Mayerstiles5860

We reconstruct the composite dynamics of Mt. Vesuvius volcano in the period 2012-2019 from the study of ground deformation, seismicity, and geofluid (groundwater and fumarolic fluids) circulation and recognize complex spatio-temporal variations in these observables at medium (years) and short (months) time-scales. We interpret the observed patterns as the combined effect of structural changes affecting the volcanic edifice and variations of the dynamics of the hydrothermal system. BAY-293 In particular, we identify a change in the activity state of Mt. Vesuvius. After the activity reached minimum levels in 2014, the centroid of the surface manifestations migrated towards the SE. Episodic variations of co-seismic and aseismic deformation and fluid release, if analysed separately, would likely have been interpreted as pseudo-random oscillations of the background geophysical and geochemical signals. When organised in a comprehensive, multiparametric fashion, they shed light on the evolution of the volcano in 4D (x,y,z, time) space. These inferences play a crucial role in the formulation of civil protection scenarios for Mt. Vesuvius, a high risk, densely urbanized volcanic area which has never experienced unrest episodes in the modern era of instrumental volcanology.Skin pigmentation is associated with skin damages and skin cancers, and ultraviolet (UV) photography is used as a minimally invasive mean for the assessment of pigmentation. Since UV photography equipment is not usually available in general practice, technologies emphasizing pigmentation in color photo images are desired for daily care. We propose a new method using conditional generative adversarial networks, named UV-photo Net, to generate synthetic UV images from color photo images. Evaluations using color and UV photo image pairs taken by a UV photography system demonstrated that pigment spots were well reproduced in synthetic UV images by UV-photo Net, and some of the reproduced pigment spots were difficult to be recognized in color photo images. In the pigment spot detection analysis, the rate of pigment spot areas in cheek regions for synthetic UV images was highly correlated with the rate for UV photo images (Pearson's correlation coefficient 0.92). We also demonstrated that UV-photo Net was effective for floating up pigment spots for photo images taken by a smartphone camera. UV-photo Net enables an easy assessment of pigmentation from color photo images and will promote self-care of skin damages and early signs of skin cancers for preventive medicine.Wearable sensing platforms have been rapidly advanced over recent years, thanks to numerous achievements in a variety of sensor fabrication techniques. However, the development of a flexible proximity sensor that can perform in a large range of object mobility remains a challenge. Here, a polymer-based sensor that utilizes a nanostructure composite as the sensing element has been presented for forthcoming usage in healthcare and automotive applications. Thermoplastic Polyurethane (TPU)/Carbon Nanotubes (CNTs) composites are capable of detecting presence of an external object in a wide range of distance. The proximity sensor exhibits an unprecedented detection distance of 120 mm with a resolution of 0.3%/mm. The architecture and manufacturing procedures of TPU/CNTs sensor are straightforward and performance of the proximity sensor shows robustness to reproducibility as well as excellent electrical and mechanical flexibility under different bending radii and over hundreds of bending cycles with variation of 4.7% and 4.2%, respectively. Tunneling and fringing effects are addressed as the sensing mechanism to explain significant capacitance changes. Percolation threshold analysis of different TPU/CNT contents indicated that nanocomposites having 2 wt% carbon nanotubes are exhibiting excellent sensing capabilities to achieve maximum detection accuracy and least noise among others. Fringing capacitance effect of the structure has been systematically analyzed by ANSYS Maxwell (Ansoft) simulation, as the experiments precisely supports the sensitivity trend in simulation. Our results introduce a new mainstream platform to realize an ultrasensitive perception of objects, presenting a promising prototype for application in wearable proximity sensors for motion analysis and artificial electronic skin.In HIV-1-infected patients, antiretroviral therapy (ART) is a key factor that may impact commensal microbiota and cause the emergence of side effects. However, it is not fully understood how long-term ART regimens have diverse impacts on the microbial compositions over time. Here, we performed 16S ribosomal RNA gene sequencing of the fecal and salivary microbiomes in patients under different long-term ART. We found that ART, especially conventional nucleotide/nucleoside reverse transcriptase inhibitor (NRTI)-based ART, has remarkable impacts on fecal microbial diversity decreased α-diversity and increased ß-diversity over time. In contrast, dynamic diversity changes in the salivary microbiome were not observed. Comparative analysis of bacterial genus compositions showed a propensity for Prevotella-enriched and Bacteroides-poor gut microbiotas in patients with ART over time. In addition, we observed a gradual reduction in Bacteroides but drastic increases in Succinivibrio and/or Megasphaera under conventional ART. These results suggest that ART, especially NRTI-based ART, has more suppressive impacts on microbiota composition and diversity in the gut than in the mouth, which potentially causes intestinal dysbiosis in patients. Therefore, NRTI-sparing ART, especially integrase strand transfer inhibitor (INSTI)- and/or non-nucleotide reverse transcriptase inhibitor (NNRTI)-containing regimens, might alleviate the burden of intestinal dysbiosis in HIV-1-infected patients under long-term ART.Dental age estimation of living individuals is difficult and challenging, and there is no consensus method in adults with permanent dentition. Thus, we aimed to provide an accurate and robust artificial intelligence (AI)-based diagnostic system for age-group estimation by incorporating a convolutional neural network (CNN) using dental X-ray image patches of the first molars extracted via panoramic radiography. The data set consisted of four first molar images from the right and left sides of the maxilla and mandible of each of 1586 individuals across all age groups, which were extracted from their panoramic radiographs. The accuracy of the tooth-wise estimation was 89.05 to 90.27%. Performance accuracy was evaluated mainly using a majority voting system and area under curve (AUC) scores. The AUC scores ranged from 0.94 to 0.98 for all age groups, which indicates outstanding capacity. The learned features of CNNs were visualized as a heatmap, and revealed that CNNs focus on differentiated anatomical parameters, including tooth pulp, alveolar bone level, or interdental space, depending on the age and location of the tooth.