The ability of cells to respond to substrate-bound protein gradients is crucial for many physiological processes, such as immune response, neurogenesis and cancer cell migration. However, the difficulty to produce well-controlled protein gradients has long been a limitation to our understanding of collective cell migration in response to haptotaxis. Here we use a photopatterning technique to create circular, square and linear fibronectin (FN) gradients on two-dimensional (2D) culture substrates. We observed that epithelial cells spread preferentially on zones of higher FN density, creating rounded or elongated gaps within epithelial tissues over circular or linear FN gradients, respectively. Using time-lapse experiments, we demonstrated that the gap closure mechanism in a 2D haptotaxis model requires a significant increase of the leader cell area. In addition, we found that gap closures are slower on decreasing FN densities than on homogenous FN-coated substrate and that fresh closed gaps are characterized by a lower cell density. KI696 price Interestingly, our results showed that cell proliferation increases in the closed gap region after maturation to restore the cell density, but that cell-cell adhesive junctions remain weaker in scarred epithelial zones. Taken together, our findings provide a better understanding of the wound healing process over protein gradients, which are reminiscent of haptotaxis.Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields.The purpose of the current study was to assess in vivo Achilles tendon (AT) mechanical loading and strain energy during locomotion. We measured AT length considering its curve-path shape. Eleven participants walked at 1.4 m/s and ran at 2.5 m/s and 3.5 m/s on a treadmill. The AT length was defined as the distance between its origin at the gastrocnemius medialis myotendinous junction (MTJ) and the calcaneal insertion. The MTJ was tracked using ultrasonography and projected to the reconstructed skin surface to account for its misalignment. Skin-to-bone displacements were assessed during a passive rotation (5°/s) of the ankle joint. Force and strain energy of the AT during locomotion were calculated by fitting a quadratic function to the experimentally measured tendon force-length curve obtained from maximum voluntary isometric contractions. The maximum AT strain and force were affected by speed (p  less then  0.05, ranging from 4.0 to 4.9% strain and 1.989 to 2.556 kN), yet insufficient in magnitude to be considered as an effective stimulus for tendon adaptation. Besides the important tendon energy recoil during the propulsion phase (7.8 to 11.3 J), we found a recoil of elastic strain energy at the beginning of the stance phase of running (70-77 ms after touch down) between 1.7 ± 0.6 and 1.9 ± 1.1 J, which might be functionally relevant for running efficiency.Rapid, accurate detection of heavy-metal content is extremely important for precise risk control and targeted remediation. Herein, a general modeling method and process based on the relationship between Pxrf measured values and site parameters are explored to construct a Pxrf correction model suitable to improve each site’s measurement accuracy. Results show a significant correlation between Pb, Mn, and Zn Pxrf measured values and actual concentrations, with correlation coefficients between 0.8 and 0.93. Through the correlation analysis, the correlation coefficient between the water content and the measured value of pxrf is in the range of 0.2-0.5. Pxrf measurement of all heavy metals was weakly affected by soil organic matter content, with correlation coefficients all lower than 0.5. Model transformation effectively improved the correlation between measured Pxrf value and actual concentration, and transformation increased the correlations of Sr, Mn, and Cu by around 0.11. Model verification results showed that the Pb, Zn, Fe, and Mn models can be used to improve Pxrf method detection accuracy.Guided by a rigorous mathematical result, we have earlier introduced a numerical algorithm, which using as input the cumulative number of deaths caused by COVID-19, can estimate the effect of easing of the lockdown conditions. Applying this algorithm to data from Greece, we extend it to the case of two subpopulations, namely, those consisting of individuals below and above 40 years of age. After supplementing the Greek data for deaths with the data for the number of individuals reported to be infected by SARS-CoV-2, we estimated the effect on deaths and infections in the case that the easing of the lockdown measures is different for these two subpopulations. We found that if the lockdown measures are partially eased only for the young subpopulation, then the effect on deaths and infections is small. However, if the easing is substantial for the older population, this effect may be catastrophic.Improved and cheaper molecular diagnostics allow the shift from “one size fits all” therapies to personalised treatments targeting the individual tumor. However, the wealth of potential targets based on comprehensive sequencing remains a yet unsolved challenge that prevents its routine use in clinical practice. Thus, we designed a workflow that selects the most promising treatment targets based on multi-omics sequencing and in silico drug prediction. In this study we demonstrate the workflow with focus on bladder cancer (BLCA), as there are, to date, no reliable diagnostics available to predict the potential benefit of a therapeutic approach. Within the TCGA-BLCA cohort, our workflow identified a panel of 21 genes and 72 drugs that suggested personalized treatment for 95% of patients-including five genes not yet reported as prognostic markers for clinical testing in BLCA. The automated predictions were complemented by manually curated data, thus allowing for accurate sensitivity- or resistance-directed drug response predictions.