Miracle-dz/UniFAR-FTUs huggingface dataset:
Crewmember and mission control personnel interactions during International Space Station missions.

INTRODUCTION Reports from astronauts and cosmonauts, studies from space analogue environments on Earth, and our previous research on the Mir Space Station have identified a number of psychosocial issues that can lead to problems during long-duration space missions. Three of these issues (time effects, displacement, leader role) were studied during a series of long-duration missions to the International Space Station (ISS). METHODS As in our previous Mir study, mood and group climate questions from the Profile of Mood States or POMS, the Group Environment Scale or GES, and the Work Environment Scale or WES were completed weekly by 17 ISS crewmembers (15 men, 2 women) in space and 128 American and Russian personnel in mission control. RESULTS The results did not support the presence of decrements in mood and group cohesion during the 2nd half of the missions or in any specific quarter. The results did support the predicted displacement of negative feelings to outside supervisors in both crew and mission control subjects on all six questionnaire subscales tested. Crewmembers related cohesion in their group to the support role of their commander. For mission control personnel, greater cohesion was linked to the support role as well as to the task role of their leader. DISCUSSION The findings from our previous study on the Mir Space Station were essentially replicated on board the ISS. The findings suggest a number of countermeasures for future on-orbit missions, some of which may not be relevant for expeditionary missions (e.g., to Mars).
The effect of age, gender, and body mass index on the pharmacokinetics and pharmacodynamics of vildagliptin in healthy volunteers.

UNLABELLED What is already known about this subject. Vildagliptin is a new, potent, and selective inhibitor of DPP-4. The efficacy and safety of vildagliptin in type 2 diabetes has been intensively studied in diverse subject populations. There has been little information published about the pharmacokinetics and pharmacodynamics of vildagliptin. What this study adds. No clinically relevant changes in pharmacokinetics or pharmacodynamics were observed between young and elderly, male and female, or high body mass index (BMI) and low BMI subjects. The results suggest that no dose modification is necessary for vildagliptin based on the age, gender, or BMI of a subject. AIMS To evaluate the effect of age, gender, and body mass index (BMI) on the pharmacokinetics and pharmacodynamics of vildagliptin. METHODS Forty healthy subjects received a single oral dose of 100 mg vildagliptin to assess the effects of age, gender, and BMI on the pharmacokinetics and pharmacodynamics, reflected by the time course of inhibition of DPP-4 activity, of vildagliptin. RESULTS Peak concentration and exposure (AUC((0-infinity))) of vildagliptin were 17% (90% CI 2, 35%) and 31% (90% CI 18, 45%) higher in elderly vs. young subjects. Renal clearance was reduced by 32% (90% CI 17, 45%) in elderly subjects. The pharmacokinetics of vildagliptin were not significantly influenced by gender or BMI. Inhibition of DPP-4 activity was similar regardless of age, gender, or BMI. CONCLUSIONS The pharmacokinetics of a single oral 100 mg dose of vildagliptin were not affected by gender and BMI. Exposure to vildagliptin was higher in elderly patients, but this was not associated with any difference in the effect of DPP-4 inhibition. Based on these results, no vildagliptin dose adjustment is necessary for age, gender, or BMI.
Expressing Death Risk as Condensed Life Experience and Death Intensity

Some risk exposures, including many medical and surgical procedures, typically carry hazards of death that are difficult to convey and appreciate in absolute terms. I propose presenting the death risk as a condensed life experience (i.e., the equivalent amount of life T that would carry the same cumulative mortality hazard for a person of the same age and sex based on life tables). For example, if the risk of death during an elective 1-hour procedure is 0.01%, and same-age and same-sex people have a 0.01% death risk over 1 month, one can inform the patient that “this procedure carries the same death risk as living 1 month of normal life. ” Comparative standards from other risky activities or from a person with the same disease at the same stage and same predictive profile could also be used. A complementary metric that may be useful to consider is the death intensity. The death intensity λ is the hazard function that shows the fold-risk estimate of dying compared with the reference person. The death intensity can vary substantially for different phases of the event, operation, or procedure (e.g., intraoperative, early postoperative, late postoperative), and this variability may also be useful to convey. T will vary depending on the time window for which it is computed. I present examples for calculating T and λ using literature data on accidents, ascent to Mount Everest, and medical and surgical procedures.
An effective method for image segmentation

This paper presents an adaptive immune genetic algorithm (AIGA) for image segmentation based on the cost minimization technique. The image segmentation problem is treated as one of combinatorial optimization. A cost function which incorporates both edge information and region gray-scale uniformity is used. The immune genetic algorithm is treated as an optimization technique to find the optimal solution. The presented algorithm recommends the use of adaptive probabilities of crossover, mutation and immune operation. Furthermore, it effectively exploits some prior knowledge of pending problem and the information of evolved individual's past history to make vaccines. The segmentation algorithm based on the AIGA is implemented and tested on several gray-scale images. The satisfactory experimental results are obtained. In addition, we compare this method with the other segmentation techniques, such as the Otsu's histogram thresholding and the fuzzy c-means clustering. AIGA is found to outperform these two methods.
Mesoscopic model for mechanical characterization of biological protein materials

Mechanical characterization of protein molecules has played a role on gaining insight into the biological functions of proteins, because some proteins perform the mechanical function. Here, we present the mesoscopic model of biological protein materials composed of protein crystals prescribed by Go potential for characterization of elastic behavior of protein materials. Specifically, we consider the representative volume element (RVE) containing the protein crystals represented by Cα atoms, prescribed by Go potential, with application of constant normal strain to RVE. The stress–strain relationship computed from virial stress theory provides the nonlinear elastic behavior of protein materials and their mechanical properties such as Young's modulus, quantitatively and/or qualitatively comparable with mechanical properties of biological protein materials obtained from experiments and/or atomistic simulations. Further, we discuss the role of native topology on the mechanical properties of protein crystals. It is shown that parallel strands (hydrogen bonds in parallel) enhance the mechanical resilience of protein materials. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2009
Cochlear Filter and Instantaneous Frequency Based Features for Spoofed Speech Detection

Vulnerability of voice biometrics systems to spoofing attacks by synthetic speech (SS) and voice converted (VC) speech has arose the need of standalone spoofed speech detection (SSD) systems. This paper is an extension of our previously proposed features (used in relatively best performing SSD system) at the first ASVspoof 2015 challenge held at INTERSPEECH 2015. For the challenge, the authors proposed novel features based on cochlear filter cepstral coefficients (CFCC) and instantaneous frequency (IF), i.e., CFCCIF. The basic motivation behind this is that human ear processes speech in subbands. The envelope of each subband and its IF is important for perception of speech. In addition, the transient information also adds to the perceptual information that is captured. We observed that subband energy variations across CFCCIF when estimated by symmetric difference (CFCCIFS) gave better discriminative properties than CFCCIF. The features are extracted at frame level and the Gaussian mixture model based classification system was used. Experiments were conducted on ASVspoof 2015 challenge database with MFCC, CFCC, CFCCIF, and CFCCIFS features. On the evaluation dataset, after score-level fusion with MFCC, the CFCCIFS features gave an overall equal error rate (EER) of 1.45% as compared to 1.87% and 1.61% with CFCCIF and CFCC, respectively. In addition to detecting the known and unknown attacks, intensive experiments have been conducted to study the effectiveness of the features under the condition that either only SS or only VC speech is available for training. It was observed that when only VC speech is used in training, both VC, as well as SS, can be detected. However, when only SS is used in training, VC speech was not detected. In general, amongst vocoder-based spoofs, it was observed that VC speech is relatively difficult to detect than SS by the SSD system. However, vocoder-independent SS was toughest with highest EER (i.e., > 10%).
High-resolution remote-sensing imagery retrieval using sparse features by auto-encoder

An unsupervised feature learning framework based on auto-encoder is proposed to learn sparse feature representations for remote-sensing imagery retrieval in this letter. The low-level feature descriptors are extracted and exploited to learn a set of feature extractors, which are then used to encode the low-level feature descriptors to generate new sparse features. The learned feature representations are applied to aerial images randomly selected from the University of California Merced data set. The results indicate that the performance of our proposed framework is comparable or superior to that of the state-of-the-art method. The framework is proved to be an effective approach to manage the huge volume of remote-sensing data and to retrieve the desired remote-sensing imagery.
Job Satisfaction and Public Service Motivation

Based on a unique case study-dataset, the paper analyses job satisfaction and public service motivation in Germany. A special issue of the investigation is related to the evaluation of performance pay scales that were introduced some years ago to German public employees within the frame of fostering New Public Management. The findings display a general dominance of intrinsic motivators. Additionally, this kind of motivators plays an important role with regard to building up and keeping job satisfaction in the public sector. Further results display the transferability of competences, autonomy, regular appraisal interviews and productivity feedback as factors incorporating a positive significance in terms of job satisfaction.
Generation of a Chimeric Hepatitis C Replicon Encoding a Genotype-6a NS3 Protease and Assessment of Boceprevir (SCH503034) Sensitivity and Drug-Associated Mutations

Background Genotype (gt)6 HCV is common amongst HCV-positive populations of the Asia–Pacific region but cell culture models for this gt have only recently been developed. Boceprevir (SCH503034) is a clinically available inhibitor of the HCV NS3 protein. We investigated the efficacy of boceprevir for inhibiting replication of a chimeric gt1b replicon encoding a gt6a NS3 protease and defined the development of mutations in the protease when boceprevir treatment was applied. Methods We constructed a chimeric gt1b subgenomic replicon encoding a gt6 NS3 protease (NS3p) sequence (gt6NS3p_gt1b). The boceprevir EC50 value against replication of this replicon was determined using quantitative reverse transcriptase PCR. Next-generation sequencing was used to identify nucleotide changes associated with boceprevir resistance. The replication capacities of chimeric replicons containing mutations associated with boceprevir resistance were determined by colony formation efficiency assays. Results The boceprevir EC50 value for the gt6NS3p_gt1b replicon was 535 ±79 nM. Boceprevir-resistant gt6NS3p_gt1b replicon cell lines could be selected and they demonstrated drug-associated amino acid changes that have previously been reported in other HCV gts. Interestingly, no mutations were observed at A156, a position defined for boceprevir resistance in gt1 NS3p, while mutation at N122, which is rarely reported in boceprevir-resistant gt1 proteases, was frequently observed. Re-introduction of these mutations into the chimeric replicon altered their replication capacity, ranging from complete abolishment of replication (A156T) to increasing replication capacity (V36A, N122S). This report provides the first characterization of gt6 HCV resistance to boceprevir. Conclusions A chimeric HCV replicon encoding gt6 NS3 protease is sensitive to boceprevir and develops drug-resistant mutations at amino acid sites previously reported for other gts. Mutation at N122 also appears to be associated with boceprevir resistance in the gt6 NS3 protease.
The competition for supplier resources

Suppliers can have a major influence on the overall competitiveness of a firm. When firms lack certain capabilities or resources within their own organization, collaborations with suppliers can help them to acquire these resources and capabilities externally and improve competitive advantage. However, for many firms, leveraging the supplier base for competitive resources remains a key challenge. This is especially the case when competing firms seek to acquire similar resources from the same supplier base. This dissertation examines how firms can acquire better resources than competitors from suppliers that are shared with these competitors. A central question is how can buying firms obtain preferential resource allocation from suppliers? This dissertation addresses the above question based on four research objectives. The first research objective includes an examination of the preferred customer status construct. The subsequent research objectives have a more theoretical focus. The chapters that link to these objectives build on the preferential resource allocation construct and theorize on the construct’s antecedents, the effects of the construct on the performance of the buying firm and the dimensions of the construct itself. The findings presented in this dissertation provide important insights to those interested in the competition for supplier resources. Practitioners can use these findings to make better decisions on the supply management practices they adopt to influence supplier resource allocation.
Investigating the Role of Hypothalamic Paraventricular Nucleus in Nociception of the Rat

The role of hypothalamic paraventricular nucleus (PVN) in nociception was investigated in the rat. Electrical stimulation of the PVN increased pain threshold, and microinjection of L-glutamate sodium into the PVN also elevated pain threshold in a dose-dependent manner, whereas cauterization of the PVN decreased pain threshold. Stimulation or cauterization of the area located within 1.0 mm of the outer perimeter of the PVN did not change pain threshold. Pituitary removal could not influence the effect of L-glutamate sodium microinjection into PVN-induced pain threshold increase. The data suggest that PVN plays a role in antinociception through the central nervous system rather than peripheral organs.
On the Use of Monte-Carlo Simulation and Deep Fourier Neural Network in Lane Departure Warning

To make improvements on vision-based lane departure warning systems (LDWS), a lane departure prediction (LDP) method based on Monte-Carlo simulation and deep Fourier neural network (DFNN) is proposed. Firstly, a closed-loop driver-vehicle-road (DVR) system model is built up and the parameters of the system, consisting of vehicle states, positioning and road conditions, are initialized by random sampling. After simulating a large number of DVR systems with random parameters, the obtained results are used as samples to train a DFNN which predicts the forthcoming maximum lateral deviation and is optimized by employing deep learning method. Then, a LDP strategy is proposed by combining the DFNN with a driver activity index, which takes driver adaptation into consideration. The experimental evaluation shows that the proposed lane departure warning algorithm can predict the lane departure event in time and reduce the false-warning rate of existing methods in a significant way. More importantly, the proposed technique enhances the system's functions of over-speed warning on curved road and over-steer warning on low-adhesion road.
Interpretation of Mammogram and Chest X-Ray Reports Using Deep Neural Networks-Preliminary Results

Radiology reports are an important means of communication between radiologists and other physicians. These reports express a radiologist’s interpretation of a medical imaging examination and are critical in establishing a diagnosis and formulating a treatment plan. In this paper, we propose a Bidirectional convolutional neural network (Bi-CNN) model for the interpretation and classification of mammograms based on breast density and chest radiographic radiology reports based on the basis of chest pathology. The proposed approach helps to organize databases of radiology reports, retrieve them expeditiously, and evaluate the radiology report that could be used in an auditing system to decrease incorrect diagnoses. Our study revealed that the proposed Bi-CNN outperforms the random forest and the support vector machine methods.
On Coresets for k-Median and k-Means Clustering in Metric and Euclidean Spaces and Their Applications

We present new approximation algorithms for the $k$-median and $k$-means clustering problems. To this end, we obtain small coresets for $k$-median and $k$-means clustering in general metric spaces and in Euclidean spaces. In $\mathbb{R}^d$, these coresets are of size with polynomial dependency on the dimension $d$. This leads to $(1+\varepsilon)$-approximation algorithms to the optimal $k$-median and $k$-means clustering in $\mathbb{R}^d$, with running time $O(ndk+2^{(k/\varepsilon)^{O(1)}}d^2\log^{k+2}n)$, where $n$ is the number of points. This improves over previous results. We use those coresets to maintain a $(1+\varepsilon)$-approximate $k$-median and $k$-means clustering of a stream of points in $\mathbb{R}^d$, using $O(d^2k^2\varepsilon^{-2}\log^8n)$ space. These are the first streaming algorithms, for those problems, that have space complexity with polynomial dependency on the dimension.
DIAMINOBENZIDINE REACTIVITY OF PEROXISOMES AND MITOCHONDRIA IN A PARASITIC AMEBA, HARTMANNELLA CULBERTSONI

Peroxisomes in Hartmannella culbertsoni were identified by localization techniques for catalase employing diaminobenzidine and hydrogen peroxide at pH 9.0. The peroxisomal reaction required hydrogen peroxide as a substrate and was sensitive to aminotriazole and partially sensitive to cyanide and azide. A strong deposit of reaction product was observed in mitochondria under almost all conditions tested. There appeared to be two enzyme systems in the mitochondria which initiated the oxidation of diaminobenzidine. Peroxisomes were approximately 1 µ in length and were irregular in outline. These organelles possessed a finely granular matrix and were surrounded by a single membrane. Cores and vacuoles were seen in these bodies.
Controller Implementation for a Class of Spatially-Varying Distributed Parameter Systems

Abstract In this paper we discuss fast implementation of the model based centralized controllers using fractional Fourier transform for large scale plant models coming from spatial discretization of a certain type of linear spatially-varying distributed parameter systems. This fast implementation reduces the computational time delay significantly when the dimension of the system is higher than 512 = 2 9 . Compared to direct implementation, the proposed method allows faster sampling. If the control design objectives are demanding fast closed loop modes, then slower sampling required by direct implementation leads to instability. The results are illustrated by an example.
Perceptual training to improve hip fracture identification in conventional radiographs

Diagnosing certain fractures in conventional radiographs can be a difficult task, usually taking years to master. Typically, students are trained ad-hoc, in a primarily-rule based fashion. Our study investigated whether students can more rapidly learn to diagnose proximal neck of femur fractures via perceptual training, without having to learn an explicit set of rules. One hundred and thirty-nine students with no prior medical or radiology training were shown a sequence of plain film X-ray images of the right hip and for each image were asked to indicate whether a fracture was present. Students were told if they were correct and the location of any fracture, if present. No other feedback was given. The more able students achieved the same level of accuracy as board certified radiologists at identifying hip fractures in less than an hour of training. Surprisingly, perceptual learning was reduced when the training set was constructed to over-represent the types of images participants found more difficult to categorise. Conversely, repeating training images did not reduce post-training performance relative to showing an equivalent number of unique images. Perceptual training is an effective way of helping novices learn to identify hip fractures in X-ray images and should supplement the current education programme for students.
Passive bistatic WiMAX radar for marine surveillance

In this study we investigate the feasibility of using passive bistatic radar to exploit WiMAX communication signals as transmitters of opportunity for marine surveillance. The range and Doppler characteristics of such a system are first assessed through ambiguity function analysis on a typical set of WiMAX data transmission waveforms. Theoretical simulations are then used to investigate the ability of passive WiMAX radar for detecting marine vessels within three busy shipping areas in the UK, and in the presence of noise and interference signals. The initial analysis shows a range resolution of 5.6 m is achievable. Additionally, the simulations demonstrate specific scenarios where WiMAX radar may be used as a low cost surveillance device for detecting both small and large marine vessels in port areas and open waters.
Reaching out : exploring SME exporting opportunities and challenges

This study by the Western Centre for Economic Research (WCER) examines small business exports to US and other international markets based on a 2007 survey of 387 firms with less than 100 employees. Most firms were in the manufacturing sector, supplemented by a few firms in professional and technical services. All participants were existing or past exporters and apart from a sprinkling of start-ups, most firms in the survey were well established. Our research was organized around four themes: the role of exports in growth strategies; the marketing chain and how firms find export markets; perceived barriers to exporting (including the thickening Canada-US border since 9/11); and indirect exporting and the role of foreign suppliers. Most of our findings did not differ by province; but where they did, the differences are noted. For interested readers, findings for each province are presented in the appendices. Our findings suggest that there is a strong role for public policy in improving Western Canada’s export performance and in addressing some of the particular challenges and barriers identified by respondents.