Validity-Preserving Delta Debugging via Generator

Reducing test inputs that trigger bugs is crucial for efficient debugging. Delta debugging is the most popular approach for this purpose. When test inputs need to conform to certain specifications, existing delta debugging practice encounters a validity problem: it blindly applies reduction rules, producing a large number of invalid test inputs that do not satisfy the required specifications. This overall diminishing effectiveness and efficiency becomes even more pronounced when the specifications extend beyond syntactical structures. Our key insight is that we should leverage input generators, which are aware of these specifications, to generate valid reduced inputs, rather than straightforwardly performing reduction on test inputs. In this paper, we propose a generator-based delta debugging method, namely GReduce, which derives validity-preserving reducers. Specifically, given a generator and its execution, demonstrating how the bug-inducing test input is generated, GReduce searches for other executions on the generator that yield reduced, valid test inputs. To evaluate the effectiveness, efficiency, and versatility of GReduce, we apply GReduce and the state-of-the-art reducer Perses in three domains: graphs, deep learning models, and JavaScript programs. The results of GReduce are 28.5%, 34.6%, 75.6% in size of those from Perses, and GReduce takes 17.5%, 0.6%, 65.4% time taken by Perses

Knockdown of MiR-20a Enhances Sensitivity of Colorectal Cancer Cells to Cisplatin by Increasing ASK1 Expression

Background/Aims: Platinum-based chemotherapy is one of the most important strategies for treatment of colorectal cancer. To improve the therapeutic efficiency, adjuvant drugs were sought to sensitize colorectal cancer cells to platinum-based agents such as cisplatin. As previous research has shown that miRNAs are associated with chemosensitivity, we aimed to alter miRNA regulation in colorectal cancer cells to increase their chemosensitivity. Methods: MTT assays were performed to determine the viability of HT29, SW480, and LoVo cells. Quantitative real time polymerase chain reaction (qRT-PCR) was performed to examine the expression of miR-20a in these cell lines. Regulation of the miR-20a/ASK1 axis was confirmed by western blotting and luciferase reporter assays. After treatment with miR-20a inhibitor (anti-miR-20a) and cisplatin, production of reactive oxygen species (ROS), mitochondrial membrane potential, and apoptosis were measured by flow cytometry. Activation of ASK1, Bcl-xl, JNK, and caspase-9, -7, and -3 was detected by western blotting. Results: miR-20a was overexpressed in colorectal cancer cell lines. Furthermore, knockdown of miR-20a increased the sensitivity of colorectal cancer cells to cisplatin treatment in vitro and in vivo. We demonstrated that the ASK1 gene was the target of miR-20a, and knockdown of miR-20a increased the expression of ASK1 in colorectal cancer cells. As cisplatin treatment induced production of ROS, knockdown of miR-20a enhanced ROS signaling through promoting the phosphorylation of ASK1. Phosphorylation of JNK and the subsequent mitochondrial apoptosis were triggered by the combination of cisplatin and anti-miR-20a. Conclusions: Knockdown of miR-20a enhanced sensitivity of colorectal cancer cells to cisplatin through the ROS/ASK1/JNK pathway

Stress-Induced Epinephrine Enhances Lactate Dehydrogenase A and Promotes Breast Cancer Stem-Like Cells

Chronic stress triggers activation of the sympathetic nervous system and drives malignancy. Using an immunodeficient murine system, we showed that chronic stress–induced epinephrine promoted breast cancer stem-like properties via lactate dehydrogenase A–dependent (LDHA-dependent) metabolic rewiring. Chronic stress–induced epinephrine activated LDHA to generate lactate, and the adjusted pH directed USP28-mediated deubiquitination and stabilization of MYC. The SLUG promoter was then activated by MYC, which promoted development of breast cancer stem-like traits. Using a drug screen that targeted LDHA, we found that a chronic stress–induced cancer stem-like phenotype could be reversed by vitamin C. These findings demonstrated the critical importance of psychological factors in promoting stem-like properties in breast cancer cells. Thus, the LDHA-lowering agent vitamin C can be a potential approach for combating stress-associated breast cancer

Toward the development of smart capabilities for understanding seafloor stretching morphology and biogeographic patterns via DenseNet from high-resolution multibeam bathymetric surveys for underwater vehicles

The increasing use of underwater vehicles facilitates deep-sea exploration at a wide range of depths and spatial scales. In this paper, we make an initial attempt to develop online computing strategies to identify seafloor categories and predict biogeographic patterns with a deep learning-based architecture, DenseNet, integrated with joint morphological cues, with the expectation of potentially developing its embedded smart capacities. We utilized high-resolution multibeam bathymetric measurements derived from MBES and denoted a collection of joint morphological cues to help with semantic mapping and localization. We systematically strengthened dominant feature propagation and promoted feature reuse via DenseNet by applying the channel attention module and spatial pyramid pooling. From our experiment results, the seafloor classification accuracy reached up to 89.87%, 82.01%, and 73.52% on average in terms of PA, MPA, and MIoU metrics, achieving comparable performances with the state-of-the-art deep learning frameworks. We made a preliminary study on potential biogeographic distribution statistics, which allowed us to delicately distinguish the functionality of probable submarine benthic habitats. This study demonstrates the premise of using underwater vehicles through unbiased means or pre-programmed path planning to quantify and estimate seafloor categories and the exhibited fine-scale biogeographic patterns

Challenges in QCD matter physics - The Compressed Baryonic Matter experiment at FAIR

Substantial experimental and theoretical efforts worldwide are devoted to explore the phase diagram of strongly interacting matter. At LHC and top RHIC energies, QCD matter is studied at very high temperatures and nearly vanishing net-baryon densities. There is evidence that a Quark-Gluon-Plasma (QGP) was created at experiments at RHIC and LHC. The transition from the QGP back to the hadron gas is found to be a smooth cross over. For larger net-baryon densities and lower temperatures, it is expected that the QCD phase diagram exhibits a rich structure, such as a first-order phase transition between hadronic and partonic matter which terminates in a critical point, or exotic phases like quarkyonic matter. The discovery of these landmarks would be a breakthrough in our understanding of the strong interaction and is therefore in the focus of various high-energy heavy-ion research programs. The Compressed Baryonic Matter (CBM) experiment at FAIR will play a unique role in the exploration of the QCD phase diagram in the region of high net-baryon densities, because it is designed to run at unprecedented interaction rates. High-rate operation is the key prerequisite for high-precision measurements of multi-differential observables and of rare diagnostic probes which are sensitive to the dense phase of the nuclear fireball. The goal of the CBM experiment at SIS100 (sqrt(s_NN) = 2.7 - 4.9 GeV) is to discover fundamental properties of QCD matter: the phase structure at large baryon-chemical potentials (mu_B > 500 MeV), effects of chiral symmetry, and the equation-of-state at high density as it is expected to occur in the core of neutron stars. In this article, we review the motivation for and the physics programme of CBM, including activities before the start of data taking in 2022, in the context of the worldwide efforts to explore high-density QCD matter.Comment: 15 pages, 11 figures. Published in European Physical Journal

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Existence of Mild Solutions to Delay Diffusion Equations with Hilfer Fractional Derivative

Because of the prevalent time-delay characteristics in real-world phenomena, this paper investigates the existence of mild solutions for diffusion equations with time delays and the Hilfer fractional derivative. This derivative extends the traditional Caputo and Riemann–Liouville fractional derivatives, offering broader practical applications. Initially, we constructed Banach spaces required to handle the time-delay terms. To address the challenge of the unbounded nature of the solution operator at the initial moment, we developed an equivalent continuous operator. Subsequently, within the contexts of both compact and non-compact analytic semigroups, we explored the existence and uniqueness of mild solutions, considering various growth conditions of nonlinear terms. Finally, we presented an example to illustrate our main conclusions