Miracle-dz/UniFAR-FTUs huggingface dataset:
Disruption of Arp2/3 Results in Asymmetric Structural Plasticity of Dendritic Spines and Progressive Synaptic and Behavioral Abnormalities

Despite evidence for a strong genetic contribution to several major psychiatric disorders, individual candidate genes account for only a small fraction of these disorders, leading to the suggestion that multigenetic pathways may be involved. Several known genetic risk factors for psychiatric disease are related to the regulation of actin polymerization, which plays a key role in synaptic plasticity. To gain insight into and test the possible pathogenetic role of this pathway, we designed a conditional knock-out of the Arp2/3 complex, a conserved final output for actin signaling pathways that orchestrates de novo actin polymerization. Here we report that postnatal loss of the Arp2/3 subunit ArpC3 in forebrain excitatory neurons leads to an asymmetric structural plasticity of dendritic spines, followed by a progressive loss of spine synapses. This progression of synaptic deficits corresponds with an evolution of distinct cognitive, psychomotor, and social disturbances as the mice age. Together, these results point to the dysfunction of actin signaling, specifically that which converges to regulate Arp2/3, as an important cellular pathway that may contribute to the etiology of complex psychiatric disorders.
Modeling of converter-fed motor drives for analyzing motor transients

The present work investigates how the components in a complex system of frequency converter, transformer, cable and motor can be modeled. Different models for the components of the system have been evaluated in order to find the best ones for analyzing the voltage stresses in a motor supplied by a PWM frequency converter. The aim is to dimension the individual components to optimize the whole system. The work is based on frequency domain analysis to detect critical harmonic frequencies, and transient- and steady-state analysis in the time domain. The developed models are valid for systems with long- as well as for shorter cable lengths, and for both VSI and CSI converters.
Dominant Path Prediction Model for Urban Scenarios

Currently, for the planning of wireless cellular networks in urban scenarios either empirical (direct ray or over rooftop ray) or ray-optical (ray tracing) propagation models are used. In this paper both approaches are compared to one another and to measurements in different urban city centers. Additionally a new concept which is called Dominant Path Model (DPM) is presented in this paper. This new concept does not focus only on the direct ray (like empirical models) and it does not consider hundreds of rays for a single pixel (like ray tracing), but it focuses on the dominant path(s) between transmitter and receiver. The parameters of these dominant paths are determined and used for the prediction of the path loss between transmitter and receiver. Thus the computational effort is far below ray tracing and in the range of empirical models. But the accuracy of the new model in very complex environments (with high shadowing of the direct ray) is even higher than the accuracy of ray tracing models (because of their limitations in the number of interactions). This very high accuracy is shown with the comparison to measurements in several international cities. Keywords—wave propagation, urban, ray tracing, dominant paths, measurements
A method for inpainting rectangular missing regions using High Dimensional Model Representation and Lagrange interpolation

Image inpainting is the process of filling missing or fixing corrupted regions in an image. The intensity values of the pixels in the missing area are expected to be associated with the pixels in the surrounding area. Interpolation-based methods that can solve the problem with a high accuracy may become inefficient when the dimension of the data increases. In this work, we propose an image inpainting method which represents a high dimensional data in lower dimensions using High Dimensional Model Representation Method (HDMR) and performs image inpainting for rectangular regions with Lagrange interpolation. Proposed approach works only on rectangular regions due to a constraint that comes from the structure of the HDMR method. Experimental results show that the proposed method produces better results than the well-known and pioneering total variation-based image inpainting method in the literature.
Using Computational Neuroscience to Define Common Input to Spinal Motor Neurons

Common input is a widely used concept in motor neurophysiology. It embodies the notion that inputs to individual spinal motor neurons (MNs) are not unique, but partly shared across MNs, and is considered the main explanation for synchronized activity of MNs (Bremner et al., 1991; Farmer et al., 1993; Boonstra and Breakspear, 2012; Farina and Negro, 2015). Motor-unit synchronization was first observed in the time domain using cross-correlation histograms from pairs of MNs (Sears and Stagg, 1976). This was later extended to the frequency domain by estimating coherence between spike trains to reveal the frequency content of common input (Farmer et al., 1993). In addition to measurements of individual MNs, coherence can also be estimated between the surface EMG of different muscles—referred to as intermuscular coherence—to assess common input shared across motor-unit pools (Boonstra and Breakspear, 2012). Common input is considered relevant for motor control as it may provide a mechanism to reduce the dimensionality of the control signal, thereby simplifying motor control (Farmer, 1998).
Template-based Automatic Segmentation of Masseter Using Prior Knowledge

In this paper, we propose a knowledge-based, fully automatic methodology for segmenting the masseter, which is a muscle of mastication, from 2-D magnetic resonance (MR) images for clinical purposes. To our knowledge, there is currently no methodology which automatically segments the masseter from MR images. Our methodology uses five ground truths, where the masseter has been manually segmented and verified by medical experts, to serve as the reference and provide prior knowledge. The prior knowledge involved is the spatial relationship between the region of interest (ROI) of the head and ROI of the masseter. In the segmentation process, anisotropic diffusion first smoothens the ROI of the latter, and thresholding removes unwanted neighboring regions of the masseter. A template of the masseter is then used to obtain an initial segmentation of the muscle, which serves as the initialization to the gradient vector flow (GVF) snake for refining the initial segmentation. We performed 2-D segmentation of the masseter on a total of 25 MR images, which belong to the mid-facial region through the mandible from five data sets. Validation was done by comparing the segmentation results obtained by using our proposed methodology against manual segmentations done by medical experts, obtaining an average accuracy of 92%
EAAC1 Gene Deletion Alters Zinc Homeostasis and Exacerbates Neuronal Injury after Transient Cerebral Ischemia

EAAC1 is a neuronal glutamate and cysteine transporter. EAAC1 uptake of cysteine provides substrate for neuronal glutathione synthesis, which plays a key role in both antioxidant defenses and intracellular zinc binding. Here we evaluated the role of EAAC1 in neuronal resistance to ischemia. EAAC1−/− mice subjected to transient cerebral ischemia exhibited twice as much hippocampal neuronal death as wild-type mice and a corresponding increase in microglial activation. EAAC1−/− mice also had elevated vesicular and cytosolic zinc concentrations in hippocampal CA1 neurons and an increased zinc translocation to postsynaptic neurons after ischemia. Treatment of the EAAC1−/− mice with N-acetyl cysteine restored neuronal glutathione concentrations and normalized basal zinc levels in the EAAC1−/− mice. Treatment of the EAAC1−/− mice with either N-acetyl cysteine or with zinc chelators reduced ischemia-induced zinc translocation, superoxide production, and neuron death. These findings suggest that cysteine uptake by EAAC1 is important for zinc homeostasis and neuronal antioxidant function under ischemic conditions.
The effect of investment tax incentives: evidence from China’s value-added tax reform

We estimate the impact of investment tax credit on firm fixed investment in a difference-in-differences-in-differences framework, using China’s 2004 value-added tax reform pilot that introduces a permanent 17%-tax credit for fixed investment in six industries in the Northeastern region. The tax credit raises significantly fixed investment of eligible firms by 28% on average during 2004–2007 relative to 2001–2003, corresponding to a user cost elasticity of 1.84. The tax incentive has larger effects on firms that are less financially constrained such as smaller firms and firms with a larger cash flow. The result is largely driven by responses of domestic private firms and is robust to specifications addressing the issue of anticipation.
Subprime Borrowers, Securitization and the Transmission of Business Cycles

A growing literature (i.e. Jaffee, Lynch, Richardson, and Van Nieuwerburgh, 2009, Acharya and Schnabl, 2009) argues that securitization improves financial stability if the securitized assets are held by capital market participants, rather than financial intermediaries. I construct a quantitative macroeconomic model with a novel specification for mortgage-backed securities (MBS) to evaluate this claim for subprime securitization during the Great Recession. I find that output in the U.S. would have dropped by only about a third and house prices by only a half of what we actually observed, if subprime MBS had been purchased by non-financial agents, rather than held by banks. This is because banks are subject to capital requirements and if MBS remain within the banking system, the fall in their value puts a strain on banks’ balance sheets. The subsequent deleveraging amplifies business cycles. My findings suggest that the existence of the securitization market stabilizes the economy under the condition that financial intermediaries do not engage in the acquisition of securitized assets.
Diminishing Prototype Size for k-Nearest Neighbors Classification

In this paper, a new classification method based on k-Nearest Neighbor (kNN) lazy classifier is proposed. This method leverages the clustering concept to reduce the size of the training set in kNN classifier and also in order to enhance its performance in terms of time complexity. The new approach is called Modified Nearest Neighbor Classifier Based on Clustering (MNNCBC). Inspiring the traditional lazy k-NN algorithm, the main idea is to classify a test instance based on the tags of its k nearest neighbors. In MNNCBC, the training set is first grouped into a small number of partitions. By obtaining a number of partitions employing several runnings of a simple clustering algorithm, MNNCBC algorithm extracts a large number of clusters out of those partitions. Then, a label is assigned to the center of each cluster produced in the previous step. The assignment is determined with use of the majority vote mechanism between the class labels of the patterns in each cluster. MNNCBC algorithm iteratively inserts a cluster into a pool of the selected clusters that are considered as the training set of the final 1-NN classifier as long as the accuracy of 1-NN classifier over a set of patterns included the training set and the validation set improves. The selected set of the most accurate clusters are considered as the training set of proposed 1-NN classifier. After that, the class label of a new test sample is determined according to the class label of the nearest cluster center. While kNN lazy classifier is computationally expensive, MNNCBC classifier reduces its computational complexity by a multiplier of 1/k. So MNNCBC classifier is about k times faster than kNN classifier. MNNCBC is evaluated on some real datasets from UCI repository. Empirical results show that MNNCBC has an excellent improvement in terms of both accuracy and time complexity in comparison with kNN classifier.
Treating thalassemia major-related iron overload: the role of deferiprone

Over the last 20 years, management for thalassemia major has improved to the point where we predict that patients’ life expectancy will approach that of the normal population. These outcomes result from safer blood transfusions, the availability of three iron chelators, new imaging techniques that allow specific organ assessment of the degree of iron overload, and improvement in the treatment of hepatitis. In October 2011, the Food and Drug Administration licensed deferiprone, further increasing the available choices for iron chelation in the US. The ability to prescribe any of the three chelators as well as their combinations has led to more effective reduction of total body iron. The ability to determine the amount of iron in the liver and heart by magnetic resonance imaging allows the prescription of the most appropriate chelation regime for patients and to reconsider what our aims with respect to total body iron should be. Recent evidence from Europe has shown that by normalizing iron stores not only are new morbidities prevented but also reversal of many complications such as cardiac failure, hypothyroidism, hypogonadism, impaired glucose tolerance, and type 2 diabetes can occur, improving survival and patients’ quality of life. The most effective way to achieve normal iron stores seems to be with the combination of deferoxamine and deferiprone. Furthermore, outcomes should continue to improve in the future. Starting relative intensive chelation in younger children may prevent short stature and abnormal pubertal maturation as well as other iron-related morbidities. Also, further information should become available on the use of other combinations in chelation treatment, some of which have been used only in a very limited fashion to date. All these advances in management require absolute cooperation and understanding of parents, children, and, subsequently, the patients themselves. Only with such cooperation can normal long-term survival be achieved, as adherence to treatment is now likely the primary barrier to longevity.
Strategy Participation in Discussions of a Transforming Organisation (Available on Internet)

Though most strategy scholars and practitioners acknowledge the importance of participation, studies of participation and participation possibilities in particular are currently very few. This study’s main aim is to contribute in the scarce research of discursive aspects shaping strategy participation. I add on the knowledge by examining the particular discursive processes shaping organisational members’ possibilities to take part in strategizing. Moreover, I provide a novel perspective to strategy participation by applying positioning theory in my analysis. I apply critical discourse analysis as a methodological framework. Furthermore, as positioning takes place within the evolvement of storylines, I also need a method to analyse the diachronic nature of the phenomenon. Therefore, I specifically apply methods narrative inquiry in analysing strategy discussions. The empirical data consists of internal social media discussions in Nokia. The dataset is vast, and final analysis includes 1980 selected message posts where strategic matters are discussed. The analysis demonstrated that modes of participation are shaped and predicted by a general strategy participation framework. The framework constitutes of a temporal dimension past-future and a strategy dimension construction-enactment. These types of participation predict how acts of positioning will be formulated. Participation in strategy discussion is typically related to giving or receiving strategy information, and to giving or receiving suggestions for strategic actions. Differently positioned participants had varying, yet equally important roles in driving discussion and promoting participation. Therefore, participation is best enabled when the whole spectrum of positions are given a voice.
ADR - Anatomy-Driven Reformation

Dedicated visualization methods are among the most important tools of modern computer-aided medical applications. Reformation methods such as Multiplanar Reformation or Curved Planar Reformation have evolved as useful tools that facilitate diagnostic and therapeutic work. In this paper, we present a novel approach that can be seen as a generalization of Multiplanar Reformation to curved surfaces. The main concept is to generate reformatted medical volumes driven by the individual anatomical geometry of a specific patient. This process generates flat views of anatomical structures that facilitate many tasks such as diagnosis, navigation and annotation. Our reformation framework is based on a non-linear as-rigid-as-possible volumetric deformation scheme that uses generic triangular surface meshes as input. To manage inevitable distortions during reformation, we introduce importance maps which allow controlling the error distribution and improving the overall visual quality in areas of elevated interest. Our method seamlessly integrates with well-established concepts such as the slice-based inspection of medical datasets and we believe it can improve the overall efficiency of many medical workflows. To demonstrate this, we additionally present an integrated visualization system and discuss several use cases that substantiate its benefits.
Risk Minimization in the Presence of Label Noise

Matrix concentration inequalities have attracted much attention in diverse applications such as linear algebra, statistical estimation, combinatorial optimization, etc. In this paper, we present new Bernstein concentration inequalities depending only on the first moments of random matrices, whereas previous Bernstein inequalities are heavily relevant to the first and second moments. Based on those results, we analyze the empirical risk minimization in the presence of label noise. We find that many popular losses used in risk minimization can be decomposed into two parts, where the first part won't be affected and only the second part will be affected by noisy labels. We show that the influence of noisy labels on the second part can be reduced by our proposed LICS (Labeled Instance Centroid Smoothing) approach. The effectiveness of the LICS algorithm is justified both theoretically and empirically.
1-1-2011 Integrating Innovation into Enterprise Architecture Management

The ability of organizations to innovate is acknowledged as an essential capability to compete in a competitive market. This paper proposes to use enterprise architecture management as a systematic approach to innovate the enterprise. The enterprise architecture approach is based on a comprehensive architecture framework which aligns the domains of business -, application and infrastructure architecture. The framework addresses all dimensions relevant for enterprise innovation like business model, organization, processes, and technology and provides appropriate design techniques. A comprehensive architecture development process is introduced which integrates innovation as a central element for the enterprise architecture design. The process encompasses all activities from business vision & strategy, architecture design to implementation. In addition, the organization of architecture management and the main stakeholders from the business as well as IT side and their respective role are explained.
Thinking Out Loud and e-Health for Coordinated Care Lessons from User Requirements Gathering in the 4C Project

e-Health is a core part of Australias strategy to address rising costs and changing demands for healthcare services. With over $1bn spent and only 6% of Australians registered, the personally controlled electronic health record (PCEHR) suggests user challenges remain. While evidence confirms the benefits from involving users in systems development there is a need for more examples of how to engage effectively in healthcare settings. This research describes the use of an agile development methodology combined with the thinking out loud technique to deliver a solution that exceeded user requirements in supporting a new model of care. The 4C project solution connected Aged Care institutions with general practices, hospitals and specialist services in Tasmanias north-west region. It was underpinned by a model of Technology Mediated Social Participation (TMSP). As a trial project for the PCEHR it remains unclear why lessons learned appear not to have been deployed more explicitly in the National roll-out.
Generation of patterns with predefined statistical properties using Cellular Neural Networks

The following paper presents an application of cellular neural networks for spontaneous generation of patterns with predefined statistical properties. The considered pattern statistical characteristics included coarseness, directionality and edge sharpness distribution. A purpose of this choice was to verify CNN capabilities in generating images that comprise structural elements significantly larger than a range of cell interconnections. Resulting CNN templates, derived using the SQP optimization algorithm, were capable of meeting majority of formulated design requirements
Extrapancreatic Autoantibody Profiles in Type I Diabetes

Type I diabetes (T1D) is an autoimmune disease characterized by destruction of insulin-producing β-cells in the pancreas. Although several islet cell autoantigens are known, the breadth and spectrum of autoantibody targets has not been fully explored. Here the luciferase immunoprecipitation systems (LIPS) antibody profiling technology was used to study islet and other organ-specific autoantibody responses in parallel. Examination of an initial cohort of 93 controls and 50 T1D subjects revealed that 16% of the diabetic subjects showed anti-gastric ATPase autoantibodies which did not correlate with autoantibodies against GAD65, IA2, or IA2-β. A more detailed study of a second cohort with 18 potential autoantibody targets revealed marked heterogeneity in autoantibody responses against islet cell autoantigens including two polymorphic variants of ZnT8. A subset of T1D subjects exhibited autoantibodies against several organ-specific targets including gastric ATPase (11%), thyroid peroxidase (14%), and anti-IgA autoantibodies against tissue transglutaminase (12%). Although a few T1D subjects showed autoantibodies against a lung-associated protein KCNRG (6%) and S100-β (8%), no statistically significant autoantibodies were detected against several cytokines. Analysis of the overall autoantibody profiles using a heatmap revealed two major subgroups of approximately similar numbers, consisting of T1D subjects with and without organ-specific autoantibodies. Within the organ-specific subgroup, there was minimal overlap among anti-gastric ATPase, anti-thyroid peroxidase, and anti-transglutaminase seropositivity, and these autoantibodies did not correlate with islet cell autoantibodies. Examination of a third cohort, comprising prospectively collected longitudinal samples from high-risk individuals, revealed that anti-gastric ATPase autoantibodies were present in several individuals prior to detection of islet autoantibodies and before clinical onset of T1D. Taken together, these results suggest that autoantibody portraits derived from islet and organ-specific targets will likely be useful for enhancing the clinical management of T1D.