A5053943810- Gyrotron and Vacuum Electronics Research
- Pulsed Power Technology Applications
- Particle accelerators and beam dynamics
- EEG and Brain-Computer Interfaces
- Speech and Audio Processing
- Neural dynamics and brain function
- Microwave Engineering and Waveguides
- Stock Market Forecasting Methods
- Blind Source Separation Techniques
- Neural Networks and Applications
- Ferroptosis and cancer prognosis
- Diabetes Management and Education
- Plant biochemistry and biosynthesis
- Ovarian cancer diagnosis and treatment
- Cancer Immunotherapy and Biomarkers
- Cardiovascular Health and Risk Factors
- Genomics and Phylogenetic Studies
- Chronic Disease Management Strategies
- Advanced Memory and Neural Computing
- Neuroscience and Music Perception
- Speech Recognition and Synthesis
- Viral Infections and Outbreaks Research
- Microbial Inactivation Methods
- Plant Gene Expression Analysis
- Genetic and phenotypic traits in livestock
New York University
2020-2025
Nanjing Municipal Center for Disease Control And Prevention
2018-2025
Fujian Institute of Oceanography
2022-2024
Fujian Normal University
2022-2024
SLAC National Accelerator Laboratory
2017-2023
Wenzhou Medical University
2021-2022
Menlo School
2017
Wuhan Institute of Virology
2014
Chinese Academy of Sciences
2014
Hubei University Of Economics
2014
Abstract Decoding human speech from neural signals is essential for brain–computer interface (BCI) technologies that aim to restore in populations with neurological deficits. However, it remains a highly challenging task, compounded by the scarce availability of corresponding speech, data complexity and high dimensionality. Here we present novel deep learning-based decoding framework includes an ECoG decoder translates electrocorticographic (ECoG) cortex into interpretable parameters...
Huang-Lian-Jie-Du decoction (HLJDD) has been widely applied to treat inflammation-associated diseases for thousands of years in China. However, the concrete molecular mechanism HLJDD treatment rheumatoid arthritis (RA) remains unclear. In this work, network pharmacology and docking were preliminarily analyze potential active ingredients, drug targets, related pathways on treating RA. A total 102 compounds with corresponding 189 targets identified from HLJDD, 41 common further by intersecting...
Speech production is a complex human function requiring continuous feedforward commands together with reafferent feedback processing. These processes are carried out by distinct frontal and temporal cortical networks, but the degree timing of their recruitment dynamics remain poorly understood. We present deep learning architecture that translates neural signals recorded directly from cortex to an interpretable representational space can reconstruct speech. leverage learned decoding networks...
Abstract Objective This study investigates speech decoding from neural signals captured by intracranial electrodes. Most prior works can only work with electrodes on a 2D grid (i.e., Electrocorticographic or ECoG array) and data single patient. We aim to design deep-learning model architecture that accommodate both surface (ECoG) depth (stereotactic EEG sEEG) The should allow training multiple participants large variability in electrode placements the trained perform well unseen during...
Abstract Objective: This study investigates speech decoding from neural signals captured by intracranial electrodes. Most prior works can only work with electrodes on a 2D grid (i.e., Electrocorticographic or ECoG array) and data single patient. We aim to design deep-learning model architecture that accommodate both surface (ECoG) depth (stereotactic EEG sEEG) The should allow training multiple participants large variability in electrode placements. not have subject-specific layers, the...
Objectives To estimate prevalence and clustering of cardiovascular risk factors (CRFs), investigate the association between relevant characteristics CRF among adults in eastern China. Design Community-based cross-sectional study. Setting Data were collected by interview survey, physical measurements laboratory examinations from 2011 Nanjing Chronic Disease Risk Factor Surveillance. Participants A representative sample 41 072 residents aged ≥18 years volunteered to participate with a response...
Exploring the capabilities of Neural Radiance Fields (NeRF) and Gaussian-based methods in context 3D scene reconstruction, this study contrasts these modern approaches with traditional Simultaneous Localization Mapping (SLAM) systems. Utilizing datasets such as Replica ScanNet, we assess performance based on tracking accuracy, mapping fidelity, view synthesis. Findings reveal that NeRF excels synthesis, offering unique generating new perspectives from existing data, albeit at slower...
When we vocalize, our brain distinguishes self-generated sounds from external ones. A corollary discharge signal supports this function in animals; however, humans, its exact origin and temporal dynamics remain unknown. We report electrocorticographic recordings neurosurgical patients a connectivity analysis framework based on Granger causality that reveals major neural communications. find reproducible source for across multiple speech production paradigms localized to the ventral motor...
Abstract When we vocalize, our brain distinguishes self-generated sounds from external ones. A corollary discharge signal supports this function in animals, however, humans its exact origin and temporal dynamics remain unknown. We report Electrocorticographic (ECoG) recordings neurosurgical patients a novel connectivity approach based on Granger-causality that reveals major neural communications. find reproducible source for across multiple speech production paradigms localized to ventral...
Background: Gastric cancer (GC) was usually associated with poor prognosis and invalid therapeutical response to immunotherapy due biological heterogeneity. It is urgent screen reliable indices especially immunotherapy-associated parameters that can predict the therapeutic responses of GC patients. Methods: Gene expression profile 854 patients were collected from The Cancer Genome Atlas (TCGA) Expression Omnibus (GEO) datasets (GSE84433) their corresponding clinical somatic mutation data....
In high-power microwave diode design, the space-charge-limited current is important because of its relation to impedance, and formation virtual cathode. Although Langmuir–Blodgett law, as a numerical solution, helpful, simple functional expression would be more convenient for practical research. this paper, physical approximation has been introduced analyze nonlinear Poisson’s equation in one-dimensional (1-D) cylindrical vacuum diode. With help approximation, solution 1-D diodes...
Abstract Background Penicillium chrysogenum has been used in producing penicillin and derived β-lactam antibiotics for many years. Although the genome of mutant strain P. Wisconsin 54-1255 already sequenced, versatility genetic diversity this species still needs to be intensively studied. In study, wild-type KF-25, which high activity against Ustilaginoidea virens , was sequenced characterized. Results The KF-25 about 29.9 Mb size contained 9,804 putative open reading frames ( orfs )....
Decoding auditory stimulus from neural activity can enable neuroprosthetics and direct communication with the brain. Some recent studies have shown successful speech decoding intracranial recording using deep learning models. However, scarcity of training data leads to low quality reconstruction which prevents a complete brain-computer-interface (BCI) application. In this work, we propose transfer approach pre-trained GAN disentangle representation generation layers for decoding. We first...
Objectives Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage B.1.617.2 (also named the Delta variant) was declared as a variant of concern by World Health Organization (WHO). This study aimed to describe outbreak that occurred in Nanjing city triggered through epidemiological parameters and understand evolving epidemiology variant. Methods We collected data all COVID-19 cases during from 20 July 2021 24 August estimated distribution serial interval, basic time-dependent...
Abstract Decoding human speech from neural signals is essential for brain-computer interface (BCI) technologies restoring function in populations with neurological deficits. However, it remains a highly challenging task, compounded by the scarce availability of corresponding speech, data complexity, and high dimensionality, limited publicly available source code. Here, we present novel deep learning-based decoding framework that includes an ECoG Decoder translates electrocorticographic...
Time-series forecasting has seen significant advancements with the introduction of token prediction mechanisms such as multi-head attention. However, these methods often struggle to achieve same performance in language modeling, primarily due quadratic computational cost and complexity capturing long-range dependencies time-series data. State-space models (SSMs), Mamba, have shown promise addressing challenges by offering efficient solutions linear RNNs capable modeling long sequences larger...
In this paper, on the basis of work about energy acquisition node design and research ZIGBEE protocol, topology structure star tree wireless sensor networks are respectively applied to collection system, which is a good solution multi-user power in cell area, as well parameters long-distance transmission problems; host computer The technologies related VC++ ACCESS database used paper. data transmitted through serial port computer, can dynamically display network real time. It plays very...
Accurate exchange rate prediction is fundamental to financial stability and international trade, positioning it as a critical focus in economic research. Traditional forecasting models often falter when addressing the inherent complexities non-linearities of data. This study explores application advanced deep learning models, including LSTM, CNN, transformer-based architectures, enhance predictive accuracy RMB/USD rate. Utilizing 40 features across 6 categories, analysis identifies TSMixer...
Accurate exchange rate prediction is fundamental to financial stability and international trade, positioning it as a critical focus in economic research. Traditional forecasting models often falter when addressing the inherent complexities non-linearities of data. This study explores application advanced deep learning models, including LSTM, CNN, transformer-based architectures, enhance predictive accuracy RMB/USD rate. Utilizing 40 features across 6 categories, analysis identifies TSMixer...
Accurate exchange rate prediction is fundamental to financial stability and international trade, positioning it as a critical focus in economic research. Traditional forecasting models often falter when addressing the inherent complexities non-linearities of data. This study explores application advanced deep learning models, including LSTM, CNN, transformer-based architectures, enhance predictive accuracy RMB/USD rate. Utilizing 40 features across 6 categories, analysis identifies TSMixer...