Large-Scale Simultaneous Inference with Hypothesis Testing: Multiple Testing Procedures in Practice

Toxicity of ciprofloxacin and ofloxacin to Moina macrocopa and investigation of p-value adjustments for (eco)toxicological studies

Ciprofloxacin (CFX) and ofloxacin (OFX) are commonly found as residual contaminants in aquatic environments, posing potential risks to various species. To ensure the safety of aquatic wildlife, it is essential to determine the toxicity of these antibiotics and establish appropriate concentration limits. Additionally, in (eco)toxicological studies, addressing the issue of multiple hypothesis testing through p-value adjustments is crucial for robust decision-making. In this study, we assessed the no observed adverse effect concentration (NOAEC) of CFX and OFX on Moina macrocopa across a concentration range of 0-400 µg L-1. Furthermore, we investigated multiple p-value adjustments to determine the NOAECs. Our analysis yielded consistent results across seven different p-value adjustments, indicating NOAECs of 100 µg CFX L-1 for age at first reproduction and 200 µg CFX L-1 for fertility. For OFX treatment, a NOAEC of 400 µg L-1 was observed for both biomarkers. However, further investigation is required to establish the NOAEC of OFX at higher concentrations with greater certainty. Our findings demonstrate that CFX exhibits higher toxicity compared to OFX, consistent with previous research. Moreover, this study highlights the differential performance of p-value adjustment methods in terms of maintaining statistical power while controlling the multiplicity problem, and their practical applicability. The study emphasizes the low NOAECs for these antibiotics in the zooplanktonic group, highlighting their significant risks to ecological and environmental safety. Additionally, our investigation of p-value adjustment approaches contributes to a deeper understanding of their performance characteristics, enabling (eco)toxicologists to select appropriate methods based on their specific needs and priorities.

Broken Rotor Bar Detection Based on Steady-State Stray Flux Signals Using Triaxial Sensor with Random Positioning

This paper investigates the detection of broken rotor bar in squirrel cage induction motors using a novel approach of randomly positioning a triaxial sensor over the motor surface. This study is conducted on two motors under laboratory conditions, where one motor is kept in a healthy state, and the other is subjected to a broken rotor bar (BRB) fault. The induced electromotive force of the triaxial coils, recorded over ten days with 100 measurements per day, is statistically analyzed. Normality tests and graphical interpretation methods are used to evaluate the data distribution. Parametric and non-parametric approaches are used to analyze the data. Both approaches show that the measurement method is valid and consistent over time and statistically distinguishes healthy motors from those with BRB defects when a reference or threshold value is specified. While the comparison between healthy motors shows a discrepancy, the quantitative analysis shows a smaller estimated difference in mean values between healthy motors than comparing healthy and BRB motors.

Plasma Protein Levels Analysis in Multiple Sclerosis Sardinian Families Identified C9 and CYP24A1 as Candidate Biomarkers

Here we investigate protein levels in 69 multiple sclerosis (MS) cases and 143 healthy controls (HC) from twenty Sardinian families to search for promising biomarkers in plasma. Using antibody suspension bead array technology, the plasma levels of 56 MS-related proteins were obtained. Differences between MS cases and HC were estimated using Linear Mixed Models or Linear Quantile Mixed Models. The proportion of proteins level variability, explained by a set of 119 MS-risk SNPs as to the literature, was also quantified. Higher plasma C9 and CYP24A1 levels were found in MS cases compared to HC (p < 0.05 after Holm multiple testing correction), with protein level differences estimated as, respectively, 0.53 (95% CI: 0.25, 0.81) and 0.42 (95% CI: 0.19, 0.65) times plasma level standard deviation measured in HC. Furthermore, C9 resulted in both statistically significantly higher relapsing-remitting MS (RRMS) and secondary-progressive MS (SPMS) compared to HC, with SPMS showing the highest differences. Instead, CYP24A1 was statistically significantly higher only in RRMS as compared to HC. Respectively, 26% (95% CI: 10%, 44%) and 16% (95% CI: 9%, 39%) of CYP24A1 and C9 plasma level variability was explained by known MS-risk SNPs. Our results highlight C9 and CYP24A1 as potential biomarkers in plasma for MS and allow us to gain insight into molecular disease mechanisms.

Comparison of sampling methods in assessing the microbiome from patients with ulcerative colitis

Background

Dysbiosis of ulcerative colitis (UC) has been frequently investigated using readily accessible stool samples. However, stool samples might insufficiently represent the mucosa-associated microbiome status. We hypothesized that luminal contents including loosely adherent luminal bacteria after bowel preparation may be suitable for diagnosing the dysbiosis of UC.

Methods

This study included 16 patients with UC (9 men and 7 women, mean age: 52.13 ± 14.09 years) and 15 sex- and age-matched healthy individuals (8 men and 7 women, mean age: 50.93 ± 14.11 years). They donated stool samples before colonoscopy and underwent luminal content aspiration and endoscopic biopsy during the colonoscopy. Then, the composition of each microbiome sample was analyzed by 16S rRNA-based next-generation sequencing.

Results

The microbiome between stool, luminal contents, and biopsy was significantly different in alpha and beta diversities. However, a correlation existed between stool and luminal contents in the Procrustes test (p = 0.001) and Mantel test (p = 0.0001). The stool microbiome was different between patients with UC and the healthy controls. Conversely, no difference was found in the microbiome of luminal content and biopsy samples between the two subject groups. The microbiome of stool and lavage predicted UC, with AUC values of 0.85 and 0.81, respectively.

Conclusion

The microbiome of stool, luminal contents, and biopsy was significantly different. However, the microbiome of luminal contents during colonoscopy can predict UC, with AUC values of 0.81. Colonoscopic luminal content aspiration analysis could determine microbiome differences between patients with UC and the healthy control, thereby beneficial in screening dysbiosis via endoscopy.

Trial registration: This trial was registered at http://cris.nih.go.kr. Registration No.: KCT0003352), Date: 2018–11-13.

Prognostic gene expression signatures of breast cancer are lacking a sensible biological meaning

The identification of prognostic biomarkers for predicting cancer progression is an important problem for two reasons. First, such biomarkers find practical application in a clinical context for the treatment of patients. Second, interrogation of the biomarkers themselves is assumed to lead to novel insights of disease mechanisms and the underlying molecular processes that cause the pathological behavior. For breast cancer, many signatures based on gene expression values have been reported to be associated with overall survival. Consequently, such signatures have been used for suggesting biological explanations of breast cancer and drug mechanisms. In this paper, we demonstrate for a large number of breast cancer signatures that such an implication is not justified. Our approach eliminates systematically all traces of biological meaning of signature genes and shows that among the remaining genes, surrogate gene sets can be formed with indistinguishable prognostic prediction capabilities and opposite biological meaning. Hence, our results demonstrate that none of the studied signatures has a sensible biological interpretation or meaning with respect to disease etiology. Overall, this shows that prognostic signatures are black-box models with sensible predictions of breast cancer outcome but no value for revealing causal connections. Furthermore, we show that the number of such surrogate gene sets is not small but very large.

Artificial Intelligence: A Clarification of Misconceptions, Myths and Desired Status

The field artificial intelligence (AI) was founded over 65 years ago. Starting with great hopes and ambitious goals the field progressed through various stages of popularity and has recently undergone a revival through the introduction of deep neural networks. Some problems of AI are that, so far, neither the "intelligence" nor the goals of AI are formally defined causing confusion when comparing AI to other fields. In this paper, we present a perspective on the desired and current status of AI in relation to machine learning and statistics and clarify common misconceptions and myths. Our discussion is intended to lift the veil of vagueness surrounding AI to reveal its true countenance.

Understanding Statistical Hypothesis Testing: The Logic of Statistical Inference

Statistical hypothesis testing is among the most misunderstood quantitative analysis methods from data science. Despite its seeming simplicity, it has complex interdependencies between its procedural components. In this paper, we discuss the underlying logic behind statistical hypothesis testing, the formal meaning of its components and their connections. Our presentation is applicable to all statistical hypothesis tests as generic backbone and, hence, useful across all application domains in data science and artificial intelligence.