A Yb:CaGd_(0.33)Y_(0.625)AlO_(4)(Yb:CGYA)laser crystal of high optical quality has been successfully synthesized via the Czochralski method.The introduction of Gd^(3+)ions preserves the original structure and efficien...A Yb:CaGd_(0.33)Y_(0.625)AlO_(4)(Yb:CGYA)laser crystal of high optical quality has been successfully synthesized via the Czochralski method.The introduction of Gd^(3+)ions preserves the original structure and efficiently generates inhomogeneous broadening of the Yb^(3+)ion emission spectra.The fluorescence emission peak wavelength of the Yb:CGYA crystal is 1053 nm,and the corresponding measured full width at halfmaximum is 93 nm.A tunable laser output ranging from 1017 nm to 1073 nm is achieved by using a birefringent filter,which represents the broadest tuning range reported in a short cavity to date.The compact laser offers significant advantages for its applications around the 1μm wavelength band.展开更多
Large language models(LLMs)and natural language processing(NLP)have significant promise to improve efficiency and refine healthcare decision-making and clinical results.Numerous domains,including healthcare,are rapidl...Large language models(LLMs)and natural language processing(NLP)have significant promise to improve efficiency and refine healthcare decision-making and clinical results.Numerous domains,including healthcare,are rapidly adopting LLMs for the classification of biomedical textual data in medical research.The LLM can derive insights from intricate,extensive,unstructured training data.Variants need to be accurately identified and classified to advance genetic research,provide individualized treatment,and assist physicians in making better choices.However,the sophisticated and perplexing language of medical reports is often beyond the capabilities of the devices we now utilize.Such an approach may result in incorrect diagnoses,which could affect a patient’s prognosis and course of therapy.This study evaluated the efficacy of the proposed model by looking at publicly accessible textual clinical data.We have cleaned the clinical textual data using various text preprocessing methods,including stemming,tokenization,and stop word removal.The important features are extracted using Bag of Words(BoW)and Term Frequency-Inverse Document Frequency(TFIDF)feature engineering methods.The important motive of this study is to predict the genetic variants based on the clinical evidence using a novel method with minimal error.According to the experimental results,the random forest model achieved 61%accuracy with 67%precision for class 9 using TFIDF features and 63%accuracy and a 73%F1 score for class 9 using Bag of Words features.The accuracy of the proposed BERT(Bidirectional Encoder Representations from Transformers)model was 70%with 5-fold cross-validation and 71%with 10-fold cross-validation.The research results provide a comprehensive overview of current LLM methods in healthcare,benefiting academics as well as professionals in the discipline.展开更多
With the advancement of deep learning in the automotive domain,more and more researchers are focusing on autonomous driving.Among these tasks,free space detection is particularly crucial.Currently,many model-based app...With the advancement of deep learning in the automotive domain,more and more researchers are focusing on autonomous driving.Among these tasks,free space detection is particularly crucial.Currently,many model-based approaches have achieved autonomous driving on well-structured urban roads,but these efforts primarily focus on urban road environments.In contrast,there are fewer deep learningmethods specifically designed for off-road traversable area detection,and their effectiveness is not yet satisfactory.This is because detecting traversable areas in complex outdoor environments poses significant challenges,and current methods often rely on single-image inputs,which do not align with contemporary multimodal approaches.Therefore,in this study,we propose a CFH-Net model for off-road traversable area detection.This model employs a Transformer architecture to enhance its capability of capturing global information.For multimodal feature extraction and fusion,we integrate the CM-FRM module for feature extraction and introduce the novel FFX module for feature fusion,thereby improving the perception capability of autonomous vehicles on unstructured roads.To address upsampling,we propose a new convolution precorrection method to reduce model parameters and computational complexity while enhancing the model’s ability to capture complex features.Finally,we conducted experiments on the ORFD off-road dataset and achieved outstanding results.展开更多
Catalytic doping is one of the economic and efficient strategies to optimize the operating temperature and kinetic behavior of magnesium hydride(MgH_(2)).Herein,efficient regulation of electronic and structural rearra...Catalytic doping is one of the economic and efficient strategies to optimize the operating temperature and kinetic behavior of magnesium hydride(MgH_(2)).Herein,efficient regulation of electronic and structural rearrangements in niobium-rich nickel oxides was achieved through precise compositional design and niobium cation functionalized doping,thereby greatly enhancing its intrinsic catalytic activity in hydrogen storage systems.As the niobium concentration increased,the Ni-Nb catalysts transformed into a mixed state of multi-phase nanoparticles(composed of nickel and niobium-rich nickel oxides)with smaller particle size and uniform distribution,thus exposing more nucleation sites and diffusion channels at the MgH_(2)/Mg interface.In addition,the additional generation of active Ni-Nb-O mixed phase induced numerous highly topical disordered and distorted crystalline,promoting the transfer and reorganization of H atoms.As a result,a stable and continuous multi-phase/component synergistic catalytic microenvironment could be constructed,exerting remarkable enhancement on MgH_(2)’s hydrogen storage performance.After comparative tests,Ni_(0.7)Nb_(0.3)-doped MgH_(2) presented the optimal low-temperature kinetics with a dehydrogenation activation energy of 78.8 kJ·mol^(−1).The onset dehydrogenation temperature of MgH_(2)+10 wt%Ni_(0.7)Nb_(0.3) was reduced to 198℃ and 6.18 wt%H_(2) could be released at 250℃ within 10 min.In addition,the dehydrogenated MgH_(2)–NiNb composites absorbed 4.87 wt%H_(2) in 10 min at 125℃ and a capacity retention rate was maintained at 6.18 wt%even after 50 reaction cycles.In a word,our work supplies fresh insights for designing novel defective-state multiphase catalysts for hydrogen storage and other energy related field.展开更多
Bimetallic surfaces play a pivotal role in heterogeneous catalysis,yet their theoretical modeling has long been hindered by the computational chal-lenges of capturing configurational disorder,a critical feature govern...Bimetallic surfaces play a pivotal role in heterogeneous catalysis,yet their theoretical modeling has long been hindered by the computational chal-lenges of capturing configurational disorder,a critical feature governing their catalytic properties.Tradition-al approaches rely on oversimplified ordered surface models or restrict dis-order to a few atomic layers,limiting their predictive power.Here,we Cu_(1-x)Zn_(x)Cu_(1-x)Zn_(x)present an accurate and efficient computational framework that integrates machine learning force fields(MLFFs)with the cluster expansion(CE)method to study configurationally dis-ordered bimetallic surfaces at finite temperatures.We have developed an efficient workflow in which the MLFF is first trained iteratively via an active learning protocol,and then used to generate accurate energetic data for thousands of configurations that enable robust CE model construction.By treating bulk and surface clusters separately,we can build CE models for surface slabs with an arbitrary number of layers.Using as a case study,our CE-based Monte Carlo simulations reveal key structural insights that are relevant to the under-standing of catalytic properties of surfaces.This work demonstrates how MLFF-aided CE can overcome traditional limitations in theoretical modeling of bimetallic surfaces and highlights pathways toward more realistic modeling of heterogeneous catalysts.展开更多
Intrinsically disordered proteins(IDPs)and their regions(IDRs)play crucial roles in cellular func-tions despite their lack of stable three-dimensional structures.In this study,we investigate the interac-tions between ...Intrinsically disordered proteins(IDPs)and their regions(IDRs)play crucial roles in cellular func-tions despite their lack of stable three-dimensional structures.In this study,we investigate the interac-tions between the C-terminal do-main of protein 4.1G(4.1G CTD)and the nuclear mitotic apparatus protein(NuMA)under varying pH and salt ion conditions to under-stand the regulatory mechanisms affecting their binding.4.1G CTD and NuMA bind effec-tively under neutral and alkaline conditions,but their interaction is disrupted under acidic conditions(pH 3.6).The protonation of positively charged residues at the C-terminal of 4.1G CTD under acidic conditions leads to increased electrostatic repulsion,weakening the overall binding free energy.Secondary structure analysis shows that specific regions of 4.1G CTD re-main stable under both pH conditions,but the C-terminal region(aa 990−1000)and the N-terminal region of NuMA(aa 1800−1810)exhibit significant reductions in secondary struc-ture probability under acidic conditions.Contact map analysis and solvent-accessible surface area analysis further support these findings by showing a reduced contact probability be-tween these regions under pH 3.6.These results provide a comprehensive understanding of how pH and ionic strength regulate the binding dynamics of 4.1G CTD and NuMA,emphasiz-ing the regulatory role of electrostatic interactions.展开更多
We investigate the mixed-state entanglement between two spins embedded in the XXZ Heisenberg chain under thermal equilibrium.By deriving an analytical expression for the entanglement of two-spin thermal states and ext...We investigate the mixed-state entanglement between two spins embedded in the XXZ Heisenberg chain under thermal equilibrium.By deriving an analytical expression for the entanglement of two-spin thermal states and extending this analysis to larger spin chains,we demonstrate that mixed-state entanglement is profoundly shaped by both disorder and temperature.Our results reveal a sharp distinction between many-body localized and ergodic phases,with entanglement vanishing above diferent fnite temperature thresholds.Furthermore,by analyzing non-adjacent spins,we uncover an approximate exponential decay of entanglement with separation.This work advances the understanding of the quantum-to-classical transition by linking the entanglement properties of small subsystems to the broader thermal environment,ofering an explanation for the absence of entanglement in macroscopic systems.These fndings provide critical insights into quantum many-body physics,bridging concepts from thermalization,localization,and quantum information theory.展开更多
Lithium-rich layered oxides (LLOs) are increasingly recognized as promising cathode materials for nextgeneration high-energy-density lithium-ion batteries (LIBs).However,they suffer from voltage decay and low initial ...Lithium-rich layered oxides (LLOs) are increasingly recognized as promising cathode materials for nextgeneration high-energy-density lithium-ion batteries (LIBs).However,they suffer from voltage decay and low initial Coulombic efficiency (ICE) due to severe structural degradation caused by irreversible O release.Herein,we introduce a three-in-one strategy of increasing Ni and Mn content,along with Li/Ni disordering and TM–O covalency regulation to boost cationic and anionic redox activity simultaneously and thus enhance the electrochemical activity of LLOs.The target material,Li_(1.2)Ni_(0.168)Mn_(0.558)Co_(0.074)O_(2)(L1),exhibits an improved ICE of 87.2%and specific capacity of 293.2 mA h g^(-1)and minimal voltage decay of less than 0.53 m V cycle-1over 300 cycles at 1C,compared to Li_(1.2)Ni_(0.13)Mn_(0.54)Co_(0.13)O_(2)(Ls)(274.4 mA h g^(-1)for initial capacity,73.8%for ICE and voltage decay of 0.84 mV/cycle over 300 cycles at 1C).Theoretical calculations reveal that the density of states (DOS) area near the Fermi energy level for L1 is larger than that of Ls,indicating higher anionic and cationic redox reactivity than Ls.Moreover,L1 exhibits increased O-vacancy formation energy due to higher Li/Ni disordering of 4.76%(quantified by X-ray diffraction Rietveld refinement) and enhanced TM–O covalency,making lattice O release more difficult and thus improving electrochemical stability.The increased Li/Ni disordering also leads to more Ni^(2+)presence in the Li layer,which acts as a pillar during Li+de-embedding,improving structural stability.This research not only presents a viable approach to designing low-Co LLOs with enhanced capacity and ICE but also contributes significantly to the fundamental understanding of structural regulation in high-performance LIB cathodes.展开更多
Disordered ferromagnets with a domain structure that exhibit a hysteresis loop when driven by the external magnetic field are essential materials for modern technological applications.Therefore,the understanding and p...Disordered ferromagnets with a domain structure that exhibit a hysteresis loop when driven by the external magnetic field are essential materials for modern technological applications.Therefore,the understanding and potential for controlling the hysteresis phenomenon in thesematerials,especially concerning the disorder-induced critical behavior on the hysteresis loop,have attracted significant experimental,theoretical,and numerical research efforts.We review the challenges of the numerical modeling of physical phenomena behind the hysteresis loop critical behavior in disordered ferromagnetic systems related to the non-equilibriumstochastic dynamics of domain walls driven by external fields.Specifically,using the extended Random Field Ising Model,we present different simulation approaches and advanced numerical techniques that adequately describe the hysteresis loop shapes and the collective nature of the magnetization fluctuations associated with the criticality of the hysteresis loop for different sample shapes and varied parameters of disorder and rate of change of the external field,as well as the influence of thermal fluctuations and demagnetizing fields.The studied examples demonstrate how these numerical approaches reveal newphysical insights,providing quantitativemeasures of pertinent variables extracted from the systems’simulated or experimentally measured Barkhausen noise signals.The described computational techniques using inherent scale-invariance can be applied to the analysis of various complex systems,both quantum and classical,exhibiting non-equilibrium dynamical critical point or self-organized criticality.展开更多
There is no unified planning standard for unstructured roads,and the morphological structures of these roads are complex and varied.It is important to maintain a balance between accuracy and speed for unstructured roa...There is no unified planning standard for unstructured roads,and the morphological structures of these roads are complex and varied.It is important to maintain a balance between accuracy and speed for unstructured road extraction models.Unstructured road extraction algorithms based on deep learning have problems such as high model complexity,high computational cost,and the inability to adapt to current edge computing devices.Therefore,it is best to use lightweight network models.Considering the need for lightweight models and the characteristics of unstructured roads with different pattern shapes,such as blocks and strips,a TMB(Triple Multi-Block)feature extraction module is proposed,and the overall structure of the TMBNet network is described.The TMB module was compared with SS-nbt,Non-bottleneck-1D,and other modules via experiments.The feasibility and effectiveness of the TMB module design were proven through experiments and visualizations.The comparison experiment,using multiple convolution kernel categories,proved that the TMB module can improve the segmentation accuracy of the network.The comparison with different semantic segmentation networks demonstrates that the TMBNet network has advantages in terms of unstructured road extraction.展开更多
While some research has explored racial and ethnic differences in disordered eating, this study may be the first to examine these differences in orthorexia nervosa, involving obsessive-compulsive thoughts and behavior...While some research has explored racial and ethnic differences in disordered eating, this study may be the first to examine these differences in orthorexia nervosa, involving obsessive-compulsive thoughts and behaviors concerning healthy eating, which negatively impact one’s life. Adult participants, recruited from college courses and social media, completed an online survey with the Orthorexia Nervosa Inventory (ONI) and the Eating Attitudes Test-26 (EAT-26). Regarding racial and ethnic background, 743 were White, 249 were Hispanic, 87 were Black, 61 were Asian or Pacific Islander, and 110 were biracial/multiracial. A MANCOVA revealed that the racial and ethnic groups did not differ on the ONI subscales assessing orthorexic behaviors, impairments, and emotions, after accounting for gender, BMI, and EAT-26 total scores that were covariates. In contrast, a second MANCOVA did reveal group differences on the EAT-26 subscales, after accounting for gender, BMI, and ONI total scores that were covariates. Black participants scored significantly lower than the other racial and ethnic groups on the subscale assessing dieting behaviors characteristic of anorexia nervosa, and the subscale assessing binge-eating and purging behaviors characteristic of bulimia nervosa. Further, Hispanic participants scored significantly lower than White participants on the latter subscale. These findings suggest that while orthorexic symptomatology does not differ based on race and ethnicity, a Black race and Hispanic ethnicity may be protective factors against disordered eating, perhaps related either to cultural norms concerning body image or to the resiliency and social support among the Black and Hispanic communities.展开更多
Storm surge is often the marine disaster that poses the greatest threat to life and property in coastal areas.Accurate and timely issuance of storm surge warnings to take appropriate countermeasures is an important me...Storm surge is often the marine disaster that poses the greatest threat to life and property in coastal areas.Accurate and timely issuance of storm surge warnings to take appropriate countermeasures is an important means to reduce storm surge-related losses.Storm surge numerical models are important for storm surge forecasting.To further improve the performance of the storm surge forecast models,we developed a numerical storm surge forecast model based on an unstructured spherical centroidal Voronoi tessellation(SCVT)grid.The model is based on shallow water equations in vector-invariant form,and is discretized by Arakawa C grid.The SCVT grid can not only better describe the coastline information but also avoid rigid transitions,and it has a better global consistency by generating high-resolution grids in the key areas through transition refinement.In addition,the simulation speed of the model is accelerated by using the openACC-based GPU acceleration technology to meet the timeliness requirements of operational ensemble forecast.It only takes 37 s to simulate a day in the coastal waters of China.The newly developed storm surge model was applied to simulate typhoon-induced storm surges in the coastal waters of China.The hindcast experiments on the selected representative typhoon-induced storm surge processes indicate that the model can reasonably simulate the distribution characteristics of storm surges.The simulated maximum storm surges and their occurrence times are consistent with the observed data at the representative tide gauge stations,and the mean absolute errors are 3.5 cm and 0.6 h respectively,showing high accuracy and application prospects.展开更多
The layeredδ-MnO_(2)(dMO)is an excellent cathode material for rechargeable aqueous zinc-ion batteries owing to its large interlayer distance(~0.7 nm),high capacity,and low cost;however,such cathodes suffer from struc...The layeredδ-MnO_(2)(dMO)is an excellent cathode material for rechargeable aqueous zinc-ion batteries owing to its large interlayer distance(~0.7 nm),high capacity,and low cost;however,such cathodes suffer from structural degradation during the long-term cycling process,leading to capacity fading.In this study,a Co-doped dMO composite with reduced graphene oxide(GC-dMO)is developed using a simple cost-effective hydrothermal method.The degree of disorderness increases owing to the hetero-atom doping and graphene oxide composites.It is demonstrated that layered dMO and GC-dMO undergo a structural transition from K-birnessite to the Zn-buserite phase upon the first discharge,which enhances the intercalation of Zn^(2+)ions,H_(2)O molecules in the layered structure.The GC-dMO cathode exhibits an excellent capacity of 302 mAh g^(-1)at a current density of 100 mAg^(-1)after 100 cycles as compared with the dMO cathode(159 mAhg^(-1)).The excellent electrochemical performance of the GC-dMO cathode owing to Co-doping and graphene oxide sheets enhances the interlayer gap and disorderness,and maintains structural stability,which facilitates the easy reverse intercalation and de-intercalation of Zn^(2+)ions and H_(2)O molecules.Therefore,GC-dMO is a promising cathode material for large-scale aqueous ZIBs.展开更多
INTRODUCTION.Depressive disorders are mental illnesses that seriously affect public health.There are approximately 320 million patients with depression worldwide,accounting for 4.4% of the total disease burden.1Depres...INTRODUCTION.Depressive disorders are mental illnesses that seriously affect public health.There are approximately 320 million patients with depression worldwide,accounting for 4.4% of the total disease burden.1Depression leads to social and occupational impairment,diminished quality of life and an elevated risk of death by suicide.展开更多
Objective This study aimed to analyse the trend of the mental disorder spectrum in children and adolescents from 2014 to 2022 in one city in Central China and to provide actionable recommendations for the prevention a...Objective This study aimed to analyse the trend of the mental disorder spectrum in children and adolescents from 2014 to 2022 in one city in Central China and to provide actionable recommendations for the prevention and management of mental disorders.Methods In this hospital-based retrospective study,we utilized child and adolescent medical records data from the Wuhan Mental Health Center from January 2014 to December 2022 and examined the top 5 mental disorders(schizophrenia,depressive episode,bipolar disorder,pervasive developmental disorder,and unspecified mood disorder)that accounted for the overall proportion of patients admitted.The rank and proportion of these mental disorders,demographic characteristics and disease indicators were analysed.Results There was a significant upwards trend in the number of children and adolescents diagnosed with mental disorders over the past 9 years,with a sharp decline in 2020 due to the COVID-19 pandemic,followed by a rebound in 2021 and a sustained level above prepandemic figures by 2022.The average age of hospitalization decreased significantly from 20.7 to 16.2 years,with a marked increase in the 12-17-year-old age group.The proportion of female hospitalizations increased from 39.2%to 55.2%,with a corresponding decrease in male hospitalizations.There was a notable decrease in the proportion of schizophrenia cases and an ascent of depressive episode to the most prevalent position.Conclusion This study emphasizes the critical need for targeted interventions and resources for severe mental disorders in children and adolescents and the importance of early detection and management of mental disorders to mitigate long-term effects on well-being and development.展开更多
Abnormal expression of microRNAs is connected to brain development and disease and could provide novel biomarkers for the diagnosis and prognosis of bipolar disorder. We performed a PubMed search for microRNA biomarke...Abnormal expression of microRNAs is connected to brain development and disease and could provide novel biomarkers for the diagnosis and prognosis of bipolar disorder. We performed a PubMed search for microRNA biomarkers in bipolar disorder and found 18 original research articles on studies performed with human patients and published from January 2011 to June 2023. These studies included microRNA profiling in bloodand brain-based materials. From the studies that had validated the preliminary findings,potential candidate biomarkers for bipolar disorder in adults could be miR-140-3p,-30d-5p,-330-5p,-378a-5p,-21-3p,-330-3p,-345-5p in whole blood, miR-19b-3p,-1180-3p,-125a-5p, let-7e-5p in blood plasma, and miR-7-5p,-23b-5p,-142-3p,-221-5p,-370-3p in the blood serum. Two of the studies had investigated the changes in microRNA expression of patients with bipolar disorder receiving treatment. One showed a significant increase in plasma miR-134 compared to baseline after 4 weeks of treatment which included typical antipsychotics, atypical antipsychotics, and benzodiazepines. The other study had assessed the effects of prescribed medications which included neurotransmitter receptorsite binders(drug class B) and sedatives, hypnotics, anticonvulsants, and analgesics(drug class C) on microRNA results. The combined effects of the two drug classes increased the significance of the results for miR-219 and-29c with miR-30e-3p and-526b* acquiring significance. MicroRNAs were tested to see if they could serve as biomarkers of bipolar disorder at different clinical states of mania, depression, and euthymia. One study showed that upregulation in whole blood of miR-9-5p,-29a-3p,-106a-5p,-106b-5p,-107,-125a-3p,-125b-5p and of miR-107,-125a-3p occurred in manic and euthymic patients compared to controls, respectively, and that upregulation of miR-106a-5p,-107 was found for manic compared to euthymic patients. In two other studies using blood plasma,downregulation of miR-134 was observed in manic patients compared to controls, and dysregulation of miR-134,-152,-607,-633,-652,-155 occurred in euthymic patients compared to controls. Finally, microRNAs such as miR-34a,-34b,-34c,-137, and-140-3p,-21-3p,-30d-5p,-330-5p,-378a-5p,-134,-19b-3p were shown to have diagnostic potential in distinguishing bipolar disorder patients from schizophrenia or major depressive disorder patients, respectively. Further studies are warranted with adolescents and young adults having bipolar disorder and consideration should be given to using animal models of the disorder to investigate the effects of suppressing or overexpressing specific microRNAs.展开更多
During pregnancy,maternal immune activation(MIA),due to infection,chronic inflammatory disorders,or toxic exposures,can result in lasting health impacts on the developing fetus.MIA has been associated with an increase...During pregnancy,maternal immune activation(MIA),due to infection,chronic inflammatory disorders,or toxic exposures,can result in lasting health impacts on the developing fetus.MIA has been associated with an increased risk of neurodevelopmental disorders,such as autism spectrum disorder(ASD)in the offspring.ASD is characterized by increased repetitive and stereotyped behaviors and decreased sociability.As of 2020,1 in 36 children are diagnosed with ASD by the age of 8 years,with ASD rates continuing to increase in prevalence in USA(Tamayo et al.,2023).Post-mortem brain studies,biomarker and transcriptomic studies,and epidemiology studies have provided compelling evidence of immune dysregulation in the circulation and brain of individuals diagnosed with ASD.Currently,the etiology of ASD is largely unknown,however,genetic components and environmental factors can contribute to increased susceptibility.Maternal allergic asthma(MAA),a form of MIA,has been identified as a potential risk factor for developing neurodevelopmental disorders(Patel et al.,2020).Asthma is a chronic inflammatory condition driven by a T-helper type(TH)2 immune response.展开更多
Tropomyosin receptor kinase B(TrkB)signaling plays a pivotal role in dendritic growth and dendritic spine formation to promote learning and memory.The activity-dependent release of brain-derived neurotrophic factor at...Tropomyosin receptor kinase B(TrkB)signaling plays a pivotal role in dendritic growth and dendritic spine formation to promote learning and memory.The activity-dependent release of brain-derived neurotrophic factor at synapses binds to pre-or postsynaptic TrkB resulting in the strengthening of synapses,reflected by long-term potentiation.Postsynaptically,the association of postsynaptic density protein-95 with TrkB enhances phospholipase Cγ-Ca^(2+)/calmodulin-dependent protein kinaseⅡand phosphatidylinositol 3-kinase-mechanistic target of rapamycin signaling required for long-term potentiation.In this review,we discuss TrkB-postsynaptic density protein-95 coupling as a promising strategy to magnify brain-derived neurotrophic factor signaling towards the development of novel therapeutics for specific neurological disorders.A reduction of TrkB signaling has been observed in neurodegenerative disorders,such as Alzheimer's disease and Huntington's disease,and enhancement of postsynaptic density protein-95 association with TrkB signaling could mitigate the observed deficiency of neuronal connectivity in schizophrenia and depression.Treatment with brain-derived neurotrophic factor is problematic,due to poor pharmacokinetics,low brain penetration,and side effects resulting from activation of the p75 neurotrophin receptor or the truncated TrkB.T1 isoform.Although TrkB agonists and antibodies that activate TrkB are being intensively investigated,they cannot distinguish the multiple human TrkB splicing isoforms or cell type-specific functions.Targeting TrkB–postsynaptic density protein-95 coupling provides an alternative approach to specifically boost TrkB signaling at localized synaptic sites versus global stimulation that risks many adverse side effects.展开更多
Alcohol use disorder(AUD)is a medical condition that impairs a person's ability to stop or manage their drinking in the face of negative social,occupational,or health consequences.AUD is defined by the National In...Alcohol use disorder(AUD)is a medical condition that impairs a person's ability to stop or manage their drinking in the face of negative social,occupational,or health consequences.AUD is defined by the National Institute on Alcohol Abuse and Alcoholism as a"severe problem".The central nervous system is the primary target of alcohol's adverse effects.It is crucial to identify various neurological disorders associated with AUD,including alcohol withdrawal syndrome,Wernicke-Korsakoff syndrome,Marchiafava-Bignami disease,dementia,and neuropathy.To gain a better understanding of the neurological environment of alcoholism and to shed light on the role of various neurotransmitters in the phenomenon of alcoholism.A comprehensive search of online databases,including PubMed,EMBASE,Web of Science,and Google Scholar,was conducted to identify relevant articles.Several neurotransmitters(dopamine,gammaaminobutyric acid,serotonin,and glutamate)have been linked to alcoholism due to a brain imbalance.Alcoholism appears to be a complex genetic disorder,with variations in many genes influencing risk.Some of these genes have been identified,including two alcohol metabolism genes,alcohol dehydrogenase 1B gene and aldehyde dehydrogenase 2 gene,which have the most potent known effects on the risk of alcoholism.Neuronal degeneration and demyelination in people with AUD may be caused by neuronal damage,nutrient deficiencies,and blood brain barrier dysfunction;however,the underlying mechanism is unknown.This review will provide a detailed overview of the neurobiology of alcohol addiction,followed by recent studies published in the genetics of alcohol addiction,molecular mechanism and detailed information on the various acute and chronic neurological manifestations of alcoholism for the Future research.展开更多
A range of neurodegenerative disorders,collectively termed parkinsonian disorders,present with a complex array of both motor and non-motor symptoms.Included in this group are Parkinson’s disease(PD),dementia with Lew...A range of neurodegenerative disorders,collectively termed parkinsonian disorders,present with a complex array of both motor and non-motor symptoms.Included in this group are Parkinson’s disease(PD),dementia with Lewy bodies(DLB),multiple system atrophy(MSA),corticobasal syndrome(CBS),and progressive supranuclear palsy(PSP).These disorders are differentiated neuropathologically by their dominant protein pathologies involvingα-synuclein(α-syn)and/or tau,the types of brain cells affected,such as neurons,oligodendroglia,and astrocytes,and the specific brain regions involved(Tolosa et al.,2021).展开更多
文摘A Yb:CaGd_(0.33)Y_(0.625)AlO_(4)(Yb:CGYA)laser crystal of high optical quality has been successfully synthesized via the Czochralski method.The introduction of Gd^(3+)ions preserves the original structure and efficiently generates inhomogeneous broadening of the Yb^(3+)ion emission spectra.The fluorescence emission peak wavelength of the Yb:CGYA crystal is 1053 nm,and the corresponding measured full width at halfmaximum is 93 nm.A tunable laser output ranging from 1017 nm to 1073 nm is achieved by using a birefringent filter,which represents the broadest tuning range reported in a short cavity to date.The compact laser offers significant advantages for its applications around the 1μm wavelength band.
基金funded by Princess Nourah bint Abdulrahman University and Researchers Supporting Project number(PNURSP2025R346),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Large language models(LLMs)and natural language processing(NLP)have significant promise to improve efficiency and refine healthcare decision-making and clinical results.Numerous domains,including healthcare,are rapidly adopting LLMs for the classification of biomedical textual data in medical research.The LLM can derive insights from intricate,extensive,unstructured training data.Variants need to be accurately identified and classified to advance genetic research,provide individualized treatment,and assist physicians in making better choices.However,the sophisticated and perplexing language of medical reports is often beyond the capabilities of the devices we now utilize.Such an approach may result in incorrect diagnoses,which could affect a patient’s prognosis and course of therapy.This study evaluated the efficacy of the proposed model by looking at publicly accessible textual clinical data.We have cleaned the clinical textual data using various text preprocessing methods,including stemming,tokenization,and stop word removal.The important features are extracted using Bag of Words(BoW)and Term Frequency-Inverse Document Frequency(TFIDF)feature engineering methods.The important motive of this study is to predict the genetic variants based on the clinical evidence using a novel method with minimal error.According to the experimental results,the random forest model achieved 61%accuracy with 67%precision for class 9 using TFIDF features and 63%accuracy and a 73%F1 score for class 9 using Bag of Words features.The accuracy of the proposed BERT(Bidirectional Encoder Representations from Transformers)model was 70%with 5-fold cross-validation and 71%with 10-fold cross-validation.The research results provide a comprehensive overview of current LLM methods in healthcare,benefiting academics as well as professionals in the discipline.
文摘With the advancement of deep learning in the automotive domain,more and more researchers are focusing on autonomous driving.Among these tasks,free space detection is particularly crucial.Currently,many model-based approaches have achieved autonomous driving on well-structured urban roads,but these efforts primarily focus on urban road environments.In contrast,there are fewer deep learningmethods specifically designed for off-road traversable area detection,and their effectiveness is not yet satisfactory.This is because detecting traversable areas in complex outdoor environments poses significant challenges,and current methods often rely on single-image inputs,which do not align with contemporary multimodal approaches.Therefore,in this study,we propose a CFH-Net model for off-road traversable area detection.This model employs a Transformer architecture to enhance its capability of capturing global information.For multimodal feature extraction and fusion,we integrate the CM-FRM module for feature extraction and introduce the novel FFX module for feature fusion,thereby improving the perception capability of autonomous vehicles on unstructured roads.To address upsampling,we propose a new convolution precorrection method to reduce model parameters and computational complexity while enhancing the model’s ability to capture complex features.Finally,we conducted experiments on the ORFD off-road dataset and achieved outstanding results.
基金financial supports from the National Key R&D Program of China(2023YFB3809103)the National Natural Science Foundation of China(U23A20128).
文摘Catalytic doping is one of the economic and efficient strategies to optimize the operating temperature and kinetic behavior of magnesium hydride(MgH_(2)).Herein,efficient regulation of electronic and structural rearrangements in niobium-rich nickel oxides was achieved through precise compositional design and niobium cation functionalized doping,thereby greatly enhancing its intrinsic catalytic activity in hydrogen storage systems.As the niobium concentration increased,the Ni-Nb catalysts transformed into a mixed state of multi-phase nanoparticles(composed of nickel and niobium-rich nickel oxides)with smaller particle size and uniform distribution,thus exposing more nucleation sites and diffusion channels at the MgH_(2)/Mg interface.In addition,the additional generation of active Ni-Nb-O mixed phase induced numerous highly topical disordered and distorted crystalline,promoting the transfer and reorganization of H atoms.As a result,a stable and continuous multi-phase/component synergistic catalytic microenvironment could be constructed,exerting remarkable enhancement on MgH_(2)’s hydrogen storage performance.After comparative tests,Ni_(0.7)Nb_(0.3)-doped MgH_(2) presented the optimal low-temperature kinetics with a dehydrogenation activation energy of 78.8 kJ·mol^(−1).The onset dehydrogenation temperature of MgH_(2)+10 wt%Ni_(0.7)Nb_(0.3) was reduced to 198℃ and 6.18 wt%H_(2) could be released at 250℃ within 10 min.In addition,the dehydrogenated MgH_(2)–NiNb composites absorbed 4.87 wt%H_(2) in 10 min at 125℃ and a capacity retention rate was maintained at 6.18 wt%even after 50 reaction cycles.In a word,our work supplies fresh insights for designing novel defective-state multiphase catalysts for hydrogen storage and other energy related field.
基金supported by the National Natural Science Foundation of China(No.22273002)the National Key Research and Development Program of China(No.2022YFB4101401).We acknowledge the High-performance Computing Platform of Peking University for providing the computational facility.
文摘Bimetallic surfaces play a pivotal role in heterogeneous catalysis,yet their theoretical modeling has long been hindered by the computational chal-lenges of capturing configurational disorder,a critical feature governing their catalytic properties.Tradition-al approaches rely on oversimplified ordered surface models or restrict dis-order to a few atomic layers,limiting their predictive power.Here,we Cu_(1-x)Zn_(x)Cu_(1-x)Zn_(x)present an accurate and efficient computational framework that integrates machine learning force fields(MLFFs)with the cluster expansion(CE)method to study configurationally dis-ordered bimetallic surfaces at finite temperatures.We have developed an efficient workflow in which the MLFF is first trained iteratively via an active learning protocol,and then used to generate accurate energetic data for thousands of configurations that enable robust CE model construction.By treating bulk and surface clusters separately,we can build CE models for surface slabs with an arbitrary number of layers.Using as a case study,our CE-based Monte Carlo simulations reveal key structural insights that are relevant to the under-standing of catalytic properties of surfaces.This work demonstrates how MLFF-aided CE can overcome traditional limitations in theoretical modeling of bimetallic surfaces and highlights pathways toward more realistic modeling of heterogeneous catalysts.
基金supported by the National Natural Science Foundation of China(No.22073018,No.22377015).
文摘Intrinsically disordered proteins(IDPs)and their regions(IDRs)play crucial roles in cellular func-tions despite their lack of stable three-dimensional structures.In this study,we investigate the interac-tions between the C-terminal do-main of protein 4.1G(4.1G CTD)and the nuclear mitotic apparatus protein(NuMA)under varying pH and salt ion conditions to under-stand the regulatory mechanisms affecting their binding.4.1G CTD and NuMA bind effec-tively under neutral and alkaline conditions,but their interaction is disrupted under acidic conditions(pH 3.6).The protonation of positively charged residues at the C-terminal of 4.1G CTD under acidic conditions leads to increased electrostatic repulsion,weakening the overall binding free energy.Secondary structure analysis shows that specific regions of 4.1G CTD re-main stable under both pH conditions,but the C-terminal region(aa 990−1000)and the N-terminal region of NuMA(aa 1800−1810)exhibit significant reductions in secondary struc-ture probability under acidic conditions.Contact map analysis and solvent-accessible surface area analysis further support these findings by showing a reduced contact probability be-tween these regions under pH 3.6.These results provide a comprehensive understanding of how pH and ionic strength regulate the binding dynamics of 4.1G CTD and NuMA,emphasiz-ing the regulatory role of electrostatic interactions.
基金supported by the National Natural Science Foundation of China(Grant Nos.92365202,12475011,and 11921005)the National Key R&D Program of China(Grant No.2024YFA1409002)Shanghai Municipal Science and Technology Major Project(Grant No.2019SHZDZX01)。
文摘We investigate the mixed-state entanglement between two spins embedded in the XXZ Heisenberg chain under thermal equilibrium.By deriving an analytical expression for the entanglement of two-spin thermal states and extending this analysis to larger spin chains,we demonstrate that mixed-state entanglement is profoundly shaped by both disorder and temperature.Our results reveal a sharp distinction between many-body localized and ergodic phases,with entanglement vanishing above diferent fnite temperature thresholds.Furthermore,by analyzing non-adjacent spins,we uncover an approximate exponential decay of entanglement with separation.This work advances the understanding of the quantum-to-classical transition by linking the entanglement properties of small subsystems to the broader thermal environment,ofering an explanation for the absence of entanglement in macroscopic systems.These fndings provide critical insights into quantum many-body physics,bridging concepts from thermalization,localization,and quantum information theory.
基金National Natural Science Foundation of China (No.52202046)Natural Science Foundation of Shaanxi Province (No.2021JQ-034)。
文摘Lithium-rich layered oxides (LLOs) are increasingly recognized as promising cathode materials for nextgeneration high-energy-density lithium-ion batteries (LIBs).However,they suffer from voltage decay and low initial Coulombic efficiency (ICE) due to severe structural degradation caused by irreversible O release.Herein,we introduce a three-in-one strategy of increasing Ni and Mn content,along with Li/Ni disordering and TM–O covalency regulation to boost cationic and anionic redox activity simultaneously and thus enhance the electrochemical activity of LLOs.The target material,Li_(1.2)Ni_(0.168)Mn_(0.558)Co_(0.074)O_(2)(L1),exhibits an improved ICE of 87.2%and specific capacity of 293.2 mA h g^(-1)and minimal voltage decay of less than 0.53 m V cycle-1over 300 cycles at 1C,compared to Li_(1.2)Ni_(0.13)Mn_(0.54)Co_(0.13)O_(2)(Ls)(274.4 mA h g^(-1)for initial capacity,73.8%for ICE and voltage decay of 0.84 mV/cycle over 300 cycles at 1C).Theoretical calculations reveal that the density of states (DOS) area near the Fermi energy level for L1 is larger than that of Ls,indicating higher anionic and cationic redox reactivity than Ls.Moreover,L1 exhibits increased O-vacancy formation energy due to higher Li/Ni disordering of 4.76%(quantified by X-ray diffraction Rietveld refinement) and enhanced TM–O covalency,making lattice O release more difficult and thus improving electrochemical stability.The increased Li/Ni disordering also leads to more Ni^(2+)presence in the Li layer,which acts as a pillar during Li+de-embedding,improving structural stability.This research not only presents a viable approach to designing low-Co LLOs with enhanced capacity and ICE but also contributes significantly to the fundamental understanding of structural regulation in high-performance LIB cathodes.
基金Djordje Spasojevic and Svetislav Mijatovic acknowledge the support from the Ministry of Science,TechnologicalDevelopment and Innovation of the Republic of Serbia(Agreement No.451-03-65/2024-03/200162)S.J.ibid.(Agreement No.451-03-65/2024-03/200122)Bosiljka Tadic from the Slovenian Research Agency(program P1-0044).
文摘Disordered ferromagnets with a domain structure that exhibit a hysteresis loop when driven by the external magnetic field are essential materials for modern technological applications.Therefore,the understanding and potential for controlling the hysteresis phenomenon in thesematerials,especially concerning the disorder-induced critical behavior on the hysteresis loop,have attracted significant experimental,theoretical,and numerical research efforts.We review the challenges of the numerical modeling of physical phenomena behind the hysteresis loop critical behavior in disordered ferromagnetic systems related to the non-equilibriumstochastic dynamics of domain walls driven by external fields.Specifically,using the extended Random Field Ising Model,we present different simulation approaches and advanced numerical techniques that adequately describe the hysteresis loop shapes and the collective nature of the magnetization fluctuations associated with the criticality of the hysteresis loop for different sample shapes and varied parameters of disorder and rate of change of the external field,as well as the influence of thermal fluctuations and demagnetizing fields.The studied examples demonstrate how these numerical approaches reveal newphysical insights,providing quantitativemeasures of pertinent variables extracted from the systems’simulated or experimentally measured Barkhausen noise signals.The described computational techniques using inherent scale-invariance can be applied to the analysis of various complex systems,both quantum and classical,exhibiting non-equilibrium dynamical critical point or self-organized criticality.
基金Supported by National Natural Science Foundation of China(Grant Nos.62261160575,61991414,61973036)Technical Field Foundation of the National Defense Science and Technology 173 Program of China(Grant Nos.20220601053,20220601030)。
文摘There is no unified planning standard for unstructured roads,and the morphological structures of these roads are complex and varied.It is important to maintain a balance between accuracy and speed for unstructured road extraction models.Unstructured road extraction algorithms based on deep learning have problems such as high model complexity,high computational cost,and the inability to adapt to current edge computing devices.Therefore,it is best to use lightweight network models.Considering the need for lightweight models and the characteristics of unstructured roads with different pattern shapes,such as blocks and strips,a TMB(Triple Multi-Block)feature extraction module is proposed,and the overall structure of the TMBNet network is described.The TMB module was compared with SS-nbt,Non-bottleneck-1D,and other modules via experiments.The feasibility and effectiveness of the TMB module design were proven through experiments and visualizations.The comparison experiment,using multiple convolution kernel categories,proved that the TMB module can improve the segmentation accuracy of the network.The comparison with different semantic segmentation networks demonstrates that the TMBNet network has advantages in terms of unstructured road extraction.
文摘While some research has explored racial and ethnic differences in disordered eating, this study may be the first to examine these differences in orthorexia nervosa, involving obsessive-compulsive thoughts and behaviors concerning healthy eating, which negatively impact one’s life. Adult participants, recruited from college courses and social media, completed an online survey with the Orthorexia Nervosa Inventory (ONI) and the Eating Attitudes Test-26 (EAT-26). Regarding racial and ethnic background, 743 were White, 249 were Hispanic, 87 were Black, 61 were Asian or Pacific Islander, and 110 were biracial/multiracial. A MANCOVA revealed that the racial and ethnic groups did not differ on the ONI subscales assessing orthorexic behaviors, impairments, and emotions, after accounting for gender, BMI, and EAT-26 total scores that were covariates. In contrast, a second MANCOVA did reveal group differences on the EAT-26 subscales, after accounting for gender, BMI, and ONI total scores that were covariates. Black participants scored significantly lower than the other racial and ethnic groups on the subscale assessing dieting behaviors characteristic of anorexia nervosa, and the subscale assessing binge-eating and purging behaviors characteristic of bulimia nervosa. Further, Hispanic participants scored significantly lower than White participants on the latter subscale. These findings suggest that while orthorexic symptomatology does not differ based on race and ethnicity, a Black race and Hispanic ethnicity may be protective factors against disordered eating, perhaps related either to cultural norms concerning body image or to the resiliency and social support among the Black and Hispanic communities.
基金The National Natural Science Foundation of China under contract No.42076214.
文摘Storm surge is often the marine disaster that poses the greatest threat to life and property in coastal areas.Accurate and timely issuance of storm surge warnings to take appropriate countermeasures is an important means to reduce storm surge-related losses.Storm surge numerical models are important for storm surge forecasting.To further improve the performance of the storm surge forecast models,we developed a numerical storm surge forecast model based on an unstructured spherical centroidal Voronoi tessellation(SCVT)grid.The model is based on shallow water equations in vector-invariant form,and is discretized by Arakawa C grid.The SCVT grid can not only better describe the coastline information but also avoid rigid transitions,and it has a better global consistency by generating high-resolution grids in the key areas through transition refinement.In addition,the simulation speed of the model is accelerated by using the openACC-based GPU acceleration technology to meet the timeliness requirements of operational ensemble forecast.It only takes 37 s to simulate a day in the coastal waters of China.The newly developed storm surge model was applied to simulate typhoon-induced storm surges in the coastal waters of China.The hindcast experiments on the selected representative typhoon-induced storm surge processes indicate that the model can reasonably simulate the distribution characteristics of storm surges.The simulated maximum storm surges and their occurrence times are consistent with the observed data at the representative tide gauge stations,and the mean absolute errors are 3.5 cm and 0.6 h respectively,showing high accuracy and application prospects.
基金supported by the National Research Foundation of Korea(NRF)grants funded by the Korean Government(NRF-2021R1A4A1030318,NRF-2022R1C1C1011386,NRF-2020M3H4A1A03084258)supported by the"Regional Innovation Strategy(RIS)"through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS-003)
文摘The layeredδ-MnO_(2)(dMO)is an excellent cathode material for rechargeable aqueous zinc-ion batteries owing to its large interlayer distance(~0.7 nm),high capacity,and low cost;however,such cathodes suffer from structural degradation during the long-term cycling process,leading to capacity fading.In this study,a Co-doped dMO composite with reduced graphene oxide(GC-dMO)is developed using a simple cost-effective hydrothermal method.The degree of disorderness increases owing to the hetero-atom doping and graphene oxide composites.It is demonstrated that layered dMO and GC-dMO undergo a structural transition from K-birnessite to the Zn-buserite phase upon the first discharge,which enhances the intercalation of Zn^(2+)ions,H_(2)O molecules in the layered structure.The GC-dMO cathode exhibits an excellent capacity of 302 mAh g^(-1)at a current density of 100 mAg^(-1)after 100 cycles as compared with the dMO cathode(159 mAhg^(-1)).The excellent electrochemical performance of the GC-dMO cathode owing to Co-doping and graphene oxide sheets enhances the interlayer gap and disorderness,and maintains structural stability,which facilitates the easy reverse intercalation and de-intercalation of Zn^(2+)ions and H_(2)O molecules.Therefore,GC-dMO is a promising cathode material for large-scale aqueous ZIBs.
基金funded by the Construction Project of the"Flagship"Department of Chinese and Western Medicine Coordination(LiuL/2024-221)the 2024 Medical Service and Security Capacity Improvement Project(National Clinical Key Specialty Construction)(LiuL/Huwei Medical/2024-65)+5 种基金the Shanghai Traditional Chinese Medicine Standardization Project(LiuL/No.2023JSP03)the Shanghai Key Discipline Construction Project of Traditional Chinese Medicine(Clinical)(LiuL/2024-No.3)the Shanghai Technical Standardization Management and Promotion Project(LiuL/No.SHDC22023212)the Shanghai Municipal Health Commission Traditional Chinese Medicine Research Project(2022)(LiuL/No.2022Cx004)Clinical research project of Shanghai Health Commission-Youth Project(LW/No.20214Y0056)Shanghai Institute of Traditional Chinese Medicine for Mental Health(LW/No.SZB2023201).
文摘INTRODUCTION.Depressive disorders are mental illnesses that seriously affect public health.There are approximately 320 million patients with depression worldwide,accounting for 4.4% of the total disease burden.1Depression leads to social and occupational impairment,diminished quality of life and an elevated risk of death by suicide.
文摘Objective This study aimed to analyse the trend of the mental disorder spectrum in children and adolescents from 2014 to 2022 in one city in Central China and to provide actionable recommendations for the prevention and management of mental disorders.Methods In this hospital-based retrospective study,we utilized child and adolescent medical records data from the Wuhan Mental Health Center from January 2014 to December 2022 and examined the top 5 mental disorders(schizophrenia,depressive episode,bipolar disorder,pervasive developmental disorder,and unspecified mood disorder)that accounted for the overall proportion of patients admitted.The rank and proportion of these mental disorders,demographic characteristics and disease indicators were analysed.Results There was a significant upwards trend in the number of children and adolescents diagnosed with mental disorders over the past 9 years,with a sharp decline in 2020 due to the COVID-19 pandemic,followed by a rebound in 2021 and a sustained level above prepandemic figures by 2022.The average age of hospitalization decreased significantly from 20.7 to 16.2 years,with a marked increase in the 12-17-year-old age group.The proportion of female hospitalizations increased from 39.2%to 55.2%,with a corresponding decrease in male hospitalizations.There was a notable decrease in the proportion of schizophrenia cases and an ascent of depressive episode to the most prevalent position.Conclusion This study emphasizes the critical need for targeted interventions and resources for severe mental disorders in children and adolescents and the importance of early detection and management of mental disorders to mitigate long-term effects on well-being and development.
文摘Abnormal expression of microRNAs is connected to brain development and disease and could provide novel biomarkers for the diagnosis and prognosis of bipolar disorder. We performed a PubMed search for microRNA biomarkers in bipolar disorder and found 18 original research articles on studies performed with human patients and published from January 2011 to June 2023. These studies included microRNA profiling in bloodand brain-based materials. From the studies that had validated the preliminary findings,potential candidate biomarkers for bipolar disorder in adults could be miR-140-3p,-30d-5p,-330-5p,-378a-5p,-21-3p,-330-3p,-345-5p in whole blood, miR-19b-3p,-1180-3p,-125a-5p, let-7e-5p in blood plasma, and miR-7-5p,-23b-5p,-142-3p,-221-5p,-370-3p in the blood serum. Two of the studies had investigated the changes in microRNA expression of patients with bipolar disorder receiving treatment. One showed a significant increase in plasma miR-134 compared to baseline after 4 weeks of treatment which included typical antipsychotics, atypical antipsychotics, and benzodiazepines. The other study had assessed the effects of prescribed medications which included neurotransmitter receptorsite binders(drug class B) and sedatives, hypnotics, anticonvulsants, and analgesics(drug class C) on microRNA results. The combined effects of the two drug classes increased the significance of the results for miR-219 and-29c with miR-30e-3p and-526b* acquiring significance. MicroRNAs were tested to see if they could serve as biomarkers of bipolar disorder at different clinical states of mania, depression, and euthymia. One study showed that upregulation in whole blood of miR-9-5p,-29a-3p,-106a-5p,-106b-5p,-107,-125a-3p,-125b-5p and of miR-107,-125a-3p occurred in manic and euthymic patients compared to controls, respectively, and that upregulation of miR-106a-5p,-107 was found for manic compared to euthymic patients. In two other studies using blood plasma,downregulation of miR-134 was observed in manic patients compared to controls, and dysregulation of miR-134,-152,-607,-633,-652,-155 occurred in euthymic patients compared to controls. Finally, microRNAs such as miR-34a,-34b,-34c,-137, and-140-3p,-21-3p,-30d-5p,-330-5p,-378a-5p,-134,-19b-3p were shown to have diagnostic potential in distinguishing bipolar disorder patients from schizophrenia or major depressive disorder patients, respectively. Further studies are warranted with adolescents and young adults having bipolar disorder and consideration should be given to using animal models of the disorder to investigate the effects of suppressing or overexpressing specific microRNAs.
基金supported by the National Institute of Environmental Health Sciences(R21ES035492,R21ES035969)National Institutes of Child Health(R01HD090214)(to PA).
文摘During pregnancy,maternal immune activation(MIA),due to infection,chronic inflammatory disorders,or toxic exposures,can result in lasting health impacts on the developing fetus.MIA has been associated with an increased risk of neurodevelopmental disorders,such as autism spectrum disorder(ASD)in the offspring.ASD is characterized by increased repetitive and stereotyped behaviors and decreased sociability.As of 2020,1 in 36 children are diagnosed with ASD by the age of 8 years,with ASD rates continuing to increase in prevalence in USA(Tamayo et al.,2023).Post-mortem brain studies,biomarker and transcriptomic studies,and epidemiology studies have provided compelling evidence of immune dysregulation in the circulation and brain of individuals diagnosed with ASD.Currently,the etiology of ASD is largely unknown,however,genetic components and environmental factors can contribute to increased susceptibility.Maternal allergic asthma(MAA),a form of MIA,has been identified as a potential risk factor for developing neurodevelopmental disorders(Patel et al.,2020).Asthma is a chronic inflammatory condition driven by a T-helper type(TH)2 immune response.
基金supported by Postdoc Fellowship from the Foundation for Angelman Syndrome Therapeutics(FT2022-005 to JM,PD2023-001 to XY,and FT2024-001 to YAH)STTR R41 MH118747(to JM)。
文摘Tropomyosin receptor kinase B(TrkB)signaling plays a pivotal role in dendritic growth and dendritic spine formation to promote learning and memory.The activity-dependent release of brain-derived neurotrophic factor at synapses binds to pre-or postsynaptic TrkB resulting in the strengthening of synapses,reflected by long-term potentiation.Postsynaptically,the association of postsynaptic density protein-95 with TrkB enhances phospholipase Cγ-Ca^(2+)/calmodulin-dependent protein kinaseⅡand phosphatidylinositol 3-kinase-mechanistic target of rapamycin signaling required for long-term potentiation.In this review,we discuss TrkB-postsynaptic density protein-95 coupling as a promising strategy to magnify brain-derived neurotrophic factor signaling towards the development of novel therapeutics for specific neurological disorders.A reduction of TrkB signaling has been observed in neurodegenerative disorders,such as Alzheimer's disease and Huntington's disease,and enhancement of postsynaptic density protein-95 association with TrkB signaling could mitigate the observed deficiency of neuronal connectivity in schizophrenia and depression.Treatment with brain-derived neurotrophic factor is problematic,due to poor pharmacokinetics,low brain penetration,and side effects resulting from activation of the p75 neurotrophin receptor or the truncated TrkB.T1 isoform.Although TrkB agonists and antibodies that activate TrkB are being intensively investigated,they cannot distinguish the multiple human TrkB splicing isoforms or cell type-specific functions.Targeting TrkB–postsynaptic density protein-95 coupling provides an alternative approach to specifically boost TrkB signaling at localized synaptic sites versus global stimulation that risks many adverse side effects.
文摘Alcohol use disorder(AUD)is a medical condition that impairs a person's ability to stop or manage their drinking in the face of negative social,occupational,or health consequences.AUD is defined by the National Institute on Alcohol Abuse and Alcoholism as a"severe problem".The central nervous system is the primary target of alcohol's adverse effects.It is crucial to identify various neurological disorders associated with AUD,including alcohol withdrawal syndrome,Wernicke-Korsakoff syndrome,Marchiafava-Bignami disease,dementia,and neuropathy.To gain a better understanding of the neurological environment of alcoholism and to shed light on the role of various neurotransmitters in the phenomenon of alcoholism.A comprehensive search of online databases,including PubMed,EMBASE,Web of Science,and Google Scholar,was conducted to identify relevant articles.Several neurotransmitters(dopamine,gammaaminobutyric acid,serotonin,and glutamate)have been linked to alcoholism due to a brain imbalance.Alcoholism appears to be a complex genetic disorder,with variations in many genes influencing risk.Some of these genes have been identified,including two alcohol metabolism genes,alcohol dehydrogenase 1B gene and aldehyde dehydrogenase 2 gene,which have the most potent known effects on the risk of alcoholism.Neuronal degeneration and demyelination in people with AUD may be caused by neuronal damage,nutrient deficiencies,and blood brain barrier dysfunction;however,the underlying mechanism is unknown.This review will provide a detailed overview of the neurobiology of alcohol addiction,followed by recent studies published in the genetics of alcohol addiction,molecular mechanism and detailed information on the various acute and chronic neurological manifestations of alcoholism for the Future research.
文摘A range of neurodegenerative disorders,collectively termed parkinsonian disorders,present with a complex array of both motor and non-motor symptoms.Included in this group are Parkinson’s disease(PD),dementia with Lewy bodies(DLB),multiple system atrophy(MSA),corticobasal syndrome(CBS),and progressive supranuclear palsy(PSP).These disorders are differentiated neuropathologically by their dominant protein pathologies involvingα-synuclein(α-syn)and/or tau,the types of brain cells affected,such as neurons,oligodendroglia,and astrocytes,and the specific brain regions involved(Tolosa et al.,2021).
