Miracle-dz/UniFAR-FTUs huggingface dataset:
Next-Generation Sequencing Identifies Transportin 3 as the Causative Gene for LGMD1F

Limb-girdle muscular dystrophies (LGMD) are genetically and clinically heterogeneous conditions. We investigated a large family with autosomal dominant transmission pattern, previously classified as LGMD1F and mapped to chromosome 7q32. Affected members are characterized by muscle weakness affecting earlier the pelvic girdle and the ileopsoas muscles. We sequenced the whole exome of four family members and identified a shared heterozygous frame-shift variant in the Transportin 3 (TNPO3) gene, encoding a member of the importin-β super-family. The TNPO3 gene is mapped within the LGMD1F critical interval and its 923-amino acid human gene product is also expressed in skeletal muscle. In addition, we identified an isolated case of LGMD with a new missense mutation in the same gene. We localized the mutant TNPO3 around the nucleus, but not inside. The involvement of gene related to the nuclear transport suggests a novel disease mechanism leading to muscular dystrophy.
Matrix-centric Neural Networks

We present a new distributed representation in deep neural nets wherein the information is represented in native form as a matrix. This differs from current neural architectures that rely on vector representations. We consider matrices as central to the architecture and they compose the input, hidden and output layers. The model representation is more compact and elegant - the number of parameters grows only with the largest dimension of the incoming layer rather than the number of hidden units. We derive feed-forward nets that map an input matrix into an output matrix, and recurrent nets which map a sequence of input matrices into a sequence of output matrices. Experiments on handwritten digits recognition, face reconstruction, sequence to sequence learning and EEG classification demonstrate the efficacy and compactness of the matrix-centric architectures.
Using Blowfish encryption to enhance security feature of an image

The use of images to hide information is a feature which leaves little doubt in a viewer's mind. Using any medium to hide information refers to a method called steganography. When we use an image as a medium then that method is called image steganography. The most famous method to now is the Least Significant Bit Algorithm (LSB). In that method, the least significant bit of each pixel is taken and information is hidden in that. This however is easily breakable. Hence an alternative and more secure solution is provided. First the data is encrypted using the Blowfish algorithm. Next an innovative method is presented. This encrypted block is broken down to `n' smaller blocks and `n' images are chosen at random and each image is made to hide a block of the encrypted data. To maintain the correct sequence of blocks a hash table is maintained. This is then encrypted using LSB to a new image called the hashing image. This hashing image is sent along with the `n' other images. To extract the data out, first the hash image is obtained and using this the encrypted block is reassembled and then original data is obtained by decryption.
Functional Specificity of Cardiolipin Synthase Revealed by the Identification of a Cardiolipin Synthase CrCLS1 in Chlamydomonas reinhardtii

Phosphatidylglycerol (PG) and cardiolipin (CL) are two essential classes of phospholipid in plants and algae. Phosphatidylglycerophosphate synthase (PGPS) and cardiolipin synthase (CLS) involved in the biosynthesis of PG and CL belong to CDP-alcohol phosphotransferase and share overall amino acid sequence homology. However, it remains elusive whether PGPS and CLS are functionally distinct in vivo. Here, we report identification of a gene encoding CLS in Chlamydomonas reinhardtii, CrCLS1, and its functional compatibility. Whereas CrCLS1 did not complement the growth phenotype of a PGPS mutant of Synechocystis sp. PCC 6803, it rescued the temperature-sensitive growth phenotype, growth profile with different carbon sources, phospholipid composition and enzyme activity of Δcrd1, a CLS mutant of Saccharomyces cerevisiae. These results suggest that CrCLS1 encodes a functional CLS of C. reinhardtii as the first identified algal CLS, whose enzyme function is distinct from that of PGPSs from C. reinhardtii. Comparison of CDP-alcohol phosphotransferase motif between PGPS and CLS among different species revealed a possible additional motif that might define the substrate specificity of these closely related enzymes.
Multimodal spike data recorded from posterior parietal cortex of non-human primates performing a reaction-time task involving combined eye and arm movements while in a virtual reality environment.

This data was collected from the Parietal Cortex of two male rhesus macaques (macaca mulatta) using single microelectrodes. For each recording session, single-cell recordings (N=343; 219 from monkey X and 124 from monkey B) were made in the superficial cortex of the SPL (area 5), as judged by recording depth and similarity to previous recordings made in this area with standard neurophysiological techniques. Activity was recorded extracellularly with varnish-coated tungsten microelectrodes (~1- to 2-MΩ impedance at 1 kHz). Single action potentials (spikes) were isolated from the amplified and filtered (600–6,000 Hz) signal via a time-amplitude window discriminator (Plexon). Spike times were sampled at 2.5 kHz. Within a virtual reality environment where visual feedback of the limb could be manipulated, animals performed a reaction-time task involving combined eye and arm movements from a single (central) starting position to one of eight peripheral targets. Results from the experiments are described in: Shi, Y. , G. Apker, and C.A. Buneo, Multimodal representation of limb endpoint position in the posterior parietal cortex. J Neurophysiol, 2013. 109(8): p. 2097-2107. DOI: 10.1152/jn.00223. 2012 And another manuscript which is currently under review.
Context-Aware Middleware Architecture for Smart Home Environment

To realize smart home vision in an equipped domain, several types of context-aware applications should be deployed. Because of complex tasks of context gathering and processing, designing context-aware applications requires middleware support. Designing a context-aware middleware is a challenging issue because of specific characteristics of context and devices such as dynamic nature of context and resource limitation of devices. Furthermore, the middleware should provide a cooperative system in which application developers could easily exchange derived contextual information. To address these issues, in this paper architectural design of a smart-home context-aware middleware is proposed, which supports cooperation among application developers. The middleware is implemented as a Java package and validated through a case study. KeywordsPervasive computing, Context-aware middleware, Context-aware application
Selling in an asymmetric retail world: perspectives from India, Russia, and the US on buyer–seller information differential, perceived adaptive selling, and purchase intention

The current research aimed to contribute to our understanding of (a) how adaptive selling is perceived by retail consumers in different types of economies when they have differing levels of buyer–seller information differential and (b) how this phenomenon influences their purchase intention. The focal countries were the US (a developed economy) and India and Russia (both developing economies). These three were selected owing to their disparate economic and cultural contexts. This investigation used a relatively new construct: perceived adaptive selling (PAS). PAS refers to the degree to which the buyer perceives that the salesperson is adapting. The study examined whether the impact of PAS was a function of a country’s level of development and whether such perceptions were influenced by the level of buyer–seller information differential in the retail transaction. The findings suggested that buyers in Russia and India PAS and were influenced by it differently from their US counterparts. This work provided a general framework for understanding tactical implementation of the salesperson’s PAS behavior.
Child differential sensitivity to parental self-efficacy improvement

This study investigates the hypothesis of a child differential sensitivity to parenting improvement. One hundred and fourteen parents of preschoolers participated in two parenting micro-trials aiming to increase parental self-efficacy in view of improving child behavior. The first micro-trial took place in a short-term laboratory experiment; the other was an eight-week parenting group intervention, both focusing on altering parental cognition. Differential effects of parental self-efficacy improvement on child’s positive and negative behaviors, depending on child temperament, were compared at post-test between control and experimental groups. Both observation and questionnaires were used to measure child behavior as well as regression and Regions of Significance analyses. Child differential sensitivity was found both in the laboratory experiment and in the parenting intervention for the temperamental trait of negative emotionality but not for the temperamental trait of activity. However, this sensitivity was in an unexpected direction. Highly emotional children benefited less from this parental cognitive improvement than children low on emotionality. These results may be explained by the specific cognitive nature of these two parenting micro-trials.
Supporting students during their undergraduate research projects using audio recordings

ABSTRACT Undergraduate final year research projects are part of many degree programmes and help students enhance a variety of transferable skills. A challenge for supervisors is encouraging students to develop as independent learners, while simultaneously providing support and structure for a successful, timely completion. In this project, we trialled a student-centred supervision approach where all supervisory meetings were concluded by producing audio recordings of students summarising the discussion. The recordings were emailed to the students who were instructed to write and return a short reflection to the supervisor. Students found that the audio summaries ensured that they stayed engaged and focused during the meetings. The recordings helped them understand and remember the issues discussed and encouraged them to take ownership of their project. Willingness and ability to reflect, however, were patchy, indicating that students may need more training in the skills of reflection earlier on in their studies.
Real-time dynamic trajectory planning for highly automated driving in highways

This paper presents the implementation of two methods for real-time trajectory planning in a dynamic environment applied to highly automated driving in a highway scenario. Both methods have been implemented for the HAVEit European project. The first method follows the Partial Motion Planning approach, and the second method uses 5th degree (quintic) polynomials to generate a detailed spatio-temporal description of a trajectory to be performed. Both implementations are integrated in a simulation environment and in an experimental research vehicle within HAVEit. Results and evaluations of the trajectory planning algorithms are presented.
The correlation of learning speed and natural foraging success in bumble-bees

Despite the widespread assumption that the learning abilities of animals are adapted to the particular environments in which they operate, the quantitative effects of learning performance on fitness remain virtually unknown. Here, we evaluate the learning performance of bumble-bees (Bombus terrestris) from multiple colonies in an ecologically relevant associative learning task under laboratory conditions, before testing the foraging performance of the same colonies under the field conditions. We demonstrate that variation in learning speed among bumble-bee colonies is directly correlated with the foraging performance, a robust fitness measure, under natural conditions. Colonies vary in learning speed by a factor of nearly five, with the slowest learning colonies collecting 40% less nectar than the fastest learning colonies. Such a steep fitness function is suggestive of strong selection for higher learning speed. Partial correlation analysis reveals that other factors such as forager body size or colour preference appear to be negligible in our study. Although our study does not directly prove causality of learning on foraging success, our approach of correlating natural within-species variation in these two factors represents a major advance over traditional between-species correlative analyses where comparability can be compromised by the fact that species vary along multiple dimensions.
Teaching an Old Dog New Tricks: Past, Present, and Future Priorities For Higher Education Physical Activity Programs

ABSTRACT Physical education programs in colleges/universities have been called on to provide students with opportunities to develop an appreciation for, and increase participation in, lifetime physical activity. Higher Education Physical Activity Programs (HEPAPs) have evolved over the past 100 years in response to changing societal and institutional expectations. The purpose of this article is to celebrate the long history of HEPAPs and recommend innovative strategies for program development that will maintain their position as a valued aspect of campus life. After tracing the historical roots and trends of HEPAPs, the authors suggest (a) adopting a public health perspective, (b) applying theoretical models as a framework for program development, (c) focusing on meaningful learner engagement, and (d) employing learner-centered instructional approaches. Within these overall themes, specific recommendations for program improvement are provided, including the use of alternative content areas, model-based instruction, universal design, instructional technology, and professional development for faculty/staff.
Long-Time Protein Folding Dynamics from Short-Time Molecular Dynamics Simulations

Protein folding involves physical timescales - microseconds to seconds - that are too long to be studied directly by straightforward molecular dynamics simulation, where the fundamental timestep is constrained to femtoseconds. Here we show how the long-time statistical dynamics of a simple solvated biomolecular system can be well described by a discrete-state Markov chain model constructed from trajectories that are an order of magnitude shorter than the longest relaxation times of the system. This suggests that such models, appropriately constructed from short molecular dynamics simulations, may have utility in the study of long-time conformational dynamics.
Target Tracking by Using Prediction and Sleep Scheduling In Sensor Networks

Lifetime maximization is one key element in the design of sensor-network-based surveillance applications. We propose a protocol for node sleep scheduling that guarantees a bounded-delay sensing coverage while maximizing network lifetime. One of the most important applications of wireless sensor Network is tracks mobile target in surveillance system. Each sensor has a duty cycling requirement of being awake for only given time slots on an average. When nodes operate in a duty cycling mode, tracking performance can be improved if the target motion can be predicted and nodes along the trajectory can be proactively awakened. However, this will negatively influence the energy efficiency and constrain the benefits of duty cycling. In this paper, we present a Probability-based Prediction and Sleep Scheduling protocol (PPSS) to improve energy efficiency of proactive wake up. Based on the prediction results, PPSS then precisely selects the nodes to awaken and reduces their active time, so as to enhance energy efficiency with limited tracking performance loss. We evaluated the efficiency of PPSS with both simulation-based experiments.
A Performance Based QoS Aware Resource Brokering Framework for the Grid

Most of the current day grid infrastructures operate on a best effort mode, in offering services to the end users. However to cater to the ever increasing performance demands of grand challenge applications, it is imperative for the grid computing technologies to evolve and mature, to deliver services with highest QoS attached with them. The work described in this paper focuses on providing a QoS aware resource brokering framework that can deliver optimal performances for the applications on the grid. Similar to the popular Amazon EC2 instance types of classifying resources based on the application requirements, the framework identifies different profiles of HPC applications, and thus chooses the right mix of QoS metrics to deliver the best possible performance with the available resources of the grid, by making dynamic Service Level Agreements with the resource and service providers. The paper gives an insight into the various performance metrics employed to optimally allocate the resources. Further it includes a detailed discussion of the test results obtained using this framework on a production grid infrastructure, highlighting the perceived performance gains by grid applications.
Being critical: an account of an early career academic working within and against neoliberalism

In 2014 I attended a symposium concerning Early Career Academics (ECAs) in the field of physical education and sport pedagogy. I was struck by the dominance of a particular theme at that symposium—that is, how to obtain a position and survive in academia. The aim of this paper is to use an inciting moment that occurred at this symposium as a means to confront some of the broader issues that I thought were at stake. Specifically I seek to interrogate the possible consequences of thinking about ECAs and academia in neoliberal terms, for the future of the field—a field that has a strong critical tradition. To this end I utilise autoethnographic techniques to connect the personal to the cultural and attempt to describe and analyse my story of becoming and being a critical scholar of sport, in a neoliberal context.
Selection of Discriminative Features for Translation Texts

Beginning in the first century AD, Buddhist texts underwent a series of translations during a period of nearly 1300 years. The identification of the translator, textual apocrypha, and translation style in Buddhist texts are always important issues. This study proposes an approach to find the most discriminative features that characterize the different Buddhist translation texts or other translation texts. We studied five different kinds of features that can be extracted from translation texts and exploited the F-score and SVM classifier to find the most discriminative features. Not only did we use the translated Buddhist texts, Kalama Sutta, for our experiment, but we also chose The Canterbury Tales to perform the same experiment and compare the results. According to our experiment results, the newly considered fifth-type features are very effective to identify translators. The selected features will be very useful for further studies of translator characteristics.
Application of Metabolomics in Traditional Chinese Medicine Differentiation of Deficiency and Excess Syndromes in Patients with Diabetes Mellitus

Metabolic profiling is widely used as a probe in diagnosing diseases. In this study, the metabolic profiling of urinary carbohydrates was investigated using gas chromatography/mass spectrometry (GC/MS) and multivariate statistical analysis. The kernel-based orthogonal projections to latent structures (K-OPLS) model were established and validated to distinguish between subjects with and without diabetes mellitus (DM). The model was combined with subwindow permutation analysis (SPA) in order to extract novel biomarker information. Furthermore, the K-OPLS model visually represented the alterations in urinary carbohydrate profiles of excess and deficiency syndromes in patients with diabetes. The combination of GC/MS and K-OPLS/SPA analysis allowed the urinary carbohydrate metabolic characterization of DM patients with different traditional Chinese medicine (TCM) syndromes, including biomarkers different from non-DM patients. The method presented in this study might be a complement or an alternative to TCM syndrome research.
Non‐linear redshift distortions: the two‐point correlation function

We consider a situation where the density and peculiar velocities in real space are linear, and we calculate ξs, the two-point correlation function in redshift space, incorporating all non-linear effects which arise as a consequence of the map from real to redshift space. Our result is non-perturbative and it includes the effects of possible multi-streaming in redshift space. We find that the deviations from the predictions of the linear redshift distortion analysis increase for the higher spherical harmonics of ξs. While the deviations are insignificant for the monopole ξ0, the hexadecapole ξ4 exhibits large deviations from the linear predictions. For a COBE normalized , cold dark matter (CDM) power spectrum, our results for ξ4 deviate from the linear predictions by a factor of two on the scale of ∼10 h−1 Mpc. The deviations from the linear predictions depend separately on f(Ω) and b. This holds the possibility of removing the degeneracy that exists between these two parameters in the linear analysis of redshift surveys which yields only . We also show that the commonly used phenomenological model, where the non-linear redshift two-point correlation function is calculated by convolving the linear redshift correlation function with an isotropic pair velocity distribution function, is a limiting case of our result.
Community Detection Algorithm Based on the Scale Free Property of Networks

Many complex networks in the real world demonstrate similar patterns, including the scale-free property and strong community structure. In this paper, we present a novel parameter-free community detection algorithm based on the scale-free property of networks, named ScaleFreeCDA. The basic idea behind it is two mechanisms, i.e., node growth and preferential attachment. Community centers are firstly determined by node degree and similarity, and then community structures are obtained accordingly. The experiments indicate that our algorithm can find high-quality communities, and can determine appropriate number of communities on most artificial and real networks.