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  1. Small RNAs (sRNAs) are short noncoding RNAs of ~50-200 nucleotides believed to primarily function in regulating crucial activities in bacteria during periods of cellular stress. This study examined the relevance of specific sRNAs on biofilm formation in nutrient starved Salmonella enterica serovar Typhimurium. Eight unique sRNAs were selected for deletion primarily based on their genomic location and/or putative targets. Quantitative and qualitative analyses confirm one of these, sRNA1186573, is required for efficient biofilm formation in S. enterica further highlighting the significance of sRNAs during Salmonella stress response. 
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    Free, publicly-accessible full text available April 21, 2024
  2. The main objective of authentic learning is to offer students an exciting and stimulating educational setting that provides practical experiences in tackling real-world security issues. Each educational theme is composed of pre-lab, lab, and post-lab activities. Through the application of authentic learning, we create and produce portable lab equipment for AI Security and Privacy on Google CoLab. This enables students to access and practice these hands-on labs conveniently and without the need for time-consuming installations and configurations. As a result, students can concentrate more on learning concepts and gain more experience in hands-on problem-solving abilities. 
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  3. The software supply chain (SSC) attack has become one of the crucial issues that are being increased rapidly with the advancement of the software development domain. In general, SSC attacks execute during the software development processes lead to vulnerabilities in software products targeting downstream customers and even involved stakeholders. Machine Learning approaches are proven in detecting and preventing software security vulnerabilities. Besides, emerging quantum machine learning can be promising in addressing SSC attacks. Considering the distinction between traditional and quantum machine learning, performance could be varies based on the proportions of the experimenting dataset. In this paper, we conduct a comparative analysis between quantum neural networks (QNN) and conventional neural networks (NN) with a software supply chain attack dataset known as ClaMP. Our goal is to distinguish the performance between QNN and NN and to conduct the experiment, we develop two different models for QNN and NN by utilizing Pennylane for quantum and TensorFlow and Keras for traditional respectively. We evaluated the performance of both models with different proportions of the ClaMP dataset to identify the f1 score, recall, precision, and accuracy. We also measure the execution time to check the efficiency of both models. The demonstration result indicates that execution time for QNN is slower than NN with a higher percentage of datasets. Due to recent advancements in QNN, a large level of experiments shall be carried out to understand both models accurately in our future research. 
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  4. Summary Lay Description

    Asphalt binder, or bitumen, is the glue that holds aggregate particles together to form a road surface. It is derived from the heavy residue that remains after distilling gasoline, diesel and other lighter products out of crude oil. Nevertheless, bitumen varies widely in composition and mechanical properties. To avoid expensive road failures, bitumen must be processed after distillation so that its mechanical properties satisfy diverse climate and load requirements. International standards now guide these mechanical properties, but yield varying long‐term performance as local source composition and preparation methods vary.In situdiagnostic methods that can predict bitumen performance independently of processing history are therefore needed. The present work focuses on one promising diagnostic candidate: microscopic observation of internal bitumen structure. Past bitumen microscopy has revealed microstructures of widely varying composition, size, shape and density. A challenge is distinguishing bulk microstructures, which directly influence a binder's mechanical properties, from surface microstructures, which often dominate optical microscopy because of bitumen's opacity and scanning‐probe microscopy because of its inherent surface specificity. In previously published work, we used infrared microscopy to enhance visibility of bulk microstructure. Here, as a foil to this work, we use visible‐wavelength microscopy together with atomic‐force microscopy (AFM) specifically to isolatesurfacemicrostructure, to understand its distinct origin and morphology, and to demonstrate its unique sensitivity to surface alterations. To this end, optical microscopy complements AFM by enabling us to observe surface microstructures form at temperatures (50°C–70°C) at which bitumen's fluidity prevents AFM, and to observe surface microstructure beneath transparent, but chemically inert, liquid (glycerol) and solid (glass) overlayers, which alter surface tension compared to free surfaces. From this study, we learned, first, that, as bitumen cools, distinctly wrinkled surface microstructures form at the same temperature at which independent calorimetric studies showed crystallization in bitumen, causing it to release latent heat of crystallization. This shows that surface microstructures are likely precipitates of the crystallizable component(s). Second, a glycerol overlayer on the cooling bitumen results in smaller, less wrinkled, sparser microstructures, whereas a glass overlayer suppresses them altogether. In contrast, underlying smaller bulk microstructures are unaffected. This shows that surface tension is the driving force behind formation and wrinkling of surface precipitates. Taken together, the work advances our ability to diagnose bitumen samples noninvasively by clearly distinguishing surface from bulk microstructure.

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