Miracle-dz/UniFAR-FTUs huggingface dataset:
Synergistic Activation of p21-activated Kinase 1 by Phosphatidylinositol 4,5-Bisphosphate and Rho GTPases*

Background: p21-activated kinase 1 (Pak1) is activated by Cdc42 as well as phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2). Results: PI(4,5)P2 and Cdc42 both contribute to Pak1 membrane recruitment and synergistically activate Pak1 but not another Cdc42 effector, Ack (activated Cdc42-associated kinase). Conclusion: Pak1 is a coincidence detector regulated by GTPase and PI(4,5)P2 binding. Significance: Coincidence detection may allow for Pak1 activation independently from other Cdc42 effectors. Autoinhibited p21-activated kinase 1 (Pak1) can be activated in vitro by the plasma membrane-bound Rho GTPases Rac1 and Cdc42 as well as by the lipid phosphatidylinositol (4,5)-bisphosphate (PIP2). Activator binding is mediated by a GTPase-binding motif and an adjacent phosphoinositide-binding motif. Whether these two classes of activators play alternative, additive, or synergistic roles in Pak1 activation is unknown, as is their contributions to Pak1 activation in vivo. To address these questions, we developed a system to mimic the membrane anchoring of Rho GTPases by creating liposomes containing both PIP2 and a Ni2+-NTA modified lipid capable of binding hexahistidine-tagged Cdc42. We find that among all biologically relevant phosphoinositides, only PIP2 is able to synergistically activate Pak1 in concert with Cdc42. Membrane binding of the kinase was highly sensitive to the spatial density of PIP2 and Pak1 demonstrated dramatically enhanced affinity for Cdc42 anchored in a PIP2 environment. To validate these findings in vivo, we utilized an inducible recruitment system to drive the ectopic synthesis of PIP2 on Golgi membranes, which normally have active Cdc42 but lack significant concentrations of PIP2. Pak1 was recruited to PIP2-containing membranes in a manner dependent on the ability of Pak1 to bind to both PIP2 and Cdc42. These findings provide a mechanistic explanation for the essential role of both phosphoinositides and GTPases in Pak1 recruitment and activation. In contrast, Ack, another Cdc42 effector kinase that lacks an analogous phosphoinositide-binding motif, fails to show the same enhancement of membrane binding and activation by PIP2, thus indicating that regulation by PIP2 and Cdc42 could provide a combinatorial code for activation of different GTPase effectors in different subcellular locations.
The Levels of the bancal Product, aDrosophila Homologue of Vertebrate hnRNP K Protein, Affect Cell Proliferation and Apoptosis in Imaginal Disc Cells

ABSTRACT We have characterized the Drosophila bancal gene, which encodes a Drosophila homologue of the vertebrate hnRNP K protein. The bancal gene is essential for the correct size of adult appendages. Reduction of appendage size in bancalmutant flies appears to be due mainly to a reduction in the number of cell divisions in the imaginal discs. Transgenes expressingDrosophila or human hnRNP K are able to rescue weakbancal phenotype, showing the functional similarity of these proteins in vivo. High levels of either human orDrosophila hnRNP K protein in imaginal discs induces programmed cell death. Expression of the antiapoptotic P35 protein suppresses this phenotype in the eye, suggesting that apoptosis is the major cellular defect caused by overexpression of K protein. Finally, the human K protein acts as a negative regulator of bancalgene expression. We propose that negative autoregulation limits the level of Bancal protein produced in vivo.
Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves

Genome association analyses have been successful in identifying quantitative trait loci (QTLs) for pig body weights measured at a single age. However, when considering the whole weight trajectories over time in the context of genome association analyses, it is important to look at the markers that affect growth curve parameters. The easiest way to consider them is via the two-step method, in which the growth curve parameters and marker effects are estimated separately, thereby resulting in a reduction of the statistical power and the precision of estimates. One efficient solution is to adopt nonlinear mixed models (NMM), which enables a joint modeling of the individual growth curves and marker effects. Our aim was to propose a genome association analysis for growth curves in pigs based on NMM as well as to compare it with the traditional two-step method. In addition, we also aimed to identify the nearest candidate genes related to significant SNP (single nucleotide polymorphism) markers. The NMM presented a higher number of significant SNPs for adult weight (A) and maturity rate (K), and provided a direct way to test SNP significance simultaneously for both the A and K parameters. Furthermore, all significant SNPs from the two-step method were also reported in the NMM analysis. The ontology of the three candidate genes (SH3BGRL2, MAPK14, and MYL9) derived from significant SNPs (simultaneously affecting A and K) allows us to make inferences with regards to their contribution to the pig growth process in the population studied.
Monitoring 3D movements for the rehabilitation of joints in physiotherapy

This article tackles several problems faced by professionals in physiotherapy: the performance of the rehabilitation exercises by the patients, the control of the course of the illness and the patient's ignorance about whether or not he is properly performing the exercises. We propose a solution based on the use of the WiiTM Controller to control the exercise movements, along with software that provides the patient with an easy, intuitive and interactive control system. Finally, web services are used to allow the remote monitoring of the treatment by physiotherapy professionals.
Accelerating Nearest Neighbor Search on Manycore Systems

We develop methods for accelerating metric similarity search that are effective on modern hardware. Our algorithms factor into easily parallelizable components, making them simple to deploy and efficient on multicore CPUs and GPUs. Despite the simple structure of our algorithms, their search performance is provably sub linear in the size of the database, with a factor dependent only on its intrinsic dimensionality. We demonstrate that our methods provide substantial speedups on a range of datasets and hardware platforms. In particular, we present results on a 48-core server machine, on graphics hardware, and on a multicore desktop.
Determining the semantic orientation of terms through gloss classification

Sentiment classification is a recent subdiscipline of text classification which is concerned not with the topic a document is about, but with the opinion it expresses. It has a rich set of applications, ranging from tracking users' opinions about products or about political candidates as expressed in online forums, to customer relationship management. Functional to the extraction of opinions from text is the determination of the orientation of ``subjective'' terms contained in text, i.e. the determination of whether a term that carries opinionated content has a positive or a negative connotation. In this paper we present a new method for determining the orientation of subjective terms. The method is based on the quantitative analysis of the glosses of such terms, i.e. the definitions that these terms are given in on-line dictionaries, and on the use of the resulting term representations for semi-supervised term classification. The method we present outperforms all known methods when tested on the recognized standard benchmarks for this task.
Improved Moves for Truncated Convex Models

We consider the problem of obtaining an approximate maximum a posteriori estimate of a discrete random field characterized by pairwise potentials that form a truncated convex model. For this problem, we propose two st-MINCUT based move making algorithms that we call Range Swap and Range Expansion. Our algorithms can be thought of as extensions of αβ-Swap and α-Expansion respectively that fully exploit the form of the pairwise potentials. Specifically, instead of dealing with one or two labels at each iteration, our methods explore a large search space by considering a range of labels (that is, an interval of consecutive labels). Furthermore, we show that Range Expansion provides the same multiplicative bounds as the standard linear programming (LP) relaxation in polynomial time. Compared to previous approaches based on the LP relaxation, for example interior-point algorithms or tree-reweighted message passing (TRW), our methods are faster as they use only the efficient st-MINCUT algorithm in their design. We demonstrate the usefulness of the proposed approaches on both synthetic and standard real data problems.
Nucleic‐Acid‐Templated Enzyme Cascades

Cellular metabolism involves complex sequences of organized enzymatic reactions, known as metabolic pathways, that convert substrates into readily usable materials. In nature, these enzymatic complexes are organized in a well‐defined manner so that the cascade reactions are more rapid and efficient than they would be if the enzymes were randomly distributed in the cytosol. Development of artificial enzyme cascades that resemble nature's organization of sequentially assembled enzymes is of current interest due to its potential applications, from diagnostics to the production of high‐value chemicals. Nucleic acids and their nanostructures have been used to organize enzyme cascades and have been shown to enhance the efficiencies and rates of sequential reactions. Here we summarize the recent progress in the development of artificial enzyme cascades and sequential reactions by arranging enzymes on various DNA/RNA templates and discuss the future directions of this research endeavour.
A Clustering Topology for Wireless Sensor Networks - New Semantics over Network Topology

Sensor networks are a primary source of massive amounts of data about the real world that surrounds us, measuring a wide range of physical parameters in real time. Given the hardware limitations and physical environment in which the sensors must operate, along with frequent changes of network topology, algorithms and protocols must be designed to provide a robust and energy efficient communications mechanism. With a view to addressing these constraints, this paper proposes a routing technique that is based on density based spatial clustering of applications with noise (DBSCAN) algorithm. This technique reveals several network topology semantics, enables the splitting of sensors responsibilities (communication/routing and sensing/monitoring), reduces the level of energy wasted on sending messages through the network by data aggregation only in cluster-head nodes and last but not the least, brings along very good results prolonging the network lifetime.
Functional analysis of the mRNA profile of neutrophil gelatinase-associated lipocalin overexpression in esophageal squamous cell carcinoma using multiple bioinformatic tools

Neutrophil gelatinase-associated lipocalin (NGAL) is a member of the lipocalin superfamily; dysregulated expression of NGAL has been observed in several benign and malignant diseases. In the present study, differentially expressed genes, in comparison with those of control cells, in the mRNA expression profile of EC109 esophageal squamous cell carcinoma (ESCC) cells following NGAL overexpression were analyzed by multiple bioinformatic tools for a comprehensive understanding. A total of 29 gene ontology (GO) terms associated with immune function, chromatin structure and gene transcription were identified among the differentially expressed genes (DEGs) in NGAL overexpressing cells. In addition to the detected GO categories, the results from the functional annotation chart revealed that the differentially expressed genes were also associated with 101 functional annotation category terms. A total of 59 subpathways associated locally with the differentially expressed genes were identified by subpathway analysis, a markedly greater total that detected by traditional pathway enrichment analysis only. Promoter analysis indicated that the potential transcription factors Snail, deltaEF1, Mycn, Arnt, MNB1A, PBF, E74A, Ubx, SPI1 and GATA2 were unique to the downregulated DEG promoters, while bZIP910, ZNF42 and SOX9 were unique for the upregulated DEG promoters. In conclusion, the understanding of the role of NGAL overexpression in ESCC has been improved through the present bioinformatic analysis.
Visual Analysis of Topic Competition on Social Media

How do various topics compete for public attention when they are spreading on social media? What roles do opinion leaders play in the rise and fall of competitiveness of various topics? In this study, we propose an expanded topic competition model to characterize the competition for public attention on multiple topics promoted by various opinion leaders on social media. To allow an intuitive understanding of the estimated measures, we present a timeline visualization through a metaphoric interpretation of the results. The visual design features both topical and social aspects of the information diffusion process by compositing ThemeRiver with storyline style visualization. ThemeRiver shows the increase and decrease of competitiveness of each topic. Opinion leaders are drawn as threads that converge or diverge with regard to their roles in influencing the public agenda change over time. To validate the effectiveness of the visual analysis techniques, we report the insights gained on two collections of Tweets: the 2012 United States presidential election and the Occupy Wall Street movement.
SAND: Semi-Supervised Adaptive Novel Class Detection and Classification over Data Stream

Most approaches to classifying data streams either divide the stream into fixed-size chunks or use gradual forgetting. Due to evolving nature of data streams, finding a proper size or choosing a forgetting rate without prior knowledge about time-scale of change is not a trivial task. These approaches hence suffer from a trade-off between performance and sensitivity. Existing dynamic sliding window based approaches address this problem by tracking changes in classifier error rate, but are supervised in nature. We propose an efficient semi-supervised framework in this paper which uses change detection on classifier confidence to detect concept drifts, and to determine chunk boundaries dynamically. It also addresses concept evolution problem by detecting outliers having strong cohesion among themselves. Experiment results on benchmark and synthetic data sets show effectiveness of the proposed approach.
Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion

Low-rank tensor estimation has been frequently applied in many real-world problems. Despite successful applications, existing Schatten 1-norm minimization (SNM) methods may become very slow or even not applicable for large-scale problems. To address this difficulty, we therefore propose an efficient and scalable core tensor Schatten 1-norm minimization method for simultaneous tensor decomposition and completion, with a much lower computational complexity. We first induce the equivalence relation of Schatten 1-norm of a low-rank tensor and its core tensor. Then the Schatten 1-norm of the core tensor is used to replace that of the whole tensor, which leads to a much smaller-scale matrix SNM problem. Finally, an efficient algorithm with a rank-increasing scheme is developed to solve the proposed problem with a convergence guarantee. Extensive experimental results show that our method is usually more accurate than the state-of-the-art methods, and is orders of magnitude faster.
Hybrid similarities for dynamic interaction recommendation

Similarity metric is the core of k-nearest neighbor collaborative filtering in recommender systems. However, traditional metrics measure the similarity among neighbors only in either direction or distance. In this paper, we propose a triangle similarity metric and two kinds of hybrid ones based on it for dynamic interaction recommendation. First, the triangle similarity metric combines both direction and distance. Second, two kinds of hybrid similarity metrics are designed to improve recommendation quality. The first hybrid one adds up the triangle, cosine and jaccard similarities, while the second one multiplies them. Third, we apply the hybrid similarity metrics to a dynamic user-recommender interaction system. Experimental results on the well-known MovieLens dataset indicate that the additive hybrid similarity outperforms traditional similarities on the Recall measure.
Algorithmic Principles for Building Efficient Overlay Computers

Preface The recent explosive growth of the Internet gives rise to the possibility of a global computer of grand-scale consisting of Internet-connected computing entities (possibly mobile, with varying computational capabilities, connected among them with different communication media), globally available and able to provide to its users a rich menu of high-level integrated services that make use of its aggregated computational power, storage space, and information resources. Achieving this efficiently and transparently is a major challenge that can be overcome by introducing an intermediate layer, the overlay computer. The goal of AEOLUS is to investigate the principles and develop the algorithmic methods for building such an overlay computer that enables this efficient and transparent access to the resources of an Internet-based global computer. In particular, the main objectives of AEOLUS are: To identify and study the important fundamental problems and investigate the corresponding algorithmic principles related to overlay computers running on global computers. • To identify the important functionalities such an overlay computer should provide as tools to the programmer, and to develop, rigorously analyze, and experimentally validate algorithmic methods that can make these functionalities efficient, scalable, fault-tolerant, and transparent to heterogeneity. • To provide improved methods for communication and computing among wireless and possibly mobile nodes so that they can transparently become part of larger Internet-based overlay computers. • To implement a set of functionalities, integrate them under a common software platform in order to provide the basic primitives of an overlay computer, as well as build sample services on this overlay computer, thus providing a proof-of-concept for our theoretical results. In order to deal with the complexity of the objectives, the research work within AE-OLUS is divided in several vertical and horizontal components. Horizontal components contain work either on the investigation of general fundamental aspects or on the development of the overlay computer. Vertical components focus on specific areas which are important for the realization of the overlay computer. These components are the following: 1. A horizontal component addressing fundamental issues of global computing with special focus on efficiency, transparency and scalability. i 2. Four vertical components addressing the issues of resource management, sharing information and computation, security and trust management and transparent extensions of overlay computers to include wireless devices, respectively. 3. A horizontal component devoted to the implementation of basic functionalities under a common software platform (the Overlay Computing Platform) and the development of an application on …
Functional hypergraph uncovers novel covariant structures over neurodevelopment

Brain development during adolescence is marked by substantial changes in brain structure and function, leading to a stable network topology in adulthood. However, most prior work has examined the data through the lens of brain areas connected to one another in large‐scale functional networks. Here, we apply a recently developed hypergraph approach that treats network connections (edges) rather than brain regions as the unit of interest, allowing us to describe functional network topology from a fundamentally different perspective. Capitalizing on a sample of 780 youth imaged as part of the Philadelphia Neurodevelopmental Cohort, this hypergraph representation of resting‐state functional MRI data reveals three distinct classes of subnetworks (hyperedges): clusters, bridges, and stars, which respectively represent homogeneously connected, bipartite, and focal architectures. Cluster hyperedges show a strong resemblance to previously‐described functional modules of the brain including somatomotor, visual, default mode, and salience systems. In contrast, star hyperedges represent highly localized subnetworks centered on a small set of regions, and are distributed across the entire cortex. Finally, bridge hyperedges link clusters and stars in a core–periphery organization. Notably, developmental changes within hyperedges are ordered in a similar core–periphery fashion, with the greatest developmental effects occurring in networked hyperedges within the functional core. Taken together, these results reveal a novel decomposition of the network organization of human brain, and further provide a new perspective on the role of local structures that emerge across neurodevelopment. Hum Brain Mapp 38:3823–3835, 2017. © 2017 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.
Children With Developmental Coordination Disorder Play Active Virtual Reality Games Differently Than Children With Typical Development

Background Active virtual reality gaming (AVG) may be useful for children with developmental coordination disorder (DCD) to practice motor skills if their movement patterns are of good quality while engaged in AVG. Objective This study aimed to examine: (1) the quality of motor patterns of children with DCD participating in AVG by comparing them with children with typical development (TD) and (2) whether differences existed in the motor patterns utilized with 2 AVG types: Sony PlayStation 3 Move and Microsoft Xbox 360 Kinect. Design This was a quasi-experimental, biomechanical laboratory–based study. Methods Twenty-one children with DCD, aged 10 to 12 years, and 19 age- and sex-matched children with TD played a match of table tennis on each AVG type. Hand path, wrist angle, and elbow angle were recorded using a motion analysis system. Linear mixed-model analyses were used to determine differences between DCD and TD groups and Move and Kinect AVG type for forehands and backhands. Results Children with DCD utilized a slower hand path speed (backhand mean difference [MD]=1.20 m/s; 95% confidence interval [95% CI]=0.41, 1.98); greater wrist extension (forehand MD=34.3°; 95% CI=22.6, 47.0); and greater elbow flexion (forehand MD=22.3°; 95% CI=7.4, 37.1) compared with children with TD when engaged in AVG. There also were differences in movement patterns utilized between AVG types. Limitations Only simple kinematic measures were compared, and no data regarding movement outcome were assessed. Conclusions If a therapeutic treatment goal is to promote movement quality in children with DCD, clinical judgment is required to select the most appropriate AVG type and determine whether movement quality is adequate for unsupervised practice.
On passivity-based sliding mode control of switched DC-to-DC power converters

In this paper we propose to incorporate, into the sliding mode controller design for the regulation of DC-to-DC power converters of the "boost" type, the energy dissipation and passivity properties of the switch-regulated system. It is announced that for the "traditional" sliding mode controller, the integral of the stored energy is infinite while for a mixed passivity-based sliding mode controller the same index becomes finite.