DEMA: a distance-bounded energy-field minimization algorithm to model and layout biomolecular networks with quantitative features.

SUMMARY: In biology, graph layout algorithms can reveal comprehensive biological contexts by visually positioning graph nodes in their relevant neighborhoods. A layout software algorithm/engine commonly takes a set of nodes and edges and produces layout coordinates of nodes according to edge constraints. However, current layout engines normally do not consider node, edge or node-set properties during layout and only curate these properties after the layout is created. Here, we propose a new layout algorithm, distance-bounded energy-field minimization algorithm (DEMA), to natively consider various biological factors, i.e., the strength of gene-to-gene association, the gene's relative contribution weight and the functional groups of genes, to enhance the interpretation of complex network graphs. In DEMA, we introduce a parameterized energy model where nodes are repelled by the network topology and attracted by a few biological factors, i.e., interaction coefficient, effect coefficient and fold change of gene expression. We generalize these factors as gene weights, protein-protein interaction weights, gene-to-gene correlations and the gene set annotations-four parameterized functional properties used in DEMA. Moreover, DEMA considers further attraction/repulsion/grouping coefficient to enable different preferences in generating network views. Applying DEMA, we performed two case studies using genetic data in autism spectrum disorder and Alzheimer's disease, respectively, for gene candidate discovery. Furthermore, we implement our algorithm as a plugin to Cytoscape, an open-source software platform for visualizing networks; hence, it is convenient. Our software and demo can be freely accessed at http://discovery.informatics.uab.edu/dema. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An automated microfluidic chemiluminescence immunoassay platform for quantitative detection of biomarkers.

A rapid, sensitive and quantitative biomarker detection platform is of great importance to the small clinic or point-of-care (POC) diagnosis. In this work, we realize that an automated diagnostic platform mainly includes two components: (1) an instrument that can complete all steps of the chemiluminescence immunoassay automatically and (2) an integrated microfluidic chip which is disposable and harmless. In the instrument, we adopt vacuum suction cups which are driven by linear motor to realize a simple, effective and convenient control. The method of acridine esterification chemiluminescence is adopted to achieve a quantitative detection, and a photomultiplier tube is used to detect photons from acridine ester producing in alkaline conditions. We use the laser cutting machine and hot press machine to accomplish the product of microfluidic chips. The automated microfluidics-based system is demonstrated by implementation of a chemiluminescence immunoassay for quantitative detection of ferritin. We observe alinear relationship between CL intensity and the concentration of ferritin from 5.1 to 1300 ng mL -1and the limit of detection (LoD) is 2.55 ng mL -1. At the same time, we also used the automated microfluidics-based system to test clinical serum samples. The whole process of chemiluminescence experiment can complete within 45 min. We realize that this lab-on-a-chip chemiluminescence immunoassay platform with features of automation and quantitation provides a promising strategy for POC diagnosis.

Decoupled neural network training with re-computation and weight prediction.

To break the three lockings during backpropagation (BP) process for neural network training, multiple decoupled learning methods have been investigated recently. These methods either lead to significant drop in accuracy performance or suffer from dramatic increase in memory usage. In this paper, a new form of decoupled learning, named decoupled neural network training scheme with re-computation and weight prediction (DTRP) is proposed. In DTRP, a re-computation scheme is adopted to solve the memory explosion problem, and a weight prediction scheme is proposed to deal with the weight delay caused by re-computation. Additionally, a batch compensation scheme is developed, allowing the proposed DTRP to run faster. Theoretical analysis shows that DTRP is guaranteed to converge to crical points under certain conditions. Experiments are conducted by training various convolutional neural networks on several classification datasets, showing comparable or better results than the state-of-the-art methods and BP. These experiments also reveal that adopting the proposed method, the memory explosion problem is effectively solved, and a significant acceleration is achieved.

Optimal selective floor cleaning using deep learning algorithms and reconfigurable robot hTetro.

Floor cleaning robots are widely used in public places like food courts, hospitals, and malls to perform frequent cleaning tasks. However, frequent cleaning tasks adversely impact the robot's performance and utilize more cleaning accessories (such as brush, scrubber, and mopping pad). This work proposes a novel selective area cleaning/spot cleaning framework for indoor floor cleaning robots using RGB-D vision sensor-based Closed Circuit Television (CCTV) network, deep learning algorithms, and an optimal complete waypoints path planning method. In this scheme, the robot will clean only dirty areas instead of the whole region. The selective area cleaning/spot cleaning region is identified based on the combination of two strategies: tracing the human traffic patterns and detecting stains and trash on the floor. Here, a deep Simple Online and Real-time Tracking (SORT) human tracking algorithm was used to trace the high human traffic region and Single Shot Detector (SSD) MobileNet object detection framework for detecting the dirty region. Further, optimal shortest waypoint coverage path planning using evolutionary-based optimization was incorporated to traverse the robot efficiently to the designated selective area cleaning/spot cleaning regions. The experimental results show that the SSD MobileNet algorithm scored 90% accuracy for stain and trash detection on the floor. Further, compared to conventional methods, the evolutionary-based optimization path planning scheme reduces 15% percent of navigation time and 10% percent of energy consumption.

A Disocclusion Inpainting Framework for Depth-Based View Synthesis.

This paper proposes a disocclusion inpainting framework for depth-based view synthesis. It consists of four modules: foreground extraction, motion compensation, improved background reconstruction, and inpainting. The foreground extraction module detects the foreground objects and removes them from both depth map and rendered video; the motion compensation module guarantees the background reconstruction model to suit for moving camera scenarios; the improved background reconstruction module constructs a stable background video by exploiting the temporal correlation information in both 2D video and its corresponding depth map; and the constructed background video and inpainting module are used to eliminate the holes in the synthesized view. The analysis and experiment indicate that the proposed framework has good generality, scalability and effectiveness, which means most of the existing background reconstruction methods and image inpainting methods can be employed or extended as the modules in our framework. Our comparison results have demonstrated that the proposed framework achieves better synthesized quality, temporal consistency, and has lower running time compared to the other methods.

High-Efficiency Plasma Separator Based on Immunocapture and Filtration.

The shortcomings of standard plasma-separation methods limit the point-of-care application of microfluidics in clinical facilities and at the patient's bedside. To overcome the limitations of this inconvenient, laborious, and costly technique, a new plasma-separation technique and device were developed. This new separation method relies on immunological capture and filtration to exclude cells from plasma, and is convenient, easy to use, and cost-effective. Most of the RBCs can be captured and immobilized by antibody which coated in separation matrix, and residue cells can be totally removed from the sample by a commercially plasma purification membranes. A 400 µL anti-coagulated whole blood sample with 65% hematocrit (Hct) can be separated by the device in 5 min with only one pipette. Up to 97% of the plasma can be recovered from the raw blood sample with a separation efficiency at 100%. The recovery rate of small molecule compounds, proteins, and nucleic acid biomarkers is evaluated; there are no obvious differences from the centrifuge method. The results demonstrate that this method is an excellent replacement for traditional plasma preparation protocols.

Fluorescence responsive conjugated poly(tetraphenylethene) and its morphological transition from micelle to vesicle.

A crown ether-functionalized poly(tetraphenylethene) (AP-TPE) is synthesized and the rotation of the TPE group is successfully restricted via the complexation of crown ether and organic ammonium salts, leading to a stepwise enhanced fluorescence accompanied by a morphological transition from micellar to vesicular.