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In silico and in vitro assessment of antimicrobial activity of Arctium lappa L. leaf and flower essential oil against WHO priority pathogens
The plague of 1720 and migration in Martigues (France) in the 17th and 18th centuries
One-off events such as wars or epidemics can change the structure of populations, by encouraging the mobility or causing the death of certain categories of people. They can also lead to a demographic slump, made up for by the arrival of new migrants. The town of Martigues (Bouches-du-Rhône, France) provides an example of this kind of local population renewal. Between the seventeenth and eighteenth centuries, its population was partially renewed after the plague of 1720. To measure the effect of the plague as a disruptive event, data on the surnames of people born in Martigues before and after the epidemic of 1720 were collected and analysed. After a delicate stage of lemmatization of the surnames, three categories of names were distinguished: those present before 1720 and which disappeared from Martigues after the plague; those absent before 1720 and which appeared afterwards; and surnames that were continually present in Martigues, but whose frequency in terms of the number of births per year could be contrasted between before and after 1720. The surname data for these three categories is compared with the list of names of the victims of the plague, which makes it possible to envisage a possible reason for their disappearance. In addition, the frequencies of surnames in the 18th century in the Bouches-du-Rhône department and in France in the 19th century make it possible to locate the possible destination of people whose surnames disappeared after 1720, as well as the probable origin of the surnames of people arriving after 1720. This study helps us to understand how the impact of an epidemic crisis can affect the evolution of a population on a local scale. The surname method used here indicates that the plague caused an exceptionally large renewal of approximately 50% of the stock of surnames, and thus of the population bearing those names. It also shows that fertility declined significantly among individuals whose surnames were already present among those who died of the plague. Finally, the results demonstrate that population renewal was achieved primarily through immigration, mainly from neighbouring municipalities.
Asundexian for Noncardioembolic Ischemic Stroke
Generative deep learning for foundational video translation in ultrasound
Abstract For deep learning (DL) to realize its potential for medical image interpretation, attention to dataset content is critical. Ultrasound presents a particular challenge because, in addition to many views and structures, it includes several sub-modalities–such as grayscale and color flow doppler (CFD)–that are often imbalanced or confounding in clinical datasets. Image translation could help, but it has not yet succeeded in noisy ultrasound. Here, we develop generative video translation for CFD-to-grayscale ultrasound. We leveraged pixel-wise, adversarial, and perceptual losses to synthesize anatomically faithful, realistic-looking ultrasound. Average SSIM between synthetic and ground-truth videos was 0.91 ± 0.04. Synthetic videos performed indistinguishably in DL classification (F1-score between real and synthetic, 0.93–0.95) and segmentation tasks (average Dice between real and synthetic segmentations, 0.97 ± 0.03). Blinded clinician accuracy in distinguishing real vs. synthetic videos was 54 ± 6%, indicating realism. Although trained only on heart videos, the model worked on ultrasound spanning clinical domains (average SSIM 0.91 ± 0.05), demonstrating foundational abilities. Applying generative translation to real-world CFD imaging recovered over seven percent more data for a clinical DL task. Together, these data expand the utility of retrospective imaging, advance rigor in medical synthetic data evaluation, and augment the dataset design toolbox for medical imaging.
A deep reinforcement based echo state network for network intrusion classification
Network intrusion classification referred to the process of monitoring and analyzing network traffic to identify suspicious activities or attacks. In this work, author proposed a novel approach to classify network intrusion by utilizing deep reinforcement learning (DRL), integrating a reservoir computing approach Echo State Network (ESN). A DRL-based approach improved upon traditional deep learning by adapting dynamically to novel/unknown and evolving attack patterns. Unlike static models, DRL continuously learned optimal strategies through interaction with the environment, allowing for better detection of previously unseen threats in real-time. To address the class imbalance often encountered in network intrusion datasets, we evaluated the performance of several advanced data balancing techniques, including Borderline-SMOTE, SMOTE-ENN, ADYSN, and K-means SMOTE. The findings demonstrated that the K-means-based data balancing method outperformed other techniques, resulting in the most robust performance across various metrics. Author conducted multi-dataset validation on benchmark datasets like NF-BoT-IoT, NF-UNSW-NB15, NF-ToN-IoT, NF-ToN-IoT-v2, NF-CSE-CIC-IDS2018 and NF-UNSW-NB15-v3 to ensure robustness across different network flow data. For adaptive modeling testing, author excluded some attack types from training data and included them in testing data (e.g., DoS, Backdoor attacks were excluded from the training data but included in the testing data (see Table 3)). The proposed approach enhanced the accuracy and reliability of intrusion detection, making it a viable solution for securing modern network infrastructures. The source code of this work is available in this Github repository ( https://github.com/codewithkhurshed/DRLZDNIDS ).
Ivermectin to Control Malaria — A Cluster-Randomized Trial
Superconductivity and electronic structures of nickelate thin film superstructures
Intravenous administration of ex vivo expanded human umbilical cord blood-derived CD34⁺ cells in a preterm hypoxic-ischemic encephalopathy mouse model
Randomized Trial of Adjunctive Prednisolone for Kawasaki Disease
Mixed-maturation fisheries compromise productivity and resilience of Chinook salmon
Nurse Scientists as Trusted Voices in Health Communication
Investigation of the operational and combustion performance of in-service gas cooktops fueled by hydrogen-enriched natural gas
The Devil Is in the Details
The KRT15 and KRT81 complex promotes lenvatinib resistance in thyroid cancer by upregulating DGKB mediated lipid metabolism
Selective Decontamination of the Digestive Tract during Ventilation in the ICU
A phantom mimicking layered biological microenvironments investigated with 7T diffusion weighted MRI
Abstract We developed a physical phantom that mimics laminar biological microstructure with controlled geometric and diffusional properties. The primary goal of this study is to characterize the diffusion properties of the phantom and to assess how laminar microstructural geometry influences diffusion-weighted (DW) magnetic resonance imaging (MRI) signals. The phantom consists of stacked polyethylene foils forming water layers with thicknesses ranging from ~ 1 to 16 μm. Water diffusion within these layers is free in-plane but restricted perpendicular to the boundaries. We initially modeled the perpendicular component of the signal using a Laplacian spectral approach, while the parallel component was described by standard Gaussian attenuation. Although this model captures the overall signal behavior, the measured data reveal systematic deviations attributable to local structural inhomogeneities, such as the formation of water pockets between foil layers. To account for these effects, we introduce an extension of the model that incorporates spatial variations in layer spacing. Our data, acquired on a 7 T MRI scanner, show good agreement with the extended model and suggest the presence of such water pockets. Their planar dimensions, which range from 28 to 1500 μm, depend on foil compression and scale proportionally with the thickness of the water layer. These findings show that accurate diffusion models are needed to correctly estimate the phantom’s microstructure, and that the developed phantom is a useful tool for studying laminar diffusion. The remaining mismatch between the model and the data suggests that additional structural factors, not captured by the model, also influence the measurements.