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Busts Adenomyoepithelioma: Ultrasonography, Elastography, Electronic digital Mammography, Contrast-Enhanced Digital camera Mammography, and also Pathology Results of the Uncommon Type of Breasts Tumor.
Well supported by Monte Carlo simulations our experimental results enable, in absence of a true elemental magnetic monopole, creation of electron vortices and studying electrodynamics in presence of a monopole-like field in a solid state environment.Collecting quantitative information on animal behaviours is difficult, especially from cryptic species or species that alter natural behaviours under observation. Using harness-mounted tri-axial accelerometers free-roaming domestic cats (Felis Catus) we developed a methodology that can precisely classify finer-scale behaviours. We further tested the effect of a prey-protector device designed to reduce prey capture. We aligned accelerometer traces collected at 50 Hz with video files (60 fps) and labelled 12 individual behaviours, then trained a supervised machine-learning algorithm using Kohonen super self-organising maps (SOM). The SOM was able to predict individual behaviours with a ~ 99.6% overall accuracy, which was slightly better than for random forest estimates using the same dataset (98.9%). There was a significant effect of sample size, with precision and sensitivity decreasing rapidly below 2000 1-s observations. We were also able to detect a behaviour specific reduction in the predictability when cats were fitted with the prey-protector device indicating it altered biomechanical gait. Our results can be applied in movement ecology, zoology and conservation, where habitat specific movement performance between predators or prey may be critical to managing species of conservation significance, or in veterinary and agricultural fields, where early detection of movement pathologies can improve animal welfare.We present a detailed study regarding the bandgap dependence on diameter and composition of spherical Ge-rich GexSi1-x nanocrystals (NCs). For this, we conducted a series of atomistic density functional theory (DFT) calculations on H-passivated NCs of Ge-rich GeSi random alloys, with Ge atomic concentration varied from 50 to 100% and diameters ranging from 1 to 4 nm. As a result of the dominant confinement effect in the DFT computations, a composition invariance of the line shape of the bandgap diameter dependence was found for the entire computation range, the curves being shifted for different Ge concentrations by ΔE(eV) = 0.651(1 - x). The shape of the dependence of NCs bandgap on the diameter is well described by a power function 4.58/d1.25 for 2-4 nm diameter range, while for smaller diameters, there is a tendency to limit the bandgap to a finite value. By H-passivation of the NC surface, the effect of surface states near the band edges is excluded aiming to accurately determine the NC bandgap. The number of H atoms necessary to fully passivate the spherical GexSi1-x NC surface reaches the total number atoms of the Ge + Si core for smallest NCs and still remains about 25% from total number of atoms for bigger NC diameters of 4 nm. The findings are in line with existing theoretical and experimental published data on pure Ge NCs and allow the evaluation of the GeSi NCs behavior required by desired optical sensor applications for which there is a lack of DFT simulation data in literature.To better understand the early response of genotypes to limited-phosphorus (P) conditions and the role of the phosphate transporter OsPHT1 gene family in the presence of PSTOL1, it is essential to characterize the level of tolerance in rice under limited-P conditions. In the present experiment, six rice genotypes were studied in three-way interactions [genotype (G) × phosphorus (P) × duration (D)] by comparing them at two instances (14 d and 28 d) under seven different concentrations of P (0.5‒10.0 ppm) in a hydroponic system. Trait differences and interactions of these traits were clearly distinguished among the various P rates. However, aboveground trait expression registered increased growth from 6.0 to 10.0 ppm of P. The major root-attributed traits in 0.5 ppm of P are significantly increased vis-à-vis 10 ppm of P. Analysis of variance displayed a significant difference between the genotypes for PSTOL1 and PHT1 expression. In low P, maximum root length with a shoot and root dry weight was observed in a new indigenous accession, IC459373, with higher expression of PSTOL1 than in Dular and IR64-Pup1 in 0.5 ppm of P at 14 d. Among the 13 PHT1 genes, OsPT1, OsPT2, OsPT6, and OsPT13 showed significant upregulation in IC459373, Dular, and IR64-Pup1. These results indicated that studying the expression levels of the PSTOL1 and PHT1 gene family at the early growth stages would be helpful in identifying superior donors to improve low-P tolerance and P-use efficiency in rice breeding programs.Despite its considerable potential in the manufacturing industry, the application of artificial intelligence (AI) in the industry still faces the challenge of insufficient trust. Since AI is a black box with operations that ordinary users have difficulty understanding, users in organizations rely on institutional cues to make decisions about their trust in AI. Therefore, this study investigates trust in AI in the manufacturing industry from an institutional perspective. We identify three institutional dimensions from institutional theory and conceptualize them as management commitment (regulative dimension at the organizational level), authoritarian leadership (normative dimension at the group level), and trust in the AI promoter (cognitive dimension at the individual level). We hypothesize that all three institutional dimensions have positive effects on trust in AI. Selleckchem AZ 960 In addition, we propose hypotheses regarding the moderating effects of AI self-efficacy on these three institutional dimensions. A survey was conducted in a large petrochemical enterprise in eastern China just after the company had launched an AI-based diagnostics system for fault detection and isolation in process equipment service. The results indicate that management commitment, authoritarian leadership, and trust in the AI promoter are all positively related to trust in AI. Moreover, the effect of management commitment and trust in the AI promoter are strengthened when users have high AI self-efficacy. The findings of this study provide suggestions for academics and managers with respect to promoting users' trust in AI in the manufacturing industry.
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