Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of directly autotransplanted tibial bone marrow aspirates on bone regeneration and osseointegration of dental implants.

OBJECTIVES Aim of the pilot trial was to evaluate applicability and effects of directly autotransplanted tibial bone marrow (BM) aspirates on the incorporation of porous bovine bone mineral in a sinus lift model and on the osseointegration of dental implants. MATERIAL AND METHODS Six edentulous patients with bilaterally severely resorbed maxillae requiring sinus augmentation and implant treatment were included. During surgery, tibial BM was harvested and added to bone substitute material (Bio-Oss(®) ) at the randomly selected test site. At control sites, augmentation was performed with Bio-Oss(®) alone. The cellular content of each BM aspirate was checked for multipotency and surface antigen expression as quality control. Histomorphometric analysis of biopsies from the augmented sites after 3 and 6 months (during implantation) was used to evaluate effects on bone regeneration. Osseointegration of implants was evaluated with Periotest(®) and radiographic means. RESULTS Multipotent cellular content in tibial BM aspirates was comparable to that in punctures from the iliac crest. No significant difference in amount of new bone formation and the integration of bone substitute particles was detected histomorphometrically. Periotest(®) values and radiographs showed successful osseointegration of inserted implants at all sites. CONCLUSION Directly autotransplanted tibial BM aspirates did not show beneficial regenerative effects in the small study population (N = 6) of the present pilot trial. However, the proximal tibia proved to be a potential donor site for small quantities of BM. Future trials should clarify whether concentration of tibial BM aspirates could effect higher regenerative potency.
Interactive coffee table for exploration of personal photos and videos

Interactive tabletops offer a unique opportunity for exploring home videos and photos. Nevertheless, there are still a number of unexplored challenges for effectively providing support for collocated group interaction around media. This paper reports on a user study involving 24 users, intended to better understanding the challenges ahead. Our volunteers (in couples) evaluated our media sharing application prototype, providing valuable feedback with regards to three key challenges: metaphor, digital ecosystem, and level of control. First, users appreciated the selected metaphor of physical photos, but without relinquishing software support, such as alignment and distribution of media items. Second, vertical auxiliary screens helped in supporting a bigger number of users and providing more comfort and a better viewing angle and stance. Third, the nature of the task (either storytelling or random exploration) had a strong influence on the control capabilities to be provided by the application. Fourth, personal spaces within the tabletop were useful for allowing independent navigation. We consider these results as relevant for the future developments of home media sharing applications for the living room.
Risk of cardiovascular event and mortality in relation to refill and guideline adherence to lipid-lowering medications among patients with type 2 diabetes mellitus in Sweden

Objective To analyze the risk of cardiovascular (CV) events and mortality in relation to adherence to lipid-lowering medications by healthcare centers and patients with type 2 diabetes mellitus (T2DM). Research design and methods We included 121 914 patients (12% secondary prevention) with T2DM reported by 1363 healthcare centers. Patients initiated lipid-lowering medications between July 2006 and December 2012 and were followed from cessation of the first filled supply until multidose dispensed medications, migration, CV events, death or December 2016. The study period was divided into 4-month intervals through 2014, followed by annual intervals through 2016. Adherence measures were assessed for each interval. Patients’ (refill) adherence was measured using the medication possession ratio (MPR). Healthcare centers’ (guideline) adherence represented the prescription prevalence of lipid-lowering medications according to guidelines. The risk of CV events and mortality was analyzed for each interval using Cox proportional hazard regression and Kaplan-Meier. Results Compared with high-adherent patients (MPR >80%), low-adherent primary prevention patients (MPR ≤80%) showed higher risk of all outcomes: 44%–51 % for CV events, doubled for all-cause mortality and 79%–90% for CV mortality. Corresponding risks for low-adherent secondary prevention patients were 17%–19% for CV events, 88%–97% for all-cause and 66%–79% for CV mortality. Primary prevention patients treated by low-adherent healthcare centers (guideline adherence <48%) had a higher risk of CV events and CV mortality. Otherwise, no difference in the risk of CV events or mortality was observed by guideline adherence level. Conclusions Our results demonstrate the importance of high refill adherence and thus the value of individualized care among patients with T2DM.
Sentiment after Translation: A Case-Study on Arabic Social Media Posts

When text is translated from one language into another, sentiment is preserved to varying degrees. In this paper, we use Arabic social media posts as stand-in for source language text, and determine loss in sentiment predictability when they are translated into English, manually and automatically. As benchmarks, we use manually and automatically determined sentiment labels of the Arabic texts. We show that sentiment analysis of English translations of Arabic texts produces competitive results, w.r. t. Arabic sentiment analysis. We discover that even though translation significantly reduces the human ability to recover sentiment, automatic sentiment systems are still able to capture sentiment information from the translations.
Brain–heart interactions: physiology and clinical implications

The brain controls the heart directly through the sympathetic and parasympathetic branches of the autonomic nervous system, which consists of multi-synaptic pathways from myocardial cells back to peripheral ganglionic neurons and further to central preganglionic and premotor neurons. Cardiac function can be profoundly altered by the reflex activation of cardiac autonomic nerves in response to inputs from baro-, chemo-, nasopharyngeal and other receptors as well as by central autonomic commands, including those associated with stress, physical activity, arousal and sleep. In the clinical setting, slowly progressive autonomic failure frequently results from neurodegenerative disorders, whereas autonomic hyperactivity may result from vascular, inflammatory or traumatic lesions of the autonomic nervous system, adverse effects of drugs and chronic neurological disorders. Both acute and chronic manifestations of an imbalanced brain–heart interaction have a negative impact on health. Simple, widely available and reliable cardiovascular markers of the sympathetic tone and of the sympathetic–parasympathetic balance are lacking. A deeper understanding of the connections between autonomic cardiac control and brain dynamics through advanced signal and neuroimage processing may lead to invaluable tools for the early detection and treatment of pathological changes in the brain–heart interaction.
Trajectory tracking for a particle in elliptical billiards

An infinitely rigid unitary mass (particle) is considered, moving on a planar region delimited by a rigid elliptical barrier (elliptical billiards) under the action of proper control forces. A class of periodic trajectories, involving an infinite sequence of non-smooth impacts between the mass and the barrier at fixed times, is found by using an LMIs based procedure. The jumps in the velocities at the impact times render difficult (if not impossible) to obtain the classical stability and attractivity properties for the dynamic system describing the tracking error behaviour. Hence, the tracking control problem is properly stated using notions similar to the quasi stability concept in V. Lakshmikantham, D.D. Bainov and P.S. Simeonov, Theory of Impulsive Differential Equations, 6, World Scientific, 1989. A controller (whose state is subject to discontinuities) based on the internal model principle is shown to solve the proposed tracking problem, giving rise to control forces that are piecewise continuous function of time, with discontinuities at the desired impact times and at the impact times of the particle with the barrier.
The Statistical Literacy Needed to Interpret School Assessment Data.

State-wide and national testing in areas such as literacy and numeracy produces reports containing graphs and tables illustrating school and individual performance. These are intended to inform teachers, principals, and education organisations about student and school outcomes, to guide change and improvement. Given the complexity of the information, it is of interest to determine the critical statistical skills required to make sense of such data. This paper examines the statistical literacy necessary to interpret the graphical presentations of school assessment data for the Australian NAPLAN testing process. A framework for professional statistical literacy that acknowledges the importance of context is used to identify different levels of data interpretation. The implications for helping users make better use of such data and for teacher education more broadly are discussed.
Individual Differences in Individualism and Collectivism Predict Ratings of Virtual Cities’ Liveability and Environmental Quality

ABSTRACT. The present research investigated individual differences in individualism and collectivism as predictors of people's reactions to cities. Psychology undergraduate students (N = 148) took virtual guided tours around historical cities. They then evaluated the cities’ liveability and environmental quality and completed measures of individualism and collectivism. Mediation analyses showed that people who scored high in self-responsibility (individualism) rated the cities as more liveable because they perceived them to be richer and better resourced. In contrast, people who scored high in collectivism rated the cities as having a better environmental quality because they perceived them to (1) provide a greater potential for community and social life and (2) allow people to express themselves. These results indicate that people's evaluations of virtual cities are based on the degree to which certain aspects of the cities are perceived to be consistent with individualist and collectivist values.
Hyperspectral Image Classification Using Deep Pixel-Pair Features

The deep convolutional neural network (CNN) is of great interest recently. It can provide excellent performance in hyperspectral image classification when the number of training samples is sufficiently large. In this paper, a novel pixel-pair method is proposed to significantly increase such a number, ensuring that the advantage of CNN can be actually offered. For a testing pixel, pixel-pairs, constructed by combining the center pixel and each of the surrounding pixels, are classified by the trained CNN, and the final label is then determined by a voting strategy. The proposed method utilizing deep CNN to learn pixel-pair features is expected to have more discriminative power. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than the conventional deep learning-based method.
EMERGING PARADIGM: TOLL-LIKE RECEPTOR 4-SENTINEL FOR THE DETECTION OF TISSUE DAMAGE

ABSTRACT The systemic inflammatory response syndrome initiated by infection shares many features in common with the trauma-induced systemic response. The toll-like receptors (TLRs) stand at the interface of innate immune activation in the settings of both infection and sterile injury by responding to a variety of microbial and endogenous ligands alike. Recently, a body of literature has evolved describing a key role for TLRs in acute injury using rodent models of hemorrhagic shock, ischemia and reperfusion, tissue trauma and wound repair, and various toxic exposures. This review will detail the observations implicating a TLR family member, TLR4, as a key component of the initial injury response.
Treatment of complex regional pain syndrome type I

Reflex sympathetic dystrophy (RSD), also known as complex regional pain syndrome type I (CRPS I), is a disabling neuropathic pain syndrome. Controversy exists about the effectiveness of therapeutic interventions for the management of RSD/CRPS I. In order to ascertain appropriate therapies we conducted a review of existing randomized controlled trials of therapies for this disabling disease. Eligible trials were identified from the Cochrane, Pubmed, Embase and MEDLINE databases from 1966 through June 2000, from references in retrieved reports and from references in review articles. Twenty‐six studies concerning treatment modalities were identified. Eighteen studies were randomized placebo‐controlled trials and eight studies were randomized active‐controlled trials. Three independent investigators reviewed articles for inclusion criteria using a 15‐item checklist. Seventeen of the trials were of high quality according to the 15‐item criteria. There was limited evidence for the effectiveness of these interventions because of the heterogeneity of treatment modalities. The search for trials concerning prevention of RSD/CRPS I resulted in two eligible studies. Both were of high quality and dealt with different interventions. There is limited evidence for their preventive effect. © 2002 European Federation of Chapters of the International Association for the Study of Pain
The Spacey Random Walk: A Stochastic Process for Higher-Order Data

Random walks are a fundamental model in applied mathematics and are a common example of a Markov chain. The limiting stationary distribution of the Markov chain represents the fraction of the time spent in each state during the stochastic process. A standard way to compute this distribution for a random walk on a finite set of states is to compute the Perron vector of the associated transition matrix. There are algebraic analogues of this Perron vector in terms of transition probability tensors of higher-order Markov chains. These vectors are nonnegative, have dimension equal to the dimension of the state space, and sum to one, and they are derived by making an algebraic substitution in the equation for the joint-stationary distribution of a higher-order Markov chain. Here, we present the spacey random walk, a non-Markovian stochastic process whose stationary distribution is given by the tensor eigenvector. The process itself is a vertex-reinforced random walk, and its discrete dynamics are related to a c.
Secure virtual architecture: a safe execution environment for commodity operating systems

This paper describes an efficient and robust approach to provide a safe execution environment for an entire operating system, such as Linux, and all its applications. The approach, which we call Secure Virtual Architecture (SVA), defines a virtual, low-level, typed instruction set suitable for executing all code on a system, including kernel and application code. SVA code is translated for execution by a virtual machine transparently, offline or online. SVA aims to enforce fine-grained (object level) memory safety, control-flow integrity, type safety for a subset of objects, and sound analysis. A virtual machine implementing SVA achieves these goals by using a novel approach that exploits properties of existing memory pools in the kernel and by preserving the kernel's explicit control over memory, including custom allocators and explicit deallocation. Furthermore, the safety properties can be encoded compactly as extensions to the SVA type system, allowing the (complex) safety checking compiler to be outside the trusted computing base. SVA also defines a set of OS interface operations that abstract all privileged hardware instructions, allowing the virtual machine to monitor all privileged operations and control the physical resources on a given hardware platform. We have ported the Linux kernel to SVA, treating it as a new architecture, and made only minimal code changes (less than 300 lines of code) to the machine-independent parts of the kernel and device drivers. SVA is able to prevent 4 out of 5 memory safety exploits previously reported for the Linux 2.4. 22 kernel for which exploit code is available, and would prevent the fifth one simply by compiling an additional kernel library.
Sound empirical evidence in software testing

Several promising techniques have been proposed to automate different tasks in software testing, such as test data generation for object-oriented software. However, reported studies in the literature only show the feasibility of the proposed techniques, because the choice of the employed artifacts in the case studies (e.g., software applications) is usually done in a non-systematic way. The chosen case study might be biased, and so it might not be a valid representative of the addressed type of software (e.g., internet applications and embedded systems). The common trend seems to be to accept this fact and get over it by simply discussing it in a threats to validity section. In this paper, we evaluate search-based software testing (in particular the EvoSuite tool) when applied to test data generation for open source projects. To achieve sound empirical results, we randomly selected 100 Java projects from SourceForge, which is the most popular open source repository (more than 300,000 projects with more than two million registered users). The resulting case study not only is very large (8,784 public classes for a total of 291,639 bytecode level branches), but more importantly it is statistically sound and representative for open source projects. Results show that while high coverage on commonly used types of classes is achievable, in practice environmental dependencies prohibit such high coverage, which clearly points out essential future research directions. To support this future research, our SF100 case study can serve as a much needed corpus of classes for test generation.
Perceptual learning of dysarthric speech: a review of experimental studies.

PURPOSE This review article provides a theoretical overview of the characteristics of perceptual learning, reviews perceptual learning studies that pertain to dysarthric populations, and identifies directions for future research that consider the application of perceptual learning to the management of dysarthria. METHOD A critical review of the literature was conducted that summarized and synthesized previously published research in the area of perceptual learning with atypical speech. Literature related to perceptual learning of neurologically degraded speech was emphasized with the aim of identifying key directions for future research with this population. CONCLUSIONS Familiarization with unfamiliar or ambiguous speech signals can facilitate perceptual learning of that same speech signal. There is a small but growing body of evidence that perceptual learning also occurs for listeners familiarized with dysarthric speech. Perceptual learning of the dysarthric signal is both theoretically and clinically significant. In order to establish the efficacy of exploiting perceptual learning paradigms for rehabilitative gain in dysarthria management, research is required to build on existing empirical evidence and develop a theoretical framework for learning to better recognize neurologically degraded speech.
Three-dimensional model-based segmentation of brain MRI

This paper presents a new technique for the automatic model-based segmentation of 3-D objects from volumetric image data. The development closely follows the seminal work of Cootes et al. (1994) but presents various new solutions to come up with a true 3-D technique rather than a slice-by-slice 2-D processing. The segmentation system includes both the building of statistical models and the automatic segmentation of new image data sets via a restricted elastic deformation of models. Geometric models are derived front a sample set of image data which have been segmented by experts. The surfaces of these binary objects are converted into a parametric surface net which is normalized to get an invariant object-centered coordinate system. Surface descriptions are expanded into series of spherical harmonics which provide parametric representations of object shapes. Gray-level information is represented by 1-D profiles normal to the surface. The alignment is based on the well-accepted stereotactic coordinate system since the driving application is the segmentation of brain objects. Shape statistics are calculated from the parametric shape representations rather than from the spatial coordinates of sets of points. After initializing the mean shape in a new data set on the basis of the alignment coordinates, the model elastically deforms in accordance to displacement forces across the surface but is restricted only by shape deformation constraints. The technique has been applied to segment left and right hippocampal structures from a large series of 3-D magnetic resonance scans taken from a schizophrenia study.
Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade

We propose a novel deep layer cascade (LC) method to improve the accuracy and speed of semantic segmentation. Unlike the conventional model cascade (MC) that is composed of multiple independent models, LC treats a single deep model as a cascade of several sub-models. Earlier sub-models are trained to handle easy and confident regions, and they progressively feed-forward harder regions to the next sub-model for processing. Convolutions are only calculated on these regions to reduce computations. The proposed method possesses several advantages. First, LC classifies most of the easy regions in the shallow stage and makes deeper stage focuses on a few hard regions. Such an adaptive and difficulty-aware learning improves segmentation performance. Second, LC accelerates both training and testing of deep network thanks to early decisions in the shallow stage. Third, in comparison to MC, LC is an end-to-end trainable framework, allowing joint learning of all sub-models. We evaluate our method on PASCAL VOC and Cityscapes datasets, achieving state-of-the-art performance and fast speed.
Maize production and field CO2 emission under different straw return rates in Northeast China

In order to understand and clarify the impacts of straw return on maize production and field CO2 emission in Northeast China, the most important agricultural base of the nation, a field experiment was conducted in 2012–2015, including no straw return (CK), straw amendment at 4000 kg/ha (S4), and at 8000 kg/ha (S8). The average grain yield was found significantly promoted by the two straw treatments, with comparably increased magnitudes of 11.0% and 12.8% for S4 and S8, respectively, and the benefits were gradually enlarged with increasing experimental duration. Although straw return tends to reduce slightly the harvest index, it was detected that it exerted significantly positive impacts on nitrogen harvest index. These results implied that added straw could lead to raising grain yield and enhancing nitrogen use efficiency simultaneously. In 2015, our monitoring showed that CO2 emission was elevated with intensified use of straw, and S4 and S8 decreased carbon emission efficiency by 7.3% and 13.6%, respectively. However, there was no statistical difference between S4 and CK. Overall, straw addition at the rate of 4000 kg/ha accompanied with inorganic fertilizer was recommended to be adopted in Northeast China, which was considered as a sustainable and relatively environment-friendly agricultural technique during maize production.