Chemical Speciation, Leaching Behavior, and Environmental Risk Assessment of Trace Elements in the Bottom Ash from Biomass Power Plant.

Biomass combustion for power generation stands as a pivotal method in energy utilization, offering a promising approach for renewable energy utilization. However, the substantial volume of slag produced by biomass burning plants poses environmental challenges, impeding sustainable energy practices. This article systematically studies the characteristics of ash generated from typical biomass direct combustion power plant ash and analyzes the chemical composition, trace element content characteristics, leaching characteristics, and chemical forms of biomass bottom ash. Furthermore, it assesses the environmental ecology and bioavailability of trace elements in bottom ash using the ecological risk assessment method and RAC method. The results demonstrate that the biomass bottom ash contains plant nutrients, such as K, Ca, Mg, and P, while the content of harmful trace elements is lower than the relevant Chinese standards. In dissolution experiments, the leaching rate of nearly all elements remains exceptionally low, primarily due to the distribution of trace elements within the lattice structure of stable minerals. Trace elements predominantly exist in the residual phase, Cu and Zn primarily found in organic compounds and sulfide bound states, while other elements mostly exist in the form of iron manganese oxide bound states. Ecological risk assessment indicates a significant risk level for Cd, contrasting with the slight risk associated with other elements. RAC results indicated no ecological risk of all of the trace elements. Consequently, the utilization of bottom ash in agricultural and forestry soils is deemed to be viable. These findings serve as a crucial foundation for biomass bottom ash resource utilization and underpin the sustainable utilization of biomass energy.

CrossHomo: Cross-Modality and Cross-Resolution Homography Estimation.

Multi-modal homography estimation aims to spatially align the images from different modalities, which is quite challenging since both the image content and resolution are variant across modalities. In this paper, we introduce a novel framework namely CrossHomo to tackle this challenging problem. Our framework is motivated by two interesting findings which demonstrate the mutual benefits between image super-resolution and homography estimation. Based on these findings, we design a flexible multi-level homography estimation network to align the multi-modal images in a coarse-to-fine manner. Each level is composed of a multi-modal image super-resolution (MISR) module to shrink the resolution gap between different modalities, followed by a multi-modal homography estimation (MHE) module to predict the homography matrix. To the best of our knowledge, CrossHomo is the first attempt to address the homography estimation problem with both modality and resolution discrepancy. Extensive experimental results show that our CrossHomo can achieve high registration accuracy on various multi-modal datasets with different resolution gaps. In addition, the network has high efficiency in terms of both model complexity and running speed. The source codes are available at https://github.com/lep990816/CrossHomo.

HyperSOR: Context-aware Graph Hypernetwork for Salient Object Ranking.

Salient object ranking (SOR) aims to segment salient objects in an image and simultaneously predict their saliency rankings, according to the shifted human attention over different objects. The existing SOR approaches mainly focus on object-based attention, e.g., the semantic and appearance of object. However, we find that the scene context plays a vital role in SOR, in which the saliency ranking of the same object varies a lot at different scenes. In this paper, we thus make the first attempt towards explicitly learning scene context for SOR. Specifically, we establish a large-scale SOR dataset of 24,373 images with rich context annotations, i.e., scene graphs, segmentation, and saliency rankings. Inspired by the data analysis on our dataset, we propose a novel graph hypernetwork, named HyperSOR, for context-aware SOR. In HyperSOR, an initial graph module is developed to segment objects and construct an initial graph by considering both geometry and semantic information. Then, a scene graph generation module with multi-path graph attention mechanism is designed to learn semantic relationships among objects based on the initial graph. Finally, a saliency ranking prediction module dynamically adopts the learned scene context through a novel graph hypernetwork, for inferring the saliency rankings. Experimental results show that our HyperSOR can significantly improve the performance of SOR.

Assessing Face Image Quality: A Large-Scale Database and a Transformer Method.

The amount of face images has been witnessing an explosive increase in the last decade, where various distortions inevitably exist on transmitted or stored face images. The distortions lead to visible and undesirable degradation on face images, affecting their quality of experience (QoE). To address this issue, this paper proposes a novel Transformer-based method for quality assessment on face images (named as TransFQA). Specifically, we first establish a large-scale face image quality assessment (FIQA) database, which includes 42,125 face images with diversifying content at different distortion types. Through an extensive crowdsource study, we obtain 712,808 subjective scores, which to the best of our knowledge contribute to the largest database for assessing the quality of face images. Furthermore, by investigating the established database, we comprehensively analyze the impacts of distortion types and facial components (FCs) on the overall image quality. Accordingly, we propose the TransFQA method, in which the FC-guided Transformer network (FT-Net) is developed to integrate the global context, face region and FC detailed features via a new progressive attention mechanism. Then, a distortion-specific prediction network (DP-Net) is designed to weight different distortions and accurately predict final quality scores. Finally, the experiments comprehensively verify that our TransFQA method significantly outperforms other state-of-the-art methods for quality assessment on face images.

3D genomic features across >50 diverse cell types reveal insights into the genomic architecture of childhood obesity.

The prevalence of childhood obesity is increasing worldwide, along with the associated common comorbidities of type 2 diabetes and cardiovascular disease in later life. Motivated by evidence for a strong genetic component, our prior genome-wide association study (GWAS) efforts for childhood obesity revealed 19 independent signals for the trait; however, the mechanism of action of these loci remains to be elucidated. To molecularly characterize these childhood obesity loci we sought to determine the underlying causal variants and the corresponding effector genes within diverse cellular contexts. Integrating childhood obesity GWAS summary statistics with our existing 3D genomic datasets for 57 human cell types, consisting of high-resolution promoter-focused Capture-C/Hi-C, ATAC-seq, and RNA-seq, we applied stratified LD score regression and calculated the proportion of genome-wide SNP heritability attributable to cell type-specific features, revealing pancreatic alpha cell enrichment as the most statistically significant. Subsequent chromatin contact-based fine-mapping was carried out for genome-wide significant childhood obesity loci and their linkage disequilibrium proxies to implicate effector genes, yielded the most abundant number of candidate variants and target genes at the BDNF, ADCY3, TMEM18 and FTO loci in skeletal muscle myotubes and the pancreatic beta-cell line, EndoC-BH1. One novel implicated effector gene, ALKAL2 - an inflammation-responsive gene in nerve nociceptors - was observed at the key TMEM18 locus across multiple immune cell types. Interestingly, this observation was also supported through colocalization analysis using expression quantitative trait loci (eQTL) derived from the Genotype-Tissue Expression (GTEx) dataset, supporting an inflammatory and neurologic component to the pathogenesis of childhood obesity. Our comprehensive appraisal of 3D genomic datasets generated in a myriad of different cell types provides genomic insights into pediatric obesity pathogenesis.

Deep M2CDL: Deep Multi-Scale Multi-Modal Convolutional Dictionary Learning Network.

For multi-modal image processing, network interpretability is essential due to the complicated dependency across modalities. Recently, a promising research direction for interpretable network is to incorporate dictionary learning into deep learning through unfolding strategy. However, the existing multi-modal dictionary learning models are both single-layer and single-scale, which restricts the representation ability. In this paper, we first introduce a multi-scale multi-modal convolutional dictionary learning ( M2CDL) model, which is performed in a multi-layer strategy, to associate different image modalities in a coarse-to-fine manner. Then, we propose a unified framework namely Deep M2CDL derived from the M2CDL model for both multi-modal image restoration (MIR) and multi-modal image fusion (MIF) tasks. The network architecture of Deep M2CDL fully matches the optimization steps of the M2CDL model, which makes each network module with good interpretability. Different from handcrafted priors, both the dictionary and sparse feature priors are learned through the network. The performance of the proposed Deep M2CDL is evaluated on a wide variety of MIR and MIF tasks, which shows the superiority of it over many state-of-the-art methods both quantitatively and qualitatively. In addition, we also visualize the multi-modal sparse features and dictionary filters learned from the network, which demonstrates the good interpretability of the Deep M2CDL network.

Development of SDS-Modified PbO2 Anode Material Based on Ti3+ Self-Doping Black TiO2NTs Substrate as a Conductive Interlayer for Enhanced Electrocatalytic Oxidation of Methylene Blue.

Efficient and stable electrode materials are urgently required for wastewater treatment in the electrocatalytic degradation of toxic and refractory organic pollutants. Ti3+ self-doping black TiO2 nanotube arrays (Ti/B-TiO2-NTs) as an interlayer were used for preparing a novel PbO2 electrode via an electrochemical reduction technology, and a sodium dodecyl sulfate (SDS)-modified PbO2 catalytic layer was successfully achieved via an electrochemical deposition technology. The physicochemical characterization tests showed that the Ti/B-TiO2-NTs/PbO2-SDS electrodes have a denser surface and finer grain size with the introduction of Ti3+ in the interlayer of Ti/TiO2-NTs and the addition of SDS in the active layer of PbO2. The electrochemical characterization results showed that the Ti3+ self-doping black Ti/TiO2-NTs/PbO2-SDS electrode had higher oxygen evolution potential (2.11 V vs. SCE), higher electrode stability, smaller charge-transfer resistance (6.74 Ω cm-2), and higher hydroxyl radical production activity, leading to it possessing better electrocatalytic properties. The above results indicated that the physicochemical and electrochemical characterization of the PbO2 electrode were all enhanced significantly with the introduction of Ti3+ and SDS. Furthermore, the Ti/B-TiO2-NTs/PbO2-SDS electrodes displayed the best performance on the degradation of methylene blue (MB) in simulated wastewater via bulk electrolysis. The removal efficiency of MB and the chemical oxygen demand (COD) could reach about 99.7% and 80.6% under the optimal conditions after 120 min, respectively. The pseudo-first-order kinetic constant of the Ti/B-TiO2-NTs/PbO2-SDS electrode was 0.03956 min-1, which was approximately 3.18 times faster than that of the Ti/TiO2-NTs/PbO2 electrode (0.01254 min-1). In addition, the Ti/B-TiO2-NTs/PbO2-SDS electrodes showed excellent stability and reusability. The degradation mechanism of MB was explored via the experimental identification of intermediates. In summary, the Ti3+ self-doping black Ti/TiO2-NTs/PbO2-SDS electrode is a promising electrode in treating wastewater.

Synthesis of Ag3PO4/Ag/g-C3N4 Composite for Enhanced Photocatalytic Degradation of Methyl Orange.

In this study, we have successfully constructed Ag3PO4/Ag/g-C3N4 heterojunctions via the hydrothermal method, which displays a wide photo-absorption range. The higher photocurrent intensity of Ag3PO4/Ag/g-C3N4 indicates that the separation efficiency of the photogenerated electron-hole pairs is higher than that of both Ag3PO4 and Ag/g-C3N4 pure substances. It is confirmed that the efficient separation of photogenerated electron-hole pairs is attributed to the heterojunction of the material. Under visible light irradiation, Ag3PO4/Ag/g-C3N4-1.6 can remove MO (~90%) at a higher rate than Ag3PO4 or Ag/g-C3N4. Its degradation rate is 0.04126 min-1, which is 4.23 and 6.53 times that of Ag/g-C3N4 and Ag3PO4, respectively. After five cycles of testing, the Ag3PO4/Ag/g-C3N4 photocatalyst still maintained high photocatalytic activity. The excellent photocatalysis of Ag3PO4/Ag/g-C3N4-1.6 under ultraviolet-visible light is due to the efficient separation of photogenerated carriers brought about by the construction of the Ag3PO4/Ag/g-C3N4 heterostructure. Additionally, Ag3PO4/Ag/g-C3N4 specimens can be easily recycled with high stability. The effects of hydroxyl and superoxide radicals on the degradation process of organic compounds were studied using electron paramagnetic resonance spectroscopy and radical quenching experiments. Therefore, the Ag3PO4/Ag/g-C3N4 composite can be used as an efficient and recyclable UV-vis spectrum-driven photocatalyst for the purification of organic pollutants.

Deep multi-task learning for nephropathy diagnosis on immunofluorescence images.

BACKGROUND AND OBJECTIVE: As an advanced technique, immunofluorescence (IF) is one of the most widely-used medical image for nephropathy diagnosis, due to its ease of acquisition with low cost. In practice, the clinically collected IF images are commonly corrupted by blurs at different degrees, mainly because of the inaccurate focus at the acquisition stage. Although deep neural network (DNN) methods achieve the great success in nephropathy diagnosis, their performance dramatically drops over the blurred IF images. This significantly limits the potential of leveraging the advanced DNN techniques in real-world nephropathy diagnosis scenarios. METHODS: This paper first establishes two IF databases with synthetic blurs (IFVB) and real-world blurs (Real-IF) for nephropathy diagnosis, respectively, including 1,659 patients and 6,521 IF images with various degrees of blurs. According to the analysis on these two databases, we propose a deep hierarchical multi-task learning based nephropathy diagnosis (DeepMT-ND) method to bridge the gap between the low-level vision and high-level medical tasks. Specifically, DeepMT-ND simultaneously handles the main task of automatic nephropathy diagnosis, as well as the auxiliary tasks of image quality assessment (IQA) and de-blurring. RESULTS: Extensive experiments show the superiority of our DeepMT-ND in terms of diagnosis accuracy and generalization ability. For instance, our method performs better than nephrologists with at least 15.4% and 6.5% accuracy improvements in IFVB and Real-IF, respectively. Meanwhile, our method also achieves comparable performance in two auxiliary tasks of IQA and de-blurring on blurred IF images. CONCLUSIONS: In this paper, we propose a new DeepMT-ND method for nephropathy diagnosis on blurred IF images. The proposed hierarchical multi-task learning framework provides the new scope to narrow the gap between the low-level vision and high-level medical tasks, and will contribute to nephropathy diagnosis in clinical scenarios. The diagnosis accuracy and generalization ability of DeepMT-ND are experimentally verified to be effective over both synthetic and real-world databases.

Magnetic CoFe1.95Y0.05O4-Decorated Ag3PO4 as Superior and Recyclable Photocatalyst for Dye Degradation.

The Ag3PO4/CoFe1.95Y0.05O4 nanocomposite with magnetic properties was simply synthesized by the hydrothermal method. The structure and morphology of the prepared material were characterized, and its photocatalytic activity for degradation of the methylene blue and rhodamine B dyes was also tested. It was revealed that the Ag3PO4 in the nanocomposite exhibited a smaller size and higher efficiency in degrading dyes than the individually synthesized Ag3PO4 when exposed to light. Furthermore, the magnetic properties of CoFe1.95Y0.05O4 enabled the nanocomposite to possess magnetic separation capabilities. The stable crystal structure and effective degradation ability of the nanocomposite were demonstrated through cyclic degradation experiments. It was shown that Ag3PO4/CoFe1.95Y0.05O4-0.2 could deliver the highest activity and stability in degrading the dyes, and 98% of the dyes could be reduced within 30 min. Additionally, the photocatalytic enhancement mechanism and cyclic degradation stability of the magnetic nanocomposites were also proposed.

Regulatory mechanisms of exopolysaccharide synthesis and biofilm formation in Streptococcus mutans.

Background: Dental caries is a chronic, multifactorial and biofilm-mediated oral bacterial infection affecting almost every age group and every geographical region. Streptococcus mutans is considered an important pathogen responsible for the initiation and development of dental caries. It produces exopolysaccharides in situ to promote the colonization of cariogenic bacteria and coordinate dental biofilm development. Objective: The understanding of the regulatory mechanism of S. mutans biofilm formation can provide a theoretical basis for the prevention and treatment of caries. Design: At present, an increasing number of studies have identified many regulatory systems in S. mutans that regulate biofilm formation, including second messengers (e.g. c-di-AMP, Ap4A), transcription factors (e.g. EpsR, RcrR, StsR, AhrC, FruR), two-component systems (e.g. CovR, VicR), small RNA (including sRNA0426, srn92532, and srn133489), acetylation modifications (e.g. ActG), CRISPR-associated proteins (e.g. Cas3), PTS systems (e.g. EIIAB), quorum-sensing signaling system (e.g. LuxS), enzymes (including Dex, YidC, CopZ, EzrA, lmrB, SprV, RecA, PdxR, MurI) and small-molecule metabolites. Results: This review summarizes the recent progress in the molecular regulatory mechanisms of exopolysaccharides synthesis and biofilm formation in S. mutans.

Domain Adaptation for Underwater Image Enhancement.

Recently, learning-based algorithms have shown impressive performance in underwater image enhancement. Most of them resort to training on synthetic data and obtain outstanding performance. However, these deep methods ignore the significant domain gap between the synthetic and real data (i.e., inter-domain gap), and thus the models trained on synthetic data often fail to generalize well to real-world underwater scenarios. Moreover, the complex and changeable underwater environment also causes a great distribution gap among the real data itself (i.e., intra-domain gap). However, almost no research focuses on this problem and thus their techniques often produce visually unpleasing artifacts and color distortions on various real images. Motivated by these observations, we propose a novel Two-phase Underwater Domain Adaptation network (TUDA) to simultaneously minimize the inter-domain and intra-domain gap. Concretely, in the first phase, a new triple-alignment network is designed, including a translation part for enhancing realism of input images, followed by a task-oriented enhancement part. With performing image-level, feature-level and output-level adaptation in these two parts through jointly adversarial learning, the network can better build invariance across domains and thus bridging the inter-domain gap. In the second phase, an easy-hard classification of real data according to the assessed quality of enhanced images is performed, in which a new rank-based underwater quality assessment method is embedded. By leveraging implicit quality information learned from rankings, this method can more accurately assess the perceptual quality of enhanced images. Using pseudo labels from the easy part, an easy-hard adaptation technique is then conducted to effectively decrease the intra-domain gap between easy and hard samples. Extensive experimental results demonstrate that the proposed TUDA is significantly superior to existing works in terms of both visual quality and quantitative metrics.

DAQE: Enhancing the Quality of Compressed Images by Exploiting the Inherent Characteristic of Defocus.

Image defocus is inherent in the physics of image formation caused by the optical aberration of lenses, providing plentiful information on image quality. Unfortunately, existing quality enhancement approaches for compressed images neglect the inherent characteristic of defocus, resulting in inferior performance. This paper finds that in compressed images, significantly defocused regions have better compression quality, and two regions with different defocus values possess diverse texture patterns. These observations motivate our defocus-aware quality enhancement (DAQE) approach. Specifically, we propose a novel dynamic region-based deep learning architecture of the DAQE approach, which considers the regionwise defocus difference of compressed images in two aspects. (1) The DAQE approach employs fewer computational resources to enhance the quality of significantly defocused regions and more resources to enhance the quality of other regions; (2) The DAQE approach learns to separately enhance diverse texture patterns for regions with different defocus values, such that texture-specific enhancement can be achieved. Extensive experiments validate the superiority of our DAQE approach over state-of-the-art approaches in terms of quality enhancement and resource savings.

Interpretable Multi-modal Image Registration Network Based on Disentangled Convolutional Sparse Coding.

Multi-modal image registration aims to spatially align two images from different modalities to make their feature points match with each other. Captured by different sensors, the images from different modalities often contain many distinct features, which makes it challenging to find their accurate correspondences. With the success of deep learning, many deep networks have been proposed to align multi-modal images, however, they are mostly lack of interpretability. In this paper, we first model the multi-modal image registration problem as a disentangled convolutional sparse coding (DCSC) model. In this model, the multi-modal features that are responsible for alignment (RA features) are well separated from the features that are not responsible for alignment (nRA features). By only allowing the RA features to participate in the deformation field prediction, we can eliminate the interference of the nRA features to improve the registration accuracy and efficiency. The optimization process of the DCSC model to separate the RA and nRA features is then turned into a deep network, namely Interpretable Multi-modal Image Registration Network (InMIR-Net). To ensure the accurate separation of RA and nRA features, we further design an accompanying guidance network (AG-Net) to supervise the extraction of RA features in InMIR-Net. The advantage of InMIR-Net is that it provides a universal framework to tackle both rigid and non-rigid multi-modal image registration tasks. Extensive experimental results verify the effectiveness of our method on both rigid and non-rigid registrations on various multi-modal image datasets, including RGB/depth images, RGB/near-infrared (NIR) images, RGB/multi-spectral images, T1/T2 weighted magnetic resonance (MR) images and computed tomography (CT)/MR images. The codes are available at https://github.com/lep990816/Interpretable-Multi-modal-Image-Registration.

In situ recombination for durable photoelectrocatalytic degradation of organic dye in wastewater.

Photoelectrocatalysis (PEC) can effectively degrade organic pollutants by using photoelectrodes without secondary pollution. However, significant mass transport resistance and decreased catalytic activity caused by the shedding of active components remain a barrier to achieving the photocatalytic system with a high degradation rate and long-term durability. Here, an in situ recombination concept is presented to overcome this challenge. The bionic coral-like electrode, obtained by in situ assembly of UIO-66 around TiO2 nanoflowers (TNF) on Ti-foam substrate, is employed as the photoanode in PEC. Ex situ evaluation of photoelectrochemical activity demonstrates that the UIO-66@TNF/Ti-foam (U@T/T) design significantly improves the light-propagation, light-absorption and charge transfer. In Situ degradation evaluations also shows that the interesting design promotes rapid and stable degradation of organic dye (e.g. Rhodamine B (RhB)). At 2.0 V of bias potential and pH 7.0 in 5 mg L-1 RhB, under the action of active species such as ·O2- and ·OH (proved by the degradation mechanism experiments), the removal rate of RhB can reach 96.1% at 120 min and almost complete removal at 200 min (99.1%).

Construction of a novel Cu1.8S/NH2-La MOFs decorated Black-TNTs photoanode electrode for high-efficiently photoelectrocatalytic degradation of 2, 4-dichlorophenol.

Photoelectrocatalysis (PEC) has long been regarded as an efficient and green method to eliminate various organic pollutants from wastewater. However, the lack of highly photoelectrocatalytic active and stable electrodes limits the development of the PEC technologies. Herein, a novel hierarchical photo-electrode with hollow Cu1.8S/NH2-La MOFs decorated black titanium dioxide nanotubes (Cu1.8S/NH2-La MOFs/Black TNTs) was fabricated by a two-step water-heating method. The prepared photoelectrode was used to degradation of 2, 4-dichlorophenol (2, 4-DCP). Analysis of photoelectrocatalytic degradation process of 2, 4-DCP was evaluated using UV-Vis absorption spectroscopy and the main degradation paths were analyzed by LC-MS. The results showed that 99.3% of the pollutant could be rapidly degraded within 180 min. Furthermore, the Cu1.8S/NH2-La MOFs/Black TNTs photoelectric pole exhibited excellent stability after 15 cycling experiments.

DeepMIH: Deep Invertible Network for Multiple Image Hiding.

Multiple image hiding aims to hide multiple secret images into a single cover image, and then recover all secret images perfectly. Such high-capacity hiding may easily lead to contour shadows or color distortion, which makes multiple image hiding a very challenging task. In this paper, we propose a novel multiple image hiding framework based on invertible neural network, namely DeepMIH. Specifically, we develop an invertible hiding neural network (IHNN) to innovatively model the image concealing and revealing as its forward and backward processes, making them fully coupled and reversible. The IHNN is highly flexible, which can be cascaded as many times as required to achieve the hiding of multiple images. To enhance the invisibility, we design an importance map (IM) module to guide the current image hiding based on the previous image hiding results. In addition, we find that the image hidden in the high-frequency sub-bands tends to achieve better hiding performance, and thus propose a low-frequency wavelet loss to constrain that no secret information is hidden in the low-frequency sub-bands. Experimental results show that our DeepMIH significantly outperforms other state-of-the-art methods, in terms of hiding invisibility, security and recovery accuracy on a variety of datasets.

Massively parallel reporter assays and variant scoring identified functional variants and target genes for melanoma loci and highlighted cell-type specificity.

The most recent genome-wide association study (GWAS) of cutaneous melanoma identified 54 risk-associated loci, but functional variants and their target genes for most have not been established. Here, we performed massively parallel reporter assays (MPRAs) by using malignant melanoma and normal melanocyte cells and further integrated multi-layer annotation to systematically prioritize functional variants and susceptibility genes from these GWAS loci. Of 1,992 risk-associated variants tested in MPRAs, we identified 285 from 42 loci (78% of the known loci) displaying significant allelic transcriptional activities in either cell type (FDR < 1%). We further characterized MPRA-significant variants by motif prediction, epigenomic annotation, and statistical/functional fine-mapping to create integrative variant scores, which prioritized one to six plausible candidate variants per locus for the 42 loci and nominated a single variant for 43% of these loci. Overlaying the MPRA-significant variants with genome-wide significant expression or methylation quantitative trait loci (eQTLs or meQTLs, respectively) from melanocytes or melanomas identified candidate susceptibility genes for 60% of variants (172 of 285 variants). CRISPRi of top-scoring variants validated their cis-regulatory effect on the eQTL target genes, MAFF (22q13.1) and GPRC5A (12p13.1). Finally, we identified 36 melanoma-specific and 45 melanocyte-specific MPRA-significant variants, a subset of which are linked to cell-type-specific target genes. Analyses of transcription factor availability in MPRA datasets and variant-transcription-factor interaction in eQTL datasets highlighted the roles of transcription factors in cell-type-specific variant functionality. In conclusion, MPRAs along with variant scoring effectively prioritized plausible candidates for most melanoma GWAS loci and highlighted cellular contexts where the susceptibility variants are functional.

From Whole Video to Frames: Weakly-Supervised Domain Adaptive Continuous-Time QoE Evaluation.

Due to the rapid increase in video traffic and relatively limited delivery infrastructure, end users often experience dynamically varying quality over time when viewing streaming videos. The user quality-of-experience (QoE) must be continuously monitored to deliver an optimized service. However, modern approaches for continuous-time video QoE estimation require densely annotating the continuous-time QoE labels, which is labor-intensive and time-consuming. To cope with such limitations, we propose a novel weakly-supervised domain adaptation approach for continuous-time QoE evaluation, by making use of a small amount of continuously labeled data in the source domain and abundant weakly-labeled data (only containing the retrospective QoE labels) in the target domain. Specifically, given a pair of videos from source and target domains, effective spatiotemporal segment-level feature representation is first learned by a combination of 2D and 3D convolutional networks. Then, a multi-task prediction framework is developed to simultaneously achieve continuous-time and retrospective QoE predictions, where a quality attentive adaptation approach is investigated to effectively alleviate the domain discrepancy without hampering the prediction performance. This approach is enabled by explicitly attending to the video-level discrimination and segment-level transferability in terms of the domain discrepancy. Experiments on benchmark databases demonstrate that the proposed method significantly improves the prediction performance under the cross-domain setting.

[Effects of Angong Niuhuang Pills against heart failure based on cross-scale polypharmacological study].

Angong Niuhuang Pills(AGNHP) are effective in clearing heat, removing the toxin, and eliminating phlegm for resuscitation. Clinically, it is widely used to treat various diseases such as febrile convulsion due to heat attacking pericardium, but its therapeutic effects on heart failure(HF) have not been well recognized. In this study, the profiles of differential metabolites regulated by AGNHP were identified by ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UPLC-Q-TOF-MS). The underlying mechanism of AGNHP against HF was illustrated based on the integrated analysis of pharmacological data and metabolic molecular network. The HF model was induced by isoproterenol in mice. After oral administration of AGNHP for one week, cardiac functions in HF mice were evaluated by echocardiography, and serum samples of mice were collected for metabolomics analysis. Eight differential metabolites of AGNHP against HF were screened out through partial least square discriminant analysis(PLS-DA) and input into MetaboAnalyst for the analysis of metabolic pathways. Moreover, the critical metabolic pathways regulated by AGNHP were enriched according to the potential targets of major compounds in AGNHP. After AGNHP treatment, the recovered index of relative content of some metabolites underwent cross-scale fusion analysis with therapeutic efficacy data, followed by "compound-reaction-enzyme-gene" network analysis. It is inferred that the anti-HF effects of AGNHP may be attributed to the metabolism of arachidonic acid, amino acid, glycerophospholipid, and linoleic acid. The cross-scale polypharmacological analysis method developed in this study provides a new method to interpret scientific principles of AGNHP against HF with modern technologies.