Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical Interventions in Aging Dovepress Late Onset Alzheimer's Disease in Older People

Dementia has become a common diagnosis in aging populations, and the numbers will increase in the forthcoming years. Alzheimer's disease (AD) is the most common form of dementia in the elderly, accounting for 50%–56% of cases at autopsy and in clinical series. Nowadays, the number of people affected by AD is rapidly increasing, and more than 35 million people worldwide have AD, a condition characterized by deterioration of memory and other cognitive domains, and leading to death 3–9 years after diagnosis. The number of patients with AD, the most common cause of disability in the elderly, is set to rise dramatically. Therefore, it is important for clinicians to recognize early signs and symptoms of dementia and to note potentially modifiable risk factors and early disease markers.
Mutational analysis of the group A streptococcal operon encoding streptolysin S and its virulence role in invasive infection

The pathogen group A Streptococcus (GAS) produces a wide spectrum of infections including necrotizing fasciitis (NF). Streptolysin S (SLS) produces the hallmark β‐haemolytic phenotype produced by GAS. The nine‐gene GAS locus (sagA–sagI) resembling a bacteriocin biosynthetic operon is necessary and sufficient for SLS production. Using precise, in‐frame allelic exchange mutagenesis and single‐gene complementation, we show sagA, sagB, sagC, sagD, sagE, sagF and sagG are each individually required for SLS production, and that sagE may further serve an immunity function. Limited site‐directed mutagenesis of specific amino acids in the SagA prepropeptide supports the designation of SLS as a bacteriocin‐like toxin. No significant pleotrophic effects of sagA deletion were observed on M protein, capsule or cysteine protease production. In a murine model of NF, the SLS‐negative M1T1 GAS mutant was markedly diminished in its ability to produce necrotic skin ulcers and spread to the systemic circulation. The SLS toxin impaired phagocytic clearance and promoted epithelial cell cytotoxicity, the latter phenotype being enhanced by the effects of M protein and streptolysin O. We conclude that all genetic components of the sag operon are required for expression of functional SLS, an important virulence factor in the pathogenesis of invasive M1T1 GAS infection.
An Approach to Self-Assembling Swarm Robots Using Multitree Genetic Programming

In recent days, self-assembling swarm robots have been studied by a number of researchers due to their advantages such as high efficiency, stability, and scalability. However, there are still critical issues in applying them to practical problems in the real world. The main objective of this study is to develop a novel self-assembling swarm robot algorithm that overcomes the limitations of existing approaches. To this end, multitree genetic programming is newly designed to efficiently discover a set of patterns necessary to carry out the mission of the self-assembling swarm robots. The obtained patterns are then incorporated into their corresponding robot modules. The computational experiments prove the effectiveness of the proposed approach.
Creating Strategic Value from Supply Chain Visibility- the Dynamic Capabilities View

To understand the role of supply chain visibility in creating strategic value, this study applies the dynamic capabilities view to investigate the nature of supply chain visibility. This research identifies four important measurable constructs of supply chain visibility that are proposed to drive supply chain reconfigurability and improve supply chain performance. They are sensing for visibility, learning for visibility, coordinating for visibility, and integrating for visibility. Implications for better understanding the nature and the role of supply chain visibility are provided based on the research model and survey results
A knowledge-based approach to domain-specific compressed video analysis

A novel approach to domain-specific video analysis is proposed. The proposed approach is based on exploiting domain-specific knowledge in the form of an ontology to detect video objects corresponding to the semantic concepts defined in the ontology. The association between the visual objects and the defined semantic concepts is performed by taking into account both qualitative attributes of the semantic objects (e.g., color homogeneity), indicating necessary preprocessing methods (color clustering, respectively), and numerical data generated via training (e.g., color models, also defined in the ontology). To enable fast and efficient processing, this methodology is applied to MPEG-2 video, requiring only its partial decoding. The proposed approach is demonstrated in the domain of Formula-1 racing video and shows promising results.
Named Entity Recognition using FOX

Unstructured data still makes up an important portion of the Web. One key task towards transforming this unstructured data into structured data is named entity recognition. We demo FOX, the Federated knOwledge eXtraction framework, a highly accurate open-source framework that implements RESTful web services for named entity recognition. Our framework achieves a higher F-measure than state-of-the-art named entity recognition frameworks by combining the results of several approaches through ensemble learning. Moreover, it disambiguates and links named entities against DBpedia by relying on the AGDISTIS framework. As a result, FOX provides users with accurately disambiguated and linked named entities in several RDF serialization formats. We demonstrate the different interfaces implemented by FOX within use cases pertaining to extracting entities from news texts.
Heuristic and intermediate features based image retrieval

In this paper, a new method for heuristics and intermediate features based image retrieval is proposed. Heuristic features are identified and directly stored into the database and easily retrieved also. An algorithm is used to convert low level features hue, saturation and intensity in HSI space to semantic based color names. Images can be retrieved by these semantic color names. For semantics based texture queries, statistical values of textures calculated from the image are used as the low level features in image retrieval. But these low level features are mostly not understandable by the general human. There is always a semantic gap between human understanding and low level features. Image database is constructed with low level texture features obtained from Gray Level Co-Occurrence 1732 Dipankar Hazra et al. Matrix (GLCM). Semantic level queries from the user mapped to the low level features at retrieval time to retrieve the required images.
Frequency-hopping based Secure Schemes in Sensornets

Frequency-hopping is a kind of secure solution in wireless communication field. We propose two novel mechanisms based on differential frequency-hopping to prevent those attacks like jamming, eavesdropping, tampering, etc. in wireless sensor networks (WSN). We also present the deployment scheme of our secure solutions. The analysis in this paper indicates that these two solutions have a number of good properties like energy efficiency, asynchrony, robustness and effectiveness to prevent the majority attacks in WSN
The Central Parsecs of the Bright Quasar PKS 1921 - 293

We report on a VLBA imaging study of the nearby bright southern blazar PKS 1921 - 293 (OV - 236). High resolution VLBA observations, made at four frequencies (5, 12, 15, and 43 GHz) over the period 1994 - 2000, have revealed a strongly curved jet extending out to about 50 parsecs from the presumed central engine. Two epoch VLBA observations, each simultaneously carried out at both 5 and 43 GHz, show a large position angle difference of 51 - 67 degrees between the jet emission at 5 and 43 GHz. Although the core of PKS 1921 - 293 has one of the highest brightness temperatures measured in any compact radio source, unlike other bright blazars it is not a source of gamma-ray emission. However, there is evidence in these images for superluminal motion within the central region (a few parsecs from the core) and within the north-east diffuse emission region. In all six-epoch 43 GHz images, two equally compact bright components within the central parsec are seen.
Prevalence of Epithelial Ovarian Cancer Stem Cells Correlates with Recurrence in Early-Stage Ovarian Cancer

Epithelial ovarian cancer stem cells (EOC stem cells) have been associated with recurrence and chemoresistance. CD44 and CK18 are highly expressed in cancer stem cells and function as tools for their identification and characterization. We investigated the association between the number of CD44+ EOC stem cells in ovarian cancer tumors and progression-free survival. EOC stem cells exist as clusters located close to the stroma forming the cancer stem cell “niche”. 17.1% of the samples reveled high number of CD44+ EOC stem cells (>20% positive cells). In addition, the number of CD44+ EOC stem cells was significantly higher in patients with early-stage ovarian cancer (FIGO I/II), and it was associated with shorter progression-free survival (P = 0.026). This study suggests that quantification of the number of EOC stem cells in the tumor can be used as a predictor of disease and could be applied for treatment selection in early-stage ovarian cancer.
Computation of curvatures using conformal parameterization

Curvatures on the surface are important geometric invariants and are widely used in different area of research. Examples include feature recognition, segmentation, or shape analysis. Therefore, it is of interest to develop an effective algorithm to approximate the curvatures accurately. The classical methods to compute these quantities involve the estimation of the normal and some involve the computation of the second derivatives of the 3 coordinate functions under the parameterization. Error is inevitably introduced because of the inaccurate approximation of the second derivatives and the normal. In this paper, we propose several novel methods to compute curvatures on the surface using the conformal parameterization. With the conformal parameterization, the conformal factor function λ can be defined on the surface. Mean curvature (H) and Gaussian curvatures (K) can then be computed with the conformal Factor (λ). It involves computing only the derivatives of the function λ, instead of the 3 coordinate functions and the normal. We also introduce a technique to compute H from K and vice versa, using the parallel surface.
A Java Virtual Machine for Runtime Reconfigurable Computing

Reconfigurable Computing (RC) is a technology that makes use of programmable logic (FPGAs) in conjunction with a traditional microprocessor to accelerate generalpurpose computations. RC machines have demonstrated impressive speedup on a variety of applications. Unfortunately, they are often difficult to program. This paper presents an experimental new RC platform, the RTR-JVM, which executes ordinary Java programs and makes use of online algorithms to select customized hardware at runtime. The RTR-JVM is thus a means of automating reconfigurable computing.
An Optimization Algorithm for Production Systems

As the scale of rule-based expert systems increases, the efficiency of production systems becomes a pressing concern. Recently developed production systems thus enable users to specify an appropriate ordering or clustering of join operations. Various efficiency heuristics have been introduced to optimize production rules manually. However, since the heuristics often conflict With each other, users have to proceed by trial and error. The problem addressed in this paper is how to automatically determine efficient join structures for production system programs. Our algorithm does not directly apply efficiency heuristics to programs, but rather enumerates possible join structures under various constraints and selects the best one. For this purpose, the cost model for production systems is introduced to estimate the run-time cost of join operations. Evaluation results demonstrate that the proposed algorithm can generate programs that are as efficient as those obtained by manual optimization, and thus can reduce the burden of manual optimization. >
Transport equations with singular coefficients. Application to the gyro-kinetic models in plasma physics

The subject matter of this paper concerns the asymptotic regimes for transport equations with singular coefficients. Such models arise for example in plasma physics, when dealing with charged particles moving under the action of strong magnetic fields. These regimes are motivated by the magnetic confinement fusion. The stiffness of the coefficients comes from the multi-scale character of the problem. According to the different possible orderings between the typical physical scales (Larmor radius, Debye length, cyclotronic frequency, plasma frequency) we distinguish several regimes. From the mathematical point of view the analysis of such regimes reduces to stability properties for transport equations whose coefficients have different magnitude orders, depending on some small parameter. The main purpose is to derive limit models by letting the small parameter vanish. In the magnetic confinement context these asymptotics can be assimilated to homogenization procedures with respect to the fast cyclotronic movement of particles around the magnetic lines. We justify rigorously the convergence towards these limit models and we investigate the well-posedness of them.
Does the Type of Resuscitative Fluid Affect Healing of Colonic Anastomosis in Experimentally Induced Hemorrhagic Shock in Rats?

Objective: The aim of the study was to investigate the effects of different resuscitative fluids on the healing of intestinal anastomosis in a hemorrhagic-shock rat model. Materials and Methods: Closed-colony Wistar male rats (n = 40; 8 rats per group) were subjected to volume-controlled hemorrhagic shock, followed by a 30-min shock phase. The animals were then resuscitated with one of the following fluids (which also corresponds to their respective groups): lactated Ringer’s solution (LR), hydroxyethyl starch (HES), 7.5% hypertonic saline (HS) and autologous blood (AB). There was also a control group (CL), which did not experience hemorrhagic shock or receive any resuscitative fluids. All rats underwent laparotomy, segmental resection and anastomosis of the left colon. Five days later, a 2nd laparotomy was performed and the anastomotic bursting pressure was measured in vivo. Thereafter, the anastomosed segment was resected to measure the tissue hydroxyproline level and the grade of anastomotic fibrosis. Results: All experimental groups (LR, HES, HS and AB) exhibited lower anastomotic bursting pressures than the CL group; however, nointergroup differences achieved statistical significance. The mean tissue hydroxyproline level and fibrosis grade also were similar across all 5 groups. Conclusion: In traumatic hemorrhagic shock, anastomosis safety does not appear to be affected by the type of fluid used for resuscitation. Moreover, LR, HES and HS all seemed to reinforce healing as effectively as transfused blood.
Analog filter circuits feature selection using MRMR and SVM

This paper addresses frequency response feature extraction of analog filter circuits. An approach for feature selection using criteria of maximum relevance minimum redundancy (MRMR) and support vector machine (SVM) is proposed. Key idea of the method is to obtain candidate feature subsets with descending order using criteria of MRMR first, and then to select the optimal feature subset through cross validation results of each feature subset using SVM. Experimental results testify that the proposed approach has the advantages of less time-consuming and the selected features have reasonable distribution.
A Novel Method of Centrality in Terrorist Network

The problem of assigning centrality values to nodes in a graph has been widely investigated during recent years. It is especially important to measure nodes' centrality in terrorist and transportation networks. Because these nodes could be anything important, could spread, accept and destroy some essential information, we should try our best to determine the key node in a network to reduce the damage and jam. The social network analysis (SNA) is used to measure the key node in a network. A terrorist network can be modeled as a generalized network (graph) consisting of nodes and links. Many methods from social network analysis and graph theory can be used to determine central nodes in a network, which are helpful for network destabilization purposes. This paper provides a centrality measure for nodes in terrorist network, which takes consideration of nodes' information from others and themselves, and Three Degree of Influence Rule. The method called Influence Index is used for known terrorist network, i.e. 9.11 hijacking. Through comparison, we get better results than some classical centrality methods, for example Degree, Between ness, Eigenvector and Laplacian Energy etc.
Increasing the Eciency of NPCs Using a Focus of Attention Based on Emotions and Personality

Several games nowadays try to improve the player immersion by representing human behavior as real as possible, generally using agent technologies to model non-player characters (NPCs). However, agent-based behavioral models representing the existing complexity of, for instance, a decision-making for a real life situation can become a very intensive computing task. For this reason, real-time simulation-based games may benefit from optimizations produced on how NPCs react to changes in the simulated game world. This paper presents an approach for speeding up the decision-making of autonomous agents representing NPCs of a game. The optimization is reached by bounding the agent perception to a subset of all agent surrounding elements, which contains only the most important elements for the agent at current time. In other words, the agent is modeled as having "focus of attention". The attention focus represented in this work is based on theories of emotions and personality.
Intelligent Community Lifecycle Support

Abstract: Knowledgesharingincommunitieshasattractedmuchattentioninthe eldof knowledge management in research and practice. In this paper we outline a viewwhere the community lifecycle is supported at di erent stages. The central componentof our framework is the community ontology SWRC+COINthat describes the typicalstructure of communities. We exemplarily show how communities in the academicdomain can be detected automatically by means of analyzing information ow in abibliographic Peer-to-Peersystemandhow theinstantiatedcommunityknowledgebasecan be exploited to support cooperative work in the communities.Key Words: Communities of Practice, Knowledge Management, Peer-to-Peer, On-tologies, Metadata, Semantic PortalsCategory: H.3. 3,H.5. 3 1 Introduction Knowledge sharing in communities has attracted much attention in the eld ofknowledge management in research and practice [Davenport and Prusak, 2000,Wenger and Snyder, 2000]. Communities are groups within or across organiza-tions who share a common set of information needs or problems. Communi-ties help participating members and their organizations to reduce transactionalcosts for knowledge acquisition, to foster organizationallearning and innovation,and to create common strategic visions. Although communities are inherentlyfuzzy, informalandself-organizing,practiceshowsthat communitiestypicallyre-quire explicit centralized organizational and technical measures that accompanytheir initialization and operation. Speci cally, this \Paradox of Management"[Wenger and Snyder, 2000] asks for supporting activities in the two phases ofdevelopment:1. Community Identi cation: identifying potential communities and initi-ating their formal implementation,2. Community Infrastructure: providing an environment that fosters com-munity operation and evolution,
Evaluation of an image-based algorithm for quantitative spectral CT applications

In this paper we describe and evaluate an image-based spectral CT method. Its central formula expresses measured CT data as a spectral integration of the spectral attenuation coefficient multiplied by a LocalWeighting Function (LWF). The LWF represents the local energy weighting in the image domain, taking into account the system and reconstruction properties and the object self attenuation. A generalized image-based formulation of spectral CT algorithms is obtained, with no need for additional corrections of e.g. beam hardening. The iterative procedure called Local Spectral Reconstruction (LSR) yields both the mass attenuation coefficients of the object and a representation of the LWF. The quantitative accuracy and precision of the method is investigated in several applications, including beam hardening correction, attenuation correction for SPECT/CT and PET/CT and a direct identification of spectral attenuation functions using the LWF result is demonstrated. In all applications the ground truth of the objects is reproduced with a quantitative accuracy in the sub-percent to two percent range. An exponential convergence behavior of the iterative procedure is observed, with one to two iteration steps as a good compromise between quantitative accuracy and precision. We conclude that the method can be used to perform image-based spectral CT reconstructions with quantitative accuracy. Existing algorithms benefit from the intrinsic treatment of beam hardening and system properties. Novel algorithms are enabled to directly compare material model functions to spectral measurement data.
Pathogenesis of tuberculosis in mice exposed to low and high doses of an environmental mycobacterial saprophyte before infection

Mycobacteria are ubiquitous in the environment, but they are not part of the normal human microbial flora. It has been suggested that variable contact with mycobacteria can influence susceptibility to mycobacterial pathogens and the efficacy of subsequent Mycobacterium bovis BCG vaccination. To test this, mice were immunized with high or low doses of an environmental saprophyte, M. vaccae, that is intensely immunogenic as an autoclaved preparation. Two months later, they received an intratracheal challenge with M. tuberculosis H37Rv. Recipients of a low Th1-inducing dose (10(7) organisms) were partially protected and maintained a high ratio of interleukin 2 (IL-2)-positive to IL-4-positive cells in the perivascular, peribronchial, and granulomatous areas of the lung, whereas in unimmunized controls the IL-4-positive cells increased markedly between days 21 and 28. In contrast, recipients of the high dose (10(9) organisms), which primes Th2 as well as Th1 cytokine production, died more rapidly than unimmunized controls and showed massive pneumonia from day 7. The ratio of IL-2-positive to IL-4-positive cells in all compartments of the lung rapidly fell to 1 by day 14 for these animals. These events correlated with cytokine mRNA profiles and with increases in the local toxicity of tumor necrosis factor alpha (TNF-alpha), demonstrable only when a major Th2 component was present. These data indicate that cross-reactive epitopes present in an environmental saprophyte can evoke either protective responses or responses that increase susceptibility to M. tuberculosis. The latter are associated with the presence of a Th2 component and increased sensitivity to TNF-alpha.