Miracle-dz/UniFAR-FTUs huggingface dataset:
Phosphorylation of Synaptic GTPase-activating Protein (synGAP) by Ca2+/Calmodulin-dependent Protein Kinase II (CaMKII) and Cyclin-dependent Kinase 5 (CDK5) Alters the Ratio of Its GAP Activity toward Ras and Rap GTPases*

Background: synGAP inactivates Ras and Rap at synapses. Results: Phosphorylation of synGAP by CaMKII increases Rap1 GAP activity more than HRas GAP activity; phosphorylation by CDK5 has the opposite effect. Conclusion: Phosphorylation by CaMKII and CDK5 alters the ratio of Rap1 and HRas GAP activities. Significance: Phosphorylation of synGAP by CaMKII and CDK5 can alter the balance of synaptic functions regulated by Ras and Rap. synGAP is a neuron-specific Ras and Rap GTPase-activating protein (GAP) found in high concentrations in the postsynaptic density (PSD) fraction from the mammalian forebrain. We have previously shown that, in situ in the PSD fraction or in recombinant form in Sf9 cell membranes, synGAP is phosphorylated by Ca2+/calmodulin-dependent protein kinase II (CaMKII), another prominent component of the PSD. Here, we show that recombinant synGAP (r-synGAP), lacking 102 residues at the N terminus, can be purified in soluble form and is phosphorylated by cyclin-dependent kinase 5 (CDK5) as well as by CaMKII. Phosphorylation of r-synGAP by CaMKII increases its HRas GAP activity by 25% and its Rap1 GAP activity by 76%. Conversely, phosphorylation by CDK5 increases r-synGAP's HRas GAP activity by 98% and its Rap1 GAP activity by 20%. Thus, phosphorylation by both kinases increases synGAP activity; CaMKII shifts the relative GAP activity toward inactivation of Rap1, and CDK5 shifts the relative activity toward inactivation of HRas. GAP activity toward Rap2 is not altered by phosphorylation by either kinase. CDK5 phosphorylates synGAP primarily at two sites, Ser-773 and Ser-802. Phosphorylation at Ser-773 inhibits r-synGAP activity, and phosphorylation at Ser-802 increases it. However, the net effect of concurrent phosphorylation of both sites, Ser-773 and Ser-802, is an increase in GAP activity. synGAP is phosphorylated at Ser-773 and Ser-802 in the PSD fraction, and its phosphorylation by CDK5 and CaMKII is differentially regulated by activation of NMDA-type glutamate receptors in cultured neurons.
HGF–MET Cascade, a Key Target for Inhibiting Cancer Metastasis: The Impact of NK4 Discovery on Cancer Biology and Therapeutics

Hepatocyte growth factor (HGF) was discovered in 1984 as a mitogen of rat hepatocytes in a primary culture system. In the mid-1980s, MET was identified as an oncogenic mutant protein that induces malignant phenotypes in a human cell line. In the early 1990s, wild-type MET was shown to be a functional receptor of HGF. Indeed, HGF exerts multiple functions, such as proliferation, morphogenesis and anti-apoptosis, in various cells via MET tyrosine kinase phosphorylation. During the past 20 years, we have accumulated evidence that HGF is an essential conductor for embryogenesis and tissue regeneration in various types of organs. Furthermore, we found in the mid-1990s that stroma-derived HGF is a major contributor to cancer invasion at least in vitro. Based on this background, we prepared NK4 as an antagonist of HGF: NK4 inhibits HGF-mediated MET tyrosine phosphorylation by competing with HGF for binding to MET. In vivo, NK4 treatments produced the anti-tumor outcomes in mice bearing distinct types of malignant cancers, associated with the loss in MET activation. There are now numerous reports showing that HGF-antagonists and MET-inhibitors are logical for inhibiting tumor growth and metastasis. Additionally, NK4 exerts anti-angiogenic effects, partly through perlecan-dependent cascades. This paper focuses on the chronology and significance of HGF-antagonisms in anti-tumor researches, with an interest in NK4 discovery. Tumor HGF–MET axis is now critical for drug resistance and cancer stem cell maintenance. Thus, oncologists cannot ignore this cascade for the future success of anti-metastatic therapy.
Large-Eddy Simulations of Longitudinal Vortices Embedded in a Turbulent Boundary Layer Donghyun You

Large-eddy simulations of pairs of artificially generated longitudinal vortices embedded in a two-dimensional turbulent boundary layer are performed to study the dynamics of the vortical structures and to provide the unsteady inflow data for use in a future turbomachinery simulation. An immersed boundarymethod is employed to represent the wall-mounted half-delta wings that generate the counter-rotating vortex pairs. Two vortex pair configurations, with “common flow” between the vortices toward the end wall (common flow down) and away from the end wall (common flow up), respectively, are investigated. Mean velocities and Reynolds stresses compare well with experimental data, demonstrating the accuracy of the numerical approach and, in particular, the efficacy of immersed boundary treatment of the vortex generators. The large coherent vortices are characterized by streamwise velocity defect and negativemean pressure. The boundary layer in the common flow region is thinned by the vortices in the common-flow-down case and thickened by them in the common-flow-up case.
AN APPLICATION OF THE FELLEGI-SUNTER MODEL OF RECORD LINKAGE TO THE 1990 U.S. DECENNIAL CENSUS

This paper describes a methodology for computer matching the Post Enumeration Survey with the Census. Computer matching is the first stage of a process for producing adjusted Census counts. All crucial matching parameters are computed solely using characteristics of the files being matched. No a priori knowledge of truth of matches is assumed. No previously created lookup tables are needed. The methods are illustrated with numerical results using files from the 1988 Dress Rehearsal Census for which the truth of matches is known.
The role of non-formal contexts in teacher education for STEM: the case of horno3 science and technology interactive centre

Abstract Teacher education can benefit directly from experiences in non-formal settings. This article presents a research study with elementary teachers who were teaching in public schools in the state of Nuevo León, México, and participated in a STEM Continuous Professional Development (CPD) workshop. The workshop provided a platform for teachers to interact with scientists and disseminators of science, allowing the appropriation of scientific knowledge applied to everyday activities and settings. Participants improved the quality of their teaching practices in classrooms and gained a new understanding of STEM subjects, enabling them to promote inspiring learning experiences with their students, where dialogue, experimentation and elucidation became an important part of their lessons. The study was carried out using ethnographic tools for analysing recorded videos, 15 sets of field notes, and 49 questionnaires. The sequential analysis of talk and gestures in their participation in the CPD workshop demonstrated high levels of involvement, creativity, and collaborative solution of STEM problems.
Filtering Motion Capture Data for Real-Time Applications

In this paper we present some custom designed filters for real-time motion capture applications. Our target application is motion controllers, i.e. systems that interpret hand motion for musical interaction. In earlier research we found effective methods to design nearly optimal filters for realtime applications. However, to be able to design suitable filters for our target application, it is necessary to establish the typical frequency content of the motion capture data we want to filter. This will again allow us to determine a reasonable cutoff frequency for the filters. We have therefore conducted an experiment in which we recorded the hand motion of 20 subjects. The frequency spectra of these data together with a method similar to the residual analysis method were then used to determine reasonable cutoff frequencies. Based on this experiment, we propose three cutoff frequencies for different scenarios and filtering needs: 5, 10 and 15 Hz, which correspond to heavy, medium and light filtering, respectively. Finally, we propose a range of real-time filters applicable to motion controllers. In particular, low-pass filters and low-pass differentiators of degrees one and two, which in our experience are the most useful filters for our target application.
A combined immersive and desktop authoring tool for virtual environments

While frameworks and application programming interfaces for virtual reality are commonplace today, designing scenarios for virtual environments still remains a tedious and time consuming task. We present a new authoring tool which combines scene assembly and visual programming in a desktop application with instant testing, tuning and planning in an immersive virtual environment. Two authors can work together, one with the desktop authoring application and the other in the immersive VR-simulation, to build a complete scenario.
E. coli Nissle 1917 Affects Salmonella Adhesion to Porcine Intestinal Epithelial Cells

Background The probiotic Escherichia coli strain Nissle 1917 (EcN) has been shown to interfere in a human in vitro model with the invasion of several bacterial pathogens into epithelial cells, but the underlying molecular mechanisms are not known. Methodology/Principal Findings In this study, we investigated the inhibitory effects of EcN on Salmonella Typhimurium invasion of porcine intestinal epithelial cells, focusing on EcN effects on the various stages of Salmonella infection including intracellular and extracellular Salmonella growth rates, virulence gene regulation, and adhesion. We show that EcN affects the initial Salmonella invasion steps by modulating Salmonella virulence gene regulation and Salmonella SiiE-mediated adhesion, but not extra- and intracellular Salmonella growth. However, the inhibitory activity of EcN against Salmonella invasion always correlated with EcN adhesion capacities. EcN mutants defective in the expression of F1C fimbriae and flagellae were less adherent and less inhibitory toward Salmonella invasion. Another E. coli strain expressing F1C fimbriae was also adherent to IPEC-J2 cells, and was similarly inhibitory against Salmonella invasion like EcN. Conclusions We propose that EcN affects Salmonella adhesion through secretory components. This mechanism appears to be common to many E. coli strains, with strong adherence being a prerequisite for an effective reduction of SiiE-mediated Salmonella adhesion.
Physical motion analysis system in driving using image processing

This paper proposes an optical motion capture system for physical motion analysis in driving. Markers for motion capture consist of two high intensity LEDs, one for position measurement and the other for pilot signal. The pilot marker flashes synchronized with the periodic pulse, which is generated by multiplying the UTC second (1PPS) obtained by the GPS. Video cameras are also triggered with the pulse and thus the proposed system can distinguish the marker every one second in the automobile in the daytime. This copes with problems in optical motion capture system, occlusions and swappings.
Classical and neural models for binocular stereoscopic reconstruction

We present a comparative study about stereoscopic reconstruction process focused in modelling for parallel axis stereoscopic cameras. We used two classical models and one based on Artificial Neural Networks for modelling the parallel axis system. Then we used the root mean square of distances between the point coordinates calculated from images and measured from the calibration pattern to evaluate the accuracy of each model. We compared the accuracy of two classical models and one based on Artificial Neural Networks. By comparing the confidence interval for every obtained model we observed that the classical model of Silven and Heikkila [1] shows average errors of 1.0 cm, however this error was reduced to 0.4 cm by an adjustment proposed in this paper. On the other hand the neural networks showed less robust to the training set. The current work can be extended to future developments in areas like photogrammetry, architecture and robotics.
‘Catholic schooling with a twist?’: a study of faith schooling in the Republic of Ireland during a period of detraditionalisation

ABSTRACT The role of faith-based schools is increasingly debated within a context of school reform, rights and plurality in multi-ethnic societies. The Catholic schooling system in the Irish Republic (always referred to as Ireland in the text) represents an interesting case internationally because of the extent to which Catholic education is structurally embedded as normative across the education system. Yet, Ireland is in a process of detraditionalisation and wider societal change. Drawing on Bourdieu and Bernstein, and a mixed methodological study of Catholic secondary schools, the article presents a typology of Catholic schooling in transition. This identifies a continuum of Catholicity among the study schools that is mediated by dynamics of social class in an increasingly competitive and diverse system. It is argued this has implications for considering the role of a recontextualised model of Catholic faith schooling, underpinned by principles of social justice in a multicultural and more secularly oriented society.
Bag-of-Visual Words for word-wise video script identification: A study

Use of multiple scripts for information communication through various media is quite common in a multilingual country. Optical character recognition of such document images or videos assists in indexing them for effective information retrieval. Hence, script identification from multi-lingual documents/images is a necessary step for selecting the appropriate OCR, due the absence of a single OCR system capable of handling multiple scripts. Script identification from printed as well as handwritten documents is a well-researched area, but script identification from video frames has not been explored much. Low resolution, blur, noisy background, to mention a few are the major bottle necks when processing video frames, and makes script identification from video images a challenging task. This paper examines the potential of Bag-of-Visual Words based techniques for word-wise script identification from video frames. Two different approaches namely, Bag-Of-Features (BoF) and Spatial Pyramid Matching (SPM), using patch based SIFT descriptors were considered for the current study. SVM Classifier was used for analysing the three popular south Indian scripts, namely Tamil, Telugu and Kannada in combination with English and Hindi. A comparative study of Bag-of-Visual words with traditional script identification techniques involving gradient based features (e.g. HoG) and texture based features (e.g. LBP) is presented. Experimental results shows that patch-based features along with SPM outperformed the traditional techniques and promising accuracies were achieved on 2534 words from the five scripts. The study reveals that patch-based feature can be used for scripts identification in-order to overcome the inherent problems with video frames.
Responsiveness to Infant Cues in Postpartum Depressed and Non-Depressed Mothers: Functional Neural Correlates and Their Relation to Behaviour

Postpartum depression (PPD) is the most common maternal birth complication, affecting approximately 5-20% of women. PPD disrupts the normal repertoire of parenting abilities and is thought to have long-term consequences for the developing infant, even after the mothers’ depression remits. Despite its prevalence and potentially enduring effects, we understand very little about the neural correlates of PPD. Recent evidence suggests that postpartum depression (PPD) is associated with reduced amygdala (AMY) response to negative stimuli. However, given the anhedonic features of PPD, it is also important to consider the brain response of depressed mothers specifically to positive infant stimuli and to other positively valenced stimuli. In the current work, mothers with clinically determined PPD (n=31) and without PPD (n=23) viewed smiling pictures of infants (both their own and other), and positive non-infant stimuli during an fMRI. Four studies were completed which examined: 1) multidimensional measures such as current parenting stress, early experiences (e.g., being parented themselves as well as experiences of trauma), current mood and anxiety levels, and relationship satisfaction to better characterize our sample of mothers with and without PPD, 2) PPD and Non-PPD mothers’ response to infants and other positive non-infant stimuli, using AMY region-of-interest as well as amygdala connectivity analyses, 3) subregion-level differences in AMY response and connectivity during the viewing of infant and non-infant stimuli 4) whether and/or how behavioural measures of maternal sensitivity and responsiveness (quality of mother-infant interaction) relate to brain response in the AMY as a whole as well as at the subregion level. iii Knowing how a depressed mother’s brain responds to her own and other infants is critical as many of the symptoms of PPD focus on the mother-infant dyad and involve a subjective evaluation of one’s parenting abilities. This work adds to the existing literature that examines the brain response of mothers with PPD to various stimuli. Furthermore, it has the long-term potential to inform treatment and intervention programs designed for PPD.
Modified differential evolution algorithm for parameter estimation in mathematical models

The parameter estimation or identification problem, which frequently arises, while developing the mathematical models, may be formulated as a nonlinear global optimization problem. Here the objective is to find the set of parameters to minimize the function quantifying the goodness of the fit subject to the system dynamics. The mathematical model of the problem is often multimodal in nature and requires a suitable global optimization method for its solution. In the present study we show the application of a Modified Differential Evolution (MDE) for solving parameter estimation problem. We have considered two test cases. A comparison of numerical results with other algorithms shows the competence of MDE over basic DE and other methods.
Decoding Algorithms for Complex Natural Language Tasks by Pawan Deshpande

This thesis focuses on developing decoding techniques for complex Natural Language Processing (NLP) tasks. The goal of decoding is to find an optimal or near optimal solution given a model that defines the goodness of a candidate. The task is challenging because in a typical problem the search space is large, and the dependencies between elements of the solution are complex. The goal of this work is two-fold. First, we are interested in developing decoding techniques with strong theoretical guarantees. We develop a decoding model based on the Integer Linear Programming paradigm which is guaranteed to compute the optimal solution and is capable of accounting for a wide range of global constraints. As an alternative, we also present a novel randomized algorithm which can guarantee an arbitrarily high probability of finding the optimal solution. We apply these methods to the task of constructing temporal graphs and to the task of title generation. Second, we are interested in carefully investigating the relations between learning and decoding. We build on the Perceptron framework to integrate the learning and decoding procedures into a single unified process. We use the resulting model to automatically generate tables-of-contents, structures with deep hierarchies and rich contextual dependencies. In all three natural language tasks, our experimental results demonstrate that theoretically grounded and stronger decoding strategies perform better than existing methods. As a final contribution, we have made the source code for these algorithms publicly available for the NLP research community. Thesis Supervisor: Regina Barzilay Title: Assistant Professor
Left Prefrontal Activity Reflects the Ability of Vicarious Fear Learning: A Functional Near-Infrared Spectroscopy Study

Fear could be acquired indirectly via social observation. However, it remains unclear which cortical substrate activities are involved in vicarious fear transmission. The present study was to examine empathy-related processes during fear learning by-proxy and to examine the activation of prefrontal cortex by using functional near-infrared spectroscopy. We simultaneously measured participants' hemodynamic responses and skin conductance responses when they were exposed to a movie. In this movie, a demonstrator (i.e., another human being) was receiving a classical fear conditioning. A neutral colored square paired with shocks (CSshock) and another colored square paired with no shocks (CSno-shock) were randomly presented in front of the demonstrator. Results showed that increased concentration of oxygenated hemoglobin in left prefrontal cortex was observed when participants watched a demonstrator seeing CSshock compared with that exposed to CSno-shock. In addition, enhanced skin conductance responses showing a demonstrator's aversive experience during learning object-fear association were observed. The present study suggests that left prefrontal cortex, which may reflect speculation of others' mental state, is associated with social fear transmission.
A random variance model for detection of differential gene expression in small microarray experiments

MOTIVATION Microarray techniques provide a valuable way of characterizing the molecular nature of disease. Unfortunately expense and limited specimen availability often lead to studies with small sample sizes. This makes accurate estimation of variability difficult, since variance estimates made on a gene by gene basis will have few degrees of freedom, and the assumption that all genes share equal variance is unlikely to be true. RESULTS We propose a model by which the within gene variances are drawn from an inverse gamma distribution, whose parameters are estimated across all genes. This results in a test statistic that is a minor variation of those used in standard linear models. We demonstrate that the model assumptions are valid on experimental data, and that the model has more power than standard tests to pick up large changes in expression, while not increasing the rate of false positives. AVAILABILITY This method is incorporated into BRB-ArrayTools version 3.0 (http://linus.nci.nih.gov/BRB-ArrayTools.html). SUPPLEMENTARY MATERIAL ftp://linus.nci. nih.gov/pub/techreport/RVM_supplement. pdf
Spectral Efficiency and Fairness for Opportunistic Round Robin Scheduling

In this paper we analyze the Opportunistic Round Robin (ORR) scheduling algorithm [1], [2]. This algorithm is able to exploit multiuser diversity (MUD) and at the same time provide short-term fairness between the users in a wireless cellular network. We analyze the spectral efficiency and the fairness for two scenarios. In the first scenario all the users have the same average carrier-to-noise ratios (CNR) and for the second scenario the users have different average CNRs. For the first scenario the absolute MUD is exploited while we exploit the so-called relative MUD between the users for the second scenario. The advantages and disadvantages of the different ORR implementations are highlighted by analyzing plots of our closed-form expressions.
A Nonconformity Approach to Model Selection for SVMs

We investigate the issue of model selection and the use of the nonconformity (strangeness) measure in batch learning. Using the nonconformity measure we propose a new training algorithm that helps avoid the need for Cross-Validation or Leave-One-Out model selection strategies. We provide a new generalisation error bound using the notion of nonconformity to upper bound the loss of each test example and show that our proposed approach is comparable to standard model selection methods, but with theoretical guarantees of success and faster convergence. We demonstrate our novel model selection technique using the Support Vector Machine.