Protein aggregates,mitochondrial import stress and neurodegenerative disorders:A salient hallmark of several neurodegenerative diseases,including Parkinson’s disease,is the abundance of protein aggregates(Goiran et a...Protein aggregates,mitochondrial import stress and neurodegenerative disorders:A salient hallmark of several neurodegenerative diseases,including Parkinson’s disease,is the abundance of protein aggregates(Goiran et al.,2022).This molecular event is believed to lead to activation of stress pathways ultimately resulting in cellular dysfunction(Eldeeb et al.,2022).Accordingly,many lines of research investigations focused on dampening the formation of protein aggregates or augmenting the clearance of protein aggregates as a potential therapeutic strategy to counteract the progression of neurodegenerative diseases,albeit with little success(Costa-Mattioli and Walter,2020).Cell stress cues such as the accumulation of protein aggregates lead to the activation of stress response pathways that aid cells in responding to the damage.Despite the notion that the transient activation of these pathways helps cells cope with stressors,persistent activation can induce unwanted apoptosis of cells and reduce overall tissue strength as well as lead to an accumulation of aggregation-prone proteins(Hetz and Papa,2018).Mutations in proteins involved in stress signaling termination can cause conditions like ataxia and early-onset dementia(Conroy et al.,2014).Therefore,it is crucial for stress response signaling to be turned off once conditions have improved.Nevertheless,the mechanisms by which cells silence these signals are still elusive.展开更多
Studies on plant diversity are usually based on the total number of species in a community.However,few studies have examined species richness(SR)of different plant life forms in a community along largescale environmen...Studies on plant diversity are usually based on the total number of species in a community.However,few studies have examined species richness(SR)of different plant life forms in a community along largescale environmental gradients.Particularly,the relative importance(RIV)of different plant life forms in a community and how they vary with environmental variables are still unclear.To fill these gaps,we determined plant diversity of ephemeral plants,annual herbs,perennial herbs,and woody plants from 187 sites across drylands in China.The SR patterns of herbaceous plants,especially perennial herbs,and their RIV in plant communities increased with increasing precipitation and soil nutrient content;however,the RIV of annual herbs was not altered along these gradients.The SR and RIV of ephemeral plants were affected mainly by precipitation seasonality.The SR of woody plants had a unimodal relationship with air temperature and exhibited the highest RIV and SR percentage in plant communities under the harshest environments.An obvious shift emerged in plant community composition,SR and their critical impact factors at 238.5 mm of mean annual precipitation(MAP).In mesic regions(>238.5 mm),herbs were the dominant species,and the SR displayed a relatively slow decreasing rate with increasing aridity,which was mediated mainly by MAP and soil nutrients.In arid regions(<238.5 mm),woody plants were the dominant species,and the SR displayed a relatively fast decreasing rate with increasing aridity,which was mediated mainly by climate variables,especially precipitation.Our findings highlight the importance of comparative life form studies in community structure and biodiversity,as their responses to gradients differed substantially on a large scale.展开更多
The traditional academic warning methods for students in higher vocational colleges are relatively backward,single,and have many influencing factors,which have a limited effect on improving their learning ability.A da...The traditional academic warning methods for students in higher vocational colleges are relatively backward,single,and have many influencing factors,which have a limited effect on improving their learning ability.A data set was established by collecting academic warning data of students in a certain university.The importance of the school,major,grade,and warning level for the students was analyzed using the Pearson correlation coefficient,random forest variable importance,and permutation importance.It was found that the characteristic of the major has a great impact on the academic warning level.Countermeasures such as dynamic adjustment of majors,reform of cognitive adaptation of courses,full-cycle academic support,and data-driven precise intervention were proposed to provide theoretical support and practical paths for universities to improve the efficiency of academic warning and enhance students’learning ability.展开更多
The authors regret that the original publication of this paper did not include Jawad Fayaz as a co-author.After further discussions and a thorough review of the research contributions,it was agreed that his significan...The authors regret that the original publication of this paper did not include Jawad Fayaz as a co-author.After further discussions and a thorough review of the research contributions,it was agreed that his significant contributions to the foundational aspects of the research warranted recognition,and he has now been added as a co-author.展开更多
Human pose estimation is a challenging task in computer vision.Most algorithms perform well in regular scenes,but lack good performance in occlusion scenarios.Therefore,we propose a multi-type feature fusion network b...Human pose estimation is a challenging task in computer vision.Most algorithms perform well in regular scenes,but lack good performance in occlusion scenarios.Therefore,we propose a multi-type feature fusion network based on importance weighting,which consists of three modules.In the first module,we propose a multi-resolution backbone with two feature enhancement sub-modules,which can extract features from different scales and enhance the feature expression ability.In the second module,we enhance the expressiveness of keypoint features by suppressing obstacle features and compensating for the unique and shared attributes of keypoints and topology.In the third module,we perform importance weighting on the adjacency matrix to enable it to describe the correlation among nodes,thereby improving the feature extraction ability.We conduct comparative experiments on the keypoint detection datasets of common objects in Context 2017(COCO2017),COCO-Wholebody and CrowdPose,achieving the accuracy of 78.9%,67.1%and 77.6%,respectively.Additionally,a series of ablation experiments are designed to show the performance of our work.Finally,we present the visualization of different scenarios to verify the effectiveness of our work.展开更多
Nirmal et al.presented a machine learning-based design of ternary organic solar cells,utilizing feature importance[1].This paper highlights the alarming potential biases in the use of feature importance in machine lea...Nirmal et al.presented a machine learning-based design of ternary organic solar cells,utilizing feature importance[1].This paper highlights the alarming potential biases in the use of feature importance in machine learning,which can lead to incorrect conclusions and outcomes.Many scientists and researchers including Nirmal et al.are unaware that feature importances in machine learning in general are model-specific and do not necessarily represent true associations between the target and features.展开更多
In this paper, we use sample average approximation with adaptive multiple importance sampling to explore moderate deviations for the optimal values. Utilizing the moderate deviation principle for martingale difference...In this paper, we use sample average approximation with adaptive multiple importance sampling to explore moderate deviations for the optimal values. Utilizing the moderate deviation principle for martingale differences and an appropriate Delta method, we establish a moderate deviation principle for the optimal value. Moreover, for a functional form of stochastic programming, we obtain a functional moderate deviation principle for its optimal value.展开更多
The condensation tracking algorithm uses a prior transition probability as the proposal distribution, which does not make full use of the current observation. In order to overcome this shortcoming, a new face tracking...The condensation tracking algorithm uses a prior transition probability as the proposal distribution, which does not make full use of the current observation. In order to overcome this shortcoming, a new face tracking algorithm based on particle filter with mean shift importance sampling is proposed. First, the coarse location of the face target is attained by the efficient mean shift tracker, and then the result is used to construct the proposal distribution for particle propagation. Because the particles obtained with this method can cluster around the true state region, particle efficiency is improved greatly. The experimental results show that the performance of the proposed algorithm is better than that of the standard condensation tracking algorithm.展开更多
Importance measures in reliability systems are used to identify weak components in contributing to a proper function of the system.Traditional importance measures mainly concerned the changing value of the system reli...Importance measures in reliability systems are used to identify weak components in contributing to a proper function of the system.Traditional importance measures mainly concerned the changing value of the system reliability caused by the change of the reliability of the component,and seldom considered the joint effect of the probability distribution,improvement rate of the object component.This paper studies the rate of the system reliability upgrading with an improvement of the component reliability for the multi-state consecutive k-out-of-n system.To verify the multi-state consecutive k-out-of-n system reliability upgrading by improving one component based on its improvement rate,an increasing potential importance(IPI)and its physical meaning are described at first.Secondly,the relationship between the IPI and Birnbaum importance measures are discussed.And the IPI for some different improvement actions of the component is further discussed.Thirdly,the characteristics of the IPI are analyzed.Finally,an application to an oil pipeline system is given.展开更多
文摘Protein aggregates,mitochondrial import stress and neurodegenerative disorders:A salient hallmark of several neurodegenerative diseases,including Parkinson’s disease,is the abundance of protein aggregates(Goiran et al.,2022).This molecular event is believed to lead to activation of stress pathways ultimately resulting in cellular dysfunction(Eldeeb et al.,2022).Accordingly,many lines of research investigations focused on dampening the formation of protein aggregates or augmenting the clearance of protein aggregates as a potential therapeutic strategy to counteract the progression of neurodegenerative diseases,albeit with little success(Costa-Mattioli and Walter,2020).Cell stress cues such as the accumulation of protein aggregates lead to the activation of stress response pathways that aid cells in responding to the damage.Despite the notion that the transient activation of these pathways helps cells cope with stressors,persistent activation can induce unwanted apoptosis of cells and reduce overall tissue strength as well as lead to an accumulation of aggregation-prone proteins(Hetz and Papa,2018).Mutations in proteins involved in stress signaling termination can cause conditions like ataxia and early-onset dementia(Conroy et al.,2014).Therefore,it is crucial for stress response signaling to be turned off once conditions have improved.Nevertheless,the mechanisms by which cells silence these signals are still elusive.
基金supported by the National Key Research and Development Program of China(2023YFF0805602)National Natural Science Foundation of China(32225032,32001192,32271597)+1 种基金the Innovation Base Project of Gansu Province(2021YFF0703904)the Science and Technology Program of Gansu Province(24JRRA515,22JR5RA525,23JRRA1157).
文摘Studies on plant diversity are usually based on the total number of species in a community.However,few studies have examined species richness(SR)of different plant life forms in a community along largescale environmental gradients.Particularly,the relative importance(RIV)of different plant life forms in a community and how they vary with environmental variables are still unclear.To fill these gaps,we determined plant diversity of ephemeral plants,annual herbs,perennial herbs,and woody plants from 187 sites across drylands in China.The SR patterns of herbaceous plants,especially perennial herbs,and their RIV in plant communities increased with increasing precipitation and soil nutrient content;however,the RIV of annual herbs was not altered along these gradients.The SR and RIV of ephemeral plants were affected mainly by precipitation seasonality.The SR of woody plants had a unimodal relationship with air temperature and exhibited the highest RIV and SR percentage in plant communities under the harshest environments.An obvious shift emerged in plant community composition,SR and their critical impact factors at 238.5 mm of mean annual precipitation(MAP).In mesic regions(>238.5 mm),herbs were the dominant species,and the SR displayed a relatively slow decreasing rate with increasing aridity,which was mediated mainly by MAP and soil nutrients.In arid regions(<238.5 mm),woody plants were the dominant species,and the SR displayed a relatively fast decreasing rate with increasing aridity,which was mediated mainly by climate variables,especially precipitation.Our findings highlight the importance of comparative life form studies in community structure and biodiversity,as their responses to gradients differed substantially on a large scale.
基金supported by the Basic Ability Improvement Project of Young and Middle-Aged Teachers in Colleges and Universities of Guangxi(2022KY1922,2021KY1938).
文摘The traditional academic warning methods for students in higher vocational colleges are relatively backward,single,and have many influencing factors,which have a limited effect on improving their learning ability.A data set was established by collecting academic warning data of students in a certain university.The importance of the school,major,grade,and warning level for the students was analyzed using the Pearson correlation coefficient,random forest variable importance,and permutation importance.It was found that the characteristic of the major has a great impact on the academic warning level.Countermeasures such as dynamic adjustment of majors,reform of cognitive adaptation of courses,full-cycle academic support,and data-driven precise intervention were proposed to provide theoretical support and practical paths for universities to improve the efficiency of academic warning and enhance students’learning ability.
文摘The authors regret that the original publication of this paper did not include Jawad Fayaz as a co-author.After further discussions and a thorough review of the research contributions,it was agreed that his significant contributions to the foundational aspects of the research warranted recognition,and he has now been added as a co-author.
基金supported by Ministry of Education Industry-University Cooperation and Collaborative Education Project(China)(220603231024713).
文摘Human pose estimation is a challenging task in computer vision.Most algorithms perform well in regular scenes,but lack good performance in occlusion scenarios.Therefore,we propose a multi-type feature fusion network based on importance weighting,which consists of three modules.In the first module,we propose a multi-resolution backbone with two feature enhancement sub-modules,which can extract features from different scales and enhance the feature expression ability.In the second module,we enhance the expressiveness of keypoint features by suppressing obstacle features and compensating for the unique and shared attributes of keypoints and topology.In the third module,we perform importance weighting on the adjacency matrix to enable it to describe the correlation among nodes,thereby improving the feature extraction ability.We conduct comparative experiments on the keypoint detection datasets of common objects in Context 2017(COCO2017),COCO-Wholebody and CrowdPose,achieving the accuracy of 78.9%,67.1%and 77.6%,respectively.Additionally,a series of ablation experiments are designed to show the performance of our work.Finally,we present the visualization of different scenarios to verify the effectiveness of our work.
文摘Nirmal et al.presented a machine learning-based design of ternary organic solar cells,utilizing feature importance[1].This paper highlights the alarming potential biases in the use of feature importance in machine learning,which can lead to incorrect conclusions and outcomes.Many scientists and researchers including Nirmal et al.are unaware that feature importances in machine learning in general are model-specific and do not necessarily represent true associations between the target and features.
基金Supported by the National Natural Science Foundation of China(Grant No.12071175)。
文摘In this paper, we use sample average approximation with adaptive multiple importance sampling to explore moderate deviations for the optimal values. Utilizing the moderate deviation principle for martingale differences and an appropriate Delta method, we establish a moderate deviation principle for the optimal value. Moreover, for a functional form of stochastic programming, we obtain a functional moderate deviation principle for its optimal value.
基金The National Natural Science Foundation of China(No60672094)
文摘The condensation tracking algorithm uses a prior transition probability as the proposal distribution, which does not make full use of the current observation. In order to overcome this shortcoming, a new face tracking algorithm based on particle filter with mean shift importance sampling is proposed. First, the coarse location of the face target is attained by the efficient mean shift tracker, and then the result is used to construct the proposal distribution for particle propagation. Because the particles obtained with this method can cluster around the true state region, particle efficiency is improved greatly. The experimental results show that the performance of the proposed algorithm is better than that of the standard condensation tracking algorithm.
基金supported by the National Natural Science Foundation of China(7127117071101116)+1 种基金the National High Technology Research and Development Program of China(863 Progrom)(2012AA040914)the Basic Research Foundation of Northwestern Polytechnical University(JC20120228)
文摘Importance measures in reliability systems are used to identify weak components in contributing to a proper function of the system.Traditional importance measures mainly concerned the changing value of the system reliability caused by the change of the reliability of the component,and seldom considered the joint effect of the probability distribution,improvement rate of the object component.This paper studies the rate of the system reliability upgrading with an improvement of the component reliability for the multi-state consecutive k-out-of-n system.To verify the multi-state consecutive k-out-of-n system reliability upgrading by improving one component based on its improvement rate,an increasing potential importance(IPI)and its physical meaning are described at first.Secondly,the relationship between the IPI and Birnbaum importance measures are discussed.And the IPI for some different improvement actions of the component is further discussed.Thirdly,the characteristics of the IPI are analyzed.Finally,an application to an oil pipeline system is given.
