Pain is a significant global health issue, and the current treatment options for pain management have limitations in terms of effectiveness, side effects, and potential for addiction. There is a ...pressing need for improved pain treatments and the development of new drugs. Voltage-gated sodium channels, particularly Nav1.3, Nav1.7, Nav1.8, and Nav1.9, play a crucial role in neuronal excitability and are predominantly expressed in the peripheral nervous system. Targeting these channels may provide a means to treat pain while minimizing central and cardiac adverse effects. In this study, we construct protein-protein interaction (PPI) networks based on pain-related sodium channels and develop a corresponding drug-target interaction network to identify potential lead compounds for pain management. To ensure reliable machine learning predictions, we carefully select 111 inhibitor data sets from a pool of more than 1000 targets in the PPI network. We employ 3 distinct machine learning algorithms combined with advanced natural language processing (NLP)-based embeddings, specifically pretrained transformer and autoencoder representations. Through a systematic screening process, we evaluate the side effects and repurposing potential of more than 150,000 drug candidates targeting Nav1.7 and Nav1.8 sodium channels. In addition, we assess the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of these candidates to identify leads with near-optimal characteristics. Our strategy provides an innovative platform for the pharmacological development of pain treatments, offering the potential for improved efficacy and reduced side effects.
The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past ...decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN), to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI) to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E.coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI) significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only effectively reduce the computational cost of BN due to much smaller sizes of local GRNs, but also identify the directions of the regulations.
•Proposing a preference learning algorithm to analyze consumer diverse preferences.•Helping users understand consumer diverse preferences from massive online reviews.•Designing experiments to ...validate the algorithm's validity, robustness and parameters.•Performing a case analysis to illustrate how to use the proposed model in business.
In the information age, there is a growing need to process and analyze the great number of online reviews to understand consumer preferences and product reputations. Instead of addressing all online reviews as a simple group decision-making problem in the existing research, we propose a new preference learning (PL) mechanism to extract preferences by analyzing the diversity of preferences across different time frames. First, we collect and process online ratings from e-commerce platforms. Then, we construct an online optimization model based on online mirror descent to learn priority vectors that reflect various consumer preferences. We incorporate multiple learners to capture evolving preferences. In addition, we design experiments to verify the model involving the validity, robustness, as well as suggested parameters ranges. The model helps consumers and businesses capture ongoing preferences from massive online reviews. Importantly, the PL mechanism is innovatively designed to detect different preferences and learn different types of priorities with generating ratings online. The model offers more accurate preference information and represents a broader range of consumers’ behaviors.
Abstract
Poor stability has long been one of the key issues that hinder the practical applications of lead-based halide perovskites. In this paper, the photoluminescence (PL) quantum yield (QY) of ...bromide-based perovskites can be increased from 2.5% to 71.54% by introducing water, and the PL QY of a sample in aqueous solution decreases minimally over 1 year. The enhanced stability and PL QY can be attributed to the water-induced methylamino lead bromide perovskite (MAPbBr
3
)@PbBr(OH). We note that this strategy is universal to MAPbBr
3
, formamidine lead bromide perovskite (FAPbBr
3
), inorganic lead bromide perovskite (CsPbBr
3
), etc. Light-emitting devices (LEDs) are fabricated by using the as-prepared perovskite as phosphors on a 365 nm UV chip. The luminance intensity of the LED is 9549 cd/m
2
when the driven current is 200 mA, and blemishes on the surface of glass are clearly observed under the illumination of the LEDs. This work provides a new strategy for highly stable and efficient perovskites.
The real-time electricity consumption data can be used in value-added service such as big data analysis, meanwhile the single user's privacy needs to be protected. How to balance the data utility and ...the privacy preservation is a vital issue, where the privacy-preserving data aggregation could be a feasible solution. Most of the existing data aggregation schemes rely on a trusted third party (TTP). However, this assumption will have negative impact on reliability, because the system can be easily knocked down by the denial of service attack. In this paper, a practical privacy-preserving data aggregation scheme is proposed without TTP, in which the users with some extent trust construct a virtual aggregation area to mask the single user's data, and meanwhile, the aggregation result almost has no effect for the data utility in large scale applications. The computation cost and communication overhead are reduced in order to promote the practicability. Moreover, the security analysis and the performance evaluation show that the proposed scheme is robust and efficient.
Vertically-aligned cobalt nickel phosphide nanowires (Co-Ni-P NWs) are synthesized on Ni foam by phosphorizing cobalt carbonate hydroxide precursor NWs in red phosphorous vapor at an elevated ...temperature. The as-fabricated self-supported integrated electrode (Ni@Co-Ni-P) exhibits outstanding electrocatalytic activity for the hydrogen evolution reaction (HER) in alkaline solution, delivering a cathodic current density of 100 mA cm−2 at a small overpotential of 137 mV and a Tafel slope of 65.1 mV dec−1. Furthermore, the electrode shows remarkable catalytic performance towards the oxygen evolution reaction (OER), affording an anodic current density of 90.2 mA cm−2 at an overpotential of 350 mV, superior to many other transition metal based OER catalysts. Given the well-defined bifunctionality, an alkaline electrolyzer is assembled using two symmetrical Ni@Co-Ni-P as the cathode and anode, respectively, which demonstrates outstanding catalytic performance for sustained water splitting at varying current densities from 10 to 240 mA cm−2. Significantly, the Ni@Co-Ni-P electrolyzer is able to operate for 3175 h (ca. 132 days) without degradation at an industry-relevant current density of 100 mA cm−2, leading to exceptionally high H2 production rate of 311 mmol h−1 g−1catalyst cm−2 with an energy efficiency of 76% at ca. 1.9 V.
•Co-Ni-P nanowire arrays are grown on Ni foam forming an integrated electrode.•The electrode can catalyze both H2 and O2 evolution showing bifunctionality.•An electrolyzer is assembled using two symmetrical integrated electrodes.•The electrolyzer shows high energy efficiency for overall water splitting.•The electrolyzer can sustain 3175 h at 100 mA cm−2 without degradation.
Drug addiction is a global public health crisis, and the design of antiaddiction drugs remains a major challenge due to intricate mechanisms. Since experimental drug screening and optimization are ...too time-consuming and expensive, there is urgent need to develop innovative artificial intelligence (AI) methods for addressing the challenge. We tackle this challenge by topology-inferred drug addiction learning (TIDAL) built from integrating multiscale topological Laplacians, deep bidirectional transformer, and ensemble-assisted neural networks (EANNs). Multiscale topological Laplacians are a novel class of algebraic topology tools that embed molecular topological invariants and algebraic invariants into its harmonic spectra and nonharmonic spectra, respectively. These invariants complement sequence information extracted from a bidirectional transformer. We validate the proposed TIDAL framework on 22 drug addiction related, 4 hERG, and 12 DAT data sets, which suggests that the proposed TIDAL is a state-of-the-art framework for the modeling and analysis of drug addiction data. We carry out cross-target analysis of the current drug addiction candidates to alert their side effects and identify their repurposing potentials. Our analysis reveals drug-mediated linear and bilinear target correlations. Finally, TIDAL is applied to shed light on relative efficacy, repurposing potential, and potential side effects of 12 existing antiaddiction medications. Our results suggest that TIDAL provides a new computational strategy for pressingly needed antisubstance addiction drug development.
Alternative pre-mRNA-splicing-induced post-transcriptional gene expression regulation is one of the pathways for tumors maintaining proliferation rates accompanying the malignant phenotype under ...stress. Here, we uncover a list of hyperacetylated proteins in the context of acutely reduced Acetyl-CoA levels under nutrient starvation. PHF5A, a component of U2 snRNPs, can be acetylated at lysine 29 in response to multiple cellular stresses, which is dependent on p300. PHF5A acetylation strengthens the interaction among U2 snRNPs and affects global pre-mRNA splicing pattern and extensive gene expression. PHF5A hyperacetylation-induced alternative splicing stabilizes KDM3A mRNA and promotes its protein expression. Pathologically, PHF5A K29 hyperacetylation and KDM3A upregulation axis are correlated with poor prognosis of colon cancer. Our findings uncover a mechanism of an anti-stress pathway through which acetylation on PHF5A promotes the cancer cells’ capacity for stress resistance and consequently contributes to colon carcinogenesis.
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•Quantitative acetylomics reveals hyperacetylated proteins upon nutrition starvation•Multiple cellular stresses result in p300-dependent PHF5A K29 acetylation•PHF5A K29 acetylation enhances KDM3A expression by stabilizing its mRNA•PHF5A acetylation and KDM3A upregulation predict poor prognosis in colon cancer
Wang et al. uncover a list of proteins that become hyperacetylated upon nutrient starvation. p300-mediated PHF5A K29 acetylation is induced by multiple cellular stresses and affects global pre-mRNA splicing. PHF5A acetylation upregulates KDM3A expression by reducing its aberrant mRNA alternative splicing, which is positively correlated with colorectal tumorigenesis.
Compared with green fluorescence protein (GFP) chromophores, the recently synthesized blue fluorescence protein (BFP) chromophore variant presents intriguing photochemical properties, for example, ...dual fluorescence emission, enhanced fluorescence quantum yield, and ultra‐slow excited‐state intramolecular proton transfer (ESIPT; J. Phys. Chem. Lett., 2014, 5, 92); however, its photochemical mechanism is still elusive. Herein we have employed the CASSCF and CASPT2 methods to study the mechanistic photochemistry of a truncated BFP chromophore variant in the S0 and S1 states. Based on the optimized minima, conical intersections, and minimum‐energy paths (ESIPT, photoisomerization, and deactivation), we have found that the system has two competitive S1 relaxation pathways from the Franck–Condon point of the BFP chromophore variant. One is the ESIPT path to generate an S1 tautomer that exhibits a large Stokes shift in experiments. The generated S1 tautomer can further evolve toward the nearby S1/S0 conical intersection and then jumps down to the S0 state. The other is the photoisomerization path along the rotation of the central double bond. Along this path, the S1 system runs into an S1/S0 conical intersection region and eventually hops to the S0 state. The two energetically allowed S1 excited‐state deactivation pathways are responsible for the in‐part loss of fluorescence quantum yield. The considerable S1 ESIPT barrier and the sizable barriers that separate the S1 tautomers from the S1/S0 conical intersections make these two tautomers establish a kinetic equilibrium in the S1 state, which thus results in dual fluorescence emission.
Blues and twos: Electronic structure calculations show that barriers for excited‐state intramolecular proton transfer (ESIPT; see figure, GSIHT=ground‐state intramolecular hydrogen transfer) and excited‐state decays cause dual emission of the blue fluorescence protein chromophore.
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