A prelude report on molecular docking of HER2 protein towards comprehending anti-cancer properties of saponins from Solanum tuberosum

Saponins are extensively known for many biological activities e.g. antimicrobial, anti-palatability, anti-cancer and hemolytic. As cancer cells have a more cholesterol-like compound in their membrane structure the saponins bind cholesterol due to their natural affinity to bind cancer cell membrane. This prevents them from entering the body through the intestinal tract, where they have the ability to attach themselves to vital organs and grow. This study reports the effective use of lower dose saponins like immunotoxin so that they can inhibit the proliferation of cancerous pancreatic cells. The investigation of pancreatic cancer metabolic pathway it was found that proteins 3H3B produced by genes HER-2 are involved in the enhancement of this type of cancer. Further docking studies showed that there is an effective interaction between saponins and cancer cells. The glide score of the saponin analogue compound with CID 21573770 (Pubchem) was -6.30 followed by score of -6.05 and -5.29 for 5-Florouracil and gemcitabine respectively. The interaction was observed in the GLU and GLN rich region, saponins made H-bonds with GLU-188, GLN-119, VAL-72 and GLN-71. This study indicates an effective way towards leading newer prospects for developing saponin analogue based cancer-fighting drugs with improved cancer cell inhibition property without killing normal cells

Parsimonious Labeling

We propose a new family of discrete energy minimization problems, which we call parsimonious labeling. Specifically, our energy functional consists of unary potentials and high-order clique potentials. While the unary potentials are arbitrary, the clique potentials are proportional to the {\em diversity} of set of the unique labels assigned to the clique. Intuitively, our energy functional encourages the labeling to be parsimonious, that is, use as few labels as possible. This in turn allows us to capture useful cues for important computer vision applications such as stereo correspondence and image denoising. Furthermore, we propose an efficient graph-cuts based algorithm for the parsimonious labeling problem that provides strong theoretical guarantees on the quality of the solution. Our algorithm consists of three steps. First, we approximate a given diversity using a mixture of a novel hierarchical $P^n$ Potts model. Second, we use a divide-and-conquer approach for each mixture component, where each subproblem is solved using an effficient $\alpha$-expansion algorithm. This provides us with a small number of putative labelings, one for each mixture component. Third, we choose the best putative labeling in terms of the energy value. Using both sythetic and standard real datasets, we show that our algorithm significantly outperforms other graph-cuts based approaches

Optimizing Emergency Department Throughput Using Best Practices to Improve Patient Flow

Emergency Department (ED) crowding and bottle necks are the reality of hospitals across the country. Patients seeking care and needing inpatient beds via the emergency rooms are facing delays with attaining the right level of care. Orchestrating a patient through an ED admission requires a multidisciplinary effort to provide safe, effective and efficient care. This quality improvement project conducted in a tertiary acute care hospital focused on Centers for Medicare and Medicaid metrics to measure Emergency Department (ED) throughput. This multidisciplinary initiative focused on reducing time stamps for patient arrival to the ED through departure to hospital or home. Outcomes showed a significant decrease in the time frame for patient arrival to being seen by a qualified provider, left without being seen rates, ED diversion, and ancillary department turnaround times. The interventions can be applied at other hospital based emergency departments

Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation

We propose an approach to discover class-specific pixels for the weakly-supervised semantic segmentation task. We show that properly combining saliency and attention maps allows us to obtain reliable cues capable of significantly boosting the performance. First, we propose a simple yet powerful hierarchical approach to discover the class-agnostic salient regions, obtained using a salient object detector, which otherwise would be ignored. Second, we use fully convolutional attention maps to reliably localize the class-specific regions in a given image. We combine these two cues to discover class-specific pixels which are then used as an approximate ground truth for training a CNN. While solving the weakly supervised semantic segmentation task, we ensure that the image-level classification task is also solved in order to enforce the CNN to assign at least one pixel to each object present in the image. Experimentally, on the PASCAL VOC12 val and test sets, we obtain the mIoU of 60.8% and 61.9%, achieving the performance gains of 5.1% and 5.2% compared to the published state-of-the-art results. The code is made publicly available

Synthesis and investigation of highly conductive Cu-Cr-MWCNT composites

There is great demand for contact materials with superior electrical and thermal conductivities and mechanical strength, for use in Vacuum Circuit Breakers (VCB). Copper (Cu) and chromium (Cr) alloy, developed in the 1970’s [1] has been the most common contact material in VCBs. Although Cu-Cr alloys possess good conductivity and mechanical properties, 50% of Cr (a strategic metal) in the alloy is resource prohibitive. A reduction in Cr usage without compromising desired properties is desirable. Previous researchers focused on developing Cu-Cr alloys with fine and uniform microstructure to enhance their physical properties. This investigation focused on the development of a Cu-Cr-MWCNT (Multi walled Carbon Nanotubes) composite with enhanced properties as compared with currently used materials. The electrical conductivity of the composite increased up to 18 times that of Cu and there was also an increase in the Vicker’s hardness

Two Wheeler Helmets with Ventilation and Metal Foam

Three different two wheeler helmets were studied to investigate their dynamic performance.First is helmet with ABS shell, second is helmet with metal foam, and third is helmet with singlegroove in the liner foam for providing ventilation. Front and side impact analyses were carriedout at 10 m/s velocity by using LS-DYNATM. Forces on the helmet and on the head due to impactwere studied with function of time. Pressure and stresses in the brain were investigated andfound not to change significantly due to the presence of groove in the liner foam, which wasprovided to improve the ventilation in helmets. The dynamic performance of a helmet with outershell as metal foam was examined and compared with ABS material

Modeling and Simulation of Single-Phase Transformer Inrush Current using Neural Network

Inrush current is a transient phenomenon which occurs during energization in transformer at no load. It depends on winding impedance, time constant of transformer circuit and core magnetization characteristics. Transient phenomenon of current represents non linear characteristics due to BH curve. Transformer circuit at no load is used to obtain various data. Data is obtained using semi – analytic solution approach. These data is used to develop neural network. Neural network shows exact modeling of inrush current. Keywords: Inrush Current, ANN, Modeling