Statistical Bias Research Articles

Deterministic Forecasting and Probabilistic Post‐Processing of Short‐Term Wind Speed Using Statistical Methods

AbstractThere is a great need for an accurate short‐term wind speed forecast, and statistical forecasts have gained increased popularity for their computational efficiency and satisfactory skill. However, there has been no systematic research to fully explore the capabilities of statistical approaches and evaluate the applicability of probabilistic information from statistical ensemble. This study first compares the skills of different statistical methods, based on linear regression, machine learning (ML), and deep learning (DL), using three strategies (i.e., direct, recursive, and multi‐output) against the three operational numerical models and their bias‐corrections, for short‐term wind speed forecast over Pearl River Estuary during 2018–2021. Inter‐comparison between statistical forecasts reveals the dominant superiority of direct strategy. On this basis, Random Forest (RF) and Support Vector Machines (SVM) perform best compared to other statistical forecasts and bias correction of numerical forecasts throughout 48 hr lead time, while the performance of methods with simplified (linear) or more complex (DL) model structures degrades significantly. Moreover, the top 10 forecasts are utilized to account for forecast uncertainties but present a substantial under‐dispersed prediction. Two traditional methods and three modern methods are implemented to perform probabilistic post‐processing. Modern methods based on ML or DL present worse skills, while traditional methods, particularly for ensemble model output statistics, show added value in discriminating binary events due to limited enhancements in calibration. Overall, RF and SVM using direct strategy are highly recommended for short‐term wind speed forecasts, and efforts are ongoing to address the issues of strong wind prediction and ensemble calibration.

The effects of antioxidant supplementation on short-term mortality in sepsis patients

BackgroundThe occurrence and development of sepsis are related to the excessive production of oxygen free radicals and the weakened natural clearance mechanism. Further dependable evidence is required to clarify the effectiveness of antioxidant therapy, especially its impact on short-term mortality. ObjectivesThe purpose of this systematic review and meta-analysis was to evaluate the effect of common antioxidant therapy on short-term mortality in patients with sepsis. MethodsAccording to PRISMA guidelines, a systematic literature search on antioxidants in adults sepsis patients was performed on PubMed/Medline, Embase, and the Cochrane Library from the establishment of the database to November 2023. Antioxidant supplements can be a single-drug or multi-drug combination: HAT (hydrocortisone, ascorbic acid, and thiamine), ascorbic acid, thiamine, N-acetylcysteine and selenium. The primary outcome was the effect of antioxidant treatment on short-term mortality, which included 28-day mortality, in-hospital mortality, intensive care unit mortality, and 30-day mortality. Subgroup analyses of short-term mortality were used to reduce statistical heterogeneity and publication bias. ResultsSixty studies of 130,986 sepsis patients fulfilled the predefined criteria and were quantified and meta-analyzed. Antioxidant therapy reduces the risk of short-term death in sepsis patients by multivariate meta-analysis of current data, including a reduction of in-hospital mortality (OR = 0.81, 95% CI 0.67 to 0.99; P = 0.040) and 28-day mortality (OR = 0.81, 95% CI 0.69 to 0.95]; P = 0.008). Particularly in subgroup analyses, ascorbic acid treatment can reduce in-hospital mortality (OR = 0.66, 95% CI 0.90 to 0.98; P = 0.006) and 28-day mortality (OR = 0.43, 95% CI 0.24 to 0.75; P = 0.003). However, the meta-analysis of RCTs found that antioxidant therapy drugs, especially ascorbic acid, did substantially reduce short-term mortality(OR = 0.78, 95% CI 0.62 to 0.98; P = 0.030; OR = 0.57, 95% CI 0.36 to 0.91; P = 0.020). ConclusionsAccording to current data of RCTs, antioxidant therapy, especially ascorbic acid, has a trend of improving short-term mortality in patients with sepsis, but the evidence remains to be further demonstrated.

The association between maternal diabetes and the risk of attention deficit/hyperactivity disorder in offspring: Updated systematic review and meta-analysis.

IntroductionThe existing body of evidence on the association between maternal diabetes and attention deficit/hyperactivity disorder (ADHD) in offspring is inconsistent and inconclusive. Thus, we need to synthesise the available evidence to examine the association between maternal diabetes and risk of ADHD in offspring.ObjectivesThe aim of this meta-analysis was to examine the association between maternal diabetes and the risk of ADHD in offspring.MethodsWe conducted a comprehensive search across PubMed, MEDLINE, EMBASE, Scopus, CINAHL and PsychINFO databases from their inception to September 8th, 2023. The methodological quality of the included studies was evaluated using Joanna Briggs Institute (JBI) and Newcastle-Ottawa Scale (NOS). Between-study heterogeneity was assessed using I2 statistic and potential publication bias was checked using both funnel plot and Egger’s test. Randomeffect model was used to calculate the pooled effect estimates and subgroup, sensitivity, and meta-regression were further performed to support our findingsResultsTwenty observational studies (two cross-sectional, five case-control and thirteen cohort studies) were included in this systematic review and meta-analysis. Our meta-analysis indicated that intra-uterine exposure to any type of maternal diabetes was associated with an increased risk ADHD in offspring [RR=1.33: 95 % CI: 1.23–1.43, I2=79.9%]. When we stratified the analysis by maternal diabetes type, we found 17%, and 37% higher risk of ADHD in offspring exposed to maternal gestational [RR=1.17: 95 % CI: 1.07–1.29] and pre-existing diabetes [RR=1.37: 95 % CI: 1.27–1.48] compared to unexposed offspring respectively. Results of subgroup and sensitivity analysis further supported the robustness of our main finding.ConclusionsOur review suggested that exposure to maternal diabetes increased the risk of ADHD in offspring. These findings underscore the need for early screening and prompt interventions for exposed offspring.Disclosure of InterestNone Declared

Validation of the MGB-IPH hydrological model for flows simulation in paired watershed in minas gerais, Brazil.

Simulating hydrological behavior makes it possible to analyze and predict water availability in river basins, anticipate potential events and carry out quantitative water balances using various scenarios. These scenarios arise from changes in land use and landscape over the years, the occurrence of extreme events and the effects of climate change. In this context, the aim of this study was to evaluate the effectiveness of the Large Basin Hydrological Model (MGB-IPH) for representing rainfall-runoff in the paired watershed of the Piracicaba River and Piranga River in the state of Minas Gerais, Brazil, using the usual calibration and validation procedures. The results of the average statistical performance indices NASH, NASH-Log, BIAS, R², D and Dr showed that the model was able to simulate the seasonality of the watersheds and presented good adjustments, with a tendency to overerestimate the flow, but within acceptable limits. It is concluded that the model performed satisfactorily in its ability to simulate the hydrological behaviour of the Piracicaba River and Piranga River watersheds, despite its limitations and the lack of input data, making it an applicable tool in local contexts.

Blood-inspired random bit generation using microfluidics system

The development of random number generators (RNGs) using speckle patterns is pivotal for secure encryption key generation, drawing from the recent statistical properties identified in speckle-based imaging. Speckle-based RNG systems generate a sequence of random numbers through the unpredictable and reproducible nature of speckle patterns, ensuring a source of randomness that is independent of algorithms. However, to guarantee their effectiveness and reliability, these systems demand a meticulous and rigorous approach. In this study, we present a blood-inspired RNG system with a microfluidics device, designed to generate random numbers at a rate of 5.5 MHz and a high-speed of 1250 fps. This process is achieved by directing a laser beam through a volumetric scattering medium to procure speckle patterns. Additionally, designed microfluidic device requires only a minimal blood sample of 5 µl to capture these speckle patterns effectively. After implementing the two-pass tuple-output von Neumann debiasing algorithm to counteract statistical biases, we utilized the randomness statistical test suite from the National Institute of Standards and Technology for validation. The generated numbers successfully passed these tests, ensuring their randomness and unpredictability. Our blood-inspired RNG, utilizing whole blood, offers a pathway for affordable, high-output applications in fields like encryption, computer security, and data protection.

Polymorphisms in two key anthocyanic genes of clivia (Clivia miniata L.) reveal evidence of selection and possible association with flower pigmentation.

Members of the genus Clivia show considerable variation in flower pigmentation and morphology. Such variation is affected by mutations that emerge in candidate flower development genes over time. Besides population history, mutations can further illuminate the effects of demographic events in populations in addition to population genetic parameters including selection, recombination, and linkage disequilibrium (LD). The current study aimed to find sequence variants in 2 anthocyanin biosynthetic genes (DFR and bHLH) of Clivia miniata and use the data to assess population genetic factors from a random collection of orange/red- and yellow-flowered specimens. Overall, average nucleotide diversity in the 2 anthocyanin genes was moderate (π = 0.00646), whereas haplotypes differed significantly (Hd ≥ 0.9). Gene evolution was seemingly driven by mutations (CmiDFR) or recombinations (CmibHLH001). LD decayed swiftly within the analyzed gene regions and supported the feasibility of assessing trait-variant associations via the association/linkage mapping approach. In the end, most associations were found to be spurious, but 1 haplotype in CmibHLH001 showed a promising correlation to the orange/red flower phenotype in Clivia specimens. In all, the present study is the first to measure gene-level diversity in C. miniata-data that had never been reported so far. Furthermore, the study also identified allelic and haplotypic variants that may be beneficial in future association genetic studies of Clivia. Such studies, however, consider large diverse populations to control for statistical bias intrinsic to the analysis of small datasets.

Nonresponse Bias of Japanese Wage Statistics

This article analyzes unit nonresponse bias of establishment surveys, drawing on two major government wage surveys in Japan. We find that unit nonresponse is prevalent among establishments with fewer employees, in urban prefectures, and operating in the service industry. Further, while low-wage establishments are less likely to respond, the resulting bias is quantitatively negligible. Regarding establishment sampling of employees, we find that the sampled establishments randomly choose workers in an appropriate manner. Overall, with proper weighting, nonresponse bias is negligible in Japanese wage statistics to the extent that the mean estimate is concerned. In addition to nonresponse bias, we also assess potential bias due to limited coverage of the sampling population and find substantial bias at the tails of the wage distribution. In particular, excluding corporate executives and establishments with few employees results in an underestimation of wage inequality.

Comparison of the application of HBV and HEC-HMS hydrology models for accessing climate change in the upper Citarum Watershed, Indonesia

Hydrologically, the upper Citarum watershed was critically damaged. This study propose to compare HBV and HEC-HMS model performance for estimating discharge in upper Citarum watershed. Besides, this study wants to know changes in discharge in the future as a result of climate changes. Discharge simulation was carried out with the HBV and HEC HMS models in the period 2006-2007 as calibration and 2008 as validation. Meanwhile, future discharge changes are calculated in 2045 based on climate model projections output from CORDEX namely MPI, CNRM, EcEarth, and CSIRO. Model performance is calculated based on the value of statistical bias, NSE, and correlation. The results showed that HEC-HMS model has bias, NSE, and corellation value of 6.33, 0.57, and 0.8. Whereas, the HBV values are 3.67, 0.61, and 0.76. Therefore, based on NSE and bias value, the HBV model performs slightly better than the HEC-HMS in upper Citarum watershed. There are an increase in the daily discharge by RCP4.5 scenario. In contrast, the daily discharge by RCP8.5 decreased in upper Citarum.

Key performance indicators for hospital planning and construction: a systematic review and meta-analysis

PurposeThe successful implementation of hospital projects (HPs) tends to confront sundry challenges in the planning and construction (P&C) phases due to their complexity and particularity. Employing key performance indicators (KPIs) facilitates the monitoring of HPs to advance their successful delivery. This study aims to comprehensively investigate the KPIs for hospital planning and construction (HPC).Design/methodology/approachThe KPIs for HPC were identified through a systematic review. Then a comprehensive assessment of these KPIs was performed utilizing a meta-analysis method. In this process, basic statistical analysis, subgroup analysis, sensitive analysis and publication bias analysis were performed.FindingsResults indicate that all 27 KPIs identified from the literature are significant for executing HPs in P&C phases. Also, some unconventional performance indicators are crucial for implementing HPs, such as “Project monitoring effectiveness” and “Industry innovation and synergy,” as their high significance is reflected in this study. Despite the fact that the findings of meta-analysis are more trustworthy than those of individual studies, a high heterogeneity still exists in the findings. It highlights the inherent uncertainty in the construction industry. Hence, this study applied subgroup analysis to explore the underlying factors causing the high level of heterogeneity and used sensitive analysis to assess the robustness of the findings.Originality/valueThere is no consensus among the prior studies on KPIs for HPC specifically and their degree of significance. Additionally, few reviews in this field have focused on the reliability of the results. This study comprehensively assesses the KPIs for HPC and explores the variability and robustness of the results, which provides a multi-dimensional perspective for practitioners and the research community to investigate the performance of HPs during the P&C stages.

Multiple Imputation When Variables Exceed Observations: An Overview of Challenges and Solutions

Missing data are a prevalent problem in psychological research that can reduce statistical power and bias parameter estimates. These problems can be mostly resolved with multiple imputation, a modern missing data treatment that is increasingly used. Imputation, however, requires the number of variables to be smaller than the number of observations (i.e., non-missing values), and this number is often exceeded due to, e.g., large assessments, high missing data rates, the inclusion of variables predictive of missing values, and the inclusion of non-linear transformations. Even when the ratio of variables to observations meets the minimum requirement, convergence failure can occur in large, complex models. Specialized techniques have been developed to overcome the challenges related to having too many variables in an imputation model, but they are still relatively unknown by researchers in psychology. Accordingly, this paper presents an overview of four imputation techniques that can be used to reduce the number of predictors in an imputation model: item aggregation with scales and parcels, passive imputation, principal component analysis (PcAux) and two-fold fully conditional specification. The purpose, advantages, limitations, and applications of each method are discussed, along with recommendations and illustrative examples, with the aims of (1) understanding different imputation methods and (2) identifying methods that could be useful for one’s imputation problem.

Evaluation of daily crop reference evapotranspiration and sensitivity analysis of FAO Penman-Monteith equation using ERA5-Land reanalysis database in Sicily, Italy

Crop evapotranspiration (ET) is one of the most important components in many hydrological processes. The crop reference evapotranspiration (ETo) represents the atmospheric water demand in each crop type, development stage, and management practices. The Penman-Monteith equation in the version suggested by the Food and Agriculture Organization (FAO56-PM), is one of the most used methods to estimate ETo. In several regions of the world, meteorological observations are not always available. The most recent reanalysis database ERA5-Land, released in 2019, can be useful to overcome this limit. The database provides, with a spatial grid of 0.1° latitude and 0.1° longitude, several hourly climate data such as air temperature, dew point temperature, solar radiation, and wind speed components all at 2.0 m above the soil surface, except wind speed components at 10 m, useful to apply the FAO56-PM equation. The objective of this research is to assess the quality of ERA5-Land climate variables data to estimate daily ETo in Sicily, Italy. The effect of the weather station’s elevation associated with the statistical indicators was also evaluated to verify how the morphology affects the measurements. Finally, the sensitivity analysis of the FAO56-PM equation was carried out to identify which climate variables have the most influence on the ETo estimation. For the period 2006–2015, the comparison between air temperature, global solar radiation, wind speed, and relative air humidity, measured from 39 ground weather stations in Sicily, and ERA5-Land was carried out and then, through FAO56-PM equation daily ETo values were estimated using both databases. The statistical indicators Root Mean Square Error (RMSE) and Mean Bias Error (MBE) confirm the possibility of considering the ERA5-Land a suitable solution to estimate ETo. The sensitivity analysis showed that good ETo estimation depends mainly on the accuracy of the relative air humidity and air temperature data.

RSPSSL: A novel high-fidelity Raman spectral preprocessing scheme to enhance biomedical applications and chemical resolution visualization

Raman spectroscopy has tremendous potential for material analysis with its molecular fingerprinting capability in many branches of science and technology. It is also an emerging omics technique for metabolic profiling to shape precision medicine. However, precisely attributing vibration peaks coupled with specific environmental, instrumental, and specimen noise is problematic. Intelligent Raman spectral preprocessing to remove statistical bias noise and sample-related errors should provide a powerful tool for valuable information extraction. Here, we propose a novel Raman spectral preprocessing scheme based on self-supervised learning (RSPSSL) with high capacity and spectral fidelity. It can preprocess arbitrary Raman spectra without further training at a speed of ~1 900 spectra per second without human interference. The experimental data preprocessing trial demonstrated its excellent capacity and signal fidelity with an 88% reduction in root mean square error and a 60% reduction in infinite norm (L∞\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${L}_{{\\infty }}$$\\end{document}) compared to established techniques. With this advantage, it remarkably enhanced various biomedical applications with a 400% accuracy elevation (ΔAUC) in cancer diagnosis, an average 38% (few-shot) and 242% accuracy improvement in paraquat concentration prediction, and unsealed the chemical resolution of biomedical hyperspectral images, especially in the spectral fingerprint region. It precisely preprocessed various Raman spectra from different spectroscopy devices, laboratories, and diverse applications. This scheme will enable biomedical mechanism screening with the label-free volumetric molecular imaging tool on organism and disease metabolomics profiling with a scenario of high throughput, cross-device, various analyte complexity, and diverse applications.

Ibicus: a new open-source Python package and comprehensive interface for statistical bias adjustment and evaluation in climate modelling (v1.0.1)

Abstract. Statistical bias adjustment is commonly applied to climate models before using their results in impact studies. However, different methods based on a distributional mapping between observational and model data can change the simulated trends as well as the spatiotemporal and inter-variable consistency of the model, and are prone to misuse if not evaluated thoroughly. Despite the importance of these fundamental issues, researchers who apply bias adjustment currently do not have the tools at hand to compare different methods or evaluate the results sufficiently to detect possible distortions. Because of this, widespread practice in statistical bias adjustment is not aligned with recommendations from the academic literature. To address the practical issues impeding this, we introduce ibicus, an open-source Python package for the implementation of eight different peer-reviewed and widely used bias adjustment methods in a common framework and their comprehensive evaluation. The evaluation framework introduced in ibicus allows the user to analyse changes to the marginal, spatiotemporal and inter-variable structure of user-defined climate indices and distributional properties as well as any alteration of the climate change trend simulated in the model. Applying ibicus in a case study over the Mediterranean region using seven CMIP6 global circulation models, this study finds that the most appropriate bias adjustment method depends on the variable and impact studied, and that even methods that aim to preserve the climate change trend can modify it. These findings highlight the importance of use-case-specific selection of the method and the need for a rigorous evaluation of results when applying statistical bias adjustment.

Evidence of COVID-19 fatalities in Swedish neighborhoods from a full population study

The COVID-19 pandemic has highlighted a debate about whether marginalized communities suffered the disproportionate brunt of the pandemic’s mortality. Empirical studies addressing this question typically suffer from statistical uncertainties and potential biases associated with uneven and incomplete reporting. We use geo-coded micro-level data for the entire population of Sweden to analyze how local neighborhood characteristics affect the likelihood of dying with COVID-19 at individual level, given the individual’s overall risk of death. We control for several individual and regional characteristics to compare the results in specific communities to overall death patterns in Sweden during 2020. When accounting for the probability to die of any cause, we find that individuals residing in socioeconomically disadvantaged neighborhoods were not more likely to die with COVID-19 than individuals residing elsewhere. Importantly, we do find that individuals show a generally higher probability of death in these neighborhoods. Nevertheless, ethnicity is an important explanatory factor for COVID-19 deaths for foreign-born individuals, especially from East Africa, who are more likely to pass away regardless of residential neighborhood.

Possibilities and limits of modelling of long-range economic consequences of air pollution – A case study

Air pollution is the biggest environmental problem in modern societies, causing considerable health damage and requiring substantial financial resources for health care. The goal of the study is to demonstrate the adverse economic consequences of air pollution on example of a small, open Central European country, Hungary, and to provide quantified financial arguments for macroeconomic decision-making for the development of a long-term energy strategy. On the basis of the Cobb-Douglas production function and Solow-Swann model of dynamic economic systems a simple and robust model was constructed to estimate and predict economic losses, caused by the pollution. On base of results it is obvious, that on base of macroeconomic theory and combination of various, publicly available, quality-controlled statistical resources quantifiable models can be constructed to characterise the economic consequences of air pollution, but it should be taken into consideration, that the reliability of economic models considerably depends on their initial parameters and practical validity of assumptions, based on which the underlying economic theories were built. The most important economic burden of air pollution is caused by the loss of working–age population, resulting in a decrease of 4.1–9.4 % a year in Gross Domestic Product (GDP) in the next fifty years. The additional burden of health care costs amounts to 0.1 % of GDP. Reducing air pollution is not only a quality of life improvement but also an investment into the economic development. Notwithstanding of statistical biases it could be proven the importance of combination health economic and econometric methods in preparation of more efficient environmental-related socio-economic decisions.

Corridor-Level Auto Occupancy in Mobility Monitoring Efforts: A Crash-Based Approach

Vehicle occupancy (the number of persons per vehicle) is fundamental to evaluations of transportation system performance focused on the movement of people rather than solely vehicles. Historically, obtaining detailed estimates of occupancy has required manual (expensive) field data collection. This paper reports on a crash-based approach for estimating occupancy. Because some characteristics, such as the presence of multiple passengers aged 16 to 21, may affect the likelihood of crash involvement, the study examined ways to control possible crash bias using two methods. The first—heuristic bias correction, which entails synthesizing missing samples—is useful for smaller jurisdictions where there is no ground truth value readily available but an area-wide occupancy measure is needed. The second—statistical bias correction, which uses manual observations for calibration followed by the creation of a model—is suitable for obtaining occupancy at the corridor level. The approach is demonstrated with 12 years of fall and spring crash data for the Hampton Roads region of southeastern Virginia, U.S., representing 1.7 million people and 50,000 crashes. Testing showed estimated occupancies were within roughly 0.05 of true occupancy when corridor-specific calibration was performed. Further, the larger number of samples used for an area-wide occupancy means that heuristic bias correction is not always necessary: for cities with more than 200 samples, the difference between uncorrected and corrected occupancies was 0.02. For states that report the number of occupants for crashes with and without injury, this approach holds promise for estimating vehicle occupancy for both corridors and regions.

A new statistical downscaling approach for short‐term forecasting of summer air temperatures through a fusion of deep learning and spatial interpolation

AbstractReliable early forecasting of extreme summer air temperatures is essential for effectively managing and mitigating the socioeconomic damage caused by thermal disasters. Numerical weather prediction models have become valuable tools for forecasting air temperature; however, they incur high computational costs, resulting in coarse spatial resolution and systematic bias owing to imperfect parametrization. To address these problems, we developed a novel statistical downscaling and bias correction method (named DeU‐Net) for the maximum and minimum air temperature (Tmax and Tmin respectively) forecasts obtained from the Global Data Assimilation and Prediction System with a spatial resolution of 10 to 1.5 km over South Korea through the fusion of deep learning (i.e., U‐Net) and spatial interpolation. In this study, we used a methodology to decompose statistically downscaled Tmax and Tmin forecasts into temporal dynamics over South Korea and spatial fluctuations by pixels. When comparing the proposed DeU‐Net with the dynamical downscaling model (i.e., Local Data Assimilation and Prediction System) and support vector regression‐based statistical downscaling model at the seen and unseen stations for forecasting the next‐day Tmax and Tmin, DeU‐Net showed the highest spatial correlation and the lowest root‐mean‐square error in all cases. In a qualitative evaluation, DeU‐Net successfully produced a detailed spatial distribution most similar to the observations. A further comparison extending the forecast lead time to 7 days indicated that the proposed DeU‐Net is a better downscaling approach than support vector regression, regardless of the forecast lead time. These results demonstrate that bias‐corrected high spatial resolution air temperature forecasts with relatively long forecast lead times in summer can be effectively produced using the proposed model for operational forecasting.

Causal role of immune cells in generalized anxiety disorder: Mendelian randomization study.

Generalized anxiety disorder (GAD) is a prevalent emotional disorder that has received relatively little attention regarding its immunological basis. Recent years have seen the widespread use of high-density genetic markers such as SNPs or CNVs for genotyping, as well as the advancement of genome-wide association studies (GWAS) technologies, which have facilitated the understanding of immunological mechanisms underlying several major psychiatric disorders. Despite these advancements, the immunological basis of GAD remains poorly understood. In light of this, we aimed to explore the causal relationship between immune cells and the disease through a Mendelian randomization study. The summary information for GAD (Ncase=4,666, Ncontrol=337,577) was obtained from the FinnGen dataset. Summary statistics for the characterization of 731 immune cells, including morphological parameters (MP=32), median fluorescence intensity (MFI=389), absolute cells (AC=118), and relative cells (RC=192), were derived from the GWAS catalog. The study involved both forward MR analysis, with immune cell traits as the exposure and GAD as the outcome, and reverse MR analysis, with GAD as the exposure and immune cell traits as the outcome. We performed extensive sensitivity analyses to confirm the robustness, heterogeneity, and potential multi-biological effects of the study results. Also, to control for false positive results during multiple hypothesis testing, we adopted a false discovery rate (FDR) to control for statistical bias due to multiple comparisons. After FDR correction, GAD had no statistically significant effect on immunophenotypes. Several phenotypes with unadjusted low P-values are worth mentioning, including decreased PB/PC levels on B cells(β=-0.289, 95%CI=0.044~0.194, P=0.002), reduced PB/PC AC in GAD patients (β=-0.270, 95% CI=0.77~0.92, P=0.000), and diminished PB/PC on lymphocytes (β=-0.315, 95% CI=0.77~0.93, P=0.001). GAD also exerted a causal effect on CD27 on IgD-CD38br (β=-0.155,95%CI=0.78~0.94,P=0.002), CD20-%B cell (β= -0.105,95% CI=0.77~0.94, P=0.002), IgD-CD38br%lymphocyte(β=-0.305, 95%CI=0.79~0.95, P=0.002), FSC-A level on granulocytes (β=0.200, 95%CI=0.75~0.91, P=8.35×10-5), and CD4RA on TD CD4+(β=-0.150, 95% CI=0.82~1.02, P=0.099). Furthermore, Two lymphocyte subsets were identified to be significantly associated with GAD risk: CD24+ CD27+ B cell (OR=1.066,95%CI=1.04~1.10,P=1.237×10-5),CD28+CD4+T cell (OR=0.927, 95%CI=0.89~0.96, P=8.085×10-5). The study has shown the close association between immune cells and GAD through genetic methods, thereby offering direction for future clinical research.

Novel approach to the global analysis of proton form factors in elastic electron-proton scattering

We present a method for the global analysis of elastic electron-proton scattering when combining data sets from experiments with different overall normalization uncertainties. The method is a modification of one employed by the NNPDF collaboration in the fitting of parton distribution data. This method is an alternative to the ‘penalty trick’ method traditionally employed in global fits to proton electric and magnetic form factors, while avoiding the statistical biases inherent in that approach. We discuss issues that arise when extending the method to nonlinear models. For data with Q2>1GeV2 we find relatively minor differences to traditional model fits when the normalization uncertainties from different experiments are correctly accounted for. We discuss implications of this method for the well-known discrepancy between the form factor ratio GE/GM extracted from the Rosenbluth and polarization transfer techniques. Published by the American Physical Society 2024

Evaluation of GSMaP Version 8 Precipitation Products on an Hourly Timescale over Mainland China

A thorough evaluation of the recently released Global Satellite Mapping of Precipitation (GSMaP) is critical for both end-users and algorithm developers. In this study, six products from three versions of GSMaP version 8, including real time (NOW-R and NOW-C), near real time (NRT-R and NRT-C), and post-real time (MVK-R and MVK-C), are systematically and quantitatively evaluated based on time-by-time observations from 2167 stations in mainland China. Among each version, both products with and without gauge correction are adopted to detect the gauge correction effect. Error quantification is carried out on an hourly timescale. Three common statistical indices (i.e., correlation coefficient (CC), relative bias (RB), and root mean square error (RMSE)) and three event detection capability indices (i.e., probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI)) were adopted to analyze the inversion errors in precipitation amount and precipitation event frequency across the various products. Additionally, in this study, we examine the dependence of GSMaP errors on rainfall intensity and elevation. The following main results can be concluded: (1) MVK-C exhibits the best ability to retrieve rainfall on the hourly timescale, with higher CC values (0.31 in XJ to 0.47 in SC), smaller RMSE values (0.14 mm/h in XJ to 0.99 mm/h in SC), and lower RB values (−4.78% in XJ to 16.03% in NC). (2) Among these three versions, the gauge correction procedure plays a crucial role in reducing errors, especially in the post-real-time version. After being corrected, MVK-C demonstrates an obvious CC value improvement (>0.3 on the hourly timescale) in various sub-regions, increasing the percentage of sites with CC values above 0.5 from 0.03% (MVK-R) to 28.47% (MVK-C). (3) GSMaP products generally exhibit error dependencies on precipitation intensity and elevation, particularly in areas with drastic elevation changes (such as 1200–1500 m and 3000–3300 m), where the accuracy of satellite precipitation estimates is significantly affected. (4) CC values decreased with an increasing rainfall intensity; RB and RMSE values increased with an increasing rainfall intensity. The results of this study may be helpful for algorithm developers and end-users and provide a scientific reference for different hydrological applications and disaster risk reduction.