nils-herrmann/clean_scirepeval huggingface dataset:
Blood oxidative stress biomarkers: influence of sex, exercise training status, and dietary intake.

Sex and lifestyle factors are known to influence the oxidation of protein, lipids, and DNA. Biomarkers such as protein carbonyls (PC), malondialdehyde (MDA), and 8-hydroxydeoxyguanosine (8-OHdG) have been commonly used in an attempt to characterize the oxidative status of human subjects.



This study compared resting blood oxidative stress biomarkers, in relation to exercise training status and dietary intake, between men and women.



Exercise-trained and sedentary men and women (with normal menstrual cycles; reporting during the early follicular phase) were recruited from the University of Memphis, Tennessee, campus and surrounding community via recruitment flyers and word of mouth. Participants were categorized by sex and current exercise training status (ie, trained or untrained). Each completed a detailed 5-day food record of all food and drink consumed. Diets were analyzed for kilocalories and macro- and micronutrient (vitamins C, E, A) intake. Venous blood samples were obtained at rest and analyzed for PC, MDA, and 8-OHdG.



In the 131 participants (89 men, of whom 74 were exercise trained and 15 untrained, and 42 women, of whom 22 were exercise trained and 20 untrained; mean [SD] age, 24  years), PC did not differ significantly between trained men and women or between untrained men and women. However, trained participants had significantly lower plasma PC (measured in nmol . mg protein(-1)) (mean [SEM] 0.0966 [0.0055]) than did untrained participants (0.1036 [0.0098]) (P < 0.05). MDA levels (measured in micromol . L(-1)) were significantly lower in trained women (0.4264 [0.0559]) compared with trained men (0.6959 [0.0593]); in trained men and women combined (0.5621 [0.0566]) compared with untrained men and women combined (0.7397 [0.0718]); and in women combined (0.5665 [0.0611]) compared with men combined (0.7338 [0.0789]) (P < 0.05 for all comparisons). No significant differences were noted between any groups for 8-OHdG. Neither PC nor 8-OHdG were correlated to any dietary variable, with the exception of PC and percent of protein in untrained men (r = 0.552; P = 0.033). MDA was positively correlated to protein intake and negatively correlated to percent of carbohydrate and vitamin C intake, primarily in trained men (P < or = 0.03).



In this sample of young healthy adults, oxidative stress was lower in women than in men and in trained compared with untrained individuals, particularly regarding MDA. With the exception of MDA primarily in trained men, dietary intake did not appear to be correlated to biomarkers of oxidative stress.
Pay for a Sliding Bloom Filter and Get Counting, Distinct Elements, and Entropy for Free

For many networking applications, recent data is more significant than older data, motivating the need for sliding window solutions. Various capabilities, such as DDoS detection and load balancing, require insights about multiple metrics including Bloom filters, per-flow counting, count distinct and entropy estimation. In this work, we present a unified construction that solves all the above problems in the sliding window model. Our single solution offers a better space to accuracy tradeoff than the state-of-the-art for each of these individual problems! We show this both analytically and by running multiple real Internet backbone and datacenter packet traces.
Detecting Emotional Contagion in Massive Social Networks

Happiness and other emotions spread between people in direct contact, but it is unclear whether massive online social networks also contribute to this spread. Here, we elaborate a novel method for measuring the contagion of emotional expression. With data from millions of Facebook users, we show that rainfall directly influences the emotional content of their status messages, and it also affects the status messages of friends in other cities who are not experiencing rainfall. For every one person affected directly, rainfall alters the emotional expression of about one to two other people, suggesting that online social networks may magnify the intensity of global emotional synchrony.
ID-Based Aggregate Signature Scheme for Wireless Sensor Networks Using Secure and Efficient Data Transmission

Data Aggregation is an important topic and a suitable technique in reducing the energy consumption of sensors nodes in wireless sensor networks (WSN’s) for affording secure and efficient big data aggregation. The wireless sensor networks have been broadly applied, such as target tracking and environment remote monitoring. However, data can be easily compromised by a vast of attacks, such as data interception and tampering of data. Data integrity protection is proposed, gives an identity-based aggregate signature scheme for wireless sensor networks with a designated verifier. The aggregate signature scheme keeps data integrity, can reduce bandwidth and storage cost. Furthermore, the security of the scheme is effectively presented based on the computation of Diffie-Hellman random oracle model.
Advertising ecotourism on the internet: commodifying environment and culture

The increased attention to environmentalism in western societies has been accompanied by a rise in ecotourism, i.e. ecologically sensitive travel to remote areas to learn about ecosystems, as well as in cultural tourism, focusing on the people who are a part of ecosystems. Increasingly, the internet has partnered with ecotourism companies to provide information about destinations and facilitate travel arrangements. This study reviews the literature linking ecotourism and sustainable development, as well as prior research showing that cultures have been historically commodified in tourism advertising for developing countries destinations. We examine seven websites advertising ecotourism and cultural tourism and conclude that: (1) advertisements for natural and cultural spaces are not always consistent with the discourse of sustainability; and (2) earlier critiques of the commodification of culture in print advertising extend to internet advertising also.
Robust adaptive backstepping controller design for DC-DC buck converters with external disturbances

In modern power electronic systems, DC-DC converter is one of the main controlled power sources for driving DC systems. But the inherent nonlinear and time-varying characteristics often result in some difficulties mostly related to the control issue. This paper presents a robust nonlinear adaptive controller design with a recursive methodology based on the pulse width modulation (PWM) to drive a DC-DC buck converter. The proposed controller is designed based on the dynamical model of the buck converter where all parameters within the model are assumed as unknown. These unknown parameters are estimated through the adaptation laws and the stability of these laws are ensured by formulating suitable control Lyapunov functions (CLFs) at different stages. The proposed control scheme also provides robustness against external disturbances as these disturbances are considered within the model. One of the main features of the proposed scheme is that it overcomes the over-parameterization problems of unknown parameters which usually appear in some conventional adaptive methods. Finally, the effectiveness of the proposed control scheme is verified through the simulation results and compared to that of an existing adaptive backstepping controller. Simulation results clearly indicate the performance improvement in terms of a faster output voltage tracking response.
Predictive Mitigation of Timing Channels - Threat Defense for Machine Codes

The security of software systems can be threatened by many internal and external threats, including data leakages due to timing channels. Even if developers manage to avoid security threats in the source code or bytecode during development and testing, new threats can arise as the compiler generates machine codes from representations at the binary code level during execution on the processor or due to operating system specifics. Current approaches either do not allow the neutralization of timing channels to be achieved comprehensively with a sufficient degree of security or require an unjustifiable amount of time and/or resources. Herein, a method is demonstrated for the protected execution of software based on a secure virtual execution environment (VEE) that combines the results from dynamic and static analyses to find timing channels through the application of code transformations. This solution complements other available techniques to prevent timing channels from being exploited. This approach helps control the appearance and neutralization of timing channels via just-in-time code modifications during all stages of program development and usage. This work demonstrates the identification of threats using timing channels as an example. The approach presented herein can be expanded to the neutralization of other types of threats.
Surface-Based Registration of Airborne and Terrestrial Mobile LiDAR Point Clouds

Light Detection and Ranging (LiDAR) is an active sensor that can effectively acquire a large number of three-dimensional (3-D) points. LiDAR systems can be equipped on different platforms for different applications, but to integrate the data, point cloud registration is needed to improve geometric consistency. The registration of airborne and terrestrial mobile LiDAR is a challenging task because the point densities and scanning directions differ. We proposed a scheme for the registration of airborne and terrestrial mobile LiDAR using the least squares 3-D surface registration technique to minimize the surfaces between two datasets. To analyze the effect of point density in registration, the simulation data simulated different conditions and estimated the theoretical errors. The test data were the point clouds of the airborne LiDAR system (ALS) and the mobile LiDAR system (MLS), which were acquired by Optech ALTM 3070 and Lynx, respectively. The resulting simulation analysis indicated that the accuracy of registration improved as the density increased. For the test dataset, the registration error of mobile LiDAR between different trajectories improved from 40 cm to 4 cm, and the registration error between ALS and MLS improved from 84 cm to 4 cm. These results indicate that the proposed methods can obtain 5 cm accuracy between ALS and MLS.
A FLEXIBLE APPROACH TO 3D RECONSTRUCTION FROM SINGLE IMAGES

Traditional image-based 3D reconstruction methods use multiple images to extract 3D geometry. However, it is not always possible to obtain such images, for example when reconstructing destroyed structures using existing photographs or paintings with proper perspective , and reconstructing objects without actually visiting the site using images from the web or postcards (figure 2). Even when multiple images are possible, parts of the scene appear in only one image due to occlusions and/or lack of features to match between images. Methods for 3D reconstruction from a single image do exist (e.g.  and ). We present a new method that is more accurate and more flexible so that it can model a wider variety of sites and structures than existing methods. Using this approach, we reconstructed in 3D many destroyed structures using old photographs and paintings. Sites all over the world have been reconstructed from tourist pictures, web pages, and postcards.
Bipolar mood cycles associated with lunar entrainment of a circadian rhythm

In bipolar disorder, episodes of depression and mania are associated with dramatic disturbances in sleep, which experiments show are likely to contribute to the pathogenesis of the episodes. A recent finding that 18 patients’ manic-depressive cycles oscillated in synchrony with biweekly surges in amplitude of the moon’s tides provided a clue to the cause of the sleep-disturbances. Analyses of one of the patients’ sleep–wake cycles suggest that his mood cycles arose when a circadian rhythm that normally is entrained to dawn and controls the daily onset of wakefulness became entrained instead to 24.8-h recurrences of every second 12.4-h tidal cycle. The finding provides the basis for a comprehensive description of the pathogenesis and pathophysiology of the mood cycle.
Immunotherapy for Chronic Hepatitis B Virus Infection

While new therapies for chronic hepatitis C virus infection have delivered remarkable cure rates, curative therapies for chronic hepatitis B virus (HBV) infection remain a distant goal. Although current direct antiviral therapies are very efficient in controlling viral replication and limiting the progression to cirrhosis, these treatments require lifelong administration due to the frequent viral rebound upon treatment cessation, and immune modulation with interferon is only effective in a subgroup of patients. Specific immunotherapies can offer the possibility of eliminating or at least stably maintaining low levels of HBV replication under the control of a functional host antiviral response. Here, we review the development of immune cell therapy for HBV, highlighting the potential antiviral efficiency and potential toxicities in different groups of chronically infected HBV patients. We also discuss the chronic hepatitis B patient populations that best benefit from therapeutic immune interventions.
Secure attachment and eudaimonic well-being in late adulthood: The mediating role of self-compassion.

Attachment style refers to a systematic pattern of emotions, behaviors, and expectations that people have for how others will respond in relationships. Extensive evidence has documented the importance of attachment security in infants, children, adolescents, and adults, but the effects of attachment among exclusively older adult populations have received less attention. The present study explored the relationships between attachment style in late adulthood and eudaimonic well-being, which refers to a life replete with meaning, productive activity, and striving to reach one's potential. It also explored the mediating role of self-compassion, which can be described as a kind and forgiving attitude toward the self.



A sample of 126 community-dwelling older adults (mean age = 70.40 years) completed measures tapping adult attachment, self-compassion, and six theoretically derived markers of eudaimonic well-being.



Attachment anxiety and avoidance were inversely related to self-acceptance, personal growth, interpersonal relationship quality, purpose in life, and environmental mastery. Mediation analyses showed that self-compassion mediated each of these relationships.



Results support the importance of attachment orientation for psychological well-being in late life and indicate that secure attachment facilitates an attitude of kindness and acceptance toward the self.
Identifying Product Defects from User Complaints: A Probabilistic Defect Model

The recent surge in using social media has created a massive amount of unstructured textual complaints about products and services. However, discovering and quantifying potential product defects from large amounts of unstructured text is a nontrivial task. In this paper, we develop a probabilistic defect model (PDM) that identifies the most critical product issues and corresponding product attributes, simultaneously. We facilitate domain-oriented key attributes (e.g., product model, year of production, defective components, symptoms, etc.) of a product to identify and acquire integral information of defect. We conduct comprehensive evaluations including quantitative evaluations and qualitative evaluations to ensure the quality of discovered information. Experimental results demonstrate that our proposed model outperforms existing unsupervised method (K-Means Clustering), and could find more valuable information. Our research has significant managerial implications for mangers, manufacturers, and policy makers.
Three-dimensional computer vision for tooth restoration

If a person with carious lesions needs or requests crowns or inlays, these dental fillings have to be manufactured for each tooth and each person individually. We survey computer vision techniques which can be used to automate this process. We introduce three particular applications which are concerned with the reconstruction of surface information. The first one aims at building up a database of normalized depth images of posterior teeth and at extracting characteristic features from these images. In the second application, a given occlusal surface of a posterior tooth with a prepared cavity is digitally reconstructed using an intact model tooth from a given database. The calculated surface data can then be used for automatic milling of a dental prosthesis, e.g. from a preshaped ceramic block. In the third application a hand-made provisoric wax inlay or crown can be digitally scanned by a laser sensor and copied three dimensionally into a different material such as ceramic. The results are converted to a format required by the computer-integrated manufacturing (CIM) system for automatic milling.
Clinical and histological comparison of polyglycolic acid suture with black silk suture after minor oral surgical procedure.

Any suture material, absorbable or nonabsorbable, elicits a kind of inflammatory reaction within the tissue. Nonabsorbable black silk suture and absorbable polyglycolic acid suture were compared clinically and histologically on various parameters.



This study consisted of 50 patients requiring minor surgical procedure, who were referred to the Department of Oral and Maxillofacial Surgery. Patients were selected randomly and sutures were placed in the oral cavity 7 days preoperatively. Polyglycolic acid was placed on one side and black silk suture material on the other. Seven days later, prior to surgical procedure the sutures will be assessed. After the surgical procedure the sutures will be placed postoperatively in the same way for 7 days, after which the sutures will be assessed clinically and histologically.



The results of this study showed that all the sutures were retained in case of polyglycolic acid suture whereas four cases were not retained in case of black silk suture. As far as polyglycolic acid suture is concerned 25 cases were mild, 18 cases moderate and seven cases were severe. Black silk showed 20 mild cases, 21 moderate cases and six severe cases. The histological results showed that 33 cases showed mild, 14 cases moderate and three cases severe in case of polyglycolic acid suture. Whereas in case of black silk suture 41 cases were mild. Seven cases were moderate and two cases were severe. Black silk showed milder response than polyglycolic acid suture histologically.



The polyglycolic acid suture was more superior because in all 50 patients the suture was retained. It had less tissue reaction, better handling characteristics and knotting capacity.
Validated LC/MS Bioanalysis of Rituximab CDR Peptides Using Nano-surface and Molecular-Orientation Limited (nSMOL) Proteolysis.

Presently, monoclonal antibodies (mAbs) therapeutics have big global sales and are starting to receive competition from biosimilars. We previously reported that the nano-surface and molecular-orientation limited (nSMOL) proteolysis which is optimal method for bioanalysis of antibody drugs in plasma. The nSMOL is a Fab-selective limited proteolysis, which utilize the difference of protease nanoparticle diameter (200 nm) and antibody resin pore diameter (100 nm). In this report, we have demonstrated that the full validation for chimeric antibody Rituximab bioanalysis in human plasma using nSMOL proteolysis. The immunoglobulin fraction was collected using Protein A resin from plasma, which was then followed by the nSMOL proteolysis using the FG nanoparticle-immobilized trypsin under a nondenaturing condition at 50°C for 6 h. After removal of resin and nanoparticles, Rituximab signature peptides (GLEWIGAIYPGNGDTSYNQK, ASGYTFTSYNMHWVK, and FSGSGSGTSYSLTISR) including complementarity-determining region (CDR) and internal standard P14R were simultaneously quantified by multiple reaction monitoring (MRM). This quantification of Rituximab using nSMOL proteolysis showed lower limit of quantification (LLOQ) of 0.586 µg/mL and linearity of 0.586 to 300 µg/mL. The intra- and inter-assay precision of LLOQ, low quality control (LQC), middle quality control (MQC), and high quality control (HQC) was 5.45-12.9% and 11.8, 5.77-8.84% and 9.22, 2.58-6.39 and 6.48%, and 2.69-7.29 and 4.77%, respectively. These results indicate that nSMOL can be applied to clinical pharmacokinetics study of Rituximab, based on the precise analysis.
A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder

The problem of identifying, in advance, the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we investigate the performance of the proposed machine learning (ML) methodology (based on the pre-treatment electroencephalogram (EEG)) for prediction of response to treatment with a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD).



A relatively small number of most discriminating features are selected from a large group of candidate features extracted from the subject's pre-treatment EEG, using a machine learning procedure for feature selection. The selected features are fed into a classifier, which was realized as a mixture of factor analysis (MFA) model, whose output is the predicted response in the form of a likelihood value. This likelihood indicates the extent to which the subject belongs to the responder vs. non-responder classes. The overall method was evaluated using a "leave-n-out" randomized permutation cross-validation procedure.



A list of discriminating EEG biomarkers (features) was found. The specificity of the proposed method is 80.9% while sensitivity is 94.9%, for an overall prediction accuracy of 87.9%. There is a 98.76% confidence that the estimated prediction rate is within the interval [75%, 100%].



These results indicate that the proposed ML method holds considerable promise in predicting the efficacy of SSRI antidepressant therapy for MDD, based on a simple and cost-effective pre-treatment EEG.



The proposed approach offers the potential to improve the treatment of major depression and to reduce health care costs.
Voltage Stability Prediction Using Active Machine Learning

An active machine learning technique for monitoring the voltage stability in transmission systems is presented. It has been shown that machine learning algorithms may be used to supplement the traditional simulation approach, but they suffer from the difficulties of online machine learning model update and offline training data preparation. We propose an active learning solution to enhance existing machine learning applications by actively interacting with the online prediction and offline training process. The technique identifies operating points where machine learning predictions based on power system measurements contradict with actual system conditions. By creating the training set around the identified operating points, it is possible to improve the capability of machine learning tools to predict future power system states. The technique also accelerates the offline training process by reducing the amount of simulations on a detailed power system model around operating points where correct predictions are made. Experiments show a significant advantage in relation to the training time, prediction time, and number of measurements that need to be queried to achieve high prediction accuracy.