We are writing in response to the article titled“Addressing the needs and rights of sex workers for HIV healthcare services in the Philippines”[1].The article calls for attention on the significant challenges faced ...We are writing in response to the article titled“Addressing the needs and rights of sex workers for HIV healthcare services in the Philippines”[1].The article calls for attention on the significant challenges faced by sex workers in the Philippines in accessing HIV healthcare.We appreciate the article’s effort to examine these issues in depth.We would like to present a constant flow of thoughts in this letter while highlighting the positive aspects,potential obstacles,and additional points that contribute to this ongoing discussion.展开更多
Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by cognitive impairments in the initial stage, which lead to severe cognitive dysfunction in the later stage. Action observation therapy (AOT) is...Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by cognitive impairments in the initial stage, which lead to severe cognitive dysfunction in the later stage. Action observation therapy (AOT) is a multisensory cognitive rehabilitation technique where the patient initially observes the actions and then tries to perform. The study aimed to examine the impact of AOT along with usual physiotherapy interventions to reduce depression, improve cognition and balance of a patient with AD. A 67 years old patient with AD was selected for this study because the patient has been suffering from depression, dementia, and physical dysfunction along with some other health conditions like diabetes and hypertension. Before starting intervention, a baseline assessment was done through the Beck Depression Inventory (BDI) tool, the Mini-Cog Scale, and the Berg Balance Scale (BBS). The patient received 12 sessions of AOT along with usual physiotherapy interventions thrice a week for four weeks, which included 45 minutes of each session. After four weeks of intervention, the patient demonstrated significant improvement in depression, cognition, and balance, whereas the BDI score declined from moderate 21/63 to mild 15/63 level of depression. The Mini-Cog score improved from 2/5 to 4/5, and the BBS score increased from 18/56 to 37/56. It is concluded that AOT along with usual physiotherapy intervention helps to reduce depression, improve cognition and balance of people with AD.展开更多
In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal ...In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal graph.Most GCNs define the graph topology by physical relations of the human joints.However,this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs,resulting in a low recognition rate for specific actions with implicit correlation between joint pairs.In addition,existing methods ignore the trend correlation between adjacent frames within an action and context clues,leading to erroneous action recognition with similar poses.Therefore,this study proposes a learnable GCN based on behavior dependence,which considers implicit joint correlation by constructing a dynamic learnable graph with extraction of specific behavior dependence of joint pairs.By using the weight relationship between the joint pairs,an adaptive model is constructed.It also designs a self-attention module to obtain their inter-frame topological relationship for exploring the context of actions.Combining the shared topology and the multi-head self-attention map,the module obtains the context-based clue topology to update the dynamic graph convolution,achieving accurate recognition of different actions with similar poses.Detailed experiments on public datasets demonstrate that the proposed method achieves better results and realizes higher quality representation of actions under various evaluation protocols compared to state-of-the-art methods.展开更多
Atractylodis Rhizoma comes from the dry rhizome of Atractylis lancea or Atractylodes chinensis in the Compositae family,and it is suitable for preventing and treating diseases such as cold,edema,night blindness and rh...Atractylodis Rhizoma comes from the dry rhizome of Atractylis lancea or Atractylodes chinensis in the Compositae family,and it is suitable for preventing and treating diseases such as cold,edema,night blindness and rheumatic arthralgia.Atractylodin is the main active component extracted and isolated from Atractylodis Rhizoma.A large number of studies have found that atractylodin has excellent drug activity in improving gastrointestinal emptying,anti-inflammation,inhibiting malignant tumor and reducing blood lipid.In this paper,the purification process and pharmacological activity of Atractylodin were summarized to provide a theoretical basis for basic research,clinical application and further development and utilization of atractylodin.展开更多
The Dirac equation γ<sub>μ</sub>(δ<sub>μ</sub>-eA<sub>μ</sub>)Ψ=mc<sup>2</sup>Ψ describes the bound states of the electron under the action of external potentials...The Dirac equation γ<sub>μ</sub>(δ<sub>μ</sub>-eA<sub>μ</sub>)Ψ=mc<sup>2</sup>Ψ describes the bound states of the electron under the action of external potentials, A<sub>μ</sub>. We assumed that the fundamental form of the Dirac equation γ<sub>μ</sub>(δ<sub>μ</sub>-S<sub>μ</sub>)Ψ=0 should describe the stable particles (the electron, the proton and the dark-matter-particle (dmp)) bound to themselves under the action of their own potentials S<sub>μ</sub>. The new equation reveals that self energy is consequence of self action, it also reveals that the spin angular momentum is consequence of the dynamic structure of the stable particles. The quantitative results are the determination of their relative masses as well as the determination of the electromagnetic coupling constant.展开更多
Lithium-sulfur battery(LSB)has brought much attention and concern because of high theoretical specific capacity and energy density as one of main competitors for next-generation energy storage systems.The widely comme...Lithium-sulfur battery(LSB)has brought much attention and concern because of high theoretical specific capacity and energy density as one of main competitors for next-generation energy storage systems.The widely commercial application and development of LSB is mainly hindered by serious“shuttle effect”of lithium polysulfides(Li PSs),slow reaction kinetics,notorious lithium dendrites,etc.In various structures of LSB materials,array structured materials,possessing the composition of ordered micro units with the same or similar characteristics of each unit,present excellent application potential for various secondary cells due to some merits such as immobilization of active substances,high specific surface area,appropriate pore sizes,easy modification of functional material surface,accommodated huge volume change,enough facilitated transportation for electrons/lithium ions,and special functional groups strongly adsorbing Li PSs.Thus many novel array structured materials are applied to battery for tackling thorny problems mentioned above.In this review,recent progresses and developments on array structured materials applied in LSBs including preparation ways,collaborative structural designs based on array structures,and action mechanism analyses in improving electrochemical performance and safety are summarized.Meanwhile,we also have detailed discussion for array structured materials in LSBs and constructed the structure-function relationships between array structured materials and battery performances.Lastly,some directions and prospects about preparation ways,functional modifications,and practical applications of array structured materials in LSBs are generalized.We hope the review can attract more researchers'attention and bring more studying on array structured materials for other secondary batteries including LSB.展开更多
Background:In 2021,the American College of Medical Genetics and Genomics(ACMG)recommended reporting actionable genotypes in 73 genes associated with diseases for which preventive or therapeutic measures are available....Background:In 2021,the American College of Medical Genetics and Genomics(ACMG)recommended reporting actionable genotypes in 73 genes associated with diseases for which preventive or therapeutic measures are available.Evaluations of the association of actionable genotypes in these genes with life span are currently lacking.Methods:We assessed the prevalence of coding and splice variants in genes on the ACMG Secondary Findings,version 3.0(ACMG SF v3.0),list in the genomes of 57,933 Icelanders.We assigned pathogenicity to all reviewed variants using reported evidence in the ClinVar database,the frequency of variants,and their associations with disease to create a manually curated set of actionable genotypes(variants).We assessed the relationship between these genotypes and life span and further examined the specific causes of death among carriers.展开更多
Background:In 2021,the American College of Medical Genetics and Genomics(ACMG)recommended reporting actionable genotypes in 73 genes associated with diseases for which preventive or therapeutic measures are available....Background:In 2021,the American College of Medical Genetics and Genomics(ACMG)recommended reporting actionable genotypes in 73 genes associated with diseases for which preventive or therapeutic measures are available.Evaluations of the association of actionable genotypes in these genes with life span are currently lacking.展开更多
This article deals with the investigation of the effects of seismic impacts on the design and dimensioning of structures in South Kivu. The starting point is the observation of an ambivalence that can be observed in t...This article deals with the investigation of the effects of seismic impacts on the design and dimensioning of structures in South Kivu. The starting point is the observation of an ambivalence that can be observed in the province, namely the non-consideration of seismic action in the study of structures by both professionals and researchers. The main objective of the study is to show the importance of dynamic analysis of structures in South Kivu. It adopts a meta-analytical approach referring to previous researches on South Kivu and proposes an efficient and optimal method. To arrive at the results, we use Eurocode 7 and 8. In addition, we conducted static analysis using the Coulomb method and dynamic analysis using the Mononobe-Okabe method and compared the results. At Nyabibwe, the results showed that we have a deviation of 24.47% for slip stability, 12.038% for overturning stability and 9.677% for stability against punching through a weight wall.展开更多
In South Africa,there is a saying,“I am because we are!”This ancient philosophy called ubuntu explains that our humanity is enriched through shared experiences,compassion and a sense of responsibility to others.Simi...In South Africa,there is a saying,“I am because we are!”This ancient philosophy called ubuntu explains that our humanity is enriched through shared experiences,compassion and a sense of responsibility to others.Similarly,the concepts of harmony,interconnectedness and mutual respect are deeply ingrained in the philosophy,values and culture of the Chinese people.展开更多
Under the combined influence of climate change and human activities,vegetation ecosystem has undergone profound changes.It can be seen that there are obvious differences in the evolution patterns and driving mechanism...Under the combined influence of climate change and human activities,vegetation ecosystem has undergone profound changes.It can be seen that there are obvious differences in the evolution patterns and driving mechanisms of vegetation ecosystem in different historical periods.Therefore,it is urgent to identify and reveal the dominant factors and their contribution rates in the vegetation change cycle.Based on the data of climate elements(sunshine hours,precipitation and temperature),human activities(population intensity and GDP intensity)and other natural factors(altitude,slope and aspect),this study explored the spatial and temporal evolution patterns of vegetation NDVI in the Yellow River Basin of China from 1989 to 2019 through a residual method,a trend analysis,and a gravity center model,and quantitatively distinguished the relative actions of climate change and human activities on vegetation evolution based on Geodetector model.The results showed that the spatial distribution of vegetation NDVI in the Yellow River Basin showed a decreasing trend from southeast to northwest.During 1981-2019,the temporal variation of vegetation NDVI showed an overall increasing trend.The gravity centers of average vegetation NDVI during the study period was distributed in Zhenyuan County,Gansu Province,and the center moved northeastwards from 1981 to 2019.During 1981-2000 and 2001-2019,the proportion of vegetation restoration areas promoted by the combined action of climate change and human activities was the largest.During the study period(1981-2019),the dominant factors influencing vegetation NDVI shifted from natural factors to human activities.These results could provide decision support for the protection and restoration of vegetation ecosystem in the Yellow River Basin.展开更多
Action recognition and detection is an important research topic in computer vision,which can be divided into action recognition and action detection.At present,the distinction between action recognition and action det...Action recognition and detection is an important research topic in computer vision,which can be divided into action recognition and action detection.At present,the distinction between action recognition and action detection is not clear,and the relevant reviews are not comprehensive.Thus,this paper summarized the action recognition and detection methods and datasets based on deep learning to accurately present the research status in this field.Firstly,according to the way that temporal and spatial features are extracted from the model,the commonly used models of action recognition are divided into the two stream models,the temporal models,the spatiotemporal models and the transformer models according to the architecture.And this paper briefly analyzes the characteristics of the four models and introduces the accuracy of various algorithms in common data sets.Then,from the perspective of tasks to be completed,action detection is further divided into temporal action detection and spatiotemporal action detection,and commonly used datasets are introduced.From the perspectives of the twostage method and one-stage method,various algorithms of temporal action detection are reviewed,and the various algorithms of spatiotemporal action detection are summarized in detail.Finally,the relationship between different parts of action recognition and detection is discussed,the difficulties faced by the current research are summarized in detail,and future development was prospected。展开更多
Recently,we have found that various intracellular cyclic adenosine monophosphate(cAMP)-e leva ting agents,both pharmacological(dibutyryl-cAMP,forskolin,and rolipram)and physiological(pituitary adenylate cyclase-activa...Recently,we have found that various intracellular cyclic adenosine monophosphate(cAMP)-e leva ting agents,both pharmacological(dibutyryl-cAMP,forskolin,and rolipram)and physiological(pituitary adenylate cyclase-activating polypeptide),decrease cell-surface levels of 67-kDa laminin receptor(67LR)and cellular prion protein(PrP~C).展开更多
There is growing evidence for the use of social prescribing as a means of facilitating healthy lifestyle behavior changes by linking patients to sources of support in the community. However, there are gaps in understa...There is growing evidence for the use of social prescribing as a means of facilitating healthy lifestyle behavior changes by linking patients to sources of support in the community. However, there are gaps in understanding about what works to facilitate and enable this behavior change i.e. , the mechanisms of actions underpinning social prescribing delivery. This study used a qualitative approach involving interviews with 18 Social Prescription Link Workers (SPLWs). Reflective thematic analysis was used to analyze the data. Through this, an overall theme of “Theoretical underpinning: Solution-focused and strengths-based” was identified. This was made up of sub-themes relating to mechanisms underpinning: the SPLWs’ role (comprising asking questions, motivational interviewing and the therapeutic alliance);the patients’ role, (empowerment and active engagement);and the approach adopted (solution building, goal and action orientated). To formalize this, a framework could be developed for SPLWs that encapsulates the solution-focused strengths-based approach for application within social prescribing. This framework can then be embedded into practice facilitating more successful healthy lifestyle behavior change for social prescribing patients.展开更多
Background: Breast cancer is the most frequently diagnosed cancer among women, making it a significant global public health issue. Although developed countries have a higher incidence of breast cancer, developing coun...Background: Breast cancer is the most frequently diagnosed cancer among women, making it a significant global public health issue. Although developed countries have a higher incidence of breast cancer, developing countries like Nigeria have a low incidence rate but higher rates of morbidity and mortality from the disease. Objective: This study explored how cues to action influenced the use of immunohistochemistry by women in Imo State and how many of them used immunohistochemistry (IHC) to diagnose their condition. Methodology: We used a descriptive study methodology for the investigation, and 121 participants were chosen at random from the 891 total population. The respondents were chosen through a systematic sampling procedure. Structured questionnaire was used as the data collection tool, and the statistical software for social science (SPSS) was used to analyze the data. Results: The mean age of the 121 participants in the study was 45.2 ± 0.7 years. The study found that awareness of IHC services is a significant influencing factor for the uptake and health personnel is the major source of awareness of IHC services. Cues to action particularly from health workers, as well as knowledge of any victim of breast cancer and user of IHC services, also significantly influenced uptake of the IHC services (P Conclusion: In our study, utilization of IHC services was also impacted by cues to action, particularly from healthcare professionals, as well as awareness of any breast cancer victims and those who used IHC services thus cues to action is a major predicator of immunohistochemistry uptake among women with breast cancer.展开更多
Based on the mass action concentration theory,a novel thermodynamic analysis for the raw material ratio in the procedure of preparing Ti-6Al-4V alloy by aluminothermic reduction process is proposed in this paper,which...Based on the mass action concentration theory,a novel thermodynamic analysis for the raw material ratio in the procedure of preparing Ti-6Al-4V alloy by aluminothermic reduction process is proposed in this paper,which is originated from TiO_(2),Al particles,and V_(2)O_(5) as feedstocks,and the relevant equilibrium thermodynamics was calculated through this new method.The results show that the range of aluminum addition coefficient in raw material to experiment should be controlled within 61.5%-100%,which can significantly reduce the number of experimental groups.This method is ready to regulate the matter of excessive aluminum content in reactants for materials preparation,especially for those reactions including elements that are effortless to combine with aluminum to form the corresponding intermetallics or alloys.In addition,it can also be used in general metallurgy or material preparation process to effectively predict the composition and proportion of equilibrium components under certain conditions.展开更多
Human action recognition(HAR)attempts to understand a subject’sbehavior and assign a label to each action performed.It is more appealingbecause it has a wide range of applications in computer vision,such asvideo surv...Human action recognition(HAR)attempts to understand a subject’sbehavior and assign a label to each action performed.It is more appealingbecause it has a wide range of applications in computer vision,such asvideo surveillance and smart cities.Many attempts have been made in theliterature to develop an effective and robust framework for HAR.Still,theprocess remains difficult and may result in reduced accuracy due to severalchallenges,such as similarity among actions,extraction of essential features,and reduction of irrelevant features.In this work,we proposed an end-toendframework using deep learning and an improved tree seed optimizationalgorithm for accurate HAR.The proposed design consists of a fewsignificantsteps.In the first step,frame preprocessing is performed.In the second step,two pre-trained deep learning models are fine-tuned and trained throughdeep transfer learning using preprocessed video frames.In the next step,deeplearning features of both fine-tuned models are fused using a new ParallelStandard Deviation Padding Max Value approach.The fused features arefurther optimized using an improved tree seed algorithm,and select the bestfeatures are finally classified by using the machine learning classifiers.Theexperiment was carried out on five publicly available datasets,including UTInteraction,Weizmann,KTH,Hollywood,and IXAMS,and achieved higheraccuracy than previous techniques.展开更多
The development of artificial intelligence(AI)and smart home technologies has driven the need for speech recognition-based solutions.This demand stems from the quest for more intuitive and natural interaction between ...The development of artificial intelligence(AI)and smart home technologies has driven the need for speech recognition-based solutions.This demand stems from the quest for more intuitive and natural interaction between users and smart devices in their homes.Speech recognition allows users to control devices and perform everyday actions through spoken commands,eliminating the need for physical interfaces or touch screens and enabling specific tasks such as turning on or off the light,heating,or lowering the blinds.The purpose of this study is to develop a speech-based classification model for recognizing human actions in the smart home.It seeks to demonstrate the effectiveness and feasibility of using machine learning techniques in predicting categories,subcategories,and actions from sentences.A dataset labeled with relevant information about categories,subcategories,and actions related to human actions in the smart home is used.The methodology uses machine learning techniques implemented in Python,extracting features using CountVectorizer to convert sentences into numerical representations.The results show that the classification model is able to accurately predict categories,subcategories,and actions based on sentences,with 82.99%accuracy for category,76.19%accuracy for subcategory,and 90.28%accuracy for action.The study concludes that using machine learning techniques is effective for recognizing and classifying human actions in the smart home,supporting its feasibility in various scenarios and opening new possibilities for advanced natural language processing systems in the field of AI and smart homes.展开更多
文摘We are writing in response to the article titled“Addressing the needs and rights of sex workers for HIV healthcare services in the Philippines”[1].The article calls for attention on the significant challenges faced by sex workers in the Philippines in accessing HIV healthcare.We appreciate the article’s effort to examine these issues in depth.We would like to present a constant flow of thoughts in this letter while highlighting the positive aspects,potential obstacles,and additional points that contribute to this ongoing discussion.
文摘Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by cognitive impairments in the initial stage, which lead to severe cognitive dysfunction in the later stage. Action observation therapy (AOT) is a multisensory cognitive rehabilitation technique where the patient initially observes the actions and then tries to perform. The study aimed to examine the impact of AOT along with usual physiotherapy interventions to reduce depression, improve cognition and balance of a patient with AD. A 67 years old patient with AD was selected for this study because the patient has been suffering from depression, dementia, and physical dysfunction along with some other health conditions like diabetes and hypertension. Before starting intervention, a baseline assessment was done through the Beck Depression Inventory (BDI) tool, the Mini-Cog Scale, and the Berg Balance Scale (BBS). The patient received 12 sessions of AOT along with usual physiotherapy interventions thrice a week for four weeks, which included 45 minutes of each session. After four weeks of intervention, the patient demonstrated significant improvement in depression, cognition, and balance, whereas the BDI score declined from moderate 21/63 to mild 15/63 level of depression. The Mini-Cog score improved from 2/5 to 4/5, and the BBS score increased from 18/56 to 37/56. It is concluded that AOT along with usual physiotherapy intervention helps to reduce depression, improve cognition and balance of people with AD.
基金supported in part by the 2023 Key Supported Project of the 14th Five Year Plan for Education and Science in Hunan Province with No.ND230795.
文摘In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal graph.Most GCNs define the graph topology by physical relations of the human joints.However,this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs,resulting in a low recognition rate for specific actions with implicit correlation between joint pairs.In addition,existing methods ignore the trend correlation between adjacent frames within an action and context clues,leading to erroneous action recognition with similar poses.Therefore,this study proposes a learnable GCN based on behavior dependence,which considers implicit joint correlation by constructing a dynamic learnable graph with extraction of specific behavior dependence of joint pairs.By using the weight relationship between the joint pairs,an adaptive model is constructed.It also designs a self-attention module to obtain their inter-frame topological relationship for exploring the context of actions.Combining the shared topology and the multi-head self-attention map,the module obtains the context-based clue topology to update the dynamic graph convolution,achieving accurate recognition of different actions with similar poses.Detailed experiments on public datasets demonstrate that the proposed method achieves better results and realizes higher quality representation of actions under various evaluation protocols compared to state-of-the-art methods.
基金Supported by Innovation and Entrepreneurship Project for College Students in Heilongjiang Province(S202210223119)the Central Fund Support for the Talent Training Project of Local University Reform and Development(2020GSP16).
文摘Atractylodis Rhizoma comes from the dry rhizome of Atractylis lancea or Atractylodes chinensis in the Compositae family,and it is suitable for preventing and treating diseases such as cold,edema,night blindness and rheumatic arthralgia.Atractylodin is the main active component extracted and isolated from Atractylodis Rhizoma.A large number of studies have found that atractylodin has excellent drug activity in improving gastrointestinal emptying,anti-inflammation,inhibiting malignant tumor and reducing blood lipid.In this paper,the purification process and pharmacological activity of Atractylodin were summarized to provide a theoretical basis for basic research,clinical application and further development and utilization of atractylodin.
文摘The Dirac equation γ<sub>μ</sub>(δ<sub>μ</sub>-eA<sub>μ</sub>)Ψ=mc<sup>2</sup>Ψ describes the bound states of the electron under the action of external potentials, A<sub>μ</sub>. We assumed that the fundamental form of the Dirac equation γ<sub>μ</sub>(δ<sub>μ</sub>-S<sub>μ</sub>)Ψ=0 should describe the stable particles (the electron, the proton and the dark-matter-particle (dmp)) bound to themselves under the action of their own potentials S<sub>μ</sub>. The new equation reveals that self energy is consequence of self action, it also reveals that the spin angular momentum is consequence of the dynamic structure of the stable particles. The quantitative results are the determination of their relative masses as well as the determination of the electromagnetic coupling constant.
基金This work was supported by the National Natural Science Foundation of China(52203066,51973157,61904123)the Tianjin Natural Science Foundation(18JCQNJC02900)+3 种基金the National innovation and entrepreneurship training program for college students(202310058007)the Tianjin Municipal college students’innovation and entrepreneurship training program(202310058088)the Science&Technology Development Fund of Tianjin Education Commission for Higher Education(Grant No.2018KJ196)the State Key Laboratory of Membrane and Membrane Separation,Tiangong University.
文摘Lithium-sulfur battery(LSB)has brought much attention and concern because of high theoretical specific capacity and energy density as one of main competitors for next-generation energy storage systems.The widely commercial application and development of LSB is mainly hindered by serious“shuttle effect”of lithium polysulfides(Li PSs),slow reaction kinetics,notorious lithium dendrites,etc.In various structures of LSB materials,array structured materials,possessing the composition of ordered micro units with the same or similar characteristics of each unit,present excellent application potential for various secondary cells due to some merits such as immobilization of active substances,high specific surface area,appropriate pore sizes,easy modification of functional material surface,accommodated huge volume change,enough facilitated transportation for electrons/lithium ions,and special functional groups strongly adsorbing Li PSs.Thus many novel array structured materials are applied to battery for tackling thorny problems mentioned above.In this review,recent progresses and developments on array structured materials applied in LSBs including preparation ways,collaborative structural designs based on array structures,and action mechanism analyses in improving electrochemical performance and safety are summarized.Meanwhile,we also have detailed discussion for array structured materials in LSBs and constructed the structure-function relationships between array structured materials and battery performances.Lastly,some directions and prospects about preparation ways,functional modifications,and practical applications of array structured materials in LSBs are generalized.We hope the review can attract more researchers'attention and bring more studying on array structured materials for other secondary batteries including LSB.
文摘Background:In 2021,the American College of Medical Genetics and Genomics(ACMG)recommended reporting actionable genotypes in 73 genes associated with diseases for which preventive or therapeutic measures are available.Evaluations of the association of actionable genotypes in these genes with life span are currently lacking.Methods:We assessed the prevalence of coding and splice variants in genes on the ACMG Secondary Findings,version 3.0(ACMG SF v3.0),list in the genomes of 57,933 Icelanders.We assigned pathogenicity to all reviewed variants using reported evidence in the ClinVar database,the frequency of variants,and their associations with disease to create a manually curated set of actionable genotypes(variants).We assessed the relationship between these genotypes and life span and further examined the specific causes of death among carriers.
文摘Background:In 2021,the American College of Medical Genetics and Genomics(ACMG)recommended reporting actionable genotypes in 73 genes associated with diseases for which preventive or therapeutic measures are available.Evaluations of the association of actionable genotypes in these genes with life span are currently lacking.
文摘This article deals with the investigation of the effects of seismic impacts on the design and dimensioning of structures in South Kivu. The starting point is the observation of an ambivalence that can be observed in the province, namely the non-consideration of seismic action in the study of structures by both professionals and researchers. The main objective of the study is to show the importance of dynamic analysis of structures in South Kivu. It adopts a meta-analytical approach referring to previous researches on South Kivu and proposes an efficient and optimal method. To arrive at the results, we use Eurocode 7 and 8. In addition, we conducted static analysis using the Coulomb method and dynamic analysis using the Mononobe-Okabe method and compared the results. At Nyabibwe, the results showed that we have a deviation of 24.47% for slip stability, 12.038% for overturning stability and 9.677% for stability against punching through a weight wall.
文摘In South Africa,there is a saying,“I am because we are!”This ancient philosophy called ubuntu explains that our humanity is enriched through shared experiences,compassion and a sense of responsibility to others.Similarly,the concepts of harmony,interconnectedness and mutual respect are deeply ingrained in the philosophy,values and culture of the Chinese people.
基金This work was supported by grants from the National Natural Science Foundation of China(42101306,4217107)the Natural Science Foundation of Shandong Province(ZR2021MD047),the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA2002040203)+2 种基金the Open Fund of the Key Laboratory of National Geographic Census and Monitoring,Ministry of Natural Resources(MNR)(2020NGCM02)the Open Fund of the Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural Resources(KF-2020-05-001)the Major Project of the High Resolution Earth Observation System of China(GFZX0404130304).
文摘Under the combined influence of climate change and human activities,vegetation ecosystem has undergone profound changes.It can be seen that there are obvious differences in the evolution patterns and driving mechanisms of vegetation ecosystem in different historical periods.Therefore,it is urgent to identify and reveal the dominant factors and their contribution rates in the vegetation change cycle.Based on the data of climate elements(sunshine hours,precipitation and temperature),human activities(population intensity and GDP intensity)and other natural factors(altitude,slope and aspect),this study explored the spatial and temporal evolution patterns of vegetation NDVI in the Yellow River Basin of China from 1989 to 2019 through a residual method,a trend analysis,and a gravity center model,and quantitatively distinguished the relative actions of climate change and human activities on vegetation evolution based on Geodetector model.The results showed that the spatial distribution of vegetation NDVI in the Yellow River Basin showed a decreasing trend from southeast to northwest.During 1981-2019,the temporal variation of vegetation NDVI showed an overall increasing trend.The gravity centers of average vegetation NDVI during the study period was distributed in Zhenyuan County,Gansu Province,and the center moved northeastwards from 1981 to 2019.During 1981-2000 and 2001-2019,the proportion of vegetation restoration areas promoted by the combined action of climate change and human activities was the largest.During the study period(1981-2019),the dominant factors influencing vegetation NDVI shifted from natural factors to human activities.These results could provide decision support for the protection and restoration of vegetation ecosystem in the Yellow River Basin.
基金supported by the National Educational Science 13th Five-Year Plan Project(JYKYB2019012)the Basic Research Fund for the Engineering University of PAP(WJY201907)the Basic Research Fund of the Engineering University of PAP(WJY202120).
文摘Action recognition and detection is an important research topic in computer vision,which can be divided into action recognition and action detection.At present,the distinction between action recognition and action detection is not clear,and the relevant reviews are not comprehensive.Thus,this paper summarized the action recognition and detection methods and datasets based on deep learning to accurately present the research status in this field.Firstly,according to the way that temporal and spatial features are extracted from the model,the commonly used models of action recognition are divided into the two stream models,the temporal models,the spatiotemporal models and the transformer models according to the architecture.And this paper briefly analyzes the characteristics of the four models and introduces the accuracy of various algorithms in common data sets.Then,from the perspective of tasks to be completed,action detection is further divided into temporal action detection and spatiotemporal action detection,and commonly used datasets are introduced.From the perspectives of the twostage method and one-stage method,various algorithms of temporal action detection are reviewed,and the various algorithms of spatiotemporal action detection are summarized in detail.Finally,the relationship between different parts of action recognition and detection is discussed,the difficulties faced by the current research are summarized in detail,and future development was prospected。
文摘There is growing evidence for the use of social prescribing as a means of facilitating healthy lifestyle behavior changes by linking patients to sources of support in the community. However, there are gaps in understanding about what works to facilitate and enable this behavior change i.e. , the mechanisms of actions underpinning social prescribing delivery. This study used a qualitative approach involving interviews with 18 Social Prescription Link Workers (SPLWs). Reflective thematic analysis was used to analyze the data. Through this, an overall theme of “Theoretical underpinning: Solution-focused and strengths-based” was identified. This was made up of sub-themes relating to mechanisms underpinning: the SPLWs’ role (comprising asking questions, motivational interviewing and the therapeutic alliance);the patients’ role, (empowerment and active engagement);and the approach adopted (solution building, goal and action orientated). To formalize this, a framework could be developed for SPLWs that encapsulates the solution-focused strengths-based approach for application within social prescribing. This framework can then be embedded into practice facilitating more successful healthy lifestyle behavior change for social prescribing patients.
文摘Background: Breast cancer is the most frequently diagnosed cancer among women, making it a significant global public health issue. Although developed countries have a higher incidence of breast cancer, developing countries like Nigeria have a low incidence rate but higher rates of morbidity and mortality from the disease. Objective: This study explored how cues to action influenced the use of immunohistochemistry by women in Imo State and how many of them used immunohistochemistry (IHC) to diagnose their condition. Methodology: We used a descriptive study methodology for the investigation, and 121 participants were chosen at random from the 891 total population. The respondents were chosen through a systematic sampling procedure. Structured questionnaire was used as the data collection tool, and the statistical software for social science (SPSS) was used to analyze the data. Results: The mean age of the 121 participants in the study was 45.2 ± 0.7 years. The study found that awareness of IHC services is a significant influencing factor for the uptake and health personnel is the major source of awareness of IHC services. Cues to action particularly from health workers, as well as knowledge of any victim of breast cancer and user of IHC services, also significantly influenced uptake of the IHC services (P Conclusion: In our study, utilization of IHC services was also impacted by cues to action, particularly from healthcare professionals, as well as awareness of any breast cancer victims and those who used IHC services thus cues to action is a major predicator of immunohistochemistry uptake among women with breast cancer.
基金the National Natural Science Foundation of China(No.U1908225)the Doctoral Scientific Research Foundation of Liaocheng University (No.318052124)。
文摘Based on the mass action concentration theory,a novel thermodynamic analysis for the raw material ratio in the procedure of preparing Ti-6Al-4V alloy by aluminothermic reduction process is proposed in this paper,which is originated from TiO_(2),Al particles,and V_(2)O_(5) as feedstocks,and the relevant equilibrium thermodynamics was calculated through this new method.The results show that the range of aluminum addition coefficient in raw material to experiment should be controlled within 61.5%-100%,which can significantly reduce the number of experimental groups.This method is ready to regulate the matter of excessive aluminum content in reactants for materials preparation,especially for those reactions including elements that are effortless to combine with aluminum to form the corresponding intermetallics or alloys.In addition,it can also be used in general metallurgy or material preparation process to effectively predict the composition and proportion of equilibrium components under certain conditions.
基金supported by“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP),granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea.(No.20204010600090).
文摘Human action recognition(HAR)attempts to understand a subject’sbehavior and assign a label to each action performed.It is more appealingbecause it has a wide range of applications in computer vision,such asvideo surveillance and smart cities.Many attempts have been made in theliterature to develop an effective and robust framework for HAR.Still,theprocess remains difficult and may result in reduced accuracy due to severalchallenges,such as similarity among actions,extraction of essential features,and reduction of irrelevant features.In this work,we proposed an end-toendframework using deep learning and an improved tree seed optimizationalgorithm for accurate HAR.The proposed design consists of a fewsignificantsteps.In the first step,frame preprocessing is performed.In the second step,two pre-trained deep learning models are fine-tuned and trained throughdeep transfer learning using preprocessed video frames.In the next step,deeplearning features of both fine-tuned models are fused using a new ParallelStandard Deviation Padding Max Value approach.The fused features arefurther optimized using an improved tree seed algorithm,and select the bestfeatures are finally classified by using the machine learning classifiers.Theexperiment was carried out on five publicly available datasets,including UTInteraction,Weizmann,KTH,Hollywood,and IXAMS,and achieved higheraccuracy than previous techniques.
基金supported by Generalitat Valenciana with HAAS(CIAICO/2021/039)the Spanish Ministry of Science and Innovation under the Project AVANTIA PID2020-114480RB-I00.
文摘The development of artificial intelligence(AI)and smart home technologies has driven the need for speech recognition-based solutions.This demand stems from the quest for more intuitive and natural interaction between users and smart devices in their homes.Speech recognition allows users to control devices and perform everyday actions through spoken commands,eliminating the need for physical interfaces or touch screens and enabling specific tasks such as turning on or off the light,heating,or lowering the blinds.The purpose of this study is to develop a speech-based classification model for recognizing human actions in the smart home.It seeks to demonstrate the effectiveness and feasibility of using machine learning techniques in predicting categories,subcategories,and actions from sentences.A dataset labeled with relevant information about categories,subcategories,and actions related to human actions in the smart home is used.The methodology uses machine learning techniques implemented in Python,extracting features using CountVectorizer to convert sentences into numerical representations.The results show that the classification model is able to accurately predict categories,subcategories,and actions based on sentences,with 82.99%accuracy for category,76.19%accuracy for subcategory,and 90.28%accuracy for action.The study concludes that using machine learning techniques is effective for recognizing and classifying human actions in the smart home,supporting its feasibility in various scenarios and opening new possibilities for advanced natural language processing systems in the field of AI and smart homes.
