Adaptive Affinity Loss and Erroneous Pseudo-Label Refinement for Weakly Supervised Semantic Segmentation

Descriptio

Semantic Attention and Scale Complementary Network for Instance Segmentation in Remote Sensing Images

In this paper, we focus on the challenging multi-category instance segmentation problem in remote sensing images(RSIs), which aims at predicting the categories of all instances and localizing them with pixel-level masks. Although many land-mark frameworks have demonstrated promising performance in instance segmentation, the complexity in the background and scale variability instances still remain challenging for in-stance segmentation of RSIs. To address the above problems, we propose an end-to-end multi-category instance segmentation model, namely Semantic Attention and Scale Complementary Network, which mainly consists of a Semantic Attention (SEA)module and a Scale Complementary Mask Branch (SCMB).The SEA module contains a simple fully convolutional semantic segmentation branch with extra supervision to strengthen the activation of interest instances on the feature map and reduce the background noise’s interference. To handle the under-segmentation of geospatial instances with large varying scales, we design the SCMB that extends the original single-scale mask branch to trident mask branches and introduces complementary mask supervision at different scales to sufficiently leverage the multi-scale information. We conduct comprehensive experiments to evaluate the effectiveness of our proposed method on the iSAID dataset and the NWPU Instance Segmentation dataset and achieve promising performance

Negative Deterministic Information based Multiple Instance Learning for Weakly Supervised Object Detection and Segmentation

Weakly  supervised  object  detection  and  semanticsegmentation  with  image-level  annotations  have  attracted  ex-tensive   attention   due   to   their  high   label   efficiency.   Multipleinstance  learning  (MIL)  offers  a  feasible  solution  forthe  twotasks by treating each image as a bag with a series of instances(object  regions  or  pixels)  and  identifying  foreground  instancesthat contribute to bag classification. However, conventional MILparadigms  often  suffer  from  issues,  e.g.,  discriminative  instancedomination  and  missing  instances.In  this  paper,  weobservethat  negative  instances  usually  contain  valuable  deterministicinformation, which is the key to solving the two issues. Motivatedby  this,  we  proposea  novel  MIL  paradigm  based  on  negativedeterministic   information   (NDI),   termed   NDI-MIL,   whichisbased  on  two  core  designs  with  a  progressive  relation:  NDIcollection  and  negative  contrastive  learning.  In  NDI  collection,we  identify  and  distill  NDI  from  negative  instances  online  bya  dynamic  feature  bank.  The  collected  NDI  is  then  utilized  ina  negative  contrastive  learning  mechanism  to  locate  and  punishthose discriminative regions, by which the discriminative instancedomination and missing instances issues are effectively addressed,leading to improved object- and pixel-level localization accuracyand completeness. In addition, we design an NDI-guided instanceselection strategy to further enhance the systematic performance.Experimental  results  on  several  public  benchmarks,  includingPASCAL VOC 2007, PASCAL VOC 2012, and MS COCO, showthat  our  method  achieves  satisfactory  performance.  The  code  isavailable at: https://github.com/GC-WSL/NDI.</p

Metabolomic biomarkers and novel dietary factors associated with gestational diabetes in China

Introduction Gestational diabetes mellitus (GDM) is impaired glucose tolerance first recognised during pregnancy; its development is associated with many adverse outcomes. Mechanisms of GDM development are not fully elucidated and few studies have used Chinese participants. Objectives The aim of this study was to investigate the maternal metabolome associated with GDM in a Chinese population,and explore the relationship with maternal diet. Methods Ninety-three participants were recruited at 26–28 weeks’ gestation from Chongqing, China. Maternal urine, serum, and hair metabolomes were analysed using gas and liquid chromatography–mass spectrometry. Dietary intake was assessed using a 96-item food frequency questionnaire. Results Of the 1064 metabolites identified, 73 were significantly different between cases and controls (P<0.05), but only 2-aminobutyric acid had both a p- and q-value<0.05. A “snack-based-dietary-pattern” was associated with an increased likelihood of GDM (odds ratio 2·1; 95% confidence interval 1.1–3.9). The association remained significant after adjustment for calorie intake but not food volume. Conclusion This study provides a comprehensive characterization of the maternal metabolome. The snack-based dietary pattern associated with GDM suggests that timing and frequency of consumption are important factors in the relationship between maternal diet and GDM

Semantics and Contour Based Interactive Learning Network For Building Footprint Extraction

Building footprint extraction plays an important role in the analysis of remote sensing images and has an extensive range of applications. Obtaining precise boundaries of buildings remains a challenge in existing building extraction methods. Some previous works have made notable efforts to address this concern. However, most of these methods require cumbersome and expensive post-processing steps. Moreover, they ignored the correlation between building semantics and contours, which we believe is crucial for building footprint extraction. To mitigate this issue, our paper presents an intuitive and effective framework that explores semantic and contour cues of buildings and fully excavates their correlation. Specifically, we construct an interactive dual-stream decoder. The Intermediate connections within this decoder interactively transmit features between branches, contributing to learning correlations between semantics and contours. We propose the Semantic Collaboration Module (SCM) to strengthen the connection between the two branches. To further boost performance, we build the Multi-Scale Semantic Context Fusion Module (MSCF) to fuse semantic information from the higher and lower layers of the network, allowing the network to obtain superior feature representations. The experimental results on the WHU, INRIA, and Massachusetts building datasets demonstrate the superior performance of our method. </p

The Gene Ontology Differs in Bursa of Fabricius Between Two Breeds of Ducks Post Hatching by Enriching the Differentially Expressed Genes

<div><p>ABSTRACT The bursa of Fabricius (BF) is the central humoral immune organ unique to birds. The present study investigated the possible difference on a molecular level between two duck breeds. The digital gene expression profiling (DGE) technology was used to enrich the differentially expressed genes (DEGs) in BF between the Jianchang and Nonghua-P strains of ducks. DGE data identified 195 DEGs in the bursa. Gene Ontology (GO) analysis suggested that DEGs were mainly enriched in the metabolic pathways and ribosome components. Pathways analysis identified the spliceosome, RNA transport, RNA degradation process, Jak-STAT signaling pathway, TNF signaling pathway and B cell receptor signaling pathway. The results indicated that the main difference in the BF between the two duck strains was in the capabilities of protein formation and B cell development. These data have revealed the main divergence in the BF on a molecular level between genetically different duck breeds and may help to perform molecular breeding programs in poultry in the future.</p></div

Wallpaper Texture Generation and Style Transfer Based on Multi-label Semantics

Textures contain a wealth of image information and are widely used in various fields such as computer graphics and computer vision. With the development of machine learning, the texture synthesis and generation have been greatly improved. As a very common element in everyday life, wallpapers contain a wealth of texture information, making it difficult to annotate with a simple single label. Moreover, wallpaper designers spend significant time to create different styles of wallpaper. For this purpose, this paper proposes to describe wallpaper texture images by using multi-label semantics. Based on these labels and generative adversarial networks, we present a framework for perception driven wallpaper texture generation and style transfer. In this framework, a perceptual model is trained to recognize whether the wallpapers produced by the generator network are sufficiently realistic and have the attribute designated by given perceptual description; these multi-label semantic attributes are treated as condition variables to generate wallpaper images. The generated wallpaper images can be converted to those with well-known artist styles using CycleGAN. Finally, using the aesthetic evaluation method, the generated wallpaper images are quantitatively measured. The experimental results demonstrate that the proposed method can generate wallpaper textures conforming to human aesthetics and have artistic characteristics

Supplemental Material, Apoptotic_pathway_of_nanosilver_Fig_S2 - Comparative cytotoxicity and apoptotic pathways induced by nanosilver in human liver HepG2 and L02 cells

<p>Supplemental Material, Apoptotic_pathway_of_nanosilver_Fig_S2 for Comparative cytotoxicity and apoptotic pathways induced by nanosilver in human liver HepG2 and L02 cells by Y Xue, J Wang, Y Huang, X Gao, L Kong, T Zhang and M Tang in Human & Experimental Toxicology</p

Supplemental Material, Apoptotic_pathway_of_nanosilver_Fig_S1 - Comparative cytotoxicity and apoptotic pathways induced by nanosilver in human liver HepG2 and L02 cells

<p>Supplemental Material, Apoptotic_pathway_of_nanosilver_Fig_S1 for Comparative cytotoxicity and apoptotic pathways induced by nanosilver in human liver HepG2 and L02 cells by Y Xue, J Wang, Y Huang, X Gao, L Kong, T Zhang and M Tang in Human & Experimental Toxicology</p

A variant NuRD complex containing PWWP2A/B excludes MBD2/3 to regulate transcription at active genes.

Transcriptional regulation by chromatin is a highly dynamic process directed through the recruitment and coordinated action of epigenetic modifiers and readers of these modifications. Using an unbiased proteomic approach to find interactors of H3K36me3, a modification enriched on active chromatin, here we identify PWWP2A and HDAC2 among the top interactors. PWWP2A and its paralog PWWP2B form a stable complex with NuRD subunits MTA1/2/3:HDAC1/2:RBBP4/7, but not with MBD2/3, p66α/β, and CHD3/4. PWWP2A competes with MBD3 for binding to MTA1, thus defining a new variant NuRD complex that is mutually exclusive with the MBD2/3 containing NuRD. In mESCs, PWWP2A/B is most enriched at highly transcribed genes. Loss of PWWP2A/B leads to increases in histone acetylation predominantly at highly expressed genes, accompanied by decreases in Pol II elongation. Collectively, these findings suggest a role for PWWP2A/B in regulating transcription through the fine-tuning of histone acetylation dynamics at actively transcribed genes