Objectives:A common side effect of inflammatory bowel disease(IBD)is intestinal fibrosis,which frequently leads to intestinal blockage and stricture formation.Although Thalidomide(THD)has shown anti-fibrotic benefits ...Objectives:A common side effect of inflammatory bowel disease(IBD)is intestinal fibrosis,which frequently leads to intestinal blockage and stricture formation.Although Thalidomide(THD)has shown anti-fibrotic benefits in hepatic and renal models,little is known about how it affects intestinal fibrosis and the underlying processes.The present research examines the molecular targets of THD and its potential as a treatment for intestinal fibrosis brought on by colitis.Methods:Clinical samples from Crohn’s disease(CD)patients with intestinal strictures treated with infliximab(IFX)and THD combined with IFX were collected.Dextran sulfate sodium(DSS)was used to develop a mouse model of intestinal fibrosis in C57BL/6 mice.Anti-tumor necrosis factor-alpha(Anti-TNFα),THD,or a combination of the two were administered to the mice.Body weight,colon length,histology,and disease activity index were used to evaluate the disease’s severity.In vitro,THD was tested on colonic fibroblast lines(CCD-18Co and MPF)to assess its effects on cell proliferation,motility,and transdifferentiation.To examine changes in gene expression and signaling pathway modifications,namely in the phosphoinositide 3-kinase/protein kinase B/mammalian target of rapamycin(PI3K/AKT/mTOR)pathway,RNA sequencing,qRT-PCR,and Western blotting were carried out.Results:In DSS-induced colitis,THD therapy lowered fibrosis,as seen by downregulated fibrotic markers(α-smooth muscle actin(α-SMA),collagen I,and collagen III)and decreased collagen deposition.Mechanistically,THD prevented fibroblasts from transdifferentiating and decreased their vitality.Furthermore,THD inhitited the PI3K/AKT/mTOR pathway in vivo and in vitro.Conclusion:THD inhibits the PI3K/AKT/mTOR signaling cascade and suppresses colonic fibroblast transdifferentiation,which protects against DSS-induced colitis-associated fibrosis,especially when combined with anti-TNFαtherapy.展开更多
Visible-infrared object detection leverages the day-night stable object perception capability of infrared images to enhance detection robustness in low-light environments by fusing the complementary information of vis...Visible-infrared object detection leverages the day-night stable object perception capability of infrared images to enhance detection robustness in low-light environments by fusing the complementary information of visible and infrared images.However,the inherent differences in the imaging mechanisms of visible and infrared modalities make effective cross-modal fusion challenging.Furthermore,constrained by the physical characteristics of sensors and thermal diffusion effects,infrared images generally suffer from blurred object contours and missing details,making it difficult to extract object features effectively.To address these issues,we propose an infrared-visible image fusion network that realizesmultimodal information fusion of infrared and visible images through a carefully designedmultiscale fusion strategy.First,we design an adaptive gray-radiance enhancement(AGRE)module to strengthen the detail representation in infrared images,improving their usability in complex lighting scenarios.Next,we introduce a channelspatial feature interaction(CSFI)module,which achieves efficient complementarity between the RGB and infrared(IR)modalities via dynamic channel switching and a spatial attention mechanism.Finally,we propose a multi-scale enhanced cross-attention fusion(MSECA)module,which optimizes the fusion ofmulti-level features through dynamic convolution and gating mechanisms and captures long-range complementary relationships of cross-modal features on a global scale,thereby enhancing the expressiveness of the fused features.Experiments on the KAIST,M3FD,and FLIR datasets demonstrate that our method delivers outstanding performance in daytime and nighttime scenarios.On the KAIST dataset,the miss rate drops to 5.99%,and further to 4.26% in night scenes.On the FLIR and M3FD datasets,it achieves AP50 scores of 79.4% and 88.9%,respectively.展开更多
Multi-modal fusion technology gradually become a fundamental task in many fields,such as autonomous driving,smart healthcare,sentiment analysis,and human-computer interaction.It is rapidly becoming the dominant resear...Multi-modal fusion technology gradually become a fundamental task in many fields,such as autonomous driving,smart healthcare,sentiment analysis,and human-computer interaction.It is rapidly becoming the dominant research due to its powerful perception and judgment capabilities.Under complex scenes,multi-modal fusion technology utilizes the complementary characteristics of multiple data streams to fuse different data types and achieve more accurate predictions.However,achieving outstanding performance is challenging because of equipment performance limitations,missing information,and data noise.This paper comprehensively reviews existing methods based onmulti-modal fusion techniques and completes a detailed and in-depth analysis.According to the data fusion stage,multi-modal fusion has four primary methods:early fusion,deep fusion,late fusion,and hybrid fusion.The paper surveys the three majormulti-modal fusion technologies that can significantly enhance the effect of data fusion and further explore the applications of multi-modal fusion technology in various fields.Finally,it discusses the challenges and explores potential research opportunities.Multi-modal tasks still need intensive study because of data heterogeneity and quality.Preserving complementary information and eliminating redundant information between modalities is critical in multi-modal technology.Invalid data fusion methods may introduce extra noise and lead to worse results.This paper provides a comprehensive and detailed summary in response to these challenges.展开更多
Mobile-edge computing(MEC)is a promising technology for the fifth-generation(5G)and sixth-generation(6G)architectures,which provides resourceful computing capabilities for Internet of Things(IoT)devices,such as virtua...Mobile-edge computing(MEC)is a promising technology for the fifth-generation(5G)and sixth-generation(6G)architectures,which provides resourceful computing capabilities for Internet of Things(IoT)devices,such as virtual reality,mobile devices,and smart cities.In general,these IoT applications always bring higher energy consumption than traditional applications,which are usually energy-constrained.To provide persistent energy,many references have studied the offloading problem to save energy consumption.However,the dynamic environment dramatically increases the optimization difficulty of the offloading decision.In this paper,we aim to minimize the energy consumption of the entireMECsystemunder the latency constraint by fully considering the dynamic environment.UnderMarkov games,we propose amulti-agent deep reinforcement learning approach based on the bi-level actorcritic learning structure to jointly optimize the offloading decision and resource allocation,which can solve the combinatorial optimization problem using an asymmetric method and compute the Stackelberg equilibrium as a better convergence point than Nash equilibrium in terms of Pareto superiority.Our method can better adapt to a dynamic environment during the data transmission than the single-agent strategy and can effectively tackle the coordination problem in the multi-agent environment.The simulation results show that the proposed method could decrease the total computational overhead by 17.8%compared to the actor-critic-based method and reduce the total computational overhead by 31.3%,36.5%,and 44.7%compared with randomoffloading,all local execution,and all offloading execution,respectively.展开更多
This study aimed to elucidate the potential mechanisms through which bone marrow-derived mesenchymal stem cells(BM-MSCs)may be effective in alleviating experimental colitis induced by treatment with 2,4,6-trinitrobenz...This study aimed to elucidate the potential mechanisms through which bone marrow-derived mesenchymal stem cells(BM-MSCs)may be effective in alleviating experimental colitis induced by treatment with 2,4,6-trinitrobenzene-sulfonate acid(TNBS),specifically through autophagy modulation.Methods:BM-MSCs were collected from BALB/c mice for subsequent experiments.The study employed cell counting kits(CCK-8)to investigate the impact of the MSC-conditioned medium(M medium)on the proliferation of RAW264.7 macrophages.The GFP-mRFP-LC3 adenovirus was transfected into RAW264.7 to detect autophagic flux.The gene expression of cytokines was assessed through quantitative reverse transcription polymerase chain reaction(qRT-PCR).Western blot analysis was employed to determine the presence of a binding interaction between NOD-like receptor protein 3(NLRP3)and autophagy.Furthermore,a colitis mouse model was established by TNBS induction.Clinical disease activity score was assessed regularly,and histological and morphometric analyses were performed on colonic tissues.Inflammatory serum cytokines were identified using an enzyme-linked immunosorbent assay.Results:BM-MSCs significantly promoted the proliferation of RAW264.7.In vitro lipopolysaccharide(LPS)-stimulated RAW264.7 cells,treated with BM-MSCs,triggered autophagy and inhibited cytokine mRNA expression.Additionally,in LPS-induced RAW264.7,BM-MSCs enhanced the Beclin1 protein expression and the microtubule-associated protein 1 light chain 3(LC3)-II to LC3-I ratio while suppressing the protein levels of NLRP3 and apoptosis-associated speck-like protein(ASC).Nevertheless,3-methyladenine(3-MA),an inhibitor of autophagy,prevented the impact of BM-MSCs by reducing the levels of NLRP3 and ASC proteins,suggesting that autophagy triggered the inhibition of the NLRP3 inflammasome.In comparison to the mice in the TNBS group,the mice in the TNBS+MSC group displayed a more acute form of colitis,and the IL1βand IL18 cytokines in their serum were lowered as well.In the meantime,3-MA raised IL1βand IL18 cytokine levels and worsened TNBS-induced experimental colitis.Conclusions:BM-MSCs can suppress inflammation in TNBS-induced experimental mice by inhibiting the NLRP3 inflammasome,thereby enhancing autophagy.展开更多
1 Introduction The LSM-tree has become a preferred solution for the storage structure of key-value(KV)NoSQL systems due to its efficient write performance[1,2].Its ability to efficiently process large-scale data write...1 Introduction The LSM-tree has become a preferred solution for the storage structure of key-value(KV)NoSQL systems due to its efficient write performance[1,2].Its ability to efficiently process large-scale data write operations makes it show high performance in handling TP(Transactional Processing)tasks.The increasing volume of real-time data processing and complex analytics tasks in current applications has made it necessary for databases to support HTAP(Hybrid Transactional/Analytical Processing)workloads.展开更多
Summarize the standards of traditional Chinese medicine formula granules issued by the National Medical Products Administration and various provinces,and keep track of the progress of the revision of the standards of ...Summarize the standards of traditional Chinese medicine formula granules issued by the National Medical Products Administration and various provinces,and keep track of the progress of the revision of the standards of traditional Chinese medicine formula granules in each province.Through a comprehensive analysis of the 179 varieties included in the published and compiled Anhui Provincial Traditional Chinese Medicine Formula Granules,this study evaluates multiple aspects,including origin selection,raw material sources,standard decoction preparation processes,control limits for heavy metals,harmful elements,and aflatoxin contamination,as well as efficacy assessments.This study aims to develop a comprehensive quality standard system for Anhui Province’s TCM formula granules,serving as the foundation for the official release of Anhui Provincial TCM Formula Granule Standards.Furthermore,it seeks to facilitate the elevation of provincial standards to national standards,thereby contributing to the continuous improvement of China’s TCM regulatory framework.展开更多
A new disordered crystal Nd:SrAl12O19(Nd:SRA)with an Nd3+doping concentration of 5%was successfully grown using the Czochralski method.A diode-pumped Nd:SRA Q-switched laser operating at 1049 nm was demonstrated for t...A new disordered crystal Nd:SrAl12O19(Nd:SRA)with an Nd3+doping concentration of 5%was successfully grown using the Czochralski method.A diode-pumped Nd:SRA Q-switched laser operating at 1049 nm was demonstrated for the first time,to the best of our knowledge.Based on an MXene Ti3C2Tx sheet,a high repetition rate of 201 kHz and a Q-switched pulse width of 346 ns were obtained when the absorbed pump power was 2.8 W.The peak power and single pulse energy were 1.87 W and 0.65μJ,respectively.展开更多
Prenatal depression,which can affect pregnant women’s physical and psychological health and cause postpartum depression,is increasing dramatically.Therefore,it is essential to detect prenatal depression early and con...Prenatal depression,which can affect pregnant women’s physical and psychological health and cause postpartum depression,is increasing dramatically.Therefore,it is essential to detect prenatal depression early and conduct an attribution analysis.Many studies have used questionnaires to screen for prenatal depression,but the existing methods lack attributability.To diagnose the early signs of prenatal depression and identify the key factors that may lead to prenatal depression from questionnaires,we present the semantically enhanced option embedding(SEOE)model to represent questionnaire options.It can quantitatively determine the relationship and patterns between options and depression.SEOE first quantifies options and resorts them,gathering options with little difference,since Word2Vec is highly dependent on context.The resort task is transformed into an optimization problem involving the traveling salesman problem.Moreover,all questionnaire samples are used to train the options’vector using Word2Vec.Finally,an LSTM and GRU fused model incorporating the cycle learning rate is constructed to detect whether a pregnant woman is suffering from depression.To verify the model,we compare it with other deep learning and traditional machine learning methods.The experiment results show that our proposed model can accurately identify pregnant women with depression and reach an F1 score of 0.8.The most relevant factors of depression found by SEOE are also verified in the literature.In addition,our model is of low computational complexity and strong generalization,which can be widely applied to other questionnaire analyses of psychiatric disorders.展开更多
1 Introduction.As applications and systems increasingly migrate to the cloud,cloud-native databaseesystems withstorage-compute disaggregatedaarchitectureand computationpushdown techniques have gained widespread suppor...1 Introduction.As applications and systems increasingly migrate to the cloud,cloud-native databaseesystems withstorage-compute disaggregatedaarchitectureand computationpushdown techniques have gained widespread support and popularity.Computation pushdown is the process of pushing operators like join and aggregation down to the storage nodes,reducing network I/O and improving query efficiency.展开更多
To the best of our knowledge,this is the first time that a mid-infrared Er^(3+):CaF_(2)-SrF_(2) laser has achieved continuous-wave mode-locked operation by a semiconductor saturable absorber mirror.The laser emits a m...To the best of our knowledge,this is the first time that a mid-infrared Er^(3+):CaF_(2)-SrF_(2) laser has achieved continuous-wave mode-locked operation by a semiconductor saturable absorber mirror.The laser emits a maximum output power of 93 mW at 2.73μm with a repetition rate of approximately 69 MHz and demonstrates a high signal-to-noise ratio of around 71 dB.In addition,a MgF_(2) birefringent plate was utilized to enable wavelength tuning of the Er^(3+):CaF_(2)-SrF_(2) laser,resulting in operation at approximately 2.73μm,2.75μm,2.79μm,and 2.81μm.These results demonstrate that Er^(3+):CaF_(2)-SrF_(2) is a promising alternative for the generation of efficient diode-pumped mode-locked lasers around 2.8μm.展开更多
文摘Objectives:A common side effect of inflammatory bowel disease(IBD)is intestinal fibrosis,which frequently leads to intestinal blockage and stricture formation.Although Thalidomide(THD)has shown anti-fibrotic benefits in hepatic and renal models,little is known about how it affects intestinal fibrosis and the underlying processes.The present research examines the molecular targets of THD and its potential as a treatment for intestinal fibrosis brought on by colitis.Methods:Clinical samples from Crohn’s disease(CD)patients with intestinal strictures treated with infliximab(IFX)and THD combined with IFX were collected.Dextran sulfate sodium(DSS)was used to develop a mouse model of intestinal fibrosis in C57BL/6 mice.Anti-tumor necrosis factor-alpha(Anti-TNFα),THD,or a combination of the two were administered to the mice.Body weight,colon length,histology,and disease activity index were used to evaluate the disease’s severity.In vitro,THD was tested on colonic fibroblast lines(CCD-18Co and MPF)to assess its effects on cell proliferation,motility,and transdifferentiation.To examine changes in gene expression and signaling pathway modifications,namely in the phosphoinositide 3-kinase/protein kinase B/mammalian target of rapamycin(PI3K/AKT/mTOR)pathway,RNA sequencing,qRT-PCR,and Western blotting were carried out.Results:In DSS-induced colitis,THD therapy lowered fibrosis,as seen by downregulated fibrotic markers(α-smooth muscle actin(α-SMA),collagen I,and collagen III)and decreased collagen deposition.Mechanistically,THD prevented fibroblasts from transdifferentiating and decreased their vitality.Furthermore,THD inhitited the PI3K/AKT/mTOR pathway in vivo and in vitro.Conclusion:THD inhibits the PI3K/AKT/mTOR signaling cascade and suppresses colonic fibroblast transdifferentiation,which protects against DSS-induced colitis-associated fibrosis,especially when combined with anti-TNFαtherapy.
基金supported by the National Natural Science Foundation of China(Grant No.62302086)the Natural Science Foundation of Liaoning Province(Grant No.2023-MSBA-070)the Fundamental Research Funds for the Central Universities(Grant No.N2317005).
文摘Visible-infrared object detection leverages the day-night stable object perception capability of infrared images to enhance detection robustness in low-light environments by fusing the complementary information of visible and infrared images.However,the inherent differences in the imaging mechanisms of visible and infrared modalities make effective cross-modal fusion challenging.Furthermore,constrained by the physical characteristics of sensors and thermal diffusion effects,infrared images generally suffer from blurred object contours and missing details,making it difficult to extract object features effectively.To address these issues,we propose an infrared-visible image fusion network that realizesmultimodal information fusion of infrared and visible images through a carefully designedmultiscale fusion strategy.First,we design an adaptive gray-radiance enhancement(AGRE)module to strengthen the detail representation in infrared images,improving their usability in complex lighting scenarios.Next,we introduce a channelspatial feature interaction(CSFI)module,which achieves efficient complementarity between the RGB and infrared(IR)modalities via dynamic channel switching and a spatial attention mechanism.Finally,we propose a multi-scale enhanced cross-attention fusion(MSECA)module,which optimizes the fusion ofmulti-level features through dynamic convolution and gating mechanisms and captures long-range complementary relationships of cross-modal features on a global scale,thereby enhancing the expressiveness of the fused features.Experiments on the KAIST,M3FD,and FLIR datasets demonstrate that our method delivers outstanding performance in daytime and nighttime scenarios.On the KAIST dataset,the miss rate drops to 5.99%,and further to 4.26% in night scenes.On the FLIR and M3FD datasets,it achieves AP50 scores of 79.4% and 88.9%,respectively.
基金supported by the Natural Science Foundation of Liaoning Province(Grant No.2023-MSBA-070)the National Natural Science Foundation of China(Grant No.62302086).
文摘Multi-modal fusion technology gradually become a fundamental task in many fields,such as autonomous driving,smart healthcare,sentiment analysis,and human-computer interaction.It is rapidly becoming the dominant research due to its powerful perception and judgment capabilities.Under complex scenes,multi-modal fusion technology utilizes the complementary characteristics of multiple data streams to fuse different data types and achieve more accurate predictions.However,achieving outstanding performance is challenging because of equipment performance limitations,missing information,and data noise.This paper comprehensively reviews existing methods based onmulti-modal fusion techniques and completes a detailed and in-depth analysis.According to the data fusion stage,multi-modal fusion has four primary methods:early fusion,deep fusion,late fusion,and hybrid fusion.The paper surveys the three majormulti-modal fusion technologies that can significantly enhance the effect of data fusion and further explore the applications of multi-modal fusion technology in various fields.Finally,it discusses the challenges and explores potential research opportunities.Multi-modal tasks still need intensive study because of data heterogeneity and quality.Preserving complementary information and eliminating redundant information between modalities is critical in multi-modal technology.Invalid data fusion methods may introduce extra noise and lead to worse results.This paper provides a comprehensive and detailed summary in response to these challenges.
基金supported by the National Natural Science Foundation of China(62162050)the Fundamental Research Funds for the Central Universities(No.N2217002)the Natural Science Foundation of Liaoning ProvincialDepartment of Science and Technology(No.2022-KF-11-04).
文摘Mobile-edge computing(MEC)is a promising technology for the fifth-generation(5G)and sixth-generation(6G)architectures,which provides resourceful computing capabilities for Internet of Things(IoT)devices,such as virtual reality,mobile devices,and smart cities.In general,these IoT applications always bring higher energy consumption than traditional applications,which are usually energy-constrained.To provide persistent energy,many references have studied the offloading problem to save energy consumption.However,the dynamic environment dramatically increases the optimization difficulty of the offloading decision.In this paper,we aim to minimize the energy consumption of the entireMECsystemunder the latency constraint by fully considering the dynamic environment.UnderMarkov games,we propose amulti-agent deep reinforcement learning approach based on the bi-level actorcritic learning structure to jointly optimize the offloading decision and resource allocation,which can solve the combinatorial optimization problem using an asymmetric method and compute the Stackelberg equilibrium as a better convergence point than Nash equilibrium in terms of Pareto superiority.Our method can better adapt to a dynamic environment during the data transmission than the single-agent strategy and can effectively tackle the coordination problem in the multi-agent environment.The simulation results show that the proposed method could decrease the total computational overhead by 17.8%compared to the actor-critic-based method and reduce the total computational overhead by 31.3%,36.5%,and 44.7%compared with randomoffloading,all local execution,and all offloading execution,respectively.
文摘This study aimed to elucidate the potential mechanisms through which bone marrow-derived mesenchymal stem cells(BM-MSCs)may be effective in alleviating experimental colitis induced by treatment with 2,4,6-trinitrobenzene-sulfonate acid(TNBS),specifically through autophagy modulation.Methods:BM-MSCs were collected from BALB/c mice for subsequent experiments.The study employed cell counting kits(CCK-8)to investigate the impact of the MSC-conditioned medium(M medium)on the proliferation of RAW264.7 macrophages.The GFP-mRFP-LC3 adenovirus was transfected into RAW264.7 to detect autophagic flux.The gene expression of cytokines was assessed through quantitative reverse transcription polymerase chain reaction(qRT-PCR).Western blot analysis was employed to determine the presence of a binding interaction between NOD-like receptor protein 3(NLRP3)and autophagy.Furthermore,a colitis mouse model was established by TNBS induction.Clinical disease activity score was assessed regularly,and histological and morphometric analyses were performed on colonic tissues.Inflammatory serum cytokines were identified using an enzyme-linked immunosorbent assay.Results:BM-MSCs significantly promoted the proliferation of RAW264.7.In vitro lipopolysaccharide(LPS)-stimulated RAW264.7 cells,treated with BM-MSCs,triggered autophagy and inhibited cytokine mRNA expression.Additionally,in LPS-induced RAW264.7,BM-MSCs enhanced the Beclin1 protein expression and the microtubule-associated protein 1 light chain 3(LC3)-II to LC3-I ratio while suppressing the protein levels of NLRP3 and apoptosis-associated speck-like protein(ASC).Nevertheless,3-methyladenine(3-MA),an inhibitor of autophagy,prevented the impact of BM-MSCs by reducing the levels of NLRP3 and ASC proteins,suggesting that autophagy triggered the inhibition of the NLRP3 inflammasome.In comparison to the mice in the TNBS group,the mice in the TNBS+MSC group displayed a more acute form of colitis,and the IL1βand IL18 cytokines in their serum were lowered as well.In the meantime,3-MA raised IL1βand IL18 cytokine levels and worsened TNBS-induced experimental colitis.Conclusions:BM-MSCs can suppress inflammation in TNBS-induced experimental mice by inhibiting the NLRP3 inflammasome,thereby enhancing autophagy.
基金supported by the Fundamental Research Funds for the National Natural Science Foundation of China under Grant No.62472077.
文摘1 Introduction The LSM-tree has become a preferred solution for the storage structure of key-value(KV)NoSQL systems due to its efficient write performance[1,2].Its ability to efficiently process large-scale data write operations makes it show high performance in handling TP(Transactional Processing)tasks.The increasing volume of real-time data processing and complex analytics tasks in current applications has made it necessary for databases to support HTAP(Hybrid Transactional/Analytical Processing)workloads.
文摘Summarize the standards of traditional Chinese medicine formula granules issued by the National Medical Products Administration and various provinces,and keep track of the progress of the revision of the standards of traditional Chinese medicine formula granules in each province.Through a comprehensive analysis of the 179 varieties included in the published and compiled Anhui Provincial Traditional Chinese Medicine Formula Granules,this study evaluates multiple aspects,including origin selection,raw material sources,standard decoction preparation processes,control limits for heavy metals,harmful elements,and aflatoxin contamination,as well as efficacy assessments.This study aims to develop a comprehensive quality standard system for Anhui Province’s TCM formula granules,serving as the foundation for the official release of Anhui Provincial TCM Formula Granule Standards.Furthermore,it seeks to facilitate the elevation of provincial standards to national standards,thereby contributing to the continuous improvement of China’s TCM regulatory framework.
基金supported by the National Natural Science Foundation of China(NSFC)(No.11974220)。
文摘A new disordered crystal Nd:SrAl12O19(Nd:SRA)with an Nd3+doping concentration of 5%was successfully grown using the Czochralski method.A diode-pumped Nd:SRA Q-switched laser operating at 1049 nm was demonstrated for the first time,to the best of our knowledge.Based on an MXene Ti3C2Tx sheet,a high repetition rate of 201 kHz and a Q-switched pulse width of 346 ns were obtained when the absorbed pump power was 2.8 W.The peak power and single pulse energy were 1.87 W and 0.65μJ,respectively.
基金the National Key R&D Program of China(No.2021YFF1201200)the National Natural Science Foundation of China(Grant Nos.61972174 and 62172187)+1 种基金the Science and Technology Planning Project of Jilin Province(No.20220201145GX,No.20200708112YY and No.20220601112FG)the Science and Technology Planning Project of Guangdong Province(No.2020A0505100018),Guangdong Universities’Innovation Team Project(No.2021KCXTD015)and Guangdong Key Disciplines Project(No.2021ZDJS138)。
文摘Prenatal depression,which can affect pregnant women’s physical and psychological health and cause postpartum depression,is increasing dramatically.Therefore,it is essential to detect prenatal depression early and conduct an attribution analysis.Many studies have used questionnaires to screen for prenatal depression,but the existing methods lack attributability.To diagnose the early signs of prenatal depression and identify the key factors that may lead to prenatal depression from questionnaires,we present the semantically enhanced option embedding(SEOE)model to represent questionnaire options.It can quantitatively determine the relationship and patterns between options and depression.SEOE first quantifies options and resorts them,gathering options with little difference,since Word2Vec is highly dependent on context.The resort task is transformed into an optimization problem involving the traveling salesman problem.Moreover,all questionnaire samples are used to train the options’vector using Word2Vec.Finally,an LSTM and GRU fused model incorporating the cycle learning rate is constructed to detect whether a pregnant woman is suffering from depression.To verify the model,we compare it with other deep learning and traditional machine learning methods.The experiment results show that our proposed model can accurately identify pregnant women with depression and reach an F1 score of 0.8.The most relevant factors of depression found by SEOE are also verified in the literature.In addition,our model is of low computational complexity and strong generalization,which can be widely applied to other questionnaire analyses of psychiatric disorders.
文摘1 Introduction.As applications and systems increasingly migrate to the cloud,cloud-native databaseesystems withstorage-compute disaggregatedaarchitectureand computationpushdown techniques have gained widespread support and popularity.Computation pushdown is the process of pushing operators like join and aggregation down to the storage nodes,reducing network I/O and improving query efficiency.
基金This work was supported by the National Natural Science Foundation of China(Nos.12374401,12104271,and 61925508)the Natural Science Foundation of Shandong Province(Nos.ZR2021LLZ008 and ZR2021QA030)the China Postdoctoral Science Foundation(No.2021M691981).
文摘To the best of our knowledge,this is the first time that a mid-infrared Er^(3+):CaF_(2)-SrF_(2) laser has achieved continuous-wave mode-locked operation by a semiconductor saturable absorber mirror.The laser emits a maximum output power of 93 mW at 2.73μm with a repetition rate of approximately 69 MHz and demonstrates a high signal-to-noise ratio of around 71 dB.In addition,a MgF_(2) birefringent plate was utilized to enable wavelength tuning of the Er^(3+):CaF_(2)-SrF_(2) laser,resulting in operation at approximately 2.73μm,2.75μm,2.79μm,and 2.81μm.These results demonstrate that Er^(3+):CaF_(2)-SrF_(2) is a promising alternative for the generation of efficient diode-pumped mode-locked lasers around 2.8μm.
