Miracle-dz/UniFAR-FTUs huggingface dataset:
The subunit 1 of the COP9 signalosome suppresses gene expression through its N-terminal domain and incorporates into the complex through the PCI domain.

The COP9 signalosome is a conserved protein complex composed of eight subunits. Individual subunits of the complex have been linked to various signal transduction pathways leading to gene expression and cell cycle control. However, it is not understood how each subunit executes these activities as part of a large protein complex. In this study, we dissected structure and function of the subunit 1 (CSN1 or GPS1) of the COP9 signalosome relative to the complex. We demonstrated that the C-terminal half of CSN1 encompassing the PCI domain is responsible for interaction with CSN2, CSN3, and CSN4 subunits and is required for incorporation of the subunit into the complex. The N-terminal fragment of CSN1 cannot stably associate with the complex but can translocate to the nucleus on its own. We further show that CSN1 or the N-terminal fragment of CSN1 (CSN1-N) can inhibit c-fos expression from either a transfected template or a chromosomal transgene ( fos-lacZ). Moreover, CSN1 as well as CSN1-N can potently suppress signal activation of a AP-1 promoter and moderately suppress serum activation of a SRE promoter, but is unable to inhibit PKA-induced CRE promoter activity. We conclude that the N-terminal half of CSN1 harbors the activity domain that confers most of the repression functions of CSN1 while the C-terminal half allows integration of the protein into the COP9 signalosome.
Sensitivity enhancement in pole-zero identification based stability analysis of microwave circuits

In this work the use of a dual Current-Voltage (I-V) probe is proposed in order to improve the sensitivity of pole-zero identification based stability analyses. The benefits of this approach are shown here by its application to a parametric oscillation in an X-band multi-stage MMIC power amplifier and to a bias oscillation in a medium power FET amplifier built in hybrid microstrip technology.
The impact of channel bonding on 802.11n network management

The IEEE 802.11n standard allows wireless devices to operate on 40MHz-width channels by doubling their channel width from standard 20MHz channels, a concept called channel bonding. Increasing channel width should increase bandwidth, but it comes at the cost of decreased transmission range and greater susceptibility to interference. However, with the incorporation of MIMO (Multiple-Input Multiple-Output) technology in 802.11n, devices can now exploit the increased transmission rates from wider channels at a reduced sacrifice to signal quality and range. The goal of our work is to understand the characteristics of channel bonding in 802.11n networks and the factors that influence that behavior to ultimately be able to predict behavior so that network performance is maximized. We discuss the impact of channel bonding choices as well as the effects of both co-channel and adjacent channel interference on network performance. We discover that intelligent channel bonding decisions rely not only on a link's signal quality, but also on the strength of neighboring links and their physical rates.
Team learning in higher education : the explication of shared mental models in relation to educational design decisions

Due to more complex tasks and changing environments in professional and educational organizations, teamwork is becoming more common since teams are expected to be more creative and expeditious in solving problems. To have teams work together in a harmonious and effective way, team learning and a shared mental model must be considered. Previous research has shown that team learning leads to a shared mental model and that this results in team effectiveness. Most research measured team learning and the shared mental model through perceptions of team members. However, it is proven that the shared mental model is continuously changing through team member interaction. But, scarce research is focussed on this. Therefore, two different measures of the explication of the shared mental model are used in the current study to see how the shared mental model changes over time and how this leads to sustained decisions. In this descriptive qualitative multiple case study data of two higher education teacher teams are collected. Observations of higher education teacher team meetings are used to measure team learning processes and the shared mental model of the team. With use of the chi-square analysis, the Fisher’s exact test and qualitative analysis there are found some interesting results. Cognitive decisions have an important role in the facilitation of the transition from engaging in team learning to arriving at sustained decisions. In addition, action decisions are an important facilitator for cognitive decisions to become sustained decisions. Thereby is found that topics that were discussed multiple times have led to more sustained decisions than topics which were discussed less. Therefore, it is concluded that the explication of the shared mental model can facilitate the transition from engaging in team learning to arriving at sustained decisions in a positive way. The combination of qualitative and quantitative research methods provides a view on how the shared mental model develops over time and how sustained decisions can be reached.
Stabilization with positive and quantized control

Asymptotic stabilization for a class of nonlinear cascades in feedforward form is achieved with positive (negative) control. For the same class of systems practical stabilization is achieved by means of a quantized control scheme, i.e. with a control action that takes values only in a discrete set. Moreover, a general stabilization result for null-controllable with positive control linear systems is given. The theory is illustrated with simulation examples.
Changes in driving patterns and worsening depressive symptoms among older adults.

OBJECTIVES This study examined whether changes in driving patterns-driving cessation and reduction-have negative consequences for the depressive symptoms of older Americans and whether these consequences are mitigated for people with a spouse who drives. METHODS The project used data from 3 waves of the Asset and Health Dynamics Among the Oldest Old (AHEAD) study. Depressive symptoms were assessed with an abbreviated Center for Epidemiologic Studies-Depression scale. Using 2 models, the project examined how driving cessation and reduction that occurred between Waves 1 and 2 contributed to increases in depressive symptoms between Waves 2 and 3. The first model included the entire sample (N = 5,239), and the second model focused on drivers only (n = 3,543). A third model added interaction terms to the analysis to consider whether respondents who stopped driving but had a spouse who drove were less at risk of worsening depressive symptoms. RESULTS Respondents who stopped driving had greater risk of worsening depressive symptoms. Drivers who restricted their driving distances before the study began also had greater risk of worsening depressive symptoms, but seemingly less so than the respondents who stopped driving altogether. For respondents who stopped driving, having a spouse available to drive them did not mitigate the risk of worsening symptoms. DISCUSSION Changes in driving patterns can be deleterious for older people's depressive symptoms. Initiatives for assisting older people should focus on strategies that help them retain driving skills, that prepare them for the possible transition from driver to ex-driver, and that ensure that they have access to mental health therapies if driving changes are imminent.
Fluid Limits for Processor-Sharing Queues with Impatience

We investigate a processor sharing queue with renewal arrivals and generally distributed service times. Impatient jobs may abandon the queue, or renege, before completing service. The corresponding stochastic processes are represented by measure valued Markov processes on R^2\_+. A scaling procedure that gives rise to a fluid model with a nontrivial, yet tractable steady state behavior, is presented. This fluid model model captures many essential features of the underlying stochastic model, and it is used to analyze the impact of impatience in processor sharing queues.
The social position of the educable mentally retarded child in the regular grades in the public schools

The purpose of the study reported here was: (1) to determine the social position of the mentally reo tarded child in the regular grades in a public school system that also provided public school special classes for the mentally retarded children; (2) to determine the relationship of other factors to this position; and (3) to see whether in these findings there existed any implications for educational plan. ning. Some of the questions that this study sought to explore were: 1. To what, extent were the educable mentally retarded children in the regular grades of the public school accepted socially? 2. What was the social position of the chilo dren of different levels of mental retardation, of chronological age, of public school grade level, and of socio-economic status? 3. What desirable or undesirable character. istics seemed to be related to the degree of eocial acceptance of the mentally retarded child? This study was made in a public school system that had provided public school special classes for the mentally retarded children for many years, and at the same time had some mentally retarded chilo dren in the regular 'grades because of a shortage of special class teachers. Although an intelligence quotient rating between 50 to 74 on an individual Intelligence test was supposedly the primary factor in
Statistical Analysis of Minimum Classification Error Learning for Gaussian and Hidden Markov Model Classifiers

Minimum classification error learning realized via generalized probabilistic descent, usually referred to as (MCE/GPD), is a very popular and powerful framework for building classifiers. This paper first presents a theoretical analysis of MCE/GPD. The focus is on a simple classification problem for estimating the means of two Gaussian classes. For this simple algorithm, we derive difference equations for the class means and decision threshold during learning, and develop closed form expressions for the evolution of both the smoothed and true error. In addition, we show that the decision threshold converges to its optimal value, and provide an estimate of the number of iterations needed to approach convergence. After convergence the class means drift towards increasing their distance to infinity without contributing to the decrease of the classification error. This behavior, referred to as mean drift, is then related to the increase of the variance of the classifier. The theoretical results perfectly agree with simulations carried out for a two-class Gaussian classification problem. In addition to the obtained theoretical results we experimentally verify, in speech recognition experiments, that MCE/GPD learning of Gaussian mixture hidden Markov models qualitatively follows the pattern suggested by the theoretical analysis. We also discuss links between MCE/GPD learning and both batch gradient descent and extended Baum-Welch re-estimation. The latter two approaches are known to be popular in large scale implementations of discriminative training. Hence, the proposed analysis can be used, at least as a rough guideline, for better understanding of the properties of discriminative training algorithms for speech recognition.
A Method of Nonlinear Manifold Classification using ISOMAP

In this paper, a new nonlinear classification algorithm is proposed using Isomap (isometric feature mapping). First, nonlinear dimensionality reduction algorithm is implemented using Isomap; then nonlinear mapping matrix is approximated by local linear mapping; next dimensionality reduction for all data sets is implemented by local linear mapping; finally linear classification is implemented. Experimental results confirm the efficiency of proposed algorithm
Action-Based Learning: Goals and Attention in the Acquisition of Market Knowledge

In this article, the authors examine the costs and benefits of action-based learning (i.e., learning that occurs as a by-product of making repeated decisions with outcome feedback). The authors report the results of three experiments that investigate the effects of different decision goals on what is learned and how transferable that learning is across related decision tasks. Contrary to popular wisdom, compared with traditional learning, experiential learning is likely to be a risky proposition because it can be either accurate and efficient or errorful and biased.
Deglycosylated anti‐amyloid beta antibodies reduce microglial phagocytosis and cytokine production while retaining the capacity to induce amyloid beta sequestration

Accumulation of amyloid beta (Abeta) is a pathological hallmark of Alzheimer's disease, and lowering Abeta is a promising therapeutic approach. Intact anti‐Abeta antibodies reduce brain Abeta through two pathways: enhanced microglial phagocytosis and Abeta transfer from the brain to the periphery (Abeta sequestration). While activation of microglia, which is essential for microglial phagocytosis, is necessarily accompanied by undesired neuroinflammatory events, the capacity for sequestration does not seem to be linked to such effects. We and other groups have found that simple Abeta binding agents are sufficient to reduce brain Abeta through the sequestration pathway. In this study, we aimed to eliminate potentially deleterious immune activation from antibodies without affecting the ability to induce sequestration. The glycan portion of immunoglobulin is critically involved in interactions with immune effectors including the Fc receptor and complement c1q; deglycosylation eliminates these interactions, while antigen (Abeta)‐binding affinity is maintained. In this study, we investigated whether deglycosylated anti‐Abeta antibodies reduce microglial phagocytosis and neuroinflammation without altering the capacity to induce Abeta sequestration. Deglycosylated antibodies maintained Abeta binding affinity. Deglycosylated antibodies did not enhance Abeta phagocytosis or cytokine release in primary cultured microglia, whereas intact antibodies did so significantly. Intravenous injection of deglycosylated antibodies elevated plasma Abeta levels and induced Abeta sequestration to a similar or greater degree compared with intact antibodies in an Alzheimer's transgenic mouse model without or with Abeta plaque pathology. We conclude that deglycosylated antibodies effectively induced Abeta sequestration without provoking neuroinflammation; thus, these deglycosylated antibodies may be optimal for sequestration therapy for Alzheimer's disease.
Advanced mapping techniques for digital signal processors

This paper is focused on the hardware modeling and the algorithms mapping on the digital signal processor (DSP) with the very long instruction word (VLIW) architecture, such as TMS320C6000. The general methods to develop an efficient application for the target processor combine high- and/or low-level programming languages. Although the hardware capabilities of the nowadays processors and compilers are persistently increasing, the programmers common practice is to hand-optimize critical parts of the digital signal processing algorithms in low-level assembly code. In the paper the benefit of the auxiliary tool for generating of semi-optimal codes for the DSP is presented. The functions for basic vector operations (addition, multiplication, and dot product) were proposed by this tool and the computing performances were compared to the corresponding functions from the TMS320C6000 DSP Library (DSPLIB). Comparing the functions' duration, the proposed routines achieve the average acceleration of 24 CPU cycles.
The Diagnostic Validity of the 13C-Urea Breath Test in the Gastrectomized Patients: Single Tertiary Center Retrospective Cohort Study

Background: This study was conducted to evaluate the diagnostic validity of the 13C-urea breath test (13C-UBT) in the remnant stomach after partial gastrectomy for gastric cancer. Methods: The 13C-UBT results after Helicobacter pylori eradication therapy was compared with the results of endoscopic biopsy-based methods in the patients who have received partial gastrectomy for the gastric cancer. Results: Among the gastrectomized patients who showed the positive 13C-UBT results (≥ 2.5‰, n = 47) and negative 13C-UBT results (< 2.5‰, n = 114) after H. pylori eradication, 26 patients (16.1%) and 4 patients (2.5%) were found to show false positive and false negative results based on biopsy-based methods, respectively. The sensitivity, specificity, false positive rate, and false negative rate for the cut-off value of 2.5‰ were 84.0%, 80.9%, 19.1%, and 16.0%, respectively. The positive and negative predictive values were 44.7% and 96.5%, respectively. In the multivariate analysis, two or more H. pylori eradication therapies (odds ratio = 3.248, 95% confidence interval= 1.088–9.695, P = 0.035) was associated with a false positive result of the 13C-UBT. Conclusions: After partial gastrectomy, a discordant result was shown in the positive 13C-UBT results compared to the endoscopic biopsy methods for confirming the H. pylori status after eradication. Additional endoscopic biopsy-based H. pylori tests would be helpful to avoid unnecessary treatment for H. pylori eradication in these cases.
An Iterative Reweighted Method for Tucker Decomposition of Incomplete Multiway Tensors

We consider the problem of low-rank decomposition of incomplete multiway tensors. Since many real-world data lie on an intrinsically low dimensional subspace, tensor low-rank decomposition with missing entries has applications in many data analysis problems such as recommender systems and image inpainting. In this paper, we focus on Tucker decomposition which represents an Nth-order tensor in terms of N factor matrices and a core tensor via multilinear operations. To exploit the underlying multilinear low-rank structure in high-dimensional datasets, we propose a group-based log-sum penalty functional to place structural sparsity over the core tensor, which leads to a compact representation with smallest core tensor. The method for Tucker decomposition is developed by iteratively minimizing a surrogate function that majorizes the original objective function, which results in an iterative reweighted process. In addition, to reduce the computational complexity, an over-relaxed monotone fast iterative shrinkage-thresholding technique is adapted and embedded in the iterative reweighted process. The proposed method is able to determine the model complexity (i.e. multilinear rank) in an automatic way. Simulation results show that the proposed algorithm offers competitive performance compared with other existing algorithms.
Feeling Love and Doing More for Distant Others: Specific Positive Emotions Differentially Affect Prosocial Consumption

Marketers often employ a variety of positive emotions to encourage consumption or promote a particular behavior (e.g., buying, donating, recycling) to benefit an organization or cause. The authors show that specific positive emotions do not universally increase prosocial behavior but, rather, encourage different types of prosocial behavior. Four studies show that whereas positive emotions (i.e., love, hope, pride, and compassion) all induce prosocial behavior toward close entities (relative to a neutral emotional state), only love induces prosocial behavior toward distant others and international organizations. Love's effect is driven by a distinct form of broadening, characterized by extending feelings of social connection and the boundary of caring to be more inclusive of others regardless of relatedness. Love—as a trait and a momentary emotion—is unique among positive emotions in fostering connectedness that other positive emotions (hope and pride) do not and broadening behavior in a way that other connected emotions (compassion) do not. This research contributes to the broaden-and-build theory of positive emotion by demonstrating a distinct type of broadening for love and adds an important qualification to the general finding that positive emotions uniformly encourage prosocial behavior.
Populating dark matter haloes with galaxies: comparing the 2dFGRS with mock galaxy redshift surveys

In two recent papers, we developed a powerful technique to link the distribution of galaxies to that of dark matter haloes by considering halo occupation numbers as a function of galaxy luminosity and type. In this paper we use these distribution functions to populate dark matter haloes in high-resolution N-body simulations of the standard LambdaCDM cosmology with Omega(m) = 0.3, Omega(Lambda) = 0.7 and sigma(8) = 0.9. Stacking simulation boxes of 100 h(-1) Mpc and 300 h(-1) Mpc with 512 3 particles each we construct mock galaxy redshift surveys out to a redshift of z = 0.2 with a numerical resolution that guarantees completeness down to 0.01L*. We use these mock surveys to investigate various clustering statistics. The predicted two-dimensional correlation function xi(r(p), pi) reveals clear signatures of redshift space distortions. The projected correlation functions for galaxies with different luminosities and types, derived from xi(r(p), pi), match the observations well on scales larger than similar to3 h(-1) Mpc. On smaller scales, however, the model overpredicts the clustering power by about a factor two. Modelling the 'finger-of-God' effect on small scales reveals that the standard LambdaCDM model predicts pairwise velocity dispersions (PVD) that are similar to400 km s(-1) too high at projected pair separations of similar to1 h(-1) Mpc. A strong velocity bias in massive haloes, with b(vel) equivalent to sigma(gal)/sigma(dm) - similar to0.6 (where sigma(gal) and sigma(dm) are the velocity dispersions of galaxies and dark matter particles, respectively) can reduce the predicted PVD to the observed level, but does not help to resolve the overprediction of clustering power on small scales. Consistent results can be obtained within the standard LambdaCDM model only when the average mass-to-light ratio of clusters is of the order of 1000 (M/L). in the B-band. Alternatively, as we show by a simple approximation, a LambdaCDM model with sigma(8) similar or equal to 0.75 may also reproduce the observational results. We discuss our results in light of the recent WMAP results and the constraints on sigma(8) obtained independently from other observations.
Update on Jehovah’s Witness cases before the European Court of Human Rights: implications of a surprising partnership

ABSTRACT This contribution details the large number of cases carried by Jehovah’s Witnesses to the European Court of Human Rights during its existence, and examines the many positive outcomes of those cases for the Witnesses and for religious freedom in the Council of Europe region. There is also discussion of theoretical debates concerning the underlying philosophy of the Court as it adjudicates the many religion cases the Witnesses have brought, and concepts from the sociology of law such as ‘courts as partners’ and ‘third party partisans’ also are examined as possible explanations of how the long-term and surprising arrangement has evolved over the life of the Court.