Monte Carlo Simulation of Magnetization Reversal in Fe Sesquilayers on W(110)

Iron sesquilayers grown at room temperature on W(110) exhibit a pronounced coercivity maximum near a coverage of 1.5 atomic monolayers. On lattices which faithfully reproduce the morphology of the real films, a kinetic Ising model is utilized to simulate the domain-wall motion. Simulations reveal that the dynamics is dominated by the second-layer islands, which act as pinning centers. The simulated dependencies of the coercivity on the film coverage, as well as on the temperature and the frequency of the applied field, are very similar to those measured in experiments. Unlike previous micromagnetic models, the presented approach provides insight into the dynamics of the domain-wall motion and clearly reveals the role of thermal fluctuations.Comment: Final version to appear in Phys. Rev. B. References to related works added. 7 pages, 5 figures, RevTex, mpeg simulations available at http://www.scri.fsu.edu/~rikvol

The genomes of two key bumblebee species with primitive eusocial organization

Background: The shift from solitary to social behavior is one of the major evolutionary transitions. Primitively eusocial bumblebees are uniquely placed to illuminate the evolution of highly eusocial insect societies. Bumblebees are also invaluable natural and agricultural pollinators, and there is widespread concern over recent population declines in some species. High-quality genomic data will inform key aspects of bumblebee biology, including susceptibility to implicated population viability threats. Results: We report the high quality draft genome sequences of Bombus terrestris and Bombus impatiens, two ecologically dominant bumblebees and widely utilized study species. Comparing these new genomes to those of the highly eusocial honeybee Apis mellifera and other Hymenoptera, we identify deeply conserved similarities, as well as novelties key to the biology of these organisms. Some honeybee genome features thought to underpin advanced eusociality are also present in bumblebees, indicating an earlier evolution in the bee lineage. Xenobiotic detoxification and immune genes are similarly depauperate in bumblebees and honeybees, and multiple categories of genes linked to social organization, including development and behavior, show high conservation. Key differences identified include a bias in bumblebee chemoreception towards gustation from olfaction, and striking differences in microRNAs, potentially responsible for gene regulation underlying social and other traits. Conclusions: These two bumblebee genomes provide a foundation for post-genomic research on these key pollinators and insect societies. Overall, gene repertoires suggest that the route to advanced eusociality in bees was mediated by many small changes in many genes and processes, and not by notable expansion or depauperation

Airborne Object Detection Using Hyperspectral Imaging: Deep Learning Review

© 2019, Springer Nature Switzerland AG. Hyperspectral images have been increasingly important in object detection applications especially in remote sensing scenarios. Machine learning algorithms have become emerging tools for hyperspectral image analysis. The high dimensionality of hyperspectral images and the availability of simulated spectral sample libraries make deep learning an appealing approach. This report reviews recent data processing and object detection methods in the area including hand-crafted and automated feature extraction based on deep learning neural networks. The accuracy performances were compared according to existing reports as well as our own experiments (i.e., re-implementing and testing on new datasets). CNN models provided reliable performance of over 97% detection accuracy across a large set of HSI collections. A wide range of data were used: a rural area (Indian Pines data), an urban area (Pavia University), a wetland region (Botswana), an industrial field (Kennedy Space Center), to a farm site (Salinas). Note that, the Botswana set was not reviewed in recent works, thus high accuracy selected methods were newly compared in this work. A plain CNN model was also found to be able to perform comparably to its more complex variants in target detection applications

Determination of c-myc amplification and overexpression in breast cancer patients: evaluation of its prognostic value against c-erbB-2, cathepsin-D and clinicopathological characteristics using univariate and multivariate analysis

C-myc and c-erbB-2 amplification and/or overexpression as well as total cathepsin-D (CD) concentration have been reported to be associated with poor prognosis in breast cancer. The prognostic significance, however, remains somewhat controversial, partly because of discrepancies among the different methodologies used. We determined the amplification and overexpression of c-myc oncogene in 152 breast cancer patients and examined its prognostic value in relation to c-erbB-2 amplification and overexpression, high concentration of CD (≥ 60 pmol mg–1 protein) and standard clinicopathological prognostic factors of the disease. High CD concentration, as well as c-myc amplification and overexpression, proved to be the best of the new variables examined for prediction of early relapse (ER; before 3 years). After multivariate analysis only CD remained significant, which suggests that the prognostic power of these variables is similar. Using univariate analysis we proved that c-myc amplification and overexpression were highly significant for disease-free survival (DFS) (P = 0.0016 and P = 0.0001 respectively) and overall survival (OS) (P < 0.0001 and P = 0.0095 respectively), although by multivariate analysis c-myc overexpression was statistically significant only for DFS (P = 0.0001) and c-myc amplification only for OS (P = 0.0006). With regard to c-erbB-2, only its overexpression appeared to be significant for DFS and OS, although after multivariate analysis its prognostic power was weaker (P = 0.030 and P = 0.024 respectively). c-myc amplification and overexpression exhibited a tendency for locoregional recurrence (LRR) (P = 0.0024 and P = 0.0075 respectively), however, their prognostic value was lower after multivariate analysis and only CD remained significant. © 1999 Cancer Research Campaig

Advances in estrogen receptor biology: prospects for improvements in targeted breast cancer therapy

Estrogen receptor (ER) has a crucial role in normal breast development and is expressed in the most common breast cancer subtypes. Importantly, its expression is very highly predictive for response to endocrine therapy. Current endocrine therapies for ER-positive breast cancers target ER function at multiple levels. These include targeting the level of estrogen, blocking estrogen action at the ER, and decreasing ER levels. However, the ultimate effectiveness of therapy is limited by either intrinsic or acquired resistance. Identifying the factors and pathways responsible for sensitivity and resistance remains a challenge in improving the treatment of breast cancer. With a better understanding of coordinated action of ER, its coregulatory factors, and the influence of other intracellular signaling cascades, improvements in breast cancer therapy are emerging