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Vacuum electrospray deposition for face-on orientation and interface preservation in organic photovoltaics
Scene categorization by Hessian-regularized active perceptual feature selection
AbstractDecoding the semantic categories of complex sceneries is fundamental to numerous artificial intelligence (AI) infrastructures. This work presents an advanced selection of multi-channel perceptual visual features for recognizing scenic images with elaborate spatial structures, focusing on developing a deep hierarchical model dedicated to learning human gaze behavior. Utilizing the BING objectness measure, we efficiently localize objects or their details across varying scales within scenes. To emulate humans observing semantically or visually significant areas within scenes, we propose a robust deep active learning (RDAL) strategy. This strategy progressively generates gaze shifting paths (GSP) and calculates deep GSP representations within a unified architecture. A notable advantage of RDAL is the robustness to label noise, which is implemented by a carefully-designed sparse penalty term. This mechanism ensures that irrelevant or misleading deep GSP features are intelligently discarded. Afterward, a novel Hessian-regularized Feature Selector (HFS) is proposed to select high-quality features from the deep GSP features, wherein (i) the spatial composition of scenic patches can be optimally maintained, and (ii) a linear SVM is learned simultaneously. Empirical evaluations across six standard scenic datasets demonstrated our method’s superior performance, highlighting its exceptional ability to differentiate various sophisticated scenery categories.
Research on the high precision hydraulic column stress monitoring method
Evaluation of the protective role of resveratrol on LPS-induced septic intestinal barrier function via TLR4/MyD88/NF-κB signaling pathways
Investigation the CMP process of 6 H-SiC in H2O2 solution with ReaxFF molecular dynamics simulation
Investigating proteogenomic divergence in patient-derived xenograft models of ovarian cancer
Unifying topological structure and self-attention mechanism for node classification in directed networks
Influence of coal gangue-aeolian sand aggregate gradation on rheological properties and pipeline transportation characteristics of filling slurry
Mechanical characteristics of roll crushing of ore materials based on discrete element method
Synthesis, biological evaluations, and in silico assessments of phenylamino quinazolinones as tyrosinase inhibitors
S100 calcium-binding protein A8 exacerbates deep vein thrombosis in vascular endothelial cells
Da Vinci’s friction for granular media
Development and validation of a nomogram for predicting postoperative fever after endoscopic submucosal dissection for colorectal lesions
Machine learning assisted classification RASAR modeling for the nephrotoxicity potential of a curated set of orally active drugs
Effectiveness of dynamic neuromuscular stabilization training on strength, endurance, and flexibility in adults with intellectual disabilities, a randomized controlled trial
In-vitro and in-vivo assessment of biocompatibility and efficacy of ostrich eggshell membrane combined with platelet-rich plasma in Achilles tendon regeneration
Leveraging U-Net and selective feature extraction for land cover classification using remote sensing imagery
The effect of fecal bile acids on the incidence and risk-stratification of colorectal cancer: an updated systematic review and meta-analysis
AbstractRecent studies suggest the role of gut microbes in bile acid metabolism in the development and progression of colorectal cancer. However, the surveys of the association between fecal bile acid concentrations and colorectal cancer (CRC) have been inconsistent. We searched online to identify relevant cross-sectional and case-control studies published online in the major English language databases (Medline, Embase, Web of Science, AMED, and CINAHL) up to January 1, 2024. We selected studies according to inclusion and exclusion criteria and extracted data from them. RevMan 5.3 was used to perform the meta-analyses. In CRC risk meta-analysis, the effect size of CA (cholic acid), CDCA (chenodeoxycholic acid), DCA (deoxycholic acid), and UDCA (ursodeoxycholic acid) were significantly higher (CA: standardized mean difference [SMD] = 0.41, 95% confidence interval [CI]: 0.5–0.76, P = 0.02; CDCA: SMD = 0.35, 95% CI: 0.09–0.62, P = 0.009; DCA: SMD = 0.33,95% CI: 0.03–0.64, P = 0.03; UDCA: SMD = 0.46, 95% CI: 0.14–0.78, P = 0.005), and the combined effect size was significantly higher in the high-risk than the low-risk CRC group (SMD = 0.36, 95% CI: 0.21–0.51, P < 0.00001). In the CRC incidence meta-analysis, the effect sizes of CA and CDCA were significantly higher (CA: SMD = 0.42, 95% CI: 0.04–0.80, P = 0.03; CDCA: SMD = 0.61, 95% CI: 0.26–0.96, P = 0.00079), and their combined effect size was also significantly higher in the high-risk compared to low-risk CRC group (SMD = 0.39, 95% CI: 0.09–0.68, P = 0.01). Only one cross-sectional study suggested a higher concentration of CDCA, DCA, and UDCA in the stool of the CRC high-risk group than the low-risk group. These findings indicate that higher fecal concentrations of bile acid may be associated with a higher risk/incidence of CRC.