Energy Minimization for IRS-Assisted SWIPT-MEC System.

With the rapid development of the internet of things (IoT) era, IoT devices may face limitations in battery capacity and computational capability. Simultaneous wireless information and power transfer (SWIPT) and mobile edge computing (MEC) have emerged as promising technologies to address these challenges. Due to wireless channel fading and susceptibility to obstacles, this paper introduces intelligent reflecting surfaces (IRS) to enhance the spectral and energy efficiency of wireless networks. We propose a system model for IRS-assisted uplink offloading computation, downlink offloading computation results, and simultaneous energy transfer. Considering constraints such as IRS phase shifts, latency, energy harvesting, and offloading transmit power, we jointly optimize the CPU frequency of IoT devices, offloading transmit power, local computation workload, power splitting (PS) ratio, and IRS phase shifts. This establishes a multi-variate coupled nonlinear problem aimed at minimizing IoT devices energy consumption. We design an effective alternating optimization (AO) iterative algorithm based on block coordinate descent, and utilize closed-form solutions, Dinkelbach-based Lagrange dual method, and semidefinite relaxation (SDR) method to minimize IoT devices energy consumption. Simulation results demonstrate that the proposed scheme achieves lower energy consumption compared to other resource allocation strategies.

Characterizing interactions of endoplasmic reticulum resident proteins in situ through the YST-PPI method.

The mutual interactions of endoplasmic reticulum (ER) resident proteins in the ER maintain its functions, prompting the protein folding, modification, and transportation. Here, a new method, named YST-PPI (YESS-based Split fast TEV protease system for Protein-Protein Interaction) was developed, targeting the characterization of protein interactions in ER. YST-PPI method integrated the YESS system, split-TEV technology, and endoplasmic reticulum retention signal peptide (ERS) to provide an effective strategy for studying ER in situ PPIs in a fast and quantitative manner. The interactions among 15 ER-resident proteins, most being identified molecular chaperones, of S. cerevisiae were explored using the YST-PPI system, and their interaction network map was constructed, in which more than 74 interacting resident protein pairs were identified. Our studies also showed that Lhs1p plays a critical role in regulating the interactions of most of the ER-resident proteins, except the Sil1p, indicating its potential role in controlling the ER molecular chaperones. Moreover, the mutual interaction revealed by our studies further confirmed that the ER-resident proteins perform their functions in a cooperative way and a multimer complex might be formed during the process.

A Semi-Supervised Adaptive Matrix Machine Approach for Fault Diagnosis in Railway Switch Machine.

The switch machine, an essential element of railway infrastructure, is crucial in maintaining the safety of railway operations. Traditional methods for fault diagnosis are constrained by their dependence on extensive labeled datasets. Semi-supervised learning (SSL), although a promising solution to the scarcity of samples, faces challenges such as the imbalance of pseudo-labels and inadequate data representation. In response, this paper presents the Semi-Supervised Adaptive Matrix Machine (SAMM) model, designed for the fault diagnosis of switch machine. SAMM amalgamates semi-supervised learning with adaptive technologies, leveraging adaptive low-rank regularizer to discern the fundamental links between the rows and columns of matrix data and applying adaptive penalty items to correct imbalances across sample categories. This model methodically enlarges its labeled dataset using probabilistic outputs and semi-supervised, automatically adjusting parameters to accommodate diverse data distributions and structural nuances. The SAMM model's optimization process employs the alternating direction method of multipliers (ADMM) to identify solutions efficiently. Experimental evidence from a dataset containing current signals from switch machines indicates that SAMM outperforms existing baseline models, demonstrating its exceptional status diagnostic capabilities in situations where labeled samples are scarce. Consequently, SAMM offers an innovative and effective approach to semi-supervised classification tasks involving matrix data.

De novo design of mini-binder proteins against IL-2 receptor ß chain.

IL-2 regulates the immune response by interacting with different IL-2 receptor (IL-2R) subunits. High dose of IL-2 binds to IL-2Rßγc heterodimer, which induce various side effects while activating immune function. Disrupting IL-2 and IL-2R interactions can block IL-2 mediated immune response. Here, we used a computational approach to de novo design mini-binder proteins against IL-2R ß chain (IL-2Rß) to block IL-2 signaling. The hydrophobic region where IL-2 binds to IL-2Rß was selected and the promising binding mode was broadly explored. Three mini-binders with amino acid numbers ranging from 55 to 65 were obtained and binder 1 showed the best effects in inhibiting CTLL-2 cells proliferation and STAT5 phosphorylation. Molecular dynamics simulation showed that the binding of binder 1 to IL-2Rß was stable; the free energy of binder1/IL-2Rß complex was lower, indicating that the affinity of binder 1 to IL-2Rß was higher than that of IL-2. Free energy decomposition suggested that the ARG35 and ARG131 of IL-2Rß might be the key to improve the affinity of binder. Our efforts provided new insights in developing of IL-2R blocker, offering a potential strategy for ameliorating the side effects of IL-2 treatment.

Developing CBTC system safety requirement hierarchy through STPA methodology.

Safety-critical systems, such as the railway signal system, are subject to potentially high costs from failures, including loss of life and property damage. The use of new technology, including communication-based train control (CBTC) systems with software and computers, has changed the types of accidents that occur. Software-related issues and dysfunctional interactions between system components controlled by the software are increasingly the cause of incidents. Developing a "safe" safety-critical system requires accurate and complete safety requirements, which are the foundation of system development. Traditional hazard analysis techniques are insufficient for identifying the causes of accidents in modern railway signaling systems. Systems-Theoretic Process Analysis (STPA) is a powerful new hazard analysis method designed to address these limitations. Building upon this foundation, a hierarchical approach to safety requirement development has been further developed. This approach combines STPA analysis with a hierarchical modeling approach to establish traceability links from safety requirements to specific architectures, refine and allocate system-level safety requirements to relevant subsystems, and abstract safety requirements at higher hierarchical levels to enable easy changes to lower-level implementations. This paper employs the aforementioned methodology within the context of the CBTC system, thereby enhancing risk management and hazard analysis, enabling early insights, and facilitating the generation of safety requirements of CBTC System.

Reed-root-based solar-driven evaporator with a faster capillary water transfer rate for effective steam generation.

Solar-driven steam evaporation technology, known for its low energy consumption and environmental friendliness, has emerged as a promising approach for seawater desalination, wastewater purification, etc. However, creating a low-cost solar evaporation system that simultaneously achieves rapid water transport, efficient light absorption, and salt tolerance remains challenging. Here, a dual-layer evaporator based on reed roots has been developed after a simple H2O2 delignification treatment and flame treatment, which exhibited enhanced water transport performance and photothermal properties. As excepted, delignification treatment enhanced the capillary water transport ability of reed roots, which is conducive to promoting the dilution of salt in the evaporator and preventing salt deposition. The evaporator demonstrates an impressive steam generation efficiency of 83.5 % and a remarkable water evaporation rate of 1.407 kg m-2 h-1 under 1 sun, thanks to its well-designed structure and optimized performance. Moreover, the evaporator exhibited excellent practical performance for outdoor applications and demonstrates a remarkable capacity for sewage purification, effectively treating heavy metal ion wastewater as well as dye wastewater. As a result, the objective of our research is to explore opportunities for the implementation of deployable, cost-effective, low-carbon-footprint solar water purification systems, particularly for some impoverished regions, to ensure the provision of high-quality water.

A combinatorial strategy for HRV 3C protease engineering to achieve the N-terminal free cleavage.

Human rhinovirus 3C protease (HRV 3CP) has a high specificity against the substrate of LEVLFQ↓G at P1' site, which plays an important role in biotechnology and academia as a fusion tag removal tool. However, a non-ignorable limitation is that an extra residue of Gly would remain at the N terminus of the recombinant target protein after cleavage with HRV 3CP, thus potentially causing protein mis-functionality or immunogenicity. Here, we developed a combinatorial strategy by integrating structure-guided library design and high-throughput screening of eYESS approach for HRV 3CP engineering to expand its P1' specificity. Finally, a C3 variant was obtained, exhibiting a broad substrate P1' specificity to recognize 20 different amino acids with the highest activity against LEVLFQ↓M (kcat/KM = 3.72 ± 0.04 mM-1∙s-1). Further biochemical and NGS-mediated substrate profiling analysis showed that C3 variant still kept its substrate stringency at P1 site and good residue tolerance at P2' site, but with an expanded P1' specificity. Structural simulation of C3 indicated a reconstructed S1' binding pocket as well as new interactions with the substrates. Overall, our studies here prompt not only the practical applications and understanding of substrate recognition mechanisms of HRV 3CP, also provide new tools for other enzyme engineering.

Asymptotic uncertainty of false discovery proportion.

Multiple testing has been a prominent topic in statistical research. Despite extensive work in this area, controlling false discoveries remains a challenging task, especially when the test statistics exhibit dependence. Various methods have been proposed to estimate the false discovery proportion (FDP) under arbitrary dependencies among the test statistics. One key approach is to transform arbitrary dependence into weak dependence and subsequently establish the strong consistency of FDP and false discovery rate under weak dependence. As a result, FDPs converge to the same asymptotic limit within the framework of weak dependence. However, we have observed that the asymptotic variance of FDP can be significantly influenced by the dependence structure of the test statistics, even when they exhibit only weak dependence. Quantifying this variability is of great practical importance, as it serves as an indicator of the quality of FDP estimation from the data. To the best of our knowledge, there is limited research on this aspect in the literature. In this paper, we aim to fill in this gap by quantifying the variation of FDP, assuming that the test statistics exhibit weak dependence and follow normal distributions. We begin by deriving the asymptotic expansion of the FDP and subsequently investigate how the asymptotic variance of the FDP is influenced by different dependence structures. Based on the insights gained from this study, we recommend that in multiple testing procedures utilizing FDP, reporting both the mean and variance estimates of FDP can provide a more comprehensive assessment of the study's outcomes.

Development of nanobodies specific to clumping factors A of Staphylococcus aureus by yeast surface display.

The Staphylococcus aureus clumping factor A (ClfA) is a fibrinogen (Fg) binding protein that plays an important role in the clumping of S. aureus in blood plasma. The current anti-infective approaches targeting ClfA are mainly based on monoclonal antibodies but showed less impressive efficacy for clinical applications. Nanobodies offer advantages in enhanced tissue penetration and a propensity to bind small epitopes. However, there is no report on generating specific nanobodies for ClfA. Here, we constructed a synthetic nanobody library based on yeast surface display to isolate nanobodies against the Fg binding domain ClfA221-550. We firstly obtained a primary nanobody directed to ClfA221-550, and then employed error-prone mutagenesis to enhance its binding affinity. Finally, 18 variants were isolated with high affinities (EC50, 1.1 ± 0.1 nM to 4.8 ± 0.3 nM), in which CNb1 presented the highest inhibition efficiency in the adhesion of S. aureus to fibrinogen. Moreover, structural simulation analysis indicated that the epitope for CNb1 partially overlapped with the binding sites for fibrinogen, thus inhibiting ClfA binding to Fg. Overall, these results indicated that the specific nanobodies generated here could prevent the adhesion of S. aureus to fibrinogen, suggesting their potential capacities in the control of S. aureus infections.

De novo design of a protein binder against Staphylococcus enterotoxin B.

Staphylococcus enterotoxin B (SEB) interacts with MHC-II molecules to overactivate immune cells and thereby to produce excessive pro-inflammatory cytokines. Disrupting the interactions between SEB and MHC-II helps eliminate the lethal threat posed by SEB. In this study, a de novo computational approach was used to design protein binders targeting SEB. The MHC-II binding domain of SEB was selected as the target, and the possible promising binding mode was broadly explored. The obtained original binder was folded into triple-helix bundles and contained 56 amino acids with molecular weight 5.9 kDa. The interface of SEB and the binder was highly hydrophobic. ProteinMPNN optimization further enlarged the hydrophobic region of the binder and improved the stability of the binder-SEB complex. In vitro study demonstrated that the optimized binder significantly inhibited the inflammatory response induced by SEB. Overall, our research demonstrated the applicability of this approach in de novo designing protein binders against SEB, and thereby providing potential therapeutics for SEB induced diseases.

Aerobic glycolysis is the predominant means of glucose metabolism in neuronal somata, which protects against oxidative damage.

It is generally thought that under basal conditions, neurons produce ATP mainly through mitochondrial oxidative phosphorylation (OXPHOS), and glycolytic activity only predominates when neurons are activated and need to meet higher energy demands. However, it remains unknown whether there are differences in glucose metabolism between neuronal somata and axon terminals. Here, we demonstrated that neuronal somata perform higher levels of aerobic glycolysis and lower levels of OXPHOS than terminals, both during basal and activated states. We found that the glycolytic enzyme pyruvate kinase 2 (PKM2) is localized predominantly in the somata rather than in the terminals. Deletion of Pkm2 in mice results in a switch from aerobic glycolysis to OXPHOS in neuronal somata, leading to oxidative damage and progressive loss of dopaminergic neurons. Our findings update the conventional view that neurons uniformly use OXPHOS under basal conditions and highlight the important role of somatic aerobic glycolysis in maintaining antioxidant capacity.

Simultaneous removal of phosphate and tetracycline using LaFeO3 functionalised magnetic biochar by obtained ultrasound-assisted sol-gel pyrolysis: Mechanisms and characterisation.

Excessive phosphate and tetracycline (TC) contaminants pose a serious risk to human health and the ecological environment. As such exploring the simultaneous adsorption of phosphate and TC is garnering increasing attention. In this study, an efficient lanthanum ferrate magnetic biochar (FLBC) was synthesised from crab shells using an ultrasound-assisted sol-gel method to study its performance and mechanisms for phosphate and TC adsorption in aqueous solutions in mono/bis systems. According to the Langmuir model, the developed exhibited a maximum adsorption capacity of 65.62 mg/g for phosphate and 234.1 mg/g for TC (pH:7.0 ± 0.1, and 25 °C). Further, it exhibited high resistance to interference and pH suitability. In practical swine wastewater applications, whereby the concentrations of phosphate and TC are 37 and 19.97 mg/L, respectively, the proposed material demonstrated excellent performance. In addition, electrostatic adsorption, chemical precipitation and ligand exchange were noted to be the main mechanisms for phosphate adsorption by FLBC, whereas hydrogen bonding and π-π interaction were the main adsorption mechanisms for TC adsorption. Therefore, this study successfully prepared a novel and efficient adsorbent for phosphate and TC.

Sphingolipid metabolism in brain insulin resistance and neurological diseases.

Sphingolipids, as members of the large lipid family, are important components of plasma membrane. Sphingolipids participate in biological signal transduction to regulate various important physiological processes such as cell growth, apoptosis, senescence, and differentiation. Numerous studies have demonstrated that sphingolipids are strongly associated with glucose metabolism and insulin resistance. Insulin resistance, including peripheral insulin resistance and brain insulin resistance, is closely related to the occurrence and development of many metabolic diseases. In addition to metabolic diseases, like type 2 diabetes, brain insulin resistance is also involved in the progression of neurodegenerative diseases including Alzheimer's disease and Parkinson's disease. However, the specific mechanism of sphingolipids in brain insulin resistance has not been systematically summarized. This article reviews the involvement of sphingolipids in brain insulin resistance, highlighting the role and molecular biological mechanism of sphingolipid metabolism in cognitive dysfunctions and neuropathological abnormalities of the brain.

Development of nanobodies against Staphylococcus enterotoxin B through yeast surface display.

Staphylococcus enterotoxin B (SEB) is one of the primary virulence factors of Staphylococcus aureus but there is still a lack of targeted drugs. SEB activates immune cells via interacting with MHC-II on antigen-presenting cells, leading to the production of large amounts of pro-inflammatory cytokines. Blocking the interaction between SEB and MHC-II can avert the overactivation of immune cells. Nanobodies are the smallest functional antibodies that can bind stably to antigens. In this study, an ideal approach to obtain specific nanobodies without immunizing camelids was introduced. We constructed a library containing up to 5 × 108 nanobodies, and then screened those targeting SEB by using yeast surface display (YSD) technique and fluorescence-activated cell sorting (FACS). A total of 8 nanobodies with divergent complementarity-determining regions (CDRs) sequences were identified and one candidate Nb8 with high affinity to SEB was isolated. In vitro study demonstrated that Nb8 significantly inhibited SEB-induced inflammatory response. Molecular docking simulation indicated that the unique CDR3 sequence contributed to the binding of Nb8 to the MHC-II binding domain of SEB and accordingly cut off the connection between SEB and MHC-II. Our efforts contributed to the development of specific nanobodies for eliminating the threats of SEB.

Cross-Domain Sentiment Analysis Based on Feature Projection and Multi-Source Attention in IoT.

Social media is a real-time social sensor to sense and collect diverse information, which can be combined with sentiment analysis to help IoT sensors provide user-demanded favorable data in smart systems. In the case of insufficient data labels, cross-domain sentiment analysis aims to transfer knowledge from the source domain with rich labels to the target domain that lacks labels. Most domain adaptation sentiment analysis methods achieve transfer learning by reducing the domain differences between the source and target domains, but little attention is paid to the negative transfer problem caused by invalid source domains. To address these problems, this paper proposes a cross-domain sentiment analysis method based on feature projection and multi-source attention (FPMA), which not only alleviates the effect of negative transfer through a multi-source selection strategy but also improves the classification performance in terms of feature representation. Specifically, two feature extractors and a domain discriminator are employed to extract shared and private features through adversarial training. The extracted features are optimized by orthogonal projection to help train the classification in multi-source domains. Finally, each text in the target domain is fed into the trained module. The sentiment tendency is predicted in the weighted form of the attention mechanism based on the classification results from the multi-source domains. The experimental results on two commonly used datasets showed that FPMA outperformed baseline models.

An impressive pristine biochar from food waste digestate for arsenic(V) removal from water: Performance, optimization, and mechanism.

Anaerobic digestion has become a global practice for valorizing food waste, but the recycling of the digestate (FWD) remains challenging. This study aimed to address this issue by utilizing FWD as a low-cost feedstock for Ca-rich biochar production. The results demonstrated that biochar pyrolyzed at 900 °C exhibited impressive As(V) adsorption performance without any modifications. Kinetic analysis suggested As(V) was chemisorbed onto CDBC9, while isotherm data conformed well to Langmuir model, indicating monolayer adsorption with a maximum capacity of 76.764 mg/g. Further analysis using response surface methodology revealed that pH value and adsorbent dosage were significant influencing factors, and density functional theory (DFT) calculation visualized the formation of ionic bonds between HAsO42- and CaO(110) and Ca(OH)2(101) surfaces. This work demonstrated the potential of using FWD for producing Ca-rich biochar, providing an effective solution for As(V) removal and highlighting the importance of waste material utilization in sustainable environmental remediation.

Human Cytomegalovirus IE1 Impairs Neuronal Migration by Downregulating Connexin 43.

Human cytomegalovirus (HCMV) is a leading cause of congenital birth defects. Though the underlying mechanisms remain poorly characterized, mouse models of congenital CMV infection have demonstrated that the neuronal migration process is damaged. In this study, we evaluated the effects of HCMV infection on connexin 43 (Cx43), a crucial adhesion molecule mediating neuronal migration. We show in multiple cellular models that HCMV infection downregulated Cx43 posttranslationally. Further analysis identified the immediate early protein IE1 as the viral protein responsible for the reduction of Cx43. IE1 was found to bind the Cx43 C terminus and promote Cx43 degradation through the ubiquitin-proteasome pathway. Deletion of the Cx43-binding site in IE1 rendered it incapable of inducing Cx43 degradation. We validated the IE1-induced loss of Cx43 in vivo by introducing IE1 into the fetal mouse brain. Noteworthily, ectopic IE1 expression induced cortical atrophy and neuronal migration defects. Several lines of evidence suggest that these damages result from decreased Cx43, and restoration of Cx43 levels partially rescued IE1-induced interruption of neuronal migration. Taken together, the results of our investigation reveal a novel mechanism of HCMV-induced neural maldevelopment and identify a potential intervention target. IMPORTANCE Congenital CMV (cCMV) infection causes neurological sequelae in newborns. Recent studies of cCMV pathogenesis in animal models reveal ventriculomegaly and cortical atrophy associated with impaired neural progenitor cell (NPC) proliferation and migration. In this study, we investigated the mechanisms underlying these NPC abnormalities. We show that Cx43, a critical adhesion molecule mediating NPC migration, is downregulated by HCMV infection in vitro and HCMV-IE1 in vivo. We provide evidence that IE1 interacts with the C terminus of Cx43 to promote its ubiquitination and consequent degradation through the proteasome. Moreover, we demonstrate that introducing IE1 into mouse fetal brains led to neuronal migration defects, which was associated with Cx43 reduction. Deletion of the Cx43-binding region in IE1 or ectopic expression of Cx43 rescued the IE1-induced migration defects in vivo. Our study provides insight into how cCMV infection impairs neuronal migration and reveals a target for therapeutic interventions.

Study on the effect of biomass on sulfur release behavior from dyeing sludge incineration: Focusing on in-situ sulfur fixation mechanism based on model compounds.

Although incineration is a recommended disposal strategy for dyeing sludge (DS), sulfurous gases problem is severe. Wood sawdust (WS) and rice husk (RH) are eco-friendly and CO2-neutral additives to relieve sulfur emission from DS incineration. However, the interaction between organic sulfur and biomass is uninterpreted. This study explores the effect of WS and RH on the combustion behavior and sulfur evolution from organic sulfur model compound combustion via thermogravimetry (TG) with mass spectrometry (MS). Results indicated that the sulfone and mercaptan combustion activities in DS were more drastic than in other forms. WS and RH additives generally deteriorated the combustibility and burnout performance of model compounds. The combustion of mercaptan and sulfone in DS contributed to most gaseous sulfur pollutants, where CH3SH and SO2 were the predominant forms. WS and RH minimized the sulfur release from mercaptan and sulfone incineration, whose in-situ retention ratios reached 20.14 % and 40.57 %. The retention mechanism to sulfur could be divided into: (1) Diffusion stage: the closed structure of biomass residue restrained sulfurous gases from escaping. (2) Chemical reaction stage: multiple sulfation occurred and inhibited sulfur release. Ca/K sulfate and compound sulfates were predisposed and thermostable sulfur-fixing products for the mercaptan-WS and sulfone-RH co-combustion systems.

Human cytomegalovirus pUL97 upregulates SOCS3 expression via transcription factor RFX7 in neural progenitor cells.

Congenital human cytomegalovirus (HCMV) infection causes severe damage to the fetal brain, and the underlying mechanisms remain elusive. Cytokine signaling is delicately controlled in the fetal central nervous system to ensure proper development. Here we show that suppressor of cytokine signaling 3 (SOCS3), a negative feedback regulator of the IL-6 cytokine family signaling, was upregulated during HCMV infection in primary neural progenitor cells (NPCs) with a biphasic expression pattern. From viral protein screening, pUL97 emerged as the viral factor responsible for prolonged SOCS3 upregulation. Further, by proteomic analysis of the pUL97-interacting host proteins, regulatory factor X 7 (RFX7) was identified as the transcription factor responsible for the regulation. Depletion of either pUL97 or RFX7 prevented the HCMV-induced SOCS3 upregulation in NPCs. With a promoter-luciferase activity assay, we demonstrated that the pUL97 kinase activity and RFX7 were required for SOCS3 upregulation. Moreover, the RFX7 phosphorylation level was increased by either UL97-expressing or HCMV-infection in NPCs, suggesting that pUL97 induces RFX7 phosphorylation to drive SOCS3 transcription. We further revealed that elevated SOCS3 expression impaired NPC proliferation and migration in vitro and caused NPCs migration defects in vivo. Taken together, these findings uncover a novel regulatory mechanism of sustained SOCS3 expression in HCMV-infected NPCs, which perturbs IL-6 cytokine family signaling, leads to NPCs proliferation and migration defects, and consequently affects fetal brain development.