LLukas22/scidocs huggingface dataset:
Biochemical Markers of Bone Turnover in Camurati–Engelmann Disease: A Report on Four Cases in One Family

Moderate increases in ``classical'' biochemical markers of bone turnover have been described only in some patients with Camurati–Engelmann disease. However, the determination of the following ``new'' markers has not been previously performed: serum osteocalcin (BGP), bone alkaline phosphatase (BAP), carboxyterminal propeptide of type I procollagen (PICP), aminoterminal propeptide of type I procollagen (PINP), tartrate-resistant acid phosphatase (TRAP), telopeptide carboxyterminal of type I collagen (ICTP), urinary pyridinoline (PYR), crosslinked N-telopeptides of type I collagen (NTX), and Crosslaps (CL). Such a determination may improve the evaluation of the disease activity. To evaluate the usefulness of biochemical markers of bone turnover reflecting Camurati–Engelmann disease activity we measured the levels of all these markers in four affected patients. The results were compared with bone scintigraphic indices of disease activity. Except for PICP and TRAP, bone formation and resorption markers were abnormal in all patients and were related to bone scan indices of disease activity. Among the markers of bone formation PINP, BAP, and BGP showed the highest values, whereas NTX and CL were the most sensitive markers of bone resorption. These results suggest that the determination of NTX or CL, and PINP or either BAP and BGP, associated with bone scan evaluation, provides the best assessment of Camurati–Engelmann disease activity.
Motivations towards environmental innovation: A conceptual framework for multiparty cooperation

Purpose – The purpose of this paper is to examine the green innovation literature using a conceptual framework developed to explain the driving forces behind environmental cooperative activities of firms. The framework links motivations to the different type of partners in the context of environmental innovations. Design/methodology/approach – Literature on innovation, environmental innovation, sustainable development and strategic management is examined in order to build the conceptual framework. Findings – This paper suggests that firms cooperate with governmental agencies, NGOs, suppliers, customers and industry associations to comply with environmental laws and regulation, obtain legitimacy as well as acquire competency (i.e. access to resources such as funds, knowledge and skills). However, only competency-oriented motivation stimulates organisations to cooperate with competitors and knowledge leaders. Research limitations/implications – The model developed is conceptual and qualitative in nature. More research that is empirical needs to be conducted to test for the validity of the six formulated propositions. Originality/value – The significance of this paper is twofold. First, it integrates two different strategic management theories: resource-based and institutional theories in explaining cooperative environmental motivations. Second, it develops a framework that provides a basis for more theoretical and empirical studies.
Early evaluation of patient risk for substantial weight gain during olanzapine treatment for schizophrenia, schizophreniform, or schizoaffective disorder

BACKGROUND
To make well informed treatment decisions for their patients, clinicians need credible information about potential risk for substantial weight gain. We therefore conducted a post-hoc analysis of clinical trial data, examining early weight gain as a predictor of later substantial weight gain.


METHODS
Data from 669 (Study 1) and 102 (Study 2) olanzapine-treated patients diagnosed with schizophrenia, schizophreniform, or schizoaffective disorder were analyzed to identify and validate weight gain cut-offs at Weeks 1-4 that were predictive of substantial weight gain (defined as an increase of > or = 5, 7, 10 kg or 7% of baseline weight) after approximately 30 weeks of treatment. Baseline characteristics alone, baseline characteristics plus weight change from baseline to Weeks 1, 2, 3 or 4, and weight change from baseline to Weeks 1, 2, 3, or 4 alone were evaluated as predictors of substantial weight gain. Similar analyses were performed to determine BMI increase cut-offs at Weeks 1-4 of treatment that were predictive of substantial increase in BMI (1, 2 or 3 kg/m2 increase from baseline).


RESULTS
At Weeks 1 and 2, predictions based on early weight gain plus baseline characteristics were more robust than those based on early weight gain alone. However, by Weeks 3 and 4, there was little difference between the operating characteristics associated with these two sets of predictors. The positive predictive values ranged from 30.1% to 73.5%, while the negative predictive values ranged from 58.1% to 89.0%. Predictions based on early BMI increase plus baseline characteristics were not uniformly more robust at any time compared to those based on early BMI increase alone. The positive predictive values ranged from 38.3% to 83.5%, while negative predictive values ranged from 42.1% to 84.7%. For analyses of both early weight gain and early BMI increase, results for the validation dataset were similar to those observed in the primary dataset.


CONCLUSION
Results from these analyses can be used by clinicians to evaluate risk of substantial weight gain or BMI increase for individual patients. For instance, negative predictive values based on data from these studies suggest approximately 88% of patients who gain less than 2 kg by Week 3 will gain less than 10 kg after 26-34 weeks of olanzapine treatment. Analysis of changes in BMI suggests that approximately 84% of patients who gain less than .64 kg/m2 in BMI by Week 3 will gain less than 3 kg/m2 in BMI after 26-34 weeks of olanzapine treatment. Further research in larger patient populations for longer periods is necessary to confirm these results.
Impact of headache on young people in a school population.

BACKGROUND
Headache is the most frequent neurological symptom and the most common manifestation of pain in childhood. Estimates of the prevalence of headache in children and adolescents vary widely (depending on the setting, methodology, and diagnostic criteria applied) and the impact is not well understood.


AIM
To quantify the impact of headache in a school population.


DESIGN OF STUDY
A questionnaire survey.


SETTING
Exeter schools.


METHOD
A total of 1037 school children between the ages of 12 and 15 years were surveyed, of whom 49% were female. Main outcome measures were headache frequency, disease-specific impact using the Pediatric Migraine Disability Assessment Score (PedMIDAS), and generic quality of life impact using the Pediatric Quality of Life Inventory (PedsQL4).


RESULTS
Twenty per cent of the study population had headache one or more times a week, with an average PedMIDAS score of over 12.1 (and an impact on over 12 days in a 3-month period). Ten per cent of the population had a PedMIDAS score of 16.8 and a PedsQL4 generic quality of life score of 70.1, indicating a poorer quality of life than that of children with asthma, diabetes, or cancer. An average of 0.6 days of school was lost in a 3-month period across all school children.


CONCLUSION
There is a significant impact of headache on the quality of life of children. This impact is both unrecognised and unmet. GPs have an important role in identification and management of this problem.
Reducing hip fracture risk with risedronate in elderly women with established osteoporosis

BACKGROUND
There is limited evidence to support the efficacy of current pharmaceutical agents in reducing the risk of hip fracture in older postmenopausal women with established osteoporosis.


OBJECTIVE
To clarify the efficacy of risedronate in reducing the risk of hip fracture in elderly postmenopausal women aged > or = 70 years with established osteoporosis, i.e., those with bone mineral density-defined osteoporosis and a prevalent vertebral fracture.


METHODS
Post hoc analysis of the Hip Intervention Program (HIP) study, a randomized controlled trial comparing risedronate with placebo for reducing the risk of hip fracture in elderly women. Women aged 70 to 100 years with established osteoporosis (baseline femoral neck T-score < or = -2.5 and > or =1 prior vertebral fracture) were included. The main outcome measure was 3-year hip fracture incidence in the risedronate and placebo groups.


RESULTS
A total of 1656 women met the inclusion criteria. After 3 years, hip fracture had occurred in 3.8% of risedronate-treated patients and 7.4% of placebo-treated patients (relative risk 0.54; 95% confidence interval 0.32-0.91; P = 0.019).


CONCLUSION
Risedronate significantly reduced the risk of hip fracture in women aged up to 100 years with established osteoporosis.
"She made up a choice for me": 22 HIV-positive women's experiences of involuntary sterilization in two South African provinces.

Since 1998 South African law has provided that adults should have access to sterilization but only with their informed consent. However, the right to sterilization and other sexual and reproductive rights have not been fully realized as women struggle to access limited services, and there are allegations of discrimination and sterilization abuses. This qualitative study explores the experiences of 22 HIV-positive women in two provinces who reported being sterilized between 1996 and 2010 without their informed consent (n=18) or without their knowledge (n=4). Key issues reported by participants included failure to respect their autonomy, lack of information given about what sterilization entailed, and subtle or overt pressure to sign the consent form. Although the legal framework was intended to ensure informed decision-making regarding sterilization, these protections appear to have failed the HIV-positive women in this study. The findings suggest that some health professionals may consider a signature on a consent form as sufficient regardless of how it was obtained. Furthermore the women's perceptions that they were singled out as needing to be sterilized simply because they were HIV-positive warrants further investigation. More research is required on the nature of the problem and on other stakeholders' perceptions.
Mining big data using parsimonious factor , machine learning , variable selection and shrinkage methods

A number of recent studies in the economics literature have focused on the usefulness of factor models in the context of prediction using ‘‘big data’’ (see Bai and Ng, 2008; Dufour and Stevanovic, 2010; Forni, Hallin, Lippi, & Reichlin, 2000; Forni et al., 2005; Kim and Swanson, 2014a; Stock and Watson, 2002b, 2006, 2012, and the references cited therein). We add to this literature by analyzing whether ‘‘big data’’ are useful for modelling low frequency macroeconomic variables, such as unemployment, inflation and GDP. In particular, we analyze the predictive benefits associated with the use of principal component analysis (PCA), independent component analysis (ICA), and sparse principal component analysis (SPCA). We also evaluate machine learning, variable selection and shrinkage methods, including bagging, boosting, ridge regression, least angle regression, the elastic net, and the non-negative garotte. Our approach is to carry out a forecasting ‘‘horse-race’’ using prediction models that are constructed based on a variety of model specification approaches, factor estimation methods, and data windowing methods, in the context of predicting 11 macroeconomic variables that are relevant to monetary policy assessment. In many instances, we find that various of our benchmark models, including autoregressive (AR)models, ARmodelswith exogenous variables, and (Bayesian) model averaging, do not dominate specifications based on factor-type dimension reduction combinedwith variousmachine learning, variable selection, and shrinkagemethods (called ‘‘combination’’ models). We find that forecast combination methods are mean square forecast error (MSFE) ‘‘best’’ for only three variables out of 11 for a forecast horizon of h = 1, and for four variables when h = 3 or 12. In addition, non-PCA type factor estimation methods yield MSFE-best predictions for nine variables out of 11 for h = 1, although PCA dominates at longer horizons. Interestingly, we also find evidence of the usefulness of combinationmodels for approximately half of our variableswhen h > 1.Most importantly, we present strong new evidence of the usefulness of factor-based dimension reduction when utilizing ‘‘big data’’ for macroeconometric forecasting. © 2016 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved. ∗ Corresponding author. E-mail addresses: khdouble@bok.or.kr (H.H. Kim), nswanson@econ.rutgers.edu (N.R. Swanson).
Learning and Game AI

The incorporation of learning into commercial games can enrich the player experience, but may concern developers in terms of issues such as losing control of their game world. We explore a number of applied research and some fielded applications that point to the tremendous possibilities of machine learning research including game genres such as real-time strategy games, flight simulation games, car and motorcycle racing games, board games such as Go, an even traditional game-theoretic problems such as the prisoners dilemma. A common trait of these works is the potential of machine learning to reduce the burden of game developers. However a number of challenges exists that hinder the use of machine learning more broadly. We discuss some of these challenges while at the same time exploring opportunities for a wide use of machine learning in games. 1998 ACM Subject Classification I.2.m Artificial Intelligence, miscellaneous
Mysteries on universe’s largest observable scales

Abstract We review recent findings that the universe on its largest scales shows hints of violations of statistical isotropy, in particular alignment with the geometry and direction of motion of the solar system, and missing power at scales greater than 60°. We present the evidence, attempts to explain it using astrophysical, cosmological or instrumental mechanisms, and prospects for future understanding.
Brain Tumor Detection using Curvelet Transform and Support Vector Machine

The prevalent cause of death in human being is brain tumor. A brain tumor is a mass or growth of anomalous cells in brain. The detection of brain tumor is difficult task. Image processing provides relevant techniques for efficient detection. In the proposed technique, first the features of MRI (Magnetic Resonance Imaging) images are extracted with curvelet transform, and then these features are applied to the support vector machine for successful identification. This proposed methodology gives efficient results. Keywords— Brain Tumor; Curvelet Transform; Support Vector Machine
A Blackboard Architecture for Control

The control problem --which of its potential actions should an A I system perform at each point in the problem-solving process?--is fundamental to all cognitive processes. This paper proposes eight behavioral goals for intelligent control and a 'blackboard control architecture' to achieve them. The architecture distinguishes domain and control problems, knowledge, and solutions. It enables A1 systems to operate upon their own knowledge and behavior and to adapt to unanticipated problemsolving situations. The paper shows how OPM, a blackboard control system for multiple-task planning, exploits these capabilities. It also shows how the architecture would replicate the control behavior of HEARSA Y-H and HASP. The paper contrasts the blackboard control architecture with three alternatives and shows how it continues an evolutionary progression of control architectures. The paper concludes with a summary of the blackboard control architecture's strengths and weaknesses. 1. The Control Problem In attempting to solve a domain problem, an AI system performs a series of problem-solving actions. Each action is triggered by data or previously generated solution elements, applies some knowledge source from the problem domain, and generates or modifies a solution element. At each point in the problem-solving process, several such actions may be possible. The control problem is: which o f its potent ial act ions s h o u M an A I sys tem perform at each po in t in the problem -solving process ? The control problem is fundamental to all cognitive processes and intelligent systems. In solving the control problem, a system decides, either implicitly or explicitly, what problems it will attempt to solve, what knowledge it will bring to bear, and what problem-solving methods and strategies it will apply. It decides how it will evaluate alternative problem solutions, how it will know when specific problems are solved, and under what circumstances it will interrupt its attention to selected problems or sub-problems. Thus, in solv ing the control problem, a sys tem determines its o w n cogni t ive behavior. Artificial Intelligence 26 (1985) 251-321 0004-3702/85/$3.30 © 1985, Elsevier Science Publishers B.V. (North-Holland)
Novel Dihedral-Based Control of Flapping-Wing Aircraft With Application to Perching

We describe the design of an aerial robot inspired by birds and the underlying theoretical developments leading to novel control and closed-loop guidance algorithms for a perching maneuver. A unique feature of this robot is that it uses wing articulation to control the flight path angle as well as the heading angle. It lacks a vertical tail for improved agility, which results in unstable lateral-directional dynamics. New closed-loop motion planning algorithms with guaranteed stability are obtained by rewriting the flight dynamic equations in the spatial domain rather than as functions of time, after which dynamic inversion is employed. It is shown that nonlinear dynamic inversion naturally leads to proportional-integral-derivative controllers, thereby providing an exact method for tuning the gains. The capabilities of the proposed bioinspired robot design and its novel closed-loop perching controller have been successfully demonstrated with perched landings on a human hand.
Comparison of fluticasone propionate and beclomethasone dipropionate on direct and indirect measurements of bronchial hyperresponsiveness in patients with stable asthma.

BACKGROUND
Fluticasone propionate is a new inhaled corticosteroid with a 2:1 efficacy ratio compared with beclomethasone dipropionate with regard to lung function and symptom scores, without increased systemic activity. The aim of this study was to investigate whether this was also the case for bronchial hyperresponsiveness, assessed by both a direct (histamine) and an indirect (ultrasonically nebulised distilled water (UNDW)) provocation test.


METHODS
Fluticasone propionate, 750 micrograms/day, and beclomethasone dipropionate, 1500 micrograms/day, were compared in a randomised, double blind, crossover study consisting of two six week treatment periods, each preceded by a three week single blind placebo period. Twenty one non-smoking asthmatics (mean forced expiratory volume in one second (FEV1) 74.7% predicted, mean PC20histamine 0.36 mg/ml) completed the study.


RESULTS
Fluticasone propionate and beclomethasone dipropionate improved FEV1, peak flow rates, asthma symptoms, and bronchial hyperresponsiveness to the same extent. Both fluticasone propionate and beclomethasone dipropionate caused an increase in PC20histamine (mean 2.29 [95% confidence interval 1.45 to 3.13] and 1.95 [1.07 to 2.84] doubling doses, respectively) and in PD20UNDW (1.12 [0.55 to 1.70] and 1.28 [0.88 to 1.70] doubling doses, respectively). Neither treatment changed morning serum cortisol levels, but fluticasone propionate decreased the number of peripheral blood eosinophils less than beclomethasone dipropionate, indicating smaller systemic effects of fluticasone propionate.


CONCLUSIONS
These findings show that fluticasone propionate is as effective as twice the dose of beclomethasone dipropionate on bronchial hyperresponsiveness, assessed by provocation with both histamine and UNDW, without increased systemic activity.
Submission to the NIPS 2016 Workshop on Machine Learning for Education Estimating student proficiency : Deep learning is not the panacea

In theoretical cognitive science, there is a tension between highly structured models whose parameters have a direct psychological interpretation and highly complex, general-purpose models whose parameters and representations are difficult to interpret. The former typically provide more insight into cognition but the latter often perform better. This tension has recently surfaced in the realm of educational data mining, where a deep learning approach to estimating student proficiency, termed deep knowledge tracing or DKT [17], has demonstrated a stunning performance advantage over the mainstay of the field, Bayesian knowledge tracing or BKT [3].
Detection and Classification of Plant Leaf Diseases Using Image processing Techniques : A Review

This paper present survey on different classification techniques that can be used for plant leaf disease classification. A classification technique deals with classifying each pattern in one of the distinct classes. A classification is a technique where leaf is classified based on its different morphological features. There are so many classification techniques such as k-Nearest Neighbor Classifier, Probabilistic Neural Network, Genetic Algorithm, Support Vector Machine, and Principal Component Analysis, Artificial neural network, Fuzzy logic. Selecting a classification method is always a difficult task because the quality of result can vary for different input data. Plant leaf disease classifications have wide applications in various fields such as in biological research, in Agriculture etc. This paper provides an overview of different classification techniques used for plant leaf disease classification. Index items— k-Nearest Neighbor, Fuzzy logic, ANN,
Computation of hemodynamics in the circle of Willis.

BACKGROUND AND PURPOSE
Wall shear stress (WSS) and pressure are important factors in the development of cerebral aneurysms. We aimed to develop a computational fluid dynamics simulator for flow in the complete circle of Willis to study the impact of variations in vessel radii and bifurcation angles on WSS and pressure on vessel walls.


METHODS
Blood flow was modeled with Navier-Stokes equations as an incompressible newtonian fluid within rigid vessel walls. A model of the circle of Willis geometry was approximated as a network of tubes around cubic curves. Pulsatile inlet flow rates and constant outlet pressure were used as boundary conditions.


RESULTS
The simulations confirmed that differences in vessel radii and asymmetric branch angles influence WSS magnitude and spatial distribution. High WSS occurred at locations where aneurysms are frequent and in anatomic variants known to be associated with an increased risk for aneurysm development.


CONCLUSIONS
Computational fluid dynamics analysis can be applied to the complete circle of Willis and should be used to study the pathophysiology of this complex vascular structure, including risk factors for aneurysm development. Further development of the method should include simulations with flexible vessel walls.