Gaze estimation,a crucial non-verbal communication cue,has achieved remarkable progress through convolutional neural networks.However,accurate gaze prediction in uncon-strained environments,particularly in extreme hea...Gaze estimation,a crucial non-verbal communication cue,has achieved remarkable progress through convolutional neural networks.However,accurate gaze prediction in uncon-strained environments,particularly in extreme head poses,partial occlusions,and abnormal lighting,remains challenging.Existing models often struggle to effectively focus on discriminative ocular features,leading to suboptimal performance.To address these limitations,this paper proposes dual-branch gaze estimation with Gaussian mixture distribution heatmaps and dynamic adaptive loss function(DMGDL),a novel dual-branch gaze estimation algorithm.By introducing Gaussian mixture distribution heatmaps centered on pupil positions as spatial attention guides,the model is enabled to prioritize ocular regions.Additionally,a dual-branch network architecture is designed to separately extract features for yaw and pitch angles,enhancing flexibility and mitigating cross-angle interference.A dynamic adaptive loss function is further formulated to address discontinuities in angle estimation,improving robustness and convergence stability.Experimental evaluations on three benchmark datasets demonstrate that DMGDL outperforms state-of-the-art methods,achiev-ing a mean angular error of 3.98°on the Max-Planck institute for informatics face gaze(MPI-IFaceGaze)dataset,10.21°on the physically unconstrained gaze estimation in the wild(Gaze360)dataset and 6.14°on the real-time eye gaze estimation in natural environments(RT-Gene)dataset,exhibiting superior generalization and robustness.展开更多
Flax(Linum usitatissimum L.)is an important oil crop in the high-altitude arid regions of China.Flaxseed is rich in various nutrients.However,the nutritional qualities of flaxseeds from different producing areas are s...Flax(Linum usitatissimum L.)is an important oil crop in the high-altitude arid regions of China.Flaxseed is rich in various nutrients.However,the nutritional qualities of flaxseeds from different producing areas are still unclear.In this study,the nutritional characteristics of flaxseed from five producing areas in China were investigated.Twenty five nutritional quality indices in flaxseed were analyzed.Subsequently,chemometric methods,including cluster analysis,principal component analysis(PCA)and partial least square discriminant analysis(PLS-DA),were employed to discover the characteristics of nutritional qualities in flaxseeds.The results revealed there are significant differences in nutritional qualities among flaxseeds from different production areas.Six quality indices includingγ-tocopherol,vitamin E,phytosterols,oleic acid,α-linolenic acid,and cycloartenol were susceptible to producing area.In detail,the superiorcharacteristic nutrients of Ningxia flaxseed,Inner Mongolia flaxseed and Hebei flaxseed are vitamin E(17.3 mg/100g),α-linolenic acid(52.6%)and cycloartenol(1738.1 mg/kg),and phytosterols(3032.0 mg/kg),respectively.This study promotes the high-value development and utilization of local flaxseed industry.展开更多
In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and ta...In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and targeted marketing.However,existing computer vision solutions often rely on facial recognition to gather such insights,raising significant privacy and ethical concerns.To address these issues,this paper presents a privacypreserving customer analytics system through two key strategies.First,we deploy a deep learning framework using YOLOv9s,trained on the RCA-TVGender dataset.Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate gender classification.Second,we apply AES-128 encryption to customer position data,ensuring secure access and regulatory compliance.Our system achieved overall performance,with 81.5%mAP@50,77.7%precision,and 75.7%recall.Moreover,a 90-min observational study confirmed the system’s ability to generate privacy-protected heatmaps revealing distinct behavioral patterns between male and female customers.For instance,women spent more time in certain areas and showed interest in different products.These results confirm the system’s effectiveness in enabling personalized layout and marketing strategies without compromising privacy.展开更多
Recognition of human gesture actions is a challenging issue due to the complex patterns in both visual andskeletal features. Existing gesture action recognition (GAR) methods typically analyze visual and skeletal data...Recognition of human gesture actions is a challenging issue due to the complex patterns in both visual andskeletal features. Existing gesture action recognition (GAR) methods typically analyze visual and skeletal data,failing to meet the demands of various scenarios. Furthermore, multi-modal approaches lack the versatility toefficiently process both uniformand disparate input patterns.Thus, in this paper, an attention-enhanced pseudo-3Dresidual model is proposed to address the GAR problem, called HgaNets. This model comprises two independentcomponents designed formodeling visual RGB (red, green and blue) images and 3Dskeletal heatmaps, respectively.More specifically, each component consists of two main parts: 1) a multi-dimensional attention module forcapturing important spatial, temporal and feature information in human gestures;2) a spatiotemporal convolutionmodule that utilizes pseudo-3D residual convolution to characterize spatiotemporal features of gestures. Then,the output weights of the two components are fused to generate the recognition results. Finally, we conductedexperiments on four datasets to assess the efficiency of the proposed model. The results show that the accuracy onfour datasets reaches 85.40%, 91.91%, 94.70%, and 95.30%, respectively, as well as the inference time is 0.54 s andthe parameters is 2.74M. These findings highlight that the proposed model outperforms other existing approachesin terms of recognition accuracy.展开更多
A research project was undertaken to collect data to study the variability in environmental parameters inside a greenhouse. The specific objectives of the project were to 1) develop the network of open-source sensor n...A research project was undertaken to collect data to study the variability in environmental parameters inside a greenhouse. The specific objectives of the project were to 1) develop the network of open-source sensor nodes, 2) evaluate the performance of the individual sensors, and 3) quantify the spatial variability of environmental parameters within the greenhouse. The sensor system consisted of a sensor node equipped with three temperature and relative humidity sensors, one light-level sensor, one barometric pressure sensor, AA batteries, and a microcontroller board with a built-in radio to transfer the data wirelessly. The sensors were controlled with open-source technology. Twelve sensor nodes were fabricated and placed at different locations in a greenhouse to evaluate variability in sensor location and environmental parameters. Data collected during February 2019 were used to test the sensors. Heatmaps were employed to assess the variability of the measurements. Variability in greenhouse temperature, relative humidity, and light level conditions was identified with the sensor system. Overall, environmental measures based on time of day appeared to be a better grouping mechanism for analysis than sensor location in the greenhouse. Similar patterns were observed between the different sensor manufacturer’s heatmaps for the temperature sensors and relative humidity sensors. This study provided a protocol for developing the inexpensive multi-sensor sensor node and showed that automated measurements obtained with the system could help monitor variation in a greenhouse setting. The costs of the system components fabricated for this study included US$76 for each sensor node and US$55 for the gateway, totaling US$967 for the 12-node study described.展开更多
Urban topsoil is the most frequent interface between human society and natural environment.The accumulation of heavy metals in the urban topsoil has a direct effect on residents'life and health.The geochemical bas...Urban topsoil is the most frequent interface between human society and natural environment.The accumulation of heavy metals in the urban topsoil has a direct effect on residents'life and health.The geochemical baseline of heavy metals is an objective description of the general level of heavy metals in the urban topsoil.Meanwhile,the determination of geochemical baseline is necessary for regional environmental management,especially in coal cities prone to heavy metal pollution.Heavy metal pollution has become an environmental problem in Fuxin City,China for a long time.To establish the geochemical baseline of heavy metals in the topsoil of Fuxin City and to evaluate the ecological risk of the topsoil,we collected 75 topsoil samples(0–20 cm)and analyzed the concentrations of Cu,Ni,Zn,Pb,Cr,Cd,Hg and As through X-ray fluorescence spectrometry,atomic absorption spectrometry and inductively coupled plasma optical emission spectrometry.We determined the geochemical baseline of heavy metals in the topsoil of Fuxin City by using iteration removal,box-whisker plot,cumulative frequency curve and reference metal normalization;evaluated the contamination risk and ecological risk of the topsoil by using the baseline factor index,Nemerow index and Hakanson potential ecological risk index;and identified the source category of heavy metals in the topsoil by using a pedigree clustering heatmap.Results showed that the geochemical baseline values were 42.86,89.34,92.23,60.55,145.21,0.09,0.08 and 4.17 mg/kg for Cu,Ni,Zn,Pb,Cr,Cd,Hg and As,respectively.The results of Nemerow index and Hakanson potential ecological risk index indicated that the urban topsoil in the study area was slightly contaminated and suffering low potential ecological risk.The main contaminated areas dominated in the middle part and northeast part of the study area,especially in the western Haizhou Strip Mine.The result of baseline factor index indicated that Hg and Cd were the major pollution elements.Using a pedigree clustering heatmap,we divided the sources of these heavy metals into three types:type I for Ni and Cr,largely represented the enrichment of heavy metals from natural sources;type II for Cu,Pb,Zn,Cd and As,mainly represented the enrichment of heavy metals from anthropogenic sources;and type III for Hg,represented the form of both natural and anthropogenic inputs.展开更多
Although compatibility is highly advocated in traditional Chinese medicine (TCM), inappropriate combination of some herbs may reduce the therapeutic action and even produce toxic effects. Kansui and licorice, one of T...Although compatibility is highly advocated in traditional Chinese medicine (TCM), inappropriate combination of some herbs may reduce the therapeutic action and even produce toxic effects. Kansui and licorice, one of TCM “Eighteen Incompatible Medicaments”, are the most representative cases of improper herbal combination, which may still be applied simultaneously under given conditions. However, the potential mechanism of their compatibility and incompatibility is unclear. In the present study, two different ratios of kansui and licorice, representing their compatibility and incompatibility respectively, were designed to elucidate their interaction by comparative plasma/tissue metabolomics and a heatmap with relative fold change. As a result, glycocholic acid, prostaglandin F2a, dihydroceramide and sphinganine were screened out as the principal alternative biomarkers of compatibility group;sphinganine, dihydroceramide, arachidonic acid, leukotriene B4, acetoacetic acid and linoleic acid were those of incompatibility group. Based on the values of biomarkers in each tissue, the liver was identified as the compatible target organ, while the heart, liver, and kidney were the incompatible target organs. Furthermore, important pathways for compatibility and incompatibility were also constructed. These results help us to better understand and utilize the two herbs, and the study was the first to reveal some innate characters of herbs related to TCM “Eighteen Incompatible Medicaments”.展开更多
The neural regeneration process is driven by a wide range of molecules and pathways. Adherens junctions are critical cellular junctions for the integrity of peripheral nerves. However, few studies have systematically ...The neural regeneration process is driven by a wide range of molecules and pathways. Adherens junctions are critical cellular junctions for the integrity of peripheral nerves. However, few studies have systematically characterized the transcript changes in the adherens junction pathway following injury. In this study, a rat model of sciatic nerve crush injury was established by forceps. Deep sequencing data were analyzed using comprehensive transcriptome analysis at 0, 1, 4, 7, and 14 days after injury. Results showed that most individual molecules in the adherens junctions were either upregulated or downregulated after nerve injury. The m RNA expression of ARPC1 B, ARPC3, TUBA8, TUBA1 C, CTNNA2, ACTN3, MET, HGF, NME1 and ARF6, which are involved in the adherens junction pathway and in remodeling of adherens junctions, was analyzed using quantitative real-time polymerase chain reaction. Most of these genes were upregulated in the sciatic nerve stump following peripheral nerve injury, except for CTNNA2, which was downregulated. Our findings reveal the dynamic changes of key molecules in adherens junctions and in remodeling of adherens junctions. These key genes provide a reference for the selection of clinical therapeutic targets for peripheral nerve injury.展开更多
Anchor-based detectors are widely used in object detection.To improve the accuracy of object detection,multiple anchor boxes are intensively placed on the input image,yet.Most of which are invalid.Although the anchor-...Anchor-based detectors are widely used in object detection.To improve the accuracy of object detection,multiple anchor boxes are intensively placed on the input image,yet.Most of which are invalid.Although the anchor-free method can reduce the number of useless anchor boxes,the invalid ones still occupy a high proportion.On this basis,this paper proposes a multiscale center point object detection method based on parallel network to further reduce the number of useless anchor boxes.This study adopts the parallel network architecture of hourglass-104 and darknet-53 of which the first one outputs heatmaps to generate the center point for object feature location on the output attribute feature map of darknet-53.Combining feature pyramid and CIoU loss function,this algorithm is trained and tested on MSCOCO dataset,increasing the detection rate of target location and the accuracy rate of small object detection.Though resembling the state-of-the-art two-stage detectors in overall object detection accuracy,this algorithm is superior in speed.展开更多
Introduction:Genome sequence plays an important role in both basic and applied studies.Gossypium raimondii,the putative contributor of the D subgenome of upland cotton(G.hirsutum,highlights the need to improve the gen...Introduction:Genome sequence plays an important role in both basic and applied studies.Gossypium raimondii,the putative contributor of the D subgenome of upland cotton(G.hirsutum,highlights the need to improve the genome quality rapidly and efficiently.Methods:We performed Hi-C sequencing of G.raimondii and reassembled its genome based on a set of new Hi-C data and previously published scaffolds.We also compared the reassembled genome sequenee with the previously published G raimondii genomes for gene and genome sequence collinearity.Result:A total of 9842%of scaffold sequences were clustered successfully,among which 99.72%of the clustered sequences were ordered and 99.92%of the ordered sequences were oriented with high-quality.Further evaluation of results by heat-map and collinearity analysis revealed that the current reassembled genome is significantly improved than the previous one(Nat Genet 44:98-1103,2012).Conclusion:This improvement in G raimondii genome not only provides a better reference to increase study efficiency but also offers a new way to assemble cotton genomes.Furthermore,Hi-C data of G.raimondii may be used for 3D structure research or regulating analysis.展开更多
Due to factors such as motion blur,video out-of-focus,and occlusion,multi-frame human pose estimation is a challenging task.Exploiting temporal consistency between consecutive frames is an efficient approach for addre...Due to factors such as motion blur,video out-of-focus,and occlusion,multi-frame human pose estimation is a challenging task.Exploiting temporal consistency between consecutive frames is an efficient approach for addressing this issue.Currently,most methods explore temporal consistency through refinements of the final heatmaps.The heatmaps contain the semantics information of key points,and can improve the detection quality to a certain extent.However,they are generated by features,and feature-level refinements are rarely considered.In this paper,we propose a human pose estimation framework with refinements at the feature and semantics levels.We align auxiliary features with the features of the current frame to reduce the loss caused by different feature distributions.An attention mechanism is then used to fuse auxiliary features with current features.In terms of semantics,we use the difference information between adjacent heatmaps as auxiliary features to refine the current heatmaps.The method is validated on the large-scale benchmark datasets PoseTrack2017 and PoseTrack2018,and the results demonstrate the effectiveness of our method.展开更多
An intelligent liquid classification system based on 77 GHz millimeter wave radar and convolution neural network are proposed in this paper.The data are collected by the AWR1843 radar platform and processed by the neu...An intelligent liquid classification system based on 77 GHz millimeter wave radar and convolution neural network are proposed in this paper.The data are collected by the AWR1843 radar platform and processed by the neural network on the host PC in real-time.The doppler heatmap generated by radar signal processing is tried for the first time as the input of the system.The information carried by the heatmap in 2 dimensions is analyzed and the reason why the doppler heatmap could be used for classification is explained.The feasible experiment proved that the proposed method can successfully classify 8 kinds of common liquid with high accuracy.The result of the experiment is explained and the limitations of the experiment are discussed.It can be drawn that the combination of FMCW millimeter wave radar and convolution neural network is a method with great potential for liquid classification.The advantages of real time,non-invasive and unilateral measurement can also be used for the detection of dangerous liquids.展开更多
基金supported by the Key Project of the NationalLanguage Commission(No.ZDI145-110)the AcademicResearch Projects of Beijing Union University(No.ZK20202514)+1 种基金the Key Laboratory Project(No.YYZN-2024-6)the Project for the Construction and Support of High-Level Innovative Teams in Beijing Municipal Institutions(No.BPHR20220121).
文摘Gaze estimation,a crucial non-verbal communication cue,has achieved remarkable progress through convolutional neural networks.However,accurate gaze prediction in uncon-strained environments,particularly in extreme head poses,partial occlusions,and abnormal lighting,remains challenging.Existing models often struggle to effectively focus on discriminative ocular features,leading to suboptimal performance.To address these limitations,this paper proposes dual-branch gaze estimation with Gaussian mixture distribution heatmaps and dynamic adaptive loss function(DMGDL),a novel dual-branch gaze estimation algorithm.By introducing Gaussian mixture distribution heatmaps centered on pupil positions as spatial attention guides,the model is enabled to prioritize ocular regions.Additionally,a dual-branch network architecture is designed to separately extract features for yaw and pitch angles,enhancing flexibility and mitigating cross-angle interference.A dynamic adaptive loss function is further formulated to address discontinuities in angle estimation,improving robustness and convergence stability.Experimental evaluations on three benchmark datasets demonstrate that DMGDL outperforms state-of-the-art methods,achiev-ing a mean angular error of 3.98°on the Max-Planck institute for informatics face gaze(MPI-IFaceGaze)dataset,10.21°on the physically unconstrained gaze estimation in the wild(Gaze360)dataset and 6.14°on the real-time eye gaze estimation in natural environments(RT-Gene)dataset,exhibiting superior generalization and robustness.
基金supported by the National Key Research and Devel-opment Project of China(2021YFD1600101)the earmarked fund for the China Agriculture Research System(CARS-12,CARS-13 and CARS-14)the Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences(CAAS-ASTIP-2024-OCRI).
文摘Flax(Linum usitatissimum L.)is an important oil crop in the high-altitude arid regions of China.Flaxseed is rich in various nutrients.However,the nutritional qualities of flaxseeds from different producing areas are still unclear.In this study,the nutritional characteristics of flaxseed from five producing areas in China were investigated.Twenty five nutritional quality indices in flaxseed were analyzed.Subsequently,chemometric methods,including cluster analysis,principal component analysis(PCA)and partial least square discriminant analysis(PLS-DA),were employed to discover the characteristics of nutritional qualities in flaxseeds.The results revealed there are significant differences in nutritional qualities among flaxseeds from different production areas.Six quality indices includingγ-tocopherol,vitamin E,phytosterols,oleic acid,α-linolenic acid,and cycloartenol were susceptible to producing area.In detail,the superiorcharacteristic nutrients of Ningxia flaxseed,Inner Mongolia flaxseed and Hebei flaxseed are vitamin E(17.3 mg/100g),α-linolenic acid(52.6%)and cycloartenol(1738.1 mg/kg),and phytosterols(3032.0 mg/kg),respectively.This study promotes the high-value development and utilization of local flaxseed industry.
文摘In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and targeted marketing.However,existing computer vision solutions often rely on facial recognition to gather such insights,raising significant privacy and ethical concerns.To address these issues,this paper presents a privacypreserving customer analytics system through two key strategies.First,we deploy a deep learning framework using YOLOv9s,trained on the RCA-TVGender dataset.Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate gender classification.Second,we apply AES-128 encryption to customer position data,ensuring secure access and regulatory compliance.Our system achieved overall performance,with 81.5%mAP@50,77.7%precision,and 75.7%recall.Moreover,a 90-min observational study confirmed the system’s ability to generate privacy-protected heatmaps revealing distinct behavioral patterns between male and female customers.For instance,women spent more time in certain areas and showed interest in different products.These results confirm the system’s effectiveness in enabling personalized layout and marketing strategies without compromising privacy.
基金the National Natural Science Foundation of China under Grant No.62072255.
文摘Recognition of human gesture actions is a challenging issue due to the complex patterns in both visual andskeletal features. Existing gesture action recognition (GAR) methods typically analyze visual and skeletal data,failing to meet the demands of various scenarios. Furthermore, multi-modal approaches lack the versatility toefficiently process both uniformand disparate input patterns.Thus, in this paper, an attention-enhanced pseudo-3Dresidual model is proposed to address the GAR problem, called HgaNets. This model comprises two independentcomponents designed formodeling visual RGB (red, green and blue) images and 3Dskeletal heatmaps, respectively.More specifically, each component consists of two main parts: 1) a multi-dimensional attention module forcapturing important spatial, temporal and feature information in human gestures;2) a spatiotemporal convolutionmodule that utilizes pseudo-3D residual convolution to characterize spatiotemporal features of gestures. Then,the output weights of the two components are fused to generate the recognition results. Finally, we conductedexperiments on four datasets to assess the efficiency of the proposed model. The results show that the accuracy onfour datasets reaches 85.40%, 91.91%, 94.70%, and 95.30%, respectively, as well as the inference time is 0.54 s andthe parameters is 2.74M. These findings highlight that the proposed model outperforms other existing approachesin terms of recognition accuracy.
文摘A research project was undertaken to collect data to study the variability in environmental parameters inside a greenhouse. The specific objectives of the project were to 1) develop the network of open-source sensor nodes, 2) evaluate the performance of the individual sensors, and 3) quantify the spatial variability of environmental parameters within the greenhouse. The sensor system consisted of a sensor node equipped with three temperature and relative humidity sensors, one light-level sensor, one barometric pressure sensor, AA batteries, and a microcontroller board with a built-in radio to transfer the data wirelessly. The sensors were controlled with open-source technology. Twelve sensor nodes were fabricated and placed at different locations in a greenhouse to evaluate variability in sensor location and environmental parameters. Data collected during February 2019 were used to test the sensors. Heatmaps were employed to assess the variability of the measurements. Variability in greenhouse temperature, relative humidity, and light level conditions was identified with the sensor system. Overall, environmental measures based on time of day appeared to be a better grouping mechanism for analysis than sensor location in the greenhouse. Similar patterns were observed between the different sensor manufacturer’s heatmaps for the temperature sensors and relative humidity sensors. This study provided a protocol for developing the inexpensive multi-sensor sensor node and showed that automated measurements obtained with the system could help monitor variation in a greenhouse setting. The costs of the system components fabricated for this study included US$76 for each sensor node and US$55 for the gateway, totaling US$967 for the 12-node study described.
基金the National Natural Science Foundation of China(41271064)the Foundation of Liaoning Educational Committee,China(L201783640)the PhD Research Startup Foundation of Liaoning University,China(BS2018L014)。
文摘Urban topsoil is the most frequent interface between human society and natural environment.The accumulation of heavy metals in the urban topsoil has a direct effect on residents'life and health.The geochemical baseline of heavy metals is an objective description of the general level of heavy metals in the urban topsoil.Meanwhile,the determination of geochemical baseline is necessary for regional environmental management,especially in coal cities prone to heavy metal pollution.Heavy metal pollution has become an environmental problem in Fuxin City,China for a long time.To establish the geochemical baseline of heavy metals in the topsoil of Fuxin City and to evaluate the ecological risk of the topsoil,we collected 75 topsoil samples(0–20 cm)and analyzed the concentrations of Cu,Ni,Zn,Pb,Cr,Cd,Hg and As through X-ray fluorescence spectrometry,atomic absorption spectrometry and inductively coupled plasma optical emission spectrometry.We determined the geochemical baseline of heavy metals in the topsoil of Fuxin City by using iteration removal,box-whisker plot,cumulative frequency curve and reference metal normalization;evaluated the contamination risk and ecological risk of the topsoil by using the baseline factor index,Nemerow index and Hakanson potential ecological risk index;and identified the source category of heavy metals in the topsoil by using a pedigree clustering heatmap.Results showed that the geochemical baseline values were 42.86,89.34,92.23,60.55,145.21,0.09,0.08 and 4.17 mg/kg for Cu,Ni,Zn,Pb,Cr,Cd,Hg and As,respectively.The results of Nemerow index and Hakanson potential ecological risk index indicated that the urban topsoil in the study area was slightly contaminated and suffering low potential ecological risk.The main contaminated areas dominated in the middle part and northeast part of the study area,especially in the western Haizhou Strip Mine.The result of baseline factor index indicated that Hg and Cd were the major pollution elements.Using a pedigree clustering heatmap,we divided the sources of these heavy metals into three types:type I for Ni and Cr,largely represented the enrichment of heavy metals from natural sources;type II for Cu,Pb,Zn,Cd and As,mainly represented the enrichment of heavy metals from anthropogenic sources;and type III for Hg,represented the form of both natural and anthropogenic inputs.
基金financially supported by the National Basic Research Program of China (2011CB505300,2011CB505303)the National Natural Science Foundation of China (81603258, 81673599,81773882)+4 种基金Key Research Project in Basic Science of Jiangsu College and University (14KJA360001)Youth Talent Project Funded by Shaanxi Higher Education Association for Science and Technology (20180307)333 High Level Talents Training Project Funded by Jiangsu Province (BRA2016387)financially supported by a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)the Discipline Innovation Team Program of Shaanxi University of Chinese Medicine (2019-YL10)
文摘Although compatibility is highly advocated in traditional Chinese medicine (TCM), inappropriate combination of some herbs may reduce the therapeutic action and even produce toxic effects. Kansui and licorice, one of TCM “Eighteen Incompatible Medicaments”, are the most representative cases of improper herbal combination, which may still be applied simultaneously under given conditions. However, the potential mechanism of their compatibility and incompatibility is unclear. In the present study, two different ratios of kansui and licorice, representing their compatibility and incompatibility respectively, were designed to elucidate their interaction by comparative plasma/tissue metabolomics and a heatmap with relative fold change. As a result, glycocholic acid, prostaglandin F2a, dihydroceramide and sphinganine were screened out as the principal alternative biomarkers of compatibility group;sphinganine, dihydroceramide, arachidonic acid, leukotriene B4, acetoacetic acid and linoleic acid were those of incompatibility group. Based on the values of biomarkers in each tissue, the liver was identified as the compatible target organ, while the heart, liver, and kidney were the incompatible target organs. Furthermore, important pathways for compatibility and incompatibility were also constructed. These results help us to better understand and utilize the two herbs, and the study was the first to reveal some innate characters of herbs related to TCM “Eighteen Incompatible Medicaments”.
基金supported by the National Natural Science Foundation of China,No.31700926the Priority Academic Program Development of Jiangsu Higher Education Institutions of China
文摘The neural regeneration process is driven by a wide range of molecules and pathways. Adherens junctions are critical cellular junctions for the integrity of peripheral nerves. However, few studies have systematically characterized the transcript changes in the adherens junction pathway following injury. In this study, a rat model of sciatic nerve crush injury was established by forceps. Deep sequencing data were analyzed using comprehensive transcriptome analysis at 0, 1, 4, 7, and 14 days after injury. Results showed that most individual molecules in the adherens junctions were either upregulated or downregulated after nerve injury. The m RNA expression of ARPC1 B, ARPC3, TUBA8, TUBA1 C, CTNNA2, ACTN3, MET, HGF, NME1 and ARF6, which are involved in the adherens junction pathway and in remodeling of adherens junctions, was analyzed using quantitative real-time polymerase chain reaction. Most of these genes were upregulated in the sciatic nerve stump following peripheral nerve injury, except for CTNNA2, which was downregulated. Our findings reveal the dynamic changes of key molecules in adherens junctions and in remodeling of adherens junctions. These key genes provide a reference for the selection of clinical therapeutic targets for peripheral nerve injury.
文摘Anchor-based detectors are widely used in object detection.To improve the accuracy of object detection,multiple anchor boxes are intensively placed on the input image,yet.Most of which are invalid.Although the anchor-free method can reduce the number of useless anchor boxes,the invalid ones still occupy a high proportion.On this basis,this paper proposes a multiscale center point object detection method based on parallel network to further reduce the number of useless anchor boxes.This study adopts the parallel network architecture of hourglass-104 and darknet-53 of which the first one outputs heatmaps to generate the center point for object feature location on the output attribute feature map of darknet-53.Combining feature pyramid and CIoU loss function,this algorithm is trained and tested on MSCOCO dataset,increasing the detection rate of target location and the accuracy rate of small object detection.Though resembling the state-of-the-art two-stage detectors in overall object detection accuracy,this algorithm is superior in speed.
文摘Introduction:Genome sequence plays an important role in both basic and applied studies.Gossypium raimondii,the putative contributor of the D subgenome of upland cotton(G.hirsutum,highlights the need to improve the genome quality rapidly and efficiently.Methods:We performed Hi-C sequencing of G.raimondii and reassembled its genome based on a set of new Hi-C data and previously published scaffolds.We also compared the reassembled genome sequenee with the previously published G raimondii genomes for gene and genome sequence collinearity.Result:A total of 9842%of scaffold sequences were clustered successfully,among which 99.72%of the clustered sequences were ordered and 99.92%of the ordered sequences were oriented with high-quality.Further evaluation of results by heat-map and collinearity analysis revealed that the current reassembled genome is significantly improved than the previous one(Nat Genet 44:98-1103,2012).Conclusion:This improvement in G raimondii genome not only provides a better reference to increase study efficiency but also offers a new way to assemble cotton genomes.Furthermore,Hi-C data of G.raimondii may be used for 3D structure research or regulating analysis.
基金supported by the National Key Research and Development Program of China(Nos.2021YFC2009200 and 2023YFC3606100)the Special Project of Technological Innovation and Application Development of Chongqing,China(No.cstc2019jscx-msxmX0167)。
文摘Due to factors such as motion blur,video out-of-focus,and occlusion,multi-frame human pose estimation is a challenging task.Exploiting temporal consistency between consecutive frames is an efficient approach for addressing this issue.Currently,most methods explore temporal consistency through refinements of the final heatmaps.The heatmaps contain the semantics information of key points,and can improve the detection quality to a certain extent.However,they are generated by features,and feature-level refinements are rarely considered.In this paper,we propose a human pose estimation framework with refinements at the feature and semantics levels.We align auxiliary features with the features of the current frame to reduce the loss caused by different feature distributions.An attention mechanism is then used to fuse auxiliary features with current features.In terms of semantics,we use the difference information between adjacent heatmaps as auxiliary features to refine the current heatmaps.The method is validated on the large-scale benchmark datasets PoseTrack2017 and PoseTrack2018,and the results demonstrate the effectiveness of our method.
基金supported in part by the Key R&D program of Shaanxi Province(2020ZDXM5-01)in part by the Fundamental Research Funds for the Central Universities.The review of this article was coordinated by Prof.Long Li.
文摘An intelligent liquid classification system based on 77 GHz millimeter wave radar and convolution neural network are proposed in this paper.The data are collected by the AWR1843 radar platform and processed by the neural network on the host PC in real-time.The doppler heatmap generated by radar signal processing is tried for the first time as the input of the system.The information carried by the heatmap in 2 dimensions is analyzed and the reason why the doppler heatmap could be used for classification is explained.The feasible experiment proved that the proposed method can successfully classify 8 kinds of common liquid with high accuracy.The result of the experiment is explained and the limitations of the experiment are discussed.It can be drawn that the combination of FMCW millimeter wave radar and convolution neural network is a method with great potential for liquid classification.The advantages of real time,non-invasive and unilateral measurement can also be used for the detection of dangerous liquids.
