Depth-Based Detection of Standing-Pigs in Moving Noise Environments

In a surveillance camera environment, the detection of standing-pigs in real-time is an important issue towards the final goal of 24-h tracking of individual pigs. In this study, we focus on depth-based detection of standing-pigs with “moving noises”, which appear every night in a commercial pig farm, but have not been reported yet. We first apply a spatiotemporal interpolation technique to remove the moving noises occurring in the depth images. Then, we detect the standing-pigs by utilizing the undefined depth values around them. Our experimental results show that this method is effective for detecting standing-pigs at night, in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (i.e., 94.47%), even with severe moving noises occluding up to half of an input depth image. Furthermore, without any time-consuming technique, the proposed method can be executed in real-time

Replacement Condition Detection of Railway Point Machines Using an Electric Current Sensor

Detecting replacement conditions of railway point machines is important to simultaneously satisfy the budget-limit and train-safety requirements. In this study, we consider classification of the subtle differences in the aging effect—using electric current shape analysis—for the purpose of replacement condition detection of railway point machines. After analyzing the shapes of after-replacement data and then labeling the shapes of each before-replacement data, we can derive the criteria that can handle the subtle differences between “does-not-need-to-be-replaced” and “needs-to-be-replaced” shapes. On the basis of the experimental results with in-field replacement data, we confirmed that the proposed method could detect the replacement conditions with acceptable accuracy, as well as provide visual interpretability of the criteria used for the time-series classification

Structural plasticity in the oestrogen receptor ligand-binding domain

The steroid hormone receptors are characterized by binding to relatively rigid, inflexible endogenous steroid ligands. Other members of the nuclear receptor superfamily bind to conformationally flexible lipids and show a corresponding degree of elasticity in the ligand-binding pocket. Here, we report the X-ray crystal structure of the oestrogen receptor α (ERα) bound to an oestradiol derivative with a prosthetic group, ortho- trifluoromethlyphenylvinyl, which binds in a novel extended pocket in the ligand-binding domain. Unlike ER antagonists with bulky side groups, this derivative is enclosed in the ligand-binding pocket, and acts as a potent agonist. This work shows that steroid hormone receptors can interact with a wider array of pharmacophores than previously thought through structural plasticity in the ligand-binding pocket

NFkappaB selectivity of estrogen receptor ligands revealed by comparative crystallographic analyses

Our understanding of how steroid hormones regulate physiological functions has been significantly advanced by structural biology approaches. However, progress has been hampered by misfolding of the ligand binding domains in heterologous expression systems and by conformational flexibility that interferes with crystallization. Here, we show that protein folding problems that are common to steroid hormone receptors are circumvented by mutations that stabilize well-characterized conformations of the receptor. We use this approach to present the structure of an apo steroid receptor that reveals a ligand-accessible channel allowing soaking of preformed crystals. Furthermore, crystallization of different pharmacological classes of compounds allowed us to define the structural basis of NFkappaB-selective signaling through the estrogen receptor, thus revealing a unique conformation of the receptor that allows selective suppression of inflammatory gene expression. The ability to crystallize many receptor-ligand complexes with distinct pharmacophores allows one to define structural features of signaling specificity that would not be apparent in a single structure.Kendall W Nettles, John B Bruning, German Gil, Jason Nowak, Sanjay K Sharma, Johnnie B Hahm, Kristen Kulp, Richard B Hochberg, Haibing Zhou, John A Katzenellenbogen, Benita S Katzenellenbogen, Younchang Kim, Andrzej Joachimiak & Geoffrey L Green