The Effect of Psychological Resilience on Cognitive Decline in Community-Dwelling Older Adults: The Korean Frailty and Aging Cohort Study.

Background: Chronic stress is associated with an increased risk of cognitive impairment and Alzheimer's disease. This study aimed to assess whether better coping with stress, as assessed using the Brief Resilience Scale (BRS), is associated with slower cognitive decline in community-dwelling older adults. Methods: This study used 2018/2019 data and 2-year follow-up data from the Korean Frailty and Aging Cohort Study. Of the 3,014 total participants, we included 1,826 participants (mean age, 77.6±3.7 years, 51.9% female) who completed BRS and Korean version of the Consortium to Establish a Registry for Alzheimer's Disease Assessment Battery and the Korean version of the Frontal Assessment Battery (FAB). Results: Higher BRS score at baseline was associated with a lesser decline in the Mini-Mental State Examination score over 2 years after adjusting for age, sex, years of education, smoking status, hypertension, diabetes, and depression (B, 0.175; 95% confidence interval, 0.025-0.325) for 2 years, which represents global cognitive function. Other cognitive function measurements (Word List Memory, Word List Recall, Word List Recognition, Digit Span, Trail Making Test-A, and FAB) did not change significantly with the BRS score at baseline. Conclusion: These findings suggest that better stress-coping ability, meaning faster termination of the stress response, may limit the decline in cognitive function.

Enhancing LOD determination in gas chromatography: Validating the Hubaux-Vos method for gas concentration measurement.

The limit of detection (LOD) is a crucial measure in analytical methods, representing the smallest amount of a substance that can be distinguished from background noise. In the realm of gas chromatography (GC), however, determining LOD can be quite subjective, leading to significant variability among researchers. In this study, we validate the Hubaux-Vos method, an International Standards Organization(ISO)-approved approach for determining LOD in gas concentration measurements, using a GC equipped with a discharge ionization detector (DID) and a dynamic dilution system. We employ a gas mixture certified reference material (CRM) of CO, CH4, and CO2 at various concentrations to generate calibration curves for each gas. Subsequently, we estimate the LODs for each gas using the Hubaux-Vos method. Surprisingly, our findings indicate a notable difference between the LODs calculated using the Hubaux-Vos method and those confirmed through experiments. This highlights the importance of critically examining the theoretical foundations of LOD determination. We strongly recommend researchers to scrutinize the principles guiding LOD determination. The method proposed in this study offers an effective way to rigorously validate theoretical approaches for estimating LODs in gas concentration measurements using GC.

PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks.

Aside from graph neural networks (GNNs) attracting significant attention as a powerful framework revolutionizing graph representation learning, there has been an increasing demand for explaining GNN models. Although various explanation methods for GNNs have been developed, most studies have focused on instance-level explanations, which produce explanations tailored to a given graph instance. In our study, we propose Prototype-bAsed GNN-Explainer ([Formula: see text]), a novel model-level GNN explanation method that explains what the underlying GNN model has learned for graph classification by discovering human-interpretable prototype graphs. Our method produces explanations for a given class, thus being capable of offering more concise and comprehensive explanations than those of instance-level explanations. First, [Formula: see text] selects embeddings of class-discriminative input graphs on the graph-level embedding space after clustering them. Then, [Formula: see text] discovers a common subgraph pattern by iteratively searching for high matching node tuples using node-level embeddings via a prototype scoring function, thereby yielding a prototype graph as our explanation. Using six graph classification datasets, we demonstrate that [Formula: see text] qualitatively and quantitatively outperforms the state-of-the-art model-level explanation method. We also carry out ystematic experimental studies by demonstrating the relationship between [Formula: see text] and instance-level explanation methods, the robustness of [Formula: see text] to input data scarce environments, and the computational efficiency of the proposed prototype scoring function in [Formula: see text].

Zero-shot test time adaptation via knowledge distillation for personalized speech denoising and dereverberation.

A personalization framework to adapt compact models to test time environments and improve their speech enhancement (SE) performance in noisy and reverberant conditions is proposed. The use-cases are when the end-user device encounters only one or a few speakers and noise types that tend to reoccur in the specific acoustic environment. Hence, a small personalized model that is sufficient to handle this focused subset of the original universal SE problem is postulated. The study addresses a major data shortage issue: although the goal is to learn from a specific user's speech signals and the test time environment, the target clean speech is unavailable for model training due to privacy-related concerns and technical difficulty of recording noise and reverberation-free voice signals. The proposed zero-shot personalization method uses no clean speech target. Instead, it employs the knowledge distillation framework, where the more advanced denoising results from an overly large teacher work as pseudo targets to train a small student model. Evaluation on various test time conditions suggests that the proposed personalization approach can significantly enhance the compact student model's test time performance. Personalized models outperform larger non-personalized baseline models, demonstrating that personalization achieves model compression with no loss in dereverberation and denoising performance.

Development of TiO2-CaCO3 Based Composites as an Affordable Building Material for the Photocatalytic Abatement of Hazardous NOx from the Environment.

This study explores the depollution activity of a photocatalytic cementitious composite comprising various compositions of n-TiO2 and CaCO3. The photocatalytic activity of the CaCO3-TiO2 composite material is assessed for the aqueous photodegradation efficiency of MB dye solution and NOx under UV light exposure. The catalyst CaCO3-TiO2 exhibits the importance of an optimal balance between CaCO3 and n-TiO2 for the highest NOx removal of 60% and MB dye removal of 74.6%. The observed trends in the photodegradation of NOx removal efficiencies suggest a complex interplay between CaCO3 and TiO2 content in the CaCO3-n-TiO2 composite catalysts. This pollutant removal efficiency is attributed to the synergistic effect between CaCO3 and n-TiO2, where a higher percentage of n-TiO2 appeared to enhance the photocatalytic activity. It is recommended that CaCO3-TiO2 photocatalysts are effectiveness in water and air purification, as well as for being cost-effective construction materials.

Microscopic theory of colour in lutetium hydride.

Nitrogen-doped lutetium hydride has recently been proposed as a near-ambient-conditions superconductor. Interestingly, the sample transforms from blue to pink to red as a function of pressure, but only the pink phase is claimed to be superconducting. Subsequent experimental studies have failed to reproduce the superconductivity, but have observed pressure-driven colour changes including blue, pink, red, violet, and orange. However, discrepancies exist among these experiments regarding the sequence and pressure at which these colour changes occur. Given the claimed relationship between colour and superconductivity, understanding colour changes in nitrogen-doped lutetium hydride may hold the key to clarifying the possible superconductivity in this compound. Here, we present a full microscopic theory of colour in lutetium hydride, revealing that hydrogen-deficient LuH2 is the only phase which exhibits colour changes under pressure consistent with experimental reports, with a sequence blue-violet-pink-red-orange. The concentration of hydrogen vacancies controls the precise sequence and pressure of colour changes, rationalising seemingly contradictory experiments. Nitrogen doping also modifies the colour of LuH2 but it plays a secondary role compared to hydrogen vacancies. Therefore, we propose hydrogen-deficient LuH2 as the key phase for exploring the superconductivity claim in the lutetium-hydrogen system. Finally, we find no phonon-mediated superconductivity near room temperature in the pink phase.

Utilization of 2D materials in aqueous zinc ion batteries for safe energy storage devices.

Aqueous rechargeable battery has been an intense topic of research recently due to the significant safety issues of conventional Li-ion batteries (LIBs). Amongst the various candidates of aqueous batteries, aqueous zinc ion batteries (AZIBs) hold great promise as a next generation safe energy storage device due to its low cost, abundance in nature, low toxicity, environmental friendliness, low redox potential, and high theoretical capacity. Yet, the promise has not been realized due to their limitations, such as lower capacity compared to traditional LIB, dendrite growth, detrimental degradation of electrode materials structure as ions intercalate/de-intercalate, and gas evolution/corrosion at the electrodes, which remains a significant challenge. To address the challenges, various 2D materials with different physiochemical characteristics have been utilized. This review explores fundamental physiochemical characteristics of widely used 2D materials in AZIBs, including graphene, MoS2, MXenes, 2D metal organic framework, 2D covalent organic framework, and 2D transition metal oxides, and how their characteristics have been utilized or modified to address the challenges in AZIBs. The review also provides insights and perspectives on how 2D materials can help to realize the full potential of AZIBs for next-generation safe and reliable energy storage devices.

Effectiveness of silver nitrate application on plant growth and bioactive compounds in Agastache rugosa (Fisch. & C.A.Mey.) kuntze.

The objective of this study was to determine the optimal dose of silver nitrate (AgNO3) for plant growth and to increase the main bioactive compounds in A. rugosa cultivated in a hydroponic system. The application of soaked diniconazole (120 µmol mol-1) to all plants at 7 days after transplanting (DAT) for dwarfing plant height, optimizing cultivation space in the plant factory. Subsequently, plants were soaked with 50, 100, 200, and 400 µmol mol-1 AgNO3 for 10 min at 25 DAT and harvested at 39 DAT. The results indicated that 200 and 400 µmol mol-1 treatments tended to severely decrease plant growth parameters compared to treatments with lower concentrations. The net photosynthetic rate was significantly reduced by the 200 and 400 µmol mol-1 treatments compared to treatments with other concentrations. The 400 µmol mol-1 treatment led to the lowest concentrations of chlorophyll a, chlorophyll a/b, total carotenoid, chlorophyll b, and the total chlorophyll. However, 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity was considerably increased in 50, 100, 200, and 400 µmol mol-1 compared to that of the control plants. A higher rosmarinic acid (RA) concentration in the whole plant was noticed with the 400 µmol mol-1 treatment compared with that of the untreated plants. The 100 µmol mol-1 treatment exhibited the highest concentration and content of tilianin in the whole plant. Concentration of acacetin 1 significantly increased in the whole plant with 100 and 200 µmol mol-1 treatments compared with that of the untreated plants. Concentrations of acacetin 2 and 3 in the whole plant were the highest with 100 and 200 µmol mol-1 treatments, respectively. The results demonstrated that 100 µmol mol-1 treatments can be used to increase bioactive compounds without severely limiting the plant growth and reducing chlorophyll concentrations of A. rugosa. Implementing this optimal dose can enable growers and researchers to cultivate A. rugosa more efficiently, enhancing bioactive compound content and overall plant performance, thus harnessing the potential health benefits of this valuable plant species.

Thermally Stable Ceramic-Salt Electrolytes for Li Metal Batteries Produced from Cold Sintering Using DMF/Water Mixture Solvents.

The cold sintering process (CSP) for synthesizing oxide-based electrolytes, which uses water transient solvents and uniaxial pressure, is a promising alternative to the conventional high temperature sintering process due to its low temperature (<200 °C) and short processing time (<2 h). However, the formation of amorphous secondary phases in the intergranular regions, which results in poor ionic conductivity (σ), remains a challenge. In this study, we introduced high-boiling solvents of dimethylformamide (DMF, b.p.: 153 °C) and dimethyl sulfoxide (DMSO, b.p.: 189 °C) as transient solvents to develop composite electrolytes of Li1.5Al0.5Ge1.5(PO4)3 (LAGP) with bis(trifluoromethane)sulfonimide lithium salt (LiTFSI). Our results show that composite electrolytes processed with the DMF/water mixture (CSP LAGP-LiTFSI DMF/H2O) yield a high σ of 10-4 S cm-1 at room temperature and high relative densities of >87%. Furthermore, the composite electrolytes exhibit good thermal stability; the σ maintains its initial value after heat treatment. In contrast, the composite electrolytes processed with the DMSO/water mixture and water alone show thermal degradation. The CSP LAGP-LiTFSI DMF/H2O composite electrolytes exhibit long-term stability, showing no signs of short circuiting after 350 h at 0.1 mAh cm-2 in Li symmetric cells. Our work highlights the importance of selecting appropriate transient solvents for producing efficient and stable composite electrolytes using CSP.

Facile and Simple Post Treatment Ball Milling Strategy for the Production of Low-Cost TiO2 Composites with Enhanced Photocatalytic Performance and Applicability to Construction Materials.

A facile and cost-effective approach assisted by ball milling (BM) of commercial titanium dioxide (TiO2), has been utilized to develop cheaper and efficient construction materials. At least three of the commercial and cheaper TiO2 samples (BA01-01, BA01-01+ and R996, designated as A1, A4 and R1, respectively) were selected and subjected to BM treatment to enhance their photocatalytic efficiencies, if possible. It was noted, that the samples A1, A4 and R1 were typical composites of TiO2 and calcium carbonate (CaCO3) and contained varying proportions of anatase, and rutile phases of TiO2 and CaCO3. Two of the highly efficient commercial TiO2 samples, Degussa P25 (simply designated as P25) and ST01 (Ishihara Ind.) were selected for making benchmark comparisons of photocatalytic efficiencies. The BM treated TiO2 samples (designated as TiO2-BM with respect to A1, A4 and R1) were evaluated for photocatalytic efficiencies both in both aqueous (methylene blue (MB)) and gaseous (NOx) photodegradation reactions. Based on detailed comparative investigations, it was observed that A1-BM photocatalyst exhibited superior photocatalytic performances over A4-BM and R1-BM, towards both MB and NOx photodegradation reactions. The difference of NOx photodegradation efficiency between the mortar mixed with A1-BM and that mixed with ST01, and P-25 at 15% were 16.6%, and 32.4%, respectively. Even though the mortar mixed with A1-BM at 15% composition exhibited a slightly lower NOx photodegradation efficiency as compared to mortar mixed with the expensive ST01 and P-25 photocatalysts, the present work promises an economic application in the eco-friendly construction materials for air purification considering the far lower cost of A1. The reasons for the superior performance of A1-BM were deduced through characterization of optical properties, surface characteristics, phase composition, morphology, microstructure and particle size distribution between pristine and BM treated A1 using characterization techniques such as diffuse reflectance spectroscopy, X-ray photoelectron spectroscopy, X-ray diffraction analysis, field emission scanning electron microscopy and particle size analysis.

Feasibility of artificial intelligence assisted quantitative muscle ultrasound in carpal tunnel syndrome.

BACKGROUND: In case of focal neuropathy, the muscle fibers innervated by the corresponding nerves are replaced with fat or fibrous tissue due to denervation, which results in increased echo intensity (EI) on ultrasonography. EI analysis can be conducted quantitatively using gray scale analysis. Mean value of pixel brightness of muscle image defined as EI. However, the accuracy achieved by using this parameter alone to differentiate between normal and abnormal muscles is limited. Recently, attempts have been made to increase the accuracy using artificial intelligence (AI) in the analysis of muscle ultrasound images. CTS is the most common disease among focal neuropathy. In this study, we aimed to verify the utility of AI assisted quantitative analysis of muscle ultrasound in CTS. METHODS: This is retrospective study that used data from adult who underwent ultrasonographic examination of hand muscles. The patient with CTS confirmed by electromyography and subjects without CTS were included. Ultrasound images of the unaffected hands of patients or subjects without CTS were used as controls. Ultrasonography was performed by one physician in same sonographic settings. Both conventional quantitative grayscale analysis and machine learning (ML) analysis were performed for comparison. RESULTS: A total of 47 hands with CTS and 27 control hands were analyzed. On conventional quantitative analysis, mean EI ratio (i.e. mean thenar EI/mean hypothenar EI ratio) were significantly higher in the patient group than in the control group, and the AUC was 0.76 in ROC analysis. In the analysis using machine learning, the AUC was the highest for the linear support vector classifier (AUC = 0.86). When recursive feature elimination was applied to the classifier, the AUC value improved to 0.89. CONCLUSION: This study showed a significant increase in diagnostic accuracy when AI was used for quantitative analysis of muscle ultrasonography. If an analysis protocol using machine learning can be established and mounted on an ultrasound machine, a noninvasive and non-time-consuming muscle ultrasound examination can be conducted as an ancillary tool for diagnosis.

Flexural Behavior Characteristics of Steel Tubes Filled with SFRCCs Incorporating Recycled Materials.

This study deals with the effect of fly ash and recycled sand on the flexural behavior of SFRCCs (steel fiber-reinforced cementitious composites)-filled steel tubes. As a result of the compressive test, the elastic modulus was reduced by the addition of micro steel fiber, and the fly ash and recycled sand replacement decreased the elastic modulus and increased the Poisson's ratio. As a result of the bending and direct tensile tests, strength enhancement by the incorporation of micro steel fibers was observed, and a smooth descending curve was confirmed after initial cracking. As a result of the flexural test on the FRCC-filled steel tube, the peak load of all specimens was similar, and the applicability of the equation presented by AISC was high. The deformation capacity of the steel tube filled with SFRCCs was slightly improved. As the elastic modulus of the FRCC material lowered and the Poisson's ratio increased, the denting depth of the test specimen deepened. This is believed to be due to the large deformation of the cementitious composite material under local pressure due to the low elastic modulus. From the results of the deformation capacities of the FRCC-filled steel tubes, it was confirmed that the contribution of indentation to the energy dissipation capacity of steel tubes filled with SFRCCs was high. From the comparison of the strain values of the steel tubes, in the steel tube filled with SFRCC incorporating recycled materials, the damage was properly distributed between the loading point and both ends through crack dispersion, and consequently, rapid curvature changes did not occur at both ends.

Monolayer Kagome metals AV3Sb5.

Recently, layered kagome metals AV3Sb5 (A = K, Rb, and Cs) have emerged as a fertile platform for exploring frustrated geometry, correlations, and topology. Here, using first-principles and mean-field calculations, we demonstrate that AV3Sb5 can crystallize in a mono-layered form, revealing a range of properties that render the system unique. Most importantly, the two-dimensional monolayer preserves intrinsically different symmetries from the three-dimensional layered bulk, enforced by stoichiometry. Consequently, the van Hove singularities, logarithmic divergences of the electronic density of states, are enriched, leading to a variety of competing instabilities such as doublets of charge density waves and s- and d-wave superconductivity. We show that the competition between orders can be fine-tuned in the monolayer via electron-filling of the van Hove singularities. Thus, our results suggest the monolayer kagome metal AV3Sb5 as a promising platform for designer quantum phases.

Correlating multimode strain and electrode configurations for high-performance gradient-index phononic crystal-based piezoelectric energy harvesting.

A gradient-index phononic crystal (GRIN PnC) capable of manipulating wave propagation can serve as an excellent input wave energy focusing platform for amplifying energy harvesting power generation. However, despite its remarkable focusing capability, the finite wavelength of the propagating elastic waves in the focal area causes voltage cancellation inside a piezoelectric element under multimode strains having opposite directions; this limits the capacity of the GRIN PnC-based energy harvesting system. This study demonstrates a rational electrode configuration for a piezoelectric energy harvesting (PEH) device that can maximize the performance of a given GRIN PnC platform. The multimode strain analysis experimentally performed on the PEHs distributed over the focusing area confirms that the patterned electrode PEH configuration is the most effective in alleviating strain and voltage cancellation while efficiently transferring the focused elastic wave energy. Furthermore, a proper combination of electrical connections between the patterned electrodes substantially increases the piezoelectric potential across the ceramic by maximizing the strain difference. The simultaneous tailoring of the piezoelectric ceramic composition and the electrode configuration leads to a maximum power generation of 7.06 mW even under off-resonance conditions, the largest ever reported in elastic wave energy harvesting.

Amplifying the dielectric constant of shellac by incorporating natural clays for organic field effect transistors (OFETs).

We demonstrate in this work the practical use of uniform mixtures of a bioresin shellac and four natural clays, i.e. montmorillonite, sepiolite, halloysite and vermiculate as dielectrics in organic field effect transistors (OFETs). We present a thorough characterization of their processability and film forming characteristic, surface characterization, elaborate dielectric investigation and the fabrication of field effect transistors with two classic organic semiconductors, i.e. pentacene and fullerene C60. We show that low operating voltage of approximately 4 V is possible for all the OFETs using several combinations of clays and shellac. The capacitance measurements show an improvement of the dielectric constant of shellac by a factor of 2, to values in excess of 7 in the uniform mixtures of sepiolite and montmorillonite with this bioresin.

Leveraging Deep Learning for Practical DoA Estimation: Experiments with Real Data Collected via USRP.

This paper presents an experimental validation of deep learning-based direction-of-arrival (DoA) estimation by using realistic data collected via universal software radio peripheral (USRP). Deep neural network (DNN) and convolutional neural network (CNN) structures are designed to estimate the DoA. Two types of data are used for training networks. One is the data synthesized by the signal model, and the other is the data collected by USRP. Here, the signal model considers both mutual coupling and multipath signals. Experimental results show that the estimation performance is most accurate when training DNN and CNN with the collected data. Furthermore, the estimation tends to be poor in the indoor environment, which suffers from the strong non-line-of-sight (NLoS) signals.

Simple Synthesis and Characterization of Shell-Thickness-Controlled Ni/Ni3C Core-Shell Nanoparticles.

Ni/Ni3C core-shell nanoparticles with an average diameter of approximately 120 nm were carburized via a chemical solution method using triethylene glycol. It was found that over time, the nanoparticles were covered with a thin Ni3C shell measuring approximately 1-4 nm, and each Ni core was composed of poly grains. The saturation magnetization of the core-shell nanopowders decreased in proportion to the amount of Ni3C. The synthesis mechanism of the Ni/Ni3C core-shell nanoparticles was proposed through X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and transmission electron microscopy (TEM) analyses.

Scale-attentional U-Net for the segmentation of the median nerve in ultrasound images.

PURPOSE: The aim of this study was to develop a neural network that accurately and effectively segments the median nerve in ultrasound (US) images. METHODS: In total, 1,305 images of the median nerve of 123 normal subjects were used to train and evaluate the model. Four datasets from two measurement regions (wrist and forearm) of the nerve and two US machines were used. The neural network was designed for high accuracy by combining information at multiple scales, as well as for high efficiency to prevent overfitting. The model was designed in two parts (cascaded and factorized convolutions), followed by selfattention over scale and channel features. The precision, recall, dice similarity coefficient (DSC), and Hausdorff distance (HD) were used as performance metrics. The area under the receiver operating characteristic curve (AUC) was also assessed. RESULTS: In the wrist datasets, the proposed network achieved 92.7% and 90.3% precision, 92.4% and 89.8% recall, DSCs of 92.3% and 89.7%, HDs of 5.158 and 4.966, and AUCs of 0.9755 and 0.9399 on two machines. In the forearm datasets, 79.3% and 87.8% precision, 76.0% and 85.0% recall, DSCs of 76.1% and 85.8%, HDs of 5.206 and 4.527, and AUCs of 0.8846 and 0.9150 were achieved. In all datasets, the model developed herein achieved better performance in terms of DSC than previous U-Net-based systems. CONCLUSION: The proposed neural network yields accurate segmentation results to assist clinicians in identifying the median nerve.

Tapered Waist Tensile Specimens for Evaluating Butt Fusion Joints of Polyethylene Pipes-Part 1: Development.

The structural integrity of butt fusion (BF) joints in thermoplastic pressure piping systems is critical to their long-term safe use. The tapered waist tensile (TWT) specimen was developed to alleviate issues associated with ISO 13953 waisted tensile (WT) specimen for evaluating BF joints. Experimental and finite element analyses were performed to obtain optimum TWT specimen designs for the BF joint destructive test. For TWT specimens, depending on the pipe size, the displacement at onset necking was reduced by 30~100%, and the tested BF area increased by 60~80% compared to the WT specimen. In addition, the transverse specimen deflection was lower thus providing better experimental stability. Furthermore, it showed the same BF displacement at the maximum force local to the BF bead, indicating that the tapered waist geometry provides equivalent deformation constraint and BF failure mode designed for the BF joint in the WT specimens. Therefore, TWT specimens offer simplicity, adaptability, stability, and accuracy in specimen preparation, testing, and analysis compared to WT specimens.

Regulator of RNase E activity modulates the pathogenicity of Salmonella Typhimurium.

RNase E-mediated RNA processing and degradation are involved in bacterial adaptation to environmental changes. The RraA regulatory protein, which is highly conserved in γ-proteobacteria, differentially modulates RNase E activity. Recent studies have revealed the association of Salmonella enterica serovar Typhimurium RNase E (STRNase E) with bacterial pathogenicity; however, the molecular mechanisms are unknown. Here, we show that the expression levels of STRraA, a protein regulator of STRNase E activity, affect S. Typhimurium pathogenicity. RNA-sequencing and RT-PCR analyses indicated positive effects of STRraA levels on the abundance of mRNA species from class II flagellar operons. Primer extension analysis further identified STRraA-regulated STRNase E cleavage in the 5' untranslated region of fliDST mRNA. The cleavage affected the stability of this polycistronic mRNA, suggesting that STRraA protects fliDST mRNA from STRNase E cleavage, leading to enhanced flagellar assembly. Accordingly, STRraA positively regulated flagellar assembly and motility. In addition, STrraA-deleted cells showed decreased invasion ability and cytotoxicity in infection of human cervical epithelial carcinoma cells and reduced mortality in a mouse infection model compared to wild-type cells. These results support an active role of STRraA in RNase E-mediated modulation of pathogenesis in S. Typhimurium.