Miracle-dz/UniFAR-FTUs huggingface dataset:
Histological composition of lumbar disc herniations related to the type of herniation and to the age.

OBJECTIVES To compare the data for histologic composition of the herniated disc material between different types of disc herniations and between patients of different age. BACKGROUND Lots of studies have investigated the histologic composition of disc herniations. Few studies have examined the presence of granulation tissue related to the type of herniations and age of the subjects. METHODS 120 patients divided in to two age groups underwent MR imaging before microsurgical removal of the herniations. Disc herniations were divided in to two groups, non migrated and large migrated disc herniations. The histologic assessment of the herniated material is done by dividing the intercellular matrix into four types. The existence of areas of granulation tissue and hyaline cartilage is evaluated too. RESULTS The tissue composition showed significant differences between patients of different age. Areas of granulation tissue and neovascularisation are found in 12% of 64 protrusions and contained extrusions, and in 29% of 56 large migrated disc extrusions (p=0.029 ). In elderly patients the tissue composition changed with a significant decrease of prevailing nucleus pulposus composition in 7% of 60 (p=0.000) and a significant increase of prevailing fibrous tissue composition in 40% of 60 patients (p=0.000). Areas of granulation tissue and neovascularisation were found in 27% of 60 patients aged ≤ 50 and in 13% of 60 patients aged >50. CONCLUSIONS The tissue composition of the herniated material showed more expressed differences between subjects of different age. Granulation tissue and neovascularisation were more frequent findings in large migrated disc herniations and in patients aged 35 to 50 years (Fig. 8, Ref. 22).
Joint Center Estimation Using Single-Frame Optimization: Part 1: Numerical Simulation

The biomechanical models used to refine and stabilize motion capture processes are almost invariably driven by joint center estimates, and any errors in joint center calculation carry over and can be compounded when calculating joint kinematics. Unfortunately, accurate determination of joint centers is a complex task, primarily due to measurements being contaminated by soft-tissue artifact (STA). This paper proposes a novel approach to joint center estimation implemented via sequential application of single-frame optimization (SFO). First, the method minimizes the variance of individual time frames’ joint center estimations via the developed variance minimization method to obtain accurate overall initial conditions. These initial conditions are used to stabilize an optimization-based linearization of human motion that determines a time-varying joint center estimation. In this manner, the complex and nonlinear behavior of human motion contaminated by STA can be captured as a continuous series of unique rigid-body realizations without requiring a complex analytical model to describe the behavior of STA. This article intends to offer proof of concept, and the presented method must be further developed before it can be reasonably applied to human motion. Numerical simulations were introduced to verify and substantiate the efficacy of the proposed methodology. When directly compared with a state-of-the-art inertial method, SFO reduced the error due to soft-tissue artifact in all cases by more than 45%. Instead of producing a single vector value to describe the joint center location during a motion capture trial as existing methods often do, the proposed method produced time-varying solutions that were highly correlated (r > 0.82) with the true, time-varying joint center solution.
Psychosocial Work Factors and Musculoskeletal Pain: A Cross-Sectional Study among Swedish Flight Baggage Handlers

Objective. Flight baggage handlers sort and load luggage to airplanes. This study aimed at investigating associations between psychosocial exposures and low back and shoulder musculoskeletal disorders (MSDs) among Swedish flight baggage handlers. Methods. A questionnaire addressing MSDs (Standardized Nordic Questionnaire) and psychosocial factors (Copenhagen Psychosocial Questionnaire, COPSOQ) was answered by 525 baggage handlers in six Swedish airports. Results. Low back (LBP) and shoulder pain (SP) were reported by 70% and 60%, respectively. Pain was reported to interfere with work (PIW) by 30% (low back) and 18% (shoulders), and intense pain (PINT) occurred in 34% and 28% of the population. Quality of leadership was the most dissatisfying psychosocial factor, while the most positive was social community at work. Low ratings in the combined domain Work organization and job content were significantly associated with PIW in both low back and shoulders (Adjusted Hazard Ratios 3.65 (95% CI 1.67–7.99) and 2.68 (1.09–6.61)) while lower ratings in the domain Interpersonal relations and leadership were associated with PIW LBP (HR 2.18 (1.06–4.49)) and PINT LBP and SP (HRs 1.95 (1.05–3.65) and 2.11 (1.08–4.12)). Conclusion. Severity of pain among flight baggage handlers was associated with psychosocial factors at work, suggesting that they may be a relevant target for intervention in this occupation.
Modelling stochastic calcium waves in cardiac myocytes based on the two-pool CICR model

In this paper, stochastic 1D and 2D models based on the two-pool calcium-induced calcium release (CICR) process are presented. An anisotropic diffusion model is presented that approximates the calcium concentration in the cytosol as well as the sarcoplasmic reticulum (SR). The relative source strengths in 1D and 2D are adjusted to simulate the behaviour of a point source array in the real 3D problem. The numerical conversion presented involves the convolution between the Green's and Heaviside functions. The models are solved using the Crank Nicolson (CN) and alternating direction implicit (ADI) for the 1D and 2D cases, respectively. These methods are known to be stable. Results are presented for the comparison between the two models.
Human skeletal muscle is refractory to the anabolic effects of leucine during the postprandial muscle-full period in older men

Leucine modulates muscle protein synthesis (MPS), with potential to facilitate accrual/maintenance of muscle mass. Animal models suggest that leucine boluses shortly after meals may prolong MPS and delay onset of a “muscle-full” state. However, the effects of nutrient “top-ups” in humans, and particularly older adults where deficits exist, have not been explored. We determined the effects of a leucine top-up after essential amino acid (EAA) feeding on anabolic signaling, MPS, and muscle energy metabolism in older men. During 13C6-phenylalanine infusion, 16 men (∼70 years) consumed 15 g of EAA with (n=8, FED + LEU) or without (n=8, FED) 3 g of leucine top-up 90 min later. Repeated blood and muscle sampling permitted measurement of fasting and postprandial plasma EAA, insulin, anabolic signaling including mTOR complex 1 (mTORC1) substrates, cellular ATP and phosphorylocreatine, and MPS. Oral EAA achieved rapid insulinemia (12.5 iU·ml−1 25 min post-feed), essential aminoacidemia (3000 μM, 45–65 min post-feed), and activation of mTORC1 signaling. Leucine top-up prolonged plasma EAA (2800 μM, 135 min) and leucine availability (1050 μM, 135 min post-feed). Fasting FSRs of 0.046 and 0.056%·h-1 (FED and FED + LEU respectively) increased to 0.085 and 0.085%·h-1 90–180 min post-feed and returned to basal rates after 180 min in both groups. Phosphorylation of mTORC1 substrates returned to fasting levels 240 min post-feed in both groups. Feeding had limited effect on muscle high-energy phosphates, but did induce eukaryotic elongation factor 2 (eEF2) phosphorylation. We demonstrate the refractoriness of muscle to nutrient-led anabolic stimulation in the postprandial period; thus, leucine supplements should be taken outside of meals, or with meals containing suboptimal protein in terms of either amount or EAA composition.
Regional urban area extraction using DMSP-OLS data and MODIS data

Stable night lights data from Defense Meteorological Satellite Program (DMSP) Operational Line-scan System (OLS) provide a unique proxy for anthropogenic development. This paper proposed two new methods of extracting regional urban extents using DMSP-OLS data, MODIS NDVI data and Land Surface Temperature (LST) data. MODIS NDVI data were used to reduce the over-glow effect, since urban areas generally have lower vegetation index values than the surrounding areas (e.g. agricultural and forest areas). On the other hand, urban areas generally show higher surface temperatures than the surrounding areas. Since urban area is the only class of interest, a one-class classifier, the One-Class Support Vector Machine (OCSVM), was selected as the classifier. The first method is classification of different data combinations for mapping: (1) OLS data and NDVI data, (2) OLS data and LST data, and (3) OLS data, NDVI data and LST data combined. The second one is a morphological reconstruction based method which combines classification results from OLS plus NDVI data and from OLS plus LST data. In the morphological reconstruction based method, the classification result using OLS and NDVI data was used as a mask image, while the classification result using OLS and LST data was used as a marker image. The north China area covering 14 provinces was selected as study area. Classification results from Landsat TM/ETM+ data from selected areas with different development levels were used as reference data to validate the proposed methods. The results show that the proposed methods effectively reduced the over-glow effect caused by DSMP-OLS data and achieved better results compared to the results from the traditional thresholding technique. The combination of all three datasets produces more accurate results than those of using any two datasets. The proposed morphological reconstruction based method achieves the best result in urban extent mapping.
Wrist and arm body surface bipolar ECG leads signal and sensor study for long-term rhythm monitoring

With cardiovascular disease and heart arrhythmias continuing to have a high mortality rate, it is important to monitor the electrocardiogram (ECG) signal in a noninvasive, long-term wearable device. In this study we investigate sensors and the ECG signal-to-noise ratio map along the left arm, for wearable arm-ECG monitoring devices. In a pilot study, 11 subjects attending a cardiology outpatient clinic, far-field left-arm ECG recordings included signals from a combination of dry and special pre-gelled BIS-Quatro™ sensor system, axially and transversally oriented along the left arm: on the wrist, upper forearm and upper arm. A total of 10 bipolar leads were recorded simultaneously (using 18 acquisition channels). Each subject was recorded for 8 minutes at rest, using the bio-potential acquisition system; all data was imported and processed using Matlab and MS Excel. Analysis was completed to evaluate signal-to-noise ratio (SNR) distribution maps. An average ECG SNR figure of 42.63 was found in the dry-electrode positioned on the upper arm bipolar lead, whilst the SNR ratio positioned on the wrist was 13.14. Similar to this, in the BIS-electrodes (gelled), there was an average ECG SNR figure of 89.25 on the upper arm and of 5.18 positioned on the wrist. This study clinically evidenced the ECG S/N map on the left arm. It reveals that bipolar arm-ECG SNR are consistently stronger on the upper arm, when recorded with the gelled BIS sensors.
Tight Bounds of Differentially and Linearly Active S-Boxes and Division Property of Lilliput

This paper provides security analysis of a lightweight block cipher called Lilliput, which was proposed in IEEE Transactions on Computers in 2015. Lilliput adopts an extended generalized Feistel network (EGFN). EGFN consists of non-linear, linear, and permutation layers, and the linear layer updates a part of the state only linearly, which causes several security concerns. Our first discovery is that the lower bounds of the number of differentially active S-boxes provided by the designers are incorrect. Thus the new bounds are derived by using mixed integer linear programming (MILP). We apply a two-stage search procedure introduced by Sun et al. that leads to tight bounds even for a large number of rounds. The search tool is then converted for linear cryptanalysis. With those updates, the challenging problem of evaluating Lilliput's security against differential and linear cryptanalysis is closed. Another contribution is the best third-party cryptanalysis. The designers expected EGFN to efficiently enhance security against integral cryptanalysis. However, security is not as enhanced as the designers expected. In fact, division property finds a 13-round distinguisher that improves on the previous distinguisher by 4 rounds. The distinguisher is further extended to a 17-round key recovery that improves on the previous best attack by 3 rounds.
Shape Completion Using Deep Boltzmann Machine

Shape completion is an important task in the field of image processing. An alternative method is to capture the shape information and finish the completion by a generative model, such as Deep Boltzmann Machine. With its powerful ability to deal with the distribution of the shapes, it is quite easy to acquire the result by sampling from the model. In this paper, we make use of the hidden activation of the DBM and incorporate it with the convolutional shape features to fit a regression model. We compare the output of the regression model with the incomplete shape feature in order to set a proper and compact mask for sampling from the DBM. The experiment shows that our method can obtain realistic results without any prior information about the incomplete object shape.
Cost-Effectiveness Analysis of Adjuvant Hormonal Treatments for Women with Postmenopausal Hormone-Receptor Positive Early Breast Cancer in the Korean Context

• Breast cancer is the most common cancer among women in South Korea. A total of 11,275 women were newly diagnosed as having breast cancer in 2006 and the agestandardized incidence rate per 100,000 was 35.5. (1) • Anastrozole and Letrozole are the third generation aromatase inhibitors, and they have been demonstrated to be more effective than tamoxifen as the first-line treatment for early breast cancer. • Anastrozole has shown more favorable disease-free survival (hazard ratio [HR], 0.83; 95% confidence interval [CI], 0.73-0.94; p=0.005) and a longer time to recurrence (HR, 0.74; 95% CI, 0.64-0.87; p=0.0002) versus tamoxifen on the completed treatment analysis of the ATAC trial, which was performed in postmenopausal women with early breast cancer. (3) Background
A big data approach to myocyte membrane analysis: Using populations of models to understand the cellular causes of heart failure

Experimentally-calibrated populations of models (ePoMs) allow to elucidate the trends hidden behind large amounts of data, but they have never been used as a means to monitor the temporal evolution of a heart's electrical activity. This work aimed at using ePoMs to understand the cell membrane anomalies that lead to heart failure. A population of the Tusscher-Noble-Noble-Panfilov model was calibrated to the activation-recovery intervals measured from epicardial electrograms acquired during a Physio-heart experiment. A Mann-Whitney-Wilcoxon U-test was performed on the statistical distributions of the calibrated parameters, at different time points during the experiment, to elucidate the physiology changes that would have led to the resulting ePoMs. The methodology developed in this paper could detect the specific pathological ion dynamics responsible for the abnormal electrical behavior observed during the experiment. Furthermore, the analysis of the electrical activities was capable of detection of pathologies at an earlier stage when compared to the analysis of the cardiac output alone. The use of big data analytics proved to be more effective than the traditional signal analysis approach in predicting heart failure; additionally, this approach accounts for variabilities in both the healthy and the pathological conditions.
Searching for the IoT Resources: Fundamentals, Requirements, Comprehensive Review, and Future Directions

Internet of Things (IoT) paradigm links physical objects in the real world to cyber world and enables the creation of smart environments and applications. A physical object is the fundamental building block of the IoT, known as a Smart Device, that can monitor the environment. These devices can communicate with each other and have data processing abilities. When deployed, smart devices collect real-time data and publish the gathered data on the Web. The functionality of smart devices can be abstracted as a service and an IoT application can be built by combining the smart devices with these services that help to address challenges of day-to-day activities. The IoT comprises billions of these intelligent communicating devices that generate enormous amount of data, and hence performing analysis on this data is a significant task. Using search techniques, the size and extent of data can be reduced and limited, so that an application can choose just the most important and valuable data items as per its necessities. It is, however, a tedious task to effectively seek and select a proper device and/or its data among a large number of available devices for a specific application. Search techniques are fundamental to IoT and poses various challenges like a large number of devices, dynamic availability, restrictions on resource utilization, real time data in various types and formats, past and historical monitoring. In the recent past, various methods and techniques have been developed by the research community to address these issues. In this paper, we present a review of the state-of-the-art search methods for the IoT, classifying them according to their design principle and search approaches as: IoT data and IoT object-based techniques. Under each classification, we describe the method adopted, their advantages and disadvantages. Finally, we identify and discuss key challenges and future research directions that will allow the next generation search techniques to recognize and respond to user queries and satisfy the information needs of users.
Prenatal Metformin Therapy Attenuates Hypertension of Developmental Origin in Male Adult Offspring Exposed to Maternal High-Fructose and Post-Weaning High-Fat Diets

Widespread consumption of a Western diet, comprised of highly refined carbohydrates and fat, may play a role in the epidemic of hypertension. Hypertension can take origin from early life. Metformin is the preferred treatment for type 2 diabetes. We examined whether prenatal metformin therapy can prevent maternal high-fructose plus post-weaning high-fat diets-induced hypertension of developmental origins via regulation of nutrient sensing signals, uric acid, oxidative stress, and the nitric oxide (NO) pathway. Gestating Sprague–Dawley rats received regular chow (ND) or chow supplemented with 60% fructose diet (HFR) throughout pregnancy and lactation. Male offspring were onto either the ND or high-fat diet (HFA) from weaning to 12 weeks of age. A total of 40 male offspring were assigned to five groups (n = 8/group): ND/ND, HFR/ND, ND/HFA, HFR/HFA, and HFR/HFA+metformin. Metformin (500 mg/kg/day) was administered via gastric gavage for three weeks during the pregnancy period. Combined maternal HFR plus post-weaning HFA induced hypertension in male adult offspring, which prenatal metformin therapy prevented. The protective effects of prenatal metformin therapy on HFR/HFA-induced hypertension, including downregulation of the renin-angiotensin system, decrease in uric acid level, and reduction of oxidative stress. Our results highlighted that the programming effects of metformin administered prenatally might be different from those reported in adults, and that deserves further elucidation.
Empirical validation of computational model of influences on physical activity

The adoption and maintenance of a healthy lifestyle is a fundamental pillar in the quest towards a healthy society. Modern (mobile) technology allows for increasingly intelligent systems that can help to optimize people’s health outcomes. One of the possible directions in such mHealth systems is the use of intelligent reasoning engines based on dynamic computational models of behavior change. In this work, we investigate the accuracy of such a model to simulate changes in physical activity levels over a period of two to twelve weeks. The predictions of the model are compared to empirical physical activity data of 108 participants. The results reveal that the model’s predictions show a moderate to strong correlation with the actual data, and it performs substantially better than a simple alternative model. Even though the implications of these findings depend strongly on the application at hand, we show that it is possible to use a computational model to predict changes in behavior. This is an important finding for developers of mHealth systems, as it confirms the relevance of model-based reasoning in such health interventions.
Exposure to physical and psychosocial stressors in relation to symptoms of common mental disorders among European professional football referees: a prospective cohort study

Objectives The study aim was to explore the association of physical and psychosocial stressors (severe injuries, surgeries, recent life events, social support) with one-season onset of symptoms of common mental disorders (CMDs) among European professional football referees. Methods An observational prospective cohort study over a follow-up period of one season (2015–2016) was conducted among professional football referees from Belgium, Finland, France, Germany, Norway, Russia, Scotland and Sweden. Based on physical and psychosocial stressors as well as symptoms of CMD, an electronic questionnaire in English and French was set up and distributed by eight football federations involved. Results The prevalence of symptoms of CMD ranged from 5.9% for distress to 19.2% for eating disorders. A higher number of severe injuries and a lower degree of satisfaction about social support were significantly related to the occurrence of symptoms of CMD with an OR of 2.63 and an OR of 1.10, respectively. Conclusion A higher number of severe injuries and a lower degree on satisfaction about social support were found to be significantly associated with the onset of symptoms of CMD among European professional football referees. Referees suffering from severe injuries were nearly three times more likely to report symptoms of anxiety and depression. Referees who reported a low satisfaction of social support were significantly more likely to report symptoms of eating disorder.
Wrist and forearm rotation of the DLR Hand Arm System: Mechanical design, shape analysis and experimental validation

The DLR Hand Arm System is based upon the variable stiffness concept which has been recently developed to improve impact robustness and energy efficiency of modern robots. This paper continues the work on the bidirectional antagonistic variable stiffness (BAVS) joint concept which is an extension of antagonistic joints. Three mechanical setups utilizing different spring and cam disc combinations to implement a desired torque-stiffness characteristic are analyzed. Two BAVS joint solutions as used for the wrist and forearm rotation of the DLR Hand Arm System are presented. Furthermore in the experimental section torque-deflection calibration and drive redundancy are validated.