Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of frontal versus temporal cortex in verbal fluency as revealed by voxel-based lesion symptom mapping

Category and letter fluency tasks have been used to demonstrate psychological and neurological dissociations between semantic and phonological aspects of word retrieval. Some previous neuroimaging and lesion studies have suggested that category fluency (semantic-based word retrieval) is mediated primarily by temporal cortex, while letter fluency (letter-based word retrieval) is mediated primarily by frontal cortex. Other studies have suggested that both letter and category fluency are mediated by frontal cortex. We tested these hypotheses using voxel-based lesion symptom mapping (VLSM) in a group of 48 left-hemisphere stroke patients. VLSM maps revealed that category and letter fluency deficits correlate with lesions in temporal and frontal cortices, respectively. Other regions, including parietal cortex, were significantly implicated in both tasks. Our findings are therefore consistent with the hypothesis that temporal cortex subserves word retrieval constrained by semantics, whereas frontal regions are more critical for strategic word retrieval constrained by phonology. (JINS, 2006, 12, 896–900.)
From Protein Sequence to Protein Function via Multi-Label Linear Discriminant Analysis

Sequence describes the primary structure of a protein, which contains important structural, characteristic, and genetic information and thereby motivates many sequence-based computational approaches to infer protein function. Among them, feature-base approaches attract increased attention because they make prediction from a set of transformed and more biologically meaningful sequence features. However, original features extracted from sequence are usually of high dimensionality and often compromised by irrelevant patterns, therefore dimension reduction is necessary prior to classification for efficient and effective protein function prediction. A protein usually performs several different functions within an organism, which makes protein function prediction a multi-label classification problem. In machine learning, multi-label classification deals with problems where each object may belong to more than one class. As a well-known feature reduction method, linear discriminant analysis (LDA) has been successfully applied in many practical applications. It, however, by nature is designed for single-label classification, in which each object can belong to exactly one class. Because directly applying LDA in multi-label classification causes ambiguity when computing scatters matrices, we apply a new Multi-label Linear Discriminant Analysis (MLDA) approach to address this problem and meanwhile preserve powerful classification capability inherited from classical LDA. We further extend MLDA by $\ell _1$ -normalization to overcome the problem of over-counting data points with multiple labels. In addition, we incorporate biological network data using Laplacian embedding into our method, and assess the reliability of predicted putative functions. Extensive empirical evaluations demonstrate promising results of our methods.
Succinoglycan Production by Rhizobium meliloti Is Regulated through the ExoS-ChvI Two-Component Regulatory System

ABSTRACT The Rhizobium meliloti exoS gene is involved in regulating the production of succinoglycan, which plays a crucial role in the establishment of the symbiosis between R. melilotiRm1021 and its host plant, alfalfa. TheexoS96::Tn5 mutation causes the upregulation of the succinoglycan biosynthetic genes, thereby resulting in the overproduction of succinoglycan. Through cloning and sequencing, we found that the exoS gene is a close homolog of theAgrobacterium tumefaciens chvG gene, which has been proposed to encode the sensor protein of the ChvG-ChvI two-component regulatory system, a member of the EnvZ-OmpR family. Further analyses revealed the existence of a newly discovered A. tumefaciens chvI homolog located just upstream of the R. meliloti exoS gene. R. meliloti ChvI may serve as the response regulator of ExoS in a two-component regulatory system. By using ExoS-specific antibodies, it was found that the ExoS protein cofractionated with membrane proteins, suggesting that it is located in the cytoplasmic membrane. By using the same antibodies, it was shown that the exoS96::Tn5 allele encodes an N-terminal truncated derivative of ExoS. The cytoplasmic histidine kinase domain of ExoS was expressed in Escherichia coli and purified, as was the R. meliloti ChvI protein. The ChvI protein autophosphorylated in the presence of acetylphosphate, and the ExoS cytoplasmic domain fragment autophosphorylated at a histidine residue in the presence of ATP. The ChvI protein was phosphorylated in the presence of ATP only when the histidine kinase domain of ExoS was also present. We propose a model for regulation of succinoglycan production by R. meliloti through the ExoS-ChvI two-component regulatory system.
QUIRC: A Quantitative Impact and Risk Assessment Framework for Cloud Security

A quantitative risk and impact assessment framework (QUIRC) is presented, to assess the security risks associated with cloud computing platforms. This framework, called QUIRC, defines risk as a combination of the Probability of a security threat event and it’s Severity, measured as its Impact. Six key Security Objectives (SO) are identified for cloud platforms, and it is proposed that most of the typical attack vectors and events map to one of these six categories. Wide-band Delphi method is proposed as a scientific means to collect the information necessary for assessing security risks. Risk assessment knowledgebases could be developed specific to each industry vertical, which then serve as inputs for security risk assessment of cloud computing platforms. QUIRC’s key advantage is its fully quantitative and iterative convergence approach, which enables stakeholders to comparatively assess the relative robustness of different cloud vendor offerings and approaches in a defensible manner.
Self-Adaptive Techniques for the Load Trend Evaluation of Internal System Resources

Modern distributed systems that have to avoid performance degradation and system overload require several runtime management decisions for load balancing and load sharing, overload and admission control, job dispatching and request redirection. As the external workload and the internal resource behavior of the modern system is highly complex and variable, self-adaptive techniques require a stable vision of the system behavior. In this paper we propose a trend model that guarantees a robust interpretation for load-aware decision algorithms. Various experimental results in a Web cluster demonstrate that the proposed models and algorithms guarantee better stability of the load and a reduction of the response time experienced by the users.
Dietary intake alters gene expression in colon tissue: possible underlying mechanism for the influence of diet on disease

Background Although the association between diet and disease is well documented, the biologic mechanisms involved have not been entirely elucidated. In this study, we evaluate how dietary intake influences gene expression to better understand the underlying mechanisms through which diet operates. Methods We used data from 144 individuals who had comprehensive dietary intake and gene expression data from RNAseq using normal colonic mucosa. Using the DESeq2 statistical package, we identified genes that showed statistically significant differences in expression between individuals in high-intake and low-intake categories for several dietary variables of interest adjusting for age and sex. We examined total calories, total fats, vegetable protein, animal protein, carbohydrates, trans-fatty acids, mutagen index, red meat, processed meat, whole grains, vegetables, fruits, fiber, folate, dairy products, calcium, and prudent and western dietary patterns. Results Using a false discovery rate of less than 0.1, meat-related foods were statistically associated with 68 dysregulated genes, calcium with three dysregulated genes, folate with four dysregulated genes, and nonmeat-related foods with 65 dysregulated genes. With a more stringent false discovery rate of less than 0.05, there were nine meat-related dysregulated genes and 23 nonmeat-related genes. Ingenuity pathway analysis identified three major networks among genes identified as dysregulated with respect to meat-related dietary variables and three networks among genes identified as dysregulated with respect to nonmeat-related variables. The top networks (Ingenuity Pathway Analysis network score >30) associated with meat-related genes were (i) cancer, organismal injury, and abnormalities, tumor morphology, and (ii) cellular function and maintenance, cellular movement, cell death, and survival. Among genes related to nonmeat consumption variables, the top networks were (i) hematological system development and function, nervous system development and function, tissue morphology and (ii) connective tissue disorders, organismal injury, and abnormalities. Conclusion Several dietary factors were associated with gene expression in our data. These findings provide insight into the possible mechanisms by which diet may influence disease processes.
Possibilistic Answer Set Programming Revisited

Possibilistic answer set programming (PASP) extends answer set programming (ASP) by attaching to each rule a degree of certainty. While such an extension is important from an application point of view, existing semantics are not well-motivated, and do not always yield intuitive results. To develop a more suitable semantics, we first introduce a characterization of answer sets of classical ASP programs in terms of possibilistic logic where an ASP program specifies a set of constraints on possibility distributions. This characterization is then naturally generalized to define answer sets of PASP programs. We furthermore provide a syntactic counterpart, leading to a possibilistic generalization of the well-known Gelfond-Lifschitz reduct, and we show how our framework can readily be implemented using standard ASP solvers.
On self-tuning networks-on-chip for dynamic network-flow dominance adaptation

Modern networks-on-chip (NoC) systems are required to handle complex run-time traffic patterns and unprecedented applications. Data traffics of these applications are difficult to be fully comprehended at design-time so as to optimize the network design. However, it has been discovered that the majority data flows in a network are dominated by less than 10% of the specific pathways. In this paper, we introduce a method that is capable of identifying critical pathways in a network at run-time and, then, can dynamically reconfigure the network to optimize for the network performance subjected to the identified dominated flows. An online learning and analysis scheme is employed to quickly discover the emerged dominated traffic flows and provides a statistical traffic prediction using regression analysis. The architecture of a self-tuning network is also discussed which can be reconfigured by setting up the identified point-to-point paths for the dominance data flows in large traffic volumes. The merits of this new approach are experimentally demonstrated using comprehensive NoC simulators. Compared to the conventional network architectures over a range of realistic applications, the proposed self-tuning network approach can effectively reduce the latency and power consumption by as much as 25% and 24%, respectively. We also evaluated the configuration time and additional hardware cost. This new approach demonstrates the capability of an adaptive NoC to handle more complex and dynamic applications.
Formal Modelling and Verification of Concurrent Systems with XCCS

XCCS is a graphical extension of CCS process algebra. To make CCS more convenient from the software engineering point of view the CCS calculus has been equipped with a graphical modelling language and some operators have been moved to the graphical layer. The proposed approach is supported by a computer tool called the Inez XCCS Editor. Among other things the tool provides a graphical editor for designing XCCS diagrams and a transformation algorithm for exporting XCCS models into CCS scripts. The paper presents both a survey of main features of the XCCS language and a description of the Inez XCCS Editor.
Phase OA LC SLMs for record of thin dynamic holographic correctors with diffraction efficiency over 50%

It was found out, that in optically addressed liquid crystal spatial light modulator with the nematic liquid crystal and photoconductor, comprised by p-i-n diode on the base of amorphous hydrogenated silicon, it is possible to record the dynamic diffraction gratings with diffraction efficiency over 50%. The gratings were recorded with the use of S- effect. Possible reasons of the discovered effect are considered in the paper.
Radioimmunoassay for the measurement of serum IL‐6 and its correlation with tumour cell mass parameters in multiple myeloma

Interleukin‐6 (IL‐6) was demonstrated to be a strong autocrine or paracrine plasmocytoma cell growth factor in humans. Using a bioassay, high serum IL‐6 (S‐IL‐6) levels were correlated with disease severity in plasma cell dyscrasias. Since other cytokines could interfere with the bioassays, we developed a specific radioimmunoassay to study S‐IL6 levels in 102 patients with monoclonal gammopathy (MG). S‐IL‐6 level was studied by a double antibody radioimmunoassay using a rabbit polyclonal anti‐IL‐6 antibody and a human recombinant IL‐6 as the standard. The lowest value of the standard significantly different from zero was found to be 78 pg/ml. Within‐run and between‐run precisions were characterized by a mean coefficient of variation of 3.72 and 5.5%, respectively. The mean analytical recovery was found to be 113% and the immunochemical identity of IL‐6 standard and S‐IL‐6 was shown by dilution tests. IL‐6 was detected in all tested sera. Sera from 66 healthy volunteers and 43 patients with acute leukemia or malignant lymphoma were tested as controls. In healthy subjects, S‐IL‐6 values were 294 ± 86 pg/ml. MG were classified as multiple myeloma (MM), macroglobulinemia, and MG of undetermined significance (MGUS). The distribution of S‐IL‐6 levels in patients with MG was significantly higher than in healthy subjects but lower than in patients with acute leukemia or Hodgkin's lymphoma. Results obtained in 55 patients with MM were related to other biological parameters. S‐IL‐6 levels correlated with bone‐marrow plasmacytosis (P < .0005), serum‐lactate dehydrogenase (S‐LDH; P < .005), serum β2 microglobulin (S −β2m; P < .01), and serum calcium (S‐Ca; P < .025) and inversely correlated with haemoglobin (P < .025). Our results indicate that 1 radioimmunoassay is suitable for the measurement of human IL‐6 in serum; 2 high S‐IL‐6 levels are observed in a small number of patients with MG; and 3 S‐IL‐6 level correlates with tumour cell mass in patients with overt MM.
Application of femtosecond lasers for subcellular nanosurgery

This dissertation offers a study of femtosecond laser disruption in single cells. Cells and tissues do not ordinarily absorb light in the near-IR wavelength range of femtosecond lasers. However, the peak intensity of a femtosecond laser pulse is very high and material disruption is possible through nonlinear absorption and plasma generation. Because the pulse duration is very short, it is possible to reach the intensity of optical breakdown at only nanojoules of energy per pulse. The low energy deposition and the high spatial localization of the nonlinear absorption, make femtosecond laser pulses an ideal tool for minimally disruptive subcellular nanosurgery. We show definitively that there can be bulk ablation within a single cell by studying the disrupted region under a transmission electron microscope. The width of the ablated area can be as small as 250 nm in diameter at energies near the ablation threshold. We also studied the effect of the laser repetition rate on the subcellular disruption threshold. We compared the pulse energies for kHz and MHz pulse trains, and found that in the MHz regime heat accumulation in the focal volume needs to be accounted for. For this repetition rate the minimum pulse energy necessary for disruption depends on the laser irradiation time. We used femtosecond laser nanosurgery to probe tension in actin stress fibers in living endothelial cells. By severing an individual stress fiber and visualizing its retraction, we showed that actin carries prestress in adherent, non-contractile cells. By plating the cells on softer, more compliant substrates, we measured the deflection of the substrate and extrapolated the force contribution of a stress filament on total amount of force exerted by the cell.
Solving Uncertain MDPs by Reusing State Information and Plans

While MDPs are powerful tools for modeling sequential decision making problems under uncertainty, they are sensitive to the accuracy of their parameters. MDPs with uncertainty in their parameters are called Uncertain MDPs. In this paper, we introduce a general framework that allows off-the-shelf MDP algorithms to solve Uncertain MDPs by planning based on currently available information and replan if and when the problem changes. We demonstrate the generality of this approach by showing that it can use the VI, TVI, ILAO*, LRTDP, and UCT algorithms to solve Uncertain MDPs. We experimentally show that our approach is typically faster than replanning from scratch and we also provide a way to estimate the amount of speedup based on the amount of information being reused.
When in Rome, do as the Romans do - Overcoming Culture Conflicts in Mobile Learning

Mobile learning allows for the embedding of learning into daily routines by means of authentic learning scenarios. In this context, IS research emphasizes the importance of considering individual differences such as cultural differences when using IT in learning scenarios. First, culture is a critical variable that strongly influences IT acceptance and use. Second, teaching concepts differ heavily across cultures. With this research-in-progress paper, we propose a theory-driven design approach to overcome culture conflicts for mobile learning applications. We derive requirements from IT-culture conflict theory and practice, which are addressed by design elements and implemented in a mobile learning application that is now being adapted for China. As a practical contribution of our research, we provide systematic guidance to overcome culture conflicts; as a theoretical contribution, we enrich culture theory by considering and resolving conflicts for mobile learning applications.
EA-Miner: a tool for automating aspect-oriented requirements identification

Aspect-Oriented requirements engineering helps to achieve early separation of concerns by supporting systematic analysis of broadly-scoped properties such as security, real-time constraints, etc. The early identification and separation of aspects and base abstractions crosscut by them helps to avoid costly refactorings at later stages such as design and code. However, if not handled effectively, the aspect identification task can become a bottleneck requiring a significant effort due to the large amount of, often poorly structured or imprecise, information available to a requirements engineer. In this paper, we describe a tool, EA-Miner, that provides effective automated support for identifying and separating aspectual and non-aspectual concerns as well as their crosscutting relationships at the requirements level. The tool utilises natural language processing techniques to reason about the properties of the concerns and model their structure and relationships.
China’s Energy Portfolio Optimization Study Based on Multi-Attribute Utility Theory and Genetic Algorithm Method

China’s energy planning problem was concluded to the energy portfolio optimization problem which was solved by multi-attribute utility theory and genetic algorithm methods. A fundamental objectives hierarchy was established to structure the energy technology alternatives. Based on this hierarchy model, weights and utilities of energy resources were calculated by the multi-attribute utility theory. The Evolver decision tool was used to find an optimal energy portfolio by the genetic algorithm. The results indicated China should reduce coal and nuclear power in the future energy portfolio.
Diffusion Study by IR Micro-Imaging of Molecular Uptake and Release on Mesoporous Zeolites of Structure Type CHA and LTA

The presence of mesopores in the interior of microporous particles may significantly improve their transport properties. Complementing previous macroscopic transient sorption experiments and pulsed field gradient NMR self-diffusion studies with such materials, the present study is dedicated to an in-depth study of molecular uptake and release on the individual particles of mesoporous zeolitic specimens, notably with samples of the narrow-pore structure types, CHA and LTA. The investigations are focused on determining the time constants and functional dependences of uptake and release. They include a systematic variation of the architecture of the mesopores and of the guest molecules under study as well as a comparison of transient uptake with blocked and un-blocked mesopores. In addition to accelerating intracrystalline mass transfer, transport enhancement by mesopores is found to be, possibly, also caused by a reduction of transport resistances on the particle surfaces.
Comparison of secure and high capacity color image steganography techniques in RGB and YCbCr domains

Steganography is one of the methods used for secret communication. Steganography attempts to hide the existence of the information. The object used to hide the secret information is called as cover object. Images are the most popular cover objects used for steganography. Different techniques have to be used for color image steganography and grey scale image steganography since they are stored in different ways. Color image are normally stored with 24 bit depth and grey scale images are stored with 8 bit depth. Color images can hold large amount of secret information since they have three color components. Different color spaces namely RGB (Red Green Blue), HSV (Hue, Saturation, Value), YUV, YIQ, YCbCr (Luminance, Chrominance) etc. are used to represent color images. Color image steganography can be done in any color space domain. In this paper color image steganography in RGB and YCbCr domain are compared. The secret information considered is grey scale image. Since RGB is the common method of representation, hiding secret information in this format is not secure.
The pepR Gene of Lactobacillus sakei Is Positively Regulated by Anaerobiosis at the Transcriptional Level

ABSTRACT Lactobacillus sakei is a lactic acid bacterium belonging to the natural flora of meat products. It constitutes the main flora of vacuum-packed meat and is largely used in western Europe as a starter for the manufacturing of fermented sausages. This species is able to grow both under aerobiosis and anaerobiosis. In many technological processes involving it, oxygen is scarce. The aim of this study was to identify the major proteins affected by growth under anaerobiosis. Using two-dimensional electrophoresis, we showed that one spot was 10-fold overexpressed when cells were grown under anaerobiosis. By N-terminal sequencing it was identified as a peptidase (PepR), and the pepR gene was cloned. Northern analysis revealed that pepR was expressed as a single 1.27-kb transcript induced under anaerobiosis. A mutant was constructed by single crossover in the pepR gene, and its growth and survival were not affected by anaerobiosis.