nils-herrmann/clean_scirepeval huggingface dataset:
Creatine synthesis and exchanges between brain cells: What can be learned from human creatine deficiencies and various experimental models?
Venue: Amino Acids

While it has long been thought that most of cerebral creatine is of peripheral origin, the last 20 years has provided evidence that the creatine synthetic pathway (AGAT and GAMT enzymes) is expressed in the brain together with the creatine transporter (SLC6A8) It has also been shown that SLC6A8 is expressed by microcapillary endothelial cells at the blood brain barrier, but is absent from surrounding astrocytes, raising the concept that the blood brain barrier has a limited permeability for peripheral creatine. The first creatine deficiency syndrome in humans was also discovered 20 years ago (GAMT deficiency) followed later by AGAT and SLC6A8 deficiencies, all three diseases being characterized by creatine deficiency in the CNS and essentially affecting the brain. By reviewing the numerous and latest experimental studies addressing creatine transport and synthesis in the CNS, as well as the clinical and biochemical characteristics of creatine deficient patients, our aim was to delineate a clearer view of the roles of the blood brain and blood cerebrospinal fluid barriers in the transport of creatine and guanidinoacetate between periphery and CNS, and on the intracerebral synthesis and transport of creatine. This review also addresses the question of guanidinoacetate toxicity for brain cells, as probably found under GAMT deficiency.
Hyaluronic Acid Fillers: Science and Clinical Uses.
Venue: Clinics in plastic surgery

Hyaluronic acid soft tissue fillers include a range of products (Juvederm Ultra, Juvederm Ultra Plus, Voluma, Restylane Silk, Restylane, Restylane Lyft, and Belotero Balance) that are used commonly for facial rejuvenation and enhancement of facial features. Although these products are similar in many ways, they are not interchangeable and have unique characteristics that need to be considered. Injection sites and techniques for facial rejuvenation are discussed.
Facial Contouring With Fillers, Neuromodulators, and Lipolysis to Achieve a Natural Look in Patients With Facial Fullness.
Venue: Journal of drugs in dermatology JDD

The desire for and use of nonsurgical injectable esthetic facial treatments is on a rise in Asia. Recent advances, including more versatile facial fillers, refined injection techniques, and adoption of a global facial approach, have in turn contributed to improved patient outcomes and increased patient satisfaction. The sought after nonsurgical treatments include the use of botulinum toxin, con touring of the face with soft tissue fillers, and thinning of the face with injection lipolysis.  To achieve a leaner, oval face shape with smooth contours.  A combination technique was applied, whereby narrowing of face was achieved with use of botulinum toxin, contouring with soft tissue llers, and thinning the face with injection lipolysis. Treatments were applied on 15 women aged between 25 and 40 years and observed with follow up for 12 weeks. Hyaluronic acid (HA) based dermal fillers (Perlane, Voluma, and Juvederm) botulinum toxin type A (Botox) and a lipolytic solution of phosphatidylcholine with deoxycholate (Dermastabilion, Aesthetic Dermal, Spain) were used to achieve a natural look in these patients with a fuller appearance of the face. The procedure was performed to reduce the facial width using botulinum toxin type A and the reduction of submental and cheek fat by injecting lipolysis injection.  All the women (n 15) had edema for up to 2 weeks after undergoing lipolysis; however, two patients had edema for 3 weeks. After injecting the patient with botulinum toxin type A, no adverse event was observed; however, after using HA based filler, one patient was observed to have bruising on the chin.  Use of HA based fillers, neuromodulators (botulinum toxin) and injectable lipolytics are well tolerated and are effective nonsurgical modalities to achieve facial recontouring for slimming of the face.
Anion Recognition and Induced Self Assembly of an a,g Cyclic Peptide To Form Spherical Clusters.
Venue: Angewandte Chemie

A cyclic octapeptide composed of hydroxy functionalized g amino acids folds in a "V shaped" conformation that allows the selective recognition of anions such as chloride, nitrate, and carbonate. The process involves the simultaneous self assembly of six peptide subunits and the recognition of four anions to form a tetrahedral structure, in which the anions are located at the corners of the resulting structure. Each anion is coordinated to three different peptides. The structure was fully characterized by several techniques, including NMR spectroscopy and X ray diffraction, and the material was able to facilitate the transmembrane transport of chloride ions.
Factors associated with car seat test failure in late preterm infants: A retrospective chart review.
Venue: Paediatrics child health

Professional organizations recommend car seat testing of preterm infants before discharge from hospital. Late preterm infants (LPIs, 34(0/7) to 36(6/7) weeks' gestational age) are at the greatest risk for failure, despite often being well.  To determine the incidence of car seat testing failure in LPIs and associative factors.  A retrospective chart review was performed of inborn LPIs admitted to all levels of newborn care between July 1, 2012 and June 30, 2013. Data collected included maternal demographics, labour and delivery history, and neonatal course. Data were analyzed using backward logistic regression.  A total of 511 charts were reviewed and 367 LPIs were eligible for inclusion. Of the 313 LPIs tested (mean SD] gestational age 36 weeks 6 days and birth weight 2614+ 405 g) 80 (26% failed (95% CI 21% to 31% Most failed due to desaturations =2) of <88% for =10 s (n=33 [41% Multiple gestation was associated with failure (adjusted OR 2.45 [95% CI 1.44 to 4.18] P=0.001) and there was a trend toward statistical significance for the variable postnatal age (0.996 [95% CI 0.99 to 1.00] P=0.05) Infants who passed their car seat test had higher postnatal ages than those who failed (mean difference 39.4 h [95% CI 12.7 h to 66.0 h] P=0.004)  Twenty six percent of LPIs failed car seat testing. Ideally, infants should be tested after an appropriate transitional period. The authors identified factors that may be important in designing future, prospective studies in this area. Future research should evaluate the clinical significance of car seat testing and resource utilization.
Octopus: An Edge fog Mutual Authentication Scheme
Venue: Int. J. Netw. Secur.

Authentication is an important and challenging issue for the security of Fog computing since, services are offered to massive scale end users (Fog users or Edge) by front Fog servers (or Fog nodes) In this paper, we propose a secure and efficient mutual authentication scheme for the Edge Fog Cloud network architecture, to mutually authenticate Fog users at the Edge of the network, with the Fog servers at the Fog layer. Our scheme requires a user roaming randomly in the network to hold only one long lived master secret key (with long enough bit length) allowing him to communicate with any of the Fog servers in the network, in a fully authenticated way. The Fog users are able to mutually authenticate with new Fog servers joining the network, without the need to re register and without any extra overheads. Moreover, the servers in the Fog are required to store only one secret key for each Fog user. On the other hand, the Fog users are totally unrelated to any public key infrastructure. The scheme requires the Fog user to perform very few hash invocations and symmetric encryptions/decryptions. Therefore, the scheme is suitable to be efficiently implemented on the Fog user's smart card/device.
A DNA Based Archival Storage System
Venue: ASPLOS 2016

Demand for data storage is growing exponentially, but the capacity of existing storage media is not keeping up. Using DNA to archive data is an attractive possibility because it is extremely dense, with a raw limit of 1 exabyte/mm3 (109 GB/mm3) and long lasting, with observed half life of over 500 years. This paper presents an architecture for a DNA based archival storage system. It is structured as a key value store, and leverages common biochemical techniques to provide random access. We also propose a new encoding scheme that offers controllable redundancy, trading off reliability for density. We demonstrate feasibility, random access, and robustness of the proposed encoding with wet lab experiments involving 151 kB of synthesized DNA and a 42 kB random access subset, and simulation experiments of larger sets calibrated to the wet lab experiments. Finally, we highlight trends in biotechnology that indicate the impending practicality of DNA storage for much larger datasets.
Programmable editing of a target base in genomic DNA without double stranded DNA cleavage
Venue: Nature

Current genome editing technologies introduce double stranded (ds) DNA breaks at a target locus as the first step to gene correction. Although most genetic diseases arise from point mutations, current approaches to point mutation correction are inefficient and typically induce an abundance of random insertions and deletions (indels) at the target locus resulting from the cellular response to dsDNA breaks. Here we report the development of 'base editing' a new approach to genome editing that enables the direct, irreversible conversion of one target DNA base into another in a programmable manner, without requiring dsDNA backbone cleavage or a donor template. We engineered fusions of CRISPR/Cas9 and a cytidine deaminase enzyme that retain the ability to be programmed with a guide RNA, do not induce dsDNA breaks, and mediate the direct conversion of cytidine to uridine, thereby effecting a C T (or G A) substitution. The resulting 'base editors' convert cytidines within a window of approximately five nucleotides, and can efficiently correct a variety of point mutations relevant to human disease. In four transformed human and murine cell lines, second and third generation base editors that fuse uracil glycosylase inhibitor, and that use a Cas9 nickase targeting the non edited strand, manipulate the cellular DNA repair response to favour desired base editing outcomes, resulting in permanent correction of ~15 75% of total cellular DNA with minimal (typically =1% indel formation. Base editing expands the scope and efficiency of genome editing of point mutations.
Electrospun nanofibers as a platform for advanced secondary batteries: a comprehensive review

Poor electrochemical performances of materials in commercial lithium ion batteries (LIBs) make it difficult to realize battery technologies for future electric power applications such as energy storage systems and electric vehicles. To reach beyond the horizon of state of the art LIBs, the exploration of next generation batteries composed of rationally designed nanomaterials in consideration of the structure, phase and element influencing the battery performance is critical. By virtue of the simple set up, versatility, size controllability and mass productivity of electrospinning, one dimensional (1D) nanofibers (NFs) produced via electrospinning are attractive candidates for the construction of advanced secondary batteries. A comprehensive review of the forefront in the development of electrospun NFs as advanced materials of next generation LIBs, sodium ion batteries (NIBs) lithium sulfur batteries and lithium air batteries with particular emphasis on synthesis and improved energy and power density, and cyclability is presented. In this review, we highlight the recent advancements in electrospun 1D nano architectures with large surface area to volume ratios and controllable morphologies as anodes, cathodes, separators, electrolytes and even catalytic materials. Furthermore, current challenges and prospects of electrospun NFs in both academia and industry are also discussed. We expect that this review opens up new research activities in a variety of research fields including advanced rechargeable batteries.
Carbon Nanofiber Based Materials as Anode Materials for Lithium Ion Batteries

Considerable efforts have been devoted to the research of high performance and longlifespan lithium ion batteries (LIBs) for their applications in large scale power units. As one of the most important components in LIBs, anode plays an important role in determining the overall performance of LIBs. Nowadays, graphite has been the most successfully commercialized anode material. However, its limited theoretical capacity (372 mA h g 1) and limited power density seems insufficient for the next generation LIBs. To overcome these problems, new materials with fundamentally higher capacity and higher power density are urgently needed. Recently, there is an ever increasing interest in developing novel carbonaceous nanomaterials to replace graphite as the anode materials for LIBs. Such materials have included carbon spheres, carbon nanotubes, carbon nanofibers (CNFs) porous monoliths, and graphene. Among these alternative forms of carbon, CNFs and its morphological controlled derivatives (such as porous or hollow CNFs) have attracted much attention due to their unique and interesting properties such as one dimensional (1D) nanostructure, good electronic conductivity, and large surface areas. Moreover, these CNFs can be used to encapsu late various second phases to form some functional composite, meeting further requirements including higher energy density, higher power density or flexible requirements, for the advanced LIB operation. Electrospinning is considered as a simple, versatile, and cost effective industry viable technology for preparing various CNFs and their composites in a continuous process, with controllable morphology. Therefore, in this chapter, we have summarized some recent progresses in electrospun nanofibrous carbon based anode for LIBs, covering the structure evolution from solid CNFs into morphology designed CNFs, and then their composites with various functional nanoparticles. We anticipate this paper can offer some useful information for some researchers in the area of energy storage and conversion and can inspire them.
The Role of Neonatal Nurses in the Prevention of Retinopathy of Prematurity.
Venue: Indian pediatrics

The main risk factors for preterm babies developing retinopathy of prematurity (ROP) are poorly administered supplemental oxygen, infections, poor weight gain and transfusion of blood products, meaning ROP is sensitive to the quality of neonatal inpatient care provided, especially the nursing care. Nurses are the primary caregivers in neonatal units and play a critical role in preventing ROP related blindness. We discuss the role of the neonatal nurse in prevention of ROP based on a framework of five dimensions of care: specialist knowledge, clinical advocacy, leadership and mentorship, service management and counselling. Developing the role in the prevention of ROP needs to be supported by a wider movement to develop core training competencies and national benchmarks for neonatal nurses. As part of the national newborn action plans, countries such as India have an opportunity to play a leading role in developing the nursing role in minimising the rates of visual impairments and blindness due to ROP.
Block Row Sparse Multiview Multilabel Learning for Image Classification
Venue: IEEE Transactions on Cybernetics

In image analysis, the images are often represented by multiple visual features (also known as multiview features) that aim to better interpret them for achieving remarkable performance of the learning. Since the processes of feature extraction on each view are separated, the multiple visual features of images may include overlap, noise, and redundancy. Thus, learning with all the derived views of the data could decrease the effectiveness. To address this, this paper simultaneously conducts a hierarchical feature selection and a multiview multilabel (MVML) learning for multiview image classification, via embedding a proposed a new block row regularizer into the MVML framework. The block row regularizer concatenating a Frobenius norm (F norm) regularizer and an l2,1 norm regularizer is designed to conduct a hierarchical feature selection, in which the F norm regularizer is used to conduct a high level feature selection for selecting the informative views (i.e. discarding the uninformative views) and the 12,1 norm regularizer is then used to conduct a low level feature selection on the informative views. The rationale of the use of a block row regularizer is to avoid the issue of the over fitting (via the block row regularizer) to remove redundant views and to preserve the natural group structures of data (via the F norm regularizer) and to remove noisy features (the 12,1 norm regularizer) respectively. We further devise a computationally efficient algorithm to optimize the derived objective function and also theoretically prove the convergence of the proposed optimization method. Finally, the results on real image datasets show that the proposed method outperforms two baseline algorithms and three state of the art algorithms in terms of classification performance.
PD Sparse A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification
Venue: ICML

We consider Multiclass and Multilabel classification with extremely large number of classes, of which only few are labeled to each instance. In such setting, standard methods that have training, prediction cost linear to the number of classes become intractable. State of the art methods thus aim to reduce the complexity by exploiting correlation between labels under assumption that the similarity between labels can be captured by structures such as low rank matrix or balanced tree. However, as the diversity of labels increases in the feature space, structural assumption can be easily violated, which leads to degrade in the testing performance. In this work, we show that a margin maximizing loss with l1 penalty, in case of Extreme Classification, yields extremely sparse solution both in primal and in dual without sacrificing the expressive power of predictor. We thus propose a Fully Corrective Block Coordinate Frank Wolfe (FC BCFW) algorithm that exploits both primal and dual sparsity to achieve a complexity sublinear to the number of primal and dual variables. A bi stochastic search method is proposed to further improve the efficiency. In our experiments on both Multiclass and Multilabel problems, the proposed method achieves significant higher accuracy than existing approaches of Extreme Classification with very competitive training and prediction time.
Multilabel Classification: Problem Analysis, Metrics and Techniques

This book offers a comprehensive review of multilabel techniques widely used to classify and label texts, pictures, videos and music in the Internet. A deep review of the specialized literature on the field includes the available software needed to work with this kind of data. It provides the user with the software tools needed to deal with multilabel data, as well as step by step instruction on how to use them. The main topics covered are: The special characteristics of multi labeled data and the metrics available to measure them. The importance of taking advantage of label correlations to improve the results. The different approaches followed to face multi label classification. The preprocessing techniques applicable to multi label datasets. The available software tools to work with multi label data. This book is beneficial for professionals and researchers in a variety of fieldsbecause of the wide range of potential applications for multilabel classification. Besides its multiple applications to classify different types of online information, it is also useful in many other areas, such as genomics and biology. No previous knowledge about the subject is required. The book introduces all the needed concepts to understand multilabel data characterization, treatment and evaluation.
Bidirectional Recurrent Convolutional Neural Network for Relation Classification
Venue: ACL

Relation classification is an important semantic processing task in the field of natural language processing (NLP) In this paper, we present a novel model BRCNN to classify the relation of two entities in a sentence. Some state of the art systems concentrate on modeling the shortest dependency path (SDP) between two entities leveraging convolutional or recurrent neural networks. We further explore how to make full use of the dependency relations information in the SDP, by combining convolutional neural networks and twochannel recurrent neural networks with long short term memory (LSTM) units. We propose a bidirectional architecture to learn relation representations with directional information along the SDP forwards and backwards at the same time, which benefits classifying the direction of relations. Experimental results show that our method outperforms the state of theart approaches on the SemEval 2010 Task 8 dataset.
Compound Type Identification in Sanskrit: What Roles do the Corpus and Grammar Play?
Venue: WSSANLP@COLING

We propose a classification framework for semantic type identification of compounds in Sanskrit. We broadly classify the compounds into four different classes namely, Avyayibhava, Tatpurusa, Bahuvrihi and Dvandva. Our classification is based on the traditional classification system followed by the ancient grammar treatise Adstadhyayi, proposed by Panini 25 centuries back. We construct an elaborate features space for our system by combining conditional rules from the grammar Adstadhyayi, semantic relations between the compound components from a lexical database Amarakosa and linguistic structures from the data using Adaptor Grammars. Our in depth analysis of the feature space highlight inadequacy of Adstadhyayi, a generative grammar, in classifying the data samples. Our experimental results validate the effectiveness of using lexical databases as suggested by Amba Kulkarni and Anil Kumar, and put forward a new research direction by introducing linguistic patterns obtained from Adaptor grammars for effective identification of compound type. We utilise an ensemble based approach, specifically designed for handling skewed datasets and we %and Experimenting with various classification methods, we achieve an overall accuracy of 0.77 using random forest classifiers.