Investigation of Candidate Loci Associated with Maize Perennialism

Developing perennial grain crops is an effective and crucial way to prevent soil erosion caused by conventional agriculture of using annual crops while meeting the increasing need of global food demand. We hypothesized that the regrowth in Zea might be controlled by two dominant complementary genes. F1 hybrids were created by crossing Zea diploperennis Iltis, Doebley & R. Guzman with annual Zea mays L. ssp. mays inbred line B73. A Total of 134 F2 plants derived from nine F1 were planted and phenotyped. A subpopulation of 94 F2 plants were genotyped with Genotype-by- Sequencing (GBS) and called 10,431 SNPs after filtering. A total number of 946 SNPs were then found related to regrowth by chi-square goodness of fit test P(χ2) \u3e 0.05. Two chromosomal regions, 24,244,192 to 28,975,747 on chromosome 2 and 2,862,253 to 6,681,861 on chromosome 7 with high LOD scores (P \u3e 0.05) are considered as the candidate loci. Candidate genes found located within these loci have no known functions or not likely to be related with regrowth. Twenty-five pairs of PCR primers were then designed on the basis of 13 SNPs and only 4 of them showed polymorphism between the parental alleles. One of the polymorphic SNP marker targeting the 27,773,017 bp on chromosome 2 showed good fitness of genotype to regrowth (P(χ2) = 0.195), yet the value is still low. More effort is needed to identify the candidate genes by SNP markers

Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework

The identification of transcription factor binding sites and cis-regulatory motifs is a frontier whereupon the rules governing protein-DNA binding are being revealed. Here, we developed a new method (DEep Sequence and Shape mOtif or DESSO) for cis-regulatory motif prediction using deep neural networks and the binomial distribution model. DESSO outperformed existing tools, including DeepBind, in predicting motifs in 690 human ENCODE ChIP-sequencing datasets. Furthermore, the deep-learning framework of DESSO expanded motif discovery beyond the state-of-the-art by allowing the identification of known and new protein-protein-DNA tethering interactions in human transcription factors (TFs). Specifically, 61 putative tethering interactions were identified among the 100 TFs expressed in the K562 cell line. In this work, the power of DESSO was further expanded by integrating the detection of DNA shape features. We found that shape information has strong predictive power for TF-DNA binding and provides new putative shape motif information for human TFs. Thus, DESSO improves in the identification and structural analysis of TF binding sites, by integrating the complexities of DNA binding into a deep-learning framework

A Survey on Transferability of Adversarial Examples across Deep Neural Networks

The emergence of Deep Neural Networks (DNNs) has revolutionized various domains, enabling the resolution of complex tasks spanning image recognition, natural language processing, and scientific problem-solving. However, this progress has also exposed a concerning vulnerability: adversarial examples. These crafted inputs, imperceptible to humans, can manipulate machine learning models into making erroneous predictions, raising concerns for safety-critical applications. An intriguing property of this phenomenon is the transferability of adversarial examples, where perturbations crafted for one model can deceive another, often with a different architecture. This intriguing property enables "black-box" attacks, circumventing the need for detailed knowledge of the target model. This survey explores the landscape of the adversarial transferability of adversarial examples. We categorize existing methodologies to enhance adversarial transferability and discuss the fundamental principles guiding each approach. While the predominant body of research primarily concentrates on image classification, we also extend our discussion to encompass other vision tasks and beyond. Challenges and future prospects are discussed, highlighting the importance of fortifying DNNs against adversarial vulnerabilities in an evolving landscape

IRIS3: integrated cell-type-specific regulon inference server from single-cell RNA-Seq

group of genes controlled as a unit, usually by the same repressor or activator gene, is known as a regulon. The ability to identify active regulons within a specific cell type, i.e., cell-type-specific regulons (CTSR), provides an extraordinary opportunity to pinpoint crucial regulators and target genes responsible for complex diseases. However, the identification of CTSRs from single-cell RNA-Seq (scRNA-Seq) data is computationally challenging. We introduce IRIS3, the first-of-its-kind web server for CTSR inference from scRNA-Seq data for human and mouse. IRIS3 is an easy-to-use server empowered by over 20 functionalities to support comprehensive interpretations and graphical visualizations of identified CTSRs. CTSR data can be used to reliably characterize and distinguish the corresponding cell type from others and can be combined with other computational or experimental analyses for biomedical studies. CTSRs can, therefore, aid in the discovery of major regulatory mechanisms and allow reliable constructions of global transcriptional regulation networks encoded in a specific cell type. The broader impact of IRIS3 includes, but is not limited to, investigation of complex diseases hierarchies and heterogeneity, causal gene regulatory network construction, and drug development

RECTA: Regulon Identification Based on Comparative Genomics and Transcriptomics Analysis

Regulons, which serve as co-regulated gene groups contributing to the transcriptional regulation of microbial genomes, have the potential to aid in understanding of underlying regulatory mechanisms. In this study, we designed a novel computational pipeline, regulon identification based on comparative genomics and transcriptomics analysis (RECTA), for regulon prediction related to the gene regulatory network under certain conditions. To demonstrate the effectiveness of this tool, we implemented RECTA on Lactococcus lactis MG1363 data to elucidate acid-response regulons. A total of 51 regulons were identified, 14 of which have computational-verified significance. Among these 14 regulons, five of them were computationally predicted to be connected with acid stress response. Validated by literature, 33 genes in Lactococcus lactis MG1363 were found to have orthologous genes which were associated with six regulons. An acid response related regulatory network was constructed, involving two trans-membrane proteins, eight regulons (llrA, llrC, hllA, ccpA, NHP6A, rcfB, regulons #8 and #39), nine functional modules, and 33 genes with orthologous genes known to be associated with acid stress. The predicted response pathways could serve as promising candidates for better acid tolerance engineering in Lactococcus lactis. Our RECTA pipeline provides an effective way to construct a reliable gene regulatory network through regulon elucidation, and has strong application power and can be effectively applied to other bacterial genomes where the elucidation of the transcriptional regulation network is needed

It is Time to Apply Biclustering: a Comprehensive Review of Biclustering Applications in Biological and Biomedical Data

Biclustering is a powerful data mining technique that allows clustering of rows and columns, simultaneously, in a matrix-format data set. It was first applied to gene expression data in 2000, aiming to identify co-expressed genes under a subset of all the conditions/samples. During the past 17 years, tens of biclustering algorithms and tools have been developed to enhance the ability to make sense out of large data sets generated in the wake of high-throughput omics technologies. These algorithms and tools have been applied to a wide variety of data types, including but not limited to, genomes, transcriptomes, exomes, epigenomes, phenomes and pharmacogenomes. However, there is still a considerable gap between biclustering methodology development and comprehensive data interpretation, mainly because of the lack of knowledge for the selection of appropriate biclustering tools and further supporting computational techniques in specific studies. Here, we first deliver a brief introduction to the existing biclustering algorithms and tools in public domain, and then systematically summarize the basic applications of biclustering for biological data and more advanced applications of biclustering for biomedical data. This review will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency

EFFECT OF AL 2 O 3 MICROPARTICLES ON THE HEAT TRANSPORT CAPABILITY IN AN OSCILLATING HEAT PIPE

ABSTRACT An experimental investigation was conducted to determine the microparticle effect on the heat transport capability of an oscillating heat pipe (OHP). The OHP was fabricated from copper tubing with inside diameter of 1.52 mm. The heat pipe consists of the evaporator, adiabatic section, and condenser. When heat load was added to the evaporator of OHP, the strong oscillating motion was generated. Due to the strong oscillation and circulation motions, the heat transport capability of OHP was significantly increased. The experimental results show that there exists an optimum volume ratio of microparticles added into the working fluid. The effects of filling ratio and tilted angle on the heat transport capacity were also conducted

Violet-Blue Aggregation-Induced Emission Emitters for Non-Doped OLEDs with CIEy Smaller than 0.046

High emission efficiency and finite molecular conjugation in aggregate state are two desirable features in violet-blue emitters. Aggregation-induced emission luminogens (AIEgens) have surfaced as promising luminescent materials that possess both features. Herein, we report the design and synthesis of a group of violet-blue emissive AIEgens with photoluminescence quantum yield higher than 98% in their film states. When utilizing these AIEgens as non-doped emitting layers, the fabricated organic light-emitting diode exhibit a maximum external quantum efficiency of 4.34% with Commission Internationale de L’Eclairage (CIE) coordinates of (0.159, 0.035), which are amenable to next generation Ultra-high Definition Television (UHDTV) display standard.</p

A Central Edge Selection Based Overlapping Community Detection Algorithm for the Detection of Overlapping Structures in Protein–Protein Interaction Networks

Overlapping structures of protein&ndash;protein interaction networks are very prevalent in different biological processes, which reflect the sharing mechanism to common functional components. The overlapping community detection (OCD) algorithm based on central node selection (CNS) is a traditional and acceptable algorithm for OCD in networks. The main content of CNS is the central node selection and the clustering procedure. However, the original CNS does not consider the influence among the nodes and the importance of the division of the edges in networks. In this paper, an OCD algorithm based on a central edge selection (CES) algorithm for detection of overlapping communities of protein&ndash;protein interaction (PPI) networks is proposed. Different from the traditional CNS algorithms for OCD, the proposed algorithm uses community magnetic interference (CMI) to obtain more reasonable central edges in the process of CES, and employs a new distance between the non-central edge and the set of the central edges to divide the non-central edge into the correct cluster during the clustering procedure. In addition, the proposed CES improves the strategy of overlapping nodes pruning (ONP) to make the division more precisely. The experimental results on three benchmark networks and three biological PPI networks of Mus. musculus, Escherichia coli, and Cerevisiae show that the CES algorithm performs well