nils-herrmann/clean_scirepeval huggingface dataset:
The antibiotic resistance and prescribing in European Children project: a neonatal and pediatric antimicrobial web-based point prevalence survey in 73 hospitals worldwide.

The neonatal and pediatric antimicrobial point prevalence survey (PPS) of the Antibiotic Resistance and Prescribing in European Children project (http://www.arpecproject.eu/) aims to standardize a method for surveillance of antimicrobial use in children and neonates admitted to the hospital within Europe. This article describes the audit criteria used and reports overall country-specific proportions of antimicrobial use. An analytical review presents methodologies on antimicrobial use.



A 1-day PPS on antimicrobial use in hospitalized children was organized in September 2011, using a previously validated and standardized method. The survey included all inpatient pediatric and neonatal beds and identified all children receiving an antimicrobial treatment on the day of survey. Mandatory data were age, gender, (birth) weight, underlying diagnosis, antimicrobial agent, dose and indication for treatment. Data were entered through a web-based system for data-entry and reporting, based on the WebPPS program developed for the European Surveillance of Antimicrobial Consumption project.



There were 2760 and 1565 pediatric versus 1154 and 589 neonatal inpatients reported among 50 European (n = 14 countries) and 23 non-European hospitals (n = 9 countries), respectively. Overall, antibiotic pediatric and neonatal use was significantly higher in non-European (43.8%; 95% confidence interval [CI]: 41.3-46.3% and 39.4%; 95% CI: 35.5-43.4%) compared with that in European hospitals (35.4; 95% CI: 33.6-37.2% and 21.8%; 95% CI: 19.4-24.2%). Proportions of antibiotic use were highest in hematology/oncology wards (61.3%; 95% CI: 56.2-66.4%) and pediatric intensive care units (55.8%; 95% CI: 50.3-61.3%).



An Antibiotic Resistance and Prescribing in European Children standardized web-based method for a 1-day PPS was successfully developed and conducted in 73 hospitals worldwide. It offers a simple, feasible and sustainable way of data collection that can be used globally.
First description of the adult stage of Clinostomum cutaneum Paperna, 1964 (Digenea: Clinostomidae) from grey herons Ardea cinerea L. and a redescription of the metacercaria from the Nile tilapia Oreochromis niloticus niloticus (L.) in Kenya

The combined use of morphological and molecular studies allowed for the first time the recognition and description of the adult stage of Clinostomum cutaneum Paperna, 1964 from the grey heron Ardea cinerea L. in Kenya. A redescription of the metacercaria that infect Nile tilapia Oreochromis niloticus niloticus (L.) from the same aquatic environment is also presented. C. cutaneum differs from all other species of Clinostomum Leidy, 1856 in the shape of its uterus. Sequencing the rRNA confirmed the morphological similarity between adults from the grey heron and the metacercarial stage from tilapia, and a level of genetic similarity with the other previously sequenced Clinostomum spp. was observed. The need for a reorganisation of Clinostomum using both morphological and molecular methods is highlighted.
Taxonomical Establishment and Compositional Studies of a New Cordyceps (Ascomycetes) Species from the Northwest Himalayas (India).

During a frequent survey in the northwest Indian Himalayan region, a new species-Cordyceps macleodganensis-was encountered. This species is described on the basis of its macromorphological features, microscopic details, and internal transcribed spacer sequencing. This species showed only 90% resemblance to Cordyceps gracilis. The chemical composition of the mycelium showed protein (14.95 ± 0.2%) and carbohydrates (59.21 ± 3.8%) as the major nutrients. This species showed appreciable amounts of P-carotene, lycopene, phenolic compounds, polysaccharides, and flavonoids. Mycelial culture of this species showed higher effectiveness for ferric-reducing antioxidant power, DPPH radical scavenging activity, ferrous ion-chelating activity, and scavenging ability on superoxide anion-derived radicals, calculated by half-maximal effective concentrations.
Cytotoxic Effects of Biosynthesized Zinc Oxide Nanoparticles on Murine Cell Lines

The aim of this study is to evaluate the in vitro cytotoxic activity and cellular effects of previously prepared ZnO-NPs on murine cancer cell lines using brown seaweed (Sargassum muticum) aqueous extract. Treated cancer cells with ZnO-NPs for 72 hours demonstrated various levels of cytotoxicity based on calculated IC50 values using MTT assay as follows: 21.7 ± 1.3 μg/mL (4T1), 17.45 ± 1.1 μg/mL (CRL-1451), 11.75 ± 0.8 μg/mL (CT-26), and 5.6 ± 0.55 μg/mL (WEHI-3B), respectively. On the other hand, ZnO-NPs treatments for 72 hours showed no toxicity against normal mouse fibroblast (3T3) cell line. On the other hand, paclitaxel, which imposed an inhibitory effect on WEHI-3B cells with IC50 of 2.25 ± 0.4, 1.17 ± 0.5, and 1.6 ± 0.09 μg/mL after 24, 48, and 72 hours treatment, respectively, was used as positive control. Furthermore, distinct morphological changes were found by utilizing fluorescent dyes; apoptotic population was increased via flowcytometry, while a cell cycle block and stimulation of apoptotic proteins were also observed. Additionally, the present study showed that the caspase activations contributed to ZnO-NPs triggered apoptotic death in WEHI-3 cells. Thus, the nature of biosynthesis and the therapeutic potential of ZnO-NPs could prepare the way for further research on the design of green synthesis therapeutic agents, particularly in nanomedicine, for the treatment of cancer.
Structure-Function Analysis of DipA, a Francisella tularensis Virulence Factor Required for Intracellular Replication

Francisella tularensis is a highly infectious bacterium whose virulence relies on its ability to rapidly reach the macrophage cytosol and extensively replicate in this compartment. We previously identified a novel Francisella virulence factor, DipA (FTT0369c), which is required for intramacrophage proliferation and survival, and virulence in mice. DipA is a 353 amino acid protein with a Sec-dependent signal peptide, four Sel1-like repeats (SLR), and a C-terminal coiled-coil (CC) domain. Here, we determined through biochemical and localization studies that DipA is a membrane-associated protein exposed on the surface of the prototypical F. tularensis subsp. tularensis strain SchuS4 during macrophage infection. Deletion and substitution mutagenesis showed that the CC domain, but not the SLR motifs, of DipA is required for surface exposure on SchuS4. Complementation of the dipA mutant with either DipA CC or SLR domain mutants did not restore intracellular growth of Francisella, indicating that proper localization and the SLR domains are required for DipA function. Co-immunoprecipitation studies revealed interactions with the Francisella outer membrane protein FopA, suggesting that DipA is part of a membrane-associated complex. Altogether, our findings indicate that DipA is positioned at the host-pathogen interface to influence the intracellular fate of this pathogen.
Aripiprazole monotherapy for maintenance therapy in bipolar I disorder: a 100-week, double-blind study versus placebo.

A 26-week, double-blind, placebo-controlled relapse prevention study of aripiprazole was designed a priori with a prospective, 74-week, double-blind, placebo-controlled extension phase. Efficacy and tolerability of aripiprazole for relapse prevention in bipolar I disorder was, therefore, evaluated for 100 weeks.



Patients with DSM-IV bipolar I disorder, recent manic or mixed episode, received open-label aripiprazole 15 or 30 mg/day (started at 30 mg/day) for 6 to 18 weeks. Patients achieving stabilization (Young Mania Rating Scale score <or= 10 and Montgomery-Asberg Depression Rating Scale score <or= 13 for 6 consecutive weeks) entered the double-blind phase, at which point they were randomly assigned to double-blind treatment with aripiprazole or placebo for 26 weeks. The primary endpoint was time to relapse for any mood episode. Patients who completed the 26-week stabilization continued in a double-blind fashion with aripipra-zole or placebo for an additional 74 weeks and were monitored for relapse, efficacy, and tolerability. The study was conducted from March 2000 to June 2003.



In total, 161 patients met the stabilization criteria and were randomly assigned to aripiprazole (N = 78) or placebo (N = 83). At 100 weeks, time to relapse was significantly longer with aripiprazole (N = 7) than placebo (N = 5; hazard ratio = 0.53 [p = .011; 95% CI = 0.32 to 0.87]); however, a further 24 patients had discontinued due to study closure. Aripiprazole was superior to placebo in delaying time to manic relapse (p = .005; hazard ratio = 0.35 [95% CI = 0.16 to 0.75]); however, no significant differences were observed in time to depressive relapse (p = .602; hazard ratio = 0.81 [95% CI = 0.36 to 1.81]). The adverse events reported during 100 weeks of treatment with aripiprazole versus placebo (>or= 5% incidence and twice placebo rate) were tremor, akathisia, dry mouth, hypertension, weight gain, vaginitis, abnormal thinking, pharyngitis, and flu syndrome. Mean weight change from baseline to 100 weeks (last observation carried forward) was +0.4 +/- 0.8 kg with aripiprazole and -1.9 +/- 0.8 kg with placebo.



Over a 100-week treatment period, aripiprazole monotherapy was effective for relapse prevention in patients who were initially stabilized on aripiprazole for 6 consecutive weeks, and it maintained a good safety and tolerability profile.
Data consistency for P2P collaborative editing

Peer-to-peer (P2P) networks are very efficient for distributing content. We want to use this potential to allow not only distribution but collaborative editing of this content. Existing collaborative editing systems are centralised or depend on the number of sites. Such systems cannot scale when deployed on P2P networks. In this paper, we propose a new model for building a collaborative editing system. This model is fully decentralised and does not depend on the number of sites.
Synthesizing program input grammars

We present an algorithm for synthesizing a context-free grammar encoding the language of valid program inputs from a set of input examples and blackbox access to the program. Our algorithm addresses shortcomings of existing grammar inference algorithms, which both severely overgeneralize and are prohibitively slow. Our implementation, GLADE, leverages the grammar synthesized by our algorithm to fuzz test programs with structured inputs. We show that GLADE substantially increases the incremental coverage on valid inputs compared to two baseline fuzzers.
CREW CLIMATE AND PERFORMANCE: USE OF GROUP DIAGRAMS BASED ON BEHAVIORAL RATINGS

A method of depicting crew climate using a group diagram based on behavioral ratings is described. Behavioral ratings were made of twelve three-person professional airline cockpit crews in full-mission simulations. These crews had been part of an earlier study in which captains had been had been grouped into three personality types, based on pencil and paper pre-tests. We found that low error rates were related to group climate variables as well as positive captain behaviors.
Capability-based access control delegation model on the federated IoT network

Flexibility is an important property for general access control system and especially in the Internet of Things (IoT), which can be achieved by access or authority delegation. Delegation mechanisms in access control that have been studied until now have been intended mainly for a system that has no resource constraint, such as a web-based system, which is not very suitable for a highly pervasive system such as IoT. To this end, this paper presents an access delegation method with security considerations based on Capability-based Context Aware Access Control (CCAAC) model intended for federated machine-to-machine communication or IoT networks. The main idea of our proposed model is that the access delegation is realized by means of a capability propagation mechanism, and incorporating the context information as well as secure capability propagation under federated IoT environments. By using the identity-based capability-based access control approach as well as contextual information and secure federated IoT, this proposed model provides scalability and flexibility as well as secure authority delegation for highly distributed system.
Implicit Parallelism through Deep Language Embedding

Parallel collection processing based on second-order functions such as map and reduce has been widely adopted for scalable data analysis. Initially popularized by Google, over the past decade this programming paradigm has found its way in the core APIs of parallel dataflow engines such as Hadoop's MapReduce, Spark's RDDs, and Flink's DataSets. We review programming patterns typical of these APIs and discuss how they relate to the underlying parallel execution model. We argue that fixing the abstraction leaks exposed by these patterns will reduce the cost of data analysis due to improved programmer productivity. To achieve that, we first revisit the algebraic foundations of parallel collection processing. Based on that, we propose a simplified API that (i) provides proper support for nested collection processing and (ii) alleviates the need of certain second-order primitives through comprehensions -- a declarative syntax akin to SQL. Finally, we present a metaprogramming pipeline that performs algebraic rewrites and physical optimizations which allow us to target parallel dataflow engines like Spark and Flink with competitive performance.
Implementing As-Rigid-As-Possible Shape Manipulation and Surface Flattening

This article provides a description of an “as-rigid-as-possible shape manipulation” implementation that is clearer and easier to understand than the original. While the original paper used triangle-based representation, we use edgebased representation to simplify the coding. We also extend the original algorithm to allow the user to place handles on arbitrary positions of the mesh. In addition, we show that the same algorithm can be used for surface flattening with quality and performance comparable to popular flattening methods.
Rational Closure for Defeasible Description Logics

In the field of non-monotonic logics, the notion of rational closure is acknowledged as a landmark, and we are going to see that such a construction can be characterised by means of a simple method in the context of propositional logic. We then propose an application of our approach to rational closure in the field of Description Logics, an important knowledge representation formalism, and provide a simple decision procedure for this case.
Model-Based Reinforcement Learning for Atari

Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. However, this typically requires very large amounts of interaction -- substantially more, in fact, than a human would need to learn the same games. How can people learn so quickly? Part of the answer may be that people can learn how the game works and predict which actions will lead to desirable outcomes. In this paper, we explore how video prediction models can similarly enable agents to solve Atari games with orders of magnitude fewer interactions than model-free methods. We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting. Our experiments evaluate SimPLe on a range of Atari games and achieve competitive results with only 100K interactions between the agent and the environment (400K frames), which corresponds to about two hours of real-time play.
Evaluation of the effects of formulation and food on the pharmacokinetics of lenvatinib (E7080) in healthy volunteers.

To evaluate the effect of formulation and a high-fat meal on the pharmacokinetics of orally administered lenvatinib (E7080).



Lenvatinib 10-mg capsule and tablet.



Pharmacokinetics and safety of a single 10-mg lenvatinib dose were evaluated in healthy subjects in two randomized, two-period, crossover, phase 1, bioavailability trials. The first compared a new capsule formulation with an older tablet formulation (n = 20 subjects); the second evaluated the influence of a standard high-fat meal on the relative bioavailability of the capsule formulation (n = 16 subjects). Geometric least squares mean ratios of AUC0-∞, maximum observed concentration (Cmax), and AUC0-t were determined. tmax, tlag (food effect only), and t1/2,z were also calculated, and descriptive statistics were provided.



A total of 36 healthy volunteers were enrolled in the two studies (mean ages 29 and 33 years). In the formulation study, AUC0-∞ and AUC0-t of the capsule formulation were ~ 10% less than the tablet formulation, and Cmax for the capsule formulation was ~ 14% lower. 90% Confidence intervals (CIs) for both AUCs were within the 80 - 125% CI, which is generally considered to denote bioequivalence, while the lower bound of the interval for Cmax was 79.8%. tmax and t1/2,z were comparable. For the capsule formulation, the mean (%CV) t1/2,z was 27.6 hours (27.3) and the median (range) tmax was 2.0 hours (2 - 4). In the food effect study, lenvatinib's AUC0-∞ and AUC0-t increased ~ 6% and 4% with the high-fat meal. Cmax following a high-fat meal was 5% lower than following administration in the fasted state. Administration with food delayed lenvatinib's tmax (2 vs. 4 hours). 90% CIs for AUCs were within the 80 - 125% CI, while the CI for Cmax was 72.1 - 126.4%. The single 10-mg dose demonstrated an acceptable tolerability profile; treatment-emergent adverse events occurred in 9 subjects (25%) overall and were typically mild in severity.



These studies show that a new capsule formulation produces slightly lower exposure (~10 - 14%) to lenvatinib compared with the original tablet formulation, and that oral administration with a high-fat meal does not significantly affect exposure, although absorption is delayed. Thus, lenvatinib can be administered without regard to the timing of meals.
Vision-Based Reaching Using Modular Deep Networks: from Simulation to the Real World

In this paper we describe a deep network architecture that maps visual input to control actions for a robotic planar reaching task with 100% reliability in real-world trials. Our network is trained in simulation and fine-tuned with a limited number of real-world images. The policy search is guided by a kinematics-based controller (K-GPS), which works more effectively and efficiently than ε-Greedy. A critical insight in our system is the need to introduce a bottleneck in the network between the perception and control networks, and to initially train these networks independently.
Brownout: building more robust cloud applications

Self-adaptation is a first class concern for cloud applications, which should be able to withstand diverse runtime changes. Variations are simultaneously happening both at the cloud infrastructure level - for example hardware failures - and at the user workload level - flash crowds. However, robustly withstanding extreme variability, requires costly hardware over-provisioning. 
 In this paper, we introduce a self-adaptation programming paradigm called brownout. Using this paradigm, applications can be designed to robustly withstand unpredictable runtime variations, without over-provisioning. The paradigm is based on optional code that can be dynamically deactivated through decisions based on control theory. 
 We modified two popular web application prototypes - RUBiS and RUBBoS - with less than 170 lines of code, to make them brownout-compliant. Experiments show that brownout self-adaptation dramatically improves the ability to withstand flash-crowds and hardware failures.
APASL consensus statements and recommendation on treatment of hepatitis C

The Asian-Pacific Association for the Study of the Liver (APASL) convened an international working party on the "APASL consensus statements and recommendation on management of hepatitis C" in March, 2015, in order to revise "APASL consensus statements and management algorithms for hepatitis C virus infection (Hepatol Int 6:409-435, 2012)". The working party consisted of expert hepatologists from the Asian-Pacific region gathered at Istanbul Congress Center, Istanbul, Turkey on 13 March 2015. New data were presented, discussed and debated to draft a revision. Participants of the consensus meeting assessed the quality of cited studies. Finalized recommendations on treatment of hepatitis C are presented in this review.
A Hybrid Frequency-domain/Image-domain Deep Network for Magnetic Resonance Image Reconstruction

Decreasing magnetic resonance (MR) image acquisition times can potentially reduce procedural cost and make MR examinations more accessible. Compressed sensing (CS)based image reconstruction methods, for example, decrease MR acquisition time by reconstructing high-quality images from data that were originally sampled at rates inferior to the NyquistShannon sampling theorem. Iterative algorithms with data regularization are the standard approach to solving ill-posed, CS inverse problems. These solutions are usually slow, therefore, preventing near-real time image reconstruction. Recently, deeplearning methods have been used to solve the CS MR reconstruction problem. These proposed methods have the advantage of being able to quickly reconstruct images in a single pass using an appropriately trained network. Some recent studies have demonstrated that the quality of their reconstruction equals and sometimes even surpasses the quality of the conventional iterative approaches. A variety of different network architectures (e.g., U-nets and Residual U-nets) have been proposed to tackle the CS reconstruction problem. A drawback of these architectures is that they typically only work on image domain data. For undersampled data, the images computed by applying the inverse Fast Fourier Transform (iFFT) are aliased. In this work we propose a hybrid architecture that works both in the k-space (or frequency-domain) and the image (or spatial) domains. Our network is composed of a complex-valued residual U-net in the k-space domain, an iFFT operation, and a real-valued Unet in the image domain. Our experiments demonstrated, using MR raw k-space data, that the proposed hybrid approach can potentially improve CS reconstruction compared to deep-learning networks that operate only in the image domain. In this study we compare our method with four previously published deep neural networks and examine their ability to reconstruct images that are subsequently used to generate regional volume estimates. We evaluated undersampling ratios of 75% and 80%. Our technique was ranked second in the quantitative analysis, but qualitative analysis indicated that our reconstruction performed the best in hard to reconstruct regions, such as the cerebellum. All images reconstructed with our method were successfully post-processed, and showed good volumetry agreement compared with the fully sampled reconstruction measures.