nils-herrmann/clean_scirepeval huggingface dataset:
Tie receptors and their angiopoietin ligands are context-dependent regulators of vascular remodeling.

Angiopoietins are ligands of the Tie2 receptor that control angiogenic remodeling in a context-dependent manner. Tie signaling is involved in multiple steps of the angiogenic remodeling process during development, including destabilization of existing vessels, endothelial cell migration, tube formation and the subsequent stabilization of newly formed tubes by mesenchymal cells. Beyond this critical role in blood vessel development, recent studies suggest a wider role for Tie2 and angiopoietins in lymphangiogenesis and the development of the hematopoietic system, as well as a possible role in the regulation of certain non-endothelial cells. The outcome of Tie signaling depends on which vascular bed is involved, and crosstalk between different VEGFs has an important modulating effect on the properties of the angiopoietins. Signaling through the Tie1 receptor is not well understood, but Tie1 may have both angiopoietin-dependent and ligand-independent functions. Changes in the expression of Tie receptors and angiopoietins occur in many pathological conditions, and mutations in the Tie2 gene are found in familial cases of vascular disease.
Long-Term L-DOPA Treatment Causes Indiscriminate Increase in Dopamine Levels at the Cost of Serotonin Synthesis in Discrete Brain Regions of Rats

(1) The treatment of choice for Parkinson’s disease (PD) is 3,4-dihydroxyphenylalanine (L-DOPA) with peripheral decarboxylase inhibitor, but long-term therapy leads to motor and psychiatric complications. In the present study we investigated 5-hydroxytryptamine (5-HT) and dopamine concentrations in serotonergic and dopaminergic nuclei following chronic administration of L-DOPA to find whether the neurotransmitter synthesis in these brain areas are compensated. (2) Rats were administered L-DOPA (250 mg/kg) and carbidopa (25 mg/kg) daily for 59 and 60 days, and killed on the 60th day, respectively at 24 h and 30 min after the last dose. L-DOPA, norepinephrine, 5-HT, 5-hydroxyindoleacetic acid (5-HIAA), dopamine, homovanillic acid (HVA), and 3,4-dihydroxyphenylacetic acid (DOPAC) were measured in striatum, nucleus raphe dorsalis (NRD), nucleus accumbens (NAc), substantia nigra, cerebellum, and cortex employing HPLC-electrochemical procedure. (3) Prolonged treatment of L-DOPA caused depression in the animals as revealed in a forced swim test. Serotonin content was significantly decreased in all brain regions studied 30 min after long-term L-DOPA, except in NAc. The cortex and striatum showed lowered levels of this indoleamine 24 h after 59 doses of L-DOPA. Dopamine, HVA, and DOPAC concentrations were significantly higher in all the regions studied after 30 min, and in the cerebellum after 24 h of L-DOPA. The levels of DOPAC were elevated in all the brain areas studied 24 h after prolonged L-DOPA treatment. (4) The present results suggest that long-term L-DOPA treatment results in significant loss of 5-HT in serotonergic and dopaminergic regions of the brain. Furthermore, while L-DOPA metabolism per se was uninfluenced, dopamine synthesis was severely impaired in all the regions. The imbalance of serotonin and dopamine formation may be the cause of overt cognitive, motor, and psychological functional aberrations seen in parkinsonian patients following prolonged L-DOPA treatment.
Chatbot using TensorFlow for small Businesses

Chatbots are software used in entertainment industry, businesses and user support. Chatbots are modeled on various techniques such as knowledge base, machine learning based. Machine learning based chatbots yields more practical results. Chatbot which gives responses based on the context of conversation tends to be more user friendly. The chatbot we are proposing demonstrates a method of developing chatbot which can follow the context of the conversation. This method uses TensorFlow for developing the neural network model of the chatbot and uses the nlp techniques to maintain the context of the conversation. This chatbots can be used in small industries or business for automating customer care as user queries will be handled by chatbots thus reducing need of human labour and expenditure.
Current results and trends in platform switching

The platform switching (PLS) concept was introduced in the literature in 2005. The biological benefits and clinical effectiveness of the PLS technique have been established by several studies. In this article different aspects of PLS concept are discussed. Crestal bone loss, biologic width, and stress distribution in this concept are comprehensively reviewed. In this article the relative published articles from 1990 to 2011 have been evaluated by electronic search. Because of controversial results especially in immediate loading and animal studies, further modified research is needed to establish the mechanism and effect of the PLS technique. Essential changes in studies including using the control group for accurate interpretation of results and long-term observation, particularly through, randomized, prospective, multicenter trials with large numbers of participants, and implants are necessary.
Kinetic analysis of the inhibitory effect of trichloroethylene (TCE) on nitrification in cometabolic degradation

In this study, the inhibitory effect of TCE on nitrification process was investigated with an enriched nitrifier culture. TCE was found to be a competitive inhibitor of ammonia oxidation and the inhibition constant (K I ) was determined as 666–802 μg/l. The TCE affinity for the AMO enzyme was significantly higher than ammonium. The effect of TCE on ammonium utilization was evaluated with linearized plots of Monod equation (e.g., Lineweaver–Burk, Hanes–Woolf and Eadie–Hofstee plots) and non-linear least square regression (NLSR). No significant differences were found among these data evaluation methods in terms of kinetic parameters obtained.
Genetic Algorithm for Solving a Dynamic Vehicle Routing Problem with Time Windows

The Vehicle Routing Problem (VRP) introduced by Dantzing and Ranser (1959) is a prominent combinatorial optimization problem. Over the last several decades, many variants of the multi-constrained vehicle routing problem have been studied and a class of problems known as rich vehicle routing problem (RVRPs), has been formed. This work is about solving a variant of RVRP with dynamically changing orders and time windows constraints. In the real world application, during the working day, new orders often occur dynamically and need to be integrated into the routes planing. A Genetic Algorithm (GA) with a simple heuristic is proposed to solve the dynamic vehicle routing problem with time windows. The performance is tested on Solomon's benchmark with different percentage of the orders revealed to the algorithm during operation time.
A Sufficient Condition for Convergences of Adam and RMSProp

Adam and RMSProp, as two of the most influential adaptive stochastic algorithms for training deep neural networks, have been pointed out to be divergent even in the convex setting via a few simple counterexamples. Many attempts, such as decreasing an adaptive learning rate, adopting a big batch size, incorporating a temporal decorrelation technique, seeking an analogous surrogate, etc., have been tried to promote Adam/RMSProp-type algorithms to converge. In contrast with existing approaches, we introduce an alternative easy-to-check sufficient condition, which merely depends on the parameters of the base learning rate and combinations of historical second-order moments, to guarantee the global convergence of generic Adam/RMSProp for solving large-scale non-convex stochastic optimization. Moreover, we show that the convergences of several variants of Adam, such as AdamNC, AdaEMA, etc., can be directly implied via the proposed sufficient condition in the non-convex setting. In addition, we illustrate that Adam is essentially a specifically weighted AdaGrad with exponential moving average momentum, which provides a novel perspective for understanding Adam and RMSProp. This observation together with this sufficient condition gives much deeper interpretations on their divergences. At last, we validate the sufficient condition by applying Adam and RMSProp to tackle the counterexamples and train deep neural networks. Numerical results are exactly in accord with the analysis in theory.
Timed Dataflow: Reducing Communication Overhead for Distributed Machine Learning Systems

Many distributed machine learning (ML) systems exhibit high communication overhead when dealing with big data sets. Our investigations showed that popular distributed ML systems could spend about an order of magnitude more time on network communication than computation to train ML models containing millions of parameters. Such high communication overhead is mainly caused by two operations: pulling parameters and pushing gradients. In this paper, we propose an approach called Timed Dataflow (TDF) to deal with this problem via reducing network traffic using three techniques: a timed parameter storage system, a hybrid parameter filter and a hybrid gradient filter. In particular, the timed parameter storage technique and the hybrid parameter filter enable servers to discard unchanged parameters during the pull operation, and the hybrid gradient filter allows servers to drop gradients selectively during the push operation. Therefore, TDF could reduce the network traffic and communication time significantly. Extensive performance evaluations in a real testbed showed that TDF could reduce up to 77% and 79% of network traffic for the pull and push operations, respectively. As a result, TDF could speed up model training by a factor of up to 4 without sacrificing much accuracy for some popular ML models, compared to systems not using TDF.
A Note on Fermat Equation’s Fascination

As former Fermatist, the author tried many times to prove Fermat’s Last Theorem in an elementary way. Just few insights of the proposed schemes partially passed the peer-reviewing and they motivated the subsequent fruitful collaboration with Prof. Mario De Paz. Among the author’s failures, there is an unpublished proof emblematic of the FLT’s charming power for the suggestive circumstances it was formulated. As sometimes happens with similar erroneous attempts, containing out-of-context hints, it provides a germinal approach to power sums yet to be refined.
Accurate estimation of correlation in InSAR observations

Interferometric synthetic aperture radar (InSAR) correlation, a measure of the similarity of two radar echoes, provides a quantitative measure of surface and subsurface scattering properties and hence surface composition and structure. Correlation is observed by comparing the radar return across several nearby radar image pixels, but estimates of correlation are biased by finite data sample size and any underlying interferometer fringe pattern. We present a method for correcting bias in InSAR correlation measurements resulting in significantly more accurate estimates, so that inverse models of surface properties are more useful. We demonstrate the value of the approach using data collected over Antarctica by the Radarsat spacecraft.
Plagiarism Detection in Arabic Documents : Approaches , Architecture and Systems

Plagiarism detection is a sensitive field of research which has gained lot of interest in the past few years. Although plagiarism detection systems are developed to check text in a variety of languages, they perform better when they are dedicated to check a specific language as they take into account the specificity of the language which leads to better quality results. Query optimization and document reduction constitute two major processing modules which play a major role in optimizing the response time and the results quality of these systems and hence determine their efficiency and effectiveness. This paper proposes an analysis of approaches, an architecture, and a system for detecting plagiarism in Arabic documents. This analysis is particularly focused on the methods and techniques used to detect plagiarism. The proposed web-based architecture exhibits the major processing modules of a plagiarism detection system which are articulated into four layers inside a processing component. The architecture has been used to develop a plagiarism detection system for the Arabic language proposing a set of functions to the user for checking a text and analyzing the results through a well-designed graphical user interface. Subject Categories and Descriptors [H.3.1 Content Analysis and Indexing]: Linguistic processing; [I.2 Artificial Intelligencd]; Natural language interfaces: [I.2.7 Natural Language Processing]; Text Analysis; [I.2.3 Clustering]; Similarity Measures General Terms: Text Analysis, Arabic Language Processing, Similarity Detection
A Novel Dual Full-Bridge LLC Resonant Converter for CC and CV Charges of Batteries for Electric Vehicles

This paper introduces a novel concept for implementing constant current (CC) and constant voltage (CV) charges of batteries for electric vehicles using a dual full-bridge LLC (FBLLC) resonant converter, which shares its primary switches. In the proposed concept, the CC and CV charges are implemented using the inherent characteristics of an LLC converter as a current source and a voltage source. One FBLLC resonant converter operates with a fixed-resonant network whereas the other operates with a variable resonant network for the CC and CV charge operations. The proposed converter can achieve zero voltage switching (ZVS) and nearly zero current switching (ZCS) for all of the primary switches in the CC charge operation, and ZVS for all of the primary switches in the CV charge operation, which lead to a high efficiency. In addition, fixed switching frequency operation is possible in both the CC and CV charges thanks to the variable resonant network. A 3.3-kW prototype converter is implemented to verify the feasibility and validity of the proposed converter and a maximum efficiency of 98% was achieved.
Bacterial meningitis in the United States, 1998-2007.

The rate of bacterial meningitis declined by 55% in the United States in the early 1990s, when the Haemophilus influenzae type b (Hib) conjugate vaccine for infants was introduced. More recent prevention measures such as the pneumococcal conjugate vaccine and universal screening of pregnant women for group B streptococcus (GBS) have further changed the epidemiology of bacterial meningitis.



We analyzed data on cases of bacterial meningitis reported among residents in eight surveillance areas of the Emerging Infections Programs Network, consisting of approximately 17.4 million persons, during 1998-2007. We defined bacterial meningitis as the presence of H. influenzae, Streptococcus pneumoniae, GBS, Listeria monocytogenes, or Neisseria meningitidis in cerebrospinal fluid or other normally sterile site in association with a clinical diagnosis of meningitis.



We identified 3188 patients with bacterial meningitis; of 3155 patients for whom outcome data were available, 466 (14.8%) died. The incidence of meningitis changed by -31% (95% confidence interval [CI], -33 to -29) during the surveillance period, from 2.00 cases per 100,000 population (95% CI, 1.85 to 2.15) in 1998-1999 to 1.38 cases per 100,000 population (95% CI 1.27 to 1.50) in 2006-2007. The median age of patients increased from 30.3 years in 1998-1999 to 41.9 years in 2006-2007 (P<0.001 by the Wilcoxon rank-sum test). The case fatality rate did not change significantly: it was 15.7% in 1998-1999 and 14.3% in 2006-2007 (P=0.50). Of the 1670 cases reported during 2003-2007, S. pneumoniae was the predominant infective species (58.0%), followed by GBS (18.1%), N. meningitidis (13.9%), H. influenzae (6.7%), and L. monocytogenes (3.4%). An estimated 4100 cases and 500 deaths from bacterial meningitis occurred annually in the United States during 2003-2007.



The rates of bacterial meningitis have decreased since 1998, but the disease still often results in death. With the success of pneumococcal and Hib conjugate vaccines in reducing the risk of meningitis among young children, the burden of bacterial meningitis is now borne more by older adults.
Control of wind turbines: Past, present, and future

We review the objectives and techniques used in the control of horizontal axis wind turbines at the individual turbine level, where controls are applied to the turbine blade pitch and generator. The turbine system is modeled as a flexible structure operating in the presence of turbulent wind disturbances. Some overview of the various stages of turbine operation and control strategies used to maximize energy capture in below rated wind speeds is given, but emphasis is on control to alleviate loads when the turbine is operating at maximum power. After reviewing basic turbine control objectives, we provide an overview of the common basic linear control approaches and then describe more advanced control architectures and why they may provide significant advantages.
Delaunay refinement algorithms for triangular mesh generation

Delaunay refinement is a technique for generating unstructured meshes of triangles for use in interpolation, the finite element method, and the finite volume method. In theory and practice, meshes produced by Delaunay refinement satisfy guaranteed bounds on angles, edge lengths, the number of triangles, and the grading of triangles from small to large sizes. This article presents an intuitive framework for analyzing Delaunay refinement algorithms that unifies the pioneering mesh generation algorithms of L. Paul Chew and Jim Ruppert, improves the algorithms in several minor ways, and most importantly, helps to solve the difficult problem of meshing nonmanifold domains with small angles. Although small angles inherent in the input geometry cannot be removed, one would like to triangulate a domain without creating any new small angles. Unfortunately, this problem is not always soluble. A compromise is necessary. A Delaunay refinement algorithm is presented that can create a mesh in which most angles are or greater and no angle is smaller than "!# , where %$'& is the smallest angle separating two segments of the input domain. New angles smaller than appear only near input angles smaller than & ( . In practice, the algorithm’s performance is better than these bounds suggest. Another new result is that Ruppert’s analysis technique can be used to reanalyze one of Chew’s algorithms. Chew proved that his algorithm produces no angle smaller than ) (barring small input angles), but without any guarantees on grading or number of triangles. He conjectures that his algorithm offers such guarantees. His conjecture is conditionally confirmed here: if the angle bound is relaxed to less than &+*-, , Chew’s algorithm produces meshes (of domains without small input angles) that are nicely graded and size-optimal.
Diagnosis of Henoch-Schönlein purpura by sonography and radionuclear scanning in a child presenting with bilateral acute scrotum.

Acute scrotum presenting as the only initial manifestation of Henoch-Schönlein purpura (HSP) is so unusual that the diagnosis can easily be missed. We report this condition in a 4-year-old boy admitted with bronchopneumonia. Bilateral painful scrotal swelling with ecchymosis occurred on the second day of hospitalization. Scrotal sonography was performed and a good blood supply was documented. Scrotal nuclear scanning was performed and was consistent with bilateral epididymoorchitis. Multiple purpuric lesions over the lower extremities and perineal region developed on the third day of hospitalization. Intermittent abdominal pain and knee pain developed thereafter. HSP was diagnosed and steroids were prescribed. The symptoms subsided gradually and no complication was noted. This case reminds us that an acute scrotum may be the only initial manifestation of HSP. Sonography and nuclear scanning can help rule out other diseases.
Sleep and Alzheimer disease pathology—a bidirectional relationship

Factors other than age and genetics may increase the risk of developing Alzheimer disease (AD). Accumulation of the amyloid-β (Aβ) peptide in the brain seems to initiate a cascade of key events in the pathogenesis of AD. Moreover, evidence is emerging that the sleep–wake cycle directly influences levels of Aβ in the brain. In experimental models, sleep deprivation increases the concentration of soluble Aβ and results in chronic accumulation of Aβ, whereas sleep extension has the opposite effect. Furthermore, once Aβ accumulates, increased wakefulness and altered sleep patterns develop. Individuals with early Aβ deposition who still have normal cognitive function report sleep abnormalities, as do individuals with very mild dementia due to AD. Thus, sleep and neurodegenerative disease may influence each other in many ways that have important implications for the diagnosis and treatment of AD.
Design optimisation and trade-offs in multi-kW DC-DC converters

This work investigates design optimisation and design trade-offs for multi-kW DC-DC Interleaved Boost Converters (IBC). A general optimisation procedure for weight minimisation is presented, and the trade-offs between the key design variables (e.g. switching frequency, topology) and performance metrics (e.g. power density, efficiency) are explored. It is shown that the optimal selection of components, switching frequency, and topology are heavily dependent on operating specifications such as voltage ratio, output voltage, and output power. With the device and component technologies considered, the single-phase boost converter is shown to be superior to the interleaved topologies in terms of power density for lower power, lower voltage specifications, whilst for higher-power specifications, interleaved designs are preferable. Comparison between an optimised design and an existing prototype for a 220 V–600 V, 40 kW specification, further illustrates the potential weight reduction that is afforded through design optimisation, with the optimised design predicting a reduction in component weight of around 33%.
Developing Predictive Models of Health Literacy

Low health literacy (LHL) remains a formidable barrier to improving health care quality and outcomes. Given the lack of precision of single demographic characteristics to predict health literacy, and the administrative burden and inability of existing health literacy measures to estimate health literacy at a population level, LHL is largely unaddressed in public health and clinical practice. To help overcome these limitations, we developed two models to estimate health literacy. We analyzed data from the 2003 National Assessment of Adult Literacy (NAAL), using linear regression to predict mean health literacy scores and probit regression to predict the probability of an individual having ‘above basic’ proficiency. Predictors included gender, age, race/ethnicity, educational attainment, poverty status, marital status, language spoken in the home, metropolitan statistical area (MSA) and length of time in U.S. All variables except MSA were statistically significant, with lower educational attainment being the strongest predictor. Our linear regression model and the probit model accounted for about 30% and 21% of the variance in health literacy scores, respectively, nearly twice as much as the variance accounted for by either education or poverty alone. Multivariable models permit a more accurate estimation of health literacy than single predictors. Further, such models can be applied to readily available administrative or census data to produce estimates of average health literacy and identify communities that would benefit most from appropriate, targeted interventions in the clinical setting to address poor quality care and outcomes related to LHL.