microRNA计算发现方法的研究进展

microRNA(miRNA)是近几年发现的一类长度为～21nt的内源非编码小RNA,在植物和动物中发挥着重要而广泛的调控功能。它的发现主要有cDNA克隆测序和计算发现两条途径。由于cDNA克隆测序方法受miRNA表达的时间和组织特异性以及表达水平的影响,而计算发现可以弥补其不足,因此miRNA的计算发现方法研究受到了广泛的重视。文章对近几年计算发现miRNA的研究进展进行了综述,根据计算发现方法的本质,将计算发现方法归纳为5类,分别是同源片段搜索方法、基于比较基因组学的预测方法、基于序列和结构特征打分的预测方法、结合作用靶标的预测方法和基于机器学习的预测方法,并对各类方法的原理、核心思想、优点和局限性进行了分析,最后探讨了进一步的发展方向。

计算科学发现:目标与方法论反思

计算科学发现是人工智能和科学交汇融合的产物,其通过计算的方式对人类科学发现过程中的认知过程进行重构并实现了对人类自然认知能力的技术增强,从而使得经过恰当编程处理的计算机能够模拟人类的发现活动,并帮助人类获得关于客观世界的新知识。计算科学发现将计算作为一种方法和视角引入科学发现的研究中,揭示了以往推理方法中更为精细的结构,也为经典问题的研究提供新的思路。通过方法论的思考可以发现,计算科学发现的限度取决于多大程度上能够将具体的科学发现活动表征为具有可计算性的问题,为该问题设计高效的算法并提供充分且正确的科学数据,同时取决于是否能够对发现系统持有合理且必要的信任。

PBL教学法在《计算机辅助药物发现》教学中的实践探索

在后基因组时代背景下,计算机辅助药物发现在现代药物研发中体现越来越重要的作用。它不仅仅是交叉理论学科,更有很强的实践性。因此,在生物信息学本科专业开展PBL教学,通过实验流程设计将教学任务从理论和实践两个层面进行模块化融合,加强理论与实践的结合,调动学生参与课堂的积极性以及独立思考的主动性,提升学生主动提出问题和解决问题能力。这一课程教学的实践探索具有十分深远的意义。

传统和机器学习策略在基于结构虚拟筛选中的应用

计算机辅助药物发现和人工智能驱动药物设计在制药行业中是减少时间和经济成本的重要策略.其中具有代表性的方法包括虚拟筛选、蛋白质-配体相互作用评估、药物药代动力学性质预测以及药物设计.通常来说,虚拟筛选是药物发现的第一步,其主要目标是识别和发现潜在的先导化合物的候选物.在过去的几十年里,已经开发了多种传统的和基于机器学习的方法来提高虚拟筛选的准确性和速度.本综述总结了传统和机器学习方法在基于结构的虚拟筛选中的应用,讨赖性较弱,且分布非常冷并在j=1处达到峰值,但(0,1,0)振动态的转论了它们的性能、优势和局限性等方面.

世纪之交的科学方法论:20世纪80年代以来科学方法论的新发展

对20世纪80年代以来科学方法论新的发展进行了系统梳理和评介,以把握世纪之交科学方法论研究的新动向.

A Filtering Approach Based on MMAE for a SINS/CNS Integrated Navigation System

This paper explores multiple model adaptive estimation(MMAE) method, and with it, proposes a novel filtering algorithm. The proposed algorithm is an improved Kalman filter— multiple model adaptive estimation unscented Kalman filter(MMAE-UKF) rather than conventional Kalman filter methods,like the extended Kalman filter(EKF) and the unscented Kalman filter(UKF). UKF is used as a subfilter to obtain the system state estimate in the MMAE method. Single model filter has poor adaptability with uncertain or unknown system parameters,which the improved filtering method can overcome. Meanwhile,this algorithm is used for integrated navigation system of strapdown inertial navigation system(SINS) and celestial navigation system(CNS) by a ballistic missile's motion. The simulation results indicate that the proposed filtering algorithm has better navigation precision, can achieve optimal estimation of system state, and can be more flexible at the cost of increased computational burden.

跨课程PBL教学法在化学小分子设计和优化平台开发中的应用

在后基因组时代背景下,计算机辅助药物发现在现代药物研发中体现越来越重要的作用。它不仅仅是交叉理论学科,涉及到计算机、生物、化工等学科的内容,更具有很强的实践性。因此,本教改论文探讨如何在生物信息学本科专业开展PBL教学,以一个药物设计中具体的问题为导向,将各不同学科中关联课程的知识串联起来,包括从实验流程设计将相关课程的内容以项目内容为导向,将相关知识进行模块化融合,加强知识的融会贯通和实践转化,加深学生对不同学科知识的综合理解,调动学生参与课堂的积极性以及独立思考的主动性,提升学生主动提出问题和解决问题的能力。同时也为类似交叉学科课程的教学和课程设计提供了新的思路。

The Future of Manufacturing： A New Perspective

Many articles have been published on intelligent manufacturing, most of which focus on hardware, soft-ware, additive manufacturing, robotics, the Internet of Things, and Industry 4.0. This paper provides a dif-ferent perspective by examining relevant challenges and providing examples of some less-talked-about yet essential topics, such as hybrid systems, redefining advanced manufacturing, basic building blocks of new manufacturing, ecosystem readiness, and technology scalahility. The first major challenge is to （re-）define what the manufacturing of the future will he, if we wish to： ① raise public awareness of new manufacturing＇s economic and societal impacts, and ② garner the unequivocal support of policy- makers. The second major challenge is to recognize that manufacturing in the future will consist of sys-tems of hybrid systems of human and robotic operators; additive and suhtractive processes; metal and composite materials; and cyher and physical systems. Therefore, studying the interfaces between con- stituencies and standards becomes important and essential. The third challenge is to develop a common framework in which the technology, manufacturing business case, and ecosystem readiness can he eval- uated concurrently in order to shorten the time it takes for products to reach customers. Integral to this is having accepted measures of ＂scalahility＂ of non-information technologies. The last, hut not least, chal-lenge is to examine successful modalities of industry-academia-government collaborations through public-private partnerships. This article discusses these challenges in detail.

Status Quo and Reform Ideas for Accounting Calculation of Social Security Fund in China

With the steady development of China＇s economic policy and social secu- rity system, social security fund has become more and more important. Therefore, in the process of establishing and implementing social security system, the status of the accounting system of social security fund has become particularly prominent. The existing problems in the system were discussed and corresponding reform ideas were proposed by analyzing the current situation of the social security fund accounting system.

Using the Fractional Order Method to Generalize Strengthening Buffer Operator and Weakening Buffer Operator

To reveal the relationship between a weakening buffer operator and strengthening buffer operator, the traditional integer order buffer operator is extended to one that is fractional order. Fractional order buffer operator not only can generalize the weakening buffer operator and the strengthening buffer operator, but also results in small adjustments of the buffer effect.The effectiveness of the grey model(GM(1,1)) with the fractional order buffer operator is validated by six cases.

Face recognition using both visible light image and near-infrared image and a deep network

In recent years, deep networks has achieved outstanding performance in computer vision, especially in the field of face recognition. In terms of the performance for a face recognition model based on deep network, there are two main closely related factors： 1） the structure of the deep neural network, and 2） the number and quality of training data. In real applications, illumination change is one of the most important factors that significantly affect the performance of face recognition algorithms. As for deep network models, only if there is sufficient training data that has various illumination intensity could they achieve expected performance. However, such kind of training data is hard to collect in the real world. In this paper, focusing on the illumination change challenge, we propose a deep network model which takes both visible light image and near-infrared image into account to perform face recognition. Near- infrared image, as we know, is much less sensitive to illuminations. Visible light face image contains abundant texture information which is very useful for face recognition. Thus, we design an adaptive score fusion strategy which hardly has information loss and the nearest neighbor algorithm to conduct the final classification. The experimental results demonstrate that the model is very effective in realworld scenarios and perform much better in terms of illumination change than other state-of-the-art models.

Investigation of Eddy Current Nondestructive Testing for Carbon Fiber-Reinforced Plastic （CFRP） Based on Electromagnetic Field Analysis

CFRP （carbon fiber reinforced plastic） is used extensively in aircraft and spacecraft structures, because of its excellent mechanical properties. Ultrasonic testing, which is used as a non-destructive testing technique for CFRP, requires a contact medium. In contrast, eddy current testing does not require a contact medium, and when used for CFRP testing it has advantages not available with other techniques. CFRP is a laminate, with each layer being anisotropically conductive, and the distribution of the induced eddy current is yet to be determined. Here, to determine the eddy current distribution in the detection of flaws in cross-ply CFRP （0°/90°） by using a cross-point probe, we performed an FEM （finite element method） analysis of electromagnetic fields. We investigated the nature of the flaw signals and the differences in eddy current distributions between materials with and without flaws.

Trajectory planning for biped robot walking on uneven terrain - Taking stepping as an example

According to the features of movements of humanoid robot, a control system for humanoid robot walking on uneven terrain is present. Constraints of stepping over stairs are analyzed and the trajectories of feet are calculated by intelligent computing methods. To overcome the shortcomings resulted from directly controlling the robot by neural network （NN） and fuzzy logic controller （FLC）, a revised particle swarm optimization （PSO） algorithm is proposed to train the weights of NN and rules of FLC. Simulations and experiments on different control methods are achieved for a detailed comparison. The results show that using the proposed methods can obtain better control effect.

3D bioprinting for cell culture and tissue fabrication

Three-dimensional （3D） bioprinting is a computer-assisted technology which precisely controls spatial position of biomaterials, growth factors and living cells, offering unprecedented possibility to bridge the gap between structurally mimic tissue constructs and functional tissues or organoids. We briefly focus on diverse bioinks used in the recent progresses of biofabrication and 3D bioprinting of various tissue architectures including blood vessel, bone, cartilage, skin, heart, liver and nerve systems. This paper provides readers a guideline with the conjunction between bioinks and the targeted tissue or organ types in structuration and final functionalization of these tissue analogues. The challenges and perspectives in 3D bioprinting field are also illustrated.

ACP-based social computing and parallel intelligence： Societies 5.0 and beyond

Social computing, as the technical foundation of future computational smart societies, has the potential to improve the effectiveness of opensource big data usage, systematically integrate a variety of elements including time, human, resources, scenarios, and organizations in the current cyber-physical-social world, and establish a novel social structure with fair information, equal rights, and a flat configuration. Meanwhile, considering the big modeling gap between the model world and the physical world, the concept of parallel intelligence is introduced. With the help of software-defined everything, parallel intelligence bridges the big modeling gap by means of constructing artificial systems where computational experiments can be implemented to verify social policies, economic strategies, and even military operations. Artificial systems play the role of ＂social laboratories＂ in which decisions are computed before they are executed in our physical society. Afterwards, decisions with the expected outputs are executed in parallel in both the artificial and physical systems to interactively sense, compute, evaluate and adjust system behaviors in real-time, leading system behaviors in the physical system converging to those proven to be optimal in the artificial ones. Thus, the smart guidance and management for our society can be achieved.

Context-aware computing-based reducing cost of service method in resource discovery and interaction

Reducing cost of service is an important goal for resource discovery and interaction technologies. The shortcomings of transhipment-method and hibernation-method are to increase holistic cost of service and to slower resource discovery respectively. To overcome these shortcomings, a context-aware computing-based method is developed. This method, firstly, analyzes the courses of devices using resource discovery and interaction technologies to identify some types of context related to reducing cost of service, then, chooses effective methods such as stopping broadcast and hibernation to reduce cost of service according to information supplied by the context but not the transhipment-method’s simple hibernations. The results of experiments indicate that under the worst condition this method overcomes the shortcomings of transhipment-method, makes the “poor” devices hibernate longer than hibernation-method to reduce cost of service more effectively, and discovers resources faster than hibernation-method; under the best condition it is far better than hibernation-method in all aspects.

Dynamic Web services discovery based on similarity computation

A new approach for dynamic Web services discovery based on similarity computation in the treatment of massive Web information resource is put forward. Firstly, three kinds of service description information, textual, semantic and structural information, were modeled to compute the similarity of services. Then a novel dynamic Web services discovery mechanism was provided and the experiment on it was carried out. Results show that the new approach achieves considerable performance on precision and efficiency metrics for dynamic Web services discovery.

Evolutionary computation in China： A literature survey

Evolutionary computation （EC） has received significant attention in China during the last two decades. In this paper, we present an overview of the current state of this rapidly growing field in China. Chinese research in theoretical foundations of EC, EC-based optimization, EC-based data mining, and EC-based real-world applications are summarized.

Sequential Bag-of-Words model for human action classification

Recently, approaches utilizing spatial-temporal features to form Bag-of-Words （BoWs） models have achieved great success due to their simplicity and effectiveness. But they still have difficulties when distinguishing between actions with high inter-ambiguity. The main reason is that they describe actions by orderless bag of features, and ignore the spatial and temporal structure information of visual words. In order to improve classification performance, we present a novel approach called sequential Bag-of-Words. It captures temporal sequential structure by segmenting the entire action into sub-actions. Meanwhile, we pay more attention to the distinguishing parts of an action by classifying sub- actions separately, which is then employed to vote for the final result. Extensive experiments are conducted on challenging datasets and real scenes to evaluate our method. Concretely, we compare our results to some state-of-the-art classification approaches and confirm the advantages of our approach to distinguish similar actions. Results show that our approach is robust and outperforms most existing BoWs based classification approaches, especially on complex datasets with interactive activities, cluttered backgrounds and inter-class action ambiguities.

An Edge-Based Approach for Semantic Matchmaking of Service Capabilities

Semantic Web Services is an emerging technology that promises to enable dynamic, execution-time discovery, composition, and invocation of Web Services. Semantic matchmaking plays a vital role in the automated and dynamic discovery process of Semantic Web Services and consists in measuring the semantic distance between a requested service and an advertised one. In this paper, an innovative approach to effectively compute the semantic distance between Ontology Web Language for Services （OWL-S） annotated services is proposed. First, an edge-based method for measuring the semantic distance between Web Ontology Language （OWL） concepts is presented. Then, a comparison of the proposed measure and the one presented in a recent related work is made in order to show that our method is more efficient and fine-grained. Finally, some equations to compute semantic matchmaking of service capabilities, which are expressed in terms of inputs and outputs, are presented.