Connecting Software Metrics across Versions to Predict Defects

Accurate software defect prediction could help software practitioners allocate test resources to defect-prone modules effectively and efficiently. In the last decades, much effort has been devoted to build accurate defect prediction models, including developing quality defect predictors and modeling techniques. However, current widely used defect predictors such as code metrics and process metrics could not well describe how software modules change over the project evolution, which we believe is important for defect prediction. In order to deal with this problem, in this paper, we propose to use the Historical Version Sequence of Metrics (HVSM) in continuous software versions as defect predictors. Furthermore, we leverage Recurrent Neural Network (RNN), a popular modeling technique, to take HVSM as the input to build software prediction models. The experimental results show that, in most cases, the proposed HVSM-based RNN model has a significantly better effort-aware ranking effectiveness than the commonly used baseline models

Branched-Chain Amino Acid Negatively Regulates KLF15 Expression via PI3K-AKT Pathway.

Recent studies have linked branched-chain amino acid (BCAA) with numerous metabolic diseases. However, the molecular basis of BCAA's roles in metabolic regulation remains to be established. KLF15 (Krüppel-like factor 15) is a transcription factor and master regulator of glycemic, lipid, and amino acids metabolism. In the present study, we found high concentrations of BCAA suppressed KLF15 expression while BCAA starvation induced KLF15 expression, suggesting KLF15 expression is negatively controlled by BCAA.Interestingly, BCAA starvation induced PI3K-AKT signaling. KLF15 induction by BCAA starvation was blocked by PI3K and AKT inhibitors, indicating the activation of PI3K-AKT signaling pathway mediated the KLF15 induction. BCAA regulated KLF15 expression at transcriptional level but not post-transcriptional level. However, BCAA starvation failed to increase the KLF15-promoter-driven luciferase expression, suggesting KLF15 promoter activity was not directly controlled by BCAA. Finally, fasting reduced BCAA abundance in mice and KLF15 expression was dramatically induced in muscle and white adipose tissue, but not in liver. Together, these data demonstrated BCAA negatively regulated KLF15 expression, suggesting a novel molecular mechanism underlying BCAA's multiple functions in metabolic regulation

Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks

Magnetic resonance image (MRI) in high spatial resolution provides detailed anatomical information and is often necessary for accurate quantitative analysis. However, high spatial resolution typically comes at the expense of longer scan time, less spatial coverage, and lower signal to noise ratio (SNR). Single Image Super-Resolution (SISR), a technique aimed to restore high-resolution (HR) details from one single low-resolution (LR) input image, has been improved dramatically by recent breakthroughs in deep learning. In this paper, we introduce a new neural network architecture, 3D Densely Connected Super-Resolution Networks (DCSRN) to restore HR features of structural brain MR images. Through experiments on a dataset with 1,113 subjects, we demonstrate that our network outperforms bicubic interpolation as well as other deep learning methods in restoring 4x resolution-reduced images.Comment: Accepted by ISBI'1

Chaotic Properties of Subshifts Generated by a Non-Periodic Recurrent Orbit

The chaotic properties of some subshift maps are investigated. These subshifts are the orbit closures of certain non-periodic recurrent points of a shift map. We first provide a review of basic concepts for dynamics of continuous maps in metric spaces. These concepts include nonwandering point, recurrent point, eventually periodic point, scrambled set, sensitive dependence on initial conditions, Robinson chaos, and topological entropy. Next we review the notion of shift maps and subshifts. Then we show that the one-sided subshifts generated by a non-periodic recurrent point are chaotic in the sense of Robinson. Moreover, we show that such a subshift has an infinite scrambled set if it has a periodic point. Finally, we give some examples and discuss the topological entropy of these subshifts, and present two open problems on the dynamics of subshifts

IRE1 phosphatase PP2Ce regulates adaptive ER stress response in the postpartum mammary gland.

We recently reported that the PPM1l gene encodes an endoplasmic reticulum (ER) membrane targeted protein phosphatase (named PP2Ce) with highly specific activity towards Inositol-requiring protein-1 (IRE1) and regulates the functional outcome of ER stress. In the present report, we found that the PP2Ce protein is highly expressed in lactating epithelium of the mammary gland. Loss of PP2Ce in vivo impairs physiological unfolded protein response (UPR) and induces stress kinase activation, resulting in loss of milk production and induction of epithelial apoptosis in the lactating mammary gland. This study provides the first in vivo evidence that PP2Ce is an essential regulator of normal lactation, possibly involving IRE1 signaling and ER stress regulation in mammary epithelium

Corpus-Steered Query Expansion with Large Language Models

Recent studies demonstrate that query expansions generated by large language models (LLMs) can considerably enhance information retrieval systems by generating hypothetical documents that answer the queries as expansions. However, challenges arise from misalignments between the expansions and the retrieval corpus, resulting in issues like hallucinations and outdated information due to the limited intrinsic knowledge of LLMs. Inspired by Pseudo Relevance Feedback (PRF), we introduce Corpus-Steered Query Expansion (CSQE) to promote the incorporation of knowledge embedded within the corpus. CSQE utilizes the relevance assessing capability of LLMs to systematically identify pivotal sentences in the initially-retrieved documents. These corpus-originated texts are subsequently used to expand the query together with LLM-knowledge empowered expansions, improving the relevance prediction between the query and the target documents. Extensive experiments reveal that CSQE exhibits strong performance without necessitating any training, especially with queries for which LLMs lack knowledge.Comment: EACL 2024 (Short

A Global-Relationship Dissimilarity Measure for the k

The k-modes clustering algorithm has been widely used to cluster categorical data. In this paper, we firstly analyzed the k-modes algorithm and its dissimilarity measure. Based on this, we then proposed a novel dissimilarity measure, which is named as GRD. GRD considers not only the relationships between the object and all cluster modes but also the differences of different attributes. Finally the experiments were made on four real data sets from UCI. And the corresponding results show that GRD achieves better performance than two existing dissimilarity measures used in k-modes and Cao’s algorithms

Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network

High-resolution (HR) magnetic resonance images (MRI) provide detailed anatomical information important for clinical application and quantitative image analysis. However, HR MRI conventionally comes at the cost of longer scan time, smaller spatial coverage, and lower signal-to-noise ratio (SNR). Recent studies have shown that single image super-resolution (SISR), a technique to recover HR details from one single low-resolution (LR) input image, could provide high-quality image details with the help of advanced deep convolutional neural networks (CNN). However, deep neural networks consume memory heavily and run slowly, especially in 3D settings. In this paper, we propose a novel 3D neural network design, namely a multi-level densely connected super-resolution network (mDCSRN) with generative adversarial network (GAN)-guided training. The mDCSRN quickly trains and inferences and the GAN promotes realistic output hardly distinguishable from original HR images. Our results from experiments on a dataset with 1,113 subjects show that our new architecture beats other popular deep learning methods in recovering 4x resolution-downgraded im-ages and runs 6x faster.Comment: 10 pages, 2 figures, 2 tables. MICCAI 201

A Novel Aptamer LL4A Specifically Targets Vemurafenib-Resistant Melanoma through Binding to the CD63 Protein.

Melanoma is a highly aggressive tumor with a poor prognosis, and half of all melanoma patients harbor BRAF mutations. A BRAF inhibitor, vemurafenib (PLX4032), has been approved by the US Food and Drug Administration (FDA) and European Medicines Agency (EMA) to treat advanced melanoma patients with BRAFV600E mutation. However, the efficacy of vemurafenib is impeded by adaptive resistance in almost all patients. In this study, using a cell-based SELEX (systematic evolution of ligands by exponential enrichment) strategy, we obtained a DNA aptamer (named LL4) with high affinity and specificity against vemurafenib-resistant melanoma cells. Optimized truncated form (LL4A) specifically binds to vemurafenib-resistant melanoma cells with dissociation constants in the nanomolar range and with excellent stability and low toxicity. Meanwhile, fluorescence imaging confirmed that LL4A significantly accumulated in tumors formed by vemurafenib-resistant melanoma cells, but not in control tumors formed by their corresponding parental cells in vivo. Further, a transmembrane protein CD63 was identified as the binding target of aptamer LL4A using a pull-down assay combined with the liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. CD63 formed a supramolecular complex with TIMP1 and β1-integrin, activated the nuclear factor кB (NF-кB) and mitogen-activated protein kinase (MAPK) signaling pathways, and contributed to vemurafenib resistance. Potentially, the aptamer LL4A may be used diagnostically and therapeutically in humans to treat targeted vemurafenib-resistant melanoma