Miracle-dz/UniFAR-FTUs huggingface dataset:
Cancer Incidence and Mortality Among Ethnic German Migrants From the Former Soviet Union

Germany is a country known for immigration. In 2015, 21% of the general population in Germany consisted of individuals with a migration background. This article focuses on cancer-specific incidence and mortality among one of the biggest migrant groups in Germany: the resettlers. Resettlers are ethnic Germans who mainly immigrated from the Russian federation and other countries of the former Soviet Union after its collapse in 1989. We investigated differences between resettlers and the general German population, regarding (i) incidence and mortality of malignant neoplasms, (ii) time trends of the corresponding incidence and mortality, and (iii) cancer stage at diagnosis. We provide data from two resettler cohorts covering an observation time of 20 years: one cohort on cancer incidence (N = 32,972), and another cohort on mortality (N = 59,390). Cancer-specific standardized incidence ratios (SIR) and standardized mortality ratios (SMR) for all malignant neoplasms combined and the most common cancer-sites were calculated between resettlers and the general German population. Time trend analyses using Poisson regression were performed to investigate the developments of SIRs and SMRs. To investigate differences in stage at diagnosis, logistic regression was performed, calculating Odds Ratios for condensed cancer stages. We observed higher incidence and mortality of stomach cancer [SIR (men) 1.62, 95%CI 1.17–2.19; SMR (men) 1.62, 95%CI 1.31–2.01; SIR (women) 1.32, 95%CI 0.86–1.94; SMR (women) 1.52, 95%CI 1.19–1.93] and higher mortality of lung cancer [SMR (men) 1.34, 95%CI 1.20–1.50] among resettlers compared to the general German population, but lower incidence and mortality of colorectal (both sexes), lung (women), prostate and female breast cancer. However, time trend analyses showed converging incidence risks of cause-specific incidence over time, whereas differences of mortality did not show changes over time. Results from logistic regression suggest that resettler men were more often diagnosed with advanced cancer stages compared to the Münster population. Our findings suggest that risk factor patterns of the most common cancer-sites among resettlers are similar to those observed within the Russian population. Such increases in prostate, colorectal and breast cancer incidence may be the consequence of improved detection measures, and/or the adaptation of resettlers to the German lifestyle.
Probabilistic Binary Neural Networks

Low bit-width weights and activations are an effective way of combating the increasing need for both memory and compute power of Deep Neural Networks. In this work, we present a probabilistic training method for Neural Network with both binary weights and activations, called BLRNet. By embracing stochasticity during training, we circumvent the need to approximate the gradient of non-differentiable functions such as sign(), while still obtaining a fully Binary Neural Network at test time. Moreover, it allows for anytime ensemble predictions for improved performance and uncertainty estimates by sampling from the weight distribution. Since all operations in a layer of the BLRNet operate on random variables, we introduce stochastic versions of Batch Normalization and max pooling, which transfer well to a deterministic network at test time. We evaluate the BLRNet on multiple standardized benchmarks.
Comparative Study on the Application of Mesenchymal Stromal Cells Combined with Tricalcium Phosphate Scaffold into Femoral Bone Defects

This prospective study sought to evaluate the healing quality of implanted ultraporous β-tricalcium phosphate sown with expanded autologous mesenchymal stromal cells (MSCs) into femoral defects during revision hip arthroplasty. A total of 37 osseous defects in 37 patients were treated and evaluated concerning bone regeneration. Nineteen subjects received β-tricalcium phosphate graft material serving as a carrier of expanded autologous MSCs (the trial group A), nine subjects received β-tricalcium phosphate graft material only (the study group B) and nine subjects received cancellous allografts only (the control group C). Clinical and radiographic evaluations were scheduled at 6 weeks, 3, 6, and 12 months post-operatively, and performed at the most recent visit as well. All observed complications were recorded during follow-up to assess the use of an ultraporous β-tricalcium phosphate synthetic graft material combined with expanded MSCs in bone defect repair. The resulting data from participants with accomplished follow-up were processed and statistically evaluated with a Freeman–Halton modification of the Fischer’s exact test, a P < 0.05 value was considered to be significant. Whereas no significant difference was observed between the trial group A with β-tricalcium phosphate synthetic graft material serving as a carrier of expanded autologous MSCs and control group C with cancellous impaction allografting in terms of the bone defect healing, significant differences were documented between the study group B with β-tricalcium phosphate graft material only and control group C. Regarding adverse effects, six serious events were recorded during the clinical trial with no causal relationship to the cell product. β-tricalcium phosphate synthetic graft material serving as a carrier of expanded autologous MSCs appears safe and promotes the healing of bone defects in a jeopardized and/or impaired microenvironment. This clinical trial was registered at the EU Clinical Trials Register before patient recruitment (Registration number: EudraCT number 2012-005599-33; Date of registration: 2013-02-04).
Deletion of Kncn Does Not Affect Kinocilium and Stereocilia Bundle Morphogenesis and Mechanotransduction in Cochlear Hair Cells

Auditory hair cells possess stunning cilia structure that composes of a bundle of stereocilia for mechano-electrical transduction and a single kinocilium for guiding the polarity of hair bundle towards maturation. However, the molecules underlying kinocilium function have not yet been fully understood. Hence, the proteins involved in hair bundle development and function are of a large interest. From a fine microarray analysis, we found that kinocilin (Kncn) was enriched in hair cell specific expression profile. Consistently, it has been reported that KNCN was a protein mainly located in the kinocilium of hair cells in the inner ear. However, the hypothesis that KNCN is a kinocilium protein has not been validated in mice with Kncn gene perturbed. In this study, we generated Kncn knockout mouse lines by CRISPR/Cas9 technique and further examined the morphology and function of cochlear hair cells. Our results showed that there was no obvious hearing loss in the knockout mice, determined by audiometry. Histological study demonstrated that the inner ear and hair cell structure were intact. Especially, there was no deficit of mechanotransduction (MET) in cochlear outer hair cells (OHCs). In summary, our work suggests that KNCN is not essential for kinocilium-oriented hair bundle function in cochlear hair cells.
Nature-inspired engineering of an F-type lectin for increased binding strength

Individual lectin-carbohydrate interactions are usually of low affinity. However, high avidity is frequently attained by the multivalent presentation of glycans on biological surfaces coupled with the occurrence of high order lectin oligomers or tandem repeats of lectin domains in the polypeptide. F-type lectins are l-fucose binding lectins with a typical sequence motif, HX(26)RXDX(4)R/K, whose residues participate in l-fucose binding. We previously reported the presence of a few eukaryotic F-type lectin domains with partial sequence duplication that results in the presence of two l-fucose-binding sequence motifs. We hypothesized that such partial sequence duplication would result in greater avidity of lectin-ligand interactions. Inspired by this example from Nature, we attempted to engineer a bacterial F-type lectin domain from Streptosporangium roseum to attain avid binding by mimicking partial duplication. The engineered lectin demonstrated 12-fold greater binding strength than the wild-type lectin to multivalent fucosylated glycoconjugates. However, the affinity to the monosaccharide l-fucose in solution was similar and partial sequence duplication did not result in an additional functional l-fucose binding site. We also cloned, expressed and purified a Branchiostoma floridae F-type lectin domain with naturally occurring partial sequence duplication and confirmed that the duplicated region with the F-type lectin sequence motif did not participate in l-fucose binding. We found that the greater binding strength of the engineered lectin from S. roseum was instead due to increased oligomerization. We believe that this Nature-inspired strategy might be useful for engineering lectins to improve binding strength in various applications.
A Systematic Review of Access to General Healthcare Services for People with Disabilities in Low and Middle Income Countries

Background: A systematic review was undertaken to explore access to general healthcare services for people with disabilities in low and middle-income countries (LMICs). Methods: Six electronic databases were searched in February 2017. Studies comparing access to general healthcare services by people with disabilities to those without disabilities from LMICs were included. Eligible measures of healthcare access included: utilisation, coverage, adherence, expenditure, and quality. Studies measuring disability using self-reported or clinical assessments were eligible. Title, abstract and full-text screening and data extraction was undertaken by the two authors. Results: Searches returned 13,048 studies, of which 50 studies were eligible. Studies were predominantly conducted in sub-Saharan Africa (30%), Latin America (24%), and East Asia/Pacific (12%). 74% of studies used cross-sectional designs and the remaining used case-control designs. There was evidence that utilisation of healthcare services was higher for people with disabilities, and healthcare expenditure was higher. There were less consistent differences between people with and without disabilities in other access measures. However, the wide variation in type and measurement of disability, and access outcomes, made comparisons across studies difficult. Conclusions: Developing common metrics for measuring disability and healthcare access will improve the availability of high quality, comparable data, so that healthcare access for people with disabilities can be monitored and improved.
Aspekte zur praxisnahen Auslegung induktiver Energieübertragungssysteme für Elektrofahrzeuge

Induktive Energieubertragungssysteme sind aufgrund der Vielzahl an Kenngrosen und Zusammenhangen auserst komplex. Deshalb mussen diese unter Berucksichtigung der vorgestellten Randbedingungen fur die jeweiligen Anforderungen und spezifischen Anwendungsfalle neu ausgelegt werden. Dazu werden Aspekte aufgezeigt, die eine Analyse der Systemanforderungen, u.a. der elektrischen und magnetischen Parameter beinhalten, speziell fur den Einsatz im Themenfeld der Elektromobilitat. Dies betrifft den zur Verfugung stehenden Bauraum, die Versatztoleranz, den Abstand, aber auch die verwendeten Materialien. Eine erste Dimensionierung greift Themen bzgl. der Spulen- und Ferritgeometrie auf. Dabei werden die vorgestellten Randfaktoren und Anforderungen berucksichtigt, die eine realitatsnahe Auslegung sicherstellen sollen.
Decoding of Neural Data Using Cohomological Feature Extraction

We introduce a novel data-driven approach to discover and decode features in the neural code coming from large population neural recordings with minimal assumptions, using cohomological feature extraction. We apply our approach to neural recordings of mice moving freely in a box, where we find a circular feature. We then observe that the decoded value corresponds well to the head direction of the mouse. Thus, we capture head direction cells and decode the head direction from the neural population activity without having to process the mouse's behavior. Interestingly, the decoded values convey more information about the neural activity than the tracked head direction does, with differences that have some spatial organization. Finally, we note that the residual population activity, after the head direction has been accounted for, retains some low-dimensional structure that is correlated with the speed of the mouse.
A Stochastic Game Framework for Analyzing Computational Investment Strategies in Distributed Computing with Application to Blockchain Mining

We study a stochastic game framework with varying number of players, for modeling and analyzing their computational investment strategies in distributed computing, for solving a problem such as in blockchain mining. In particular, we propose a continuous time Markov chain model, where players arrive and depart according to a stochastic process, and determine their investment strategies based on the state of the system. The players obtain a certain reward for being the first to solve the problem, while incur a certain cost based on the time and computational power invested in the attempt to solve it. We consider that the players are Markovian, that is, they determine their strategies which maximize their expected utilities, while ignoring past payoffs. We first study a scenario where the rate of problem getting solved is proportional to the total computational power invested by the players. We show that, in statewise Nash equilibrium, players with costs exceeding a certain threshold do not invest, while players with costs less than this threshold invest maximum power. Further, we show that Markov perfect equilibrium follows a similar threshold policy, and the players do not need to have knowledge of the system state. We then consider a scenario where the rate of problem getting solved is independent of the computational power invested by players. Here, we show that, in statewise Nash equilibrium, only the players with cost parameters in a relatively low range, invest. We also show that, in Markov perfect equilibrium, players invest proportionally to the reward-cost ratio. Using simulations, we quantify the effects of arrival and departure rates on players' expected utilities and provide insights
Atomistic Investigation of Anisotropic Nanoindentation Behavior of Nanotwinned Aluminum Containing Inclined Twin Boundaries

Nanotwinned metals exhibit superior mechanical properties due to unique dislocation–twin boundary interactions. In the present work, we elucidate the microscopic deformation mechanisms and their correlations with the macroscopic mechanical response of nanotwinned Al containing inclined twin boundaries under nanoindentation by means of molecular dynamics simulations. The effect of twin boundary orientation with respect to the indented surface on the nanoindentation is evaluated. Simulation results reveal that dislocation slip, dislocation–twin boundary interaction, and twin boundary migration operate in parallel in the plastic deformation of nanotwinned Al. The inclination angle of twin boundaries with respect to indented surface has a strong influence on the interaction between individual deformation modes, which in turn leads to the anisotropic indentation behavior of nanotwinned Al.
CloudMAC: towards software defined WLANs

Traditional enterprise WLAN management systems are hard to extend and require powerful access points (APs). In this paper we introduce and evaluate CloudMAC, an architecture for enterprise WLANs in which MAC frames are generated and processed on virtual APs hosted in a datacenter. The APs only need to forward MAC frames. The APs and the servers are connected via an OpenFlow-enabled network, which allows to control where and how MAC frames are transmitted.
Cardiovascular outcomes and safety with antidiabetic drugs

Type 2 diabetes is a debilitating disease that impacts the life expectancy, quality of life, and health of an individual. Cardiovascular disease (CVD) is a common diabetes-associated complication and a principal cause for death in diabetic patients. This review aims to investigate and summarize the effect of Type 2 diabetes mellitus (T2DM) medications on CVD issues. A comprehensive literature review mainly from level 1 evidence was performed. Thirty-seven articles were extracted from Google Scholar, ScienceDirect, ProQuest, and PubMed Database using a combination of keywords. The findings suggest that different glucose-lowering agents have been tested for their efficacy and safety in T2DM with CVD. Some of the recent trials such as the “United Kingdom Prospective Diabetes Study,” “Empagliflozin (EMPA) Cardiovascular (CV) Outcome Event Trial in T2DM Patients-Removing Excess Glucose” (EMPA-REG OUTCOME), “Liraglutide Effect and Action in Diabetes: Evaluation of CV Outcome Results,” and “Trial to Evaluate CV and Other Long-term Outcomes with Semaglutide in Subjects with Type 2 Diabetes” (SUSTAIN6) have shed important light on this vital clinical concern, thus demonstrating a convincing effect of liraglutide, semaglutide, and EMPA on CVD outcomes, while metformin is thought to be the first-line optimal oral agent to manage Type 2 diabetics. Some classes of drugs demonstrate CV protection, some of them may be a result of a class effect, and some differences might be based on the population enrolled individually. Most of the trials failed to show a significant benefit with regard to mortality and morbidity in spite of intensive glycemic control. This study, therefore, enabled us to develop a guide of potential antidiabetic medication that can influence or promote CV health. Health professionals in future should weigh the CV risk against possible advantages while prescribing antidiabetic medications.
Comparative Study of Outlier Detection Algorithms for Machine Learning

Outliers are unusual data points which are inconsistent with other observations. Human error, mechanical faults, fraudulent behavior, instrument error, and changes in the environment are some reasons to arise outliers. Several types of outlier detection algorithms are developed and a number of surveys and overviews are performed to distinguish their advantages and disadvantages. Multivariate outlier detection algorithms are widely used among other types, therefore we concentrate on this type. In this work a comparison between effects of multivariate outlier detection algorithms on machine learning problems is performed. For this purpose, three multivariate outlier detection algorithms namely distance based, statistical based and clustering based are evaluated. Benchmark datasets of Heart disease, Breast cancer and Liver disorder are used for the experiments. To identify the effectiveness of mentioned algorithms, the above datasets are classified by Support Vector Machines (SVM) before and after outlier detection. Finally a comparative review is performed to distinguish the advantages and disadvantages of each algorithm and their respective effects on accuracy of SVM classifiers.
Challenges in the Diagnosis of Magnesium Status

Magnesium is a critical mineral in the human body and is involved in ~80% of known metabolic functions. It is currently estimated that 60% of adults do not achieve the average dietary intake (ADI) and 45% of Americans are magnesium deficient, a condition associated with disease states like hypertension, diabetes, and neurological disorders, to name a few. Magnesium deficiency can be attributed to common dietary practices, medications, and farming techniques, along with estimates that the mineral content of vegetables has declined by as much as 80–90% in the last 100 years. However, despite this mineral’s importance, it is poorly understood from several standpoints, not the least of which is its unique mechanism of absorption and sensitive compartmental handling in the body, making the determination of magnesium status difficult. The reliance on several popular sample assays has contributed to a great deal of confusion in the literature. This review will discuss causes of magnesium deficiency, absorption, handling, and compartmentalization in the body, highlighting the challenges this creates in determining magnesium status in both clinical and research settings.
Analysis of Multi-Sensor Fusion for Mobile and Wearable Sensor Based Human Activity Recognition

Sensor-based human activity monitoring and detection have become an emerging field of intense research and development in recent years due to its immense applications in wide area of human endeavors. Human activity recognition integrates diverse sensors with machine learning algorithms to provide contextual information on relative activity details for health-related feedbacks and lifestyles changes. However, there are varieties of sensors for implementing human activity recognition with diverse capabilities and types of activities they provide best performances. Also, the diverse nature of human activities and nature in which they are performed by the individual makes them challenging to recognize. Therefore, determining the impact of these sensors in human activity recognition using machine learning techniques are of immense advantages in human activity monitoring and detection. The objective of this paper is to comprehensively evaluate the performance of single and multi-sensor fusion for human activity recognition using accelerometer and gyroscope sensors. Firstly, the performances of these sensors were extensively analyzed individually using seven classification algorithms. Secondly, we conducted a comprehensive experimental evaluation of sensor fusion attached on the same location and on different locations of the body. The extensive evaluation with 10-fold cross validation demonstrates that highest average F-measures for single sensor and fusion are 0.908 and 0.938 with Random Forest and Voting ensemble algorithms respectively. Furthermore, the fusion of heterogeneous sensors attached to different locations of the body shows Chest and Hip sensors fusion achieves an average F-measure of 0.942 and classification accuracy of 94.23% using Random Forest algorithm. The outcome of our experimental evaluation shows the significant impact of multi-sensor fusion for human activity monitoring and detection.