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Introduction: Ototoxicity is a debilitating side effect of over 150 medications with diverse mechanisms of action, many of which could be taken concurrently to treat multiple conditions. Approaches for preclinical evaluation of drug-drug interactions that might impact ototoxicity would facilitate design of safer multi-drug regimens and mitigate unsafe polypharmacy by flagging combinations that potentially cause adverse interactions for monitoring. They may also identify protective agents that antagonize ototoxic injury.Methods: To address this need, we have developed a novel workflow that we call Parallelized Evaluation of Protection and Injury for Toxicity Assessment (PEPITA), which empowers high-throughput, semi-automated quantification of ototoxicity and otoprotection in zebrafish larvae via microscopy. We used PEPITA and confocal microscopy to characterize in vivo the consequences of drug-drug interactions on ototoxic drug uptake and cellular damage of zebrafish lateral line hair cells.Results and discussion: By applying PEPITA to measure ototoxic drug interaction outcomes, we discovered antagonistic interactions between macrolide and aminoglycoside antibiotics that confer protection against aminoglycoside-induced damage to lateral line hair cells in zebrafish larvae. Co-administration of either azithromycin or erythromycin in zebrafish protected against damage from a broad panel of aminoglycosides, at least in part via inhibiting drug uptake into hair cells via a mechanism independent from hair cell mechanotransduction. Conversely, combining macrolides with aminoglycosides in bacterial inhibition assays does not show antagonism of antimicrobial efficacy. The proof-of-concept otoprotective antagonism suggests that combinatorial interventions can potentially be developed to protect against other forms of toxicity without hindering on-target drug efficacy.

Information is the cornerstone of research, from experimental (meta)data and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems to transform this large information load into useful scientific findings.

Halting tuberculosis transmission is crucial to TB elimination. Here the authors implicate IL-1R dependent T cell priming as the underlying mechanism determining variability in transmission of Mycobacterium tuberculosis strains.

Woven fabric composite structures are applied in a wide range of industrial applications. Composite structures are vulnerable to damage from working in complex conditions and environments, which threatens the safety of the in-service structure. Damage detection based on Lamb waves is one of the most promising structural health monitoring (SHM) techniques for composite materials. In this paper, based on guided Lamb waves, a lightweight deep-learning approach is proposed for identifying damaged regions in woven fabric composite structures. The designed deep neural networks are built using group convolution and depthwise separated convolution, which can reduce the parameters considerably. The input of this model is a multi-channel matrix transformed by a one-dimensional guided wave signal. In addition, channel shuffling is introduced to increase the interaction between features, and a multi-head self-attention module is designed to increase the model’s global modeling capabilities. The relevant experimental results show that the proposed SHM approach can achieve a recognition accuracy of 100% after only eight epochs of training, and the proposed LCANet has only 4.10% of the parameters of contrastive SHM methods, which further validates the effectiveness and reliability of the proposed method.

ABSTRACTBrucellosis caused by Brucella melitensis is considered to be one of the most important zoonotic diseases in China. In this study, Conventional bio-typing, MLVA (multiple locus variable-number tandem repeat analysis), and WGS (whole-genome sequencing)-SNP (single nucleotide polymorphism) were used to study the genetic similarity of B. melitensis in northern and southern China and analyze its relationship with worldwide lineages. Currently, the distribution of species/biovars of B. melitensis has obviously changed, and B. melitensis has become the dominant species in southern regions of China. Strains from the southern had a common geographic origin with strains from the northern. Many MLVA-16 events were shared in the genotypes of the southern and northern strains, suggest that genotypic movement occurred from north to south. Based on WGS-SNP analysis, strains from different provinces were closely related and may have descended from one common ancestor, suggests that the southern strains originated from northern China. These data indicate that B. melitensis is a latent “travel bacterium” that spread and expanded from North China to South China. Moreover, B. melitensis strains from China are also genetically related to strains from other Asian regions (Kazakhstan, Russia, Mongolia, and India). The movement of infected sheep and their products requires control.

The mutual interactions of Lamb waves in nonlinear elastic plates are studied in this article. Many researchers have investigated the interactions of Lamb wave modes at nonlinear higher harmonics. However, little is known about nonlinearity-driven Lamb modulations from two primary modes with different frequencies. In this study, the existence of symmetric or antisymmetric mode due to Lamb wave mutual interactions is firstly theoretically formulated. Then, an approach is proposed to evaluate the intensity of phase velocity matching for selecting primary modes. Finally, the characteristics of the modulated wave generation are investigated and demonstrated. The generation of modulated waves in an aluminum plate and fatigue crack can be detected by mutual interactions of Lamb waves. The main contribution of this work is the proposed mutual interaction theory of Lamb waves in fatigue plates, which can guide fatigue detection in the metal plate.

According to the lack of theoretical research on the spatial effect of the foundation pit, the concept of the reasonable arch curve (bending moment on the curve is zero everywhere) is introduced to establish a new three-dimensional instability failure theoretical model of the water-rich foundation pit. Taking the K6 + 650 ∼ K6 + 880 section of the GUI River Foundation Pit as an example, the active earth pressure acting on the supporting structure per unit length, the critical length–depth ratio, the main influence range, and the influence coefficient of spatial effect are calculated and compared with previous research results and field monitoring data, in order to explore the influence law of the length, depth, and moisture content of soil layers on the spatial effect. The results show the following: (1) When the length–depth ratio is greater than 1, the foundation pit presents the failure mode of the end arch curve and middle straight line, and when the length–depth ratio is greater than 3, the main influence range has a linear relationship with the depth and a negative correlation with the moisture content, but little relationship with the length. (2) The active earth pressure is approximately a quadratic function of the depth, and there is a more than 16% increase when the moisture content increases from 21% to 25%. (3) The change law of the horizontal displacement of the pile top monitored verifies the rationality and progressiveness of the new instability failure model. Through this study, the spatial effect revealed can provide the theoretical basis for pile support of the foundation pit.

Bars are significant load-carrying components in engineering structures. In particular, L-bars are typical structural components commonly used in truss structures and have typical irregular asymmetric cross-sections. To ensure the safety of load-carrying bars, much research has been done for non-destructive testing (NDT). Ultrasonic guided waves have been widely applied in various NDT techniques for bars as a result of the long-range propagation, low attenuation, and high sensitivity to damages. Though good for inspection of ultrasonic guided waves in symmetric cross-section bar-like structures, the application in asymmetric ones lacks further research. Moreover, traditional damage detection in bars using ultrasonic guided waves usually depends on a single-mode at a lower frequency with lower sensitivity and accuracy. To make full use of all frequencies and modes, a multi-mode characteristic-based damage detection method is presented with the sum of multiple signals (SoM) strategy for L-bars with asymmetric cross-section. To control the desired mode in multi-mode ultrasonic guided waves, excitation optimization and weighted gathering are carried out by the analysis of the semi-analytical finite element (SAFE) method and the normal mode expansion (NME) method. An L-bar example with the asymmetric cross-section of 35 mm × 20 mm × 3 mm is used to specialize the proposed method, and some finite element (FE) models have been simulated to validate the mode control. In addition, one PZT is applied as a contrast in order to validate the multielement mode control. Then, more FE simulations experiments for damage detection have been performed to validate the damage detection method and verify the improvement in detection accuracy and damage sensitivity.

ABSTRACT The rapid spread of multidrug-resistant strains has created a pressing need for new drug regimens to treat tuberculosis (TB), which kills 1.8 million people each year. Identifying new regimens has been challenging due to the slow growth of the pathogen Mycobacterium tuberculosis (MTB), coupled with the large number of possible drug combinations. Here we present a computational model (INDIGO-MTB) that identified synergistic regimens featuring existing and emerging anti-TB drugs after screening in silico more than 1 million potential drug combinations using MTB drug transcriptomic profiles. INDIGO-MTB further predicted the gene Rv1353c as a key transcriptional regulator of multiple drug interactions, and we confirmed experimentally that Rv1353c upregulation reduces the antagonism of the bedaquiline-streptomycin combination. A retrospective analysis of 57 clinical trials of TB regimens using INDIGO-MTB revealed that synergistic combinations were significantly more efficacious than antagonistic combinations (P value = 1 × 10−4) based on the percentage of patients with negative sputum cultures after 8 weeks of treatment. Our study establishes a framework for rapid assessment of TB drug combinations and is also applicable to other bacterial pathogens. IMPORTANCE Multidrug combination therapy is an important strategy for treating tuberculosis, the world’s deadliest bacterial infection. Long treatment durations and growing rates of drug resistance have created an urgent need for new approaches to prioritize effective drug regimens. Hence, we developed a computational model called INDIGO-MTB that identifies synergistic drug regimens from an immense set of possible drug combinations using the pathogen response transcriptome elicited by individual drugs. Although the underlying input data for INDIGO-MTB was generated under in vitro broth culture conditions, the predictions from INDIGO-MTB correlated significantly with in vivo drug regimen efficacy from clinical trials. INDIGO-MTB also identified the transcription factor Rv1353c as a regulator of multiple drug interaction outcomes, which could be targeted for rationally enhancing drug synergy.

Axial stress monitoring in arbitrary cross sections is a challenging task. Stringers are the main axial load carrying components of aircraft skin structures and have typical complex cross sections. This paper investigates the strategy of axial stress monitoring in an arbitrary cross section based on acoustoelastic guided waves using piezoelectric lead zirconate titanate (PZT) sensors. To select appropriate guided wave frequencies and modes sensitive for axial stress monitoring in an arbitrary cross section, the feature guided waves are investigated using acoustoelastic theory combined with the semianalytical finite element method. The mode shapes are derived, which show that these longitudinal-like modes are more sensitive to axial stress. A PZT transducer array is also considered to maximize desired modes. Piezoelectric sensors are used to excite and detect the guided waves in the experiments. Results from acoustoelastic measurements on a T-type stringer are presented, showing the feasibility of this method for axial stress monitoring.

Research on the dynamic compressive characteristics of sandstone under radial gradient stress and confining pressure is conducive to understanding the characteristics of the surrounding rock, especially in an excavation operation for an underground mine roadway and tunnel. The present work aimed at studying the effects of radial gradient stress and confining pressure on the impact of compression of sandstone using a large-diameter split Hopkinson pressure bar. The results showed that the dynamic strength of sandstone under radial gradient stress increased with strain rate following a power function, and the dynamic strength of the sandstone under radial gradient stress was lower and more sensitive to strain rate. The increase in strain at peak stress (peak strain) was linearly correlated with the strain rate under different confining pressures. The sensitivity of the peak strain to confining pressure was lower for the sandstone with a hole, while the values of the elastic modulus were decreased. However, further increasing the stain rate would lead to an increase in the elastic modulus. Also, the ductility of the sandstone with a hole tested in this study was found to improve more significantly. Finally, with an increase in the incident energy, the absorbed energy per unit volume would increase, but would not be affected obviously by the radial gradient stress.

Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.

The importance of fucoidan as a functional ingredient in food, health products, and pharmaceutics is well-recognized due to its beneficial biological effects. Fucoidan is usually extracted from brown seaweeds, including Undaria pinnatifida. Fucoidan exhibits beneficial bio-activity and has antioxidant, anticancer, and anticoagulant properties. This review focuses on the biological activity of U. pinnatifida-derived fucoidan and investigates its structure–activity or fraction–activity relationship. It also describes several fucoidan extracts, along with their claimed anticancer effects. It aims to provide information and thoughts for future research such as the development of fucoidan into functional foods or nutraceuticals.

Mycobacterium tuberculosis (MTB) is the causative bacterium of tuberculosis, a disease responsible for over a million deaths worldwide annually with a growing number of strains resistant to antibiotics. The development of better therapeutics would greatly benefit from improved understanding of the mechanisms associated with MTB responses to different genetic and environmental perturbations. Therefore, we expanded a genome-scale regulatory-metabolic model for MTB using the Probabilistic Regulation of Metabolism (PROM) framework. Our model, MTBPROM2.0, represents a substantial knowledge base update and extension of simulation capability. We incorporated a recent ChIP-seq based binding network of 2555 interactions linking to 104 transcription factors (TFs) (representing a 3.5-fold expansion of TF coverage). We integrated this expanded regulatory network with a refined genome-scale metabolic model that can correctly predict growth viability over 69 source metabolite conditions and predict metabolic gene essentiality more accurately than the original model. We used MTBPROM2.0 to simulate the metabolic consequences of knocking out and overexpressing each of the 104 TFs in the model. MTBPROM2.0 improves performance of knockout growth defect predictions compared to the original PROM MTB model, and it can successfully predict growth defects associated with TF overexpression. Moreover, condition-specific models of MTBPROM2.0 successfully predicted synergistic growth consequences of overexpressing the TF whiB4 in the presence of two standard anti-TB drugs. MTBPROM2.0 can screen in silico condition-specific transcription factor perturbations to generate putative targets of interest that can help prioritize future experiments for therapeutic development efforts.

BACKGROUND:The biomarker discovery field is replete with molecular signatures that have not translated into the clinic despite ostensibly promising performance in predicting disease phenotypes. One widely cited reason is lack of classification consistency, largely due to failure to maintain performance from study to study. This failure is widely attributed to variability in data collected for the same phenotype among disparate studies, due to technical factors unrelated to phenotypes (e.g., laboratory settings resulting in "batch-effects") and non-phenotype-associated biological variation in the underlying populations. These sources of variability persist in new data collection technologies. METHODS:Here we quantify the impact of these combined "study-effects" on a disease signature's predictive performance by comparing two types of validation methods: ordinary randomized cross-validation (RCV), which extracts random subsets of samples for testing, and inter-study validation (ISV), which excludes an entire study for testing. Whereas RCV hardwires an assumption of training and testing on identically distributed data, this key property is lost in ISV, yielding systematic decreases in performance estimates relative to RCV. Measuring the RCV-ISV difference as a function of number of studies quantifies influence of study-effects on performance. RESULTS:As a case study, we gathered publicly available gene expression data from 1,470 microarray samples of 6 lung phenotypes from 26 independent experimental studies and 769 RNA-seq samples of 2 lung phenotypes from 4 independent studies. We find that the RCV-ISV performance discrepancy is greater in phenotypes with few studies, and that the ISV performance converges toward RCV performance as data from additional studies are incorporated into classification. CONCLUSIONS:We show that by examining how fast ISV performance approaches RCV as the number of studies is increased, one can estimate when "sufficient" diversity has been achieved for learning a molecular signature likely to translate without significant loss of accuracy to new clinical settings.

We utilized abundant transcriptomic data for the primary classes of brain cancers to study the feasibility of separating all of these diseases simultaneously based on molecular data alone. These signatures were based on a new method reported herein--Identification of Structured Signatures and Classifiers (ISSAC)--that resulted in a brain cancer marker panel of 44 unique genes. Many of these genes have established relevance to the brain cancers examined herein, with others having known roles in cancer biology. Analyses on large-scale data from multiple sources must deal with significant challenges associated with heterogeneity between different published studies, for it was observed that the variation among individual studies often had a larger effect on the transcriptome than did phenotype differences, as is typical. For this reason, we restricted ourselves to studying only cases where we had at least two independent studies performed for each phenotype, and also reprocessed all the raw data from the studies using a unified pre-processing pipeline. We found that learning signatures across multiple datasets greatly enhanced reproducibility and accuracy in predictive performance on truly independent validation sets, even when keeping the size of the training set the same. This was most likely due to the meta-signature encompassing more of the heterogeneity across different sources and conditions, while amplifying signal from the repeated global characteristics of the phenotype. When molecular signatures of brain cancers were constructed from all currently available microarray data, 90% phenotype prediction accuracy, or the accuracy of identifying a particular brain cancer from the background of all phenotypes, was found. Looking forward, we discuss our approach in the context of the eventual development of organ-specific molecular signatures from peripheral fluids such as the blood.

Introduction: Ototoxicity is a debilitating side effect of over 150 medications with diverse mechanisms of action, many of which could be taken concurrently to treat multiple conditions. Approaches for preclinical evaluation of drug-drug interactions that might impact ototoxicity would facilitate design of safer multidrug regimens and mitigate unsafe polypharmacy by flagging combinations that potentially cause adverse interactions for monitoring. They may also identify protective agents that antagonize ototoxic injury. Methods: To address this need, we have developed a novel workflow that we call Parallelized Evaluation of Protection and Injury for Toxicity Assessment (PEPITA), which empowers high-throughput, semi-automated quantification of ototoxicity and otoprotection in zebrafish larvae via microscopy. We used PEPITA and confocal microscopy to characterize in vivo the consequences of drug-drug interactions on ototoxic drug uptake and cellular damage of zebrafish lateral line hair cells. Results and discussion: By applying PEPITA to measure ototoxic drug interaction outcomes, we discovered antagonistic interactions between macrolide and aminoglycoside antibiotics that confer protection against aminoglycosideinduced damage to lateral line hair cells in zebrafish larvae. Co-administration of either azithromycin or erythromycin in zebrafish protected against damage from a broad panel of aminoglycosides, at least in part via inhibiting drug uptake into hair cells via a mechanism independent from hair cell mechanotransduction. Conversely, combining macrolides with aminoglycosides in bacterial inhibition assays does not show antagonism of antimicrobial efficacy. The proof-of-concept otoprotective antagonism suggests that combinatorial interventions can potentially be developed to protect against other forms of toxicity without hindering on-target drug efficacy. [ABSTRACT FROM AUTHOR]

Gaseous nitrous acid (HONO) is a critical source of hydroxyl radicals (OH) in the troposphere. While both direct and secondary sources contribute to atmospheric HONO, direct emissions have traditionally been considered minor contributors. In this study, we developed δ15N and δ18O isotopic fingerprints to identify six direct HONO emission sources and conducted a 1-y case study on the isotopic composition of atmospheric HONO at rural and urban sites. Interestingly, we identified that livestock farming is a previously overlooked direct source of HONO and determined its HONO to ammonia (NH3) emission ratio. Additionally, our results revealed that spatial and temporal variations in atmospheric HONO isotopic composition can be partially attributed to direct emissions. Through a detailed HONO budget analysis incorporating agricultural sources, we found that direct HONO emissions accounted for 39~45% of HONO production in rural areas across different seasons. The findings were further confirmed by chemistry transport model simulations, highlighting the significance of direct HONO emissions and their impact on air quality in the North China Plain. These findings provide compelling evidence that direct HONO emissions play a more substantial role in contributing to atmospheric HONO than previously believed. Moreover, the δ15N and δ18O isotopic fingerprints developed in this study may serve as a valuable tool for further research on the atmospheric chemistry of reactive nitrogen gases. [ABSTRACT FROM AUTHOR]