Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Debris Formation with a Porous Tantalum Glenoid Component: Radiographic Analysis with 2-Year Minimum Follow-up.

BACKGROUND Porous tantalum has been used effectively in hip, knee, and reverse shoulder arthroplasty implants. However, a first-generation porous tantalum glenoid component for use in anatomic shoulder arthroplasty previously demonstrated failure, with failure usually preceded by the appearance of intra-articular metallic debris. After redesign, the component was reintroduced in 2009. The purpose of the current study was to evaluate the radiographic and clinical outcomes of the redesigned glenoid component. METHODS Sixty-eight patients undergoing total shoulder arthroplasty received a Trabecular Metal porous tantalum glenoid component (73 components; 5 patients underwent staged bilateral procedures). No polymethylmethacrylate cement was used (off-label usage in the U.S.). A grading system to assess metallic debris formation was developed using radiographs of the previous generation of porous tantalum glenoid components that failed. Radiographs from the current series were independently reviewed by 2 shoulder arthroplasty specialists, and their results were compared. Glenoid components were evaluated for signs of bone ingrowth and metallic debris formation. RESULTS Sixty-six (90%) of the 73 components were evaluated at a minimum of 2 years of follow-up (mean radiographic follow-up of 50.8 months; range, 24 to 68 months). Of these, 92.4% demonstrated minimal or no glenoid radiolucency. Overall, the prevalence of metallic tantalum debris formation was 44% (29 of 66). Sequential radiograph review demonstrated that the incidence of metallic debris formation increased for each year of follow-up, with radiographs from 2, 3, 4, and ≥5 years of follow-up demonstrating a metallic debris incidence of 23%, 36%, 49%, and 52%, respectively. Additionally, the severity of metallic debris formation increased with follow-up duration. There was no component dissociation or revision due to implant breakage in this series. CONCLUSIONS The porous tantalum glenoid component studied had excellent short-term component fixation. However, the development of metallic debris, increasing in both overall incidence and degree of severity over time, raises concern for potential failure of this glenoid component. Longer follow-up is required. LEVEL OF EVIDENCE Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.
Geodetically Inferred Locking State of the Cascadia Megathrust Based on a Viscoelastic Earth Model

In a viscoelastic Earth, stresses slowly built up due to fault locking are relaxed concurrently during the entire interseismic period. This interseismic stress relaxation causes crustal deformation much farther away from the locked fault than can be explained using elastic models that neglect the relaxation. Here we develop a viscoelastic geodetic inversion model to address this problem at Cascadia. We invert ~500 horizontal velocity vectors based on continuous and campaign geodetic measurements over the past two decades. Ambiguities arising from long‐term rotation of upper‐plate crustal blocks are addressed by test‐correcting the geodetic velocities with two different block‐motion models. Fault back slip (i.e., slip deficit) Green's functions are derived using a Maxwell viscoelastic finite element model with realistic subduction zone structure and megathrust geometry. The preferred model features a narrow and shallow megathrust locked zone, consistent with earlier thermorheological reasoning. For an elastic model to fit the data to the same fidelity, megathrust locking has to extend to much greater depths. However, even with the viscoelastic model, the land‐based geodetic data still cannot resolve whether there is some creep (incomplete locking) in the shallowest part of the megathrust far offshore. Neither can the land data fully resolve along‐strike variations of the locking state. These ambiguities can be resolved only when adequate seafloor geodetic data are obtained.
Computationally Efficient Neural Feature Extraction for Spike Sorting in Implantable High-Density Recording Systems

Modern microelectrode arrays acquire neural signals from hundreds of neurons in parallel that are subsequently processed for spike sorting. It is important to identify, extract, and transmit appropriate features that allow accurate spike sorting while using minimum computational resources. This paper describes a new set of spike sorting features, explicitly framed to be computationally efficient and shown to outperform principal component analysis (PCA)-based spike sorting. A hardware friendly architecture, feasible for implantation, is also presented for detecting neural spikes and extracting features to be transmitted for off chip spike classification. The proposed feature set does not require any off-chip training, and requires about 5% of computations as compared to the PCA-based features for the same classification accuracy, tested for spike trains with a broad range of signal-to-noise ratio. Our simulations show a reduction of required bandwidth to about 2% of original data rate, with an average classification accuracy of greater than 94% at a typical signal to noise ratio of 5 dB.
Joint Eurographics -ieee Tcvg Symposium on Visualization (2003) Smart Hardware-accelerated Volume Rendering

For volume rendering of regular grids the display of view-plane aligned slices has proven to yield both good quality and performance. In this paper we demonstrate how to merge the most important extensions of the original 3D slicing approach, namely the pre-integration technique, volumetric clipping, and advanced lighting. Our approach allows the suppression of clipping artifacts and achieves high quality while offering the flexibility to explore volume data sets interactively with arbitrary clip objects. We also outline how to utilize the proposed volumetric clipping approach for the display of segmented data sets. Moreover, we increase the rendering quality by implementing efficient over-sampling with the pixel shader of consumer graphics accelerators. We give prove that at least 4-times over-sampling is needed to reconstruct the ray integral with sufficient accuracy even with pre-integration. As an alternative to this brute-force over-sampling approach we propose a hardware-accelerated ray caster which is able to perform over-sampling only where needed and which is able to gain additional speed by early ray termination and space leaping.
Filtering: a method for solving graph problems in MapReduce

The MapReduce framework is currently the de facto standard used throughout both industry and academia for petabyte scale data analysis. As the input to a typical MapReduce computation is large, one of the key requirements of the framework is that the input cannot be stored on a single machine and must be processed in parallel. In this paper we describe a general algorithmic design technique in the MapReduce framework called filtering. The main idea behind filtering is to reduce the size of the input in a distributed fashion so that the resulting, much smaller, problem instance can be solved on a single machine. Using this approach we give new algorithms in the MapReduce framework for a variety of fundamental graph problems for sufficiently dense graphs. Specifically, we present algorithms for minimum spanning trees, maximal matchings, approximate weighted matchings, approximate vertex and edge covers and minimum cuts. In all of these cases, we parameterize our algorithms by the amount of memory available on the machines allowing us to show tradeoffs between the memory available and the number of MapReduce rounds. For each setting we will show that even if the machines are only given substantially sublinear memory, our algorithms run in a constant number of MapReduce rounds. To demonstrate the practical viability of our algorithms we implement the maximal matching algorithm that lies at the core of our analysis and show that it achieves a significant speedup over the sequential version.
An Elevated IL10 mRNA Combined with Lower TNFA mRNA Level in Active Rheumatoid Arthritis Peripheral Blood

We aimed to investigate the expression of pro-inflammatory cytokine genes TNFA, IL6, IL12B, IL23, IL18 and immunoregulatory genes FOXP3, TGFB1, and IL10 in the peripheral blood of patients with rheumatoid arthritis (RA) at messenger ribonucleic acid (mRNA) level. The total RNA was isolated from peripheral blood samples. Real-time quantitative PCR was used to perform TaqMan-based assays to quantify mRNAs from 8 target genes. IL23A was upregulated (1.7-fold), whereas IL6 (5-fold), FOXP3 (4-fold), and IL12B (2.56-fold) were downregulated in patients compared to controls. In addition, we found a strong positive correlation between the expression of FOXP3 and TNFA and a moderate correlation between FOXP3 and TGFB1. These data showed the imbalance of the T helper (Th) 1/Th17/ T regulatory (Treg) axis at a systemic level in RA. In cases with active disease, the IL10 gene expression was approximately 2-fold higher; in contrast, the expression of FOXP3 was significantly decreased (3.38-fold). The main part of patients with higher disease activity expressed upregulation of IL10 and downregulation of TNFA. Different disease activity cohorts could be separated based on IL10, TNFA and IL12B expression combinations. In conclusion, our results showed that active disease is associated with an elevated IL10 and lower TNFA mRNA level in peripheral blood cells of RA patients.
Imaging of Bing-Neel syndrome

A 64-year-old man with IgM kappa restricted Waldenstrom macroglobulinemia (WM) presented with right frontal headache and transient expressive aphasia. MRI demonstrated regions of enhancement within periventricular white matter with associated FLAIR hyperintensity and no restricted diffusion (figure). CSF analysis showed leukocytes 2 cells/mm3 (71% lymphocytes), …
Modelling the impacts of the Pacific Ocean sea surface temperature anomalies on a drought event in southwestern China with a piecewise‐integration method

Model‐based sensitivity experiments are a widely used method for studying climate change attributions. In traditional climate sensitivity studies, “climate drift” occurs because of the accumulation of model errors during long‐term integrations, both in “actual” and “hypothetical” states. A more accurate piecewise‐integration method is used to reduce the model errors, dividing the continuous simulation into a series of sequential short‐term simulations. The model fields are updated at the end of each subinterval with analysis data for the “actual” state run, and with the sum of the analysis data and the difference between “hypothetical” and “actual” states for the “hypothetical” state run. This paper conducts sensitivity experiments with the continuous‐integration method and the piecewise‐integration method to evaluate the impacts of the central and eastern Pacific Ocean sea surface temperature anomalies (SSTAs) on the severe drought that occurred in southwestern China (SWC) in the winter of 2009/2010. The results show the following. (a) Model errors produced by the piecewise‐integration method for the actual state simulation are significantly less than those produced by the continuous‐integration method. (b) The intensity of the drought is significantly overestimated in continuous‐integration experiments. However, the drought can be accurately simulated in its spatial distribution and intensity via the piecewise‐integration method. Thus, climate sensitivity to changes in external forcing can be studied with greater credibility. (c) The warm central Pacific Ocean SSTAs may be the main cause of the drought over SWC. Adequate precipitation occurred over SWC when the centre of the SSTAs shifted to the east. (d) Warm SSTAs over the central Pacific Ocean influenced precipitation over SWC by weakening the water vapour transport branch from South China Sea to SWC. With a deficit of water vapour, the pronounced subsidence and warm temperatures were the main dynamic and thermodynamic factors that caused and maintained the drought.
Fishery Interaction Modeling of Cetacean Bycatch in the California Drift Gillnet Fishery to Inform a Dynamic Ocean Management Tool

FISHERY INTERACTION MODELING OF CETACEAN BYCATCH IN THE CALIFORNIA DRIFT GILLNET FISHERY TO INFORM A DYNAMIC OCEAN MANAGEMENT TOOL Nicholas B. Sisson Old Dominion University, 2018 Director: Dr. Sara M. Maxwell Understanding the drivers that lead to interaction between target species in a fishery and marine mammals is a critical aspect in efforts to reduce bycatch. In the California drift gillnet fishery static management approaches and gear changes have reduced bycatch but neither measure ascertains the underlying dynamics causing bycatch events. To avoid further potentially drastic measures such as hard caps, dynamic management approaches that consider the scales relevant to physical dynamics, animal movement and human use could be implemented. A key component to this approach is determining the factors that lead to fisheries interactions. Using 25 years (1990-2014) of National Oceanic and Atmospheric Administration fisheries’ observer data from the California drift gillnet fishery, we model the relative probability of bycatch (presence– absence) of four cetacean species in the California Current System (short-beaked common dolphin Delphinus delphis, northern right whale dolphins Lissodelphis borealis, Risso’s dolphins Grampus griseus, and Pacific white-sided dolphins Lagenorhynchus obliquidens). Due to the nature of protected species bycatch, these are rare-events, which cause a large amount of absences (zeros) in each species’ dataset. Using a data-assimilative configuration of the Regional Ocean Modeling System, we determined the capabilities of a flexible machine-learning algorithm to handle these zero-inflated datasets in order to explore the physical drivers of cetacean bycatch in the California drift gillnet fishery. Results suggest that cetacean bycatch probability has a complex relationship with the physical environment, with mesoscale variability acting as a strong driver. Through the modeling process, we observed varied responses to the range of sample sizes in the zero-inflated datasets, determining the minimum number of presences capable of building an accurate model. The selection of predictor variables and model evaluation statistics were found to play an important role in assessing the biological significance of our species distribution models. These results highlight the statistical capability (and incapability) of modeling techniques to predict the complex nature driving fishery interaction of cetacean bycatch in the California drift gillnet fishery. By determining where fisheries interactions are most likely to occur, we can inform near real-time management approaches to reduce bycatch while still allowing fishermen to meet their catch quotas.
GLOBAL ANALYSIS OF AGE-STRUCTURED WITHIN-HOST VIRUS MODEL

A mathematical model of a within-host viral infection with explicit age-since-infection structure for infected cells is presented. A global analysis of the model is conducted. It is shown that when the basic reproductive number falls below unity, the infection dies out. On the contrary, when the basic reproductive number exceeds unity, there exists a unique positive equilibrium that attracts all positive solutions of the model. The global stability analysis combines the existence of a compact global attractor and a Lyapunov function.
FOL-Based Approach for Improving Legal-GRL Modeling Framework: A Case for Requirements Engineering of Legal Regulations of Social Media

Requirements engineers need to have a comprehensive requirements modeling framework for modeling legal requirements, particularly for privacy-related regulations, which are required for IT systems. The nature of law demands a special approach for dealing with the complexity of regulations. In this paper, we integrate different approaches for modeling legal requirements into one unified framework. We use semantic parameterization technique and first-order logic (FOL) approach for extracting legal requirements from legal documents. We then use Goal-oriented Requirements Language (GRL) to illustrate and evaluate the models. The aim of this paper is to improve and extend the existing Legal-GRL framework using semantic parameterization process and FOL. We use social media as the example to illustrate our approach.
Quantification of Nanoparticle Dose and Vesicular Inheritance in Proliferating Cells

Assessing dose in nanoparticle–cell interactions is inherently difficult due to a complex multiplicity of possible mechanisms and metrics controlling particle uptake. The fundamental unit of nanoparticle dose is the number of particles internalized per cell; we show that this can be obtained for large cell populations that internalize fluorescent nanoparticles by endocytosis, through calibration of cytometry measurements to transmission electron microscopy data. Low-throughput, high-resolution electron imaging of quantum dots in U-2 OS cells is quantified and correlated with high-throughput, low-resolution optical imaging of the nanoparticle-loaded cells. From the correlated data, we obtain probability distribution functions of vesicles per cell and nanoparticles per vesicle. Sampling of these distributions and comparison to fluorescence intensity histograms from flow cytometry provide the calibration factor required to transform the cytometry metric to total particle dose per cell, the mean value of which is 2.4 million. Use of the probability distribution functions to analyze particle partitioning during cell division indicates that, while vesicle inheritance is near symmetric, highly variable vesicle loading leads to a highly asymmetric particle dose within the daughter cells.
The International Journal of Biostatistics A Targeted Maximum Likelihood Estimator of a Causal Effect on a Bounded Continuous Outcome

Targeted maximum likelihood estimation of a parameter of a data generating distribution, known to be an element of a semi-parametric model, involves constructing a parametric model through an initial density estimator with parameter ε representing an amount of fluctuation of the initial density estimator, where the score of this fluctuation model at ε = 0 equals the efficient influence curve/canonical gradient. The latter constraint can be satisfied by many parametric fluctuation models since it represents only a local constraint of its behavior at zero fluctuation. However, it is very important that the fluctuations stay within the semi-parametric model for the observed data distribution, even if the parameter can be defined on fluctuations that fall outside the assumed observed data model. In particular, in the context of sparse data, by which we mean situations where the Fisher information is low, a violation of this property can heavily affect the performance of the estimator. This paper presents a fluctuation approach that guarantees the fluctuated density estimator remains inside the bounds of the data model. We demonstrate this in the context of estimation of a causal effect of a binary treatment on a continuous outcome that is bounded. It results in a targeted maximum likelihood estimator that inherently respects known bounds, and consequently is more robust in sparse data situations than the targeted MLE using a naive fluctuation model. When an estimation procedure incorporates weights, observations having large weights relative to the rest heavily influence the point estimate and inflate the variance. Truncating these weights is a common approach to reducing the variance, but it can also introduce bias into the estimate. We present an alternative targeted maximum likelihood estimation (TMLE) approach that dampens the effect of these heavily weighted observations. As a substitution estimator, TMLE respects the global constraints of the observed data model. For example, when outcomes are binary, a fluctuation of an initial density estimate on the logit scale constrains predicted probabilities to be between 0 and 1. This inherent enforcement of bounds has been extended to continuous outcomes. Simulation study results indicate that this approach is on a par with, and many times superior to, fluctuating on the linear scale, and in particular is more robust when there is sparsity in the data.
Nonparametric Bayesian estimation structures in the wavelet domain of multiple noisy image copies

This paper addresses the recovery of an image from its multiple noisy copies using a nonparametric Bayesian estimator in the wavelet domain. Boubchir et al have proposed a prior statistical model based on the α-stable densities adapted to capture the sparseness of the wavelet detail coefficients. They used the scale mixture of Gaussians theorem as an analytical approximation for α-stable densities, which is not known in general, in order to obtain a closed-form expression of their Bayesian denoiser. Since the proposed estimator has worked well for one copy of corrupted image, we consider its extension to multiple copies in this paper. So, our contribution is to design two fusion structures based on the Bayesian denoiser and the traditional averaging operation, in order to combine all multiple noisy image copies to recover the noise free image. Because of the nonlinearity of the Bayesian denoiser, averaging then Bayesian denoising or Bayesian denoising then averaging will produce different estimators. We will demonstrate the effectiveness of our Bayesian denoiser fusion structures compared to other denoising approaches. Better performance comes at the expense of higher complexity.
Scaling the Si MOSFET: from bulk to SOI to bulk

Scaling the Si MOSFET is reconsidered. Requirements on subthreshold leakage control force conventional scaling to use high doping as the device dimension penetrates into the deep-submicrometer regime, leading to an undesirably large junction capacitance and degraded mobility. By studying the scaling of fully depleted SOI devices, the important concept of controlling horizontal leakage through vertical structures is highlighted. Several structural variations of conventional SOI structures are discussed in terms of a natural length scale to guide the design. The concept of vertical doping engineering can also be realized in bulk Si to obtain good subthreshold characteristics without large junction capacitance or heavy channel doping. >
Game Development: Harder Than You Think

The hardest part of making a game has always been the engineering. In times past, game engineering was mainly about low-level optimization—writing code that would run quickly on the target computer, leveraging clever little tricks whenever possible. But in the past ten years, games have ballooned in complexity. Now the primary technical challenge is simply getting the code to work to produce an end result that bears some semblance to the desired functionality. To the extent that we optimize, we are usually concerned with high-level algorithmic choices. There’s such a wide variety of algorithms to know about, so much experience required to implement them in a useful way, and so much work overall that just needs to be done, that we have a perpetual shortage of qualified people in the industry.