Division Gets Better: Learning Brightness-Aware and Detail-Sensitive Representations for Low-Light Image Enhancement

Low-light image enhancement strives to improve the contrast, adjust the visibility, and restore the distortion in color and texture. Existing methods usually pay more attention to improving the visibility and contrast via increasing the lightness of low-light images, while disregarding the significance of color and texture restoration for high-quality images. Against above issue, we propose a novel luminance and chrominance dual branch network, termed LCDBNet, for low-light image enhancement, which divides low-light image enhancement into two sub-tasks, e.g., luminance adjustment and chrominance restoration. Specifically, LCDBNet is composed of two branches, namely luminance adjustment network (LAN) and chrominance restoration network (CRN). LAN takes responsibility for learning brightness-aware features leveraging long-range dependency and local attention correlation. While CRN concentrates on learning detail-sensitive features via multi-level wavelet decomposition. Finally, a fusion network is designed to blend their learned features to produce visually impressive images. Extensive experiments conducted on seven benchmark datasets validate the effectiveness of our proposed LCDBNet, and the results manifest that LCDBNet achieves superior performance in terms of multiple reference/non-reference quality evaluators compared to other state-of-the-art competitors. Our code and pretrained model will be available.Comment: 14 pages, 16 figure

Coupled analysis of non-contacting profiled finger seals taking into account friction effects

A computational model for coupled fluid-structure interaction analysis taking into account friction contacts is developed to study lifting capability and leakage performance of non-contacting finger seals. A cyclic finger seal segment consisting of one low-pressure finger, halves of two high-pressure fingers, front and back plates is considered. Compressible air flow in the finger seal is modeled using a computational fluid dynamics approach. A non-linear three-dimensional solid model takes into account friction contacts between the fingers and seal plates

Predicting 1-, 3-, 5-, and 8-year all-cause mortality in a community-dwelling older adult cohort: relevance for predictive, preventive, and personalized medicine

Background: Population aging is a global public health issue involving increased prevalence of age-related diseases, and concomitant burden on medical resources and the economy. Ninety-two diseases have been identified as age-related, accounting for 51.3% of the global adult disease burden. The economic cost per capita for older people over 60 years is 10 times that of the younger population. From the aspects of predictive, preventive, and personalized medicine (PPPM), developing a risk-prediction model can help identify individuals at high risk for all-cause mortality and provide an opportunity for targeted prevention through personalized intervention at an early stage. However, there is still a lack of predictive models to help community-dwelling older adults do well in healthcare. Objectives: This study aims to develop an accurate 1-, 3-, 5-, and 8-year all-cause mortality risk-prediction model by using clinical multidimensional variables, and investigate risk factors for 1-, 3-, 5-, and 8-year all-cause mortality in community-dwelling older adults to guide primary prevention. Methods: This is a two-center cohort study. Inclusion criteria: (1) community-dwelling adult, (2) resided in the districts of Chaonan or Haojiang for more than 6 months in the past 12 months, and (3) completed a health examination. Exclusion criteria: (1) age less than 60 years, (2) more than 30 incomplete variables, (3) no signed informed consent. The primary outcome of the study was all-cause mortality obtained from face-to-face interviews, telephone interviews, and the medical death database from 2012 to 2021. Finally, we enrolled 5085 community-dwelling adults, 60 years and older, who underwent routine health screening in the Chaonan and Haojiang districts, southern China, from 2012 to 2021. Of them, 3091 participants from Chaonan were recruited as the primary training and internal validation study cohort, while 1994 participants from Haojiang were recruited as the external validation cohort. A total of 95 clinical multidimensional variables, including demographics, lifestyle behaviors, symptoms, medical history, family history, physical examination, laboratory tests, and electrocardiogram (ECG) data were collected to identify candidate risk factors and characteristics. Risk factors were identified using least absolute shrinkage and selection operator (LASSO) models and multivariable Cox proportional hazards regression analysis. A nomogram predictive model for 1-, 3-, 5- and 8-year all-cause mortality was constructed. The accuracy and calibration of the nomogram prediction model were assessed using the concordance index (C-index), integrated Brier score (IBS), receiver operating characteristic (ROC), and calibration curves. The clinical validity of the model was assessed using decision curve analysis (DCA). Results: Nine independent risk factors for 1-, 3-, 5-, and 8-year all-cause mortality were identified, including increased age, male, alcohol status, higher daily liquor consumption, history of cancer, elevated fasting glucose, lower hemoglobin, higher heart rate, and the occurrence of heart block. The acquisition of risk factor criteria is low cost, easily obtained, convenient for clinical application, and provides new insights and targets for the development of personalized prevention and interventions for high-risk individuals. The areas under the curve (AUC) of the nomogram model were 0.767, 0.776, and 0.806, and the C-indexes were 0.765, 0.775, and 0.797, in the training, internal validation, and external validation sets, respectively. The IBS was less than 0.25, which indicates good calibration. Calibration and decision curves showed that the predicted probabilities were in good agreement with the actual probabilities and had good clinical predictive value for PPPM. Conclusion: The personalized risk prediction model can identify individuals at high risk of all-cause mortality, help offer primary care to prevent all-cause mortality, and provide personalized medical treatment for these high-risk individuals from the PPPM perspective. Strict control of daily liquor consumption, lowering fasting glucose, raising hemoglobin, controlling heart rate, and treatment of heart block could be beneficial for improving survival in elderly populations

RNA-Seq reveals the key pathways and genes involved in the light-regulated flavonoids biosynthesis in mango (Mangifera indica L.) peel

IntroductionFlavonoids are important water soluble secondary metabolites in plants, and light is one of the most essential environmental factors regulating flavonoids biosynthesis. In the previous study, we found bagging treatment significantly inhibited the accumulation of flavonols and anthocyanins but promoted the proanthocyanidins accumulation in the fruit peel of mango (Mangifera indica L.) cultivar ‘Sensation’, while the relevant molecular mechanism is still unknown.MethodsIn this study, RNA-seq was conducted to identify the key pathways and genes involved in the light-regulated flavonoids biosynthesis in mango peel.ResultsBy weighted gene co-expression network analysis (WGCNA), 16 flavonoids biosynthetic genes were crucial for different flavonoids compositions biosynthesis under bagging treatment in mango. The higher expression level of LAR (mango026327) in bagged samples might be the reason why light inhibits proanthocyanidins accumulation in mango peel. The reported MYB positively regulating anthocyanins biosynthesis in mango, MiMYB1, has also been identified by WGCNA in this study. Apart from MYB and bHLH, ERF, WRKY and bZIP were the three most important transcription factors (TFs) involved in the light-regulated flavonoids biosynthesis in mango, with both activators and repressors. Surprisingly, two HY5 transcripts, which are usually induced by light, showed higher expression level in bagged samples.DiscussionOur results provide new insights of the regulatory effect of light on the flavonoids biosynthesis in mango fruit peel

Anti-tumor Immunity of Newcastle Disease Virus HN Protein is Influenced by Differential Subcellular Targeting

Background and objective Hemagglutinin-neuraminidase (HN) protein of newcastle disease virus is an important immunogen for oncolysis. We designed three different expression plasmids encoding the HN protein targeted to different subcellular compartments: cytoplasmic (Cy-HN), secreted (Sc-HN) and membrane-anchored (M-HN). On the basis of antitumor effect in vitro, the aim of this study is to investigate the anti-tumor immunity effect of HN protein in vivo. Methods In the present study, we developed a mouse model in order to evaluate the anti-tumor effect of the intratumorally injected modified HN proteins and the anti-tumor immunity by lymphocyte proliferative response and CTL activity test. Results Although all three DNA constructs elicited an immune response, tumor-bearing mice intratumorally injected with M-HN demonstrated a significantly better anti-tumor effect than those injected with Cy-HN or Sc-HN (Day 18: P=0.022; Day 21: P < 0.01). It also showed that this anti-tumor effect was mediated by higher lymphocyte proliferative response and CTL activity in mice intratumorally injected with M-HN [M-HN vs Cy-HN, P=0.019; M-HN vs Sc-HN, P=0.043; M-HN vs pcDNA3.1(+), P < 0.01]. Conclusion The anti-tumor immunity of Newcastle disease virus HN protein is influenced by differential subcellular targeting. The membrane-anchored form of the HN protein appears to be an ideal candidate to improve the specific cellular immunity

Deformable channel non‐local network for crowd counting

Abstract Both global dependency and local correlation are crucial for solving the scale variation of crowd. However, most of previous methods fail to take two factors into consideration simultaneously. Against the aforementioned issue, a deformable channel non‐local network, abbreviated as DCNLNet for crowd counting, which can simultaneously learn global context information and adaptive local receptive field is proposed. Specifically, the proposed DCNLNet consists of two well‐crafted designed modules: deformable channel non‐local block (DCNL) and spatial attention feature fusion block (SAFF). The DCNL encodes long‐range dependencies between pixels and the adaptive local correlation with channel non‐local and deformable convolution, respectively, benefiting for improving the spatial discrimination of features. While the SAFF aims to aggregate the cross‐level information, which interacts these features from different depths and learns specific weights for the feature maps with spatial attention. Extensive experiments are performed on three crowd counting benchmark datasets and experimental results indicate that the proposed DCNLNet achieves compelling performance compared to other representative counting models

Characteristics of Mango Leaf Photosynthetic Inhibition by Enhanced UV-B Radiation

To investigate the photosynthetic change characteristics of mango leaves under enhanced UV-B radiation, adult ‘Tainong No. 1′ mango (Mangifera indica) trees were treated (N = nine individuals) with simulated enhanced UV-B radiation [24 and 96 kJ/(m2·d)] in the field, and the photochemical reactions, activities of key enzymes in carbon assimilation, and the expression of genes were observed. The results showed that compared with the control, there was a decrease in tree yield, soluble sugar, sugar–acid ratio, and vitamin C of the fruits under the 96 kJ/(m2·d) treatment, while no significant changes were observed under 24 kJ/(m2·d). After 20 or 40 days, the leaves’ net photosynthetic rate (Pn), stomatal conductance (Sc), transpiration rate (Tr), intercellular CO2 concentration (Ci), and chlorophyll a/b under exposure to 96 kJ/(m2·d) of UV-B were significantly lower than in the control, whereas chlorophyll a, chlorophyll b, carotenoids, Hill reaction activity, photochemical quenching coefficient (qP), and Rubisco activities were significantly higher. In contrast, the Hill activity and Rubisco activity under 24 kJ/(m2·d) were significantly higher than the control, and increased by 350% and 30.8%, respectively, while Pn, Sc, Tr, Ci, and the content of photosynthetic pigments were similar to the control. The expression of gene coding the Rubisco big subunit (rbcL) was inhibited by the 96 kJ/(m2·d) treatment. We conclude that stomatal limitation was directly induced by 96 kJ/(m2·d), resulting in the inhibition of photosynthesis and the reduction in yield and deterioration of the quality of mango

Antioxidant Activity of Papaya Seed Extracts

The antioxidant activities of the ethanol, petroleum ether, ethyl acetate, n-butanol and water extract fractions from the seeds of papaya were evaluated in this study. The ethyl acetate fraction showed the strongest DPPH and hydroxyl free radical-scavenging activities, and its activities were stronger than those of ascorbic acid and sodium benzoate, respectively. The n-butanol fraction demonstrated the greatest ABTS+ radicals scavenging activity. The ethyl acetate fraction and the n-butanol fraction not only showed higher antioxidant activities than the petroleum ether fraction, water fraction and ethanol fraction, but also showed higher superoxide anion and hydrogen peroxide radicals scavenging activities than those of the other extract fractions. The high amount of total phenolics and total flavonoids in the ethyl acetate and n-butanol fractions contributed to their antioxidant activities. The ethyl acetate fraction was subjected to column chromatography, to yield two phenolic compounds, p-hydroxybenzoic acid (1) and vanillic acid (2), which possessed significant antioxidant activities. Therefore, the seeds of papaya and these compounds might be used as natural antioxidants