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Abstract The aim of this research was to determine the residues of dioxins and dioxin-like polychlorinated biphenyls in cultured eel and find out the source of pollution and the distribution pattern of eels. One hundred samples of eel, 4 fodder samples, and 12 environmental samples (water, plants, and soil) were collected from 4 cities and counties in Jiangxi Province, China. The contents of 17 kinds of dioxins (PCDD/Fs) and 12 kinds of dioxin-like polychlorinated biphenyls (dl-PCBs) were determined by high-resolution gas chromatography-tandem mass spectrometry, and the exposure risk was evaluated by using risk index. The total toxicity equivalence quantity (TEQ) of dioxin and dioxin-like polychlorinated biphenyls in eel and fodder samples collected in the study area were 0.65 ± 0.31 pg/g and 0.10 ± 0.02 pg/g, respectively. Dl-PCBs were the main dioxin pollution in eel and fodder samples. 2,3',4,4',5-pentachlorodiphenyl and 2,3,3',4,4'-pentachlorobiphenyl were the main contributing monomers. The environmental samples were mainly polluted by polychlorinated dibenzo-p-dioxins (PCDDs), with the main contributing monomer being Octachlorodibenzodioxins (OCDD), while 3,3',4,4',5-pentachlorobiphenyl and 2,3,4,7,8-pentachlorodibenzofuran were the main toxic compounds in eel and fodder. The dioxin pollution of eels cultured in Jiangxi Province was mainly from fodder polluted via dl-PCBs. The meat segment VI (tail) exhibited a strong enrichment effect of polychlorinated benzofurans (PCDFs). It should be the key part for dioxins and dioxin-like polychlorinated biphenyls assessment. Further, the results were helpful to improve the edible safety of eel products and the efficiency of the risk assessment of dioxins and dioxin-like polychlorinated biphenyls in fish. Graphical Abstract

Abstract Aggregation‐induced emission (AIE) is a phenomenon where a molecule that is weakly or non‐luminescent in a diluted solution becomes highly emissive when aggregated. AIE luminogens (AIEgens) hold promise in diverse applications like bioimaging, chemical sensing, and optoelectronics. Investigation in AIE luminescence is also critical for understanding aggregation kinetics as the aggregation process is an essential component of AIE emission. Experimental investigation of AIEgen aggregation is challenging due to the fast timescale of the aggregation and the amorphous aggregate structures. Computer simulations such as molecular dynamics (MD) simulation provide a valuable approach to complement experiments with atomic‐level knowledge to study the fast dynamics of aggregation processes. However, individual simulations still struggle to systematically elucidate heterogeneous kinetics of the formation of amorphous AIEgen aggregates. Kinetic network models (KNMs), constructed from an ensemble of MD simulations, hold great potential in addressing this challenge. In these models, dynamic processes are modeled as a series of Markovian transitions occurring among metastable conformational states at discrete time intervals. In this perspective article, we first review previous studies to characterize the AIEgen aggregation kinetics and their limitations. We then introduce KNMs as a promising approach to elucidate the complex kinetics of aggregations to address these limitations. More importantly, we discuss our perspective on linking the output of KNMs to experimental observations of time‐resolved AIE luminescence. We expect that this approach can validate the computational predictions and provide great insights into the aggregation kinetics for AIEgen aggregates. These insights will facilitate the rational design of improved AIEgens in their applications in biology and materials sciences.

The spread of the FluA virus poses significant public health concerns worldwide. Fluorescent lateral flow immunoassay (LFIA) test strips have emerged as vital tools for the early detection and monitoring of influenza infections. However, existing quantitative virus-detection methods, particularly those utilizing smartphone-based sensing platforms, encounter accessibility challenges in resource-limited areas and among the elderly population. Despite their advantages in speed and portability, these platforms often lack user-friendliness for these demographics, impeding their widespread utilization. To address these challenges, this study proposes leveraging the optical pick-up unit (OPU) sourced from commercial optical drives as a readily available fluorescence excitation module for the quantitative detection of antibodies labeled with quantum-dot fluorescent microspheres. Additionally, we utilize miniaturized and high-performance optical components and 3D-printed parts, along with a customized control system, to develop an affordable point-of-care testing (POCT) device. Within the system, a stepping motor scans the test strip from the T-line to the C-line, enabling the calculation of the fluorescence-intensity ratio between the two lines. This simple yet effective design facilitates rapid and straightforward field or at-home testing for FluA. The proposed prototype platform demonstrates promising performance, achieving a limit of detection (LOD) of 2.91 ng/mL, a total detection time of no more than 15 min, and dimensions of 151 mm × 11.2 mm × 10.8 mm3. We believe that the proposed approach holds great potential for improving access to an accurate influenza diagnosis.

Existing data-driven object detection methods use the Constant False Alarm Rate (CFAR) principle to achieve more robust detection performance using supervised learning. This study systematically proposes a data-driven target detection framework based on the measured echo data from the ground early warning radar for low-altitude slow dim target detection. This framework addresses two key problems in this field: (1) aiming at the problem that current data-driven object detection methods fail to make full use of feature representation learning to exert its advantages, a representation learning method of echo temporal dependency is proposed, and two implementations, including unsupervised- and supervised-learning are given; (2) Low-altitude slow dim targets show extreme sparsity in the radar detection range, such unevenness of target-clutter sample scale causes the trained model to seriously tilt to the clutter samples, resulting in the decision deviation. Therefore, we further propose incorporating the data balancing policy of abnormal detection into the framework. Finally, ablation experiments are performed on the measured X-band echo data for each component in the proposed framework. Experimental results completely validate the effectiveness of our echo temporal representation learning and balancing policy. Additionally, under real sequential validation, our proposed method achieves comprehensive detection performance that is superior to multiple CFAR methods.

Egg products after thermal treatment possess a unique flavor and are favored by consumers. In this study, the key aroma-active compounds of egg yolk products and their formation mechanism during thermal treatment were investigated. The volatile aroma compounds in egg yolks were monitored using an electronic nose, gas chromatography-mass spectrometry (GC–MS) and gas chromatography–olfactometry–mass spectrometry (GC–O–MS), and the lipid molecular species were explored using ultra-high-performance liquid chromatography– mass spectrometry with a Q-Exactive HF-X Orbitrap (UPLC-Q-Exactive HF-X). A total of 68 volatile compounds were identified. Boiled eggs mainly derived their flavor from hexanal, 2-pentyl-furan, 2-butanone, 3-methyl-butanal and heptane. Meanwhile, fried eggs relied mainly on 14 compounds, the most important of which were 2-ethyl-3-methyl-pyrazine, 3-ethyl-2,5-dimethyl-pyrazine, 2-ethyl-3,5-dimethyl-pyrazine, nonanal and 2,3-diethyl-5-methyl-pyrazine, providing a baked and burnt sugar flavor. A total of 201 lipid molecules, belonging to 21 lipid subclasses, were identified in egg yolks, and 13 oxidized lipids were characterized using a molecular network. Phosphoethanolamines (PEs) containing polyunsaturated fatty acids were the primary flavor precursors contributing to the development of egg yolks’ flavor, participating in lipid oxidation reactions and the Maillard reaction and regulating the production of aldehydes and pyrazine compounds. This study provides reference and guidance for the development of egg yolk flavor products.

The spiking neural network (SNN), as a promising brain-inspired computational model with binary spike information transmission mechanism, rich spatially-temporal dynamics, and event-driven characteristics, has received extensive attention. However, its intricately discontinuous spike mechanism brings difficulty to the optimization of the deep SNN. Since the surrogate gradient method can greatly mitigate the optimization difficulty and shows great potential in directly training deep SNNs, a variety of direct learning-based deep SNN works have been proposed and achieved satisfying progress in recent years. In this paper, we present a comprehensive survey of these direct learning-based deep SNN works, mainly categorized into accuracy improvement methods, efficiency improvement methods, and temporal dynamics utilization methods. In addition, we also divide these categorizations into finer granularities further to better organize and introduce them. Finally, the challenges and trends that may be faced in future research are prospected.

Abstract The three-dimensional target position is vital to automatic driving, which can efficiently detect the geometrical shape of obstacles and achieve automatic environment recognition for both indoor and outdoor scenarios. In this paper, we propose an RFID 3D localization prototype based on the polarization phase model. To eliminate the impacts of tag angle, we leverage the polarization effect to obtain polarized phases. After that, we use polarized phase decomposition to get pure phases, i.e., the phase corresponding to the line of sight path. Hence, we can calculate the raw phases using the HMFCW algorithm. Finally, we can estimate the target’s 3D position according to unwrapped phase information. In this way, we implement a low-cost, high-precision, easy-for-deployment 3D position solution using commercial UHF RFID hardware. The experiments show that the method can obtain effective positioning results in various environments and achieve positioning accuracy with an average error of about 9 cm.

Basement excavation may induce deformations of the adjacent tunnels. The response of existing tunnels to basement excavation considering the critical unloading ratio is rarely studied. In this study, a three-dimensional numerical model is established to investigate basement–tunnel interaction. Then, the numerical model is validated by simulating the centrifuge model test. Thereafter, the influences of basement geometry and tunnel location relative to the basement on the vertical deformation of the tunnel are studied. The results show that the vertical deformation of the tunnel increases linearly with the unloading ratio, which describes the degree of excavation depth above the tunnel. But there exists a critical unloading ratio of 0.6, beyond which the vertical deformation of the tunnel increases significantly. On this basis, an empirical model is proposed to predict the vertical deformation of the tunnel considering the unloading ratio.

Lizhe Zhu et al. use a quasi Markov State Model built from extensive molecular dynamics simulations to elucidate the dynamics of RISC-mRNA interactions. Their results provide further insight on how a target nucleotide is positioned before recognition by Argonaute.

Classically, ice nucleation on foreign surfaces is considered a one-step process. Here, Li et al. uncover a two-barrier pathway which becomes competitive at lower temperatures, facilitated by synergistic, entropic effects of rhombic and hexagonal ice structures.

Introduction The prevalence of heart failure (HF) is increasing. Exercise-based cardiac rehabilitation (CR) reduces mortality and further improves the prognosis of patients with HF. However, the effect of different modes of CR delivery on HF remains unclear. Thus, the purpose of this study is to find out the relative efficacy and safety of different modes of CR delivery for individuals with HF using a network meta-analysis.Methods and analysis We will perform a systematic review and network meta-analysis of randomised controlled trials which compare different modes of exercise-based CR delivery for patients with HF. Databases including Embase, Medline, the Cochrane Central Register of Controlled Trials and Web of Science will be searched up to May 2022. The primary outcomes will focus on the functional capacity and the health-related quality of life (hr-QOL). Functional capacity will be evaluated by peak oxygen consumption (mL/kg/min) and 6 min walking test (metres). The Minnesota Living with Heart Failure questionnaire, Short Form-36, Psychometric properties of the Kansas City cardiomyopathy questionnaire and EuroQol five dimensions questionnaire will serve as measures of hr-QOL. As secondary outcomes, we will assess hospital admissions (all-cause and cardiac) and all-cause mortality, which required a minimum follow-up of 6 months, as well as adverse events during exercise training. The risk of bias for individual studies will be evaluated according to the Cochrane Handbook. The quality of evidence will be assessed by the Grading of Recommendations, Assessment, Development and Evaluation approach.Ethics and dissemination This study does not require ethics approval as it is based on published trials. Results of this systematic review and network meta-analysis will be submitted to a peer-reviewed journal for future publication.Trial registration number CRD42021278351.

The interaction perception between aroma and oral chewing during food consumption has always been a hot topic in exploring consumers’ preferences and purchase desires. A chewing simulation system was set to find out the effect of key saliva components and chewing time on odorants released with grilled eel meat. Odor release did not always enhance with the degree of chewing, or the amount of saliva released. The breaking up of the tissue structure of the fish meat by the teeth encourages the release of odorants and the participation of saliva partially blocks this process. The release of pyrazine, alcohol, and acid compounds in grilled eel meat peaked within 20–60 s after chewing. Sufficient exposure of saliva to grilled eel meat will inhibit aromatic, ketone, ester, hydrocarbon, and sulfur compounds release. 3-methyl-2-butanol contributed to the subtle aroma differences that arise before and after eating grilled eel meat. Naphthalene, 2-acetylthiazole, 2-decenal, 2-undecanone, 5-ethyldihydro-2(3H)-furanone were the main odorants released in large quantities in the early stages of eating grilled eel and affected the top note. Consequently, the results provided the odorants information in aroma perception during grilled eel consumption and benefited the objective evaluation of grilled eel product optimization.

Background: Considerable evidence implicates myeloid-derived suppressor cells (MDSCs) promote tumor progression and drug resistance. Sorafenib is the standard first-line therapy for advanced hepatocellular carcinoma (HCC). Clinical evidence indicates that sorafenib resistance is associated with increased MDSCs, by which MDSCs exerts these effects is obscure. This study aimed to investigate the mechanism of sorafenib resistance mediated by MDSCs. Methods: A syngeneic mouse-liver cancer cell line BNL was subcutaneously injected to build a tumor-bearing mouse model, and syngeneic MDSCs were adoptive transferred into the tumor-bearing mouse. Tumor tissue was obtained, and transcriptomic analysis of the tumor was carried out on RNAseq data. A coculture system was used to verify the crosstalk between MDSCs and BNL cells. Results: Adoptive MDSCs transfer into tumor-bearing mice induced an increase of tumor-infiltrating MDSCs, which led to tumor growth and impaired antitumor activity of sorafenib in BNL HCC models. MDSCs transfer contributed to tumor fibrosis and tumor-associated fibroblast (CAF) activation, associated with fibroblast growth factor (FGF1) upregulation. In contrast, MDSC depletion by anti-Ly6G+ reduced fibrosis and increased sorafenib antitumor efficacy. Intriguingly, tumor-infiltrating MDSCs barely expressed FGF1. IL-6 derived from MDSCs increased FGF1 expression in BNL liver cancer cells, and anti-IL-6 attenuated this effect in vitro. MAPK pathway, one of the sorafenib targets, is the downstream signaling of FGF1 and is reactivated by MDSCs-mediated FGF1 upregulation. Conclusions: Our finding demonstrated that MDSCs led to tumor growth and sorafenib resistance via FGF1 upregulation and subsequent indirect CAF activation. We offered a novel mechanism of MDSCs-driven HCC progression and sorafenib resistance.

Wheat powdery mildew (Blumeria graminis f. sp. tritici, Bgt, recently clarified as B. graminis s. str.), is one of the most destructive diseases of wheat. Pm60 is a nucleotide-binding leucine-rich repeat (NLR) gene that confers race-specific resistance to Bgt. Allelic variants (Pm60, Pm60a, and Pm60b) were found in Triticum urartu and T. dicoccoides, the wild progenitors of wheat. In the present study, we studied the diversity of the Pm60 locus in a large set of wheat germplasm and found 20 tetraploid wheats harboring the Pm60 alleles, which correspond to three novel haplotypes (HapI–HapIII). HapI (Pm60 allele) and HapII (Pm60a allele) were present in domesticated tetraploid wheats, whereas HapIII (Pm60a allele) was identified in wild tetraploid T. araraticum. A sequence comparison of HapII and HapIII revealed that they differed by three SNPs and a GCC deletion. Results of the phylogenetic analysis revealed that HapII was more closely related to the functional haplotype MlIW172. Infection tests showed that HapII-carrying lines display a partial resistance response to Bgt#GH, while HapI was susceptible. Our results provide insights into the genetic evolution of the Pm60 locus and potential valuable alleles for powdery mildew resistance breeding.

The Maillard reaction (MR) is affected by lipid oxidation, the intermediate products of which are key to understanding this process. Herein, nine aliphatic aldehyde–glutathione–ribose models were designed to explore the influence of lipid oxidation products with different structures on the MR. The browning degree, fluorescence degree, and antioxidant activity of the MR products were determined, and the generated volatile organic compounds (VOCs) and nonvolatile compounds were analyzed by gas chromatography-mass spectrometry and ultra-performance liquid chromatography-mass spectrometry. A total of 108 VOCs and 596 nonvolatile compounds were detected. The principal component and hierarchical clustering analyses showed that saturated aldehydes mainly affected the VOCs generated by the MR, while unsaturated aldehydes significantly affected the nonvolatile compounds, which changed the taste attributes of the MR products. Compared with the control group, the addition of unsaturated aldehydes significantly increased the sourness score and decreased the umami score. In addition, the addition of unsaturated aldehydes decreased the antioxidant activity and changed the composition of nonvolatile compounds, especially aryl thioethers and medium chain fatty acids, with a strong correlation with umami and sourness in the electronic tongue analysis (p < 0.05). The addition of aliphatic aldehydes reduces the ultraviolet absorption of the intermediate products of MR browning, whereas saturated aldehydes reduce the browning degree of the MR products. Therefore, the flavor components of processed foods based on the MR can be effectively modified by the addition of lipid oxidation products.

Background: There is an urgent need for effective treatment for advanced hepatocellular carcinoma (HCC). Sorafenib, a multi-kinase inhibitor, is the first-line treatment for advanced HCC, which has a modest efficacy due to severe adverse effects (AEs), acquired resistance, and others. Jianpi Huayu Decoction (JHD) is a traditional Chinese medicine formulation, effective as an alternative and complementary therapy of HCC. Combined sorafenib with JHD is a potential approach to overcome sorafenib resistance. Methods: Effect and AEs of clinical-dose sorafenib combined JHD were evaluated in a subcutaneous model. Diarrhea, the most frequently reported AEs, was evaluated by diarrhea score. Illumina NovaSeq analyzed the gut microbiota (GM) composition of the mice. Results: JHD administration enhanced anti-tumor response to sorafenib and prevented the occurrence of sorafenib-induced diarrhea and body weight loss. Proinflammatory GM Helicobacter positively correlated with sorafenib treatment while anti-inflammatory GM, Muribaculum, Fusicatenibacter, and Dorea positively correlated with JHD treatment. In addition, JHD decreased the infiltration of inflammatory cells and downregulated signaling of interleukin 6/signal transducer and activator of the transcription-3 pathway. Conclusions: Our finding suggested that JHD with microbiota modulation properties could significantly potentiate sorafenib and provide a promising treatment for advanced HCC.

Interactions between proteins and RNA are an important mechanism for post-transcriptional regulation, but predicting these interactions is difficult. Through a deep learning approach, here the authors predict RNA-binding sites and binding preference based on the local physicochemical properties of the protein surface.

Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of immature myeloid cells that are activated under pathological conditions, such as cancer, or mature myeloid cells that are converted immune-suppressive cells via tumor-derived exosomes, and potently support the tumor processes at different levels. Currently, multiple studies have demonstrated that MDSCs induce immune checkpoint blockade (ICB) therapy resistance through their contribution to the immunosuppressive network in the tumor microenvironment. In addition, non-immunosuppressive mechanisms of MDSCs such as promotion of angiogenesis and induction of cancer stem cells also exert a powerful role in tumor progression. Thus, MDSCs are potential therapeutic targets to enhance the antitumor efficacy of ICB therapy in cases of multiple cancers. This review focuses on the tumor-promoting mechanism of MDSCs and provides an overview of current strategies that target MDSCs with the objective of enhancing the antitumor efficacy of ICB therapy.

There is little known about kinetic changes in anterior cruciate ligament deficiency (ACLD) combined with meniscal tears during jogging. Therefore, 29 male patients with injured ACLs and 15 healthy male volunteers were recruited for this study to investigate kinetic abnormalities in male patients after ACL deficiency combined with a meniscal injury during jogging. Based on experimental data measured by an optical tracking system, a subject-specific musculoskeletal model was employed to estimate the tibiofemoral joint kinetics during jogging. Between-limb and interpatient differences were compared by the analysis of variance. The results showed that decreased knee joint forces and moments of both legs in ACLD patients were detected during the stance phase compared to the control group. Meanwhile, compared with ACLD knees, significantly fewer contact forces and flexion moments in ACLD combined with lateral and medial meniscal injury groups were found at the mid-stance, and ACLD with medial meniscal injury group showed a lower axial moment in the loading response (p < 0.05). In conclusion, ACLD knees exhibit reduced tibiofemoral joint forces and moments during jogging when compared with control knees. A combination of meniscus injuries in the ACLD-affected side exhibited abnormal kinetic alterations at the loading response and mid-stance phase.

Lipid oxidation and the Maillard reaction are two of the most important reactions affecting the flavor of foods that have been heat-processed. To investigate the contribution of lipids to the mussel’s flavor, the mussel’s Maillard reaction products (MRPs) were prepared with polar lipids (mainly phospholipids) and nonpolar lipids (mainly glycerides), respectively. The effects of polar and nonpolar lipids on the flavor of the MRPs were investigated by sensory evaluation, electronic tongue, electronic nose, ultra-performance liquid chromatography-mass-spectrometry (UPLC-MS) and gas chromatography-mass-spectrometry (GC-MS). From the sensory evaluation results, the polar lipid MRPs had the highest scores. The tastes of polar lipid MRPs and nonpolar lipid MRPs were mainly umami, saltiness and sourness, and there were significant differences in their sour tastes. The flavor compounds in the MRPs were mainly inorganic sulfides, organic sulfides and nitrogen oxides. The odor of polar lipid MRPs was stronger than that of nonpolar lipid MRPs, and the seafood flavor was more obvious. A total of 37 volatile compounds were detected by GC-MS, mainly aldehydes, alcohols and ketones. The addition of polar lipids helped the MRPs to produce more volatile compounds. A total of 177 non-volatile compounds (including amino acids and their derivatives and oligopeptides, etc.) were detected in the samples using UPLC-MS. The non-volatile compounds contained in the no-lipid MRPs, polar lipid MRPs and nonpolar lipid MRPs were significantly different. This study provides a theoretical basis and technical support for the production of mussel MRPs.

Molecular systems displaying aggregation-induced emission (AIE) have important biomedical and optoelectronic applications. Here the authors report a further mechanism for AIE, through aromaticity reversal from the ground state to the excited state, in the non-aromatic annulene derivative of cyclooctatetrathiophene.

The low fiber digestibility and unbalanced amino acids restricted the use of DDGS in swine diets. Key nutrition components dynamic monitoring and key regulatory pathways analysis were performed to find the rules of nutrition changes for DDGS fermented by Aspergillus niger. Cellulose and hemicellulose were reduced to 15.3% and 15.2%. 1,4-D-Xylobiose was decreased from 16.8 μg/mL to 0.2 μg/mL. Lys, Arg, and Thr were increased to 3.00%, 2.89%, and 4.40%, and met the requirements of pigs. The whole fermentation process was divided into three stages. Cellulose degradation and Lys and Arg synthesis occurred in the early stage, while Asp synthesis occurred in the last stage. α-Ketoglutarate was the key factor for Aspergillus niger degrading cellulose to synthesize Lys and Arg. The key active metabolic pathways that respond to the changes in nutrition were identified which preliminarily revealed the rules of nutrition adjustment of DDGS during fermentation with Aspergillus niger.

Transcription errors can substantially affect metabolic processes in organisms by altering the epigenome and causing misincorporations in mRNA, which is translated into aberrant mutant proteins. Moreover, within eukaryotic genomes there are specific Transcription Error-Enriched genomic Loci (TEELs) which are transcribed by RNA polymerases with significantly higher error rates and hypothesized to have implications in cancer, aging, and diseases such as Down syndrome and Alzheimer’s. Therefore, research into transcription errors is of growing importance within the field of genetics. Nevertheless, methodological barriers limit the progress in accurately identifying transcription errors. Pro-Seq and NET-Seq can purify nascent RNA and map RNA polymerases along the genome but cannot be used to identify transcriptional mutations. Here we present background Error Model-coupled Precision nuclear run-on Circular-sequencing (EmPC-seq), a method combining a nuclear run-on assay and circular sequencing with a background error model to precisely detect nascent transcription errors and effectively discern TEELs within the genome.

The rapid urbanization in China has already put heavy pressures on imperfect infrastructure, especially for fundamental urban functions such as power and water supply, traffic, education, and healthcare. The emergence of smart cities can help cope with the rapidly expanding demands on urban infrastructure. However, the development of smart cities in China is just in its infancy, and there is still a lack of clear understanding of the development path of smart cities. This article focuses on the development of smart cities in China. It aims to (a) judge whether there is spatial autoregression in the construction of smart cities in 83 Chinese cities and (b) identify key influencing factors in the development of smart cities in China through a spatial econometric model developed by GeoDa software. The results show that there exists spatial autoregression in the development of smart cities in China. Four key influencing factors (governmental support, innovative level, economic development, and human capital) are identified. Based on these findings, suggestions for future promoting development of smart cities in China are put forward. This research can deepen the understanding of the spatial effects of smart cities and provide valuable decision-making references for policy makers.

Tumor-induced immunosuppressive microenvironment in which myeloid-derived suppressor cells (MDSCs) plays an important role, remains an obstacle for effective oncotherapy currently. Inducing MDSCs into maturation was confirmed as an effective method to reduce the tumor-bearing host's immunosuppression. Traditional Chinese medicines (TCM) possess characteristics of alleviating immunosuppression of cancer patients and low toxicity. Jianpi Huayu Decoction (JHD) was an experienced formula of TCM for oncotherapy based on TCM theory and clinical practice. We previously observed that JHD attenuated the expression of interleukin-10 (IL-10) and transforming growth factor beta (TGF-β) in tumor. IL-10 and TGF-β were found to be cytokines positively related to immunosuppression induced by MDSCs. Here, our study was designed to further investigate the regulation of JHD on the immune system in the H22 liver-cancer mouse model. Mainly, flow cytometry was used to detect the proportion of immune cells, to analyze the apoptosis, differentiation and reactive oxygen species of MDSCs. We found that JHD significantly reduced the destruction of spleen structure, reduced the proportion of regulatory T cells (Treg) and T helper 17 cells (Th17), and increased the proportion of cytotoxic T lymphotes (CTL), Dendritic cells (DC) and CD11b+Gr-1+cells in spleen, but with no significant change of T helper 1 cells (Th1), T helper 2 cells (Th2) and macrophages. In vitro experiments showed that apoptosis of MDSCs was decreased as the time of JHD stimulation increased, which partly explained the increase of CD11b+Gr-1+cells in the spleen. Meanwhile, JHD could promote the differentiation of MDSCs into macrophages and dendritic cells, attenuate expression of ROS in MDSCs and reduce its inhibition on the proliferation of CD4+ T cells, in vitro. Therefore, that the proportion of CD11b+Gr-1+ cells increased in the spleen of tumor-bearing hosts may not be villainy after treatment, when these drugs suppress the immunosuppressive ability of CD11b+Gr-1+ cells and promote it mature to replenish dendritic cell, at the same time. Generally, JHD may be a complementary and alternative drug for attenuating the immunosuppressive status induced by hepatocellular carcinoma, possibly by promoting differentiation and inhibiting the immunosuppressive activity of MDSCs.

The traditional Chinese medicine formula Jianpi-Huayu (JPHY) has been reported to be effective in the treatment of hepatocellular carcinoma (HCC). However, its underlying mechanism remains unclear. In this article, we employed an orthotopic transplantation model in nude mice to explore whether JPHY could inhibit the development of HCC by regulating miR-602, which targets the Ras association domain-containing protein 1A (RASSF1A) pathway. HCC SMMC-7721 cells were treated with JPHY to test whether the RASSF1A gene as mediated by miR-602 affected the proliferation and apoptosis of tumor cells. We subsequently detected miR-602, RASSF1A, and tumor cell apoptosis–related markers in cells and liver tumor tissues. We observed that mice treated with JPHY had smaller tumors and higher survival rates than untreated ones. Similarly, JPHY-treated SMMC-7721 cells exhibited alterations in morphology and higher cytotoxicity compared with the control group. Furthermore, we found that JPHY decreased overexpression of miR-602 and increased protein expression levels of the RASS1A gene, which in turn altered protein expression levels of tumor cell apoptosis–related genes in the cells and liver tumor tissues of drug-treated mice. These results indicated that JPHY could potentially be used to treat HCC by targeting miR-602, which targets the RASSF1A gene, which in turn plays a major role in HCC pathogenesis.

Abstract The receptor tyrosine kinase, erythropoietin-producing hepatocellular A4 (EphA4), was recently identified as a molecular target for Alzheimer’s disease (AD). We found that blockade of the interaction of the receptor and its ligands, ephrins, alleviates the disease phenotype in an AD transgenic mouse model, suggesting that targeting EphA4 is a potential approach for developing AD interventions. In this study, we identified five FDA-approved drugs—ergoloid, cyproheptadine, nilotinib, abiraterone, and retapamulin—as potential inhibitors of EphA4 by using an integrated approach combining virtual screening with biochemical and cellular assays. We initially screened a database of FDA-approved drugs using molecular docking against the ligand-binding domain of EphA4. Then, we selected 22 candidate drugs and examined their inhibitory activity towards EphA4. Among them, five drugs inhibited EphA4 clustering induced by ephrin-A in cultured primary neurons. Specifically, nilotinib, a kinase inhibitor, inhibited the binding of EphA4 and ephrin-A at micromolar scale in a dosage-dependent manner. Furthermore, nilotinib inhibited the activation of EphA4 and EphA4-dependent growth cone collapse in cultured hippocampal neurons, demonstrating that the drug exhibits EphA4 inhibitory activity in cellular context. As demonstrated in our combined computational and experimental approaches, repurposing of FDA-approved drugs to inhibit EphA4 may provide an alternative fast-track approach for identifying and developing new treatments for AD.

The PSD-95/SAPAP/Shank complex functions as the major scaffold in orchestrating the formation and plasticity of the post-synaptic densities (PSDs). We previously demonstrated that the exquisitely specific SAPAP/Shank interaction is critical for Shank synaptic targeting and Shank-mediated synaptogenesis. Here, we show that the PSD-95/SAPAP interaction, SAPAP synaptic targeting, and SAPAP-mediated synaptogenesis require phosphorylation of the N-terminal repeat sequences of SAPAPs. The atomic structure of the PSD-95 guanylate kinase (GK) in complex with a phosphor-SAPAP repeat peptide, together with biochemical studies, reveals the molecular mechanism underlying the phosphorylation-dependent PSD-95/SAPAP interaction, and it also provides an explanation of a PSD-95 mutation found in patients with intellectual disabilities. Guided by the structural data, we developed potent non-phosphorylated GK inhibitory peptides capable of blocking the PSD-95/SAPAP interaction and interfering with PSD-95/SAPAP-mediated synaptic maturation and strength. These peptides are genetically encodable for investigating the functions of the PSD-95/SAPAP interaction in vivo.

Hydrophobic interactions occur between nonpolar molecules in water and their experimental quantification can help the understanding of biological self-assembly. Here Jianget al. examine the kinetics and thermodynamics of hydrophobic aggregation in a bulk environment and characterize its cooperativity.

Abstract Hospital‐acquired infection causes many deaths worldwide and calls for the urgent need for antibacterial biomaterials used in clinic that can selectively kill harmful bacteria. The present study rationally designs fusion peptides capable of undergoing 2D self‐assembly on the poly(methyl methacrylate) surface to form a smart surface, which can maintain a desirable orientation via electrostatic interactions. The in vitro assay shows that the smart surface can recognize bacteria to exert antibacterial activity and is nontoxic toward mouse bone mesenchymal stem cells. Excitingly, the smart surface can distinguish different bacterial strains. This selective feature, from being broad‐spectrum to being highly selective against S. aureus, can be altered by varying the number of amino acids in the recognition sequences. By all‐atom molecular dynamics simulations, it is also found that the recognition sequence in the peptide is critical for the selectivity toward specific bacterial strains, in which a less accessible surface area for the bacteria in the antimicrobial peptide sequence is responsible for such selectivity. Finally, the smart surface can inhibit S. aureus infection in vivo with much more rapid tissue‐healing compared to the control.

The prefrontal cortex (PFC), which plays key roles in many higher cognitive processes, is a hierarchical system consisting of multi-scale organizations. Optimizing the working state at each scale is essential for PFC's information processing. Typical optimal working states at different scales have been separately reported, including the dopamine-mediated inverted-U profile of the working memory (WM) at the system level, critical dynamics at the network level, and detailed balance of excitatory and inhibitory currents (E/I balance) at the cellular level. However, it remains unclear whether these states are scale-specific expressions of the same optimal state and, if so, what is the underlying mechanism for its regulation traversing across scales. Here, by studying a neural network model, we show that the optimal performance of WM co-occurs with the critical dynamics at the network level and the E/I balance at the level of individual neurons, suggesting the existence of a unified, multi-scale optimal state for the PFC. Importantly, such a state could be modulated by dopamine at the synaptic level through a series of U or inverted-U profiles. These results suggest that seemingly different optimal states for specific scales are multi-scale expressions of one condition regulated by dopamine. Our work suggests a cross-scale perspective to understand the PFC function and its modulation.

RNA Polymerase II can detect and cleave mis-incorporated nucleotides by a proofreading mechanism that requires backtracking of the enzyme. Here the authors show that Pol II backtracking occurs in a stepwise mode that involves two intermediate states where the fraying of the terminal RNA nucleotide is a prerequisite.

Speech recognition (SR) has been improved significantly by artificial neural networks (ANNs), but ANNs have the drawbacks of biologically implausibility and excessive power consumption because of the nonlocal transfer of real-valued errors and weights. While spiking neural networks (SNNs) have the potential to solve these drawbacks of ANNs due to their efficient spike communication and their natural way to utilize kinds of synaptic plasticity rules found in brain for weight modification. However, existing SNN models for SR either had bad performance, or were trained in biologically implausible ways. In this paper, we present a biologically inspired convolutional SNN model for SR. The network adopts the time-to-first-spike coding scheme for fast and efficient information processing. A biological learning rule, spike-timing-dependent plasticity (STDP), is used to adjust the synaptic weights of convolutional neurons to form receptive fields in an unsupervised way. In the convolutional structure, the strategy of local weight sharing is introduced and could lead to better feature extraction of speech signals than global weight sharing. We first evaluated the SNN model with a linear support vector machine (SVM) on the TIDIGITS dataset and it got the performance of 97.5%, comparable to the best results of ANNs. Deep analysis on network outputs showed that, not only are the output data more linearly separable, but they also have fewer dimensions and become sparse. To further confirm the validity of our model, we trained it on a more difficult recognition task based on the TIMIT dataset, and it got a high performance of 93.8%. Moreover, a linear spike-based classifier-tempotron-can also achieve high accuracies very close to that of SVM on both the two tasks. These demonstrate that an STDP-based convolutional SNN model equipped with local weight sharing and temporal coding is capable of solving the SR task accurately and efficiently.

An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of 0.094 s in comparison with HSV, HIS, and 2R-G-B color spaces. The guided filtering method can effectively distinguish the boundary between the tillage soil compared to other competing vanilla methods such as Tarel, multi-scale retinex, wavelet-based retinex, and homomorphic filtering in spite of having the fastest processing speed of 0.113 s . The extracted soil boundary line of the improved anti-noise morphology algorithm has the best precision and speed compared to other operators such as Sobel, Roberts, Prewitt, and Log. After comparing different sizes of image templates, the optimal template with the size of 140 × 260 pixels could achieve high-precision vision navigation while the course deviation angle was not more than 7.5 ° . The maximum tractor speed of the optimal template and global template were 51.41 km / h and 27.47 km / h , respectively, which can meet the real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the tillage soil boundary line extracted by the proposed improved anti-noise morphology algorithm, which has broad application prospect.

The RNA polymerase II (Pol II) is a eukaryotic enzyme that catalyzes the synthesis of the messenger RNA using a DNA template. Despite numerous biochemical and biophysical studies, it remains elusive whether the "secondary channel" is the only route for NTP to reach the active site of the enzyme or if the "main channel" could be an alternative. On this regard, crystallographic structures of Pol II have been extremely useful to understand the structural basis of transcription, however, the conformation of the unpaired non-template DNA part of the full transcription bubble (TB) is still unknown. Since diffusion routes of the nucleoside triphosphate (NTP) substrate through the main channel might overlap with the TB region, gaining structural information of the full TB is critical for a complete understanding of Pol II transcription process. In this study, we have built a structural model of Pol II with a complete transcription bubble based on multiple sources of existing structural data and used Molecular Dynamics (MD) simulations together with structural analysis to shed light on NTP entry pathways. Interestingly, we found that although both channels have enough space to allow NTP loading, the percentage of MD conformations containing enough space for NTP loading through the secondary channel is twice higher than that of the main channel. Further energetic study based on MD simulations with NTP loaded in the channels has revealed that the diffusion of the NTP through the main channel is greatly disfavored by electrostatic repulsion between the NTP and the highly negatively charged backbones of nucleotides in the non-template DNA strand. Taken together, our results suggest that the secondary channel is the major route for NTP entry during Pol II transcription.

Argonaute (Ago) proteins and microRNAs (miRNAs) are central components in RNA interference, which is a key cellular mechanism for sequence-specific gene silencing. Despite intensive studies, molecular mechanisms of how Ago recognizes miRNA remain largely elusive. In this study, we propose a two-step mechanism for this molecular recognition: selective binding followed by structural re-arrangement. Our model is based on the results of a combination of Markov State Models (MSMs), large-scale protein-RNA docking, and molecular dynamics (MD) simulations. Using MSMs, we identify an open state of apo human Ago-2 in fast equilibrium with partially open and closed states. Conformations in this open state are distinguished by their largely exposed binding grooves that can geometrically accommodate miRNA as indicated in our protein-RNA docking studies. miRNA may then selectively bind to these open conformations. Upon the initial binding, the complex may perform further structural re-arrangement as shown in our MD simulations and eventually reach the stable binary complex structure. Our results provide novel insights in Ago-miRNA recognition mechanisms and our methodology holds great potential to be widely applied in the studies of other important molecular recognition systems.

Protein-ligand recognition plays key roles in many biological processes. One of the most fascinating questions about protein-ligand recognition is to understand its underlying mechanism, which often results from a combination of induced fit and conformational selection. In this study, we have developed a three-pronged approach of Markov State Models, Molecular Dynamics simulations, and flux analysis to determine the contribution of each model. Using this approach, we have quantified the recognition mechanism of the choline binding protein (ChoX) to be ∼90% conformational selection dominant under experimental conditions. This is achieved by recovering all the necessary parameters for the flux analysis in combination with available experimental data. Our results also suggest that ChoX has several metastable conformational states, of which an apo-closed state is dominant, consistent with previous experimental findings. Our methodology holds great potential to be widely applied to understand recognition mechanisms underlining many fundamental biological processes.

The dynamics of the PPi release during the transcription elongation of bacterial RNA polymerase and its effects on the Trigger Loop (TL) opening motion are still elusive. Here, we built a Markov State Model (MSM) from extensive all-atom molecular dynamics (MD) simulations to investigate the mechanism of the PPi release. Our MSM has identified a simple two-state mechanism for the PPi release instead of a more complex four-state mechanism observed in RNA polymerase II (Pol II). We observed that the PPi release in bacterial RNA polymerase occurs at sub-microsecond timescale, which is ∼3-fold faster than that in Pol II. After escaping from the active site, the (Mg-PPi)(2-) group passes through a single elongated metastable region where several positively charged residues on the secondary channel provide favorable interactions. Surprisingly, we found that the PPi release is not coupled with the TL unfolding but correlates tightly with the side-chain rotation of the TL residue R1239. Our work sheds light on the dynamics underlying the transcription elongation of the bacterial RNA polymerase.

Chaos should occur often in gene regulatory networks (GRNs) which have been widely described by nonlinear coupled ordinary differential equations, if their dimensions are no less than 3. It is therefore puzzling that chaos has never been reported in GRNs in nature and is also extremely rare in models of GRNs. On the other hand, the topic of motifs has attracted great attention in studying biological networks, and network motifs are suggested to be elementary building blocks that carry out some key functions in the network. In this paper, chaotic motifs (subnetworks with chaos) in GRNs are systematically investigated. The conclusion is that: (i) chaos can only appear through competitions between different oscillatory modes with rivaling intensities. Conditions required for chaotic GRNs are found to be very strict, which make chaotic GRNs extremely rare. (ii) Chaotic motifs are explored as the simplest few-node structures capable of producing chaos, and serve as the intrinsic source of chaos of random few-node GRNs. Several optimal motifs causing chaos with atypically high probability are figured out. (iii) Moreover, we discovered that a number of special oscillators can never produce chaos. These structures bring some advantages on rhythmic functions and may help us understand the robustness of diverse biological rhythms. (iv) The methods of dominant phase-advanced driving (DPAD) and DPAD time fraction are proposed to quantitatively identify chaotic motifs and to explain the origin of chaotic behaviors in GRNs.

Molecular recognition is determined by the structure and dynamics of both a protein and its ligand, but it is difficult to directly assess the role of each of these players. In this study, we use Markov State Models (MSMs) built from atomistic simulations to elucidate the mechanism by which the Lysine-, Arginine-, Ornithine-binding (LAO) protein binds to its ligand. We show that our model can predict the bound state, binding free energy, and association rate with reasonable accuracy and then use the model to dissect the binding mechanism. In the past, this binding event has often been assumed to occur via an induced fit mechanism because the protein's binding site is completely closed in the bound state, making it impossible for the ligand to enter the binding site after the protein has adopted the closed conformation. More complex mechanisms have also been hypothesized, but these have remained controversial. Here, we are able to directly observe roles for both the conformational selection and induced fit mechanisms in LAO binding. First, the LAO protein tends to form a partially closed encounter complex via conformational selection (that is, the apo protein can sample this state), though the induced fit mechanism can also play a role here. Then, interactions with the ligand can induce a transition to the bound state. Based on these results, we propose that MSMs built from atomistic simulations may be a powerful way of dissecting ligand-binding mechanisms and may eventually facilitate a deeper understanding of allostery as well as the prediction of new protein-ligand interactions, an important step in drug discovery.

Neural networks can generate spatiotemporal patterns of spike activity. Sequential activity learning and retrieval have been observed in many brain areas, and e.g. is crucial for coding of episodic memory in the hippocampus or generating temporal patterns during song production in birds. In a recent study, a sequential activity pattern was directly entrained onto the neural activity of the primary visual cortex (V1) of rats and subsequently successfully recalled by a local and transient trigger. It was observed that the speed of activity propagation in coordinates of the retinotopically organized neural tissue was constant during retrieval regardless how the speed of light stimulation sweeping across the visual field during training was varied. It is well known that spike-timing dependent plasticity (STDP) is a potential mechanism for embedding temporal sequences into neural network activity. How training and retrieval speeds relate to each other and how network and learning parameters influence retrieval speeds, however, is not well described. We here theoretically analyze sequential activity learning and retrieval in a recurrent neural network with realistic synaptic short-term dynamics and STDP. Testing multiple STDP rules, we confirm that sequence learning can be achieved by STDP. However, we found that a multiplicative nearest-neighbor (NN) weight update rule generated weight distributions and recall activities that best matched the experiments in V1. Using network simulations and mean-field analysis, we further investigated the learning mechanisms and the influence of network parameters on recall speeds. Our analysis suggests that a multiplicative STDP rule with dominant NN spike interaction might be implemented in V1 since recall speed was almost constant in an NMDA-dominant regime. Interestingly, in an AMPA-dominant regime, neural circuits might exhibit recall speeds that instead follow the change in stimulus speeds. This prediction could be tested in experiments.