Salient Object Detection in RGB-D Videos

Given the widespread adoption of depth-sensing acquisition devices, RGB-D videos and related data/media have gained considerable traction in various aspects of daily life. Consequently, conducting salient object detection (SOD) in RGB-D videos presents a highly promising and evolving avenue. Despite the potential of this area, SOD in RGB-D videos remains somewhat under-explored, with RGB-D SOD and video SOD (VSOD) traditionally studied in isolation. To explore this emerging field, this paper makes two primary contributions: the dataset and the model. On one front, we construct the RDVS dataset, a new RGB-D VSOD dataset with realistic depth and characterized by its diversity of scenes and rigorous frame-by-frame annotations. We validate the dataset through comprehensive attribute and object-oriented analyses, and provide training and testing splits. Moreover, we introduce DCTNet+, a three-stream network tailored for RGB-D VSOD, with an emphasis on RGB modality and treats depth and optical flow as auxiliary modalities. In pursuit of effective feature enhancement, refinement, and fusion for precise final prediction, we propose two modules: the multi-modal attention module (MAM) and the refinement fusion module (RFM). To enhance interaction and fusion within RFM, we design a universal interaction module (UIM) and then integrate holistic multi-modal attentive paths (HMAPs) for refining multi-modal low-level features before reaching RFMs. Comprehensive experiments, conducted on pseudo RGB-D video datasets alongside our RDVS, highlight the superiority of DCTNet+ over 17 VSOD models and 14 RGB-D SOD models. Ablation experiments were performed on both pseudo and realistic RGB-D video datasets to demonstrate the advantages of individual modules as well as the necessity of introducing realistic depth. Our code together with RDVS dataset will be available at https://github.com/kerenfu/RDVS/

Light Field Salient Object Detection: A Review and Benchmark

Salient object detection (SOD) is a long-standing research topic in computer vision and has drawn an increasing amount of research interest in the past decade. This paper provides the first comprehensive review and benchmark for light field SOD, which has long been lacking in the saliency community. Firstly, we introduce preliminary knowledge on light fields, including theory and data forms, and then review existing studies on light field SOD, covering ten traditional models, seven deep learning-based models, one comparative study, and one brief review. Existing datasets for light field SOD are also summarized with detailed information and statistical analyses. Secondly, we benchmark nine representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets, from which insightful discussions and analyses, including a comparison between light field SOD and RGB-D SOD models, are achieved. Besides, due to the inconsistency of datasets in their current forms, we further generate complete data and supplement focal stacks, depth maps and multi-view images for the inconsistent datasets, making them consistent and unified. Our supplemental data makes a universal benchmark possible. Lastly, because light field SOD is quite a special problem attributed to its diverse data representations and high dependency on acquisition hardware, making it differ greatly from other saliency detection tasks, we provide nine hints into the challenges and future directions, and outline several open issues. We hope our review and benchmarking could help advance research in this field. All the materials including collected models, datasets, benchmarking results, and supplemented light field datasets will be publicly available on our project site https://github.com/kerenfu/LFSOD-Survey

Dynamics of a deformable self-propelled particle under external forcing

We investigate dynamics of a self-propelled deformable particle under external field in two dimensions based on the model equations for the center of mass and a tensor variable characterizing deformations. We consider two kinds of external force. One is a gravitational-like force which enters additively in the time-evolution equation for the center of mass. The other is an electric-like force supposing that a dipole moment is induced in the particle. This force is added to the equation for the deformation tensor. It is shown that a rich variety of dynamics appears by changing the strength of the forces and the migration velocity of self-propelled particle

Precursors and Pathways Leading to Enhanced Secondary Organic Aerosol Formation during Severe Haze Episodes

Publisher Copyright: © 2021 American Chemical SocietyMolecular analyses help to investigate the key precursors and chemical processes of secondary organic aerosol (SOA) formation. We obtained the sources and molecular compositions of organic aerosol in PM2.5in winter in Beijing by online and offline mass spectrometer measurements. Photochemical and aqueous processing were both involved in producing SOA during the haze events. Aromatics, isoprene, long-chain alkanes or alkenes, and carbonyls such as glyoxal and methylglyoxal were all important precursors. The enhanced SOA formation during the severe haze event was predominantly contributed by aqueous processing that was promoted by elevated amounts of aerosol water for which multifunctional organic nitrates contributed the most followed by organic compounds having four oxygen atoms in their formulae. The latter included dicarboxylic acids and various oxidation products from isoprene and aromatics as well as products or oligomers from methylglyoxal aqueous uptake. Nitrated phenols, organosulfates, and methanesulfonic acid were also important SOA products but their contributions to the elevated SOA mass during the severe haze event were minor. Our results highlight the importance of reducing nitrogen oxides and nitrate for future SOA control. Additionally, the formation of highly oxygenated long-chain molecules with a low degree of unsaturation in polluted urban environments requires further research.Peer reviewe

RBM4 down-regulates PTB and antagonizes its activity in muscle cell–specific alternative splicing

RBM4 activates exon skipping of PTB transcripts to suppress PTB expression and counteracts PTB-mediated inhibition of alternative splicing during myogenesis

Dormancy within Staphylococcus epidermidis biofilms : a transcriptomic analysis by RNA-seq

The proportion of dormant bacteria within Staphylococcus epidermidis biofilms may determine its inflammatory profile. Previously, we have shown that S. epidermidis biofilms with higher proportions of dormant bacteria have reduced activation of murine macrophages. RNA-sequencing was used to identify the major transcriptomic differences between S. epidermidis biofilms with different proportions of dormant bacteria. To accomplish this goal, we used an in vitro model where magnesium allowed modulation of the proportion of dormant bacteria within S. epidermidis biofilms. Significant differences were found in the expression of 147 genes. A detailed analysis of the results was performed based on direct and functional gene interactions. Biological processes among the differentially expressed genes were mainly related to oxidation-reduction processes and acetyl-CoA metabolic processes. Gene set enrichment revealed that the translation process is related to the proportion of dormant bacteria. Transcription of mRNAs involved in oxidation-reduction processes was associated with higher proportions of dormant bacteria within S. epidermidis biofilm. Moreover, the pH of the culture medium did not change after the addition of magnesium, and genes related to magnesium transport did not seem to impact entrance of bacterial cells into dormancy.The authors thank Stephen Lorry at Harvard Medical School for providing CLC Genomics software. This work was funded by Fundacao para a Ciencia e a Tecnologia (FCT) and COMPETE grants PTDC/BIA-MIC/113450/2009, FCOMP-01-0124-FEDER-014309, FCOMP-01-0124-FEDER-022718 (FCT PEst-C/SAU/LA0002/2011), QOPNA research unit (project PEst-C/QUI/UI0062/2011), and CENTRO-07-ST24-FEDER-002034. The following authors had an individual FCT fellowship: VC (SFRH/BD/78235/2011) and AF (2SFRH/BD/62359/2009)

Iron Deficiency Increases Growth and Nitrogen-Fixation Rates of Phosphorus-Deficient Marine Cyanobacteria

Marine dinitrogen (N2)-fixing cyanobacteria have large impacts on global biogeochemistry as they fix carbon dioxide (CO2) and fertilize oligotrophic ocean waters with new nitrogen. Iron (Fe) and phosphorus (P) are the two most important limiting nutrients for marine biological N2 fixation, and their availabilities vary between major ocean basins and regions. A long-standing question concerns the ability of two globally dominant N2-fixing cyanobacteria, unicellular Crocosphaera and filamentous Trichodesmium, to maintain relatively high N2-fixation rates in these regimes where both Fe and P are typically scarce. We show that under P-deficient conditions, cultures of these two cyanobacteria are able to grow and fix N2 faster when Fe deficient than when Fe replete. In addition, growth affinities relative to P increase while minimum concentrations of P that support growth decrease at low Fe concentrations. In Crocosphaera, this effect is accompanied by a reduction in cell sizes and elemental quotas. Relatively high growth rates of these two biogeochemically critical cyanobacteria in low-P, low-Fe environments such as those that characterize much of the oligotrophic ocean challenge the common assumption that low Fe levels can have only negative effects on marine primary producers. The closely interdependent influence of Fe and P on N2-fixing cyanobacteria suggests that even subtle shifts in their supply ratio in the past, present and future oceans could have large consequences for global carbon and nitrogen cycles