale(DAS)and Pain Coping Style Questionnaire(CSQ)instruments,and the intervention effect was analyzed using SPSS 17.0 statistical software.RESULTS At baseline,the scores on the various scales showed that all subjects had cognitive Bias and adverse coping styles.The IBS Symptom Severity Scale(IBSSSS)scores,ATQ total scores,DAS scores and CSQ scores of the two groups were not significantly different(P>0.05).Compared with baseline,after 6 wk of the CBT+E intervention,there were significant differences in the ATQ scores,the dependence and total scores on the DAS,and the catastrophization,distraction and prayer scor

关键词： Irritable bowel syndrome Cognitive behavioral therapy Exercise Cognitive Bias Coping style

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Capture efficiency and Bias from the defect dynamics near grain boundaries in BCC Fe using mesoscale simulations

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Journal of Materials Science & Technology 2021年第34期93卷 169-177页

作者： Jun Chai Shuo Jin Ziang Yu Haixuan Xu Guang-Hong Lu School of Physics.Beihang University Beijing 100191China Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang UniversityBeijing 100191 China Department of Materials Science and Engineering University of TennesseeKnoxville.TN 37996United States Joint Institute for Advanced Materials KnoxvilleTN 37996United States

The capture efficiency describes the capability of a sink,such as a grain boundary(GB),dislocation,and void,to absorb point defects(PDs).The Bias defines the difference in capture efficiency between the absorption of a vacancy and dumbbell at a sink.Complete kinetic information on PDs,including diffusion barriers and diffusion orientations,as well as accurate saddle points,are needed to determine the capture efficiency and Bias at a sink accurately,which is computationally demanding.In the present study,the Self-Evolving Atomistic Kinetic Monte Carlo(SEAKMC)method was used to investigate the defect dynamics of PDs near different types of grain boundaries(GBs)(with both 100 and 110 families)accurately in body-centered cubic(BCC)iron(Fe).The capture efficiency,sink strength,and Bias factor of different types of GBs were determined in Fe,which,different from traditional rate theory estimation,showed a distinct capture efficiency,sink strength,and Bias in different GBs.The results demonstrate a strong positive correlation between the capture efficiency and the GB strain width,instead of the GB misorientation,GB energy,or GB-PD binding energy,which have been investigated previously.This work provides valuable insight into the radiation-induced microstructural evolution of GBs.

关键词： Iron Grain boundary Capture efficiency Bias SEAKMC

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Effectiveness of mindfulness based cognitive therapy on weight loss, improvement of hypertension and attentional Bias to eating cues in overweight people

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International Journal of Nursing Sciences 2020年第1期7卷 35-40页

作者： Mercedeh Masoumi Alarnout Mahdieh Rahmanian Vahideh Aghamohammadi Elahe Mohammadi Khadijeh Nasiri National Nutrition and Food Technology Research Institute Faculty of Nutrition Science and Food TechnologyShahid Beheshti University of Medical ScienceTehranIran Faculty of Psychology Payame Noor UniversityTehranIran Department of Nutrition Khalkhal University of Medical SciencesKhalkhalIran Department of Nursing Khalkhal University of Medical SciencesKhalkhalIran

Objectives:Prevalence rates of overweight and obesity are dramatically ever-increasing across the world.Therefore,this study was to evaluate the effect of mindfulness-based cognitive therapy(MBCT)on weight loss,hypertension,and attentional Bias towards food cues in a group of women affected with this condition.Methods:A total of 45 participants were selected out of women referring to the Nutrition and Diet Therapy Clinic affiliated to Shahid Beheshti University of Medical Sciences,Iran,and then randomized into three groups of 15.The first experimental group was subjected to an energy-restricted diet therapy together with MBCT during 8 sessions,the second group took the diet therapy alone,and the third group received no intervention.Body mass index(BMI),hypertension,and attentional Bias towards food cues were correspondingly evaluated before,at the end,and four weeks after the completion of the interventions.Results:The results of this study revealed that MBCT,along with diet therapy,had been significantly more effective in weight loss,decrease in BMI,lower systolic blood pressure(SBP),and attentional Bias towards food cues compared with the diet therapy alone(P≤0.01).MBCT had no significant impact on the decline in diastolic blood pressure(DBP)in participants in the follow-up phase.Conclusion:This study demonstrated that MBCT along with the conventional diet therapy was more effective in weight loss,decrease in BMI,hypertension control,as well as attentional Bias towards food cues than the diet therapy alone.

关键词： Attentional Bias Body mass index Cognitive-behavioral therapy Diet Hypertension Iran Mindfulness

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Modeling and analysis of solar Doppler difference Bias with arbitrary rotation axis

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Chinese Journal of Aeronautics 2020年第12期33卷 3331-3343页

作者： Jin LIU Yangyang LI Xiaolin NING Xiao CHEN Zhiwei KANG College of Information Science and Engineering Wuhan University of Science and TechnologyWuhan 430081China College of Instrument Science and Optoelectronic Engineering Beihang UniversityBeijing 100191China Shanghai Institution of Satellite Engineering Shanghai 200240China College of Information Science and Engineering Hunan UniversityChangsha 410082China

The solar rotation causes the solar Doppler difference Bias,which leads to the decline of the velocity measurement accuracy.Modeling and compensation are an effective solution.The limited model with specific geometric direction,where the solar rotation axis is perpendicular to the plane through the Sun,the Earth and Mars,was established.However,in fact,the geometric relationship among the Sun,Mars and the spacecraft is not fully in line with the hypothesis of the model due to the spacecraft orbital angle and the solar rotation axis drift.Thus,this model is not consistent with the fact.In order to solve this problem,a universal solar Doppler difference Bias model,which provides the expression with arbitrary rotation axis,is established in this paper.In this method,for any point at the solar surface,four variables including the direction of the solar rotation linear velocity at this point,the distance from this point to the rotation axis,the vector from this point to Mars,and the vector from this point to the spacecraft are calculated.Based on these four variables,the solar Doppler difference Bias corresponding to this point is obtained.The theoretical analysis and simulation results demonstrate that the solar Doppler difference Bias model with the actual rotation axis is different from that with one of the specific rotation axes.Therefore,it is indispensable to build the proposed model for compensation.Besides,the direction of the solar rotation axis,the spacecraft-Mars-Sun angle and the spacecraft-to-Mars distance are important impact factors for the proposed model.

关键词： Bias Doppler navigation Measurement Modeling Rotation

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The methodological challenge in high-throughput profiling and quantifying microRNAs

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Quantitative Biology 2022年第4期10卷 321-332页

作者： Mengya Chai Xueyang Xiong Huimin Wang Lida Xu College of Life Science and Technology Beijing University of Chemical TechnologyBeijing 100029China

Background:MicroRNAs(miRNAs)play an essential role in various biological processes and signaling pathways through the regulation of gene expression and genome stability.Recent data indicated that the next-generation sequencing(NGS)-based high-throughput quantification of miRNAs from biofluids provided exciting possibilities for discovering biomarkers of various diseases and might help promote the development of the early diagnosis of cancer.However,the complex process of library construction for sequencing always introduces Bias,which may twist the actual expression levels of miRNAs and reach misleading conclusions.Results:We discussed the deviation issue in each step during constructing miRNA sequencing libraries and suggested many strategies to generate high-quality data by avoiding or minimizing Bias.For example,improvement of adapter design(a blocking element away from the ligation end,a randomized fragment adjacent to the ligation junction and UMI)and optimization of ligation conditions(a high concentration of PEG 8000,reasonable incubation temperature and time,and the selection of ligase)in adapter ligation,high-quality input RNA samples,removal of adapter dimer(solid phase reverse immobilization(SPRI)magnetic bead,locked nucleic acid(LNA)oligonucleotide,and Phi29 DNA polymerase),PCR(linear amplification,touch-down PCR),and product purification are essential factors for achieving high-quality sequencing data.Moreover,we described several protocols that exhibit significant advantages using combinatorial optimization and commercially available low-input miRNA library preparation kits.Conclusions:Overall,our work provides the basis for unBiased high-throughput quantification of miRNAs.These data will help achieve optimal design involving miRNA profiling and provide reliable guidance for clinical diagnosis and treatment by significantly increasing the credibility of potential biomarkers.

关键词： microRNA next-generation sequencing library preparation Bias

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Homeopathic potencies of Arnica montana L. change gene expression in a Tamm-Horsfall protein-1 cell line in vitro model: the role of ethanol as a possible confounder and statistical Bias

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Journal of Integrative Medicine 2017年第4期15卷 255-264页

作者： Salvatore Chirumbolo Geir Bj?rklund Department of Neurological and Movement Sciences University of Verona Council for Nutritional and Environmental Medicine

Marzotto et al. showed that homeopathic preparations of Amica montana L. acted directly on gene expression of Tamm-Horsfall protein- 1 （THP-1） monocyte/macrophage cell lines activated with phorbol 12-mydstate 13- acetate and interleukin-4 （IL-4）. A. montana homeopathic dilutions are used in complementary and alternative medicine to treat inflammation disorders and post-traumatic events as well as for wound repair. The French Pharmacopoeia of these remedies uses 0.3% ethanol in each centesimal dilution. In this paper, we discuss how ethanol-containing A. montana homeopathic centesimal dilutions can change gene expression in IL-4- treated monocyte/macrophage THP-I. We assessed the role of ethanol in the Arnica homeopathic dilutions containing this alcohol by investigating its action on gene expression of THP-1 cell. Evidence would strongly suggest that the presence of ethanol in these remedies might play a fundamental role in the dilutions ability to affect gene expression, particularly for doses from 5c to 15c. Where, rather than playing a major role in the mesoscopic structure of water, the ethanol might have a chemical-physical role in the induction of THP-1 gene expression, apoptosis, and deoxyribonucleic acid function. This evidence generates a debate about the suggestion that the use of a binary-mixed solvent in homeopathic chemistry, used by Hahnemann since 1810, may be fundamental to explain the activity of homeopathy on cell models.

关键词： Arnica homeopathy Bias statistics gene expression Tamm-Horsfall protein cell line

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Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction

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Financial Innovation 2022年第1期8卷 1930-1964页

作者： Andrés Alonso Robisco JoséManuel CarbóMartínez Banco de España Calle de Alcalá4828014 MadridSpain

Implementing new machine learning(ML)algorithms for credit default prediction is associated with better predictive performance;however,it also generates new model risks,particularly concerning the supervisory validation process.Recent industry surveys often mention that uncertainty about how supervisors might assess these risks could be a barrier to innovation.In this study,we propose a new framework to quantify model risk-adjustments to compare the performance of several ML methods.To address this challenge,we first harness the internal ratings-based approach to identify up to 13 risk components that we classify into 3 main categories—statistics,technology,and market conduct.Second,to evaluate the importance of each risk category,we collect a series of regulatory documents related to three potential use cases—regulatory capital,credit scoring,or provisioning—and we compute the weight of each category according to the intensity of their mentions,using natural language processing and a risk terminology based on expert knowledge.Finally,we test our framework using popular ML models in credit risk,and a publicly available database,to quantify some proxies of a subset of risk factors that we deem representative.We measure the statistical risk according to the number of hyperparameters and the stability of the predictions.The technological risk is assessed through the transparency of the algorithm and the latency of the ML training method,while the market conduct risk is quantified by the time it takes to run a post hoc technique(SHapley Additive exPlanations)to interpret the output.

关键词： Artificial intelligence Machine learning Credit risk Interpretability Bias Internal ratings based model IRB model Natural language processing NLP

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