The relationship among metabolic rate of tree shrews (Tupaia belangeri) under cold acclimation

Many small mammals inhabiting cold environments display enhanced capacity for seasonal changes in nonshivering thermogenesis (NST) and thermoregulatory maximum metabolic rate (MMR). However, it is not known how this plasticity remains in a mammal that rarely experiences extreme cold fluctuations. In order to answer this question, we determined body mass ( Mb), basal metabolic rate (BMR), NST, and MMR on a tree shrews (Tupaia belangeri), acclimated to cold (5 ºC) conditions. NST was measured as the maximum response of metabolic rate (NSTmax) after injection of norepinephrine (NE) in thermoneutrality minus BMR. Maximum metabolic rate was assessed in animals exposed to enhanced heat-loss atmosphere (He-O2) connected with an open-flow respirometer. Body mass and metabolic variables increased significantly after cold acclimation with respect to control group but to a high extent (BMR, 87.97%; NST, 69.77%; and MMR, 32.35%). However, aerobic scope (MMR/BMR), and calculated shivering thermogenesis (ST) did not significantly change with control group. Our data suggest: 1). The body mass and the capacity of heat production in the cold acclimated group were higher; 2). The increase of BMR and MMR during cold acclimation was the main pattern of heat production in the tree shrews

Peak-First CTC: Reducing the Peak Latency of CTC Models by Applying Peak-First Regularization

The CTC model has been widely applied to many application scenarios because of its simple structure, excellent performance, and fast inference speed. There are many peaks in the probability distribution predicted by the CTC models, and each peak represents a non-blank token. The recognition latency of CTC models can be reduced by encouraging the model to predict peaks earlier. Existing methods to reduce latency require modifying the transition relationship between tokens in the forward-backward algorithm, and the gradient calculation. Some of these methods even depend on the forced alignment results provided by other pretrained models. The above methods are complex to implement. To reduce the peak latency, we propose a simple and novel method named peak-first regularization, which utilizes a frame-wise knowledge distillation function to force the probability distribution of the CTC model to shift left along the time axis instead of directly modifying the calculation process of CTC loss and gradients. All the experiments are conducted on a Chinese Mandarin dataset AISHELL-1. We have verified the effectiveness of the proposed regularization on both streaming and non-streaming CTC models respectively. The results show that the proposed method can reduce the average peak latency by about 100 to 200 milliseconds with almost no degradation of recognition accuracy.Comment: Submitted to ICASSP 2023(5 pages, 2 figures

MobilePortrait: Real-Time One-Shot Neural Head Avatars on Mobile Devices

Existing neural head avatars methods have achieved significant progress in the image quality and motion range of portrait animation. However, these methods neglect the computational overhead, and to the best of our knowledge, none is designed to run on mobile devices. This paper presents MobilePortrait, a lightweight one-shot neural head avatars method that reduces learning complexity by integrating external knowledge into both the motion modeling and image synthesis, enabling real-time inference on mobile devices. Specifically, we introduce a mixed representation of explicit and implicit keypoints for precise motion modeling and precomputed visual features for enhanced foreground and background synthesis. With these two key designs and using simple U-Nets as backbones, our method achieves state-of-the-art performance with less than one-tenth the computational demand. It has been validated to reach speeds of over 100 FPS on mobile devices and support both video and audio-driven inputs

IL-36-related genes predict prognosis of gastric cancer

IntroductionGastric cancer (GC) is one of the most frequently encountered malignant tumors in the clinic. Because effective early screening techniques are lacking, most patients have advanced disease at first diagnosis. The interleukin (IL)-36 family plays a vital role in regulating the immune system, inflammatory responses, and the occurrence and development of cancer. Hence, this study explored the potential role of IL-36 related genes (IL-36RGs) in GC and built a prognostic risk assessment model for GC based on IL-36RGs, which can help evaluate treatment and prognosis.MethodsFirst, relevant datasets were downloaded from public databases. After processing the datasets to remove batch effects, perform differential analysis, and take intersections, IL-36-related differentially expressed genes (IL-36RDEGs) were screened. A prognostic risk model containing nine model genes was constructed based on univariate Cox and least absolute shrinkage and selection operator (LASSO) regression methods. Then, to investigate the potential biological activities of the model genes in GC, we conducted enrichment, PPI interaction network, and immune infiltration analyses. Immunohistochemical staining was conducted to validate the expression of IL-36A in GC.ResultsThe prognostic risk model analysis revealed that mortality events in the high-risk group were substantially elevated compared to those in the low-risk group. The model demonstrated excellent predictive capability at 1, 2, and 3 years and showed the best clinical predictive performance at 3 years. Bioinformatics analysis of the model genes indicate that they primarily participate in mechanisms that promote the synthesis and secretion of cytokines in GC. And hub genes may be strongly correlated with host immune response mechanisms. According to the immunohistochemical staining results, IL-36A expression was higher in the STAD group than in the control group.ConclusionsThe results of the above analysis suggest that IL-36RDEGs can serve as independent prognostic biomarkers for GC and provide insights into IL-36RGs from both bioinformatics and experimental validation perspectives

Macrophage polarization states in atherosclerosis

Atherosclerosis, a chronic inflammatory condition primarily affecting large and medium arteries, is the main cause of cardiovascular diseases. Macrophages are key mediators of inflammatory responses. They are involved in all stages of atherosclerosis development and progression, from plaque formation to transition into vulnerable plaques, and are considered important therapeutic targets. Increasing evidence suggests that the modulation of macrophage polarization can effectively control the progression of atherosclerosis. Herein, we explore the role of macrophage polarization in the progression of atherosclerosis and summarize emerging therapies for the regulation of macrophage polarization. Thus, the aim is to inspire new avenues of research in disease mechanisms and clinical prevention and treatment of atherosclerosis

Hyperconjugated side chained benzodithiophene and 4,7-di-2-thienyl-2,1,3-benzothiadiazole based polymer for solar cells

A novel donor-acceptor (D-A) copolymer (P3TBDTDTBT), including hyperconjugated side chained benzodithiophene as a donor and 4,7-di-2-thienyl-2,1,3-benzothiadiazole (DTBT) as an acceptor, was designed and synthesized. Due to the introduction of the hyperconjugated side chain, the resultant polymer exhibited good thermal stability with a high decomposition temperature of 437 degrees C, a low band-gap of 1.67 eV with an absorption onset of 742 nm in the solid film, and a deep highest occupied molecular orbital (HOMO) energy level of -5.26 eV. Finally, the polymer solar cell (PSC) device based on this polymer and [6,6]-phenyl-C-61-butyric acid methyl ester (PCBM) showed the best power conversion efficiency (PCE) of 3.57% with an open-circuit voltage (V-oc) of 0.78 V, a short-circuit current density (J(sc)) of 8.83 mA cm(-2) and a fill factor (FF) of 53%

The effect of a therapeutic regimen of Traditional Chinese Medicine rehabilitation for post-stroke cognitive impairment: study protocol for a randomized controlled trial

Background Post-stroke cognitive impairment (PSCI) lessens quality of life, restricts the rehabilitation of stroke, and increases the social and economic burden stroke imposes on patients and their families. Therefore effective treatment is of paramount importance. However, the treatment of PSCI is very limited. The primary aim of this protocol is to propose a lower cost and more effective therapy, and to confirm the long-term effectiveness of a therapeutic regimen of Traditional Chinese Medicine (TCM) rehabilitation for PSCI. Methods/Design A prospective, multicenter, large sample, randomized controlled trial will be conducted. A total of 416 eligible patients will be recruited from seven inpatient and outpatient stroke rehabilitation units and randomly allocated into a therapeutic regimen of TCM rehabilitation group or cognitive training (CT) control group. The intervention period of both groups will last 12 weeks (30 minutes per day, five days per week). Primary and secondary outcomes will be measured at baseline, 12 weeks (at the end of the intervention), and 36 weeks (after the 24-week follow-up period). Discussion This protocol presents an objective design of a multicenter, large sample, randomized controlled trial that aims to put forward a lower cost and more effective therapy, and confirm the long-term effectiveness of a therapeutic regimen of TCM rehabilitation for PSCI through subjective and objective assessments, as well as highlight its economic advantages