Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensory neurons derived from diabetic rats have diminished internal Ca2+ stores linked to impaired re-uptake by the endoplasmic reticulum

Distal symmetrical sensory neuropathy in diabetes involves the dying back of axons, and the pathology equates with axonal dystrophy generated under conditions of aberrant Ca2+ signalling. Previous work has described abnormalities in Ca2+ homoeostasis in sensory and dorsal horn neurons acutely isolated from diabetic rodents. We extended this work by testing the hypothesis that sensory neurons exposed to long-term Type 1 diabetes in vivo would exhibit abnormal axonal Ca2+ homoeostasis and focused on the role of SERCA (sarcoplasmic/endoplasmic reticulum Ca2+-ATPase). DRG (dorsal root ganglia) sensory neurons from age-matched normal and 3–5-month-old STZ (streptozotocin)-diabetic rats (an experimental model of Type 1 diabetes) were cultured. At 1–2 days in vitro an array of parameters were measured to investigate Ca2+ homoeostasis including (i) axonal levels of intracellular Ca2+, (ii) Ca2+ uptake by the ER (endoplasmic reticulum), (iii) assessment of Ca2+ signalling following a long-term thapsigargin-induced blockade of SERCA and (iv) determination of expression of ER mass and stress markers using immunocytochemistry and Western blotting. KCl- and caffeine-induced Ca2+ transients in axons were 2-fold lower in cultures of diabetic neurons compared with normal neurons indicative of reduced ER calcium loading. The rate of uptake of Ca2+ into the ER was reduced by 2-fold (P<0.05) in diabetic neurons, while markers for ER mass and ER stress were unchanged. Abnormalities in Ca2+ homoeostasis in diabetic neurons could be mimicked via long-term inhibition of SERCA in normal neurons. In summary, axons of neurons from diabetic rats exhibited aberrant Ca2+ homoeo<1? show=[fo]? >stasis possibly triggered by sub-optimal SERCA activity that could contribute to the distal axonopathy observed in diabetes.
Changing flights in mid-air: a model for safely modifying continuous queries

Continuous queries can run for unpredictably long periods of time. During their lifetime, these queries may need to be adapted either due to changes in application semantics (e.g., the implementation of a new alert detection policy), or due to changes in the system's behavior (e.g., adapting performance to a changing load). While in previous works query modification has been implicitly utilized to serve specific purposes (e.g., load management), to date no research has been done that defines a general-purpose, reliable, and efficiently implementable model for modifying continuous queries at run-time. In this paper, we introduce a punctuation-based framework that can formally express arbitrary lifecycle operations on the basis of input-output mappings and basic control elements such as start or stop of queries. On top of this foundation, we derive all possible query change methods, each providing different levels of correctness guarantees and performance. We further show how these models can be efficiently realized in a state-of-the-art stream processing engine; we also provide experimental results demonstrating the key performance tradeoffs of the change methods.
Scalability evaluation of the Yima streaming media architecture

Over the last decade research has been pursued on all aspects of streaming media. While many theoretical results have been reported in the literature, few performance results of large‐scale systems have been published. In this report we specifically explore the scalability aspects of our Yima streaming media architecture in an end‐to‐end test environment. With Yima, it was our goal to design and implement an architecture that would scale in performance from small to large systems. Some of the design features include (1) a multi‐node cluster architecture based on commodity hardware and custom software, (2) media type independence (support ranges from 500 Kb s$^{-1}$ MPEG‐4 to 45 Mb s$^{-1}$ HDTV, at both variable and constant bitrates), (3) fine‐grained online scale up/down capabilities, and (4) a client‐controlled rate smoothing protocol. We briefly discuss the design and implementation of these capabilities of Yima and then thoroughly evaluate its scalability through several sets of experiments. Our results show that Yima scales linearly (within the range of our test parameters) as a function of the cluster size and also as a function of available resources such as network bandwidth and CPU performance. Copyright © 2004 John Wiley & Sons, Ltd.
Developing an Appreciative Management Scale (AMS 1.0)

In working life, ageing and retiring staff and managers are being replaced by younger generations which come from different working life cultures. This may give rise to different management expectations. As a result, this creates a need to assess how the concept of appreciative management is implemented in health care. The aim was to develop a valid and reliable instrument to assess appreciative management. A multi-phase, mixed-method and psychometric evaluation of the Appreciative Management Scale (AMS) was conducted. A concept analysis and systematic literature review were carried out. The instrument’s development employed a two-phase Delphi study approach including essays, survey iteration rounds and expert panel evaluation. The instrument was pre-tested and tested empirically in a survey completed by staff respondents and managers. AMS 1.0 has 83 items that are categorised into Systematic Management, Equality, Appreciation of Know-How, and the Promotion of Wellbeing at Work. The instrument was found to be valid and reliable. The AMS 1.0 scale needs to be tested internationally in order to conduct evaluative surveys of appreciative management in other countries. By using the AMS 1.0 instrument to assess managers’ management practices, managers receive valuable feedback on their own management skills and also the skills of workers.
Bayesian Browsing Model: Exact Inference of Document Relevance from Petabyte-Scale Data

A fundamental challenge in utilizing Web search click data is to infer user-perceived relevance from the search log. Not only is the inference a difficult problem involving statistical reasonings but the bulky size, together with the ever-increasing nature, of the log data imposes extra requirements on scalability. In this paper, we propose the Bayesian Browsing Model (BBM), which performs exact inference of the document relevance, only requires a single pass of the data (i.e., the optimal scalability), and is shown effective. We present two sets of experiments to evaluate the model effectiveness and scalability. On the first set of over 50 million search instances of 1.1 million distinct queries, BBM outperforms the state-of-the-art competitor by 29.2% in log-likelihood while being 57 times faster. On the second click log set, spanning a quarter of petabyte, we showcase the scalability of BBM: we implemented it on a commercial MapReduce cluster, and it took only 3 hours to compute the relevance for 1.15 billion distinct query-URL pairs.
Effects of Terrorism Threat on Economic Preferences: The Role of Personality

Certain threats, such as terrorism tend to have a low probability of affecting us directly, yet reminders of them (such as media news) are striking. Could individuals with particular personality traits find them distractive from the usual priorities in daily decision-making? This study explored the putative effects of media-driven terrorism threat, on two economic preferences (risk aversion and delay discounting) - using a sample reasonably closely representative of a modern Western city. Participants (N = 78) higher on impulsivity showed lower risk-seeking under terrorism threat. Discussed are directions for further research and the implications for media coverage of terrorist threat.
Patients using vitamin K antagonists show increased levels of coronary calcification: an observational study in low-risk atrial fibrillation patients.

AIMS Vitamin K antagonists (VKA) are currently the most frequently used drug to prevent ischaemic stroke in atrial fibrillation (AF) patients. However, VKA use has been associated with increased vascular calcification. The aim of this study was to investigate the contribution of VKA use to coronary artery calcification in low-risk AF patients. METHODS AND RESULTS A prospective coronary calcium scan was performed in 157 AF patients without significant cardiovascular disease (108 males; mean age 57 ± 9 years). A total of 71 (45%) patients were chronic VKA users. The duration of VKA treatment varied between 6 and 143 months (mean 46 months). No significant differences in clinical characteristics were found between patients on VKA treatment and non-anticoagulated patients. However, median coronary artery calcium scores differed significantly between patients without and patients with VKA treatment [0, inter-quartile range (IQR) 0-40, vs. 29, IQR 0-184; P = 0.001]. Mean coronary calcium scores increased with the duration of VKA use (no VKA: 53 ± 115, 6-60 months on VKA: 90 ± 167, and >60 months on VKA: 236 ± 278; P < 0.001). Multivariable logistic regression analysis revealed that age and VKA treatment were significantly related to increased coronary calcium score. CONCLUSION Patients using VKA show increased levels of coronary calcification. Age and VKA treatment were independently related to increased coronary calcium score.
The Contingent Magnetic Variation in Migraine

Migraine is characterized by an increased contingent negative variation. Separate comparisons of the early and late component showed that it was the former rather than the latter that was elevated. The function and localization of the early component are as yet poorly understood. The magneto-encephalogram was recorded in 16 patients with migraine and 17 healthy control subjects during a forewarned reaction time task. Neuromag- netic data were recorded with a 37-channel neuromagnetometer with the sensors being placed above the left hemisphere contralaterally to the side of the acoustic stimulation and the motor reaction. Subjects responded to the second of two low intensity sound stimuli that were administered with an interval of 4.5 s. Migraine patients exhibited a significantly higher amplitude of the early component of the contingent magnetic variation (CMV) than controls. Groups did not differ with regard to the amplitude of the late component. Modeling the sources with a single moving equivalent current dipole (ECD) provided a high goodness of fit for the M100 (magnetic N1) in both groups and for the early component of the CMV in migraineurs but not controls. The activity of the early component was centered medially with respect to the M100-ECDs and more laterally so in migraine patients than in controls.
Helping Families Deal with Anger: A Biblical Perspective

The spiritual effects of anger in families are devastating. However, the Bible offers hope; it provides guidance on how to deal with anger constructively. This article will address the dynamics of angry families and the crucial role of parents. It will explore the contemplative method and its potential to provide biblical guidance and restoration to angry children. Specifically, the article proposes meditation and journaling as viable solutions to help children with habitual anger.
The Relationship between HEXACO Personality Traits and Iranian EFL Learners’ Speaking Ability

This study aimed to investigate the relationship between HEXACO personality traits and Iranian EFL learners’ speaking ability. In order to achieve the purpose of the study, among all the language institutes (fifteen institutes) in Amol, Mazandaran Province, 8 were selected randomly (four institutes for female and four institutes for male language learners). Then, two classes were selected randomly from each institute. Among all the language learners in these eight institutes and classes, 250 learners (125 male and 125 female learners) were selected using a random cluster sampling method. The participants were asked to complete the HEXACO personality traits questionnaire. Speaking ability of all the learners was checked via scored interview to examine the relationship between HEXACO personality traits (Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness, and Openness to Experience) and their speaking performance. Speaking ability of the learners were evaluated by three interviewers (the researcher and two trained raters) based on IELTS speaking bands in four areas: 1) Fluency and Coherence 2) Lexical Resource 3) Grammatical Resource 4) Pronunciation. The interview was scored out of 9 in this study. To analyze the collected data, Pearson’s correlation coefficient and multiple regressions with stepwise method were used in the study. The findings of the study showed a positive and significant relationship between HEXACO personality traits and speaking performance of the learners. Extraversion, conscientiousness and altruism could predict 90.3 percent of variance of speaking ability.
Injecting mechanical faults to localize developer faults for evolving software

This paper presents a novel methodology for localizing faults in code as it evolves. Our insight is that the essence of failure-inducing edits made by the developer can be captured using mechanical program transformations (e.g., mutation changes). Based on the insight, we present the FIFL framework, which uses both the spectrum information of edits (obtained using the existing FaultTracer approach) as well as the potential impacts of edits (simulated by mutation changes) to achieve more accurate fault localization. We evaluate FIFL on real-world repositories of nine Java projects ranging from 5.7KLoC to 88.8KLoC. The experimental results show that FIFL is able to outperform the state-of-the-art FaultTracer technique for localizing failure-inducing program edits significantly. For example, all 19 FIFL strategies that use both the spectrum information and simulated impact information for each edit outperform the existing FaultTracer approach statistically at the significance level of 0.01. In addition, FIFL with its default settings outperforms FaultTracer by 2.33% to 86.26% on 16 of the 26 studied version pairs, and is only inferior than FaultTracer on one version pair.
Depth map generation for a reconnaissance robot via sensor fusion

In this paper design and implementation of an accurate positioning system in indoor environments for an Omni directional Robot and a stereo vision based depth estimation system is discussed. The positioning system which is robust for wheel slip and jerky motion was implemented fusing odometry, Inertial Navigation System (INS) and magnetic compass based measurements. The vision system was implemented using two off the shelf web cameras. Here, Semi-Global Block Matching (SGBM) method was further developed considering multiple realizations of the same scenery to increase the accuracy of the depth estimation.
Emotional Speech Synthesis

The goal of this MSc project was to build unit selection voice that could portray emotions in various intensities. A suitable definition of emotion was developed along with a descriptive framework that supported the work carried out. Two speakers were recorded portraying happy and angry speaking styles, additionally a neutral database was also recorded. One voice was built for each speaker that included all the speech from that speaker. A target cost function was implemented that choses units from the database according to emotion mark-up in the database. The Dictionary of Affect [30] supported the emotional target cost function by providing an emotion rating for words in the target utterance. If a word was particularly emotional, units from that emotion were favoured. In addition intensity could be varied which resulted in a bias to select more emotional units. A perceptual evaluation was carried out and subjects were able to recognise reliably, emotions with varying amounts of emotional units present in the target utterance.
An Improved Method for Combination Feature Selection in Web Click-Through Data Mining

An important way to analyze the web click-through data is to build up a 2-class linear classifier, and select a key subset from user's features which mainly decided the hit result. But in many circumstances, the fitting accuracy is not good as the model only considers original features. We often add combination features which are products of the original features to the classifier model to improve the accuracy. Meanwhile, the combination features cause a serious problem. They dramatically increase the number of features, which is called "feature dimension explosion". Traditional algorithms can hardly afford this because they need to input all the features at the beginning of processing. Grafting method provides an incremental way to solve the problem, which only adds one feature at a time. However, Grafting method has very low efficiency when the dimension of the feature space is huge and sparse. In this paper, we propose an adaptive Grafting algorithm and PV filter method to solve the feature dimension explosion problem. Our algorithm significantly improves the computational efficiency by educing the steps of model optimizing, and reduces the scale of feature space by applying a very simple filter strategy to make the algorithm work effectively. Our experiments on real data show that we can easily generate and select ombination features by using the adaptive Grafting algorithm and PV filter method, which significantly raises the fitting accuracy of the model.
Saturated chains in composition posets

We study three different poset structures on the set of all compositions. In the first case, the covering relation consists of inserting a part of size one to the left or to the right, or increasing the size of some part by one. The resulting poset was studied by the author in "A poset classifying non-commutative term orders", and then in "Standard paths in another composition poset" where some results about generating functions for standard paths in this poset was established. The latter article was inspired by the work of Bergeron, Bousquet-M{\'e}lou and Dulucq on "Standard paths in the composition poset", where they studied a poset where there are additional cover relations which allows the insertion of a part of size one anywhere in the composition. Finally, following a suggestion by Richard Stanley we study yet a third which is an extension of the previous two posets. This poset is related to quasi-symmetric functions. For these posets, we study generating functions for saturated chains of fixed width k. We also construct ``labeled'' non-commutative generating functions and their associated languages.
Demand Response Management for Smart Grids With Wind Power

In this paper, we study demand response (DR) management when a wind farm is connected to a smart grid. Total social welfare, defined in terms of consumer utility and cost of power production, is maximized while the probability of power deficit due to the uncertainty in renewable production is limited by an upper bound. We find the optimal deficit-limiting conventional power production and consumption schedules, and demonstrate their superiority over competing policies, particularly when the renewable share is high. It is also noted that geographically diverse turbines (or wind farms) should be connected to the grid to ensure less variable power output and low enough probability of deficit.
Estimating photometric redshifts with artificial neural networks

A new approach to estimating photometric redshifts ‐ using artificial neural networks (ANNs) ‐ is investigated. Unlike the standard template-fitting photometric redshift technique, a large spectroscopically-identified training set is required but, where one is available, ANNs produce photometric redshift accuracies at least as good as and often better than the template-fitting method. The Bayesian priors on the underlying redshift distribution are automatically taken into account. Furthermore, inputs other than galaxy colours ‐ such as morphology, angular size and surface brightness ‐ may be easily incorporated, and their utility assessed. Different ANN architectures are tested on a semi-analytic model galaxy catalogue and the results are compared with the template-fitting method. Finally, the method is tested on a sample of ∼20 000 galaxies from the Sloan Digital Sky Survey. The rms redshift error in the range z 0.35 is σ z ∼ 0.021.
Application of Image Texture for Discrimination of Tea Categories Using Multi-spectral Imaging Technique and Support Vector Machine

This study investigated multi-spectral imaging technique as a rapid method to discriminate the tea category. Tea was spread over the whole images. The images for each sample were captured using a red, near infrared and green channel multi-spectral camera. 320 images were obtained. Three texture features were obtained through the entropy of three channels and then set as the input variables for pattern recognition. Principal components analysis (PCA) and least squares-support vector machine (LS-SVM) were used for the pattern recognition. The cluster ability of PCA cluster plot was not good while the discrimination rate of LS-SVM model was 97.5%. We used one channel image to subtract another one, and six images of each sample were obtained. Then six new entropy values were obtained. The cluster ability of the new PCA cluster plot is better than the old one and the discrimination rate of LS-SVM model was 100%. It is concluded that multi-spectral imaging technique can identify categories of green tea fast and non-destructively.
Deletions of multidrug resistance gene loci in breast cancer leads to the down-regulation of its expression and predict tumor response to neoadjuvant chemotherapy

Neoadjuvant chemotherapy (NAC) is intensively used for the treatment of primary breast cancer. In our previous studies, we reported that clinical tumor response to NAC is associated with the change of multidrug resistance (MDR) gene expression in tumors after chemotherapy. In this study we performed a combined analysis of MDR gene locus deletions in tumor DNA, MDR gene expression and clinical response to NAC in 73 BC patients. Copy number variations (CNVs) in biopsy specimens were tested using high-density microarray platform CytoScanTM HD Array (Affymetrix, USA). 75%–100% persons having deletions of MDR gene loci demonstrated the down-regulation of MDR gene expression. Expression of MDR genes was 2–8 times lower in patients with deletion than in patients having no deletion only in post-NAC tumors samples but not in tumor tissue before chemotherapy. All patients with deletions of ABCB1 ABCB 3 ABCC5 gene loci – 7q21.1, 6p21.32, 3q27 correspondingly, and most patients having deletions in ABCC1 (16p13.1), ABCC2 (10q24), ABCG1 (21q22.3), ABCG2 (4q22.1), responded favorably to NAC. The analysis of all CNVs, including both amplification and deletion showed that the frequency of 13q14.2 deletion was 85% among patients bearing tumor with the deletion at least in one MDR gene locus versus 9% in patients with no deletions. Differences in the frequency of 13q14.2 deletions between the two groups were statistically significant (p = 2.03 ×10−11, Fisher test, Bonferroni-adjusted p = 1.73 × 10−8). In conclusion, our study for the first time demonstrates that deletion MDR gene loci can be used as predictive marker for tumor response to NAC.
Improved BP Decoding Algorithm for Nonbinary LDPC Codes Based on Bit-flipping Method

Nonbinary LDPC codes demonstrated excellent performance. However, how to reduce the decoding complexity for such codes without performance degradation is still a problem to be solved. In this paper, the bit-flipping method is introduced into the standard Fourier transform decoding algorithm for decoding nonbinary LDPC codes. Simulation results show that the proposed method gains better BER performance with efficiently reduce of the decoding computation complexity in high Eb/N0 regions.