Miracle-dz/UniFAR-FTUs huggingface dataset:
Transforming Growth Factor β-Independent Shuttling of Smad4 between the Cytoplasm and Nucleus

ABSTRACT Smad4 plays a pivotal role in all transforming growth factor β (TGF-β) signaling pathways. Here we describe six widely expressed alternatively spliced variants of human Smad4 with deletions of different exons in the linker, the region of Smad4 that separates the two well-conserved MH1 and MH2 domains. All these Smad4 variants form complexes with activated Smad2 and Smad3 and are incorporated into DNA-binding complexes with the transcription factor Fast-1, regardless of the amount of linker they contain. However, sequences encoded by exons 5 to 7 in the linker are essential for transcriptional activation. Most importantly, our observation that different Smad4 isoforms have different subcellular localizations has led us to the identification of a functional CRM1-dependent nuclear export signal in the Smad4 linker and a constitutively active nuclear localization signal in the N-terminal MH1 domain. In the absence of TGF-β signaling, we conclude that Smad4 is rapidly and continuously shuttling between the nucleus and the cytoplasm, the distribution of Smad4 between the nucleus and the cytoplasm being dictated by the relative strengths of the nuclear import and export signals. We demonstrate that inhibition of CRM1-mediated nuclear export by treatment of cells with leptomycin B results in endogenous Smad4 accumulating very rapidly in the nucleus. Endogenous Smad2 and Smad3 are completely unaffected by leptomycin B treatment, indicating that the nucleocytoplasmic shuttling is specific for Smad4. We propose that, upon TGF-β signaling, complex formation between Smad4 and activated Smad2 or -3 leads to nuclear accumulation of Smad4 through inhibition of its nuclear export. We demonstrate that after prolonged TGF-β signaling Smad2 becomes dephosphorylated and Smad2 and Smad4 accumulate back in the cytoplasm.
Study on the Optimal QoS Utility Scheduling in Multiple-Plane and Multiple-Stage Switching Network

As Internet grows exponentially, scalable routers are the next step on the backbone with multiple-plane and multiple-stage (MPMS) switching network to provide QoS guarantees. Until now, most scheduling schemes in switching networks are simply designed to guarantee countable priority services, which are not suitable for the QoS requirements of increasing applications, namely voice (VoIP), video (IPTV) and data. Different from traditional strict-priority based scheduling intensively used in industry, in this paper, we try to discuss a novel issue from the objective of optimal QoS utility scheduling (OQUS). We first provide the graphic model of MPMS switching network, and then construct its OQUS model based on utility functions of multiple QoS parameters for different service levels. We proved the necessary and sufficient conditions for the optimal solution to the OQUS model in the MPMS switching network. Lastly, we discussed an effective punishing function solution (PFS) to the OQUS model and provided the iteration steps for the optimal solution to the OQUS model in the MPMS switching network.
Resource Placement in Cloud Computing and Network Applications

In this thesis we address the problem of resource allocation in geographically distributed systems, in particular cloud computing. We consider a large-scale system and regionally distributed demands for various resources of multiple types. The system operator aims at placing the resources across regions to maximize the profit, thus needing to address the problem of how to place the resources in response to the demand. The system is faced with an arbitrary, multi-dimensional, stochastic demand, thus the operator should optimize the profit while taking into account the full demand distributions. Cloud computing and online services, utilizing regional datacenters and facing the problem of where to place various servers, fall under this paradigm. Our problems are developed under a very wide cost model, thus allowing the accommodation of many systems. These problems have various formulations based on the systems they represent. We provide efficient algorithms running in a fast polynomial time1. Our algorithms and solutions can be used for: 1. Exact system optimization; 2. Deriving lower bounds for heuristic based analysis, and; 3. Sensitivity analysis. Our solutions are based on analytic techniques utilizing stochastic analysis as well as graph theory methodologies that can be applied to other optimization/combinatorial problems. 1Polynomial in the number of resources the operator places or reposition. iii
The image torque operator: A new tool for mid-level vision

Contours are a powerful cue for semantic image understanding. Objects and parts of objects in the image are delineated from their surrounding by closed contours which make up their boundary. In this paper we introduce a new bottom-up visual operator to capture the concept of closed contours, which we call the `Torque' operator. Its computation is inspired by the mechanical definition of torque or moment of force, and applied to image edges. The torque operator takes as input edges and computes over regions of different size a measure of how well the edges are aligned to form a closed, convex contour. We explore fundamental properties of this measure and demonstrate that it can be made a useful tool for visual attention, segmentation, and boundary edge detection by verifying its benefits on these applications.
Edge and Texture Fusion for Plant Leaf Classification

Automatic digital plant classification and retrieval can be achieved by extracting features from its leaves. There are ample opportunities to improve plant species identification due to computerization through the designing of a convenient automatic plant recognition system. Many different approaches are used to classify the species of plants based on the plants features. In this research, the plants are classified on the basis of shape and texture. In this paper, it is proposed to extract edge and texture features using Gabor filter and fuse them for image classification. Radial Basis Function is used to measure the classification accuracy of the proposed method.
Power Spectra of Regular Languages and Cellular Automata

The spatial structure of attractors produced by many one­ d ime ns ional cellular automata can be described by regu lar languages. This paper gives simulations and analytical resu lts for the power spec­ tra of such at t ractors. The power spectra are Fourier t ran sfor ms of autocorrelati on functions which ar e expone ntially damped (sometimes with oscillations) . The characteristic length scal e is related to non ­ trivial eigenvalues of th e arc-to-arc transition matrix in the regular langua ge graph.
Novel Suppressive Effects of Ketotifen on Migration and Invasion of MDA-MB-231 and HT-1080 Cancer Cells

The high mortality rates associated with cancer reflect the metastatic spread of tumor cells from the site of their origin. Metastasis, in fact, is the cause of 90% of cancer deaths. Therefore, considerable effort is being made to inhibit metastasis. In the present study, we screened ketotifen for anti-migratory and anti-invasive activities against MDA-MB-231 breast cancer and HT-1080 fibrosarcoma cancer cells. Cancer cell migration and invasion were measured using multi-well chambers. Additionally, western blots were used to examine the effects of ketotifen on the expressions of CDC42, Rho, Rac, and matrix metalloproteinase 9 (MMP-9). The results showed that ketotifen dose-dependently suppressed the migration and invasion of MDA-MB-231 and HT-1080 cells. Ketotifen also suppressed the expressions of CDC42, Rac, and Rho, which, significantly, are involved in MDA-MB-231 and HT-1080 cancer cell migration. Moreover, ketotifen suppressed the expression and activity of MMP-9, which is involved in degradation of the extracellular matrix leading to invasion. The overall data suggested that ketotifen suppresses the migration and invasion of MDA-MB-231 and HT-1080 cancer cells via inhibition of CDC42, Rac, Rho, and MMP-9 expression.
MUSA: A Scalable Multi-touch and Multi-perspective Variability Management Tool

Variability management is one of the main activities in the Software Product Line Engineering process. Common and varied features of related products are modelled along with the dependencies and relationships among them. With the increase in size and complexity of product lines and the more holistic systems approach to the design process, managing the ever-growing variability models has become a challenge. In this paper, we present MUSA, a tool for managing variability and features in large-scale models. MUSA adopts the Separation of Concerns design principle by providing multiple perspectives to the model, each conveying different set of information. The demonstration is conducted using a real-life model (comprising of 1000+ features) particularly showing the Structural View, which is displayed using a mind-mapping visualisation technique (hyperbolic trees), and the Dependency View, which is displayed graphically using logic gates.
Challenges in using system-level models for RTL verification

In modern day digital design flow, high-level models written in C and C++ serve multiple purposes, one of which is to aid verification of register-transfer level (RTL) hardware models. These high-level models, also called system-level models (SLMs), act as reference models for hardware designs created at the RTL level. They define the correct behavior for the RTL hardware design under verification. Written in a programming language (or similar) and therefore executable, they are used extensively in both simulation-based verification and formal equivalence checking. This paper presents how SLMs fit into the different RTL verification schemes and the challenges involved in the various verification flows. Input stimulus generation based on formal verification technology is introduced as a new way to improve simulation coverage. This paper also covers other techniques engineers use to meet various challenges encountered in RTL verification.
Emotion Perception from Dynamic and Static Body Expressions in Point-Light and Full-Light Displays

Research on emotion recognition has been dominated by studies of photographs of facial expressions. A full understanding of emotion perception and its neural substrate will require investigations that employ dynamic displays and means of expression other than the face. Our aims were: (i) to develop a set of dynamic and static whole-body expressions of basic emotions for systematic investigations of clinical populations, and for use in functional-imaging studies; (ii) to assess forced-choice emotion-classification performance with these stimuli relative to the results of previous studies; and (iii) to test the hypotheses that more exaggerated whole-body movements would produce (a) more accurate emotion classification and (b) higher ratings of emotional intensity. Ten actors portrayed 5 emotions (anger, disgust, fear, happiness, and sadness) at 3 levels of exaggeration, with their faces covered. Two identical sets of 150 emotion portrayals (full-light and point-light) were created from the same digital footage, along with corresponding static images of the ‘peak’ of each emotion portrayal. Recognition tasks confirmed previous findings that basic emotions are readily identifiable from body movements, even when static form information is minimised by use of point-light displays, and that full-light and even point-light displays can convey identifiable emotions, though rather less efficiently than dynamic displays. Recognition success differed for individual emotions, corroborating earlier results about the importance of distinguishing differences in movement characteristics for different emotional expressions. The patterns of misclassifications were in keeping with earlier findings on emotional clustering. Exaggeration of body movement (a) enhanced recognition accuracy, especially for the dynamic point-light displays, but notably not for sadness, and (b) produced higher emotional-intensity ratings, regardless of lighting condition, for movies but to a lesser extent for stills, indicating that intensity judgments of body gestures rely more on movement (or form-from-movement) than static form information.
Big-data analytics for Arrhythmia Classification using data compression and kernel methods

Big data analytics is broadly used today in multiple research fields to discover and analyze hidden patterns and other useful information in large databases. Although Cardiac Arrhythmia Classification (CAC) has been studied in depth to date, new CAC methods need to be still designed. In this work, we propose a new big data analytics method for automatic CAC of intracardiac Electrograms (EGMs) stored in Implantable Cardioverter Defibrillators (ICDs). The proposed method combines the effectiveness of a measure based on data compression concepts (Jaccard dictionary similarity), which exploits the information among EGMs, and the classification power of kernel methods. It also requires minimal EGM preprocessing and allows us to deal with EGMs of different duration. A database of 6848 EGMs extracted from a national scientific big data service for ICDs, named SCOOP platform, were used in our experiments. Performance for two classifiers (k-Nearest Neighbors or k-NN, and Support Vector Machines or SVM) were compared in two CAC scenarios using four different input spaces. Results showed that k-NN worked better than SVM when previous episodes from the same patient were available in the classifier design, and vice-versa. For the best cases, k-NN and SVM yielded accuracies near to 95% and 85%, respectively. These results suggest that the proposed method can be used as a high-quality big data service for CAC, providing a support to cardiologists for improving the knowledge on patient diagnosis.
Reframing the mediation debate in Irish all-issues divorce disputes: from mediation vs. litigation to mediation and litigation

Abstract Mediation currently plays a minor role in the Irish family justice system, yet a policy consensus exists that more couples should be encouraged to mediate and that increased rates of mediation will reduce the numbers seeking redress through the courts. The Mediation Act 2017 adopts this position, assuming that the provision of information on mediation will increase uptake and that mediation offers an alternative to litigation for most civil disputes. This article reviews attempts in Ireland, England and Wales to encourage family disputants to mediate, identifying weaknesses in the information strategy. It also examines the legal framework governing all-issues divorce and dissolution in Ireland, pointing to the limited potential for mediation to act as an alternative to litigation. It concludes by arguing that policy focus must shift away from encouraging mediation as an alternative to litigation towards more nuanced understanding of mediation as a support to court based dispute resolution.
Integrating global and local boosting

Several data analysis problems require investigations of relationships between attributes in related heterogeneous databases, where different prediction models can be more appropriate for different regions. A new technique of integrating global and local boosting is proposed. A comparison with other well known and widely used combining methods on standard benchmark datasets has shown that the proposed technique leads to more accurate results.
On Testing Statistics of Harmonic New Better Than Renewal Used in Expectation Class of Life Distributions

The harmonic new better or worse than renewal used in expectation classes of life distributions are considered in this paper. Relations of these classes with other existing classes of life distributions are explored and their closure properties under some reliability operations are studied. Test statistics of exponentiality for these classes are established under random censoring. Percentiles and the power estimates are simulated. The estimated risks of the statistics are calculated and the asymptotic normality of these tests is tested.
An evaluation of student learning in a web-supported unit on plant diversity

This paper reports on a study of the effectiveness of a basic botany unit, which has been enhanced by online support materials. A WebCT site was developed to provide digital access to materials studied in practical sessions. Whereas previously, students only saw practical material once, now they have access to high quality, colour images, to study at their leisure. The online materials were evaluated in the context of the unit of study. The nature of the unit encouraged students to adopt a surface learning approach, but the evaluation process encouraged teaching staff to explore alternative teaching strategies. Students found the online materials very useful, and some students used them for deep approaches to learning. However, the majority of use of the online materials was for ‘cramming’ prior to examinations. The use of the online materials, in this case, reinforced the surface-learning nature of the unit.
Energy Efficient Assignment of Events in Wireless Sensor and Mobile Actor Networks

In this work, we address the problem of optimal assignment of mobile actors in wireless sensor and actor networks (WSANs) to the places of events in real-time with energy and resource constraints. Given the location information about the actors and the places of events, finding the optimal assignment of actors towards the events with the objective of minimizing the overall movement of actors can be shown to be NP-complete. We formulate the problem of assigning the events to the actors in real-time with resource constraints as mixed integer non-linear program (MINLP). We also propose inter and intra zone based localized heuristics for finding the near optimal schedule of the actors to the appropriate events. From the simulations, we observed that the combination of the heuristics, "move to the earliest deadline first" (EDF) and "assign the tasks to the nearest actor" performs fairly better than others by minimizing the overall movement required by the actors and reducing the deadline miss ratio. The simulation of inter-zone deadline based heuristic gives results as close as 7% to the objective value obtained by the MINLP problem
Review of type-logical semantics

One of the many roles of linguistics is to address the semanti cs of natural languages, that is, the meaning of sentences in natural languages. An important part of the meaning of sen tences can be characterized by stating the conditions that need to hold for the sentence to be true. Necessarily, th is approach, called truth-conditional semantics, disregards some relevant aspects of meaning, but has been very use ful in the analysis of natural languages. Structuralist views of language (the kind held by Saussure, for instance, a nd later Chomsky) have typically focused on phonology, morphology, and syntax. Little progress, however, has been shown towards the structure of meaning, or content. A common tool for the study of content, and structure in gener al for that matter, has been logic. During most of the 20th century, an important role of logic has been to stu dy the structure of content of mathematical languages. Many logicians have moved on to apply the techniques develop ed to the analysis of natural languages—Frege, Russell, Carnap, Reichenbach, and Montague. An early introduction t o such classical approaches can be found in [2]. As an illustration of the kind of problems that need to be addr essed, consider the following examples. The following two sentences assert the existence of a man that both walk s and talks:
Relative brain signature: a population-based feature extraction procedure to identify functional biomarkers in the brain of alcoholics

A novel feature extraction technique, Relative‐Brain‐Signature (RBS), which characterizes subjects' relationship to populations with distinctive neuronal activity, is presented. The proposed method transforms a set of Electroencephalography's (EEG) time series in high dimensional space to a space of fewer dimensions by projecting time series onto orthogonal subspaces.
Can the Human Brain Predict the Consequences of Arm Movement Corrections When Transporting an Object? Hints from Grip Force Adjustments

It is well established that motor prediction is crucial for many of our daily actions. However, it is still unclear whether the brain generates motor prediction in real time. To challenge this idea, grip force was monitored while subjects had to transport a hand-held object to a visual target that could move unexpectedly. In agreement with previous reports, subjects triggered fast arm movement corrections to bring the object to the new target location. In addition, we found that subjects initiated grip force adjustments before or in synchrony with arm movement corrections. Throughout the movement, grip force anticipated the mechanical consequences resulting from arm motion, even when it was substantially corrected. Moreover, the predictive control of grip force did not interfere with the on-line control of arm trajectory. Altogether, our results suggest that motor prediction is an automatic, real-time process operating during movement execution and correction.
The loi de position and the acoustics of French mid vowels

This paper reports the results of two experiments on the acoustics of French mid vowels in a variety in which close-mid vowels ([e], [o], [o]) occur in open syllables and open-mid vowels ([ɛ], [oe], [ɔ]) in closed syllables, according to the loi de position . Open-mid allophones have consistently higher F1 realizations and more central F2 realizations than their close-mid counterparts, but are not consistently shorter. These results are problematic for accounts of the loi de position as a pattern of vowel reduction, with mid-vowel lowering and centralizing being caused by shortening. F1 and F2 distances between close-mid and open-mid allophones vary across different prosodic and consonantal contexts and these variations can be analyzed as resulting from duration-based undershoot. More broadly, the results have implications for the typology of closed-syllable vowel laxing: they suggest that tense and lax realizations cannot generally be derived from the same acoustic target via closed-syllable vowel shortening but have distinct acoustic targets.
H∞ estimation for optimization of rational-powered membership functions

Determination of membership functions of a fuzzy logic system to achieve the best performance is of great importance. Constraining the membership functions to a specific shape which is parameterized by a few variables and then applying a parameter optimization method can be a solution. The H∞ filter has been applied to optimize only parameters of triangular membership functions. In this study, the H∞ filter is extended to optimize the input rational-powered membership functions and output singletons. The corresponding equations and derivatives are given and finally the simulation results are shown and compared with Gradient Descent and extended Kalman filtering methods to discuss the efficiency of this approach.
Occluded Face Detection Based on Adaboost Technology

This paper proposes an occluded face detection technology based on the Adaboost algorithm. In this paper, we select moving regions for detection using a background subtraction method. The upper half and lower half parts of human face are detected respectively in moving regions by facial detector which was trained based on Adaboost algorithm and Haar features. Our experimental results indicate the occluded face detection can be built in the Adaboost algorithm for detect human faces in front of ATM machine effectively and efficiently.