GDNet-EEG: An attention-aware deep neural network based on group depth-wise convolution for SSVEP stimulation frequency recognition

BackgroundSteady state visually evoked potentials (SSVEPs) based early glaucoma diagnosis requires effective data processing (e.g., deep learning) to provide accurate stimulation frequency recognition. Thus, we propose a group depth-wise convolutional neural network (GDNet-EEG), a novel electroencephalography (EEG)-oriented deep learning model tailored to learn regional characteristics and network characteristics of EEG-based brain activity to perform SSVEPs-based stimulation frequency recognition.MethodGroup depth-wise convolution is proposed to extract temporal and spectral features from the EEG signal of each brain region and represent regional characteristics as diverse as possible. Furthermore, EEG attention consisting of EEG channel-wise attention and specialized network-wise attention is designed to identify essential brain regions and form significant feature maps as specialized brain functional networks. Two publicly SSVEPs datasets (large-scale benchmark and BETA dataset) and their combined dataset are utilized to validate the classification performance of our model.ResultsBased on the input sample with a signal length of 1 s, the GDNet-EEG model achieves the average classification accuracies of 84.11, 85.93, and 93.35% on the benchmark, BETA, and combination datasets, respectively. Compared with the average classification accuracies achieved by comparison baselines, the average classification accuracies of the GDNet-EEG trained on a combination dataset increased from 1.96 to 18.2%.ConclusionOur approach can be potentially suitable for providing accurate SSVEP stimulation frequency recognition and being used in early glaucoma diagnosis

Rapid detection and structural characterization of verapamil metabolites in rats by UPLC-MSE and UNIFI platform.

High-resolution mass spectrometry (HRMS) is an important technology for studying biotransformations of drugs in biological systems. In order to process complex HRMS data, bioinformatics, including data-mining techniques for identifying drug metabolites from liquid chromatography/high-resolution mass spectrometry (LC/HRMS) or multistage mass spectrometry (MSn ) datasets as well as elucidating the detected metabolites’ structure by spectral interpretation software, are important tools. Data-mining technologies have widely been used in drug metabolite identification, including mass defect filters, product ion filters, neutral-loss filters, control sample comparisons and extracted ion chromatographic analysis. However, the metabolites identified by current different technologies are not the same, indicating the importance of technique integration for efficient and complete identification of metabolic products. In this study, a universal, high-throughput workflow for identifying and verifying metabolites by applying the drug metabolite identification software UNIFI is reported, to study the biotransformation of verapamil in rats. A total of 71 verapamil metabolites were found in rat plasma, urine and faeces, including two metabolites that have not been reported in the literature. Phase I metabolites of verapamil were identified as N-demethylation, O-demethylation, N-dealkylation and oxidation and dehydrogenation metabolites; phase II metabolites were mainly glucuronidation and sulfate conjugates, indicating that UNIFI software could be effective and valuable in identifying drug metabolites

Instant-3D: Instant Neural Radiance Field Training Towards On-Device AR/VR 3D Reconstruction

Neural Radiance Field (NeRF) based 3D reconstruction is highly desirable for immersive Augmented and Virtual Reality (AR/VR) applications, but achieving instant (i.e., < 5 seconds) on-device NeRF training remains a challenge. In this work, we first identify the inefficiency bottleneck: the need to interpolate NeRF embeddings up to 200,000 times from a 3D embedding grid during each training iteration. To alleviate this, we propose Instant-3D, an algorithm-hardware co-design acceleration framework that achieves instant on-device NeRF training. Our algorithm decomposes the embedding grid representation in terms of color and density, enabling computational redundancy to be squeezed out by adopting different (1) grid sizes and (2) update frequencies for the color and density branches. Our hardware accelerator further reduces the dominant memory accesses for embedding grid interpolation by (1) mapping multiple nearby points' memory read requests into one during the feed-forward process, (2) merging embedding grid updates from the same sliding time window during back-propagation, and (3) fusing different computation cores to support the different grid sizes needed by the color and density branches of Instant-3D algorithm. Extensive experiments validate the effectiveness of Instant-3D, achieving a large training time reduction of 41x - 248x while maintaining the same reconstruction quality. Excitingly, Instant-3D has enabled instant 3D reconstruction for AR/VR, requiring a reconstruction time of only 1.6 seconds per scene and meeting the AR/VR power consumption constraint of 1.9 W.Comment: Accepted by ISCA'2

Targeting Inhibition of Accumulation and Function of Myeloid-Derived Suppressor Cells by Artemisinin via PI3K/AKT, mTOR, and MAPK Pathways Enhances Anti-PD-L1 Immunotherapy in Melanoma and Liver Tumors

Despite the remarkable success and efficacy of immune checkpoint blockade (ICB) therapy such as anti-PD-L1 antibody in treating cancers, myeloid-derived suppressor cells (MDSCs) that lead to the formation of the protumor immunosuppressive microenvironment are one of the major contributors to ICB resistance. Therefore, inhibition of MDSC accumulation and function is critical for further enhancing the therapeutic efficacy of anti-PD-L1 antibody in a majority of cancer patients. Artemisinin (ART), the most effective antimalarial drug with tumoricidal and immunoregulatory activities, is a potential option for cancer treatment. Although ART is reported to reduce MDSC levels in 4T1 breast tumor model and improve the therapeutic efficacy of anti-PD-L1 antibody in T cell lymphoma-bearing mice, how ART influences MDSC accumulation, function, and molecular pathways as well as MDSC-mediated anti-PD-L1 resistance in melanoma or liver tumors remains unknown. Here, we reported that ART blocks the accumulation and function of MDSCs by polarizing M2-like tumor-promoting phenotype towards M1-like antitumor one. This switch is regulated via PI3K/AKT, mTOR, and MAPK signaling pathways. Targeting MDSCs by ART could significantly reduce tumor growth in various mouse models. More importantly, the ART therapy remarkably enhanced the efficacy of anti-PD-L1 immunotherapy in tumor-bearing mice through promoting antitumor T cell infiltration and proliferation. These findings indicate that ART controls the functional polarization of MDSCs and targeting MDSCs by ART provides a novel therapeutic strategy to enhance anti-PD-L1 cancer immunotherapy

Determination of incommensurate modulated structure in Bi2Sr1.6La0.4CuO6+{\delta} by aberration-corrected transmission electron microscopy

Incommensurate modulated structure (IMS) in Bi2Sr1.6La0.4CuO6+{\delta} (BSLCO) has been studied by aberration corrected transmission electron microscopy in combination with high-dimensional (HD) space description. Two images in the negative Cs imaging (NCSI) and passive Cs imaging (PCSI) modes were deconvoluted, respectively. Similar results as to IMS have been obtained from two corresponding projected potential maps (PPMs), but meanwhile the size of dots representing atoms in the NCSI PPM is found to be smaller than that in PCSI one. Considering that size is one of influencing factors of precision, modulation functions for all unoverlapped atoms in BSLCO were determined based on the PPM obtained from the NCSI image in combination with HD space description

Coseeded Schwann cells myelinate neurites from differentiated neural stem cells in neurotrophin-3-loaded PLGA carriers

Biomaterials and neurotrophic factors represent promising guidance for neural repair. In this study, we combined poly-(lactic acid-co-glycolic acid) (PLGA) conduits and neurotrophin-3 (NT-3) to generate NT-3-loaded PLGA carriers in vitro. Bioactive NT-3 was released stably and constantly from PLGA conduits for up to 4 weeks. Neural stem cells (NSCs) and Schwann cells (SCs) were coseeded into an NT-releasing scaffold system and cultured for 14 days. Immunoreactivity against Map2 showed that most of the grafted cells (>80%) were differentiated toward neurons. Double-immunostaining for synaptogenesis and myelination revealed the formation of synaptic structures and myelin sheaths in the coculture, which was also observed under electron microscope. Furthermore, under depolarizing conditions, these synapses were excitable and capable of releasing synaptic vesicles labeled with FM1-43 or FM4-64. Taken together, coseeding NSCs and SCs into NT-3-loaded PLGA carriers increased the differentiation of NSCs into neurons, developed synaptic connections, exhibited synaptic activities, and myelination of neurites by the accompanying SCs. These results provide an experimental basis that supports transplantation of functional neural construction in spinal cord injury

An AT-hook gene is required for palea formation and floral organ number control in rice

AbstractGrasses have highly specialized flowers and their outer floral organ identity remains unclear. In this study, we identified and characterized rice mutants that specifically disrupted the development of palea, one of the outer whorl floral organs. The depressed palea1 (dp1) mutants show a primary defect in the main structure of palea, implying that palea is a fusion between the main structure and marginal tissues on both sides. The sterile lemma at the palea side is occasionally elongated in dp1 mutants. In addition, we found a floral organ number increase in dp1 mutants at low penetration. Both the sterile lemma elongation and the floral organ number increase phenotype are enhanced by the mutation of an independent gene SMALL DEGENERATIVE PALEA1 (SDP1), whose single mutation causes reduced palea size. E function and presumable A function floral homeotic genes were found suppressed in the dp1–2 mutant. We identified the DP1 gene by map-based cloning and found it encodes a nuclear-localized AT-hook DNA binding protein, suggesting a grass-specific role of chromatin architecture modification in flower development. The DP1 enhancer SDP1 was also positional cloned, and was found identical to the recently reported RETARDED PALEA1 (REP1) gene encoding a TCP family transcription factor. We further found that SDP1/REP1 is downstreamly regulated by DP1

PM_{2.5} reductions in Chinese cities from 2013 to 2019 remain significant despite the inflating effects of meteorological conditions

Air pollution is a major environmental issue in China and imposes severe health burdens on Chinese citizens. Consequently, China has deployed a series of control measures to mitigate fine particulate matter (PM_{2.5}). However, the extent to which these measures have been effective is obscured by the existence of confounding meteorological effects. Here, we use a newly developed reduced-form model that can address emission-driven PM_{2.5} trends and control for meteorological effects to examine the level of PM_{2.5} reduction across 367 cities since the introduction of the Air Pollution Prevention and Control Action Plan (the Plan) in 2013. Our findings show that, on average, the national annual mean level of PM_{2.5} decreased by 34% from 2013 to 2019 after the removal of meteorological effects, about 10% less than the reduction level officially observed. Despite this difference, assuming that current control efforts continue through 2035, the long-term air-quality target of 35 μg/m^{3} as determined by the recently updated Plan will be met