Miracle-dz/UniFAR-FTUs huggingface dataset:
Solanum lycopersicum microRNA1916 targets multiple target genes and negatively regulates the immune response in tomato.

MicroRNA1916 (miR1916) is one of the nonconserved miRNAs that respond to various stresses in plants, but little has been known at present about its mechanisms in biotic stresses. In this study, the expression of Solanum lycopersicum (sly)-miR1916 in tomato was found to be down-regulated after infection with Phytophthora infestans or Botrytis cinerea. Tomato plants that overexpressed sly-miR1916 displayed significant enhancement in susceptibility to P. infestans and B. cinerea infection, as well as increased tendency to produce reactive oxygen species. Silencing of sly-miR1916 by short tandem target mimic and artificial microRNA strategies caused the tomato plants to become more tolerant to adverse conditions. In addition, lower sly-miR1916 expression could up-regulate the expression of strictosidine synthase (STR-2), UDP-glycosyltransferases (UGTs), late blight resistance protein homolog R1B-16, disease resistance protein RPP13-like, and MYB transcription factor (MYB12), which ultimately resulted in the accumulation of α-tomatine and anthocyanins via STR-2, UGT, and MYB12. Furthermore, ectopic expression of sly-miR1916/STR-2 significantly changed the tolerance of tobacco to B. cinerea. Taken together, the results demonstrated that sly-miR1916 might regulate the expression of STR-2, UGT, and MYB12 in tomato plant, conferring sensitivity to biotic stress via modulating α-tomatine and anthocyanins.
Segmentation of cervical nuclei using SLIC and pairwise regional contrast

A framework to detect and segment nuclei from cervical cytology images is proposed in this study. Poor contrast, spurious edges, degree of overlap, and intensity inhomogeneity make the nuclei segmentation task more complex in overlapping cell images. The proposed technique segments cervical nuclei by merging over-segmented SLIC superpixel regions using a novel region merging criteria based on pairwise regional contrast and image gradient contour evaluations. The framework was evaluated using the first overlapping cervical cytology image segmentation challenge - ISBI 2014 dataset. The result shows that the proposed framework outperforms the state-of-the-art algorithms in nucleus detection and segmentation accuracies.
Back bias impact on effective mobility of p-type nanowire SOI MOSFETs

In this work we investigated the impact of back bias on the effective mobility of p-type Ω-gate nanowire SOI MOSFETs. Evaluation is performed through both measurements and 3D numerical simulations. Electrostatic potential, electric field and holes density are studied through simulations to explain transconductance degradation with back bias increase. Holes mobility linear dependence on back bias is found to be related to the inversion channel density and its position along the silicon thickness. Besides, this work also sheds light on the dependence of the drain current in vertically stacked NW with back bias, as its behavior is determined by the bottom Ω-gate level.
Neurite orientation and dispersion density imaging (NODDI) detects cortical and corticospinal tract degeneration in ALS

Background Corticospinal tract (CST) degeneration and cortical atrophy are consistent features of amyotrophic lateral sclerosis (ALS). We hypothesised that neurite orientation dispersion and density imaging (NODDI), a multicompartment model of diffusion MRI, would reveal microstructural changes associated with ALS within the CST and precentral gyrus (PCG) ‘in vivo’. Methods 23 participants with sporadic ALS and 23 healthy controls underwent diffusion MRI. Neurite density index (NDI), orientation dispersion index (ODI) and free water fraction (isotropic compartment (ISO)) were derived. Whole brain voxel-wise analysis was performed to assess for group differences. Standard diffusion tensor imaging (DTI) parameters were computed for comparison. Subgroup analysis was performed to investigate for NODDI parameter differences relating to bulbar involvement. Correlation of NODDI parameters with clinical variables were also explored. The results were accepted as significant where p<0.05 after family-wise error correction at the cluster level, clusters formed with p<0.001. Results In the ALS group NDI was reduced in the extensive regions of the CST, the corpus callosum and the right PCG. ODI was reduced in the right anterior internal capsule and the right PCG. Significant differences in NDI were detected between subgroups stratified according to the presence or absence of bulbar involvement. ODI and ISO correlated with disease duration. Conclusions NODDI demonstrates that axonal loss within the CST is a core feature of degeneration in ALS. This is the main factor contributing to the altered diffusivity profile detected using DTI. NODDI also identified dendritic alterations within the PCG, suggesting microstructural cortical dendritic changes occur together with CST axonal damage.
Too much medicine: not enough trust?

As many studies around the theme of ‘too much medicine’ attest, investigations are being ordered with increasing frequency; similarly the threshold for providing treatment has lowered. Our contention is that trust (or lack of it) is a significant factor in influencing this, and that understanding the relationship between trust and investigations and treatments will help clinicians and policymakers ensure ethical decisions are more consistently made. Drawing on the philosophical literature, we investigate the nature of trust in the patient–doctor relationship, arguing that at its core it involves a transfer of discretion. We show that there is substantial empirical support for the idea that more trust will reduce the problem of too much medicine. We then investigate ways in which trust can be built, concentrating on issues of questioning, of acknowledging uncertainty and of shouldering responsibility for it. We argue that offering investigations or treatments as a way of generating trust may itself be an untrustworthy way of proceeding, and that healthcare systems should provide the institutional support for facilitating continuity, questioning and the entrusting of uncertainty.
A Natural Animal Model System of Craniofacial Anomalies: The Blind Mexican Cavefish

Natural model systems evolving under extreme environmental pressures provide the opportunity to advance our knowledge of how the craniofacial complex evolves in nature. Unlike traditional models, natural systems are less inbred, and, therefore, better model the complex variation of the human population. Owing to the nature of certain craniofacial aberrations in blind Mexican cavefish, we suggest that this organism can provide new insights to a variety of craniofacial changes. Diverse cranial features have evolved in natural cave‐dwelling Astyanax fish, which have thrived in the unforgiving darkness and nutrient‐poor environment of the cave for countless generations. While the genetic and environmental underpinnings of various cranial anomalies have been investigated for decades, a comprehensive characterization of their molecular and developmental origins remains incomplete. Cavefish provide numerous advantages given the availability of genomic resources, developmental and molecular tools, and the presence of a normative surface‐dwelling “ancestral” surrogate for comparative studies. By leveraging the frequency of abnormal and asymmetric cranial features in cavefish, we anticipate advances in our knowledge of the etiologies of irregular cranial features. Extreme adaptations in cavefish are expected to offer new insights into the complex and multifactorial nature of craniofacial disorders and facial asymmetry. Anat Rec, 2018. © 2018 American Association for Anatomy.
Maximizing Explanatory Power in Stereological Data Collection: A Protocol for Reliably Integrating Optical Fractionator and Multiple Immunofluorescence Techniques

With the promise of greater reliability and replicability of estimates, stereological techniques have revolutionized data collection in the neurosciences. At the same time, improvements in immunohistochemistry and fluorescence imaging technologies have facilitated easy application of immunofluorescence protocols, allowing for isolation of multiple target proteins in one tissue sample. Combining multiple immunofluorescence labeling with stereological data collection can provide a powerful tool to maximize explanatory power and efficiency, while minimizing tissue use. Multiple cell classes, subtypes of larger populations, or different cell states can be quantified in one case and even in one sampling run. Here, we present a protocol integrating stereological data collection and multiple immunofluorescence using commonly employed widefield epifluorescence filter sets, optimized for blue (DAPI), green (FITC), and far red (CY5) channels. Our stereological protocol has been designed to accommodate the challenges of fluorescence imaging to overcome limitations like fixed filter sets, photobleaching, and uneven immunolabeling. To enhance fluorescence signal for stereological sampling, our immunolabeling protocol utilizes both high temperature antigen retrieval to improve primary antibody binding and secondary antibodies conjugated to optimally stable fluorophores. To illustrate the utility of this approach, we estimated the number of Ctip2 immunoreactive subcerebral projection neurons and NeuN immunoreactive neurons in rat cerebral cortex at postnatal day 10. We used DAPI (blue) to define the neocortex, anti-NeuN (far red) to identify neurons, and co-labeling of anti-Ctip2 (green) and anti-NeuN (far red) to isolate only subcerebral projection neurons. Our protocol resulted in estimates with low sampling error (CE < 0.05) and high intrarater reliability (ICC > 0.98) that fall within the range of published values, attesting to its efficacy. We show our immunofluorescence techniques can be used to reliably identify other cell types, e.g., different glial cell classes, to highlight the broader applications of our approach. The flexibility of the technique, increasingly reduced costs of fluorescence technologies, and savings in experimental time and tissue use make this approach valuable for neuroscientists interested in incorporating stereology to ask precise neurophysiological and neuroanatomical questions.
Parameter Estimation in Synaptic Coupling Model Using a Point Process Modeling Framework*

Biophysical models are widely used to characterize temporal dynamics of the brain networks on different topological and spatial scales. In parallel, the state-space modeling framework with point process observations has been successfully applied in characterizing spiking activity of neuronal ensembles in response to different dynamical covariates. Parameter estimation in biophysical models is generally done heuristically, which hampers their applicability and interpretability. Heuristic parameter estimation becomes an intractable problem when the number of model parameters grows. Here, we propose an algorithm for estimating biophysical model parameters using point-process models and a state-space framework. The framework provides methods for parameter estimation as well as model validation. We demonstrate the application of this methodology to the problem of estimating the parameters of a dynamic synapse model. We generate simulation data for the dynamic synapse across a range of parameters values and assess the estimation accuracy of our method using a combination of goodness-of-fit measures. The proposed methodology can be applied broadly to parameter estimation problems across a broad range of biophysical models, including Hodgkin-Huxley models and network models.
Are There Categories of Corneal Shapes?

This study investigates the possible existence of different natural corneal shape categories. This is important to better describe cornea for both diagnostic and therapeutic assessments. We started by describing corneal shape of different populations as a function of influencing clinical data i.e. age, ametropia and gender. This was done by averaging Zernike polynomial (ZP) decomposition of the anterior surfaces in each subgroup. The results showed small but significant differences of shape that are supported by the literature. This motivated us to examine the feasibility of characterizing the normal corneal shape with an automatic method of clustering independent of any clinical a priori knowledge. Since we did not know beforehand the number of corneal categories, agglomerative hierarchical clustering was applied on ZP coefficients for a large database. The dendrogram based on the Ward’s distance was evaluated with two different clustering validity indexes (coefficient of determination R2 and semi partial R2 (SPR2). The optimal number of categories was around four showing corneal shapes ranging from flatter to steeper.
A Solid-State 0.56 THz Near-Field Array for μM-Scale Surface Imaging

We demonstrate a fully-integrated 0.56 THz 128-pixel near-field 1-D sensor array with 10–12 μm lateral resolution. The sensing elements are based two parallel cross-bridged split-ring resonators that utilize a 3-D topology to exhibit high field confinement in the sensing spot. The sensor comprises a scalable excitation scheme that is designed to maximize the fill factor of the imager for large 1-D arrays. The THz front-end is co-integrated with a digital-read out chain. This enables rapid THz near-field image acquisition of planar and soft surfaces. Furthermore, the imager can be operated as a standalone device that is only powered with a USB cable. As an exemplary application, this work demonstrates THz near-field sensing as a tool for biometric fingerprint reading for the first time.
Prediction of One-Year Transplant-Free Survival after Norwood Procedure Based on the Pre-Operative Data

This paper discusses computational modeling of predictive risk factors for neonates undergoing a Norwood surgical procedure, a multi-stage cardiac procedure that restores functional systemic circulation in patients such as neonates with Hypoplastic Left Heart Syndrome (HLHS). In this model, we apply machine learning based binary classication to 549 cases reported by the Pediatric Heart Networks Single Ventricle Reconstruction Trial. We use neural networks classier to predict risk factors for individual patients undergoing a Norwood procedure for the repair of HLHS. Results indicate that independent risk can be calculated with 85% accuracy and 0.94 area under the receiver operating characteristics curve. This model may help physicians provide counseling for families and medically optimize patients prior to surgery by modifying individual risk factors.
Load curve characterization based on real time measurements: Case of study in Colombia

The aim of this paper was test the performance of different features and four clustering algorithms: K-Means, Hierarchical clustering, Density-based spatial clustering and Gaussian Mixture Models applied to load curve characterization with real data of energy consumption in Colombia. Clustering algorithms were compared using a common data mining methodology, where K-Means produced the best performance. The results show that it is possible to implement machine learning algorithms using different types of features, in order to obtain relevant information for the energy sector. Also, this work presents possible applications using load curve characterization.
Ginger and Testosterone

Enhancing and protecting testosterone production is one target for many scientists because of its crucial role as a primary sex hormone in males. Several in vivo trials have utilized different dietary supplements and medicinal plants to enhance testosterone production in males. Since 1991, various in-vivo, as well as basic research studies, have discovered a link between ginger (Zingiber officinale) and testosterone. However, such a link has not yet been collectively reviewed. This review systematically discusses and summarizes the effect of ginger and ginger extracts on testosterone. To achieve this contribution, we searched the PubMed, Scopus, and Web of Science databases for English language articles (full texts or abstracts) from November 1991 through August 2018 using the keywords “ginger” and “Zingiber officinale” versus “testosterone”. Additionally, the references from related published articles were also reviewed, only if relevant. In conclusion, the mainstream of research that links ginger to testosterone demonstrated that ginger supplementation, particularly in oxidative stress conditions, enhances testosterone production in males. The mechanisms by which this occurs mainly by enhancing luteinizing hormone (LH) production, increasing the level of cholesterol in the testes, reducing oxidative stress and lipid peroxidation in the testes, enhancing the activity of the antioxidant enzymes, normalizing blood glucose, increasing blood flow in the testes, increasing testicular weight, and recycling testosterone receptors. However, the effect of ginger on testosterone is not yet confirmed in humans. Therefore, clinical studies in this context of research are imperative.
A Novel Variable Stiffness Compliant Finger Exoskeleton for Rehabilitation Based on Electromagnet Control

In this study, we present the design of a variable stiffness finger exoskeleton for hand rehabilitation, to meet the requirements of different human users and versatile rehabilitation task. This paper describes the design principle and fabrication of the variable stiffness finger exoskeleton which combines a variable stiffness beam and a 3D printed compliant finger exoskeleton. Experimental studies have shown that by using the electromagnetic force, exoskeleton stiffness variation is achievable. Therefore, the proposed variable stiffness finger exoskeleton is capable of adapting the versatile tasks and providing a soft, wearable device for hand rehabilitation of different human users.
Similarity Dependent Chinese Restaurant Process for Cognate Identification in Multilingual Wordlists

We present and evaluate two similarity dependent Chinese Restaurant Process (sd-CRP) algorithms at the task of automated cognate detection. The sd-CRP clustering algorithms do not require any predefined threshold for detecting cognate sets in a multilingual word list. We evaluate the performance of the algorithms on six language families (more than 750 languages) and find that both the sd-CRP variants performs as well as InfoMap and better than UPGMA at the task of inferring cognate clusters. The algorithms presented in this paper are family agnostic and can be applied to any linguistically under-studied language family.
Test–Retest Reliability of a Social Interaction Task

Accurate repeatability of experimental data is the basis of professional scientific research. In this study we analyzed three consecutive experiments: The subjects had to complete a questionnaire three times under similar conditions within a 2–3 week interval to ensure reproducibility of the original data from experiment to experiment, using the method of test–retest reliability. Absolute reliability was assessed by the standard error of measurement (SEM) and smallest real difference (SRD). The relative reliability was estimated by calculating the intraclass correlation coefficients (ICC3,1) (average measures) and the results demonstrate almost perfect agreement. The social interaction model was applied for organization of the experimental study. In this virtual model, the participants had to choose one of three types of reactions (i.e., attacking, avoiding, or friendly) to stimuli-facial expressions (i.e., angry, fearful, sad, neutral, and happy). The results show significant correlation between personal characteristics and social interactions. The results of the influence of such personal characteristics as agreeableness, collectivism, extraversion, neuroticism, and those shown on the Relational-interdependent Self-Construal Scale and State-Trait Anxiety Inventory are highly consistent with other researchers’ data and common sense.
Physical Activity, Residential Environment, and Nature Relatedness in Young Men—A Population-Based MOPO Study

Background: In general, nature relatedness is positively associated with physical activity, health, and subjective well-being. However, increased residence in urban areas, and the decrease in natural spaces, may affect the younger generation most adversely. The associated environmental changes can increase youths' risk of spending most of their time indoors, and weaken their nature relatedness, making them less likely to enjoy nature's health benefits. This is a serious public health issue, since inadequate physical activity, combined with minimum time spent in green space, can affect health across the whole lifespan. Thus, to develop effective interventions for physical activation and promote health and well-being among young men, further knowledge of the determinants of their nature relatedness is necessary. Aims: To explore factors related to nature relatedness, including physical activity, physical activity with parents, and residential environment. Methods: The study population consisted of all 914 young men (mean-17.8 years; SD-0.5) who participated in mandatory call-ups for military service and completed the study questionnaire in 2013. The questionnaire inquired about their nature relatedness, demographic characteristics, socioeconomic status, physical activity, health, and subjective well-being. A geographic information system (GIS) was used to assess the features of their residential environments. Multivariable linear regression was used to analyze the data. Results: Physical activity (p = 0.021) and physical activity with parents at primary school age (p = 0.007), and currently (p = 0.001) as well as good self-rated health (p = 0.001), and father's higher socioeconomic status (p = 0.041), were positively connected to nature relatedness. Conclusions: Physical activity in general, physical activity with parents, and nature relatedness were positively related. This knowledge can be utilized in promoting physical activity and health among young men.
Automatic extraction of the 3D symmetry line of back surface: application on scoliotic adolescents

We propose a new method to extract automatically the symmetry line of the 3D back surface of patients affected by scoliosis. Our method is based on the detection of local symmetry planes computed on thick layers of the back. Results have been obtained on a sample of 112 scoliotic adolescents and we compare the symmetry line obtained by our method with a reference line defined by clinicians. We also study the influence of the scoliosis severity and of the Body Mass Index on the results.
Building a Nasa Yuwe Language Corpus and Tagging with a Metaheuristic Approach

Nasa Yuwe is the language of the Nasa indigenous community in Colombia. It is currently threatened with extinction. In this regard, a range of computer science solutions have been developed to the teaching and revitalization of the language. One of the most suitable approaches is the construction of a Part-Of-Speech Tagging (POST), which encourages the analysis and advanced processing of the language. Nevertheless, for Nasa Yuwe no tagged corpus exists, neither is there a POS Tagger and no related works have been reported. This paper therefore concentrates on building a linguistic corpus tagged for the Nasa Yuwe language and generating the first tagging application for Nasa Yuwe. The main results and findings are 1) the process of building the Nasa Yuwe corpus, 2) the tagsets and tagged sentences, as well as the statistics associated with the corpus, 3) results of two experiments to evaluate several POS Taggers (a Random tagger, three versions of HSTAGger, a tagger based on the harmony search metaheuristic, and three versions of a memetic algorithm GBHS Tagger, based on Global-Best Harmony Search (GBHS), Hill Climbing and an explicit Tabu memory, which obtained the best results in contrast with the other methods considered over the Nasa Yuwe language corpus.