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Salmonella enterica is frequently implicated in foodborne disease outbreaks associated with fresh-cut fruits. In the U.S., more than one third of fruit-related outbreaks have been linked to two S. enterica serotypes Newport and Typhimurium. Approximately 80% of fruit-related human salmonellosis cases were associated with tomatoes, cantaloupes and cucumbers. In this study, we investigated the population dynamics of S. Newport and S. Typhimurium on fresh-cut tomato, cantaloupe, cucumber and apple under short-term storage conditions. We further compared the transcriptomic profiles of a S. Newport strain on fresh-cut tomato and cantaloupe using high-throughput RNA-seq. We demonstrated that both S. enterica Newport and Typhimurium survived well on various fresh-cut fruit items under refrigeration storage conditions, independent of inoculation levels. However, S. enterica displayed variable survival behaviors on different types of fruits. For example, at 7 d storage, the population of S. enterica reduced less than 0.2 log (p &gt; 0.05) on fresh-cut tomato and cantaloupe, in contrast to ~0.5 log (p less then 0.05) on cucumber and apple. RNA-seq analysis suggested that S. enterica mediates its survival on fresh-cut fruits through differentially regulating genes involved in specific carbon utilization and metabolic pathways. Several known bacterial virulence factors (e.g., pag gene) were found to be differentially regulated on fresh-cut tomato and cantaloupe, suggesting a link between the events of food contamination and subsequent human infection. Findings from this study contribute to a better understanding of S. enterica survival mechanisms on fresh-cut produce.In the UK and Northern Europe, ripening oats can become contaminated with T-2 and HT-2 mycotoxins, produced mainly by Fusarium langsethiae. There are indicative levels related to the maximum limits for oat grain for these toxins. The objectives of this study were to examine the effect of interacting conditions of temperature (10-30 °C) and water activity (aw, 0.995-0.90) on (a) lag times prior to growth, (b) growth and (c) T-2 and HT-2 toxins by two strains of F. langsethiae isolated from oats in the UK and compare this with the type strain (Fl201059) which has been genomically sequenced, and (d) develop (and validated with published data) a probabilistic models for impacts of temperature × aw on growth and toxin production. All three strains had an optimum aw range and temperature of 0.995-0.98 and 25 °C for growth. For T-2 + HT-2 production these were 0.995 aw and 20 °C. Overall, the type strain produced higher amounts of T-2 + HT-2 with a HT-2/T-2 ratio of up to 76. Using this study data sets and those from the literature, probabilistic models were developed and validated for growth and T-2 + HT-2 toxin production in relation to temperature × aw conditions. These models, when applied in stored oats, will be beneficial in determining the conditions on the relative level of risk of contamination with these two toxins in the context of the EU indicative maximum levels.Studying the in-vivo mechanical and electrophysiological cochlear responses in several species helps us to have a comprehensive view of the sensitivity and frequency selectivity of the cochlea. Different species might use different mechanisms to achieve the sharp frequency-place map. The outer hair cells (OHC) play an important role in mediating frequency tuning. In the present work, we measured the OHC-generated local cochlear microphonic (LCM) and the motion of different layers in the organ of Corti using optical coherence tomography (OCT) in the first turn of the cochlea in guinea pig. In the best frequency (BF) band, our observations were similar to our previous measurements in gerbil a nonlinear peak in LCM responses and in the basilar membrane (BM) and OHC-region displacements, and higher motion in the OHC region than the BM. Sub-BF the responses in the two species were different. In both species the sub-BF displacement of the BM was linear and LCM was nonlinear. Sub-BF in the OHC-region, nonlinearity was only observed in a subset of healthy guinea pig cochleae while in gerbil, robust nonlinearity was observed in all healthy cochleae. The differences suggest that gerbils and guinea pigs employ different mechanisms for filtering sub-BF OHC activity from BM responses. However, it cannot be ruled out that the differences are due to technical measurement differences across the species.In the face of unrelenting climate change and insufficient mitigation, experts are increasingly considering using geoengineering - carbon dioxide removal and solar radiation management - to manipulate the Earth's climate. So far, most laypeople are unaware of geoengineering, and many are resistant to these technologies when told about them. A growing literature finds that these initial reactions are tied to psychological traits, beliefs, and identities including trust in the actors involved, political and social identities, beliefs about tampering with the natural world, and perceived trade-offs between geoengineering and alternative approaches. Finally, given the lack of existing knowledge of geoengineering, public acceptance is highly susceptible to how these technologies are framed, offering both risks and opportunities for climate communication.Researchers and practitioners have used virtual reality (VR) as a tool to understand attitudes and behaviors around climate change for decades. As VR has become more immersive, mainstream, and commercially available, it has also become a medium for education about climate issues, a way to indirectly expose users to novel stimuli, and a tool to tell stories about antienvironmental activity. This review explicates the relationship between VR and climate change from a psychological perspective and offers recommendations to make virtual experiences engaging, available, and impactful for users. Climate change is perhaps the most urgent global issue of our lifetime with irreversible consequences. It therefore requires innovative experiential approaches to teach its effects and modify attitudes in support of proenvironmental actions.With the recent technological advances, biological datasets, often represented by networks (i.e., graphs) of interacting entities, proliferate with unprecedented complexity and heterogeneity. Although modern network science opens new frontiers of analyzing connectivity patterns in such datasets, we still lack data-driven methods for extracting an integral connectional fingerprint of a multi-view graph population, let alone disentangling the typical from the atypical variations across the population samples. We present the multi-view graph normalizer network (MGN-Net2), a graph neural network based method to normalize and integrate a set of multi-view biological networks into a single connectional template that is centered, representative, and topologically sound. We demonstrate the use of MGN-Net by discovering the connectional fingerprints of healthy and neurologically disordered brain network populations including Alzheimer's disease and Autism spectrum disorder patients. Additionally, by comparing the learned templates of healthy and disordered populations, we show that MGN-Net significantly outperforms conventional network integration methods across extensive experiments in terms of producing the most centered templates, recapitulating unique traits of populations, and preserving the complex topology of biological networks. Our evaluations showed that MGN-Net is powerfully generic and easily adaptable in design to different graph-based problems such as identification of relevant connections, normalization and integration.Multi-Delay single-shot arterial spin labeling (ASL) imaging provides accurate cerebral blood flow (CBF) and, in addition, arterial transit time (ATT) maps but the inherent low SNR can be challenging. Especially standard fitting using non-linear least squares often fails in regions with poor SNR, resulting in noisy estimates of the quantitative maps. State-of-the-art fitting techniques improve the SNR by incorporating prior knowledge in the estimation process which typically leads to spatial blurring. To this end, we propose a new estimation method with a joint spatial total generalized variation regularization on CBF and ATT. This joint regularization approach utilizes shared spatial features across maps to enhance sharpness and simultaneously improves noise suppression in the final estimates. [https://www.selleckchem.com/products/AZD0530.html selleck] The proposed method is evaluated at three levels, first on synthetic phantom data including pathologies, followed by in vivo acquisitions of healthy volunteers, and finally on patient data following an ischemic stroke. The quantitative estimates are compared to two reference methods, non-linear least squares fitting and a state-of-the-art ASL quantification algorithm based on Bayesian inference. The proposed joint regularization approach outperforms the reference implementations, substantially increasing the SNR in CBF and ATT while maintaining sharpness and quantitative accuracy in the estimates.Deep learning techniques hold promise to develop dense topography reconstruction and pose estimation methods for endoscopic videos. However, currently available datasets do not support effective quantitative benchmarking. In this paper, we introduce a comprehensive endoscopic SLAM dataset consisting of 3D point cloud data for six porcine organs, capsule and standard endoscopy recordings, synthetically generated data as well as clinically in use conventional endoscope recording of the phantom colon with computed tomography(CT) scan ground truth. A Panda robotic arm, two commercially available capsule endoscopes, three conventional endoscopes with different camera properties, two high precision 3D scanners, and a CT scanner were employed to collect data from eight ex-vivo porcine gastrointestinal (GI)-tract organs and a silicone colon phantom model. In total, 35 sub-datasets are provided with 6D pose ground truth for the ex-vivo part 18 sub-datasets for colon, 12 sub-datasets for stomach, and 5 sub-datasets formance of Endo-SfMLearner is extensively compared with the state-of-the-art SC-SfMLearner, Monodepth2, and SfMLearner. The codes and the link for the dataset are publicly available at https//github.com/CapsuleEndoscope/EndoSLAM. A video demonstrating the experimental setup and procedure is accessible as Supplementary Video 1.Structural magnetic resonance imaging (MRI) has shown great clinical and practical values in computer-aided brain disorder identification. Multi-site MRI data increase sample size and statistical power, but are susceptible to inter-site heterogeneity caused by different scanners, scanning protocols, and subject cohorts. Multi-site MRI harmonization (MMH) helps alleviate the inter-site difference for subsequent analysis. Some MMH methods performed at imaging level or feature extraction level are concise but lack robustness and flexibility to some extent. Even though several machine/deep learning-based methods have been proposed for MMH, some of them require a portion of labeled data in the to-be-analyzed target domain or ignore the potential contributions of different brain regions to the identification of brain disorders. In this work, we propose an attention-guided deep domain adaptation (AD2A) framework for MMH and apply it to automated brain disorder identification with multi-site MRIs. The proposed framework does not need any category label information of target data, and can also automatically identify discriminative regions in whole-brain MR images.