Bayesian Testing Research Articles

Statistical Signal Processing for Radar Systems

Through measurable models, "Factual Flag Handling for Radar Frameworks" goes into detail around the progressed procedures and strategies utilized to see at and progress radar information. This field employments thoughts from both measurable hypothesis and flag preparing to unravel difficult issues in radar framework execution, like finding targets, taking after them, and speculating what they are. This article talks approximately distinctive factual models and strategies that can be utilized with radar. These incorporate Bayesian gauges, versatile sifting, and theory testing. This paper talks around how factual apparatuses can be utilized to create radar frameworks more precise, dependable, and clear by mimicking and decreasing the impacts of commotion, impedances, and outside components. Creating factual flag preparing methods for lessening disarray, distinguishing targets, and estimating parameters are a few of the foremost critical subjects. Other imperative points incorporate ways to analyze and get it radar information in genuine time. It gives a careful see at both the hypothetical bases and real-world applications, appearing how measurable strategies can be utilized to make strides radar framework capabilities. There are case thinks about and real-life examples that appear how these strategies can be utilized totally different radar circumstances, such as military, flying machine, car, and climate radars. This book gives engineers and understudies the instruments they got to move forward radar framework plan and execution in settings that are getting more complicated and changing rapidly by combining factual flag preparing with radar innovation.

CCN3/NOV as a potential therapeutic target for diverticular disease: A proteome-wide Mendelian randomization study.

Genome-wide association studies (GWAS) identified over 100 susceptibility loci and candidate causal genes for diverticular disease (DD) at the transcriptional level. However, effective therapeutics or preventions based on underlying disease mechanisms remain to be elucidated. In this study, we explored potential causal genes for DD at the protein level. We used 2 GWAS summary statistics of DD; 1 was obtained from the United Kingdom Biobank (UKBB) with 31,917 cases and 419,135 controls, and the other from the FinnGen consortium with 30,649 cases and 301,931 controls. For the primary analysis, we employed proteome-wide Mendelian randomization (MR) studies using 738 cis-acting protein quantitative trait loci (pQTLs) for 735 plasma proteins from the 5 published studies. For external validation, we conducted 2-sample MR analyses using plasma pQTLs of the screened proteins from another study by deCODE genetics. Moreover, we performed a series of sensitivity analyses including reverse MR and Bayesian colocalization tests. The primary MR identified 4 plasma proteins that were associated with DD risk including CCN3/NOV (odds ratio [OR], 0.98; 95% confidence interval [CI], 0.97-0.99; P = 1.2 × 10-11 for UKBB. OR, 0.73; 95% CI, 0.66-0.81; P = 7.2 × 10-10 for FinnGen). The validation MR well replicated the primary result of CCN3/NOV (OR, 0.95; 95% CI, 0.93-0.96; P = 1.9 × 10-11 for UKBB. OR, 0.43; 95% CI, 0.33-0.56; P = 7.0 × 10-10 for FinnGen). Sensitivity analyses supported the causal association. We prioritized plasma CCN3/NOV protein as a protective factor for DD for follow-up functional studies to elucidate the disease mechanisms and therapeutics.

Assessing the effect of model specification and prior sensitivity on Bayesian tests of temporal signal.

Our understanding of the evolution of many microbes has been revolutionised by the molecular clock, a statistical tool to infer evolutionary rates and timescales from analyses of biomolecular sequences. In all molecular clock models, evolutionary rates and times are jointly unidentifiable and 'calibration' information must therefore be used. For many organisms, sequences sampled at different time points can be employed for such calibration. Before attempting to do so, it is recommended to verify that the data carry sufficient information for molecular dating, a practice referred to as evaluation of temporal signal. Recently, a fully Bayesian approach, BETS (Bayesian Evaluation of Temporal Signal), was proposed to overcome known limitations of other commonly used techniques such as root-to-tip regression or date randomisation tests. BETS requires the specification of a full Bayesian phylogenetic model, posing several considerations for untangling the impact of model choice on the detection of temporal signal. Here, we aimed to (i) explore the effect of molecular clock model and tree prior specification on the results of BETS and (ii) provide guidelines for improving our confidence in molecular clock estimates. Using microbial molecular sequence data sets and simulation experiments, we assess the impact of the tree prior and its hyperparameters on the accuracy of temporal signal detection. In particular, highly informative priors that are inconsistent with the data can result in the incorrect detection of temporal signal. In consequence, we recommend: (i) using prior predictive simulations to determine whether the prior generates a reasonable expectation of parameters of interest, such as the evolutionary rate and age of the root node, (ii) conducting prior sensitivity analyses to assess the robustness of the posterior to the choice of prior, and (iii) selecting a molecular clock model that reasonably describes the evolutionary process.

Shared genetic associations and etiology between obstructive sleep apnea and CVDs: A genome-wide cross-trait analysis and bidirectional Mendelian randomization analysis.

To investigate the genetic correlations and potential causal relationships between obstructive sleep apnea (OSA) and various cardiovascular diseases (CVDs), aiming to enhance understanding of shared genetic mechanisms and improve recognition and treatment of OSA in patients with CVDs. Utilizing genome-wide association study (GWAS) data, we analyzed shared genetics between OSA and CVDs using linkage disequilibrium score regression (LDSC), multi-trait analysis of GWAS (MTAG), and genotype-tissue expression analysis (GTEx TSEA). We further investigated causal relationships using Bayesian colocalization tests, bidirectional Mendelian randomization (MR), and latent causal variable (LCV) analysis. We found strong associations between OSA and multiple CVDs: coronary artery disease (CAD), heart failure (HF), myocardial infarction (MI), stroke, and atrial fibrillation (AF). Novel SNPs related to CVDs were identified during single-trait MTAG analysis. By applying cross-trait MTAG, we identified 15 shared loci between OSA and CAD, 25 shared loci between OSA and MI, and 7 shared loci between OSA and HF. Shared genes are primarily expressed in the blood, heart, kidney, liver, muscle, and pancreas. MR analysis indicated a significant causal effect of OSA on HF and AF as a causal factor for OSA. LCV analysis suggested that AF was causally associated with OSA, while HF showed partial causality. Our study suggests strong genetic correlations between OSA and several CVDs. Further research is needed on the associations between OSA and CVDs, as well as the mechanisms of the identified loci.

Hypometric genetics: Improved power in genetic discovery by incorporating quality control flags.

Balancing the tradeoff between quantity and quality of phenotypic data is critical in omics studies. Measurements below the limit of quantification (BLQ) are often tagged in quality control fields, but these flags are currently underutilized in human genetics studies. Extreme phenotype sampling is advantageous for mapping rare variant effects. We hypothesize that genetic drivers, along with environmental and technical factors, contribute to the presence of BLQ flags. Here, we introduce "hypometric genetics" (hMG) analysis and uncover a genetic basis for BLQ flags, indicating an additional source of genetic signal for genetic discovery, especially from phenotypic extremes. Applying our hMG approach to n= 227,469UK Biobank individuals with metabolomic profiles, we reveal more than 5% heritability for BLQ flags and report biologically relevant associations, for example, at APOC3, APOA5, and PDE3B loci. For common variants, polygenic scores trained only for BLQ flags predict the corresponding quantitative traits with 91% accuracy, validating the genetic basis. For rare coding variant associations, we find an asymmetric 65.4% higher enrichment of metabolite-lowering associations for BLQ flags, highlighting the impact of putative loss-of-function variants with large effects on phenotypic extremes. Joint analysis of binarized BLQ flags and the corresponding quantitative metabolite measurements improves power in Bayesian rare variant aggregation tests, resulting in an average of 181% more prioritized genes. Our approach is broadly applicable to omics profiling. Overall, our results underscore the benefit of integrating quality control flags and quantitative measurements and highlight the advantage of joint analysis of population-based samples and phenotypic extremes in human genetics studies.

Design of Bayesian evaluation test for missile damage effectiveness based on multiple damage level standards

The qualified missile damage effectiveness is an important guarantee for giving full play to combat effectiveness. In order to test whether the missile damage effectiveness meets the standard, it is particularly necessary to carry out damage effectiveness identification test research. In view of the limitation that the existing "single-shot damage probability" damage effectiveness quantitative characterization index is difficult to fully describe the missile damage effectiveness based on a single damage standard, considering the different damage level results formed by a single missile hitting the target, the damage effectiveness is characterized by the probability of occurrence of different damage level results. Missile identification from the perspective of multiple damage level standards is conducive to comprehensive inspection of missile damage effectiveness. In addition, in order to overcome the problem of insufficient information in small sample missile assembly tests, the Bayesian method is combined with the system contribution degree to fuse multi-source prior information in the identification test design, and a test scheme with full information utilization and controllable risks for both parties is designed. The example shows that compared with the quantitative characterization index of a single damage standard, this study can fully describe the missile damage effectiveness, and compared with the improved binomial distribution hypothesis test method, the superiority of this method is verified.

Plasma genome-wide mendelian randomization identifies potentially causal genes in idiopathic pulmonary fibrosis

BackgroundIdiopathic pulmonary fibrosis (IPF) is a complex lung disease with a very poor prognosis. Existing drugs for the treatment of IPF are still insufficient. Therefore, there is still a need to explore new drug targets for preventing and treating IPF.MethodsWe included quantitative trait loci (QTL) for genes, DNA methylation, and proteins in plasma, as well as the summary statistics for IPF. Genetic variants located within 500 kb of the gene and strongly associated with plasma exposure were used as instrumental variables. The causal association between plasma exposures and IPF was primarily estimated using summary-data-based Mendelian randomization (SMR) analysis. Five other MR methods and sensitivity analyses were employed to validate the SMR results. Bayesian tests for colocalization between QTL and IPF risk loci further strengthen the MR results.ResultsWe identified three genes and five DNA methylation sites causally associated with IPF by SMR analysis, validation of MR analysis, sensitivity analysis, and colocalization analysis. BTRC and LINC01252 were negatively associated with IPF risk (OR: 0.30, 95% CI: 0.17–0.54, FDRSMR = 0.029; OR: 0.85, 95% CI: 0.78–0.92, FDRSMR = 0.043), and RIPK4 was positively associated with IPF risk (OR: 2.60, 95% CI: 1.64–4.12, FDRSMR = 0.031). cg00045227 (OR8U8, OR: 1.16, 95% CI: 1.08–1.24, FDRSMR = 0.010), cg00577578 (GBAP1, OR: 1.23, 95% CI: 1.12–1.36, FDRSMR = 0.014), cg14222479 (ARPM1, OR: 3.17, 95% CI: 1.98–5.08, FDRSMR = 0.001), and cg19263494 (PMF1, OR: 1.20, 95% CI: 1.10–1.30, FDRSMR = 0.012) were positively associated with the risk of IPF, whereas cg07163735 (MAPT, OR: 0.22, 95% CI: 0.11–0.45, FDRSMR = 0.013) was negatively correlated with the risk of IPF.ConclusionsThis study demonstrated that genetically determined plasma levels of the BTRC, RIPK4, and LINC01252 genes, as well as methylation levels of cg00045227 (OR8U8), cg00577578 (GBAP1), cg07163735 (MAPT), cg14222479 (ARPM1), and cg19263494 (PMF1), have causal influences on the risk of IPF.

Bayesian estimation and comparison of idiographic network models.

Idiographic network models are estimated on time series data of a single individual and allow researchers to investigate person-specific associations between multiple variables over time. The most common approach for fitting graphical vector autoregressive (GVAR) models uses least absolute shrinkage and selection operator (LASSO) regularization to estimate a contemporaneous and a temporal network. However, estimation of idiographic networks can be unstable in relatively small data sets typical for psychological research. This bears the risk of misinterpreting differences in estimated networks as spurious heterogeneity between individuals. As a remedy, we evaluate the performance of a Bayesian alternative for fitting GVAR models that allows for regularization of parameters while accounting for estimation uncertainty. We also develop a novel test, implemented in the tsnet package in R, which assesses whether differences between estimated networks are reliable based on matrix norms. We first compare Bayesian and LASSO approaches across a range of conditions in a simulation study. Overall, LASSO estimation performs well, while a Bayesian GVAR without edge selection may perform better when the true network is dense. In an additional simulation study, the novel test is conservative and shows good false-positive rates. Finally, we apply Bayesian estimation and testing in an empirical example using daily data on clinical symptoms for 40 individuals. We additionally provide functionality to estimate Bayesian GVAR models in Stan within tsnet. Overall, Bayesian GVAR modeling facilitates the assessment of estimation uncertainty which is important for studying interindividual differences of intraindividual dynamics. In doing so, the novel test serves as a safeguard against premature conclusions of heterogeneity. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Exploring Estimation Procedures for Reducing Dimensionality in Psychological Network Modeling.

To understand psychological data, it is crucial to examine the structure and dimensions of variables. In this study, we examined alternative estimation algorithms to the conventional GLASSO-based exploratory graph analysis (EGA) in network psychometric models to assess the dimensionality structure of the data. The study applied Bayesian conjugate or Jeffreys' priors to estimate the graphical structure and then used the Louvain community detection algorithm to partition and identify groups of nodes, which allowed the detection of the multi- and unidimensional factor structures. Monte Carlo simulations suggested that the two alternative Bayesian estimation algorithms had comparable or better performance when compared with the GLASSO-based EGA and conventional parallel analysis (PA). When estimating the multidimensional factor structure, the analytically based method (i.e., EGA.analytical) showed the best balance between accuracy and mean biased/absolute errors, with the highest accuracy tied with EGA but with the smallest errors. The sampling-based approach (EGA.sampling) yielded higher accuracy and smaller errors than PA; lower accuracy but also lower errors than EGA. Techniques from the two algorithms had more stable performance than EGA and PA across different data conditions. When estimating the unidimensional structure, the PA technique performed the best, followed closely by EGA, and then EGA.analytical and EGA.sampling. Furthermore, the study explored four full Bayesian techniques to assess dimensionality in network psychometrics. The results demonstrated superior performance when using Bayesian hypothesis testing or deriving posterior samples of graph structures under small sample sizes. The study recommends using the EGA.analytical technique as an alternative tool for assessing dimensionality and advocates for the usefulness of the EGA.sampling method as a valuable alternate technique. The findings also indicated encouraging results for extending the regularization-based network modeling EGA method to the Bayesian framework and discussed future directions in this line of work. The study illustrated the practical application of the techniques to two empirical examples in R.

Bayesian Optimal Two-Sample Tests for High-Dimensional Gaussian Populations

We propose minimax optimal Bayesian two-sample tests for testing equality of high-dimensional mean vectors and covariance matrices between two populations. In many applications including genomics and medical imaging, it is natural to assume that only a few entries of two mean vectors or covariance matrices are different. Many existing tests that rely on aggregating the difference between empirical means or covariance matrices are not optimal or yield low power under such setups. Motivated by this, we develop Bayesian two-sample tests employing a divide-and-conquer idea, which is powerful especially when the differences between two populations are rare but large. The proposed two-sample tests manifest closed forms of Bayes factors and allow scalable computations even in high-dimensions. We prove that the proposed tests are consistent under relatively mild conditions compared to existing tests in the literature. Furthermore, the testable regions from the proposed tests turn out to be minimax optimal in terms of rates. Simulation studies show clear advantages of the proposed tests over other state-of-the-art methods in various scenarios. Our tests are also applied to the analysis of the gene expression data of two cancer data sets.

Chest X-ray Findings and Prognostic Factors in Survival Analysis in Peritoneal Dialysis and Hemodialysis Patients: A Retrospective Cross-Sectional Study.

Background and Objectives: This study aims to analyze survival in peritoneal and hemodialysis patients using chest radiography and biochemical parameters, determine common dialysis etiologies and causes of death, reveal prognostic factors, and contribute to clinical practice. Materials and Methods: A retrospective cross-sectional study was conducted with data from 33 peritoneal dialysis and 37 hemodialysis patients collected between October 2018 and February 2020. Survival and mortality were retrospectively tracked over 70 months (October 2018-June 2024). Chest X-ray measurements (cardiothoracic index, pulmonary vascular pedicle width, right pulmonary artery diameter, diaphragmatic height) and biochemical parameters (urea, albumin, creatinine, parathormone, ferritin, hemoglobin, arterial blood gas, potassium) were analyzed for their impact on survival. Statistical analyses included descriptive statistics, chi-square test, Fisher's exact test, Bayesian analysis, McNemar test, Kaplan-Meier survival analysis, Cox regression, Bayesian correlation test, linear regression analysis (scatter plot), and ROC analysis. SPSS 20.0 was used for data analysis, with p < 0.05 considered statistically significant. Results: Hypertension, type 2 diabetes, and urogenital disorders were the main dialysis etiologies. Peritonitis (38.5%) and cardiovascular diseases (47.4%) were the leading causes of death in peritoneal and hemodialysis patients, respectively. Significant chest X-ray differences included pulmonary vascular pedicle width and pulmonary artery diameter in hemodialysis and diaphragm height in peritoneal dialysis. Kaplan-Meier showed no survival difference between methods. Cox regression identified age, intact parathormone levels, iPTH/PVPW ratio, and clinical status as survival and mortality factors. The iPTH/PVPW ratio cut-off for mortality prediction was ≤6.8. Conclusions: Age, intact parathormone levels, pulmonary vascular pedicle width, and clinical status significantly impact survival in dialysis patients. Management of hypertension and diabetes, management and follow-up of urogenital disorders, infection control, patient education, and regular cardiovascular check-ups may improve survival rates. Additionally, the iPTH/PVPW ratio can predict mortality risk.

A Bayesian hypothesis testing model for quality control of mass customized cabinet manufacturing process

The demand for customization of cabinets and homes has increased, and customized product components are cumbersome in size and time-consuming. In order to realize the mass production of customized cabinet furniture, research from the custom design, optimization of factory machine production process and quality inspection, as well as the flow of the way, is to realize the fundamental method of large-scale production of cabinet furniture. Based on modern intelligent information processing functions instead of manual ordering, order splitting, and unit parts for reasonable division of classification, optimize the topology of the production line, to achieve modern intelligent production of panels. However, in the production process, there is a lack of on-line inspection of some links, and the yield of the product is affected. This thesis establishes a Bayesian hypothesis testing model based on the topological model of the production line process, and through the actual production data, the defective nodes of the manufacturing process are retraced to achieve the purpose of optimizing the customized production.

Right anterior insula ASL hypoperfusion as a diagnostic biomarker of prodromal and mild dementia with Lewy bodies: preliminary evidence using a Bayesian approach.

Identifying and validating a biomarker with high specificity in early-stage dementia with Lewy bodies (DLB) using a feasible method is crucial to enhance the current suboptimal diagnostic procedure. Previous research revealed abnormalities, including hypoperfusion in the right anterior insular cortex at group level, in prodromal DLB. Exploring hypoperfusion of the right anterior insula, at an individual-level and assessing its relevance as a potential imaging biomarker in early DLB, has, to our knowledge, not been investigated. Our preliminary study aims to assess the feasibility of the technique and to provide a methodological framework for further investigation. We assessed the feasibility and accuracy of the hypoperfusion of the right anterior insula per arterial spin labelling magnetic resonance imaging (ASL-MRI) as a diagnostic biomarker in early DLB and provided rough estimates of its sensitivity and specificity. Defining the region of interest based on previous research, we established the biomarker as the hypoperfusion of the right anterior insula. Discriminative and analytical performances were assessed in comparison to a control group of treatment-resistant depression patients. Bayesian diagnostic reasoning was employed to assess the biomarker diagnostic usability in early DLB in two scenarios: healthy elderly controls and mild cognitive impairment. Additionally, we updated probabilities by integrating data from the Mayo-clinic cognitive fluctuations scale and real-time quaking-induced conversion (RT-QuIC) α-synuclein data. Lastly, a whole-brain perfusion analysis of DLB patients was conducted to identify further brain regions with discriminative abilities. We successfully replicated the right anterior insular hypoperfusion (RAI-Hypo) in all DLB patients at the individual level. The overall sensitivity of the biomarker was 96%, and the specificity was 92%. Bayesian testing revealed the biomarker's highest performance in early-stage DLB with cognitive fluctuations, showcasing a diagnostic potential associated with a high precision and moderate accuracy. In a cognitively non-impaired population, the RAI-Hypo showed a limited usability and lacked in selectivity to qualify as a screening tool. The exploratory whole-brain analysis revealed perfect discriminative capacities in the bilateral anterior insulae and the left inferior parietal lobule. Further studies are needed to confirm our preliminary results. If performance is maintained in subsequent studies and is compared to a more suitable control population, the proposed biomarker may be eventually sufficient to discriminate early-stage DLB from non-DLB.

A Bayesian flexible model for testing Granger causality

A new Bayesian hypothesis testing procedure for evaluating the Granger causality between two or more time series is proposed. The test is based on a flexible model for the joint evolution of multiple series, where a latent binary matrix indicates whether there is a Granger-causal relationship between such time series. The model is specified through a dependent Geometric stick-breaking process that generalizes the standard parametric Gaussian vector autoregression model, whereas the prior distribution of the latent matrix ensures a multiple testing correction. A Monte Carlo simulation study is provided for comparing the robustness of the proposed hypothesis test with state-of-the-art alternatives. The results show that this proposal performs better than competing approaches. Finally, the new test is applied to real economic data.

Bayesian group testing regression models for spatial data

Spatial patterns are common in infectious disease epidemiology. Disease mapping is essential to infectious disease surveillance. Under a group testing protocol, biomaterial from multiple individuals is physically combined into a pooled specimen, which is then tested for infection. If the pool tests negative, all contributing individuals are generally assumed to be uninfected. If the pool tests positive, the individuals are usually retested to determine who is infected. When the prevalence of infection is low, group testing provides significant cost savings over traditional individual testing by reducing the number of tests required. However, the lack of statistical methods capable of producing maps from group testing data has limited the use of group testing in disease mapping. We develop a Bayesian methodology that can simultaneously map disease prevalence using group testing data and identify risk factors for infection. We illustrate its real-world utility using two datasets from vector-borne disease surveillance.

Bayesian non-parametric modeling by mixture of Kibble-Pólya tree to detect Low-Activity uranium contamination

Accurate detection of low-level radioactivity is critical for decommissioning projects in nuclear facilities, particularly in the design of radiation monitoring systems with a low false alarm rate. Utilizing a non-parametric Bayesian continuous probability distribution enables reliable mapping of potential contamination. Our method introduces a statistical test based on a Pólya tree prior, applied to radiation detection. The detection efficiency of this proposed Bayesian test is quantified using receiver-operating characteristic (ROC) curves and compared to a Bayesian test based on the Kibble bivariate gamma distribution developed for the same purpose. The results demonstrate that the new Bayesian test generally outperforms the previous method in terms of detection performance under very low signal-to-noise ratios, with improvements ranging from 3% to 28% against both stationary and non-stationary radiological backgrounds, respectively. This superiority is further reaffirmed through comparisons with alternative Bayesian and frequentist hypothesis tests, with gains estimated at 52% and 4%, respectively.

A class of sequential multi-hypothesis tests

In this article, we deal with sequential testing of multiple hypotheses. In the general scheme of construction of optimal tests based on the backward induction, we propose a modification which provides a simplified (generally speaking, and suboptimal) version of the optimal test, for any particular criterion of optimization. We call this DBC version (the one with Dropped Backward Control) of the optimal test. In particular, for the case of two simple hypotheses, dropping backward control in the Bayesian test produces the classical sequential probability ratio test (SPRT). Similarly, dropping backward control in the modified Kiefer-Weiss solutions produces Lorden’s 2-SPRTs. In the case of more than two hypotheses, we obtain in this way new classes of sequential multi-hypothesis tests, and investigate their properties. The efficiency of the DBC-tests is evaluated with respect to the optimal Bayesian multi-hypothesis test and with respect to the matrix sequential probability ratio test (MSPRT) by Armitage. In a multihypothesis variant of the Kiefer-Weiss problem for binomial proportions the performance of the DBC-test is numerically compared with that of the exact solution. In a model of normal observations with a linear trend, the performance of the DBC-test is numerically compared with that of the MSPRT. Some other numerical examples are presented. In all the cases the proposed tests exhibit a very high efficiency with respect to the optimal tests (more than 99.3% when sampling from Bernoulli populations) and/or with respect to the MSPRT (even outperforming the latter in some scenarios).

Comparative host transcriptomics as a tool to identify candidate biomarkers for immune reactions in leprosy using meta-analysis.

Background Leprosy is no longer considered an imprecation, as an effective multidrug therapy regimen is available worldwide for its cure. However, its diverse clinical manifestations sometimes involve acute inflammatory reactions. These complications result in irreversible nerve damage, neuritis and anatomical deformities that emerge before, during the treatment or after the completion of treatment. Reversal reaction (Type-I) and erythema nodosum leprosum (Type-II) are the leprosy reactions generally seen in patients with lepromatous and borderline forms of leprosy. At present, there is no accurate diagnostic test available to detect these leprosy reactions. Objectives To identify potential biomarkers indicative of Type-I and Type-II leprosy reactions that could help in their early diagnosis. Methods and Results Host-transcriptomics investigations have been utilised in this study to decipher a correlation between host-gene expression-based biomarkers and exacerbation of leprosy reactions. We present a comparative analysis of publicly available host transcriptomics datasets (from Gene Expression Omnibus) related to leprosy reactions. Individual datasets were analysed and integration of results was carried out using meta-analysis. Common differentially expressed genes (DEGs) were identified using the frequentist and Bayesian ratio association test methods. We have identified several genes - ADAMTS5, ADAMTS9, IFITM2, IFITM3, KIRREL, ANK3, CD1E, CTSF, DOCK9 and KRT73 to name a few - which can serve as potential biomarkers for Type-II reaction. Similarly, ACP5, APOC1, CCL17, S100B, SLC11A1 among others may likely serve as biomarkers for Type-I reaction. Limitations The number of datasets related to leprosy reactions found after the systematic search is less (n = 4) and may limit the accuracy of identified biomarker genes. This could be resolved by including more studies in the data analysis. Conclusion We provide a comprehensive list of gene candidates which could be prioritised further in research focusing on immune reactions in leprosy, as they are likely important in understanding its complexities and could be useful in its early diagnosis.

Modeling compositional heterogeneity resolves deep phylogeny of flowering plants

Angiosperms experienced one of the most remarkable radiations of land plants and are now the dominant autotrophs in terrestrial ecosystems. Recent phylogenomic studies based on large-scale data from plastid, mitochondrial, or nuclear transcriptomes/genomes and increased taxon sampling have provided unprecedent resolution into the phylogeny of flowering plants. However, owing to ancient rapid radiations, the interrelationships among the five lineages of Mesangiospermae, the vast majority of angiosperms, remain contentious. Here we show that, although plastid and mitochondrial genomes lack sufficient phylogenetic signal for resolving deeper phylogeny, the relationships among five mesangiosperm lineages can be confidently resolved under better-fitting models using genome-scale data. According to our Bayesian cross-validation and model test in a maximum likelihood framework, site-heterogeneous models (e.g., CAT-GTR + G4, LG + C20 + F + G) outperform site-homogeneous or partition models often used in previous studies. Under site-heterogeneous models, the approximately unbiased test favored our preferred tree recovered from various datasets: Ceratophyllales (coontails) are robustly recovered as sister to monocots, and they together are sister to the clade comprising magnoliids, Chloranthales, and eudicots. Our phylogenomic analyses resolve the last enigma of the deeper phylogeny of angiosperms and emphasize the efficacy of modeling compositional heterogeneity in resolving rapid radiations of plants.

A Sustainable Revolution in Sisal Fiber with Enhanced Mechanical Properties of Concrete

Introduction This study provides a comprehensive comparative analysis of conventional concrete (CC) and Sisal Fiber Concrete (SFC) and incorporates sisal fiber into a concrete blend for the M25 grade concrete mix. Methods In order to evaluate the efficacy of both variations of concrete, mechanical and durability parameters were examined. As compared to CC, the results indicated that SFC had a substantially greater compressive strength. The average compressive strength of SFC at the 28-day was 29.47 N/mm2, which signified a significant incremental percentage growth of 9.58% in comparison to CC. In the same way, SFC exhibited an exceptional flexural strength, as evidenced by its mean value of 7.81 N/mm2, which represented a significant 34.42% improvement in comparison to CC. The Bayesian factor independent sample test yielded a t-test value of 12.495 for compressive strength, accompanied by a p-value below 0.001. These results suggest that the observed difference was statistically significant. Conversely, a t-test value of 19.380 and a p-value below 0.001 were produced by the Bayesian factor independent sample test for flexural strength, both of which further supported the existence of a significant difference. The mean disparity in compressive strength between CC and SFC was 5.1522 N/mm2, with a 95% confidence interval encompassing values between 4.2856 and 6.0188 N/mm2. In a similar manner, the mean discrepancy in flexural strength was 2.0000 N/mm2, accompanied by a 95% confidence interval that varied between 1.7831 and 2.2169 N/mm2. Results The obtained results provide further evidence that SFC is stronger than CC in both compressive and flexural strength, which is consistent with the results obtained from the frequentist statistical analysis. Conclusion With its eco-friendly properties, sisal fiber concrete could indeed play a significant role in the future of sustainable construction.