Equilibrium reconstruction method for self-organized plasmas on reversed field pinches with polarimeter-interferometer

In the reversed field pinch(RFP),plasmas exhibit various self-organized states.Among these,the three-dimensional(3D)helical state known as the“quasi-single-helical”(QSH)state enhances RFP confinement.However,accurately describing the equilibrium is challenging due to the presence of 3D structures,magnetic islands,and chaotic regions.It is difficult to obtain a balance between the available diagnostic and the real equilibrium structure.To address this issue,we introduce KTX3DFit,a new 3D equilibrium reconstruction code specifically designed for the Keda Torus eXperiment(KTX)RFP.KTX3DFit utilizes the stepped-pressure equilibrium code(SPEC)to compute 3D equilibria and uses polarimetric interferometer signals from experiments.KTX3DFit is able to reconstruct equilibria in various states,including axisymmetric,doubleaxis helical(DAx),and single-helical-axis(SHAx)states.Notably,this study marks the first integration of the SPEC code with internal magnetic field data for equilibrium reconstruction and could be used for other 3D configurations.

Unveiling the brain’s symphony:exploring the necessity and sufficiency of neural networks in behavior control

Since the pioneering work by Broca and Wernicke in the 19th century,who examined individuals with brain lesions to associate them with specific behaviors,it was evident that behaviors are complex and cannot be fully attributable to specific brain areas alone.Instead,they involve connectivity among brain areas,whether close or distant.At that time,this approach was considered the optimal way to dissect brain circuitry and function.These pioneering efforts opened the field to explore the necessity or sufficiency of brain areas in controlling behavior and hence dissecting brain function.However,the connectivity of the brain and the mechanisms through which various brain regions regulate specific behaviors,either individually or collaboratively,remain largely elusive.Utilizing animal models,researchers have endeavored to unravel the necessity or sufficiency of specific brain areas in influencing behavior;however,no clear associations have been firmly established.

Women in visual neural regeneration research

The year 2024 marks the 60^(th)anniversary of Title IX and 25 years since the New York Times revealed bias against female faculty members at the Massachusetts Institute of Technology.We take an opportunity here to examine the state of gender bias in a relatively new yet already prominent field,neural regeneration in the visual system,for which there is a well-defined context useful for this purpose.The National Eye Institute(NEI)provided the first round of research funding for its Audacious Goals Initiative(AGI)on visual neural regeneration in 2013 and the last round in 2021.Therefore,we focus on this timespan.Data sources included PubMed,the National Science Foundation(NSF),the NEI,the Blue Ridge Institute for Medical Research and data from the major professional organization for eye and vision research,the Association for Research in Vision and Ophthalmology(ARVO).

Commentary on:“Human neural stem cell-derived artificial organelles to improve oxidative phosphorylation”

Mitochondrial function is fundamental to neuroregeneration,particularly in neurons,where high energy demands are essential for repair and recovery(Patrón and Zinsmaier,2016;Beckervordersandforth et al.,2017;Iwata et al.,2023).Mitochondrial dysfunction,characterized by an imbalance in ATP levels and excessive production of mitochondrial reactive oxygen species,is a key factor that impedes neural regeneration in neurodegenerative diseases and after neuronal injury(Han et al.,2016,2020;Zheng et al.,2016;Zong et al.,2024).

Unlocking the future:Mitochondrial genes and neural networks in predicting ovarian cancer prognosis and immunotherapy response

BACKGROUND Mitochondrial genes are involved in tumor metabolism in ovarian cancer(OC)and affect immune cell infiltration and treatment responses.AIM To predict prognosis and immunotherapy response in patients diagnosed with OC using mitochondrial genes and neural networks.METHODS Prognosis,immunotherapy efficacy,and next-generation sequencing data of patients with OC were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus.Mitochondrial genes were sourced from the MitoCarta3.0 database.The discovery cohort for model construction was created from 70% of the patients,whereas the remaining 30% constituted the validation cohort.Using the expression of mitochondrial genes as the predictor variable and based on neural network algorithm,the overall survival time and immunotherapy efficacy(complete or partial response)of patients were predicted.RESULTS In total,375 patients with OC were included to construct the prognostic model,and 26 patients were included to construct the immune efficacy model.The average area under the receiver operating characteristic curve of the prognostic model was 0.7268[95% confidence interval(CI):0.7258-0.7278]in the discovery cohort and 0.6475(95%CI:0.6466-0.6484)in the validation cohort.The average area under the receiver operating characteristic curve of the immunotherapy efficacy model was 0.9444(95%CI:0.8333-1.0000)in the discovery cohort and 0.9167(95%CI:0.6667-1.0000)in the validation cohort.CONCLUSION The application of mitochondrial genes and neural networks has the potential to predict prognosis and immunotherapy response in patients with OC,providing valuable insights into personalized treatment strategies.

Is it possible to foot reflexology for infants with sensorineural hearing loss?

Foot reflexology is a non-invasive and safe complementary therapy that works by massaging the reflex zones of the feet and exerts systemic or whole-body regulation through meridian nerve conduction.This therapy is commonly used in the treatment of various conditions such as autism and Parkinson's disease.However,there is limited reporting on the use of foot reflexology therapy for infants with sensorineural hearing loss(SNHL).Currently,there is no definitive conclusion on how foot reflexology therapy can influence hearing.This editorial holds some guiding significance regarding this clinical issue.The aim is to present physiological evidence of how foot reflexology therapy can impact infants with SNHL,thereby enhancing clinician’s awareness of foot reflexology in treating infants with SNHL.

Induced neural stem cells regulate microglial activation through Akt-mediated upregulation of CXCR4 and Crry in a mouse model of closed head injury

Microglial activation that occurs rapidly after closed head injury may play important and complex roles in neuroinflammation-associated neuronal damage and repair.We previously reported that induced neural stem cells can modulate the behavior of activated microglia via CXCL12/CXCR4 signaling,influencing their activation such that they can promote neurological recovery.However,the mechanism of CXCR4 upregulation in induced neural stem cells remains unclear.In this study,we found that nuclear factor-κB activation induced by closed head injury mouse serum in microglia promoted CXCL12 and tumor necrosis factor-αexpression but suppressed insulin-like growth factor-1 expression.However,recombinant complement receptor 2-conjugated Crry(CR2-Crry)reduced the effects of closed head injury mouse serum-induced nuclear factor-κB activation in microglia and the levels of activated microglia,CXCL12,and tumor necrosis factor-α.Additionally,we observed that,in response to stimulation(including stimulation by CXCL12 secreted by activated microglia),CXCR4 and Crry levels can be upregulated in induced neural stem cells via the interplay among CXCL12/CXCR4,Crry,and Akt signaling to modulate microglial activation.In agreement with these in vitro experimental results,we found that Akt activation enhanced the immunoregulatory effects of induced neural stem cell grafts on microglial activation,leading to the promotion of neurological recovery via insulin-like growth factor-1 secretion and the neuroprotective effects of induced neural stem cell grafts through CXCR4 and Crry upregulation in the injured cortices of closed head injury mice.Notably,these beneficial effects of Akt activation in induced neural stem cells were positively correlated with the therapeutic effects of induced neural stem cells on neuronal injury,cerebral edema,and neurological disorders post–closed head injury.In conclusion,our findings reveal that Akt activation may enhance the immunoregulatory effects of induced neural stem cells on microglial activation via upregulation of CXCR4 and Crry,thereby promoting induced neural stem cell–mediated improvement of neuronal injury,cerebral edema,and neurological disorders following closed head injury.

Establishment of human cerebral organoid systems to model early neural development and assess the central neurotoxicity of environmental toxins

Human brain development is a complex process,and animal models often have significant limitations.To address this,researchers have developed pluripotent stem cell-derived three-dimensional structures,known as brain-like organoids,to more accurately model early human brain development and disease.To enable more consistent and intuitive reproduction of early brain development,in this study,we incorporated forebrain organoid culture technology into the traditional unguided method of brain organoid culture.This involved embedding organoids in matrigel for only 7 days during the rapid expansion phase of the neural epithelium and then removing them from the matrigel for further cultivation,resulting in a new type of human brain organoid system.This cerebral organoid system replicated the temporospatial characteristics of early human brain development,including neuroepithelium derivation,neural progenitor cell production and maintenance,neuron differentiation and migration,and cortical layer patterning and formation,providing more consistent and reproducible organoids for developmental modeling and toxicology testing.As a proof of concept,we applied the heavy metal cadmium to this newly improved organoid system to test whether it could be used to evaluate the neurotoxicity of environmental toxins.Brain organoids exposed to cadmium for 7 or 14 days manifested severe damage and abnormalities in their neurodevelopmental patterns,including bursts of cortical cell death and premature differentiation.Cadmium exposure caused progressive depletion of neural progenitor cells and loss of organoid integrity,accompanied by compensatory cell proliferation at ectopic locations.The convenience,flexibility,and controllability of this newly developed organoid platform make it a powerful and affordable alternative to animal models for use in neurodevelopmental,neurological,and neurotoxicological studies.

Repetitive transcranial magnetic stimulation in Alzheimer’s disease:effects on neural and synaptic rehabilitation

Alzheimer’s disease is a neurodegenerative disease resulting from deficits in synaptic transmission and homeostasis.The Alzheimer’s disease brain tends to be hyperexcitable and hypersynchronized,thereby causing neurodegeneration and ultimately disrupting the operational abilities in daily life,leaving patients incapacitated.Repetitive transcranial magnetic stimulation is a cost-effective,neuro-modulatory technique used for multiple neurological conditions.Over the past two decades,it has been widely used to predict cognitive decline;identify pathophysiological markers;promote neuroplasticity;and assess brain excitability,plasticity,and connectivity.It has also been applied to patients with dementia,because it can yield facilitatory effects on cognition and promote brain recovery after a neurological insult.However,its therapeutic effectiveness at the molecular and synaptic levels has not been elucidated because of a limited number of studies.This study aimed to characterize the neurobiological changes following repetitive transcranial magnetic stimulation treatment,evaluate its effects on synaptic plasticity,and identify the associated mechanisms.This review essentially focuses on changes in the pathology,amyloidogenesis,and clearance pathways,given that amyloid deposition is a major hypothesis in the pathogenesis of Alzheimer’s disease.Apoptotic mechanisms associated with repetitive transcranial magnetic stimulation procedures and different pathways mediating gene transcription,which are closely related to the neural regeneration process,are also highlighted.Finally,we discuss the outcomes of animal studies in which neuroplasticity is modulated and assessed at the structural and functional levels by using repetitive transcranial magnetic stimulation,with the aim to highlight future directions for better clinical translations.

Small extracellular vesicles derived from human induced pluripotent stem cell-differentiated neural progenitor cells mitigate retinal ganglion cell degeneration in a mouse model of optic nerve injury

Several studies have found that transplantation of neural progenitor cells(NPCs)promotes the survival of injured neurons.However,a poor integration rate and high risk of tumorigenicity after cell transplantation limits their clinical application.Small extracellular vesicles(sEVs)contain bioactive molecules for neuronal protection and regeneration.Previous studies have shown that stem/progenitor cell-derived sEVs can promote neuronal survival and recovery of neurological function in neurodegenerative eye diseases and other eye diseases.In this study,we intravitreally transplanted sEVs derived from human induced pluripotent stem cells(hiPSCs)and hiPSCs-differentiated NPCs(hiPSC-NPC)in a mouse model of optic nerve crush.Our results show that these intravitreally injected sEVs were ingested by retinal cells,especially those localized in the ganglion cell layer.Treatment with hiPSC-NPC-derived sEVs mitigated optic nerve crush-induced retinal ganglion cell degeneration,and regulated the retinal microenvironment by inhibiting excessive activation of microglia.Component analysis further revealed that hiPSC-NPC derived sEVs transported neuroprotective and anti-inflammatory miRNA cargos to target cells,which had protective effects on RGCs after optic nerve injury.These findings suggest that sEVs derived from hiPSC-NPC are a promising cell-free therapeutic strategy for optic neuropathy.

Maintaining moderate levels of hypochlorous acid promotes neural stem cell proliferation and differentiation in the recovery phase of stroke

It has been shown clinically that continuous removal of ischemia/reperfusion-induced reactive oxygen species is not conducive to the recovery of late stroke.Indeed,previous studies have shown that excessive increases in hypochlorous acid after stroke can cause severe damage to brain tissue.Our previous studies have found that a small amount of hypochlorous acid still exists in the later stage of stroke,but its specific role and mechanism are currently unclear.To simulate stroke in vivo,a middle cerebral artery occlusion rat model was established,with an oxygen-glucose deprivation/reoxygenation model established in vitro to mimic stroke.We found that in the early stage(within 24 hours)of ischemic stroke,neutrophils produced a large amount of hypochlorous acid,while in the recovery phase(10 days after stroke),microglia were activated and produced a small amount of hypochlorous acid.Further,in acute stroke in rats,hypochlorous acid production was prevented using a hypochlorous acid scavenger,taurine,or myeloperoxidase inhibitor,4-aminobenzoic acid hydrazide.Our results showed that high levels of hypochlorous acid(200μM)induced neuronal apoptosis after oxygen/glucose deprivation/reoxygenation.However,in the recovery phase of the middle cerebral artery occlusion model,a moderate level of hypochlorous acid promoted the proliferation and differentiation of neural stem cells into neurons and astrocytes.This suggests that hypochlorous acid plays different roles at different phases of cerebral ischemia/reperfusion injury.Lower levels of hypochlorous acid(5 and 100μM)promoted nuclear translocation ofβ-catenin.By transfection of single-site mutation plasmids,we found that hypochlorous acid induced chlorination of theβ-catenin tyrosine 30 residue,which promoted nuclear translocation.Altogether,our study indicates that maintaining low levels of hypochlorous acid plays a key role in the recovery of neurological function.

Human-induced pluripotent stem cell-derived neural stem cell exosomes improve blood-brain barrier function after intracerebral hemorrhage by activating astrocytes via PI3K/AKT/MCP-1 axis

Cerebral edema caused by blood-brain barrier injury after intracerebral hemorrhage is an important factor leading to poor prognosis.Human-induced pluripotent stem cell-derived neural stem cell exosomes(hiPSC-NSC-Exos)have shown potential for brain injury repair in central nervous system diseases.In this study,we explored the impact of hiPSC-NSC-Exos on blood-brain barrier preservation and the underlying mechanism.Our results indicated that intranasal delivery of hiPSC-NSC-Exos mitigated neurological deficits,enhanced blood-brain barrier integrity,and reduced leukocyte infiltration in a mouse model of intracerebral hemorrhage.Additionally,hiPSC-NSC-Exos decreased immune cell infiltration,activated astrocytes,and decreased the secretion of inflammatory cytokines like monocyte chemoattractant protein-1,macrophage inflammatory protein-1α,and tumor necrosis factor-αpost-intracerebral hemorrhage,thereby improving the inflammatory microenvironment.RNA sequencing indicated that hiPSC-NSC-Exo activated the PI3K/AKT signaling pathway in astrocytes and decreased monocyte chemoattractant protein-1 secretion,thereby improving blood-brain barrier integrity.Treatment with the PI3K/AKT inhibitor LY294002 or the monocyte chemoattractant protein-1 neutralizing agent C1142 abolished these effects.In summary,our findings suggest that hiPSC-NSC-Exos maintains blood-brain barrier integrity,in part by downregulating monocyte chemoattractant protein-1 secretion through activation of the PI3K/AKT signaling pathway in astrocytes.

A Self-organization Mapping Neural Network Algorithm and Its Application to Identify Ecosystem Service Zones

The self-organization mapping （SOM） neural network algorithm is a new method used to identify the ecosystem service zones at regional extent. According to the ecosystem assessment framework of Millennium Ecosystem Assessment （ MA）, this paper develops an indicator system and conducts a spatial cluster analysis at the 1km by I km grid pixel scale with the SOM neural network algorithm to sort the core ecosystem services over the vertical and horizontal dimensions. A case study was carried out in Xilingol League. The ecosystem services in Xilingol League could be divided to six different ecological zones. The SOM neural network algorithm was capable of identifying the similarities among the input data automatically. The research provides both spatially and temporally valuable information targeted sustainable ecosystem management for decision-makers.

Application of Self-Organizing Feature Map Neural Network Based on K-means Clustering in Network Intrusion Detection

Due to the widespread use of the Internet,customer information is vulnerable to computer systems attack,which brings urgent need for the intrusion detection technology.Recently,network intrusion detection has been one of the most important technologies in network security detection.The accuracy of network intrusion detection has reached higher accuracy so far.However,these methods have very low efficiency in network intrusion detection,even the most popular SOM neural network method.In this paper,an efficient and fast network intrusion detection method was proposed.Firstly,the fundamental of the two different methods are introduced respectively.Then,the selforganizing feature map neural network based on K-means clustering(KSOM)algorithms was presented to improve the efficiency of network intrusion detection.Finally,the NSLKDD is used as network intrusion data set to demonstrate that the KSOM method can significantly reduce the number of clustering iteration than SOM method without substantially affecting the clustering results and the accuracy is much higher than Kmeans method.The Experimental results show that our method can relatively improve the accuracy of network intrusion and significantly reduce the number of clustering iteration.

Waterlogging risk assessment based on self-organizing map(SOM)artificial neural networks:a case study of an urban storm in Beijing

Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annuallyinthe urban area of Beijing, the capital of China. Based on a selforganizing map(SOM) artificial neural network(ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product(GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANNis suitable for automatically and quantitatively assessing risks associated with waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors,producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. The points that were assigned risk grades of IV or Vwere located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.

SELF-ORGANIZED SEMANTIC FEATURE EVOLUTION FOR AXIOMATIC DESIGN

Aiming at the problem existing in the computer aided design process that how to express the design intents with high-level engineering terminologies, a mechanical product self-organized semantic feature evolution technology for axiomatic design is proposed, so that the constraint relations between mechanical parts could be expressed in a semantic form which is more suitable for designers. By describing the evolution rules for semantic constraint information, the abstract expression of design semantics in mechanical product evolution process is realized and the constraint relations between parts are mapped to the geometric level from the semantic level; With semantic feature relation graph, the abstract semantic description, the semantic relative structure and the semantic constraint information are linked together; And the methods of semantic feature self-organized evolution are classified. Finally, combining a design example of domestic high-speed elevator, how to apply the theory to practical product development is illustrated and this method and its validity is described and verified. According to the study results, the designers are able to represent the design intents at an advanced semantic level in a more intuitional and natural way and the automation, recursion and visualization for mechanical product axiomatic design are also realized.

Adaptive prediction system of sintering through point based on self-organize artificial neural network

A soft sensing method of burning through point (BTP) was described and a new predictive parameter—the mathematics inflexion point of waste gas temperature curve in the middle of the strand was proposed. The artificial neural network was used in predicting BTP, modification on backpropagation algorithm was made in order to improve the convergence and self organize the hidden layer neurons. The adaptive prediction system developed on these techniques shows its characters such as fast, accuracy, less dependence on production data. The prediction of BTP can be used as operation guidance or control parameter.[

Self-organizing fuzzy clustering neural network and application to electronic countermeasures effectiveness evaluation

A self-organizing fuzzy clustering neural network by combining the self-organizing Kohonen clustering network with the fuzzy theory is proposed. This network model is designed for the effectiveness evaluation of electronic countermeasures, which not only exerts the advantages of the fuzzy theory, but also has a good ability in machine learning and data analysis. The subjective value of sample versus class is computed by the fuzzy computing theory, and the classified results obtained by self-organizing learning of Kohonen neural network are represented on output layer. Meanwhile, the fuzzy competition learning algorithm keeps the similar information between samples and overcomes the disadvantages of neural network which has fewer samples. The simulation result indicates that the proposed algorithm is feasible and effective.

Self-Organized Public-Key Management for Mobile Ad Hoc Networks Based on a Bidirectional Trust Model

In traditional networks , the authentication is performed by certificate authoritys（CA）,which can＇t be built in distributed mobile Ad Hoc Networks however. In this pa per, we propose a fully self-organized public key management based on bidirectional trust model without any centralized authority that allows users to generate their public-private key pairs, to issue certificates, and the trust relation spreads rationally according to the truly human relations. In contrast with the traditional self-organized public-key management, the average certificates paths get more short, the authentication passing rate gets more high and the most important is that the bidirectional trust based model satisfys the trust re quirement of hosts better.

A Fairness Resource Allocation Algorithm for Coverage and Capacity Optimization in Wireless Self-Organized Network

To achieve the higher resource efficiency, Coverage and Capacity Optimization(CCO) as an important role of the network self-healing and self-optimization, has become a focus topic in wireless Self-Organized Network(SON). In this paper, a novel CCO scheme is proposed to maximize utility function of the integrated coverage and capacity. It starts with the analysis on the throughput proportional fairness(PF) algorithm and then proposes the novel Coverage and Capacity Proportional Fairness(CCPF) allocation algorithm along with a proof of the algorithms convergence. This proposed algorithm is applied in a coverage capacity optimization scheme which can guarantee the reasonable network capacity by the coverage range accommodation. Next, we simulate the proposed CCO scheme based on telecom operators' real network data and compare with three typical resource allocation algorithms: round robin(RR), proportional fairness(PF) and max C/I. In comparison of the PF algorithm, the numerical results show that our algorithm increases the average throughput by 1.54 and 1.96 times with constructed theoretical data and derived real network data respectively.