Miracle-dz/UniFAR-FTUs huggingface dataset:
Characteristics of nonsyndromic supernumerary teeth in children and adolescents

'Supernumerary teeth' (ST) is a term used to describe extra teeth in both dentitions than those that we are predisposed to have and considered to be one of the most significant dental anomalies affecting children and adolescents. Diagnosis and management of ST is one of the main responsibilities of dentists and also physicians. The aims of this study were to document the prevalence, characteristics, and associated clinical complications of ST among children and adolescents and to summarize the management and intervention time for ST. Materials and methods: Thirteen deciduous ST in 13 patients and 219 permanent ST in 179 patients aged up to 18 years were included in this study (in total, 192 patients with 232 ST). Results: The general prevalence of ST determined in our study was 1.148% (deciduous ST: 0.077%, permanent ST: 1.07%). Boys (66%) were affected more frequently than girls (34%). Of the permanent ST, 183 (92%) were located in the maxilla and 56.8% were conical. Most of the ST were considered fully developed (134, 67.3%), while 60 (30.2%) had partial root formation with complete crown formation. Conclusion: ST can cause a variety of complications. Early diagnosis of ST facilitates early intervention, minimal complications, and more favorable prognosis.
Severe Acute Axonal Neuropathy Induced by Ciprofloxacin: A Case Report

Fluoroquinolones increase the risk of peripheral neuropathy. The present work aims to report a case of fluoroquinolone-related severe axonal neuropathy. The subject of this study was a 62-year-old man who exhibited generalized sensory disturbances 4 days after treatment by ciprofloxacin prescribed for urinary infection. Electrodiagnostic studies revealed severe motor-sensory axonal neuropathy with widespread fibrillation potentials in support of generalized motor polyradiculopathy. There was no evidence of conduction blocks or albuminocytologic dissociation in favor of an autoimmune inflammatory reaction. The only pathological biomarker was the reduction of serum folate. According to this case, we suggest that folate level could be routinely measured and supplementation should be performed in patients with fluoroquinolone-induced neuropathy.
Learning ReLU Networks via Alternating Minimization

We propose and analyze a new family of algorithms for training neural networks with ReLU activations. Our algorithms are based on the technique of alternating minimization: estimating the activation patterns of each ReLU for all given samples, interleaved with weight updates via a least-squares step. The main focus of our paper are 1-hidden layer networks with $k$ hidden neurons and ReLU activation. We show that under standard distributional assumptions on the $d-$dimensional input data, our algorithm provably recovers the true `ground truth' parameters in a linearly convergent fashion. This holds as long as the weights are sufficiently well initialized; furthermore, our method requires only $n=\widetilde{O}(dk^2)$ samples. We also analyze the special case of 1-hidden layer networks with skipped connections, commonly used in ResNet-type architectures, and propose a novel initialization strategy for the same. For ReLU based ResNet type networks, we provide the first linear convergence guarantee with an end-to-end algorithm. We also extend this framework to deeper networks and empirically demonstrate its convergence to a global minimum.
Young But Not Old Adult African Striped Mice Reduce Their Activity in the Dry Season When Food Availability is Low

An individual′s survival and fitness depend on its ability to effectively allocate its time between competing behaviors. Sex, social tactic, season and food availability are important factors influencing activity budgets. However, few field studies have tested their influences. The African striped mouse (Rhabdomys pumilio) lives in highly seasonal habitats in southern Africa, and individuals can adopt different social tactics. We investigated seasonal changes in activity budgets of different tactics and predicted that individuals will reduce their activity in the non-breeding season to save energy when food availability is low and that young non-breeding adults (‘philopatrics’) invest mainly in activities related to gaining body mass to increase survival probability. We predicted old adults (‘breeders’), which bred during the previous breeding season, to invest mainly in maintenance of their social status. We conducted 90 focal observations during the non-breeding season and 73 during the breeding season. Activity budgets of striped mice were season and tactic specific, with philopatrics, but not breeders, reducing activity when food availability was low, possibly to decrease energy expenditure. Philopatrics of both sexes foraged and basked more in the breeding season than during the non-breeding season. Male philopatrics gained body mass and female philopatrics maintained their body mass in both seasons. Sex-specific differences occurred during the breeding season, when female breeders foraged more than male breeders, while male breeders chased other individuals more than female breeders. These findings indicate that individuals adopting different social tactics display distinct behaviors to fulfill tactic-specific energetic needs.
Principles of Functional Circuit Connectivity: Insights From Spontaneous Activity in the Zebrafish Optic Tectum

The brain is continuously active, even in the absence of external stimulation. In the optic tectum of the zebrafish larva, this spontaneous activity is spatially organized and reflects the circuit’s functional connectivity. The structure of the spontaneous activity displayed patterns associated with aspects of the larva’s preferences when engaging in complex visuo-motor behaviors, suggesting that the tectal circuit is adapted for the circuit’s functional role in detecting visual cues and generating adequate motor behaviors. Further studies in sensory deprived larvae suggest that the basic structure of the functional connectivity patterns emerges even in the absence of retinal inputs, but that its fine structure is affected by visual experience.
Investigation of extracted Sclerocarya birrea seed oil as a bioenergy resource for compression ignition engines

Sclerocarya birrea (Marula) seed oil was extracted and characterized for its physico-chemical properties and fatty acid compositions, respectively, by using standardized laboratory methods of the Association of Official and Analytical Chemist (AOAC). The fuel and lubrication properties of marula oil were also determined by using the ASTM methods, and the oil was evaluated in terms of its antiwear, viscometrics, volatility, stability, environmental compatibility properties and energy content. It was found that the high percentage of mono-unsaturated oleic acid (73.6%) provided the oiliness that makes marula oil a natural alternative to genetically modify high oleic acid sunflower oil used in biodiesel production. The aggregate properties of seed oiliness as exemplified by the high oleic acid content, high saponification value (178.6 mg/KOH) and viscosity (41 mm2/s) makes marula oil to be prospective based oil for engine crank case biolubricants with antiwear and friction reduction properties. However, the higher oil viscosity exhibited by marula seed oil in comparison to diesel could pose some durability problems to compression ignition engines, when used directly as fuel. Nonetheless, the reduction of oil viscosity would be required by heating, blending with diesel fuel, or by transesterification to forestall the risk of engine failure resulting from the use of unmodified marula oil. The flash point of marula oil (235
SEASONAL DYNAMICS OF TOTAL SOLUBLE PROTEINS IN ADULT TREES OF Quercus petraea (Matts.) Liebl. AND Fagus sylvatica L.

The complete distribution of total soluble proteins was investigated in 40-year-old oak and beech trees, felled at two dates (October 1999 and June 2000), to estimate seasonal variations in protein content at tree level. The concentration of total soluble proteins was nearly twice as high in oak compared to beech (755 mg.g-1 Dry Mass vs. 4.2 mg.g-1 Dry Mass, respectively) and 10 times lower than total non structural carbohydrates. Scaling from samples to total tree biomass, the contribution of C stored as total soluble proteins accounted for 500 gC in oak trees and only for 250 gC in beech trees. The stem was the major storage compartment in both species. Soluble proteins made up most of nitrogen at the stem and roots of oaks, while in its branches and in all beech organs, several N compounds predominated. These concentrations varied before bud break and stem growth in oak and beech. The seasonal progression of total soluble proteins in twigs of both species showed opposite patterns, especially during Spring, probably due to internal redistribution of proteins from upper stem and large branches. The dates of minimum and maximum concentrations were different for total soluble proteins and total non structural carbohydrates.
Tocotrienol-Rich Fraction Supplementation Modulates Antioxidant Enzymes Activity and Reduces DNA Damage in APPswe/PS1dE9 Alzheimer's Disease Mouse Model (Suplementasi Fraksi Kaya Tokotrienol Memodulasi Aktiviti Enzim Antioksidan dan Mengurangkan Kerosakan DNA pada APPswe/PS1dE9 Model Mencit Penyakit

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by deterioration of the brain functions that result in impairment of memory, cognition and behavioural functions. Oxidative stress is well known to be one of the causative factors for AD. Thus this disease is potentially modulated by natural antioxidants such as vitamin E. The aim of this study was to evaluate the effect of tocotrienol-rich fraction (TRF) supplementation on antioxidant enzymes and DNA damage using APPswe/PS1dE9 transgenic mouse model of AD. Animals were supplemented with TRF (200 mg/kg) or alpha-tocopherol (αT) (200 mg/kg) for six months starting from nine months old. We found that superoxide dismutase (SOD) activity in AD mouse was decreased by supplementation of TRF and αT as compared with AD control mouse with no significant differences in glutathione peroxidise (GPx) activity in all groups. TRF supplementation significantly increased catalase (CAT) activity. The level of DNA damage of AD mouse shows significant decrease with supplementation of TRF and αT. In conclusion, TRF was able to modulate antioxidant enzymes activity and decreased the level of DNA damage of AD transgenic mouse model.
CO Tully–Fisher relation of star-forming galaxies at = 0.05–0.3

The Tully–Fisher relation (TFR) is an empirical relation between galaxy luminosity and rotation velocity. We present here the first TFR of galaxies beyond the local Universe that uses carbon monoxide (CO) as the kinematic tracer. Our final sample includes 25 isolated, non-interacting star-forming galaxies with double-horned or boxy CO integrated line profiles located at redshifts z ≤ 0.3, drawn from a larger ensemble of 67 detected objects. The best reverse Ks-band, stellar mass and baryonic mass CO TFRs are, respectively, MKs = (−8.4 ± 2.9)[log ( W50/km s−1 sin i ) − 2.5] + (−23.5 ± 0.5), log (M/M) = (5.2 ± 3.0)[log ( W50/km s−1 sin i ) − 2.5] + (10.1 ± 0.5) and log (Mb/M) = (4.9 ± 2.8)[log ( W50/km s−1 sin i ) − 2.5] + (10.2 ± 0.5), where MKs is the total absolute Ks-band magnitude of the objects, M and Mb their total stellar and baryonic masses, and W50 the width of their line profile at 50 per cent of the maximum. Dividing the sample into different redshift bins and comparing to the TFRs of a sample of local (z = 0) star-forming galaxies from the literature, we find no significant evolution in the slopes and zero-points of the TFRs since z ≈ 0.3, this in either luminosity or mass. In agreement with a growing number of CO TFR studies of nearby galaxies, we more generally find that CO is a suitable and attractive alternative to neutral hydrogen (HI). Our work thus provides an important benchmark for future higher redshift CO TFR studies.
Emotional Attention: A Study of Image Sentiment and Visual Attention

Image sentiment influences visual perception. Emotion-eliciting stimuli such as happy faces and poisonous snakes are generally prioritized in human attention. However, little research has evaluated the interrelationships of image sentiment and visual saliency. In this paper, we present the first study to focus on the relation between emotional properties of an image and visual attention. We first create the EMOtional attention dataset (EMOd). It is a diverse set of emotion-eliciting images, and each image has (1) eye-tracking data collected from 16 subjects, (2) intensive image context labels including object contour, object sentiment, object semantic category, and high-level perceptual attributes such as image aesthetics and elicited emotions. We perform extensive analyses on EMOd to identify how image sentiment relates to human attention. We discover an emotion prioritization effect: for our images, emotion-eliciting content attracts human attention strongly, but such advantage diminishes dramatically after initial fixation. Aiming to model the human emotion prioritization computationally, we design a deep neural network for saliency prediction, which includes a novel subnetwork that learns the spatial and semantic context of the image scene. The proposed network outperforms the state-of-the-art on three benchmark datasets, by effectively capturing the relative importance of human attention within an image. The code, models, and dataset are available online at https://nus-sesame.top/emotionalattention/.
Deep learning and interpolation for featured-based pattern classification

Deep machine learning has received significant attention over the past decade, especially in terms of dealing with information that may span large scales. By employing a hierarchical architecture, consisting of simple computational nodes of similar characteristic, such a network helps to partition large data structures into relatively smaller, more manageable units, and to discover any dependencies that may exist between the resulting units. However, the process of running this type of network which has a layered structure, to perform tasks such as feature extraction, and subsequent feature pattern-based recognition, typically involves significant computation. To tackle this problem, two approaches are proposed in this thesis. The first novel approach developed is for image classification, by integrating deep learning and feature interpolation, supported with advanced learning classification techniques. The recently introduced Deep Spatio-Temporal Inference Network (DeSTIN) is employed to carry out limited original feature extraction. Simple interpolation is then employed to artificially increase the dimensionality of the extracted feature sets for accurate classification, without incurring heavy computational cost. The work is tested against the popular MNIST dataset of handwritten digits, demonstrating the potential of the proposed work. The second approach, which is a substantially simplified 2-layer learning network, is introduced that exploits unsupervised learning for pattern representation, capable of extracting effective features efficiently. Experimental results, in comparison with the use of popular deep learning networks, again on the application to handwritten digit classification demonstrate that the proposed approach is of significant potential in dealing with real-world problems. The generation of effective feature pattern-based classification rules from data is essential to the development of intelligent classifiers which are readily comprehensible to the user. Unfortunately, a sparse rule base may be generated when there is missing information in the experienced dataset. This hinders classification systems that work based on such sparse knowledge effectively performing their tasks in many real-world applications, where complete historical data cannot be assumed. This thesis further proposes an innovative approach by integrating fuzzy rule interpolation within a data-driven classification mechanism, such that conclusions can be approximately derived even if no matched rule can be found from a given sparse rule base when given a certain observation. The proposed technique is simple conceptually, directly exploiting the recently developed fuzzy rule interpolation techniques. However, the resulting integrated system offers a powerful means to develop robust classifiers, significantly enhancing the effectiveness of intelligent classification systems, as demonstrated by systematic comparative experimental results and also, by an application to the challenging problem of mammographic risk analysis.
Long-term field studies on rodents

Long-term studies on rodents have been conducted for longer periods (up to 70 years) and more generations (up to 88 generations) than for most other mammalian taxa. These studies have been instrumental in furthering our understanding of ecophysiology, social systems, and population and community processes. Studies on African striped mice (Rhabdomys pumilio) revealed that basal blood glucose levels span a far greater homeostatic range than previously thought. Studies on American red squirrels (Tamiasciurus hudsonicus) demonstrated how endocrine pathways underlying phenotypic plasticity allow individuals to respond to different environments. Long-term studies on African striped mice, marmots (Marmota), tuco-tucos (Ctenomys sociabilis), and degus (Octodon degus) revealed that ecological constraints on dispersal are drivers of group formation in some species but not others. Social flexibility, when the social system of an entire population can change due to individuals changing their social tactics, has been demonstrated in striped mice. Long-term studies on prairie voles (Microtus ochrogaster) found that males and females often live in pairs, leading to subsequent studies of the neural mechanisms underlying social monogamy. Long-term studies on other arvicoline rodents contributed more to our understanding of the factors influencing population dynamics than studies on any other mammalian order. While food availability and predation have been identified as factors influencing population dynamics, no single factor alone drives population dynamics in any species. We encourage researchers to incorporate manipulative experiments into long-term studies and to take integrative approaches to inform cross-disciplinary theory.
A Large-Scale Study on Source Code Reviewer Recommendation

Context: Software code reviews are an important part of the development process, leading to better software quality and reduced overall costs. However, finding appropriate code reviewers is a complex and time-consuming task. Goals: In this paper, we propose a large-scale study to compare performance of two main source code reviewer recommendation algorithms (RevFinder and a Naive Bayes-based approach) in identifying the best code reviewers for opened pull requests. Method: We mined data from Github and Gerrit repositories, building a large dataset of 51 projects, with more than 293K pull requests analyzed, 180K owners and 157K reviewers. Results: Based on the large analysis, we can state that i) no model can be generalized as best for all projects, ii) the usage of a different repository (Gerrit, GitHub) can have impact on the the recommendation results, iii) exploiting sub-projects information available in Gerrit can improve the recommendation results.
Accumulation of T-helper 22 cells, interleukin-22 and myeloid-derived suppressor cells promotes gastric cancer progression in elderly patients

Chronic inflammation and immunosuppression lead to aging and tumorigenesis. T-helper 22 (Th22) cells, interleukin 22 (IL-22) and myeloid-derived suppressor cells (MDSCs) serve an important role in inflammatory-immune diseases and cancer. However, the status of Th22 cells, IL-22 and MDSCs in aging and elderly gastric cancer progression is unknown. In the present study, 39 elderly patients with gastric cancer (EGC), 32 elderly healthy controls (HE) and 31 young healthy controls (HY) were enrolled, and the peripheral Th22, Th17 and Th1 cells, and MDSCs, were examined using flow cytometry. Plasma levels of IL-22, IL-6 and tumor necrosis factor-α (TNF-α) were examined using ELISA. IL-22 protein levels in tumor tissues were examined using immunohistochemistry. There were increased numbers of peripheral Th22 and Th17 cells, and MDSCs, as well as increased plasma levels of IL-22, IL-6 and TNF-α in EGC compared with HE and HY. However, HE exhibited significantly increased levels of peripheral Th22 and Th17 cells as well as IL-6 and TNF-α compared with HY. Immunohistochemical analysis demonstrated that IL-22 protein accumulated in tumor cells and lymphocytes in the tumor microenvironment. The results obtained demonstrated that peripheral Th22 and Th17 cells as well as IL-6 and TNF-α plasma levels increased with aging. Furthermore, Th22 and Th17 cells, MDSCs, and IL-22 may be used as prognostic markers for identifying gastric cancer in elderly patients.
The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News

A large body of research work and efforts have been focused on detecting fake news and building online fact-check systems in order to debunk fake news as soon as possible. Despite the existence of these systems, fake news is still wildly shared by online users. It indicates that these systems may not be fully utilized. After detecting fake news, what is the next step to stop people from sharing it? How can we improve the utilization of these fact-check systems? To fill this gap, in this paper, we (i) collect and analyze online users called guardians, who correct misinformation and fake news in online discussions by referring fact-checking URLs; and (ii) propose a novel fact-checking URL recommendation model to encourage the guardians to engage more in fact-checking activities. We found that the guardians usually took less than one day to reply to claims in online conversations and took another day to spread verified information to hundreds of millions of followers. Our proposed recommendation model outperformed four state-of-the-art models by 11%~33%. Our source code and dataset are available at http://web.cs. wpi.edu/~kmlee/data/gau.html.
Euler-Equation-Based Drag Minimization of Unconventional Aircraft Configurations

This study investigates the potential of unconventional aircraft transports through numerical optimization. Three distinct configurations are investigated: a box wing, a C-tip blended wing–body, and a braced wing. Each transport is sized for the same regional mission and is subjected to the same optimization strategy based on the Euler equations. The figure of merit is inviscid pressure drag at transonic speed; the nonlinear constraints are lift, pitching moment, and internal volume. The design variables include the section shape and twist distribution of the main lifting surfaces. It is found that the box-wing, C-tip blended-wing–body, and braced-wing configurations investigated here are, respectively, 34.1, 36.2, and 40.3% more efficient than a similarly optimized conventional tube-and-wing configuration. Each optimization revealed, in one way or another, the importance of accounting for flow nonlinearity during the early stages of unconventional aircraft design. For the blended wing–body, the C tip doe.