nils-herrmann/clean_scirepeval huggingface dataset:
Cardiac Hypertrophy and Fibrosis in the Metabolic Syndrome: A Role for Aldosterone and the Mineralocorticoid Receptor

Obesity and hypertension, major risk factors for the metabolic syndrome, render individuals susceptible to an increased risk of cardiovascular complications, such as adverse cardiac remodeling and heart failure. There has been much investigation into the role that an increase in the renin-angiotensin-aldosterone system (RAAS) plays in the pathogenesis of metabolic syndrome and in particular, how aldosterone mediates left ventricular hypertrophy and increased cardiac fibrosis via its interaction with the mineralocorticoid receptor (MR). Here, we review the pertinent findings that link obesity with elevated aldosterone and the development of cardiac hypertrophy and fibrosis associated with the metabolic syndrome. These studies illustrate a complex cross-talk between adipose tissue, the heart, and the adrenal cortex. Furthermore, we discuss findings from our laboratory that suggest that cardiac hypertrophy and fibrosis in the metabolic syndrome may involve cross-talk between aldosterone and adipokines (such as adiponectin).
Influence of the implant-abutment connection design and diameter on the screw joint stability

This study was conducted to evaluate the influence of the implant-abutment connection design and diameter on the screw joint stability.



Regular and wide-diameter implant systems with three different joint connection designs: an external butt joint, a one-stage internal cone, and a two-stage internal cone were divided into seven groups (n=5, in each group). The initial removal torque values of the abutment screw were measured with a digital torque gauge. The postload removal torque values were measured after 100,000 cycles of a 150 N and a 10 Hz cyclic load had been applied. Subsequently, the rates of the initial and postload removal torque losses were calculated to evaluate the effect of the joint connection design and diameter on the screw joint stability. Each group was compared using Kruskal-Wallis test and Mann-Whitney U test as post-hoc test (α=0.05).



THE POSTLOAD REMOVAL TORQUE VALUE WAS HIGH IN THE FOLLOWING ORDER WITH REGARD TO MAGNITUDE: two-stage internal cone, one-stage internal cone, and external butt joint systems. In the regular-diameter group, the external butt joint and one-stage internal cone systems showed lower postload removal torque loss rates than the two-stage internal cone system. In the wide-diameter group, the external butt joint system showed a lower loss rate than the one-stage internal cone and two-stage internal cone systems. In the two-stage internal cone system, the wide-diameter group showed a significantly lower loss rate than the regular-diameter group (P<.05).



The results of this study showed that the external butt joint was more advantageous than the internal cone in terms of the postload removal torque loss. For the difference in the implant diameter, a wide diameter was more advantageous in terms of the torque loss rate.
A Framework of Mining Semantic Regions from Trajectories

With the pervasive use of mobile devices with location sensing and positioning functions, such as Wi-Fi and GPS, people now are able to acquire present locations and collect their movement. As the availability of trajectory data prospers, mining activities hidden in raw trajectories becomes a hot research problem. Given a set of trajectories, prior works either explore density-based approaches to extract regions with high density of GPS data points or utilize time thresholds to identify users’ stay points. However, users may have different activities along with trajectories. Prior works only can extract one kind of activity by specifying thresholds, such as spatial density or temporal time threshold. In this paper, we explore both spatial and temporal relationships among data points of trajectories to extract semantic regions that refer to regions in where users are likely to have some kinds of activities. In order to extract semantic regions, we propose a sequential clustering approach to discover clusters as the semantic regions from individual trajectory according to the spatial-temporal density. Based on semantic region discovery, we develop a shared nearest neighbor (SNN) based clustering algorithm to discover the frequent semantic region where the moving object often stay, which consists of a group of similar semantic regions from multiple trajectories. Experimental results demonstrate that our techniques are more accurate than existing clustering schemes.
Kinect vs. Low-cost Inertial Sensing for Gesture Recognition

In this paper, we investigate efficient recognition of human gestures / movements from multimedia and multimodal data, including the Microsoft Kinect and translational and rotational acceleration and velocity from wearable inertial sensors. We firstly present a system that automatically classifies a large range of activities (17 different gestures) using a random forest decision tree. Our system can achieve near real time recognition by appropriately selecting the sensors that led to the greatest contributing factor for a particular task. Features extracted from multimodal sensor data were used to train and evaluate a customized classifier. This novel technique is capable of successfully classifying various gestures with up to 91 % overall accuracy on a publicly available data set. Secondly we investigate a wide range of different motion capture modalities and compare their results in terms of gesture recognition accuracy using our proposed approach. We conclude that gesture recognition can be effectively performed by considering an approach that overcomes many of the limitations associated with the Kinect and potentially paves the way for low-cost gesture recognition in unconstrained environments.
The generalized stochastic preference choice model

We propose a new discrete choice model that generalizes the random utility model (RUM). We show that this model, called the Generalized Stochastic Preference (GSP) model can explain several choice phenomena that can’t be represented by a RUM. In particular, the model can easily (and also exactly) replicate some well known examples that are not RUM, as well as controlled choice experiments carried out since 1980’s that possess strong regularity violations. One of such regularity violation is the decoy effect in which the probability of choosing a product increases when a similar, but inferior product is added to the choice set. An appealing feature of the GSP is that it is non-parametric and therefore it has very high flexibility. The model has also a simple description and interpretation: it builds upon the well known representation of RUM as a stochastic preference, by allowing some additional consumer types to be non-rational.
The prevalence of carriage of meticillin-resistant staphylococci by veterinary dermatology practice staff and their respective pets.

It has been shown that people and pets can harbour identical strains of meticillin-resistant (MR) staphylococci when they share an environment. Veterinary dermatology practitioners are a professional group with a high incidence of exposure to animals infected by Staphylococcus spp. The objective of this study was to assess the prevalence of carriage of MR Staphylococcus aureus (MRSA), MR S. pseudintermedius (MRSP) and MR S. schleiferi (MRSS) by veterinary dermatology practice staff and their personal pets. A swab technique and selective media were used to screen 171 veterinary dermatology practice staff and their respective pets (258 dogs and 160 cats). Samples were shipped by over-night carrier. Human subjects completed a 22-question survey of demographic and epidemiologic data relevant to staphylococcal transmission. The 171 human-source samples yielded six MRSA (3.5%), nine MRSP (5.3%) and four MRSS (2.3%) isolates, while 418 animal-source samples yielded eight MRSA (1.9%) 21 MRSP (5%), and two MRSS (0.5%) isolates. Concordant strains (genetically identical by pulsed-field gel electrophoresis) were isolated from human subjects and their respective pets in four of 171 (2.9%) households: MRSA from one person/two pets and MRSP from three people/three pets. In seven additional households (4.1%), concordant strains were isolated from only the pets: MRSA in two households and MRSP in five households. There were no demographic or epidemiologic factors statistically associated with either human or animal carriage of MR staphylococci, or with concordant carriage by person-pet or pet-pet pairs. Lack of statistical associations may reflect an underpowered study.
Amyloid cascade hypothesis: Pathogenesis and therapeutic strategies in Alzheimer's disease

Alzheimer's disease is an irreversible, progressive neurodegenerative disorder. Various therapeutic approaches are being used to improve the cholinergic neurotransmission, but their role in AD pathogenesis is still unknown. Although, an increase in tau protein concentration in CSF has been described in AD, but several issues remains unclear. Extensive and accurate analysis of CSF could be helpful to define presence of tau proteins in physiological conditions, or released during the progression of neurodegenerative disease. The amyloid cascade hypothesis postulates that the neurodegeneration in AD caused by abnormal accumulation of amyloid beta (Aβ) plaques in various areas of the brain. The amyloid hypothesis has continued to gain support over the last two decades, particularly from genetic studies. Therefore, current research progress in several areas of therapies shall provide an effective treatment to cure this devastating disease. This review critically evaluates general biochemical and physiological functions of Aβ directed therapeutics and their relevance.
Printed quadrifilar helix antenna with integrated feed network

A new printed quadrifilar helix antenna with integrated feed network was designed for satellite mobile communication systems. The input impedance of this antenna in each port is about 50Ω without a matching circuit. It makes the feed network simplified and miniaturized. Parameter tradeoffs have a direct bearing on antenna performance in antenna design. A trial and error approach to this problem is both costly and unreliable. The purpose of this work is to use computer electromagnetic simulation to predict antenna properties for optimum performance. The proposed antenna displays small size, light weight, low cost, large bandwidth, hemispherical radiation pattern and excellent circular polarization.
Classification of healthcare data using genetic fuzzy logic system and wavelets

Healthcare plays an important role in promoting the general health and well-being of people around the world. The difficulty in healthcare data classification arises from the uncertainty and the high-dimensional nature of the medical data collected. This paper proposes an integration of fuzzy standard additive model (SAM) with genetic algorithm (GA), called GSAM, to deal with uncertainty and computational challenges. GSAM learning process comprises three continual steps: rule initialization by unsupervised learning using the adaptive vector quantization clustering, evolutionary rule optimization by GA and parameter tuning by the gradient descent supervised learning. Wavelet transformation is employed to extract discriminative features for high-dimensional datasets. GSAM becomes highly capable when deployed with small number of wavelet features as its computational burden is remarkably reduced. The proposed method is evaluated using two frequently-used medical datasets: the Wisconsin breast cancer and Cleveland heart disease from the UCI Repository for machine learning. Experiments are organized with a five-fold cross validation and performance of classification techniques are measured by a number of important metrics: accuracy, F-measure, mutual information and area under the receiver operating characteristic curve. Results demonstrate the superiority of the GSAM compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. The proposed approach is thus helpful as a decision support system for medical practitioners in the healthcare practice.
Expect the unexpected: Harnessing Sentence Completion for Sarcasm Detection

The trigram ‘I love being’ is expected to be followed by positive words such as ‘happy’. In a sarcastic sentence, however, the word ‘ignored’ may be observed. The expected and the observed words are, thus, incongruous. We model sarcasm detection as the task of detecting incongruity between an observed and an expected word. In order to obtain the expected word, we use Context2Vec, a sentence completion library based on Bidirectional LSTM. However, since the exact word where such an incongruity occurs may not be known in advance, we present two approaches: an Allwords approach (which consults sentence completion for every content word) and an Incongruous words-only approach (which consults sentence completion for the 50% most incongruous content words). The approaches outperform reported values for tweets but not for discussion forum posts. This is likely to be because of redundant consultation of sentence completion for discussion forum posts. Therefore, we consider an oracle case where the exact incongruous word is manually labeled in a corpus reported in past work. In this case, the performance is higher than the all-words approach. This sets up the promise for using sentence completion for sarcasm detection. This paper has been selected for publication at 2017 CONFERENCE OF THE PACIFIC ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (http://pacling.ucsy.edu.mm/ pacling/)
Auto-calibration of cylindrical multi-projector systems

In this paper we present a novel technique to calibrate multiple casually aligned projectors on a fiducial-free cylindrical curved surface using a single camera. We impose two priors to the cylindrical display: (a) cylinder is a vertically extruded surface; and (b) the aspect ratio of the rectangle formed by the four corners of the screen is known. Using these priors, we can estimate the display's 3D surface geometry and camera extrinsic parameters using a single image without any explicit display to camera correspondences. Using the estimated camera and display properties, we design a novel deterministic algorithm to recover the intrinsic and extrinsic parameters of each projector using a single projected pattern seen by the camera which is then used to register the images on the display from any arbitrary viewpoint making it appropriate for virtual reality systems. Finally, our method can be extended easily to handle sharp corners — making it suitable for the common CAVE like VR setup. To the best of our knowledge, this is the first method that can achieve accurate geometric auto-calibration of multiple projectors on a cylindrical display without performing an extensive stereo reconstruction.
1 A Survey of Mixed Data Clustering Algorithms

Most of the datasets normally contain either numeric or categorical features. Mixed data comprises of both numeric and categorical features, and they frequently occur in various domains, such as health, nance, marketing, etc. Clustering is oen sought on mixed data to nd structures and to group similar objects. However, clustering mixed data is challenging because it is dicult to directly apply mathematical operations, such as summation, average etc. on the feature values of these datasets. In this paper, we review various types of mixed data clustering techniques in detail. We present a taxonomy to identify ten types of dierent mixed data clustering techniques. We also compare the performance of several mixed data clustering methods on publicly available datasets. e paper further identies challenges in developing dierent mixed data clustering algorithms and provides guidelines for future directions in this area.
Primary Cutaneous Peripheral T-Cell Lymphoma Not Otherwise Specified: A Rapidly Progressive Variant of Cutaneous T-Cell Lymphoma

Primary Cutaneous Peripheral T-Cell Lymphoma NOS (PTL-NOS) is a rare, progressive, fatal dermatologic disease that presents with features similar to many common benign plaque-like skin conditions, making recognition of its distinguishing features critical for early diagnosis and treatment (Bolognia et al., 2008). A 78-year-old woman presented to ambulatory care with a single 5 cm nodule on her shoulder that had developed rapidly over 1-2 weeks. Examination was suspicious for malignancy and a biopsy was performed. Biopsy results demonstrated CD4 positivity, consistent with Mycosis Fungoides with coexpression of CD5, CD47, and CD7. Within three months her cancer had progressed into diffuse lesions spanning her entire body. As rapid progression is usually uncharacteristic of Mycosis Fungoides, her diagnosis was amended to PTL-NOS. Cutaneous T-Cell Lymphoma (CTCL) should be suspected in patients with patches, plaques, erythroderma, or papules that persist or multiply despite conservative treatment. Singular biopsies are often nondiagnostic, requiring a high degree of suspicion if there is deviation from the anticipated clinical course. Multiple biopsies are often necessary to make the diagnosis. Physicians caring for patients with rapidly progressive, nonspecific dermatoses with features described above should keep more uncommon forms of CTCL in mind and refer for early biopsy.
Top-down approach to vestibular compensation: Translational lessons from vestibular rehabilitation

This review examines vestibular compensation and vestibular rehabilitation from a unified translational research perspective. Laboratory studies illustrate neurobiological principles of vestibular compensation at the molecular, cellular and systems levels in animal models that inform vestibular rehabilitation practice. However, basic research has been hampered by an emphasis on 'naturalistic' recovery, with time after insult and drug interventions as primary dependent variables. The vestibular rehabilitation literature, on the other hand, provides information on how the degree of compensation can be shaped by specific activity regimens. The milestones of the early spontaneous static compensation mark the re-establishment of static gaze stability, which provides a common coordinate frame for the brain to interpret residual vestibular information in the context of visual, somatosensory and visceral signals that convey gravitoinertial information. Stabilization of the head orientation and the eye orientation (suppression of spontaneous nystagmus) appear to be necessary by not sufficient conditions for successful rehabilitation, and define a baseline for initiating retraining. The lessons from vestibular rehabilitation in animal models offer the possibility of shaping the recovery trajectory to identify molecular and genetic factors that can improve vestibular compensation.
A 500 fW/bit 14 fJ/bit-access 4kb standard-cell based sub-VT memory in 65nm CMOS

Ultra-low power (ULP) biomedical implants and sensor nodes typically require small memories of a few kb, while previous work on reliable subthreshold (sub-VT) memories targets several hundreds of kb. Standard-cell based memories (SCMs) are a straightforward approach to realize robust sub-VT storage arrays and fill the gap of missing sub-VT memory compilers. This paper presents an ultra-low-leakage 4kb SCM manufactured in 65nm CMOS technology. To minimize leakage power during standby, a single custom-designed standard-cell (D-latch with 3-state output buffer) addressing all major leakage contributors of SCMs is seamlessly integrated into the fully automated SCM compilation flow. Silicon measurements of a 4kb SCM indicate a leakage power of 500fW per stored bit (at a data-retention voltage of 220 mV) and a total energy of 14 fJ per accessed bit (at energy-minimum voltage of 500mV), corresponding to the lowest values in 65nm CMOS reported to date.
Spherical Convolutional Neural Network for 3D Point Clouds

We propose a neural network for 3D point cloud processing that exploits spherical convolution kernels and octree partitioning of space. The proposed metric-based spherical kernels systematically quantize point neighborhoods to identify local geometric structures in data, while maintaining the properties of translation-invariance and asymmetry. The network architecture itself is guided by octree data structuring that takes full advantage of the sparse nature of irregular point clouds. We specify spherical kernels with the help of neurons in each layer that in turn are associated with spatial locations. We exploit this association to avert dynamic kernel generation during network training, that enables efficient learning with high resolution point clouds. We demonstrate the utility of the spherical convolutional neural network for 3D object classification on standard benchmark datasets.
Anoxic treatment of phenolic wastewater in sequencing batch reactor.

Studies were conducted on the anoxic phenol removal using granular denitrifying sludge in sequencing batch reactor at different cycle lengths and influent phenol concentrations. Results showed that removal exceeded 80% up to an influent phenol concentration of 1050 mg/l at 6 h cycle length, which corresponded to 6.4 kg COD/m3/d. Beyond this, there was a steep decrease in phenol and COD removal efficiencies. This was accompanied by an increase in nitrite concentration in the effluent. On an average, 1 g nitrate-N was consumed per 3.4 g phenol COD removal. Fraction of COD available for sludge growth was calculated to be 11%.
Sleep and delinquency: does the amount of sleep matter?

Sleep, a key indicator of health, has been linked to a variety of indicators of well-being such that people who get an adequate amount generally experience greater well-being. Further, a lack of sleep has been linked to a wide range of negative developmental outcomes, yet sleep has been largely overlooked among researchers interested in adolescent delinquency. The purpose of this study was to explore the relationship between hours of sleep and delinquent behavior among adolescents by using data from Wave 1 of the National Longitudinal Study of Adolescent Health (n = 14,382; 50.2% female, 63.5% white). A series of negative binomial regressions showed that youth who typically sleep seven or fewer hours per night reported significantly more property delinquency than youth who sleep the recommended 8-10 h. Further, youth who reported sleeping 5 or fewer hours per night reported significantly more violent delinquency than youth who reported sleeping the recommended number of hours per night. The findings suggest that sleep is an important, and overlooked, dimension of delinquent behavior and studies that focus on adolescent health should further investigate the effects of insufficient sleep. Finally, the authors recommend that sleep and other relevant health behaviors be considered in the context of more comprehensive approaches to delinquency prevention and intervention.
Novel Color LBP Descriptors for Scene and Image Texture Classification

Four novel color Local Binary Pattern (LBP) descriptors are presented in this paper for scene image and image texture classification with applications to image search and retrieval. The oRGB-LBP descriptor is derived by concatenating the LBP features of the component images in the oRGB color space. The Color LBP Fusion (CLF) descriptor is constructed by integrating the LBP descriptors from different color spaces; the Color Grayscale LBP Fusion (CGLF) descriptor is derived by integrating the grayscale-LBP descriptor and the CLF descriptor; and the CGLF+PHOG descriptor is obtained by integrating the Pyramid of Histogram of Orientation Gradients (PHOG) and the CGLF descriptor. Feature extraction applies the Enhanced Fisher Model (EFM) and image classification is based on the nearest neighbor classification rule (EFM-NN). The proposed image descriptors and the feature extraction and classification methods are evaluated using three grand challenge databases and are shown to improve upon the classification performance of existing methods.