nils-herrmann/clean_scirepeval huggingface dataset:
Effect of astaxanthin supplementation on muscle damage and oxidative stress markers in elite young soccer players.

The purpose of the current study was to examine the effect of Astaxanthin (Asx) supplementation on muscle enzymes as indirect markers of muscle damage, oxidative stress markers and antioxidant response in elite young soccer players.



Thirty-two male elite soccer players were randomly assigned in a double-blind fashion to Asx and placebo (P) group. After the 90 days of supplementation, the athletes performed a 2 hour acute exercise bout. Blood samples were obtained before and after 90 days of supplementation and after the exercise at the end of observational period for analysis of thiobarbituric acid-reacting substances (TBARS), advanced oxidation protein products (AOPP), superoxide anion (O2•¯), total antioxidative status (TAS), sulphydril groups (SH), superoxide-dismutase (SOD), serum creatine kinase (CK) and aspartate aminotransferase (AST).



TBARS and AOPP levels did not change throughout the study. Regular training significantly increased O2•¯ levels (main training effect, P<0.01). O2•¯ concentrations increased after the soccer exercise (main exercise effect, P<0.01), but these changes reached statistical significance only in the P group (exercise x supplementation effect, P<0.05). TAS levels decreased significantly post- exercise only in P group (P<0.01). Both Asx and P groups experienced increase in total SH groups content (by 21% and 9%, respectively) and supplementation effect was marginally significant (P=0.08). Basal SOD activity significantly decreased both in P and in Asx group by the end of the study (main training effect, P<0.01). All participants showed a significant decrease in basal CK and AST activities after 90 days (main training effect, P<0.01 and P<0.001, respectively). CK and AST activities in serum significantly increased as result of soccer exercise (main exercise effect, P<0.001 and P<0.01, respectively). Postexercise CK and AST levels were significantly lower in Asx group compared to P group (P<0.05)



The results of the present study suggest that soccer training and soccer exercise are associated with excessive production of free radicals and oxidative stress, which might diminish antioxidant system efficiency. Supplementation with Asx could prevent exercise induced free radical production and depletion of non-enzymatic antioxidant defense in young soccer players.
Possible State Approaches to Cryptocurrencies

Cryptocurrencies are a type of digital currencies that are relying on cryptographic proofs for confirmation of transactions. Cryptocurrencies usually achieve a unique combination of three features: ensuring limited anonymity, independence from central authority and double spending attack protection. No other group of currencies, including fiat currencies, has this combination of features. We will define cryptocurrency ownership and account anonymity. We will define cryptocurrency ownership and account anonymity. We will introduce a classification of the types of approaches to regulation of cryptocurrencies by various individual countries. We will present the risks that the use of cryptocurrencies involves and the possibilities of prevention of those risks. We will present the possible use of cryptocurrencies for the benefit of the state. The conclusion addresses the implications of adoption of a cryptocurrency as a national currency.
Elliptic Curve Cryptography

For a reference on elliptic curves and their cryptographic applications, see: • Alfred J. Menezes, Elliptic curve public key cryptosystems, 1993. • Joseph H. Silverman and John Tate, Rational points on elliptic curves, 1992. (An undergraduate mathematics text on elliptic curves.) • J.W.S. Cassels, Lectures on elliptic curves, 1991. (Informal and mathematical.) An elliptic curve is not an ellipse! An ellipse is a degree 2 equation of the form x + ay = b. (However, given such an ellipse, you could try to compute the arc length of a certain portion of the curve; the integral which arises can be associated to an elliptic curve.
DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy. Using a deep learning approach to track user-defined body parts during various behaviors across multiple species, the authors show that their toolbox, called DeepLabCut, can achieve human accuracy with only a few hundred frames of training data.
Impact of Anesthesia and Euthanasia on Metabolomics of Mammalian Tissues: Studies in a C57BL/6J Mouse Model

A critical application of metabolomics is the evaluation of tissues, which are often the primary sites of metabolic dysregulation in disease. Laboratory rodents have been widely used for metabolomics studies involving tissues due to their facile handing, genetic manipulability and similarity to most aspects of human metabolism. However, the necessary step of administration of anesthesia in preparation for tissue sampling is not often given careful consideration, in spite of its potential for causing alterations in the metabolome. We examined, for the first time using untargeted and targeted metabolomics, the effect of several commonly used methods of anesthesia and euthanasia for collection of skeletal muscle, liver, heart, adipose and serum of C57BL/6J mice. The data revealed dramatic, tissue-specific impacts of tissue collection strategy. Among many differences observed, post-euthanasia samples showed elevated levels of glucose 6-phosphate and other glycolytic intermediates in skeletal muscle. In heart and liver, multiple nucleotide and purine degradation metabolites accumulated in tissues of euthanized compared to anesthetized animals. Adipose tissue was comparatively less affected by collection strategy, although accumulation of lactate and succinate in euthanized animals was observed in all tissues. Among methods of tissue collection performed pre-euthanasia, ketamine showed more variability compared to isoflurane and pentobarbital. Isoflurane induced elevated liver aspartate but allowed more rapid initiation of tissue collection. Based on these findings, we present a more optimal collection strategy mammalian tissues and recommend that rodent tissues intended for metabolomics studies be collected under anesthesia rather than post-euthanasia.
Knee rehabilitation using an intelligent robotic system

There is an increasing trend in using robots for medical purposes. One specific area is the rehabilitation. There are some commercial exercise machines used for rehabilitation purposes. However, these machines have limited use because of their insufficient motion freedom. In addition, these types of machines are not actively controlled and therefore can not accommodate complicated exercises required during rehabilitation. In this study, a rule based intelligent control methodology is proposed to imitate the faculties of an experienced physiotherapist. These involve interpretation of patient reactions, storing the information received, acting according to the available data, and learning from the previous experiences. Robot manipulator is driven by a servo motor and controlled by a computer using force/torque and position sensor information. Impedance control technique is selected for the force control.
Irradiance-Adaptive PV Module Integrated Converter for High Efficiency and Power Quality in Standalone and DC Microgrid Applications

The strive for efficient and cost-effective photovoltaic (PV) systems motivated the power electronic design developed here. The work resulted in a dc–dc converter for module integration and distributed maximum power point tracking (MPPT) with a novel adaptive control scheme. The latter is essential for the combined features of high energy efficiency and high power quality over a wide range of operating conditions. The switching frequency is optimally modulated as a function of solar irradiance for power conversion efficiency maximization. With the rise of irradiance, the frequency is reduced to reach the conversion efficiency target. A search algorithm is developed to determine the optimal switching frequency step. Reducing the switching frequency may, however, compromise MPPT efficiency. Furthermore, it leads to increased ripple content. Therefore, to achieve a uniform high power quality under all conditions, interleaved converter cells are adaptively activated. The overall cost is kept low by selecting components that allow for implementing the functions at low cost. Simulation results show the high value of the module integrated converter for dc standalone and microgrid applications. A 400-W prototype was implemented at 0.14 Euro/W. Testing showed efficiencies above 95 %, taking into account all losses from power conversion, MPPT, and measurement and control circuitry.
Ouroboros Praos: An Adaptively-Secure, Semi-synchronous Proof-of-Stake Blockchain

We present “Ouroboros Praos”, a proof-of-stake blockchain protocol that, for the first time, provides security against fully-adaptive corruption in the semi-synchronous setting : Specifically, the adversary can corrupt any participant of a dynamically evolving population of stakeholders at any moment as long the stakeholder distribution maintains an honest majority of stake; furthermore, the protocol tolerates an adversarially-controlled message delivery delay unknown to protocol participants. To achieve these guarantees we formalize and realize in the universal composition setting a suitable form of forward secure digital signatures and a new type of verifiable random function that maintains unpredictability under malicious key generation. Our security proof develops a general combinatorial framework for the analysis of semi-synchronous blockchains that may be of independent interest. We prove our protocol secure under standard cryptographic assumptions in the random oracle model.
ByteFreq: Malware clustering using byte frequency

Increased number of malware samples have created many challenges for Antivirus companies. One of these challenges is clustering the large number of malware samples they receive daily. Malware authors use malware generation kits to create different instances of the same malware. So most of these malicious samples are polymorphic instances of previously known malware family only. Clustering these large number of samples rapidly and accurately without spending much time on processing the sample have become a critical requirement. In this paper we proposed, implemented and evaluated a method, called ByteFreq that can cluster large number of samples using byte frequency. Byte frequency is represented as time series and SAX (Symbolic Aggregation approXimation)[1] is used to convert the time series in symbolic representation. We evaluated proposed system on real world malware samples and achieved 0.92 precision and 0.96 recall accuracy.
A tool for generating synthetic data

It is popular to use real-world data to evaluate data mining techniques. However, there are some disadvantages to use real-world data for such purposes. Firstly, real-world data in most domains is difficult to obtain for several reasons, such as budget, technical or ethical. Secondly, the use of many of the real-world data is restricted, those data sets do either not contain specific patterns that are easy to mine or the data needs special preparation and the algorithm needs very specific settings in order to find patterns in it. The solution to this could be the generation of synthetic, "meaningful data" (data with intrinsic patterns). This paper presents a novel approach for generating synthetic data by developing a tool, including novel algorithms for specific data mining patterns, and a user-friendly interface, which is able to create large data sets with predefined classification rules, multilinear regression patterns. A preliminary run of the prototype proves that the generation of large amounts of such "meaningful data" is possible. Also the proposed approach could be extended to a further development for generating synthetic data with other intrinsic patterns.
Taxonomy and physiology of probiotic lactic acid bacteria.

The current taxonomy of probiotic lactic acid bacteria is reviewed with special focus on the genera Lactobacillus, Bifidobacterium and Enterococcus. The physiology and taxonomic position of species and strains of these genera were investigated by phenotypic and genomic methods. In total, 176 strains, including the type strains, have been included. Phenotypic methods applied were based on biochemical, enzymatical and physiological characteristics, including growth temperatures, cell wall analysis and analysis of the total soluble cytoplasmatic proteins. Genomic methods used were pulsed field gel electrophoresis (PFGE), randomly amplified polymorphic DNA-PCR (RAPD-PCR) and DNA-DNA hybridization for bifidobacteria. In the genus Lactobacillus the following species of importance as probiotics were investigated: L. acidophilus group, L. casei group and L. reuteri/L. fermentum group. Most strains referred to as L. acidophilus in probiotic products could be identified either as L. gasseri or as L. johnsonii, both members of the L. acidophilus group. A similar situation could be shown in the L. casei group, where most of the strains named L. casei belonged to L. paracasei subspp. A recent proposal to reject the species L. paracasei and to include this species in the restored species L. casei with a neotype strain was supported by protein analysis. Bifidobacterium spp. strains have been reported to be used for production of fermented dairy and recently of probiotic products. According to phenotypic features and confirmed by DNA-DNA hybridization most of the bifidobacteria strains from dairy origin belonged to B. animalis, although they were often declared as B. longum by the manufacturer. From the genus Enterococcus, probiotic Ec. faecium strains were investigated with regard to the vanA-mediated resistance against glycopeptides. These unwanted resistances could be ruled out by analysis of the 39 kDa resistance protein. In conclusion, the taxonomy and physiology of probiotic lactic acid bacteria can only be understood by using polyphasic taxonomy combining morphological, biochemical and physiological characteristics with molecular-based phenotypic and genomic techniques.
ArtLinks: fostering social awareness and reflection in museums

Technologies in museums often support learning goals, providing information about exhibits. However, museum visitors also desire meaningful experiences and enjoy the social aspects of museum-going, values ignored by most museum technologies. We present ArtLinks, a visualization with three goals: helping visitors make connections to exhibits and other visitors by highlighting those visitors who share their thoughts; encouraging visitors' reflection on the social and liminal aspects of museum-going and their expectations of technology in museums; and doing this with transparency, aligning aesthetically pleasing elements of the design with the goals of connection and reflection. Deploying ArtLinks revealed that people have strong expectations of technology as an information appliance. Despite these expectations, people valued connections to other people, both for their own sake and as a way to support meaningful experience. We also found several of our design choices in the name of transparency led to unforeseen tradeoffs between the social and the liminal.
Transforming Online Learning through Narrative and Student Agency

Efforts to improve online learning have focused primarily on the technology platform for delivering educational content and supporting student discourse. In this paper we describe an alternative approach that invokes two powerful forces behind human learning: narrative and agency. Each of these constructs and their potential impacts on learning is reviewed, and the design of a large-enrollment online undergraduate course that leverages these elements is described. A study of student learning and engagement was conducted using surveys and an analysis of student projects on 96 students enrolled in the new course. Surveys were also administered to 129 students taking a traditional online course in other departments to provide a comparison data set. Results of this study indicated significant benefits of employing narrative and increasing student choice on interest and perceived relevance of the course material, critical thinking, and the acquisition of design skills. We conclude by discussing the implications of these results on the design of online instructional environments generally, and call for the increased adoption of interactive storytelling elements into web-based learning platforms.
Understanding the cycle: childhood maltreatment and future crime.

Child maltreatment is a major social problem. This paper focuses on measuring the relationship between child maltreatment and crime using data from the National Longitudinal Study of Adolescent Health (Add Health). We focus on crime because it is one of the most costly potential outcomes of maltreatment. Our work addresses two main limitations of the existing literature on child maltreatment. First, we use a large national sample, and investigate different types of maltreatment in a unified framework. Second, we pay careful attention to controlling for possible confounders using a variety of statistical methods that make differing assumptions. The results suggest that maltreatment greatly increases the probability of engaging in crime and that the probability increases with the experience of multiple forms of maltreatment.
Spontaneous bone formation after mandible segmental resection in “krokodil” drug-related jaw osteonecrosis patient: case report

We report a case of a 48-year-old male patient with “krokodil” drug-related osteonecrosis of both jaws. Patient history included 1.5 years of “krokodil” use, with 8-month drug withdrawal prior to surgery. The patient was HCV positive. On the maxilla, sequestrectomy was performed. On the mandible, sequestrectomy was combined with bone resection. From ramus to ramus, segmental defect was formed, which was not reconstructed with any method. Post-operative follow-up period was 3 years and no disease recurrence was noted. On 3-year post-operative orthopantomogram, newly formed mandibular bone was found. This phenomenon shows that spontaneous bone formation is possible after mandible segmental resection in osteonecrosis patients.
CREATING AGILE SUPPLY CHAINS IN THE FASHION INDUSTRY by

Fashion markets are synonymous with rapid change and, as a result, commercial success or failure in those markets is largely determined by the organisation’s flexibility and responsiveness. Responsiveness is characterised by short time-to-market, the ability to scale up (or down) quickly and the rapid incorporation of consumer preferences into the design process. In this paper it is argued that conventional organisational structures and forecast-driven supply chains are not adequate to meet the challenges of volatile and turbulent demand which typify fashion markets today. Instead, the requirement is for the creation of an agile organisation embedded within an agile supply chain INTRODUCTION Fashion markets have long attracted the interest of researchers. More often the focus of their work was the psychology and sociology of fashion and with the process by which fashions were adopted across populations (see for example Wills and Midgley, 1973). In parallel with this, a body of work has developed seeking to identify cycles in fashions (e.g. Carman, 1966). Much of this earlier work was intended to create insights and even tools to help improve the demand forecasting of fashion products. However, the reality that is now gradually being accepted both by those who work in the industry and those who study it, is that the demand for fashion products cannot be forecast. Instead, we need to recognise that fashion markets are complex open systems that frequently demonstrate high levels of ‘chaos’. In such conditions managerial effort may be better expended on devising strategies
Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks

Many real-world problems, e.g. object detection, have outputs that are naturally expressed as sets of entities. This creates a challenge for traditional deep neural networks which naturally deal with structured outputs such as vectors, matrices or tensors. We present a novel approach for learning to predict sets with unknown permutation and cardinality using deep neural networks. Specifically, in our formulation we incorporate the permutation as unobservable variable and estimate its distribution during the learning process using alternating optimization. We demonstrate the validity of this new formulation on two relevant vision problems: object detection, for which our formulation outperforms state-of-the-art detectors such as Faster R-CNN and YOLO, and a complex CAPTCHA test, where we observe that, surprisingly, our set based network acquired the ability of mimicking arithmetics without any rules being coded.
Osteotomy for congenital elevation of the scapula (Sprengel's deformity).

We report 12 consecutive cases of vertical scapular osteotomy to correct Sprengel's deformity, performed during a 16-year period, with a mean follow-up of 10.4 years. The mean increase in abduction of the shoulder was 53 degrees . The cosmetic appearance improved by a mean of 1.5 levels on the Cavendish scale. Neither function nor cosmesis deteriorated with time. We recommend the procedure for correction of moderate deformities with a functional deficit.
An Extension of the String-to-String Correction Problem

The string-to-string correction problem asks for a sequence S of "edit operations" of minimal cost such that ~(A) = B, for given strings A and B. The edit operations previously investigated allow changing one symbol of a string into another single symbol, deleting one symbol from a string, or inserting a single symbol into a string. This paper extends the set of allowable edit operations to include the operation of interchanging the positions of two adjacent characters Under certain restrictions on edit-operation costs, it is shown that the extended problem can still be solved in time proportional to the product of the lengths of the given strings.