Drug discovery and development affects various aspects of human health and dramatically impacts the pharmaceutical market.However,investments in a new drug often go unrewarded due to the long and complex process of dr...Drug discovery and development affects various aspects of human health and dramatically impacts the pharmaceutical market.However,investments in a new drug often go unrewarded due to the long and complex process of drug research and development(R&D).With the advancement of experimental technology and computer hardware,artificial intelligence(AI)has recently emerged as a leading tool in analyzing abundant and high-dimensional data.Explosive growth in the size of biomedical data provides advantages in applying AI in all stages of drug R&D.Driven by big data in biomedicine,AI has led to a revolution in drug R&D,due to its ability to discover new drugs more efficiently and at lower cost.This review begins with a brief overview of common AI models in the field of drug discovery;then,it summarizes and discusses in depth their specific applications in various stages of drug R&D,such as target discovery,drug discovery and design,preclinical research,automated drug synthesis,and influences in the pharmaceutical market.Finally,the major limitations of AI in drug R&D are fully discussed and possible solutions are proposed.展开更多
Sparse representation is an effective data classification algorithm that depends on the known training samples to categorise the test sample.It has been widely used in various image classification tasks.Sparseness in ...Sparse representation is an effective data classification algorithm that depends on the known training samples to categorise the test sample.It has been widely used in various image classification tasks.Sparseness in sparse representation means that only a few of instances selected from all training samples can effectively convey the essential class-specific information of the test sample,which is very important for classification.For deformable images such as human faces,pixels at the same location of different images of the same subject usually have different intensities.Therefore,extracting features and correctly classifying such deformable objects is very hard.Moreover,the lighting,attitude and occlusion cause more difficulty.Considering the problems and challenges listed above,a novel image representation and classification algorithm is proposed.First,the authors’algorithm generates virtual samples by a non-linear variation method.This method can effectively extract the low-frequency information of space-domain features of the original image,which is very useful for representing deformable objects.The combination of the original and virtual samples is more beneficial to improve the clas-sification performance and robustness of the algorithm.Thereby,the authors’algorithm calculates the expression coefficients of the original and virtual samples separately using the sparse representation principle and obtains the final score by a designed efficient score fusion scheme.The weighting coefficients in the score fusion scheme are set entirely automatically.Finally,the algorithm classifies the samples based on the final scores.The experimental results show that our method performs better classification than conventional sparse representation algorithms.展开更多
Reconfigurable intelligent surface(RIS)is an emerging meta-surface that can provide additional communications links through reflecting the signals,and has been recognized as a strong candidate of 6G mobile communicati...Reconfigurable intelligent surface(RIS)is an emerging meta-surface that can provide additional communications links through reflecting the signals,and has been recognized as a strong candidate of 6G mobile communications systems.Meanwhile,it has been recently admitted that implementing artificial intelligence(AI)into RIS communications will extensively benefit the reconfiguration capacity and enhance the robustness to complicated transmission environments.Besides the conventional model-driven approaches,AI can also deal with the existing signal processing problems in a data-driven manner via digging the inherent characteristic from the real data.Hence,AI is particularly suitable for the signal processing problems over RIS networks under unideal scenarios like modeling mismatching,insufficient resource,hardware impairment,as well as dynamical transmissions.As one of the earliest survey papers,we will introduce the merging of AI and RIS,called AIRIS,over various signal processing topics,including environmental sensing,channel acquisition,beamforming design,and resource scheduling,etc.We will also discuss the challenges of AIRIS and present some interesting future directions.展开更多
In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utiliz...In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information(CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real CSI.The capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.展开更多
To simplify the fabrication process and increase the versatility of neuromorphic systems,the reconfiguration concept has attracted much attention.Here,we developed a novel electrochemical VO_(2)(EC-VO_(2))device,which...To simplify the fabrication process and increase the versatility of neuromorphic systems,the reconfiguration concept has attracted much attention.Here,we developed a novel electrochemical VO_(2)(EC-VO_(2))device,which can be reconfigured as synapses or LIF neurons.The ionic dynamic doping contributed to the resistance changes of VO_(2),which enables the reversible modulation of device states.The analog resistance switching and tunable LIF functions were both measured based on the same device to demonstrate the capacity of reconfiguration.Based on the reconfigurable EC-VO_(2),the simulated spiking neural network model exhibited excellent performances by using low-precision weights and tunable output neurons,whose final accuracy reached 91.92%.展开更多
Surface-wave tomography is an important and widely used method for imaging the crust and upper mantle velocity structure of the Earth.In this study,we proposed a deep learning(DL)method based on convolutional neural n...Surface-wave tomography is an important and widely used method for imaging the crust and upper mantle velocity structure of the Earth.In this study,we proposed a deep learning(DL)method based on convolutional neural network(CNN),named SfNet,to derive the vS model from the Rayleigh wave phase and group velocity dispersion curves.Training a network model usually requires large amount of training datasets,which is labor-intensive and expensive to acquire.Here we relied on synthetics generated automatically from various spline-based vS models instead of directly using the existing vS models of an area to build the training dataset,which enhances the generalization of the DL method.In addition,we used a random sampling strategy of the dispersion periods in the training dataset,which alleviates the problem that the real data used must be sampled strictly according to the periods of training dataset.Tests using synthetic data demonstrate that the proposed method is much faster,and the results for the vS model are more accurate and robust than those of conventional methods.We applied our method to a dataset for the Chinese mainland and obtained a new reference velocity model of the Chinese continent(ChinaVs-DL1.0),which has smaller dispersion misfits than those from the traditional method.The high accuracy and efficiency of our DL approach makes it an important method for vS model inversions from large amounts of surface-wave dispersion data.展开更多
The purpose of this study was to establish a high-performance liquid chromatography (HPLC) method for the simultaneous determination of sodium danshensu, protocatechuic aldehyde, rosmarinic acid, salvianolic acid B, a...The purpose of this study was to establish a high-performance liquid chromatography (HPLC) method for the simultaneous determination of sodium danshensu, protocatechuic aldehyde, rosmarinic acid, salvianolic acid B, and 4-coumaric acid in Danhong injection. The chromatographic method employed was as follows: the column was a Welch Ultimate XB-C18 column (250 mm × 4.6 mm, 10 μm), the mobile phase was a gradient elution of 0.4% formic acid aqueous solution (A) and acetonitrile (B), the detection wavelengths were 280 nm for sodium danshensu, protocatechuic aldehyde, and salvianolic acid B and 326 nm for 4-coumaric acid and rosmarinic acid, the sample volume was 10 μL, the flow rate was 1.0 mL/min, and the column temperature was 35°C. This method can realize the separation and determination of sodium danshensu, protocatechuic aldehyde, rosmarinic acid, salvianolic acid B, and 4-coumaric acid within 50 minutes. The linear relationships of the five peak areas and their concentrations are good (R<sup>2</sup>> 0.9997). The precision RSD values are all less than 1.0%. The reproducibility RSD values are all less than 1.3%. The stability RSD values are all less than 2.2%. The recovery values ranged from 92.4% to 99.4%. This method is simple, accurate, and reproducible. It can be used for the determination of sodium danshensu, protocatechuic aldehyde, rosmarinic acid, salvianolic acid B, and 4-coumaric acid in Danhong injection.展开更多
Recently,backscatter communication(BC)has been introduced as a green paradigm for Internet of Things(IoT).Meanwhile,unmanned aerial vehicles(UAVs)can serve as aerial base stations(BSs)to enhance the performance of BC ...Recently,backscatter communication(BC)has been introduced as a green paradigm for Internet of Things(IoT).Meanwhile,unmanned aerial vehicles(UAVs)can serve as aerial base stations(BSs)to enhance the performance of BC system thanks to their high mobility and flexibility.In this paper,we investigate the problem of energy efficiency(EE)for an energy-limited backscatter communication(BC)network,where backscatter devices(BDs)on the ground harvest energy from the wireless signal of a flying rotary-wing quadrotor.Specifically,we first reformulate the EE optimization problem as a Markov decision process(MDP)and then propose a deep reinforcement learning(DRL)algorithm to design the UAV trajectory with the constraints of the BD scheduling,the power reflection coefficients,the transmission power,and the fairness among BDs.Simulation results show the proposed DRL algorithm achieves close-to-optimal performance and significant EE gains compared to the benchmark schemes.展开更多
Wireless power transfer(WPT) to support mobile and portable devices is an emerging wireless technique.Among all kinds of approaches,magnetic resonance coupling(MRC) is an excellent one for mid-range WPT,which provides...Wireless power transfer(WPT) to support mobile and portable devices is an emerging wireless technique.Among all kinds of approaches,magnetic resonance coupling(MRC) is an excellent one for mid-range WPT,which provides better mobility,flexibility,and convenience due to its simplicity in hardware implementation and longer transmission distances.In this paper,we consider an MRCWPT system with multiple power transmitters,one intended power receiver and multiple unintended power receivers.We investigate the probabilistic robust beamforming designs and provide efficient algorithms to achieve the local optimums under two different criteria,i.e.,total source power minimization problem and min-max unintended receiving power restriction problem.As the problems are quite typical in robust design situations,our proposed robust beamformers can be conveniently applied to other probabilistic robust design problems,thus reduce the complexity as well as improve the beamforming performance.Numerical results demonstrate that the proposed algorithms can significantly improve the performance as well as the robustness of the WPT system.展开更多
Most human-secreted and membrane-bound proteins have covalently attached oligosaccharide chains or glycans.Glycosylation influences the physical and chemical properties of proteins,as well as their biological function...Most human-secreted and membrane-bound proteins have covalently attached oligosaccharide chains or glycans.Glycosylation influences the physical and chemical properties of proteins,as well as their biological functions.Unsurprisingly,alterations in protein glycosylation have been implicated in a growing number of human diseases,and glycans are increasingly being considered as potential therapeutic targets,an essential part of therapeutics,and biomarkers.Although glycosylation pathways are biochemically well-studied,little is known about the networks of genes that guide the cell-and tissue-specific regulation of these biochemical reactions in humans in vivo.The lack of a detailed understanding of the mechanisms regulating glycome variation and linking the glycome to human health and disease is slowing progress in clinical applications of human glycobiology.Two of the tools that can provide much sought-after knowledge of human in vivo glycobiology are human genetics and genomics,which offer a powerful data-driven agnostic approach for dissecting the biology of complex traits.This review summarizes the current state of human populational glycogenomics.In Section 1,we provide a brief overview of the N-glycan’s structural organization,and in Section 2,we give a description of the major blood plasma glycoproteins.Next,in Section 3,we summarize,systemize,and generalize the results from current N-glycosylation genome-wide association studies(GWASs)that provide novel knowledge of the genetic regulation of the populational variation of glycosylation.Until now,such studies have been limited to an analysis of the human blood plasma N-glycome and the N-glycosylation of immunoglobulin G and transferrin.While these three glycomes make up a rather limited set compared with the enormous multitude of glycomes of different tissues and glycoproteins,the study of these three does allow for powerful analysis and generalization.Finally,in Section 4,we turn to genes in the established loci,paying particular attention to genes with strong support in Section 5.At the end of the review,in Sections 6 and 7,we describe special cases of interest in light of new discoveries,focusing on possible mechanisms of action and biological targets of genetic variation that have been implicated in human protein N-glycosylation.展开更多
Pre-harvest yield prediction of ratoon rice is critical for guiding crop interventions in precision agriculture.However,the unique agronomic practice(i.e.,varied stubble height treatment)in rice ratooning could lead t...Pre-harvest yield prediction of ratoon rice is critical for guiding crop interventions in precision agriculture.However,the unique agronomic practice(i.e.,varied stubble height treatment)in rice ratooning could lead to inconsistent rice phenology,which had a significant impact on yield prediction of ratoon rice.Multi-temporal unmanned aerial vehicle(UAV)-based remote sensing can likely monitor ratoon rice productivity and reflect maximum yield potential across growing seasons for improving the yield prediction compared with previous methods.Thus,in this study,we explored the performance of combination of agronomic practice information(API)and single-phase,multi-spectral features[vegetation indices(VIs)and texture(Tex)features]in predicting ratoon rice yield,and developed a new UAV-based method to retrieve yield formation process by using multi-temporal features which were effective in improving yield forecasting accuracy of ratoon rice.The results showed that the integrated use of VIs,Tex and API(VIs&Tex+API)improved the accuracy of yield prediction than single-phase UAV imagery-based feature,with the panicle initiation stage being the best period for yield prediction(R^(2) as 0.732,RMSE as 0.406,RRMSE as 0.101).More importantly,compared with previous multi-temporal UAV-based methods,our proposed multi-temporal method(multi-temporal model VIs&Tex:R^(2) as 0.795,RMSE as 0.298,RRMSE as 0.072)can increase R^(2) by 0.020-0.111 and decrease RMSE by 0.020-0.080 in crop yield forecasting.This study provides an effective method for accurate pre-harvest yield prediction of ratoon rice in precision agriculture,which is of great significance to take timely means for ensuring ratoon rice production and food security.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
Objective To evaluate the association between serum uric acid(SUA)and kidney function decline.Methods Data was obtained from the China Health and Retirement Longitudinal Study on the Chinese middle-aged and older popu...Objective To evaluate the association between serum uric acid(SUA)and kidney function decline.Methods Data was obtained from the China Health and Retirement Longitudinal Study on the Chinese middle-aged and older population for analysis.The kidney function decline was defined as an annual estimated glomerular filtration rate(e GFR)decrease by>3 mL/min per 1.73 m^(2).Multivariable logistic regression was applied to determine the association between SUA and kidney function decline.The shape of the association was investigated by restricted cubic splines.Results A total of 7,346 participants were included,of which 1,004 individuals(13.67%)developed kidney function decline during the follow-up of 4 years.A significant dose-response relation was recorded between SUA and the kidney function decline(OR 1.14,95%CI 1.03-1.27),as the risk of kidney function decline increased by 14%per 1 mg/d L increase in SUA.In the subgroup analyses,such a relation was only recorded among women(OR 1.22,95%CI 1.03-1.45),those aged<60 years(OR 1.22,95%CI 1.05-1.42),and those without hypertension and without diabetes(OR 1.22,95%CI 1.06-1.41).Although the dose-response relation was not observed in men,the high level of SUA was related to kidney function decline(OR 1.83,95%CI 1.05-3.17).The restricted cubic spline analysis indicated that SUA>5 mg/dL was associated with a significantly higher risk of kidney function decline.Conclusion The SUA level was associated with kidney function decline.An elevation of SUA should therefore be addressed to prevent possible kidney impairment and dysfunction.展开更多
Accurate profiling of microscopy images from small scale to high throughput is an essential procedure in basic and applied biological research.Here,we present Microsnoop,a novel deep learning–based representation too...Accurate profiling of microscopy images from small scale to high throughput is an essential procedure in basic and applied biological research.Here,we present Microsnoop,a novel deep learning–based representation tool trained on large-scale microscopy images using masked self-supervised learning.Microsnoop can process various complex and heterogeneous images,and we classified images into three categories:single-cell,full-field,and batch-experiment images.Our benchmark study on 10 high-quality evaluation datasets,containing over 2,230,000 images,demonstrated Microsnoop’s robust and state-ofthe-art microscopy image representation ability,surpassing existing generalist and even several custom algorithms.Microsnoop can be integrated with other pipelines to perform tasks such as superresolution histopathology image and multimodal analysis.Furthermore,Microsnoop can be adapted to various hardware and can be easily deployed on local or cloud computing platforms.We will regularly retrain and reevaluate the model using communitycontributed data to consistently improve Microsnoop.展开更多
Deep learning has achieved great success in a variety of research fields and industrial applications.However,when applied to seismic inversion,the shortage of labeled data severely influences the performance of deep l...Deep learning has achieved great success in a variety of research fields and industrial applications.However,when applied to seismic inversion,the shortage of labeled data severely influences the performance of deep learning-based methods.In order to tackle this problem,we propose a novel seismic impedance inversion method based on a cycle-consistent generative adversarial network(Cycle-GAN).The proposed Cycle-GAN model includes two generative subnets and two discriminative subnets.Three kinds of loss,including cycle-consistent loss,adversarial loss,and estimation loss,are adopted to guide the training process.Benefit from the proposed structure,the information contained in unlabeled data can be extracted,and adversarial learning further guarantees that the prediction results share similar distributions with the real data.Moreover,a neural network visualization method is adopted to show that the proposed CNN model can learn more distinguishable features than the conventional CNN model.The robustness experiments on synthetic data sets show that the proposed method can achieve better performances than other methods in most cases.And the blind-well experiments on real seismic profiles show that the predicted impedance curve of the proposed method maintains a better correlation with the true impedance curve.展开更多
In recent years,deep learning has made tremendous achievements in computer vision,natural language processing,man-machine games and so on,where artificial intelligence can reach or go beyond the level of human beings....In recent years,deep learning has made tremendous achievements in computer vision,natural language processing,man-machine games and so on,where artificial intelligence can reach or go beyond the level of human beings.However,behind so many glories,some serious challenges exist in the bottom hardware,hindering the further development of Artificial Intelligence.展开更多
Lipids have been found to modulate tumor biology,including proliferation,survival,and metastasis.With the new understanding of tumor immune escape that has developed in recent years,the influence of lipids on the can...Lipids have been found to modulate tumor biology,including proliferation,survival,and metastasis.With the new understanding of tumor immune escape that has developed in recent years,the influence of lipids on the cancer—immunity cycle has also been gradually discovered.First,regarding antigen presentation,cholesterol prevents tumor antigens from being identified by antigen presenting cells.Fatty acids reduce the expression of major histocompatibility complex class I and costimulatory factors in dendritic cells,impairing antigen presentation to T cells.Prostaglandin E2(PGE2)reduce the accumulation of tumor-infiltrating dendritic cells.Regarding T-cell priming and activation,cholesterol destroys the structure of the T-cell receptor and reduces immunodetection.In contrast,cholesterol also promotes T-cell receptor clustering and relative signal transduction.PGE2 represses T-cell proliferation.Finally,regarding T-cell killing of cancer cells,PGE2 and cholesterol weaken granule-dependent cytotoxicity.Moreover,fatty acids,cholesterol,and PGE2 can improve the activity of immunosuppressive cells,increase the expression of immune checkpoints and promote the secretion of immunosuppressive cytokines.Given the regulatory role of lipids in the cancer—immunity cycle,drugs that modulate fatty acids,cholesterol and PGE2 have been envisioned as effective way in restoring antitumor immunity and synergizing with immunotherapy.These strategies have been studied in both preclinical and clinical studies.展开更多
Acid-base dissociation constant(pK_(a)) is a key physicochemical parameter in chemical science, especially in organic synthesis and drug discovery. Current methodologies for pK_(a) prediction still suffer from limited...Acid-base dissociation constant(pK_(a)) is a key physicochemical parameter in chemical science, especially in organic synthesis and drug discovery. Current methodologies for pK_(a) prediction still suffer from limited applicability domain and lack of chemical insight. Here we present MF-SuP-pK_(a)(multi-fidelity modeling with subgraph pooling for pK_(a) prediction), a novel pK_(a) prediction model that utilizes subgraph pooling, multi-fidelity learning and data augmentation. In our model, a knowledgeaware subgraph pooling strategy was designed to capture the local and global environments around the ionization sites for micro-pK_(a) prediction. To overcome the scarcity of accurate pK_(a) data, lowfidelity data(computational pK_(a)) was used to fit the high-fidelity data(experimental pK_(a)) through transfer learning. The final MF-SuP-pK_(a) model was constructed by pre-training on the augmented ChEMBL data set and fine-tuning on the DataWarrior data set. Extensive evaluation on the DataWarrior data set and three benchmark data sets shows that MF-SuP-pK_(a) achieves superior performances to the state-of-theart pK_(a) prediction models while requires much less high-fidelity training data. Compared with Attentive FP, MF-SuP-pK_(a) achieves 23.83% and 20.12% improvement in terms of mean absolute error(MAE) on the acidic and basic sets, respectively.展开更多
基金funded by the Natural Science Foundation of Zhejiang Province(LR21H300001)National Key R&D Program of China(2022YFC3400501)+4 种基金National Natural Science Foundation of China(22220102001,U1909208,81872798,and 81825020)Leading Talent of the“Ten Thousand Plan”-National High-Level Talents Special Support Plan of ChinaFundamental Research Fund of Central University(2018QNA7023)Key R&D Program of Zhejiang Province(2020C03010)“Double Top-Class”University(181201*194232101)。
文摘Drug discovery and development affects various aspects of human health and dramatically impacts the pharmaceutical market.However,investments in a new drug often go unrewarded due to the long and complex process of drug research and development(R&D).With the advancement of experimental technology and computer hardware,artificial intelligence(AI)has recently emerged as a leading tool in analyzing abundant and high-dimensional data.Explosive growth in the size of biomedical data provides advantages in applying AI in all stages of drug R&D.Driven by big data in biomedicine,AI has led to a revolution in drug R&D,due to its ability to discover new drugs more efficiently and at lower cost.This review begins with a brief overview of common AI models in the field of drug discovery;then,it summarizes and discusses in depth their specific applications in various stages of drug R&D,such as target discovery,drug discovery and design,preclinical research,automated drug synthesis,and influences in the pharmaceutical market.Finally,the major limitations of AI in drug R&D are fully discussed and possible solutions are proposed.
文摘Sparse representation is an effective data classification algorithm that depends on the known training samples to categorise the test sample.It has been widely used in various image classification tasks.Sparseness in sparse representation means that only a few of instances selected from all training samples can effectively convey the essential class-specific information of the test sample,which is very important for classification.For deformable images such as human faces,pixels at the same location of different images of the same subject usually have different intensities.Therefore,extracting features and correctly classifying such deformable objects is very hard.Moreover,the lighting,attitude and occlusion cause more difficulty.Considering the problems and challenges listed above,a novel image representation and classification algorithm is proposed.First,the authors’algorithm generates virtual samples by a non-linear variation method.This method can effectively extract the low-frequency information of space-domain features of the original image,which is very useful for representing deformable objects.The combination of the original and virtual samples is more beneficial to improve the clas-sification performance and robustness of the algorithm.Thereby,the authors’algorithm calculates the expression coefficients of the original and virtual samples separately using the sparse representation principle and obtains the final score by a designed efficient score fusion scheme.The weighting coefficients in the score fusion scheme are set entirely automatically.Finally,the algorithm classifies the samples based on the final scores.The experimental results show that our method performs better classification than conventional sparse representation algorithms.
基金This work was supported in part by National Key Research and Development Program of China under Grant 2017YFB1010002in part by National Natural Science Foundation of China under Grant 61871455,61831013.
文摘Reconfigurable intelligent surface(RIS)is an emerging meta-surface that can provide additional communications links through reflecting the signals,and has been recognized as a strong candidate of 6G mobile communications systems.Meanwhile,it has been recently admitted that implementing artificial intelligence(AI)into RIS communications will extensively benefit the reconfiguration capacity and enhance the robustness to complicated transmission environments.Besides the conventional model-driven approaches,AI can also deal with the existing signal processing problems in a data-driven manner via digging the inherent characteristic from the real data.Hence,AI is particularly suitable for the signal processing problems over RIS networks under unideal scenarios like modeling mismatching,insufficient resource,hardware impairment,as well as dynamical transmissions.As one of the earliest survey papers,we will introduce the merging of AI and RIS,called AIRIS,over various signal processing topics,including environmental sensing,channel acquisition,beamforming design,and resource scheduling,etc.We will also discuss the challenges of AIRIS and present some interesting future directions.
基金supported in part by the National Science Fund for Distinguished Young Scholars under Grant 61925102in part by the National Natural Science Foundation of China(62201087&92167202&62101069&62201086)in part by the Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Center。
文摘In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information(CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real CSI.The capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.
基金Project supported by the National Natural Science Foundation of China (Grant Nos.61925401,92064004,61927901,and 92164302)the 111 Project (Grant No.B18001)+1 种基金support from the Fok Ying-Tong Education Foundationthe Tencent Foundation through the XPLORER PRIZE。
文摘To simplify the fabrication process and increase the versatility of neuromorphic systems,the reconfiguration concept has attracted much attention.Here,we developed a novel electrochemical VO_(2)(EC-VO_(2))device,which can be reconfigured as synapses or LIF neurons.The ionic dynamic doping contributed to the resistance changes of VO_(2),which enables the reversible modulation of device states.The analog resistance switching and tunable LIF functions were both measured based on the same device to demonstrate the capacity of reconfiguration.Based on the reconfigurable EC-VO_(2),the simulated spiking neural network model exhibited excellent performances by using low-precision weights and tunable output neurons,whose final accuracy reached 91.92%.
基金the Open Fund from SinoProbe Laboratory(Grant No.Sinoprobe Lab 202201)the National Natural Science Foundation of China(No.U1939204).
文摘Surface-wave tomography is an important and widely used method for imaging the crust and upper mantle velocity structure of the Earth.In this study,we proposed a deep learning(DL)method based on convolutional neural network(CNN),named SfNet,to derive the vS model from the Rayleigh wave phase and group velocity dispersion curves.Training a network model usually requires large amount of training datasets,which is labor-intensive and expensive to acquire.Here we relied on synthetics generated automatically from various spline-based vS models instead of directly using the existing vS models of an area to build the training dataset,which enhances the generalization of the DL method.In addition,we used a random sampling strategy of the dispersion periods in the training dataset,which alleviates the problem that the real data used must be sampled strictly according to the periods of training dataset.Tests using synthetic data demonstrate that the proposed method is much faster,and the results for the vS model are more accurate and robust than those of conventional methods.We applied our method to a dataset for the Chinese mainland and obtained a new reference velocity model of the Chinese continent(ChinaVs-DL1.0),which has smaller dispersion misfits than those from the traditional method.The high accuracy and efficiency of our DL approach makes it an important method for vS model inversions from large amounts of surface-wave dispersion data.
文摘The purpose of this study was to establish a high-performance liquid chromatography (HPLC) method for the simultaneous determination of sodium danshensu, protocatechuic aldehyde, rosmarinic acid, salvianolic acid B, and 4-coumaric acid in Danhong injection. The chromatographic method employed was as follows: the column was a Welch Ultimate XB-C18 column (250 mm × 4.6 mm, 10 μm), the mobile phase was a gradient elution of 0.4% formic acid aqueous solution (A) and acetonitrile (B), the detection wavelengths were 280 nm for sodium danshensu, protocatechuic aldehyde, and salvianolic acid B and 326 nm for 4-coumaric acid and rosmarinic acid, the sample volume was 10 μL, the flow rate was 1.0 mL/min, and the column temperature was 35°C. This method can realize the separation and determination of sodium danshensu, protocatechuic aldehyde, rosmarinic acid, salvianolic acid B, and 4-coumaric acid within 50 minutes. The linear relationships of the five peak areas and their concentrations are good (R<sup>2</sup>> 0.9997). The precision RSD values are all less than 1.0%. The reproducibility RSD values are all less than 1.3%. The stability RSD values are all less than 2.2%. The recovery values ranged from 92.4% to 99.4%. This method is simple, accurate, and reproducible. It can be used for the determination of sodium danshensu, protocatechuic aldehyde, rosmarinic acid, salvianolic acid B, and 4-coumaric acid in Danhong injection.
基金the National Natural Science Foundation of China 61661021,61971191,61902214,and 61871321,in part by the Beijing Natural Science Foundation under Grant L182018,in part by the National Science and Technology Major Project of the Ministry of Science and Technology of China under Grant 2016ZX03001014-006in part by the open project of Shanghai Institute of Microsystem and Information Technology(20190910)+1 种基金in part by the Key project of Natural Science Foundation of Jiangxi Province(20202ACBL202006)in part by the open project of Key Laboratory of Wireless Sensor Network&Communication,Shanghai Institute of Microsystem and Information Technology,Chinese Academy of Sciences,865 Changning Road,Shanghai 200050 China,and in part by the Tsinghua University Initiative Scientific Research Program 2019Z08QCX19.
文摘Recently,backscatter communication(BC)has been introduced as a green paradigm for Internet of Things(IoT).Meanwhile,unmanned aerial vehicles(UAVs)can serve as aerial base stations(BSs)to enhance the performance of BC system thanks to their high mobility and flexibility.In this paper,we investigate the problem of energy efficiency(EE)for an energy-limited backscatter communication(BC)network,where backscatter devices(BDs)on the ground harvest energy from the wireless signal of a flying rotary-wing quadrotor.Specifically,we first reformulate the EE optimization problem as a Markov decision process(MDP)and then propose a deep reinforcement learning(DRL)algorithm to design the UAV trajectory with the constraints of the BD scheduling,the power reflection coefficients,the transmission power,and the fairness among BDs.Simulation results show the proposed DRL algorithm achieves close-to-optimal performance and significant EE gains compared to the benchmark schemes.
基金supported by National Natural Science Foundation of China(Grant No.61771185,61831013)Science and Technology Research Project of Henan Province(Grant No.182102210044)+1 种基金Key Scientific Research Program of Henan Higher Education(Grant No.18A510009)Beijing Municipal Natural Science Foundation(Grant No.4182030)
文摘Wireless power transfer(WPT) to support mobile and portable devices is an emerging wireless technique.Among all kinds of approaches,magnetic resonance coupling(MRC) is an excellent one for mid-range WPT,which provides better mobility,flexibility,and convenience due to its simplicity in hardware implementation and longer transmission distances.In this paper,we consider an MRCWPT system with multiple power transmitters,one intended power receiver and multiple unintended power receivers.We investigate the probabilistic robust beamforming designs and provide efficient algorithms to achieve the local optimums under two different criteria,i.e.,total source power minimization problem and min-max unintended receiving power restriction problem.As the problems are quite typical in robust design situations,our proposed robust beamformers can be conveniently applied to other probabilistic robust design problems,thus reduce the complexity as well as improve the beamforming performance.Numerical results demonstrate that the proposed algorithms can significantly improve the performance as well as the robustness of the WPT system.
基金an output of a research project implemented as part of the Research Program at the Moscow State University (MSU) Institute for Artificial Intelligence.
文摘Most human-secreted and membrane-bound proteins have covalently attached oligosaccharide chains or glycans.Glycosylation influences the physical and chemical properties of proteins,as well as their biological functions.Unsurprisingly,alterations in protein glycosylation have been implicated in a growing number of human diseases,and glycans are increasingly being considered as potential therapeutic targets,an essential part of therapeutics,and biomarkers.Although glycosylation pathways are biochemically well-studied,little is known about the networks of genes that guide the cell-and tissue-specific regulation of these biochemical reactions in humans in vivo.The lack of a detailed understanding of the mechanisms regulating glycome variation and linking the glycome to human health and disease is slowing progress in clinical applications of human glycobiology.Two of the tools that can provide much sought-after knowledge of human in vivo glycobiology are human genetics and genomics,which offer a powerful data-driven agnostic approach for dissecting the biology of complex traits.This review summarizes the current state of human populational glycogenomics.In Section 1,we provide a brief overview of the N-glycan’s structural organization,and in Section 2,we give a description of the major blood plasma glycoproteins.Next,in Section 3,we summarize,systemize,and generalize the results from current N-glycosylation genome-wide association studies(GWASs)that provide novel knowledge of the genetic regulation of the populational variation of glycosylation.Until now,such studies have been limited to an analysis of the human blood plasma N-glycome and the N-glycosylation of immunoglobulin G and transferrin.While these three glycomes make up a rather limited set compared with the enormous multitude of glycomes of different tissues and glycoproteins,the study of these three does allow for powerful analysis and generalization.Finally,in Section 4,we turn to genes in the established loci,paying particular attention to genes with strong support in Section 5.At the end of the review,in Sections 6 and 7,we describe special cases of interest in light of new discoveries,focusing on possible mechanisms of action and biological targets of genetic variation that have been implicated in human protein N-glycosylation.
基金supported by a National Key Research and Development Program of China(2022ZD0114900)the works at University of California,Los Angeles were supported by Multidisciplinary Research Program of the University Research Initiative Office of Naval Research(MURI ONR+1 种基金N00014-16-1-2007)Defense Advanced Research Projects Agency Explainable Artificial Intelligence DARPA XAI(N66001-17-2-4029)。
基金supported by the Key Research and Development Program of Heilongjiang,China(Grant No.2022ZX01A25)Cooperative Funding between Huazhong Agricultural University and Shenzhen Institute of Agricultural Genomics(Grant No.SZYJY2022014)+2 种基金Fundamental Research Funds for the Central Universities,Beijing,China(Grant Nos.2662022JC006 and 2662022ZHYJ002)National Natural Science Foundation of China(Grant No.32101819)Huazhong Agriculture University Research Startup Fund,China(Grant Nos.11041810340 and 11041810341).
文摘Pre-harvest yield prediction of ratoon rice is critical for guiding crop interventions in precision agriculture.However,the unique agronomic practice(i.e.,varied stubble height treatment)in rice ratooning could lead to inconsistent rice phenology,which had a significant impact on yield prediction of ratoon rice.Multi-temporal unmanned aerial vehicle(UAV)-based remote sensing can likely monitor ratoon rice productivity and reflect maximum yield potential across growing seasons for improving the yield prediction compared with previous methods.Thus,in this study,we explored the performance of combination of agronomic practice information(API)and single-phase,multi-spectral features[vegetation indices(VIs)and texture(Tex)features]in predicting ratoon rice yield,and developed a new UAV-based method to retrieve yield formation process by using multi-temporal features which were effective in improving yield forecasting accuracy of ratoon rice.The results showed that the integrated use of VIs,Tex and API(VIs&Tex+API)improved the accuracy of yield prediction than single-phase UAV imagery-based feature,with the panicle initiation stage being the best period for yield prediction(R^(2) as 0.732,RMSE as 0.406,RRMSE as 0.101).More importantly,compared with previous multi-temporal UAV-based methods,our proposed multi-temporal method(multi-temporal model VIs&Tex:R^(2) as 0.795,RMSE as 0.298,RRMSE as 0.072)can increase R^(2) by 0.020-0.111 and decrease RMSE by 0.020-0.080 in crop yield forecasting.This study provides an effective method for accurate pre-harvest yield prediction of ratoon rice in precision agriculture,which is of great significance to take timely means for ensuring ratoon rice production and food security.
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.
文摘Objective To evaluate the association between serum uric acid(SUA)and kidney function decline.Methods Data was obtained from the China Health and Retirement Longitudinal Study on the Chinese middle-aged and older population for analysis.The kidney function decline was defined as an annual estimated glomerular filtration rate(e GFR)decrease by>3 mL/min per 1.73 m^(2).Multivariable logistic regression was applied to determine the association between SUA and kidney function decline.The shape of the association was investigated by restricted cubic splines.Results A total of 7,346 participants were included,of which 1,004 individuals(13.67%)developed kidney function decline during the follow-up of 4 years.A significant dose-response relation was recorded between SUA and the kidney function decline(OR 1.14,95%CI 1.03-1.27),as the risk of kidney function decline increased by 14%per 1 mg/d L increase in SUA.In the subgroup analyses,such a relation was only recorded among women(OR 1.22,95%CI 1.03-1.45),those aged<60 years(OR 1.22,95%CI 1.05-1.42),and those without hypertension and without diabetes(OR 1.22,95%CI 1.06-1.41).Although the dose-response relation was not observed in men,the high level of SUA was related to kidney function decline(OR 1.83,95%CI 1.05-3.17).The restricted cubic spline analysis indicated that SUA>5 mg/dL was associated with a significantly higher risk of kidney function decline.Conclusion The SUA level was associated with kidney function decline.An elevation of SUA should therefore be addressed to prevent possible kidney impairment and dysfunction.
基金This study was supported by the National Key R&D Program of China(2021YFC1712905)the National Natural Science Foundation of China(nos.82173941 and 61872319)+2 种基金the Key R&D Program of Zhejiang Province(no.2023C01039)Y.W.was supported by the Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine(no.ZYYCXTD-D-202002)the Fundamental Research Funds for the Central Universities(no.226-2023-00114).We thank L.Cai at the California Institute of Technology for providing the seqFISH+image data.We thank T.Walter for providing the pretrained MoCo model on the TCGA dataset.We thank W.K.Wang and L.Sun at Amazon Web Services China for their indispensable support in terms of computing resources and technology.We are grateful for the support from the ZJU PII-Molecular Devices Joint Laboratory and support from the“Medicine+X”interdisciplinary Center of Zhejiang University.
文摘Accurate profiling of microscopy images from small scale to high throughput is an essential procedure in basic and applied biological research.Here,we present Microsnoop,a novel deep learning–based representation tool trained on large-scale microscopy images using masked self-supervised learning.Microsnoop can process various complex and heterogeneous images,and we classified images into three categories:single-cell,full-field,and batch-experiment images.Our benchmark study on 10 high-quality evaluation datasets,containing over 2,230,000 images,demonstrated Microsnoop’s robust and state-ofthe-art microscopy image representation ability,surpassing existing generalist and even several custom algorithms.Microsnoop can be integrated with other pipelines to perform tasks such as superresolution histopathology image and multimodal analysis.Furthermore,Microsnoop can be adapted to various hardware and can be easily deployed on local or cloud computing platforms.We will regularly retrain and reevaluate the model using communitycontributed data to consistently improve Microsnoop.
基金financially supported by the NSFC(Grant No.41974126 and 41674116)the National Key Research and Development Program of China(Grant No.2018YFA0702501)the 13th 5-Year Basic Research Program of China National Petroleum Corporation(CNPC)(2018A-3306)。
文摘Deep learning has achieved great success in a variety of research fields and industrial applications.However,when applied to seismic inversion,the shortage of labeled data severely influences the performance of deep learning-based methods.In order to tackle this problem,we propose a novel seismic impedance inversion method based on a cycle-consistent generative adversarial network(Cycle-GAN).The proposed Cycle-GAN model includes two generative subnets and two discriminative subnets.Three kinds of loss,including cycle-consistent loss,adversarial loss,and estimation loss,are adopted to guide the training process.Benefit from the proposed structure,the information contained in unlabeled data can be extracted,and adversarial learning further guarantees that the prediction results share similar distributions with the real data.Moreover,a neural network visualization method is adopted to show that the proposed CNN model can learn more distinguishable features than the conventional CNN model.The robustness experiments on synthetic data sets show that the proposed method can achieve better performances than other methods in most cases.And the blind-well experiments on real seismic profiles show that the predicted impedance curve of the proposed method maintains a better correlation with the true impedance curve.
基金Project(2017QHZ031)supported by Scientific Research Starting Project of Southwest Petroleum University,ChinaProject(18TD0013)supported by Science and Technology Innovation Team of Education Department of Sichuan for Dynamical System and Its Applications,ChinaProject(2017CXTD02)supported by Youth Science and Technology Innovation Team of Southwest Petroleum University for Nonlinear Systems,China。
基金the National Key R&D Program of China(2017YFA0207600)National Outstanding Youth Science Fund Project of National Natural Science Foundation of China(61925401)+2 种基金PKU-Baidu Fund Project(2019BD002)National Natural Science Foundation of China(92064004,61927901,61421005,61674006)the 111 Project(B18001).
文摘In recent years,deep learning has made tremendous achievements in computer vision,natural language processing,man-machine games and so on,where artificial intelligence can reach or go beyond the level of human beings.However,behind so many glories,some serious challenges exist in the bottom hardware,hindering the further development of Artificial Intelligence.
基金supported by the National Natural Science Foundation of China(No.81930102 to Bo Yang),the National Natural Science Foundation of China(No.82273949 to Ling Ding),the National Natural Science Foundation of China(No.82104196 to Xi Chen)。
文摘Lipids have been found to modulate tumor biology,including proliferation,survival,and metastasis.With the new understanding of tumor immune escape that has developed in recent years,the influence of lipids on the cancer—immunity cycle has also been gradually discovered.First,regarding antigen presentation,cholesterol prevents tumor antigens from being identified by antigen presenting cells.Fatty acids reduce the expression of major histocompatibility complex class I and costimulatory factors in dendritic cells,impairing antigen presentation to T cells.Prostaglandin E2(PGE2)reduce the accumulation of tumor-infiltrating dendritic cells.Regarding T-cell priming and activation,cholesterol destroys the structure of the T-cell receptor and reduces immunodetection.In contrast,cholesterol also promotes T-cell receptor clustering and relative signal transduction.PGE2 represses T-cell proliferation.Finally,regarding T-cell killing of cancer cells,PGE2 and cholesterol weaken granule-dependent cytotoxicity.Moreover,fatty acids,cholesterol,and PGE2 can improve the activity of immunosuppressive cells,increase the expression of immune checkpoints and promote the secretion of immunosuppressive cytokines.Given the regulatory role of lipids in the cancer—immunity cycle,drugs that modulate fatty acids,cholesterol and PGE2 have been envisioned as effective way in restoring antitumor immunity and synergizing with immunotherapy.These strategies have been studied in both preclinical and clinical studies.
基金financially supported by National Key Research and Development Program of China (2021YFF1201400)National Natural Science Foundation of China (22220102001)Natural Science Foundation of Zhejiang Province (LZ19H300001, LD22H300001, China)。
文摘Acid-base dissociation constant(pK_(a)) is a key physicochemical parameter in chemical science, especially in organic synthesis and drug discovery. Current methodologies for pK_(a) prediction still suffer from limited applicability domain and lack of chemical insight. Here we present MF-SuP-pK_(a)(multi-fidelity modeling with subgraph pooling for pK_(a) prediction), a novel pK_(a) prediction model that utilizes subgraph pooling, multi-fidelity learning and data augmentation. In our model, a knowledgeaware subgraph pooling strategy was designed to capture the local and global environments around the ionization sites for micro-pK_(a) prediction. To overcome the scarcity of accurate pK_(a) data, lowfidelity data(computational pK_(a)) was used to fit the high-fidelity data(experimental pK_(a)) through transfer learning. The final MF-SuP-pK_(a) model was constructed by pre-training on the augmented ChEMBL data set and fine-tuning on the DataWarrior data set. Extensive evaluation on the DataWarrior data set and three benchmark data sets shows that MF-SuP-pK_(a) achieves superior performances to the state-of-theart pK_(a) prediction models while requires much less high-fidelity training data. Compared with Attentive FP, MF-SuP-pK_(a) achieves 23.83% and 20.12% improvement in terms of mean absolute error(MAE) on the acidic and basic sets, respectively.
