Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of the Metabolic Syndrome: Study Design and Baseline Data of the Lufthansa Prevention Study (LUPS), A Prospective Observational Cohort Survey

Abstract The Lufthansa Prevention Study (LUPS) study is a prospective observation of a healthy worker cohort to identify early changes in metabolism leading to the Metabolic Syndrome (MetS) and to analyze their relation to behavioral factors like nutrition, physical activity, psychological status, and to underlying genetic conditions. The LUPS study recruited a sample of 1.962 non-diabetic healthy adults between 25–60 years, employed at a flight base of Lufthansa Technik GmbH in Hamburg, Germany. Baseline assessments included anthropometric measures, blood and urine samples and medical history. Psychosocial variables, dietary habits and life-style risk factors were assessed via self-reported questionnaires. In this report we describe the study design and present baseline parameters including the prevalence of the MetS using different classification criteria. The MetS was present in 20% of male and 12% of female subjects according to the ‘Harmonizing the metabolic syndrome’ definition. The prevalence varies between 2.6% in male and 2.3% in female subjects up to 48% in male and 41% in female subjects according to different classification criteria of MetS. In conclusion, this first cross-sectional view on the LUPS data confirms the expectation that this cohort is rather healthy and thus provides the opportunity to analyze early changes associated with the development of the MetS. The LUPS study is registered as a clinical trial NCT01313156.
Pantothenate auxotrophy of Methylobacterium spp. isolated from living plants

ABSTRACT A number of pink-pigmented facultative methylotrophs (PPFMs) belonging to Methylobacterium spp. isolated from living plant samples were found to require B vitamins for their growth in minimal medium, and most B vitamin-auxotrophic PPFMs required pantothenate (vitamin B5). Further investigation of pantothenate auxotrophy using the representative strain Methylobacterium sp. OR01 demonstrated that this strain cannot synthesize β-alanine, one of the precursors of pantothenate. β-alanine and several precursors of pantothenate restored the growth of Methylobacterium sp. OR01 in minimal medium. Furthermore, this strain could colonize leaves of Arabidopsis thaliana cultivated in medium without pantothenate or its precursors. Pantothenate, β-alanine and several precursors were detected in the suspension of A. thaliana leaves. These results suggest that pantothenate-auxotrophic PPFMs can symbiotically colonize the surface of plant leaves by acquiring β-alanine and other precursors, in addition to pantothenate. Finally, the fitness advantage of B vitamin auxotrophy of PPFMs in the phyllosphere environment is discussed. Graphical Abstract Pantothenate-auxotrophic strains of Methylobacterium show symbiotic colonization on plants through acquisition of pantothenate and its precursors.
Identification of Cognitive Navigational Commands for Mobile Robots Based on Hand Gestures and Vocal Commands

Assistive robots are developed to uplift living standards of human beings. An assistive robot needs to be friendly, reliable, and understandable in order to be a human like companion. A robot should be able to navigate in an environment according to users' commands. Users' commands can be vocal or gestures. Combining users' command types to identify the navigation information will enhance the user-robot interaction and makes navigation more effective and purposeful. Moreover, understanding the relationship between the navigation commands makes the robot a more human friendly companion. A robot should be capable of interpreting spatial information in uncertain terms. Moreover it should be able to improve the knowledge through communicating with the user, itself. The Cognitive Map Creator (CMC) has been introduced to create a cognitive map of direct navigational commands to enhance the interaction between the human and the robot which will help to make a robot more human-friendly. The Conversation Management Module (CMM), Spatial Data Interpreter (SDI) and Navigational Command Identifier (NCI) have been introduced in order to create a cognitive map of navigational commands. The capabilities of the robot have been demonstrated and evaluated from experimental results.
Realistic and efficient 2D crack simulation

Although numerical algorithms for 2D crack simulation have been studied in Modeling and Simulation (M&S) and computer graphics for decades, realism and computational efficiency are still major challenges. In this paper, we introduce a high-fidelity, scalable, adaptive and efficient/runtime 2D crack/fracture simulation system by applying the mathematically elegant Peano-Cesaro triangular meshing/remeshing technique to model the generation of shards/fragments. The recursive fractal sweep associated with the Peano-Cesaro triangulation provides efficient local multi-resolution refinement to any level-of-detail. The generated binary decomposition tree also provides efficient neighbor retrieval mechanism used for mesh element splitting and merging with minimal memory requirements essential for realistic 2D fragment formation. Upon load impact/contact/penetration, a number of factors including impact angle, impact energy, and material properties are all taken into account to produce the criteria of crack initialization, propagation, and termination leading to realistic fractal-like rubble/fragments formation. The aforementioned parameters are used as variables of probabilistic models of cracks/shards formation, making the proposed solution highly adaptive by allowing machine learning mechanisms learn the optimal values for the variables/parameters based on prior benchmark data generated by off-line physics based simulation solutions that produce accurate fractures/shards though at highly non-real time paste. Crack/fracture simulation has been conducted on various load impacts with different initial locations at various impulse scales. The simulation results demonstrate that the proposed system has the capability to realistically and efficiently simulate 2D crack phenomena (such as window shattering and shards generation) with diverse potentials in military and civil M&S applications such as training and mission planning.
Laterality and rehabilitation: differences in left and right hand productions in aphasic agraphic hemiplegics

Abstract The typical picture of a patient suffering from a left hemisphere cerebral vascular accident (CVA) is that of an aphasic with a right hemiplegia. In such patients the impairment in speech is generally mirrored in written production (Basso et al. 1978). By necessity this agraphia is demonstrated in the left, non-dominant, non-paralysed hand. Due to the concomitant hemiplegia, writing is not normally examined in the dominant right hand. (This situation also holds for praxis.) This raises an important question: in patients with right hemiplegia, could it be demonstrated that the writing disturbance reflects a unilateral left agraphia? In other words, could the dominant, paralysed hand produce written language better than the non-dominant but unparalysed left hand?
A standard comparison of adaptive controllers to solve the cart pole problem

The cart-pole problem is an example of an inherently unstable dynamical system and has been widely used to test adaptive controllers. While various researchers have applied a range of adaptive controllers to solve the problem, there has been little work done on comparing the results of these methods. To compound this, researchers have used differing configurations of the cart-pole system. This work compares three common techniques used to solve the cart-pole problem. The cart-pole controllers are assessed on the basis of balancing time and centring/oscillation ability for a standardised configuration. Comparative results are given. <<ETX>>
Performance Analysis of GNSS/INS Loosely Coupled Integration Systems under Spoofing Attacks

The loosely coupled integration of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) have been widely used to improve the accuracy, robustness and continuity of navigation services. However, the integration systems possibly affected by spoofing attacks, since integration algorithms without spoofing detection would feed autonomous INSs with incorrect compensations from the spoofed GNSSs. This paper theoretically analyzes and tests the performances of GNSS/INS loosely coupled integration systems with the classical position fusion and position/velocity fusion under typical meaconing (MEAC) and lift-of-aligned (LOA) spoofing attacks. Results show that the compensations of Inertial Measurement Unit (IMU) errors significantly increase under spoofing attacks. The compensations refer to the physical features of IMUs and their unreasonable increments likely result from the spoofing-induced inconsistency of INS and GNSS measurements. Specially, under MEAC attacks, the IMU error compensations in both the position-fusion-based system and position/velocity-fusion-based system increase obviously. Under LOA attacks, the unreasonable compensation increments are found from the position/velocity-fusion-based integration system. Then a detection method based on IMU error compensations is tested and the results show that, for the position/velocity-fusion-based integration system, it can detect both MEAC and LOA attacks with high probability using the IMU error compensations.
MicroRNA-140-5p aggravates hypertension and oxidative stress of atherosclerosis via targeting Nrf2 and Sirt2

In the present study, the function of microRNA (miR)-140-5p on oxidative stress in mice with atherosclerosis was investigated. A reverse transcription-quantitative polymerase chain reaction assay was used to determine the expression of miR-140-5p. Oxidative stress kits and reactive oxygen species (ROS) kits were used to analyze alterations in oxidative stress and ROS levels. The alterations in protein expression were determined using western blot analysis and an immunofluorescence assay. miR-140-5p expression was increased in mice with atherosclerosis with hypertension. Consistently, miR-140-5p expression was also increased in mice with atherosclerosis. Upregulation of miR-140-5p increased oxidative stress and ROS levels by suppressing the protein expression of nuclear factor erythroid 2-related factor 2 (Nrf2), sirtuin 2 (Sirt2), Kelch-like enoyl-CoA hydratase-associated protein 1 (Keap1) and heme oxygenase 1 (HO-1) in vitro. By contrast, downregulation of miR-140-5p decreased oxidative stress and ROS levels by activating the protein expression of Nrf2, Sirt2, Keap1 and HO-1 in vitro. Sirt2 agonist or Nrf2 agonist inhibited the effects of miR-140-5p on oxidative stress in vitro. Collectively, these results suggested that miR-140-5p aggravated hypertension and oxidative stress of mice with atherosclerosis by targeting Nrf2 and Sirt2.
Cancers classification based on deep neural networks and emotional learning approach

In the present era, enormous factors contribute to causing cancer. So cancer classification cannot rely only on doctor's thoughts. As a result, intelligent algorithms concerning doctor's help are inevitable. Therefore, the authors are motivated to suggest a novel algorithm to classify three cancer datasets; colon, ALL‐AML, and leukaemia cancers. Their proposed algorithm is based on the deep neural network and emotional learning process. First of all, by applying the principal component analysis, they had a feature reduction. Then, they used deep neural as a feature extraction. Then, they implemented different classifiers; multi‐layer perceptron, support vector machine (SVM), decision tree, and Gaussian mixture model. In the end, because in the real world, especially when working on systems biology, unpredictable events, and uncertainties are undeniable, the robustness of their model against uncertainties is important. So they added Gaussian noise to the input features of the first encoder in each dataset, then, they applied the stacked denoising method. Experimental results disclosed that, generally, using emotional learning increased the accuracy. In addition, the highest accuracy was gained by SVM, 91.66, 92.27, and 96.56% for colon, ALL‐AML, and leukaemia, respectively. However, GMM led to the lowest accuracy. The best accuracy gained by GMM was 60%.
A distributed framework for information retrieval, processing and presentation of data

The Internet is ever growing and the information is highly unstructured. Finding the best priced product when shopping online is a difficult task, but shopping search engines help the user identify the desired product. Starting from this reality, this paper presents a distributed framework for information retrieval, processing and presentation of data, which has three main components: a data server cluster, a distributed crawler with a product extraction feature, and a web server for presenting the processed data. The considered use case consists of extracting information about board and card games from specialized websites and presenting the data in a user-friendly website. Efficiency is determined by means of performance evaluation metrics that include crawl and extraction times, and a discussion in terms of scalability. The high modularization of the framework allows it to be an effective teaching tool in classrooms; each module can be easily swapped with a custom implementation. The proposed framework offers students and researchers an environment for developing and testing a wide range of algorithms and programming solutions, e.g., load balancer, crawler, reverse engineering a web template, product extractor from web pages, data normalization, notification services, web APIs, custom database.
Management of Adult Neurogenic Dysphagia

Foreword by John C. Rosenbek, Ph.D. Issues in Dysphagia Management. Framework for Intervention. Rehabilitation of Dysphagia. Biofeedback Modalities: Surface Electromyography. Other Biofeedback Modalities: Endoscopy, Ultrasound, Auscultation, Oximetry. Compensatory Interventions for Dysphagia. Malnutrition and Dehydration in Dysphagia. Non-Oral Nutrition. Oral Nutrition Interventions for Dysphagia. Special Issues of Feeding Dependency. Surgical Management of Dysphagia of Neurogenic Origin. Legal and Reimbursement Issues of Documentation. The Patients Perspective. Appendix A: American Speech-Language-Hearing Association Code of Ethics. Appendix B: American Speech-Language-Hearing Association Statement of Practices and Procedures of the Board of Ethics. Appendix C: Medicare Intermediary Manual Part 3-Claims Process.
Inhibition of cell growth by cellular differentiation into adipocyte-like cells in dexamethasone sensitive cancer cell lines

ABSTRACT The stress responses in human body lead to secretion of cortisol hormone. The present study investigated the cellular responses on cell growth and cellular differentiation into adipocytes by exposure of synthetic stress hormone, dexamethasone (DEX) in various human cancer and normal cells. After prolonged exposure of cells with 1 μg/ml DEX for 2 weeks, population doubling time (PDT) was significantly (P < .05) increased by inhibited cell growth in A-549 and MCF-7 cancer cells, and was unchanged in MDA-MB-231 cancer cells, normal MRC-5 fibroblasts, umbilical cord matrix-derived mesenchymal stem cells (UCMSCs) and dental papilla tissue-derived mesenchymal stem cells (DSCs). Whereas, PDT was significantly (P < .05) decreased in U87-MG cancer cells by increased cell growth. Glucose uptake was significantly (P < .05) increased in all the cancer cell lines compared to that in normal cell lines. Further, adiposome-like vesicles were noted in A-549 and MCF-7 cancer cells indicating retarded cell growth by DEX treatment, and the vesicles were stained with Oil-Red O solution. Further, the expression of adipocyte-specific genes such as glucose transporter type 4 (GLUT4), glucocorticoid receptors β (GRβ) and peroxisome proliferator-activated receptor γ (PPARγ) were significantly (P < .05) increased in A-549 and MCF-7 with lipid vesicles. The level of telomerase activity was found to be significantly (P < .05) downregulated in DEX-treated A-549 and MCF-7 cancer cells. Our results have clearly shown that DEX treatment induces inhibition of cell growth by differentiating into adipocyte-like cells in dexamethasone sensitive cancer cells.
A spectroscopic study of IRAS F 10214 + 4724 Journal Item

The z 1⁄4 2:286 IRAS galaxy F10214 þ 4724 remains one of the most luminous galaxies in the Universe, despite its gravitational lens magnification. We present optical and near-infrared spectra of F10214 þ 4724, with clear evidence for three distinct components: lines of width ,1000 km s from a Seyfert 2 nucleus; & 200 km s lines which are likely to be associated with star formation; and a broad (,4000 km s) C III] 1909-Å emission line which is blueshifted by ,1000 km s with respect to the Seyfert 2 lines. Our study of the Seyfert 2 component leads to several new results. (i) From the double-peaked structure in the Lya line, and the lack of Lyb, we argue that the Lya photons have emerged through a neutral column of NH , 2:5 × 10 m, possibly located within the AGN narrow-line region, as proposed for several high-redshift radio galaxies. (ii) The resonant O VI 1032, 1036-Å doublet (previously identified as Lyb) is in an optically thick (1:1) ratio. At face value this implies an extreme density (ne , 10 17 m) more typical of broad-line region clouds. However, we attribute this instead to the damping wings of Lyb from the resonant absorption. (iii) A tentative detection of He II 1086 suggests little extinction in the rest frame ultraviolet.
ExpandNets: Exploiting Linear Redundancy to Train Small Networks

While very deep networks can achieve great performance, they are ill-suited to applications in resource-constrained environments. Knowledge transfer, which leverages a deep teacher network to train a given small network, has emerged as one of the most popular strategies to address this problem. In this paper, we introduce an alternative approach to training a given small network, based on the intuition that parameter redundancy facilitates learning. We propose to expand each linear layer of a small network into multiple linear layers, without adding any nonlinearity. As such, the resulting expanded network can be compressed back to the small one algebraically, but, as evidenced by our experiments, consistently outperforms training the small network from scratch. This strategy is orthogonal to knowledge transfer. We therefore further show on several standard benchmarks that, for any knowledge transfer technique, using our expanded network as student systematically improves over using the small network.
Learning Conditional Random Fields with Augmented Observations for Partially Observed Action Recognition

This paper aims at recognizing partially observed human actions in videos. Action videos acquired in uncontrolled environments often contain corrupt frames, which make actions partially observed. Furthermore, these frames can last for arbitrary lengths of time and appear irregularly. They are inconsistent with training data and degrade the performance of pre-trained action recognition systems. We present an approach to address this issue. For each training and testing actions, we divide it into segments and explore the mutual dependency between temporal segments. This property states that the similarity of two actions at one segment often implies their similarity at another. We augment each segment with extra alternatives retrieved from training data. The augmentation algorithm is designed in a way where a few alternatives are good enough to replace the original segment where corrupt frames occur. Our approach is developed upon hidden conditional random fields and leverages the flexibility of hidden variables for uncertainty handling. It turns out that our approach integrates corrupt segment detection and alternative selection into the process of prediction, and can recognize partially observed actions more accurately. It is evaluated on both fully observed actions and partially observed ones with either synthetic or real corrupt frames. The experimental results manifest its general applicability and superior performance, especially when corrupt frames are present in the action videos.
Engagement Recognition using Deep Learning and Facial Expression

Engagement is a key indicator of the quality of learning experience, and one that plays a major role in developing intelligent educational interfaces. Any such interface requires the ability to recognise the level of engagement in order to respond appropriately; however, there is very little existing data to learn from, and new data is expensive and difficult to acquire. This paper presents a deep learning model to improve engagement recognition from face images captured in the wild that overcomes the data sparsity challenge by pre-training on readily available basic facial expression data, before training on specialised engagement data. In the first of two steps, a facial expression recognition model is trained to provide a rich face representation using deep learning. In the second step, we use the model's weights to initialize our deep learning based model to recognize engagement; we term this the Transfer model. We train the model on our new engagement recognition (ER) dataset with 4627 engaged and disengaged samples. We find that the Transfer model outperforms effective deep learning architectures that we apply for the first time to engagement recognition, as well as approaches using histogram of oriented gradients and support vector machines.