Immune regulation of the gut-brain axis and lung-brain axis involved in ischemic stroke

Local ischemia often causes a series of inflammatory reactions when both brain immune cells and the peripheral immune response are activated.In the human body,the gut and lung are regarded as the key reactional targets that are initiated by brain ischemic attacks.Mucosal microorganisms play an important role in immune regulation and metabolism and affect blood-brain barrier permeability.In addition to the relationship between peripheral organs and central areas and the intestine and lung also interact among each other.Here,we review the molecular and cellular immune mechanisms involved in the pathways of inflammation across the gut-brain axis and lung-brain axis.We found that abnormal intestinal flora,the intestinal microenvironment,lung infection,chronic diseases,and mechanical ventilation can worsen the outcome of ischemic stroke.This review also introduces the influence of the brain on the gut and lungs after stroke,highlighting the bidirectional feedback effect among the gut,lungs,and brain.

Unraveling the gut-brain axis:the impact of steroid hormones and nutrition on Parkinson's disease

This comprehensive review explores the intricate relationship between nutrition,the gut microbiome,steroid hormones,and Parkinson's disease within the context of the gut-brain axis.The gut-brain axis plays a pivotal role in neurodegenerative diseases like Parkinson's disease,encompassing diverse components such as the gut microbiota,immune system,metabolism,and neural pathways.The gut microbiome,profoundly influenced by dietary factors,emerges as a key player.Nutrition during the first 1000 days of life shapes the gut microbiota composition,influencing immune responses and impacting both child development and adult health.High-fat,high-sugar diets can disrupt this delicate balance,contributing to inflammation and immune dysfunction.Exploring nutritional strategies,the Mediterranean diet's anti-inflammatory and antioxidant properties show promise in reducing Parkinson's disease risk.Microbiome-targeted dietary approaches and the ketogenic diet hold the potential in improving brain disorders.Beyond nutrition,emerging research uncovers potential interactions between steroid hormones,nutrition,and Parkinson's disease.Progesterone,with its anti-inflammatory properties and presence in the nervous system,offers a novel option for Parkinson's disease therapy.Its ability to enhance neuroprotection within the enteric nervous system presents exciting prospects.The review addresses the hypothesis thatα-synuclein aggregates originate from the gut and may enter the brain via the vagus nerve.Gastrointestinal symptoms preceding motor symptoms support this hypothesis.Dysfunctional gut-brain signaling during gut dysbiosis contributes to inflammation and neurotransmitter imbalances,emphasizing the potential of microbiota-based interventions.In summary,this review uncovers the complex web of interactions between nutrition,the gut microbiome,steroid hormones,and Parkinson's disease within the gut-brain axis framework.Understanding these connections not only offers novel therapeutic insights but also illuminates the origins of neurodegenerative diseases such as Parkinson's disease.

Cellular interplay to 3D in vitro microphysiological disease model:cell patterning microbiota-gut-brain axis

The microbiota-gut-brain axis(MGBA)has emerged as a key prospect in the bidirectional communication between two major organ systems:the brain and the gut.Homeostasis between the two organ systems allows the body to function without disease,whereas dysbiosis has long-standing evidence of etiopathological conditions.The most common communication paths are the microbial release of metabolites,soluble neurotransmitters,and immune cells.However,each pathway is intertwined with a complex one.With the emergence of in vitro models and the popularity of three-dimensional(3D)cultures and Transwells,engineering has become easier for the scientific understanding of neurodegenerative diseases.This paper briefly retraces the possible communication pathways between the gut microbiome and the brain.It further elaborates on three major diseases:autism spectrum disorder,Parkinson’s disease,and Alzheimer’s disease,which are prevalent in children and the elderly.These diseases also decrease patients’quality of life.Hence,understanding them more deeply with respect to current advances in in vitro modeling is crucial for understanding the diseases.Remodeling of MGBA in the laboratory uses many molecular technologies and biomaterial advances.Spheroids and organoids provide a more realistic picture of the cell and tissue structure than monolayers.Combining them with the Transwell system offers the advantage of compartmentalizing the two systems(apical and basal)while allowing physical and chemical cues between them.Cutting-edge technologies,such as bioprinting and microfluidic chips,might be the future of in vitro modeling,as they provide dynamicity.

Software Defect Prediction Method Based on Stable Learning

The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data distribution.However,target projects often lack sufficient data,which affects the performance of the transfer learning model.In addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model.To address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual networks.This method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code images.During the model training process,target project data are not required as prior knowledge.Following the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the data.This approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction performance.To evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE dataset.The experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction methods.Therefore,SDP-SL can effectively enhance within-and cross-project defect predictions.

Irisin/BDNF signaling in the muscle-brain axis and circadian system: A review

In mammals,the timing of physiological,biochemical and behavioral processes over a 24-h period is controlled by circadian rhythms.To entrain the master clock located in the suprachiasmatic nucleus of the hypothalamus to a precise 24-h rhythm,environmental zeitgebers are used by the circadian system.This is done primarily by signals from the retina via the retinohypothalamic tract,but other cues like exercise,feeding,temperature,anxiety,and social events have also been shown to act as non-photic zeitgebers.The recently identified myokine irisin is proposed to serve as an entraining non-photic signal of exercise.Irisin is a product of cleavage and modification from its precursor membrane fibronectin typeⅢdomain-containing protein 5(FNDC5)in response to exercise.Apart from well-known peripheral effects,such as inducing the"browning"of white adipocytes,irisin can penetrate the blood-brain barrier and display the effects on the brain.Experimental data suggest that FNDC5/irisin mediates the positive effects of physical activity on brain functions.In several brain areas,irisin induces the production of brain-derived neurotrophic factor(BDNF).In the master clock,a significant role in gating photic stimuli in the retinohypothalamic synapse for BDNF is suggested.However,the brain receptor for irisin remains unknown.In the current review,the interactions of physical activity and the irisin/BDNF axis with the circadian system are reconceptualized.

Low-intensity pulsed ultrasound reduces alveolar bone resorption during orthodontic treatment via Lamin A/C-Yes-associated protein axis in stem cells

BACKGROUND The bone remodeling during orthodontic treatment for malocclusion often requires a long duration of around two to three years,which also may lead to some complications such as alveolar bone resorption or tooth root resorption.Low-intensity pulsed ultrasound(LIPUS),a noninvasive physical therapy,has been shown to promote bone fracture healing.It is also reported that LIPUS could reduce the duration of orthodontic treatment;however,how LIPUS regulates the bone metabolism during the orthodontic treatment process is still unclear.AIM To investigate the effects of LIPUS on bone remodeling in an orthodontic tooth movement(OTM)model and explore the underlying mechanisms.METHODS A rat model of OTM was established,and alveolar bone remodeling and tooth movement rate were evaluated via micro-computed tomography and staining of tissue sections.In vitro,human bone marrow mesenchymal stem cells(hBMSCs)were isolated to detect their osteogenic differentiation potential under compression and LIPUS stimulation by quantitative reverse transcription-polymerase chain reaction,Western blot,alkaline phosphatase(ALP)staining,and Alizarin red staining.The expression of Yes-associated protein(YAP1),the actin cytoskeleton,and the Lamin A/C nucleoskeleton were detected with or without YAP1 small interfering RNA(siRNA)application via immunofluorescence.RESULTS The force treatment inhibited the osteogenic differentiation potential of hBMSCs;moreover,the expression of osteogenesis markers,such as type 1 collagen(COL1),runt-related transcription factor 2,ALP,and osteocalcin(OCN),decreased.LIPUS could rescue the osteogenic differentiation of hBMSCs with increased expression of osteogenic marker inhibited by force.Mechanically,the expression of LaminA/C,F-actin,and YAP1 was downregulated after force treatment,which could be rescued by LIPUS.Moreover,the osteogenic differentiation of hBMSCs increased by LIPUS could be attenuated by YAP siRNA treatment.Consistently,LIPUS increased alveolar bone density and decreased vertical bone absorption in vivo.The decreased expression of COL1,OCN,and YAP1 on the compression side of the alveolar bone was partially rescued by LIPUS.CONCLUSION LIPUS can accelerate tooth movement and reduce alveolar bone resorption by modulating the cytoskeleton-Lamin A/C-YAP axis,which may be a promising strategy to reduce the orthodontic treatment process.

Open-Source Software Defined Networking Controllers:State-of-the-Art,Challenges and Solutions for Future Network Providers

Software Defined Networking(SDN)is programmable by separation of forwarding control through the centralization of the controller.The controller plays the role of the‘brain’that dictates the intelligent part of SDN technology.Various versions of SDN controllers exist as a response to the diverse demands and functions expected of them.There are several SDN controllers available in the open market besides a large number of commercial controllers;some are developed tomeet carrier-grade service levels and one of the recent trends in open-source SDN controllers is the Open Network Operating System(ONOS).This paper presents a comparative study between open source SDN controllers,which are known as Network Controller Platform(NOX),Python-based Network Controller(POX),component-based SDN framework(Ryu),Java-based OpenFlow controller(Floodlight),OpenDayLight(ODL)and ONOS.The discussion is further extended into ONOS architecture,as well as,the evolution of ONOS controllers.This article will review use cases based on ONOS controllers in several application deployments.Moreover,the opportunities and challenges of open source SDN controllers will be discussed,exploring carriergrade ONOS for future real-world deployments,ONOS unique features and identifying the suitable choice of SDN controller for service providers.In addition,we attempt to provide answers to several critical questions relating to the implications of the open-source nature of SDN controllers regarding vendor lock-in,interoperability,and standards compliance,Similarly,real-world use cases of organizations using open-source SDN are highlighted and how the open-source community contributes to the development of SDN controllers.Furthermore,challenges faced by open-source projects,and considerations when choosing an open-source SDN controller are underscored.Then the role of Artificial Intelligence(AI)and Machine Learning(ML)in the evolution of open-source SDN controllers in light of recent research is indicated.In addition,the challenges and limitations associated with deploying open-source SDN controllers in production networks,how can they be mitigated,and finally how opensource SDN controllers handle network security and ensure that network configurations and policies are robust and resilient are presented.Potential opportunities and challenges for future Open SDN deployment are outlined to conclude the article.

Static Analysis Techniques for Fixing Software Defects in MPI-Based Parallel Programs

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed memorysystems.However, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

Towards Improving the Quality of Requirement and Testing Process in Agile Software Development:An Empirical Study

Software testing is a critical phase due to misconceptions about ambiguities in the requirements during specification,which affect the testing process.Therefore,it is difficult to identify all faults in software.As requirement changes continuously,it increases the irrelevancy and redundancy during testing.Due to these challenges;fault detection capability decreases and there arises a need to improve the testing process,which is based on changes in requirements specification.In this research,we have developed a model to resolve testing challenges through requirement prioritization and prediction in an agile-based environment.The research objective is to identify the most relevant and meaningful requirements through semantic analysis for correct change analysis.Then compute the similarity of requirements through case-based reasoning,which predicted the requirements for reuse and restricted to error-based requirements.Afterward,the apriori algorithm mapped out requirement frequency to select relevant test cases based on frequently reused or not reused test cases to increase the fault detection rate.Furthermore,the proposed model was evaluated by conducting experiments.The results showed that requirement redundancy and irrelevancy improved due to semantic analysis,which correctly predicted the requirements,increasing the fault detection rate and resulting in high user satisfaction.The predicted requirements are mapped into test cases,increasing the fault detection rate after changes to achieve higher user satisfaction.Therefore,the model improves the redundancy and irrelevancy of requirements by more than 90%compared to other clustering methods and the analytical hierarchical process,achieving an 80%fault detection rate at an earlier stage.Hence,it provides guidelines for practitioners and researchers in the modern era.In the future,we will provide the working prototype of this model for proof of concept.

HV Process Model of Software Development

Software Development Life Cycle (SDLC) is one of the major ingredients for the development of efficient software systems within a time frame and low-cost involvement. From the literature, it is evident that there are various kinds of process models that are used by the software industries for the development of small, medium and long-term software projects, but many of them do not cover risk management. It is quite obvious that the improper selection of the software development process model leads to failure of the software products as it is time bound activity. In the present work, a new software development process model is proposed which covers the risks at any stage of the development of the software product. The model is named a Hemant-Vipin (HV) process model and may be helpful for the software industries for development of the efficient software products and timely delivery at the end of the client. The efficiency of the HV process model is observed by considering various kinds of factors like requirement clarity, user feedback, change agility, predictability, risk identification, practical implementation, customer satisfaction, incremental development, use of ready-made components, quick design, resource organization and many more and found through a case study that the presented approach covers many of parameters in comparison of the existing process models. .

A Hybrid Model for Improving Software Cost Estimation in Global Software Development

Accurate software cost estimation in Global Software Development(GSD)remains challenging due to reliance on historical data and expert judgments.Traditional models,such as the Constructive Cost Model(COCOMO II),rely heavily on historical and accurate data.In addition,expert judgment is required to set many input parameters,which can introduce subjectivity and variability in the estimation process.Consequently,there is a need to improve the current GSD models to mitigate reliance on historical data,subjectivity in expert judgment,inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns.This study introduces a novel hybrid model that synergizes the COCOMO II with Artificial Neural Networks(ANN)to address these challenges.The proposed hybrid model integrates additional GSD-based cost drivers identified through a systematic literature review and further vetted by industry experts.This article compares the effectiveness of the proposedmodelwith state-of-the-artmachine learning-basedmodels for software cost estimation.Evaluating the NASA 93 dataset by adopting twenty-six GSD-based cost drivers reveals that our hybrid model achieves superior accuracy,outperforming existing state-of-the-artmodels.The findings indicate the potential of combining COCOMO II,ANN,and additional GSD-based cost drivers to transform cost estimation in GSD.

In situ calibrated angle between the quantization axis and the propagating direction of the light field for trapping neutral atoms

The recently developed magic-intensity trapping technique of neutral atoms efficiently mitigates the detrimental effect of light shifts on atomic qubits and substantially enhances the coherence time. This technique relies on applying a bias magnetic field precisely parallel to the wave vector of a circularly polarized trapping laser field. However, due to the presence of the vector light shift experienced by the trapped atoms, it is challenging to precisely define a parallel magnetic field, especially at a low bias magnetic field strength, for the magic-intensity trapping of85Rb qubits. In this work, we present a method to calibrate the angle between the bias magnetic field and the trapping laser field with the compensating magnetic fields in the other two directions orthogonal to the bias magnetic field direction. Experimentally, with a constantdepth trap and a fixed bias magnetic field, we measure the respective resonant frequencies of the atomic qubits in a linearly polarized trap and a circularly polarized one via the conventional microwave Rabi spectra with different compensating magnetic fields and obtain the corresponding total magnetic fields via the respective resonant frequencies using the Breit–Rabi formula. With known total magnetic fields, the angle is a function of the other two compensating magnetic fields.Finally, the projection value of the angle on either of the directions orthogonal to the bias magnetic field direction can be reduced to 0(4)° by applying specific compensating magnetic fields. The measurement error is mainly attributed to the fluctuation of atomic temperature. Moreover, it also demonstrates that, even for a small angle, the effect is strong enough to cause large decoherence of Rabi oscillation in a magic-intensity trap. Although the compensation method demonstrated here is explored for the magic-intensity trapping technique, it can be applied to a variety of similar precision measurements with trapped neutral atoms.

Development and Evaluation of 3D Delivery Animation Software Designed to Improve the Mother’s and Spouse’s Satisfaction with Delivery

Purpose: To clarify the effectiveness of 3-D delivery animation software for the mother’s and husband’s satisfaction with delivery. Subjects and Method: We independently developed a software application used to display the pelvic region and explain the labor process. The study involved a collaboration with hospital staff who recruited 18 primiparous and 18 multiparous mothers who were hospitalized for delivery at Facility A. The midwife explained the process of delivery using the “Delivery Animation Software”. A self-administered, anonymous questionnaire was distributed and analyzed separately for primiparous and multiparous mothers and their husbands. Results: 1) For both primiparous and multiparous couples, both mothers and their husbands gained a significantly higher level of understanding after delivery than during pregnancy. 2) The Self-Evaluation Scale for Experience of Delivery results were as follows: “I did my best for the baby even if it was painful” was selected more often for “birth coping skills”;“reliable medical staff” was selected more often for “physiological birth process”;“the birth progressed as I expected” was selected frequently by primiparous mothers;and “the birth progressed smoothly” was selected often by multiparous mothers. 3) In terms of husbands’ satisfaction with the delivery, “I was satisfied with the delivery”, “I was given an easy-to-understand explanation”, and “They explained the process to me” was selected of primiparous and multiparous fathers. 4) All primiparous and multiparous mothers positively evaluated whether the delivery animation was helpful in understanding the process of delivery. Conclusion: The delivery animation was effective in improving the understanding and satisfaction of both the mothers and their husbands.

Single nucleus/cell RNA-seq of the chicken hypothalamicpituitaryovarian axis offers new insights into the molecular regulatory mechanisms of ovarian development

The hypothalamic-pituitary-ovarian(HPO)axis represents a central neuroendocrine network essential for reproductive function.Despite its critical role,the intrinsic heterogeneity within the HPO axis across vertebrates and the complex intercellular interactions remain poorly defined.This study provides the first comprehensive,unbiased,cell type-specific molecular profiling of all three components of the HPO axis in adult Lohmann layers and Liangshan Yanying chickens.Within the hypothalamus,pituitary,and ovary,seven,12,and 13 distinct cell types were identified,respectively.Results indicated that the pituitary adenylate cyclase activating polypeptide(PACAP),follicle-stimulating hormone(FSH),and prolactin(PRL)signaling pathways may modulate the synthesis and secretion of gonadotropin-releasing hormone(GnRH),FSH,and luteinizing hormone(LH)within the hypothalamus and pituitary.In the ovary,interactions between granulosa cells and oocytes involved the KIT,CD99,LIFR,FN1,and ANGPTL signaling pathways,which collectively regulate follicular maturation.The SEMA4 signaling pathway emerged as a critical mediator across all three tissues of the HPO axis.Additionally,gene expression analysis revealed that relaxin 3(RLN3),gastrin-releasing peptide(GRP),and cocaine-and amphetamine regulated transcripts(CART,also known as CARTPT)may function as novel endocrine hormones,influencing the HPO axis through autocrine,paracrine,and endocrine pathways.Comparative analyses between Lohmann layers and Liangshan Yanying chickens demonstrated higher expression levels of GRP,RLN3,CARTPT,LHCGR,FSHR,and GRPR in the ovaries of Lohmann layers,potentially contributing to their superior reproductive performance.In conclusion,this study provides a detailed molecular characterization of the HPO axis,offering novel insights into the regulatory mechanisms underlying reproductive biology.

A Proposed Approach for Measuring Maturity Level of Software Delivery

Software delivery is vital for modern organizations, driving innovation and competitiveness. Measuring an organization’s maturity in software delivery is crucial for efficiency and quality. The Capability Maturity Model (CMM) framework provides a roadmap for improvement but assessing an organization’s CMM Level is challenging. This paper offers a quantitative approach tailored to the CMM framework, using Goal-Question-Metric (GQM) frame-works for each key process area (KPA). These frameworks include metrics and questions to compute maturity scores effectively. The study also refines practices into questions for a thorough assessment. The result is an Analysis Matrix that calculates weighted scores and an overall maturity score. This approach helps organizations assess and enhance their software delivery processes systematically, aiming for improved practices and growth.

A Tutorial on Federated Learning from Theory to Practice:Foundations,Software Frameworks,Exemplary Use Cases,and Selected Trends

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties.This paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources.By virtue of FL,models can be learned from all such distributed data sources while preserving data privacy.The aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software frameworks.Furthermore,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape.The ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

Software Vulnerability Mining and Analysis Based on Deep Learning

In recent years,the rapid development of computer software has led to numerous security problems,particularly software vulnerabilities.These flaws can cause significant harm to users’privacy and property.Current security defect detection technology relies on manual or professional reasoning,leading to missed detection and high false detection rates.Artificial intelligence technology has led to the development of neural network models based on machine learning or deep learning to intelligently mine holes,reducing missed alarms and false alarms.So,this project aims to study Java source code defect detection methods for defects like null pointer reference exception,XSS(Transform),and Structured Query Language(SQL)injection.Also,the project uses open-source Javalang to translate the Java source code,conducts a deep search on the AST to obtain the empty syntax feature library,and converts the Java source code into a dependency graph.The feature vector is then used as the learning target for the neural network.Four types of Convolutional Neural Networks(CNN),Long Short-Term Memory(LSTM),Bi-directional Long Short-Term Memory(BiLSTM),and Attention Mechanism+Bidirectional LSTM,are used to investigate various code defects,including blank pointer reference exception,XSS,and SQL injection defects.Experimental results show that the attention mechanism in two-dimensional BLSTM is the most effective for object recognition,verifying the correctness of the method.

SCIRD: Revealing Infection of Malicious Software in Edge Computing-Enabled IoT Networks

The Internet of Things(IoT)has characteristics such as node mobility,node heterogeneity,link heterogeneity,and topology heterogeneity.In the face of the IoT characteristics and the explosive growth of IoT nodes,which brings about large-scale data processing requirements,edge computing architecture has become an emerging network architecture to support IoT applications due to its ability to provide powerful computing capabilities and good service functions.However,the defense mechanism of Edge Computing-enabled IoT Nodes(ECIoTNs)is still weak due to their limited resources,so that they are susceptible to malicious software spread,which can compromise data confidentiality and network service availability.Facing this situation,we put forward an epidemiology-based susceptible-curb-infectious-removed-dead(SCIRD)model.Then,we analyze the dynamics of ECIoTNs with different infection levels under different initial conditions to obtain the dynamic differential equations.Additionally,we establish the presence of equilibrium states in the SCIRD model.Furthermore,we conduct an analysis of the model’s stability and examine the conditions under which malicious software will either spread or disappear within Edge Computing-enabled IoT(ECIoT)networks.Lastly,we validate the efficacy and superiority of the SCIRD model through MATLAB simulations.These research findings offer a theoretical foundation for suppressing the propagation of malicious software in ECIoT networks.The experimental results indicate that the theoretical SCIRD model has instructive significance,deeply revealing the principles of malicious software propagation in ECIoT networks.This study solves a challenging security problem of ECIoT networks by determining the malicious software propagation threshold,which lays the foundation for buildingmore secure and reliable ECIoT networks.

Curcumin inhibits colorectal cancer development by blocking the YAP/TAZ signaling axis

Background:Curcumin is a plant polyphenol with antitumor properties and inhibits the development of colorectal cancer(CRC).However,as the molecular mechanism associated is still unclear,our study aimed to explore the underlying molecular mechanisms by which curcumin inhibits CRC.Methods:HT29 and SW480 cells were treated with curcumin or/and Doxycycline(DOX),and cell viability,colony forming ability,migration and invasion were confirmed by cell counting kit-8(CCK-8),colony forming,Transwell assays.And Yes-associated protein 1(YAP)and PDZ-binding motif(TAZ)signaling-related genes or proteins were analyzed using reverse transcription quantitative real-time PCR(RT-qPCR),western blot,and immunofluorescence assays.Then nude mice xenograft tumor model was constructed,YAP and Ki67 expressions were tested by immunohistochemistry(IHC)staining.Results:In our study,we proved that curcumin significantly inhibited the CRC cell viability,cell migration,and cell invasion abilities.In addition,curcumin inhibited YAP and Transcriptional coactivator with TAZ or the YAP/TAZ signaling axis in CRC cells.Further,in the nude mice model,curcumin treatment significantly decreased the size and weight of xenotransplant tumors.Conclusion:Therefore,curcumin significantly inhibited CRC development and invasion by regulating the YAP/TAZ signaling axis.

Research progress on insomnia treated by traditional Chinese medicine and acupuncture based on microbial-gut-brain axis theory

Insomnia,as one of the emotional diseases,has been increasing in recent years,which has a great impact on people's life and work.Therefore,researchers are eager to find a more perfect treatment.The microbiome-gut-brain axis is a new theory that has gradually become popular abroad in recent years and has a profound impact in the field of insomnia.In recent years,traditional Chinese medicine(TCM)has played an increasingly important role in the treatment of insomnia,especially acupuncture and Chinese herbal medicine.It is the main method of TCM in the treatment of insomnia.This paper mainly reviews the combination degree of"microorganism-gut-brain axis"theory with TCM and acupuncture under the system of TCM.To explore the mechanism of TCM and acupuncture in the treatment of insomnia under the guidance of"microorganismgut-brain axis"theory,in order to provide a new idea for the diagnosis and treatment of insomnia.