Comprehensive profiling of zebrafish hepatic proximal promoter CpG island methylation and its modification during chemical carcinogenesis

Background\ud DNA methylation is an epigenetic mechanism associated with regulation of gene expression and it is modulated during chemical carcinogenesis. The zebrafish is increasingly employed as a human disease model; however there is a lack of information on DNA methylation in zebrafish and during fish tumorigenesis. \ud \ud Results\ud A novel CpG island tiling array containing 44,000 probes, in combination with immunoprecipitation of methylated DNA, was used to achieve the first comprehensive methylation profiling of normal adult zebrafish liver. DNA methylation alterations were detected in zebrafish liver tumors induced by the environmental carcinogen 7, 12-dimethylbenz(a)anthracene. Genes significantly hypomethylated in tumors were associated particularly with proliferation, glycolysis, transcription, cell cycle, apoptosis, growth and metastasis. Hypermethylated genes included those associated with anti-angiogenesis and cellular adhesion. Of 49 genes that were altered in expression within tumors, and which also had appropriate CpG islands and were co-represented on the tiling array, approximately 45% showed significant changes in both gene expression and methylation. \ud \ud Conclusion\ud The functional pathways containing differentially methylated genes in zebrafish hepatocellular carcinoma have also been reported to be aberrantly methylated during tumorigenesis in humans. These findings increase the confidence in the use of zebrafish as a model for human cancer in addition to providing the first comprehensive mapping of DNA methylation in the normal adult zebrafish liver. \ud \u

SCVCNet: Sliding cross-vector convolution network for cross-task and inter-individual-set EEG-based cognitive workload recognition

This paper presents a generic approach for applying the cognitive workload recognizer by exploiting common electroencephalogram (EEG) patterns across different human-machine tasks and individual sets. We propose a neural network called SCVCNet, which eliminates task- and individual-set-related interferences in EEGs by analyzing finer-grained frequency structures in the power spectral densities. The SCVCNet utilizes a sliding cross-vector convolution (SCVC) operation, where paired input layers representing the theta and alpha power are employed. By extracting the weights from a kernel matrix's central row and column, we compute the weighted sum of the two vectors around a specified scalp location. Next, we introduce an inter-frequency-point feature integration module to fuse the SCVC feature maps. Finally, we combined the two modules with the output-channel pooling and classification layers to construct the model. To train the SCVCNet, we employ the regularized least-square method with ridge regression and the extreme learning machine theory. We validate its performance using three databases, each consisting of distinct tasks performed by independent participant groups. The average accuracy (0.6813 and 0.6229) and F1 score (0.6743 and 0.6076) achieved in two different validation paradigms show partially higher performance than the previous works. All features and algorithms are available on website:https://github.com/7ohnKeats/SCVCNet.Comment: 12 page

PPT4J: Patch Presence Test for Java Binaries

The number of vulnerabilities reported in open source software has increased substantially in recent years. Security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the ability to test whether a patch is applied to the target binary, a.k.a. patch presence test, is crucial for practitioners. However, it is challenging to obtain accurate semantic information from patches, which could lead to incorrect results. In this paper, we propose a new patch presence test framework named PPT4J ($\textbf{P}$atch $\textbf{P}$resence $\textbf{T}$est $\textbf{for}$ $\textbf{J}$ava Binaries). PPT4J is designed for open-source Java libraries. It takes Java binaries (i.e. bytecode files) as input, extracts semantic information from patches, and uses feature-based techniques to identify patch lines in the binaries. To evaluate the effectiveness of our proposed approach PPT4J, we construct a dataset with binaries that include 110 vulnerabilities. The results show that PPT4J achieves an F1 score of 98.5% with reasonable efficiency, improving the baseline by 14.2%. Furthermore, we conduct an in-the-wild evaluation of PPT4J on JetBrains IntelliJ IDEA. The results suggest that a third-party library included in the software is not patched for two CVEs, and we have reported this potential security problem to the vendor.Comment: 12 page

Intra- and intertrench variations in flexural bending of the Manila, Mariana and global trenches : implications on plate weakening in controlling trench dynamics

Author Posting. © Author(s), 2017. This article is posted here by permission of Oxford University Press for personal use, not for redistribution. The definitive version was published in Geophysical Journal International 212 (2018): 1429–1449, doi:10.1093/gji/ggx488.We conducted detailed analyses of a global array of trenches, revealing systematic intra- and intertrench variations in plate bending characteristics. The intratrench variations of the Manila and Mariana Trenches were analysed in detail as end-member cases of the relatively young (16–36 Ma) and old (140–160 Ma) subducting plates, respectively. Meanwhile, the intertrench variability was investigated for a global array of additional trenches including the Philippine, Kuril, Japan, Izu-Bonin, Aleutian, Tonga-Kermadec, Middle America, Peru, Chile, Sumatra and Java Trenches. Results of the analysis show that the trench relief (W0) and width (X0) of all systems are controlled primarily by the faulting-reduced elastic thickness near the trench axis (Tme) and affected only slightly by the initial unfaulted thickness (TMe) of the incoming plate. The reduction in Te has caused significant deepening and narrowing of trench valleys. For the cases of relatively young or old plates, the plate age could be a dominant factor in controlling the trench bending shape, regardless the variations in axial loadings. Our calculations also show that the axial loading and stresses of old subducting plates can vary significantly along the trench axis. In contrast, the young subducting plates show much smaller values and variations in axial loading and stresses.This work was supported by Chinese Academy of Sciences Grants (Y4SL021001, QYZDY-SSW-DQC005, YZ201325 and YZ201534), National Natural Science Foundation of China Grants (91628301, U1606401, 41376063 and 41706056) and HKSAR Research Grant Council Grants (24601515, 14313816)

Engineering Colloidal Lithography and Nanoskiving to Fabricate Rows of Opposing Crescent Nanogaps

A scalable fabrication route combining colloidal lithography and nanoskiving is reported for generating free-standing asymmetric metal nanostructures of crescent-shaped gold nanowires and rows of opposing crescents with and without nanogaps. Strong localized surface plasmon resonances and propagating surface plasmon polaritons are excited at the sharp tips of the crescent and in the sub-10 nm nanogaps. High-order resonance modes are excited due to the coupling between the resonances in the tips and gaps. The Raman signals are greatly enhanced due to the strong electric fields. In addition, the optical responses and electric field distributions can be controlled by the polarization of the incident light. The strong electric field enhancement coupled with facile, scalable fabrication make crescent-shaped nanostructures promising in nonlinear optics, optical trapping, and surface-enhanced spectroscopy

TeacherLM: Teaching to Fish Rather Than Giving the Fish, Language Modeling Likewise

Large Language Models (LLMs) exhibit impressive reasoning and data augmentation capabilities in various NLP tasks. However, what about small models? In this work, we propose TeacherLM-7.1B, capable of annotating relevant fundamentals, chain of thought, and common mistakes for most NLP samples, which makes annotation more than just an answer, thus allowing other models to learn "why" instead of just "what". The TeacherLM-7.1B model achieved a zero-shot score of 52.3 on MMLU, surpassing most models with over 100B parameters. Even more remarkable is its data augmentation ability. Based on TeacherLM-7.1B, we augmented 58 NLP datasets and taught various student models with different parameters from OPT and BLOOM series in a multi-task setting. The experimental results indicate that the data augmentation provided by TeacherLM has brought significant benefits. We will release the TeacherLM series of models and augmented datasets as open-source.Comment: 5 figures, 15 page