Tau plays a crucial role in several neurodegenerative diseases,collectively referred to as tauopathies.Therefore,targeting potential pathological changes in tau could enable useful therapeutic interventions.However,ta...Tau plays a crucial role in several neurodegenerative diseases,collectively referred to as tauopathies.Therefore,targeting potential pathological changes in tau could enable useful therapeutic interventions.However,tau is not an easy target because it dynamically interacts with microtubules and other cellular components,which presents a challenge for tau-targeted drugs.New cellular models could aid the development of mechanism-based tau-targeted therapies.展开更多
Roof falls due to geological conditions are major hazards in the mining industry,causing work time loss,injuries,and fatalities.There are roof fall problems caused by high horizontal stress in several largeopening lim...Roof falls due to geological conditions are major hazards in the mining industry,causing work time loss,injuries,and fatalities.There are roof fall problems caused by high horizontal stress in several largeopening limestone mines in the eastern and midwestern United States.The typical hazard management approach for this type of roof fall hazards relies heavily on visual inspections and expert knowledge.In this context,we proposed a deep learning system for detection of the roof fall hazards caused by high horizontal stress.We used images depicting hazardous and non-hazardous roof conditions to develop a convolutional neural network(CNN)for autonomous detection of hazardous roof conditions.To compensate for limited input data,we utilized a transfer learning approach.In the transfer learning approach,an already-trained network is used as a starting point for classification in a similar domain.Results show that this approach works well for classifying roof conditions as hazardous or safe,achieving a statistical accuracy of 86.4%.This result is also compared with a random forest classifier,and the deep learning approach is more successful at classification of roof conditions.However,accuracy alone is not enough to ensure a reliable hazard management system.System constraints and reliability are improved when the features used by the network are understood.Therefore,we used a deep learning interpretation technique called integrated gradients to identify the important geological features in each image for prediction.The analysis of integrated gradients shows that the system uses the same roof features as the experts do on roof fall hazards detection.The system developed in this paper demonstrates the potential of deep learning in geotechnical hazard management to complement human experts,and likely to become an essential part of autonomous operations in cases where hazard identification heavily depends on expert knowledge.Moreover,deep learning-based systems reduce expert exposure to hazardous conditions.展开更多
In this study,we systematically tested the hypothesis that during the critical developmental period of adolescence,on a macro scale,the concentrations of major excitatory and inhibitory neurotransmitters(glutamate/glu...In this study,we systematically tested the hypothesis that during the critical developmental period of adolescence,on a macro scale,the concentrations of major excitatory and inhibitory neurotransmitters(glutamate/glutamine andγ‑aminobutyric acid[GABA])in the dorsal and ventral lateral prefrontal cortex are associated with the brain’s functional connectivity and an individual’s psychopathology.Neurotransmitters were measured via magnetic resonance spectroscopy while functional connectivity was measured with resting-state fMRI(n=121).Seed-based and network-based analyses revealed associations of neurotransmitter concentrations and functional connectivities between regions/networks that are connected to prefrontal cortices via structural connections that are thought to be under dynamic development during adolescence.These regions tend to be boundary areas between functional networks.Furthermore,several connectivities were found to be associated with individual’s levels of internalizing psychopathology.These findings provide insights into specific neurochemical mechanisms underlying the brain’s macroscale functional organization,its development during adolescence,and its potential associations with symptoms associated with internalizing psychopathology.展开更多
Objectives:Virtues have been recognized as central to human flourishing and psychological well-being.This study tested whether three dispositional virtues,i.e.,mindfulness,gratitude,and forgiveness,show distinct and o...Objectives:Virtues have been recognized as central to human flourishing and psychological well-being.This study tested whether three dispositional virtues,i.e.,mindfulness,gratitude,and forgiveness,show distinct and overlapping associations with psychological distress,subjective well-being,and emotion-regulation difficulties in adults.Methods:A sample of Italian community adults(N=211;151 women,60 men;mean age=28.63,standard deviation[SD]=10.89)completed self-report questionnaires assessing mindfulness,gratitude,forgiveness,psychological distress(stress,anxiety,and depression),psychological well-being(subjective happiness,life satisfaction),and emotion regulation difficulties.Sex,age,and lifetime meditation experience were covariates.Results:Correlation analysis showed higher virtues related to lower distress and higher well-being.In multivariable models,mindfulness and gratitude uniquely predicted lower depression,anxiety,and stress,whereas forgiveness was non-significant for distress.For well-being,all three virtues were positive,unique predictors,with gratitude and forgiveness showing comparatively stronger links than mindfulness.Emotion-regulation difficulties were lower with higher mindfulness and forgiveness,whereas gratitude was non-significant.Mindfulness,gratitude,and forgiveness form a complementary virtues profile,where different virtues reinforce each other,i.e.,mindfulness and gratitude align more with reduced distress,gratitude and forgiveness with enhanced well-being,and mindfulness together with forgiveness with better emotion regulation.Conclusion:Mindfulness,gratitude,and forgiveness each contribute uniquely to mental health:mindfulness and gratitude relate more to reduced distress,gratitude and forgiveness to enhanced well-being,and mindfulness and forgiveness to better emotion regulation.Together,they form a complementary virtues profile that supports psychological flourishing and may inform future virtue-based prevention and intervention programs.展开更多
Traditionally, the optimization algorithm based on physics principles has some shortcomings such as low population diversity and susceptibility to local extrema. A new optimization algorithm based on kinetic-molecular...Traditionally, the optimization algorithm based on physics principles has some shortcomings such as low population diversity and susceptibility to local extrema. A new optimization algorithm based on kinetic-molecular theory(KMTOA) is proposed. In the KMTOA three operators are designed: attraction, repulsion and wave. The attraction operator simulates the molecular attraction, with the molecules moving towards the optimal ones, which makes possible the optimization. The repulsion operator simulates the molecular repulsion, with the molecules diverging from the optimal ones. The wave operator simulates the thermal molecules moving irregularly, which enlarges the searching spaces and increases the population diversity and global searching ability. Experimental results indicate that KMTOA prevails over other algorithms in the robustness, solution quality, population diversity and convergence speed.展开更多
文摘Tau plays a crucial role in several neurodegenerative diseases,collectively referred to as tauopathies.Therefore,targeting potential pathological changes in tau could enable useful therapeutic interventions.However,tau is not an easy target because it dynamically interacts with microtubules and other cellular components,which presents a challenge for tau-targeted drugs.New cellular models could aid the development of mechanism-based tau-targeted therapies.
基金partially supported by the National Institute for Occupational Safety and Health,contract number 0000HCCR-2019-36403。
文摘Roof falls due to geological conditions are major hazards in the mining industry,causing work time loss,injuries,and fatalities.There are roof fall problems caused by high horizontal stress in several largeopening limestone mines in the eastern and midwestern United States.The typical hazard management approach for this type of roof fall hazards relies heavily on visual inspections and expert knowledge.In this context,we proposed a deep learning system for detection of the roof fall hazards caused by high horizontal stress.We used images depicting hazardous and non-hazardous roof conditions to develop a convolutional neural network(CNN)for autonomous detection of hazardous roof conditions.To compensate for limited input data,we utilized a transfer learning approach.In the transfer learning approach,an already-trained network is used as a starting point for classification in a similar domain.Results show that this approach works well for classifying roof conditions as hazardous or safe,achieving a statistical accuracy of 86.4%.This result is also compared with a random forest classifier,and the deep learning approach is more successful at classification of roof conditions.However,accuracy alone is not enough to ensure a reliable hazard management system.System constraints and reliability are improved when the features used by the network are understood.Therefore,we used a deep learning interpretation technique called integrated gradients to identify the important geological features in each image for prediction.The analysis of integrated gradients shows that the system uses the same roof features as the experts do on roof fall hazards detection.The system developed in this paper demonstrates the potential of deep learning in geotechnical hazard management to complement human experts,and likely to become an essential part of autonomous operations in cases where hazard identification heavily depends on expert knowledge.Moreover,deep learning-based systems reduce expert exposure to hazardous conditions.
基金supported by NIMH grant R01105501(PI:MTB and BLK).
文摘In this study,we systematically tested the hypothesis that during the critical developmental period of adolescence,on a macro scale,the concentrations of major excitatory and inhibitory neurotransmitters(glutamate/glutamine andγ‑aminobutyric acid[GABA])in the dorsal and ventral lateral prefrontal cortex are associated with the brain’s functional connectivity and an individual’s psychopathology.Neurotransmitters were measured via magnetic resonance spectroscopy while functional connectivity was measured with resting-state fMRI(n=121).Seed-based and network-based analyses revealed associations of neurotransmitter concentrations and functional connectivities between regions/networks that are connected to prefrontal cortices via structural connections that are thought to be under dynamic development during adolescence.These regions tend to be boundary areas between functional networks.Furthermore,several connectivities were found to be associated with individual’s levels of internalizing psychopathology.These findings provide insights into specific neurochemical mechanisms underlying the brain’s macroscale functional organization,its development during adolescence,and its potential associations with symptoms associated with internalizing psychopathology.
基金Salvatore G.Chiarella,Alessandro Frolli,and Antonella Cavallaro have been supported by Ministero dell’Universitàe della Ricerca(Italy),grant number PROBEN_0000012(WAVE2)supported by Bial Foundation(Portugal),grant number 351/2024.
文摘Objectives:Virtues have been recognized as central to human flourishing and psychological well-being.This study tested whether three dispositional virtues,i.e.,mindfulness,gratitude,and forgiveness,show distinct and overlapping associations with psychological distress,subjective well-being,and emotion-regulation difficulties in adults.Methods:A sample of Italian community adults(N=211;151 women,60 men;mean age=28.63,standard deviation[SD]=10.89)completed self-report questionnaires assessing mindfulness,gratitude,forgiveness,psychological distress(stress,anxiety,and depression),psychological well-being(subjective happiness,life satisfaction),and emotion regulation difficulties.Sex,age,and lifetime meditation experience were covariates.Results:Correlation analysis showed higher virtues related to lower distress and higher well-being.In multivariable models,mindfulness and gratitude uniquely predicted lower depression,anxiety,and stress,whereas forgiveness was non-significant for distress.For well-being,all three virtues were positive,unique predictors,with gratitude and forgiveness showing comparatively stronger links than mindfulness.Emotion-regulation difficulties were lower with higher mindfulness and forgiveness,whereas gratitude was non-significant.Mindfulness,gratitude,and forgiveness form a complementary virtues profile,where different virtues reinforce each other,i.e.,mindfulness and gratitude align more with reduced distress,gratitude and forgiveness with enhanced well-being,and mindfulness together with forgiveness with better emotion regulation.Conclusion:Mindfulness,gratitude,and forgiveness each contribute uniquely to mental health:mindfulness and gratitude relate more to reduced distress,gratitude and forgiveness to enhanced well-being,and mindfulness and forgiveness to better emotion regulation.Together,they form a complementary virtues profile that supports psychological flourishing and may inform future virtue-based prevention and intervention programs.
基金Project(61174140)supported by the National Natural Science Foundation of ChinaProject(13JJA002)supported by Hunan Provincial Natural Science Foundation,ChinaProject(20110161110035)supported by the Doctoral Fund of Ministry of Education of China
文摘Traditionally, the optimization algorithm based on physics principles has some shortcomings such as low population diversity and susceptibility to local extrema. A new optimization algorithm based on kinetic-molecular theory(KMTOA) is proposed. In the KMTOA three operators are designed: attraction, repulsion and wave. The attraction operator simulates the molecular attraction, with the molecules moving towards the optimal ones, which makes possible the optimization. The repulsion operator simulates the molecular repulsion, with the molecules diverging from the optimal ones. The wave operator simulates the thermal molecules moving irregularly, which enlarges the searching spaces and increases the population diversity and global searching ability. Experimental results indicate that KMTOA prevails over other algorithms in the robustness, solution quality, population diversity and convergence speed.
