FG-Depth: Flow-Guided Unsupervised Monocular Depth Estimation

The great potential of unsupervised monocular depth estimation has been demonstrated by many works due to low annotation cost and impressive accuracy comparable to supervised methods. To further improve the performance, recent works mainly focus on designing more complex network structures and exploiting extra supervised information, e.g., semantic segmentation. These methods optimize the models by exploiting the reconstructed relationship between the target and reference images in varying degrees. However, previous methods prove that this image reconstruction optimization is prone to get trapped in local minima. In this paper, our core idea is to guide the optimization with prior knowledge from pretrained Flow-Net. And we show that the bottleneck of unsupervised monocular depth estimation can be broken with our simple but effective framework named FG-Depth. In particular, we propose (i) a flow distillation loss to replace the typical photometric loss that limits the capacity of the model and (ii) a prior flow based mask to remove invalid pixels that bring the noise in training loss. Extensive experiments demonstrate the effectiveness of each component, and our approach achieves state-of-the-art results on both KITTI and NYU-Depth-v2 datasets.Comment: Accepted by ICRA202

Dynamics of Panax ginseng

The bacterial communities of 1- to 6-year ginseng rhizosphere soils were characterized by culture-independent approaches, random amplified polymorphic DNA (RAPD), and amplified ribosomal DNA restriction analysis (ARDRA). Culture-dependent method (Biolog) was used to investigate the metabolic function variance of microbe living in rhizosphere soil. Results showed that significant genetic and metabolic function variance were detected among soils, and, with the increasing of cultivating years, genetic diversity of bacterial communities in ginseng rhizosphere soil tended to be decreased. Also we found that Verrucomicrobia, Acidobacteria, and Proteobacteria were the dominants in rhizosphere soils, but, with the increasing of cultivating years, plant disease prevention or plant growth promoting bacteria, such as Pseudomonas, Burkholderia, and Bacillus, tended to be rare

Semi-Supervised Learning for Visual Bird's Eye View Semantic Segmentation

Visual bird's eye view (BEV) semantic segmentation helps autonomous vehicles understand the surrounding environment only from images, including static elements (e.g., roads) and dynamic elements (e.g., vehicles, pedestrians). However, the high cost of annotation procedures of full-supervised methods limits the capability of the visual BEV semantic segmentation, which usually needs HD maps, 3D object bounding boxes, and camera extrinsic matrixes. In this paper, we present a novel semi-supervised framework for visual BEV semantic segmentation to boost performance by exploiting unlabeled images during the training. A consistency loss that makes full use of unlabeled data is then proposed to constrain the model on not only semantic prediction but also the BEV feature. Furthermore, we propose a novel and effective data augmentation method named conjoint rotation which reasonably augments the dataset while maintaining the geometric relationship between the front-view images and the BEV semantic segmentation. Extensive experiments on the nuScenes and Argoverse datasets show that our semi-supervised framework can effectively improve prediction accuracy. To the best of our knowledge, this is the first work that explores improving visual BEV semantic segmentation performance using unlabeled data. The code is available at https://github.com/Junyu-Z/Semi-BEVsegComment: Accepted by ICRA202

Polymer Electret Improves the Performance of the Oxygen-Doped Organic Field-Effect Transistors

Chemical doping is widely used in the electronic devices. In p-type semiconductor thin films, oxygen doping fills the hole traps and increases hole concentrations, improving the performance of the organic field-effect transistors (OFETs). Due to the low ionization potential for p-type semiconductors, the superfluous holes induced by the oxygen doping degrades the OFETs off-state leakage performance. On the other hand, for p-type semiconductors with high ionization potential (up to 5.5-6.0 eV), the limited oxidation of oxygen is hard to achieve satisfactory doping concentrations to fill the trap states. This refers to the well-known intrinsic incompatibility between the oxygen doping and high-performance OFETs. Herein, a novel strategy is introduced to overcome the incompatibility and achieve high-performance OFETs by using the structural polymer electret. That is, moderate hole concentrations induced by low-pressure (30 Pa) oxygen plasma fill the hole traps within semiconductor. And the built-in field resulted from polymer electret accumulates the holes inside semiconductor near the semiconductor/electret interface, thus improving the OFETs performance. Using a model organic semiconductor with high ionization potential-2,7-didodecyl[1]benzothieno [3,2-b][1]benzothiophene (C12-BTBT) as an example, the high-performance OFETs with field-effect mobility (μFET) of 3.5 cm 2 V -1 s -1 , subthreshold-swing (SS) of 110 mV decade -1 , on-off ratio of 10 4 , and widely-tunable threshold voltage (V t ) are realized at a low voltage below 2 V in the open air

Self-supervised Event-based Monocular Depth Estimation using Cross-modal Consistency

An event camera is a novel vision sensor that can capture per-pixel brightness changes and output a stream of asynchronous ``events''. It has advantages over conventional cameras in those scenes with high-speed motions and challenging lighting conditions because of the high temporal resolution, high dynamic range, low bandwidth, low power consumption, and no motion blur. Therefore, several supervised monocular depth estimation from events is proposed to address scenes difficult for conventional cameras. However, depth annotation is costly and time-consuming. In this paper, to lower the annotation cost, we propose a self-supervised event-based monocular depth estimation framework named EMoDepth. EMoDepth constrains the training process using the cross-modal consistency from intensity frames that are aligned with events in the pixel coordinate. Moreover, in inference, only events are used for monocular depth prediction. Additionally, we design a multi-scale skip-connection architecture to effectively fuse features for depth estimation while maintaining high inference speed. Experiments on MVSEC and DSEC datasets demonstrate that our contributions are effective and that the accuracy can outperform existing supervised event-based and unsupervised frame-based methods.Comment: Accepted by IROS202

Comprehensive characterization of a time-course transcriptional response induced by autotoxins in Panax ginseng using RNA-Seq

The lists for all the differentially expressed genes. (XLS 1362 kb

Genome-Wide Analysis of mRNAs and lncRNAs of Intramuscular Fat Related to Lipid Metabolism in Two Pig Breeds

Background/Aims: Long non-coding RNAs (lncRNAs) can regulate adipogenesis and lipid accumulation. Intramuscular fat deposition appears to vary in different pig breeds, and the regulation mechanism has not yet been fully elucidated at molecular level. Moreover, little is known about the function and profile of lncRNAs in intramuscular fat deposition and metabolism in pig. The aim of this study was thus to explore the regulatory functions of lncRNAs in intramuscular fat deposition. Methods: In this study, Laiwu (LW) pig and Large White (LY) pig with significant difference in fat deposition were selected for use. RNA-seq technology and bioinformatics methods were used to comparatively analyze the gene expression profiles of intramuscular fat between LW and LY pigs to identify key mRNAs and lncRNAs associated with lipid metabolism and adipogenesis. Real-time fluorescence-based quantitative PCR was applied to verify the expression level of the differentially expressed mRNAs and lncRNAs. Results: A total of 513 mRNAs and 55 lncRNAs were differentially expressed between two pig breeds. By co-expression network construction as well as cis- and trans-regulated target gene analysis, 31 key lncRNAs were identified. Gene Ontology and KEGG pathway analyses revealed that differentially expressed genes and lncRNAs were mainly involved in the biological processes and pathways related to adipogenesis and lipid metabolism. Conclusion: XLOC_046142, XLOC_004398 and XLOC_015408 may target MAPKAPK2, NR1D2 and AKR1C4, respectively, and play critical regulatory roles in intramuscular adipogenesis and lipid accumulation in pig. XLOC_064871 and XLOC_011001 may play a role in lipid metabolism-related disease via regulating TRIB3 and BRCA1. This study provides a valuable resource for lncRNA study and improves our understanding of the biological roles of lipid metabolism- related genes and molecular mechanism of intramuscular fat metabolism and deposition

Identification and Characterization of CircRNAs of Two Pig Breeds as a New Biomarker in Metabolism-Related Diseases

Background/Aims: CircRNAs, as miRNA sponges, participate in many important biological processes. However, it remains unclear whether circRNAs can regulate lipid metabolism. This paper aims to study the molecular mechanism of fat deposition and provide useful information for the prevention and therapy of lipid metabolism-related diseases. Methods: CircRNA sequencing was performed to investigate the expression of circRNAs in the subcutaneous adipose tissues of Large White pig and Laiwu pig. The expression of circRNAs was further validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Furthermore, circRNA-microRNAs (miRNA)-mRNA interaction networks were constructed using bioinformatics tools. In addition, GO and KEGG enrichment analyses were performed for the target genes of circRNAs. Results: In the subcutaneous adipose tissue of Laiwu pig, 70 up-regulated circRNAs and 205 down-regulated circRNAs were identified. Two circRNAs (up-regulated circRNA_26852 and down-regulated circRNA_11897), the expressions of which were confirmed by qRT-PCR, were selected for subsequent analysis. CircRNA-miRNA-mRNA interaction networks were constructed for circRNA_26852 and its target genes as well as circRNA_11897 and its target genes. GO and KEGG enrichment analyses reveal that the target genes of circRNA_26852 and circRNA_11897 are enriched in pathways related to adipocyte differentiation and lipid metabolism, as well as in disease-related pathways. Conclusions: In this study, circRNA sequencing and bioinformatics technique were used to analyze, for the first time, the expression of circRNAs in the subcutaneous adipose tissues of Large White pig and Laiwu pig. It is inferred that circRNAs might regulate adipogenic differentiation and lipid metabolism. The results provide a theoretical basis for further study on fat deposition mechanism and provide potential therapy targets for metabolism-related diseases

A diploid wheat TILLING resource for wheat functional genomics

Citation: Rawat, N., . . . & Gill, B. (2012). A diploid wheat TILLING resource for wheat functional genomics. BMC Plant Biology, 12(1), 205. https://doi.org/10.1186/1471-2229-12-205Background: Triticum monococcum L., an A genome diploid einkorn wheat, was the first domesticated crop. As a diploid, it is attractive genetic model for the study of gene structure and function of wheat-specific traits. Diploid wheat is currently not amenable to reverse genetics approaches such as insertion mutagenesis and post-transcriptional gene silencing strategies. However, TILLING offers a powerful functional genetics approach for wheat gene analysis. Results: We developed a TILLING population of 1,532 M[subscript 2] families using EMS as a mutagen. A total of 67 mutants were obtained for the four genes studied. Waxy gene mutation frequencies are known to be 1/17.6 - 34.4 kb DNA in polyploid wheat TILLING populations. The T. monococcum diploid wheat TILLING population had a mutation frequency of 1/90 kb for the same gene. Lignin biosynthesis pathway genes- COMT1, HCT2, and 4CL1 had mutation frequencies of 1/86 kb, 1/92 kb and 1/100 kb, respectively. The overall mutation frequency of the diploid wheat TILLING population was 1/92 kb. Conclusion: The mutation frequency of a diploid wheat TILLING population was found to be higher than that reported for other diploid grasses. The rate, however, is lower than tetraploid and hexaploid wheat TILLING populations because of the higher tolerance of polyploids to mutations. Unlike polyploid wheat, most mutants in diploid wheat have a phenotype amenable to forward and reverse genetic analysis and establish diploid wheat as an attractive model to study gene function in wheat. We estimate that a TILLING population of 5, 520 will be needed to get a non-sense mutation for every wheat gene of interest with 95% probability