Statistical Approach Research Articles

MAGICAL: A multi-class classifier to predict synthetic lethal and viable interactions using protein-protein interaction network.

Synthetic lethality (SL) and synthetic viability (SV) are commonly studied genetic interactions in the targeted therapy approach in cancer. In SL, inhibiting either of the genes does not affect the cancer cell survival, but inhibiting both leads to a lethal phenotype. In SV, inhibiting the vulnerable gene makes the cancer cell sick; inhibiting the partner gene rescues and promotes cell viability. Many low and high-throughput experimental approaches have been employed to identify SLs and SVs, but they are time-consuming and expensive. The computational tools for SL prediction involve statistical and machine-learning approaches. Almost all machine learning tools are binary classifiers and involve only identifying SL pairs. Most importantly, there are limited properties known that best describe and discriminate SL from SV. We developed MAGICAL (Multi-class Approach for Genetic Interaction in Cancer via Algorithm Learning), a multi-class random forest based machine learning model for genetic interaction prediction. Network properties of protein derived from physical protein-protein interactions are used as features to classify SL and SV. The model results in an accuracy of ~80% for the training dataset (CGIdb, BioGRID, and SynLethDB) and performs well on DepMap and other experimentally derived reported datasets. Amongst all the network properties, the shortest path, average neighbor2, average betweenness, average triangle, and adhesion have significant discriminatory power. MAGICAL is the first multi-class model to identify discriminatory features of synthetic lethal and viable interactions. MAGICAL can predict SL and SV interactions with better accuracy and precision than any existing binary classifier.

Active components and mechanisms of action of psychological interventions in bipolar disorder: A systematic literature review.

The efficacy of psychological interventions for bipolar disorder (BD) is well established, but much remains unknown about how change occurs. The primary objective of this exploratory study was to audit what is known about active components and mechanisms of action of psychological interventions for BD. We conducted a systematic review (PROSPERO CRD42022323276). Two independent reviewers screened references from four databases and extracted data from eligible studies. We included four component studies, six studies with mediation analyses and 26 studies presenting subjective experiences of how psychological interventions bring change. Ten mediators were examined across six studies, with only one putative mediator, medication adherence, tested in more than one study. Some initial support for mediation of varied outcomes by control over thoughts, positive non-verbal behaviour, self-esteem, post-trauma growth and medication adherence. Some preliminary support was found in two components, human support and IPT. Studies exploring participant experiences of therapeutic change enumerated a range of potential active components, mechanisms of action and contextual factors potentially warranting investigation in future research. However, the evidence base for active components and mechanisms of action in psychological interventions for BD is unsatisfactory. Findings were inconsistent, studies homogenous with significant methodological limitations and statistical approaches failed to meet quality criteria. Preliminary identification of potential components and mechanisms via qualitative analyses and the insights emerging from this review will inform future research aimed at investigating how psychological interventions work in BD.

Long-term effects of widespread pharmaceutical pollution on trade-offs between behavioural, life-history and reproductive traits in fish.

In our rapidly changing world, understanding how species respond to shifting conditions is of paramount importance. Pharmaceutical pollutants are widespread in aquatic ecosystems globally, yet their impacts on animal behaviour, life-history and reproductive allocation remain poorly understood, especially in the context of intraspecific variation in ecologically important traits that facilitate species' adaptive capacities. We test whether a widespread pharmaceutical pollutant, fluoxetine (Prozac), disrupts the trade-off between individual-level (co)variation in behavioural, life-history and reproductive traits of freshwater fish. We exposed the progeny of wild-caught guppies (Poecilia reticulata) to three field-relevant levels of fluoxetine (mean measured concentrations: 0, 31.5 and 316 ng/L) for 5 years, across multiple generations. We used 12 independent laboratory populations and repeatedly quantified activity and risk-taking behaviour of male guppies, capturing both mean behaviours and variation within and between individuals across exposure treatments. We also measured key life-history traits (body condition, coloration and gonopodium size) and assessed post-copulatory sperm traits (sperm vitality, number and velocity) that are known to be under strong sexual selection in polyandrous species. Intraspecific (co)variation of these traits was analysed using a comprehensive, multivariate statistical approach. Fluoxetine had a dose-specific (mean) effect on the life-history and sperm trait of guppies: low pollutant exposure altered male body condition and increased gonopodium size, but reduced sperm velocity. At the individual level, fluoxetine reduced the behavioural plasticity of guppies by eroding their within-individual variation in both activity and risk-taking behaviour. Fluoxetine also altered between-individual correlations in pace-of-life syndrome traits: it triggered the emergence of correlations between behavioural and life-history traits (e.g. activity and body condition) and between life-history and sperm traits (e.g. gonopodium size and sperm vitality), but collapsed other between-individual correlations (e.g. activity and gonopodium size). Our results reveal that chronic exposure to global pollutants can affect phenotypic traits at both population and individual levels, and even alter individual-level correlations among such traits in a dose-specific manner. We discuss the need to integrate individual-level analyses and test behaviour in association with life-history and reproductive traits to fully understand how animals respond to human-induced environmental change.

Human sleeping pose estimation from IR images for in-bed patient monitoring using image processing and deep learning techniques

Human Pose Estimation (HPE) is a crucial step towards understanding people in images and videos. HPE provides geometric and motion information of the human body, which has been applied to a wide range of applications (e.g., human-computer interaction, motion analysis, augmented reality, virtual reality, healthcare, etc.). An extremely useful task of this kind is the 2D pose estimation of bedridden patients from infrared (IR) images. Here, the IR imaging modality is preferred due to privacy concerns and the need for monitoring both uncovered and covered patients at different levels of illumination. The major drawback of this research problem is the unavailability of covered examples, which are very costly to collect and time-consuming to label. In this work, a deep learning-based framework was developed for human sleeping pose estimation on covered images using only the uncovered training images. In the training scheme, two different image augmentation techniques, a statistical approach as well as a GAN-based approach, were explored for domain adaptation, where the statistical approach performed better. The accuracy of the model trained on the statistically augmented dataset was improved by 124 % as compared with the model trained on non-augmented images. To handle the scarcity of training infrared images, a transfer learning strategy was used by pre-training the model on an RGB pose estimation dataset, resulting in a further increment in accuracy of 4 %. Semi-supervised learning techniques, with a novel pose discriminator model in the loop, were adopted to utilize the unannotated training data, resulting in a further 3 % increase in accuracy. Thus, significant improvement has been shown in the case of 2D pose estimation from infrared images, with a comparatively small amount of annotated data and a large amount of unannotated data by using the proposed training pipeline powered by heavy augmentation.

RSM-BASED STATISTICAL APPROACH TO ENHANCE THE COMPRESSIVE PERFORMANCE OF ABS P400 PARTS FABRICATED VIA FDM TECHNOLOGY

This research investigates the performance of ABS P400 parts under compressive loading. These ABS P400 parts are fabricated by fused deposition modeling (FDM) process using omnidirectional printing. The effects of three critical parameters, raster angle (A), air gap (B), and raster width (C), each at three different levels, were analyzed on three different performance measures, i.e. total deformation up to failure ([Formula: see text]) measured in %, maximum energy ([Formula: see text]) in MJ/m3 and break energy ([Formula: see text]) in MJ/m3. Results of experimentations were analyzed using analysis of variance (ANOVA), perturbation curve and 3D surface plots. The research established the anisotropic nature, durability and less brittleness of the FDM fabricated ABSP400 part, which leads to lower strength and also influence the energy absorbing behavior while deformation under compression. Complex relationships were exhibited between the chosen parameters and studied measures. It was also observed that [Formula: see text] is noticeable due to less brittleness of ABS P400 however, [Formula: see text] and [Formula: see text] vary in a contradictory fashion with changes in process parameters. The models were validated using normality plots. Multi-objective optimization was done using the desirability approach to determine the optimal combination of FDM process parameters for optimum responses. The desirability result showed that the optimized input parameter is [Formula: see text][Formula: see text]mm, and [Formula: see text][Formula: see text]mm which yield maximized optimum responses as [Formula: see text]%, [Formula: see text][Formula: see text]MJ/m3, and [Formula: see text][Formula: see text]MJ/m3. The work not only reflects the effect of three FDM parameters on the total deformation up to failure ([Formula: see text] in %) but, in addition, energy absorption behavior of FDM parts under compression is also investigated. The study on the behavior of energy at ultimate stress or ultimate load and breaking stress or breaking load were rarely investigated in earlier research which reflects the novelty of our research.

Analyzing the heterogenous effects of factors on high-range speeding likelihood of taxi speeders: Does explainable deep learning provides more insights than random parameter approach?

The random parameters Generalized Linear Model (GLM) is frequently used to model speeding characteristics and capture the heterogenous effects of factors. However, this statistical approach is seldom employed for prediction and generalization due to the challenge of transferring its predefined errors. Recently, the emergence of explainable AI techniques has illuminated a new path for analyzing factors associated with risky driving behaviors. Despite this, there remains a gap that comparing results from machine and deep learning (ML/DL) approaches with those from random parameters GLM. This study aims to apply the random parameter GLM and explainable deep learning to evaluate the heterogenous effects of factors on the taxis’ high-range speeding likelihood. Initially, a Beta GLM with random parameters (BGLM-RP) is developed to model the high-range speeding likelihood among taxi drivers. Additionally, XGBoost, a simple convolutional neural network (Simple-CNN), a deeper CNN (DCNN), and a deeper CNN with self-attention (DCNN-SA) are developed. The quantified explanations and illustrations of the factors’ heterogenous effects from ML/DL models are derived from pseudo coefficients by decomposing factors’ SHapley Additive exPlanations (SHAP) values. All the developed statistical, ML, and DL models are compared in terms of mean absolute errors and mean square errors on testing and full data. Results show that DCNN-SA excels in prediction on testing data, indicating its superior generalization capabilities, while BGLM-RP outperforms other models on full data. The DCNN-SA can reveal the heterogenous effects of factors for both in-sample and out-of-sample data, which is not possible for the random parameter GLM. However, BGLM-RP can reveal larger magnitudes of the factors’ heterogenous effects for in-sample data. The signs and significances are identical between the varying coefficients from BGLM-RP and the pseudo coefficients from the ML/DL models, demonstrating the validity and rationale of using the proposed explanation framework to quantify the factors’ effects in ML/DL models. The study also discusses the contributions of various factors to the high-range speeding likelihood of taxi drivers.

Leaching of Neodymium From Hard Disk Drive Magnet Waste Using Citric Acid‐ Design of Experiments and Optimization

AbstractNeodymium magnet waste recycling and precious metal recovery have drawn a lot of researchers’ attention because of their vital role in contemporary technology. In this work, the potential of citric acid for the leaching of neodymium from hard disk drive waste magnets has been examined using statistical approaches like full factorial design(FFD) and central composite design (CCD).The variables have been screened and conditions are optimized to obtain higher leaching. The polynomial quadratic Model proves effective to predict optimized conditions for neodymium leaching. Leachant concentration and temperature were screened as the most effective factors. Optimization conditions for the leaching process were achieved through central composite design, revealing that 2 mol/L citric acid and a temperature of 333 K provide the ideal conditions for the complete dissolution of neodymium. This extensive study contributes to sustainable materials management and resource conservation by illuminating the effective recovery of important metals from neodymium magnets.

Developing a new methodology for determining typologies of peri-urban agriculture: A case study of the Jakarta Bandung Mega Urban Region Indonesia

Peri-urban agriculture (PUA) has different ecological, socio-institutional, and economic conditions. The dynamic characteristics of PUA are driven by the location in a rural-to-urban transitional zone. In this context, a new approach should be developed for identifying PUA typologies to facilitate more effective and relatively quick policy management and planning. Therefore, this study aims to develop new categories and a set of methodologies to determine PUA typologies using statistical approaches such as variable simplification (Principal Component Analysis), cluster analysis (K-means), and village-based spatial characterization mapping. A structured selection was conducted to determine the case studies, and two adjacent suburban districts in the Jakarta Bandung Mega Urban Region (JBMUR) corridor were obtained. The results show that there is a new insight into the use of spatial approaches and the relationship between empirical conditions at the local level and policies related to PUA. The developed typology approach describes significant variations in agricultural conditions based on the intensity. Strategies and recommendations for PUA development based on typology are also proposed.

Complex Interactions between the Human Major Histocompatibility Complex (MHC) and Microbiota: Their Roles in Disease Pathogenesis and Immune System Regulation.

The relationship between microbiota and the immune system is complex and characterized by the ways in which microbiota directs immune function interactions, both innate and acquired and also keeps activating the immune system throughout an individual's life. In this respect, the human Major Histocompatibility Complex (MHC, referred to as HLA in humans) plays a crucial role and is also established in self-defense against microbes by presenting microbial-derived peptides to the immune cells. However, this assumption has some unclear aspects that should be investigated. For example, how is the microbiota shaped by microbe species diversity, quantity and functions of the immune system, as well as the role and molecular mechanisms of the HLA complex during this process. There are autoimmune diseases related to both HLA and specific microbiota changes or alterations, many of which are mentioned in the present review. In addition, the HLA peptide presenting function should be put in a framework together with its linkage to diseases and also with HLA compatibility necessary for transplants to be successful. These are still quite an enigmatically statistical and phenomenological approach, but no firm pathogenic mechanisms have been described; thus, HLA's real functioning is still to be fully unveiled. After many years of HLA single-genes studies, firm pathogenesis mechanisms underlying disease linkage have been discovered. Finally, microbiota has been defined as conformed by bacteria, protozoa, archaea, fungi, and viruses; notwithstanding, endogenous viral sequences integrated into the human genome and other viral particles (obelisks) recently found in the digestive mucosa should be taken into account because they may influence both the microbiome and the immune system and their interactions. In this context, we propose to integrate these microbial-genetic particle components into the microbiome concept and designate it as "microgenobiota".

Optimisation of Convolution-Based Image Lightness Processing.

In the convolutional retinex approach to image lightness processing, an image is filtered by a centre/surround operator that is designed to mitigate the effects of shading (illumination gradients), which in turn compresses the dynamic range. Typically, the parameters that define the shape and extent of the filter are tuned to provide visually pleasing results, and a mapping function such as a logarithm is included for further image enhancement. In contrast, a statistical approach to convolutional retinex has recently been introduced, which is based upon known or estimated autocorrelation statistics of the image albedo and shading components. By introducing models for the autocorrelation matrices and solving a linear regression, the optimal filter is obtained in closed form. Unlike existing methods, the aim is simply to objectively mitigate shading, and so image enhancement components such as a logarithmic mapping function are not included. Here, the full mathematical details of the method are provided, along with implementation details. Significantly, it is shown that the shapes of the autocorrelation matrices directly impact the shape of the optimal filter. To investigate the performance of the method, we address the problem of shading removal from text documents. Further experiments on a challenging image dataset validate the method.

Development of temperament types from infancy to adolescence: Genetic and environmental influences with an economically and racially/ethnically diverse sample.

Kagan theorized biologically based temperament types that are present in infancy, stable across development, and essential for understanding individual differences. Despite evidence, temperament research remains focused on a few prominent dimensions of temperament, without adequately addressing covariance among dimensions and temperament types. Using longitudinal twin data, we took a person-centered statistical approach to identify temperament types and examined continuity and change across five developmental periods (Ninfancy = 602; Ntoddlerhood = 522; Nearly childhood = 390; Nlate childhood = 718; Nearly adolescence = 700). We then examined the genetic and environmental etiology of temperament types. Twins were boys and girls (51-53% female), primarily Hispanic/Latinx (23-30%) and non-Hispanic/Latinx White (56-63%), and from socioeconomically diverse families (28-38% near-or-below the poverty line). Using latent profile analysis, we identified three temperament types at each age characterized by negative reactivity and dysregulation, positive reactivity and strong self-regulation, and moderate reactivity and regulation. Latent transition analyses revealed considerable continuity in membership type for "negative dysregulated" beginning in infancy, log odds = 1.58 (SE = .65) to 3.16 (SE = .77), p < .01, of remaining relative to transitioning to "typical expressive", and "positive well-regulated" beginning in early childhood, log odds = 1.41 (SE = .56) to 2.25 (SE = .47), p < .05. Twin analyses revealed moderate heritability and a consistent role of the shared environment on positive well-regulated, with negative dysregulated and typical expressive also moderately heritable with the shared environment being important at some ages. Findings support the presence of theorized biologically based temperament types that develop rapidly in infancy and toddlerhood and provide a foundation for the study of individual differences and risk and resilience processes across the lifespan. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Complementary approaches to dissect late leaf rust resistance in an interspecific raspberry population.

Over the last ten years, global raspberry production has increased by 47.89%, based mainly on the red raspberry species (Rubus idaeus). However, the black raspberry (Rubus occidentalis), although less consumed, is resistant to one of the most important diseases for the crop, the late leaf rust caused by Acculeastrum americanum fungus. In this context, genetic resistance is the most sustainable way to control the disease, mainly because there are no registered fungicides for late leaf rust in Brazil. Therefore, the aim was to understand the genetic architecture that controls resistance to late leaf rust in raspberries. For that, we used an interspecific multi-parental population using the species mentioned above as parents, two different statistical approaches to associate the phenotypes with markers (GWAS and copula graphical models), and two phenotyping methodologies from the first to the seventeenth Day After Inoculation (D.A.I.) (high-throughput phenotyping with a multispectral camera and traditional phenotyping by disease severity scores). Our findings indicate that a locus of higher effect, at position 13.3 Mb on chromosome five, possibly controls late leaf rust resistance, as both GWAS and the network suggested the same marker. Of the twelve genes flanking its region, four were possible receptors, three were likely defense executors, one gene was likely part of signaling cascades, and four were classified as non-defense related. Although the network and GWAS indicated the same higher effect genomic region, the network identified other different candidate regions, potentially complementing the genetic control comprehension.

Erosive Rainfall Thresholds Identification Using Statistical Approaches in a Karst Yellow Soil Mountain Erosion-Prone Region in Southwest China

Karst yellow soil is one of the most important cultivated soils in southwest China. At present, only a few studies have dealt with rainfall erosivity and erosive rainfall thresholds in the karst yellow soil region. This paper utilizes statistical methods to identify erosive rainfall thresholds and slope erosion-prone areas in the Qianzhong region. This analysis is based on long-term experimental data from 10 experimental stations and 69 experimental plots within the region in 2006 to 2022. The findings show the following: The rainfall amount threshold was 12.66 mm for woodland plots, 10.57 mm for grassland plots, 9.94 mm for farmland plots, and 8.93 mm for fallow plots. Soil and water conservation measures in forestry and grassland effectively increase the rainfall amount thresholds. Compared to farmland, the rainfall threshold increased by 27.32% for woodland and 6.32% for grassland. Bare land and farmland are erosion-prone areas in the karst yellow soil region. The erosive rainfall thresholds for farmland plots with slopes of 13°, 15°, 20°, 23°, and 25° were 10.41 mm, 10.28 mm, 9.66 mm, 9.52 mm, and 9.15 mm, respectively. With the increase in the 13–25° slope gradient of farmland, the initial rainfall required for runoff generation leads to a reduction. The wrong selection indices (WSI) of all landcover plots were less than 10%, and the efficiency indices (EFF) were between 80.43% and 90.25%. The relative error index (REI) of the erosive rainfall thresholds for all landcover runoff plots was less than 0.50%, very close to 0, indicating that these thresholds have small errors and high accuracy. This study gained a better understanding of natural rainfall-induced erosion characteristics in the study area, determined rainfall thresholds for distinguishing erosive rainfall events from non-erosive across different landcover types, and reduced the workload of calculating rainfall erosivity while enhancing the accuracy of soil erosion forecasting and simulation in the karst mountain yellow soil area.

Correction: Rearing carob moth, Ectomyelois ceratoniae (Zell.), on pistachio nuts: biological and statistical approaches

Correction: Rearing carob moth, Ectomyelois ceratoniae (Zell.), on pistachio nuts: biological and statistical approaches

Statistics in Personalized Medicine: Challenges and Innovations

Personalized medicine represents a paradigm shift in healthcare, aiming to tailor medical treatments to individual patients based on their unique genetic profiles, lifestyle factors, and environmental influences. At the forefront of this transformation lies statistics, which plays a pivotal role in integrating diverse data sources, identifying biomarkers, and developing predictive models that guide personalized treatment decisions. However, statistics in personalized medicine face challenges such as data integration complexities, small sample sizes, and ethical considerations. Despite these challenges, innovative statistical approaches including machine learning, Bayesian inference, and multi-omics integration are driving advancements. The future of statistics in personalized medicine lies in integrating multi-omics data, adopting artificial intelligence for predictive modeling, enhancing quantitative pharmacology, leveraging real-world evidence, and addressing ethical and regulatory frameworks. By advancing these fronts, statistics holds the promise to optimize treatment outcomes, improve patient care, and redefine the landscape of healthcare delivery in the 21st century. Keywords: Personalized medicine, Statistics, Data integration, Machine learning, Ethical considerations

New Clerodane Diterpenoids from Tinospora Capillipes with Antibacterial and Anti-Inflammatory Properties.

Four new clerodane diterpenoids, namely tinocapills A-D (1-4), and one known analogue (5) were isolated from the roots of Tinospora capillipes in the present study. The structures of these new compounds, including their absolute configurations, were determined through a combination of detailed spectroscopic analysis and theoretical statistical approaches, including electronic circular dichroism (ECD) analyses and quantum mechanical (QM)-NMR methods. Additionally, the stereostructure of 5 was confirmed via X-ray diffraction analysis. Furthermore, all these isolates were evaluated for their antibacterial and anti-inflammatory activities. Compounds 1, 2 and 5 demonstrated antibacterial activity against methicillin-resistant Staphylococcus aureus (MRSA) with MICs ranging from 4-64 μg/mL, and compounds 3 and 4 exhibited potential anti-inflammatory effects by suppressing LPS-induced TNF-α and NO releases in RAW264.7 cells.

Intoxication in the city: Investigating spatial patterns and determinants of drugs and alcohol-related illegal activities in India's geostrategic corridor

Illicit drug and alcohol-related activities pose significant challenges to metropolitan centres globally, impacting public safety and socio-economic stability. This research analyzes the spatial and temporal patterns of drugs and alcohol-related illegal activities (DARIA) in Siliguri, known as the "Gateway of North-East India." The primary objectives are to explore DARIA patterns, identify clusters and concentrated areas, and investigate contributing factors. Using GIS-based spatial analysis and geographical models, we studied incidents recorded over three years from 2021 to 2023. Advanced spatial statistical approaches were applied to identify specific clusters of DARIA occurrences. The pure temporal analysis highlighted periods of increased illegal activity. Various geographical models, including ordinary least squares (OLS), geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR), were used to examine the spatial association. The MGWR model, with an explanatory power of R2 = 0.816, provided a detailed understanding of spatial relationships and variations in DARIA. Our findings indicate specific zones with frequent DARIA occurrences, mainly near major transportation hubs, commercial points, and areas with significant mobility. Key exploratory factors such as illiteracy rate, population density, proximity to playgrounds and slums were highly associated with DARIA incidents. These findings contribute to developing safer, more resilient, and more sustainable cities worldwide.

Let's include the ladies: Do morphology-performance relationships vary between sexes in lizards?

An animal's morphology influences its ability to perform essential tasks, such as locomoting to obtain prey or escape predators. While morphology-performance relationships are well-studied in lizards, most conclusions have been based only on male study subjects, leaving unanswered questions about females. Sex-specific differences are important to understand because females carry the bulk of the physiological demands of reproduction. Consequently, their health and survival can determine the fate of the population as a whole. To address this knowledge gap, we sampled introduced populations of common wall lizards (Podarcis muralis) in Ohio, USA. We measured a complete suite of limb and body dimensions of both males and females, and we measured sprint speeds while following straight paths and curved paths on different substrates. Using a multivariate statistical approach, we identified that body dimensions relative to snout-to-vent length in males were much larger compared to females and that body dimensions of P. muralis have changed over time in both sexes. We found that sprint speed along curved paths increased with relative limb size in both males and females. When following straight paths, male speed similarly increased as body dimensions increased; conversely, female speed decreased as body dimensions increased. Female sprint speed was also found to have less variation than that of males and was less affected by changes in body size and hindfoot length compared to males. This study thus provides insights into how selective pressures might shape males and females differently and the functional implications of sexual dimorphism.

Establishing Healthy Breath Baselines With Tin Oxide Sensors: Fundamental Building Blocks for Noninvasive Health Monitoring.

Volatile organic compounds (VOCs) in breath serve as a source of biomarkers for medical conditions relevant to warfighter health including Corona Virus Disease and other potential biological threats. Electronic noses are integrated arrays of gas sensors that are cost-effective and miniaturized devices that rapidly respond to VOCs in exhaled breath. The current study seeks to qualify healthy breath baselines of exhaled VOC profiles through analysis using a commercialized array of metal oxide (MOX) sensors. Subjects were recruited/consented through word of mouth and using posters. For each sample, breath was analyzed using an array of MOX sensors with parameters that were previously established. Data were also collected using a lifestyle questionnaire and from a blood test to assess markers of general health. Sensor data were processed using a feature extraction algorithm, which were analyzed through statistical approaches to identify correlations with confounding factors. Reproducibility was also assessed through relative standard deviation values of sensor features within a single subject and between different volunteers. A total of 164 breath samples were collected from different individuals, and 10 of these volunteers provided an additional 9 samples over 6 months for the longitudinal study. First, data from different subjects were analyzed, and the trends of the 17 extracted features were elucidated. This revealed not only a high degree of correlation between sensors within the array but also between some of the features extracted within a single sensor. This helped guide the removal of multicollinear features for multivariate statistical analyses. No correlations were identified between sensor features and confounding factors of interest (age, body mass index, smoking, and sex) after P-value adjustment, indicating that these variables have an insignificant impact on the observed sensor signal. Finally, the longitudinal replicates were analyzed, and reproducibility assessment showed that the variability between subjects was significantly higher than within replicates of a single volunteer (P-value = .002). Multivariate analyses within the longitudinal data displayed that subjects could not be distinguished from one another, indicating that there may be a universal healthy breath baseline that is not specific to particular individuals. The current study sought to qualify healthy baselines of VOCs in exhaled breath using a MOX sensor array that can be leveraged in the future to detect medical conditions relevant to warfighter health. For example, the results of the study will be useful, as the healthy breath VOC data from the sensor array can be cross-referenced in future studies aiming to use the device to distinguish disease states. Ultimately, the sensors may be integrated into a portable breathalyzer or current military gear to increase warfighter readiness through rapid and noninvasive health monitoring.

Circular code identified by the codon usage

Since 1996, circular codes in genes have been identified thanks to the development of 6 statistical approaches: trinucleotide frequencies per frame (Arquès and Michel, 1996), correlation functions per frame (Arquès and Michel, 1997), frame permuted trinucleotide frequencies (Frey and Michel, 2003, 2006), advanced statistical functions at the gene population level (Michel, 2015) and at the gene level (Michel, 2017). All these 3-frame statistical methods analyse the trinucleotide information in the 3 frames of genes: the reading frame and the 2 shifted frames. Notably, codon usage does not allow for the identification of circular codes (Michel, 2020). This has been a long-standing problem since 1996, hindering biologists’ access to circular code theory.By considering circular code conditions resulting from code theory, particularly the concept of permutation class, and building upon previous statistical work, a new statistical approach based solely on the codon usage, i.e. a 1-frame statistical method, surprisingly reveals the maximal C3 self-complementary trinucleotide circular code X in bacterial genes and in average (bacterial, archaeal, eukaryotic) genes, and almost in archaeal genes. Additionally, a new parameter definition indicates that bacterial and archaeal genes exhibit codon usage dispersion of the same order of magnitude, but significantly higher than that observed in eukaryotic genes. This statistical finding may explain the greater variability of codes in eukaryotic genes compared to bacterial and archaeal genes, an issue that has been open for many years. Finally, biologists can now search for new (variant) circular codes at both the genome level (across all genes in a given genome) and the gene level using only codon usage, without the need for analysing the shifted frames.