nils-herrmann/clean_scirepeval huggingface dataset:
Signaling pathways effecting crosstalk between cartilage and adjacent tissues: Seminars in cell and developmental biology: The biology and pathology of cartilage.

Endochondral ossification, the mechanism responsible for the development of the long bones, is dependent on an extremely stringent coordination between the processes of chondrocyte maturation in the growth plate, vascular expansion in the surrounding tissues, and osteoblast differentiation and osteogenesis in the perichondrium and the developing bone center. The synchronization of these processes occurring in adjacent tissues is regulated through vigorous crosstalk between chondrocytes, endothelial cells and osteoblast lineage cells. Our knowledge about the molecular constituents of these bidirectional communications is undoubtedly incomplete, but certainly some signaling pathways effective in cartilage have been recognized to play key roles in steering vascularization and osteogenesis in the perichondrial tissues. These include hypoxia-driven signaling pathways, governed by the hypoxia-inducible factors (HIFs) and vascular endothelial growth factor (VEGF), which are absolutely essential for the survival and functioning of chondrocytes in the avascular growth plate, at least in part by regulating the oxygenation of developing cartilage through the stimulation of angiogenesis in the surrounding tissues. A second coordinating signal emanating from cartilage and regulating developmental processes in the adjacent perichondrium is Indian Hedgehog (IHH). IHH, produced by pre-hypertrophic and early hypertrophic chondrocytes in the growth plate, induces the differentiation of adjacent perichondrial progenitor cells into osteoblasts, thereby harmonizing the site and time of bone formation with the developmental progression of chondrogenesis. Both signaling pathways represent vital mediators of the tightly organized conversion of avascular cartilage into vascularized and mineralized bone during endochondral ossification.
Version Management of XML Documents

The problem of ensuring e cient storage and fast retrieval for multi-version structured documents is important because of the recent popularity of XML documents and semistructured information on the web. Traditional document version control systems, e.g. RCS, which model documents as a sequence of lines of text and use the shortest edit script to represent version di erences are inadequate since they do not preserve the logical structure of the original document. Instead we propose a new approach where the structure of the document is preserved intact, and their sub-objects are timestamped hierarchically for e cient reconstruction of current and past versions. Our technique, called the Usefulness Based Copy Control (UBCC), is geared towards efcient version reconstruction while using small space overhead. Our analysis and experiments illustrate the e ectiveness of the overall approach to version control for structured documents.
Adaptive Engagement of Cognitive Control in Context-Dependent Decision Making.

Many decisions require a context-dependent mapping from sensory evidence to action. The capacity for flexible information processing of this sort is thought to depend on a cognitive control system in frontoparietal cortex, but the costs and limitations of control entail that its engagement should be minimized. Here, we show that humans reduce demands on control by exploiting statistical structure in their environment. Using a context-dependent perceptual discrimination task and model-based analyses of behavioral and neuroimaging data, we found that predictions about task context facilitated decision making and that a quantitative measure of context prediction error accounted for graded engagement of the frontoparietal control network. Within this network, multivariate analyses further showed that context prediction error enhanced the representation of task context. These results indicate that decision making is adaptively tuned by experience to minimize costs while maintaining flexibility.
Comparative study of machine learning algorithms for breast cancer detection and diagnosis

Breast cancer is one of the most widespread diseases among women in the UAE and worldwide. Correct and early diagnosis is an extremely important step in rehabilitation and treatment. However, it is not an easy one due to several uncertainties in detection using mammograms. Machine Learning (ML) techniques can be used to develop tools for physicians that can be used as an effective mechanism for early detection and diagnosis of breast cancer which will greatly enhance the survival rate of patients. This paper compares three of the most popular ML techniques commonly used for breast cancer detection and diagnosis, namely Support Vector Machine (SVM), Random Forest (RF) and Bayesian Networks (BN). The Wisconsin original breast cancer data set was used as a training set to evaluate and compare the performance of the three ML classifiers in terms of key parameters such as accuracy, recall, precision and area of ROC. The results obtained in this paper provide an overview of the state of art ML techniques for breast cancer detection.
Molecular biology of atopic dermatitis.

Atopic dermatitis (AD) is a chronic inflammatory skin disease with specific genetic and immunological mechanisms. The rapid development of new techniques in molecular biology had ushered in new discoveries on the role of cytokines, chemokines, and immune cells in the pathogenesis of AD. New polymorphisms of AD are continually being reported in different populations. The physical and immunological barrier of normal intact skin is an important part of the innate immune system that protects the host against microbials and allergens that are associated with AD. Defects in the filaggrin gene FLG may play a role in facilitating exposure to allergens and microbial pathogens, which may induce Th2 polarization. Meanwhile, Th22 cells also play roles in skin barrier impairment through IL-22, and AD is often considered to be a Th2/Th22-dominant allergic disease. Mast cells and eosinophils are also involved in the inflammation via Th2 cytokines. Release of pruritogenic substances by mast cells induces scratching that further disrupts the skin barrier. Th1 and Th17 cells are mainly involved in chronic phase of AD. Keratinocytes also produce proinflammatory cytokines such as thymic stromal lymphopoietin (TSLP), which can further affect Th cells balance. The immunological characteristics of AD may differ for various endotypes and phenotypes. Due to the heterogeneity of the disease, and the redundancies of these mechanisms, our knowledge of the pathophysiology of the disease is still incomplete, which is reflected by the absence of a cure for the disease.
Fuzzy control of semi-active automotive suspensions

This paper presents a new fuzzy controller for semi-active vehicle suspension systems, which has a significantly fewer number of rules in comparison to existing fuzzy controllers. The proposed fuzzy controller has only nine fuzzy rules, whose performance is equivalent to the existing fuzzy controller with 49 fuzzy rules. The proposed controller with less number of fuzzy rules will be more feasible and cost-efficient in hardware implementation. For comparison, a linear quadratic regulator controlled semi-active suspension, and a passive suspension are also implemented and simulated. Simulation results show that the ride comfort and road holding are improved by 28% and 31%, respectively, with the fuzzy controlled semi-active suspension system, in comparison to the linear quadratic regulator controlled semi-active suspension.
Development of a psychometrically sound internet addiction scale: a preliminary step.

The Internet Addiction Scale (IAS) is a self-report instrument based on the 7 Diagnostic and Statistical Manual of Mental Disorders (4th ed.; American Psychiatric Association, 1994) substance dependence criteria and 2 additional criteria recommended by Griffiths (1998). The IAS was administered to 233 undergraduates along with 4 measures pertaining to loneliness and boredom proneness. An item reliability analysis reduced the initial scale from 36 to 31 items (with a Cronbach's alpha of .95). A principal-components analysis indicated that the IAS consisted mainly of one factor. Multiple regression analyses revealed that Family and Social Loneliness and Boredom Proneness were significantly correlated with the IAS; Family and Social Loneliness uniquely predicted IAS scores. No evidence for widespread Internet addiction was found.
Sensing array of radically coupled genetic biopixels

Although there has been considerable progress in the development of engineering principles for synthetic biology, a substantial challenge is the construction of robust circuits in a noisy cellular environment. Such an environment leads to considerable intercellular variability in circuit behaviour, which can hinder functionality at the colony level. Here we engineer the synchronization of thousands of oscillating colony ‘biopixels’ over centimetre-length scales through the use of synergistic intercellular coupling involving quorum sensing within a colony and gas-phase redox signalling between colonies. We use this platform to construct a liquid crystal display (LCD)-like macroscopic clock that can be used to sense arsenic via modulation of the oscillatory period. Given the repertoire of sensing capabilities of bacteria such as Escherichia coli, the ability to coordinate their behaviour over large length scales sets the stage for the construction of low cost genetic biosensors that are capable of detecting heavy metals and pathogens in the field.
Towards Photo Watercolorization with Artistic Verisimilitude

We present a novel artistic-verisimilitude driven system for watercolor rendering of images and photos. Our system achieves realistic simulation of a set of important characteristics of watercolor paintings that have not been well implemented before. Specifically, we designed several image filters to achieve: 1) watercolor-specified color transferring; 2) saliency-based level-of-detail drawing; 3) hand tremor effect due to human neural noise; and 4) an artistically controlled wet-in-wet effect in the border regions of different wet pigments. A user study indicates that our method can produce watercolor results of artistic verisimilitude better than previous filter-based or physical-based methods. Furthermore, our algorithm is efficient and can easily be parallelized, making it suitable for interactive image watercolorization.
Collaborative Compressive Sensing Based Dynamic Spectrum Sensing and Mobile Primary User Localization in Cognitive Radio Networks

In wideband cognitive radio (CR) networks, spectrum sensing is one of the key issues that enable the whole network functionality. Collaborative spectrum sensing among the cognitive radio nodes can greatly improve the sensing performance, and is also able to obtain the location information of primary radios (PRs). Most existing work merely studies the cognitive radio networks with static PRs, yet how to deal with the situations for mobile PRs remains less addressed. In this paper, we propose a collaborative compressive sensing based approach to estimate both the power spectrum and locations of the PRs by exploiting the sparsity facts: the relative narrow band nature of the transmitted signals compared with the broad bandwidth of available spectrum and the mobile PRs located sparsely in the operational space. To effectively track mobile PRs, we implement a Kalman filter using the current estimations to update the location information. To handle dynamics in spectrum usage, a dynamic compressive spectrum sensing algorithm is proposed. Joint consideration of the above two techniques is also investigated. Simulation results validate the effectiveness and robustness of the proposed approach.
Consensus Recommendations for Optimal Augmentation of the Asian Face with Hyaluronic Acid and Calcium Hydroxylapatite Fillers.

Although the use of filling agents for soft-tissue augmentation has increased worldwide, most consensus statements do not distinguish between ethnic populations. There are, however, significant differences between Caucasian and Asian faces, reflecting not only cultural disparities, but also distinctive treatment goals. Unlike aesthetic patients in the West, who usually seek to improve the signs of aging, Asian patients are younger and request a broader range of indications.



Members of the Asia-Pacific Consensus group-comprising specialists from the fields of dermatology, plastic surgery, anatomy, and clinical epidemiology-convened to develop consensus recommendations for Asians based on their own experience using cohesive polydensified matrix, hyaluronic acid, and calcium hydroxylapatite fillers.



The Asian face demonstrates differences in facial structure and cosmetic ideals. Improving the forward projection of the "T zone" (i.e., forehead, nose, cheeks, and chin) forms the basis of a safe and effective panfacial approach to the Asian face. Successful augmentation may be achieved with both (1) high- and low-viscosity cohesive polydensified matrix/hyaluronic acid and (2) calcium hydroxylapatite for most indications, although some constraints apply.



The Asia-Pacific Consensus recommendations are the first developed specifically for the use of fillers in Asian populations.



Therapeutic, V.
Health consequences of obesity.

The recent epidemic of childhood obesity(1) has raised concern because of the possible clinical and public health consequences.(2,)(3) However, there remains a widespread perception among health professionals that childhood obesity is a largely cosmetic problem, with minor clinical effects. No systematic review has yet focused on the diverse array of possible consequences of childhood obesity, though older non-systematic reviews are available.(4,)(5) In addition, no review to date has considered the vast body of evidence on the health impact of childhood obesity which has been published recently. The aim of the present review was therefore to provide a critically appraised, evidence based, summary of the consequences of childhood obesity in the short term (for the child) and longer term (in adulthood).
The effects of the Mulligan Sustained Natural Apophyseal Glide (SNAG) mobilisation in the lumbar flexion range of asymptomatic subjects as measured by the Zebris CMS20 3-D motion analysis system

Mulligan's mobilisation techniques are thought to increase the range of movement (ROM) in patients with low back pain. The primary aim of this study was to investigate the application of the Mulligan's Sustained Natural Apophyseal Glide (SNAG) technique on lumbar flexion ROM. The secondary aim was to measure the intra- and inter-day reliability of lumbar ROM employing the same procedure.



49 asymptomatic volunteers participated in this double-blinded study. Subjects were randomly assigned to receive either SNAG mobilisation (n = 25), or a sham mobilisation (n = 24). The SNAG technique was applied at the L3and L4 spinal levels with active flexion in sitting by an experienced manual therapist. Three sets of 10 repetitions at each of the two spinal levels were performed. The sham mobilisation was similar to the SNAG but did not apply the appropriate direction or force. Lumbar ROM was measured by a three dimensional electronic goniometer (Zebris CMS20), before and after each technique. For the reliability, five measurements in two different days (one week apart) were performed in 20 healthy subjects.



When both interventions were compared, independent t tests yielded no statistically significant results in ROM between groups (p = 0.673). Furthermore no significant within group differences were observed: SNAG (p = 0.842), sham (p = 0.169). Intra- and inter-day reliability of flexion measurements was high (ICC(1,1) > 0.82, SEM < 4.0 degrees , SDD<16.3%) indicating acceptable clinical applicability.



While the Zebris proved to be a reliable device for measuring lumbar flexion ROM, SNAG mobilisation did not demonstrate significant differences in flexion ROM when compared to sham mobilisation.
A factor graph approach to estimation and model predictive control on Unmanned Aerial Vehicles

In this paper, we present a factor graph framework to solve both estimation and deterministic optimal control problems, and apply it to an obstacle avoidance task on Unmanned Aerial Vehicles (UAVs). We show that factor graphs allow us to consistently use the same optimization method, system dynamics, uncertainty models and other internal and external parameters, which potentially improves the UAV performance as a whole. To this end, we extended the modeling capabilities of factor graphs to represent nonlinear dynamics using constraint factors. For inference, we reformulate Sequential Quadratic Programming as an optimization algorithm on a factor graph with nonlinear constraints. We demonstrate our framework on a simulated quadrotor in an obstacle avoidance application.
Survey on Emotional Body Gesture Recognition

Automatic emotion recognition has become a trending research topic in the past decade. While works based on facial expressions or speech abound recognizing affect from body gestures remains a less explored topic. We present a new comprehensive survey hoping to boost research in the field. We first introduce emotional body gestures as a component of what is commonly known as "body language" and comment general aspects as gender differences and culture dependence. We then define a complete framework for automatic emotional body gesture recognition. We introduce person detection and comment static and dynamic body pose estimation methods both in RGB and 3D. We then comment the recent literature related to representation learning and emotion recognition from images of emotionally expressive gestures. We also discuss multi-modal approaches that combine speech or face with body gestures for improved emotion recognition. While pre-processing methodologies (e.g. human detection and pose estimation) are nowadays mature technologies fully developed for robust large scale analysis, we show that for emotion recognition the quantity of labelled data is scarce, there is no agreement on clearly defined output spaces and the representations are shallow and largely based on naive geometrical representations.
Thick legs - not always lipedema.

Due to its increased presence in the press and on television, the diagnosis of lipedema is on the way to becoming a trendy diagnosis for those with thick legs. Despite this, one must recognize that lipedema is a very rare disease. It is characterized by disproportional obesity of the extremities, especially in the region of the hip and the legs, hematoma development after minimal trauma, and increased pressure-induced or spontaneous pain. Aids for making the correct diagnosis are (duplex) sonography, the waist-hip index or the waist-height index and lymphoscintigraphy. Important differential diagnoses are constitutional variability of the legs, lipohypertrophy in obesity, edema in immobility, edema in chronic venous insufficiency and rheumatic diseases. The symptom-based therapy of lipedema consists of conservative (compression, manual lymphatic drainage, exercise) and surgical treatments (liposuction). Until now there is no curative therapy. Obesity is an important risk factor for the severity and prognosis of lipedema. Further studies for a better understanding of the pathogenesis of lipedema and in the end possible curative treatments are urgently needed.
Context-aware services adaptation for a smart living room

Pervasive or ubiquitous computing was developed thanks to the technological evolution of embedded systems and computer communication means. Ubiquitous computing has given birth to the concept of smart spaces that facilitate our daily life and increase our comfort where devices provide proactively adpated services. In spite of the significant previous works done in this domain, there still a lot of work and enhancement to do in particular the taking into account of current user's context when providing adaptable services. In this paper we propose an approach for context-aware services adaptation for a smart living room using two machine learning methods.
Multi-Level Intrusion Detection System (ML-IDS)

As the deployment of network-centric systems increases, network attacks are proportionally increasing in intensity as well as complexity. Attack detection techniques can be broadly classified as being signature-based, classification-based, or anomaly-based. In this paper we present a multi level intrusion detection system (ML-IDS) that uses autonomic computing to automate the control and management of ML-IDS. This automation allows ML-IDS to detect network attacks and proactively protect against them. ML-IDS inspects and analyzes network traffic using three levels of granularities (traffic flow, packet header, and payload), and employs an efficient fusion decision algorithm to improve the overall detection rate and minimize the occurrence of false alarms. We have individually evaluated each of our approaches against a wide range of network attacks, and then compared the results of these approaches with the results of the combined decision fusion algorithm.
The next generation of scenarios for climate change research and assessment

Advances in the science and observation of climate change are providing a clearer understanding of the inherent variability of Earth’s climate system and its likely response to human and natural influences. The implications of climate change for the environment and society will depend not only on the response of the Earth system to changes in radiative forcings, but also on how humankind responds through changes in technology, economies, lifestyle and policy. Extensive uncertainties exist in future forcings of and responses to climate change, necessitating the use of scenarios of the future to explore the potential consequences of different response options. To date, such scenarios have not adequately examined crucial possibilities, such as climate change mitigation and adaptation, and have relied on research processes that slowed the exchange of information among physical, biological and social scientists. Here we describe a new process for creating plausible scenarios to investigate some of the most challenging and important questions about climate change confronting the global community.
Homomorphic Filtering of colour images using a Spatial Filter Kernel in the HSI colour space

This paper describes a solution to the issue of providing a balance between reduced computational complexity and image quality for homomorphic filtering of colour images. The solution eliminates frequency-domain transforms and calculation of the filter kernels dependent on image size in the frequency domain. It also reduces the overall computational complexity associated with frequency domain filtering and is easier to implement in hardware for real-time applications. The solution also reduces demands on hardware memory by processing the image in the HSI colour space. Additionally, the HSI domain helps reduce computational complexity further since only one channel is processed. Furthermore, the spatial domain filter kernel provides excellent contrast enhancement and sharper edges while the HSI colour space processing gives good colour rendition, thus improving colour image quality and removing the need for colour restoration postprocessing.