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Matthew Shupler, William Godwin, Joseph Frostad, Paul Gustafson, Raphael E Arku, Michael Brauer
BACKGROUND: Exposure to household air pollution (HAP) from cooking with dirty fuels is a leading health risk factor within Asia, Africa and Central/South America. The concentration of particulate matter of diameter ≤ 2.5 μm (PM2.5 ) is an important metric to evaluate HAP risk, however epidemiological studies have demonstrated significant variation in HAP-PM2.5 concentrations at household, community and country levels. To quantify the global risk due to HAP exposure, novel estimation methods are needed, as financial and resource constraints render it difficult to monitor exposures in all relevant areas...
August 14, 2018: Environment International
Krishna K Gali, Yong Liu, Anoop Sindhu, Marwan Diapari, Arun S K Shunmugam, Gene Arganosa, Ketema Daba, Carolyn Caron, Reddy V B Lachagari, Bunyamin Tar'an, Thomas D Warkentin
BACKGROUND: The objective of this research was to map quantitative trait loci (QTLs) of multiple traits of breeding importance in pea (Pisum sativum L.). Three recombinant inbred line (RIL) populations, PR-02 (Orb x CDC Striker), PR-07 (Carerra x CDC Striker) and PR-15 (1-2347-144 x CDC Meadow) were phenotyped for agronomic and seed quality traits under field conditions over multiple environments in Saskatchewan, Canada. The mapping populations were genotyped using genotyping-by-sequencing (GBS) method for simultaneous single nucleotide polymorphism (SNP) discovery and construction of high-density linkage maps...
August 16, 2018: BMC Plant Biology
Shixiang Wang, Chifai Cheung, Mingjun Ren, Mingyu Liu
There are still significant challenges in the accurate positioning of optical freeform surfaces on the machine tool and the measurement instrument due to the high accuracy requirement and their complex shapes. This paper proposes a Fiducial-aided On-machine Positioning method (FAOPM) that combines on-machine measurement and off-machine measurement to precisely position optical freeform surfaces during the precision manufacturing cycle including rough machining, fine machining, measurement, and error compensation...
July 23, 2018: Optics Express
Shigeyoshi Inoue, Daniel Franz
Boron and aluminum share one main group, they are found in daily life commodities, and considered environmentally benign. Nevertheless, they markedly differ in their element properties (e.g. metal character, atomic radius). The use of Lewis acidic complexes of boron and aluminum for methods of bond activation and catalysis (e.g. hydrogenation of unsaturated substrates, polymerization of olefins and epoxides) is quickly expanding. The introduction of cationic charge may boost the metalloid-centered Lewis acidity and allows for its fine-tuning particularly with regard to preference for "hard" or "soft" Lewis bases (i...
August 16, 2018: Chemistry: a European Journal
Hye Won Suk, Stephen G West, Kimberly L Fine, Kevin J Grimm
This didactic article aims to provide a gentle introduction to penalized splines as a way of estimating nonlinear growth curves in which many observations are collected over time on a single or multiple individuals. We begin by presenting piecewise linear models in which the time domain of the data is divided into consecutive phases and a separate linear regression line is fitted in each phase. Linear splines add the feature that the regression lines fitted in adjacent phases are always joined at the boundary so there is no discontinuity in level between phases...
August 16, 2018: Psychological Methods
Faisal Mahmood, Richard Chen, Sandra Sudarsky, Daphne Yu, Nicholas J Durr
Deep learning has emerged as a powerful artificial intelligence tool to interpret medical images for a growing variety of applications. However, the paucity of medical imaging data with high-quality annotations that is necessary for training such methods ultimately limits their performance. Medical data is challenging to acquire due to privacy issues, shortage of experts available for annotation, limited representation of rare conditions and cost. This problem has previously been addressed by using synthetically generated data...
August 16, 2018: Physics in Medicine and Biology
Xiaoqing Wang, Xiangjun Wang, Yubo Ni
In the facial expression recognition task, a good-performing convolutional neural network (CNN) model trained on one dataset (source dataset) usually performs poorly on another dataset (target dataset). This is because the feature distribution of the same emotion varies in different datasets. To improve the cross-dataset accuracy of the CNN model, we introduce an unsupervised domain adaptation method, which is especially suitable for unlabelled small target dataset. In order to solve the problem of lack of samples from the target dataset, we train a generative adversarial network (GAN) on the target dataset and use the GAN generated samples to fine-tune the model pretrained on the source dataset...
2018: Computational Intelligence and Neuroscience
Keisuke Danno, Takuto Nakamura, Natsumi Okoso, Naohiko Nakamura, Kohta Iguchi, Yoshiaki Iwadate, Takahiro Kenmotsu, Masaya Ikegawa, Shinji Uemoto, Kenichi Yoshikawa
Although biopsy is one of the most important methods for diagnosis in diseases, there is ambiguity based on the information obtained from the visual inspection of tissue slices. Here, we studied the effect of external extension on tissue slices from mouse liver with different stages of disease: Healthy normal state, Simple steatosis, Non-alcoholic steatohepatitis and Hepatocellular carcinoma. We found that the cracking pattern of a tissue slice caused by extension can provide useful information for distinguishing among the disease states...
August 15, 2018: Scientific Reports
Liyan Wu, Zhibin Jiao, Yuqiu Song, Cuihong Liu, Huan Wang, Yuying Yan
Biological surfaces with unique wettability in nature have provided an enormous innovation for scientists and engineers. More specifically, materials possessing various wetting properties have drawn considerable attention owing to their promising application prospects. Recently, great efforts have been concentrated on the researches on wetting-induced drag-reduction materials inspired by biology because of their ability to save energy. In this work, the drag-reduction characteristics of the bionic surface with delicate water-trapping microstructures of fish Ctenopharyngodon idellus scales were explored by experimental method...
August 15, 2018: Scientific Reports
Qiudan Li, Can Wang, Ruoran Liu, Lei Wang, Daniel Dajun Zeng, Scott James Leischow
BACKGROUND: E-liquid is one of the main components in electronic nicotine delivery systems (ENDS). ENDS review comments could serve as an early warning on use patterns and even function to serve as an indicator of problems or adverse events pertaining to the use of specific e-liquids-much like types of responses tracked by the Food and Drug Administration (FDA) regarding medications. OBJECTIVE: This study aimed to understand users' "vaping" experience using sentiment opinion summarization techniques, which can help characterize how consumers think about specific e-liquids and their characteristics (eg, flavor, throat hit, and vapor production)...
August 15, 2018: Journal of Medical Internet Research
Yuan Yuan, Shirley Xie, Jennifer C Darnell, Andrew J Darnell, Yuhki Saito, Hemali Phatnani, Elisabeth A Murphy, Chaolin Zhang, Tom Maniatis, Robert B Darnell
BACKGROUND: Alternative RNA processing plays an essential role in shaping cell identity and connectivity in the central nervous system. This is believed to involve differential regulation of RNA processing in various cell types. However, in vivo study of cell type-specific post-transcriptional regulation has been a challenge. Here, we describe a sensitive and stringent method combining genetics and CLIP (crosslinking and immunoprecipitation) to globally identify regulatory interactions between NOVA and RNA in the mouse spinal cord motoneurons...
August 15, 2018: Genome Biology
David C Andersson, Nicolas Martinez, Dominik Zeller, Anders Allgardsson, Michael Marek Koza, Bernhard Frick, Fredrik J Ekström, Judith Peters, Anna Linusson
The enzyme acetylcholinesterase (AChE) is essential in human and animals since it catalyzes the breakdown of nerve signaling substance acetylcholine. Small molecules that inhibit the function of AChE are important for their use as drugs in, e.g., symptomatic treatment of Alzheimer's disease. New and improved inhibitors are warranted, mainly due to severe side effects of current drugs. In the present study, we have investigated if and how two enantiomeric inhibitors of AChE influences the overall dynamics of the non-covalent complexes, using elastic incoherent neutron scattering, A fruitful combination of univariate models including a newly developed non-Gaussian model for atomic fluctuations and multivariate methods (principal component analysis and discriminant analysis) was crucial to analyze the fine details of the data...
August 15, 2018: Journal of Physical Chemistry. B
Nailiang Yang, Hongfei Cheng, Xiaozhi Liu, Qinbai Yun, Ye Chen, Bing Li, Bo Chen, Zhicheng Zhang, Xiaoping Chen, Qipeng Lu, Jingtao Huang, Ying Huang, Yun Zong, Yanhui Yang, Lin Gu, Hua Zhang
Similar to heterostructures composed of different materials, possessing unique properties due to the synergistic effect between different components, the crystal-phase heterostructures, one variety of hetero-phase structures, composed of different crystal phases in monometallic nanomaterials are herein developed, in order to explore crystal-phase-based applications. As novel hetero-phase structures, amorphous/crystalline heterostructures are highly desired, since they often exhibit unique properties, and hold promise in various applications, but these structures have rarely been studied in noble metals...
August 14, 2018: Advanced Materials
Tomohiro Kajikawa, Noriyuki Kadoya, Kengo Ito, Yoshiki Takayama, Takahito Chiba, Seiji Tomori, Ken Takeda, Keiichi Jingu
The quality of radiotherapy has greatly improved due to the high precision achieved by intensity-modulated radiation therapy (IMRT). Studies have been conducted to increase the quality of planning and reduce the costs associated with planning through automated planning method; however, few studies have used the deep learning method for optimization of planning. The purpose of this study was to propose an automated method based on a convolutional neural network (CNN) for predicting the dosimetric eligibility of patients with prostate cancer undergoing IMRT...
August 14, 2018: Radiological Physics and Technology
Hervé Hogues, Francis Gaudreault, Christopher R Corbeil, Christophe Deprez, Traian Sulea, Enrico O Purisima
Despite decades of development, protein-protein docking remains a largely unsolved problem. The main difficulties are the immense space spanned by the translational and rotational degrees of freedom and the prediction of the conformational changes of proteins upon binding. FFT is generally the preferred method to exhaustively explore the translation-rotation space at a fine grid resolution, albeit with the tradeoff of approximating force fields with correlation functions. This work presents a direct search alternative that samples the states in Cartesian space at the same resolution and computational cost as standard FFT methods...
August 14, 2018: Journal of Chemical Theory and Computation
Yi Yu, Suhua Tang, Kiyoharu Aizawa, Akiko Aizawa
In this work, travel destinations and business locations are taken as venues. Discovering a venue by a photograph is very important for visual context-aware applications. Unfortunately, few efforts paid attention to complicated real images such as venue photographs generated by users. Our goal is fine-grained venue discovery from heterogeneous social multimodal data. To this end, we propose a novel deep learning model, category-based deep canonical correlation analysis. Given a photograph as input, this model performs: 1) exact venue search (find the venue where the photograph was taken) and 2) group venue search (find relevant venues that have the same category as the photograph), by the cross-modal correlation between the input photograph and textual description of venues...
August 10, 2018: IEEE Transactions on Neural Networks and Learning Systems
Kelei He, Xiaohuan Cao, Yinghuan Shi, Dong Nie, Yang Gao, Dinggang Shen
Accurate segmentation of pelvic organs (i.e., prostate, bladder and rectum) from CT image is crucial for effective prostate cancer radiotherapy. However, it is a challenging task due to 1) low soft tissue contrast in CT images and 2) large shape and appearance variations of pelvic organs. In this paper, we employ a two-stage deep learning based method, with a novel distinctive curve guided fully convolutional network (FCN), to solve the aforementioned challenges. Specifically, the first stage is for fast and robust organ detection in the raw CT images...
August 13, 2018: IEEE Transactions on Medical Imaging
Sarah E Gerard, Taylor J Patton, Gary E Christensen, John E Bayouth, Joseph M Reinhardt
Pulmonary fissure detection in computed tomography (CT) is a critical component for automatic lobar segmentation. The majority of fissure detection methods use feature descriptors that are hand-crafted, low-level, and have local spatial extent. The design of such feature detectors is typically targeted towards normal fissure anatomy, yielding low sensitivity to weak and abnormal fissures that are common in clinical datasets. Furthermore, local features commonly suffer from low specificity, as the complex textures in the lung can be indistinguishable from the fissure when global context is not considered...
August 10, 2018: IEEE Transactions on Medical Imaging
Chao Ma, Jia-Bin Huang, Xiaokang Yang, Ming-Hsuan Yang
Visual tracking is challenging as target objects often undergo significant appearance changes caused by deformation, abrupt motion, background clutter and occlusion. In this paper, we propose to exploit the rich hierarchical features of deep convolutional neural networks to improve the accuracy and robustness of visual tracking. Deep neural networks trained on object recognition datasets consist of multiple convolutional layers. These layers encode target appearance with different levels of abstraction. For example, the outputs of the last convolutional layers encode the semantic information of targets and such representations are invariant to significant appearance variations...
August 13, 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Meng Yang, Juntao Ye, Frank Ding, Yubo Zhang, Dong-Ming Yan
We introduce a new method to efficiently track complex interfaces among multi-phase immiscible fluids. Unlike existing techniques, we use a mesh-based representation for global liquid surfaces, while selectively modeling some local surficial regions with regional level sets (RLS) to handle complex geometry that is difficult to resolve with explicit topology operations. Such a semi-explicit surface mechanism can preserve volume, fine features and foam-like thin films under a relatively low computational expenditure...
August 7, 2018: IEEE Transactions on Visualization and Computer Graphics
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