Null Value Research Articles

Pengaruh Penggunaan Alat Peraga Pembentukan Urine Terhadap Kemampuan Literasi Sains Siswa SD

In Indonesia, according to the 2022 PISA results, students' scientific literacy is in a low category, equivalent to the results obtained in 2006, and is ranked 68th out of 81 countries. Based on existing problems, innovation is needed to increase scientific literacy, one of which is using science teaching aids. This research examines the effect of using urine formation teaching aids on the scientific literacy abilities of grade IV students at SDN 2 Riang Bandung. In this research, the method used is a quantitative method in which the research experiments with a one-group pretest-posttest design, and the data collection techniques are in the form of observation, tests, and documentation. The data processing technique was obtained through test instrument testing and hypothesis testing. From processing this data, the normality test value was obtained, which had a pretest value of 0.171, then the post-test value was 0.051, which stated that the value was normal. Then the homogeneity test which has a pretest value of 0.075 which is explained that the value is greater than 0.05 which is stated that the value is homogeneous. Finally, the paired sample t-test has a pretest value of 0.000, which means the value is smaller than 0.05. Thus it can be stated that the null hypothesis value (Ho) is rejected and the hypothesis (Ha) is accepted. Hypothesis testing proves that using urine formation props on the scientific literacy skills of elementary school students has a great influence on class IV of SDN 2 Riang Bandung.

Enhanced Deep Autoencoder-Based Reinforcement Learning Model with Improved Flamingo Search Policy Selection for Attack Classification

Intrusion detection has been a vast-surveyed topic for many decades as network attacks are tremendously growing. This has heightened the need for security in networks as web-based communication systems are advanced nowadays. The proposed work introduces an intelligent semi-supervised intrusion detection system based on different algorithms to classify the network attacks accurately. Initially, the pre-processing is accomplished using null value dropping and standard scaler normalization. After pre-processing, an enhanced Deep Reinforcement Learning (EDRL) model is employed to extract high-level representations and learn complex patterns from data by means of interaction with the environment. The enhancement of deep reinforcement learning is made by associating a deep autoencoder (AE) and an improved flamingo search algorithm (IFSA) to approximate the Q-function and optimal policy selection. After feature representations, a support vector machine (SVM) classifier, which discriminates the input into normal and attack instances, is employed for classification. The presented model is simulated in the Python platform and evaluated using the UNSW-NB15, CICIDS2017, and NSL-KDD datasets. The overall classification accuracy is 99.6%, 99.93%, and 99.42% using UNSW-NB15, CICIDS2017, and NSL-KDD datasets, which is higher than the existing detection frameworks.

Counternull Sets in Randomized Experiments

Consider a study whose primary results are “not statistically significant”. How often does it lead to the following published conclusion that “there is no effect of the treatment/exposure on the outcome”? We believe too often and that the requirement to report counternull values could help to avoid this! In statistical parlance, the null value of an estimand is a value that is distinguished in some way from other possible values, for example a value that indicates no difference between the general health status of those treated with a new drug versus a traditional drug. A counternull value is a nonnull value of that estimand that is supported by the same amount of evidence that supports the null value. Of course, such a definition depends critically on how “evidence” is defined. Here, we consider the context of a randomized experiment where evidence is summarized by the randomization-based p-value associated with a specified sharp null hypothesis. Consequently, a counternull value has the same p-value from the randomization test as does the null value; the counternull value is rarely unique, but rather comprises a set of values. We explore advantages to reporting a counternull set in addition to the p-value associated with a null value; a first advantage is pedagogical, in that reporting it avoids the mistake of implicitly accepting a not-rejected null hypothesis; a second advantage is that the effort to construct a counternull set can be scientifically helpful by encouraging thought about nonnull values of estimands. Two examples are used to illustrate these ideas.

SURG-36. ACCURACY OF THE ROSA ONE BRAIN ROBOTIC PLATFORM FOR THE PLACEMENT OF NEUROBLATE AND VISUALASE LASER INTERSTITIAL THERMAL THERAPY PROBES

Abstract Laser interstitial thermal therapy (LITT) is a minimally invasive treatment option for patients with intracranial lesions. The success of LITT is dependent on accurate placement of the laser probe in target tissue. Placement can be facilitated by various stereotactic methods, including the Robotic Surgical Assistant (ROSA) ONE Brain robotic platform. The aim of this study is to examine the accuracy of stereotactic probe placement utilizing the ROSA robot. Primary outcomes were entry distance, target distance, and entry angle error. Accuracy differences between two major LITT platforms, the Neuroblate (Monteris Medical) and Visualase Systems, were compared. A prospective institutional database was used to identify all patients who underwent LITT for intracranial tumors utilizing the ROSA robot between 2013-2023. The sign test was used to compare error values to test their difference from a null value of 0. Spearman correlation was used to assess the relationship between angle of error and trajectory length. Kuskal-Wallis test was used to compare errors and trajectory length between the two companies. Of the 105 patients who underwent LITT during the study period, 337 probes were placed. For Visualase probes, median entry distance (1.1mm, IQR=0.4-2.0), target distance (1.6mm, IQR =0.9-2.4), and entry angle error (1.5°, IQR=0.9-2.5), were significantly different than zero (p=<.0001). For Neuroblate probes, median entry distance (1.5mm, IQR=1.0-2.1), target distance (1.8mm, IQR=1.1-2.4), and entry angle error (1.8°, IQR=1.1-2.5) were significantly different than zero (p=<.0001). When comparing laser systems, there was a significant difference in median entry distance error with Neuroblate having a higher error (1.5mm, IQR=1.0-2.1) than Visualase (1.1mm IQR=0.4-2.0, p=0.0041). Stereotactic placement of LITT probes utilizing the ROSA robotic system was not able to achieve perfect alignment with the planned trajectory. When comparing the systems, Visualase probes were placed more accurately at the entry point than Neuroblate probes.

Increased Risk of Alzheimer's Disease With Glycemic Variability: A Systematic Review and Meta-Analysis.

There is increasing evidence that establishes a connection between fluctuations in glucose metabolism and the onset of Alzheimer's disease (AD). Current research supports the notion that this metabolic imbalance significantly affects cognitive health. However, the specific mechanisms through which these fluctuations influence neurodegeneration, eventually leading to AD, require further exploration. This study aims to determine the risk of AD among individuals with fluctuations in blood glucose levels,with or without type 2 diabetes mellitus (T2DM), further providing the most recent and thorough overview of the evidence in this area. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, a thorough search was carried out utilizing particular phrases in the PubMed, Elsevier, Research Gate, and Cochrane databases: ("glucose variability" or "glycemic variability" or "glucose fluctuation" or "glucose instability" or "glycemic fluctuation") and ("Alzheimer's disease" or "Alzheimer disease" or "Alzheimer dementia" or "Alzheimer"). Studies published between January 2014 to January 2024, written in English, and examining the relationship between glucose variability and AD, were included. The outcomes measured were risk of cognitive impairment and AD, cognitive performance, and risk of AD. The results of the literature search produced 142 records, with six studies meeting the eligibility criteria. Parameters for glycemic variability included fasting plasma glucose (FPG) variability, glycated hemoglobin (HbA1c) variability, FPG variability independent of the mean (VIM), FPG coefficient of variation (CV), and FPG standard deviation (SD). The studies revealed a positive correlation between glycemic variability and the risk of AD over time, and the findings indicated that maintaining stable glycemic levels may reduce the risk of cognitive decline among individuals with or without T2DM. Due to the small number of studies that are currently available, despite a calculated relative risk of 2.65 indicating a higher risk of AD among subjects with glycemic variability, the inclusion of the null value in the confidence interval (0.61-11.45) renders these findings not statistically significant. This comprehensive review demonstrated that, in people with or without diabetes, glycemic variability influences cognitive decline and the risk of AD. The studies demonstrated a correlation between higher fluctuations in glucose levels and an increased risk of AD, highlighting the importance of managing blood sugar levels to mitigate dementia risks. Despite these strong associations, the actual incidence rates of AD in the studied populations remained relatively low. Overall, the results were not statistically significant. Further research is recommended to explore the risk of AD among individuals with fluctuations in blood glucose levels.

B-011 Intermethod Comparison Verification between Atellica Solution® and CI® Analyzers for Amylase and Glucose Methods

Abstract Background The objective of this study is to verify if the results of the Amylase and Glucose methods, measured by two different analyzers: Atellica Solution® and CI® (Siemens Healthineers), are interchangeable. Methods To carry out the intermethod comparison study of the Amylase and Glucose methods, 40 patient samples were selected for each assay, with concentrations across the entire measurement range. Statistical analysis was performed using Method Validator v.19 software, through Bland-Altman mean difference analysis and Passing-Bablok regression. Statistics are expressed with their 95% confidence intervals (CI). Results AMYLASE: After analyzing the Bland-Altman differences, a statistically significant systematic error was found, as the mean difference between the results obtained by Atellica Solution® and CI® was 28.1 with a 95% CI that does not include the null value, with higher results in CI®. Regarding the Passing Bablok analysis, systematic differences, both proportional and constant, were observed as the confidence interval of the intercept does not include the zero value, and that of the slope does not include the value 1.GLUCOSE: After Bland-Altman analysis, a systematic error was found, as the mean difference between the results obtained by Atellica Solution® and CI® was 2.1, with slightly higher results in CI®. Regarding the Passing Bablok analysis, there were no systematic differences of constant type since the confidence interval of the intercept includes the zero value, and a slight proportional systematic error based on the slope as its 95% CI does not include the value 1. Conclusions After evaluating the results, it is concluded that the Atellica Solution® and CI® analyzers are not interchangeable for the Amylase assay, so new reference values should be established. For the Glucose assay, both analyzers are interchangeable as the observed difference does not exceed our quality specification.

A-219 Method Intercomparison Study between Atellica Solution® and CI® Analyzers for Total Cholesterol and Triglycerides

Abstract Background Method intercomparison is an essential requirement that every Laboratory must perform when considering a method or instrument change for a specific analyte. Objective verify if the results of two analytical techniques (Total Cholesterol and Triglycerides) measured by Atellica Solution® and CI® analyzers (Siemens Healthineers) are interchangeable. Methods 40 patient samples were selected for each assay, with results distributed across the entire measurement range and analyzed by both instruments. Estatistical program Method Validator was used. To determine the degree of agreement between both instruments, Bland-Altman difference analysis and Passing-Bablok regression were performed. Statistics are expressed with their 95% confidence intervals. Results Total Cholesterol: After analyzing Bland-Altman differences, a systematic error was found as the mean difference between the results obtained by Atellica Solution® and CI 1900® was 13.8 with a 95% CI that does not include the null value. Regarding Passing Bablok analysis, a constant systematic difference was observed as the confidence interval of the intercept does not include the zero value, and also a proportional difference based on the 95% CI of the slope that does not include the value 1.• Triglycerides: After analyzing the Bland-Altman differences, a systematic error was found as the mean difference between the results obtained by Atellica Solution® and CI 1900® was -15.6, with higher results in Atellica Solution®. Regarding the Passing Bablok analysis, constant systematic differences were observed as the confidence interval of the intercept does not include the zero value, and also proportional differences based on the slope, whose 95% CI does not include the value 1 Conclusions It´s concluded that Atellica Solution® and CI® analyzers are not interchangeable for Total Cholesterol and Triglycerides assays due to the presence of a constant systematic error. To use them on both instruments, it would be advisable to modify the reference values for both methods.

B-009 Verification of Result Interchangeability Between Two Analyzers in the Determination of Uric Acid and Urea

Abstract Background Uric acid and urea are two parameters widely used in the diagnosis and management of renal pathology. In our laboratory, they are determined using Atellica Solution® analyzers (Siemens Healthineers). After validating a new Siemens equipment, Atellica CI®, it is essential to ensure result interchangeability in our laboratory accredited by ISO 15189:2012. Objectives evaluate the interchangeability of results for uric acid and urea measured by Atellica Solution® and CI® analyzers. Methods A total of 40 serum samples from patients with uric acid values between 1.3 and 15.2 mg/dL, and another 40 serum samples from patients with urea values between 7 and 142 mg/dL were processed in parallel. Statistical analysis of the results was performed using Passing-Bablok regression and Bland Altman mean difference analysis with Method Validator Version 1.19. Results are expressed with their 95% confidence interval. Results Uric Acid: After analyzing Bland-Altman differences, a statistically significant slight systematic error was found, as the 95% CI does not include 0, with slightly higher results in CI 1900®. Regarding Passing-Bablok regression analysis, there were no systematic errors of constant type (since the 95% CI of the intercept includes 0) but a discreet significant proportional systematic error was observed (the confidence interval of the slope does not include 1).Urea: In Bland-Altman analysis, a discreet systematic error was observed as the 95% CI of the mean difference does not include the null value. Regarding Passing-Bablok regression analysis, there were no systematic errors of constant type (since the 95% CI of the intercept includes 0) or proportional type (since the 95% CI of the slope includes 1), indicating interchangeability between both analyzers. Conclusions The results obtained by both Atellica Solution® and CI® analyzers for uric acid and urea assays are interchangeable since the statistically significant differences observed can be considered analytically and clinically insignificant.

Arsenic exposure and measures of glucose tolerance in Bangladeshi adults: A cross-sectional study.

Arsenic has been associated with diabetes and impaired glucose tolerance in many studies, although some reports have shown null findings. We conducted a cross-sectional study among 300 adults in Bangladesh. Participants were randomly selected from a roster of 1800 people who previously participated in studies of arsenic and skin lesions. We measured fasting glucose and insulin levels. We assessed drinking water arsenic concentration using graphite furnace atomic absorption spectrophotometry (GF-AAS) and toenail arsenic concentration using inductively coupled mass spectrometry (ICP-MS). We ran covariant-adjusted, linear regression and spline models to examine associations of arsenic concentrations with the homeostatic model assessment of insulin resistance (HOMA-IR), a marker of insulin resistance, and HOMA of beta-cell function (HOMA-β), a marker of beta-cell function. Among 285 participants with complete data, the median (IQR) arsenic concentration was 4.0 (6.9) μg/g in toenails and 39.0 (188.5) μg/L in drinking water. Arsenic concentrations were not associated with insulin resistance or beta-cell function. HOMA-IR was 0.67% lower and HOMA-β was 0.28% lower per µg/g increment in toenail arsenic, but these effect estimates were small, and confidence intervals crossed the null value. Although arsenic exposure has been associated with diabetes, we found no evidence of an adverse effect on insulin resistance or beta-cell function.

Hybridization of Machine Learning Model with Bee Colony based Feature Selection for Medical Data Classification

Nowadays, an important count of biomedical data is created continuously in several biomedical equipment and experiments because of quick technical enhancements in biomedical science. The study of clinical and health data is vital to enhance the analysis precision, prevention, and treatment. Initial analysis and treatment are extremely important approaches for preventing deaths in many diseases. Accordingly, the data mining and machine learning (ML) approaches are helpful tools for utilizing minimization error and for providing helpful data for analysis. But the data obtained in digital machines takes higher dimensionality, and not every data attained in digital machines is significant to specific diseases. This article develops an artificial bee colony-based feature selection with optimal hybrid ML model for medical data classification (ABCFS-OHML) technique. The presented ABCFS-OHML technique mainly aims to identify and classify the presence of disease using medical data. To attain this, the presented ABCFS-OHML technique initially pre-processes the input data in two ways namely null value removal and data transformation. Furthermore, the presented ABCFS-OHML technique uses ABCFS model for the choice of effectual subset of features. At last, root means square propagation with convolutional neural network-Hop field neural network (CNN-HFNN) model for classification purposes. The usage of RMSProp optimizer assists in attaining optimal hyperparameter selection of the CNN-HFNN method. The performance validation of the ABCFS-OHML technique takes place using three medical datasets. The comparison study reported that the ABCFS-OHML technique has accurately classified the medical data over other recent approaches.

Comparative Efficacy of 21 Treatment Strategies for Resectable Pancreatic Cancer: A Network Meta-Analysis.

The primary treatment for operable pancreatic cancer (PC) involves surgery followed by adjuvant therapy. Nevertheless, perioperative or neoadjuvant chemotherapy (CT) may be used to mitigate the likelihood of recurrence and mortality. This network meta-analysis (NMA) assesses the comparative efficacy of various treatment approaches for resectable PC. A thorough search was carried out on January 31, 2023, encompassing PubMed/MEDLINE, Cochrane Library, and Embase databases. We incorporated randomized clinical trials (RCTs) that compared surgical interventions with or without (neo)adjuvant or perioperative therapies for operable PC. We conducted a fixed-effects Bayesian NMA. We presented the effect sizes in terms of hazard ratios (HRs) for overall survival (OS) along with 95% credible intervals (95% CrIs). The treatment was deemed statistically superior when the 95% credible interval (CrI) did not encompass a null value (hazard ratio < 1). Treatment rankings were established based on the surface under the cumulative ranking curve (SUCRA). A total of 24 studies were incorporated, comparing 21 treatments with surgery in isolation. Eleven treatments showed superior efficacy compared to surgery alone, with HRs ranging from 0.38 for perioperative treatments to 0.73 for adjuvant 5-fluorouracil. After the exclusion of studies conducted in Asia, it was found that the perioperative regimen of gemcitabine combined with nab-paclitaxel was the most effective regimen (SUCRA, p = 0.99). The findings endorse the utilization of perioperative CT, especially multi-agent CT, as the favored intervention for operable PC in Western nations.

Gestational urinary concentrations of glyphosate and aminomethylphosphonic acid in relation to preterm birth: the MIREC study.

Few high-quality studies have evaluated associations between urinary glyphosate or its environmental degradate (aminomethylphosphonic acid (AMPA)] and preterm birth (PTB). To quantify associations between urinary glyphosate and AMPA and preterm birth in the pan-Canadian Maternal-Infant Research on Environmental Chemicals (MIREC) study and determine if associations differ by fetal sex. We measured first trimester urinary glyphosate and AMPA concentrations in MIREC participants who were recruited between 2008-2011 from 10 Canadian cities. Of the 1880 participants whose first trimester urine samples were analyzed for glyphosate or AMPA, 1765 delivered a singleton, live birth. Our primary outcome was preterm birth (PTB) defined as births occurring between 20 and <37 weeks. Secondary outcomes were spontaneous preterm births (sPTB) and gestational age. We modelled the hazard of PTB and sPTB using discrete time survival analysis with multivariable logistic regression to calculate odds ratios (OR). We used multivariable linear regression models to quantify associations between analytes and gestational age. To assess effect modification by fetal sex, we stratified all models and calculated interaction terms. In the logistic regressions models we additionally calculated the relative excess risk due to interaction. Six percent (n = 106) of the study population delivered preterm, and 4.7% (n = 83) had a spontaneous preterm birth. Median specific-gravity standardized concentrations of glyphosate and AMPA were 0.25 and 0.21 µg/L. Associations between both glyphosate or AMPA and PTB, sPTB, and gestational age centered around the null value. The adjusted ORs of PTB for each doubling of glyphosate and AMPA concentrations were 0.98 (95% CI: 0.94, 1.03) and 0.99 (95% CI: 0.92, 1.06) respectively. We observed no evidence of differences by fetal sex. In this Canadian pregnancy cohort, neither glyphosate nor AMPA urinary concentrations was associated with PTB or reduced gestational length.

Sustainability Indicators to MSW Treatment Assessment: The Rio de Janeiro Case Study

The Brazilian Policy foresees the waste management hierarchy, according to which energy reuse from waste is preferred to final disposal. However, less than 0.2% of the country’s waste goes to energy production. This paper proposes sustainability indicators to support the decision to choose the best process to treat municipal solid waste (MSW) through bioenergy generation technologies. Then, we conduct a case study for Rio de Janeiro. Incineration and gasification were not economically feasible—despite TRL 9 and 8. However, the projects presented a null net present value by increasing the gate fee to 94.69 and 255.39 USD/ton of MSW, respectively. The social indicators (job creation, salary increase with the absorption of waste pickers, population served, reduction in MSW sent to landfill) did not indicate the best technology. The results of the environmental indicators for incineration and gasification were, respectively, 0.45 and 0.37 t CO2eq/tMSW for GWP, 1.49 and 1.23 MWh/tMSW for energy intensity, 1.24 and 6.14 m3/tMSW for water intensity, 39.3 and 27.9 m2/tMSW for land use and 0.135 and 0.088 t SO2eq/tMSW for acidification. Gasification presented better results on 60% of the environmental indicators. However, incineration scored better in the important ones, water and energy intensities, in addition to the technical–economic aspect.

An efficient learning based approach for automatic record deduplication with benchmark datasets

With technological innovations, enterprises in the real world are managing every iota of data as it can be mined to derive business intelligence (BI). However, when data comes from multiple sources, it may result in duplicate records. As data is given paramount importance, it is also significant to eliminate duplicate entities towards data integration, performance and resource optimization. To realize reliable systems for record deduplication, late, deep learning could offer exciting provisions with a learning-based approach. Deep ER is one of the deep learning-based methods used recently for dealing with the elimination of duplicates in structured data. Using it as a reference model, in this paper, we propose a framework known as Enhanced Deep Learning-based Record Deduplication (EDL-RD) for improving performance further. Towards this end, we exploited a variant of Long Short Term Memory (LSTM) along with various attribute compositions, similarity metrics, and numerical and null value resolution. We proposed an algorithm known as Efficient Learning based Record Deduplication (ELbRD). The algorithm extends the reference model with the aforementioned enhancements. An empirical study has revealed that the proposed framework with extensions outperforms existing methods.

Horizon phase spaces in general relativity

We derive a prescription for the phase space of general relativity on two intersecting null surfaces using the null initial value formulation. The phase space allows generic smooth initial data, and the corresponding boundary symmetry group is the semidirect product of the group of arbitrary diffeomorphisms of each null boundary which coincide at the corner, with a group of reparameterizations of the null generators. The phase space can be consistently extended by acting with half-sided boosts that generate Weyl shocks along the initial data surfaces. The extended phase space includes the relative boost angle between the null surfaces as part of the initial data.We then apply the Wald-Zoupas framework to compute gravitational charges and fluxes associated with the boundary symmetries. The non-uniqueness in the charges can be reduced to two free parameters by imposing covariance and invariance under rescalings of the null normals. We show that the Wald-Zoupas stationarity criterion cannot be used to eliminate the non-uniqueness. The different choices of parameters correspond to different choices of polarization on the phase space. We also derive the symmetry groups and charges for two subspaces of the phase space, the first obtained by fixing the direction of the normal vectors, and the second by fixing the direction and normalization of the normal vectors. The second symmetry group consists of Carrollian diffeomorphisms on the two boundaries.Finally we specialize to future event horizons by imposing the condition that the area element be non-decreasing and become constant at late times. For perturbations about stationary backgrounds we determine the independent dynamical degrees of freedom by solving the constraint equations along the horizons. We mod out by the degeneracy directions of the presymplectic form, and apply a similar procedure for weak non-degeneracies, to obtain the horizon edge modes and the Poisson structure. We show that the area operator of the black hole generates a shift in the relative boost angle under the Poisson bracket.

Association of ambient air pollution and pesticide mixtures on respiratory inflammatory markers in agricultural communities

Air pollution exposure is associated with adverse respiratory health outcomes. Evidence from occupational and community-based studies also suggests agricultural pesticides have negative health impacts on respiratory health. Although populations are exposed to multiple inhalation hazards simultaneously, multidomain mixtures (e.g. environmental and chemical pollutants of different classes) are rarely studied. We investigated the association of ambient air pollution-pesticide exposure mixtures with urinary leukotriene E4 (LTE4), a respiratory inflammation biomarker, for 75 participants in four Central California communities over two seasons. Exposures included three criteria air pollutants estimated via the Community Multiscale Air Quality model (fine particulate matter, ozone, and nitrogen dioxide) and urinary metabolites of organophosphate (OP) pesticides (total dialkyl phosphates (DAPs), total diethyl phosphates (DE), and total dimethyl phosphates (DM)). We implemented multiple linear regression models to examine associations in single pollutant models adjusted for age, sex, asthma status, occupational status, household member occupational status, temperature, and relative humidity, and evaluated whether associations changed seasonally. We then implemented Bayesian kernel machine regression (BKMR) to analyse these criteria air pollutants, DE, and DM as a mixture. Our multiple linear regression models indicated an interquartile range (IQR) increase in total DAPs was associated with an increase in urinary LTE4 in winter (β: 0.04, 95% CI: [0.01, 0.07]). Similarly, an IQR increase in total DM was associated with an increase in urinary LTE4 in winter (β:0.03, 95% CI: [0.004, 0.06]). Confidence intervals for all criteria air pollutant effect estimates included the null value. BKMR analysis revealed potential non-linear interactions between exposures in our air pollution-pesticide mixture, but all confidence intervals contained the null value. Our analysis demonstrated a positive association between OP pesticide metabolites and urinary LTE4 in a low asthma prevalence population and adds to the limited research on the joint effects of ambient air pollution and pesticides mixtures on respiratory health.

The HDI + ROPE decision rule is logically incoherent but we can fix it.

The Bayesian highest-density interval plus region of practical equivalence (HDI + ROPE) decision rule is an increasingly common approach to testing null parameter values. The decision procedure involves a comparison between a posterior highest-density interval (HDI) and a prespecified region of practical equivalence. One then accepts or rejects the null parameter value depending on the overlap (or lack thereof) between these intervals. Here, we demonstrate, both theoretically and through examples, that this procedure is logically incoherent. Because the HDI is not transformation invariant, the ultimate inferential decision depends on statistically arbitrary and scientifically irrelevant properties of the statistical model. The incoherence arises from a common confusion between probability density and probability proper. The HDI + ROPE procedure relies on characterizing posterior densities as opposed to being based directly on probability. We conclude with recommendations for alternative Bayesian testing procedures that do not exhibit this pathology and provide a "quick fix" in the form of quantile intervals. This article is the work of the authors and is reformatted from the original, which was published under a CC-BY Attribution 4.0 International license and is available at https://psyarxiv.com/5p2qt/. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

ABO and Rhesus blood groups and multiple health outcomes: an umbrella review of systematic reviews with meta-analyses of observational studies

BackgroundNumerous studies have been conducted to investigate the relationship between ABO and Rhesus (Rh) blood groups and various health outcomes. However, a comprehensive evaluation of the robustness of these associations is still lacking.MethodsWe searched PubMed, Web of Science, Embase, Scopus, Cochrane, and several regional databases from their inception until Feb 16, 2024, with the aim of identifying systematic reviews with meta-analyses of observational studies exploring associations between ABO and Rh blood groups and diverse health outcomes. For each association, we calculated the summary effect sizes, corresponding 95% confidence intervals, 95% prediction interval, heterogeneity, small-study effect, and evaluation of excess significance bias. The evidence was evaluated on a grading scale that ranged from convincing (Class I) to weak (Class IV). We assessed the certainty of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation criteria (GRADE). We also evaluated the methodological quality of included studies using the A Measurement Tool to Assess Systematic Reviews (AMSTAR). AMSTAR contains 11 items, which were scored as high (8–11), moderate (4–7), and low (0–3) quality. We have gotten the registration for protocol on the PROSPERO database (CRD42023409547).ResultsThe current umbrella review included 51 systematic reviews with meta-analysis articles with 270 associations. We re-calculated each association and found only one convincing evidence (Class I) for an association between blood group B and type 2 diabetes mellitus risk compared with the non-B blood group. It had a summary odds ratio of 1.28 (95% confidence interval: 1.17, 1.40), was supported by 6870 cases with small heterogeneity (I2 = 13%) and 95% prediction intervals excluding the null value, and without hints of small-study effects (P for Egger’s test > 0.10, but the largest study effect was not more conservative than the summary effect size) or excess of significance (P < 0.10, but the value of observed less than expected). And the article was demonstrated with high methodological quality using AMSTAR (score = 9). According to AMSTAR, 18, 32, and 11 studies were categorized as high, moderate, and low quality, respectively. Nine statistically significant associations reached moderate quality based on GRADE.ConclusionsOur findings suggest a potential relationship between ABO and Rh blood groups and adverse health outcomes. Particularly the association between blood group B and type 2 diabetes mellitus risk.

Reservoir temperature prediction based on characterization of water chemistry data—case study of western Anatolia, Turkey

Reservoir temperature estimation is crucial for geothermal studies, but traditional methods are complex and uncertain. To address this, we collected 83 sets of water chemistry and reservoir temperature data and applied four machine learning algorithms. These models considered various input factors and underwent data preprocessing steps like null value imputation, normalization, and Pearson coefficient calculation. Cross-validation addressed data volume issues, and performance metrics were used for model evaluation. The results revealed that our machine learning models outperformed traditional fluid geothermometers. All machine learning models surpassed traditional methods. The XGBoost model, based on the F-3 combination, demonstrated the best prediction accuracy with an R2 of 0.9732, while the Bayesian ridge regression model using the F-4 combination had the lowest performance with an R2 of 0.8302. This study highlights the potential of machine learning for accurate reservoir temperature prediction, offering geothermal professionals a reliable tool for model selection and advancing our understanding of geothermal resources.

Psychosocial working conditions as determinants of slips and lapses, and poor social interactions with patients among medical assistants in Germany: A cohort study.

We sought to examine the relationship of unfavorable psychosocial working conditions with slips and lapses and poor patient interaction as well as potential intermediate factors among medical assistants (MAs) in Germany based on prospective data. We used data from 408 MAs from a 4-year cohort study (follow-up: 2021). At baseline, psychosocial working conditions were assessed by the established effort-reward-imbalance questionnaire and a MA-specific questionnaire with 7 subscales. Frequency of slips and lapses (e.g., pertaining to measurements and documentation) and the quality of patient interactions (e.g., unfriendliness or impatience) due to work stress were assessed at follow-up with three items each (potential score ranges = 3-15). Potential intermediate factors at baseline included work engagement (i.e., vitality and dedication (UWES)), work satisfaction (COPSOQ), anxiety (GAD-2), depressiveness (PHQ-2), and self-reported health. We ran multivariable linear regression using z-standardized exposures to estimate unstandardized coefficients (B) and 95% confidence intervals (CI). Potential intermediate factors were added separately to the regression models. Attenuation of the association between exposure and outcome toward the null value (B = 0) was interpreted as mediation. High reward and lack of resources were weakly associated with the frequency of slips and lapses (the Bs were -0.18 and 0.23, respectively; p<0.05), with little evidence of mediation. With the exception of low recognition, all unfavorable psychosocial working conditions predicted a higher frequency of poor interactions with patients (p-values<0.01). These associations were attenuated by work engagement, work satisfaction, and health outcomes. We found mostly non-significant associations between adverse psychosocial working conditions and the frequency of slips and lapses. However, unfavorable psychosocial working conditions among MAs predicted a higher frequency of poor interaction with patients due to stress.