Miracle-dz/UniFAR-FTUs huggingface dataset:
Roles of CatR and cis,cis-muconate in activation of the catBC operon, which is involved in benzoate degradation in Pseudomonas putida

In Pseudomonas putida, the catBC operon encodes enzymes involved in benzoate degradation. Previous studies have determined that these enzymes are induced when P. putida is grown in the presence of benzoate. Induction of the enzymes of the catBC operon requires an intermediate of benzoate degradation, cis,cis-muconate, and a regulatory protein, CatR. It has been determined that CatR binds to a 27-bp region of the catBC promoter in the presence or absence of inducer. We have called this the repression binding site. In this study, we used a gel shift assay to demonstrate that the inducer, cis,cis-muconate, increases the affinity of CatR for the catBC promoter region by 20-fold. Furthermore, in the absence of cis,cis-muconate, CatR forms two complexes in the gel shift assay. The inducer cis,cis-muconate confers specificity primarily for the formation of complex 2. DNase I footprinting showed that an additional 27 bp of the catBC promoter region is protected by CatR in the presence of cis,cis-muconate. We have named this second binding site the activation binding site. Methylation interference footprinting determined that in the presence or absence of inducer, five G nucleotides of the catBC promoter region were necessary for CatR interaction with the repression binding site, while a single G residue was important for CatR interaction with the activation binding site in the presence of cis,cis-muconate. Using polymerase chain reaction-generated constructs, we found that the binding of CatR to the repression binding site is independent of the activation binding site. However, binding of CatR to the activation binding site required an intact repression binding site.
Investigation on the Space Vector PWM for Large Power Three-Level DC-Link Voltage Source Inverter Equipped with IGCTs

In high performance AC drive systems, the large power three-level voltage source inverters equipped with IGCTs are widely used now and have promising future. In this paper, the principle of space vector PWM (SVPWM) for three-level inverter is introduced. Some problems of SVPWM for three-level inverter are discussed. Based on the system simulation, a high power three-level inverter equipped with IGCTs is designed. The working performance of the inverter is verified with system experiment. The experiment results show that the inverter has excellent performance
Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid

In this paper, we consider deployment of energy consumption scheduling (ECS) devices in smart meters for autonomous demand side management within a neighborhood, where several buildings share an energy source. The ECS devices are assumed to be built inside smart meters and to be connected to not only the power grid, but also to a local area network which is essential for handling two-way communications in a smart grid infrastructure. They interact automatically by running a distributed algorithm to find the optimal energy consumption schedule for each subscriber, with an aim at reducing the total energy cost as well as the peak-to-average-ratio (PAR) in load demand in the system. Incentives are also provided for the subscribers to actually use the ECS devices via a novel pricing model, derived from a game-theoretic analysis. Simulation results confirm that our proposed distributed algorithm significantly reduces the PAR and the total cost in the system.
Detection of Cerebral Vasculopathy by Transcranial Doppler in Children With Neurofibromatosis Type 1

Neurofibromatosis type 1 is characterized by nerve sheath neurofibromas associated with a number of additional clinical features, including cerebrovascular disease. The aim of this study was to use transcranial Doppler as a screening method for identifying cerebral vasculopathy in children with neurofibromatosis type 1. Forty children with neurofibromatosis type 1, aged 5 to 18 years old, were examined by transcranial Doppler. Patients presenting with hemodynamic features of arterial stenosis/occlusion on transcranial Doppler underwent magnetic resonance angiography to confirm the findings. Magnetic resonance angiography was performed on 4 children who exhibited a transcranial Doppler hemodynamic pattern indicative of cerebral vasculopathy. Among these cases, 2 presented internal carotid artery stenosis/occlusion, 1 had bilateral middle cerebral artery stenosis, and 1 presented a normal magnetic resonance angiography result. Transcranial Doppler can be used routinely in the investigation of cerebrovascular disease in neurofibromatosis type 1 patients, where magnetic resonance angiography can be subsequently applied to confirm the diagnosis, further contributing to the prevention of cerebrovascular events.
Mouse, Tactile, and Tangible Input for 3D Manipulation

We evaluate the performance and usability of mouse-based, touch-based, and tangible interaction for manipulating objects in a 3D virtual environment. This comparison is a step toward a better understanding of the limitations and benefits of these existing interaction techniques, with the ultimate goal of facilitating an easy transition between the different 3D data exploration environments. For this purpose we analyze participants' performance in 3D manipulation using a docking task. We measured completion times, docking accuracy, as well as subjective criteria such as fatigue, workload, and preference. Our results show that the three input modalities provide similar levels of precision but require different completion times. We also discuss our qualitative observations as well as people's preferences and put our findings into context of the application domain of 3D data analysis environments.
Novel Attack Detection Using Fuzzy Logic and Data Mining

Intrusion Detection Systems are increasingly a key part of systems defense. Various approaches to Intrusion Detection are currently being used, but they are relatively ineffective. Artificial Intelligence plays a driving role in security services. This paper proposes a dynamic Intelligent Intrusion Detection System model, based on specific AI approach for intrusion detection. The technique that is being investigated includes fuzzy logic with network profiling, which uses simple data mining techniques to process the network data. The proposed hybrid system combines anomaly and misuse detection. Simple fuzzy rules, allow us to construct if-then rules that reflect common ways of describing security attacks. Suspicious intrusions can be traced back to its original source and any traffic from that particular source will be redirected back to them in future. Both network traffic and system audit data are used as inputs for the
Assessing the Denominational Identity of American Evangelical Colleges and Universities, Part II: Faculty Perspectives and Practices

This article presents results from Phase II of the Council for Christian Colleges & Universities (CCCU) Denominational Study, a multi-institutional examination of the state of denominational identity at evangelical Christian colleges, their faculty, and the students they serve. This phase of the study involved the administration of an online survey that was completed by 1,557 full-time faculty serving in 37 denominationally-affiliated evangelical Christian colleges. On the whole, faculty expressed a strong sense of affinity toward the sponsoring denominations of their respective institutions, though less than half attended a local congregation in the same denominational tradition. In addition, personal theological traditions were reported to influence faculty classroom practice on a number of dimensions. Faculty also perceived that college leaders and administrators placed a significant amount of importance on the denominational identities of their respective institutions, reporting that denominational identity was emphasized across many facets of institutional life, including campus ethos, curriculum, corporate worship, institutional governance, and public rhetoric. Finally, though respondents valued denominational identity in the faculty search process, a large majority expressed openness toward hiring colleagues from differing denominational traditions.
Potential antiproliferative effect of isoxazolo- and thiazolo coumarin derivatives on breast cancer mediated bone and lung metastases

Abstract The study highlights the current progress in the development of coumarin scaffolds for drug discovery as novel anticancer agents in metastatic breast cancer. Eight compounds, combining the coumarin core and five membered heterocycles (isoxazoles and thiazoles) in hydrazinyldiene- -chroman-2,4-diones, were characterized in terms of a potential antiproliferative effect on bone (SCP1833) and lung (SCP4175) metastatic breast cancer cell lines using the 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide assay. Cell viability was evaluated after 48 and 72 h of treatment and the 50 % inhibitory concentrations were determined. The results demonstrated dose- and time-dependent activity, with the most potent molecules having a thiazole moiety, without or with additional methyl group(s) attached to the carbon(s) at position(s) 5 and/or 4 in the thiazole ring. These molecules possessed significantly higher potency against both test cell lines compared to 4-hydroxycoumarin
Validation of the Values in Action Inventory of Strengths for Youth (VIA-Youth) Among South African learners

This study investigated the reliability and validity of the Values in Action Inventory for Youth (VIA-Youth) of Park and Peterson (2006) in a total multi-cultural group of South African learners aged 13 to 17 years (N=1691: male = 703, female = 988). Descriptive statistics, reliability coefficients and correlations were calculated, and confirmatory and exploratory factor analyses were performed. Acceptable reliability coefficients were obtained for most of the strengths subscales. Criterion-related validity was supported by correlations in expected directions between VIA-Youth subscales and indices of psychological well-being and pathology. Exploration of construct validity by testing of structural equation models and confirmatory and exploratory factor analyses suggested that the VIA-Youth is more homogeneous or uni-dimensional than multi-dimensional. Findings did not support the theoretically hypothesized six virtue-cluster model of character strengths.
Temporal transcriptional response to ethylene gas drives growth hormone cross-regulation in Arabidopsis

The gaseous plant hormone ethylene regulates a multitude of growth and developmental processes. How the numerous growth control pathways are coordinated by the ethylene transcriptional response remains elusive. We characterized the dynamic ethylene transcriptional response by identifying targets of the master regulator of the ethylene signaling pathway, ETHYLENE INSENSITIVE3 (EIN3), using chromatin immunoprecipitation sequencing and transcript sequencing during a timecourse of ethylene treatment. Ethylene-induced transcription occurs in temporal waves regulated by EIN3, suggesting distinct layers of transcriptional control. EIN3 binding was found to modulate a multitude of downstream transcriptional cascades, including a major feedback regulatory circuitry of the ethylene signaling pathway, as well as integrating numerous connections between most of the hormone mediated growth response pathways. These findings provide direct evidence linking each of the major plant growth and development networks in novel ways. DOI: http://dx.doi. org/10.7554/eLife.00675. 001
Prospective backward Oracle matching algorithm for network intrusion detection system

Pattern matching algorithms are the core component of most of the Network Intrusion Detection Systems (NIDS) search engines. With the rapid advancements in technology, there is an exponential growth in network speed. NIDS must inspect the packets in a faster manner without degrading performance during heavy traffic. Hence performance of NIDS mostly depends on the selection of pattern matching algorithms. Large number of patterns that are of different lengths and case insensitive, can be efficiently handled by multiple pattern matching methods. Several algorithms exist, but deciding performance of which pattern matching algorithm is best is not known. These algorithms allow NIDS detection engines to rapidly search for several patterns concurrently in traffic of network systems, but frequently consume lot of time. This paper provides a brief overview on major pattern matching algorithms and variations of Backward Oracle Matching (BOM) Algorithm, which is significantly faster than other algorithms. This paper proposes modifications to the BOM algorithm for better pattern matching that can be applied at several levels of the efficient NIDS architecture to achieve better results. Sample experimental evaluation of proposed BOM was done with Snort NIDS tool and performance shown graphically.
Improved Heuristics for Optimal Path-finding on Game Maps

As computer game worlds get more elaborate the more visible pathfinding performance bottlenecks become. The heuristic functions typically used for guiding A*-based path inding are too simplistic to provide the search with the necessary guidance in such large and complex game worlds. This may result in A*-search exploring the entire game map in order to find a path between two distant locations. This article presents two effective heuristics for estimating distances between locations in large and complex game maps. The former, the dead-end heuristic, eliminates from the search map areas that are provably irrele- vant for the current query, whereas the second heuristic uses so-called gateways to improve its estimates. Empirical evaluation on actual game maps shows that both heuristics reduce the exploration and time complexity of A* search significantly over a standard octile distance metric.
A novel framework for modeling and mitigating distributed link flooding attacks

Distributed link-flooding attacks constitute a new class of attacks with the potential to segment large areas of the Internet. Their distributed nature makes detection and mitigation very hard. This work proposes a novel framework for the analytical modeling and optimal mitigation of such attacks. The detection is modeled as a problem of relational algebra, representing the association of potential attackers (bots) to potential targets. The analysis seeks to optimally dissolve all but the malevolent associations. The framework is implemented at the level of online Traffic Engineering (TE), which is naturally triggered on link-flooding events. The key idea is to continuously re-route traffic in a manner that makes persistent participation to link-flooding events highly improbable for any benign source. Thus, bots are forced to adopt a suspicious behavior to remain effective, revealing their presence. The load-balancing objective of TE is not affected at all. Extensive simulations on various topologies validate our analytical findings.
Multipartite Entanglement Among Single Spins in Diamond

Robust entanglement at room temperature is a necessary requirement for practical applications in quantum technology. We demonstrate the creation of bipartite- and tripartite-entangled quantum states in a small quantum register consisting of individual 13C nuclei in a diamond lattice. Individual nuclear spins are controlled via their hyperfine coupling to a single electron at a nitrogen-vacancy defect center. Quantum correlations are of high quality and persist on a millisecond time scale even at room temperature, which is adequate for sophisticated quantum operations.
Adaptive Learning in a World of Projections

This article presents a general tool for convexly constrained parameter/function estimation both for classification and regression tasks, in a timeadaptive setting and in (infinite dimensional) reproducing kernel Hilbert spaces (RKHS). The thematical framework is that of the set theoretic estimation formulation and the classical projections onto convex sets (POCS) theory. However, in contrast to the classical POCS methodology, which assumes a finite number of convex sets, our method builds upon our recent extension of the theory, which considers an infinite number of convex sets. Such a context is necessary to cope with the adaptive setting rationale, where data arrive sequentially. This article's goal is to review the advances that have taken place in this area over the years and present them, in simple geometric arguments, as an integral part and natural evolution of the classical POCS methodology. The structure of the resulting algorithms is such that it allows extension to general RKHS. In this perspective, two very powerful techniques, convex optimization and (implicit) mapping to RKHS, are combined, which provide a framework for a unifying treatment of linear and nonlinear modeling of both classification and regression tasks. Typical signal processing problems, such as filtering, smoothing, equalization, and beamforming, fall under this common umbrella. The methodology allows for the incorporation of a set of convex constraints, which encode a priori information. Convexity, rather than differentiability, is the only prerequisite for adopting error measures that quantify the model's fit against a set of training data points. Moreover, the complexity per iteration step remains linear with respect to the number of unknown parameters. The potential of the theory is demonstrated via numerical simulations for two typical problems; adaptive equalization and adaptive robust beamforming.
Grammatical inference in bioinformatics

Bioinformatics is an active research area aimed at developing intelligent systems for analyses of molecular biology. Many methods based on formal language theory, statistical theory, and learning theory have been developed for modeling and analyzing biological sequences such as DNA, RNA, and proteins. Especially, grammatical inference methods are expected to find some grammatical structures hidden in biological sequences. In this article, we give an overview of a series of our grammatical approaches to biological sequence analyses and related researches and focus on learning stochastic grammars from biological sequences and predicting their functions based on learned stochastic grammars.
A Phase-Intensity Surface Plasmon Resonance Biosensor for Avian Influenza A (H5N1) Detection

In this paper, we present a phase-intensity surface plasmon resonance (SPR) biosensor and demonstrate its use for avian influenza A (H5N1) antibody biomarker detection. The sensor probes the intensity variation produced by the steep phase response at surface plasmon excitation. The prism sensor head is fixed between a pair of polarizers with a perpendicular orientation angle and a forbidden transmission path. At SPR, a steep phase change is introduced between the p- and s-polarized light, and this rotates the polarization ellipse of the transmission beam. This allows the light at resonance to be transmitted and a corresponding intensity change to be detected. Neither time-consuming interference fringe analysis nor a phase extraction process is required. In refractive index sensing experiments, the sensor resolution was determined to be 6.3 × 10−6 refractive index values (RIU). The sensor has been further applied for H5N1 antibody biomarker detection, and the sensor resolution was determined to be 193.3 ng mL−1, compared to 1 μg mL−1 and 0.5 μg mL−1, as reported in literature for influenza antibody detection using commercial Biacore systems. It represents a 517.3% and 258.7% improvement in detection limit, respectively. With the unique features of label-free, real-time, and sensitive detection, the phase-intensity SPR biosensor has promising potential applications in influenza detection.
Personalizing universal recurrent neural network language model with user characteristic features by social network crowdsourcing

With the popularity of mobile devices, personalized speech recognizer becomes more realizable today and highly attractive. Each mobile device is primarily used by a single user, so it's possible to have a personalized recognizer well matching to the characteristics of individual user. Although acoustic model personalization has been investigated for decades, much less work have been reported on personalizing language model, probably because of the difficulties in collecting enough personalized corpora. Previous work used the corpora collected from social networks to solve the problem, but constructing a personalized model for each user is troublesome. In this paper, we propose a universal recurrent neural network language model with user characteristic features, so all users share the same model, except each with different user characteristic features. These user characteristic features can be obtained by crowdsouring over social networks, which include huge quantity of texts posted by users with known friend relationships, who may share some subject topics and wording patterns. The preliminary experiments on Facebook corpus showed that this proposed approach not only drastically reduced the model perplexity, but offered very good improvement in recognition accuracy in n-best rescoring tests. This approach also mitigated the data sparseness problem for personalized language models.
Public Involvement in Social Policy Reform: Seen from the Perspective of Japan's Elderly-Care Insurance Scheme

Japan has undergone drastic demographic changes in the past few decades. To cope with the needs of being an ageing society, the government has enacted a Long-term Care Insurance Law for the elderly that was implemented from 1 April 2000. The new legislation was conceived as a political compromise to appease two strongly opposed forces: reformists and the old guard. In the process of drafting reform, new political players, including ordinary citizens and mayors of small-scale municipal governments, have emerged. Two citizen action groups participated in the reform process, and succeeded in reflecting their preferences in its policymaking. The mayors who supported the new system started reforming social welfare administration systems, challenging traditional local politics. This article focuses on a few of these groups and how they have changed the Japanese political scene. It concludes that their political activities have contributed not only to promoting social policy reform, but also to revitalising politics in this country.
Hybrid Membranes of PLLA/Collagen for Bone Tissue Engineering: A Comparative Study of Scaffold Production Techniques for Optimal Mechanical Properties and Osteoinduction Ability

Synthetic and natural polymer association is a promising tool in tissue engineering. The aim of this study was to compare five methodologies for producing hybrid scaffolds for cell culture using poly-l-lactide (PLLA) and collagen: functionalization of PLLA electrospun by (1) dialkylamine and collagen immobilization with glutaraldehyde and by (2) hydrolysis and collagen immobilization with carbodiimide chemistry; (3) co-electrospinning of PLLA/chloroform and collagen/hexafluoropropanol (HFP) solutions; (4) co-electrospinning of PLLA/chloroform and collagen/acetic acid solutions and (5) electrospinning of a co-solution of PLLA and collagen using HFP. These materials were evaluated based on their morphology, mechanical properties, ability to induce cell proliferation and alkaline phosphatase activity upon submission of mesenchymal stem cells to basal or osteoblastic differentiation medium (ODM). Methods (1) and (2) resulted in a decrease in mechanical properties, whereas methods (3), (4) and (5) resulted in materials of higher tensile strength and osteogenic differentiation. Materials yielded by methods (2), (3) and (5) promoted osteoinduction even in the absence of ODM. The results indicate that the scaffold based on the PLLA/collagen blend exhibited optimal mechanical properties and the highest capacity for osteodifferentiation and was the best choice for collagen incorporation into PLLA in bone repair applications.