The quality and reliability of short videos about thyroid nodules on BiliBili and TikTok: Cross-sectional study.

Background: The detection rate of thyroid nodules has witnessed a significant surge in recent years, triggering heightened public apprehension. Short video platforms such as TikTok and BiliBili have showed tremendous potential in the dissemination of health information. There is a plethora of videos about thyroid nodules on TikTok and BiliBili, but the quality and reliability of videos concerning thyroid nodules remains unknown. Methods: On December 3rd, 2023, the top 100 short videos related to thyroid nodules on BiliBili and TikTok were collected through a comprehensive search in Chinese. After extracting the basic information, the quality and reliability of each video was assessed by using the global quality score (GQS) and DISCERN score. Further, Spearman correlation analyses were applied to examine the correlation among video variables, GQS and DISCERN score. Results: Compared to BiliBili, TikTok exhibits greater popularity, as evidenced by higher counts of likes (P = 0.021), comments (P = 0.008) and shares (P = 0.017). The median (interquartile range) scores of GQS and DISCERN score were 3 (2-3) on TikTok while 2 (2-3) on BiliBili. Both reviewers exhibited good consistency in GQS and DISCERN score. Moreover, it was observed that the videos shared by thyroid specialists demonstrated higher scores both in GQS (P = 0.014) and DISCERN score (P = 0.022) than others on TikTok. Spearman correlation analysis revealed no significant correlation between video variables and the scores of GQS and DISCERN score. Conclusions: The quality and reliability of thyroid nodules videos on BiliBili and TikTok were unsatisfactory. Notably, videos shared by thyroid specialists are more likely to exhibit superior quality and reliability. People should exercise caution when perusing short videos.

Enhancing SNN-based spatio-temporal learning: A benchmark dataset and Cross-Modality Attention model.

Spiking Neural Networks (SNNs), renowned for their low power consumption, brain-inspired architecture, and spatio-temporal representation capabilities, have garnered considerable attention in recent years. Similar to Artificial Neural Networks (ANNs), high-quality benchmark datasets are of great importance to the advances of SNNs. However, our analysis indicates that many prevalent neuromorphic datasets lack strong temporal correlation, preventing SNNs from fully exploiting their spatio-temporal representation capabilities. Meanwhile, the integration of event and frame modalities offers more comprehensive visual spatio-temporal information. Yet, the SNN-based cross-modality fusion remains underexplored. In this work, we present a neuromorphic dataset called DVS-SLR that can better exploit the inherent spatio-temporal properties of SNNs. Compared to existing datasets, it offers advantages in terms of higher temporal correlation, larger scale, and more varied scenarios. In addition, our neuromorphic dataset contains corresponding frame data, which can be used for developing SNN-based fusion methods. By virtue of the dual-modal feature of the dataset, we propose a Cross-Modality Attention (CMA) based fusion method. The CMA model efficiently utilizes the unique advantages of each modality, allowing for SNNs to learn both temporal and spatial attention scores from the spatio-temporal features of event and frame modalities, subsequently allocating these scores across modalities to enhance their synergy. Experimental results demonstrate that our method not only improves recognition accuracy but also ensures robustness across diverse scenarios.

DOCTer: a novel EEG-based diagnosis framework for disorders of consciousness.

Objective. Accurately diagnosing patients with disorders of consciousness (DOC) is challenging and prone to errors. Recent studies have demonstrated that EEG (electroencephalography), a non-invasive technique of recording the spontaneous electrical activity of brains, offers valuable insights for DOC diagnosis. However, some challenges remain: (1) the EEG signals have not been fully used; and (2) the data scale in most existing studies is limited. In this study, our goal is to differentiate between minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS) using resting-state EEG signals, by proposing a new deep learning framework.Approach. We propose DOCTer, an end-to-end framework for DOC diagnosis based on EEG. It extracts multiple pertinent features from the raw EEG signals, including time-frequency features and microstates. Meanwhile, it takes clinical characteristics of patients into account, and then combines all the features together for the diagnosis. To evaluate its effectiveness, we collect a large-scale dataset containing 409 resting-state EEG recordings from 128 UWS and 187 MCS cases.Main results. Evaluated on our dataset, DOCTer achieves the state-of-the-art performance, compared to other methods. The temporal/spectral features contributes the most to the diagnosis task. The cerebral integrity is important for detecting the consciousness level. Meanwhile, we investigate the influence of different EEG collection duration and number of channels, in order to help make the appropriate choices for clinics.Significance. The DOCTer framework significantly improves the accuracy of DOC diagnosis, helpful for developing appropriate treatment programs. Findings derived from the large-scale dataset provide valuable insights for clinics.

Mechanically adaptive and deployable intracortical probes enable long-term neural electrophysiological recordings.

Flexible intracortical probes offer important opportunities for stable neural interfaces by reducing chronic immune responses, but their advances usually come with challenges of difficult implantation and limited recording span. Here, we reported a mechanically adaptive and deployable intracortical probe, which features a foldable fishbone-like structural design with branching electrodes on a temperature-responsive shape memory polymer (SMP) substrate. Leveraging the temperature-triggered soft-rigid phase transition and shape memory characteristic of SMP, this probe design enables direct insertion into brain tissue with minimal footprint in a folded configuration while automatically softening to reduce mechanical mismatches with brain tissue and deploying electrodes to a broader recording span under physiological conditions. Experimental and numerical studies on the material softening and structural folding-deploying behaviors provide insights into the design, fabrication, and operation of the intracortical probes. The chronically implanted neural probe in the rat cortex demonstrates that the proposed neural probe can reliably detect and track individual units for months with stable impedance and signal amplitude during long-term implantation. The work provides a tool for stable neural activity recording and creates engineering opportunities in basic neuroscience and clinical applications.

Emerging insights into thyroid cancer from immunotherapy perspective: A bibliometric analysis.

Thyroid cancer is a common endocrine malignancy that poses considerable therapeutic challenges in treating anaplastic carcinoma and advanced aggressive disease. Immunotherapy has become a prominent strategy for cancer treatment, and has shown remarkable advancements in recent years. In this study, we utilized visualization and bibliometric tools to analyze publications on thyroid cancer immunotherapy from the Web of Science Core Collection (WoSCC). A total of 409 articles were included, with an annual increase in both publications and citations since 2016. China leads research efforts in this area, while the University of Texas System and UTMD Anderson Cancer Center rank first in publication output. The journal Thyroid has garnered the highest citations. Notable authors contributing to this field include Antonelli Alessandro, Fallahi Poupak, and Wang Yu. Current research hotspots include immune checkpoint inhibitors, combination therapies involving immunotherapy with targeted therapy, CAR-T cell therapy, and modulation of the tumor microenvironment, all of which underscore the evolving landscape and potential for innovative treatments in thyroid cancer.

Impact of COVID-19 pandemic on microplastic occurrence in aquatic environments: A three-year study in Taihu Lake Basin, China.

The impact of the Coronavirus Disease 2019 (COVID-19) pandemic on microplastic (MP) occurrence in aquatic environments deserves an in-depth study. In this study, the occurrence of MPs and environmental flux of plastics before (2019) and during (2020 and 2021) the pandemic were comparatively investigated in various aquatic compartments in the Taihu Lake Basin in China. The field-based investigations from 2019 to 2021 for Taihu Lake have shown that, at the onset of the outbreak, the MP abundance declined at a rate of 62.3 %, but gradually recovered to the pre-pandemic level. However, the amount of plastics being released into aquatic environments showed a declining trend in 2020 and 2021 compared to those in 2019, with decrease rates of 13.7 % and 15.8 %, respectively. Characterization analysis of MP particles and source apportionment framework implied that while the contributions of tire abrasion and domestic waste to MP occurrence were depleted owing to the reduction in human activity during the pandemic, weathering and fragmentation of retained plastics contributed to the recovery of stored MPs. This study provides insights into the anthropogenic influences on MP occurrence, and supports policymakers in managing and controlling plastic contamination in large freshwater systems in the "new normal" phase.

Suitable fermentation temperature of forage sorghum silage increases greenhouse gas production: Exploring the relationship between temperature, microbial community, and gas production.

Silage is an excellent method of feed preservation; however, carbon dioxide, methane and nitrous oxide produced during fermentation are significant sources of agricultural greenhouse gases. Therefore, determining a specific production method is crucial for reducing global warming. The effects of four temperatures (10 °C, 20 °C, 30 °C, and 40 °C) on silage quality, greenhouse gas yield and microbial community composition of forage sorghum were investigated. At 20 °C and 30 °C, the silage has a lower pH value and a higher lactic acid content, resulting in higher silage quality and higher total gas production. In the first five days of ensiling, there was a significant increase in the production of carbon dioxide, methane, and nitrous oxide. After that, the output remained relatively stable, and their production at 20 °C and 30 °C was significantly higher than that at 10 °C and 40 °C. Firmicutes and Proteobacteria were the predominant silage microorganisms at the phylum level. Under the treatment of 20 °C, 30 °C, and 40 °C, Lactobacillus had already dominated on the second day of silage. However, low temperatures under 10 °C slowed down the microbial community succession, allowing, bad microorganisms such as Chryseobacterium, Pantoea and Pseudomonas dominate the fermentation, in the early stage of ensiling, which also resulted in the highest bacterial network complexity. According to random forest and structural equation model analysis, the production of carbon dioxide, methane and nitrous oxide is mainly affected by microorganisms such as Lactobacillus, Klebsiella and Enterobacter, and temperature influences the activity of these microorganisms to mediate gas production in silage. This study helps reveal the relationship between temperature, microbial community and greenhouse gas production during silage fermentation, providing a reference for clean silage fermentation.

Role and Molecular Mechanism of miR-586 in the Differentiation of Dental Pulp Stem Cells into Odontoblast-like Cells.

Dental pulp stem cells (DPSCs) are a class of cells with the potential of self-replication and multi-directional differentiation, which are widely considered to have great application value. It was to investigate miR-586 in DPSCs differentiated into odontoblast-like cells. In this article, human dental pulp stem cells (hDPSCs) were used as samples, and hDPSCs were co-cultured with endothelial progenitor cells (EPCs). Furthermore, a lentiviral expression vector for the miR-586 inhibitor was established. The effect of miR-586 inhibitor expression vector on the activity of hDPSCs was detected by Cell Counting Kit-8 (CCK-8). The differentiation of hDPSCs was tested by mineralized nodule staining. The expression of miR-586 and a gene related to dental cell differentiation in the pulp was subjected to detection by real-time quantitative PCR (qRT-PCR). As against the normal hDPSCs and the empty vector, the miR-586 lentivirus expression inhibition vector could visibly raise the expression of dentin sialophosphoprotein (DSPP) in hDPSCs; and the cell proliferation activity was visibly enhanced; In addition, the mRNA expressions of dentin-matrix acidic phosphoprotein 1 (DMP-1) and alkaline phosphatase (ALP) were visibly raised in the miR-586 lentivirus expression inhibition vector (all P < 0.05). Additionally, ALP activity was significantly enhanced (P < 0.05). The number of mineralized nodules was significantly increased (P < 0.05). MiR-586 plays a key regulatory function in DPSCs differentiated into odontoblast-like cells and is associated with specific molecular mechanisms.

CareSleepNet: A Hybrid Deep Learning Network for Automatic Sleep Staging.

Sleep staging is essential for sleep assessment and plays an important role in disease diagnosis, which refers to the classification of sleep epochs into different sleep stages. Polysomnography (PSG), consisting of many different physiological signals, e.g. electroencephalogram (EEG) and electrooculogram (EOG), is a gold standard for sleep staging. Although existing studies have achieved high performance on automatic sleep staging from PSG, there are still some limitations: 1) they focus on local features but ignore global features within each sleep epoch, and 2) they ignore cross-modality context relationship between EEG and EOG. In this paper, we propose CareSleepNet, a novel hybrid deep learning network for automatic sleep staging from PSG recordings. Specifically, we first design a multi-scale Convolutional-Transformer Epoch Encoder to encode both local salient wave features and global features within each sleep epoch. Then, we devise a Cross-Modality Context Encoder based on co-attention mechanism to model cross-modality context relationship between different modalities. Next, we use a Transformer-based Sequence Encoder to capture the sequential relationship among sleep epochs. Finally, the learned feature representations are fed into an epoch-level classifier to determine the sleep stages. We collected a private sleep dataset, SSND, and use two public datasets, Sleep-EDF-153 and ISRUC to evaluate the performance of CareSleepNet. The experiment results show that our CareSleepNet achieves the state-of-the-art performance on the three datasets. Moreover, we conduct ablation studies and attention visualizations to prove the effectiveness of each module and to analyze the influence of each modality.

Emergence and reconfiguration of modular structure for artificial neural networks during continual familiarity detection.

Advances in artificial intelligence enable neural networks to learn a wide variety of tasks, yet our understanding of the learning dynamics of these networks remains limited. Here, we study the temporal dynamics during learning of Hebbian feedforward neural networks in tasks of continual familiarity detection. Drawing inspiration from network neuroscience, we examine the network's dynamic reconfiguration, focusing on how network modules evolve throughout learning. Through a comprehensive assessment involving metrics like network accuracy, modular flexibility, and distribution entropy across diverse learning modes, our approach reveals various previously unknown patterns of network reconfiguration. We find that the emergence of network modularity is a salient predictor of performance and that modularization strengthens with increasing flexibility throughout learning. These insights not only elucidate the nuanced interplay of network modularity, accuracy, and learning dynamics but also bridge our understanding of learning in artificial and biological agents.

The impact of marine heatwaves on surface phytoplankton chlorophyll-a in the South China Sea.

According to previous studies, marine heatwaves (MHWs) significantly suppress the phytoplankton chlorophyll-a concentration (Chl a) in tropical oceans. However, pre-MHW Chl a has rarely been considered as a reference value. In this study, the Chl a for the periods preceding and during MHWs events was used to explore the impact of MHWs on Chl a from 1998 to 2022 in the South China Sea (SCS). The Chl a response to MHWs in different regions was further discussed based on the Chl a variation characteristics. The results showed that the Chl a response to MHWs exhibited regional variability. Interestingly, there was a large proportion of positive Chl a anomalies (∼0.55) in the estuary and offshore regions during MHWs; however, Chl a anomalies were mostly negative in the upwelling regions. These different response patterns are related to background conditions, including nutrient concentrations, wind-driven dynamics, and light availability. In upwelling regions, negative Chl a anomalies were primarily due to the weakening of wind speeds, Ekman pumping velocities, and upwelling intensities. In estuarine regions, positive Chl a anomalies were caused by enhanced light availability, whereas in offshore regions, there were attributed to the increased atmospheric wet deposition. These results have improved our understanding of the impact of MHWs on marine ecosystems.

The Impact of the VEGF/VEGFR2/PI3K/AKT Signaling Axis on the Proliferation and Migration Abilities of Human Dental Pulp Stem Cells.

The potential therapeutic benefits of human dental pulp stem cells (HDPSCs) in dental regenerative medicine have been demonstrated. However, little is known about the molecular mechanisms regulating the biological characteristics of HDPSCs. The experiment aims to explore whether VEGF activates signaling pathways such as FAK, PI3K, Akt, and p38 in HDPSCs, and to investigate the molecular mechanisms by which VEGF influences proliferation and migration of HDPSCs. Normal and inflamed human dental pulp (HDP) samples were collected, and the levels of VEGF in HDP were assessed. HDPSCs were cultured and purified. HDPSCs were stimulated with lipopolysaccharide (LPS) at gradient concentrations, and real-time quantitative polymerase chain reaction (qPCR) was used to assess changes in VEGF mRNA. Gradient concentrations of VEGF were used to stimulate HDPSCs, and cell migration ability was evaluated through scratch assays and Transwell chamber experiments. Phosphorylation levels of FAK, AKT, and P38 were assessed using Western blotting. Inhibitors of VEGFR2, FAK, AKT, P38, and VEGF were separately applied to HDPSCs, and cell migration ability and phosphorylation levels of FAK, AKT, and P38 were determined. The results indicated significant differences in VEGF levels between normal and inflamed HDP tissues, with levels in the inflamed state reaching 435% of normal levels (normal: 87.91 ng/mL, inflamed: 382.76 ng/mL, P < 0.05). LPS stimulation of HDPSCs showed a significant increase in VEGF mRNA expression with increasing LPS concentrations (LPS concentrations of 0.01, 0.1, 1, and 10 µg/mL resulted in VEGF mRNA expressions of 181.2%, 274.2%, 345.8%, and 460.9%, respectively, P < 0.05). VEGF treatment significantly enhanced the migration ability of HDPSCs in Transwell chamber experiments, with migration rates increasing with VEGF concentrations (VEGF concentrations of 0, 1, 10, 20, 50, and 100 ng/mL resulted in migration rates of 8.41%, 9.34%, 21.33%, 28.41%, 42.87%, and 63.15%, respectively, P < 0.05). Inhibitors of VEGFR2, FAK, AKT, P38, and combined VEGF stimulation demonstrated significant migration inhibition, with migration rates decreasing to 8.31%, 12.64%, 13.43%, 18.32%, and 74.17%, respectively. The migration rate with combined VEGF stimulation showed a significant difference (P < 0.05). The analysis of phosphorylation levels revealed that VEGF stimulation significantly activated phosphorylation of FAK, AKT, and P38, with phosphorylation levels increasing with VEGF concentrations (P < 0.05). The VEGF/VEGFR2 signaling axis regulated the migration ability of HDPSCs through the FAK/PI3K/AKT and P38MAPK pathways. This finding highlighted not only the crucial role of VEGF in injury repair of HDPSCs but also provided important clues for a comprehensive understanding of the potential applications of this signaling axis in dental regenerative medicine.

Neural Dielet 2.0: A 128-Channel 2mm×2mm Battery-Free Neural Dielet Merging Simultaneous Multi-Channel Transmission through Multi-Carrier Orthogonal Backscatter.

Miniaturization of wireless neural-recording systems enables minimally-invasive surgery and alleviates the rejection reactions for implanted brain-computer interface (BCI) applications. Simultaneous massive-channel recording capability is essential to investigate the behaviors and inter-connections in billions of neurons. In recent years, battery-free techniques based on wireless power transfer (WPT) and backscatter communication have reduced the sizes of neural-recording implants by battery eliminating and antenna sharing. However, the existing battery-free chips realize the multi-channel merging in the signal-acquisition circuits, which leads to large chip area, signal attenuation, insufficient channel number or low bandwidth, etc. In this work, we demonstrate a 2mm×2mm battery-free neural dielet, which merges 128 channels in the wireless part. The neural dielet is fabricated with 65nm CMOS process, and measured results show that: 1) The proposed multi-carrier orthogonal backscatter technique achieves a high data rate of 20.16Mb/s and an energy efficiency of 0.8pJ/bit. 2) A self-calibrated direct digital converter (SC-DDC) is proposed to fit the 128 channels in the 2mm×2mm die, and then the all-digital implementation achieves 0.02mm2 area and 9.87µW power per channel.

Ferroptosis in thyroid cancer: Potential mechanisms, effective therapeutic targets and predictive biomarker.

Thyroid cancer is a prevalent endocrine malignancy whose global incidence has risen over the past several decades. Ferroptosis, a regulated form of cell death distinguished by the excessive buildup of iron-dependent lipid peroxidates, stands out from other programmed cell death pathways in terms of morphological and molecular characteristics. Increasing evidence suggests a close association between thyroid cancer and ferroptosis, that is, inducing ferroptosis effectively suppresses the proliferation of thyroid cancer cells and impede tumor advancement. Therefore, ferroptosis represents a promising therapeutic target for the clinical management of thyroid cancer in clinical settings. Alterations in ferroptosis-related genes hold potential for prognostic prediction in thyroid cancer. This review summarizes current studies on the role of ferroptosis in thyroid cancer, elucidating its mechanisms, therapeutic targets, and predictive biomarkers. The findings underscore the significance of ferroptosis in thyroid cancer and offer valuable insights into the development of innovative treatment strategies and accurate predictors for the thyroid cancer.

Robust Sensory Information Reconstruction and Classification With Augmented Spikes.

Sensory information recognition is primarily processed through the ventral and dorsal visual pathways in the primate brain visual system, which exhibits layered feature representations bearing a strong resemblance to convolutional neural networks (CNNs), encompassing reconstruction and classification. However, existing studies often treat these pathways as distinct entities, focusing individually on pattern reconstruction or classification tasks, overlooking a key feature of biological neurons, the fundamental units for neural computation of visual sensory information. Addressing these limitations, we introduce a unified framework for sensory information recognition with augmented spikes. By integrating pattern reconstruction and classification within a single framework, our approach not only accurately reconstructs multimodal sensory information but also provides precise classification through definitive labeling. Experimental evaluations conducted on various datasets including video scenes, static images, dynamic auditory scenes, and functional magnetic resonance imaging (fMRI) brain activities demonstrate that our framework delivers state-of-the-art pattern reconstruction quality and classification accuracy. The proposed framework enhances the biological realism of multimodal pattern recognition models, offering insights into how the primate brain visual system effectively accomplishes the reconstruction and classification tasks through the integration of ventral and dorsal pathways.

Rice myo-inositol-3-phosphate synthase 2 (RINO2) alleviates heat injury-induced impairment in pollen germination and tube growth by modulating Ca2+ signaling and actin filament cytoskeleton.

Low phytic acid (lpa) crop is considered as an effective strategy to improve crop nutritional quality, but a substantial decrease in phytic acid (PA) usually has negative effect on agronomic performance and its response to environment adversities. Myo-inositol-3-phosphate synthase (MIPS) is the rate-limiting enzyme in PA biosynthesis pathway, and regarded as the prime target for engineering lpa crop. In this paper, the rice MIPS gene (RINO2) knockout mutants and its wild type were performed to investigate the genotype-dependent alteration in the heat injury-induced spikelet fertility and its underlying mechanism for rice plants being imposed to heat stress at anthesis. Results indicated that RINO2 knockout significantly enhanced the susceptibility of rice spikelet fertility to heat injury, due to the severely exacerbated obstacles in pollen germination and pollen tube growth in pistil for RINO2 knockout under high temperature (HT) at anthesis. The loss of RINO2 function caused a marked reduction in inositol and phosphatidylinositol derivative concentrations in the HT-stressed pollen grains, which resulted in the strikingly lower content of phosphatidylinositol 4,5-diphosphate (PI (4,5) P2) in germinating pollen grain and pollen tube. The insufficient supply of PI (4,5) P2 in the HT-stressed pollen grains disrupted normal Ca2+ gradient in the apical region of pollen tubes and actin filament cytoskeleton in growing pollen tubes. The severely repressed biosynthesis of PI (4,5) P2 was among the regulatory switch steps leading to the impaired pollen germination and deformed pollen tube growth for the HT-stressed pollens of RINO2 knockout mutants.

Attention deficit hyperactivity disorder in children with epilepsy: a multicenter cross-sectional analysis in China.

BACKGROUND: The diagnosis and treatment of attention deficit hyperactivity disorder (ADHD) comorbid with epilepsy have been insufficiently addressed in China. We conducted a study in China to investigate the current status, diagnosis, and treatment of ADHD in children to further our understanding of ADHD comorbid with epilepsy, strengthen its management, and improve patients' quality of life. METHODS: We carried out a multicenter cross-sectional survey of children with epilepsy across China between March 2022 and August 2022. We screened all patients for ADHD and compared various demographic and clinical factors between children with and without ADHD, including gender, age, age at epilepsy onset, duration of epilepsy, seizure types, seizure frequency, presence of epileptiform discharges, and treatment status. Our objective was to explore any possible associations between these characteristics and the prevalence of ADHD. RESULTS: Overall, 395 epilepsy patients aged 6-18 years were enrolled. The age at seizure onset and duration of epilepsy ranged from 0.1-18 to 0.5-15 years, respectively. Focal onset seizures were observed in 212 (53.6%) patients, while 293 (76.3%) patients had epileptiform interictal electroencephalogram (EEG) abnormalities. Among the 370 patients treated with anti-seizure medications, 200 (54.1%) had monotherapy. Although 189 (47.8%) patients had ADHD, only 31 received treatment for it, with the inattentive subtype being the most common. ADHD was more common in children undergoing polytherapy compared to those on monotherapy. Additionally, poor seizure control and the presence of epileptiform interictal EEG abnormalities may be associated with a higher prevalence of ADHD. CONCLUSIONS: While the prevalence of ADHD was higher in children with epilepsy than in normal children, the treatment rate was notably low. This highlights the need to give more importance to the diagnosis and treatment of ADHD in children with epilepsy.

Darwin3: a large-scale neuromorphic chip with a novel ISA and on-chip learning.

Spiking neural networks (SNNs) are gaining increasing attention for their biological plausibility and potential for improved computational efficiency. To match the high spatial-temporal dynamics in SNNs, neuromorphic chips are highly desired to execute SNNs in hardware-based neuron and synapse circuits directly. This paper presents a large-scale neuromorphic chip named Darwin3 with a novel instruction set architecture, which comprises 10 primary instructions and a few extended instructions. It supports flexible neuron model programming and local learning rule designs. The Darwin3 chip architecture is designed in a mesh of computing nodes with an innovative routing algorithm. We used a compression mechanism to represent synaptic connections, significantly reducing memory usage. The Darwin3 chip supports up to 2.35 million neurons, making it the largest of its kind on the neuron scale. The experimental results showed that the code density was improved by up to 28.3× in Darwin3, and that the neuron core fan-in and fan-out were improved by up to 4096× and 3072× by connection compression compared to the physical memory depth. Our Darwin3 chip also provided memory saving between 6.8× and 200.8× when mapping convolutional spiking neural networks onto the chip, demonstrating state-of-the-art performance in accuracy and latency compared to other neuromorphic chips.

Association of coagulation function with the risk of in-hospital mortality in patients with severe acute respiratory distress syndrome.

BACKGROUND: To evaluate the association of coagulation disorder score with the risk of in-hospital mortality in acute respiratory distress syndrome (ARDS) patients. METHODS: In this cohort study, 7,001 adult patients with ARDS were identified from the Medical Information Mart for Intensive Care Database-IV (MIMIC-IV). Univariate and multivariate Logistic stepwise regression models were used to explore the associations of coagulation-associated biomarkers with the risk of in-hospital mortality in patients with ADRS. Restricted cubic spline (RCS) was plotted to explore the association between coagulation disorder score and in-hospital mortality of ARDS patients. RESULTS: The follow-up time for in-hospital death was 7.15 (4.62, 13.88) days. There were 1,187 patients died and 5,814 people survived in hospital. After adjusting for confounding factors, increased risk of in-hospital mortality in ARDS patients was observed in those with median coagulation disorder score [odds ratio (OR) = 1.22, 95% confidence interval (CI): 1.01-1.47) and high coagulation disorder score (OR = 1.38, 95% CI: 1.06-1.80). The results of RCS indicated that when the coagulation disorder score >2, the trend of in-hospital mortality rose gradually, and OR was >1. CONCLUSIONS: Poor coagulation function was associated with increased risk of in-hospital mortality in ARDS patients. The findings implied that clinicians should regularly detect the levels of coagulation-associated biomarkers for the management of ARDS patients.

Simplifying Multimodal With Single EOG Modality for Automatic Sleep Staging.

Polysomnography (PSG) recordings have been widely used for sleep staging in clinics, containing multiple modality signals (i.e., EEG and EOG). Recently, many studies have combined EEG and EOG modalities for sleep staging, since they are the most and the second most powerful modality for sleep staging among PSG recordings, respectively. However, EEG is complex to collect and sensitive to environment noise or other body activities, imbedding its use in clinical practice. Comparatively, EOG is much more easily to be obtained. In order to make full use of the powerful ability of EEG and the easy collection of EOG, we propose a novel framework to simplify multimodal sleep staging with a single EOG modality. It still performs well with only EOG modality in the absence of the EEG. Specifically, we first model the correlation between EEG and EOG, and then based on the correlation we generate multimodal features with time and frequency guided generators by adopting the idea of generative adversarial learning. We collected a real-world sleep dataset containing 67 recordings and used other four public datasets for evaluation. Compared with other existing sleep staging methods, our framework performs the best when solely using the EOG modality. Moreover, under our framework, EOG provides a comparable performance to EEG.