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Buruli ulcer (BU) is an emerging infectious disease that causes disfiguring skin ulcers. The causative agent, Mycobacterium ulcerans , secretes toxin called mycolactone that triggers inflammation and immunopathology. Existing treatments are lengthy and consist of drugs developed for tuberculosis. Here, we report that a pyrazolo[1,5-a]pyridine-3-carboxamide, TB47, is highly bactericidal against M. ulcerans both in vitro and in vivo. In the validated mouse model of BU, TB47 alone reduces M. ulcerans burden in mouse footpads by more than 2.5 log 10 CFU compared to the standard BU treatment regimen recommended by the WHO. We show that mutations of ubiquinol-cytochrome C reductase cytochrome subunit B confer resistance to TB47 and the dissimilarity of CydABs from different mycobacteria may account for their differences in susceptibility to TB47. TB47 is highly potent against M. ulcerans and possesses desirable pharmacological attributes and low toxicity that warrant further assessment of this agent for treatment of BU. Combination therapy for Buruli ulcer (BU) is suboptimal. Here, Liu et al. show that the candidate drug TB47 has potent bactericidal activity against Mycobacterium ulcerans in vitro and in a mouse model, which underscores its potential for shortening the course of BU and treating other mycobacterial diseases.

Metallic alloys are routinely subjected to nonequilibrium processes during manufacturing, such as rapid solidification and thermomechanical processing. It has been suggested in the high-entropy alloy literature that chemical short-range order (SRO) could offer a new knob to tailor materials properties. While evidence of the effect of SRO on materials properties accumulates, the state of SRO evolution during alloy manufacturing remains obscure. Here, we employ high-fidelity atomistic simulations to track SRO evolution during the solidification and thermomechanical processing of alloys. Our investigation reveals that alloy processing can lead to nonequilibrium steady-states of SRO that are different from any equilibrium state. The mechanism behind nonequilibrium SRO formation is shown to be an inherent ordering bias present in nonequilibrium events. These results demonstrate that conventional manufacturing processes provide pathways for tuning SRO that lead to a broad nonequilibrium spectrum of SRO states beyond the equilibrium design space of alloys. This study reveals that traditional manufacturing can create nonequilibrium short-range order in metallic alloys, offering an additional dimension for tailoring alloy properties beyond composition and microstructure.

Much research has been done on time series of financial market in last two decades using linear and non-linear correlation of the returns of stocks. In this paper, we design a method of network reconstruction for the financial market by using the insights from machine learning tool. To do so, we analyze the time series of financial indices of S&P 500 around some financial crises from 1998 to 2012 by using feature ranking approach where we use the returns of stocks in a certain day to predict the feature ranks of the next day. We use two different feature ranking approaches—Random Forest and Gradient Boosting—to rank the importance of each node for predicting the returns of each other node, which produces the feature ranking matrix. To construct threshold network, we assign a threshold which is equal to mean of the feature ranking matrix. The dynamics of network topology in threshold networks constructed by new approach can identify the financial crises covered by the monitored time series. We observe that the most influential companies during global financial crisis were in the sector of energy and financial services while during European debt crisis, the companies are in the communication services. The Shannon entropy is calculated from the feature ranking which is seen to increase over time before market crash. The rise of entropy implies the influences of stocks to each other are becoming equal, can be used as a precursor of market crash. The technique of feature ranking can be an alternative way to infer more accurate network structure for financial market than existing methods, can be used for the development of the market.

Counting the number of people in a room is crucial for optimizing smart buildings, enhancing energy efficiency, and ensuring security while preserving privacy. This study introduces a novel radar-based occupancy estimation method leveraging a 24-GHz Continuous Wave (CW) radar system integrated with time-frequency mapping techniques using Continuous Wavelet Transform (CWT) and power spectrum analysis. Unlike previous studies that rely on WiFi or PIR-based sensors, this approach provides a robust alternative without privacy concerns. The time-frequency scalograms generated from radar echoes were used to train deep-learning models, including DarkNet19, MobileNetV2, and ResNet18. Experiments conducted with sedentary occupants over 4 hours and 40 minutes resulted in 1680 image samples. The proposed approach achieved high accuracy, with DarkNet19 performing the best, reaching 92.7% on CWT images and 92.3% on power spectrum images. Additionally, experiments in a walking environment with another continuous 1 hour of data achieved 86.5% accuracy, demonstrating the method’s effectiveness beyond static scenarios. These results confirm that CW radar with deep learning can enable non-intrusive, privacy-preserving occupancy estimation for smart building applications.

Mycotoxins are secondary metabolites of filamentous fungi that contaminate food products such as fruits, vegetables, cereals, beverages, and other agricultural commodities. Their occurrence in the food chain, especially in beverages, can pose a serious risk to human health, due to their toxicity, even at low concentrations. Mycotoxins, such as aflatoxins (AFs), ochratoxin A (OTA), patulin (PAT), fumonisins (FBs), trichothecenes (TCs), zearalenone (ZEN), and the alternaria toxins including alternariol, altenuene, and alternariol methyl ether have largely been identified in fruits and their derived products, such as beverages and drinks. The presence of mycotoxins in beverages is of high concern in some cases due to their levels being higher than the limits set by regulations. This review aims to summarize the toxicity of the major mycotoxins that occur in beverages, the methods available for their detection and quantification, and the strategies for their control. In addition, some novel techniques for controlling mycotoxins in the postharvest stage are highlighted.

Photovoltaic (PV) fault detection is crucial because undetected PV faults can lead to significant energy losses, with some cases experiencing losses of up to 10%. The efficiency of PV systems depends upon the reliable detection and diagnosis of faults. The integration of Artificial Intelligence (AI) techniques has been a growing trend in addressing these issues. The goal of this systematic review is to offer a comprehensive overview of the recent advancements in AI-based methodologies for PV fault detection, consolidating the key findings from 31 research papers. An initial pool of 142 papers were identified, from which 31 were selected for in-depth review following the PRISMA guidelines. The title, objective, methods, and findings of each paper were analyzed, with a focus on machine learning (ML) and deep learning (DL) approaches. ML and DL are particularly suitable for PV fault detection because of their capacity to process and analyze large amounts of data to identify complex patterns and anomalies. This study identified several AI techniques used for fault detection in PV systems, ranging from classical ML methods like k-nearest neighbor (KNN) and random forest to more advanced deep learning models such as Convolutional Neural Networks (CNNs). Quantum circuits and infrared imagery were also explored as potential solutions. The analysis found that DL models, in general, outperformed traditional ML models in accuracy and efficiency. This study shows that AI methodologies have evolved and been increasingly applied in PV fault detection. The integration of AI in PV fault detection offers high accuracy and effectiveness. After reviewing these studies, we proposed an Artificial Neural Network (ANN)-based method for PV fault detection and classification.

This study employs the feature ranking network method to investigate the foreign exchange (FX) market to uncover the underlying structural transition by observing the dependencies and stability of currencies. For this purpose, the FX market’s time series of 50 currencies is examined from January 2020 to October 2023 against the US dollar, covering the COVID‐19 pandemic and the Russia–Ukraine war. Using the random forest regressor, the feature ranking matrix is determined by utilizing the returns of currencies on a given day to predict the feature ranks for the following day. The dependency network is constructed using the threshold method, revealing that the topological properties of the networks undergo significant changes, especially during the war. Asian currencies grab the central positions of the dependency network, indicating their high reliance. We select four representative currencies to provide a clearer and more focused analysis of currency dependency, stability, and entropic trends. It is observed that the war triggers instability in currencies and increases the developing countries’ currency dependence. The global entropy increases with minor fluctuations during the war, and a sharp decline in entropy was observed at the beginning of 2023, indicating an extremely high dependence of the currencies of Russia (RUB), the Philippines (PHP), and Bangladesh (BDT) on others. For comparative analysis, we discuss the topological properties of the EUR‐based network alongside those of the USD‐referred market. The proposed dependency network–based analytical framework provides valuable and sustainable insights for observing currency resilience and contagion in pandemic and geopolitical events.

The feature ranking method of machine learning is applied to investigate the feature ranking and network properties of 21 world stock indices. The feature ranking is the probability of influence of each index on the target. The feature ranking matrix is determined by using the returns of indices on a certain day to predict the price returns of the next day using Random Forest and Gradient Boosting. We find that the North American indices influence others significantly during the global financial crisis, while during the European sovereign debt crisis, the significant indices are American and European. The US stock indices dominate the world stock market in most periods. The indices of two Asian countries (India and China) influence remarkably in some periods, which occurred due to the unrest state of these markets. The networks based on feature ranking are constructed by assigning a threshold at the mean of the feature ranking matrix. The global reaching centrality of the threshold network is found to increase significantly during the global financial crisis. Finally, we determine Shannon entropy from the probabilities of influence of indices on the target. The sharp drops of entropy are observed during big crises, which are due to the dominance of a few indices in these periods that can be used as a measure of the overall distribution of influences. Through this technique, we identify the indices that are influential in comparison to others, especially during crises, which can be useful to study the contagions of the global stock market.

Recent years, Burkholderia species have emerged as a new source of natural products (NPs) with increasing attractions. Genome mining suggests the Burkholderia genomes include many natural product biosynthetic gene clusters (BGCs) which are new targets for drug discovery. In order to collect more Burkholderia , here, a strain S-53 was isolated from the soil samples on a mountain area in Changde, P.R. China and verified by comparative genetic analysis to belong to Burkholderia . The complete genome of Burkholderia strain S-53 is 8.2 Mbps in size with an average G + C content of 66.35%. Its taxonomy was both characterized by 16S rRNA- and whole genome-based phylogenetic trees. Bioinformatic prediction in silico revealed it has a total of 15 NP BGCs, some of which may encode unknown products. It is expectable that availability of these BGCs will speed up the identification of new secondary metabolites from Burkholderia and help us understand how sophisticated BGC regulation works.

Microbial genome sequencing has uncovered a myriad of natural products (NPs) that have yet to be explored. Bacteria in the genus Pseudomonas serve as pathogens, plant growth promoters, and therapeutically, industrially, and environmentally important microorganisms. Though most species of Pseudomonas have a large number of NP biosynthetic gene clusters (BGCs) in their genomes, it is difficult to link many of these BGCs with products under current laboratory conditions. In order to gain new insights into the diversity, distribution, and evolution of these BGCs in Pseudomonas for the discovery of unexplored NPs, we applied several bioinformatic programming approaches to characterize BGCs from Pseudomonas reference genome sequences available in public databases along with phylogenetic and genomic comparison. Our research revealed that most BGCs in the genomes of Pseudomonas species have a high diversity for NPs at the species and subspecies levels and built the correlation of species with BGC taxonomic ranges. These data will pave the way for the algorithmic detection of species- and subspecies-specific pathways for NP development.