Miracle-dz/UniFAR-FTUs huggingface dataset:
SLPFA: Protein Structure-Label Embedding Attention Network for Protein Function Annotation

Gene Ontology (GO) is a framework that utilizes a series of GO terms in a Directed Acyclic Graph (DAG) to describe protein functions. Proteins are typically annotated with several or dozens of GO terms. However, existing methods often struggle to simultaneously annotate multiple relevant GO terms with hierarchical dependencies to proteins, as they solely rely on protein sequences or structures. To better utilize the hierarchical information of GO terms and improve protein function annotation performance, we propose the Protein Structure-Label Embedding Attention Network for Protein Function Annotation (SLPFA). SLPFA embeds proteins and GO terms into a joint latent space using attention mechanisms to bridge the semantic gap between them. Specifically, we employ a soft-mask GNN to learn the topological structure of proteins, allowing simultaneous focus on key nodes while remaining invariant to irrelevant parts. Additionally, we encode the ancestral information for each GO term in its embedding and utilize a learnable matrix to capture the hierarchical dependencies. Finally, SLPFA employs protein structure-label embedding attention to project the protein structure and label embedding together into a joint latent space. This enables the model to learn the high-level semantics of proteins and hierarchical GO terms, resulting in a reduced semantic gap between proteins and their functions. Experimental results demonstrate that SLPFA outperforms state-of-the-art deep learning-based methods on the PDB-cdhit dataset, which yields Fmax of 0.604, 0.478, 0.524 and the AUPRC of 0.630, 0.357, 0.452 for the MF, BP, CC ontology domains, respectively. Furthermore, when the training and testing proteins have less than 15% sequence identity, SLPFA also achieves competitive results in the MF, BP, and CC ontology domains.
Modeling and State Estimation for Lithium Sulfur Batteries as a Piecewise Affine System

Lithium-sulfur (Li-S) is a promising battery chemistry for applications demanding high energy densities, such as electrified aircraft and heavy-duty trucks, among others. A critical challenge in modeling the Li-S chemistry lies in the use of differential algebraic (DAE) equations for representing the electrochemical dynamics. Due to their constrained and stiff nature, these equations are not conducive to real-time state estimation. In this study, we propose a novel approach to constrained state estimation for Li-S batteries by integrating a piecewise affine (PWA) model into a moving horizon estimation (MHE) framework. We begin by deriving the PWA model using a linear tree algorithm based on data obtained from simulations of a calibrated DAE model. We further leverage the unique structural advantages of the proposed PWA model to formulate a real-time state estimation algorithm grounded in a mixed-integer quadratic program. Overall, our initial findings, based on a single constant current trajectory, demonstrate that our approach offers an accurate and computationally efficient method for modeling and state estimation of Li-S batteries. The coupled PWA-MHE framework effectively captures the dynamics of the DAE system, even in the presence of high observational noise (20mV).
Construction of Virtual Simulation Practice Teaching Platform for Business Majors Based on Fuzzy Control Algorithm

Simulation plays an important role in control research. Information technology and various related technologies, the research of simulation technology is also deepening. At present, there is no unified platform for the design and simulation of adaptive fuzzy controller, and the simulation algorithms of various controllers are different. With the strong advocacy of national education departments, virtual simulation technology has been widely used in academic education, and has gradually become an important means to improve traditional teaching. Cross-professional comprehensive training of business has almost become the preferred course of combining theory with practice in general colleges and universities. It requires students from different majors to participate together, cooperate and communicate deeply in teams, and compete and confront each other among groups, which helps to improve graduates' innovative and entrepreneurial ability. Through teaching practice, the design of teaching system, the joint training between schools and enterprises, and the consideration of virtual and actual combat are further improved. Explain the teaching application of virtual simulation experiment teaching platform. The virtual simulation experiment teaching platform is convenient for students to complete intelligent control experiments, and carry out secondary development and innovative experiments
Highly-Digital 0.0018-mm2/channel Multiplexed Neural Frontend with Time-Based Incremental ADC for In-Brain-Stroke-Cavity LFP Monitoring

Abnormal brain cavities are of primary interest in the treatment of stroke symptoms and traumatic brain injuries because of the highly plastic behavior of the neurons along the cavity wall. Their complex and hollow geometry, however, renders the use of rigid needle probes or traditional μECoG arrays ineffective. The deeply-seated implant also dictates strict area and heat dissipation constraints that most published designs do not meet. This paper presents the design of an ultra-compact neural frontend that is part of the fully implantable SCATMAN system, tailored for in-cavity Local Field Potential (LFP) monitoring. The prototype has been fabricated in a 180-nm bulk CMOS technology. To fit the cavity size constraints, this design combines time multiplexing and time-domain conversion in a novel 13-bit incremental ADC, requiring only 0.0018 mm2/channel of readout area, the smallest reported for stimulation-voltage-compatible technologies, while consuming 4.51 μW/channel. The use of a time-based ∆Σ modulator renders the implementation highly digital and ensures a very small silicon footprint. The design also yields a best-in-class peak Total Harmonic Distortion (THD) of -72.6 dB @2.5-mVpp input, thanks to its implicit DAC mismatch-error shaping property.
Multi-view Brain Networks Construction for Alzheimer’s Disease Diagnosis

Alzheimer’s disease (AD) is a progressive neurodegenerative disease which has a serious impact on patients’ daily lives. Early detection, diagnosis and treatment of AD remain a major challenge in clinical practice. Current methods typically construct brain functionally connectivity networks based on similarities between regions of interest (ROIs) for each individual, neglecting the potential information from identical ROIs. We contend that considering similarities among identical ROIs holds crucial implications for AD classification. To address this issue, we propose a multi-view brain network construction method, which constructs brain network based on the similarity of identical ROIs for AD diagnosis. Firstly, the time series of the same ROIs of all subjects are extracted and recombined into a new set. For the newly composed set the brain connectivity network is obtained based on the similarity between the time series. Next, this constructed network undergoes graph convolutional network (GCN) processing to produce node embeddings. Finally, these embeddings are extracted for all ROIs within each subject, converted into one-dimensional feature vectors, and utilized for disease detection with support vector machine (SVM). To demonstrate the effectiveness of our method, we evaluate the performance of the proposed method in the widely recognized Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and the results show that our method exhibits higher performance in identifying AD and MCI compared to existing methods.
Rethinking Low-Dose CT Synthesis: Degrading Normal-Dose CT from Origin for Pairwise Training of CT Denoiser

Training a denoiser for translating low-dose to normal-dose computed tomography (LDCT to NDCT) requires collecting spatially corresponded paired data, yet it is impractical. Existing works aim to ‘add’ noise to real NDCT for synthesizing paired LDCT, but often failed to model the complex noise-structure entanglement, and therefore led to a suboptimal trained denoiser. In this paper, we propose a fresh solution to mimic more reasonable LDCT by modeling the structure-entangled noise patterns. Our motivation is based on a fact that the noise is originally carried by the source sinogram, and then passed to the reconstructed CT and intertwined with the structures via the filtered back-projection (FBP). In light of this, we first convert a NDCT image to the source sinogram by forward projection, and then design a Degrading CT Network (DeCTNet) to learn noise from the origin. Specifically, DeCTNet consists of two sequential networks, i.e., sinogram and reconstruction networks (SinoNet and RecoNet). SinoNet learns to directly add noise to the NDCT-converted sinogram, and RecoNet learns to further process the reconstructed CT image guided by real but unpaired LDCT images. DeCTNet utilizes a differentiable FBP operator to naturally reinvent the sinogram noise to the entangled noise-structure patterns in CT, and thus bridge SinoNet and RecoNet in an end-to-end training. Moreover, adversarial loss and content-fidelity loss are jointly minimized to effectively learn the noise characteristics and content retention in LDCT synthesis. Both quantitative and qualitative evaluations demonstrate that the denoiser trained using DeCTNet-synthesized pairs outperforms those trained using pairs synthesized by other state-of-the-arts, and radiologists are often not able to distinguish our denoised LDCTs from the real NDCTs.
Learning a Gaussian Process Approximation of a Model Predictive Controller with Guarantees

Model predictive control effectively handles complex dynamical systems with constraints, but its high computational demand often makes real-time application infeasible. We propose using Gaussian process regression to learn an approximation of the controller offline for online use. Our approach incorporates a robust predictive control scheme and provides bounds on approximation errors to ensure recursive feasibility and input-to-state stability. Exploiting a sampling-based scenario approach, we develop an efficient sampling strategy and guarantee that, with high probability, the approximation error remains within acceptable bounds. Our method demonstrates enhanced efficiency and reduced computational demand in an example application.
Sleep Model: A Sequence Model for Predicting the Next Sleep Stage

As sleep disorders are becoming more prevalent there is an urgent need to classify sleep stages in a less disturbing way. In particular, sleep-stage classification using simple sensors, such as single-channel electroencephalography (EEG), electrooculography (EOG), electromyography (EMG), or electrocardiography (ECG) has gained substantial interest. In this study, we proposed a sleep model that predicts the next sleep stage and used it to improve sleep classification accuracy. The sleep models were built using sleep-sequence data and employed either statistical n-gram or deep neural network-based models. We developed beam-search decoding to combine the information from the sensor and the sleep models. Furthermore, we evaluated the performance of the n-gram and long short-term memory (LSTM) recurrent neural network (RNN)-based sleep models and demonstrated the improvement of sleep-stage classification using an EOG sensor. The developed sleep models significantly improved the accuracy of sleep-stage classification, particularly in the absence of an EEG sensor.
A Study of Medical Decision Recommendation Generation and Similarity Fusion Based on CDSS and ChatGPT-4

This paper describes the process of embedding ChatGPT-4 (Large Scale Natural Language Modeling) in a CDSS (Clinical Decision Support System) to generate multiple types of decision recommendations. First, given enquiry data, the CDSS generates a specific type of decision recommendations and gives the questions and answers to ChatGPT-4 to generate the related type of decision recommendations. To categorize the same type of decision recommendations in CDSS and ChatGPT-4 together, Word2Vec model was used to learn the semantic relationships of words in medical texts and evaluate the model. Then, the similarity between the decision recommendations generated by CDSS and ChatGPT-4 is determined by calculating the cosine similarity, and a suitable threshold is set to decide whether to fuse these decision recommendations or not. Through this method, the decision recommendations generated by CDSS and ChatGPT-4 can be effectively fused to provide more comprehensive and precise clinical decision support.
Attention Fusion and Abnormal Brain Topology Neural Network for Mild Depression Recognition

Many studies attempt to explore the underlying mechanisms of depression and distinguish between depression patients and normal controls (NC) using electroencephalography (EEG) techniques. With the advancement of deep learning methods, an increasing number of studies aim to design Computer-Aided Diagnosis (CAD) systems for mild depression (MD) to achieve early identification. However, few studies construct models based on abnormal brain topological structures specific to MD patients. In this study, we investigate the abnormal brain topological structures of individuals with MD based on EEG data obtained during an emotional face paradigm. Functional connectivity analysis reveals a higher proportion of inter-hemispheric connections in the MD group compared to intra-hemispheric connections. Additionally, intra-hemispheric connections are primarily observed within the frontal and parietal lobes of both groups. Hierarchical clustering analysis results indicate impairments in the frontal and parietal lobes in the MD group compared to the NC group. Based on these findings, we propose a novel feature called "cross-brain feature" and introduce a multi-cross-brain attention fusion mechanism to integrate information between brain regions. We train and test our models using 5-fold cross-validation. The results demonstrate that the classification model based on abnormal brain topological structures achieves the highest performance among the three state-of-the-art algorithms, with an accuracy of 80.1%, an area under the ROC curve (AUC) of 80%, and a sensitivity (SEN) of 86.3%. These findings suggest that combining abnormal brain topological structures derived from functional connectivity matrices with deep learning techniques can provide an effective objective approach to the early detection of depression.
An Inductorless 40.5V High-Voltage Generator for Integrated Neuromuscular Electrical Stimulators

Neuromuscular electrical stimulation (NMES) requires voltages exceeding several tens of volts which are typically obtained by using DC-DC boost converters. However, these converters incorporate a large external inductor, which hinders integration and severely restricts the minimum size of the stimulator. This paper presents a charge pump (CP) high-voltage generator particularly designed for NMES applications. The proposed hybrid CP comprises a low-voltage latched CP followed by a high-voltage Dickson CP with boosted pumping clocks. This architecture generates an output voltage of 40.5 V from a 3.3 V supply, enabling the delivery of up to 30 mA stimulation pulses at a maximum frequency of 50 Hz. The circuit is designed in a 180 nm HV CMOS process and occupies a silicon area of 2.7 mm2. By eliminating the inductor and requiring only one external storage capacitor, this design has the potential of being used in compact stimulators for wearable medical applications.
Towards Cognitive Load-Based Decision Making in VR Training

We present a methodology to achieve adaptive, cognitive load-based decision making in the context of virtual reality training and assessed in the scope of medical training. The aim is to create dynamic training scenarios that adapt according to the cognitive load of the trainee, adjusting training parameters to keep the trainee at an optimal learning level, where they are appropriately challenged and engaged, but not overexerted. This is achieved by monitoring the trainee's cognitive load via eye tracking and pupillometry, employing an adaptive algorithm compensating for the effect of ambient light on the human eye. The cognitive load measurement has been validated in a real-life training study using a sim lab with haptic dolls, and the same training study is currently being evaluated in VR. In this paper, we report the findings of the real-life study, detail preliminary results from the VR study, and describe the methodology used to achieve adaptive cognitive load-based decision making in the VR scenario.
Echoes of the Mind: Conformer Augmentation and Contrastive Loss in Audio-EEG Convergence

Deciphering the intricate neural mechanisms behind human speech perception mandates the seamless alignment of auditory signals with electroencephalogram (EEG) data. Yet, the unpredictable fluctuations and inherent noise within EEG, intertwined with the delicate dance between sound and neural response, cast a daunting challenge before researchers. Within the realm of this challenge, our study presents a groundbreaking iteration of the Conformer architecture, meticulously fine-tuned for the exacting task of audio-EEG matching.Where the conventional Conformer pivots on a self-attention mechanism, our enhanced version echoes a more nuanced tune, introducing what we’ve termed "local attention. " This deliberate design choice emphasizes nearby inputs, capturing those elusive local patterns and relationships pivotal to understanding the spatial interplay between auditory and EEG features. Such a precision-focused approach guides the model to spotlight vital data sectors during its training voyage, unearthing features rich in meaning and contextual relevance.Our empirical symphony resonates with the brilliance of this enhanced Conformer. It consistently surpasses traditional methods, echoing superior prowess in audio-EEG matching accuracy, and in turn, amplifying the resonance of our innovative approach.
Explainable Depression Recognition from EEG Signals via Graph Convolutional Network

Depression is a prevalent mental disorder that poses significant risks to human health and social stability. Current methods for diagnosing depression heavily rely on patient descriptions and psychiatrist observations, which are susceptible to interference from subjective factors and carry the risk of misdiagnosis and missed diagnosis. Therefore, it is crucial to develop an objective method for recognizing depression based on objective criteria. Recently, combining EEG signals with deep learning techniques for depression recognition has become a popular research topic. However, existing EEG-based depression recognition methods are poorly interpretable, making it challenging to explain the neural mechanisms of depression disorders. Consequently, we propose an explainable framework for depression recognition from EEG signals based on a GCN. In this method, a hybrid module of 1DCNN, LSTM and GCN is utilized to extract features from EEG signals, which can effectively capture spatiotemporal correlations between different brain regions. The EEG subgraph construction module explores the differences in crucial connectivity patterns of the brain between different groups, enhancing the interpretability of our model. The experimental results on the MODMA dataset show that our model outperforms the baseline model in all metrics, thus verifying the validity of the proposed model. Additionally, compared with existing explainable algorithms, our method consistently yielded nearly identical experimental results, demonstrating its ability to capture the correlation between depression and neuroscience, and has good interpretability.
The interaction between mutated DYRK1A and APP suggests a potential target for vascular cognitive impairment

Vascular cognitive impairment, being a cerebrovascular condition that is more common in the aging population, results in a deterioration in cognitive capacities. Regarding these aspects, the identification of associated proteins, pathways, and treatment targets is crucial. The interactions that result in Vascular Cognitive Impairment can be better understood by looking into the interactors of the well-researched APP, which has been linked to dementia. Therefore, our study focused on identifying novel APP interactions by protein-protein interaction networks. After constructing the APP-centered dynamic-structural PPI network we investigated these interactions with PRISM. We suggested a number of interactions that could be relevant to VCI and found that APP binds more selectively to SORT1, MAPK8, and UBQLN1 proteins. Additionally, we propose that three mutations in the kinase DYRK1A (V165I, S337P, and D401G) may be essential for VCI through its interaction with APP.
Temporal and Spatial Changes in Snow Cover and the Corresponding Radiative Forcing Analysis in Siberia from the 1970s to the 2010s

In the context of global climate change, the extent of snow cover in Siberia has significantly decreased since the 1970s, especially in spring. The changes of snow cover at middle and high latitudes have significant impacts on the meteorological and hydrological processes because the snow cover can affect the surface energy, water balance, and the development of the atmospheric boundary layer. In this paper, the temporal and spatial changes in snow cover were firstly estimated based on a long time series of remote sensing snow cover data, both showing a decreased trend. Based on this, we estimated the radiative forcing caused by the snow cover changes from the 1970s to the 2010s and compared it with the radiative forcing caused by the vegetation cover changes over the same time period in Siberia, indicating that the snow cover changes in Siberia can accelerate climate warming and the vegetation cover changes here have the opposite effect. Furthermore, the snow cover changes may play a more important role than the vegetation cover changes in regulating the surface radiation balance in Siberia on the regional scale.
A Graph Method for Predicting Protein-RNA Interaction Using Reduced Amino Acid Alphabets

Protein-RNA interactions play important roles in the biological systems. Searching for regular patterns at the protein-RNA binding interfaces is important for understanding how protein and RNA recognize each other and bind to form a complex. Herein we present a graph mining approach to discovering patterns at the protein-RNA interfaces. We represented known protein-RNA interfaces using graph and then discovered graph patterns enriched in the interfaces. To evaluate the effectiveness of our method, we conducted two experiments. The first one was to use the graph patterns as input features to a Support Vector Machine to automatically classify protein surface patches into RNA-binding sites and non-RNA-binding sites. In the second experiment, we built a simple scoring function that calculated the total number of the graph patterns that occurred in a protein-RNA interface. That scoring function was able to discriminate near native protein-RNA complexes from docking decoys with a performance comparable with a state-of-the-art complex scoring function. Our work also compared the performances of the proposed method using full amino acid alphabet with reduced alphabets. The results indicate that our method yields strikingly good result in terms of hit count.