LARaLink 2.0: a comprehensive aid to basic and clinical cytogenetic research

LARaLink 2.0 (Loci Analysis for Rearrangement Link) is an enabling web technology that permits the rapid retrieval of clinical cytogenetic and molecular data. New data mining capabilities have been incorporated into version 2.0, building upon LARaLink 1.0, to extend the utility of the system for applications in both the clinical and basic sciences. These include access to the Chromosomal Variation in Man database and the GEO database. Together these new resources enhance the user's ability to associate genotype with phenotype to identify potential gene candidates. Unlimited access for researchers exploring disease-gene relationships and for clinicians extending practice in patient care is available at LARaLink.bioinformatics.wayne.edu:8080/ unigene.     

The Prerequisites of Personality Growth in Developmental Education

The Elkonin-Davydov system of developmental education focuses on theoretical generalization in knowledge domains. The present article emphasizes the developmental potential of the approach. A parallel is drawn between Carl Rogers's approach to "free learning" and the Elkonin-Davydov system. Based on a comparative analysis, the article presents a sketch of a new philosophy of education.     

植被叶面积指数遥感反演的尺度效应及空间变异性

遥感作为宏观生态学研究中数据获取的一种便捷手段,有助于把握较大尺度内生态学现象的特征.应用遥感数据反演LAI时,由于像元的异质性,不同尺度遥感数据之间的转换是遥感发展的一个重要问题.以河北省黄骅市为研究区,在利用TM和MODIS遥感数据对芦苇LAI反演误差产生原因进行分析的基础上,利用半变异函数对像元空间异质性进行了定量描述.发现NDVI算法的非线性带给LAI尺度转换的误差很小,而LAI的空间异质性则是引起LAI尺度效应的根本原因.并且当像元内空间异质性很大时半变异函数的基台值比纯像元要大得多,空间自相关的程度是引起LAI尺度转换误差的主要原因;反之,像元内空间异质性不大时,随机误差是引起LAI尺度转换误差的主要原因.当像元为纯像元时,由像元异质性引起的反演误差基本可以忽略.此外,研究区芦苇的空间相关有效尺度约为360m,超过此距离空间相关性则不复存在.     

Content-Based Image Retrieval Using Spatial Layout Information in Brain Tumor T1-Weighted Contrast-Enhanced MR Images

This study aims to develop content-based image retrieval (CBIR) system for the retrieval of T1-weighted contrast-enhanced MR (CE-MR) images of brain tumors. When a tumor region is fed to the CBIR system as a query, the system attempts to retrieve tumors of the same pathological category. The bag-of-visual-words (BoVW) model with partition learning is incorporated into the system to extract informative features for representing the image contents. Furthermore, a distance metric learning algorithm called the Rank Error-based Metric Learning (REML) is proposed to reduce the semantic gap between low-level visual features and high-level semantic concepts. The effectiveness of the proposed method is evaluated on a brain T1-weighted CE-MR dataset with three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor). Using the BoVW model with partition learning, the mean average precision (mAP) of retrieval increases beyond 4.6% with the learned distance metrics compared with the spatial pyramid BoVW method. The distance metric learned by REML significantly outperforms three other existing distance metric learning methods in terms of mAP. The mAP of the CBIR system is as high as 91.8% using the proposed method, and the precision can reach 93.1% when the top 10 images are returned by the system. These preliminary results demonstrate that the proposed method is effective and feasible for the retrieval of brain tumors in T1-weighted CE-MR Images.     

Mapping Diffusion in a Living Cell via the Phasor Approach

Diffusion of a fluorescent protein within a cell has been measured using either fluctuation-based techniques (fluorescence correlation spectroscopy (FCS) or raster-scan image correlation spectroscopy) or particle tracking. However, none of these methods enables us to measure the diffusion of the fluorescent particle at each pixel of the image. Measurement using conventional single-point FCS at every individual pixel results in continuous long exposure of the cell to the laser and eventual bleaching of the sample. To overcome this limitation, we have developed what we believe to be a new method of scanning with simultaneous construction of a fluorescent image of the cell. In this believed new method of modified raster scanning, as it acquires the image, the laser scans each individual line multiple times before moving to the next line. This continues until the entire area is scanned. This is different from the original raster-scan image correlation spectroscopy approach, where data are acquired by scanning each frame once and then scanning the image multiple times. The total time of data acquisition needed for this method is much shorter than the time required for traditional FCS analysis at each pixel. However, at a single pixel, the acquired intensity time sequence is short; requiring nonconventional analysis of the correlation function to extract information about the diffusion. These correlation data have been analyzed using the phasor approach, a fit-free method that was originally developed for analysis of FLIM images. Analysis using this method results in an estimation of the average diffusion coefficient of the fluorescent species at each pixel of an image, and thus, a detailed diffusion map of the cell can be created.     

Online Multi-Modal Robust Non-Negative Dictionary Learning for Visual Tracking

Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality.     

Not seeing is not believing: improving the visibility of your fluorescence images

The digital age has brought both technical advances and ethical quandaries regarding data acquisition and image presentation in the field of cell biology. Image manipulation has drawn considerable attention in the past decade, leading to general guidelines for ethical data processing. However, effective methods of image presentation have been discussed only cursorily and have been largely overlooked. Under standard viewing conditions, the human visual system imposes limitations for readers analyzing fluorescence images. In this paper, I discuss the advantages and limitations of image-manipulation techniques with respect to the human visual system, including contrast stretching, nonlinear grayscale transformations, and pseudocoloring. While online data viewing presents innovative ways to access image information, most images continue to be viewed in static publications, in which image presentation is critical for effective information transmission.     

Phase conjugation model for information transfer in the nervous system

In the nervous system, dispersion in propagation time sometimes brings delay distortion or phase distortion on the information transmission. Also in the memory retrieval processes in the brain, some parts of images may be retrieved more slowly than others. For smooth control of fast movements as well as for keeping exact thinking, these distortions have to be taken out. To understand the distortion cancelling mechanism, new neural network models for compensating the phase distortion are proposed. The models stand on the concept of "phase conjugate mirror" which is used in optical image processing. Simulation studies based on the model resulted in successful cancellation of the delay dispersion involved in the information transmission in the nervous system.     

环境减灾卫星多光谱CCD影像自动几何精纠正与正射校正系统

我国环境减灾光学卫星(HJ)多光谱CCD影像由于具有高时空分辨率、大幅宽等优势,能够提供同一区域不同生长阶段的植被信息,是提取具有生态学意义土地覆盖及开展植被生理生态参量反演的重要数据源之一。然而,目前该数据产品的几何定位不准确及山区地形畸变误差使其难以满足应用需求。高定位精度是遥感影像信息提取、参数反演与应用分析的前提,遥感影像的几何精纠正与正射校正是遥感数据预处理面临的首要问题。在分析国内外卫星影像自动化处理系统研究现状的基础上,结合HJ CCD影像幅宽大的特点,构建了HJ CCD影像的自动几何精纠正与正射校正处理系统。与目前商业软件相比,自动几何精纠正与正射校正处理系统采用了自动化的控制点搜索与过滤方法,能有效提高控制点选取的效率与精度。同时,结合DEM数据,系统自动拟合卫星飞行路径并纠正由偏离星下观测导致的山体位移。系统应用结果表明,自动几何精纠正和正射校正系统能够有效的提高处理效率,节省人力和时间成本。该系统已被成功应用于全国生态十年(2000—2010年)变化遥感调查与评估专项土地覆盖遥感监测的环境减灾卫星多光谱遥感影像预处理工作。     

A model for integrating elementary neural functions into delayed-response behavior

It is well established that various cortical regions can implement a wide array of neural processes, yet the mechanisms which integrate these processes into behavior-producing, brain-scale activity remain elusive. We propose that an important role in this respect might be played by executive structures controlling the traffic of information between the cortical regions involved. To illustrate this hypothesis, we present a neural network model comprising a set of interconnected structures harboring stimulus-related activity (visual representation, working memory, and planning), and a group of executive units with task-related activity patterns that manage the information flowing between them. The resulting dynamics allows the network to perform the dual task of either retaining an image during a delay (delayed-matching to sample task), or recalling from this image another one that has been associated with it during training (delayed-pair association task). The model reproduces behavioral and electrophysiological data gathered on the inferior temporal and prefrontal cortices of primates performing these same tasks. It also makes predictions on how neural activity coding for the recall of the image associated with the sample emerges and becomes prospective during the training phase. The network dynamics proves to be very stable against perturbations, and it exhibits signs of scale-invariant organization and cooperativity. The present network represents a possible neural implementation for active, top-down, prospective memory retrieval in primates. The model suggests that brain activity leading to performance of cognitive tasks might be organized in modular fashion, simple neural functions becoming integrated into more complex behavior by executive structures harbored in prefrontal cortex and/or basal ganglia.     

Imaging technique implemented in CellTracks system

BACKGROUND: We developed the CellTracks cell analysis system that, similar to flow cytometry, yields multiparameter information by which the cells can be differentiated. We describe the implementation of a laser scanning imaging method in the system. Image analysis of the cells improves the specificity of cell classification, especially in cases where the particular cells are found relatively infrequently and one has to discriminate between artifacts and real events. METHODS: Fluorescent images of immunomagnetically labeled and aligned cells are obtained by passing the cells through a laser focus. The laser focus is smaller than the objects and subsequent frames captured by a regular surveillance CCD camera with a frame grabber board represent different parts of the cells. Complete images of the cells are constructed by shifting each image with respect to each other and adding individual pixel values. RESULTS: The power of combining a fluorescent image with multiparametric data is demonstrated by imaging fluorescent and magnetically labeled beads and cells. The image gives additional information about the dye distribution across the objects. Changes in dye distribution as a function of time were observed in leukocytes labeled with the red fluorescent label, Oxazine750, which are imaged at different time intervals. CONCLUSIONS: An imaging technique implemented in the CellTracks system provides high-resolution fluorescent images of events previously identified by the system. The images of the fluorescent cells enhance the ability to classify rare events.     

Blockchain-based federated learning methodologies in smart environments

Blockchain technology is an undeniable ledger technology that stores transactions in high-security chains of blocks. Blockchain can solve security and privacy issues in a variety of domains. With the rapid development of smart environments and complicated contracts between users and intelligent devices, federated learning (FL) is a new paradigm to improve accuracy and precision factors of data mining by supporting information privacy and security. Much sensitive information such as patient health records, safety industrial information, and banking personal information in various domains of the Internet of Things (IoT) including smart city, smart healthcare, and smart industry should be collected and gathered to train and test with high potential privacy and secured manner. Using blockchain technology to the adaption of intelligent learning can influence maintaining and sustaining information security and privacy. Finally, blockchain-based FL mechanisms are very hot topics and cut of scientific edge in data science and artificial intelligence. This research proposes a systematic study on the discussion of privacy and security in the field of blockchain-based FL methodologies on the scientific databases to provide an objective road map of the status of this issue. According to the analytical results of this research, blockchain-based FL has been grown significantly during these 5 years and blockchain technology has been used more to solve problems related to patient healthcare records, image retrieval, cancer datasets, industrial equipment, and economical information in the field of IoT applications and smart environments.

     

Visual one-shot learning as an ‘anti-camouflage device’: a novel morphing paradigm

Once people perceive what is in the hidden figure such as Dallenbach’s cow and Dalmatian, they seldom seem to come back to the previous state when they were ignorant of the answer. This special type of learning process can be accomplished in a short time, with the effect of learning lasting for a long time (visual one-shot learning). Although it is an intriguing cognitive phenomenon, the lack of the control of difficulty of stimuli presented has been a problem in research. Here we propose a novel paradigm to create new hidden figures systematically by using a morphing technique. Through gradual changes from a blurred and binarized two-tone image to a blurred grayscale image of the original photograph including objects in a natural scene, spontaneous one-shot learning can occur at a certain stage of morphing when a sufficient amount of information is restored to the degraded image. A negative correlation between confidence levels and reaction times is observed, giving support to the fluency theory of one-shot learning. The correlation between confidence ratings and correct recognition rates indicates that participants had an accurate introspective ability (metacognition). The learning effect could be tested later by verifying whether or not the target object was recognized quicker in the second exposure. The present method opens a way for a systematic production of “good” hidden figures, which can be used to demystify the nature of visual one-shot learning.     

The possible molecular basis for memory processes in the central nervous system

The brain is able to record the messages that arrive from the external world and memory is the specific mechanism of this recording which can leave either a transient or a permanent trace.It is likely that the structural basis of such a mechanism is a modification of macromolecular conformation induced by electric events concomitant with the neural discharge.Nucleic acids and proteins are candidates for the role of basic molecules in the engram because of their ability to undergo transient structural modifications such as conformational changes and to render permanent the above modifications through the system of protein biosynthesis.Short-term memory is a transient modification established within very short time intervals which can be wiped out quite easily. It might in fact correspond to a single interference with the synaptic activity, dependent on a transient and labile influence of macromolecules present in synaptic membrane and modified by the electric field created by neural discharge within the membrane.Long-term memory is the basis of a global condition referred to as experience and requires longer times to be established. It is definitely associated with protein synthesis and results as a permanent modification of the number and structure of the synapses.The mechanism of the recording and retrieval of information has been described with an attempt to inter-relate different models and hypotheses.     

Improving the accuracy of burn-surface estimation

A user-friendly computer-assisted method of calculating total body surface area burned (TBSAB) has been developed. This method is more accurate, faster, and subject to less error than conventional methods. For comparison, the ability of 30 physicians to estimate TBSAB was tested. Parameters studied included the effect of prior burn care experience, the influence of burn size, the ability to accurately sketch the size of burns on standard burn charts, and the ability to estimate percent TBSAB from the sketches. Despite the ability for physicians of all levels of training to accurately sketch TBSAB, significant burn size over-estimation (p less than 0.01) and large interrater variability of potential consequence was noted. Direct benefits of a computerized system are many. These include the need for minimal user experience and the ability for wound-trend analysis, permanent record storage, calculation of fluid and caloric requirements, hemodynamic parameters, and the ability to compare meaningfully the different treatment protocols.     

Forgetting, reminding, and remembering: the retrieval of lost spatial memory

Retrograde amnesia can occur after brain damage because this disrupts sites of storage, interrupts memory consolidation, or interferes with memory retrieval. While the retrieval failure account has been considered in several animal studies, recent work has focused mainly on memory consolidation, and the neural mechanisms responsible for reactivating memory from stored traces remain poorly understood. We now describe a new retrieval phenomenon in which rats' memory for a spatial location in a watermaze was first weakened by partial lesions of the hippocampus to a level at which it could not be detected. The animals were then reminded by the provision of incomplete and potentially misleading information—an escape platform in a novel location. Paradoxically, both incorrect and correct place information reactivated dormant memory traces equally, such that the previously trained spatial memory was now expressed. It was also established that the reminding procedure could not itself generate new learning in either the original environment, or in a new training situation. The key finding is the development of a protocol that definitively distinguishes reminding from new place learning and thereby reveals that a failure of memory during watermaze testing can arise, at least in part, from a disruption of memory retrieval.     

GeVaDSs - decision support system for novel Genetic Vaccine development process

ABSTRACT: BACKGROUND: The lack of a uniform way for qualitative and quantitative evaluation of vaccine candidates under development led us to set up a standardized scheme for vaccine efficacy and safety evaluation. We developed and implemented molecular and immunology methods, and designed support tools for immunization data storage and analyses. Such collection can create a unique opportunity for immunologists to analyse data delivered from their laboratories. RESULTS: We designed and implemented GeVaDSs (Genetic Vaccine Decision Support system)- an interactive system for efficient storage, integration, retrieval and representation of data. Moreover, GeVaDSs allows for relevant association and interpretation of data, and thus for knowledge-based generation of testable hypotheses of vaccine responses. CONCLUSIONS: GeVaDSs has been tested by several laboratories in Europe, and proved its usefulness in vaccine analysis. Case study of its application is presented in the additional files. The system is available at: http://gevads.cs.put.poznan.pl/preview/ (login: viewer, password: password).