Efficient Spiking Transformer Enabled By Partial Information

Spiking neural networks (SNNs) have received substantial attention in recent years due to their sparse and asynchronous communication nature, and thus can be deployed in neuromorphic hardware and achieve extremely high energy efficiency. However, SNNs currently can hardly realize a comparable performance to that of artificial neural networks (ANNs) because their limited scalability does not allow for large-scale networks. Especially for Transformer, as a model of ANNs that has accomplished remarkable performance in various machine learning tasks, its implementation in SNNs by conventional methods requires a large number of neurons, notably in the self-attention module. Inspired by the mechanisms in the nervous system, we propose an efficient spiking Transformer (EST) framework enabled by partial information to address the above problem. In this model, we not only implemented the self-attention module with a reasonable number of neurons, but also introduced partial-information self-attention (PSA), which utilizes only partial input signals, further reducing computational resources compared to conventional methods. The experimental results show that our EST can outperform the state-of-the-art SNN model in terms of accuracy and the number of time steps on both Cifar-10/100 and ImageNet datasets. In particular, the proposed EST model achieves 78.48% top-1 accuracy on the ImageNet dataset with only 16 time steps. In addition, our proposed PSA reduces flops by 49.8% with negligible performance loss compared to a self-attention module with full information

Tenascin-C predicts IVIG non-responsiveness and coronary artery lesions in kawasaki disease in a Chinese cohort

ObjectivesTo assess the predictive value of tenascin-C (TN-C) for intravenous immunoglobulin (IVIG) non-responsiveness and coronary artery lesions (CALs) development at the acute stage of Kawasaki disease, and to build novel scoring systems for identifying IVIG non-responsiveness and CALs.MethodsA total of 261 patients in acute-stage Kawasaki disease were included. Serum samples before IVIG initiation were collected and TN-C expression levels were measured using an enzyme-linked immunosorbent assay. In addition to TN-C, another fifteen clinical and laboratory parameters collected before treatment were compared between IVIG responsive and non-responsive groups, and between groups with and without CALs. Multiple logistic regression analyses were performed to construct new scoring systems for the prediction of IVIG non-responsiveness and CALs development.ResultsIVIG non-responsive group (n = 51) had significantly higher TN-C level compared to IVIG responsive group (n = 210) (15.44 vs. 12.38 IU/L, P < 0.001). A novel scoring system composed of TN-C, total bilirubin, serum sodium and albumin was established to predict IVIG non-responsiveness. Patients with a total score ≥ 2 points were classified as high-risk cases. With the sensitivity of 78.4% and specificity of 73.8%, the efficiency of our scoring system for predicting IVIG non-responsiveness was comparable to the Kobayashi system. Consistently, the group developing CALs at the acute stage (n = 42) had significantly higher TN-C level compared to the group without CALs (n = 219) (19.76 vs. 12.10 IU/L, P < 0.001). A new scoring system showed that patients with elevated TN-C, platelet count ≥ 450 × 109/L, and delayed initial infusion of IVIG had a higher risk of developing CALs. Individuals with a total score ≥ 3 points were classified as high-risk cases. The sensitivity and specificity of the novel simple system for predicting CALs development were 83.3% and 74.0%, respectively, yielding a better efficiency than the Harada score.ConclusionElevated TN-C appeared to be an independent risk factor for both IVIG non-responsiveness and CALs in Chinese children with KD. Our scoring systems containing TN-C is simple and efficient in the early identification of high-risk KD cases that could benefit from more individualized medications

Knowledge, Attitudes, and Social Responsiveness Toward Corona Virus Disease 2019 (COVID-19) Among Chinese Medical Students—Thoughts on Medical Education

Purpose: To assess knowledge, attitudes, and social responsiveness toward COVID-19 among Chinese medical students.Methods: Self-administered questionnaires were used to collect data from 889 medical students in three well-known Chinese medical universities. The questionnaire was comprised of three domains which consisted of demographic characteristic collection, seven items for knowledge, and eight items for attitudes and social responsiveness toward COVID-19. Data from different universities were lumped together and were divided into different groups to compare the differences, including (1) students at the clinical learning stage (Group A) or those at the basic-medicine stage (Group B) and (2) students who have graduated and worked (Group C) or those newly enrolled (Group D).Results: Medical students at group B had a weaker knowledge toward COVID-19 than did students at group A, especially in the question of clinical manifestations (p < 0.001). The percentage of totally correct answers of COVID-19 knowledge in group C was higher than that in Group D (p < 0.001). There were significant differences between groups C and D in the attitudes and social responsiveness toward COVID-19. Surprisingly, we found that the idea of newly enrolled medical students could be easily affected by interventions.Conclusions: In light of this information, medical education should pay attention not only to the cultivation of professional knowledge and clinical skills but also to the positive interventions to better the comprehensive qualities including communicative abilities and empathy

Antibacterial, injectable, and adhesive hydrogel promotes skin healing

With the development of material science, hydrogels with antibacterial and wound healing properties are becoming common. However, injectable hydrogels with simple synthetic methods, low cost, inherent antibacterial properties, and inherent promoting fibroblast growth are rare. In this paper, a novel injectable hydrogel wound dressing based on carboxymethyl chitosan (CMCS) and polyethylenimine (PEI) was discovered and constructed. Since CMCS is rich in -OH and -COOH and PEI is rich in -NH2, the two can interact through strong hydrogen bonds, and it is theoretically feasible to form a gel. By changing their ratio, a series of hydrogels can be obtained by stirring and mixing with 5 wt% CMCS aqueous solution and 5 wt% PEI aqueous solution at volume ratios of 7:3, 5:5, and 3:7. Characterized by morphology, swelling rate, adhesion, rheological properties, antibacterial properties, in vitro biocompatibility, and in vivo animal experiments, the hydrogel has good injectability, biocompatibility, antibacterial (Staphylococcus aureus: 56.7 × 107 CFU/mL in the blank group and 2.5 × 107 CFU/mL in the 5/5 CPH group; Escherichia coli: 66.0 × 107 CFU/mL in the blank group and 8.5 × 107 CFU/mL in the 5/5 CPH group), and certain adhesion (0.71 kPa in the 5/5 CPH group) properties which can promote wound healing (wound healing reached 98.02% within 14 days in the 5/5 CPH group) and repair of cells with broad application prospects

Transmittance contrast‐induced photocurrent: A general strategy for self‐powered photodetectors based on MXene electrodes

Abstract The regulation of carrier generation and transport by Schottky junctions enables effective optoelectronic conversion in optoelectronic devices. A simple and general strategy to spontaneously generate photocurrent is of great significance for self‐powered photodetectors but is still being pursued. Here, we propose that a photocurrent can be induced at zero bias by the transmittance contrast of MXene electrodes in MXene/semiconductor Schottky junctions. Two MXene electrodes with a large transmittance contrast (84%) between the thin and thick zones were deposited on the surface of a semiconductor wafer using a simple and robust solution route. Kelvin probe force microscopy tests indicated that the photocurrent at zero bias could be attributed to asymmetric carrier generation and transport between the two Schottky junctions under illumination. As a demonstration, the MXene/GaN ultraviolet (UV) photodetector exhibits excellent performance superior to its counterpart without transmittance contrast, including high responsivity (81 mA W–1), fast response speed (less than 31 and 29 ms) and ultrahigh on/off ratio (1.33 × 106), and good UV imaging capability. Furthermore, this strategy has proven to be universal for first‐ to third‐generation semiconductors such as Si and GaAs. These results provide a facile and cost‐effective route for high‐performance self‐powered photodetectors and demonstrate the versatile and promising applications of MXene electrodes in optoelectronics

Metric Learning-enhanced Optimal Transport for Biochemical Regression Domain Adaptation

Generalizing knowledge beyond source domains is a crucial prerequisite for many biomedical applications such as drug design and molecular property prediction. To meet this challenge, researchers have used optimal transport (OT) to perform representation alignment between the source and target domains. Yet existing OT algorithms are mainly designed for classification tasks. Accordingly, we consider regression tasks in the unsupervised and semi-supervised settings in this paper. To exploit continuous labels, we propose novel metrics to measure domain distances and introduce a posterior variance regularizer on the transport plan. Further, while computationally appealing, OT suffers from ambiguous decision boundaries and biased local data distributions brought by the mini-batch training. To address those issues, we propose to couple OT with metric learning to yield more robust boundaries and reduce bias. Specifically, we present a dynamic hierarchical triplet loss to describe the global data distribution, where the cluster centroids are progressively adjusted among consecutive iterations. We evaluate our method on both unsupervised and semi-supervised learning tasks in biochemistry. Experiments show the proposed method significantly outperforms state-of-the-art baselines across various benchmark datasets of small molecules and material crystals