Hybrid Tractable Classes of Binary Quantified Constraint Satisfaction Problems

In this paper, we investigate the hybrid tractability of binary Quantified Constraint Satisfaction Problems (QCSPs). First, a basic tractable class of binary QCSPs is identified by using the broken-triangle property. In this class, the variable ordering for the broken-triangle property must be same as that in the prefix of the QCSP. Second, we break this restriction to allow that existentially quantified variables can be shifted within or out of their blocks, and thus identify some novel tractable classes by introducing the broken-angle property. Finally, we identify a more generalized tractable class, i.e., the min-of-max extendable class for QCSPs

iAssembler: a package for de novo assembly of Roche-454/Sanger transcriptome sequences

<p>Abstract</p> <p>Background</p> <p>Expressed Sequence Tags (ESTs) have played significant roles in gene discovery and gene functional analysis, especially for non-model organisms. For organisms with no full genome sequences available, ESTs are normally assembled into longer consensus sequences for further downstream analysis. However current <it>de novo </it>EST assembly programs often generate large number of assembly errors that will negatively affect the downstream analysis. In order to generate more accurate consensus sequences from ESTs, tools are needed to reduce or eliminate errors from <it>de novo </it>assemblies.</p> <p>Results</p> <p>We present iAssembler, a pipeline that can assemble large-scale ESTs into consensus sequences with significantly higher accuracy than current existing assemblers. iAssembler employs MIRA and CAP3 assemblers to generate initial assemblies, followed by identifying and correcting two common types of transcriptome assembly errors: 1) ESTs from different transcripts (mainly alternatively spliced transcripts or paralogs) are incorrectly assembled into same contigs; and 2) ESTs from same transcripts fail to be assembled together. iAssembler can be used to assemble ESTs generated using the traditional Sanger method and/or the Roche-454 massive parallel pyrosequencing technology.</p> <p>Conclusion</p> <p>We compared performances of iAssembler and several other <it>de novo </it>EST assembly programs using both Roche-454 and Sanger EST datasets. It demonstrated that iAssembler generated significantly more accurate consensus sequences than other assembly programs.</p

CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization

Low-dose computed tomography (CT) images suffer from noise and artifacts due to photon starvation and electronic noise. Recently, some works have attempted to use diffusion models to address the over-smoothness and training instability encountered by previous deep-learning-based denoising models. However, diffusion models suffer from long inference times due to the large number of sampling steps involved. Very recently, cold diffusion model generalizes classical diffusion models and has greater flexibility. Inspired by the cold diffusion, this paper presents a novel COntextual eRror-modulated gEneralized Diffusion model for low-dose CT (LDCT) denoising, termed CoreDiff. First, CoreDiff utilizes LDCT images to displace the random Gaussian noise and employs a novel mean-preserving degradation operator to mimic the physical process of CT degradation, significantly reducing sampling steps thanks to the informative LDCT images as the starting point of the sampling process. Second, to alleviate the error accumulation problem caused by the imperfect restoration operator in the sampling process, we propose a novel ContextuaL Error-modulAted Restoration Network (CLEAR-Net), which can leverage contextual information to constrain the sampling process from structural distortion and modulate time step embedding features for better alignment with the input at the next time step. Third, to rapidly generalize to a new, unseen dose level with as few resources as possible, we devise a one-shot learning framework to make CoreDiff generalize faster and better using only a single LDCT image (un)paired with NDCT. Extensive experimental results on two datasets demonstrate that our CoreDiff outperforms competing methods in denoising and generalization performance, with a clinically acceptable inference time. Source code is made available at https://github.com/qgao21/CoreDiff.Comment: IEEE Transactions on Medical Imaging, 202

Expression of ethylene biosynthetic and receptor genes in rose floral tissues during ethylene-enhanced flower opening

Ethylene production, as well as the expression of ethylene biosynthetic (Rh-ACS1–4 and Rh-ACO1) and receptor (Rh-ETR1–5) genes, was determined in five different floral tissues (sepals, petals, stamens, gynoecia, and receptacles) of cut rose (Rosa hybrida cv. Samantha upon treatment with ethylene or the ethylene inhibitor 1-methylcyclopropene (1-MCP). Ethylene-enhanced ethylene production occurred only in gynoecia, petals, and receptacles, with gynoecia showing the greatest enhancement in the early stage of ethylene treatment. However, 1-MCP did not suppress ethylene production in these three tissues. In sepals, ethylene production was highly decreased by ethylene treatment, and increased dramatically by 1-MCP. Ethylene production in stamens remained unchanged after ethylene or 1-MCP treatment. Induction of certain ethylene biosynthetic genes by ethylene in different floral tissues was positively correlated with the ethylene production, and this induction was also not suppressed by 1-MCP. The expression of Rh-ACS2 and Rh-ACS3 was quickly induced by ethylene in gynoecia, but neither Rh-ACS1 nor Rh-ACS4 was induced by ethylene in any of the five tissues. In addition, Rh-ACO1 was induced by ethylene in all floral tissues except sepals. The induced expression of ethylene receptor genes by ethylene was much faster in gynoecia than in petals, and the expression of Rh-ETR3 was strongly suppressed by 1-MCP in all floral tissues. These results indicate that ethylene biosynthesis in gynoecia is regulated developmentally, rather than autocatalytically. The response of rose flowers to ethylene occurs initially in gynoecia, and ethylene may regulate flower opening mainly through the Rh-ETR3 gene in gynoecia

Quad-Net: Quad-domain Network for CT Metal Artifact Reduction

Metal implants and other high-density objects in patients introduce severe streaking artifacts in CT images, compromising image quality and diagnostic performance. Although various methods were developed for CT metal artifact reduction over the past decades, including the latest dual-domain deep networks, remaining metal artifacts are still clinically challenging in many cases. Here we extend the state-of-the-art dual-domain deep network approach into a quad-domain counterpart so that all the features in the sinogram, image, and their corresponding Fourier domains are synergized to eliminate metal artifacts optimally without compromising structural subtleties. Our proposed quad-domain network for MAR, referred to as Quad-Net, takes little additional computational cost since the Fourier transform is highly efficient, and works across the four receptive fields to learn both global and local features as well as their relations. Specifically, we first design a Sinogram-Fourier Restoration Network (SFR-Net) in the sinogram domain and its Fourier space to faithfully inpaint metal-corrupted traces. Then, we couple SFR-Net with an Image-Fourier Refinement Network (IFR-Net) which takes both an image and its Fourier spectrum to improve a CT image reconstructed from the SFR-Net output using cross-domain contextual information. Quad-Net is trained on clinical datasets to minimize a composite loss function. Quad-Net does not require precise metal masks, which is of great importance in clinical practice. Our experimental results demonstrate the superiority of Quad-Net over the state-of-the-art MAR methods quantitatively, visually, and statistically. The Quad-Net code is publicly available at https://github.com/longzilicart/Quad-Net

Morphology, phylogeny and lipid components of an oil-rich microalgal strain

Microalgae have attracted much more attentions for their roles in biofuel exploration recently. In this report, one oil-rich microalgal strain (TY02) was isolated from the lawn soil in a park, Taiyuan, Shanxi, China, and the morphology and phylogeny characters of the strain was systematically analyzed. Observed by light microscopy, scanning electron microscopy, transmission electron microscopy and fluorescent microscopy, the lipid bodies were observed clearly. After extracting the total lipid by chloroform-methanol method, the fatty acid content and composition of the lipid bodies in the strain were detected and analyzed through gas chromatography-mass spectrometry. The results demonstrated that the total lipid content of TY02 was 33.27 ± 1.13%, among the total 7 kinds of fatty acids identified in TY02, the major constituents are C16 and C18 fatty acids, which taking up to 88.15%. Moreover, the predominant fatty acids were Hexadecanoic acid (C16: 0), 9, 12-Octadecadienoic acid (C18: 2) and 9, 12, 15-Octade mcatrienoic acid (C18: 3). Based on the molecular markers of 18S rDNA, rbcL and ITS genes, phylogenetic trees and ITS2 secondary structure analysis all showed that the strain closed to Parachlorella kessleri. All results might bring a new look  that some  microalgae with  potential values can be a raw biodiesel material