Miracle-dz/UniFAR-FTUs huggingface dataset:
Ocular developmental abnormalities and glaucoma associated with interstitial 6p25 duplications and deletions.

PURPOSE Mutations in the forkhead transcription factor gene FOXC1 on 6p25 cause a range of ocular developmental abnormalities, with associated glaucoma. However, FOXC1 mutations have not been found in all similarly affected pedigrees mapping to this interval. This study was undertaken to investigate the potential role of 6p25 rearrangements in causing such phenotypes. METHODS Two large families with autosomal dominant iris hypoplasia and early-onset glaucoma, 21 probands with Axenfeld-Rieger phenotypes not attributable to PITX2 mutations, and 7 individuals with documented 6p25 cytogenetic rearrangements, were investigated by genotyping and fluorescence in situ hybridization, with markers and probes from the 6p25 region. RESULTS Interstitial 6p25 duplications were present in the unrelated families with iris hypoplasia, whereas an interstitial 6p25 deletion was identified in one Axenfeld-Rieger pedigree. Larger cytogenetic rearrangements, leading to trisomy or monosomy of the 6p25 region, resulted in microcornea and Rieger syndrome phenotypes, respectively. All the rearrangements encompassed FOXC1, increasing or decreasing the number of FOXC1 copies present, and appeared to correlate with the phenotypes observed. CONCLUSIONS These findings represent the first example of both interstitial duplications and deletions cosegregating with a human developmental disorder that is attributable to altered dose of transcription factor. The data presented provide additional evidence for the pathogenicity of altered gene dosage of FOXC1 and suggest that a common mechanism is responsible for rearrangements of 6p25.
Developing a Machine Learning Approach to Controlling Musical Synthesizer Parameters in Real-Time Live Performance

Musicians who play synthesizers often adjust synthesis parameters during live performance to achieve a more expressive sound. Training a computer to make automatic parameter adjustments based on examples provided by the performer frees the performer from this responsibility while maintaining an expressive sound in line with the performer’s desired aesthetic. This paper is an overview of ongoing research to explore the effectiveness of using Long Short-Term Memory (LSTM) recurrent neural networks to accomplish this task.
A Bottom Up approach to Persian Stemming

Stemmers have many applications in natural language processing and some fields such as information retrieval. Many algorithms have been proposed for stemming. In this paper, we propose a new algorithm for Persian language. Our algorithm is a bottom up algorithm that is capable to reorganize without changing the implementation. Our experiments show that the proposed algorithm has a suitable result in stemming and flexibility.
Equivariant Nonstationary Source Separation

Most of source separation methods focus on stationary sources, so higher-order statistics is necessary for successful separation, unless sources are temporally correlated. For nonstationary sources, however, it was shown [Neural Networks 8 (1995) 411] that source separation could be achieved by second-order decorrelation. In this paper, we consider the cost function proposed by Matsuoka et al. [Neural Networks 8 (1995) 411] and derive natural gradient learning algorithms for both fully connected recurrent network and feedforward network. Since our algorithms employ the natural gradient method, they possess the equivariant property and ®nd a steepest descent direction unlike the algorithm [Neural Networks 8 (1995) 411]. We also show that our algorithms are always locally stable, regardless of probability distributions of nonstationary sources.
Distributed Nesterov gradient methods for random networks: Convergence in probability and convergence rates

We consider distributed optimization where N nodes in a generic, connected network minimize the sum of their individual, locally known, convex costs. Existing literature proposes distributed gradient-like methods that are attractive due to computationally cheap iterations and provable resilience to random inter-node communication failures, but such methods have slow theoretical and empirical convergence rates. Building from the centralized Nesterov gradient methods, we propose accelerated distributed gradient-like methods and establish that they achieve strictly faster rates than existing distributed methods. At the same time, our methods maintain cheap iterations and resilience to random communication failures. Specifically, for convex, differentiable local costs with Lipschitz continuous and bounded derivative, we establish (with respect to the cost function optimality) convergence in probability and convergence rates in expectation and in second moment.
Metrics for evaluating concern separation and composition

This paper discusses an approach to evaluating the separation of concerns for an object-oriented software system. For assessing this separation, the developer is asked to specify the nature of classes through annotations. Automatic identification of some structural characteristics (e.g., inheritance, libraries, synchronisation) is used to appraise the composition and intertwining of concerns inside a class.
Towards Efficient and Flexible KNN Query Processing in Real-Life Road Networks

Along with the developments of mobile services, effectively modeling road networks and efficiently indexing and querying network constrained objects has become a challenging problem. In this paper, we first introduce a road network model which captures real-life road networks better than previous models. Then, based on the proposed model, we propose a novel index named the RNG (road network grid) index for accelerating KNN queries and continuous KNN queries over road network constrained data points. In contrast to conventional methods, speed limitations and blocking information of roads are included into the RNG index, which enables the index to support both distance-based and time-based KNN queries and continuous KNN queries. Our work extends previous ones by taking into account more practical scenarios, such as complexities in real-life road networks and time-based KNN queries. Extensive experimental study shows that our methods are efficient in terms of both CPU time and disk I/Os.
Applications of Silk in Drug Delivery: Advancement in Pharmaceutical Dosage Forms

Silk is one of the important natural protein fibre produced by certain insect larvae, Major source are mulberry silkworm Bombyx mori, Tasar silkworm Antheraea mylitta, Oak tasar silkworm Antheraea proyeli, Eri silkworm Philosamia ricini and Muga silkworm Antheraea assamensis. Silk is having pleotropic effect of being novel aid in drug delivery because of its properties like self-assembly, mechanical toughness, processing flexibility, biodegradability and biocompatibility. Silkworm silk has been widely used as biomedical sutures, 3D- silk fibroin scaffolds, films, nanofibers, nanoparticles, microspheres, for coatings, microneedles etc. Present review article covers these with sufficient information. © 2011 IGJPS. All rights reserved.
Multilevel latent variable models for global health‐related quality of life assessment

Quality of life (QOL) assessment is a key component of many clinical studies and frequently requires the use of single global summary measures that capture the overall balance of findings from a potentially wide‐ranging assessment of QOL issues. We propose and evaluate an irregular multilevel latent variable model suitable for use as a global summary tool for health‐related QOL assessments. The proposed model is a multiple indicator and multiple cause style of model with a two‐level latent variable structure. We approach the modeling from a general multilevel modeling perspective, using a combination of random and nonrandom cluster types to accommodate the mixture of issues commonly evaluated in health‐related QOL assessments—overall perceptions of QOL and health, along with specific psychological, physical, social, and functional issues. Using clinical trial data, we evaluate the merits and application of this approach in detail, both for mean global QOL and for change from baseline. We show that the proposed model generally performs well in comparing global patterns of treatment effect and provides more precise and reliable estimates than several common alternatives such as selecting from or averaging observed global item measures. A variety of computational methods could be used for estimation. We derived a closed‐form expression for the marginal likelihood that can be used to obtain maximum likelihood parameter estimates when normality assumptions are reasonable. Our approach is useful for QOL evaluations aimed at pharmacoeconomic or individual clinical decision making and in obtaining summary QOL measures for use in quality‐adjusted survival analyses. Copyright © 2012 John Wiley & Sons, Ltd.
Depth - Based Image Segmentation

In this paper I investigate light field imaging as it might relate to the problem of image segmentation in cell culture, time-lapse microscopy. I discuss the current field of light field imaging, depth-based imaging segmentation, and light field microscopy. I then discuss the process of gathering data that lends itself well to this problem, calibrating depth map data with ground-truth measurements, generating heat map overlays for quick error estimation, image data segmentation performance, and depth discretization. Finally, I remark on how light field imaging might be applied to the world of microscopy, and in particular, automatic cell tracking.
Global scheduler properties derived from local restrictions

The VoDka server is a video-on-demand system for a Spanish cable company. We look at the distributed scheduler of this system. This scheduler enables that whenever a user agent is asking for a certain movie, this request is transferred through the system and a set of possible play-back qualities is returned to the agent. In case of a non-empty set, the agent selects one and the movie is streamed to the user.The storage subsystem of the server is composed by a hierarchy of different storage systems, i.e. disks, CD players or tapes. These devices all have restrictions of which the process controlling the device is aware of. A second layer of processes controls a set of devices in one machine and has restrictions, for example, the bandwidth of its connection. A third layer may be further out in the network and serve as a cache to store more popular movies.Every process in the scheduler of the system has a function determining local restrictions, given the configuration and present state of the system. We have built a tool to construct complete models of several configurations. With techniques from the area of formal methods (in particular model checking) these models are used to determine global properties of the system, such as the maximum number of a certain class of movies that can be served in parallel.
CytoGEDEVO - global alignment of biological networks with Cytoscape

MOTIVATION In the systems biology era, high-throughput omics technologies have enabled the unraveling of the interplay of some biological entities on a large scale (e.g. genes, proteins, metabolites or RNAs). Huge biological networks have emerged, where nodes correspond to these entities and edges between them model their relations. Protein-protein interaction networks, for instance, show the physical interactions of proteins in an organism. The comparison of such networks promises additional insights into protein and cell function as well as knowledge-transfer across species. Several computational approaches have been developed previously to solve the network alignment (NA) problem, but only a few concentrate on the usability of the implemented tools for the evaluation of protein-protein interactions by the end users (biologists and medical researchers). RESULTS We have created CytoGEDEVO, a Cytoscape app for visual and user-assisted NA. It extends the previous GEDEVO methodology for global pairwise NAs with new graphical and functional features. Our main focus was on the usability, even by non-programmers and the interpretability of the NA results with Cytoscape. AVAILABILITY AND IMPLEMENTATION CytoGEDEVO is publicly available from the Cytoscape app store at http://apps.cytoscape. org/apps/cytogedevo In addition, we provide stand-alone command line executables, source code, documentation and step-by-step user instructions at http://cytogedevo.compbio. sdu.dk CONTACT malek@tugraz.at SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Nonnormal Frontal Dynamics

The generalized stability of the secondary atmospheric circulation over strong SST fronts is studied using a hydrostatic, Boussinesq, two-dimensional f-plane model. It is shown that even in a parameter regime in which these circulations are stable to small perturbations, significant nonnormal growth of optimal initial perturbations occurs. The maximum growth factor in perturbation total energy is 250 and is dominated by the potential energy, which obtains a growth factor of 219 two to five hours after the beginning of the integration. This domination of potential energy growth is consistent with the observation that the available potential energy (APE) of the secondary circulation is larger by two orders of magnitude than the kinetic energy as well as with the transfer of kinetic to potential perturbation energy at the beginning of the growth of the perturbations. The norm kernel is found to significantly influence the structure of the optimal initial perturbation as well as the energy obtained by the mature perturbations, but the physical mechanism of growth and the structure of the mature perturbations are robust.
Current Emotion Research in Anthropology: Reporting the Field

An internal critique of anthropology in recent decades has shifted the focus and scope of anthropological work on emotion. In this article I review the changes, explore the pros and cons of leading anthropological approaches and theories, and argue that—so far as anthropology is concerned—only detailed narrative accounts can do full justice to the complexity of emotions. A narrative approach captures both the particularity and the temporal dimension of emotion with greater fidelity than semantic, synchronic, and discourse-based approaches.
MEMS Multi-Fields Uniform Model Simulation and Optimization Method Research and Application

To solve the coupling and optimizing problems in MEMS designing and analyzing process, the paper mainly put forward to the uniform model construction simulation method. The method adopt uniform constraints to describe MEMS multi-fields simulation model, firstly construct MEMS model library which includes structure, electricity, magnetic, thermal and fluid elements in multi-field uniform language modeling; through these elements, MEMS physics simulation model can be directly constructed; after model construction, some prosperities of the MEMS devices can be simulated. The aim of the method of paper is trying to conquer the computing efficiency of numerical simulation and abstraction problems of hardware describe language (HDL) and realizes MEMS multi-fields uniform constrained model construction and solving theory system. At last, an application example of the method has been realized, which shows that the method is effective to the early design of MEMS product. Copyright © 2013 IFSA.
Qualitative Evaluation of a Parent Education Program for Divorcing Parents

Abstract In response to the significant increase in divorce and the expanding number of children affected by this stressor, parent education programs for divorcing individuals have been developed. There are over 500 of these programs nationwide, yet few of these programs have been evaluated through quantitative methods, and none have been evaluated through use of extensive qualitative means. It was the purpose of the current study to explore, in depth, the following areas related to divorce education programs through a qualitative approach: (a) how parents constructed the knowledge from the program, (b) the degree to which the participants felt the program was helpful, (c) which program components were most effective, and (d) whether the participants had any specific suggestions on how to improve the program. Suggestions for practice are also provided based upon the findings from this study.
Some Personality and Motivational Correlates of Children's Prosocial Behavior

Abstract The prosocial behavior of 76 male and female fourth-graders was investigated by observing their sharing, caring, and helping actions on the playground and by obtaining sociometric measures (peer nomination) of a variety of prosocial actions. Interrelations among all the prosocial acts were examined. Personal characteristics such as assertiveness, empathy, self-concept, value orientation, and moral-motivational reasoning based on role taking were also assessed. The number and pattern of significant correlations obtained among the observed prosocial behaviors, among the peer nominations, and between these two sets of scores indicated that, for girls, prosocial responding was moderately coherent, but that, for boys, caring, sharing, and helping were relatively independent domains. The hypotheses about the associations between prosocial behavior and personal characteristics were at least moderately supported. Sex differences in these relationships suggest that females' predominant prosocial responses.
‘Inspiration, ideas, encouragement’: teacher development and improved use of technology in language teaching through open educational practice

This paper describes a study undertaken with language tutors who were engaged in a project to publish and create open educational resources. We sought to investigate how far working with open content could offer language tutors opportunities to develop professionally and acquire new technical knowledge for language teaching. Language educators face particular motivational challenges, and often have a lack of training in the use of technology for teaching. We applied a self-efficacy theory of motivation to understand the extent to which tutors felt confident and capable about open practice, and whether they perceived development benefits. On the whole our findings suggest that open practice may be an effective vehicle for professional development, for enhancing knowledge of technology in teaching and for alleviating some specific motivational barriers faced by language educators. However, they also revealed significant issues which challenge tutors’ likelihood of continuing with open practice, which would need to be addressed for the benefits of open working to be fully realised.
The Interaction of Large Bowel Microflora with the Colonic Mucus Barrier

The colonic mucus barrier is the first line of defence that the underlying mucosa has against the wide range of potentially damaging agents of microbial, endogenous, and dietary origin that occur within the colonic lumen. The functional component of mucus is the secreted, polymeric glycoprotein mucin. The mucus barrier can either act as an energy source or a support medium for growth to the intestinal microflora. The mucus barrier appears to effectively partition the vast number of microbial cells from the underlying epithelium. The normal functionality and biochemistry of this mucus barrier appears to be lost in diseases of the colorectal mucosa. Germ-free animal studies have highlighted the necessity of the presence of the colonic microflora to drive the maturation of the colonic mucosa and normal mucus production. A number of by-products of the microflora have been suggested to be key luminal drivers of colonic mucus secretion.
M.HhaI binds tightly to substrates containing mismatches at the target base.

The (cytosine-5) DNA methyltransferase M.HhaI causes its target cytosine base to be flipped completely out of the DNA helix upon binding. We have investigated the effects of replacing the target cytosine by other, mismatched bases, including adenine, guanine, thymine and uracil. We find that M.HhaI binds more tightly to such mismatched substrates and can even transfer a methyl group to uracil if a G:U mismatch is present. Other mismatched substrates in which the orphan guanine is changed exhibit similar behavior. Overall, the affinity of DNA binding correlates inversely with the stability of the target base pair, while the nature of the target base appears irrelevant for complex formation. The presence of a cofactor analog. S-adenosyl-L-homocysteine, greatly enhances the selectivity of the methyltransferase for cytosine at the target site. We propose that the DNA methyltransferases have evolved from mismatch binding proteins and that base flipping was, and still is, a key element in many DNA-enzyme interactions.
Factor Topographic Latent Source Analysis : Factor Analysis for Brain Images ?

Traditional approaches to analyzing experimental functional magnetic resonance imaging (fMRI) data entail fitting per-voxel parameters to explain how the observed images reflect the thoughts and stimuli a participant experienced during the experiment. These methods implicitly assume that voxel responses are independent and that the unit of analysis should be the voxel. However, both of these assumptions are known to be untrue: it is well known that voxel activations exhibit strong spatial correlations, and common sense tells us that the true underlying brain activations are independent of the resolution at which the brain image happened to be taken. Here we propose a fundamentally different approach, whereby brain images are represented as weighted sums of spatial functions. Our technique yields compact representations of the brain images that leverage spatial correlations in the data and are independent of the image resolution.
Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints

Recent spiking network models of Bayesian inference and unsupervised learning frequently assume either inputs to arrive in a special format or employ complex computations in neuronal activation functions and synaptic plasticity rules. Here we show in a rigorous mathematical treatment how homeostatic processes, which have previously received little attention in this context, can overcome common theoretical limitations and facilitate the neural implementation and performance of existing models. In particular, we show that homeostatic plasticity can be understood as the enforcement of a 'balancing' posterior constraint during probabilistic inference and learning with Expectation Maximization. We link homeostatic dynamics to the theory of variational inference, and show that nontrivial terms, which typically appear during probabilistic inference in a large class of models, drop out. We demonstrate the feasibility of our approach in a spiking Winner-Take-All architecture of Bayesian inference and learning. Finally, we sketch how the mathematical framework can be extended to richer recurrent network architectures. Altogether, our theory provides a novel perspective on the interplay of homeostatic processes and synaptic plasticity in cortical microcircuits, and points to an essential role of homeostasis during inference and learning in spiking networks.