Miracle-dz/UniFAR-FTUs huggingface dataset:
Insulin action in growth hormone-deficient and age-matched control rats: effect of growth hormone treatment.

The isolated effect of growth hormone on carbohydrate metabolism in rat skeletal muscle was studied in growth hormone-deficient dwarf rats (dw/dw) treated with either recombinant human growth hormone or saline for 10 days. In addition, age-matched heterozygous (DW/dw) (normal weight and plasma IGF-I) control rats were treated with saline. Growth hormone increased weight gain from 0.1+/-0.1 (s.e.m) to 3.6+/-0.1 g/day and plasma IGF-I concentration from 364+/-23 to 451+/-32 ng/ml. Glucose metabolism in skeletal muscle perfused with basal, submaximal and maximal concentrations (0, 600 and 60 000 pmol/l respectively) of insulin was not changed by growth hormone. No change could be detected in the total number of glucose transporters (GLUT1 and GLUT4) in the skeletal muscles, except from a lower amount of GLUT4 in the soleus muscle in the heterozygous control group. However, at submaximal insulin concentrations, skeletal muscle glucose uptake and transport were significantly lower in the heterozygous control group compared with the growth hormone-deficient group. This could indicate either a direct long-term effect of growth hormone or more likely a secondary effect attributable to the difference in body weight (205.2+/-3.1 vs 361. 6+/-5.9 g for dwarf rats and heterozygous controls respectively), and thereby muscle fibre size, between the groups probably resulting in lower average interstitial insulin and glucose concentrations at a given plasma concentration in the heterozygous rats. It is concluded that restoration of subnormal growth hormone concentrations for 10 days has no effect on insulin-stimulated glucose metabolism in skeletal muscle in vitro.
C6 Peptide-Based Multiplex Phosphorescence Analysis (PHOSPHAN) for Serologic Confirmation of Lyme Borreliosis

Background A single-tier immunoassay using the C6 peptide of VlsE (C6) from Borrelia burgdorferi sensu stricto (Bb) has been proposed as a potential alternative to conventional two-tier testing for the serologic diagnosis of Lyme disease in the United States and Europe. Objective To evaluate the performance of C6 peptide based multiplex Phosphorescence Analysis (PHOSPHAN) for the serologic confirmation of Lyme borreliosis (LB) in Russian patients. Methods Serum samples (n = 351) were collected from 146 patients with erythema migrans (EM); samples from 131 of these patients were taken several times prior to treatment and at different stages of recovery. The control group consisted of 197 healthy blood donors and 31 patients with other diseases, all from the same highly endemic region of Russia. All samples were analyzed by PHOSPHAN for IgM and IgG to Bb C6, recombinant OspC and VlsE proteins, and C6 peptides from B. garinii and B. afzelii. Results IgM and IgG to Bb C6 were identified in 43 and 95 out of 131 patients (32.8 and 72.5%, respectively); seroconversion of IgM antibodies was observed in about half of the patients (51.2%), and of IgG antibodies, in almost all of them (88.4%). Additional detection of OspC-IgM and VlsE-IgM or IgG to C6 from B. garinii or B. afzelii did not contribute significantly to the overall sensitivity of the multiplex immunoassay. Conclusions The multiplex phosphorescence immunoassay is a promising method for simultaneously revealing the spectrum of antibodies to several Borrelia antigens. Detection of IgM and IgG to Bb C6 in the sera of EM patients provides effective serologic confirmation of LB and, with high probability, indicates an active infection process.
High-Performance XML Filtering: An Overview of YFilter

We have developed YFilter, an XML filtering system that provides fast, on-the-fly matching of XMLencoded data to large numbers of query specifications containing constraints on both structure and content. YFilter encodes path expressions using a novel NFA-based approach that enables highly-efficient, shared processing for large numbers of XPath expressions. In this paper, we provide a brief technical overview of YFilter, focusing on the NFA model, its implementation, and its performance characteristics.
An exercise in the management of change: identifying factors which affect the likelihood of an engineering company in the nottingham area choosing a national vocational qualification route as the means of meeting its training needs

Abstract The aim of this research has been to examine the implementation of National Vocational Qualifications (NVQs) in engineering in the Greater Nottingham area from the perspective of management of change. It has concentrated on the effects of change on those directly responsible for managing training programmes within their companies, and their reactions to it. A model for the management of change has been developed, which has been used to interrogate the data obtained from semi-structured interviews. By coincidence, or fate, the research was completed shortly before the introduction of the Learning and Skills Council (LSC). A number of similarities have been discovered in the training backgrounds of those involved, despite differences in such factors as the engineering sectors in which they work, the size and age of their company. The major conclusions drawn from the results have been the need to identify key people at an operational level who are required to implement change practically, and to ensure that readiness, relevance and resources are identified for them, through the context, nature and politics of the change, if they are to engage with the vision. The implication of this study is that this has not been addressed in a systematic way for engineering in the Greater Nottingham conurbation. The result has been a flawed implementation of the original intention, which has led to the development of informal, ad hoc relationships to fill the vacuum. These are matters to be addressed by the LSC if the situation is not to be perpetuated.
Efficient Differentiation of Mycobacterium avium Complex Species and Subspecies by Use of Five-Target Multiplex PCR

ABSTRACT Infections caused by the Mycobacterium avium complex (MAC) are on the rise in both human and veterinary medicine. A means of effectively discriminating among closely related yet pathogenetically diverse members of the MAC would enable better diagnosis and treatment as well as further our understanding of the epidemiology of these pathogens. In this study, a five-target multiplex PCR designed to discriminate MAC organisms isolated from liquid culture media was developed. This MAC multiplex was designed to amplify a 16S rRNA gene target common to all Mycobacterium species, a chromosomal target called DT1 that is unique to M. avium subsp. avium serotypes 2 and 3, to M. avium subsp. silvaticum, and to M. intracellulare, and three insertion sequences, IS900, IS901, and IS1311. The pattern of amplification results allowed determination of whether isolates were mycobacteria, whether they were members of the MAC, and whether they belonged to one of three major MAC subspecies, M. avium subsp. paratuberculosis, M. avium subsp. avium, and M. avium subsp. hominissuis. Analytical sensitivity was 10 fg of M. avium subsp. paratuberculosis genomic DNA, 5 to 10 fg of M. avium subsp. avium genomic DNA, and 2 to 5 fg of DNA from other mycobacterial species. Identification accuracy of the MAC multiplex was evaluated by testing 53 bacterial reference strains consisting of 28 different mycobacterial species and 12 nonmycobacterial species. Identification accuracy in a clinical setting was evaluated for 223 clinical MAC isolates independently identified by other methods. Isolate identification agreement between the MAC multiplex and these comparison assays was 100%. The novel MAC multiplex is a rapid, reliable, and simple assay for discrimination of MAC species and subspecies in liquid culture media.
Dynamic Framed-ALOHA for Energy-Constrained Wireless Sensor Networks with Energy Harvesting

The Dynamic Framed-ALOHA (DFA) protocol is studied for wireless sensor networks with energy limitations and energy-harvesting capability. The performance of DFA in this scenario is evaluated in terms of the time efficiency (or throughput), which is routinely used to evaluate medium access protocols, and by introducing a new metric, referred to as detection efficiency, which is tailored to scenarios with energy constraints. Specifically, detection efficiency measures the ability of a multiple access protocol to collect data from nodes without depleting their energy reserves. Analysis is first performed by assuming that DFA is operated with a perfect backlog (i.e., number of sensors left to be interrogated) knowledge. Then, a low-complexity backlog estimation algorithm is presented, which is shown by numerical results to perform close to the ideal case of perfect backlog knowledge.
The outbreak of cooperation

We study the ongoing collective action problem among intentional agents whose choices depend not only on the past but also on their expectations as to how their actions will affect those of others. In this model agents act on the basis of imperfect information. We show that under these conditions the onset of overall cooperation can take place in a sudden and unexpected way. Likewise, defection can appear out of nowhere in very large, previously cooperating groups. These outbreaks mark the end of long transient states in which defection or cooperation persists in groups that cannot sustain it indefinitely.
Focused ultrasound ablation using real time ultrasound image guidance

Focused ultrasound surgery (FUS) is showing significant potential as a non-invasive medical intervention without percutaneous access for ablation (cell destruction by heating) of tumours. MRI is frequently used to guide FUS surgery (MRgFUS). Although the MRI can acquire multiple slices per second, organ motion is not accounted for in the repositioning of the sonication using MRI. Ultrasound, with realtime imaging, and which is less costly and easier to use than MRI, may become an alternative. In this paper we present a real-time ultrasound image guidance method suitable for tracking the motion of organs and ablating tissue. To demonstrate the potential of ultrasound imaging for real-time guidance of FUS, a 6-degree-of-freedom (DOF) robot has been used to control a FUS transducer and position the focus at a target that has been segmented from a real-time ultrasound video. A moving phantom is designed to test the feasibility of the tracking and the control method.
Skype traffic identification based SVM using optimized feature set

Skype traffic recognition is a challenging problem due to the encryption and dynamic port number. Accuracy and timely traffic classification is critical in network security monitoring and traffic engineering. In this paper, we propose an online recognition method based on SVM (support vector machine) machine learning method. As the feature set is optimized instead of redundant, our method is able to compute faster and more accuracy. Experimental results on Collage campus data sets show that our method performs better on both speed and efficiency. Moreover, the robustness of our method is demonstrated on the other non-Skype traffic such as MSN (Microsoft Service Network), PPLive (Peer to Peer LIVE) application.
A concept of quality assurance for metrics in CASE-tools

The quality of software has become more important to software companies in the past years. Software measurement is one of many approaches that is used to check the quality of software [18]. This may involve measuring some attributes of a software product or a software process and comparing these measurements to each other or to some desirable level. A software metric is "any type of measurement that relates to a software system, process or related documentation" [7]. Software metrics can help to improve the quality of the produced software. However, metrics and metrics tools are still not used in most software companies -- for example in the 3 companies where we cooperated in last 5 years. One reason is that there is lack of knowledge about metrics and hence software metrics are still unknown or difficult to use for some developers, and software measurement is still time-consuming for managers. Another reason is that, good metrics tools are still expensive for small and middle companies. In our opinion the effectiveness of metrics can be improved by simple organizational expedients. In this paper we present new/adapted requirements on metrics in CASE tools to define flexible product quality models. For this quality model we tried to use some standard techniques, e.g. metrics suite, metrics visualization or metrics filtering to show how metrics in CASE tools can be defined and how it can benefit different people who are involved in a software deployment.
Distance Effects in the Demand for Wildland Recreational Services: The Case of National Parks in the United States

Distance decay in spatial demand usually is taken as axiomatic. There are, however, a number of situations in which distance decay cannot be taken for granted. In recreational pursuits, for example, spatial interaction is often marked by a confounding distance effect in which nearby and more distant destinations are equally attractive. The research reported in this paper concerns an examination of the distance effect in the spatial demand for a specific type of recreation: the use of national parks in the USA. The paper contains a review of the related spatial demand literature, including the travel cost model used in calculating economic values of national parks and related places. A central-place-type model of park use is described and put into operation in the form of two linear spatial demand models. One focuses on regional demand for park use and the other focuses on a national market. The initial model specifications are expanded in order to examine the drift of their distance parameters over two variables intended to quantify park quality: age and area. Empirical tests of the models indicate distance decay in the demand for park use is pronounced when distance is considered in the context of park quality.
Classification of EEG Signals from Four Subjects During Five Mental Tasks

Neural networks are trained to classify half-second segments of six-channel, EEG data into one of five classes corresponding to five cognitive tasks performed by four subjects. Two and three-layer feedforward neural networks are trained using 10-fold cross-validation and early stopping to control over-fitting. EEG signals were represented as autoregressive (AR) models. The average percentage of test segments correctly classified ranged from 71% for one subject to 38% for another subject. Cluster analysis of the resulting neural networks’ hidden-unit weight vectors identifies which EEG channels are most relevant to this discrimination problem.
A Comparative Study of Different Level Interpolations for Improving Spatial Resolution in Diffusion Tensor Imaging

This paper studies and evaluates the feasibility and the performance of different level interpolations for improving spatial resolution of diffusion tensor magnetic resonance imaging (DT-MRI or DTI). In particular, the following techniques are investigated: anisotropic interpolation operating on scalar gray-level images, log-Euclidean interpolation method, and the quaternion interpolation method, which operate on diffusion tensor fields. The performance is evaluated both qualitatively and quantitatively using criteria such as tensor determinant, fractional anisotropy (FA), mean diffusivity (MD), fiber length, etc. We conclude that tensor field interpolations allow avoiding undesirable swelling effect in DTI, which is not the case with scalar gray-level interpolation, and that scalar gray-level image interpolation and log-Euclidean tensor field interpolation suffer from decrease in FA and MD, which may mislead the interpretation of the clinical parameters FA and MD. In contrast, the quaternion tensor field interpolation avoids such FA and MD decrease, which suggests its use for clinical applications.
Embedded optimization-based controllers for industrial processes

Due to the growth of computational capabilities and the proliferation of field-programmable gate arrays (FPGA), the utilization of model predictive control (MPC) for embedded applications in the industry has become a possibility and a fact. This paper presents and discusses the possibilities of the use of online MPC, embedded in an educational device from National Instruments, using two different optimization algorithms and code generators, which have come out in recent years by the academia: CVXGEN, which implements a primal-dual interior-point algorithm, and qpOASES, which relies on the online active-set strategy algorithm. Both algorithms have been tested both in simulation and in real-time experimentation to control a four-tank pilot plant.
Improved Large Margin Dependency Parsing via Local Constraints and Laplacian Regularization

We present an improved approach for learning dependency parsers from tree-bank data. Our technique is based on two ideas for improving large margin training in the context of dependency parsing. First, we incorporate local constraints that enforce the correctness of each individual link, rather than just scoring the global parse tree. Second, to cope with sparse data, we smooth the lexical parameters according to their underlying word similarities using Laplacian Regularization. To demonstrate the benefits of our approach, we consider the problem of parsing Chinese treebank data using only lexical features, that is, without part-of-speech tags or grammatical categories. We achieve state of the art performance, improving upon current large margin approaches.
University Patenting in Wales, Scotland and Northern Ireland: a Comparative Analysis

Using data on the patent portfolios of UK universities, the paper compares the levels of patenting activity (filings), success (grants) and quality (patents with commercial co-assignees and patent citations) at Welsh, Scottish and Northern Irish institutions. Patent activity, per researcher, in Wales is on a par with that in Scotland and about twice the rate in Northern Ireland, but the numbers of researchers relative to population in Wales and Northern Ireland are around half of the number in Scotland. Patent quality indicators are less favourable to Wales, while both activity levels and conversion of applications into grants are poor in Northern Ireland. It is suggested that greater cross-university collaboration in Wales and Northern Ireland would help improve performance.
On the Emergent Behaviors of a Robot Controlled by a Real-Time Evolving Neural Network

In this paper we apply a real-time evolving neural network which uses a hill-climbing algorithm capable of adapting not only a network’s synaptic weights but also its topology (creating a recurrent neural network). We then apply this network to a robot in a simulated environment. By equipping the robot with a minimal set of instincts and a short-term memory system (to facilitate reinforcement learning), we observe that several strategies developed which pass the emergent behavior test of (Ronald et al., 1999). In particular, we see robots learning behaviors that are not rewarded by the environment. Of course a hill-climbing algorithm is more likely than a genetic-algorithm to get stuck at a local optimum, we argue that, despite this, the method described here has several unique advantages. In particular, it allows us to create a single persistent robot that slowly learns and “grows up” as described in (Ross et al., 2003). With our system, it is an individual that learns not a population of individuals, and our learning is continual (e.g. there is no need to reset the robot to some starting position to evaluate the fitness of a particular network). We conclude with several future problems and applications. For instance, we describe a simple mechanism allowing a network to be copied to embedded hardware whenever a network connection is available to a PC (which is responsible for the memory and time intensive task of evolving the network). This mechanism does not require a continual link to a PC. We also discuss the possibility of creating a distributed evolving neural network system.
Risk Communication: A Two-Community Analysis of Proximity, Dread, Trust, Involvement, Uncertainty, Openness/Accessibility, and Knowledge on Support/Opposition Toward Chemical Companies

The research project examined how people's levels of trust, involvement, uncertainty, perceptions of openness, support, dread, and knowledge were affected by proximity to chemical manufacturing facilities. The study, included a telephone survey of 501 respondents, divided into 2 groups. Residents who live within 5 miles of a large complex of chemical plants were compared to people living at least 20 miles from chemical facilities Findings indicated that proximity affected people's levels of uncertainty, dread, and support Residents close to chemical plants expressed less uncertainty, less dread, and greater support for the chemical industry. Dread appears to play a significant role in a risk assessment, management, and communication scenario. It correlated negatively with support for chemical companies, trust, and perceptions of openness and positively with uncertainty and cognitive involvement. Dread is concern over dire and stigmatizing consequences from explosions and exposure to hazardous material. Fu.
Determinants of echocardiographic left atrial volume: implications for normalcy.

AIMS The relative role of multiple determinants of left atrial volume index (LAVi) in athletes and non-athletes is not fully defined. Thus, we decided to prospectively assess the determinants of LAVi in healthy individuals and competitive athletes over a wide age range. METHODS AND RESULTS Four hundred and eighteen healthy individuals (mean age 41.7 ± 15.6 years, range 16-84, 65% males, 38% competitive athletes) underwent Doppler echocardiography including assessment of LAVi by the biplane area-length method and of left ventricular (LV) diastolic function including the ratio of early diastolic peak LV inflow velocity to peak myocardial early diastolic velocity (E/e'). Mean LAVi was 32.2 ± 9.0 mL/m(2) in the pooled population. LAVi was larger in athletes than in non-athletes (38.9 ± 9.6 mL/m(2) vs. 28.4 ± 5.8 mL/m(2), P < 0.0001). In the pooled population a stepwise multiple linear regression analysis identified LV end-diastolic volume index (LVEDVi) (β = 0.378, P < 0.0001), LV mass index (LVMi) (β = 0.260, P < 0.0001), competitive sport activity (β = 0.258, P < 0.0001), and age (β = 0.222, P < 0.0001) as independent determinants of LAVi (model R(2) = 0.54, P < 0.0001). By separate analyses, although LVEDVi, age, and LVMi were predictors of LAVi in both groups, body mass index and the E/e' ratio were additional predictors of LAVi only in non-athletes. CONCLUSIONS In healthy individuals LV size, competitive sport, age, and LV mass are independent determinants of LAVi. Body mass index and the E/e' ratio affect LAVi only in non-athletes. These findings may have practical implications when assessing normalcy of LA size in the clinical setting.