Miracle-dz/UniFAR-FTUs huggingface dataset:
Micromorphological study of Centaurea L. (Asteraceae); some taxa of eight sections analysed with scanning electron microscopy and light microscopy

This study deals with the pollen morphological characterization of 36 taxa (Sections; Cheirolepis 3, Cynaroides 14, Rhizocalathium 5, Grossheimia 3, Chartolepis 3, Pseudophaeopappus 1, Plumosipappus 1 and Phaeopappus 6) belonging to the genus Centaurea. Pollen slides were prepared using the Acetolysis technique. Light Microscope examination showed pollen grains to be tricolporate, isopolar, radially symmetrical, oblate-spheroidal, prolate-spheroidal, subprolate, tectum perforate and scabrate. We identified two basic types of pollen grains, designated type I (Scabrate), type II (Microechinate). 34 taxa evaluated resemble Wagenitz’s Jacea pollen type and 2 taxa Montana pollen type. We present micrographs of pollen grains, together with descriptions of the characteristics of their structure. We found that the pollen characters (shape, exine structure, sculpturing) were useful for classification.
Testing Pathological Variation of White Matter Tract in Adult Rats after Severe Spinal Cord Injury with MRI

The purpose of this study was to assess the pathological variation in white matter tracts in the adult severe thoracic contusion spinal cord injury (SCI) rat models combined with in vivo magnetic resonance imaging (MRI), as well as the effect of spared white matter (WM) quantity on hindlimb motor function recovery. 7.0T MRI was conducted for all experimental animals before SCI and 1, 3, 7, and 14 days after SCI. The variation in the white matter tract in different regions of the spinal cord after SCI was examined by luxol fast blue (LFB) staining, NF200 immunochemistry, and diffusion tensor imaging (DTI) parameters, including fraction anisotropy, mean diffusivity, axial diffusion, and radial diffusivity. Meanwhile, Basso-Beattie-Bresnahan (BBB) open-field scoring was performed to evaluate the behavior of the paraplegic hind limbs. The quantitative analysis showed that spared white matter measures assessed by LFB and MRI had a close correlation (R2 = 0.8508). The percentage of spared white matter area was closely correlated with BBB score (R2 = 0.8460). After SCI, spared white matter in the spinal cord, especially the ventral column WM, played a critical role in motor function restoration. The results suggest that the first three days provides a key time window for SCI protection and treatment; spared white matter, especially in the ventral column, plays a key role in motor function recovery in rats. Additionally, DTI may be an important noninvasive technique to diagnose acute SCI degree as well as a tool to evaluate functional prognosis. During the transition from nerve protection toward clinical treatment after SCI, in vivo DTI may serve as an emerging noninvasive technique to diagnose acute SCI degree and predict the degree of spontaneous functional recovery after SCI.
Metabolomic Fingerprinting of Salinispora From Atlantic Oceanic Islands

Salinispora (Micromonosporaceae) is an obligate marine bacterium genus consisting of three species that share over 99% 16S rRNA identity. The genome and biosynthetic pathways of the members of this genus have been widely investigated due to their production of species-specific metabolites. However, despite the species’ high genetic similarity, site-specific secondary metabolic gene clusters have been found in Salinispora strains collected at different locations. Therefore, exploring the metabolic expression of Salinispora recovered from different sites may furnish insights into their environmental adaptation or their chemical communication and, further, may lead to the discovery of new natural products. We describe the first occurrence of Salinispora strains in sediments from the Saint Peter and Saint Paul Archipelago (a collection of islets in Brazil) in the Atlantic Ocean, and we investigate the metabolic profiles of these strains by employing mass-spectrometry-based metabolomic approaches, including molecular networking from the Global Natural Products Social Molecular Networking platform. Furthermore, we analyze data from Salinispora strains recovered from sediments from the Madeira Archipelago (Portugal, Macaronesia) in order to provide a wider metabolomic investigation of Salinispora strains from the Atlantic Oceanic islands. Overall, our study evidences a broader geographic influence on the secondary metabolism of Salinispora than was previously proposed. Still, some biosynthetic gene clusters, such as those corresponding to typical chemical signatures of S. arenicola, like saliniketals and rifamycins, are highly conserved among the assessed strains.
Efficient Condition-based Representations for Long-Term Visual Localization

We propose an approach to localization from images that is designed to explicitly handle the strong variations in appearance happening when capturing conditions change throughout the day or across seasons. As revealed by recent long-term localization benchmarks, both traditional feature-based and retrieval-based approaches still struggle to handle such changes. Our novel retrieval-based method introduces condition-specific sub-networks allowing the computation of global image descriptors that are explicitly dependent of the capturing conditions. We compare our approach to previous localization methods on very recent challenging benchmarks, and observe that our method outperforms them by a large margin in case of day-night variation, where repeatable feature points cannot be identified or matched.
Theory of Curriculum Learning, with Convex Loss Functions

Curriculum Learning - the idea of teaching by gradually exposing the learner to examples in a meaningful order, from easy to hard, has been investigated in the context of machine learning long ago. Although methods based on this concept have been empirically shown to improve performance of several learning algorithms, no theoretical analysis has been provided even for simple cases. To address this shortfall, we start by formulating an ideal definition of difficulty score - the loss of the optimal hypothesis at a given datapoint. We analyze the possible contribution of curriculum learning based on this score in two convex problems - linear regression, and binary classification by hinge loss minimization. We show that in both cases, the expected convergence rate decreases monotonically with the ideal difficulty score, in accordance with earlier empirical results. We also prove that when the ideal difficulty score is fixed, the convergence rate is monotonically increasing with respect to the loss of the current hypothesis at each point. We discuss how these results bring to term two apparently contradicting heuristics: curriculum learning on the one hand, and hard data mining on the other.
DNA-Mediated Interferon Signature Induction by SLE Serum Occurs in Monocytes Through Two Pathways: A Mechanism to Inhibit Both Pathways

A primary mechanism for activation of innate immunity is recognition of damage or pathogen associated molecular patterns by pattern recognition receptors (PRRs). Nucleic acid is a damage associated molecular pattern molecule that when internalized into a monocyte and recognized by intracellular nucleic acid sensing toll like receptors will cause production of type 1 interferon. The process by which DNA or RNA is delivered into the cytosol of monocytes in systemic lupus erythematosus remains incompletely understood, and therapeutic approaches to prevent DNA-mediated monocyte activation are needed. We identified two mechanisms for internalization of DNA by monocytes. IgG-bound DNA was internalized by interacting with Fc gamma receptor IIa, while high-mobility group box-1 protein-bound DNA was internalized by interacting with the receptor for advanced glycation end products. Both pathways contribute to an inflammatory phenotype in monocytes exposed to serum from patients with SLE. Moreover, both of these pathways can be inhibited by a pentapeptide, DWEYS, which is a DNA mimetope. In one instance DWEYS directly competes with DNA for antibody binding and in the other DWEYS binds high-mobility group box-1 and blocks its interaction with RAGE. Our data highlight distinct pathways involved in nucleic acid enters monocytes in SLE, and identify a potential therapeutic to prevent nucleic acid internalization in SLE.
Stand-Alone Heartbeat Detection in Multidimensional Mechanocardiograms

We describe a home health monitoring solution with cardiac beat-to-beat detection using accelerometer and gyroscope signal fusion. The proposed method measures both the precordial translational and rotational motions of the chest using miniaturized inertial sensors. The algorithm removes motion artefacts, selects the best axis from multi-axial accelerometric and gyroscopic signals and detects the location of beats using two detection principles based on the signal envelope and signal morphology. We consider the beat-to-beat detection accuracy, estimate the heart rate and compare the detection performance between the sensor modalities in two study groups: i) healthy subjects and ii) heart disease patients. The average sensitivity and precision of the beat detection were 99.9% and 99.6% for the healthy subjects and 96.1% and 95.6% for the heart disease patients, respectively. Although high-accuracy beat detection was achieved for the heart disease patients, location matching in these patients was found to be less accurate compared to that of the healthy subjects. The average root mean square error (RMSE) between the mechanical and ECG interbeat intervals was 5.6 ms for the healthy patients; this error increased approximately 10-fold for the heart disease patients. Similarly, the RMSE for the averaged heart rate estimation showed about a 10-fold difference at 1.05 beats per minute for the heart disease patients. The used sensor modalities are found in many electronic devices, such as smartphones and wearable technologies and this method provides a step towards ubiquitous cardiac monitoring.
Sequential Congruency Effects in Monolingual and Bilingual Adults: A Failure to Replicate Grundy et al. (2017)

Previous research suggests bilingual adults show smaller sequential congruency effects than monolingual adults. Here we re-examined these findings by administering an Eriksen flanker task to monolingual and bilingual adults. The task produced robust conventional and sequential congruency effects. Neither effect differed for monolingual and bilingual adults. Results are discussed in terms of current debates concerning differences in cognitive control between monolingual and bilingual adults.
A Resistorless High-Precision Compensated CMOS Bandgap Voltage Reference

A resistorless high-precision compensated CMOS bandgap voltage reference (BGR), which is compatible with a standard CMOS process, is presented in this paper. A higher-order curvature correction method called base-emitter voltage linearization is adapted to directly compensate the thermal nonlinearity of base–emitter voltage. With proper mathematical operations of high-order temperature currents, most of the nonlinear temperature terms in <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {BE}}$ </tex-math></inline-formula> can be greatly eliminated. The proposed BGR, which is implemented in 0.35-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS technology, is capable of working down to 2 V supply voltages with 1.14055 V mean output voltage. A minimum temperature coefficient of 1.01 ppm/°C with a temperature range from −40 °C to 125 °C is realized, and a power-supply noise attenuation of 61 dB is achieved without any filter capacitors. The line regulation is better than 2 mV/V from 2 V to 5 V supply voltage while dissipating a maximum supply current of <inline-formula> <tex-math notation="LaTeX">$33~\mu \text{A}$ </tex-math></inline-formula>. The active area of the presented BGR is 180 <inline-formula> <tex-math notation="LaTeX">$\mu \text {m}\times 220\,\,\mu \text{m}$ </tex-math></inline-formula>.
An Explorative Comparison of Blame Attributions to Companion Robots Across Various Moral Dilemmas

We report results from an exploratory study with a humanoid robot asking participants (n = 30) to attribute blameworthiness to other robots that made decisions in moral dilemmas. Drawing from current research in machine ethics, we identify three ethical theories that have been formalized for the use in robots: Utilitarianism, Deontology, and Value-based ethics. We aligned these ethical theories with the attributions of blame. Our results suggest that a utilitarian robot, although attractive from a computational point of view because of its calculative nature, accumulates most blame across several dilemmas as compared to its alternatives-most significantly in dilemmas that occur in everyday life. Therefore ethical decision making for companion robots may best be implemented using rule-based or value-based procedures rather than utilitarian calculi.
A Framework for Driver Emotion Recognition using Deep Learning and Grassmann Manifolds

In this paper, we propose a novel, real-time, camera based framework for determining the drivers emotions through facial expression recognition. Studies have established that driver's emotions play an important role in driving behavior. Therefore, continuous monitoring of the driver's emotions and requisite warning to the driver will help in maintaining safety on the roads. In our framework, at regular intervals, we detect the driver's face in the current frame and recognize the driver's emotions. For expression recognition, we extract features from the face image using two standard pre-trained deep neural networks, AlexNet and VGG16, that we fine-tune on facial expression data. We extract the features from the fully connected layer from these two networks for each frame and concatenate the two feature vectors to form a single feature vector. The novelty of our framework lies in creating distinct subspaces of each expression, using these feature vectors and applying Grassmann graph embedding based discriminant analysis to recognize the expression. The subspaces accommodate the variations in multiple instances of an expression of the same person as well as across multiple people. Our experimental results on standard datasets show that our proposed framework outperforms state-of-the-art methods.
Automated Detection of Handwritten Whiteboard Content in Lecture Videos for Summarization

Online lecture videos are a valuable resource for students across the world. The ability to find videos based on their content could make them even more useful. Methods for automatic extraction of this content reduce the amount of manual effort required to make indexing and retrieval of such videos possible. We adapt a deep learning based method for scene text detection, for the purpose of detection of handwritten text, math expressions and sketches in lecture videos. We detect handwritten elements on the whiteboard to generate a summary of all content over time in the lecture, while also dealing with occluded content due to motion of the lecturer. We train, test on the publicly available AccessMath lecture video dataset and evaluate our framework on the basis of number of summary frames, as well as recall and precision of all whiteboard content in the set of test lecture videos. We found that our method increases the precision of the state-of-the-art while there is potential to increase recall as well. We have added to the existing ground truth in the AccessMath dataset by providing timestamp-based, semantically meaningful bounding box annotations for the handwritten whiteboard content, which has been released.
Suppression of Parkinsonian Beta Oscillations by Deep Brain Stimulation: Determination of Effective Protocols

A neural field model of the corticothalamic-basal ganglia system is developed that describes enhanced beta activity within subthalamic and pallidal circuits in Parkinson's disease (PD) via system resonances. A model of deep brain stimulation (DBS) of typical clinical targets, the subthalamic nucleus (STN) and globus pallidus internus (GPi), is added and studied for several distinct stimulation protocols that are used for treatment of the motor symptoms of PD and that reduce pathological beta band activity (13–30 Hz) in the corticothalamic-basal ganglia network. The resulting impact of DBS on enhanced beta activity in the STN and GPi, as well as cortico-subthalamic and cortico-pallidal coherence, are studied. Both STN-DBS and GPi-DBS are found to be effective for suppressing peak STN and GPi power in the beta band, with GPi-DBS being slightly more effective in both the STN and the GPi for all stimulus protocols tested. The largest decrease in cortico-STN coherence is observed during STN-DBS, whereas GPi-DBS is most effective for reducing cortico-GPi coherence. A reduction of the pathologically large STN connection strengths that define the parkinsonian state results in enhanced 6 Hz activity and could thus represent a compensatory mechanism that has the side effect of driving parkinsonian tremor-like oscillations. This model provides a method for systematically testing effective DBS protocols that agrees with experimental and clinical findings. Furthermore, the model suggests GPi-DBS and STN-DBS have distinct impacts on elevated synchronization between the basal ganglia and motor cortex in PD.
Iron in Glacial Systems: Speciation, Reactivity, Freezing Behavior, and Alteration During Transport

A more insightful view of iron in glacial systems requires consideration of iron speciation and mineralogy, the potential for iron minerals to undergo weathering in ice-water environments, the impact of freezing on concentration and speciation, and potential for glacial delivery to undergo alteration during transport into the ocean. A size fractionation approach improves recognition of iron speciation by separating dissolved Fe (0.1 wt. %) which represent samples in which the on-going transformation of ferrihydrite to goethite/hematite is incomplete. Numerical models of freezing in subglacial systems show that the nanomolar levels of soluble Fe in icebergs cannot be achieved solely by freezing, and must indicate the presence of nanoparticulate Fe and/or iron desorbed from ice or sediments during melting. Models of freezing effects in sea ice show that nanomolar levels of Fe are achievable because high concentrations of hydroxide and chloride ions maintain dissolved iron as soluble complexes. Delivery of iron through fjords is temporally and spatially variable due to circulation patterns, mixing of different sources and aggregation through salinity gradients.
Smartphone Application for Physical Activity Enhancement at Workplace: Would Office Workers Actually Use It?

The proliferation of smartphone into daily life could potentially become an effective medium for the delivery of workplace-based physical activity intervention. Based on Unified Theory of Acceptance and Use of Technology model (UTAUT), this study investigates factors influencing office workers' intention to use smartphone application to enhance their physical activity at workplace. A total of 195 valid surveys were collected and subsequently analyzed using Partial Least Square Structural Equation Modelling (PLS-SEM) approach. The outcome demonstrated that both Performance Expectancy (PE) and Social Influence (SI) have significant positive effect on Behavioural Intention. Effort Expectancy (EE) in contrast has no significant influence on office workers' intention to use. This study provides a preliminary evidence on factors that should be consider by researchers, developers and employers in developing smartphone applications aiming to enhance physical activity at workplace among office workers.
Theoretical Analysis of Auto Rate-Tuning by Batch Normalization

Batch Normalization (BN) has become a cornerstone of deep learning across diverse architectures, appearing to help optimization as well as generalization. While the idea makes intuitive sense, theoretical analysis of its effectiveness has been lacking. Here theoretical support is provided for one of its conjectured properties, namely, the ability to allow gradient descent to succeed with less tuning of learning rates. It is shown that even if we fix the learning rate of scale-invariant parameters (e.g., weights of each layer with BN) to a constant (say, $0.3$), gradient descent still approaches a stationary point (i.e., a solution where gradient is zero) in the rate of $T^{-1/2}$ in $T$ iterations, asymptotically matching the best bound for gradient descent with well-tuned learning rates. A similar result with convergence rate $T^{-1/4}$ is also shown for stochastic gradient descent.
Miro1 – the missing link to peroxisome motility

ABSTRACT Peroxisomes are ubiquitous, highly dynamic, multifunctional compartments in eukaryotic cells, which perform key roles in cellular lipid metabolism and redox balance. Like other membrane-bound organelles, peroxisomes must move in the cellular landscape to perform localized functions, interact with other organelles and to properly distribute during cell division. However, our current knowledge of peroxisome motility in mammalian cells is still very limited. Recently, three independent studies have identified Miro1 as a regulator of peroxisome motility in mammalian cells. In these studies, the authors show that Miro1 is targeted to peroxisomes in several cell lines, in a process that relies on its interaction with the peroxisomal chaperone Pex19. Interestingly, however, different conclusions are drawn about which Miro1 isoforms are targeted to peroxisomes, how it interacts with Pex19 and most importantly, the type of motility Miro1 is regulating.
Comparison of 22 kHz and 85 kHz 50 kW Wireless Charging System Using Si and SiC Switches for Electric Vehicle

Fast charging in transportation application requires high-power (>50 kW) operation. The overall size of wireless charging systems (WCS) must increase to meet the high-power requirement. As a result, high power systems produce more electromagnetic field (EMF) emissions. Although higher operating frequencies (85 kHz) reduce the size of inductors and capacitors, system losses tend to increase. High-power systems are traditionally investigated for 10 kHz to 22 kHz operation to reduce the switching loss in the converter, and conductive and magnetic losses in the power pad. The use of SiC MOSFETs reduces conduction and switching losses in the converter even at higher frequencies. In this paper, a comprehensive loss comparison of 50 kW wireless charging system is conducted for 85 kHz and 22 kHz operation of Si MOSFET and SiC MOSFET converters. Detailed performance indexes are compared analytically as a function of frequency and verified using FEA and PLECS. This comprehensive system level study shows that magnetic losses can be reduced significantly, and that EMF emission can be reduced by 50% by moving from 22 kHz to 85 kHz. Moreover, the system requires 50% less copper and allows a significant reduction in the size of the primary and secondary filter capacitor at 85 kHz. The results show that the system efficiency at 85 kHz can be increased by 21% compared to 22 kHz operation by using a SiC MOSFET converter.