nils-herrmann/clean_scirepeval huggingface dataset:
Root fractures: the influence of type of healing and location of fracture on tooth survival rates - an analysis of 492 cases.

The purpose of this study was to analyze tooth loss after root fractures and to assess the influence of the type of healing and the location of the root fracture. Furthermore, the actual cause of tooth loss was analyzed.



Long-term survival rates were calculated using data from 492 root-fractured teeth in 432 patients. The cause of tooth loss was assessed as being the result of either pulp necrosis (including endodontic failures), new traumas or excessive mobility. The statistics used were Kaplan-Meier and the log rank method.



The location of the root fracture had a strong significant effect on tooth survival (P = 0.0001). The 10-year tooth survival of apical root fractures was 89% [95% confidence interval (CI), 78-99%], of mid-root fractures 78% (CI, 64-92%), of cervical-mid-root fractures 67% (CI, 50-85%), and of cervical fractures 33% (CI, 17-49%). The fracture-healing type offered further prognostic information. No tooth loss was observed in teeth with hard tissue fracture healing regardless of the position of the fracture. For teeth with interposition of connective tissue, the location of the fracture had a significant influence on tooth loss (P = 0.0001). For teeth with connective tissue healing, the estimated 8-year survival of apical, mid-root, and cervical-mid-root fractures were all more than 80%, whereas the estimated 8-year survival of cervical fractures was 25% (CI, 7-43%). For teeth with non-healing with interposition of granulation tissue, the location of the fracture showed a significant influence on tooth loss (P = 0.0001). The cause of tooth loss was found to be very dependent upon the location of the fracture. In conclusion, the long-term tooth survival of root fractures was strongly influenced by the type of healing and the location of the fracture.
Proposal : Scalable , Active and Flexible Learning on Distributions

A wide range of machine learning problems, including astronomical inference about galaxy clusters, natural image scene classification, parametric statistical inference, and predictions of public opinion, can be well-modeled as learning a function on (samples from) distributions. This thesis explores problems in learning such functions via kernel methods. The first challenge is one of computational efficiency when learning from large numbers of distributions: the computation of typicalmethods scales between quadratically and cubically, and so they are not amenable to large datasets. We investigate the approach of approximate embeddings into Euclidean spaces such that inner products in the embedding space approximate kernel values between the source distributions. We present a new embedding for a class of information-theoretic distribution distances, and evaluate it and existing embeddings on several real-world applications. We also propose the integration of these techniques with deep learning models so as to allow the simultaneous extraction of rich representations for inputs with the use of expressive distributional classifiers. In a related problem setting, common to astrophysical observations, autonomous sensing, and electoral polling, we have the following challenge: when observing samples is expensive, but we can choose where we would like to do so, how do we pick where to observe? We propose the development of a method to do so in the distributional learning setting (which has a natural application to astrophysics), as well as giving a method for a closely related problem where we search for instances of patterns by making point observations. Our final challenge is that the choice of kernel is important for getting good practical performance, but how to choose a good kernel for a given problem is not obvious. We propose to adapt recent kernel learning techniques to the distributional setting, allowing the automatic selection of good kernels for the task at hand. Integration with deep networks, as previously mentioned, may also allow for learning the distributional distance itself. Throughout, we combine theoretical results with extensive empirical evaluations to increase our understanding of the methods.
Expanded haemodialysis: from operational mechanism to clinical results

Recent advances in chemical composition and new production techniques resulted in improved biocompatibility and permeability of dialysis membranes. Among these, the creation of a new class of membranes called medium cut-off (MCO) represents an important step towards improvement of clinical outcomes. Such membranes have been developed to improve the clearance of medium to high molecular weight (MW) solutes (i.e. uraemic toxins in the range of 5-50 kDa). MCO membranes have peculiar retention onset and cut-off characteristics. Due to a modified sieving profile, MCO membranes have also been described as high-retention onset. The significant internal filtration achieved in MCO haemodialysers provides a remarkable convective clearance of medium to high MW solutes. The marginal loss of albumin observed in MCO membranes compared with high cut-off membranes is considered acceptable, if not beneficial, producing a certain clearance of protein-bound solutes. The application of MCO membranes in a classic dialysis modality characterizes a new technique called expanded haemodialysis. This therapy does not need specific software or dedicated hardware, making its application possible in every setting where the quality of dialysis fluid meets current standards.
Sequence Based Classification for Predictive Maintenance

PACCAR, a commercial vehicles company, has provided a dataset of sensor snapshots that were taken using communication networks during the operation of several vehicles, as well as repairs that were performed for specific failures on these vehicles. Our objective is to predict future repairs for predictive maintenance. Most approaches in the literature involve predicting repairs with only one time window. We show that in addition to the repair, time window prediction is significantly more difficult due to various inherent challenges associated with the sensor snapshot and imbalanced data. Models employed for prediction are Random Forest (industry standard), LSTM RNN, and GMM (Gaussian Mixture Models). Our findings show that while time window is intractable, repair code prediction is best at 43% recall with Random Forest.
Papulopustular rosacea, skin immunity and Demodex: pityriasis folliculorum as a missing link.

Papulopustular rosacea (PPR) is a common facial skin disease, characterized by erythema, telangiectasia, papules and pustules. Its physiopathology is still being discussed, but recently several molecular features of its inflammatory process have been identified: an overproduction of Toll-Like receptors 2, of a serine protease, and of abnormal forms of cathelicidin. The two factors which stimulate the Toll-like receptors to induce cathelicidin expression are skin infection and cutaneous barrier disruption: these two conditions are, at least theoretically, fulfilled by Demodex, which is present in high density in PPR and creates epithelial breaches by eating cells. So, the major pathogenic mechanisms of Demodex and its role in PPR are reviewed here in the context of these recent discoveries. In this review, the inflammatory process of PPR appears to be a consequence of the proliferation of Demodex, and strongly supports the hypothesis that: (1) in the first stage a specific (innate or acquired) immune defect against Demodex allows the proliferation of the mite; (2) in the second stage, probably when some mites penetrate into the dermis, the immune system is suddenly stimulated and gives rise to an exaggerated immune response against the Demodex, resulting in the papules and the pustules of the rosacea. In this context, it would be very interesting to study the immune molecular features of this first stage, named "pityriasis folliculorum", where the Demodex proliferate profusely with no, or a low immune reaction from the host: this entity appears to be a missing link in the understanding of rosacea.
Relative N-gram signatures: Document visualization at the level of character N-grams

The Common N-Gram (CNG) classifier is a text classification algorithm based on the comparison of frequencies of character n-grams (strings of characters of length n) that are the most common in the considered documents and classes of documents. We present a text analytic visualization system that employs the CNG approach for text classification and uses the differences in frequency values of common n-grams in order to visually compare documents at the sub-word level. The visualization method provides both an insight into n-gram characteristics of documents or classes of documents and a visual interpretation of the workings of the CNG classifier.
Augmenting data when training a CNN for retinal vessel segmentation: How to warp?

The retinal vascular condition is a trustworthy biomarker of several ophthalmologic and cardiovascular diseases, so automatic vessel segmentation is a crucial step to diagnose and monitor these problems. Deep Learning models have recently revolutionized the state-of-the-art in several fields, since they can learn features with multiple levels of abstraction from the data itself. However, these methods can easily fall into overfitting, since a huge number of parameters must be learned. Having bigger datasets may act as regularization and lead to better models. Yet, acquiring and manually annotating images, especially in the medical field, can be a long and costly procedure. Hence, when using regular datasets, people heavily need to apply artificial data augmentation. In this work, we use a fully convolutional neural network capable of reaching the state-of-the-art. Also, we investigate the benefits of augmenting data with new samples created by warping retinal fundus images with nonlinear transformations. Our results hint that may be possible to halve the amount of data, while maintaining the same performance.
A Diversity-Guided Particle Swarm Optimizer - the ARPSO

The particle swarm optimization (PSO) algorithm is a new population based search strategy, which has exhibited good performance on well-known numerical test problems. However, on strongly multi-modal test problems the PSO tends to suffer from premature convergence. This is due to a decrease of diversity in search space that leads to a total implosion and ultimately fitness stagnation of the swarm. An accepted hypothesis is that maintenance of high diversity is crucial for preventing premature convergence in multi-modal optimization. We introduce the attractive and repulsive PSO (ARPSO) in trying to overcome the problem of premature convergence. It uses a diversity measure to control the swarm. The result is an algorithm that alternates between phases of attraction and repulsion. The performance of the ARPSO is compared to a basic PSO (bPSO) and a genetic algorithm (GA). The results show that the ARPSO prevents premature convergence to a high degree, but still keeps a rapid convergence like the basic PSO. Thus, it clearly outperforms the basic PSO as well as the implemented GA in multi-modal optimization.
EPSSR: Energy preserving system for smart rooms

Wastage of electricity is one of the main problems which we are facing now a day. In our home, school, colleges or industry we see that lights are kept on even if there is nobody in the room or area. This happens due to negligence or because we forgot to turn the lights off or when we are in hurry. In this paper, an Energy Preserving System for Smart Rooms (EPSSR) is proposed to save energy in smart rooms. Using the ESP8266 chip which is a Wi-Fi chip with full TCP/IP stack and MCU capability we developed a lighting controls to reduce electrical usage. Based on the technology of the Internet of Things (IoT), a lot of solutions may be done to control smart rooms light without the need of accessing the electrical sockets or plug. Our research idea focuses on measuring the number of persons entering any room like seminar hall, conference room and classroom using pair of Infrared sensors and the chip. When a person enters the room, counter will be incremented with lightening the room and the light continue lightening while persons counter greater than zero. When a person leaves the room, the counter is decreased by one. If the persons counter reaches zero, the lights inside the room will be turned off using a relay interface. This paper provides a real energy preserving model that could be used in daily life.
On positivity-preserving high order discontinuous Galerkin schemes for compressible Navier-Stokes equations

We construct a local Lax-Friedrichs type positivity-preserving flux for compressible Navier-Stokes equations, which can be easily extended to high dimensions for generic forms of equations of state, shear stress tensor and heat flux. With this positivity-preserving flux, any finite volume type schemes including discontinuous Galerkin (DG) schemes with strong stability preserving Runge-Kutta time discretizations satisfy a weak positivity property. With a simple and efficient positivity-preserving limiter, high order explicit Runge-Kutta DG schemes are rendered preserving the positivity of density and internal energy without losing local conservation or high order accuracy. Numerical tests suggest that the positivity-preserving flux and the positivity-preserving limiter do not induce excessive artificial viscosity, and the high order positivity-preserving DG schemes without other limiters can produce satisfying non-oscillatory solutions when the nonlinear diffusion in compressible Navier-Stokes equations is accurately resolved.
Oral manifestations of cyclic neutropenia in a Japanese child: case report with a 5-year follow-up.

Cyclic neutropenia is an uncommon hematologic disorder characterized by a marked decrease in the number of neutrophils in the peripheral blood occurring at regular intervals. The neutropenic phase is characteristically associated with clinical symptoms such as recurrent fever, malaise, headaches, anorexia, pharyngitis, ulcers of the oral mucous membrane, and gingival inflammation. This case report describes a Japanese girl who has this disease and suffers from periodontitis and oral ulceration. Her case has been followed up for the past 5 years from age 7 to 12. The importance of regular oral hygiene, careful removal of subgingival plaque and calculus, and periodic and thorough professional mechanical tooth cleaning was emphasized to arrest the progress of periodontal breakdown. Local antibiotic application with minocycline ointment in periodontal pockets was beneficial as an ancillary treatment, especially during neutropenic periods.
Prevalence and incidence of Parkinson's disease in Europe

To provide an overview on the prevalence and incidence of Parkinson's disease (PD) in selected European countries.



PD is a common disease of unknown etiology. Accurate information on the epidemiology of PD is critical to inform health policy. An aging population will lead to more patients with PD; thus, the high financial burden PD places on society will increase.



A systematic literature search was performed to identify studies on the prevalence and incidence of PD in the following European countries: Austria, the Czech Republic, France, Germany, Italy, The Netherlands, Portugal, Spain, Sweden and United Kingdom. Only published studies were included. Abstracts, reviews, meta-analyses and letters to the editor were excluded. There were no language restrictions. Data were extracted using a standardized assessment form, and evidence tables were used to systematically report and compare the data.



Of 39 identified studies, most (87%) reported estimates of PD prevalence rates, while only a few (13%) reported estimates of PD annual incidence rates. Crude prevalence rate estimates ranged from 65.6 per 100,000 to 12,500 per 100,000 and annual incidence estimates ranged from 5 per 100,000 to 346 per 100,000. No publications could be identified for Austria or the Czech Republic.



The observed variations in prevalence and incidence rates may result from environmental or genetic factors, but might also be a consequence of differences in methodologies for case ascertainment, diagnostic criteria, or age distributions of the study populations. The comparability of existing studies is limited.
Deep borehole disposal of nuclear waste : engineering challenges

In recent years, geological disposal of radioactive waste has focused on placement of highand intermediate-level wastes in mined underground caverns at depths of 500–800 m. Notwithstanding the billions of dollars spent to date on this approach, the difficulty of finding suitable sites and demonstrating to the public and regulators that a robust safety case can be developed has frustrated attempts to implement disposal programmes in several countries, and no disposal facility for spent nuclear fuel exists anywhere. The concept of deep borehole disposal was first considered in the 1950s, but was rejected as it was believed to be beyond existing drilling capabilities. Improvements in drilling and associated technologies and advances in sealing methods have prompted a re-examination of this option for the disposal of high-level radioactive wastes, including spent fuel and plutonium. Since the 1950s, studies of deep boreholes have involved minimal investment. However, deep borehole disposal offers a potentially safer, more secure, cost-effective and environmentally sound solution for the long-term management of high-level radioactive waste than mined repositories. Potentially it could accommodate most of the world’s spent fuel inventory. This paper discusses the concept, the status of existing supporting equipment and technologies and the challenges that remain.
Global report on preterm birth and stillbirth (1 of 7): definitions, description of the burden and opportunities to improve data

This is the first of seven articles from a preterm birth and stillbirth report. Presented here is an overview of the burden, an assessment of the quality of current estimates, review of trends, and recommendations to improve data.


PRETERM BIRTH
Few countries have reliable national preterm birth prevalence data. Globally, an estimated 13 million babies are born before 37 completed weeks of gestation annually. Rates are generally highest in low- and middle-income countries, and increasing in some middle- and high-income countries, particularly the Americas. Preterm birth is the leading direct cause of neonatal death (27%); more than one million preterm newborns die annually. Preterm birth is also the dominant risk factor for neonatal mortality, particularly for deaths due to infections. Long-term impairment is an increasing issue.


STILLBIRTH
Stillbirths are currently not included in Millennium Development Goal tracking and remain invisible in global policies. For international comparisons, stillbirths include late fetal deaths weighing more than 1000g or occurring after 28 weeks gestation. Only about 2% of all stillbirths are counted through vital registration and global estimates are based on household surveys or modelling. Two global estimation exercises reached a similar estimate of around three million annually; 99% occur in low- and middle-income countries. One million stillbirths occur during birth. Global stillbirth cause-of-death estimates are impeded by multiple, complex classification systems.


RECOMMENDATIONS TO IMPROVE DATA
(1) increase the capture and quality of pregnancy outcome data through household surveys, the main data source for countries with 75% of the global burden; (2) increase compliance with standard definitions of gestational age and stillbirth in routine data collection systems; (3) strengthen existing data collection mechanisms--especially vital registration and facility data--by instituting a standard death certificate for stillbirth and neonatal death linked to revised International Classification of Diseases coding; (4) validate a simple, standardized classification system for stillbirth cause-of-death; and (5) improve systems and tools to capture acute morbidity and long-term impairment outcomes following preterm birth.



Lack of adequate data hampers visibility, effective policies, and research. Immediate opportunities exist to improve data tracking and reduce the burden of preterm birth and stillbirth.
Selective activation of the infraspinatus muscle.

To improve selective infraspinatus muscle strength and endurance, researchers have recommended selective shoulder external-rotation exercise during rehabilitation or athletic conditioning programs. Although selective strengthening of the infraspinatus muscle is recommended for therapy and training, limited information is available to help clinicians design a selective strengthening program.



To determine the most effective of 4 shoulder external-rotation exercises for selectively stimulating infraspinatus muscle activity while minimizing the use of the middle trapezius and posterior deltoid muscles.



Cross-sectional study.



University research laboratory.



A total of 30 healthy participants (24 men, 6 women; age = 22.6 ± 1.7 years, height = 176.2 ± 4.5 cm, mass = 65.6 ± 7.4 kg) from a university population.



The participants were instructed to perform 4 exercises: (1) prone horizontal abduction with external rotation (PER), (2) side-lying wiper exercise (SWE), (3) side-lying external rotation (SER), and (4) standing external-rotation exercise (STER).



Surface electromyography signals were recorded from the infraspinatus, middle trapezius, and posterior deltoid muscles. Differences among the exercise positions were tested using a 1-way repeated-measures analysis of variance with Bonferroni adjustment.



The infraspinatus muscle activity was greater in the SWE (55.98% ± 18.79%) than in the PER (46.14% ± 15.65%), SER (43.38% ± 22.26%), and STER (26.11% ± 15.00%) (F3,87 = 19.97, P < .001). Furthermore, the SWE elicited the least amount of activity in the middle trapezius muscle (F3,87 = 20.15, P < .001). Posterior deltoid muscle activity was similar in the SWE and SER but less than that measured in the PER and STER (F3,87 = 25.10, P < .001).



The SWE was superior to the PER, SER, and STER in maximizing infraspinatus activity with the least amount of middle trapezius and posterior deltoid activity. These findings may help clinicians design effective exercise programs.