SVCNet: Scribble-based Video Colorization Network with Temporal Aggregation

In this paper, we propose a scribble-based video colorization network with temporal aggregation called SVCNet. It can colorize monochrome videos based on different user-given color scribbles. It addresses three common issues in the scribble-based video colorization area: colorization vividness, temporal consistency, and color bleeding. To improve the colorization quality and strengthen the temporal consistency, we adopt two sequential sub-networks in SVCNet for precise colorization and temporal smoothing, respectively. The first stage includes a pyramid feature encoder to incorporate color scribbles with a grayscale frame, and a semantic feature encoder to extract semantics. The second stage finetunes the output from the first stage by aggregating the information of neighboring colorized frames (as short-range connections) and the first colorized frame (as a long-range connection). To alleviate the color bleeding artifacts, we learn video colorization and segmentation simultaneously. Furthermore, we set the majority of operations on a fixed small image resolution and use a Super-resolution Module at the tail of SVCNet to recover original sizes. It allows the SVCNet to fit different image resolutions at the inference. Finally, we evaluate the proposed SVCNet on DAVIS and Videvo benchmarks. The experimental results demonstrate that SVCNet produces both higher-quality and more temporally consistent videos than other well-known video colorization approaches. The codes and models can be found at https://github.com/zhaoyuzhi/SVCNet.Comment: accepted by IEEE Transactions on Image Processing (TIP

Effectiveness and minimum effective dose of app-based mobile health interventions for anxiety and depression symptom reduction: Systematic review and meta-analysis

BACKGROUND: Mobile health (mHealth) apps offer new opportunities to deliver psychological treatments for mental illness in an accessible, private format. The results of several previous systematic reviews support the use of app-based mHealth interventions for anxiety and depression symptom management. However, it remains unclear how much or how long the minimum treatment dose is for an mHealth intervention to be effective. Just-in-time adaptive intervention (JITAI) has been introduced in the mHealth domain to facilitate behavior changes and is positioned to guide the design of mHealth interventions with enhanced adherence and effectiveness. OBJECTIVE: Inspired by the JITAI framework, we conducted a systematic review and meta-analysis to evaluate the dose effectiveness of app-based mHealth interventions for anxiety and depression symptom reduction. METHODS: We conducted a literature search on 7 databases (ie, Ovid MEDLINE, Embase, PsycInfo, Scopus, Cochrane Library (eg, CENTRAL), ScienceDirect, and ClinicalTrials, for publications from January 2012 to April 2020. We included randomized controlled trials (RCTs) evaluating app-based mHealth interventions for anxiety and depression. The study selection and data extraction process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We estimated the pooled effect size using Hedge g and appraised study quality using the revised Cochrane risk-of-bias tool for RCTs. RESULTS: We included 15 studies involving 2627 participants for 18 app-based mHealth interventions. Participants in the intervention groups showed a significant effect on anxiety (Hedge g=-.10, 95% CI -0.14 to -0.06, I2=0%) but not on depression (Hedge g=-.08, 95% CI -0.23 to 0.07, I2=4%). Interventions of at least 7 weeks\u27 duration had larger effect sizes on anxiety symptom reduction. CONCLUSIONS: There is inconclusive evidence for clinical use of app-based mHealth interventions for anxiety and depression at the current stage due to the small to nonsignificant effects of the interventions and study quality concerns. The recommended dose of mHealth interventions and the sustainability of intervention effectiveness remain unclear and require further investigation

The molecular gas kinematics in the host galaxy of non-repeating FRB 180924B

Fast radio bursts (FRBs) are millisecond-duration transients with large dispersion measures. The origin of FRBs is still mysterious. One of the methods to comprehend FRB origin is to probe the physical environments of FRB host galaxies. Mapping molecular-gas kinematics in FRB host galaxies is critical because it results in star formation that is likely connected to the birth of FRB progenitors. However, most previous works of FRB host galaxies have focused on its stellar component. Therefore, we, for the first time, report the molecular gas kinematics in the host galaxy of the non-repeating FRB 180924B at $z= 0.3216$. Two velocity components of the CO (3-2) emission line are detected in its host galaxy with the Atacama Large Millimeter/submillimeter Array (ALMA): the peak of one component ($-155.40$ km s$^{-1}$) is near the centre of the host galaxy, and another ($-7.76$ km s$^{-1}$) is near the FRB position. The CO (3-2) spectrum shows asymmetric profiles with A$_{\rm peak}$ $=2.03\pm 0.39$, where A$_{\rm peak}$ is the peak flux density ratio between the two velocity components. The CO (3-2) velocity map also indicates an asymmetric velocity gradient from $-180$ km s$^{-1}$ to 8 km s$^{-1}$. These results indicate a disturbed kinetic structure of molecular gas in the host galaxy. Such disturbed kinetic structures are reported for repeating FRB host galaxies using HI emission lines in previous works. Our finding indicates that non-repeating and repeating FRBs could commonly appear in disturbed kinetic environments, suggesting a possible link between the gas kinematics and FRB progenitors.Comment: 5 pages, 4 figures, Accepted for publication in MNRAS, https://www.youtube.com/watch?v=CldxLE7Pdwk&t=1

easyExon – A Java-based GUI tool for processing and visualization of Affymetrix exon array data

<p>Abstract</p> <p>Background</p> <p>Alternative RNA splicing greatly increases proteome diversity and thereby contribute to species- or tissue-specific functions. The possibility to study alternative splicing (AS) events on a genomic scale using splicing-sensitive microarrays, including the Affymetrix GeneChip Exon 1.0 ST microarray (exon array), has appeared very recently. However, the application of this new technology is hindered by the lack of free and user-friendly software devoted to these novel platforms.</p> <p>Results</p> <p>In this study we present a Java-based freeware, easyExon <url>http://microarray.ym.edu.tw/easyexon</url>, to process, filtrate and visualize exon array data with an analysis pipeline. This tool implements the most commonly used probeset summarization methods as well as AS-orientated filtration algorithms, e.g. MIDAS and PAC, for the detection of alternative splicing events. We include a biological filtration function according to GO terms, and provide a module to visualize and interpret the selected exons and transcripts. Furthermore, easyExon can integrate with other related programs, such as Integrate Genome Browser (IGB) and Affymetrix Power Tools (APT), to make the whole analysis more comprehensive. We applied easyExon on a public accessible colon cancer dataset as an example to illustrate the analysis pipeline of this tool.</p> <p>Conclusion</p> <p>EasyExon can efficiently process and analyze the Affymetrix exon array data. The simplicity, flexibility and brevity of easyExon make it a valuable tool for AS event identification in genomic research.</p

Serotype Competence and Penicillin Resistance in Streptococcus pneumoniae

Enhanced molecular surveillance of virulent clones with higher competence can detect serotype switching

Role of Pigment Epithelium-Derived Factor in Stem/Progenitor Cell-Associated Neovascularization

Pigment epithelium-derived factor (PEDF) was first identified in retinal pigment epithelium cells. It is an endogenously produced protein that is widely expressed throughout the human body such as in the eyes, liver, heart, and adipose tissue; it exhibits multiple and varied biological activities. PEDF is a multifunctional protein with antiangiogenic, antitumorigenic, antioxidant, anti-inflammatory, antithrombotic, neurotrophic, and neuroprotective properties. More recently, PEDF has been shown to be the most potent inhibitor of stem/progenitor cell-associated neovascularization. Neovascularization is a complex process regulated by a large, interacting network of molecules from stem/progenitor cells. PEDF is also involved in the pathogenesis of angiogenic eye disease, tumor growth, and cardiovascular disease. Novel antiangiogenic agents with tolerable side effects are desired for the treatment of patients with various diseases. Here, we review the value of PEDF as an important endogenous antiangiogenic molecule; we focus on the recently identified role of PEDF as a possible new target molecule to influence stem/progenitor cell-related neovascularization

Automated Facial Recognition for Noonan Syndrome Using Novel Deep Convolutional Neural Network With Additive Angular Margin Loss

BackgroundNoonan syndrome (NS), a genetically heterogeneous disorder, presents with hypertelorism, ptosis, dysplastic pulmonary valve stenosis, hypertrophic cardiomyopathy, and small stature. Early detection and assessment of NS are crucial to formulating an individualized treatment protocol. However, the diagnostic rate of pediatricians and pediatric cardiologists is limited. To overcome this challenge, we propose an automated facial recognition model to identify NS using a novel deep convolutional neural network (DCNN) with a loss function called additive angular margin loss (ArcFace).MethodsThe proposed automated facial recognition models were trained on dataset that included 127 NS patients, 163 healthy children, and 130 children with several other dysmorphic syndromes. The photo dataset contained only one frontal face image from each participant. A novel DCNN framework with ArcFace loss function (DCNN-Arcface model) was constructed. Two traditional machine learning models and a DCNN model with cross-entropy loss function (DCNN-CE model) were also constructed. Transfer learning and data augmentation were applied in the training process. The identification performance of facial recognition models was assessed by five-fold cross-validation. Comparison of the DCNN-Arcface model to two traditional machine learning models, the DCNN-CE model, and six physicians were performed.ResultsAt distinguishing NS patients from healthy children, the DCNN-Arcface model achieved an accuracy of 0.9201 ± 0.0138 and an area under the receiver operator characteristic curve (AUC) of 0.9797 ± 0.0055. At distinguishing NS patients from children with several other genetic syndromes, it achieved an accuracy of 0.8171 ± 0.0074 and an AUC of 0.9274 ± 0.0062. In both cases, the DCNN-Arcface model outperformed the two traditional machine learning models, the DCNN-CE model, and six physicians.ConclusionThis study shows that the proposed DCNN-Arcface model is a promising way to screen NS patients and can improve the NS diagnosis rate