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Quantitatively identifying direct dependencies between variables is an important task in data analysis, in particular for reconstructing various types of networks and causal relations in science and engineering. One of the most widely used criteria is partial correlation, but it can only measure linearly direct association and miss nonlinear associations. However, based on conditional independence, conditional mutual information (CMI) is able to quantify nonlinearly direct relationships among variables from the observed data, superior to linear measures, but suffers from a serious problem of underestimation, in particular for those variables with tight associations in a network, which severely limits its applications. In this work, we propose a new concept, “partial independence,” with a new measure, “part mutual information” (PMI), which not only can overcome the problem of CMI but also retains the quantification properties of both mutual information (MI) and CMI. Specifically, we first defined PMI to measure nonlinearly direct dependencies between variables and then derived its relations with MI and CMI. Finally, we used a number of simulated data as benchmark examples to numerically demonstrate PMI features and further real gene expression data from Escherichia coli and yeast to reconstruct gene regulatory networks, which all validated the advantages of PMI for accurately quantifying nonlinearly direct associations in networks.

Background Current gene regulatory network (GRN) inference methods are notorious for a great number of indirect interactions hidden in the predictions. Filtering out the indirect interactions from direct ones remains an important challenge in the reconstruction of GRNs. To address this issue, we developed a redundancy silencing and network enhancement technique (RSNET) for inferring GRNs. Results To assess the performance of RSNET method, we implemented the experiments on several gold-standard networks by using simulation study, DREAM challenge dataset and Escherichia coli network. The results show that RSNET method performed better than the compared methods in sensitivity and accuracy. As a case of study, we used RSNET to construct functional GRN for apple fruit ripening from gene expression data. Conclusions In the proposed method, the redundant interactions including weak and indirect connections are silenced by recursive optimization adaptively, and the highly dependent nodes are constrained in the model to keep the real interactions. This study provides a useful tool for inferring clean networks.

A new coronavirus SARS-CoV-2, also called novel coronavirus 2019 (2019-nCoV), started to circulate among humans around December 2019, and it is now widespread as a global pandemic. The disease caused by SARS-CoV-2 virus is called COVID-19, which is highly contagious and has an overall mortality rate of 6.35% as of May 26, 2020. There is no vaccine or antiviral available for SARS-CoV-2. In this study, we report our discovery of inhibitors targeting the SARS-CoV-2 main protease (M pro ). Using the FRET-based enzymatic assay, several inhibitors including boceprevir, GC-376, and calpain inhibitors II, and XII were identified to have potent activity with single-digit to submicromolar IC 50 values in the enzymatic assay. The mechanism of action of the hits was further characterized using enzyme kinetic studies, thermal shift binding assays, and native mass spectrometry. Significantly, four compounds (boceprevir, GC-376, calpain inhibitors II and XII) inhibit SARS-CoV-2 viral replication in cell culture with EC 50 values ranging from 0.49 to 3.37 µM. Notably, boceprevir, calpain inhibitors II and XII represent novel chemotypes that are distinct from known substrate-based peptidomimetic M pro inhibitors. A complex crystal structure of SARS-CoV-2 M pro with GC-376, determined at 2.15 Å resolution with three protomers per asymmetric unit, revealed two unique binding configurations, shedding light on the molecular interactions and protein conformational flexibility underlying substrate and inhibitor binding by M pro . Overall, the compounds identified herein provide promising starting points for the further development of SARS-CoV-2 therapeutics.

The ongoing SARS-CoV-2 pandemic continues to be a significant threat to global health. First reported in November 2021, the Omicron variant (B.1.1.529) is more transmissible and can evade immunity better than previous SARS-CoV-2 variants, fueling an unprecedented surge in cases. To produce functional proteins from its polyprotein, SARS-CoV-2 relies on the cysteine proteases Nsp3/papain-like protease (PLpro) and Nsp5/main protease (Mpro)/3C-like protease to cleave at three and more than 11 sites, respectively. Therefore, Mpro and PLpro inhibitors are considered to be one of the most promising SARS-CoV-2 antivirals. On December 22, 2021, the Food and Drug Administration (FDA) issued an Emergency Use Authorization (EUA) for PAXLOVID, a ritonavir-boosted formulation of nirmatrelvir. Nirmatrelvir is a first-in-class orally bioavailable SARS-CoV-2 Mpro inhibitor. Thus, the scientific community must vigilantly monitor potential mechanisms of drug resistance, especially because SARS-CoV-2 is naïve to Mpro inhibitors. Mutations have been well identified in variants to this point. Notably, Omicron Mpro (OMpro) harbors a single mutation—P132H. Here, we characterized the enzymatic activity, drug inhibition, and structure of OMpro while evaluating the past and future implications of Mpro mutations.

There is a graph G  = ( V , E ), which has n vertices and l edges. Color the vertices of G black or white, ensuring no black vertex is adjacent to any white vertex, thus partitioning them into disjoint black and white sets. The optimal solution of the black and white coloring (BWC) problem is defined as the coloring scheme that maximizes the number of white vertices in the corresponding set, given a fixed number of black vertices. This problem is a NP-complete problem, widely used in reagent product storage in chemical industry and the solution to the problem of black and white queens in chess. The paper presents a swarm evolution algorithm based on improved simulated annealing search and evolutionary operation with probability learning mechanism. Furthermore, crossover operation, perturbation operation, and tabu search strategy improve the search ability of the algorithm, while evolutionary operation with probability learning mechanism increases the probability of the algorithm finding better solutions. Using Cayley graphs, random graphs, semi-random graphs, and benchmark DIMACS graphs, experiments are conducted to compare the finding results from swarm evolution algorithm and other classical heuristic algorithms. Experimental results show that the swarm evolution algorithm outperforms other heuristic algorithms in solving the BWC problem, and the swarm evolution algorithm can improve the known best results of the BWC problem.

Background Methyl jasmonate (MeJA), which has been identified as a lipid-derived stress hormone, mediates plant resistance to biotic/abiotic stress. Understanding MeJA-induced plant defense provides insight into how they responding to environmental stimuli. Result In this work, the dynamic network analysis method was used to quantitatively identify the tipping point of growth-to-defense transition and detect the associated genes. As a result, 146 genes were detected as dynamic network biomarker (DNB) members and the critical defense transition was identified based on dense time-series RNA-seq data of MeJA-treated Arabidopsis thaliana . The GO functional analysis showed that these DNB genes were significantly enriched in defense terms. The network analysis between DNB genes and differentially expressed genes showed that the hub genes including SYP121, SYP122, WRKY33 and MPK11 play a vital role in plant growth-to-defense transition. Conclusions Based on the dynamic network analysis of MeJA-induced plant resistance, we provide an important guideline for understanding the growth-to-defense transition of plants’ response to environment stimuli. This study also provides a database with the key genes of plant defense induced by MeJA.

Cellulose nanofibril (CNF) is a sort of novel nanomaterial directly extracted from plant resources, inheriting the advantages of cellulose as a cheap, green and renewable material for the development of new-generation eco-friendly electronics. In recent years, CNF-based triboelectric nanogenerator (TENG) has attracted increasing research interests, as the unique chemical, morphological, and electrical properties of CNF render the device with considerable flexibility, mechanical strength, and triboelectric output. In this study, we explore the use of isoreticular metal-organic frameworks (IRMOF) as functional filler to improve the performance of CNF based TENGs. Two types of IRMOFs that own the same network topology, namely IRMOF-1 and its aminated version IRMOF-3, are embedded with CNF to fabricated TENGs; their contribution to triboelectric output enhancement, including the roughness effect induced by large particles as well as the charge induction effect arisen from -NH2 groups, are discussed. The performance-enhanced CNF-based TENG with 0.6 wt.% of IRMOF-3 is utilized to harvest mechanical energy from human activities and charge commercial capacitors, from which the electrical energy is sufficient to light up light-emitting diodes (LEDs) and drive low-power electronic devices. In addition, a locomotor analysis system is established by assembling the above TENGs and capacitors into a 3 × 3 sensing array, which allowed signal extraction from each sensing unit to display a motion distribution map. These results demonstrate the great potential of CNF/IRMOF-based TENGs for development of self-powered sensing devices for long-term motion monitoring.

Vitiligo is a chronic autoimmune condition that leads to the loss of skin pigmentation in certain areas due to the destruction of melanocytes, which produce pigment. A topological index is a numerical value obtained from the structure of a chemical graph and is useful for studying the theoretical characteristics of organic molecules. It can also help determine the physico-chemical and biological aspects of various drugs. This article uses novel neighborhood degree-based topological indices to study vitiligo drugs and demonstrates a strong correlation with physico-chemical properties. Additionally, the results are compared with those obtained through degree-based topological indices.

Driven by the rapid development of the Internet of Things (IoT), deploying deep learning models on resource-constrained hardware has become an increasingly critical challenge, which has propelled the emergence of TinyML as a viable solution. This study aims to deploy lightweight deep learning models for human activity recognition (HAR) using TinyML on edge devices. We designed and evaluated three models: a 2D Convolutional Neural Network (2D CNN), a 1D Convolutional Neural Network (1D CNN), and a DeepConv LSTM. Among these, the DeepConv LSTM outperformed existing lightweight models by effectively capturing both spatial and temporal features, achieving an accuracy of 98.24% and an F1 score of 98.23%. After performing full integer quantization on the best model, its size was reduced from 513.23 KB to 136.51 KB and was successfully deployed on the Arduino Nano 33 BLE Sense Rev2 using the Edge Impulse platform. The device’s memory usage was 29.1 KB, flash usage was 189.6 KB, and the model’s average inference time was 21 milliseconds, requiring approximately 0.01395 GOP, with a computational performance of around 0.664 GOPS. Even after quantization, the model maintained an accuracy of 97% and an F1 score of 97%, ensuring efficient utilization of computational resources. This deployment highlights the potential of TinyML in achieving low-latency and efficient HAR systems, making it suitable for real-time human activity recognition applications.

The Cartesian product and join are two classical operations in graphs. Let dL(G)(e) be the degree of a vertex e in line graph L(G) of a graph G. The edge versions of atom-bond connectivity (ABCe) and geometric arithmetic (GAe) indices of G are defined as ∑ef∈E(L(G))dL(G)(e)+dL(G)(f)−2dL(G)(e)×dL(G)(f) and ∑ef∈E(L(G))2dL(G)(e)×dL(G)(f)dL(G)(e)+dL(G)(f), respectively. In this paper, ABCe and GAe indices for certain Cartesian product graphs (such as Pn□Pm, Pn□Cm and Pn□Sm) are obtained. In addition, ABCe and GAe indices of certain join graphs (such as Cm+Pn+Sr, Pm+Pn+Pr, Cm+Cn+Cr and Sm+Sn+Sr) are deduced. Our results enrich and revise some known results.