Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features

Synthesis and applications of MOF - derived porous nanostructures

Metal organic frameworks (MOFs) represent a class of porous material which is formed by strong bonds between metal ions and organic linkers. By careful selection of constituents, MOFs can exhibit very high surface area, large pore volume, and excellent chemical stability. Research on synthesis, structures and properties of various MOFs has shown that they are promising materials for many applications, such as energy storage, gas storage, heterogeneous catalysis and sensing. Apart from direct use, MOFs have also been used as support substrates for nanomaterials or as sacrificial templates/precursors for preparation of various functional nanostructures. In this review, we aim to present the most recent development of MOFs as precursors for the preparation of various nanostructures and their potential applications in energy-related devices and processes. Specifically, this present survey intends to push the boundaries and covers the literatures from the year 2013 to early 2017, on supercapacitors, lithium-ion batteries, electrocatalysts, photocatalyst, gas sensing, water treatment, solar cells, and carbon dioxide capture. Finally, an outlook in terms of future challenges and potential prospects towards industrial applications are also discussed

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

A Capillary-Evaporation Micropump for Real-Time Sweat Rate Monitoring with an Electrochemical Sensor

Sweat collection and real time monitoring of sweat rate play essential roles in physiology monitoring and assessment of an athlete’s performance during exercise. In this paper, we report a micropump for sweat simulant collection based on the capillary–evaporation effect. An electrochemical sensor is integrated into the micropump, which monitors the flow rate in real-time by detecting the current using three electrodes. The evaporation rate from micropore array, equivalent to the sweat rate, was theoretically and numerically investigated. The designed micropump yields the maximum collection rate as high as 0.235 μ L/min. In addition, the collection capability of the micropump was validated experimentally; the flow rate through the microchannel was further detected in real-time with the electrochemical sensor. The experimental maximum collection rate showed good consistency with the theoretical data. Our proposed device shows the potential for sweat collection and real-time monitoring of sweat rate, which is a promising candidate for being a wearable platform for real-time physiology and performance monitoring during exercise