Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion

Most of the traditional convolutional neural networks (CNNs) implements bottom-up approach (feed-forward) for image classifications. However, many scientific studies demonstrate that visual perception in primates rely on both bottom-up and top-down connections. Therefore, in this work, we propose a CNN network with feedback structure for Solar power plant detection on middle-resolution satellite images. To express the strength of the top-down connections, we introduce feedback CNN network (FB-Net) to a baseline CNN model used for solar power plant classification on multi-spectral satellite data. Moreover, we introduce a method to improve class activation mapping (CAM) to our FB-Net, which takes advantage of multi-channel pulse coupled neural network (m-PCNN) for weakly-supervised localization of the solar power plants from the features of proposed FB-Net. For the proposed FB-Net CAM with m-PCNN, experimental results demonstrated promising results on both solar-power plant image classification and detection task.Comment: 9 pages, 9 figures, 4 table

Nonthermal Emission Associated with Strong AGN Outbursts at the Centers of Galaxy Clusters

Recently, strong AGN outbursts at the centers of galaxy clusters have been found. Using a simple model, we study particle acceleration around a shock excited by an outburst and estimate nonthermal emission from the accelerated particles. We show that emission from secondary electrons is consistent with the radio observations of the minihalo in the Perseus cluster, if there was a strong AGN outburst >~10^8 yrs ago with an energy of ~1.8x10^62 erg. The validity of our model depends on the frequency of the large outbursts. We also estimate gamma-ray emission from the accelerated particles and show that it could be detected with GLAST.Comment: Accepted for publication in ApJ

Identification of hepta-histidine as a candidate drug for Huntington's disease by in silico-in vitro- in vivo-integrated screens of chemical libraries.

We identified drug seeds for treating Huntington's disease (HD) by combining in vitro single molecule fluorescence spectroscopy, in silico molecular docking simulations, and in vivo fly and mouse HD models to screen for inhibitors of abnormal interactions between mutant Htt and physiological Ku70, an essential DNA damage repair protein in neurons whose function is known to be impaired by mutant Htt. From 19,468 and 3,010,321 chemicals in actual and virtual libraries, fifty-six chemicals were selected from combined in vitro-in silico screens; six of these were further confirmed to have an in vivo effect on lifespan in a fly HD model, and two chemicals exerted an in vivo effect on the lifespan, body weight and motor function in a mouse HD model. Two oligopeptides, hepta-histidine (7H) and Angiotensin III, rescued the morphological abnormalities of primary neurons differentiated from iPS cells of human HD patients. For these selected drug seeds, we proposed a possible common structure. Unexpectedly, the selected chemicals enhanced rather than inhibited Htt aggregation, as indicated by dynamic light scattering analysis. Taken together, these integrated screens revealed a new pathway for the molecular targeted therapy of HD

Serum macrophage migration inhibitory factor reflects adrenal function in the hypothalamo-pituitary-adrenal axis of septic patients: an observational study

<p>Abstract</p> <p>Background</p> <p>The hypothalamo-pituitary-adrenal (HPA) axis modulates the inflammatory response during sepsis. Macrophage migration inhibitory factor (MIF), which counteracts the anti-inflammatory activity of glucocorticoid (GC), is one of the mediators of the development of inflammation. An inflammatory imbalance involving GC and MIF might be the cause or result of adrenal insufficiency. Our objective was to clarify the relationship between serum MIF and adrenal function in the HPA axis of sepsis patients using the adrenocorticotropic hormone (ACTH) stimulation test.</p> <p>Methods</p> <p>An observational study was performed in a university intensive care unit over a two-year period. Of 64 consecutive sepsis patients, 41 were enrolled. The enrolled patients underwent an ACTH stimulation test within 24 h of the diagnosis of severe sepsis or septic shock. Clinical and laboratory parameters, including serum MIF and cortisol, were measured.</p> <p>Results</p> <p>Based on their responses to the ACTH stimulation test, the patients were divided into a normal adrenal response (NAR) group (n = 22) and an adrenal insufficiency (AI) group (n = 19). The AI group had significantly more septic shock patients and higher prothrombin time ratios, serum MIF, and baseline cortisol than did the NAR group (<it>P </it>< 0.05). Serum MIF correlated significantly with the SOFA (Sequential Organ Failure Assessment) score, prothrombin time ratio, and delta max cortisol, which is maximum increment of serum cortisol concentration after ACTH stimulation test (rs = 0.414, 0.355, and -0.49, respectively, <it>P </it>< 0.05). Serum MIF also correlated significantly with the delta max cortisol/albumin ratio (rs = -0.501, <it>P </it>= 0.001). Receiver operating characteristic curve analysis identified the threshold serum MIF concentration (19.5 ng/mL, <it>P </it>= 0.01) that segregated patients into the NAR and AI groups.</p> <p>Conclusions</p> <p>The inverse correlation between serum MIF and delta max cortisol or the delta max cortisol/albumin ratio suggests that high serum MIF reflects an insufficient adrenal response in the HPA axis. Serum MIF could be a valuable clinical marker of adrenal insufficiency in sepsis patients.</p

Development of Three-Dimensional Soil-Amplification Analysis Method for Screening for Seismic Damage to Buried Water-Distribution Pipeline Networks

A soil-amplification analysis method is developed that uses high-resolution ground data and a three-dimensional nonlinear dynamic finite-element method to screen for possible areas of seismic damage to buried water-distribution pipeline networks. The method is applied to a cut-and-fill developed area in Japan, whose water-distribution pipeline network was severely damaged in the 2011 off the Pacific Coast of Tohoku Earthquake. The obtained soil amplification is compared with known points of pipeline damage to check the validity of the analysis. A sensitivity test is also conducted to account for uncertainties in the properties of the ground material. From the results, it is expected that the developed soil-amplification method could be used to screen for possible damage to buried pipelines in a given area, and used to support methods for estimating damage to buried pipelines based on observations and seismic indices