A Study On The Water Quality Improvement Of The Songdo Waterfront\u27s Canal System

This study aims to investigate the flow conditions in the proposed canal system to be located in Songdo District, Incheon. Incheon Free Economic Zone (IFEZ) is concerned about the potential issues like water quality and algal problems, which will greatly affect the success of the Songdo waterfront development.Thus there is a need to ensure that natural stream flow and good water quality will maintain. In order to do this, a 3D numerical model, MIKE 3 FM was setup and used to investigate the water circulation system with respect to the operation of the four water gates present at the ends of the canal system, where ideal configurations of the gate operations were determined. The model was computed water quality change under various tidal conditions. The results given by the numerical model will be used as indications for a preconstruction plan of the Songdo canal system.By annual period simulation using real measured data from Incheon coast, analyse the polluted water from songdo city’s land inflow which is large influence to canal water quality. BOD, Nitrogen and phosphorous parameter from land are increased and influence to WQI(Water Quality Index). In canal WQI is 3~4 points that is higher than costal WQI which is increased by polluted water from land inflow. So we analyse the water quality change impacted by polluted land inflow and suggest a method to alleviate polluted water in Songdo waterfront’s canal system. Acknowledgments This research was supported by a grant (12-TI-C01) from Advanced Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government

Comparing a Query Compound with Drug Target Classes Using 3D-Chemical Similarity

3D similarity is useful in predicting the profiles of unprecedented molecular frameworks that are 2D dissimilar to known compounds. When comparing pairs of compounds, 3D similarity of the pairs depends on conformational sampling, the alignment method, the chosen descriptors, and the similarity coefficients. In addition to these four factors, 3D chemocentric target prediction of an unknown compound requires compound–target associations, which replace compound-to-compound comparisons with compound-to-target comparisons. In this study, quantitative comparison of query compounds to target classes (one-to-group) was achieved via two types of 3D similarity distributions for the respective target class with parameter optimization for the fitting models: (1) maximum likelihood (ML) estimation of queries, and (2) the Gaussian mixture model (GMM) of target classes. While Jaccard–Tanimoto similarity of query-to-ligand pairs with 3D structures (sampled multi-conformers) can be transformed into query distribution using ML estimation, the ligand pair similarity within each target class can be transformed into a representative distribution of a target class through GMM, which is hyperparameterized via the expectation–maximization (EM) algorithm. To quantify the discriminativeness of a query ligand against target classes, the Kullback–Leibler (K–L) divergence of each query was calculated and compared between targets. 3D similarity-based K–L divergence together with the probability and the feasibility index, (Fm), showed discriminative power with regard to some query–class associations. The K–L divergence of 3D similarity distributions can be an additional method for (1) the rank of the 3D similarity score or (2) the p-value of one 3D similarity distribution to predict the target of unprecedented drug scaffolds

Automated Serial Optical Coherence Microscopy toward Statistical 3D Digital Histopathology

we introduce automated serial OCM toward statistical 3D digital histopathology. Our research is the extension of previous work in order to enhance the process of imaging acquisition. Our approach has three unique features, (1) surface tracking, (2) single body and automated system combined vibratome and microscopic imaging head, and (3) selection of magnification. In validation test, various mouse organs were imaged and quantified at the region of interest which presented less labor and shorten image acquisition time compared to previous works

Additional file 1 of Random-forest model for drug–target interaction prediction via Kullbeck–Leibler divergence

Additional file 1. Supplementary Information File

Memory Harvesting in Multi-GPU Systems with Hierarchical Unified Virtual Memory

With the ever-growing demands for GPUs, most organizations allow users to share the multi-GPU servers. However, we observe that the memory space across GPUs is not effectively utilized enough when consolidating various workloads that exhibit highly varying resource demands. This is because the current memory management techniques were designed solely for individual GPUs rather than shared multi-GPU environments. This study introduces a novel approach to provide an illusion of virtual memory space for GPUs, called hierarchical unified virtual memory (HUVM), by incorporating the temporarily idle memory of neighbor GPUs. Since modern GPUs are connected to each other through a fast interconnect, it provides lower access latency to neighbor GPU???s memory compared to the host memory via PCIe. On top of HUVM, we design a new memory manager, called memHarvester, to effectively and efficiently harvest the temporarily available neighbor GPUs??? memory. For diverse consolidation scenarios with DNN training and graph analytics workloads, our experimental result shows up to 2.71?? performance improvement compared to the prior approach in multi-GPU environments

Multi-contrast digital histopathology of mouse organs using quantitative phase imaging and virtual staining

Quantitative phase imaging (QPI) has emerged as a new digital histopathologic tool as it provides structural information of conventional slide without staining process. It is also capable of imaging biological tissue sections with sub-nanometer sensitivity and classifying them using light scattering properties. Here we extend its capability further by using optical scattering properties as imaging contrast in a wide-field QPI. In our first step towards validation, QPI images of 10 major organs of a wild-type mouse have been obtained followed by H&E-stained images of the corresponding tissue sections. Furthermore, we utilized deep learning model based on generative adversarial network (GAN) architecture for virtual staining of phase delay images to a H&E-equivalent brightfield (BF) image analogues. Using the structural similarity index, we demonstrate similarities between virtually stained and H&E histology images. Whereas the scattering-based maps look rather similar to QPI phase maps in the kidney, the brain images show significant improvement over QPI with clear demarcation of features across all regions. Since our technology provides not only structural information but also unique optical property maps, it could potentially become a fast and contrast-enriched histopathology technique

Digital histopathology using optical coherence microscopy(OCM) and deep learning

Histological optical imaging is a gold standard method to observe biological tissues. However, this technique is a time-consuming and labor-intensive process. In this study, we introduce the new approach for digital histopathology which is based on OCM and deep learning. We developed a fully automated multi-scale OCM system equipped with user-friendly operating software and a deep learning module. Various tissues including the cancer model were imaged by OCM, which was further virtually stained. In conclusion, our system offers an efficient process in terms of acquisition time, digitalization and interpretatio