Background: In response to the escalating burden of cardiovascular diseases (CVDs) worldwide, exacerbated by lifestyle changes and socioeconomic shifts, acute coronary syndromes (ACS) stand out as a leading cause of m...Background: In response to the escalating burden of cardiovascular diseases (CVDs) worldwide, exacerbated by lifestyle changes and socioeconomic shifts, acute coronary syndromes (ACS) stand out as a leading cause of morbidity and mortality. The pivotal role of insulin resistance in the pathogenesis of atherosclerosis, independent of traditional risk factors, has garnered significant interest. Objective: This review aims to synthesize the recent advancements in the utilization of the triglyceride glucose index (TyG index) as a biomarker for assessing the severity and predicting the prognosis of ACS lesions. Methods: A systematic search was conducted across PubMed, Embase, and Scopus databases, incorporating keywords such as “triglyceride glucose index”, “TyG index”, “acute coronary syndrome”, “cardiovascular disease”, “insulin resistance”, “coronary artery calcification”, “SYNTAX score”, “Gensini score”, and “major adverse cardiac events”. Studies were included from the inception of each database up to July 2024. Selection criteria encompassed observational studies, case-control studies, and randomized controlled trials, with a particular emphasis on evaluating the diagnostic and prognostic value of the TyG index in patients with acute coronary syndromes. Ultimately, 46 publications met the inclusion criteria. Data extraction and quality assessment were performed in accordance with established guidelines. Results: Evidence suggests that the TyG index, reflecting insulin resistance, blood glucose, and lipid levels, is significantly associated with lesion severity in ACS, including coronary artery calcification, SYNTAX score, and Gensini score. Moreover, it demonstrates predictive power for major adverse cardiovascular events, underscoring its potential as a valuable tool in clinical decision-making. Conclusion: The review highlights the emerging role of the TyG index in the assessment and prognosis of ACS, advocating for its incorporation into clinical practice as a complement to existing diagnostic modalities. However, the establishment of standardized reference ranges and further validation across diverse populations are warranted to refine its applicability in personalized medicine. The interdisciplinary approach is essential to advance our understanding of the complex interplay between insulin resistance and cardiovascular disease, paving the way for the development of more effective prevention and treatment strategies.展开更多
Automatic and accurate classification is a fundamental problem to the analysis and modeling of LiDAR(Light Detection and Ranging)data.Recently,convolutional neural network(ConvNet or CNN)has achieved remarkable perfor...Automatic and accurate classification is a fundamental problem to the analysis and modeling of LiDAR(Light Detection and Ranging)data.Recently,convolutional neural network(ConvNet or CNN)has achieved remarkable performance in image recognition and computer vision.While significant efforts have also been made to develop various deep networks for satellite image scene classification,it still needs to further investigate suitable deep learning network frameworks for 3D dense mobile laser scanning(MLS)data.In this paper,we present a simple deep CNN for multiple object classification based on multi-scale context representation.For the pointwise classification,we first extracted the neighboring points within spatial context and transformed them into a three-channel image for each point.Then,the classification task can be treated as the image recognition using CNN.The proposed CNN architecture adopted common convolution,maximum pooling and rectified linear unit(ReLU)layers,which combined multiple deeper network layers.After being trained and tested on approximately seven million labeled MLS points,the deep CNN model can classify accurately into nine classes.Comparing with the widely used ResNet algorithm,this model performs better precision and recall rates,and less processing time,which indicated the significant potential of deep-learning-based methods in MLS data classification.展开更多
Multilayer ceramic sheets composed of Li1.075Nb0.625Ti0.45O3 （LNT） layers and silver metal layers were fabricated by aqueous tape-casting method. LNT green tape was prepared using PVA （polyvinyl alcohol） as binde...Multilayer ceramic sheets composed of Li1.075Nb0.625Ti0.45O3 （LNT） layers and silver metal layers were fabricated by aqueous tape-casting method. LNT green tape was prepared using PVA （polyvinyl alcohol） as binder and ethylene glycol as plasticizer. The influence of the slurry composition on the rheological properties of the slurries and the properties of the resultant green tapes were studied. The slurry exhibited a typical shear thinning behavior. The increase in the PVA content increased the tensile strength of the tapes. The slip compositions with 5 wt pct PVA produced green tapes with satisfactory tensile strength. Ethylene glycol additions enhanced the flexibility of the green tapes but also produced a decrease in the tensile strength. Sliver inner-electrode was pasted on LNT green tapes and the sheets were stacked, pressed and sintered at 900℃ for 2 h. SEM （scanning electron microscopy） micrographs showed that the multilayer sheets were fully dense with fairly uniform microstructure and no reaction was observed between LNT and sliver layers.展开更多
Blood pressure(BP)is an important indicator of an individuaPs health status and is closely related to daily behaviors.Thus,a continuous daily measurement of BP is critical for hypertension control.To assist continuous...Blood pressure(BP)is an important indicator of an individuaPs health status and is closely related to daily behaviors.Thus,a continuous daily measurement of BP is critical for hypertension control.To assist continuous measurement,BP prediction based on non-physiological data(ubiquitous mobile phone data)was studied in the research.An algorithm was proposed that predicts BP based on patients'daily routine,which includes activities such as sleep,work,and commuting.The aim of the research is to provide insight into the application of mobile data in telemonitoring and the continuous unobtrusive daily measurement of BP.A half-year data set from October 2017 of 320 individuals,including telecom data and BP measurement data,was analyzed.Two hierarchical Bayesian topic models were used to extract individuals,location-driven daily routine patterns(topics)and calculate probabilities among these topics from their day-level mobile trajectories.Based on the topic probability distribution and patients'contextual data,their BP were predicted using different models.The prediction model comparison shows that the long short-term memory(LSTM)method exceeds others when the data has a high dependency.Otherwise,the Random Forest regression model outperforms the LSTM method.Also,the experimental results validate the effectiveness of the topics in BP prediction.展开更多
In this study we successfully intercalated potassium(K) atoms into single ZrTe_5 crystals by liquid ammonia method, and found a semimetal-to-semiconductor transition at low temperatures in K-intercalated ZrTe_5. As th...In this study we successfully intercalated potassium(K) atoms into single ZrTe_5 crystals by liquid ammonia method, and found a semimetal-to-semiconductor transition at low temperatures in K-intercalated ZrTe_5. As the K concentration increased, the resistance anomalous peak was gradually suppressed until finally disappearing. Whilst, the corresponding Hall resistance measurements consistently showed a sign reversal. The semimetal-to-semiconductor transition can be attributed to a lattice expansion induced by atom intercalation, leading to a larger energy band gap.展开更多
文摘Background: In response to the escalating burden of cardiovascular diseases (CVDs) worldwide, exacerbated by lifestyle changes and socioeconomic shifts, acute coronary syndromes (ACS) stand out as a leading cause of morbidity and mortality. The pivotal role of insulin resistance in the pathogenesis of atherosclerosis, independent of traditional risk factors, has garnered significant interest. Objective: This review aims to synthesize the recent advancements in the utilization of the triglyceride glucose index (TyG index) as a biomarker for assessing the severity and predicting the prognosis of ACS lesions. Methods: A systematic search was conducted across PubMed, Embase, and Scopus databases, incorporating keywords such as “triglyceride glucose index”, “TyG index”, “acute coronary syndrome”, “cardiovascular disease”, “insulin resistance”, “coronary artery calcification”, “SYNTAX score”, “Gensini score”, and “major adverse cardiac events”. Studies were included from the inception of each database up to July 2024. Selection criteria encompassed observational studies, case-control studies, and randomized controlled trials, with a particular emphasis on evaluating the diagnostic and prognostic value of the TyG index in patients with acute coronary syndromes. Ultimately, 46 publications met the inclusion criteria. Data extraction and quality assessment were performed in accordance with established guidelines. Results: Evidence suggests that the TyG index, reflecting insulin resistance, blood glucose, and lipid levels, is significantly associated with lesion severity in ACS, including coronary artery calcification, SYNTAX score, and Gensini score. Moreover, it demonstrates predictive power for major adverse cardiovascular events, underscoring its potential as a valuable tool in clinical decision-making. Conclusion: The review highlights the emerging role of the TyG index in the assessment and prognosis of ACS, advocating for its incorporation into clinical practice as a complement to existing diagnostic modalities. However, the establishment of standardized reference ranges and further validation across diverse populations are warranted to refine its applicability in personalized medicine. The interdisciplinary approach is essential to advance our understanding of the complex interplay between insulin resistance and cardiovascular disease, paving the way for the development of more effective prevention and treatment strategies.
基金National Natural Science Foundation of China(Nos.41971423,31972951,41771462)Hunan Provincial Natural Science Foundation of China(No.2020JJ3020)+1 种基金Science and Technology Planning Project of Hunan Province(No.2019RS2043,2019GK2132)Outstanding Youth Project of Education Department of Hunan Province(No.18B224)。
文摘Automatic and accurate classification is a fundamental problem to the analysis and modeling of LiDAR(Light Detection and Ranging)data.Recently,convolutional neural network(ConvNet or CNN)has achieved remarkable performance in image recognition and computer vision.While significant efforts have also been made to develop various deep networks for satellite image scene classification,it still needs to further investigate suitable deep learning network frameworks for 3D dense mobile laser scanning(MLS)data.In this paper,we present a simple deep CNN for multiple object classification based on multi-scale context representation.For the pointwise classification,we first extracted the neighboring points within spatial context and transformed them into a three-channel image for each point.Then,the classification task can be treated as the image recognition using CNN.The proposed CNN architecture adopted common convolution,maximum pooling and rectified linear unit(ReLU)layers,which combined multiple deeper network layers.After being trained and tested on approximately seven million labeled MLS points,the deep CNN model can classify accurately into nine classes.Comparing with the widely used ResNet algorithm,this model performs better precision and recall rates,and less processing time,which indicated the significant potential of deep-learning-based methods in MLS data classification.
基金supported by the Key Scientificand Technological Project of Zhejiang Province, China(2006C21071).
文摘Multilayer ceramic sheets composed of Li1.075Nb0.625Ti0.45O3 （LNT） layers and silver metal layers were fabricated by aqueous tape-casting method. LNT green tape was prepared using PVA （polyvinyl alcohol） as binder and ethylene glycol as plasticizer. The influence of the slurry composition on the rheological properties of the slurries and the properties of the resultant green tapes were studied. The slurry exhibited a typical shear thinning behavior. The increase in the PVA content increased the tensile strength of the tapes. The slip compositions with 5 wt pct PVA produced green tapes with satisfactory tensile strength. Ethylene glycol additions enhanced the flexibility of the green tapes but also produced a decrease in the tensile strength. Sliver inner-electrode was pasted on LNT green tapes and the sheets were stacked, pressed and sintered at 900℃ for 2 h. SEM （scanning electron microscopy） micrographs showed that the multilayer sheets were fully dense with fairly uniform microstructure and no reaction was observed between LNT and sliver layers.
基金the National Natural Science Foundation of China(Grants No.91646205 and 71421002)the Fundamental Research Funds for the Central Universities of China(Grant No.16JCCS08)。
文摘Blood pressure(BP)is an important indicator of an individuaPs health status and is closely related to daily behaviors.Thus,a continuous daily measurement of BP is critical for hypertension control.To assist continuous measurement,BP prediction based on non-physiological data(ubiquitous mobile phone data)was studied in the research.An algorithm was proposed that predicts BP based on patients'daily routine,which includes activities such as sleep,work,and commuting.The aim of the research is to provide insight into the application of mobile data in telemonitoring and the continuous unobtrusive daily measurement of BP.A half-year data set from October 2017 of 320 individuals,including telecom data and BP measurement data,was analyzed.Two hierarchical Bayesian topic models were used to extract individuals,location-driven daily routine patterns(topics)and calculate probabilities among these topics from their day-level mobile trajectories.Based on the topic probability distribution and patients'contextual data,their BP were predicted using different models.The prediction model comparison shows that the long short-term memory(LSTM)method exceeds others when the data has a high dependency.Otherwise,the Random Forest regression model outperforms the LSTM method.Also,the experimental results validate the effectiveness of the topics in BP prediction.
基金supported by the Ministry of Science and Technology of China(Grant Nos.2014CB921103,and 2015CB921203)the National Natural Science Foundation of China(Grant Nos.11774149,11790311,11674157,11674154,51032003,1171101156,11374149,and 11374140)
文摘In this study we successfully intercalated potassium(K) atoms into single ZrTe_5 crystals by liquid ammonia method, and found a semimetal-to-semiconductor transition at low temperatures in K-intercalated ZrTe_5. As the K concentration increased, the resistance anomalous peak was gradually suppressed until finally disappearing. Whilst, the corresponding Hall resistance measurements consistently showed a sign reversal. The semimetal-to-semiconductor transition can be attributed to a lattice expansion induced by atom intercalation, leading to a larger energy band gap.
