Miracle-dz/UniFAR-FTUs huggingface dataset:
CRITICA: coding region identification tool invoking comparative analysis.

Gene recognition is essential to understanding existing and future DNA sequence data. CRITICA (Coding Region Identification Tool Invoking Comparative Analysis) is a suite of programs for identifying likely protein-coding sequences in DNA by combining comparative analysis of DNA sequences with more common noncomparative methods. In the comparative component of the analysis, regions of DNA are aligned with related sequences from the DNA databases; if the translation of the aligned sequences has greater amino acid identity than expected for the observed percentage nucleotide identity, this is interpreted as evidence for coding. CRITICA also incorporates noncomparative information derived from the relative frequencies of hexanucleotides in coding frames versus other contexts (i.e., dicodon bias). The dicodon usage information is derived by iterative analysis of the data, such that CRITICA is not dependent on the existence or accuracy of coding sequence annotations in the databases. This independence makes the method particularly well suited for the analysis of novel genomes. CRITICA was tested by analyzing the available Salmonella typhimurium DNA sequences. Its predictions were compared with the DNA sequence annotations and with the predictions of GenMark. CRITICA proved to be more accurate than GenMark, and moreover, many of its predictions that would seem to be errors instead reflect problems in the sequence databases. The source code of CRITICA is freely available by anonymous FTP (rdp.life.uiuc.edu in/pub/critica) and on the World Wide Web (http:/(/)rdpwww.life. uiuc.edu).
Cav1.3 channels control D2-autoreceptor responses via NCS-1 in substantia nigra dopamine neurons

See Borgkvist et al. (doi:10.1093/brain/awu150) for a scientific commentary on this article. D2 autoreceptors and L-type calcium channels are both implicated in Parkinson’s disease, but how they interact is unclear. Dragicevic et al. reveal that L-type calcium channels can modulate D2-autoreceptor responses via the neuronal calcium sensor NCS-1. This dopamine-dependent signalling network is altered in Parkinson’s disease and could represent a therapeutic target.
HLA-F Is a Predominantly Empty, Intracellular, TAP-Associated MHC Class Ib Protein with a Restricted Expression Pattern1

HLA-F is currently the most enigmatic of the human MHC-encoded class Ib genes. We have investigated the expression of HLA-F using a specific Ab raised against a synthetic peptide corresponding to amino acids 61–84 in the α1 domain of the predicted HLA-F protein. HLA-F is expressed as a β2-microglobulin-associated, 42-kDa protein that shows a restricted tissue distribution. To date, we have detected this product only in peripheral blood B cells, B cell lines, and tissues containing B cells, in particular adult tonsil and fetal liver, a major site of B cell development. Thermostability assays suggest that HLA-F is expressed as an empty heterodimer devoid of peptide. Consistent with this, studies using endoglycosidase-H and cell surface immunoprecipitations also indicate that the overwhelming majority of HLA-F contains an immature oligosaccharide component and is expressed inside the cell. We have found that IFN-γ treatment induces expression of HLA-F mRNA and HLA-F protein, but that this does not result in concomitant cell surface expression. HLA-F associates with at least two components of the conventional class I assembly pathway, calreticulin and TAP. The unusual characteristics of the predicted peptide-binding groove together with the predominantly intracellular localization raise the possibility that HLA-F may be capable of binding only a restricted set of peptides.
Automatic Quantification of Myocardial Infarction from Delayed Enhancement MRI

In this study, we developed a novel method for a fullyautomatic segmentation and quantification of myocardialinfarction from delay enhancement - magnetic resonanceimaging. Hyper-enhanced region corresponding to the infarctcore is separated from normal tissue via a Gaussian mixturemodel. Then, peri-infarct area is determined by spatial-weightedfuzzy clustering. No-reflow area inside the infarct core is alsodefined by an improved thresholding. Results of quantificationare presented in per-slice and global volumetric analysis, as wellas in bull's eye of 16-segment model. Promising results have beenachieved in quantification and segmentation of myocardialinfarct. Our software could be used in clinical practice after acomplete validation.
Answering natural language queries on spoken dialogs in meeting discussions

In this article, we focus on the analysis of human-human multimodal dialogue and aim at structuring human discussions in meetings in order to develop a computational environment to answer user natural language queries related to the content of these discussions. We ﬁrst de-ﬁne an annotation schema that describes from an argumentative point of view how segments of human discussions are coherently organized and related to each other to expose the content and the dynamics of the dialogue. Second, we describe a rule-based computational environment to semantically analyze natural language queries in the restricted domain of the meetings argumentative description. Finally, we show results of excerpts of selected queries and we discuss the entailed issues
Improved 3D modeling using multistage feature extraction and matching

Recovering a complete, detailed, accurate and realistic 3D model from images is still a difficult task. This paper proposes an improved 3D modeling using multistage feature extraction and matching. A 3D modeling technique is applied based on the features extracted from images of the same scene taken from different directions. Matching of the images are used to construct the 3D model. Using multistage features extraction and matching algorithms, multiple 3D models can be obtained. This paper will focus on modeling from reality rather than computer graphics creation of artificial world models. Experimental results are illustrated to explore the effectiveness of the proposed method.
Low Energy Physical Activity Recognition System on Smartphones

An innovative approach to physical activity recognition based on the use of discrete variables obtained from accelerometer sensors is presented. The system first performs a discretization process for each variable, which allows efficient recognition of activities performed by users using as little energy as possible. To this end, an innovative discretization and classification technique is presented based on the χ2 distribution. Furthermore, the entire recognition process is executed on the smartphone, which determines not only the activity performed, but also the frequency at which it is carried out. These techniques and the new classification system presented reduce energy consumption caused by the activity monitoring system. The energy saved increases smartphone usage time to more than 27 h without recharging while maintaining accuracy.
Cell Stores

Cell stores provide a relational-like, tabular level of abstraction to business users while leveraging recent database technologies, such as key-value stores and document stores. This allows to scale up and out the efficient storage and retrieval of highly dimensional data. Cells are the primary citizens and exist in different forms, which can be explained with an analogy to the state of matter: as a gas for efficient storage, as a solid for efficient retrieval, and as a liquid for efficient interaction with the business users. Cell stores were abstracted from, and are compatible with the XBRL standard for importing and exporting data. The first cell store repository contains roughly 200GB of SEC filings data, and proves that retrieving data cubes can be performed in real time (the threshold acceptable by a human user being at most a few seconds).
The Transformative Potential of Role-Playing Games—: From Play Skills to Human Skills

Background. Several authors from different fields have already mentioned the educational potential of role-playing games (RPG). As tabletop role-playing games (TRPG) present some similarities with small adult groups in learning and personal development situations, what about their transformative potential? Aim. The purpose of this article is to describe the tabletop role-playing game’s emerging context, a few of its specificities and functions, to show links with several education, play and game, and personal development theories, and to raise awareness about its transformative potential. Methods. Three complementary approaches were used: a literature review (academics and role-players), action-researches through a transformative role-playing game (“TF-RPG” – a TRPG plus a debriefing), and data cross-analysis. Results. Participants are involved in the TF-RPG through four levels of reality, namely the character, player, person, and human being, which can be associated with four dimensions of learning: knowing, doing, being, and relating. The unveiling of links between the TF-RPG experience and their personal journey offers the participants various ways of learning and paths towards personal development. Conclusion. TRPGs are particularly effective to foster knowledge acquisition, develop role-play skills, strengthen team building, encourage collaborative creativity, and explore one’s personal development.
Smart Proxies in LuaOrb : Automatic Adaptation and Monitoring

The LuaOrb project uses reflective features offered by the Lua programming language and by CORBA itself to create a platform that combines simplicity and flexibility in its support for dynamic behavior. In this paper we describe ongoing work on smart proxies. Smart proxies, based on simple QoS descriptions and event-based monitoring facilities, substitute traditional stubs, but automatically react to changes in run-time conditions, The paper also discusses support for dynamic stubs, which can implement generic adaptation procedures or performance-enhanced access to servers. Finally, we discuss an example of dynamic adaptation with smart proxies.
Plan B: a buffered memory model for Java

Recent advances in verification have made it possible to envision trusted implementations of real-world languages. Java with its type-safety and fully specified semantics would appear to be an ideal candidate; yet, the complexity of the translation steps used in production virtual machines have made it a challenging target for verifying compiler technology. One of Java's key innovations, its memory model, poses significant obstacles to such an endeavor. The Java Memory Model is an ambitious attempt at specifying the behavior of multithreaded programs in a portable, hardware agnostic, way. While experts have an intuitive grasp of the properties that the model should enjoy, the specification is complex and not well-suited for integration within a verifying compiler infrastructure. Moreover, the specification is given in an axiomatic style that is distant from the intuitive reordering-based reasonings traditionally used to justify or rule out behaviors, and ill suited to the kind of operational reasoning one would expect to employ in a compiler. This paper takes a step back, and introduces a Buffered Memory Model (BMM) for Java. We choose a pragmatic point in the design space sacrificing generality in favor of a model that is fully characterized in terms of the reorderings it allows, amenable to formal reasoning, and which can be efficiently applied to a specific hardware family, namely x86 multiprocessors. Although the BMM restricts the reorderings compilers are allowed to perform, it serves as the key enabling device to achieving a verification pathway from bytecode to machine instructions. Despite its restrictions, we show that it is backwards compatible with the Java Memory Model and that it does not cripple performance on TSO architectures.
Heritage language learners' perceptions of acquiring and maintaining the Spanish language

ABSTRACT The number of heritage language learners in American universities is increasing each year. Similar to monolingual students, this subgroup of learners is required to complete university level ‘foreign language’ coursework, which can be a source of anxiety. Particularly, this subgroup of learners may experience high levels of anxiety due to their low levels of heritage language proficiency because they may think they should already know their heritage language or they may think others will think they should already know their heritage language. This qualitative study investigated 11 university-level Hispanic heritage speakers’ experiences of acquiring and maintaining the Spanish language. The model developed from the interview sessions illustrates that participants’ perceptions of their Spanish language background impacted their Spanish language experiences. Although participants in this study had various levels of Spanish proficiency, they expressed how their lack of formal educational experiences was the main hindrance to their ability to obtain advanced levels of Spanish language skills.
A Delaunay triangulation based node connection strategy for probabilistic roadmap planners

In this article, we revisit one of the key issues, i.e., node connection, in probabilistic roadmap (PRM) planners, which have been shown effective in high dimensional motion planning problems. We propose a new method of neighborhood selection strategy based on certain empirically observed properties of Delaunay triangulation of a random uniformly distributed point set. Our method allows a node in the network to have neighbors that are close to itself in the sense of Delaunay neighborhood. The algorithm introduced in this article is easy to implement and we show the boost in performance with the idea proposed in some preliminary experiments.
On the solution of data association problem using rollout algorithms

The quality and precision of tracking maneuvering targets under heavy clutter is highly dependent on both the data association and the state estimation algorithms. In this study, measurement-to-track association problem for a single target when PD = 1 is discussed. The problem considers the batch set of measurements in a time interval. An approximate stochastic optimization algorithm for data association is presented. To reduce the computational load, the rollout algorithm is utilized. The algorithm is applied to a tracking scenario and GNN, JPDA and MHT algorithms are compared with their rollout versions. In the comparison several different track quality measures are used to demonstrate the efficiency of the algorithm.
Volatile STT-RAM Scratchpad Design and Data Allocation for Low Energy

On-chip power consumption is one of the fundamental challenges of current technology scaling. Cache memories consume a sizable part of this power, particularly due to leakage energy. STT-RAM is one of several new memory technologies that have been proposed in order to improve power while preserving performance. It features high density and low leakage, but at the expense of write energy and performance. This article explores the use of STT-RAM--based scratchpad memories that trade nonvolatility in exchange for faster and less energetically expensive accesses, making them feasible for on-chip implementation in embedded systems. A novel multiretention scratchpad partitioning is proposed, featuring multiple storage spaces with different retention, energy, and performance characteristics. A customized compiler-based allocation algorithm suitable for use with such a scratchpad organization is described. Our experiments indicate that a multiretention STT-RAM scratchpad can provide energy savings of 53% with respect to an iso-area, hardware-managed SRAM cache.
Fingerprint pre-segmentation method based on Mean Shift Algorithm

In the research or application of fingerprint images, people focus on the region where the ridges are seen. The background region of the whole image contains various degrees of noise caused by unevenly pressing of the fingerprint and the contaminated camera lens. This phenomenon calls for the considerations of noise elimination, image enhancement, and pre-segmentation. Mean Shift algorithm is a nonparametric statistics method that searches for the most approximate mode to the sample distribution. The fingerprint pre-segmentation method based on mean shift algorithm was studied and applied in a number of images stored in FVC2004 image Database. The results show that the Mean Shift method is applicable to most images. There are very few results are out of the optimal ones.
Synthesizing Dynamical Behaviors for Physical Systems Using Phase-Space Method

Desired dynamics for physical systems can be synthesized from the phase-space geometric point of view. We have developed a phase-space method for synthesizing control strategies and implemented the method for a class of nonlinear dynamical systems. In this paper, we examine how the phase-space method can be applied to analyze and synthesize control laws for physical systems that exhibit distinct phases of operation. Examples of such systems includes walking and hopping machines successfully constructed by Marc Raibert and power regulators containing switching elements. We outline how useful control strategies for these systems can be explored in phase space using geometric constraints. The phase-space synthesis relies on knowledge of applied mathematics and control theory and techniques of geometric reasoning. We describe additional components that are necessary for making this approach practical. Background We address the problem of synthesizing desired dynamics for physical systems, using numerical explorations guided by domain knowledge of dynamical systcms and control theory. In the past several years, we have developed ¯ computational techniques for automatically analyzing qualitative behaviors of nonlinear dynamical systems in terms of asymptotic and equivalence classes of behaviors, using a geometric analysis of phase space; ¯ a computational mechanism for automatically synthesizing control laws for nonlinear dynamical systems using a graph of equivalence classes of behaviors. We have successfully applied our qualitative techniques to the design of a maglev controller and to the analysis of a nonlinear power regulator. The phase-space modeling approach for analyzing and synthesizing nonlinear dynamics relies on
Centralized and decentralized balance group optimization in electricity markets with demand response

In this paper, the potential of using Demand Response (DR) to minimize balancing energy costs of Balance Groups (BGs) in electricity markets is investigated. Two algorithms are developed based on direct and price-based control concepts, respectively, to control an aggregated pool of office buildings. The direct control algorithm is set up as a centralized Model Predictive Control (MPC) problem yielding an optimal control sequence. This is used as a benchmark for a decentralized price control scheme, which is suboptimal, but still provides a good performance with much lower communication requirements compared to the benchmark. The two approaches are compared using a case study and conclusions regarding their advantages and disadvantages are drawn based on simulation results. The results show that with proper exploitation of the flexibility of office building aggregations significant balancing cost reductions can be achieved with only limited communication which is, in particular, respecting privacy requirements.
Putative DHHC-Cysteine-Rich Domain S-Acyltransferase in Plants

Protein S-acyltransferases (PATs) containing Asp-His-His-Cys within a Cys-rich domain (DHHC-CRD) are polytopic transmembrane proteins that are found in eukaryotic cells and mediate the S-acylation of target proteins. S-acylation is an important secondary and reversible modification that regulates the membrane association, trafficking and function of target proteins. However, little is known about the characteristics of PATs in plants. Here, we identified 804 PATs from 31 species with complete genomes. The analysis of the phylogenetic relationships suggested that all of the PATs fell into 8 groups. In addition, we analysed the phylogeny, genomic organization, chromosome localisation and expression pattern of PATs in Arabidopsis, Oryza sative, Zea mays and Glycine max. The microarray data revealed that PATs genes were expressed in different tissues and during different life stages. The preferential expression of the ZmPATs in specific tissues and the response of Zea mays to treatments with phytohormones and abiotic stress demonstrated that the PATs play roles in plant growth and development as well as in stress responses. Our data provide a useful reference for the identification and functional analysis of the members of this protein family.
Identifying Predictors of Compulsive Hoarding Tendencies in Young Adult Consumers

Compulsive hoarding becomes a problem when the spaces hoarded items are stored in become unusable due to clutter, health, or safety issues. Our research purpose was to document relationships between two non-normative consumer behaviors (compulsive hoarding, compulsive buying tendency) and two shopping-related variables: hedonic shopping motives and emotional attachment to possessions with everyday consumers. As hedonic shopping motives have been related to compulsive buying, we predicted these motives (e.g., adventure, gratification, role, value, social, and idea) are related to compulsive buying. We also examined the relationship between compulsive buying and compulsive hoarding tendency and whether emotional attachment to possessions moderated this relationship. Participants were 280 undergraduate and graduate students attending a Midwestern university in the U.S. Regression analysis revealed the enhancing emotion motive (a combined motive of adventure and gratification) positively influenced compulsive buying whereas the value motive negatively influenced compulsive buying. All other hedonic shopping motives were non-significant. Participants who tended to buy compulsively were likely to hoard compulsively. This relationship, however, was moderated by participants`` emotional attachment to possessions. Participants with high emotional attachment to possessions showed a higher level of hoarding behavior than those with low emotional attachment to possessions. However, the increase in hoarding tendency among participants with low emotional attachment to possessions was larger between those who were low in compulsive buying and those who were high in compulsive buying than the increase between these two groups among participants with high emotional attachment to possessions.