Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of surface treatments on the shear bond strength of luting cements to zirconia

Abstract Objective: The aim of this in vitro study was to evaluate the effect of surface treatments on the shear bond strength of resin cements to zirconia. Material and methods: Sintered zirconia specimens (n = 192) were divided into four different surface treatment groups: control (no treatment); airborne-particle abrasion; glaze layer and hydrofluoric acid (HF) application, and hot etching solution application. Then, each group was divided into four subgroups (n = 12), and three different resin cements were applied to the zirconia surfaces. The shear bond strength value of each specimen was measured after 5000 thermo cycles. The failure types were examined with a stereomicroscope and the effects of the surface treatments were evaluated with a scanning electron microscope. Results were analyzed using analysis of variance and Tukey’s post hoc tests (α = 0.05). Results: The surface treatment and resin cement type significantly affected the bond strength results (p < 0.05). For all resin cements, the airborne-particle abrasion treatment increased the shear bond strength values (p < 0.05). The glaze layer & HF application increased shear bond strength values for all groups, except the Single Bond Universal-RelyX Unicem Aplicap group (p < 0.05). The surface roughness values of airborne-particle abraded specimens were similar to comparable values for specimens from the control group and the hot etching solution group (p > 0.05). The glaze layer & HF application group produced the highest surface roughness values (p < 0.05). Conclusion: The results of this study recommend using the appropriate combination of surface treatment and adhesive/silane coupling agent to achieve durable zirconia-resin bonding.
Improving Camera Pose Estimation via Temporal EWA Surfel Splatting

Camera pose estimation is a fundamental problem of Augmented Reality and 3D reconstruction systems. Recently, despite the new better performing direct methods being developed, state-of-the-art methods are still estimating erroneous poses due to sensor noise, environmental conditions and challenging trajectories. Adding a back-end mapping process, SLAM systems achieve better performance and are more robust, but require higher computational resources, limiting their applicability. Therefore, lighter solutions to improve the accuracy of pose estimates are required. In this work we demonstrate the effectiveness of lighter data structures, namely surface elements, and exploit the temporality of sensor data streams to accumulate moving camera frames and improve tracking. This representation allows us to splat a photometric and geometric model simultaneously and use it to improve the performance of dense RGB-D camera pose estimation methods. Exploiting Elliptical Weighted Average splatting to produce high quality photometric results also allows us to detect erroneous poses through a novel visual quality analysis process. We show evidence of the EWA temporal model's effectiveness in publicly available datasets and argue that point-based representations are a good candidate for building lighter systems that should be further explored.
Surface Properties of Laser-etched LTCC Ceramic

In the LTCC process, metal layers are usually deposited by screen printing conductor material on unfired tape substrate. Despite being widely used, this method has certain disadvantages, such as limited resolution of the printing process. An alternative method of producing conductor patterns is to deposit a uniform conductor paste layer on the substrate and the use a Nd:YAG laser to selectively ablate the conductor material, producing a desired pattern. This method allows achieving a higher patterning precision and also eliminates photochemical process of screen preparation, which makes it an attractive choice for rapid prototyping applications. Laser ablation step can be performed either post-firing (for a top conductor layer) or pre-firing (which allows for patterning buried layers). The aim of this paper is to investigate surface properties of LTCC ceramics processed by laser etching. Precision optical imaging and SEM are used to determine etched surface properties, and EDS analysis is used to determin.
Machine to Machine Trust in Smart Cities

In the coming decades, we will live in a world surrounded by tens of billions of devices that will interoperate and collaborate in an effort to deliver personalized and autonomic services. This paradigm of smart objects and smart things interconnected and ubiquitously surrounding us is called the Internet of Things (IoT). Cities may be the first to benefit from the IoT, but reliance on these machines to make decisions has profound implications for trust, and makes mechanisms for expressing and reasoning about trust essential. This paper introduces the project funded by the Georgia Tech Research Institute to look at several dimensions of Machine to Machine Trust in the context of Smart Cities.
Discrete Wildfire Simulation Case Study

Wildfire activity around the world has been increasing for the past several decades. This has led to increased frequency of wildfires and longer fire seasons. When a fire is detected, one of the most important considerations is where to allocate limited firefighting resources to best contain the fire and protect infrastructure and urban areas. This is where robust, high-resolution fire simulations can play a critical role. Many software applications currently exist that predict the propagation of wildfires, each using a variety of input data and techniques. In this case study, we examine and implement a discrete probabilistic fire model developed and validated for the 2012 Tavira wildfire in Portugal. We then simulate a hypothetical fire in western Wyoming, USA, analyze the probable burn scar, and determine where firefighting resources are best allocated.
Paper Chemistry: François Dagognet and the Chemical Graph

In two books published in 1969 and 1973, the philosopher François Dagognet articulated a sharp contrast between the verbal and the visual in the history of chemical representation. Ursula Klein took up Dagognet’s argument as both inspiration and foil in her account of Berzelian formulas as productive “paper tools. ” Building on Klein’s work, I show how Dagognet portrayed chemical names and formulas not just as representations and paper tools, but as material abstractions that were objects of inquiry in themselves. Dagognet associated this way of doing chemistry with chemists’ use of computers, citing the work of the physical organic chemist Jacques-Émile Dubois. However, I show that chemical editors and mathematicians had begun to treat chemical names and formulas in this way long before anyone used computers for such studies. Indeed, some of the techniques of graph theory central to the application of computers to chemistry in the mid-twentieth century were themselves in part derived half a century earlier from the application of chemical formulas to mathematical reasoning.
The Diabetes Center of Excellence: A Model to Emulate.

INTRODUCTION Diabetes is an epidemic that deserves a strategic approach to its prevention and management in our military health system (MHS). The Centers for Disease Control and Prevention estimates that 29.1 million Americans have diabetes and 86 million, or 1 in 3 adults, have prediabetes. More troubling is the notion that 9 out of 10 of these individuals with prediabetes are unaware of their current condition and future risk. If consistent lifestyle changes are not enacted, 15 to 30% of these individuals will progress to Type 2 diabetes within 5 years. The Air Force, whose beneficiary population is not immune to the diabetes epidemic, finds itself with an ever-widening gap between the number of empaneled patients with diabetes (approximately 50,000) and the number of providers that can successfully manage the complex and ever-evolving diabetes standards. The AF Medical Operations Agency in 2011 determined that the Air Force lacked standardization in diabetes care and prevention among the Air Force medical treatment facilities (MTFs). This deficiency highlighted the need to bolster the AF Diabetes Center of Excellence (DCOE) mission and vision to encompass a three-tiered approach: Clinical Excellence, Translational Research, and Outreach/Training. While the DCOE is funded by the Air Force, housed at Wilford Hall Ambulatory Surgical Center in San Antonio, the intent is to develop Tri-Service participation and impact.
Effect of alternating postures on cognitive performance for healthy people performing sedentary work

Abstract Prolonged sitting is a risk factor for several diseases and the prevalence of worksite-based interventions such as sit-to-stand workstations is increasing. Although their impact on sedentary behaviour has been regularly investigated, the effect of working in alternating body postures on cognitive performance is unclear. To address this uncertainty, 45 students participated in a two-arm, randomised controlled cross-over trial under laboratory conditions. Subjects executed validated cognitive tests (working speed, reaction time, concentration performance) either in sitting or alternating working postures on two separate days (ClinicalTrials.gov Identifier: NCT02863731). MANOVA results showed no significant difference in cognitive performance between trials executed in alternating, standing or sitting postures. Perceived workload did not differ between sitting and alternating days. Repeated measures ANOVA revealed significant learning effects regarding concentration performance and working speed for both days. These results suggest that working posture did not affect cognitive performance in the short term. Practitioner Summary: Prior reports indicated health-related benefits based on alternated (sit/stand) body postures. Nevertheless, their effect on cognitive performance is unknown. This randomised controlled trial showed that working in alternating body postures did not influence reaction time, concentration performance, working speed or workload perception in the short term.
Causal Inference Lecture Notes : Causal Inference with Repeated Measures in Observational Studies

We start with the simple panel data of two waves and assume that the outcome variables are measured before and after the administration of the treatment. We use Y0i and Y1i to denote the observed outcomes for time 0 (pre-treatment) and time 1 (post-treatment). Finally, let Gi be the binary indicator variable which indicates the group membership of unit i (Gi = 1 for the treatment group and Gi = 0 for the control group). Thus, the binary treatment variable can be defined as Zti = tGi for t = 0, 1. That is, the units in the treatment group (Gi = 1) receive the treatment but only at time 1, while the units in both groups do not receive the treatment at time 0. In particular, we have Z1i = Gi. The set-up implies that the following potential outcomes exist; i.e., (Y0i(0), Y0i(1), Y1i(0), Y1i(1)). Now, we can express the observed outcomes as a function of potential outcomes Y0i = Y0i(0) and Y1i = GiY1i(1) + (1−Gi)Y1i(0) for all i. Or more generally, for t = 0, 1, we have, Yti = ZtiYti(1) + (1− Zti)Yti(0). (1)
The Post-Buckling Behaviour of Simply-Supported Square Plates

The post-buckling behaviour of flat square plates loaded along two opposite straight edges is analysed using the von Karman equations, trigonometric series and Galerkin’s method. The unloaded edges are considered to be either free to distort in the plane of the plate or maintained straight but allowed to move bodily; all edges are simply-supported in the out-of-plane direction. The non-linear algebraic equations are solved approximately using a McLaurin’s series expansion technique which facilitates the development of explicit expressions for ultimate load, end shortening and stiffness. The effects of initial geometric imperfections are also studied and it is shown that the results for ultimate load prediction and end shortening are in good agreement with test results.
A Dual-Color Genetically Engineered Mouse Model for Multispectral Imaging of the Pancreatic Microenvironment

Objectives To develop a mouse model for multispectral fluorescence imaging of the pancreas and pancreatic microenvironment. Methods Cre/loxP technology was used to develop this model. We crossed mT/mG indicator mice, engineered to constitutively express a conditional tdTomato transgene that converts to green fluorescent protein (GFP) expression after exposure to Cre recombinase, with Pdx1-Cre transgenic mice. To characterize this model for studies of pancreas biology, we performed bright light and fluorescence imaging of body cavities and intact organs and confocal microscopy of pancreata from offspring of Pdx1-Cre and mT/mG crosses. Results Pdx1-Cre–mT/mG mice demonstrated bright GFP expression within the pancreas and duodenum and intense tdTomato expression in all other organs. Green fluorescent protein expression was mosaic in Pdx1-Cre–mT/mG pancreata, with most showing extensive conversion from tdTomato to GFP expression within the epithelial-derived elements of the pancreatic parenchyma. Because both GFP and tdTomato are membrane targeted, individual cell borders were clearly outlined in confocal images of mT/mG pancreata. Conclusions This mouse model enables multispectral fluorescence imaging of individual cells and cell processes at the microscopic level of the pancreatic microenvironment; it should prove valuable for a variety of fluorescence imaging studies, ranging from pancreatic development to pancreatic cancer biology.
Optimal fixed-point algorithms for service differentiation in wireless networks

We study network utility maximization problems in wireless networks for service differentiation that optimize the Signal-to-Interference-plus-Noise Radio (SINR) and reliability under Rayleigh fading. Though seemingly nonconvex, we show that these problems can be solved using an optimization decomposition where each user calculates a payment for a given resource allocation, and the network uses the payment to optimize the performance of the user. We study three important examples of this utility maximization, namely the weighted sum logarithmic SINR maximization, the weighted sum inverse SINR minimization and the weighted sum logarithmic reliability maximization. These problems have hitherto been solved suboptimally in the literature. By exploiting the positivity, quasi-concavity and homogeneity properties in these problems using the nonlinear Perron-Frobenius theory, we propose fixed-point algorithms that converge geometrically fast to the globally optimal solution. Numerical evaluations show that our algorithms are stable (free of parameter configuration) and computationally fast.
A-44G transition in SMN2 intron 6 protects patients with spinal muscular atrophy

Spinal muscular atrophy (SMA) is a neuromuscular disease caused by reduced expression of survival of motor neuron (SMN), a protein expressed in humans by two paralogous genes, SMN1 and SMN2. These genes are nearly identical, except for 10 single-nucleotide differences and a 5-nucleotide insertion in SMN2. SMA is subdivided into four main types, with type I being the most severe. SMN2 copy number is a key positive modifier of the disease, but it is not always inversely correlated with clinical severity. We previously reported the c.859G > C variant in SMN2 exon 7 as a positive modifier in several patients. We have now identified A-44G as an additional positive disease modifier, present in a group of patients carrying 3 SMN2 copies but displaying milder clinical phenotypes than other patients with the same SMN2 copy number. One of the three SMN2 copies appears to have been converted from SMN1, but except for the C6T transition, no other changes were detected. Analyzed with minigenes, SMN1C6T displayed a ∼20% increase in exon 7 inclusion, compared to SMN2. Through systematic mutagenesis, we found that the improvement in exon 7 splicing is mainly attributable to the A-44G transition in intron 6. Using RNA-affinity chromatography and mass spectrometry, we further uncovered binding of the RNA-binding protein HuR to the -44 region, where it acts as a splicing repressor. The A-44G change markedly decreases the binding affinity of HuR, resulting in a moderate increase in exon 7 inclusion.
Exploiting State-of-the-Art Deep Learning Methods for Document Image Analysis

This paper provides details of our (partially award-winning) methods submitted to four competitions of ICDAR 2017. In particular, they are designed to (i) classify scripts, (ii) perform pixel-based labeling for layout analysis, (iii) identify writers, and (iv) recognize font size and types. The methods build on the current state-of-the-art in Deep Learning and have been adapted to the specific needs of the individual tasks. All methods are variants of Convolutional Neural Network (CNN) with specialized architectures, initialization, and other tricks which have been introduced in the field of deep learning within the last few years.
Distributively Computing Random Walk Betweenness Centrality in Linear Time

Betweenness centrality of a node represents its influence over the spread of information in the network. It is normally defined as the ratio of the number of shortest paths passing through the node among all shortest paths. However, the spread of information may not just pass through the shortest paths which is captured by a new measure of betweenness centrality based on random walks [1]. The random walk betweenness centrality of a node means how often it is traversed by a random walk between all pairs of other nodes. In this paper, we propose an O(n log n) time distributed randomized approximation algorithm for calculating each node's random walk betweenness centrality with an approximation ratio (1-∊ where n is the number of nodes and ∊ is an arbitrarily small constant between 0 and 1. Our distributed algorithm is designed under the widely used CONGEST model, where each edge can only transfer O(log n) bits in each round. To our best knowledge, this is the first distributed algorithm for computing the random walk betweenness centrality. Moreover, we give a non-trivial lower bound for distributively computing the exact random walk betweenness centrality under the CONGEST model, which is Ω(n\log n +D) where D is the network diameter. This means exactly computing random walk betweenness cannot be done in sublinear time.
Design, Expression, and Processing of Epitomized Hepatitis C Virus-Encoded CTL Epitopes1

Hepatitis C virus (HCV) vaccine efficacy may crucially depend on immunogen length and coverage of viral sequence diversity. However, covering a considerable proportion of the circulating viral sequence variants would likely require long immunogens, which for the conserved portions of the viral genome, would contain unnecessarily redundant sequence information. In this study, we present the design and in vitro performance analysis of a novel “epitome” approach that compresses frequent immune targets of the cellular immune response against HCV into a shorter immunogen sequence. Compression of immunological information is achieved by partial overlapping shared sequence motifs between individual epitopes. At the same time, sequence diversity coverage is provided by taking advantage of emerging cross-reactivity patterns among epitope variants so that epitope variants associated with the broadest variant cross-recognition are preferentially included. The processing and presentation analysis of specific epitopes included in such a compressed, in vitro-expressed HCV epitome indicated effective processing of a majority of tested epitopes, although re-presentation of some epitopes may require refined sequence design. Together, the present study establishes the epitome approach as a potential powerful tool for vaccine immunogen design, especially suitable for the induction of cellular immune responses against highly variable pathogens.
New tRNA contacts facilitate ligand binding in a Mycobacterium smegmatis T box riboswitch

Significance T box riboswitches regulate the expression of amino acid-related genes using a specific uncharged tRNA as their ligand. Previous work demonstrated that the conserved Stem II S-turn motif and Stem IIA/B pseudoknot element of the riboswitch are important for tRNA-dependent regulation; however, their function remained unknown. We identified contacts between these RNA elements and the tRNA ligand, providing evidence that they directly interact with tRNA. This represents an example of tRNA recognition by an S-turn motif or pseudoknot element, contributing to the understanding of alternative roles for tRNA and other RNA–RNA interactions. Furthermore, the novelty of these contacts makes them optimal targets for the ongoing development of antibiotics that target T box riboswitches. T box riboswitches are RNA regulatory elements widely used by organisms in the phyla Firmicutes and Actinobacteria to regulate expression of amino acid-related genes. Expression of T box family genes is down-regulated by transcription attenuation or inhibition of translation initiation in response to increased charging of the cognate tRNA. Three direct contacts with tRNA have been described; however, one of these contacts is absent in a subclass of T box RNAs and the roles of several structural domains conserved in most T box RNAs are unknown. In this study, structural elements of a Mycobacterium smegmatis ileS T box riboswitch variant with an Ultrashort (US) Stem I were sequentially deleted, which resulted in a progressive decrease in binding affinity for the tRNAIle ligand. Selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) revealed structural changes in conserved riboswitch domains upon interaction with the tRNA ligand. Cross-linking and mutational analyses identified two interaction sites, one between the S-turn element in Stem II and the T arm of tRNAIle and the other between the Stem IIA/B pseudoknot and the D loop of tRNAIle. These newly identified RNA contacts add information about tRNA recognition by the T box riboswitch and demonstrate a role for the S-turn and pseudoknot elements, which resemble structural elements that are common in many cellular RNAs.
Stress and marital adjustment in families of children with cancer

Pediatric cancer is highly stressful for parents. The current prospective study examines the impact of several stressors (financial strain, life threat, treatment intensity, treatment‐related events, and negative life events) on the trajectory of marital adjustment across the first year following diagnosis. We examined whether average level of stressors across the year was related to (1) levels of marital adjustment at the end of the first year of treatment and ( ) the rate of change in marital adjustment.