Remote Sensing Scene Classification Based on Convolutional Neural Networks Pre-Trained Using Attention-Guided Sparse Filters

Open access articleSemantic-level land-use scene classification is a challenging problem, in which deep learning methods, e.g., convolutional neural networks (CNNs), have shown remarkable capacity. However, a lack of sufficient labeled images has proved a hindrance to increasing the land-use scene classification accuracy of CNNs. Aiming at this problem, this paper proposes a CNN pre-training method under the guidance of a human visual attention mechanism. Specifically, a computational visual attention model is used to automatically extract salient regions in unlabeled images. Then, sparse filters are adopted to learn features from these salient regions, with the learnt parameters used to initialize the convolutional layers of the CNN. Finally, the CNN is further fine-tuned on labeled images. Experiments are performed on the UCMerced and AID datasets, which show that when combined with a demonstrative CNN, our method can achieve 2.24% higher accuracy than a plain CNN and can obtain an overall accuracy of 92.43% when combined with AlexNet. The results indicate that the proposed method can effectively improve CNN performance using easy-to-access unlabeled images and thus will enhance the performance of land-use scene classification especially when a large-scale labeled dataset is unavailable

Manipulating droplet jumping behaviors on hot substrates with surface topography by controlling vapor bubble growth: from vibration to explosion

A major challenge in surface science is rapid removal of sessile liquid droplets from a substrate with complex three-dimensional structures. However, our understanding of interfacial phenomena including droplet wetting dynamics and phase changes on engineered surfaces remains elusive, impeding dexterous designs for agile droplet purging. Here we present a surface topography strategy to modulate droplet jumping behaviors on micropillared substrates at moderate superheat of 20-30 {\deg}C. Specifically, sessile droplets usually dwell in the Wenzel state and therefore the micropillar matrix functions as fin array for heat transfer enhancement. By tuning the feature sizes of micropillars, one can adjust the vapor bubble growth at the droplet base from the heat-transfer-controlled mode to the inertia-controlled mode. As opposed to the relatively slow vibration jumping in seconds, the vapor bubble growth in the inertia-controlled mode on tall-micropillared surface leads to droplet out-of-plane jumping in milliseconds. Such rapid droplet detachment stems from the swift Wenzel-to Cassie transition incurred by vapor bubble burst (explosion), during which the bubble expanding velocity can reach as fast as ~4 m/s. Vapor bubble growth in a droplet and bubble-burst-induced droplet jumping have been less explored. This study unveils the underpinning mechanisms of versatile jumping behaviors of boiling droplets from a hot micro-structured surface and opens up further possibilities for the design of engineered surfaces that mitigate potential damage of vapor explosion or alleviate condensate flooding

Characterization of four vaccine-related polioviruses including two intertypic type 3/type 2 recombinants associated with aseptic encephalitis

Temperature sensitivity of 4 poliovirus type 3 isolates. (DOC 31 kb

Optimal Design of Low-Density SNP Arrays for Genomic Prediction: Algorithm and Applications

Low-density (LD) single nucleotide polymorphism (SNP) arrays provide a cost-effective solution for genomic prediction and selection, but algorithms and computational tools are needed for the optimal design of LD SNP chips. A multiple-objective, local optimization (MOLO) algorithm was developed for design of optimal LD SNP chips that can be imputed accurately to medium-density (MD) or high-density (HD) SNP genotypes for genomic prediction. The objective function facilitates maximization of non-gap map length and system information for the SNP chip, and the latter is computed either as locus-averaged (LASE) or haplotype-averaged Shannon entropy (HASE) and adjusted for uniformity of the SNP distribution. HASE performed better than LASE with more computing time. Nevertheless, the differences diminished when \u3e5,000 SNPs were selected. Optimization was accomplished conditionally on the presence of SNPs that were obligated to each chromosome. The frame location of SNPs on a chip can be either uniform (evenly spaced) or non-uniform. For the latter design, a tunable empirical Beta distribution was used to guide location distribution of frame SNPs such that both ends of each chromosome were enriched with SNPs. The SNP distribution on each chromosome was finalized through the objective function that was locally and empirically maximized. This MOLO algorithm was capable of selecting a set of approximately evenly-spaced and highly-informative SNPs, which in turn led to increased imputation accuracy compared with selection solely of evenly-spaced SNPs. Imputation accuracy increased with LD chip size, and imputation error rate was extremely low for chips with \u3e3,000 SNPs. Assuming that genotyping or imputation error occurs at random, imputation error rate can be viewed as the upper limit for genomic prediction error. Our results show that about 25% of imputation error rate was propagated to genomic prediction in an Angus population. The utility of this MOLO algorithm was also demonstrated in a real application, in which a 6K SNP panel was optimized conditional on 5,260 obligatory SNP selected based on SNP-trait association in U.S. Holstein animals. With this MOLO algorithm, both imputation error rate and genomic prediction error rate were minimal

Preparation and Characterization of New Nano-Composite Scaffolds Loaded With Vascular Stents

In this study, vascular stents were fabricated from poly (lactide-ɛ-caprolactone)/collagen/nano-hydroxyapatite (PLCL/Col/nHA) by electrospinning, and the surface morphology and breaking strength were observed or measured through scanning electron microscopy and tensile tests. The anti-clotting properties of stents were evaluated for anticoagulation surfaces modified by the electrostatic layer-by-layer self-assembly technique. In addition, nano-composite scaffolds of poly (lactic-co-glycolic acid)/polycaprolactone/nano-hydroxyapatite (PLGA/PCL/nHA) loaded with the vascular stents were prepared by thermoforming-particle leaching and their basic performance and osteogenesis were tested in vitro and in vivo. The results show that the PLCL/Col/nHA stents and PLGA/PCL/nHA nano-composite scaffolds had good surface structures, mechanical properties, biocompatibility and could guide bone regeneration. These may provide a new way to build vascularized-tissue engineered bone to repair large bone defects in bone tissue engineering

Correlation between dietary patterns and cognitive function in older Chinese adults: A representative cross-sectional study

ObjectiveThe objective of this study was to investigate the relationship between dietary patterns and cognitive function in older adults (≥60 years old).MethodsFood intake was quantitatively assessed by the Food Frequency Questionnaire (FFQ), and cognitive function was assessed by the Chinese version of the Simple Mental State Examination Scale (MMSE). Four major dietary patterns were identified by the factor analysis (FA) method. The relationship between dietary patterns and cognitive function was evaluated by logistic regression.ResultsA total of 884 participants were included in the study. Four dietary patterns (vegetable and mushroom, oil and salt, seafood and alcohol, and oil tea dietary patterns) were extracted. In the total population, Model III results showed that the fourth quartile of dietary pattern factor scores for the vegetable and mushroom pattern was 0.399 and 7.056. The vegetable and mushroom dietary pattern may be a protective factor for cognitive function, with p-value = 0.033, OR (95% CI): 0.578 (0.348, 0.951) in Model III (adjusted for covariates: sex, ethnic, marital, agricultural activities, smoking, drinking, hypertension, diabetes, dyslipidemia, BMI, and dietary fiber). In the ethnic stratification analysis, the scores of dietary pattern factors of the vegetable and mushroom among the Yao participants were 0.333 and 5.064. The Vegetable and mushroom diet pattern may be a protective factor for cognitive function, p-value = 0.012, OR (95% CI): 0.415 (0.206, 0.815).ConclusionThe fourth quartile of the vegetable and mushroom dietary pattern scores showed dose-dependent and a strong correlation with cognitive function. Currently, increasing vegetable and mushroom intake may be one of the effective ways to prevent and mitigate cognitive decline. It is recommended to increase the dietary intake of vegetables and mushroom foods

Genomic Analyses Reveal Mutational Signatures and Frequently Altered Genes in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide and the fourth most lethal cancer in China. However, although genomic studies have identified some mutations associated with ESCC, we know little of the mutational processes responsible. To identify genome-wide mutational signatures, we performed either whole-genome sequencing (WGS) or whole-exome sequencing (WES) on 104 ESCC individuals and combined our data with those of 88 previously reported samples. An APOBEC-mediated mutational signature in 47% of 192 tumors suggests that APOBEC-catalyzed deamination provides a source of DNA damage in ESCC. Moreover, PIK3CA hotspot mutations (c.1624G>A [p.Glu542Lys] and c.1633G>A [p.Glu545Lys]) were enriched in APOBEC-signature tumors, and no smoking-associated signature was observed in ESCC. In the samples analyzed by WGS, we identified focal (<100 kb) amplifications of CBX4 and CBX8. In our combined cohort, we identified frequent inactivating mutations in AJUBA, ZNF750, and PTCH1 and the chromatin-remodeling genes CREBBP and BAP1, in addition to known mutations. Functional analyses suggest roles for several genes (CBX4, CBX8, AJUBA, and ZNF750) in ESCC. Notably, high activity of hedgehog signaling and the PI3K pathway in approximately 60% of 104 ESCC tumors indicates that therapies targeting these pathways might be particularly promising strategies for ESCC. Collectively, our data provide comprehensive insights into the mutational signatures of ESCC and identify markers for early diagnosis and potential therapeutic targets