Network Sketching: Exploiting Binary Structure in Deep CNNs

Convolutional neural networks (CNNs) with deep architectures have substantially advanced the state-of-the-art in computer vision tasks. However, deep networks are typically resource-intensive and thus difficult to be deployed on mobile devices. Recently, CNNs with binary weights have shown compelling efficiency to the community, whereas the accuracy of such models is usually unsatisfactory in practice. In this paper, we introduce network sketching as a novel technique of pursuing binary-weight CNNs, targeting at more faithful inference and better trade-off for practical applications. Our basic idea is to exploit binary structure directly in pre-trained filter banks and produce binary-weight models via tensor expansion. The whole process can be treated as a coarse-to-fine model approximation, akin to the pencil drawing steps of outlining and shading. To further speedup the generated models, namely the sketches, we also propose an associative implementation of binary tensor convolutions. Experimental results demonstrate that a proper sketch of AlexNet (or ResNet) outperforms the existing binary-weight models by large margins on the ImageNet large scale classification task, while the committed memory for network parameters only exceeds a little.Comment: To appear in CVPR201

Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation

In this paper, we propose an alternative method to estimate room layouts of cluttered indoor scenes. This method enjoys the benefits of two novel techniques. The first one is semantic transfer (ST), which is: (1) a formulation to integrate the relationship between scene clutter and room layout into convolutional neural networks; (2) an architecture that can be end-to-end trained; (3) a practical strategy to initialize weights for very deep networks under unbalanced training data distribution. ST allows us to extract highly robust features under various circumstances, and in order to address the computation redundance hidden in these features we develop a principled and efficient inference scheme named physics inspired optimization (PIO). PIO's basic idea is to formulate some phenomena observed in ST features into mechanics concepts. Evaluations on public datasets LSUN and Hedau show that the proposed method is more accurate than state-of-the-art methods.Comment: To appear in CVPR 2017. Project Page: https://sites.google.com/view/st-pio

Immune interference in effectiveness of influenza and COVID-19 vaccination

Vaccines are known to function as the most effective interventional therapeutics for controlling infectious diseases, including polio, smallpox, rabies, tuberculosis, influenza and SARS-CoV-2. Smallpox has been eliminated completely and polio is almost extinct because of vaccines. Rabies vaccines and Bacille Calmette-Guérin (BCG) vaccines could effectively protect humans against respective infections. However, both influenza vaccines and COVID-19 vaccines are unable to eliminate these two infectious diseases of their highly variable antigenic sites in viral proteins. Vaccine effectiveness (VE) could be negatively influenced (i.e., interfered with) by immune imprinting of previous infections or vaccinations, and repeated vaccinations could interfere with VE against infections due to mismatch between vaccine strains and endemic viral strains. Moreover, VE could also be interfered with when more than one kind of vaccine is administrated concomitantly (i.e., co-administrated), suggesting that the VE could be modulated by the vaccine-induced immunity. In this review, we revisit the evidence that support the interfered VE result from immune imprinting or repeated vaccinations in influenza and COVID-19 vaccine, and the interference in co-administration of these two types of vaccines is also discussed. Regarding the development of next-generation COVID-19 vaccines, the researchers should focus on the induction of cross-reactive T-cell responses and naive B-cell responses to overcome negative effects from the immune system itself. The strategy of co-administrating influenza and COVID-19 vaccine needs to be considered more carefully and more clinical data is needed to verify this strategy to be safe and immunogenic

Heteroaromatic organic compound with conjugated multi-carbonyl as cathode material for rechargeable lithium batteries

The heteroaromatic organic compound, N,N\u27-diphenyl-1,4,5,8-naphthalenetetra-carboxylic diimide (DP-NTCDI-250) as the cathode material of lithium batteries is prepared through a simple one-pot N-acylation reaction of 1,4,5,8-naphthalenetetra-carboxylic dianhydride (NTCDA) with phenylamine (PA) in DMF solution followed by heat treatment in 250 °C. The as prepared sample is characterized by the combination of elemental analysis, NMR, FT-IR, TGA, XRD, SEM and TEM. The electrochemical measurements show that DP-NTCDI-250 can deliver an initial discharge capacity of 170 mAh g-1 at the current density of 25 mA g-1. The capacity of 119 mAh g-1 can be retained after 100 cycles. Even at the high current density of 500 mA g-1, its capacity still reaches 105 mAh g-1, indicating its high rate capability. Therefore, the as-prepared DP-NTCDI-250 could be a promising candidate as low cost cathode materials for lithium batteries

A Combined Risk Score Model to Assess Prognostic Value in Patients with Soft Tissue Sarcomas

A study by Tsvetkov et al. recently published a proposed novel form of copper-induced cell death in Science; however, few studies have looked into the possible mechanism in soft tissue sarcoma (STS). Herein, this study sought to investigate the function of cuproptosis-related genes (CRGs) in the development of tumor-associated immune cells and the prognosis of sarcoma. Herein, this study aimed to explore the role of cuproptosis-related genes (CRGs) in the development, tumor-associated immune cells, and the prognosis of sarcoma. Methods: The prognostic model was established via the least absolute shrinkage and selection operator (LASSO) algorithm as well as multivariate Cox regression analysis. The stromal scores, immune scores, ESTIMA scores, and tumor purity of sarcoma patients were evaluated by the ESTIMATE algorithm. Functional analyses were performed to investigate the underlying mechanisms of immune cell infiltration and the prognosis of CRGs in sarcoma. Results: Two molecular subgroups with different CRG expression patterns were recognized, which showed that patients with a higher immune score and more active immune status were prone to have better prognostic survival. Moreover, GO and KEGG analyses showed that these differentially expressed CRGs were mainly enriched in metabolic/ions-related signaling pathways, indicating that CRGs may have impacts on the immune cell infiltration and prognosis of sarcoma via regulating the bioprocess of mitochondria and consequently affecting the immune microenvironment. The expression levels of CRGs were closely correlated to the immunity condition and prognostic survival of sarcoma patients. Conclusions: The interaction between cuproptosis and immunity in sarcoma may provide a novel insight into the study of molecular mechanisms and candidate biomarkers for the prognosis, resulting in effective treatments for sarcoma patients

Effects of rainfall intensity on runoff and nutrient loss of gently sloping farmland in a karst area of SW China

Nutrient losses from sloping farmland in karst areas lead to the decline in land productivity and nonpoint source pollution. A specially tailored steel channel with an adjustable slope and underground hole fissures was used to simulate the microenvironment of the "dual structure" of the surface and underground of sloping farmland in a karst area. The artificial rainfall simulation method was used to explore the surface and underground runoff characteristics and nutrient losses from sloping farmland under different rainfall intensities. The effect of rainfall intensity on the nutrient loss of farmland on karst sloping land was clarified. The results showed that the surface was the main route of runoff and nutrient loss during the rainy season on sloping farmland in karst areas. The influence of rainfall intensity on the nutrients in surface runoff was more substantial than that on underground runoff nutrients. Nutrient loss was more likely to occur underground than on the surface. The losses of total nitrogen, total phosphorus, and total potassium in surface and underground runoff initially increased and then gradually stabilized with the extension of rainfall duration and increased with increasing rainfall intensity and the amount of nutrient runoff. The output of nutrients through surface runoff accounted for a high proportion of the total, and underground runoff was responsible for a low proportion. Although the amount of nutrients output by underground runoff was small, it could directly cause groundwater pollution. The research results provide a theoretical reference for controlling land source pollution from sloping farming in karst areas

Biochemical and transcriptomic evaluation of a 3D lung organoid platform for pre-clinical testing of active substances targeting senescence

Chronic lung diseases such as chronic obstructive pulmonary disease and cystic fibrosis are incurable. Epithelial senescence, a state of dysfunctional cell cycle arrest, contributes to the progression of such diseases. Therefore, lung epithelial cells are a valuable target for therapeutic intervention. Here, we present a 3D airway lung organoid platform for the preclinical testing of active substances with regard to senescence, toxicity, and inflammation under standardized conditions in a 96 well format. Senescence was induced with doxorubicin and measured by activity of senescence associated galactosidase. Pharmaceutical compounds such as quercetin antagonized doxorubicininduced senescence without compromising organoid integrity. Using single cell sequencing, we identified a subset of cells expressing senescence markers which was decreased by quercetin. Doxorubicin induced the expression of detoxification factors specifically in goblet cells independent of quercetin. In conclusion, our platform enables for the analysis of senescence-related processes and will allow the pre-selection of a wide range of compounds (e.g. natural products) in preclinical studies, thus reducing the need for animal testing

Chinese Herbal Medicine for Postpartum Depression: A Systematic Review of Randomized Controlled Trials

Background. Postpartum depression (PPD) does great harm to women following childbirth. The aim of this study was to conduct a systematic review of the literature to assess the efficacy and safety of CHM for the treatment of PPD. Methods. Published or ongoing registered trials were searched for from the inception of the various databases to December 31, 2015. Data extraction and methodology assessment were conducted independently by two researchers. RevMan 5.3 software was used to analyze the data. Results. Forty-seven registered clinical trials (RCTs) were identified and reviewed. The results showed CHM alone or in combination with routine treatments could reduce HAMD score, EPDS score, incidence of adverse events, TESS, and SERS. CHM combined with routine treatment was more effective in increasing serum estradiol levels and reducing progesterone levels than routine treatment alone. Meanwhile, pooled data revealed that MRLQS combined with routine treatments or MRLQS plus MSHS combined with routine treatments were more effective than other therapeutic methods in TCM. MRLQS plus MSHS alone was found to be an effective alternative when compared to routine treatments. Conclusions. This review suggested that CHM was safe and effective in the treatment of PPD. However, this could not be proven conclusively. To ensure evidence-based clinical practice, more rigorously designed trials are warranted

Outer membrane vesicles from bacteria: Role and potential value in the pathogenesis of chronic respiratory diseases

Infectious diseases are the leading cause of death in both adults and children, with respiratory infections being the leading cause of death. A growing body of evidence suggests that bacterially released extracellular membrane vesicles play an important role in bacterial pathogenicity by targeting and (de)regulating host cells through the delivery of nucleic acids, proteins, lipids, and carbohydrates. Among the many factors contributing to bacterial pathogenicity are the outer membrane vesicles produced by the bacteria themselves. Bacterial membrane vesicles are being studied in more detail because of their potential role as deleterious mediators in bacterial infections. This review provides an overview of the most current information on the emerging role of bacterial membrane vesicles in the pathophysiology of pneumonia and its complications and their adoption as promising targets for future preventive and therapeutic approaches

Spatio-temporal interactive fusion based visual object tracking method

Visual object tracking tasks often struggle with utilizing inter-frame correlation information and handling challenges like local occlusion, deformations, and background interference. To address these issues, this paper proposes a spatio-temporal interactive fusion (STIF) based visual object tracking method. The goal is to fully utilize spatio-temporal background information, enhance feature representation for object recognition, improve tracking accuracy, adapt to object changes, and reduce model drift. The proposed method incorporates feature-enhanced networks in both temporal and spatial dimensions. It leverages spatio-temporal background information to extract salient features that contribute to improved object recognition and tracking accuracy. Additionally, the model’s adaptability to object changes is enhanced, and model drift is minimized. A spatio-temporal interactive fusion network is employed to learn a similarity metric between the memory frame and the query frame by utilizing feature enhancement. This fusion network effectively filters out stronger feature representations through the interactive fusion of information. The proposed tracking method is evaluated on four challenging public datasets. The results demonstrate that the method achieves state-of-the-art (SOTA) performance and significantly improves tracking accuracy in complex scenarios affected by local occlusion, deformations, and background interference. Finally, the method achieves a remarkable success rate of 78.8% on TrackingNet, a large-scale tracking dataset