Miracle-dz/UniFAR-FTUs huggingface dataset:
Assurance of monoclonality in one round of cloning through cell sorting for single cell deposition coupled with high resolution cell imaging

Regulatory authorities require that cell lines used in commercial production of recombinant proteins must be derived from a single cell progenitor or clone. The limiting dilution method of cell cloning required multiple rounds of low‐density cell plating and microscopic observation of a single cell in order to provide evidence of monoclonality. Other cloning methods rely on calculating statistical probability of monoclonality rather than visual microscopic observation of cells. We have combined the single cell deposition capability of the Becton Dickinson Influx™ cell sorter with the microscopic imaging capability of the SynenTec Cellavista to create a system for producing clonal production cell lines. The efficiency of single cell deposition by the Influx™ was determined to be 98% using fluorescently labeled cells. The centrifugal force required to settle the deposited cells to the bottom of the microplate well was established to be 1,126g providing a 98.1% probability that all cells will be in the focal plane of the Cellavista imaging system. The probability that a single cell was deposited by the cell sorter combined with the probability of every cell settling into the focal plane of the imager yield a combined >99% probability of documented monoclonality. © 2015 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog. , 31:1172–1178, 2015
Graph Cut Based Multiple View Segmentation for 3D Reconstruction

In this paper we propose a novel framework for efficiently extracting foreground objects in so called short-baseline image sequences. We apply the obtained segmentation to improve subsequent 3D reconstruction results. Essentially, our framework combines a graph cut based optimization algorithm with an intuitive user interface. At first a meanshift segmentation algorithm partitions each image of the sequence into a certain number of regions. Additionally we provide an intelligent graphical user interface for easy specification of foreground as well as background regions across all images of the sequence. Within the graph cut optimization algorithm we define new energy terms to increase the robustness and to keep the segmentation of the foreground object coherent across all images of the sequence. Finally, a refined graph cut segmentation and several adjustment operations allow an accurate and effective foreground extraction. The obtained results are demonstrated on several real world data sets.
Research on Credit Card Fraud Detection Model Based on Distance Sum

Along with increasing credit cards and growing trade volume in China, credit card fraud rises sharply. How to enhance the detection and prevention of credit card fraud becomes the focus of risk control of banks. This paper proposes a credit card fraud detection model using outlier detection based on distance sum according to the infrequency and unconventionality of fraud in credit card transaction data, applying outlier mining into credit card fraud detection. Experiments show that this model is feasible and accurate in detecting credit card fraud.
Expressive Probability Models For Speech Recognition And Understanding

The paper is a brief summary of an invited talk given at the ASRU 99 conference. The principal points are as follows: first, that the expressive power of the probabilitymodels available for use in speech recognition and understanding has expanded significantly; second, that using expressive models such as dynamic Bayesian networks can result in improved learning rates and recognition results; and finally that still further expansion is required to tackle many problems of interest in the area of speech understanding. This further expansion should combine probability theory with the expressive power of first-order logical languages. The paper sketches an approximate inference method for representation systems of this kind.
Personalized recommendation based on the personal innovator degree

This paper proposes a novel Collaborative Filtering scheme; it focuses on the dynamics and precedence of user preference to recommend items that match the latest preference of the target user. In predicting which items this user will purchase in the near future, the proposed algorithm identifies purchase history logs of users who have similar preferences and a high degree of purchase precedence (i.e., purchasing the same items earlier) relative to the target user. We call this metric the Personal Innovator Degree (PID). Experiments using real online sales data show that the proposed method outperforms existing methods.
Efficient memory access in 2D Mesh NoC architectures using high bandwidth routers

Networks on Chip (NoC) have emerged as a promising interconnection technology for scalable many-core architectures. Proposed NoC-architectures and topologies often assume uniform distribution of traffic, where all tiles produce and consume the same amount of data. However, even in homogeneous many-core architectures the Network on Chip is used to access peripheral buses and memory controllers for off-chip memory access. These components can consume and generate a significant part of the overall traffic, thus having higher bandwidth requirements than processing tiles. In this work we propose High Bandwidth Routers replacing conventional routers within the NoC at the positions, where components with high bandwidth requirements are attached. A High Bandwidth Router design is proposed and investigated with respect to performance and implementation costs. The position of memory nodes within a tiled architecture is analyzed. The results show a significant improvement of throughput and reduction of latency for memory communication, with moderate additional implementation costs.
A combined classification-clustering framework for identifying disruptive events

Twitter is a popular micro-blogging web application serving hundreds of millions of users. Users publish short messages to communicate with friends and families, express their opinions and broadcast news and information about a variety of topics all in real-time. User-generated content can be utilized as a rich source of real-world event identification as well as extract useful knowledge about disruptive events for a given region. In this paper, we propose a novel detection framework for identifying real-time events, including a main event and associated disruptive events, from Twitter data. Theapproach is based on five steps:data collection, pre-processing,classification, online clustering and summarization. We use a Naive Bayes classification model and an Online Clustering method to validate our model on a major real-world event (Formula 1 Abu Dhabi Grand Prix 2013).
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Given explosive data traÆc in the world-wide web (WWW), it is crucial to achieve the scalable performance of web servers. The overall performance and resource utilization can be improved by spreading document requests among a group of web servers. This leads to the design and implementation of Distributed Cooperative Apache (DC-Apache) web server. In this paper, we describe the unique features of the DC-Apache system (1) to migrate and replicate documents among cooperating servers, (2) using dynamic hyperlink generation to distribute requests for documents to balance the load, and (3) to maintain replicated copies in a consistent state. We also address the issue of storage management for more e ective document replication under limited capacity. In the experiments, the DC-Apache system demonstrated its ability to achieve high performance and scalability by e ectively distributing load among a group of cooperating Apache servers and by eliminating hot spots and performance bottleneck with replicated documents. The DC-Apache system is an e ective and practical solution to provide high performance and scalability to cope with ever increasing demands from clients all over the web. keywords: WWW, Scalable Web server, Apache, DCApache, Distributed Web server, Replication, Load balancing
Responses to EPA Notice of Data Availability From Ocean Carbon and Biogeochemistry Program

The following comments are compiled from the U.S. Ocean Carbon and Biogeochemistry (OCB) community of scientists (see http://www.us-ocb.org/). The goal of the Ocean Carbon and Biogeochemistry Program is to help coordinate and facilitate U.S. marine biogeochemistry and related ecological research working in conjunction with relevant federal science agencies (e.g, NSF, NASA and NOAA). The OCB is a community of scientists, primarily at academic and research institutions. Much of this information is a summary of the following reviews on the state of ocean acidification research findings, but includes updates from the recent literature.
An algorithm for improving graph partitions

We present an algorithm called Improve that improves a proposed partition of a graph, taking as input a subset of vertices and returning a new subset of vertices with a smaller quotient cut score. The most powerful previously known method for improving quotient cuts, which is based on parametric flow, returns a partition whose quotient cut score is at least as small as any set contained within the proposed set. For our algorithm, we can prove a stronger guarantee: the quotient score of the set returned is nearly as small as any set in the graph with which the proposed set has a larger-than-expected intersection. The algorithm finds such a set by solving a sequence of polynomially many s -- t minimum cut problems, a sequence that cannot be cast as a single parametric flow problem. We demonstrate empirically that applying Improve to the output of various graph partitioning algorithms greatly improves the quality of cuts produced without significantly impacting the running time.
Innate Immune Response in Brain, NF-Kappa B Signaling and Cystatins

Recently several reports have demonstrated that innate immune response and inflammation have an important role in major neurodegenerative diseases. The activation of the NF-κB family of transcription factors is a key step in the regulation of pro inflammatory cytokine expression. Microglia and other cell types in the brain can be activated in response to endogenous danger molecules as well as aggregated proteins and brain injury. During the past couple of years several studies reported the role of cystatins in neuroinflammation and neurodegeneration. In the present review, I will summarize and analyze recent findings regarding the role of cystatins in inflammation and NF-κB activation. Type I cystatin stefin B (cystatin B) is an endogenous cysteine cathepsin inhibitor localized in the cytosol, mitochondria and nucleus. Mutations in the gene of stefin B are associated with the neurodegenerative disease known as Unverricht-Lundborg disease and microglial activation plays an important role in the pathogenesis of the disease. Stefin B deficient mice have increased caspase-11 expression and secreted higher amounts of pro-inflammatory cytokines. The increased caspase-11 gene expression, was a consequence of increased NF-κB activation.
A tool for the numerical solution of cooperating Markov chains in product-form

Performance modelling of complex and heterogeneous systems based on analytical models are often solved by the analysis of underlying Markovian models. We consider performa nce models based on Continuous Time Markov Chains (CTMCs) and their solution, that is the analysis of the steady -st te distribution, to efficiently derive a set of performance indices. This paper presents a tool that is able to decide whether a set of cooperating CTMCs yields a product-form stationary distribution. In this case, the tool c mputes the unnormalised steadystate distribution. The algorithm underlying the tool has been presented in [1 0] by exploiting the recent advances in the theory of product-form models such as the Reversed Compound A gent Theorem (RCAT) [5]. In this paper, we focus on the peculiarities of the formalism adopted to describe the interac ting CTMCs and on the software design that may have interesting consequences for the performance c ommunity.
IAPT and Learning Disabilities

Citing this paper Please note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details. And where the final published version is provided on the Research Portal, if citing you are again advised to check the publisher's website for any subsequent corrections.
Highly Specific Gene Silencing by Artificial miRNAs in Rice

Background Endogenous microRNAs (miRNAs) are potent negative regulators of gene expression in plants and animals. Artificial miRNAs (amiRNAs)–designed to target one or several genes of interest–provide a new and highly specific approach for effective post-transcriptional gene silencing (PTGS) in plants. Methodology We devised an amiRNA-based strategy for both japonica and indica type strains of cultivated rice, Oryza sativa. Using an endogenous rice miRNA precursor and customized 21mers, we designed amiRNA constructs targeting three different genes (Pds, Spl11, and Eui1/CYP714D1). Upon constitutive expression of these amiRNAs in the varieties Nipponbare (japonica) and IR64 (indica), the targeted genes are down-regulated by amiRNA-guided cleavage of the transcripts, resulting in the expected mutant phenotypes. The effects are highly specific to the target gene, the transgenes are stably inherited and they remain effective in the progeny. Conclusion/Significance Our results not only show that amiRNAs can efficiently trigger gene silencing in a monocot crop, but also that amiRNAs can effectively modulate agronomically important traits in varieties used in modern breeding programs. We provide all software tools and a protocol for the design of rice amiRNA constructs, which can be easily adapted to other crops. The approach is suited for candidate gene validation, comparative functional genomics between different varieties, and for improvement of agronomic performance and nutritional value.
Social Network and Sentiment Analysis on Twitter: Towards a Combined Approach

Twitter is a platform which may contain opinions, thoughts, facts and other information. Within it, many and various communities are originated by users with common interests, or with similar ways to feel part of the community. This paper presents a possible combined approach between Social Network Analysis and Sentiment Analysis. In particular, we have tried to associate a sentiment to the nodes of the graphs showing the social connections, and this may highlight the potential correlations. The idea behind it is that, on the one hand, the network topology can contextualize and then, in part, unmask some incorrect results of the Sentiment Analysis; on the other hand, the polarity of the feeling on the network can highlight the role of semantic connections in the hierarchy of the communities that are present in the network. In this work, we illustrate the approach to the issue, together with the system architecture and, then, we discuss our first results.
GaitEnable: An omnidirectional robotic system for gait rehabilitation

This paper introduces GaitEnable, a robotic gait trainer composed of an actuated omnidirectional mobile base, a passive body weight support (BWS) system, and a reactive control system that can initiate, sustain, stabilize or perturb a user's gait. The device is designed to provide minimal constraints to the user's natural motion, and its actuated mobile base can move cooperatively with the user in any direction. Data from preliminary experiments performed by a healthy male subject confirm that the reactive control system can compensate for the device's inertial effects and that the device's omnidirectional mobile base reduces pelvis and torso motion constraints. The results also demonstrate that GaitEnable can easily be programmed to simulate different types of behaviours or motion constraints.
WWICE: an architecture for in-home digital networks

The transition to digital information and networking of new digital devices lead to considerable changes in the consumer electronic industry. New applications will arise, offering more entertainment, comfort and flexibility. To achieve this, complex problems in communication and distributed systems need to be solved. High requirements on stability, usability, quality and price call for new solutions. This paper describes the concept of In-Home Digital Networks and will then in detail address the WWICE system. This new architecture provides a coherent system environment for the home. It focuses on services and applications, which can easily be accessed and controlled by the user. Application framework and middleware services of the layered software architecture efficiently support development of IHDN applications as well as flexible application control at runtime.
Self-optimization of LTE networks utilizing Celnet Xplorer

In order to meet demanding performance objectives in Long Term Evolution (LTE) networks, it is mandatory to implement highly efficient, autonomic self-optimization and configuration processes. Self-optimization processes have already been studied in second generation (2G) and third generation (3G) networks, typically with the objective of improving radio coverage and channel capacity. The 3rd Generation Partnership Project (3GPP) standard for LTE self-organization of networks (SON) provides guidelines on self-configuration of physical cell ID and neighbor relation function and self-optimization for mobility robustness, load balancing, and inter-cell interference reduction. While these are very important from an optimization perspective of local phenomenon (i.e., the eNodeB's interaction with its neighbors), it is also essential to architect control algorithms to optimize the network as a whole. In this paper, we propose a Celnet Xplorer-based SON architecture that allows detailed analysis of network performance combined with a SON control engine to optimize the LTE network. The network performance data is obtained in two stages. In the first stage, data is acquired through intelligent non-intrusive monitoring of the standard interfaces of the Evolved UMTS Terrestrial Radio Access Network (E-UTRAN) and Evolved Packet Core (EPC), coupled with reports from a software client running in the eNodeBs. In the second stage, powerful data analysis is performed on this data, which is then utilized as input for the SON engine. Use cases involving tracking area optimization, dynamic bearer profile reconfiguration, and tuning of network-wide coverage and capacity parameters are presented.
A Method for Distinctly Marking Honey Bees, Apis mellifera, Originating from Multiple Apiary Locations

Abstract Inexpensive and non-intrusive marking methods are essential to track natural behavior of insects for biological experiments. An inexpensive, easy to construct, and easy to install bee marking device is described in this paper. The device is mounted at the entrance of a standard honey bee Apis mellifera L. (Hymenoptera: Apidae) hive and is fitted with a removable tube that dispenses a powdered marker. Marking devices were installed on 80 honey bee colonies distributed in nine separate apiaries. Each device held a tube containing one of five colored fluorescent powders, or a combination of a fluorescent powder (either green or magenta) plus one of two protein powders, resulting in nine unique marks. The powdered protein markers included egg albumin from dry chicken egg whites and casein from dry powdered milk. The efficacy of the marking procedure for each of the unique markers was assessed on honey bees exiting each apiary. Each bee was examined, first by visual inspection for the presence of colored fluorescent powder and then by egg albumin and milk casein specific enzyme-linked immunosorbent assays (ELISA). Data indicated that all five of the colored fluorescent powders and both of the protein powders were effective honey bee markers. However, the fluorescent powders consistently yielded more reliable marks than the protein powders. In general, there was less than a 1% chance of obtaining a false positive colored or protein-marked bee, but the chance of obtaining a false negative marked bee was higher for “protein-marked” bees.
Adaptive winner-update search for efficient motion vector estimation in video coding

A kind of fast block-matching algorithms which do not introduce any prediction error as compared with the full-search algorithm is to reduce the number of necessary matching evaluations for every searching point in the search window based on partial distortion elimination (PDE), among which winner-update search (WUS) algorithm is a well-known technique. Based on correlation characteristic between gradient magnitudes and matching errors of pixels, this paper proposes an adaptive WUS (AWUS) algorithm which significantly improves the computation efficiency of the original WUS algorithm. This approach can eliminate inappropriate motion vectors faster using an efficient lower bounds set computed according to the edge features of the predicted image. Experimental results indicate that it can accelerate search process by more than 10%, compared with the original WUS algorithm without any loss of estimation accuracy. Furthermore, the proposed algorithm is most suitable for estimating motion vectors for video sequences with rich texture information.
A Decision Support System Using Semantic Web Technologies

News can be an important source of information to support decision making, the emergence of the internet made more and more news sources and consequently news items available. However only a very small subset of all these news items are relevant for the decision to be made. Therefore decision makers need an information system that is capable of extracting a set of relevant news items automatically, as it is impossible to sort out the huge amount of news items manually. This paper proposes a solution that provides decision makers with the ability to extract a set of news items related to specific concepts of interest, that aid them in the process of making a decision. This is accomplished by creating a knowledge base and developing a system that classifies news with respect to the knowledge base. This knowledge based is then visualized through an innovative graph representation, which can be used for retrieving relevant news items. A characteristic feature of our approach is the consideration of both concepts and concept relationships for the retrieval of relevant sets of news-items. As the time aspect of news items is an important facet, for example to build historic overviews or to take only recent developments into account, our solution will also be able to
Self-Configuring Heterogeneous Server Clusters

Previous research on cluster-based servers has focused on homogeneous systems. However, real-life clusters are almost invariably heterogeneous in terms of the performance, capacity, and power consumption of their hardware components. In this paper, we describe a self-configuring Web server for a heterogeneous cluster. The self-configuration is guided by analytical models of throughput and power consumption. Our experimental results for a cluster comprised of traditional and blade nodes show that the modelbased server can consume 29% less energy than an energyoblivious server, with only a negligible loss in throughput. The results also show that our server conserves more than twice as much energy as an energy-conscious server that we previously proposed for homogeneous clusters.