nils-herrmann/clean_scirepeval huggingface dataset:
Stem cell bioprocessing: fundamentals and principles.

In recent years, the potential of stem cell research for tissue engineering-based therapies and regenerative medicine clinical applications has become well established. In 2006, Chung pioneered the first entire organ transplant using adult stem cells and a scaffold for clinical evaluation. With this a new milestone was achieved, with seven patients with myelomeningocele receiving stem cell-derived bladder transplants resulting in substantial improvements in their quality of life. While a bladder is a relatively simple organ, the breakthrough highlights the incredible benefits that can be gained from the cross-disciplinary nature of tissue engineering and regenerative medicine (TERM) that encompasses stem cell research and stem cell bioprocessing. Unquestionably, the development of bioprocess technologies for the transfer of the current laboratory-based practice of stem cell tissue culture to the clinic as therapeutics necessitates the application of engineering principles and practices to achieve control, reproducibility, automation, validation and safety of the process and the product. The successful translation will require contributions from fundamental research (from developmental biology to the 'omics' technologies and advances in immunology) and from existing industrial practice (biologics), especially on automation, quality assurance and regulation. The timely development, integration and execution of various components will be critical-failures of the past (such as in the commercialization of skin equivalents) on marketing, pricing, production and advertising should not be repeated. This review aims to address the principles required for successful stem cell bioprocessing so that they can be applied deftly to clinical applications.
RINSE: the real-time immersive network simulation environment for network security exercises

The RINSE simulator is being developed to support large-scale network security preparedness and training exercises, involving hundreds of players and a modeled network composed of hundreds of LANs. The simulator must be able to present a realistic rendering of network behavior as attacks are launched and players diagnose events and try counter measures to keep network services operating. We describe the architecture and function of RINSE and outline how techniques like multiresolution traffic modeling and new routing simulation methods are used to address the scalability challenges of this application. We also describe in more detail new work on CPU/memory models necessary for the exercise scenarios and a latency absorption technique that will help when extending the range of client tools usable by the players.
“Nomophobia”: Impact of Cell Phone Use Interfering with Symptoms and Emotions of Individuals with Panic Disorder Compared with a Control Group 

Panic disorder refers to the frequent and recurring acute attacks of anxiety.



This study describes the routine use of mobiles phones (MPs) and investigates the appearance of possible emotional alterations or symptoms related to their use in patients with panic disorder (PD).



We compared patients with PD and agoraphobia being treated at the Panic and Respiration Laboratory of The Institute of Psychiatry, Federal University of Rio de Janeiro, Brazil, to a control group of healthy volunteers.



An MP-use questionnaire was administered to a consecutive sample of 50 patients and 70 controls.



People with PD showed significant increases in anxiety, tachycardia, respiratory alterations, trembling, perspiration, panic, fear and depression related to the lack of an MP compared to the control group.



Both groups exhibited dependence on and were comforted by having an MP; however, people with PD and agoraphobia showed significantly more emotional alterations as well as intense physical and psychological symptoms when they were apart from or unable to use an MP compared to healthy volunteers.
Epigastric hernia and its etiology

Controversies still prevail as to how exactly epigastric hernia occurs. Both the vascular lacunae hypothesis and the tendinous fibre decussation hypothesis have proved to be widely accepted as possible explanations for the etiology. Patient. We present a patient who suffered from early-onset epigastric hernia.  We believe the identification of the ligamentum teres and its accompanying vessel at its fascial defect supports the vascular lacunae hypothesis. However, to further our understanding, biopsy of the linea alba in patients with epigastric hernias is indicated.
Restoring function in exhausted CD8 T cells during chronic viral infection

Functional impairment of antigen-specific T cells is a defining characteristic of many chronic infections, but the underlying mechanisms of T-cell dysfunction are not well understood. To address this question, we analysed genes expressed in functionally impaired virus-specific CD8 T cells present in mice chronically infected with lymphocytic choriomeningitis virus (LCMV), and compared these with the gene profile of functional memory CD8 T cells. Here we report that PD-1 (programmed death 1; also known as Pdcd1) was selectively upregulated by the exhausted T cells, and that in vivo administration of antibodies that blocked the interaction of this inhibitory receptor with its ligand, PD-L1 (also known as B7-H1), enhanced T-cell responses. Notably, we found that even in persistently infected mice that were lacking CD4 T-cell help, blockade of the PD-1/PD-L1 inhibitory pathway had a beneficial effect on the ‘helpless’ CD8 T cells, restoring their ability to undergo proliferation, secrete cytokines, kill infected cells and decrease viral load. Blockade of the CTLA-4 (cytotoxic T-lymphocyte-associated protein 4) inhibitory pathway had no effect on either T-cell function or viral control. These studies identify a specific mechanism of T-cell exhaustion and define a potentially effective immunological strategy for the treatment of chronic viral infections.
Computer Aided Business Model Design: Analysis of Key Features Adopted by Users

Business model innovation comprises many theoretical models, frameworks and methodologies; however, there is still little in the way of digital support for them using Computer Aided Design software. In this paper, we present an analysis of real-world usage data from the first generation of such tools that were designed to support the business model canvas. We first present how two artifacts implement features such as colors for grouping, custom attributes and positioning of elements. An examination of how digital support brings about new opportunities in business modeling allows us to compare this with a paper-based version. We then analyze how variations of these features have been used in the real world. Finally, we examine the implications of our observations for the next generation of tools and the advancement of research for dedicated Computer Aided Design tools to support strategic objects such as business models.
Toward a Wireless Open Source Instrument: Functional Near-infrared Spectroscopy in Mobile Neuroergonomics and BCI Applications

Brain-Computer Interfaces (BCIs) and neuroergonomics research have high requirements regarding robustness and mobility. Additionally, fast applicability and customization are desired.  EEG acquisition technology, currently one of the main modalities used for mobile brain activity assessment, is widely spread and open for access and thus easily customizable. fNIRS technology on the other hand has either to be bought as a predefined commercial solution or developed from scratch using published literature. To help reducing time and effort of future custom designs for research purposes, we present our approach toward an open source multichannel stand-alone fNIRS instrument for mobile NIRS-based neuroimaging, neuroergonomics and BCI/BMI applications. The instrument is low-cost, miniaturized, wireless and modular and openly documented on www.opennirs.org. It provides features such as scalable channel number, configurable regulated light intensities, programmable gain and lock-in amplification. In this paper, the system concept, hardware, software and mechanical implementation of the lightweight stand-alone instrument are presented and the evaluation and verification results of the instrument's hardware and physiological fNIRS functionality are described. Its capability to measure brain activity is demonstrated by qualitative signal assessments and a quantitative mental arithmetic based BCI study with 12 subjects.
To Copy or Not to Copy: Making In-Memory Databases Fast on Modern NICs

When databases resided primarily on disks, the problem of data layout was focused on structures that enabled e cient reads and writes from that medium, as well the as e↵ective use of main memory as a cache. With in-memory databases, this part of the I/O problem is largely gone, but that does not mean that I/O considerations can be completely ignored. We argue for the importance of considering the “other” side of the I/O equation—network communication with clients—when designing in-memory databases. Modern NICs include a number of optimizations intended to improve I/O performance, including kernel bypass, zero-copy and scatter-gather DMA. Applied carefully, these features can reduce the involvement of the CPU in network transfers and can save memory bandwidth. However, our experiments show that some optimizations do not always provide a benefit in a database context, and using them can be tricky, as they a↵ect strategies for processing updates and managing data lifetimes. In this paper, we explore the application of zero-copy NIC DMA to in-memory databases, and we explore how the NIC can influence and explicitly leverage data layout and concurrency control. We apply these results to the Bw-Tree structure by proposing a client-assisted design for transmitting large range scan results. Overall, the approach boosts throughput by 1.7⇥ and reduces CPU overhead by 75% compared to simple zero-copy DMA.
Why We Read Wikipedia

Wikipedia is one of the most popular sites on the Web, with millions of users relying on it to satisfy a broad range of information needs every day. Although it is crucial to understand what exactly these needs are in order to be able to meet them, little is currently known about why users visit Wikipedia. The goal of this paper is to fill this gap by combining a survey of Wikipedia readers with a log-based analysis of user activity. Based on an initial series of user surveys, we build a taxonomy of Wikipedia use cases along several dimensions, capturing users’ motivations to visit Wikipedia, the depth of knowledge they are seeking, and their knowledge of the topic of interest prior to visiting Wikipedia. Then, we quantify the prevalence of these use cases via a large-scale user survey conducted on live Wikipedia with almost 30,000 responses. Our analyses highlight the variety of factors driving users to Wikipedia, such as current events, media coverage of a topic, personal curiosity, work or school assignments, or boredom. Finally, we match survey responses to the respondents’ digital traces in Wikipedia’s server logs, enabling the discovery of behavioral patterns associated with specific use cases. For instance, we observe long and fast-paced page sequences across topics for users who are bored or exploring randomly, whereas those using Wikipedia for work or school spend more time on individual articles focused on topics such as science. Our findings advance our understanding of reader motivations and behavior on Wikipedia and can have implications for developers aiming to improve Wikipedia’s user experience, editors striving to cater to their readers’ needs, third-party services (such as search engines) providing access to Wikipedia content, and researchers aiming to build tools such as recommendation engines.
Towards genomic selection in apple (Malus × domestica Borkh.) breeding programmes: Prospects, challenges and strategies

The apple genome sequence and the availability of high-throughput genotyping technologies have initiated a new era where SNP markers are abundant across the whole genome. Genomic selection (GS) is a statistical approach that utilizes all available genome-wide markers simultaneously to estimate breeding values or total genetic values. For breeding programmes, GS is a promising alternative to the traditional marker-assisted selection for manipulating complex polygenic traits often controlled by many small-effect genes. Various factors, such as genetic architecture of selection traits, population size and structure, genetic evaluation systems, density of SNP markers and extent of linkage disequilibrium, have been shown to be the key drivers of the accuracy of GS. In this paper, we provide an overview of the status of these aspects in current apple-breeding programmes. Strategies for GS for fruit quality and disease resistance are discussed, and an update on an empirical genomic selection study in a New Zealand apple-breeding programme is provided, along with a foresight of expected accuracy from such selection.
An Optimization Framework for Conformal Radiation Treatment Planning

A optimization framework for three-dimensional conformal radiation therapy is presented. In conformal therapy, beams of radiation are applied to a patient from different directions, where the aperture through which the beam is delivered from each direction is chosen to match the shape of the tumor, as viewed from that direction. Wedge filters may be used to produce a gradient in beam intensity across the aperture. Given a set of equispaced beam angles, a mixed-integer linear program can be solved to determine the most effective angles to be used in a treatment plan, the weight (exposure time) to be used for each beam, and the type and orientation of wedges to be used. Practical solution techniques for this problem are described; they include strengthening of the formulation and solution of smaller approximate problems obtained by a reduced parametrization of the treatment region. In addition, techniques for controlling the dose-volume histogram implicitly for various parts of the treatment region using hotand cold-spot control parameters are presented. Computational results are given that show the effectiveness of the proposed approach on practical data sets.
Synthetic Aperture Radar Autofocus via Semidefinite Relaxation

The autofocus problem in synthetic aperture radar imaging amounts to estimating unknown phase errors caused by unknown platform or target motion. At the heart of three state-of-the-art autofocus algorithms, namely, phase gradient autofocus, multichannel autofocus (MCA), and Fourier-domain multichannel autofocus (FMCA), is the solution of a constant modulus quadratic program (CMQP). Currently, these algorithms solve a CMQP by using an eigenvalue relaxation approach. We propose an alternative relaxation approach based on semidefinite programming, which has recently attracted considerable attention in other signal processing problems. Experimental results show that our proposed methods provide promising performance improvements for MCA and FMCA through an increase in computational complexity.
Automatically Recognizing On-ball Screens

The pick and roll is a powerful tool; as former coach Stan Van Gundy once said of his Magic team, "[The pick and roll is] what we're going to be in when the game's on the line. [...] I don't care how good you are, you can't take away everything" . In today's perimeter oriented NBA, the pick and roll is more important than ever before. The player tracking data that is now being collected across all arenas in the NBA holds out the promise of deepening our understanding of offensive strategies. In this paper we approach part of that problem by introducing a pattern recognition framework for identifying onball screens. We use a machine learning classifier on top of a rule-based algorithm to recognize on-ball screens. Tested on 21 quarters from 14 NBA games from last season our algorithm achieved a sensitivity of 82% and positive predictive value of 80%
Programming moves: Design and evaluation of applying embodied interaction in virtual environments to enhance computational thinking in middle school students

We detail the design, implementation, and an initial evaluation of a virtual reality education and entertainment (edutainment) application called Virtual Environment Interactions (VEnvI). VEnvI is an application in which students learn computer science concepts through the process of choreographing movement for a virtual character using a fun and intuitive interface. In this exploratory study, 54 participants as part of a summer camp geared towards promoting participation of women in science and engineering programmatically crafted a dance performance for a virtual human. A subset of those students participated in an immersive embodied interaction metaphor in VEnvI. In creating this metaphor that provides first-person, embodied experiences using self-avatars, we seek to facilitate engagement, excitement and interest in computational thinking. We qualitatively and quantitatively evaluated the extent to which the activities of the summer camp, programming the dance moves, and the embodied interaction within VEnvI facilitated students' edutainment, presence, interest, excitement, and engagement in computing, and the potential to alter their perceptions of computing and computer scientists. Results indicate that students enjoyed the experience and successfully engaged the virtual character in the immersive embodied interaction, thus exhibiting high telepresence and social presence. Students also showed increased interest and excitement regarding the computing field at the end of their summer camp experience using VEnvI.
Architecture-driven Modelling and Analysis

Over the past 15 years there has been increasing recognition that careful attention to the design of a system’s software architecture is critical to satisfying its requirements for quality attributes such as performance, security, and dependability. As a consequence, during this period the field of software architecture has matured significantly. However, current practices of software architecture rely on relatively informal methods, limiting the potential for fully exploiting architectural designs to gain insight and improve the quality of the resulting system. In this paper we draw from a variety of research results to illustrate how formal approaches to software architecture can lead to enhancements in software quality, including improved clarity of design, support for analysis, and assurance that implementations conform to their intended architecture.
CHALLENGES IN IMPLEMENTING ENTERPRISE RISK MANAGEMENT

Since the financial crisis in 2008 organisations have been forced to rethink their risk management. Therefore entities have changed from silo-based Traditional Risk Management to the overarching framework Enterprise Risk Management. Yet Enterprise Risk Management is a young model and it has to contend with various challenges. At the moment there are just a few research papers but they claim that this approach is reasonable. The two frameworks COSO and GRC try to support Enterprise Risk Management. Research does not provide studies about their efficiency. The challenges of Enterprise Risk Management are the composition of the system, suitable metrics, the human factor and the complex environment.
N4ITK: Improved N3 bias correction with robust B-spline approximation

A variant of the popular non-parametric non-uniform intensity normalization (N3) algorithm is proposed for bias field correction. Several studies have been performed under a variety of conditions evaluating the performance of N3. These studies have demonstrated the importance of certain parameters on the results such as those of the B-spline approximation strategy. We propose the substitution of a fast and robust Bspline approximation routine for improved bias field correction over the B-spline fitting approach of the original N3 algorithm. Our strategy features additional advantages such as hierarchical B-spline fitting within a multiresolution framework. Similar to the original N3 algorithm, we also make the source code, testing, and technical documentation of our contribution available to the public albeit through the Insight Toolkit of the National Institutes of Health.
Automated generation of directed tests for transition coverage in cache coherence protocols

Processors with multiple cores and complex cache coherence protocols are widely employed to improve the overall performance. It is a major challenge to verify the correctness of a cache coherence protocol since the number of reachable states grows exponentially with the number of cores. In this paper, we propose an efficient test generation technique, which can be used to achieve full state and transition coverage in simulation based verification for a wide variety of cache coherence protocols. Based on effective analysis of the state space structure, our method can generate more efficient test sequences (50% shorter) compared with tests generated by breadth first search. Moreover, our proposed approach can generate tests on-the-fly due to its space efficient design.
Anxiety and depression lowers blood pressure: 22-year follow-up of the population based HUNT study, Norway

For decades, symptoms of anxiety and depression have been included among psychological factors associated with development of hypertension. Although this has been questioned in recent studies, most findings have been based on a single assessment of mental distress at baseline. We examined these associations using repeated assessments of anxiety, depression and blood pressure.



Data on 17,410 men and women aged 20 to 67 participating in the Nord-Trøndelag Health Study (HUNT) in Norway in 1984-86 were re-examined 11 and 22 years later. The main outcome was change in mean blood pressure (mm Hg) during follow-up.



We found that a high symptom level score (≥80th percentile) of combined anxiety and depression at baseline, as compared to a lower symptom level, was associated with lower mean systolic (-0.67 mm Hg, p = 0.044) and diastolic (-0.25 mm Hg, p = 0.201) blood pressure at year 22. A high symptom level present at all three examinations was associated with a stronger decrease in mean systolic (-1.59 mm Hg, p = 0.004) and diastolic (-0.78 mm Hg, p = 0.019) blood pressure and with a 20% (p = 0.001) lower risk of developing hypertension (BP ≥140/90 mm Hg) at year 22. The associations were only slightly attenuated in multivariate analyses, with no evidence of a mediating effect of alteration in heart rate.



This study do not support previous hypothesis that emotional stress may be a cause of hypertension. Our findings indicate that symptoms of anxiety and depression are associated with decrease in blood pressure, particularly when a high symptom level can be detected over decades.
Parallel labeling of massive XML data with MapReduce

The volume of XML data has become enormous and still grows very quickly as many data have been typed in XML by virtue of its simplicity and extensibility. While a tree labeling algorithm has a crucial role in XML query processing, conventional algorithms are all sequential so that they fail to label a large volume of XML data in a timely manner. To address this issue, we devise parallel tree labeling algorithms for massive XML data. Specifically, we focus on how to efficiently label a single large XML file in parallel. We first propose parallel versions of two prominent tree labeling schemes based on the MapReduce framework. We then present techniques for runtime workload balancing and data repartition to solve performance issues caused by data skewness and MapReduce’s inherited limitation. Through extensive experiments with synthetic and real-world datasets on 15 nodes, we show that our parallel labeling algorithms are up to 17 times faster than conventional algorithms, providing strong durability against data skewness.