Automatically detecting and tracking free‐ranging Japanese macaques in video recordings with deep learning and particle filters

Recently, automated observation systems for animals using artificial intelligence have been proposed. In the wild, animals are difficult to detect and track automatically because of lamination and occlusions. Our study proposes a new approach to automatically detect and track wild Japanese macaques (Macaca fuscata) using deep learning and a particle filter algorithm. Macaque likelihood is derived through deep learning and used as an observation model in a particle filter to predict the macaques’ position and size in an image. By using deep learning as an observation model, it is possible to simplify the observation model and improve the accuracy of the classifier. We investigated whether the algorithm could find body regions of macaques in video recordings of free‐ranging groups at Katsuyama, Japan to evaluate our model. Experimental results showed that our method with deep learning as an observation model had higher tracking accuracy than a method that uses a support vector machine. More generally, our study will help researchers to develop automatic observation systems for animals in the wild.     

Development and implementation of a general compiler to produce specific programs for minicomputer control of physiological experiments in real time

A method for automatic generation of specific FORTRAN programs to control physiological experiments with a computer was developed. The general real-time software package is built into a high level language (MAB = MAcro Basic). From this package, the scientist can automatically generate for him/herself specific programs for controlling his own experiments by a simple procedure. The generated programs contain only the relevant code, adjusted dimensions of arrays, names of parameters, and formatting for printing of tables and graphics for the particular experiment. Therefore, the resulting program is efficient both in terms of memory utilization and in execution time.     

Remote Safety Monitoring for Elderly Persons Based on Omni-Vision Analysis

Remote monitoring service for elderly persons is important as the aged populations in most developed countries continue growing. To monitor the safety and health of the elderly population, we propose a novel omni-directional vision sensor based system, which can detect and track object motion, recognize human posture, and analyze human behavior automatically. In this work, we have made the following contributions: (1) we develop a remote safety monitoring system which can provide real-time and automatic health care for the elderly persons and (2) we design a novel motion history or energy images based algorithm for motion object tracking. Our system can accurately and efficiently collect, analyze, and transfer elderly activity information and provide health care in real-time. Experimental results show that our technique can improve the data analysis efficiency by 58.5% for object tracking. Moreover, for the human posture recognition application, the success rate can reach 98.6% on average.     

单片机控制的颈动脉窦区自动灌流系统的研究

目的：建立一种自动控制颈动脉窦区灌流压的新方法。方法：采用AT89C2051单片计算机构成谦价实用的灌流控制器,钭它与LDB-M型电子蠕动泵、实验动物的颈动脉窦区构成自动灌流系统。结果及结论：该系统能对大鼠颈动脉窦区进行均匀斜坡升压,阶梯升压-降压灌流,并已用于压力感受器反射效应研究中,此系统能保证灌流曲线的准确性和可重复性。进一步提高了实验的技术水平。     

Classification of C2C12 cells at differentiation by convolutional neural network of deep learning using phase contrast images

In the field of regenerative medicine, tremendous numbers of cells are necessary for tissue/organ regeneration. Today automatic cell-culturing system has been developed. The next step is constructing a non-invasive method to monitor the conditions of cells automatically. As an image analysis method, convolutional neural network (CNN), one of the deep learning method, is approaching human recognition level. We constructed and applied the CNN algorithm for automatic cellular differentiation recognition of myogenic C2C12 cell line. Phase-contrast images of cultured C2C12 are prepared as input dataset. In differentiation process from myoblasts to myotubes, cellular morphology changes from round shape to elongated tubular shape due to fusion of the cells. CNN abstract the features of the shape of the cells and classify the cells depending on the culturing days from when differentiation is induced. Changes in cellular shape depending on the number of days of culture (Day 0, Day 3, Day 6) are classified with 91.3% accuracy. Image analysis with CNN has a potential to realize regenerative medicine industry.     

Automatic Detection of Calcaneal-Fifth Metatarsal Angle Using Radiograph: A Computer-Aided Diagnosis of Flat Foot for Military New Recruits in Taiwan

Flatfoot (pes planus) is one of the most important physical examination items for military new recruits in Taiwan. Currently, the diagnosis of flatfoot is mainly based on radiographic examination of the calcaneal-fifth metatarsal (CA–MT5) angle, also known as the arch angle. However, manual measurement of the arch angle is time-consuming and often inconsistent between different examiners. In this study, seventy male military new recruits were studied. Lateral radiographic images of their right and left feet were obtained, and mutual information (MI) registration was used to automatically calculate the arch angle. Images of two critical bones, the calcaneus and the fifth metatarsal bone, were isolated from the lateral radiographs to form reference images, and were then compared with template images to calculate the arch angle. The result of this computer-calculated arch angle was compared with manual measurement results from two radiologists, which showed that our automatic arch angle measurement method had a high consistency. In addition, this method had a high accuracy of 97% and 96% as compared with the measurements of radiologists A and B, respectively. The findings indicated that our MI registration measurement method cannot only accurately measure the CA–MT5 angle, but also saves time and reduces human error. This method can increase the consistency of arch angle measurement and has potential clinical application for the diagnosis of flatfoot.     

Extracting optimal tempo-spatial features using local discriminant bases and common spatial patterns for brain computer interfacing

Brain computer interfaces (BCI) provide a new approach to human computer communication, where the control is realised via performing mental tasks such as motor imagery (MI). In this study, we investigate a novel method to automatically segment electroencephalographic (EEG) data within a trial and extract features accordingly in order to improve the performance of MI data classification techniques. A new local discriminant bases (LDB) algorithm using common spatial patterns (CSP) projection as transform function is proposed for automatic trial segmentation. CSP is also used for feature extraction following trial segmentation. This new technique also allows to obtain a more accurate picture of the most relevant temporal–spatial points in the EEG during the MI. The results are compared with other standard temporal segmentation techniques such as sliding window and LDB based on the local cosine transform (LCT).     

Use of Indocyanine Green for Detecting the Sentinel Lymph Node in Breast Cancer Patients: From Preclinical Evaluation to Clinical Validation

Assessment of the sentinel lymph node (SLN) in patients with early stage breast cancer is vital in selecting the appropriate surgical approach. However, the existing methods, including methylene blue and nuclides, possess low efficiency and effectiveness in mapping SLNs, and to a certain extent exert side effects during application. Indocyanine green (ICG), as a fluorescent dye, has been proved reliable usage in SLN detection by several other groups. In this paper, we introduce a novel surgical navigation system to detect SLN with ICG. This system contains two charge-coupled devices (CCD) to simultaneously capture real-time color and fluorescent video images through two different bands. During surgery, surgeons only need to follow the fluorescence display. In addition, the system saves data automatically during surgery enabling surgeons to find the registration point easily according to image recognition algorithms. To test our system, 5 mice and 10 rabbits were used for the preclinical setting and 22 breast cancer patients were utilized for the clinical evaluation in our experiments. The detection rate was 100% and an average of 2.7 SLNs was found in 22 patients. Our results show that the usage of our surgical navigation system with ICG to detect SLNs in breast cancer patients is technically feasible.     

Implementation of controlling strategy in a biomechanical lower limb model with active muscles for coupling multibody dynamics and finite element analysis

Computational biomechanics for human body modeling has generally been categorized into two separated domains: finite element analysis and multibody dynamics. Combining the advantages of both domains is necessary when tissue stress and physical body motion are both of interest. However, the method for this topic is still in exploration. The aim of this study is to implement unique controlling strategies in finite element model for simultaneously simulating musculoskeletal body dynamics and in vivo stress inside human tissues. A finite element lower limb model with 3D active muscles was selected for the implementation of controlling strategies, which was further validated against in-vivo human motion experiments. A unique feedback control strategy that couples together a basic Proportion-Integration-Differentiation (PID) controller and generic active signals from Computed Muscle Control (CMC) method of the musculoskeletal model or normalized EMG singles was proposed and applied in the present model. The results show that the new proposed controlling strategy show a good correlation with experimental test data of the normal gait considering joint kinematics, while stress distribution of local lower limb tissue can be also detected in real-time with lower limb motion. In summary, the present work is the first step for the application of active controlling strategy in the finite element model for concurrent simulation of both body dynamics and tissue stress. In the future, the present method can be further developed to apply it in various fields for human biomechanical analysis to monitor local stress and strain distribution by simultaneously simulating human locomotion.     

基于深度学习的肝脏病理图像中肝脂肪变性分级研究

摘要 目的：设计基于深层神经网络模型用来分析肝脏全景病理切片图像（Whole slide images, WSI）的肝脂肪变性分级方法,以实现对非酒精性脂肪性肝病（Non-alcoholic fatty liver disease, NAFLD）病程的辅助诊断。方法：结合临床诊断,以非酒精性脂肪肝活动度积分（NAFLD activity score, NAS）为评价标准,将肝脂肪变性程度分为无、轻度、中度和重度等四级病程,本研究采用多示例学习的策略构建并训练深度神经网络模型,将训练获得的人工智能模型用来实现计算机自动化诊断肝脏病理切片中肝脂肪变性程度分级。结果：通过使用本研究中的人工智能方法可以在3分钟内对一张WSI进行完整的分析,得到该病患肝脏病理切片中肝脂肪变性分级,训练获得的人工智能模型的AUC为0.97,肝脂肪变性分级的平均准确率为78.18%,macro-F1 score、macro-Precision和macro-Recall分别为79.49、82.03和77.10,其结果展示获得的人工智能模型已满足可辅助临床诊断的水平。结论：本研究基于深度学习技术开发的人工智能方法初步实现快速自动化诊断肝脂肪变性分级,展现了其潜在的临床使用价值。     

Detecting Memory and Structure in Human Navigation Patterns Using Markov Chain Models of Varying Order

One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Google''s PageRank algorithm and others. Recently, new studies suggested that human navigation may better be modeled using higher order Markov chain models, i.e., the next page depends on a longer history of past clicks. Yet, this finding is preliminary and does not account for the higher complexity of higher order Markov chain models which is why the memoryless model is still widely used. In this work we thoroughly present a diverse array of advanced inference methods for determining the appropriate Markov chain order. We highlight strengths and weaknesses of each method and apply them for investigating memory and structure of human navigation on the Web. Our experiments reveal that the complexity of higher order models grows faster than their utility, and thus we confirm that the memoryless model represents a quite practical model for human navigation on a page level. However, when we expand our analysis to a topical level, where we abstract away from specific page transitions to transitions between topics, we find that the memoryless assumption is violated and specific regularities can be observed. We report results from experiments with two types of navigational datasets (goal-oriented vs. free form) and observe interesting structural differences that make a strong argument for more contextual studies of human navigation in future work.     

Acquiring lexical knowledge using raw corpora and unsupervised clustering method

In this paper, we propose a computational model for automatic acquisition of lexical knowledge based on the principles of human language information processing. The proposed model assumes a hybrid model for the human lexical representation including full-list and decomposition forms. The proposed method automatically acquires lexical entries and its grammatical knowledge by unsupervised learning techniques. For the purposes of evaluating performance of the proposed method, a large-scale corpus of over 10 million lexical was used, the lexical knowledge acquisition process was tested, and the results were analyzed.     

Autonomous Navigation for Unmanned Aerial Vehicles Based on Chaotic Bionics Theory

In this paper a new reactive mechanism based on perception-action bionics for multi-sensory integration applied to Un-manned Aerial Vehicles (UAVs) navigation is proposed.The strategy is inspired by the olfactory bulb neural activity observed inrabbits subject to external stimuli.The new UAV navigation technique exploits the use of a multiscroll chaotic system which isable to be controlled in real-time towards less complex orbits,like periodic orbits or equilibrium points,considered as perceptiveorbits.These are subject to real-time modifications on the basis of environment changes acquired through a Synthetic ApertureRadar (SAR) sensory system.The mathematical details of the approach are given including simulation results in a virtual en-vironment.The results demonstrate the capability of autonomous navigation for UAV based on chaotic bionics theory in com-plex spatial environments.     

Automatic dendritic spine analysis in two-photon laser scanning microscopy images.

Dendritic spine expression plays an important role in the central nervous system. Modern fluorescence microscopy and green fluorescent protein technology have facilitated the research on spines. To quantitatively analyze the spines in fluorescence microscopy images, an automatic dendritic spine analysis method is proposed. Because of the limit of axial resolution, our method is designed to process the projection image along the z-axis and analyze the lateral spines. The method can automatically extract the dendrite centerlines and segment the spines along the dendrites according to width-based criteria. The criteria utilize a common morphological feature of the spines. It can detect some shapes of spines missed by previous methods. In addition, the proposed method is automatic once a few parameters are set. Spine numbers, lengths, and densities, which biologists are interested in, are analyzed both manually and automatically. The results of the two methods match well. The proposed method provides automatic and accurate dendritic spine analysis. It can serve as a useful tool for spine image analysis to avoid tedious manual labor.     

基于植保大数据的病虫害移动智能采集新设备

为提高农作物重大病虫害发生信息自动化、智能化采集能力,全面提升监测预警水平,笔者基于大数据、人工智能和深度学习技术,研发了一款农作物病虫害移动智能采集设备——智宝,主要实现了3个方面的功能:一是病虫害发生信息自动采集上报.通过该产品进行人工拍照,可实现对田间农作物重大病虫害发生图像、发生位置、发生数量、微环境因子等数据的实时采集和上报.二是自动识别计数.基于植保大数据与人工智能技术,通过构建病虫害自动识别系统,可实现重大病虫害精准识别与分析,只要拍摄照片,即可快速、精确地识别病虫害种类,并自动计数、上报到指定的测报系统.三是自动分析判别分级.针对拍摄采集上报的重大病虫害发生信息,系统可在自动识别和计数的基础上,进一步对病虫害发生严重程度进行智能判别分级,甚至根据相关预测模型,对病虫害的发生趋势进行辅助分析预测,提出预测建议.通过2016—2019年组织多地植保机构进行试验改进,该技术产品日趋成熟,有望在未来的农作物病虫害发生信息采集和预测预报工作中推广使用.     

Error tolerant NMR backbone resonance assignment and automated structure generation

Error tolerant backbone resonance assignment is the cornerstone of the NMR structure determination process. Although a variety of assignment approaches have been developed, none works sufficiently well on noisy fully automatically picked peaks to enable the subsequent automatic structure determination steps. We have designed an integer linear programming (ILP) based assignment system (IPASS) that has enabled fully automatic protein structure determination for four test proteins. IPASS employs probabilistic spin system typing based on chemical shifts and secondary structure predictions. Furthermore, IPASS extracts connectivity information from the inter-residue information and the (automatically picked) (15)N-edited NOESY peaks which are then used to fix reliable fragments. When applied to automatically picked peaks for real proteins, IPASS achieves an average precision and recall of 82% and 63%, respectively. In contrast, the next best method, MARS, achieves an average precision and recall of 77% and 36%, respectively. The assignments generated by IPASS are then fed into our protein structure calculation system, FALCON-NMR, to determine the 3D structures without human intervention. The final models have backbone RMSDs of 1.25?, 0.88?, 1.49?, and 0.67? to the reference native structures for proteins TM1112, CASKIN, VRAR, and HACS1, respectively. The web server is publicly available at http://monod.uwaterloo.ca/nmr/ipass.     

RFID analytics for hospital ward management

In this paper, we present an RFID-enabled platform for hospital ward management. Active RFID tags are attached to individuals and assets in the wards. Active RFID readers communicate with the tags continuously and automatically to keep track of the real-time information about the locations of the tagged objects. The data regarding the locations and other transmitted information are stored in the ward management system. This platform enables capabilities of real-time monitoring and tracking of individuals and assets, reporting of ward statistics, and providing intelligence and analytics for hospital ward management. All of these capabilities benefit hospital ward management by enhanced patient safety, increased operational efficiency and throughput, and mitigation of risk of infectious disease widespread. A prototype developed based on our proposed architecture of the platform was tested in a pilot study, which was conducted in two medical wards of the intensive care unit of one of the largest public general hospitals in Hong Kong. This pilot study demonstrates the feasibility of the implementation of this RFID-enabled platform for practical use in hospital wards. Furthermore, the data collected from the pilot study are used to provide data analytics for hospital ward management.     

Early Stage Chronic Kidney Disease Diagnosis by Applying Data Mining Methods to Urinalysis,Blood Analysis and Disease History

Background

Chronic kidney disease (CKD) is a disorder associated with breakdown of kidney structure and function. CKD can be diagnosed in its early stage only by experienced nephrologists and urologists (medical experts) using the disease history, symptoms and laboratory tests. There are few studies related to the automatic diagnosis of CKD in the literature. However, these methods are not adequate to help the medical experts.

Computer analysis of cervical cells. Automatic feature extraction and classification

A prescreening instrument for cervical smears using computerized image processing and pattern recognition techniques requires that single cells in the specimen can be automatically isolated and analyzed. This paper describes a dual wavelength method for automatic isolation of the cytoplasm and nuclei of cells. Density-oriented, shape-oriented and texture-oriented parameters were calculated and evaluated for more than 600 cells. It is shown that the computer can be taught to distinguish between normal and atypical cells with an accuracy of ca. 97%, while human classification reproducibility is ca. 95%. In addition, an attempt to assign a measure of atypia to individual cells is described.     

A distributed knowledge-based approach to flexible automation: The contract net framework

This article applied distributed artificial intelligence to the real-time planning and control of flexible manufacturing systems (FMS) consisting of asynchronous manufacturing cells. A knowledge-based approach is used to determine the course of action, resource sharing, and processor assignments. Within each cell there is an embedded automatic planning system that executes dynamic scheduling and supervises manufacturing operations. Because of the decentralized control, real-time task assignments are carried out by a negotiation process among cell hosts. The negotiation process is modeled by augmented Petri nets —the combination of production rules and Petri nets—and is excuted by a distributed, rule-based algorithm.