关键词： MASPs   RCA family   complement regulation   lectin pathway   chimeric proteins  

摘要： Dysregulation of the complement system is involved in the pathogenesis of several diseases, and its inhibition has been shown to be a feasible therapeutic option. Therefore, there is an interest in the development of complement modulators to treat complement activation-related inflammatory pathologies. Mannose-binding lectin (MBL)/ficolin/collectin-associated protein-1 (MAP-1) is a regulatory molecule of the lectin pathway (LP), whereas complement receptor 1 (CD35) and decay-accelerating factor (CD55) are membrane-anchored regulators with effects on the central effector molecule C3. In this study, we developed 2 novel soluble chimeric inhibitors by fusing MAP-1 to the 3 first domains of CD35 (CD35(1-3)) or the 4 domains of CD55 (CD55(1-4)) to modulate the complement cascade at 2 different stages. The constructs showed biologic properties similar to those of the parent molecules. In functional complement activation assays, the constructs were very efficient in inhibiting LP activation at the level of C3 and in the formation of terminal complement complex. This activity was enhanced when coincubated with recombinant LP recognition molecules MBL and ficolin-3. Recombinant MAP-1 fusion proteins, combined with recombinant LP recognition molecules to target sites of inflammation, represent a novel and effective therapeutic approach involving the initiation and the central and terminal effector functions of the complement cascade.

关键词： Structural health monitoring   Machine learning   Low-rank matrix completion   Pattern recognition   Self-powered sensors   Plate-like structures   Incomplete signals   Energy harvesting  

摘要： Data mining methods have been widely used for structural health monitoring (SHM) and damage identification for analysis of continuous signals. Nonetheless, the applicability and effectiveness of these techniques cannot be guaranteed when dealing with discrete binary and incomplete/missing signals (i.e., not continuous in time). In this paper a novel data interpretation framework for SHM with noisy and incomplete signals, using a through-substrate self-powered sensing technology, is presented within the context of artificial intelligence (AI). AI methods, namely, machine learning and pattern recognition, were integrated within the data interpretation framework developed for use in a practical engineering problem: data-driven SHM of plate-like structures. Finite element simulations on an aircraft stabilizer wing and experimental vibration tests on a dynamically loaded plate were conducted to validate the proposed framework. Machine learning algorithms, including support vector machine, k-nearest neighbor, and artificial neural networks, were integrated within the developed learning framework for performance assessment of the monitored structures. Different levels of harvested energy were considered to evaluate the robustness of the SHM system with respect to such variations. Results demonstrate that the SHM methodology employing the proposed machine learning-based data interpretation framework is efficient and robust for damage detection with incomplete and sparse/missing binary signals, overcoming the notable issue of energy availability for smart damage identification platforms being used in structural/infrastructure and aerospace health monitoring. The present study aims to advance data mining and interpretation techniques in the SHM domain, promoting the practical application of machine learning and pattern recognition with incomplete and missing/sparse signals in smart cities and smart infrastructure monitoring.

关键词： MDA5   Squaliobarbus curriculus   Grass carp reovirus   Expression level   Overexpression  

摘要： MDA5 is a cytoplasmic viral double-stranded RNA recognition receptor that plays a pivotal role in the aquatic animal innate immune system. To decipher the role of MDA5 of Squaliobarbus curriculus (ScMDA5) in the immune response, full-length cDNA of ScMDA5 was cloned using the RACE technology, mRNA and protein expression levels of ScMDA5 signalling pathway members in response to stimulation were detected and effects of overexpression of ScMDA5 on the immune response were investigated. ScMDA5 comprises 3597 bp and is composed of an open reading frame (2958 nucleotides long) that translates into a putative peptide of 985 amino acid residues. ScMDA5 possesses two N-terminal caspase-recruiting domains, DEAD-like helicases superfamily, helicase superfamily C-terminal and RIG-I_C-RD domains, and differences in these domains among species were mainly observed with respect to their length and location. ScMDA5 was closely clustered with those of Carassius meatus, Ctenoptuayngodon idellus and Mylophwyngodon piceus. ScMDA5 transcripts were most abundant in the spleen and the lowest in the liver. Expression levels of ScMDA5 in healthy tissues were significantly correlated with those of ScIRF3, ScIRF7 and ScIFN. Besides, mRNA expression levels of ScIRF3 were significantly correlated with those of ScIRF7 (0.956, P < 0.01). Expression level changes, including downregulation, upregulation and initial upregulation followed by downregulation, were found in ScMDA5 signalling pathway molecules in tissues after grass carp reovirus infection. Protein levels of ScMDA5 were the highest in the liver and the lowest in the spleen in detected healthy tissues. Overexpression of ScMDA5 led to significantly enhanced CURF7 and CiMx, bc transcription in grass carp ovary cells (P < 0.05). The results of this study helped to clarify the role of ScMDA5 in the immune reaction against grass carp reovirus and provided fundamental information for fish breeding to achieve strong resistance to infection.

关键词： N6-methyladenosine site   Deep learning   Convolution neural network   RNA methylation   word2vec  

摘要： N6-methyladenosine (m(6)A) is an RNA methylation modification and it is involved in various biological progresses such as translation, alternative splicing, degradation, stability, etc. Therefore, it is highly recommended to develop computational models for detecting N6-methyldenosine sites in RNA as experimental technologies, such as m(6)A-seq and MeRIP-Seq, are both expensive and time consuming. Previous works start with features design step, which requires domain knowledge, followed by a classifier or cascade of classifiers for m(6)A sites identification. In this paper, on the other hand, we utilize an automatic feature learning approach based on the widely used natural language technique "word2vec". The learnt features are extracted automatically from the human genome without any explicit definition. Then, these learnt features are fed to a simple convolution neural network model for classification. The proposed model is denoted as "iN6-Methyl (5-step)". It has been evaluated on three publicly available benchmark datasets and outperformed the current state-of-the-art methods. It is anticipated that the proposed model could be helpful for both academia and drug discovery. Finally, a user-friendly webserver has been established and made freely available at: https://***/NSCL/***.

关键词： Chemiluminescence   Immunoassay   Enhanced chemiluminescence   Digital image processing   Pattern recognition   Horseradish peroxidase  

摘要： Herein we report the combination of enzyme-linked immunoassay and pattern recognition analysis for extracting both chemical and spatial information from latent fingermarks (LFMs). The development approach basically involves two steps, namely, specific recognition of protein and polypeptide secretions present in the ridge residues of LFMs by horseradish peroxidase (HRP)-labeled antibodies and the HRP-catalyzed chemiluminescent (CL) reaction between luminol and H2O2. The emitted light can spatially resolve the ridges, generating a bright image against the dark object surface for visualization of an LFM. Meanwhile, thanks to the molecular specificity of the immunoassay step, the emission also provides us additional information on the existence of specific substances in LFMs. The developed LFMs are further processed by a set of digital image processing procedures. Quantitative analysis based on minutia features shows that even poorly developed fingermarks can be matched successfully. This work offers the promise of facilitating cross-disciplinary studies between data-processing approaches and fingermark development techniques, such as the extraction of more information from LFM evidence, as well as the establishment of evaluation criteria for an enhancement technique.

关键词： Ballast box test   Ballast fouling   SmartRock   Pattern recognition   Autoregressive (AR) model   X-bar control chart  

摘要： In railroad, unfavorable ballast performances (e.g., ballast fouling, loss of lateral confinement) can lead to deterioration of upper structures such as the rail and tie. Therefore, accurate and timely monitoring of ballast condition is critical for rail safety operation and effective maintenance. In this paper, a series of ballast box tests were conducted to investigate the ballast particle movement pattern inside railway ballast under different ballast, loading, moisture, and shoulder confinement conditions. Eight wireless embedded devices - "SmartRocks" - were used in the laboratory tests in three different locations to study different ballast movement patterns under different conditions. A statistical Autoregressive (AR) model with X-bar control chart method was used to identify changes in particle movement patterns under different conditions. The results show that 1) the ballast particle movements are much more sensitive to moisture content for the fouled ballast than for the clean ballast;and 2) the AR model is capable of identifying ballast fouling and shoulder instability. In addition, a threshold value of 20% for the percentage of outliers of ballast particle movement patterns is suggested for the test conditions considered in this study. This study represents a preliminary step towards developing a reliable ballast condition identification index and further field studies are needed. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Acromegaly   Artificial intelligence   Biology and life sciences   Clinical genetics   Computer and information sciences   Diagnostic medicine   Endocrine disorders   Endocrinology   Fabry disease   Genetic diseases   Health care   Health care providers   Machine learning   Medical doctors   Medical personnel   Medicine and health sciences   Patient advocacy   People and places   Physicians   Population groupings   Professions   Psychology   Psychometrics   Questionnaires   Research and analysis methods   Research Article   Research design   Social sciences   Survey research  

摘要： Background Rare diseases (RD) result in a wide variety of clinical presentations, and this creates a significant diagnostic challenge for health care professionals. We hypothesized that there exist a set of consistent and shared phenomena among all individuals affected by (different) RD during the time before diagnosis is established. Objective We aimed to identify commonalities between different RD and developed a machine learning diagnostic support tool for RD. Methods 20 interviews with affected individuals with different RD, focusing on the time period before their diagnosis, were performed and qualitatively analyzed. Out of these pre-diagnostic experiences, we distilled key phenomena and created a questionnaire which was then distributed among individuals with the established diagnosis of i.) RD, ii.) other common nonrare diseases (NRO) iii.) common chronic diseases (CD), iv.), or psychosomatic/somatoform disorders (PSY). Finally, four combined single machine learning methods and a fusion algorithm were used to distinguish the different answer patterns of the questionnaires. Results The questionnaire contained 53 questions. A total sum of 1763 questionnaires (758 RD, 149 CD, 48 PSY, 200 NRO, 34 healthy individuals and 574 not evaluable questionnaires) were collected. Based on 3 independent data sets the 10-fold stratified cross-validation method for the answer-pattern recognition resulted in sensitivity values of 88.9% to detect the answer pattern of a RD, 86.6% for NRO, 87.7% for CD and 84.2% for PSY. Conclusion Despite the great diversity in presentation and pathogenesis of each RD, patients with RD share surprisingly similar pre-diagnosis experiences. Our questionnaire and data-mining based approach successfully detected unique patterns in groups of individuals affected by a broad range of different rare diseases. Therefore, these results indicate distinct patterns that may be used for diagnostic support in RD.

关键词： Myo-prosthesis   EMG   Pattern recognition   myosignals  

摘要： Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations.

关键词： 背景建模   行人检测   人脸检测   Ada   Boost   模式识别  

摘要： 通过深入研究高斯混合模型、HOG+SVM分类器和Haar+Ada Boost分类器的算法原理,提出了一种基于前景提取和模式识别的行人检测算法,首先使用高斯混合模型进行背景建模,通过前景提取的方法提取出运动物体,对运动物体进行行人检测,最后对判断为行人的对象进行人脸检测,分类区分出蒙面的可疑行人,解决基于背景建模方法的误判问题和基于统计学习方法的效率问题.实验结果表明,相对于单独使用模式识别算法,新算法显著降低了漏检率,且在运行速度和检测率方面表现良好.

关键词： 城市道路交通   空间交互  

摘要： 中国科学院深圳先进技术研究院高性能计算技术研究中心博士后刘康与中国科学院地理科学与资源研究所陆锋研究员团队、美国威斯康辛-麦迪逊大学高松助理教授合作的研究在城市道路交通交互模式识别方法取得进展。相应成果为“Liu K,Gao S,Lu *** spatial interaction patterns of vehicle movements on urban road networks by topic modelling[J].Computers,Environment and Urban Systems,2019,74:50-61(基于主题模型的城市道路交通空间交互模式识别方法)”。