Prenatal programing of motivated behaviors:can innate immunity prime behavior?

Prenatal programming during pregnancy sets physiological outcomes in the offspring by integrating external or internal stimuli.Accordingly,pregnancy is an important stage of physiological adaptations to the environment where the fetus becomes exposed and adapted to the maternal milieu.Maternal exposure to high-energy dense diets can affect motivated behavior in the offs p ring leading to addiction and impaired sociability.A high-energy dense exposure also increases the pro-inflammatory cytokines profile in plasma and brain and favors microglia activation in the offspring.While still under investigation,prenatal exposure to high-energy dense diets promotes structural abnormalities in selective brain regions regulating motivation and social behavior in the offspring.The current review addresses the role of energy-dense foods programming central and peripheral inflammatory profiles during embryonic development and its effect on motivated behavior in the offspring.We provide preclinical and clinical evidence that supports the contribution of prenatal programming in shaping immune profiles that favor structural and brain circuit disruption leading to aberrant motivated behaviors after birth.We hope this minireview encourages future research on novel insights into the mechanisms underlying maternal programming of motivated behavior by central immune networks.

PROPORTION OF THE CEMENT RAW MATERIAL CALCULATED BY THE LINEAR PROGRAMING

This paper presents a new method .linear programming method. to calculate the proportion of the cement raw material. Its advanlages are as following : good practicability, convenience for analysis, and being easy for controlling. The mathematical model given in the paper is apt to be realized on computer.

Automatic Generation of UML Class Diagrams for Object-oriented Design and Programing Course

UML Class diagram generation from textual requirements is an important task in object-oriented design and programing course.This study proposes a method for automatically generating class diagrams from Chinese textual requirements on the basis of Natural Language Processing(NLP)and mapping rules for sentence pattern matching.First,classes are identified through entity recognition rules and candidate class pruning rules using NLP from requirements.Second,class attributes and relationships between classes are extracted using mapping rules for sentence pattern matching on the basis of NLP.Third,we developed an assistant tool integrated into a precision micro classroom system for automatic generation of class diagram,to effectively assist the teaching of object-oriented design and programing course.Results are evaluated with precision,accuracy and recall from eight requirements of object-oriented design and programing course using truth values created by teachers.Our research should benefit beginners of object-oriented design and programing course,who may be students or software developers.It helps them to create correct domain models represented in the UML class diagram.

EDISON-WMW: Exact Dynamic Programing Solution of the Wilcoxon–Mann–Whitney Test

In many research disciplines, hypothesis tests are applied to evaluate whether findings are statistically significant or could be explained by chance. The Wilcoxon-Mann-Whitney （WMW） test is among the most popular hypothesis tests in medicine and life science to analyze if two groups of samples are equally distributed. This nonparametric statistical homogeneity test is commonly applied in molecular diagnosis. Generally, the solution of the WMW test takes a high combinatorial effort for large sample cohorts containing a significant number of ties. Hence, P value is frequently approximated by a normal distribution. We developed EDISON-WMW, a new approach to calcu- late the exact permutation of the two-tailed unpaired WMW test without any corrections required and allowing for ties. The method relies on dynamic programing to solve the combinatorial problem of the WMW test efficiently. Beyond a straightforward implementation of the algorithm, we pre- sented different optimization strategies and developed a parallel solution. Using our program, the exact P value for large cohorts containing more than 1000 samples with ties can be calculated within minutes. We demonstrate the performance of this novel approach on randomly-generated data, benchmark it against 13 other commonly-applied approaches and moreover evaluate molec- ular biomarkers for lung carcinoma and chronic obstructive pulmonary disease （COPD）. We foundthat approximated P values were generally higher than the exact solution provided by EDISON- WMW. Importantly, the algorithm can also be applied to high-throughput omics datasets, where hundreds or thousands of features are included. To provide easy access to the multi-threaded version of EDISON-WMW, a web-based solution of our algorithm is freely available at http：// www.ccb.uni-saarland.de/software/wtest/.

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.

Optimization of the Deployment of Temperature Nodes Based on Linear Programing in the Internet of Things

The Internet of Things emphasizes the concept of objects connected with each other, which includes all kinds of wireless sensor networks. An important issue is to reduce the energy consumption in the sensor networks since sensor nodes always have energy constraints. Deployment of thousands of wireless sensors in an appropriate pattern will simultaneously satisfy the application requirements and reduce the sensor network energy consumption. This article deployed a number of sensor nodes to record temperature data. The data was then used to predict the temperatures of some of the sensor node using linear programming. The predictions were able to reduce the node sampling rate and to optimize the node deployment to reduce the sensor energy consumption. This method can compensate for the temporarily disabled nodes. The main objective is to design the objective function and determine the constraint condition for the linear programming. The result based on real experiments shows that this method successfully predicts the values of unknown sensor nodes and optimizes the node deployment. The sensor network energy consumption is also reduced by the optimized node deployment.

An improved PSO algorithm for solving nonlinear programing problems with constrained conditions

Engineering optimization problems can be always classified into two main categories including the linear programming(LP)and nonlinear programming(NLP)problems.Each programming problem further involves the unconstrained conditions and constrained conditions for design variables of the optimized system.This paper will focus on the issue about the design problem of NLP with the constrained conditions.The employed method for such NLP problems is a variant of particle swarm optimization(PSO),named improved particle swarm optimization(IPSO).The developed IPSO is to modify the velocity updating formula of the algorithm to enhance the search ability for given optimization problems.In this work,many different kinds of physical engineering optimization problems are examined and solved via the proposed IPSO algorithm.Simulation results compared with various optimization methods reported in the literature will show the effectiveness and feasibility for solving NLP problems with the constrained conditions.

Reprograming yeast for anti-cancer vinblastine synthesis

In a recent study published in Nature,Zhang et al.employed an innovative approach by reprogramming and engineering yeast strain for combined biosynthesis of vindoline and catharanthine,followed by an additional in vitro chemical step for the successful synthesis of the anti-cancer vinblastine.Development and utilization of plant resources have played indispensable roles in the treatment of human diseases,which had been an amazing focus of biomedical research for decades.Monoterpene indole alkaloids(MIAs),the plant secondary metabolites that mainly exist in Gentianales,had been a popular family of the medicinal drug research because of their remarkable structural diversities and biological activities.

A trust region algorithm for bilevel programing problems

A trust region algorithm is proposed for solving bilevel programming problems where the lower level programming problem is a strongly convex programming problem with linear constraints. This algorithm is based on a trust region algorithm for nonsmooth unconstrained optimization problems, and its global convergence is also proved.

从故事生成程序到大语言模型:人工智能编剧创作的历史与实践

人工智能生成内容(AI Generated Content,简称AIGC)电影包括内涵丰富的多种维度,从编剧角度可以分为AI生成故事(AI Generated Storyline,简称AIGSL)与AI生成剧本(AI Generated Screenplay,简称AIGSP)。AIGSL研究始于20世纪60年代初,按照创作模式可以分为基于结构模型的故事生成、基于规则的故事生成和基于机器学习的故事生成三类程序。2022年底以来,以Chat GPT为代表的大语言模型将AIGC的内容从简单的AIGSL拓展到了AIGSP领域,同时催生了基于大语言模型的专用故事与剧本生成工具。大语言模型的故事和剧本生产存在事实错误、陈词滥调以及主观偏见等天然缺陷,但AIGSL与AIGSP已经开始实现产业化。目前的人工智能编剧创作绝大多数并不完全由AI创作,属于弱人工智能领域。中国电影工作者与软件工程师有必要利用机器学习等AI技术,学习中国传统叙事文本并总结其叙事风格,为具有中国叙事特色的编剧创作提供智能助力。

Hepatic arterial infusion chemotherapy with anti-angiogenesis agents and immune checkpoint inhibitors for unresectable hepatocellular carcinoma and meta-analysis

BACKGROUND Hepatic arterial infusion chemotherapy(HAIC)has been proven to be an ideal choice for treating unresectable hepatocellular carcinoma(uHCC).HAIC-based treatment showed great potential for treating uHCC.However,large-scale studies on HAIC-based treatments and meta-analyses of first-line treatments for uHCC are lacking.AIM To investigate better first-line treatment options for uHCC and to assess the safety and efficacy of HAIC combined with angiogenesis inhibitors,programmed cell death of protein 1(PD-1)and its ligand(PD-L1)blockers(triple therapy)under real-world conditions.METHODS Several electronic databases were searched to identify eligible randomized controlled trials for this meta-analysis.Study-level pooled analyses of hazard ratios(HRs)and odds ratios(ORs)were performed.This was a retrospective single-center study involving 442 patients with uHCC who received triple therapy or angiogenesis inhibitors plus PD-1/PD-L1 blockades(AIPB)at Sun Yat-sen University Cancer Center from January 2018 to April 2023.Propensity score matching(PSM)was performed to balance the bias between the groups.The Kaplan-Meier method and cox regression were used to analyse the survival data,and the log-rank test was used to compare the suvival time between the groups.RESULTS A total of 13 randomized controlled trials were included.HAIC alone and in combination with sorafenib were found to be effective treatments(P values for ORs:HAIC,0.95;for HRs:HAIC+sorafenib,0.04).After PSM,176 HCC patients were included in the analysis.The triple therapy group(n=88)had a longer median overall survival than the AIPB group(n=88)(31.6 months vs 14.6 months,P<0.001)and a greater incidence of adverse events(94.3%vs 75.4%,P<0.001).CONCLUSION This meta-analysis suggests that HAIC-based treatments are likely to be the best choice for uHCC.Our findings confirm that triple therapy is more effective for uHCC patients than AIPB.

Efficacy and predictive factors of transarterial chemoembolization combined with lenvatinib plus programmed cell death protein-1 inhibition for unresectable hepatocellular carcinoma

BACKGROUND The efficacy and safety of transarterial chemoembolization(TACE)combined with lenvatinib plus programmed cell death protein-1(PD-1)for unresectable hepato-cellular carcinoma(HCC)have rarely been evaluated and it is unknown which factors are related to efficacy.AIM To evaluate the efficacy and independent predictive factors of TACE combined with lenvatinib plus PD-1 inhibitors for unresectable HCC.METHODS This study retrospectively enrolled patients with unresectable HCC who received TACE/lenvatinib/PD-1 treatment between March 2019 and April 2022.Overall survival(OS)and progression-free survival(PFS)were determined.The objective response rate(ORR)and disease control rate(DCR)were evaluated in accordance with the modified Response Evaluation Criteria in Solid Tumors.Additionally,the prognostic factors affecting the clinical outcome were assessed.RESULTS One hundred and two patients were enrolled with a median follow-up duration of 12.63 months.The median OS was 26.43 months(95%CI:17.00-35.87),and the median PFS was 10.07 months(95%CI:8.50-11.65).The ORR and DCR were 61.76%and 81.37%,respectively.The patients with Barcelona Clinic Liver Cancer Classification(BCLC)B stage,early neutrophil-to-lymphocyte ratio(NLR)response(decrease),or early alpha-fetoprotein(AFP)response(decrease>20%)had superior OS and PFS than their counterparts.CONCLUSION This study showed that TACE/lenvatinib/PD-1 treatment was well tolerated with encouraging efficacy in patients with unresectable HCC.The patients with BCLC B-stage disease with early NLR response(decrease)and early AFP response(decrease>20%)may achieve better clinical outcomes with this triple therapy.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

Rehabilitation care for pain in elderly knee replacement patients

BACKGROUND Total knee arthroplasty(TKA)is recognized as the most effective surgical intervention for relieving pain and improving joint mobility and deformity in patients with knee osteoarthritis and other synovial diseases.The application of accelerated postoperative rehabilitation(enhanced recovery after surgery)has demonstrated its efficacy in improving patient outcomes,and early postoperative joint function exercise has become a key prognostic factor in knee replacement.The unexpected appearance of limb pain and swelling hindered the patient’s tendency for early mobilization,leading in prolonged hospitalization,delayed functional recovery and negative psychological responses.A retrospective analysis was conducted on a cohort of 116 patients who under-went TKA at our hospital between July 2019 and July 2021.The patients were divided into two groups:A control group(n=58)receiving programmatic nur-sing,and an observed group(n=58)receiving programmed nursing combined with a collaborative nursing model.A pain management team consisting of attending physicians,head nurses,and responsible nurses was established.Outcome measures included visual analogue scale(VAS)scores,activities of daily living(ADL)scale scores,and functional scores.The ADL scores of patients in both groups exhibited a continuous increase.However,there was no statistically significant difference in the ADL scores between the two groups at 48 h and the 7th d post-surgery(P>0.05).Upon reexamination at the 3rd mo,the observation group demonstrated higher ADL scores compared to the control group(67.48±14.69 vs 59.40±16.06,P<0.05).The VAS scores of both groups significantly decreased,with no significant difference observed between the groups at each time point(P>0.05).The functional status of patients in both groups exhibited a gradual increase prior to intervention and at the 1st,2nd,and 3rd month following discharge(P<0.05).There was no statistically significant difference in knee joint function scores between the two groups at the 1st month after discharge(47.52 vs 45.81,P>0.05).However,the knee joint function scores of patients in the observation group were significantly higher than those in the control group at the 2nd(59.38 vs 53.19,P<0.05)and 3rd month(71.92 vs 64.34,P<0.05)following discharge.CONCLUSION The utilization of programmed pain nursing in conjunction with collaborative nursing for out-of-hospital care of TKA patients has demonstrated favorable outcomes,encompassing pain reduction,enhanced prognosis,and improved nursing quality for patients.

Cytokine release syndrome triggered by programmed death 1 blockade(sintilimab)therapy in a psoriasis patient:A case report

BACKGROUND In recent years,immune checkpoint inhibitors(ICIs)have demonstrated remarkable efficacy across diverse malignancies.Notably,in patients with advanced gastric cancer,the use of programmed death 1(PD-1)blockade has significantly prolonged overall survival,marking a pivotal advancement comparable to the impact of Herceptin over the past two decades.While the therapeutic benefits of ICIs are evident,the increasing use of immunotherapy has led to an increase in immune-related adverse events.CASE SUMMARY This article presents the case of a patient with advanced gastric cancer and chronic plaque psoriasis.Following sintilimab therapy,the patient developed severe rashes accompanied by cytokine release syndrome(CRS).Fortunately,effective management was achieved through the administration of glucocorticoid,tocilizumab,and acitretin,which resulted in favorable outcomes.CONCLUSION Glucocorticoid and tocilizumab therapy was effective in managing CRS after PD-1 blockade therapy for gastric cancer in a patient with chronic plaque psoriasis.

Immunotherapy for esophageal cancer:Where are we now and where can we go

Immune checkpoint inhibitor therapy has dramatically improved patient prognosis,and thereby transformed the treatment in various cancer types including esophageal squamous cell carcinoma(ESCC)in the past decade.Monoclonal antibodies that selectively inhibit programmed cell death-1(PD-1)activity has now become standard of care in the treatment of ESCC in metastatic settings,and has a high expectation to provide clinical benefit during perioperative period.Further,anti-cytotoxic T-lymphocyte–associated protein 4(CTLA-4)monoclonal antibody has also been approved in the treatment of recurrent/metastatic ESCC in combination with anti-PD-1 antibody.Well understanding of the existing evidence of immune-based treatments for ESCC,as well as recent clinical trials on various combinations with chemotherapy for different clinical settings including neoadjuvant,adjuvant,and metastatic diseases,may provide future prospects of ESCC treatment for better patient outcomes.

Crosstalk among mitophagy,pyroptosis,ferroptosis,and necroptosis in central nervous system injuries

Central nervous system injuries have a high rate of resulting in disability and mortality;however,at present,effective treatments are lacking.Programmed cell death,which is a genetically determined fo rm of active and ordered cell death with many types,has recently attra cted increasing attention due to its functions in determining the fate of cell survival.A growing number of studies have suggested that programmed cell death is involved in central nervous system injuries and plays an important role in the progression of brain damage.In this review,we provide an ove rview of the role of programmed cell death in central nervous system injuries,including the pathways involved in mitophagy,pyroptosis,ferroptosis,and necroptosis,and the underlying mechanisms by which mitophagy regulates pyroptosis,ferroptosis,and necro ptosis.We also discuss the new direction of therapeutic strategies to rgeting mitophagy for the treatment of central nervous system injuries,with the aim to determine the connection between programmed cell death and central nervous system injuries and to identify new therapies to modulate programmed cell death following central nervous system injury.In conclusion,based on these properties and effects,interventions targeting programmed cell death could be developed as potential therapeutic agents for central nervous system injury patients.

An integrated MINLP model for multi-party coordination in downstream oil supply chain

Cooperation among enterprises can bring overall and individual performance improvement,and a smooth coordination method is indispensable.However,due to the lack of customized coordination methods,cooperation in the downstream oil supply chain cannot be carried out smoothly.This paper intends to propose a multi-party coordination method to promote cooperation between oil shippers and pipeline operator by optimizing oil transportation,oil substitution and pipeline pricing schemes.An integrated game-theoretic modeling and analysis approach is developed to characterize the operation behaviors of all stakeholders in the downstream oil supply chain.The proposed mixed integer nonlinear programming model constrains supply and demand capacity,transportation routes,oil substitution rules and pipeline freight levels.Logarithm transformation and price discretization are introduced for model linear approximation.Simulation experiments are carried out in the oil distribution system in South China.The results show that compared to the business-as-usual scheme,the new scheme saves transportation cost by 3.48%,increases pipeline turnover by 5.7%,and reduces energy consumption and emissions by 7.66%and 6.77%.It is proved that the proposed method improves the revenue of the whole system,achieves fair revenue distribution,and also improves the energy and environmental benefits of the oil supply chain.

Uncertainty quantification of inverse analysis for geomaterials using probabilistic programming

Uncertainty is an essentially challenging for safe construction and long-term stability of geotechnical engineering.The inverse analysis is commonly utilized to determine the physico-mechanical parameters.However,conventional inverse analysis cannot deal with uncertainty in geotechnical and geological systems.In this study,a framework was developed to evaluate and quantify uncertainty in inverse analysis based on the reduced-order model(ROM)and probabilistic programming.The ROM was utilized to capture the mechanical and deformation properties of surrounding rock mass in geomechanical problems.Probabilistic programming was employed to evaluate uncertainty during construction in geotechnical engineering.A circular tunnel was then used to illustrate the proposed framework using analytical and numerical solution.The results show that the geomechanical parameters and associated uncertainty can be properly obtained and the proposed framework can capture the mechanical behaviors under uncertainty.Then,a slope case was employed to demonstrate the performance of the developed framework.The results prove that the proposed framework provides a scientific,feasible,and effective tool to characterize the properties and physical mechanism of geomaterials under uncertainty in geotechnical engineering problems.