Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Frequency Bands Analysis of Large Scale Intrinsic Brain Networks and Its Application in Schizotypal Personality Disorder

The human brain is a complex system composed by several large scale intrinsic networks with distinct functions. The low frequency oscillation (LFO) signal of blood oxygen level dependent (BOLD), measured through resting-state fMRI, reflects the spontaneous neural activity of these networks. We propose to characterize these networks by applying the multiple frequency bands analysis (MFBA) to the LFO time courses (TCs) resulted from the group independent component analysis (ICA). Specifically, seven networks, including the default model network (DMN), dorsal attention network (DAN), control executive network (CEN), salience network, sensorimotor network, visual network and limbic network, are identified. After the power spectral density (PSD) analysis, the amplitude of low frequency fluctuation (ALFF) and the fractional amplitude of low frequency fluctuation (fALFF) is determined in three bands: <0.1 Hz; slow-5; and slow-4. Moreover, the MFBA method is applied to reveal the frequency-dependent alternations of fALFF for seven networks in schizotypal personality disorder (SPD). It is found that seven networks can be divided into three categories: the advanced cognitive networks, primary sensorimotor networks and limbic networks, and their fALFF successively decreases in both slow-4 and slow-5 bands. Comparing to normal control group, the fALFF of DMN, DAN and CEN in SPD tends to be higher in slow-5 band, but lower in slow-4. Higher fALFF of sensorimotor and visual networks in slow-5, higher fALFF of limbic network in both bands have been observed for SPD group. The results of ALFF are consistent with those of fALFF. The proposed MFBA method may help distinguish networks or oscillators in the human brain, reveal subtle alternations of networks through locating their dominant frequency band, and present potential to interpret the neuropathology disruptions.
A Hierarchical Model of Inhibitory Control

Inhibitory control describes the suppression of goal-irrelevant stimuli and behavioral responses. Current developmental taxonomies distinguish between Response Inhibition – the ability to suppress a prepotent motor response, and Attentional Inhibition – the ability to resist interference from distracting stimuli. Response Inhibition and Attentional Inhibition have exhibited moderately strong positive correlations in previous studies, suggesting they are closely related cognitive abilities. These results may reflect the use of cognitive tasks combining Stimulus–Stimulus- and Stimulus–Response-conflict as indicators of both constructs, which may have conflated their empirical association. Additionally, previous statistical modeling studies have not controlled for individual differences in Working Memory Capacity, which may account for some of the empirical overlap between Response Inhibition and Attentional Inhibition. The aim of the current study was to test a hierarchical model of inhibitory control that specifies Working Memory Capacity as a higher-order cognitive construct. Response Inhibition and Attentional Inhibition were conceptualized as lower-order cognitive mechanisms that should be empirically independent constructs apart from their shared reliance on Working Memory Capacity for active maintenance of goal-relevant representations. Measures of performance on modified stimulus–response compatibility tasks, complex memory span, and non-selective stopping tasks were obtained from 136 preadolescent children (M = 11 years, 10 months, SD = 8 months). Consistent with hypotheses, results from Structural Equation Modeling demonstrated that the Response Inhibition and Attentional Inhibition factors were empirically independent constructs that exhibited partial statistical dependence on the Working Memory Capacity factor. These findings have important implications for current theories and models of inhibitory control during development.
Detection of Generalized-Roughness and Single-Point Bearing Faults Using Linear Prediction-Based Current Noise Cancellation

The dominant components in the stator current of a typical induction motor contain a substantial amount of information that is not related to bearing faults and can be considered as “noise” to bearing fault detection. The presence of the noise in the current signal creates the possibility of missed and false alarms. This paper develops a current noise cancellation method. When noise cancellation is used for recovering a desired signal, the distinguishing characteristics of both the desired signal and the noise need to be considered. Contrary to conventional current noise cancellation methods, “predictability” is considered as the distinguishing characteristic. Linear prediction theory with an optimum prediction order is applied to efficiently model the predictable components as noise. The developed method is able to efficiently cancel out the noise from the current signal. The information about the mechanical load level, the faulty part of the bearing, machine parameters, nameplate values, the current spectrum distribution, and the current data associated with the healthy bearing is not required in this method. Simulation and experimental results confirm the merits and effectiveness of the developed method to detect the bearing fault types through kurtosis and spectral analysis.
Hierarchical Cluster Analysis of Cytokeratins and Stem Cell Expression Profiles of Canine Cutaneous Epithelial Tumors

The diagnosis of cutaneous epithelial tumors (CETs) in dogs is based on predominant histological differentiation patterns. However, the expression of a broad panel of antigens has not been comprehensively examined with immunohistochemistry. The present study aims to establish a comprehensive expression profile and identify useful diagnostic markers for each CET type. Cytokeratin (CK), stem cell, and other associated markers were immunohistochemically examined in 110 canine CETs. Among these, CK16 was useful for differentiating between basal and squamous cell carcinomas. Acantholytic squamous cell carcinomas were positive for CK8, CK18, and CK19, suggesting their close association with the apocrine duct. Unlike their benign counterparts, sebaceous carcinomas coexpressed CK5/6 and adipophilin. Smooth muscle actin (SMA) and p63 immunostaining were useful for accurately distinguishing between glandular and ductal differentiation in apocrine tumors. A case of apocrine carcinoma and malignant myoepithelioma was identified using anti-SMA antibodies. Stem cell expression profiles (CK8, CK15, CK19, and CD34) of hair follicle tumors were discrete and indicative of their anatomic origins. The effectiveness of immunohistochemistry for tumor diagnosis was further confirmed by hierarchical cluster analysis, through which selected markers were able to sort CETs into specific groups: CK5/6, CK8, CK14, CK16, CK18, CK19, p63, adipophilin, and SMA sorted tumors of epidermal, apocrine, or sebaceous origin; while CK8, CK14, CK15, CK16, CK19, CD34, and p63 sorted hair follicle tumors in agreement with their histological differentiation. In conclusion, the present study provides comprehensive immunohistochemical information, which could complement histomorphological features for the future classification of canine CETs.
Maternal Regulation of Pups’ Cortical Activity: Role of Serotonergic Signaling

Visual Abstract A developing brain shows intense reorganization and heightened neuronal plasticity allowing for environmental modulation of its development. During early life, maternal care is a key factor of this environment and defects in this care can derail adaptive brain development and may result in susceptibility to neuropsychiatric disorders. Nevertheless, the mechanisms by which those maternal interactions immediately impact the offspring’s brain activity to initiate the pathway to pathology are not well understood. We do know that multiple neurotransmitter systems are involved, including the serotonergic system, a key neuromodulator involved in brain development and emotional regulation. We tested the importance of the serotonergic system and pups’ immediate neural response to maternal presence using wireless electrophysiological recordings, a novel approach allowing us to record neural activity during pups’ interactions with their mother. We found that maternal contact modulates the P10–P12 rat pups’ anterior cingulate cortex (ACC) activity by notably increasing local-field potential (LFP) power in low-frequency bands. We demonstrated, by blocking serotonergic receptors, that this increase is mediated through 5-HT2 receptors (5-HT2Rs). Finally, we showed in isolated pups that enhancing serotonergic transmission, using a selective-serotonin-reuptake-inhibitor, is sufficient to enhance LFP power in low-frequency bands in a pattern similar to that observed when the mother is in the nest. Our results highlight a significant contribution of the serotonergic system in mediating changes of cortical activity in pups related to maternal presence.
Learning-Based Adaptive Attitude Control of Spacecraft Formation With Guaranteed Prescribed Performance

This paper investigates a novel leader-following attitude control approach for spacecraft formation under the preassigned two-layer performance with consideration of unknown inertial parameters, external disturbance torque, and unmodeled uncertainty. First, two-layer prescribed performance is preselected for both the attitude angular and angular velocity tracking errors. Subsequently, a distributed two-layer performance controller is devised, which can guarantee that all the involved closed-loop signals are uniformly ultimately bounded. In order to tackle the defect of statically two-layer performance controller, learning-based control strategy is introduced to serve as an adaptive supplementary controller based on adaptive dynamic programming technique. This enhances the adaptiveness of the statically two-layer performance controller with respect to unexpected uncertainty dramatically, without any prior knowledge of the inertial information. Furthermore, by employing the robustly positively invariant theory, the input-to-state stability is rigorously proven under the designed learning-based distributed controller. Finally, two groups of simulation examples are organized to validate the feasibility and effectiveness of the proposed distributed control approach.
Differential effects of makeup on perceived age

Makeup accentuates three youth-related visual features - skin homogeneity, facial contrast, and facial feature size. By manipulating these visual features, makeup should make faces appear younger. We tested this hypothesis in an experiment in which participants estimated the age of carefully controlled photographs of faces with and without makeup. We found that 40- and especially 50-year-old women did appear significantly younger when wearing makeup. Contrary to our hypothesis, 30-year-old women looked no different in age with or without makeup, while 20-year-old women looked older with makeup. Two further studies replicated these results, finding that makeup made middle-aged women look younger, but made young women look older. Seeking to better understand why makeup makes young women look older, we ran a final study and found evidence that people associate makeup use with adulthood. By activating associations with adulthood, makeup may provide an upward bias on age estimations of women who are not clearly adult. We propose that makeup affects social perceptions through bottom-up routes, by modifying visual cues such as facial contrast, facial feature size, and skin homogeneity, and also through top-down routes, by activating social representations and norms associated with makeup use.
Circularly Polarized Substrate-Integrated Cylindrical Dielectric Resonator Antenna Array for 60 GHz Applications

A 60 GHz circularly polarized (CP) substrate-integrated cylindrical dielectric resonator antenna (DRA) and its 2 × 2 array are studied. Each DRA element is excited in its HEM11δ mode by an aperture-coupled source. To obtain CP fields, each element is top-loaded by a patch, which has a pair of slits. For the array, a sequential-rotation feeding network is used to widen the bandwidth of the axial ratio. Reasonable agreement between the measured and simulated results is observed. It is found that the array can be used for the 60 GHz band (57–64 GHz), with a peak measured antenna gain of 11.43 dBic.
From Office Environmental Stressors to Work Performance: The Role of Work Patterns

Background: Different studies have shown a relationship between office environmental stressors and performance. However, studying environmental stress in the workplace requires analyzing more specific patterns to generate knowledge about the type of employees who are more or less vulnerable to environmental stressors. The present study analyzes the mediating role of health symptoms and negative emotions in the relationship between stressors and performance in different work patterns (task complexity and interactivity). Methods: There were 83 office workers (n = 603 time points) that took part in a diary study with multilevel design. Results: The appraisal of the environmental stressors is positively related to health-related symptoms, which in turn increase negative emotions, and then decrease the performance of workers who perform complex tasks and interact frequently with other people at work. This mediation is not significant when office workers do not interact frequently with other people at work and/or perform simple, rather than complex tasks. Conclusions: Work patterns play an important role when studying the mediating role of health-related symptoms and negative emotions in the relationship between the appraisal of environmental stressors and performance in office workers. In other words, employees in the ‘interactive and complex’ pattern are more vulnerable to the negative effects of office stressors on performance.
Automatic detection of motion artifacts on MRI using Deep CNN

Motion artifacts on brain Magnetic Resonance Images (MRI) constitute an important factor that degrades the image quality, impacting the quantitative analysis based on structural segmentation. Thus, assessing the image quality is essential to determine if the image fulfills the minimal quality level necessary to the research analysis. Nowadays an MR expert, responsible for quality control, performs a visual check on every acquired image. The MRI database is huge, and this quality screening is time-consuming and fatiguing. We propose to automatically detect the images containing motion artifacts using Deep Convolutional Neural Networks (CNN), currently presenting the best performance on image classification contests. Four renowned architectures were chosen to be fine-tuned, and have their results combined to report the motion artifacts presence on the acquisition. Besides, as Deep CNN filters from lower layers map to smaller regions in the original input and our goal is to detect fine-grained image corruption, the CNNs were adapted to use the output from lower intermediate level as features to the binary classifier. The adapted CNNs were trained and tested using an annotated dataset composed of MRI T1-weighted volumetric acquisitions. The training subset contains 48 images, while the testing subset has 20 images. The method consists of two steps. Firstly CNNs were trained using patches from the three MRI planes (sagittal, axial and coronal). Secondly, the results from the patches are combined to provide the result per acquisition. On the second step, an Artificial Neural Network (ANN) classifier was trained combining the patches results from the four modified Deep CNNs and the patch location information. The overall performance on the test set was 88.27% per patch and 100% per acquisition. The proposed technique can be applied to large datasets, providing, to the quality control expert, the motion artifact presence probability, minimizing the time spent on manual quality control.
Women in Academia and Research: An Overview of the Challenges Toward Gender Equality in Colombia and How to Move Forward

Throughout history and over the last years, women have strongly intended to play central roles in addressing major aspects of the world’s most urgent problems such as global poverty, health and climate change. Women have been in a continuous effort to promote health, educational and environmental sustainability programs. In exchange, women are facing countless obstacles to ensure gender equality. Colombia has until recently been highly affected by decades of violent armed conflicts. Today, peace processes are having considerable success on solving these conflicts, but several social problems still persist. One of these problems is gender inequality that systematically hampers the ability of women to progress and develop their full potential. This reborn Colombia following the peace negotiations, should be the key for having a country where men and women are treated equally and with justice. Instead women rights are still being continuously affected by violence, social exclusion, exploitation and discrimination. Gender inequality creates a big barrier that needs to be overcome by women that want to effectively contribute to a so necessary development of Colombia. Science is not an exception on the field of gender-equality, being a great disparity between men and women present in the Colombian scientific community. In this review we discuss the hurdles that scientific women face in Colombia. Furthermore, and by learning from the experiences of some successful female Colombian Scientists, we shed new lights on how women can overcome these challenges. We will also show how these female scientific leaders from different disciplines have combined efforts to build a better country with regards to science, education, technology and innovation. Lastly, we propose new ways to reach gender equality in the scientific community. We hope that this review will motivate many young female scientists to invest in their careers, and ultimately help in the social development of Colombia.
Hysteresis Loss in Ferrite Permanent Magnets in Rotating Electrical Machinery

In this paper, hysteresis loss in magnetic poles of electrical machines with ferrite permanent magnets is studied. A vibrating sample magnetometer is used to observe the hysteresis behavior of three distinct commercial ferrite magnet grades. The measurement results indicate the presence of additional magnetic phases with reduced coercivity in the samples under study. These phases can act as an additional source of loss in ferrite magnets. A new modeling concept is introduced to simulate the hysteresis behavior of the ferrite magnet grades in the second and first quadrants of the intrinsic BH plane. Finite-element-calculated magnetic field distribution is used as an input for the analytical model to determine the hysteresis loss in the magnets of two distinct permanent magnet machine designs. The results indicate that some hysteresis loss in the ferrite permanent magnet magnetic poles of electrical machines may occur, but it plays a minor role in practice.
Origin and Evolution of Flavin-Based Electron Bifurcating Enzymes

Twelve evolutionarily unrelated oxidoreductases form enzyme complexes that catalyze the simultaneous coupling of exergonic and endergonic oxidation–reduction reactions to circumvent thermodynamic barriers and minimize free energy loss in a process known as flavin-based electron bifurcation. Common to these 12 bifurcating (Bf) enzymes are protein-bound flavin, the proposed site of bifurcation, and the electron carrier ferredoxin. Despite the documented role of Bf enzymes in balancing the redox state of intracellular electron carriers and in improving the efficiency of cellular metabolism, a comprehensive description of the diversity and evolutionary history of Bf enzymes is lacking. Here, we report the taxonomic distribution, functional diversity, and evolutionary history of Bf enzyme homologs in 4,588 archaeal, bacterial, and eukaryal genomes and 3,136 community metagenomes. Bf homologs were primarily detected in the genomes of anaerobes, including those of sulfate-reducers, acetogens, fermenters, and methanogens. Phylogenetic analyses of Bf enzyme catalytic subunits (oxidoreductases) suggest they were not a property of the Last Universal Common Ancestor of Archaea and Bacteria, which is consistent with the limited and unique taxonomic distributions of enzyme homologs among genomes. Further, phylogenetic analyses of oxidoreductase subunits reveal that non-Bf homologs predate Bf homologs. These observations indicate that multiple independent recruitments of flavoproteins to existing oxidoreductases enabled coupling of numerous new electron Bf reactions. Consistent with the role of these enzymes in the energy metabolism of anaerobes, homologs of Bf enzymes were enriched in metagenomes from subsurface environments relative to those from surface environments. Phylogenetic analyses of homologs from metagenomes reveal that the earliest evolving homologs of most Bf enzymes are from subsurface environments, including fluids from subsurface rock fractures and hydrothermal systems. Collectively, these data suggest strong selective pressures drove the emergence of Bf enzyme complexes via recruitment of flavoproteins that allowed for an increase in the efficiency of cellular metabolism and improvement in energy capture in anaerobes inhabiting a variety of subsurface anoxic habitats where the energy yield of oxidation-reduction reactions is generally low.
Sexual Harassment at Institutions of Higher Education: Prevalence, Risk, and Extent

Sexual harassment is a pervasive problem on college campuses. Across eight academic campuses, 16,754 students participated in an online study that included questions about sexual harassment victimization by a faculty/staff member or by a peer since enrollment at their Institution of Higher Education (IHE). Utilizing an intersectional theory and hurdle models, this study explored the effects of gender, race/ethnicity, sexual orientation, age at enrollment, student status, and time spent at institution on students’ risk for peer- and faculty/staff-perpetrated sexual harassment victimization, as well as the extent of victimization for students who experience harassment. Across institutions, 19% of students reported experiencing faculty/staff-perpetrated sexual harassment and 30% reported experiencing peer-perpetrated sexual harassment. Hypotheses related to intersectional impacts were partially supported, with most significant findings in main effects. Time at institution was found to increase both risk and extent of victimization of both types of harassment. Traditional undergraduate students, non-Latinx White students, female students, and gender and sexual minority students were found to be at increased risk for harassment. Being female increases the odds of experiencing both faculty/staff and peer sexual harassment by 86% and 147%, respectively. Latinx students and students with an ethnicity other than White reported less victimization, but those who reported sexual harassment faced greater extent of harassing behaviors. A discussion of these findings for institutional program planning and policy is explored.