Memristors have many uses in machine learning and neuromorphic hardware. From memory elements in dot product engines to replicating both synapse and neuron wall behaviors, the memristor has proved a versatile component. Here we demonstrate an analog mode of operation observed in our silicon oxide memristors and apply this to the problem of edge detection. We demonstrate how a potential divider exploiting this analog behavior can prove a scalable solution to edge detection. We confirm its behavior experimentally and simulate its performance on a standard testbench. We show good performance comparable to existing memristor based work with a benchmark score of 0.465 on the BSDS500 dataset, while simultaneously maintaining a lower component count.

The human gut microbiota has now been associated with drug responses and efficacy, while chemical compounds present in these drugs can also impact the gut bacteria. However, drug–microbe interactions are still understudied in the clinical context, where polypharmacy and comorbidities co-occur. Here, we report relations between commonly used drugs and the gut microbiome. We performed metagenomics sequencing of faecal samples from a population cohort and two gastrointestinal disease cohorts. Differences between users and non-users were analysed per cohort, followed by a meta-analysis. While 19 of 41 drugs are found to be associated with microbial features, when controlling for the use of multiple medications, proton-pump inhibitors, metformin, antibiotics and laxatives show the strongest associations with the microbiome. We here provide evidence for extensive changes in taxonomy, metabolic potential and resistome in relation to commonly used drugs. This paves the way for future studies and has implications for current microbiome studies by demonstrating the need to correct for multiple drug use. Here, via a metagenomics analysis of population-based and disease cohorts, Vich Vila et al. study the impact of 41 commonly used medications on the taxonomic structures, metabolic potential and resistome of the gut microbiome, underscoring the importance of correcting for multiple drug use in microbiome studies.

The aim of this research is to determine the effects of a ten-week modern and recreational dance exercise program and trunk and leg muscle strengthening exercises on the coordination and explosive power of student-age female dancers. The total number of participants was 54, of which 27 made up the experimental group who participated in an experimental exercise program and 27 the control group. The experimental group performed Hip Hop and Dancehall dances and trunk and leg muscle strengthening exercises 3 times a week for 90 min each. The control group had no additional forms of exercise other than regular daily activities. The coordination of the participants was evaluated on the basis of six tests (Side Steps, 20 Steps forward Twirling a Baton, Skipping the Horizontal Jump Rope, Turning in 6 squares, Hand-Foot Drumming and Agility test with a Baton) and two tests for determining explosive power parameters (the squat jump and countermovement jump). Results showed statistical significance between the groups in 5 variables of coordination at the multivariate and univariate level (p<.05, p<.01), and in both variables of explosive power at the univariate level (p<.05). A large and intermediate effect size of the experimental program was determined for 5 variables of coordination, and intermediate effect size for both variables of explosive power. The results of this study showed that a ten-week exercise program for recreational and modern dance and exercises for strengthening the muscles of the torso and legs have a positive effect on the changes in the parameters of coordination and explosive power in student-age female dancers.

The aim of this research was to determine the differences in specific race performance characteristics of male swimmers in the 50-m and 100-m freestyle at the Serbia Open Championship 2017 (long-course). The overall sample included in this study consisted of 40 swimmers divided by a K-Means Cluster Analysis into three groups in relation to the results in the 50-m freestyle (G1_50, T50=23.53±.27 s, n=6; G2_50, T50=24.54±.33 s, n=18; G3_50, T50=25.52±.33 s, n=16), and 55 swimmers also divided into three groups in relation to the results in the 100-m freestyle (G1_100, T100=50.99±.82s, n=10; G2_100, T100=53.41±.48 s, n=17; G3_100, T100=56.13±1.32 s, n=28). The research results indicate that there is a difference in the specific race performance characteristics in relation to the achieved results in the 50-m freestyle, including: t10_50 (F=16.79, p=.000), SL2_50 (F=4.44, p=.019) and SI2_50 (F=13.49, p=.000), also in the 100-m freestyle, including: t10_100 (F=36.45, p=.000), SL1_100 (F=5.77, p=.005), SL2_100 (F=17.47, p=.000), SL3_100 (F=7.72, p=.001), SL4_100 (F=9.84, p=.000), SI1_100 (F=5.12, p=.009), SI2_100 (F=45.97, p=.000), SI3_100 (F=13.86, p=.000), SI4_100 (F=31.23, p=.000), SR1_100 (F=4.12, p=.022) and SR2_100 (F=6.37, p=.003). Based on these results we can draw the conclusion that swimmers who have better control over their race performance characteristics during all the segments of the race, including stroke length, stroke index and stroke rate have the potential of being faster in the 50-m and 100-m freestyle.

Animal studies have shown that intestinal barrier function is compromised with aging. We aimed to assess the effects of aging on intestinal barrier function in humans in vivo and ex vivo. In this cross-sectional study, healthy subjects and subjects with irritable bowel syndrome (IBS) of older (65–75 years) and young adult age (18–40 years) were compared. In vivo gastrointestinal site-specific permeability was assessed by a multi-sugar test, taking into account potential confounders. Sigmoid biopsies were collected from subgroups of healthy young adults and elderly for ex vivo Ussing chamber experiments, gene transcription of barrier-related genes and staining of junctional proteins. No significant differences between healthy young adults and elderly were found for small intestinal, colonic and whole gut permeability (P ≥ 0.142). In IBS patients, gastroduodenal and colonic permeability did not differ significantly (P ≥ 0.400), but small intestinal and whole gut permeability were higher in elderly versus young adults (P ≤ 0.009), mainly driven by the IBS-diarrhea subtype. Ussing chamber experiments with or without stressor (P ≥ 0.052), and relative expression of intestinal barrier-related genes (P ≥ 0.264) showed no significant differences between healthy elderly and young adults, as confirmed by immunofluorescent stainings. Overall, the functional capacity of the intestinal barrier is maintained in elderly.

Dentoalveolar trauma is considered an emergency condition and is challenging for every dentist. As primary and permanent teeth may suffer repercussions from an injury, a therapist must be mindful of which situations the use of splinting methods is required. In dentistry, a splint is a rigid or flexible device with the function of supporting, protecting, and immobilizing teeth that have been weakened (endodon-tically, periodontally), traumatically injured, replanted, or fractured. Generally, splinting is not recommended for primary teeth injuries such as luxation and avulsion. In permanent dentition, splint appliances are indicated for periodontal injuries, such as subluxation, luxation and avulsion, and hard tissue injuries such as class IV root fractures. Nowadays, there are many appliances that may be used for immobilization of traumatized teeth. Since this issue may sometimes be confusing for dental practitioners, this chapter deals with splint classification (rigid and flexible), the basic characteristics of splints, the indications, and methods of application.

Model extrapolation to unseen flow is one of the biggest challenges facing data-driven turbulence modeling, especially for models with high dimensional inputs that involve many flow features. In this study we review previous efforts on data-driven Reynolds-Averaged Naiver Stokes (RANS) turbulence modeling and model extrapolation, with main focus on the popular methods being used in the field of transfer learning. Several potential metrics to measure the dissimilarity between training flows and testing flows are examined. Different Machine Learning (ML) models are compared to understand how the capacity or complexity of the model affects its behavior in the face of dataset shift. Data preprocessing schemes which are robust to covariate shift, like normalization, transformation, and importance re-weighted likelihood, are studied to understand whether it is possible to find projections of the data that attenuate the differences in the training and test distributions while preserving predictability. Three metrics are proposed to assess the dissimilarity between training/testing dataset. To attenuate the dissimilarity, a distribution matching framework is used to align the statistics of the distributions. These modifications also allow the regression tasks to have better accuracy in forecasting under-represented extreme values of the target variable. These findings are useful for future ML based turbulence models to evaluate their model predictability and provide guidance to systematically generate diversified high-fidelity simulation database.