nils-herrmann/clean_scirepeval huggingface dataset:
Drug Treatment of Sleep Disorders

Sleep is a complex behavior that cyclically alternates with wakefulness and is fundamental for both mental and physical wellness. During normal sleep the body acquires a specific posture; any active behavior is absent and mental activity typically oscillates between a state of “loss of consciousness” and the experience of dreaming. Sleep is, by itself, a cyclical process characterized by the alternation of two phases, non-REM (NREM) sleep and “rapid eye movement” (REM) sleep, during which the typical synchronized electroencephalographic (EEG) pattern of NREM sleep is substituted by a “paradoxical” EEG desynchronization. NREM sleep is a state of minimal energy expenditure and motor activity, during which cardiorespiratory and thermoregulatory variables are driven by the autonomic nervous system at a lower and more stable level compared to wakefulness. During REM sleep, beyond the occurrence of REMs, posture control is lost and dreams become more vivid. Furthermore, autonomic activity is highly unstable leading to centrally driven surges in heart rate and blood pressure, breathing becomes irregular, and thermoregulation is suspended or depressed, suggesting a derangement of the integrative regulation of physiological functions. Overall sleep quality is one of the most important parameters considered for the subjective assessment of quality of life and general health status. However, although sleep function(s) is intuitively associated with some kind of rest that seems to be mostly required by the central nervous system, and many theories on sleep function have been proposed, a full comprehension of sleep function has not yet been achieved and is probably not imminent. C. Berteotti • M. Cerri • M. Luppi • A. Silvani • R. Amici (*) Department of Biomedical and Neuromotor Sciences—Physiology, Alma Mater Studiorum-University of Bologna, Piazza P.ta S. Donato, 2, 40126 Bologna, Italy e-mail: chiara.berteotti@unibo.it; matteo.cerri@unibo.it; marco.luppi@unibo.it; alessandro.silvani3@unibo.it; roberto.amici@unibo.it; http://www.unibo.it/Faculty/default.  A. Guglietta (ed.), Drug Treatment of Sleep Disorders, Milestones in Drug Therapy, DOI 10.1007/978-3-319-11514-6_1 3 1 Neurophysiology and Neurobiology of Sleep
Analysing road accident data using association rule mining

Road accident is one of the crucial areas of research in India. A variety of research has been done on data collected through police records covering a limited portion of highways. The analysis of such data can only reveal information regarding that portion only; but accidents are scattered not only on highways but also on local roads. A different source of road accident data in India is Emergency Management research Institute (EMRI) which serves and keeps track of every accident record on every type of road and cover information of entire State's road accidents. In this paper, we have used data mining techniques to analyze the data provided by EMRI in which we first cluster the accident data and further association rule mining technique is applied to identify circumstances in which an accident may occur for each cluster. The results can be utilized to put some accident prevention efforts in the areas identified for different categories of accidents to overcome the number of accidents.
How to Perform a Transrectal Ultrasound Examination of the Lumbosacral and Sacroiliac Joints

There is increasing interest in pathology of the lumbosacral and sacroiliac joints giving rise to stiffness and/or lameness and decreased performance in equine sports medicine. Pain arising from these regions can be problematic alone or in conjunction with lameness arising from other sites (thoracolumbar spine, hind limbs, or forelimbs). Localization of pain to this region is critically important through clinical assessment, diagnostic anesthesia, and imaging. In general, diagnostic imaging of the axial skeleton and pelvis is difficult to perform and to interpret. Radiography is infrequently performed. To obtain good-quality diagnostic radiographs, general anesthesia, a high-output radiographic generator, and special techniques must be performed. Variation in the size and shape of the sacroiliac joints and sacral wings and caudal sacral osteophytes are common; special techniques for taking radiographs have allowed for identification of these structures and the inter-transverse joints. These authors urge caution in the interpretation of lesions identified on radiography in the absence of other diagnostic imaging and clinical examination. Nuclear scintigraphy is an important component of work-up for sacroiliac region pain, but limitations exist. Several reports exist detailing the anatomy and technique findings in normal horses and findings in lame horses. Patient motion, camera positioning, and muscle asymmetry can cause errors in interpretation. In normal horses, the appearance of the sacroiliac region varies with age but is generally symmetric. In horses with sacroiliac problems, it is more difficult to distinguish the tubera sacrale from the sacroiliac joint than in normal horses, and, in horses with lameness, there is more asymmetry detected. Techniques for percutaneous and transrectal ultrasound examination have been described, and
The Pervasive Effects of an Antibiotic on the Human Gut Microbiota, as Revealed by Deep 16S rRNA Sequencing

The human intestinal microbiota is essential to the health of the host and plays a role in nutrition, development, metabolism, pathogen resistance, and regulation of immune responses. Antibiotics may disrupt these coevolved interactions, leading to acute or chronic disease in some individuals. Our understanding of antibiotic-associated disturbance of the microbiota has been limited by the poor sensitivity, inadequate resolution, and significant cost of current research methods. The use of pyrosequencing technology to generate large numbers of 16S rDNA sequence tags circumvents these limitations and has been shown to reveal previously unexplored aspects of the "rare biosphere." We investigated the distal gut bacterial communities of three healthy humans before and after treatment with ciprofloxacin, obtaining more than 7,000 full-length rRNA sequences and over 900,000 pyrosequencing reads from two hypervariable regions of the rRNA gene. A companion paper in PLoS Genetics (see Huse et al., doi: 10.1371/journal.pgen.1000255) shows that the taxonomic information obtained with these methods is concordant. Pyrosequencing of the V6 and V3 variable regions identified 3,300-5,700 taxa that collectively accounted for over 99% of the variable region sequence tags that could be obtained from these samples. Ciprofloxacin treatment influenced the abundance of about a third of the bacterial taxa in the gut, decreasing the taxonomic richness, diversity, and evenness of the community. However, the magnitude of this effect varied among individuals, and some taxa showed interindividual variation in the response to ciprofloxacin. While differences of community composition between individuals were the largest source of variability between samples, we found that two unrelated individuals shared a surprising degree of community similarity. In all three individuals, the taxonomic composition of the community closely resembled its pretreatment state by 4 weeks after the end of treatment, but several taxa failed to recover within 6 months. These pervasive effects of ciprofloxacin on community composition contrast with the reports by participants of normal intestinal function and with prior assumptions of only modest effects of ciprofloxacin on the intestinal microbiota. These observations support the hypothesis of functional redundancy in the human gut microbiota. The rapid return to the pretreatment community composition is indicative of factors promoting community resilience, the nature of which deserves future investigation.
Treating a maxillary midline diastema in adult patients: a general dentist's perspective.

A maxillary midline diastema (MMD) often is a primary concern of patients during a dental consultation. Although an MMD can be transient owing to the developing dentition and, thus, requires no active treatment, management of MMDs in the permanent dentition requires a detailed examination and appropriate care.



. The authors present five cases of MMDs in adults to illustrate a range of restorative and orthodontic options. In the first case, the clinician used resin-based composite buildup to close an MMD resulting from small teeth and generalized spacing in the dental arch. In the second case, the clinician placed porcelain veneers to treat an MMD in a patient with discolored dentition. In the third case, the clinician fitted a removable appliance to close an MMD by tipping the incisors palatally. In the fourth case, the clinician fitted a sectional fixed appliance to promote mesial bodily movement of the incisors. In the fifth case, the clinician placed a full-arch fixed appliance to treat an MMD caused by tilted incisors.



and



Effective treatment requires an accurate diagnosis and appropriate intervention. General dentists can perform a range of restorative and orthodontic treatments in appropriate clinical situations to address patients' concerns.
Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet

Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy model in which invariant representations can be built by self-organizing learning based on the temporal and spatial statistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associative synaptic learning rule with a short-term memory trace, and/or it can use spatial continuity in continuous spatial transformation learning which does not require a temporal trace. The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and also lighting. The model has been extended to provide an account of invariant representations in the dorsal visual system of the global motion produced by objects such as looming, rotation, and object-based movement. The model has been extended to incorporate top-down feedback connections to model the control of attention by biased competition in, for example, spatial and object search tasks. The approach has also been extended to account for how the visual system can select single objects in complex visual scenes, and how multiple objects can be represented in a scene. The approach has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus.
Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns

This paper presents a theoretically v ery simple yet ef ficient approach for gray scale and rotation in variant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distrib utions. The proposed approach is v ery robust in terms of gray scale v ariations, since the operators are by definition in variant against any monotonic transformation of the gray scale. Another adv antage is computational simplicity , as the operators can be realized with a fe w operations in a small neighborhood and a lookup table. Excellent e xperimental results obtained in tw o true problems of rotation invariance, where the classifier is trained at one particular rotation angle and tested with samples from other rotation angles, demonstrate that good discrimination can be achie v d with the statistics of simple rotation in variant local binary patterns. These operators characterize the spatial configuration of local image texture and the performance can be further impro ved by combining them with rotation invariant variance measures that characterize the contrast of local image texture. The joint distrib utions of these orthogonal measures are sho wn t be very powerful tools for rotation in variant texture analysis.
Clustering based multi-label classification for image annotation and retrieval

This paper presents a novel multi-label classification framework for domains with large numbers of labels. Automatic image annotation is such a domain, as the available semantic concepts are typically hundreds. The proposed framework comprises an initial clustering phase that breaks the original training set into several disjoint clusters of data. It then trains a multi-label classifier from the data of each cluster. Given a new test instance, the framework first finds the nearest cluster and then applies the corresponding model. Empirical results using two clustering algorithms, four multi-label classification algorithms and three image annotation data sets suggest that the proposed approach can improve the performance and reduce the training time of standard multi-label classification algorithms, particularly in the case of large number of labels.
Hand Pose Estimation via Latent 2.5D Heatmap Regression

Estimating the 3D pose of a hand is an essential part of human-computer interaction. Estimating 3D pose using depth or multiview sensors has become easier with recent advances in computer vision, however, regressing pose from a single RGB image is much less straightforward. The main difficulty arises from the fact that 3D pose requires some form of depth estimates, which are ambiguous given only an RGB image. In this paper we propose a new method for 3D hand pose estimation from a monocular image through a novel 2.5D pose representation. Our new representation estimates pose up to a scaling factor, which can be estimated additionally if a prior of the hand size is given. We implicitly learn depth maps and heatmap distributions with a novel CNN architecture. Our system achieves state-of-the-art accuracy for 2D and 3D hand pose estimation on several challenging datasets in presence of severe occlusions.
Graph theoretical analysis of complex networks in the brain

Since the discovery of small-world and scale-free networks the study of complex systems from a network perspective has taken an enormous flight. In recent years many important properties of complex networks have been delineated. In particular, significant progress has been made in understanding the relationship between the structural properties of networks and the nature of dynamics taking place on these networks. For instance, the 'synchronizability' of complex networks of coupled oscillators can be determined by graph spectral analysis. These developments in the theory of complex networks have inspired new applications in the field of neuroscience. Graph analysis has been used in the study of models of neural networks, anatomical connectivity, and functional connectivity based upon fMRI, EEG and MEG. These studies suggest that the human brain can be modelled as a complex network, and may have a small-world structure both at the level of anatomical as well as functional connectivity. This small-world structure is hypothesized to reflect an optimal situation associated with rapid synchronization and information transfer, minimal wiring costs, as well as a balance between local processing and global integration. The topological structure of functional networks is probably restrained by genetic and anatomical factors, but can be modified during tasks. There is also increasing evidence that various types of brain disease such as Alzheimer's disease, schizophrenia, brain tumours and epilepsy may be associated with deviations of the functional network topology from the optimal small-world pattern.
Distributed Language Modeling for N-best List Re-ranking

In this paper we describe a novel distributed language model for N -best list re-ranking. The model is based on the client/server paradigm where each server hosts a portion of the data and provides information to the client. This model allows for using an arbitrarily large corpus in a very efficient way. It also provides a natural platform for relevance weighting and selection. We applied this model on a 2.97 billion-word corpus and re-ranked theN -best list from Hiero, a state-of-theart phrase-based system. Using BLEU as a metric, the re-ranked translation achieves a relative improvement of 4.8%, significantly better than the model-best translation.
Safeguarding 5G wireless communication networks using physical layer security

The fifth generation (5G) network will serve as a key enabler in meeting the continuously increasing demands for future wireless applications, including an ultra-high data rate, an ultrawide radio coverage, an ultra-large number of devices, and an ultra-low latency. This article examines security, a pivotal issue in the 5G network where wireless transmissions are inherently vulnerable to security breaches. Specifically, we focus on physical layer security, which safeguards data confidentiality by exploiting the intrinsic randomness of the communications medium and reaping the benefits offered by the disruptive technologies to 5G. Among various technologies, the three most promising ones are discussed: heterogenous networks, massive multiple-input multiple-output, and millimeter wave. On the basis of the key principles of each technology, we identify the rich opportunities and the outstanding challenges that security designers must tackle. Such an identification is expected to decisively advance the understanding of future physical layer security.
HiEve: A Corpus for Extracting Event Hierarchies from News Stories

In news stories, event mentions denote real-world events of different spatial and temporal granularity. Narratives in news stories typically describe some real-world event of coarse spatial and temporal granularity along with its subevents. In this work, we present HiEve, a corpus for recognizing relations of spatiotemporal containment between events. In HiEve, the narratives are represented as hierarchies of events based on relations of spatiotemporal containment (i.e., superevent–subevent relations). We describe the process of manual annotation of HiEve. Furthermore, we build a supervised classifier for recognizing spatiotemporal containment between events to serve as a baseline for future research. Preliminary experimental results are encouraging, with classifier performance reaching 58% F1-score, only 11% less than the inter-annotator agreement.
Opportunities and Challenges Presented by Wikidata in the Context of Biocuration

Wikidata is a world readable and writable knowledge base maintained by the Wikimedia Foundation. It offers the opportunity to collaboratively construct a fully open access knowledge graph spanning biology, medicine, and all other domains of knowledge. To meet this potential, social and technical challenges must be overcome most of which are familiar to the biocuration community. These include community ontology building, high precision information extraction, provenance, and license management. By working together with Wikidata now, we can help shape it into a trustworthy, unencumbered central node in the Semantic Web of biomedical data. wikidata; semantic web; ontology; crowdsourcing; wiki; biocuration; knowledge graph;
Modeling Human Motion Using Binary Latent Variables

We propose a non-linear generative model for human motion data that uses an undirected model with binary latent variables and real-valued “visible” variables that represent joint angles. The latent and visible variables at each time step receive directed connections from the visible variables at the last few time-steps. Such an architecture makes on-line inference efficient and allows us to use a simple approximate learning procedure. After training, the model finds a single set of parameters that simultaneously capture several different kinds of motion. We demonstrate the power of our approach by synthesizing various motion sequences and by performing on-line filling in of data lost during motion capture.  http://www.cs.toronto.edu/ ∼gwtaylor/publications/nips2006mhmublv/
Autophagy and Aging

Genetic inhibition of autophagy induces degenerative changes in mammalian tissues that resemble those associated with aging, and normal and pathological aging are often associated with a reduced autophagic potential. Pharmacological or genetic manipulations that increase life span in model organisms often stimulate autophagy, and its inhibition compromises the longevity-promoting effects of caloric restriction, Sirtuin 1 activation, inhibition of insulin/insulin growth factor signaling, or the administration of rapamycin, resveratrol, or spermidine. Here, we discuss the probable cause and effect relationship between perturbed autophagy and aging, as well as possible molecular mechanisms that may mediate the anti-aging effects of autophagy.
Knowledge Management through Ontologies

Most enterprises agree that knowledge is an essential asset for success and survival on a increasingly competitive and global market. This awareness is one of the main reasons for the exponential growth of knowledge management in the past decade. Our approach to knowledge management is based on ontolo-gies, and makes knowledge assets intelligently accessible to people in organizations. Most company-vital knowledge resides in the heads of people, and thus successful knowledge management does not only consider technical aspects , but also social ones. In this paper, we describe an approach to intelligent knowledge management that explicitly takes into account the social issues involved. The proof of concept is given by a large-scale initiative involving knowledge management of a virtual organization .
The draft genome of the transgenic tropical fruit tree papaya (Carica papaya Linnaeus)

Papaya, a fruit crop cultivated in tropical and subtropical regions, is known for its nutritional benefits and medicinal applications. Here we report a 3× draft genome sequence of ‘SunUp’ papaya, the first commercial virus-resistant transgenic fruit tree to be sequenced. The papaya genome is three times the size of the Arabidopsis genome, but contains fewer genes, including significantly fewer disease-resistance gene analogues. Comparison of the five sequenced genomes suggests a minimal angiosperm gene set of 13,311. A lack of recent genome duplication, atypical of other angiosperm genomes sequenced so far, may account for the smaller papaya gene number in most functional groups. Nonetheless, striking amplifications in gene number within particular functional groups suggest roles in the evolution of tree-like habit, deposition and remobilization of starch reserves, attraction of seed dispersal agents, and adaptation to tropical daylengths. Transgenesis at three locations is closely associated with chloroplast insertions into the nuclear genome, and with topoisomerase I recognition sites. Papaya offers numerous advantages as a system for fruit-tree functional genomics, and this draft genome sequence provides the foundation for revealing the basis of Carica’s distinguishing morpho-physiological, medicinal and nutritional properties.