Proximal small intestinal bypass outperforms Roux-en-Y and jejunoileal bypass in glucose regulation in streptozotocin induced diabetic rats

BACKGROUND The efficacy of various bariatric surgeries varies in reducing blood glucose levels.Given the distinct mechanisms and anatomical alterations associated with each procedure,it is crucial to compare their glycemic control outcomes.We hypothesize that proximal small intestinal bypass(PSIB)is superior in blood glucose reduction over Roux-en-Y gastric bypass(RYGB)and jejunoileal bypass(JIB).AIM To compare the effectiveness of PSIB,RYGB,and JIB in lowering blood glucose.METHODS Rats with streptozotocin-induced diabetes were randomly divided into PSIB,RYGB,JIB,and sham-operated groups.Body weight,food intake,fasting blood glucose level,oral glucose tolerance test,insulin tolerance test,liver enzymes,and blood lipids were measured.RESULTS Postoperatively,only the JIB group had a lower body weight compared to the sham group.The food intake of the rats in all three surgical groups was significantly less than that in the sham group.Fasting blood glucose was reduced in all surgical groups and was lower in the PSIB group than in the RYGB and JIB groups.Glucose tolerance and insulin sensitivity improved in all three surgical groups compared to the sham group,but the improvement appeared earliest in the PSIB group.At six weeks postsurgery,the PSIB group showed a reduction in alanine transaminase levels and maintained a normal lipid profile.CONCLUSION PSIB demonstrated excellent hypoglycemic effects in the early postoperative period,and had better efficacy than RYGB and JIB.

Data Driven Vibration Control:A Review

With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing,aviation,and healthcare,the data driven vibration control(DDVC)has attracted broad interests from both the industrial and academic communities.Input shaping(IS),as a simple and effective feedforward method,is greatly demanded in DDVC methods.It convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure,thereby suppressing the residual vibration separately.Based on a thorough investigation into the state-of-the-art DDVC methods,this survey has made the following efforts:1)Introducing the IS theory and typical input shapers;2)Categorizing recent progress of DDVC methods;3)Summarizing commonly adopted metrics for DDVC;and 4)Discussing the engineering applications and future trends of DDVC.By doing so,this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives,aiming at promoting future research regarding this emerging and vital issue.

A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable requirements.However, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational efficiency.Hence, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

Influence of thermomechanical treatment on recrystallization and softening resistance of Cu-6.5Fe-0.3Mg alloy

The recrystallization and softening resistance of a Cu-6.5Fe-0.3Mg(mass fraction,%)alloy prepared by Process 1(cold rolling heat treatment)and Process 2(hot/cold rolling heat treatment)were studied using Vickers hardness tests,tensile tests,scanning electron microscopy and transmission electron microscopy.The softening temperature,hardness and tensile strength of the alloy prepared by Process 2 were 110°C,HV 15 and 114 MPa higher,respectively,than those of the alloy prepared by Process 1 after aging at 300°C.The recrystallization activation energy of the alloys prepared by Process 1 and Process 2 were 72.83 and 98.11 kJ/mol,respectively.The pinning effects of the precipitates of the two alloys on grain boundaries and dislocations were basically the same.The softening mechanism was mainly attributed to the loss of dislocation strengthening.The higher Fe fiber density inhibited the average free migration path of dislocations and grain boundary migration in the alloy,which was the main reason for higher softening temperature of the alloy prepared by Process 2.

A Novel Tensor Decomposition-Based Efficient Detector for Low-Altitude Aerial Objects With Knowledge Distillation Scheme

Unmanned aerial vehicles(UAVs) have gained significant attention in practical applications, especially the low-altitude aerial(LAA) object detection imposes stringent requirements on recognition accuracy and computational resources. In this paper, the LAA images-oriented tensor decomposition and knowledge distillation-based network(TDKD-Net) is proposed,where the TT-format TD(tensor decomposition) and equalweighted response-based KD(knowledge distillation) methods are designed to minimize redundant parameters while ensuring comparable performance. Moreover, some robust network structures are developed, including the small object detection head and the dual-domain attention mechanism, which enable the model to leverage the learned knowledge from small-scale targets and selectively focus on salient features. Considering the imbalance of bounding box regression samples and the inaccuracy of regression geometric factors, the focal and efficient IoU(intersection of union) loss with optimal transport assignment(F-EIoU-OTA)mechanism is proposed to improve the detection accuracy. The proposed TDKD-Net is comprehensively evaluated through extensive experiments, and the results have demonstrated the effectiveness and superiority of the developed methods in comparison to other advanced detection algorithms, which also present high generalization and strong robustness. As a resource-efficient precise network, the complex detection of small and occluded LAA objects is also well addressed by TDKD-Net, which provides useful insights on handling imbalanced issues and realizing domain adaptation.

Seed dispersal by wintering ducks in a coastal wetland of eastern China

Duck species are important vectors for seed dispersal of many plants,contributing significantly to the regeneration of wetland plant communities.However,research on the temporal changes in diet and the dynamics and differences of seed dispersal among different duck species is still limited.In this study,we analyzed the diversity of duck community and the diversity of seeds in the feces of different duck species from December to February for 2022–23 and 2023–24 in the coastal wetland of Dafeng,eastern Jiangsu Province,China.A total of 13 duck species were recorded in the four habitats,of which Spot-billed Ducks(Anas zonorhyncha)and Mallards(A.platyrhynchos)were the most abundant.The diversity and abundance of ducks vary across different habitats,and tidal flat supports the greatest diversity and abundance of ducks.We collected fecal samples from Spot-billed Ducks,Gadwalls(Mareca strepera),Mallards,and Eurasian Teals(A.crecca)in the tidal flat,from which seeds belonging to 7 families,13 genera and 15 different plant species were obtained.There were significant differences in seed dispersal among the four duck species,which varied with the season.Gadwalls and Common Teals showed more significant diversity in seed dispersal,with their feces containing a greater variety of plant seeds,which is related to their broader dietary range and ecological adaptability.Furthermore,the seasonal variation in the number of seeds per feces reflected the availability of seeds in the habitat and the response of ducks to environmental changes,while variations in seed intake among different duck species may be associated with inter-annual weather condition changes.The results of this study will provide a new perspective for understanding the mechanisms of bird-mediated seed dispersal in coastal wetland and offer preliminary insights for the seed dispersal by Asian ducks.

Enhancing Na^(+) diffusion dynamics and structural stability of O3-NaMn_(0.5)Ni_(0.5)O_(2)cathode by Sc and Zn dual-substitution

Sc and Zn were introduced into O3-NaMn_(0.5)Ni_(0.5)O_(2)(NaMN)using the combination of solution combustion and solid-state method.The effect of Sc and Zn dual-substitution on Na^(+) diffusion dynamics and structural stability of NaMN was investigated.The physicochemical characterizations suggest that the introduction of Sc and Zn broaden Na^(+) diffusion channels and weaken the Na—O bonds,thereby facilitating the diffusion of sodium ions.Simulations indicate that the Sc and Zn dual-substitution decreases the diffusion barrier of Na-ions and improves the conductivity of the material.The dual-substituted NaMn_(0.5)Ni_(0.4)Sc_(0.04)Zn_(0.04)O_(2)(Na MNSZ44)cathode delivers impressive cycle stability with capacity retention of 71.2%after 200 cycles at 1C and 54.8%after 400 cycles at 5C.Additionally,the full cell paired with hard carbon anode exhibits a remarkable long-term cycling stability,showing capacity retention of 64.1%after 250 cycles at 1C.These results demonstrate that Sc and Zn dual-substitution is an effective strategy to improve the Na^(+) diffusion dynamics and structural stability of NaMN.

A Distributed Adaptive Second-Order Latent Factor Analysis Model

Dear Editor,This letter presents a distributed adaptive second-order latent factor(DAS) model for addressing the issue of high-dimensional and incomplete data representation. Compared with first-order optimizers, a second-order optimizer has stronger ability in approaching a better solution when dealing with the non-convex optimization problems, thus obtaining better performance in extracting the latent factors(LFs) well representing the known information from high-dimensional and incomplete data.

SDGNN: Symmetry-Preserving Dual-Stream Graph Neural Networks

Dear Editor,This letter proposes a symmetry-preserving dual-stream graph neural network(SDGNN) for precise representation learning to an undirected weighted graph(UWG). Although existing graph neural networks(GNNs) are influential instruments for representation learning to a UWG, they invariably adopt a unique node feature matrix for illustrating the sole node set of a UWG.

Hydroxysafflor yellow A induced ferroptosis of Osteosarcoma cancer cells by HIF-1α/HK2 and SLC7A11 pathway

Osteosarcoma is a very serious primary bone cancer with a high death rate and a dismal prognosis.Since there is no permanent therapy for this condition,it is necessary to develop a cure.Therefore,this investigation was carried out to assess the impacts and biological functions of hydroxysafflor yellow A(HYSA)in osteosarcoma cell lines(MG63).In this investigational study,MG63 cells were utilized.Microarray experiments,quantitative polymerase chain reaction(qPCR),immunofluorescent staining,extracellular acidification rate(ECAR),oxygen consumption rate(OCR),glucose consumption,lactate production,and ATP levels,proliferation assay,5-Ethynyl-2′-deoxyuridine(EDU)staining,and Western blot were performed.In MG63 cells,HYSA lowered cell proliferation and metastasis rates,suppressed EDU cell number,and enhanced caspase-3/9 activity levels.HYSA reduced the Warburg effect and induced ferroptosis(FPT)in MG63 cells.Inhibiting ferroptosis diminished HYSA’s anti-cancer activities in MG63 cells.The stimulation of the HIF-1α/SLC7A11 pathway decreased HYSA’s anti-cancer activities in MG63 cells.HIF-1αis one target spot for HYSA in a model of osteosarcoma cancer(OC).HYSA altered HIF-1α’s thermophoretic activity;following binding with HYSA,HIF-1α’s melting point increased from~55°C to~60°C.HYSA significantly enhanced the thermal stability of exogenous WT HIF-1αwhile not affecting Mut HIF-1α,suggesting that ARG-311,GLY-312,GLN-347,and GLN-387 may be involved in the interaction between HIF-1αand HYSA.Conclusively,our study revealed that HYSA induced FPT and reduced the Warburg effect of OC through mitochondrial damage by HIF-1α/HK2/SLC7A11 pathway.HYSA is a possible therapeutic option for OC or other cancers.

Quick Weighing of Passing Vehicles Using the Transfer-Learning-Enhanced Convolutional Neural Network

Transfer learning could reduce the time and resources required by the training of new models and be therefore important for generalized applications of the trainedmachine learning algorithms.In this study,a transfer learningenhanced convolutional neural network(CNN)was proposed to identify the gross weight and the axle weight of moving vehicles on the bridge.The proposed transfer learning-enhanced CNN model was expected to weigh different bridges based on a small amount of training datasets and provide high identification accuracy.First of all,a CNN algorithm for bridge weigh-in-motion(B-WIM)technology was proposed to identify the axle weight and the gross weight of the typical two-axle,three-axle,and five-axle vehicles as they crossed the bridge with different loading routes and speeds.Then,the pre-trained CNN model was transferred by fine-tuning to weigh themoving vehicle on another bridge.Finally,the identification accuracy and the amount of training data required were compared between the two CNN models.Results showed that the pre-trained CNN model using transfer learning for B-WIM technology could be successfully used for the identification of the axle weight and the gross weight for moving vehicles on another bridge while reducing the training data by 63%.Moreover,the recognition accuracy of the pre-trained CNN model using transfer learning was comparable to that of the original model,showing its promising potentials in the actual applications.

Research on the Application of Psychology in the Ideological and Political Education of Counselors in Higher Vocational Colleges

As an important base for the training of skilled personnel,the ideological and political education of counselors plays a vital role in the process of personnel training in higher vocational colleges.Based on the analysis of the current situation of ideological and political education of counselors in higher vocational colleges,this paper analyzes the application value and strategies of psychology in ideological and political education,aiming at providing a set of systematic psychological application frameworks for counselors in higher vocational colleges,so as to promote the all-round development of students and realize the fundamental task of cultivating morality and educating people.

An Overview of Calibration Technology of Industrial Robots

With the continuous improvement of automation,industrial robots have become an indispensable part of automated production lines.They widely used in a number of industrial production activities,such as spraying,welding,handling,etc.,and have a great role in these sectors.Recently,the robotic technology is developing towards high precision,high intelligence.Robot calibration technology has a great significance to improve the accuracy of robot.However,it has much work to be done in the identification of robot parameters.The parameter identification work of existing serial and parallel robots is introduced.On the one hand,it summarizes the methods for parameter calibration and discusses their advantages and disadvantages.On the other hand,the application of parameter identification is introduced.This overview has a great reference value for robot manufacturers to choose proper identification method,points further research areas for researchers.Finally,this paper analyzes the existing problems in robot calibration,which may be worth researching in the future.

Secondary-Atom-Doping Enables Robust Fe-N-C Single-Atom Catalysts with Enhanced Oxygen Reduction Reaction

Single-atom catalysts(SACs) with nitrogen-coordinated nonprecious metal sites have exhibited inimitable advantages in electrocatalysis.However,a large room for improving their activity and durability remains.Herein,we construct atomically dispersed Fe sites in N-doped carbon supports by secondary-atom-doped strategy.Upon the secondary doping,the density and coordination environment of active sites can be efficiently tuned,enabling the simultaneous improvement in the number and reactivity of the active site.Besides,structure optimizations in terms of the enlarged surface area and improved hydrophilicity can be achieved simultaneously.Due to the beneficial microstructure and abundant highly active FeN_5 moieties resulting from the secondary doping,the resultant catalyst exhibits an admirable half-wave potential of 0.81 V versus 0.83 V for Pt/C and much better stability than Pt/C in acidic media.This work would offer a general strategy for the design and preparation of highly active SACs for electrochemical energy devices.

Lipotoxic hepatocyte-derived exosomal miR-1297 promotes hepatic stellate cell activation through the PTEN signaling pathway in metabolic-associated fatty liver disease

BACKGROUND Exosomes play an important role in metabolic-associated fatty liver disease(MAFLD),but the mechanism by which exosomes participate in MAFLD still remain unclear.AIM To figure out the function of lipotoxic exosomal miR-1297 in MAFLD.METHODS MicroRNA sequencing was used to detect differentially expressed miRNAs(DEmiR)in lipotoxic exosomes derived from primary hepatocytes.Bioinformatic tools were applied to analyze the target genes and pathways regulated by the DE-miRs.Quantitative real-time PCR(qPCR)was conducted for the verification of DEmiRs.qPCR,western blot,immunofluorescence staining and ethynyl-20-deoxyuridine assay were used to evaluate the function of lipotoxic exosomal miR-1297 on hepatic stellate cells(LX2 cells).A luciferase reporter experiment was performed to confirm the relationship of miR-1297 and its target gene PTEN.RESULTS MicroRNA sequencing revealed that there were 61 exosomal DE-miRs(P<0.05)with a fold-change>2 from palmitic acid treated primary hepatocytes compared with the vehicle control group.miR-1297 was the most highly upregulated according to the microRNA sequencing.Bioinformatic tools showed a variety of target genes and pathways regulated by these DE-miRs were related to liver fibrosis.miR-1297 was overexpressed in exosomes derived from lipotoxic hepatocytes by qPCR.Fibrosis promoting genes(α-SMA,PCNA)were altered in LX2 cells after miR-1297 overexpression or miR-1297-rich lipotoxic exosome incubation via qPCR and western blot analysis.Immunofluorescence staining and ethynyl-20-deoxyuridine staining demonstrated that the activation and proliferation of LX2 cells were also promoted after the above treatment.PTEN was found to be the target gene of miR-1297 and knocking down PTEN contributed to the activation and proliferation of LX2 cells via modulating the PI3K/AKT signaling pathway.CONCLUSION miR-1297 was overexpressed in exosomes derived from lipotoxic hepatocytes.The lipotoxic hepatocyte-derived exosomal miR-1297 could promote the activation and proliferation of hepatic stellate cells through the PTEN/PI3K/AKT signaling pathway,accelerating the progression of MAFLD.

Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning

A recommender system(RS)relying on latent factor analysis usually adopts stochastic gradient descent(SGD)as its learning algorithm.However,owing to its serial mechanism,an SGD algorithm suffers from low efficiency and scalability when handling large-scale industrial problems.Aiming at addressing this issue,this study proposes a momentum-incorporated parallel stochastic gradient descent(MPSGD)algorithm,whose main idea is two-fold:a)implementing parallelization via a novel datasplitting strategy,and b)accelerating convergence rate by integrating momentum effects into its training process.With it,an MPSGD-based latent factor(MLF)model is achieved,which is capable of performing efficient and high-quality recommendations.Experimental results on four high-dimensional and sparse matrices generated by industrial RS indicate that owing to an MPSGD algorithm,an MLF model outperforms the existing state-of-the-art ones in both computational efficiency and scalability.

Zinc-α2-glycoprotein 1 attenuates non-alcoholic fatty liver disease by negatively regulating tumour necrosis factor-α

BACKGROUND Zinc-α2-glycoprotein 1 (AZGP1) plays important roles in metabolism-related diseases. The underlying molecular mechanisms and therapeutic effects of AZGP1 remain unknown in non-alcoholic fatty liver disease (NAFLD). AIM To explore the effects and potential mechanism of AZGP1 on NAFLD in vivo and in vitro. METHODS The expression of AZGP1 and its effects on hepatocytes were examined in NAFLD patients, CCl4-treated mice fed a high fat diet (HFD), and human LO2 cells. RESULTS AZGP1 levels were significantly decreased in liver tissues of NAFLD patients and mice. AZGP1 knockdown was found to activate inflammation;enhance steatogenesis, including promoting lipogenesis [sterol regulatory elementbinding protein (SREBP)-1c, liver X receptor (LXR), fatty acid synthase (FAS), acetyl-CoA carboxylase (ACC), and stearoyl CoA desaturase 1 (SCD)-1], increasing lipid transport and accumulation [fatty acid transport protein (FATP), carnitine palmitoyl transferase (CPT)-1A, and adiponectin], and reducing fatty acid β-oxidation [farnesoid X receptor (FXR) and peroxisome proliferator-activated receptor (PPAR)-α];accelerate proliferation;and reverse apoptosis in LO2 cells. AZGP1 overexpression (OV-AZGP1) had the opposite effects. Furthermore, AZGP1 alleviated NAFLD by blocking TNF-α-mediated inflammation and intracellular lipid deposition, promoting proliferation, and inhibiting apoptosis in LO2 cells. Finally, treatment with OV-AZGP1 plasmid dramatically improved liver injury and eliminated liver fat in NAFLD mice. CONCLUSION AZGP1 attenuates NAFLD with regard to ameliorating inflammation, accelerating lipolysis, promoting proliferation, and reducing apoptosis by negatively regulating TNF-α. AZGP1 is suggested to be a novel promising therapeutic target for NAFLD.

Neural Dynamics for Distributed Collaborative Control of Manipulators With Time Delays

Time-delay phenomena extensively exist in practical systems,e.g.,multi-agent systems,bringing negative impacts on their stabilities.This work analyzes a collaborative control problem of redundant manipulators with time delays and proposes a time-delayed and distributed neural dynamics scheme.Under assumptions that the network topology is fixed and connected and the existing maximal time delay is no more than a threshold value,it is proved that all manipulators in the distributed network are able to reach a desired motion.The proposed distributed scheme with time delays considered is converted into a time-variant optimization problem,and a neural dynamics solver is designed to solve it online.Then,the proposed neural dynamics solver is proved to be stable,convergent and robust.Additionally,the allowable threshold value of time delay that ensures the proposed scheme’s stability is calculated.Illustrative examples and comparisons are provided to intuitively prove the validity of the proposed neural dynamics scheme and solver.

A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis

A large-scale dynamically weighted directed network(DWDN)involving numerous entities and massive dynamic interaction is an essential data source in many big-data-related applications,like in a terminal interaction pattern analysis system(TIPAS).It can be represented by a high-dimensional and incomplete(HDI)tensor whose entries are mostly unknown.Yet such an HDI tensor contains a wealth knowledge regarding various desired patterns like potential links in a DWDN.A latent factorization-of-tensors(LFT)model proves to be highly efficient in extracting such knowledge from an HDI tensor,which is commonly achieved via a stochastic gradient descent(SGD)solver.However,an SGD-based LFT model suffers from slow convergence that impairs its efficiency on large-scale DWDNs.To address this issue,this work proposes a proportional-integralderivative(PID)-incorporated LFT model.It constructs an adjusted instance error based on the PID control principle,and then substitutes it into an SGD solver to improve the convergence rate.Empirical studies on two DWDNs generated by a real TIPAS show that compared with state-of-the-art models,the proposed model achieves significant efficiency gain as well as highly competitive prediction accuracy when handling the task of missing link prediction for a given DWDN.

MicroRNA-194 inactivates hepatic stellate cells and alleviates liver fibrosis by inhibiting AKT2

BACKGROUND Activation of hepatic stellate cells(HSCs)is a pivotal event in the onset and progression of liver fibrosis.Loss of microRNA-194(miR-194)has been reported in activated HSCs,but the actual role of miR-194 in liver fibrosis remains uncertain.AIM To explore the role and potential mechanism of miR-194-mediated regulation of liver fibrosis in vitro and in vivo.METHODS The expression of miR-194 was examined in human fibrotic liver tissues,activated HSCs,and a carbon tetrachloride(CCl4)mouse model by qPCR.The effects of AKT2 regulation by miR-194 on the activation and proliferation of HSCs were assessed in vitro.For in vivo experiments,we reintroduced miR-194 in mice using a miR-194 agomir to investigate the functions of miR-194 in liver fibrosis.RESULTS MiR-194 expression was notably lacking in activated HSCs from both humans and mice.Overexpression of miR-194(OV-miR-194)inhibitedα-smooth muscle actin(α-SMA)and type I collagen(Col I)expression and suppressed cell proliferation in HSCs by causing cell cycle arrest in G0/G1 phase.AKT2 was predicted to be a target of miR-194.Notably,the effects of miR-194 knockdown in HSCs were almost blocked by AKT2 deletion,indicating that miR-194 plays a role in HSCs via regulation of AKT2.Finally,miR-194 agomir treatment dramatically ameliorated liver fibrosis in CCl4-treated mice.CONCLUSION We revealed that miR-194 plays a protective role by inhibiting the activation and proliferation of HSCs via AKT2 suppression.Our results further propose miR-194 as a potential therapeutic target for liver fibrosis.