Acupuncture State Detection at Zusanli (ST-36) Based on Scalp EEG and Transformer.

In clinical acupuncture practice, needle twirling (NT) and needle retention (NR) are strategically combined to achieve different therapeutic effects, highlighting the importance of distinguishing between different acupuncture states. Scalp EEG has been proven significantly relevant to brain activity and acupuncture stimulation. In this work, we designed an acupuncture paradigm to collect scalp EEG to study the differences in EEG changes during different acupuncture states.

REI-Net: A Reference Electrode Standardization Interpolation Technique Based 3D CNN for Motor Imagery Classification.

High-quality scalp EEG datasets are extremely valuable for motor imagery (MI) analysis. However, due to electrode size and montage, different datasets inevitably experience channel information loss, posing a significant challenge for MI decoding. A 2D representation that focuses on the time domain may loss the spatial information in EEG.

Cross-Species Prediction of Transcription Factor Binding by Adversarial Training of a Novel Nucleotide-Level Deep Neural Network.

Cross-species prediction of TF binding remains a major challenge due to the rapid evolutionary turnover of individual TF binding sites, resulting in cross-species predictive performance being consistently worse than within-species performance. In this study, a novel Nucleotide-Level Deep Neural Network (NLDNN) is first proposed to predict TF binding within or across species. NLDNN regards the task of TF binding prediction as a nucleotide-level regression task, which takes DNA sequences as input and directly predicts experimental coverage values.

Input density tunes Kenyon cell sensory responses in the Drosophila mushroom body.

The ability to discriminate sensory stimuli with overlapping features is thought to arise in brain structures called expansion layers, where neurons carrying information about sensory features make combinatorial connections onto a much larger set of cells. For 50 years, expansion coding has been a prime topic of theoretical neuroscience, which seeks to explain how quantitative parameters of the expansion circuit influence sensory sensitivity, discrimination, and generalization. Here, we investigate the developmental events that produce the quantitative parameters of the arthropod expansion layer, called the mushroom body.

Deep multi-modal intermediate fusion of clinical record and time series data in mortality prediction.

In intensive care units (ICUs), mortality prediction is performed by combining information from these two sources of ICU patients by monitoring patient health. Respectively, time series data generated from each patient admission to the ICU and clinical records consisting of physician diagnostic summaries. However, existing mortality prediction studies mainly cascade the multimodal features of time series data and clinical records for prediction, ignoring thecross-modal correlation between the underlying features in different modal data.

Fabrication of an ultra-high quality MgF micro-resonator for a single soliton comb generation.

Crystalline micro-resonators are attractive for a wide range of applications due to their extremely high quality (Q) factor. In this paper, we develop a semi-automatic method for fabricating ultra-high Q-factor MgF crystalline micro-resonators. By utilizing a force feedback sensor and corresponding control, we made a semi-automatic precision grind-and-polishing machine, and successfully fabricated trapezoid MgF resonators with diameter of 9.

Hacking brain development to test models of sensory coding.

Animals can discriminate myriad sensory stimuli but can also generalize from learned experience. You can probably distinguish the favorite teas of your colleagues while still recognizing that all tea pales in comparison to coffee. Tradeoffs between detection, discrimination, and generalization are inherent at every layer of sensory processing.

Inverse design of soliton microcomb based on genetic algorithm and deep learning.

Soliton microcombs generated by the third-order nonlinearity of microresonators exhibit high coherence, low noise, and stable spectra envelopes, which can be designed for many applications. However, conventional dispersion engineering based design methods require iteratively solving Maxwell's equations through time-consuming electromagnetic field simulations until a local optimum is obtained. Moreover, the overall inverse design from soliton microcomb to the microcavity geometry has not been systematically investigated.

A multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy.

Magnetoencephalography is a noninvasive neuromagnetic technology to record epileptic activities for the pre-operative localization of epileptogenic zones, which has received increasing attention in the diagnosis and surgery of epilepsy. As reported by recent studies, pathological high frequency oscillations (HFOs), when utilized as a biomarker to localize the epileptogenic zones, result in a significant reduction in seizure frequency, even seizure elimination in around 80% of cases. Thus, objective, rapid, and automatic detection and recommendation of HFOs are highly desirable for clinicians to alleviate the burden of reviewing a large amount of MEG data from a given patient.

Automatic BASED scoring on scalp EEG in children with infantile spasms using convolutional neural network.

In recent years, the Burden of Amplitudes and Epileptiform Discharges (BASED) score has been used as a reliable, accurate, and feasible electroencephalogram (EEG) grading scale for infantile spasms. However, manual EEG annotation is, in general, very time-consuming, and BASED scoring is no exception. Convolutional neural networks (CNNs) have proven their great potential in many EEG classification problems.

Gating mechanism and a modulatory niche of human GluN1-GluN2A NMDA receptors.

N-methyl-D-aspartate (NMDA) receptors are glutamate-gated calcium-permeable ion channels that are widely implicated in synaptic transmission and plasticity. Here, we report a gallery of cryo-electron microscopy (cryo-EM) structures of the human GluN1-GluN2A NMDA receptor at an overall resolution of 4 Å in complex with distinct ligands or modulators. In the full-length context of GluN1-GluN2A receptors, we visualize the competitive antagonists bound to the ligand-binding domains (LBDs) of GluN1 and GluN2A subunits, respectively.

Detecting High Frequency Oscillations for Stereoelectroencephalography in Epilepsy via Hypergraph Learning.

Successful epilepsy surgeries depend highly on pre-operative localization of epileptogenic zones. Stereoelectroencephalography (SEEG) records interictal and ictal activities of the epilepsy in order to precisely find and localize epileptogenic zones in clinical practice. While it is difficult to find distinct ictal onset patterns generated the seizure onset zone from SEEG recordings in a confined region, high frequency oscillations are commonly considered as putative biomarkers for the identification of epileptogenic zones.

Multi-Head Self-Attention Model for Classification of Temporal Lobe Epilepsy Subtypes.

As a long-standing chronic disease, Temporal Lobe Epilepsy (TLE), resulting from abnormal discharges of neurons and characterized by recurrent episodic central nervous system dysfunctions, has affected more than 70% of drug-resistant epilepsy patients across the world. As the etiology and clinical symptoms are complicated, differential diagnosis of TLE mainly relies on experienced clinicians, and specific diagnostic biomarkers remain unclear. Though great effort has been made regarding the genetics, pathology, and neuroimaging of TLE, an accurate and effective diagnosis of TLE, especially the TLE subtypes, remains an open problem.

Automatic and Accurate Epilepsy Ripple and Fast Ripple Detection via Virtual Sample Generation and Attention Neural Networks.

About 1% of the population around the world suffers from epilepsy. The success of epilepsy surgery depends critically on pre-operative localization of epileptogenic zones. High frequency oscillations including ripples (80-250 Hz) and fast ripples (250-500 Hz) are commonly used as biomarkers to localize epileptogenic zones.

Photonic thermometer with a sub-millikelvin resolution and broad temperature range by waveguide-microring Fano resonance.

Fano resonance theoretically is an effective approach for sensitivity enhancement in photonic sensing applications, but the reported methods suffer from complicated structure and fabrication, narrow dynamic range, etc. In this article, we propose a photonic thermometer with sub-millikelvin resolution and broad temperature measurement range implemented by a simple waveguide-microring Fano structure. An air hole is introduced at the center of the coupling region of the waveguide of an all-pass microring resonator.

Deep learning in bioinformatics: Introduction, application, and perspective in the big data era.

Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics. With the advances of the big data era in biology, it is foreseeable that deep learning will become increasingly important in the field and will be incorporated in vast majorities of analysis pipelines. In this review, we provide both the exoteric introduction of deep learning, and concrete examples and implementations of its representative applications in bioinformatics.

[De novo construction of mammalian synthetic inhibitory transcription factor and promoter pairs].

Transcriptional regulation is crucial for regulated gene expression. Due to the complexity, it has been difficult to engineer eukaryotic transcription factor (TF) and promoter pairs. The few availabilities of eukaryotic TF and promotor pairs limit their practical use for clinical or industrial applications.

Edge extraction using a time-varying vortex beam in incoherent digital holography.

Edge extraction using a time-varying vortex beam (TV-VB) is demonstrated in optical scanning holography (OSH) operating in an incoherent mode. OSH is a two-pupil heterodyne scanning optical system. We propose that one of the pupil functions used is a delta function and the other pupil function is a spiral phase plate (SPP).

Improved full analytical polygon-based method using Fourier analysis of the three-dimensional affine transformation.

Previous research [Appl. Opt.52, A290 (2013)] has revealed that Fourier analysis of three-dimensional affine transformation theory can be used to improve the computation speed of the traditional polygon-based method.

3D dynamic holographic display by modulating complex amplitude experimentally.

Complex amplitude modulation method is presented theoretically and performed experimentally for three-dimensional (3D) dynamic holographic display with reduced speckle using a single phase-only spatial light modulator. The determination of essential factors is discussed based on the basic principle and theory. The numerical simulations and optical experiments are performed, where the static and animated objects without refinement on the surfaces and without random initial phases are reconstructed successfully.

Analytical brightness compensation algorithm for traditional polygon-based method in computer-generated holography.

In three-dimensional (3D) holographic display, current brightness compensation algorithm of the traditional polygon-based method experimentally obtains the compensation factor, which depends on the fabrication process. In this paper, we proposed an analytical brightness compensation method discarding the influence of the fabrication. The surface property function with the flat power spectral density and the compensation factor obtained from the simplified relationship between the original and the rotated frequencies are used to analytically compensate the radiant energy of the tilted polygon.

Reducing the memory usage for effective computer-generated hologram calculation using compressed look-up table in full-color holographic display.

A fast algorithm with low memory usage is proposed to generate the hologram for full-color 3D display based on a compressed look-up table (C-LUT). The C-LUT is described and built to reduce the memory usage and speed up the calculation of the computer-generated hologram (CGH). Numerical simulations and optical experiments are performed to confirm this method, and several other algorithms are compared.

Fast polygon-based method for calculating computer-generated holograms in three-dimensional display.

In the holographic three-dimensional (3D) display, the numerical synthesis of the computer-generated holograms needs tremendous calculation. To solve the problem, a fast polygon-based method based on two-dimensional Fourier analysis of 3D affine transformation is proposed. From one primitive polygon, the proposed method calculates the diffracted optical field of each arbitrary polygon in the 3D model, where the pseudo-inverse matrix, the interpolation, and the compensation of the power spectral density are employed.

Designing and fabricating diffractive optical elements with a complex profile by interference.

We demonstrate a novel (to our knowledge) method for the design and the fabrication of diffractive optical elements (DOEs) with an arbitrary complex phase profile based on interference. The DOEs are designed to modulate the complex light wave by the analytical formulas, and an asymmetric holographic DOE with cubic phase modulation is fabricated by a two-step exposure technique. The desired Airy beams are produced experimentally, which demonstrates the validity of this method.