Glia-to-neuron reprogramming to the rescue?

Over the last two decades,the dogma that cell fate is immutable has been increasingly challenged,with important implications for regenerative medicine.The brea kth rough discovery that induced pluripotent stem cells could be generated from adult mouse fibroblasts is powerful proof that cell fate can be changed.An exciting extension of the discovery of cell fate impermanence is the direct cellular reprogram ming hypothesis-that terminally differentiated cells can be reprogrammed into other adult cell fates without first passing through a stem cell state.

Neurocircuit regeneration by extracellular matrix reprogramming

The brain's extracellular matrix(ECM),which is comprised of protein and glycosaminoglycan(GAG)scaffolds,constitutes 20%-40% of the human brain and is considered one of the largest influencers on brain cell functioning(Soles et al.,2023).Synthesized by neural and glial cells,the brain's ECM regulates a myriad of homeostatic cellular processes,including neuronal plasticity and firing(Miyata et al.,2012),cation buffering(Moraws ki et al.,2015),and glia-neuron interactions(Anderson et al.,2016).Considering the diversity of functions,dynamic remodeling of the brain's ECM indicates that this understudied medium is an active participant in both normal physiology and neurological diseases.

Interleukin-17A facilitates tumor progression via upregulating programmed death ligand-1 expression in hepatocellular carcinoma

BACKGROUND Hepatocellular carcinoma(HCC)is an inflammation-associated tumor with a dismal prognosis.Immunotherapy has become an important treatment strategy for HCC,as immunity is closely related to inflammation in the tumor microenvir-onment.Inflammation regulates the expression of programmed death ligand-1(PD-L1)in the immunosuppressive tumor microenvironment and affects im-munotherapy efficacy.Interleukin-17A(IL-17A)is involved in the remodeling of the tumor microenvironment and plays a protumor or antitumor role in different tumors.We hypothesized that IL-17A participates in tumor progression by affe-cting the level of immune checkpoint molecules in HCC.The upregulation of PD-L1 expression in HCC cells by IL-17A was assessed by reverse transcription PCR,western blotting,and flow cytometry.Mechanistic studies were conducted with gene knockout models and pathway inhibitors.The function of IL-17A in immune evasion was explored through coculture of T cells and HCC cells.The effects of IL-17A on the malignant biological behaviors of HCC cells were evaluated in vitro,and the antitumor effects of an IL-17A inhibitor and its synergistic effects with a PD-L1 inhibitor were studied in vivo.RESULTS IL-17A upregulated PD-L1 expression in HCC cells in a dose-dependent manner,whereas IL-17A receptor knockout or treatment with a small mothers against decapentaplegic 2 inhibitor diminished the PD-L1 expression induced by IL-17A.IL-17A enhanced the survival of HCC cells in the coculture system.IL-17A increased the viability,G2/M ratio,and migration of HCC cells and decreased the apoptotic index.Cyclin D1,VEGF,MMP9,and Bcl-1 expression increased after IL-17A treatment,whereas BAX expression decreased.The combination of IL-17A and PD-L1 inhibitors showed synergistic antitumor efficacy and increased cluster of differentiation 8+T lymphocyte infiltration in an HCC mouse model.CONCLUSION IL-17A upregulates PD-L1 expression via the IL-17A receptor/phosphorylation-small mothers against decapenta-plegic 2 signaling pathway in HCC cells.Blocking IL-17A enhances the therapeutic efficacy of PD-L1 antibodies in HCC in vivo.

Programmed cell death 1 inhibitor sintilimab plus S-1 and gemcitabine for liver metastatic pancreatic ductal adenocarcinoma

BACKGROUND Pancreatic ductal adenocarcinoma(PDAC)is a highly aggressive cancer with poor prognosis.When it metastasizes to the liver,treatment options become particularly limited and challenging.Current treatment options for liver metastatic PDAC are limited,and chemotherapy alone often proves insufficient.Immunotherapy,particularly programmed cell death 1(PD-1)inhibitors like sintilimab,shows potential efficacy for various cancers but has limited reports on PDAC.This study compares the efficacy and safety of sintilimab plus S-1 and gemcitabine vs S-1 and gemcitabine alone in liver metastatic PDAC.AIM To explore the feasibility and effectiveness of combined PD-1 inhibitor sintilimab and S-1 and gemcitabine(combination group)vs S-1 and gemcitabine used alone(chemotherapy group)for treating liver metastatic pancreatic adenocarcinoma.METHODS Eligible patients were those with only liver metastatic PDAC,an Eastern Cooperative Oncology Group performance status of 0-1,adequate organ and marrow functions,and no prior anticancer therapy.Participants in the combination group received intravenous sintilimab 200 mg every 3 weeks,oral S-140 mg/m²twice daily on days 1-14 of a 21-day cycle,and intravenous gemcitabine 1000 mg/m²on days 1 and 8 of the same cycle for up to eight cycles or until disease progression,death,or unacceptable toxicity.Participants in the chemotherapy group received oral S-140 mg/m²twice daily on days 1-14 of a 21-day cycle and intravenous gemcitabine 1000 mg/m²on days 1 and 8 of the same cycle for up to eight cycles.Between June 2020 and December 2021,66 participants were enrolled,with 32 receiving the combination treatment and 34 receiving chemotherapy alone.RESULTS The group receiving the combined therapy exhibited a markedly prolonged median overall survival(18.8 months compared to 10.3 months,P<0.05)and progression-free survival(9.6 months vs 5.4 months,P<0.05).compared to the chemotherapy group.The incidence of severe adverse events did not differ significantly between the two groups(P>0.05).CONCLUSION The combination of PD-1 inhibitor sintilimab with S-1 and gemcitabine demonstrated effectiveness and safety for treating liver metastatic PDAC,meriting further investigation.

Designing and Optimization of an Off-line Programming System for Robotic Belt Grinding Process

Off-line programming （OLP） system becomes one of the most important programming modules for the robotic belt grinding process, however there lacks research on increasing the grinding dexterous space depending on the OLP system. A new type of grinding robot and a novel robotic belt grinding workcell are forwarded, and their features are briefly introduced. An open and object-oriented off-line programming system is developed for this robotic belt grinding system. The parameters of the trimmed surface are read from the initial graphics exchange specification （IGES） file of the CAD model of the workpiece. The deBoor-Cox basis function is used to sample the grinding target with local contact frame on the workpiece. The numerical formula of inverse kinematics is set up based on Newton＇s iterative procedure, to calculate the grinding robot configurations corresponding to the grinding targets. After the grinding path is obtained, the OLP system turns to be more effective than the teach-by-showing system. In order to improve the grinding workspace, an optimization algorithm for dynamic tool frame is proposed and performed on the special robotic belt grinding system. The initial tool frame and the interval of neighboring tool frames are defined as the preparation of the algorithm. An optimized tool local frame can be selected to grind the complex surface for a maximum dexterity index of the robot. Under the optimization algorithm, a simulation of grinding a vane is included and comparison of grinding workspace is done before and after the tool frame optimization. By the algorithm, the grinding workspace can be enlarged. Moreover the dynamic tool frame can be considered to add one degree-of-freedom to the grinding kinematical chain, which provides the theoretical support for the improvement of robotic dexterity for the complex surface grinding.

Trade-offs and Synergies of Ecosystem Services in Karst Area of China Driven by Grain-for-Green Program

As an important means regulating the relationship between human and natural ecosystem,ecological restoration program plays a key role in restoring ecosystem functions.The Grain-for-Green Program(GFGP,One of the world’s most ambitious ecosystem conservation set-aside programs aims to transfer farmland on steep slopes to forestland or grassland to increase vegetation coverage)has been widely implemented from 1999 to 2015 and exerted significant influence on land use and ecosystem services(ESs).In this study,three ecological models(In VEST,RUSLE,and CASA)were used to accurately calculate the three key types of ESs,water yield(WY),soil conservation(SC),and net primary production(NPP)in Karst area of southwestern China from 1982 to 2015.The impact of GFGP on ESs and trade-offs was analyzed.It provides practical guidance in carrying out ecological regulation in Karst area of China under global climate change.Results showed that ESs and trade-offs had changed dramatically driven by GFGP.In detail,temporally,SC and NPP exhibited an increasing trend,while WY exhibited a decreasing trend.Spatially,SC basically decreased from west to east;NPP basically increased from north to south;WY basically increased from west to east;NPP and SC,SC and WY developed in the direction of trade-offs driven by the GFGP,while NPP and WY developed in the direction of synergy.Therefore,future ecosystem management and restoration policy-making should consider trade-offs of ESs so as to achieve sustainable provision of ESs.

Programmed cell death-1 inhibitor-related sclerosing cholangitis:A systematic review

BACKGROUND Programmed cell death-1(PD-1)inhibitor has been indicated for many types of malignancies.However,these inhibitors also cause immune-related adverse events.Hepatobiliary disorder is a phenotype of immune-related adverse event affecting 0%–4.5%of patients treated with PD-1 inhibitors.Recent studies have reported PD-1 inhibitor-related sclerosing cholangitis(SC);however,the associated clinical and pathological features are unclear.AIM To evaluate the clinical and pathological features of PD-1 inhibitor-related SC through a systematic review of the literature.METHODS The review,conducted using electronic databases in PubMed,was restricted to the period from January 2014 to September 2019 and focused on case reports/series on PD-1 inhibitor-related SC published in English.We scanned the references of the selected literature to identify any further relevant studies.Six cases previously studied by us,including three that have not yet been published,were included in this review.RESULTS Thirty-one PD-1 inhibitor-related SC cases were evaluated.Median age of patients was 67 years(range,43–89),with a male to female ratio of 21:10.The main disease requiring PD-1 inhibitor treatment was non-small cell lung cancer.Agents that caused PD-1 inhibitor-related SC were nivolumab(19 cases),pembrolizumab(10 cases),avelumab(1 case),and durvalumab(1 case).The median number of cycles until PD-1 inhibitor-related SC onset was 5.5(range,1–27).Abdominal pain or discomfort(35.5%,11/31)was the most frequent symptom.Blood serum tests identified liver dysfunction with a notable increase in biliary tract enzymes relative to hepatic enzymes,and a normal level of serum immunoglobulin G4.Biliary dilation without obstruction(76.9%,20/26),diffuse hypertrophy of the extrahepatic biliary tract(90.5%,19/21),and multiple strictures of the intrahepatic biliary tract(30.4%,7/23)were noted.In 11/23(47.8%)cases,pathological examination indicated that CD8+T cells were the dominant inflammatory cells in the bile duct or peribiliary tract.Although corticosteroids were mainly used for PD inhibitor-related SC treatment,the response rate was 11.5%(3/26).CONCLUSION Some clinical and pathological features of PD-1 inhibitor-related SC were revealed.To establish diagnostic criteria for PD-1 inhibitor-related SC,more cases need to be evaluated.

A Stochastic Programming Strategy in Microgrid Cyber Physical Energy System for Energy Optimal Operation

This paper focuses on the energy optimal operation problem of microgrids (MGs) under stochastic environment. The deterministic method of MGs operation is often uneconomical because it fails to consider the high randomness of unconventional energy resources. Therefore, it is necessary to develop a novel operation approach combining the uncertainty in the physical world with modeling strategy in the cyber system. This paper proposes an energy scheduling optimization strategy based on stochastic programming model by considering the uncertainty in MGs. The goal is to minimize the expected operation cost of MGs. The uncertainties are modeled based on autoregressive moving average (ARMA) model to expose the effects of physical world on cyber world. Through the comparison of the simulation results with deterministic method, it is shown that the effectiveness and robustness of proposed stochastic energy scheduling optimization strategy for MGs are valid. © 2014 Chinese Association of Automation.

Optimal Control for a Class of Complex Singular System Based on Adaptive Dynamic Programming

This paper presents a new design approach to achieve decentralized optimal control of high-dimension complex singular systems with dynamic uncertainties. Based on robust adaptive dynamic programming(robust ADP) method, controllers for solving the singular systems optimal control problem are designed. The proposed algorithm can work well when the system model is not exactly known but the input and output data can be measured. The policy iteration of each controller only uses their own states and input information for learning,and do not need to know the whole system dynamics. Simulation results on the New England 10-machine 39-bus test system show the effectiveness of the designed controller.

Optimization of circulating cooling water systems based on chance constrained programming

Recent research on deterministic methods for circulating cooling water systems optimization has been well developed. However, the actual operating conditions of the system are mostly variable, so the system obtained under deterministic conditions may not be stable and economical. This paper studies the optimization of circulating cooling water systems under uncertain circumstance. To improve the reliability of the system and reduce the water and energy consumption, the influence of different uncertain parameters is taken into consideration. The chance constrained programming method is used to build a model under uncertain conditions, where the confidence level indicates the degree of constraint violation. Probability distribution functions are used to describe the form of uncertain parameters. The objective is to minimize the total cost and obtain the optimal cooling network configuration simultaneously.An algorithm based on Monte Carlo method is proposed, and GAMS software is used to solve the mixed integer nonlinear programming model. A case is optimized to verify the validity of the model. Compared with the deterministic optimization method, the results show that when considering the different types of uncertain parameters, a system with better economy and reliability can be obtained(total cost can be reduced at least 2%).

Effects of mindfulness-based intervention programs on sleep among people with common mental disorders:A systematic review and meta-analysis

BACKGROUND Sleep problems are particularly prevalent in people with depression or anxiety disorder.Although mindfulness has been suggested as an important component in alleviating insomnia,no comprehensive review and meta-analysis has been conducted to evaluate the effects of different mindfulness-based intervention(MBI)programs on sleep among people with depression or anxiety disorder.AIM To compare the effects of different MBI programs on sleep among people with depression or anxiety disorder.METHODS Related publications in Embase,Medline,PubMed and PsycINFO databases were systematically searched from January 2010 to June 2020 for randomised controlled trials.Data were synthesized using a random-effects or a fixed-effects model to analyse the effects of various MBI programs on sleep problems among people with depression or anxiety disorder.The fixed-effects model was used when heterogeneity was negligible,and the random-effects model was used when heterogeneity was significant to calculate the standardised mean differences(SMDs)and 95%confidence intervals(CIs).RESULTS We identified 397 articles,of which 10 randomised controlled trials,involving a total of 541 participants,were included in the meta-analysis.Studies of internet mindfulness meditation intervention(IMMI),mindfulness meditation(MM),mindfulness-based cognitive therapy(MBCT),mindfulness-based stress reduction(MBSR)and mindfulness-based touch therapy(MBTT)met the inclusion criteria.The greatest effect sizes are reported in favour of MBTT,with SMDs of-1.138(95%CI:-1.937 to-0.340;P=0.005),followed by-1.003(95%CI:-1.645 to-0.360;P=0.002)for MBCT.SMDs of-0.618(95%CI:-0.980 to-0.257;P=0.001)and-0.551(95%CI:-0.842 to-0.260;P<0.0001)were reported for IMMI and MBSR in the pooling trials,respectively.Significant effects on sleep problem improvement are shown in all reviewed MBI programs,except MM,for which the effect size was shown to be nonsignificant.CONCLUSION All MBI programs(MBTT,MBCT,IMMI and MBSR),except MM,are effective options to improve sleep problems among people with depression or anxiety disorder.

Policy iteration optimal tracking control for chaotic systems by using an adaptive dynamic programming approach

A policy iteration algorithm of adaptive dynamic programming（ADP） is developed to solve the optimal tracking control for a class of discrete-time chaotic systems. By system transformations, the optimal tracking problem is transformed into an optimal regulation one. The policy iteration algorithm for discrete-time chaotic systems is first described. Then,the convergence and admissibility properties of the developed policy iteration algorithm are presented, which show that the transformed chaotic system can be stabilized under an arbitrary iterative control law and the iterative performance index function simultaneously converges to the optimum. By implementing the policy iteration algorithm via neural networks,the developed optimal tracking control scheme for chaotic systems is verified by a simulation.

Coupling between the Grain for Green Program and a household level-based agricultural eco-economic system in Ansai,Shaanxi Province of China

The implementation of the Grain for Green Program(GGP)has changed the development track of the agricultural eco-economic system in China.In response to the results of a lag study that investigated the coupling between the GGP and the agricultural eco-economic system in a loess hilly region,we used a structural equation model to analyze the survey data from 494 households in Ansai,a district of Yan’an City in Shaanxi Province of China in 2015.The model clarified the direction and intensity of the coupling between the GGP and the agricultural eco-economic system.The coupling benefits were derived through linkages between the program and various chains in the agricultural eco-economic system.The GGP,the agroecosystem of Ansai and their potential coupling effects were in a state of general coordination.The agroecosystem directly affected the coupling effect,with the standardized path coefficient of 0.87,indicating that the agroecosystem in Ansai at this stage provided basic material support for the coupling between the GGP and the agricultural eco-economic system.The direct path coefficient of agroeconomic system impacted on the coupling effect was-0.76,indicating that partial contradictions occurred between the agroeconomic system and the coupling effect.Therefore,although the current agroecosystem in Ansai should be provided sufficient agroecological resources for the benign coupling between the program and the agricultural eco-economic system,agricultural development failed to effectively transform agroecological resources into agricultural economic advantages in this region,which resulted in a relative lag in the development of the agricultural economic system.Thus,the coupling between the GGP and the agricultural eco-economic system was poor.To improve the coupling and the sustainable development of the agricultural eco-economic system in cropland retirement areas,the industrial structure needs to be diversified,the agricultural resources(including agroecological resources,agricultural economic resources and agricultural social resources)need to be rationally allocated,and the chain structure of the agricultural eco-economic system needs to be continuously improved.

A Partial Parallel Airport Gate Assignment Supported by a Knowledge－based System Combined with Mathematical Programming

The gate assignment at an airport is one of the major activities in airport operations.With the increase of passenger traffic volumes and the number of flights, the complexity of this task and the factors to be considered have increased significantly, and an efficient gate utilizationhas received considerable attention. For overcoming the shortcomings of previous gate assignmentapproaches, this paper presents a partial parallel gate assignment approach, by which more factorsconcerning aircraft and gates can be collsidered at the same time. This paper also presents themethod of using a knowledge-based system combined with a mathematical programming method forgetting an optimized feasible assignment solution. By this way, it is more easily to get the solutionthat satisfies both the static and dynamic situations,and thus it may adapt well to meet the needsof actual use to rea-time operations. An experimental prototype has been implemented, and a casestudy is presented at the end of the paper.

A Neuron-Oriented Programming System

A neruon-oriented programming system based on parallel neural information processing has been presented. With the neural programming system built upon 4～8 process elements(TMS C30), the system has thus provided users high speed, general purpose and large scale neural network application development platforms etc.

Stability of Nonlinear Systems Using Optimal Fuzzy Controllers and Its Simulation by Java Programming

In this paper, at first, the single input rule modules(SIRMs) dynamically connected fuzzy inference model is used to stabilize a double inverted pendulum system. Then, a multiobjective particle swarm optimization(MOPSO) is implemented to optimize the fuzzy controller parameters in order to decrease the distance error of the cart and summation of the angle errors of the pendulums, simultaneously. The feasibility and efficiency of the proposed Pareto front is assessed in comparison with results reported in literature and obtained from other algorithms.Finally, the Java programming with applets is utilized to simulate the stability of the nonlinear system and explain the internetbased control.

Synthesis of refrigeration system based on generalized disjunctive programming model

Refrigeration system holds an important role in process industries. The optimal synthesis cannot only reduce the energy consumption, but also save the production costs. In this study, a general methodology is developed for the optimal design of refrigeration cycle and heat exchanger network(HEN) simultaneously. Taking the heat integration between the external heat sources/sinks and the refrigeration cycle into consideration, a superstructure with sub-coolers is developed. Through defining logical variables that indicate the relative temperature positions of refrigerant streams after sub-coolers, the synthesis is formulated as a Generalized Disjunctive Programming(GDP) problem based on LP transshipment model, with the target of minimizing the total compressor shaft work in the refrigeration system. The GDP model is then reformulated as a Mixed Integer Nonlinear Programming(MINLP) problem with the aid of binary variables and Big-M Constraint Method. The efficacy of the process synthesis model is demonstrated by a case study of ethylene refrigeration system. The result shows that the optimization can significantly reduce the exergy loss as well as the total compression shaft work.

An Intelligent MMRP Constraint Programming System

In this paper,an intelligent constraint programming system for manufacturing material resource planning (MMRP) was presented.It is aimed to tackling large,particularly combinatorial,problems during the MMRP process,which increasingly involves complex sets of objectives and constraints in today’s industrial manufacturing.The system consists of a do- main-specific architecture,an algorithm library,and a pre-defined solution library,based on which intelligent agents can effi- ciently construct MMRP problem specifications,select suitable algorithms to solve problems,and evolve a population of solutions to- wards a Pareto-optimal frontier.Our system significantly improves the efficiency,effectiveness,and reliability of MMRP prob- lem solving.

An expert system for predicting shear stress distribution in circular open channels using gene expression programming

The shear stress distribution in circular channels was modeled in this study using gene expression programming(GEP). 173 sets of reliable data were collected under four flow conditions for use in the training and testing stages. The effect of input variables on GEP modeling was studied and 15 different GEP models with individual, binary, ternary, and quaternary input combinations were investigated. The sensitivity analysis results demonstrate that dimensionless parameter y/P, where y is the transverse coordinate, and P is the wetted perimeter, is the most influential parameter with regard to the shear stress distribution in circular channels. GEP model 10, with the parameter y/P and Reynolds number(Re) as inputs, outperformed the other GEP models, with a coefficient of determination of 0.7814 for the testing data set. An equation was derived from the best GEP model and its results were compared with an artificial neural network(ANN) model and an equation based on the Shannon entropy proposed by other researchers. The GEP model, with an average RMSE of 0.0301, exhibits superior performance over the Shannon entropy-based equation, with an average RMSE of 0.1049, and the ANN model, with an average RMSE of 0.2815 for all flow depths.

Applying the Socio-Ecological Model to barriers to implementation of ACL injury prevention programs: A systematic review

Background: Preventing anterior cruciate ligament(ACL) injuries is important to avoid long-term adverse health consequences. Identifying barriers to implementation of these prevention programs is crucial to reducing the incidence of these injuries. Our purpose was to identify barriers of implementation for ACL injury prevention programs and suggest mechanisms for reducing the barriers through application of a SocioEcological Model(SEM).Methods: Studies investigating ACL prevention program effectiveness were searched in Medline via PubMed and the Cochrane Library, and a subsequent review of the references of the identified articles, yielded 15 articles total. Inclusion criteria encompassed prospective controlled trials, published in English, with ACL injuries as the primary outcome. Studies were independently appraised by 2 reviewers for methodological quality using the PEDro scale. Barriers to implementation were identified when reported in at least 2 separate studies. A SEM was used to suggest ways to reduce the identified barriers.Results: Five barriers were identified: motivation, time requirements, skill requirements for program facilitators, compliance, and cost. The SEM suggested ways to minimize the barriers at all levels of the model from the individual through policy levels.Conclusion: Identification of barriers to program implementation and suggesting how to reduce them through the SEM is a critical first step toward enabling ACL prevention programs to be more effective and ultimately reducing the incidence of these injuries.