nils-herrmann/clean_scirepeval huggingface dataset:
Presence of two tumor necrosis factor (tnf)-α homologs on different chromosomes of zebrafish (Danio rerio) and medaka (Oryzias latipes).

Two or more isoforms of several cytokines including tumor necrosis factors (tnfs) have been reported from teleost fish. Although zebrafish (Danio rerio) and medaka (Oryzias latipes) possess two tnf-α genes, their genomic location and existence are yet to be described and confirmed. Therefore, we conducted in silico identification, synteny analysis of tnf-α and tnf-n from both the fish with that of human TNF/lymphotoxin loci and their expression analysis in zebrafish. We identified two homologs of tnf-α (named as tnf-α1 and tnf-α2) and a tnf-n gene from zebrafish and medaka. Genomic location of these genes was found to be as: tnf-α1, and tnf-n and tnf-α2 genes on zebrafish chromosome 19 and 15 and medaka chromosome 11 and 16, respectively. Several features such as existence of TNF family signature, conservation of genes in TNF loci with human chromosome, phylogenetic clustering and amino acid similarity with other teleost TNFs confirmed their identity as tnf-α and tnf-n. There were a constitutive expression of all three genes in different tissues, and an increased expression of tnf-α1 and -α2 and a varied expression of tnf-n ligand in zebrafish head kidney cells induced with 20 μg mL(-1) LPS in vitro. Our results suggest the presence of two tnf-α homologs on different chromosomes of zebrafish and medaka and correlate this incidence arising from the fish whole genome duplication event.
Selective sampling on graphs for classification

Selective sampling is an active variant of online learning in which the learner is allowed to adaptively query the label of an observed example. The goal of selective sampling is to achieve a good trade-off between prediction performance and the number of queried labels. Existing selective sampling algorithms are designed for vector-based data. In this paper, motivated by the ubiquity of graph representations in real-world applications, we propose to study selective sampling on graphs. We first present an online version of the well-known Learning with Local and Global Consistency method (OLLGC). It is essentially a second-order online learning algorithm, and can be seen as an online ridge regression in the Hilbert space of functions defined on graphs. We prove its regret bound in terms of the structural property (cut size) of a graph. Based on OLLGC, we present a selective sampling algorithm, namely Selective Sampling with Local and Global Consistency (SSLGC), which queries the label of each node based on the confidence of the linear function on graphs. Its bound on the label complexity is also derived. We analyze the low-rank approximation of graph kernels, which enables the online algorithms scale to large graphs. Experiments on benchmark graph datasets show that OLLGC outperforms the state-of-the-art first-order algorithm significantly, and SSLGC achieves comparable or even better results than OLLGC while querying substantially fewer nodes. Moreover, SSLGC is overwhelmingly better than random sampling.
Hierarchical Reinforcement Learning with Hindsight

Reinforcement Learning (RL) algorithms can suffer from poor sample efficiency when rewards are delayed and sparse. We introduce a solution that enables agents to learn temporally extended actions at multiple levels of abstraction in a sample efficient and automated fashion. Our approach combines universal value functions and hindsight learning, allowing agents to learn policies belonging to different time scales in parallel. We show that our method significantly accelerates learning in a variety of discrete and continuous tasks. A video illustrating our results is available at https://www.youtube.com/watch?v=jQ5FkDgTBLI.
Towards Unsupervised Ontology Learning from Data

Data-driven elicitation of ontologies from structured data is a well-recognized knowledge acquisition bottleneck. The development of efficient techniques for (semi-)automating this task is therefore practically vital — yet, hindered by the lack of robust theoretical foundations. In this paper, we study the problem of learning Description Logic TBoxes from interpretations, which naturally translates to the task of ontology learning from data. In the presented framework, the learner is provided with a set of positive interpretations (i.e., logical models) of the TBox adopted by the teacher. The goal is to correctly identify the TBox given this input. We characterize the key constraints on the models that warrant finite learnability of TBoxes expressed in selected fragments of the Description Logic EL and define corresponding learning algorithms.
Representation of Dupin cyclides using quaternions

Dupin cyclides are surfaces characterized by the property that all their curvature lines are circles or lines. Spheres, circular cylinders, cones and tori are particular examples. We introduce a bilinear rational Bézier-like formula with quaternion weights for parametrizing principal patches of Dupin cyclides. The proposed construction is not affine invariant but it is Möbius invariant, has lower degrees compared with the standard representation, and it is convenient for offsetting. Several important properties of Dupin cyclides can be recovered in terms of closed quaternion formulas: implicit equation, principal curvatures, representation as canal surfaces. Advantages of this approach are demonstrated by deriving a new formula for the Willmore energy of a principal patch.
Who Cares about Others' Privacy: Personalized Anonymization of Moving Object Trajectories

The preservation of privacy when publishing spatiotemporal traces of mobile humans is a field that is receiving growing attention. However, while more and more services offer personalized privacy options to their users, few trajectory anonymization algorithms are able to handle personalization effectively, without incurring unnecessary information distortion. In this paper, we study the problem of Personalized (K,∆)anonymity, which builds upon the model of (k,δ)-anonymity, while allowing users to have their own individual privacy and service quality requirements. First, we propose efficient modifications to state-of-the-art (k,δ)-anonymization algorithms by introducing a novel technique built upon users’ personalized privacy settings. This way, we avoid over-anonymization and we decrease information distortion. In addition, we utilize datasetaware trajectory segmentation in order to further reduce information distortion. We also study the novel problem of Bounded Personalized (Κ,∆)-anonymity, where the algorithm gets as input an upper bound the information distortion being accepted, and introduce a solution to this problem by editing the (k,δ) requirements of the highest demanding trajectories. Our extensive experimental study over real life trajectories shows the effectiveness of the proposed techniques.
Fasciola hepática infection in humans: overcoming problems for the diagnosis

Fascioliasis is a zoonosis actually considered as a foodborne trematode disease priority by the World Health Organization. Our study presents three cases of F. hepatica infection diagnosed by direct, indirect and/or imaging diagnostic techniques, showing the need of the combined use of them. In order to overcome some difficulties of the presently available methods we show for the first time the application of molecular tools to improve human fascioliasis diagnosis by employing a PCR protocol based on a repetitive element as target sequence. In conclusion, diagnosis of human fascioliasis has to be carried out by the combination of diagnostic techniques that allow the detection of infection in different disease phases, different epidemiological situations and known/new transmission patterns in the actual scenario.
Ultra-Wideband Scanning Antenna Array With Rotman Lens

An ultra-wideband (6–18 GHz) phased-array antenna with a beam scanning angle of ±28° is proposed. A step-by-step design procedure consisting of beamforming network (BFN), end-launcher feed adapter, and the radiating element is presented. Microstrip Rotman lens has been designed to act as the BFN, and optimized to achieve minimum phase-error over the whole frequency range. In order to satisfy the condition needed for avoiding grating lobes, as well as achieving a wide radiation bandwidth and a high power handling capability, an  $E$ -plane double-ridged horn antenna is used as the radiating element. A novel wideband end-launcher coaxial to double-ridged waveguide transition has also been developed for connecting the BFN to the antenna array. Extensive optimization procedures have been applied to the end-launched adapter together with the antenna to achieve the best return loss over the frequency band of operation. The whole system has been simulated using CST full-wave simulator. An excellent agreement between the measurements of the fabricated system and the simulated results is observed.
Drops of madness? Recreational misuse of tropicamide collyrium; early warning alerts from Russia and Italy.

Tropicamide is an antimuscarinic drug usually prescribed as an ophthalmic solution to induce short-term mydriasis and cycloplegia. Over the last 2 years, tropicamide has been reported in both Russia and Italy to be self-administered intravenously (IV) for recreational purposes.



The literature on tropicamide was searched in PsycInfo and Pubmed databases. Considering the absence of peer-reviewed data, results were integrated with a multilingual qualitative assessment of a range of Web sites, drug fora and other online resources (i.e., e-newsgroups, chat rooms, mailing lists, e-newsletters and bulletin boards): between January 2012 and January 2013, exploratory qualitative searches of more than 100 Web sites have been carried out in English and Italian using generic and specific keywords such as "legal highs," "research chemicals," "online pharmacy," "tropicamide," "mydriacil," "tropicacyl," "visumidriatic," "online pharmacies" and "tropicamide recreational abuse" in the Google search engine.



Misuse of tropicamide typically occurs through IV injection; its effects last from 30 min to 6 h, and it is often taken in combination with other psychoactive compounds, most typically alcohol, marijuana and opiates. Medical effects of tropicamide misuse include slurred speech, persistent mydriasis, unconsciousness/unresponsiveness, hallucinations, kidney pain, dysphoria, "open eye dreams," hyperthermia, tremors, suicidal feelings, convulsions, psychomotor agitation, tachycardia and headache.



More large-scale studies need to be carried out to confirm and better describe the extent of tropicamide misuse in the European Union and elsewhere. Health and other professionals should be rapidly informed about this new and alerting trend of misuse.
Characterizing and Optimizing Cellular Network Performance During Crowded Events

During crowded events, cellular networks face voice and data traffic volumes that are often orders of magnitude higher than what they face during routine days. Despite the use of portable base stations for temporarily increasing communication capacity and free Wi-Fi access points for offloading Internet traffic from cellular base stations, crowded events still present significant challenges for cellular network operators looking to reduce dropped call events and improve Internet speeds. For an effective cellular network design, management, and optimization, it is crucial to understand how cellular network performance degrades during crowded events, what causes this degradation, and how practical mitigation schemes would perform in real-life crowded events. This paper makes a first step toward this end by characterizing the operational performance of a tier-1 cellular network in the U.S. during two high-profile crowded events in 2012. We illustrate how the changes in population distribution, user behavior, and application workload during crowded events result in significant voice and data performance degradation, including more than two orders of magnitude increase in connection failures. Our findings suggest two mechanisms that can improve performance without resorting to costly infrastructure changes: radio resource allocation tuning and opportunistic connection sharing. Using trace-driven simulations, we show that more aggressive release of radio resources via 1-2 s shorter radio resource control timeouts as compared with routine days helps to achieve better tradeoff between wasted radio resources, energy consumption, and delay during crowded events, and opportunistic connection sharing can reduce connection failures by 95% when employed by a small number of devices in each cell sector.
Research on quality of life in female patients with congenital adrenal hyperplasia and issues in developing nations.

Congenital adrenal hyperplasia (CAH) is the commonest cause of ambiguous genitalia for female newborns and is one of the conditions under the umbrella term of "Disorders of Sex Development" (DSD). Management of these patients require multidisciplinary collaboration and is challenging because there are many aspects of care, such as the most appropriate timing and extent of feminizing surgery required and attention to psychosexual, psychological, and reproductive issues, which still require attention and reconsideration, even in developed nations. In developing nations, however, additional challenges prevail: poverty, lack of education, lack of easily accessible and affordable medical care, traditional beliefs on intersex, religious, and cultural issues, as well as poor community support. There is a paucity of long-term outcome studies on DSD and CAH to inform on best management to achieve optimal outcome. In a survey conducted on 16 patients with CAH and their parents in a Malaysian tertiary center, 31.3% of patients stated poor knowledge of their condition, and 37.5% did not realize that their medications were required for life. This review on the research done on quality of life (QOL) of female patients with CAH aims: to discuss factors affecting QOL of female patients with CAH, especially in the developing population; to summarize the extant literature on the quality of life outcomes of female patients with CAH; and to offer recommendations to improve QOL outcomes in clinical practice and research.
Mobile Cloud Computing: The Future of Cloud

Mobile Cloud Computing (MCC) has revolutionized the way in which mobile subscribers across the globe leverage services on the go. The mobile devices have evolved from mere devices that enabled voice calls only a few years back to smart devices that enable the user to access value added services anytime, anywhere. MCC integrates cloud computing into the mobile environment and overcomes obstacles related to performance (e.g. battery life, storage, and bandwidth), environment (e.g. heterogeneity, scalability, availability) and security (e.g. reliability and privacy).
On How to Design Dataflow FPGA-Based Accelerators for Convolutional Neural Networks

In the past few years we have experienced an extremely rapid growth of modern applications based on deep learning algorithms such as Convolutional Neural Network (CNN), and consequently, an intensification of academic and industrial research focused on the optimization of their imple- mentation. Among the different alternatives that have been ex- plored, FPGAs seems to be one of the most attractive, as they are able to deliver high performance and energy-efficiency, thanks to their inherent parallelism and direct hardware execution, while retaining extreme flexibility due to their reconfigurability.In this paper we present a design methodology of a dataflow accelerator for the implementation of CNNs on FPGAs, that ensures scalability – and achieve a higher degree of parallelism as the size of the CNN increases – and an efficient exploitation of the available resources. Furthermore, we analyze resource consumption of the layers of the CNN as well as latency in relation to the implementation's hyperparameters. Finally, we show that the proposed design implements a high-level pipeline between the different network layers, and as a result, we can improve the latency to process an image by feeding the CNN with batches of multiple images.
Multidisciplinary perspectives on banana (Musa spp.) domestication.

Original multidisciplinary research hereby clarifies the complex geodomestication pathways that generated the vast range of banana cultivars (cvs). Genetic analyses identify the wild ancestors of modern-day cvs and elucidate several key stages of domestication for different cv groups. Archaeology and linguistics shed light on the historical roles of people in the movement and cultivation of bananas from New Guinea to West Africa during the Holocene. The historical reconstruction of domestication processes is essential for breeding programs seeking to diversify and improve banana cvs for the future.
Optimization of Triboelectric Nanogenerator Charging Systems for Efficient Energy Harvesting and Storage

Triboelectric nanogenerator (TENG) technology has emerged as a new mechanical energy harvesting technology with numerous advantages. This paper analyzes its charging behavior together with a load capacitor. Through numerical and analytical modeling, the charging performance of a TENG with a bridge rectifier under periodic external mechanical motion is completely analogous to that of a dc voltage source in series with an internal resistance. An optimum load capacitance that matches the TENGs impedance is observed for the maximum stored energy. This optimum load capacitance is theoretically detected to be linearly proportional to the charging cycle numbers and the inherent TENG capacitance. Experiments were also performed to further validate our theoretical anticipation and show the potential application of this paper in guiding real experimental designs.
State of the Art in Edge and Trail Bundling Techniques

Bundling techniques provide a visual simplification of a graph drawing or trail set, by spatially grouping similar graph edges or trails. This way, the structure of the visualization becomes simpler and thereby easier to comprehend in terms of assessing relations that are encoded by such paths, such as finding groups of strongly interrelated nodes in a graph, finding connections between spatial regions on a map linked by a number of vehicle trails, or discerning the motion structure of a set of objects by analyzing their paths. In this state of the art report, we aim to improve the understanding of graph and trail bundling via the following main contributions. First, we propose a data-based taxonomy that organizes bundling methods on the type of data they work on (graphs vs trails, which we refer to as paths). Based on a formal definition of path bundling, we propose a generic framework that describes the typical steps of all bundling algorithms in terms of high-level operations and show how existing method classes implement these steps. Next, we propose a description of tasks that bundling aims to address. Finally, we provide a wide set of example applications of bundling techniques and relate these to the above-mentioned taxonomies. Through these contributions, we aim to help both researchers and users to understand the bundling landscape as well as its technicalities.
Implementation and evaluation of an interprofessional simulation-based education program for undergraduate nursing students in operating room nursing education: a randomized controlled trial

The present study was designed to implement an interprofessional simulation-based education program for nursing students and evaluate the influence of this program on nursing students' attitudes toward interprofessional education and knowledge about operating room nursing.



Nursing students were randomly assigned to either the interprofessional simulation-based education or traditional course group. A before-and-after study of nursing students' attitudes toward the program was conducted using the Readiness for Interprofessional Learning Scale. Responses to an open-ended question were categorized using thematic content analysis. Nursing students' knowledge about operating room nursing was measured.



Nursing students from the interprofessional simulation-based education group showed statistically different responses to four of the nineteen questions in the Readiness for Interprofessional Learning Scale, reflecting a more positive attitude toward interprofessional learning. This was also supported by thematic content analysis of the open-ended responses. Furthermore, nursing students in the simulation-based education group had a significant improvement in knowledge about operating room nursing.



The integrated course with interprofessional education and simulation provided a positive impact on undergraduate nursing students' perceptions toward interprofessional learning and knowledge about operating room nursing. Our study demonstrated that this course may be a valuable elective option for undergraduate nursing students in operating room nursing education.
A knowledge-based decision system for healthcare diagnosis and advisory

In this paper, we present a knowledge-based decision system for healthcare. It not only performs intelligent diagnoses but also produces inferential advices for the interrelated diseases involving overweight or obese, diabetes, high blood pressure and high cholesterol conditions. Moreover, it performs deep diagnoses for the pregnant Asian women; for the unknown type of diabetes and for the risk of a heart attack and stroke increases. Also, it generates a risk report to remind patients to pay attention on their health. Our knowledge-based decision system provides the efficient and effective way to take care of the patient's health, to promote the human's quality of life and to provide disease monitoring and control to alleviate or to reduce the medical condition. In the long-term, this system will help us to reduce our medicare investments and to provide high quality healthy lives.
Evolving spiking neural networks for audiovisual information processing

This paper presents a new modular and integrative sensory information system inspired by the way the brain performs information processing, in particular, pattern recognition. Spiking neural networks are used to model human-like visual and auditory pathways. This bimodal system is trained to perform the specific task of person authentication. The two unimodal systems are individually tuned and trained to recognize faces and speech signals from spoken utterances, respectively. New learning procedures are designed to operate in an online evolvable and adaptive way. Several ways of modelling sensory integration using spiking neural network architectures are suggested and evaluated in computer experiments.
Fracture Property Studies of Paracetamol Single Crystals Using Microindentation Techniques

To study the fracture behavior of the major habit faces of paracetamol single crystals using microindentation techniques and to correlate this with crystal structure and molecular packing.  Vicker's microindentation techniques were used to measure the hardness and crack lengths. The development of all the major radial cracks was analyzed using the Laugier relationship and fracture toughness values evaluated.  Paracetamol single crystals showed severe cracking and fracture around all Vicker's indentations with a limited zone of plastic deformation close to the indent. This is consistent with the material being a highly brittle solid that deforms principally by elastic deformation to fracture rather than by plastic flow. Fracture was associated predominantly with the (010) cleavage plane, but was also observed parallel to other lattice planes including (110), (210) and (100). The cleavage plane (010) had the lowest fracture toughness value, Kc = 0.041MPa m1/2, while the greatest value, Kc = 0.105MPa m1/2; was obtained for the (210) plane.  Paracetamol crystals showed severe cracking and fracture because of the highly brittle nature of the material. The fracture behavior could be explained on the basis of the molecular packing arrangement and the calculated attachment energies across the fracture planes.