Miracle-dz/UniFAR-FTUs huggingface dataset:
Age and growth of three endemic threatened guitarfishes Pseudobatos horkelii, P. percellens and Zapteryx brevirostris in the western South Atlantic Ocean.

The age and growth of three endemic threatened guitarfish species were analysed using vertebrae of Pseudobatos horkelii, P. percellens and Zapteryx brevirostris. Edge and marginal-increment analyses were used to evaluate the periodicity of the formation of the band-pairs, suggesting deposition of one band-pair per year, from late winter to late spring. The von Bertalanffy growth model was used to describe the growth of these species with the following parameters, for pooled sexes: P. horkelii L∞ = 126.93, k = 0.19 and t0 = -1.51; P. percellens L∞ = 109.31, k = 0.16 and t0 = -1.78; Z. brevirostris L∞ = 60.37, k = 0.24 and t0 = -1.42. Our results are essential to understanding the resilience and vulnerability of these species to harvest, which can contribute to management and conservation actions of these species. This article is protected by copyright. All rights reserved.
Rbfox1 up-regulation impairs BDNF-dependent hippocampal LTP by dysregulating TrkB isoform expression levels

Brain-derived neurotrophic factor (BDNF) is a potent modulator of brain synaptic plasticity. Signaling defects caused by dysregulation of its Ntrk2 (TrkB) kinase (TrkB.FL) and truncated receptors (TrkB.T1) have been linked to the pathophysiology of several neurological and neurodegenerative disorders. We found that upregulation of Rbfox1, an RNA binding protein associated with intellectual disability, epilepsy and autism, increases selectively hippocampal TrkB.T1 isoform expression. Physiologically, increased Rbfox1 impairs BDNF-dependent LTP which can be rescued by genetically restoring TrkB.T1 levels. RNA-seq analysis of hippocampi with upregulation of Rbfox1 in conjunction with the specific increase of TrkB.T1 isoform expression also shows that the genes affected by Rbfox1 gain of function are surprisingly different from those influenced by Rbfox1 deletion. These findings not only identify TrkB as a major target of Rbfox1 pathophysiology but also suggest that gain or loss of function of Rbfox1 regulate different genetic landscapes.
Local and Widespread Pressure Pain Hyperalgesia Is Not Side Specific in Females with Unilateral Neck Pain that Can Be Reproduced during Passive Neck Rotation

Current evidence for widespread hyperalgesia in non-specific neck pain (NSNP) is unclear. It is currently recommended to group NSNP patients according to pain-provoking movements. The aim of this study was to investigate local and widespread pain sensitivity in females with unilateral NSNP that is reproducible during passive neck rotation compared with matched controls, and to compare the side specific effect of pain location on pressure pain sensitivity among females with unilateral NSNP. Thirty-six females with unilateral NSNP evoked during passive ipsilateral (n = 20) or contralateral (n = 16) rotation toward the painful side were compared with 20 controls. Participants reported their pain intensity at rest and during passive neck rotation and completed the Neck Disability Index. Pressure pain thresholds (PPTs) were assessed bilaterally over the anterior scalene; the sternocleidomastoid; the levator scapulae; lateral to the spinous process of C6; the median, ulnar, and radial nerves; and the tibialis anterior. The ANOVA revealed lower PPTs in females with unilateral NSNP compared with the controls (all at p < 0.001), but no differences were found between the sides, nor was there any Group × side interaction. Among females with NSNP, those with higher pain intensity during ipsilateral rotation toward the painful side showed lower PPTs over the anterior scalene, median nerve, ulnar nerve, and tibialis anterior (all, p < 0.05) than females with higher pain intensity during contralateral rotation toward the painful side. These findings demonstrated bilateral local and widespread pressure pain hyperalgesia in females with unilateral NSNP that was reproducible during passive neck rotation compared with controls. There was no side specific effect of pain location on PPTs among females with unilateral NSNP.
Rapid predictive simulations with complex musculoskeletal models suggest that diverse healthy and pathological human gaits can emerge from similar control strategies

Physics-based predictive simulations of human movement have the potential to support personalized medicine, but large computational costs and difficulties to model control strategies have limited their use. We have developed a computationally efficient optimal control framework to predict human gaits based on optimization of a performance criterion without relying on experimental data. The framework generates three-dimensional muscle-driven simulations in 36 min on average—more than 20 times faster than existing simulations—by using direct collocation, implicit differential equations and algorithmic differentiation. Using this framework, we identified a multi-objective performance criterion combining energy and effort considerations that produces physiologically realistic walking gaits. The same criterion also predicted the walk-to-run transition and clinical gait deficiencies caused by muscle weakness and prosthesis use, suggesting that diverse healthy and pathological gaits can emerge from the same control strategy. The ability to predict the mechanics and energetics of a broad range of gaits with complex three-dimensional musculoskeletal models will allow testing novel hypotheses about gait control and hasten the development of optimal treatments for neuro-musculoskeletal disorders.
Current Research of the Renin-Angiotensin System Effect on Stem Cell Therapy

The renin-angiotensin system (RAS) is a chief regulator of the cardiovascular system and body fluid homeostasis. Stem/progenitor cell therapy has pointed towards a novel tool for medical and therapeutic intervention. In addition to the physiological and pathologi- cal role of the RAS and its pharmacological inhibitors, the proliferation, differentiation in stem cells is mediated through various cell-signalling pathways. This book chapter reviews the new role of RAS components, distinct from other common roles by consider-ing its regulating impact on the several signalling pathways involved in different body tissues, as well as in stem cell therapy.
Assessing Sign Restrictions

This paper assesses sign restrictions via a controlled experiment. A researcher estimates a VAR on an infinite amount of data generated by a DSGE model. He or she then imposes sign restrictions on impulse responses to identify a structural shock while being agnostic about the response of a key variable of interest to this shock. Can such an agnostic identification procedure pin down the correct sign of this unconstrained response? The DSGE models of Erceg, Henderson, and Levin (2000), as well as of Smets and Wouters (2003), are used as data generating processes. Two conditions must be met for the method to unambiguously deliver the correct sign of the unconstrained impulse response. First, a sufficiently large number of restrictions must be imposed – more than what is typically imposed in applied work. Second, the variance of the shock under study must be sufficiently large – larger than the values provided by Bayesian estimations of the DSGE models. Hence, sign restrictions can be a useful tool to recover structural shocks from VAR residuals.
The Shape of Demand: What Does It Tell Us about Direct-to-Consumer Marketing of Antidepressants?

Much of the debate surrounding Direct-to-Consumer Advertising (DTCA) of pharmaceuticals centers on whether DTCA conveys useful information to consumers or indiscriminately increases requests for the advertised medication. By identifying how DTCA changes the shape of the demand curve for antidepressants, we seek to infer the promotional objectives of manufacturers. Using data from the 1996-2003 Medical Expenditure Panel Survey (MEPS), we find that advertising shifts the demand curve for antidepressants outward and rotates it counter-clockwise. DTCA increases the probability that an individual will initiate use of antidepressants, particularly when out-of-pocket medication costs are low, but does not necessarily increase utilization levels among those already taking antidepressants. This is consistent with a promotional campaign that seeks to alert consumers to the product’s existence, but conveys no real information that would allow them to learn their true match with the product.
Can Stricter Environmental Regulations Increase Export of the Polluting Good?

According to the Pollution Haven Hypothesis (PHH), weak environmental policies improve a country’s comparative advantage in the polluting sector, thus promoting its expansion. In this paper, we develop a neo-classical general equilibrium model with two goods and two factors and show that the relationship between environmental policies and comparative advantage can be ambiguous. We focus entirely on emission caps or command and control (CAC) programs of regulation and treat abatement as equivalent to a technological retardation. We show that when the technological retardation is Hick neutral, the PHH holds and the Heckscher Ohlin Samuelson (HOS) theorem determines trade patterns between a capital-abundant country and a labor-abundant country that follow different environmental policies. If pollution abatement is capital biased in the polluting sector, the standard trade theorems and the PHH may not hold. The paper derives a sufficient condition under which the PHH would hold. However, if this condition is violated, the PHH as well as the HOS theorem may not hold.
Motion periodicity and bifurcation of a wave excited hopping ball

We consider the problem of a hopping ball excited by a travelling harmonic wave on an elastic surface. The ball, considered as a particle, is assumed to interact with the surface through inelastic collisions. The surface motion due to the wave induces a horizontal drift in the ball. The problem is treated analytically under certain approximations. The phase space of the hopping motion is captured by constructing a phase-velocity return map. The fixed points of the return map and its compositions represent periodic hopping solutions. The linear stability of the obtained periodic solution is studied in detail. The minimum frequency for the onset of periodic hops, and the subsequent loss of stability at the bifurcation frequency, have been determined analytically. Interestingly, for small values of wave amplitude, the analytical solutions reveal striking similarities with the results of the classical bouncing ball problem.
Statistical Analysis of Reliability Field Data When Working Conditions are Imprecisely Reported

Statistical Analysis of Reliability Field Data When Working Conditions are Imprecisely Reported In contrast to laboratory lifetime tests reliability field tests are usually performed in conditions which vary in time in a random way. We consider the case when users are asked about their description of their vague perceptions of the usage conditions. In the paper we use interval-valued variables for the description of imprecisely known test conditions that may be used as covariates in classical reliability models. We present a simple approximate algorithm for a proportional hazard lifetime model. Statystyczna Analiza Danych Niezawodnościowych W Przypadku Nieprecyzyjnie Określonych Warunków Eksploatacji W przeciwieństwie do badań niezawodnościowych prowadzonych w warunkach laboratoryjnych badania prowadzone w warunkach normalnej eksploatacji prowadzone są w warunkach, które w sposób losowy zmieniają się w czasie. W pracy rozpatrywany jest przypadek, gdy warunki eksploatacji opisane w sposób nieprecyzyjny w postaci przedziałowej. Użyte do tego celu zmienne przedziałowe zostały zastosowane jako zmienne stowarzyszone w klasycznych modelach niezawodnościowych typu proporcjonalnego hazardu.
Modeling a User-Oriented Ontology on Accessible Homes for Supporting Activities of Daily Living (ADL) in Healthy Aging

Inaccessibility of the buildings is the most common obstacle which presents barriers for older adults with different motor abilities. An inclusive design process, where elderly and designers work together, is required to overcome this obstacle. To do so, this study proposes a user-oriented model (i) to define a knowledge presentation for designers; (ii) to assist them during the development of accessible homes and (iii) to accommodate exemplary home attributes for activities of daily living (ADL). The ontology for this model was first constructed by collecting user information through LEGO® Serious Play® on the four subdomains of motor abilities: (1) strength; (2) balance; (3) locomotion; and (4) endurance. The findings of this study are significant for future aging studies and mobile computing researches in terms of indicating that diverse motor ability difficulties are associated with different requirements of accessibility attributes, and structured knowledge is required to diagrammatize their association with ADL.
Applying Deep Learning in Word Embedding for Making a Diagnosis Prediction Model from Orthopedic Clinical Note

We propose deep learning in word embedding for making a diagnostic prediction model. One factor that causes uncertainties in diagnostic is the inexperience of physicians. The diagnosis errors lead to incorrect and delay in treatment, waste of time and money. To solve the problem, a differential diagnosis is a critical tool. It is powerful and does not introduce additional work to physician. Our method applied a deep learning tool together with word embedding from existing diagnosis texts in medical system. The model takes the clinical notes from a physician. The note is then used to analyze the possibilities of diseases. The output is sorted by model confidence. We validate our model with True Positive Rate (Recall), False Positive Rate (Precision) and accuracy. Our model achieves a new record of accuracy at 99.95% The highest recall rate is at 86.64% in top first prediction.
Estimation of Data Propagation Time on the Bitcoin Network

The goal of this research is to estimate the data propagation time on the Bitcoin network. Using network coordinates, we estimate the communication latency between computers. Such latency estimation contributes future optimization of data propagation. In this research, we report an experiment on computing the network coordinates. In the current Bitcoin network, it is very difficult to acquire internode delay because the network topology is not available. In this study, we calculate the delay based on our topology estimation and describe the effectiveness of the network coordinates using various topology estimation parameters.
Building the Language Resource for a Cebuano-Filipino Neural Machine Translation System

Parallel corpus is a critical resource in machine learning based translation. The task of collecting, extracting, and aligning texts in order to build an acceptable corpus for doing translation is very tedious most especially for low-resource languages. In this paper, we present the efforts made to build a parallel corpus for Cebuano and Filipino from two different domains: biblical texts and the web. For the biblical resource, subword unit translation for verbs and copy-able approach for nouns were applied to correct inconsistencies in translation. This correction mechanism was applied as a preprocessing technique. On the other hand, for Wikipedia being the main web resource, commonly occurring topic segments were extracted from both the source and the target languages. These observed topic segments are unique in 4 different categories. The identification of these topic segments may be used for automatic extraction of sentences. A Recurrent Neural Network was used to implement the translation using OpenNMT sequence modeling tool in TensorFlow. The two different corpora were then evaluated by using them as two separate inputs in the neural network. Results have shown a difference in BLEU score in both corpora.
Android Malware Prediction Using Extreme Learning Machine with Different Kernel Functions

Android is currently the most popular smartphone platform which occupied 88% of global sale by the end of 2nd quarter 2018. With the popularity of these applications, it is also inviting cybercriminals to develop malware application for accessing important information from smartphones. The major objective of cybercriminals to develop Malware apps or Malicious apps to threaten the organization privacy data, user privacy data, and device integrity. Early identification of such malware apps can help the android user to save private data and device integrity. In this study, features extracted from intermediate code representations obtained using decompilation of APK file are used for providing requisite input data to develop the models for predicting android malware applications. These models are trained using extreme learning with multiple kernel functions ans also compared with the model trained using most frequently used classifiers like linear regression, decision tree, polynomial regression, and logistic regression. This paper also focuses on the effectiveness of data sampling techniques for balancing data and feature selection methods for selecting right sets of significant uncorrelated metrics. The high-value of accuracy and AUC confirm the predicting capability of data sampling, sets of metrics, and training algorithms to malware and normal applications.
Identifying Plant Pentatricopeptide Repeat Proteins Using a Variable Selection Method

Motivation: Pentatricopeptide repeat (PPR), which is a triangular pentapeptide repeat domain, plays an important role in plant growth. Features extracted from sequences are applicable to PPR protein identification using certain classification methods. However, which components of a multidimensional feature (namely variables) are more effective for protein discrimination has never been discussed. Therefore, we seek to select variables from a multidimensional feature for identifying PPR proteins. Method: A framework of variable selection for identifying PPR proteins is proposed. Samples representing PPR positive proteins and negative ones are equally split into a training and a testing set. Variable importance is regarded as scores derived from an iteration of resampling, training, and scoring step on the training set. A model selection method based on Gaussian mixture model is applied to automatic choice of variables which are effective to identify PPR proteins. Measurements are used on the testing set to show the effectiveness of the selected variables. Results: Certain variables other than the multidimensional feature they belong to do work for discrimination between PPR positive proteins and those negative ones. In addition, the content of methionine may play an important role in predicting PPR proteins.
Adapting a Classic Platform Video Game to the Carbohydrate Counting Method for Insulin-Dependent Diabetics

Digital games designed for children and adolescents in the Diabetics Education (DE) context aim at positively influencing diabetics' behavior or teaching DE knowledge and skills. Previous work has not considered playful learning of difficult content, such as the Carbohydrate Counting Method, which involves counting the number of carbohydrate grams in a meal and matching that to the appropriate dose of insulin in order to manage the blood glucose levels. Sugar Mario was designed to have a familiar and simple gameplay based on classical platform video games, such as Super Mario, Donkey Kong, and other similar games. The carbohydrate counting method was seamlessly integrated in the gameplay in order to provide an educating, entertaining, and compelling experience to diabetics of all ages, even family members and friends. Our implementation suggests that a classic and engaging platform video game is malleable to the infusion of serious content without significant dilution of the gameplay. Notably, a diabetic person has designed Sugar Mario and the source code has been shared online for inspection and contribution by interested parties.
A Strategy Integrating Iterative Filtering and Convolution Neural Network for Time Series Feature Extraction

Time series processing is a vital issue that is encountered in various fields. However, such data are mostly non-stationary on account of the fact that they are affected by a variety of factors. In this paper, we present a supervised strategy by integrating the iterative filtering (IF) method and convolution neural network (CNN) to automatically extract features of time series, where the IF technique can decompose the raw time series into intrinsic mode functions (IMFs), and then the CNN aims to extract the features from the images constructed by the IMFs under specific task. To illustrate the performance of the proposed strategy, we apply it in one-step and multi-step predictive tasks on the national association of securities dealers automated quotations (NASDAQ) data. Furthermore, we compute the importance of the extracted and raw features by the combined decision trees, such as random forest (RF) and gradient boosted decision trees (GBDT). The results indicate the significant improvement of the proposed strategy.
Comparison of video shot detection methods using higher order local descriptor

Video Shot Detection plays a vital role in the analysis of the contents in Video. The algorithms and methodologies learnt from Video Shot Detection has a wide range of applications starting from Video Browsing, Content-based Video Retrieval and Storage, Surveillance and many more. Video Shot Detection is the temporal segmentation of Video stream to determine the transitions of Video. Out of the two categories of transition viz., Hard Cut and Soft Transition, in this paper we propose methods to determine Hard Cut. Three Video Shot Detection Methods have been used in this work, they are Mutual Information, Weighted Variance, Likelihood Ratio and Edge Change Ratio. A comparative study has been conducted to find out the best Video Shot Detection method out of the three. The essential factor that drives the above three methods is extraction of appropriate features from the frames of video stream which would be used for the temporal segmentation of video to determine the transition. In this proposed paper we are using Higher Order Local Descriptors such as Local Binary Pattern(LBP), Local Derivative Pattern(LDP), Local Tetra Pattern(LTP) and Local Vector Pattern(LVP) and convert the original video into these feature videos. Frames from input video sequence are converted to texture domain and for each video sequence we generate four video corresponding to four Higher Order Local Descriptors. These video sequences are used to determine the `CUT' transition. Using QM Parameters, we found out the best feature among four Higher Order Local Descriptors for a given class of video.
Data Augmentation is More Important Than Model Architectures for Retinal Vessel Segmentation

While various deep learning models have recently been proposed or applied to improve the segmentation of vessels in retinal images, the performance gap between different models are often quite small. Such small difference may come from their limited generalization capabilities due to small training data. By simply augmenting data with oriented image patches extracted from the limited training images, we are surprised to observe that even a very simple U-Net with these augmented training patches can outperform the state-of-the-art models with much more complicated architectures or training schemes, and initial gaps between models have become negligible or disappeared. This suggests that it might be more crucial to explore effective data augmentations to extract richer visual information from limited training data, rather than solely focusing on developing other novel deep learning techniques for retinal vessel segmentation.