Miracle-dz/UniFAR-FTUs huggingface dataset:
Quorum sensing and Chromobacterium violaceum: exploitation of violacein production and inhibition for the detection of N-acylhomoserine lactones.

Quorum sensing relies upon the interaction of a diffusible signal molecule with a transcriptional activator protein to couple gene expression with cell population density. In Gram-negative bacteria, such signal molecules are usually N-acylhomoserine lactones (AHLs) which differ in the structure of their N-acyl side chains. Chromobacterium violaceum, a Gram-negative bacterium commonly found in soil and water, produces the characteristic purple pigment violacein. Previously the authors described a violacein-negative, mini-Tn5 mutant of C. violaceum (CV026) in which pigment production can be restored by incubation with supernatants from the wild-type strain. To develop this mutant as a general biosensor for AHLs, the natural C. violaceum AHL molecule was first chemically characterized. By using solvent extraction, HPLC and mass spectrometry, a single AHL, N-hexanoyl-L-homoserine lactone (HHL), was identified in wild-type C. violaceum culture supernatants which was absent from CV026. Since the production of violacein constitutes a simple assay for the detection of AHLs, we explored the ability of CV026 to respond to a series of synthetic AHL and N-acylhomocysteine thiolactone (AHT) analogues. In CV026, violacein is inducible by all the AHL and AHT compounds evaluated with N-acyl side chains from C4 to C8 in length, with varying degrees of sensitivity. Although AHL compounds with N-acyl side chains from C10 to C14 are unable to induce violacein production, if an activating AHL (e.g. HHL) is incorporated into the agar, these long-chain AHLs can be detected by their ability to inhibit violacein production. The versatility of CV026 in facilitating detection of AHL mixtures extracted from culture supernatants and separated by thin-layer chromatography is also demonstrated. These simple bioassays employing CV026 thus greatly extend the ability to detect a wide spectrum of AHL signal molecules.
Human Motion Extrapolation via Motion Analysis and 2D-3D Motion Estimation

The 3D virtual reality is a very popular research area over the last decade. The 3D products are proposed in video game industries. But most of the 3D products or researches didn¡¦t use the real images or videos to generate, so we proposed a method to adopt the real 2D images to render 3D-liked vision. Our goal is to show the object like Google Map, we can rotate the object we load and according to the angle to show the corresponding object. The main idea is shooting a human at multi-angle, then we observe the regular pattern of human and analyze the motion of human. Finaly, infer a rule for the relationship between the change of viewpoint.
Evaluation of pinching effort by a tendon-driven robot hand

In this paper, we focus on the hand posture when a human subject pinches an object, and the correlation between the results of sensory evaluation by the subject and the tendon force of the subject's fingers are investigated. Surface electromyogram (surface EMG) and pinching force are measured during the pinching motion of human subjects. Experimental results show that subjects feel comfortable pinching a 60 [mm] cylinder. The surface EMGs are lower in the vicinity of 60 [mm]. Furthermore, a tendon-driven robot hand is developed as a sensing hand prototype. The surface EMGs and the motor torque are compared in a pinching experiment using the tendon-driven robot hand. Experimental results show that the tendon-driven robot hand is effective for quantitatively evaluating pinching effort. Simulation of the pinching motion is conducted using a simple finger model. The simulation results show the importance of finger posture and the moment arm in estimating tendon force. Taken together, these results demonstrate the possibility of conducting quantitative evaluation using the tendon-driven robot hand.
Characteristics and Outcomes of Elderly Patients Refused to ICU

Background. There are few data regarding the process of deciding which elderly patients are refused to ICU admission, their characteristics, and outcome. Methods. Prospective longitudinal observational cohort study. We included all consecutive patients older than 75 years, who were evaluated for admission to but were refused to treatment in ICU, during 18 months, with 12-month followup. We collected demographic data, ICU admission/refusal reasons, previous functional and cognitive status, comorbidity, severity of illness, and hospital and 12-month mortality. Results. 338 elderly patients were evaluated for ICU admission and 88 were refused to ICU (26%). Patients refused because they were “too ill to benefit” had more comorbidity and worse functional and mental situation than those admitted to ICU; there were no differences in illness severity. Hospital mortality rate of the whole study cohort was 36.3%, higher in patients “too ill to benefit” (55.6% versus 35.8%, P < 0.01), which also have higher 1-year mortality (73.7% versus 42.5%, P < 0.01). High comorbidity, low functional status, unavailable ICU beds, and age were associated with refusal decision on multivariate analysis. Conclusions. Prior functional status and comorbidity, not only the age or severity of illness, can help us more to make the right decision of admitting or refusing to ICU patients older than 75 years.
Early identification and early intervention in autism spectrum disorders: Accurate and effective?

Abstract Over the past decade, there has been increased interest in identifying autism and autism spectrum disorder (ASD) in toddlers. Although there is a strong rationale for identifying ASD early and delivering effective intervention, a recent report in the journal Pediatrics raises important questions about the scientific evidence currently available supporting early intervention. In addition, the British National Health Service (NHS) has not adopted universal screening for autism, even though the American (US) Academy of Pediatrics endorsed a recommendation that all toddlers be screened for ASD by the age of 24 months (in 2007). The goal of this initiative is to identify and, where indicated, provide early intervention for autism and ASD. Although it is inarguable that this is a worthwhile and laudable goal, the systematic study of this goal is confounded by the inherent difficulty in reliably identifying autism in 24-month-old toddlers. It is challenging to demonstrate intervention effects in the absence of randomly assigned control groups in an increasingly heterogeneous ASD population. The purpose of this paper is to examine the current literature on early identification and early intervention in autism and ASD and to provide a framework for examining these issues.
Cyclic Sequence Alignments: Approximate Versus Optimal Techniques

The problem of cyclic sequence alignment is considered. Most existing optimal methods for comparing cyclic sequences are very time consuming. For applications where these alignments are intensively used, optimal methods are seldom a feasible choice. The alternative to an exact and costly solution is to use a close-to-optimal but cheaper approach. In previous works, we have presented three suboptimal techniques inspired on the quadratic-time suboptimal algorithm proposed by Bunke and Buhler. Do these approximate approaches come sufficiently close to the optimal solution, with a considerable reduction in computing time? Is it thus worthwhile investigating these approximate methods? This paper shows that approximate techniques are good alternatives to optimal methods.
Configuration and tuning of sensor network applications through virtual sensors

Writing software for sensor networks requires full understanding of the physical phenomenon under observation. Nevertheless, in many cases knowing the details of the area of operation is difficult or unfeasible. This leads to the necessity of configuring and tuning the application executed on the sensor nodes even after the network has been deployed. We present a solution where a layer of computation is inserted between the application and the sensing equipment. This layer acts like a tiny interpreter and can be used to customize the behavior of already running applications. Experimental validation of the architecture and examples of use are also shown
Mannose-Binding Lectin Inhibits the Motility of Pathogenic Salmonella by Affecting the Driving Forces of Motility and the Chemotactic Response

Mannose-binding lectin (MBL) is a key pattern recognition molecule in the lectin pathway of the complement system, an important component of innate immunity. MBL functions as an opsonin which enhances the sequential immune process such as phagocytosis. We here report an inhibitory effect of MBL on the motility of pathogenic bacteria, which occurs by affecting the energy source required for motility and the signaling pathway of chemotaxis. When Salmonella cells were treated with a physiological concentration of MBL, their motile fraction and free-swimming speed decreased. Rotation assays of a single flagellum showed that the flagellar rotation rate was significantly reduced by the addition of MBL. Measurements of the intracellular pH and membrane potential revealed that MBL affected a driving force for the Salmonella flagellum, the electrochemical potential difference of protons. We also found that MBL treatment increased the reversal frequency of Salmonella flagellar rotation, which interfered with the relative positive chemotaxis toward an attractive substrate. We thus propose that the motility inhibition effect of MBL may be secondarily involved in the attack against pathogens, potentially facilitating the primary role of MBL in the complement system.
Mining User Query Logs to Refine Component Description

How to help reusers retrieve components efficiently and conveniently is critical to the success of the component-based software development (CBSD). In the literature, many research efforts have been put into the improvement of component retrieval mechanisms. Although many different retrieval methods have been proposed, nowadays the component description text is still an important information source for retrieval. Unfortunately, the descriptions of components often contain improper or even noisy information which could deteriorate the effectiveness of the retrieval mechanism. To alleviate the problem, in this paper, we propose an approach which can refine the component description by mining user query logs. The key idea of our approach is to extract proper information from the user query logs to amend the description of the component. Besides, logs from similar components are also taken into consideration. To evaluate the effectiveness of our approach, we performed an experimental study on the JDK. The experimental results demonstrated an improvement in terms of retrieval effectiveness.
An artificial neural network based model for online prediction of potential deadlock in multithread programs

In this paper we introduce a novel approach for online potential deadlock detection in multithread programs. Our approach is based on reasoning about deadlock possibility using the prediction of future behavior of threads. Predicting the future behavior of threads is not a trivial task. Due to the nondeterministic nature of multithread programs the future behavior of these programs, in most of cases, cannot be easily specified. In this work we extracted some specific behaviors of threads at runtime and then we converted extracted behaviors into a predictable format. Time series is a proper choice to this conversion. Many Statistical and also Artificial Intelligence techniques have been developed to predict the future members of time series. Among all the prediction techniques, artificial neural networks showed applicable performance and flexibility in predicting complex behavioral patterns which are the most usual cases in real world applications. We experimented our approach on some Java multithread programs which was deadlock prone. Applying our approach to this test suit, about 74% of deadlocks were predicted.
Pricing, implementation and calibration of credit derivatives in incomplete market

After the financial crisis of 2008, it is observed that the liquidity of many credit derivatives has dried up. The existence of illiquid credit derivatives that can not be perfectly hedged means that the market is incomplete. Therefore, the classical risk-neutral approach to valuing the credit derivatives will not take account the risks that remain after the hedging. In this thesis, we address these issues by modifying the classical intensity-based models for credit derivatives by embedding them into the framework of optimal portfolio problems, a methodology that takes into account the investor’s risk aversion. Through stochastic optimal control methods, we use indifference pricing with exponential utility function to determine the defaultable bond prices and Credit Default Swap (CDS) spreads. The Hamilton-Jacobi-Bellman (HJB) equations for the value functions are derived. The bid and ask spreads are numerically solved based on the indifference between the investor’s two utility maximization problems. We examine how these quantities are affected qualitatively by the risk aversion, the default intensity, the correlation coefficient and the recovery rate. Moreover, the asymptotic behavior of the spread curves is studied. The calibration of the indifference pricing model of CDS spreads in the context of CIR-intensity is investigated. This gives rise to nonlinear inverse problems since the price of the CDS spreads depends in a non-linear way on the intensity’s parameters and the risk aversion coefficient. In a first step using the data of CDS spreads from Markit, the estimates of time series for the default intensity for a given rating class are obtained. In a second step, including the default intensities constructed in the first step, the parameters of the CIR intensity model and the absolute risk aversion coefficient are estimated. The estimation results reveal that during the subprime crisis, more risk averse investors prefer to buy the protection against the default of low credit quality firms. In addition, estimates for the parameters of CIRintensity are in general significant and are increasing when the credit quality is decreasing.
A storage-free data parasitizing scheme for wireless body area networks

With the increasing sophistication and maturity of biomedical sensors and the significant advances on low-power circuits and wireless communications technologies, wireless body area networks (WBANs) have emerged recently to provide pervasive health monitoring for humans. In WBANs, smart phones can serve as data sinks to forward the sensing data to back-end servers. Due to the battery concern of smart phones and the postural changes of humans, temporary disconnection between sensors and their associated smart phones may frequently happen in WBANs. In this case, the sensing data would be lost when the limited memory space of sensors overflows. To prevent excessive data loss, this paper proposes a scheme to parasitize the data on existing public Wi-Fi networks, once the links from sensors to the smart phones become unavailable. Specifically, an optimization problem to maximize the time during which data loss can be avoided by exploiting the data parasitizing scheme is formulated, where a decision set of the packets' size and sending timing to public Wi-Fi networks needs to be determined. We develop an offline algorithm to obtain an optimal decision set and present an efficient online algorithm for practical implementations. The feasibility of the proposed scheme and the efficacy of the algorithms are demonstrated through prototype implementations on a WBAN testbed with biomedical sensor devices for real-world experiments.
Residential solar hot water: Determinants of demand in New Hampshire

As New Hampshire pursues public policy goals embedded in the Renewable Portfolio Standard, the Regional Greenhouse Gas Initiative, the Climate Action Plan, and other legislation and documentation, many advocates and policy makers are looking for reductions in fossil fuel use in the residential sector. This paper analyzes the results of a survey of New Hampshire residents undertaken in the autumn of 2009 regarding attitudes toward energy policy, and willingness to invest in renewable energy. Regarding residential solar hot water, the survey finds that the price at which half of New Hampshire homeowners would consider purchasing such a system is $5536. Seriousness of commitment is also tested, showing significant barriers to followthrough. These barriers and potential means of overcoming them are examined, based on concepts from economics and related fields. The paper concludes with recommendations for further research.
Musical Style Identification Using Self-Organising Maps

In this paper the capability of using self-organising neural maps (SOM) as music style classifiers from symbolic specifications of musical fragments is studied. From MIDI file sources, the monophonic melody track is extracted and cut into fragments of equal length. From these sequences, melodic, harmonic, and rhythmic numerical descriptors are computed and presented to the SOM. Their performance is analysed in terms of separability in different music classes from the activations of the map, obtaining different degrees of success for classical and jazz music. This scheme has a number of applications like indexing and selecting musical databases or the evaluation of style-specific automatic composition systems.
Solving K-Coverage Problem in Wireless Sensor Networks Using Improved Harmony Search

Coverage and energy efficiency metrics are two fundamental issues for distribution of wireless sensor nodes. Coverage reflects how well an area is monitored by sensor nodes and in energy efficient networks less energy is consumed to provide the same level of services. These twin specifics are presented to evaluate the performance of a wireless sensor network. On the other hand, connectivity and k-coverage are prerequisites for a k-covered Wireless Sensor Network. Connectivity shows how nodes in a sensor network can effectively communicate and kcoverage means hotspot regions are covered by at least k sensors. In this article, we propose an Improved Harmony Search algorithm in a k-covered and connected wireless sensor network to achieve a sensor node deployment such that the covered area is optimal and data transfer has low energy consumption. Through computer simulations, experimental results verified that the proposed method improved the coverage of area in compare to some related methods.
Generating Better Decision Trees

A new decision tree learning algorithm called IDX is described. More general than existing algorithms, IDX addresses issues of decision tree quality largely overlooked in the artificial intelligence and machine learning literature. Decision tree size, error rate, and expected classification cost are just a few of the quality measures it can exploit. Furthermore, decision trees of varying quality can be induced simply by adjusting the complexity of the algorithm. Quality should be addressed during decision tree construction since retrospective pruning of the tree, or of a derived rule set, may be unable to compensate for inferior splitting decisions. The complexity of the algorithm, the basis for the heuristic it embodies, and the results of three different sets of experiments are described.
An efficient femtocell deployment scheme for mitigating interference in two-tier networks

In two-tier networks, which consist of macrocells and femtocells, femtocells can offload the traffic from macrocells and provide better indoor signal coverage, but the dynamic deployment feature of femtocells may result in signal interference. In this paper, an efficient femtocell deployment scheme is presented to mitigate interference. Both functions of frequency channel allocation and transmission power adjustment are proposed. Signal coverage of a set of femtocell base stations with interference can also be extended by re-allocation frequency channels. The simulation results show that the proposed deployment scheme mitigates the interference efficiently and has better network performance as compared with co-channel and cognitive deployment approaches.
Providing QoS in a Cognitive Radio Network

We consider the problem of wireless channel allocation (whenever the channels are free) to multiple cognitive radio users in a Cognitive Radio Network (CRN) so as to satisfy their Quality of Service (QoS) requirements efficiently. The CRN base station may not know the channel states of all the users. The multiple channels are available at random times. In this setup Opportunistic Splitting can be an attractive solution. A disadvantage of this algorithm is that it requires the metrics of all users to be an independent, identically distributed sequence. However we use a recently generalized version of this algorithm in which the optimal parameters are learnt on-line through stochastic approximation and metrics can be Markov. We provide scheduling algorithms which maximize weighted-sum system throughput or are throughput or delay optimal. We also consider the scenario when some traffic streams are delay sensitive.
Paper submitted to : Third National Symposium onLarge-Scale Structural Analysis forHigh-Performance Computers and WorkstationsComputational Results for Parallel Unstructured

The majority of nite element models in structural engineering are composed of unstructured meshes. These unstructured meshes are often very large and require signiicant computational resources; hence they are excellent candidates for massively parallel computation. Parallel solution of the sparse matrices that arise from such meshes has been studied heavily, and many good algorithms have been developed. Unfortunately, many of the other aspects of parallel unstructured mesh computation have gone largely ignored. We present a set of algorithms that allow the entire unstructured mesh computation process to execute in parallel|including adaptive mesh reenement, equation reordering, mesh partitioning, and sparse linear system solution. We brieey describe these algorithms and state results regarding their running-time and performance. We then give results from the 512-processor Intel DELTA for a large-scale structural analysis problem. These results demonstrate that the new algorithms are scal-able and eecient. The algorithms are able to achieve up to 2.2 gigaaops for this unstructured mesh problem.