Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of JC Virus Large T Antigen with Myelin Basic Protein Transcription Factor (MEF-1/Purα) in Hypomyelinated Brains of Mice Transgenically Expressing T Antigen

ABSTRACT Progressive multifocal leukoencephalopathy (PML) is a fatal demyelinating disease caused by cytolytic destruction of oligodendrocytes, the myelin-producing cells of the central nervous system, by the human neurotropic JC virus (JCV). The early protein of JCV, T antigen, which is produced at the early stage of infection, is important for orchestrating the events leading to viral lytic infection and cytolytic destruction of oligodendrocytes. Results from transgenic mouse studies, however, have revealed that, in the absence of lytic infection, this protein can induce brain hypomyelination and suppression of myelin gene expression. Since expression of the gene encoding myelin basic protein, the major component of myelin, can be regulated by a DNA-binding transcription factor, MEF-1/Purα, (Purα), we have examined the level of this protein in transgenic mouse brains. Results from immunoprecipitation and Western blots showed that while there was no drastic decrease in the level of MEF-1/Purα in transgenic mouse brains, JCV T antigen was found in a complex with MEF-1/Purα. Immunohistological studies revealed abnormal oligodendrocytes in white matter, where MEF-1/Purα and T antigen were detected. Furthermore, immunogold electron microscopic studies revealed that Purα and T antigen are colocalized in the nucleus of the oligodendrocytes and in hippocampal neurons. Interestingly, results from cell culture studies revealed that incubation of oligodendrocytes with JCV led to a drastic decrease in the level of MEF-1/Purα protein. These observations provide insight into the molecular pathogenesis of PML and support a model for a dual effect of JCV on inducing hypomyelination by (i) affecting myelin gene expression via interaction of JCV T antigen and the myelin gene transcription factor, MEF-1/Purα, and (ii) causing a decline in the level of the host regulatory proteins, including MEF-1/Purα, which are involved in myelin gene expression.
Reachable Independent Contact Regions for precision grasps

Independent Contact Regions allow a robust finger placement on the object, despite of potential errors in finger position. They are computed without considering the kinematics of the end-effector, and are usually applied to off-line grasp planners. This paper presents an approach to obtain Reachable Independent Contact Regions by including the hand kinematics in the computational loop. The regions are computed in a short time, which allows real-time applications in virtual grasping. Potential applications of the proposed approach include regrasp planning, and dual-hand manipulation of objects.
Camera Calibration with a Viewfinder

To answer the industrial need for simple camera calibration procedure, we propose a new method that requires a simple calibration object composed simply of a box and two crosses. The box is opened in the front where a large cross, made of wires, is attached while another is drawn (or attached) at the bottom. Both crosses are perfectly aligned similarly to a viewfinder. The viewfinder is first oriented with respect to camera such that the optical axis of the camera passes by the center of both crosses, allowing the display of a single (superimposed) cross and an immediate reading of the coordinates of the optical axis. Then, using the similar triangles theorem, the focal distance can be easily estimated. In addition, if necessary, the method can determine the orientation of the CCD matrix if it is not perfectly perpendicular to the optical axis by solving a simple linear system. This method should be particularly useful for calibration of cameras in situ, such as microscopes or embedded cameras.
Improving Full-Text Precision on Short Queries using Simple Constraints

We show that two simple constraints, when applied to short user queries (on the order of 5{10 words) can yield precision scores comparable to or better than those achieved using long queries (50{85 words) at low document cuto levels. These constraints are meant to detect documents that have subtopic passages that includes the most important components of the query. The constraints are: (i) a simple Boolean constraint which requires the user to specify the query as a list of topics; this list is converted into a conjunct of disjuncts by the system, and (ii) a subtopic-sized proximity constraint imposed over the Boolean constraint. The vector space model is used to rank the documents that satisfy both constraints. Experiments run over 45 TREC queries show signi cant, almost consistent improvements over rankings that use no constraints. These results have important rami cations for interactive systems intended for casual users, such as those searching on the World Wide Web.
3D Scene Segmentation and Object Tracking in Multiocular Image Sequences

In this contribution we describe a vision-based system for the 3D detection and tracking of moving persons and objects in complex scenes. A 3D point cloud of the scene is extracted by a combined stereo technique consisting of a correlation-based block-matching approach and a spacetime stereo approach based on spatio-temporally local intensity modelling, resulting in a 3D point cloud attributed with motion infor- mation. For localising persons and objects in the scene the point cloud is segmented into clusters by applying a hierarchical clustering algorithm, using velocity information as an additional discrimination criterion. Ini- tial object hypotheses are obtained by partitioning the observed scene with cylinders, including the tracking results of the previous frame. Mul- tidimensional unconstrained nonlinear minimisation is then applied to refine the initial object hypotheses, such that neighbouring clusters with similar velocity vectors are grouped to form a compact object. A particle filter is applied to select hypotheses which generate consistent trajecto- ries. The described system is evaluated based on real-world sequences acquired in an industrial production environment and from a tabletop scene, using manually obtained ground truth data. We find that even in the presence of moving objects closely neighbouring the person, all objects are detected and tracked in a robust and stable manner. The av- erage tracking accuracy is of the order of several percent of the distance to the scene.
Extent and severity of Caliciopsis canker in New England, USA: an emerging disease of eastern white pine (Pinus strobus L.).

Caliciopsis canker is an emerging problem in Pinus growing regions of Eastern North America. The fungal disease caused by Caliciopsis pinea is associated with overstocked stands and poor sites, but few quantitative data are available. The objective of this study, therefore, was to assess the extent and severity of Caliciopsis canker and to explore environmental variables associated with disease to identify areas at risk of damage. During 2014, 58 sites across New England with >75% P. strobus basal area in public lands were surveyed. Most sites (72%) had Caliciopsis canker signs or symptoms. Caliciopsis pinea was successfully identified with molecular techniques. In sites with Caliciopsis canker, 36% of the mature pines were symptomatic. Pole sized and suppressed trees were more likely to be damaged than larger trees with dominant crown positions (p < 0.05). Pinus strobus density for sites with Caliciopsis canker was 311 trees/ha (mean P. strobus stand diameter = 40 cm) compared to 220 trees/ha (mean white pine stand diameter = 43 cm) for sites without Caliciopsis canker (p = 0.1). Caliciopsis canker symptoms tended to appear more frequently in stands with excessively drained, coarse textured soils derived from glacial outwash (86%) or stands with poorly drained soils and low fertility (78%) than in stands with well drained, more fertile soils (59%) (p = 0.1). The severity of symptoms varied among soil groups and was greater for excessively drained, nutrient poor soils than for well-drained, more fertile soils (p = 0.027).
HMM sampling and applications to gene finding and alternative splicing

The standard method of applying hidden Markov models to biological problems is to find a Viterbi (maximal weight) path through the HMM graph. The Viterbi algorithm reduces the problem of finding the most likely hidden state sequence that explains given observations, to a dynamic programming problem for corresponding directed acyclic graphs. For example, in the gene finding application, the HMM is used to find the most likely underlying gene structure given a DNA sequence. In this note we discuss the applications of sampling methods for HMMs. The standard sampling algorithm for HMMs is a variant of the common forward-backward and backtrack algorithms, and has already been applied in the context of Gibbs sampling methods. Nevetheless, the practice of sampling state paths from HMMs does not seem to have been widely adopted, and important applications have been overlooked. We show how sampling can be used for finding alternative splicings for genes, including alternative splicings that are conserved between genes from related organisms. We also show how sampling from the posterior distribution is a natural way to compute probabilities for predicted exons and gene structures being correct under the assumed model. Finally, we describe a new memory efficient sampling algorithm for certain classes of HMMs which provides a practical sampling alternative to the Hirschberg algorithm for optimal alignment. The ideas presented have applications not only to gene finding and HMMs but more generally to stochastic context free grammars and RNA structure prediction.
Learn Multiple-Kernel SVMs for Domain Adaptation in Hyperspectral Data

This letter presents a novel semisupervised method for addressing a domain adaptation problem in the classification of hyperspectral data. To overcome the influence of distribution bias between the source and target domains, we introduce the domain transfer multiple-kernel learning to simultaneously minimize the maximum mean discrepancy criterion and the structural risk functional of support vector machines. Then, the pairwise binary classifiers are merged as the multiclass classifier for solving the classification problem in hyperspectral data. Both bias and nonbias sampling strategies are introduced to evaluate the robustness of the proposed method against the spectral distribution bias. The results obtained from real data sets show that the proposed method can achieve higher classification accuracy even with cross-domain distribution bias and provide robust solutions with different labeled and unlabeled data sizes.
Entrepreneurship and Rent Seeking under Credit Constraints ∗

In many countries, stringent regulations constrain firm efficiency and present opportunities for bribing. Still, little is known about the economy-wide consequences of corruption. I study the aggregate effects of business regulations that encourage bribery through the lens of a model in which financially constrained entrepreneurs are subject to both idiosyncratic productivity shocks and a policy distortion that reduces the optimal size of the firm. These entrepreneurs have access to a rent-seeking technology that allows them to bribe to mitigate the policy distortion. While bribery eliminates some of the “sand in the gears,” its full private benefits accrue to the richest—and not necessarily to the most productive entrepreneurs. This misallocation magnifies the aggregate productivity losses from financial frictions and slows down both capital and wealth accumulation. Further, my results suggest that reducing the regulatory burden on firms achieves far better results than “crackdowns” on corruption, which are aimed at reducing bribery without changing the underlying regulations that motivate it. Finally, holding constant the economy’s access to external financing, I show that a low external debt-to-GDP ratio can arise from policies that restrict firm size and encourage bribery. Thus, inefficient institutions potentially dampen the gains from financial development.
Quantitative Analysis of the Self-Assembly Strategies of Intermediate Filaments from Tetrameric Vimentin

In vitro assembly of intermediate filaments from tetrameric vimentin consists of a very rapid phase of tetramers laterally associating into unit-length filaments and a slow phase of filament elongation. We focus in this paper on a systematic quantitative investigation of two molecular models for filament assembly, recently proposed in (Kirmse et al. J. Biol. Chem. 282, 52 (2007), 18563-18572), through mathematical modeling, model fitting, and model validation. We analyze the quantitative contribution of each filament elongation strategy: with tetramers, with unit-length filaments, with longer filaments, or combinations thereof. In each case, we discuss the numerical fitting of the model with respect to one set of data, and its separate validation with respect to a second, different set of data. We introduce a high-resolution model for vimentin filament self-assembly, able to capture the detailed dynamics of filaments of arbitrary length. This provides much more predictive power for the model, in comparison to previous models where only the mean length of all filaments in the solution could be analyzed. We show how kinetic observations on low-resolution models can be extrapolated to the high-resolution model and used for lowering its complexity.
Using location for personalized POI recommendations in mobile environments

Internet-based recommender systems have traditionally employed collaborative filtering techniques to deliver relevant "digital" results to users. In the mobile Internet however, recommendations typically involve "physical" entities (e.g., restaurants), requiring additional user effort for fulfillment. Thus, in addition to the inherent requirements of high scalability and low latency, we must also take into account a "convenience" metric in making recommendations. In this paper, we propose an enhanced collaborative filtering solution that uses location as a key criterion for generating recommendations. We frame the discussion in the context of our "restaurant recommender" system, and describe preliminary results that indicate the utility of such an approach. We conclude with a look at open issues in this space, and motivate a future discussion on the business impact and implications of mining the data in such systems
Association Between XRCC3 Thr241Met Polymorphism and Risk of Breast Cancer: Meta-Analysis of 23 Case-Control Studies

Background Studies have shown that gene and environmental factors, such as BRCA1/2 mutations, ionized radiation, and chemical carcinogens, are related with breast cancer. X-ray repair cross-complementing group 3 (XRCC3) is involved in homologous repair of double DNA breaks. It was reported that Thr241Met single-nucleotide polymorphism (SNP) in XRCC3 is associated with increased risk of breast cancer. However, the finding remains controversial. The current meta-analysis aims to determine whether XRCC3 Thr241Met polymorphism is associated with increased risk of breast cancer. Material/Methods We performed a meta-analysis of association between XRCC3 T241M polymorphism and the risk of breast cancer. Crude odds ratios (ORs) together with 95% confidence intervals (CIs) were used to assess the strength of association in dominant, recessive, and homozygote models. Results We included 23 studies consisting of 13513 cases and 14100 controls in our study. For meta-analysis on the entire database, association of the SNP and breast cancer risk was observed in recessive (OR=1.10, 95% CI: 1.03–1.18, p=0.005) and homozygote (OR=1.09, 95% CI: 1.01–1.18, p=0.023) models. For the analysis on the Asian population subgroup, association of the SNP and breast cancer risk was also observed in recessive (OR=1.615, 95% CI: 1.17–2.228, p=0.004) and homozygote (OR=1.609, 95% CI: 1.154-2.241, p=0.005) models. For the evaluation of the patients without family history of breast cancer, association of the SNP and breast cancer risk was observed in dominant (OR=1.364, 95% CI: 1.096–1.698, p=0.005), recessive (OR=1.336, 95% CI: 0.999–1.788, p=0.051) and homozygote (OR=1.492, 95% CI: 1.085–2.051, p=0.014) models. Conclusions We can conclude that XRCC3 Thr241Met polymorphism might be associated with breast cancer risk, especially in Asian populations and in patients without family history of breast cancer.
The Approximability and Integrality Gap of Interval Stabbing and Independence Problems

Motivated by problems such as rectangle stabbing in the plane, we study the minimum hitting set and maximum independent set problems for families of d-intervals and d-union-intervals. We obtain the following: (1) constructions yielding asymptotically tight lower bounds on the integrality gaps of the associated natural linear programming relaxations; (2) an LP-relative dapproximation for the hitting set problem ond-intervals; (3) a proof that the approximation ratios for independent set on families of 2-intervals and 2-union-intervals can be improved to match tight duality gap lower bounds obtained via topological arguments, if one has access to an oracle for a PPAD-complete problem related to nding Borsuk-Ulam xed-points.
Characterization of solar panel using capacitive load

This work aims to design and assembly a characterization of solar panel using capacitive load. This device provides information that allows the user to increase the efficiency of power generation in photovoltaic systems. Detection of operation issues or abnormal conditions such as partial shading caused by dirt or objects can affect adversely the maximum power point. Simulation results were used to validate the model and allowed the development of the device. Tests were realized with the prototype in a panel of 48 W. Measured results present accuracy when compared with the simulated results validating the prototype.
Reconstruction of light field with spectral information from compressive 2D projections

A light field is an image with two spatial and two angular dimensions. Different acquisition systems have been used to capture light fields such as cameras and microlenses arrays. Compressive light field imaging is a recent technique that allows to recover a light field from just certain measurements sensed in a single 2-dimensional focal plane array (FPA). The essential optical element in this system is a binary coded aperture that blocks and unblocks the light rays before they impinge on the detector. Currently, it is studied the inclusion of a spectral dimension on the 4D light field with the aim to capture different spectral bands. This work presents a new acquisition model that includes the spectral dimension. The proposed acquisition model replaces the traditional binary coded apertures used in compressive light field by an array of optical filters which modulate the scene not only in the spatio - angular dimensions but spectrally as well. Simulations show that it is possible to reconstruct a light field with spectral information with only certain measures of the scene.
High-Resolution Quantification of Focal Adhesion Spatiotemporal Dynamics in Living Cells

Focal adhesions (FAs) are macromolecular complexes that provide a linkage between the cell and its external environment. In a motile cell, focal adhesions change size and position to govern cell migration, through the dynamic processes of assembly and disassembly. To better understand the dynamic regulation of focal adhesions, we have developed an analysis system for the automated detection, tracking, and data extraction of these structures in living cells. This analysis system was used to quantify the dynamics of fluorescently tagged Paxillin and FAK in NIH 3T3 fibroblasts followed via Total Internal Reflection Fluorescence Microscopy (TIRF). High content time series included the size, shape, intensity, and position of every adhesion present in a living cell. These properties were followed over time, revealing adhesion lifetime and turnover rates, and segregation of properties into distinct zones. As a proof-of-concept, we show how a single point mutation in Paxillin at the Jun-kinase phosphorylation site Serine 178 changes FA size, distribution, and rate of assembly. This study provides a detailed, quantitative picture of FA spatiotemporal dynamics as well as a set of tools and methodologies for advancing our understanding of how focal adhesions are dynamically regulated in living cells. A full, open-source software implementation of this pipeline is provided at http://gomezlab.bme. unc.edu/tools.
Local Jet Based Similarity for NL-Means Filtering

Reducing the dimension of local descriptors in images is useful to perform pixels comparison faster. We show here that, for computing the NL-means denoising filter, image patches can be favourably replaced by a vector of spatial derivatives (local jet), to calculate the similarity between pixels. First, we present the basic, limited range implementation, and compare it with the original NL-means. We use a fast estimation of the noise variance to automatically adjust the decay parameter of the filter. Next, we present the unlimited range implementation using nearest neighbours search in the local jet space, based on a binary search tree representation.
The simulation of the Antarctic ozone hole by chemistry-climate models

While chemistry-climate models are able to reproduce many characteristics of the global total column ozone field and its long-term evolution, they have fared less well in simulating the commonly used diagnostic of the area of the Antarctic ozone hole i.e. the area within the 220 Dobson Unit (DU) contour. Two possible reasons for this are: (1) the underlying Global Climate Model (GCM) does not correctly simulate the size of the polar vortex, and (2) the stratospheric chemistry scheme incorporated into the GCM, and/or the model dynamics, results in systematic biases in the total column ozone fields such that the 220 DU contour is no longer appropriate for delineating the edge of the ozone hole. Both causes are examined here with a view to developing ozone hole area diagnostics that better suit measurement-model inter-comparisons. The interplay between the shape of the meridional mixing barrier at the edge of the vortex and the meridional gradients in total column ozone across the vortex edge is investigated in measurements and in 5 chemistry-climate models (CCMs). Analysis of the simulation of the polar vortex in the CCMs shows that the first of the two possible causes does play a role in some models. This in turn affects the ability of the models to simulate the large observed meridional gradients in total column ozone. The second of the two causes also strongly affects the ability of the CCMs to track the observed size of the ozone hole. It is shown that by applying a common algorithm to the CCMs for selecting a delineating threshold unique to each model, a more appropriate diagnostic of ozone hole area can be generated that shows better agreement with that derived from observations.
An architecture for user modeling on Intelligent and Adaptive Scheduling Systems

User modeling and user adaptive interaction research areas are becoming crucial applied issues to understand and support users as they interact with technology. Modeling the decisions to be made and the constraints placed by market globalization in a way that can address the needs of all stakeholders has been a long time area of academic and industrial research, mainly for Planning, Scheduling, and Strategic decision making areas. Business analysts, developers, and organizations involved in all phases of the business value chain have requirements for applied business insight through modeling. In this paper, an architecture for user modeling on Intelligent and Adaptive Scheduling System is proposed.