Miracle-dz/UniFAR-FTUs huggingface dataset:
Association between Dietary Xanthophyll (Lutein and Zeaxanthin) Intake and Early Age-Related Macular Degeneration: The Atherosclerosis Risk in Communities Study

ABSTRACT Purpose: To examine the association between xanthophyll intake and prevalent early age-related macular degeneration (AMD) using data from the Atherosclerosis Risk in Communities Study (n = 10,295). Potential effect modification by genetic polymorphisms and biomarkers of high-density lipoprotein (HDL) metabolism was explored. Methods: Xanthophyll intake was assessed at visit 1 (1987–1989) using food frequency questionnaires. Prevalent early AMD was assessed at visit 3 (1993–1995) via retinal photographs. Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI) for AMD by quintiles of xanthophyll intake, adjusted for age, sex, race, field center, and pack-years of smoking. To evaluate effect modification, the association between tertiles (T) of xanthophyll intake and AMD was stratified by complement factor H (CFH) rs1061170 and age-related maculopathy susceptibility 2 (ARMS2) rs10490924 genotypes, as well as by median cutpoints of HDL biomarkers. Results: Xanthophyll intake was not associated with AMD in the overall sample, Caucasians (n = 8257), or African-Americans (n = 2038). Exploratory analyses observed that the association between xanthophyll intake and AMD varied statistically significantly by CFH rs1061170 genotype among Caucasians (p for interaction = 0.045) but not African Americans. No interactions were observed between xanthophyll intake and ARMS2 rs10490924. Moreover, higher xanthophyll intake was associated with decreased odds of AMD among participants with lower HDL (OR = 0.79, 95% CI 0.57–1.09) but not higher HDL (p for interaction = 0.048). Conclusion: Xanthophyll intake was not associated with early AMD. Further studies to investigate this association by genetic susceptibility or variations in HDL metabolism are needed.
A mutagenesis-derived Lrp5 mouse mutant with abnormal retinal vasculature and low bone mineral density

Purpose Familial exudative vitreoretinopathy (FEVR) is caused by mutations in the genes encoding low-density lipoprotein receptor-related protein (LRP5) or its interacting partners, namely frizzled class receptor 4 (FZD4) and norrin cystine knot growth factor (NDP). Mouse models for Lrp5, Fzd4, and Ndp have proven to be important for understanding the retinal pathophysiology underlying FEVR and systemic abnormalities related to defective Wnt signaling. Here, we report a new mouse mutant, tvrm111B, which was identified by electroretinogram (ERG) screening of mice generated in the Jackson Laboratory Translational Vision Research Models (TVRM) mutagenesis program. Methods ERGs were used to examine outer retinal physiology. The retinal vasculature was examined by in vivo retinal imaging, as well as by histology and immunohistochemistry. The tvrm111B locus was identified by genetic mapping of mice generated in a cross to DBA/2J, and subsequent sequencing analysis. Gene expression was examined by real-time PCR of retinal RNA. Bone mineral density (BMD) was examined by peripheral dual-energy X-ray absorptiometry. Results The tvrm111B allele is inherited as an autosomal recessive trait. Genetic mapping of the decreased ERG b-wave phenotype of tvrm111B mice localized the mutation to a region on chromosome 19 that included Lrp5. Sequencing of Lrp5 identified the insertion of a cytosine (c.4724_4725insC), which is predicted to cause a frameshift that disrupts the last three of five conserved PPPSPxS motifs in the cytoplasmic domain of LRP5, culminating in a premature termination. In addition to a reduced ERG b-wave, Lrp5tvrm111B homozygotes have low BMD and abnormal features of the retinal vasculature that have been reported previously in Lrp5 mutant mice, including persistent hyaloid vessels, leakage on fluorescein angiography, and an absence of the deep retinal capillary bed. Conclusions The phenotype of the Lrp5tvrm111B mutant includes abnormalities of the retinal vasculature and of BMD. This model may be a useful resource to further our understanding of the biological role of LRP5 and to evaluate experimental therapies for FEVR or other conditions associated with LRP5 dysfunction.
Groupwise Retargeted Least-Squares Regression

In this brief, we propose a new groupwise retargeted least squares regression (GReLSR) model for multicategory classification. The main motivation behind GReLSR is to utilize an additional regularization to restrict the translation values of ReLSR, so that they should be similar within same class. By analyzing the regression targets of ReLSR, we propose a new formulation of ReLSR, where the translation values are expressed explicitly. On the basis of the new formulation, discriminative least-squares regression can be regarded as a special case of ReLSR with zero translation values. Moreover, a groupwise constraint is added to ReLSR to form the new GReLSR model. Extensive experiments on various machine leaning data sets illustrate that our method outperforms the current state-of-the-art approaches.
Scheduling in D2D Underlaid Cellular Networks with Deadline Constraints

In this paper, we develop a scheduling algorithm for device-to-device (D2D) cellular networks with deadline constraints via the convex delay cost approach. At the beginning of each time slot, the algorithm allocates all available channels to the users, and each user can choose to transmit in different modes. After characterizing the transmission rates and defining the utility for each possible scheduling decision, we propose power optimization algorithms to maximize the utility for each type of decision. Our scheduling algorithm allocates each channel according to the decision that provides the maximum utility value, and it manages mode selection, channel allocation and power optimization. Via simulation results, we discuss the parameter selection for our algorithm and verify the performance improvements by allowing D2D users to share channels with other users.
Widar: Decimeter-Level Passive Tracking via Velocity Monitoring with Commodity Wi-Fi

Various pioneering approaches have been proposed for Wi-Fi-based sensing, which usually employ learning-based techniques to seek appropriate statistical features, yet do not support precise tracking without prior training. Thus to advance passive sensing, the ability to track fine-grained human mobility information acts as a key enabler. In this paper, we propose Widar, a Wi-Fi-based tracking system that simultaneously estimates a human's moving velocity (both speed and direction) and location at a decimeter level. Instead of applying statistical learning techniques, Widar builds a theoretical model that geometrically quantifies the relationships between CSI dynamics and the user's location and velocity. On this basis, we propose novel techniques to identify frequency components related to human motion from noisy CSI readings and then derive a user's location in addition to velocity. We implement Widar on commercial Wi-Fi devices and validate its performance in real environments. Our results show that Widar achieves decimeter-level accuracy, with a median location error of 25 cm given initial positions and 38 cm without them and a median relative velocity error of 13%.
Dynamic Air Permeability Behavior of Nylon 66 Airbag Fabrics

A novel dynamic air permeability tester (Textest FX3350) is designed to project air at high pressure through automotive airbag fabrics. The abilities of the machine are studied, and a standard operating procedure is defined. A series of fabrics is tested with the machine, and the results are correlated with readings obtained from the conventional static tester, which operates at a lower pressure. A nonlinear relationship exists between air pressure and fabric air permeability.
Datasize-Aware High Dimensional Configurations Auto-Tuning of In-Memory Cluster Computing

In-Memory cluster Computing (IMC) frameworks (e.g., Spark) have become increasingly important because they typically achieve more than 10× speedups over the traditional On-Disk cluster Computing (ODC) frameworks for iterative and interactive applications. Like ODC, IMC frameworks typically run the same given programs repeatedly on a given cluster with similar input dataset size each time. It is challenging to build performance model for IMC program because: 1) the performance of IMC programs is more sensitive to the size of input dataset, which is known to be difficult to be incorporated into a performance model due to its complex effects on performance; 2) the number of performance-critical configuration parameters in IMC is much larger than ODC (more than 40 vs. around 10), the high dimensionality requires more sophisticated models to achieve high accuracy. To address this challenge, we propose DAC, a datasize-aware auto-tuning approach to efficiently identify the high dimensional configuration for a given IMC program to achieve optimal performance on a given cluster. DAC is a significant advance over the state-of-the-art because it can take the size of input dataset and 41 configuration parameters as the parameters of the performance model for a given IMC program, --- unprecedented in previous work. It is made possible by two key techniques: 1) Hierarchical Modeling (HM), which combines a number of individual sub-models in a hierarchical manner; 2) Genetic Algorithm (GA) is employed to search the optimal configuration. To evaluate DAC, we use six typical Spark programs, each with five different input dataset sizes. The evaluation results show that DAC improves the performance of six typical Spark programs, each with five different input dataset sizes compared to default configurations by a factor of 30.4x on average and up to 89x. We also report that the geometric mean speedups of DAC over configurations by default, expert, and RFHOC are 15.4x, 2.3x, and 1.5x, respectively.
Interacting with hidden content using content-aware free-space transparency

We present <i>content-aware free-space transparency</i>, an approach to viewing and manipulating the otherwise hidden content of obscured windows through unimportant regions of overlapping windows. Traditional approaches to interacting with otherwise obscured content in a window system render an entire window uniformly transparent. In contrast, content-aware free-space transparency uses opaque-to-transparent gradients and image-processing filters to minimize the interference from overlapping material, based on properties of that material. By increasing the amount of simultaneously visible content and allowing basic interaction with otherwise obscured content, without modifying window geometry, we believe that free-space transparency has the potential to improve user productivity.
Delft University of Technology A Module-System Discipline for Model-Driven Software Development

Model-driven development is a pragmatic approach to software development that embraces domainspecific languages (DSLs), where models correspond to DSL programs. A distinguishing feature of modeldriven development is that clients of a model can select from an open set of alternative semantics of the model by applying different model transformations. However, in existing model-driven frameworks, dependencies between models, model transformations, and generated code artifacts are either implicit or globally declared in build scripts, which impedes modular reasoning, separate compilation, and programmability in general. We propose the design of a new module system that incorporates models and model transformations as modules. A programmer can apply transformations in import statements, thus declaring a dependency on generated code artifacts. Our design enables modular reasoning and separate compilation by preventing hidden dependencies, and it supports mixing modeling artifacts with conventional code artifacts as well as higher-order transformations. We have formalized our design and the aforementioned properties and have validated it by an implementation and case studies that show that our module system successfully integrates model-driven development into conventional programming languages.
Formulation and invitro characterization of domperidone loaded solid lipid nanoparticles

Domperidone (DOM) is a dopamine- receptor (D2) antagonist, widely used in the treatment of motion-sickness. The pharmacokinetic parameters of DOM make it a suitable candidate for development of Solid Lipid Nanoparticles (SLN). The purpose of the present investigation was to prepare and evaluate Domperidone loaded solid lipid nanoparticles (DOM-SLN). DOM loaded SLN were prepared by hot homogenization followed by ultrasonication technique. DOM- SLN were characterized for particle size, polydispersity index (PDI), zeta potential and entrapment efficiency and invitro drug release behaviour were investigated. P-XRD and DSC analysis were performed to characterize the state of drug and lipid modification. Shape and surface morphology were determined by transmission electron microscopy (TEM). SLN formulations were subjected to stability study over a period of 30 days. The mean particle size, PDI, Zeta potential and entrapment efficiency of optimised SLN were found to be 56 nm, 0.154, 34 mV, 98.5 %. P-XRD and DSC studies revealed that DOM was in an amorphous state. Shape and surface morphology was determined by TEM revealed fairly spherical shape of nanoparticles. Invitro release studies demonstrated that the SLN formulation possessed a controlled release over a period of 48 hrs. SLN formulations were subjected to stability over a period of 30 days. Fairly spherical shaped, stable and controlled release DOM-SLN could be prepared by hot homogenization followed by ultrasonication technique.
MR-IoT: An information centric MapReduce framework for IoT

The number of devices connected by the Internet of Things (IoT) has exceeded billions today. As IoT grows, so do the volumes of data it produces. Distributed data process (such as: MapReduce) performs better than a single central server by considering the big data set. Moreover, many IoT network spans widely in geographical areas, such as smart cities and supply chain management. Thus, the collected data can be processed in distributed way before transmission. This paper studies how to develop a MapReduce framework to process massive IoT data. Because in many cases IoT consumers desire more to get the meaningful knowledge than to build connections with multiple devices. The proposed framework should also fits well with the information-centric nature of IoT applications. As a result, our design (MR-IoT) is built upon a novel Information Centric Networking (ICN) architecture — NDN. It defines two schemes to execute MapReduce tasks on IoT: computational tree construction and computational task dissemination. A testbed is built on ndnSIM to verify the design and the result shows the defined schemes work correctly and the network traffic is significantly decreased.
Rapid Reconstruction of 3D Neuronal Morphology from Light Microscopy Images with Augmented Rayburst Sampling

Digital reconstruction of three-dimensional (3D) neuronal morphology from light microscopy images provides a powerful technique for analysis of neural circuits. It is time-consuming to manually perform this process. Thus, efficient computer-assisted approaches are preferable. In this paper, we present an innovative method for the tracing and reconstruction of 3D neuronal morphology from light microscopy images. The method uses a prediction and refinement strategy that is based on exploration of local neuron structural features. We extended the rayburst sampling algorithm to a marching fashion, which starts from a single or a few seed points and marches recursively forward along neurite branches to trace and reconstruct the whole tree-like structure. A local radius-related but size-independent hemispherical sampling was used to predict the neurite centerline and detect branches. Iterative rayburst sampling was performed in the orthogonal plane, to refine the centerline location and to estimate the local radius. We implemented the method in a cooperative 3D interactive visualization-assisted system named flNeuronTool. The source code in C++ and the binaries are freely available at http://sourceforge.net/projects/flneurontool/. We validated and evaluated the proposed method using synthetic data and real datasets from the Digital Reconstruction of Axonal and Dendritic Morphology (DIADEM) challenge. Then, flNeuronTool was applied to mouse brain images acquired with the Micro-Optical Sectioning Tomography (MOST) system, to reconstruct single neurons and local neural circuits. The results showed that the system achieves a reasonable balance between fast speed and acceptable accuracy, which is promising for interactive applications in neuronal image analysis.
Seasonal Variation in the Spatial Distribution of Basking Sharks (Cetorhinus maximus) in the Lower Bay of Fundy, Canada

The local distribution of basking sharks in the Bay of Fundy (BoF) is unknown despite frequent occurrences in the area from May to November. Defining this species’ spatial habitat use is critical for accurately assessing its Special Concern conservation status in Atlantic Canada. We developed maximum entropy distribution models for the lower BoF and the northeast Gulf of Maine (GoM) to describe spatiotemporal variation in habitat use of basking sharks. Under the Maxent framework, we assessed model responses and distribution shifts in relation to known migratory behavior and local prey dynamics. We used 10 years (2002-2011) of basking shark surface sightings from July-October acquired during boat-based surveys in relation to chlorophyll-a concentration, sea surface temperature, bathymetric features, and distance to seafloor contours to assess habitat suitability. Maximum entropy estimations were selected based on AICc criterion and used to predict habitat utilizing three model-fitting routines as well as converted to binary suitable/non-suitable habitat using the maximum sensitivity and specificity threshold. All models predicted habitat better than random (AUC values >0.796). From July-September, a majority of habitat was in the BoF, in waters >100 m deep, and in the Grand Manan Basin. In October, a majority of the habitat shifted southward into the GoM and to areas >200 m deep. Model responses suggest that suitable habitat from July - October is dependent on a mix of distance to the 0, 100, 150, and 200 m contours but in some models on sea surface temperature (July) and chlorophyll-a (August and September). Our results reveal temporally dynamic habitat use of basking sharks within the BoF and GoM. The relative importance of predictor variables suggests that prey dynamics constrained the species distribution in the BoF. Also, suitable habitat shifted minimally from July-September providing opportunities to conserve the species during peak abundance in the region.
Transient Conjugate Heat Transfer Analysis of a Turbine Stage using CFD

Radial turbine stage in a turbocharger is subjected in the field to a time varying turbine inlet temperature. Accurate fatigue life calculation of the turbine wheel and the turbine housing requires an accurate prediction of the metal temperature distribution that is varying in time. Conjugate Heat Transfer analysis of a radial turbine stage was performed using Computational Fluid Dynamics (CFD) to achieve the goal of accurately predicting metal temperature distribution. The flow model consisted of a radial turbine wheel and the housing with both the fluid and metal zones modeled, and non-conformal interfaces between solid-fluid zones. The wheel fluid and metal part were modeled using hexahedral cells, while the housing fluid and metal parts were meshed using tetrahedral cells. A mixing plane was created between the rotating wheel fluid zone and the non-rotating housing fluid zone.
Dietary fructose exacerbates hepatocellular injury when incorporated into a methionine‐choline‐deficient diet

Background: Methionine‐choline‐deficient (MCD) diets cause steatohepatitis in rodents and are used to model fatty liver disease in human beings. Recent studies have identified sucrose as a major contributor to MCD‐related liver disease through its ability to promote hepatic de novo lipogenesis.
Undulation Training for Development of Hierarchical Fitness and Improved Firefighter Job Performance

Peterson, MD, Dodd, DJ, Alvar, BA, Rhea, MR, and Favre, M. Undulation training for development of hierarchical fitness and improved firefighter job performance. J Strength Cond Res 22(5): 1683-1695, 2008-Firefighters routinely encounter physical demands that contribute to countless musculoskeletal injuries. Seemingly, a progressive prescription for fitness would offer superior protection against intrinsic job risks. The purpose of this study was to investigate the influence of two resistance training interventions on fitness adaptations among firefighters, and to assess the degree of transfer to job-specific tasks. Firefighter trainees were recruited for participation in this experimental study. Two distinct, periodized training models-undulation training (UT; n = 7) and standard training control (STCo; n = 7)-were used to determine the differential affects for muscular fitness and transference to firefighter performance batteries. Specific tests were administered to evaluate 1) upper- and lower-body muscular strength, 2) lower-body power output, 3) sprint speed and jumping ability, 4) anthropometry, and 5) firefighter Grinder performance (i.e., firefighter-specific job tests). The 9-week UT experimental treatment prescription was characterized by daily “nonlinear” fluctuations in training to preferentially elicit specific and distinct muscular fitness components, whereas the STCo treatment conformed to a traditional model, in which each fitness component was systematically targeted during a specified mesocycle. For both treatments, nearly all fitness and performance measures significantly increased from baseline (p < 0.05), with a trend in favor of UT. Further, the UT group experienced significantly greater improvements (p < 0.05) in Grinder performance over the STCo group. Calculation of effect sizes identified meaningful differences in the magnitude of changes in outcomes (effect size > 0.50) in favor of UT for measures of thigh circumference, vertical jump, 1RM squat, Grinder performance, and peak power output. These findings suggest a potentially greater stimulus for multidimensional muscular fitness development with UT, over a periodized STCo. This study effectively establishes that UT may offer a greater transference to performance for firefighter-specific job tasks.
Compact Physical Models for AlGaN/GaN MIS-FinFET on Threshold Voltage and Saturation Current

Physical models for threshold voltage and drain saturation current of AlGaN/GaN MIS-FinFETs have been established and verified by TCAD simulation to provide guidelines for the GaN MIS-FinFETs-based designs. The depletion effect on the two dimensional electron gases (2DEG) channel by sidewall fin gate is modeled based on the numerical solutions of Poisson equation with proper boundary conditions. The conduction band at the 2DEG zone lifted above the Fermi level by the gate bias is modeled for getting the threshold voltage. The device saturation current, comprising 2DEG channel current and sidewall channel current, as a function of fin width has also been modeled. The influences of device design parameters on the threshold voltage and saturation current are analyzed by the proposed models with verification.