nils-herrmann/clean_scirepeval huggingface dataset:
Robotic Assisted Gait Training Therapies for Pediatric Cerebral Palsy: A Review

Children and adolescents with CP experience many types of disability and functional impairment that effect the gait cycle. New robotic gait therapies adapted to pediatric patients provide a safe, highly repetitive, and task specific therapeutic venue for the rehabilitation and elicitation of a more natural walking gait. While the use of robotic assisted gait training (RAGT) is a relatively novel therapeutic approach to gait therapy, several studies have examined the efficacy of this therapeutic modality in pediatric patients with CP.  The purpose of this review is to examine the trends in the therapeutic efficacy of utilizing RAGT therapy as a gait restorative modality for children with CP.  The present studies show that RAGT therapy may provide multiple therapeutic benefits to children with CP, including statistically significant improvements in gross motor function and multiple gait characteristics. Also, RAGT therapy may be a safe and favorable complement to current physiotherapy regimens.  As various degrees of functional improvement are a noticeable trend among all presented studies, further study in this therapeutic technique is warranted, and implementation of similar therapeutic protocol may be valuable to a rehabilitation care plan. The highly repetitive and task specific nature of RAGT may provide a valuable paradigm for children with CP whom have never learned a normal gait pattern.
Species variation in the degree of sex differences in brain and behaviour related to birdsong: adaptations and constraints
Venue: Philosophical Transactions of the Royal Society B: Biological Sciences

The song control system, a neural circuit that controls the learning and production of birdsong, provided the first example in vertebrates of prominent macro morphological sex differences in the brain. Forebrain nuclei HVC, robust nucleus of the arcopallium (RA) and area X all exhibit prominent male biased sex differences in volume in zebra finches and canaries. Subsequent studies compared species that exhibited different degrees of a sex difference in song behaviour and revealed an overall positive correlation between male biases in song behaviour and male biases in the volume of the song nuclei. However, several exceptions have been described in which male biases in HVC and RA are observed even though song behaviour is equal or even female biased. Other phenotypic measures exhibit lability in both sexes. In the duetting plain tailed wren (Pheugopedius euophrys) males and females have auditory cells in the song system that are tuned to the joint song the two sexes produce rather than just male or female components. These findings suggest that there may be constraints on the adaptive response of the song system to ecological conditions as assessed by nucleus volume but that other critical variables regulating song can respond so that each sex can modify its song behaviour as needed.
Recent developments in rare earth free wrought magnesium alloys having high strength: A review

To further expand industrial application of Magnesium alloys, developments of the low cost, Rare Earth free Magnesium alloys with high strength were strongly desired. In this review, recent researches on the Rare Earth free Magnesium alloys will be critically reviewed from the viewpoint of the novel alloying designs, e.g. Magnesium Zinc, Magnesium Tin and Magnesium Calcium systems, and advanced processing technologies to summarize the experimental progresses in pursuing high strength. Considering the large strength gap between the current Rare Earth free Magnesium alloys and state of the art Magnesium Rare Earth alloy or high strength Aluminum alloy, the new strengthening mechanism, such as the solute clustering which was recently observed in Magnesium Rare Earth alloys and the other Rare Earth free Magnesium systems, was suggested. Accordingly, the new alloy design and processing routes, and also the future research directions for Rare Earth free Magnesium alloys would be proposed.
Semantic Labeling: A Domain Independent Approach
Venue: SEMWEB

Semantic labeling is the process of mapping attributes in data sources to classes in an ontology and is a necessary step in heterogeneous data integration. Variations in data formats, attribute names and even ranges of values of data make this a very challenging task. In this paper, we present a novel domain independent approach to automatic semantic labeling that uses machine learning techniques. Previous approaches use machine learning to learn a model that extracts features related to the data of a domain, which requires the model to be re trained for every new domain. Our solution uses similarity metrics as features to compare against labeled domain data and learns a matching function to infer the correct semantic labels for data. Since our approach depends on the learned similarity metrics but not the data itself, it is domain independent and only needs to be trained once to work effectively across multiple domains. In our evaluation, our approach achieves higher accuracy than other approaches, even when the learned models are trained on domains other than the test domain.
Site Specific Bioorthogonal Labeling for Fluorescence Imaging of Intracellular Proteins in Living Cells.
Venue: Journal of the American Chemical Society

Over the past years, fluorescent proteins (e.g. green fluorescent proteins) have been widely utilized to visualize recombinant protein expression and localization in live cells. Although powerful, fluorescent protein tags are limited by their relatively large sizes and potential perturbation to protein function. Alternatively, site specific labeling of proteins with small molecule organic fluorophores using bioorthogonal chemistry may provide a more precise and less perturbing method. This approach involves site specific incorporation of unnatural amino acids (UAAs) into proteins via genetic code expansion, followed by bioorthogonal chemical labeling with small organic fluorophores in living cells. While this approach has been used to label extracellular proteins for live cell imaging studies, site specific bioorthogonal labeling and fluorescence imaging of intracellular proteins in live cells is still challenging. Herein, we systematically evaluate site specific incorporation of diastereomerically pure bioorthogonal UAAs bearing stained alkynes or alkenes into intracellular proteins for inverse electron demand Diels Alder cycloaddition reactions with tetrazine functionalized fluorophores for live cell labeling and imaging in mammalian cells. Our studies show that site specific incorporation of axial diastereomer of trans cyclooct 2 ene lysine robustly affords highly efficient and specific bioorthogonal labeling with monosubstituted tetrazine fluorophores in live mammalian cells, which enabled us to image the intracellular localization and real time dynamic trafficking of IFITM3, a small membrane associated protein with only 137 amino acids, for the first time. Our optimized UAA incorporation and bioorthogonal labeling conditions also enabled efficient site specific fluorescence labeling of other intracellular proteins for live cell imaging studies in mammalian cells.
Overcoming Calibration Problems in Pattern Labeling with Pairwise Ratings: Application to Personality Traits
Venue: ECCV Workshops

We address the problem of calibration of workers whose task is to label patterns with continuous variables, which arises for instance in labeling images of videos of humans with continuous traits. Worker bias is particularly difficult to evaluate and correct when many workers contribute just a few labels, a situation arising typically when labeling is crowd sourced. In the scenario of labeling short videos of people facing a camera with personality traits, we evaluate the feasibility of the pairwise ranking method to alleviate bias problems. Workers are exposed to pairs of videos at a time and must order by preference. The variable levels are reconstructed by fitting a Bradley Terry Luce model with maximum likelihood. This method may at first sight, seem prohibitively expensive because for N videos, (p=N(N 1)/2\ pairs must be potentially processed by workers rather that N videos. However, by performing extensive simulations, we determine an empirical law for the scaling of the number of pairs needed as a function of the number of videos in order to achieve a given accuracy of score reconstruction and show that the pairwise method is affordable. We apply the method to the labeling of a large scale dataset of 10,000 videos used in the ChaLearn Apparent Personality Trait challenge.
Image Labeling by Assignment
Venue: Journal of Mathematical Imaging and Vision

We introduce a novel geometric approach to the image labeling problem. Abstracting from specific labeling applications, a general objective function is defined on a manifold of stochastic matrices, whose elements assign prior data that are given in any metric space, to observed image measurements. The corresponding Riemannian gradient flow entails a set of replicator equations, one for each data point, that are spatially coupled by geometric averaging on the manifold. Starting from uniform assignments at the barycenter as natural initialization, the flow terminates at some global maximum, each of which corresponds to an image labeling that uniquely assigns the prior data. Our geometric variational approach constitutes a smooth non convex inner approximation of the general image labeling problem, implemented with sparse interior point numerics in terms of parallel multiplicative updates that converge efficiently.
Uncertainty in Deep Learning

Deep learning has attracted tremendous attention from researchers in various fields of information engineering such as AI, computer vision, and language processing [Kalchbrenner and Blunsom, 2013; Krizhevsky et al. 2012; Mnih et al. 2013] but also from more traditional sciences such as physics, biology, and manufacturing [Anjos et al. 2015; Baldi et al. 2014; Bergmann et al. 2014] Neural networks, image processing tools such as convolutional neural networks, sequence processing models such as recurrent neural networks, and regularisation tools such as dropout, are used extensively. However, fields such as physics, biology, and manufacturing are ones in which representing model uncertainty is of crucial importance [Ghahramani, 2015; Krzywinski and Altman, 2013] With the recent shift in many of these fields towards the use of Bayesian uncertainty [Herzog and Ostwald, 2013;  2014; Trafimow and Marks, 2015] new needs arise from deep learning. In this work we develop tools to obtain practical uncertainty estimates in deep learning, casting recent deep learning tools as Bayesian models without changing either the models or the optimisation. In the first part of this thesis we develop the theory for such tools, providing applications and illustrative examples. We tie approximate inference in Bayesian models to dropout and other stochastic regularisation techniques, and assess the approximations empirically. We give example applications arising from this connection between modern deep learning and Bayesian modelling such as active learning of image data and data efficient deep reinforcement learning. We further demonstrate the method's practicality through a survey of recent applications making use of the suggested tools in language applications, medical diagnostics, bioinformatics, image processing, and autonomous driving. In the second part of the thesis we explore its theoretical implications, and the insights stemming from the link between Bayesian modelling and deep learning. We discuss what determines model uncertainty properties, analyse the approximate inference analytically in the linear case, and theoretically examine various priors such as spike and slab priors.
Active Learning for High Dimensional Inputs using Bayesian Convolutional Neural Networks

The recent advances of deep learning in applied machine learning gained tremendous success, addressing the problem of learning from massive amounts of data. However, the challenge now is to learn data efficiently with the ability to learn in complex domains without requiring deep learning models to be trained with large quantities of data. We present the novel framework of achieving data efficiency in deep learning through active learning. We develop active learning algorithms for collecting the most informative data for training deep neural network models. Our work is the first to propose active learning algorithms for image data using convolutional neural networks. Recent work showed that the Bayesian approach to CNNs can offer robustness of these models to overfitting on small datasets. By using dropout in neural networks to avoid overfitting as a Bayesian approximation, we can represent model uncertainty from CNNs for image classification tasks. Our proposed Bayesian active learning algorithms use the predictive distribution from the output of a CNN to query most useful datapoints for image classification with least amount of training data. We present information theoretic acquisition functions which incorporates model uncertainty information, namely Dropout Bayesian Active Learning by Disagreement (Dropout BALD) along with several new acquisition functions, and demonstrate their performance on image classification tasks using MNIST as an example. Since our approach is the first to propose active learning in a deep learning framework, we compare our results with several semi supervised learning methods which also focuses on learning data efficiently using least number of training samples. Our results demonstrate that we can perform active learning in a deep learning framework which has previously not been done for image data. This allows us to achieve data efficiency in training. We illustrate that compared to standard semi supervised learning methods, we achieve a considerable improvement in classification accuracy. Using our Bayesian active learning framework using 1000 training samples only, we achieve classification error rate of 0.57% while the state of the art under purely supervised setting with significantly larger training data is 0.3% on MNIST.
Direct comparison of elastic incoherent neutron scattering experiments with molecular dynamics simulations of DMPC phase transitions
Venue: The European physical journal. E, Soft matter

Neutron scattering techniques have been employed to investigate 1,2 dimyristoyl sn glycero 3 phosphocholine (DMPC) membranes in the form of multilamellar vesicles (MLVs) and deposited, stacked multilamellar bilayers (MLBs) covering transitions from the gel to the liquid phase. Neutron diffraction was used to characterise the samples in terms of transition temperatures, whereas elastic incoherent neutron scattering (EINS) demonstrates that the dynamics on the sub macromolecular length scale and pico to nano second time scale are correlated with the structural transitions through a discontinuity in the observed elastic intensities and the derived mean square displacements. Molecular dynamics simulations have been performed in parallel focussing on the length time and temperature scales of the neutron experiments. They correctly reproduce the structural features of the main gel liquid phase transition. Particular emphasis is placed on the dynamical amplitudes derived from experiment and simulations. Two methods are used to analyse the experimental data and mean square displacements. They agree within a factor of 2 irrespective of the probed time scale, i.e. the instrument utilized. Mean square displacements computed from simulations show a comparable level of agreement with the experimental values, albeit, the best match with the two methods varies for the two instruments. Consequently, experiments and simulations together give a consistent picture of the structural and dynamical aspects of the main lipid transition and provide a basis for future, theoretical modelling of dynamics and phase behaviour in membranes. The need for more detailed analytical models is pointed out by the remaining variation of the dynamical amplitudes derived in two different ways from experiments on the one hand and simulations on the other.
Pharmacological Inhibition of the Protein Kinase MRK/ZAK Radiosensitizes Medulloblastoma
Venue: Molecular Cancer Therapeutics

Medulloblastoma is a cerebellar tumor and the most common pediatric brain malignancy. Radiotherapy is part of the standard care for this tumor, but its effectiveness is accompanied by significant neurocognitive sequelae due to the deleterious effects of radiation on the developing brain. We have previously shown that the protein kinase MRK/ZAK protects tumor cells from radiation induced cell death by regulating cell cycle arrest after ionizing radiation. Here, we show that siRNA mediated MRK depletion sensitizes medulloblastoma primary cells to radiation. We have, therefore, designed and tested a specific small molecule inhibitor of MRK, M443, which binds to MRK in an irreversible fashion and inhibits its activity. We found that M443 strongly radiosensitizes UW228 medulloblastoma cells as well as UI226 patient derived primary cells, whereas it does not affect the response to radiation of normal brain cells. M443 also inhibits radiation induced activation of both p38 and Chk2, two proteins that act downstream of MRK and are involved in DNA damage induced cell cycle arrest. Importantly, in an animal model of medulloblastoma that employs orthotopic implantation of primary patient derived UI226 cells in nude mice, M443 in combination with radiation achieved a synergistic increase in survival. We hypothesize that combining radiotherapy with M443 will allow us to lower the radiation dose while maintaining therapeutic efficacy, thereby minimizing radiation induced side effects.
Classification of topological quantum matter with symmetries

Topological materials have become the focus of intense research in recent years, since they exhibit fundamentally new physical phenomena with potential applications for novel devices and quantum information technology. One of the hallmarks of topological materials is the existence of protected gapless surface states, which arise due to a nontrivial topology of the bulk wave functions. This review provides a pedagogical introduction into the field of topological quantum matter with an emphasis on classification schemes. Both fully gapped and gapless topological materials and their classification in terms of nonspatial symmetries, such as time reversal, as well as spatial symmetries, such as reflection, are considered. Furthermore, the classification of gapless modes localized on topological defects is surveyed. The classification of these systems is discussed by use of homotopy groups, Clifford algebras, K theory, and nonlinear sigma models describing the Anderson (de)localization at the surface or inside a defect of the material. Theoretical model systems and their topological invariants are reviewed together with recent experimental results in order to provide a unified and comprehensive perspective of the field. While the bulk of this article is concerned with the topological properties of noninteracting or mean field Hamiltonians, a brief overview of recent results and open questions concerning the topological classifications of interacting systems is also provided.
Informatics and Nursing in a Post Nursing Informatics World: Future Directions for Nurses in an Automated, Artificially Intelligent, Social Networked Healthcare Environment.
Venue: Nursing leadership

The increased adoption and use of technology within healthcare and society has influenced the nursing informatics specialty in a multitude of fashions. Namely, the nursing informatics specialty currently faces a range of important decisions related to its knowledge base, established values and future directions all of which are in need of development and future proofing. In light of the increased use of automation, artificial intelligence and big data in healthcare, the specialty must also reconceptualize the roles of both nurses and informaticians to ensure that the nursing profession is ready to operate within future digitalized healthcare ecosystems. To explore these goals, the author of this manuscript outlines an examination of technological advancements currently taking place within healthcare, and also proposes implications for the nursing role and the nursing informatics specialty. Finally, recommendations and insights towards how the roles of nurses and informaticians might evolve or be shaped in the growing post nursing informatics era are presented.
Effectiveness of a Nursing Intervention Protocol for Chemotherapy Induced Neutropenia Nurses and Patients Related Outcome

Chemotherapy induced neutropenia (CIN) is a significant problem among cancer patients receiving chemotherapy. CIN is causing serious complications in approximately 80% of patients. There is a pressing need to design evidence based nursing protocols to provide safe and effective care for patients with neutropenia.  The aim of present study has three folds. First was the assessment of nurses' knowledge and practices for management of CIN patients; the second was the development of an evidence based nursing intervention protocol according to most recent strong evidences of neutropenia management and the nurses' educational needs; and finally applying the developed protocol and thereafter evaluating its impact on nurses, as well as patients' knowledge and practice. A Quasiexperimental research design was utilized in this study. A convenient sample of 30 oncology nurses, as well as thirty patients was recruited from Oncology Department of Assiut University Hospital. Tools: data collection instruments included; nurses CIN knowledge test; neutropenia guideline observation checklist for evaluating nurses' practice compliance with designed protocols; patients' CIN test; and checklist to assess patient's' practices related to oral care and hand washing.  Mean scores for nurses' and patients' post protocol knowledge as well as practice level were significantly higher than the pre protocol scores. A positive significant correlation between total knowledge and practice scores was observed.  Designing and implementing evidence based nursing intervention protocol is important for improvement of nurses' knowledge and practice and enhancement of patients' outcomes. Providing patients and their families with the necessary knowledge and practice through implementation of educational programs is a must to equip them with the necessary skills for prevention and management of problems related to alteration in protective mechanisms.