Miracle-dz/UniFAR-FTUs huggingface dataset:
Universal motor efficiency improvement using evolutionary optimization

We present a new design procedure that improves the efficiency of a universal motor, the type of motor that is typically used in home appliances and power tools. The goal of our optimization was to find the independent geometrical parameters of the rotor and the stator with the aim of reducing the motor's power losses, which occur in the iron and the copper. Our procedure is based on a genetic algorithm (GA) and by using this procedure we were able to significantly improve the motor's efficiency-the ratio of the motor's output power to its input power. The GA proved to be a simple and efficient search-and-optimization method for solving this day-to-day design problem in industry. It significantly outperformed a conventional "direct" design procedure that we had used previously.
ERP Indices of Attentional Disengagement and Filtering Following Reflexive Attentional Capture

CASSIE BARASH FORD: ERP Indices of Attentional Disengagement and Filtering Following Reflexive Attentional Capture (Under the direction of Joseph B. Hopfinger) The goal of this study was dissociate the mechanism of target selection when distractor suppression is required from spatial disengagement and reorienting. Specifically, we aimed to elicit and compare online event-related potential (ERP) indices of these attention mechanisms, the N2pc, IIN and P4pc. Combining the classic spatial cuing paradigm with a modified visual search design allowed the comparison of these components when attention was either reflexively oriented to the location of an upcoming target, or was reflexively captured to the opposite visual field, requiring disengagement and reorienting to the target. Reaction times and early sensory processing ERP components indicated that attention was reflexively captured by the cue, extending established reflexive attention effects from simple cuing paradigms to visual search. The ERP results suggest that the N2pc reliably indexes the direction of spatial attention, but is not an appropriate marker of the shifting of spatial attention. Significant IIN and P4pc components were found only for conditions requiring disengagement, suggesting that both may reflect the disengagement and reorienting of attention, indexing these mechanisms better than the N2pc.
S6 Kinase Inhibits Intrinsic Axon Regeneration Capacity via AMP Kinase in Caenorhabditis elegans

The ability of axons to regrow after injury is determined by the complex interplay of intrinsic growth programs and external cues. In Caenorhabditis elegans mechanosensory neuron, axons exhibit robust regenerative regrowth following laser axotomy. By surveying conserved metabolic signaling pathways, we have identified the ribosomal S6 kinase RSKS-1 as a new cell-autonomous inhibitor of axon regeneration. RSKS-1 is not required for axonal development but inhibits axon regrowth after injury in multiple neuron types. Loss of function in rsks-1 results in more rapid growth cone formation after injury and accelerates subsequent axon extension. The enhanced regrowth of rsks-1 mutants is partly dependent on the DLK-1 MAPK cascade. An essential output of RSKS-1 in axon regrowth is the metabolic sensor AMP kinase, AAK-2. We further show that the antidiabetic drug phenformin, which activates AMP kinase, can promote axon regrowth. Our data reveal a new function for an S6 kinase acting through an AMP kinase in regenerative growth of injured axons.
Cold-blooded women can detect lies with greater accuracy than other women

ABSTRACT Lies are notoriously difficult to detect. But it appears that some people are better at accomplishing this task than others even though the factors contributing to deception detection accuracy are not well understood. This study explored the influence of empathy on the detection of deception as a function of the detectors’ gender while dark personality traits were statistically controlled. Eighty men and 80 women were requested to judge whether individuals viewed in videos were giving their true opinion or not on current debatable issues (50% truthful and 50% deceptive narratives). Judges were divided into four groups according to their gender and their degree of empathy, as assessed using the Questionnaire Measure of Emotional Empathy. It was found that women with lower levels of empathy distinguished false from true opinions better than women with higher empathy, whereas no such difference was found in men. These results suggest that the degree of empathy in women influences their ability to detect deception and supports recent studies showing that emotional skills negatively affect deception detection ability. We suggest that less empathic women are less affected by emotional contagion and thus may be more able to focus on non-emotional cues that might reveal deception.
Systemic Inflammatory Markers Are Closely Associated with Atherogenic Lipoprotein Subfractions in Patients Undergoing Coronary Angiography

Objective. To investigate the relationship between inflammatory markers and atherogenic lipoprotein subfractions. Methods. We studied 520 eligible subjects who were not receiving any lipid-lowering therapy. The inflammatory markers including white blood cell (WBC) count, high-sensitivity C-reactive protein (hs-CRP), fibrinogen, erythrocyte sedimentation rate (ESR), and D-dimer were measured. A multimarker inflammatory index was developed. Low-density lipoprotein (LDL) and high-density lipoprotein (HDL) separation processes were performed using Lipoprint System. Results. In age- and sex-adjusted analysis, several inflammatory markers (WBC count, hs-CRP, fibrinogen, and ESR) were positively related to circulating non-HDL cholesterol and remnant cholesterol (p < 0.05, all). Among lipoprotein subfractions, we observed a positive association of inflammatory markers with very low-density lipoprotein cholesterol, small LDL cholesterol, and LDL score (p < 0.05, all). Meanwhile, a negative association was detected between inflammatory markers and mean LDL particle size (p < 0.05) or large HDL cholesterol (p < 0.05). Moreover, we found that the relationships between multimarker index quartiles and small LDL cholesterol, LDL score, and mean LDL particle size were slightly stronger in patients with CAD. Conclusions. Systemic inflammatory markers are positively correlated with small LDL cholesterol and LDL score while being negatively linked with mean LDL particle size and large HDL cholesterol, highlighting the potential contribution to increased cardiovascular risk.
CONSTRAINTS ON THE EXTRAGALACTIC BACKGROUND LIGHT FROM VERY HIGH ENERGY GAMMA-RAY OBSERVATIONS OF BLAZARS

The extragalactic background light (EBL) from the infrared to the ultraviolet is difficult to measure directly, but can be constrained with a variety of methods. The EBL photons absorb γ-rays from distant blazars, allowing one to use blazar spectra from atmospheric Cherenkov telescopes (ACTs) to put upper limits on the EBL by assuming a blazar source spectrum. Here, we apply a simple technique, similar to that developed by Schroedter, to the most recent very high energy (VHE) γ-ray observations of blazars to put upper limits on the EBL energy density. This technique is independent of the EBL model and has well defined errors on the constraints. Our results are consistent with EBL measurements and constraints but marginally inconsistent with several EBL models.
Action planning and coping planning for long-term lifestyle change: Theory and assessment

Planning is regarded as highly valuable in the process of health behaviour change. It bridges the gap between behavioural intentions and health behaviour. To further develop this concept, a distinction is made between action planning and coping planning. The latter refers to the mental simulation of overcoming anticipated barriers to action. Action planning and coping planning for physical exercise were examined in a longitudinal study with 352 cardiac patients. They were approached during rehabilitation treatment and followed up at two and four months after discharge. Both planning cognitions were psychometrically identified, and it was found that they operated differently in the behavioural change process. Action plans were more influential early in the rehabilitation process, whereas coping plans were more instrumental later on. Participants with higher levels of coping planning after discharge were more likely to report higher levels of exercise four months after discharge. It is suggested to include both kinds of planning in interventions at different stages in health behaviour change.
Modulation of B-Lymphocyte and NK Cell Activities by Glycoinositolphospholipid Purified from Trypanosoma cruzi

ABSTRACT Glycoinositolphospholipids (GIPLs) are some of the major glycolipids of the Trypanosoma cruzi surface that were previously shown to activate B cells. In the present study, we investigated whether (i) T. cruzi GIPLs could induce immunoglobulin secretion from B cells in the absence of T cells and NK cells and whether (ii) NK cells are also stimulated by the GIPLs. B cells purified from mice deficient in both T and NK cells (CD3ɛ transgenic mice) secreted immunoglobulin in response to the GIPL. This response was increased by coculture with a murine NK cell line. TheT. cruzi GIPL also increased the NK cell (interleukin-2 induced) proliferative response. Our data indicate that the T. cruzi GIPL has a direct stimulatory effect on NK cells and induces immunoglobulin secretion in the absence of T lymphocytes and NK cells. These findings suggest that this T. cruzi-derived molecule may be one of the stimulators that lead to NK cell activation during T. cruzi infection.
Glutamate Receptors in Neuroinflammatory Demyelinating Disease

Multiple sclerosis (MS) is a chronic demyelinating disease of the human central nervous system (CNS). The condition predominantly affects young adults and is characterised by immunological and inflammatory changes in the periphery and CNS that contribute to neurovascular disruption, haemopoietic cell invasion of target tissues, and demyelination of nerve fibres which culminate in neurological deficits that relapse and remit or are progressive. The main features of MS can be reproduced in the inducible animal counterpart, experimental autoimmune encephalomyelitis (EAE). The search for new MS treatments invariably employs EAE to determine drug activity and provide a rationale for exploring clinical efficacy. The preclinical development of compounds for MS has generally followed a conventional, immunotherapeutic route. However, over the past decade, a group of compounds that suppress EAE but have no apparent immunomodulatory activity have emerged. These drugs interact with the N-methyl-D-aspartate (NMDA) and α-amino-3-hydroxy-5-isoxazolepropionic acid (AMPA)/kainate family of glutamate receptors reported to control neurovascular permeability, inflammatory mediator synthesis, and resident glial cell functions including CNS myelination. The review considers the importance of the glutamate receptors in EAE and MS pathogenesis. The use of receptor antagonists to control EAE is also discussed together with the possibility of therapeutic application in demyelinating disease.
Strategic Classification

Machine learning relies on the assumption that unseen test instances of a classification problem follow the same distribution as observed training data. However, this principle can break down when machine learning is used to make important decisions about the welfare (employment, education, health) of strategic individuals. Knowing information about the classifier, such individuals may manipulate their attributes in order to obtain a better classification outcome. As a result of this behavior -- often referred to as gaming -- the performance of the classifier may deteriorate sharply. Indeed, gaming is a well-known obstacle for using machine learning methods in practice; in financial policy-making, the problem is widely known as Goodhart's law. In this paper, we formalize the problem, and pursue algorithms for learning classifiers that are robust to gaming. We model classification as a sequential game between a player named "Jury" and a player named "Contestant. " Jury designs a classifier, and Contestant receives an input to the classifier drawn from a distribution. Before being classified, Contestant may change his input based on Jury's classifier. However, Contestant incurs a cost for these changes according to a cost function. Jury's goal is to achieve high classification accuracy with respect to Contestant's original input and some underlying target classification function, assuming Contestant plays best response. Contestant's goal is to achieve a favorable classification outcome while taking into account the cost of achieving it. For a natural class of "separable" cost functions, and certain generalizations, we obtain computationally efficient learning algorithms which are near optimal, achieving a classification error that is arbitrarily close to the theoretical minimum. Surprisingly, our algorithms are efficient even on concept classes that are computationally hard to learn. For general cost functions, designing an approximately optimal strategy-proof classifier, for inverse-polynomial approximation, is NP-hard.
Effects of gold and PCL- or PLLA-coated silica nanoparticles on brain endothelial cells and the blood–brain barrier

Nanomedicine is a constantly expanding field, facilitating and improving diagnosis and treatment of diseases. As nanomaterials are foreign objects, careful evaluation of their toxicological and functional aspects prior to medical application is imperative. In this study, we aimed to determine the effects of gold and polymer-coated silica nanoparticles used in laser tissue soldering on brain endothelial cells and the blood–brain barrier using rat brain capillary endothelial cells (rBCEC4). All types of nanoparticles were taken up time-dependently by the rBCEC4 cells, albeit to a different extent, causing a time- and concentration-dependent decrease in cell viability. Nanoparticle exposure did not change cell proliferation, differentiation, nor did it induce inflammation. rBCEC4 cells showed blood–brain barrier characteristics including tight junctions. None of the nanoparticles altered the expression of tight junctions or impaired the blood–brain barrier permeability. The findings suggest that effects of these nanoparticles on the metabolic state of cells have to be further characterized before use for medical purposes.
Raman study of lattice vibrations in type II superlattice InAs/InAs1-xSbx

In this work, we report a polarized Raman study on the vibrational properties of the InAs/InAs1-xSbx SLs as well as selected InAs1-xSbx alloys, all grown on GaSb substrates by either MBE or MOCVD, from both growth surface and cleaved edge.
Collective vs Independent Classiﬁcation in Statistical Relational Learning

Statistical Relational Learning (SRL) addresses the problem of performing probabilistic inference on data instances that are correlated. Collective classiﬁcation is an important SRL task, in which related data instances are classiﬁed simultaneously as opposed to independently which is done in independent Machine Learning. In several studies conducted in the last decade, it has been shown that collective classiﬁcation, by exploiting relational information, tends to outperform independent (supervised) classiﬁcation on various relational datasets. However, owing to their ability to exploit relational information collective classiﬁcation algorithms are invariably more complex than their independent counterparts. Moreover, independent classiﬁcation algorithms have been around for a considerably longer period of time and hence have been thoroughly studied both theoretically and experimentally. Consequently, a natural question that arises is: Under what circumstances should we perform collective classiﬁcation? Previously, it had been argued that on relational datasets which exhibit high auto-correlation between related instances linked through arbitrary paths in a relational data graph, collective classiﬁcation is superior. In this paper however, we partition the feature space into (a) Direct features – links from an object type (which contains the class attribute) to itself (b) Indirect features – the remaining set of features (i.e. other links and object types) and argue that high auto-correlation between instances linked through Direct features and low/medium auto-correlation between instances linked through Indirect features is essential if collective classiﬁcation is to signiﬁcantly outperform independent classiﬁcation. Moreover, based on this argument and depending on the setting (i.e. level of auto-correlation) we motivate simple baseline classiﬁcation algorithms which can be used as yardsticks to evaluate the more sophisticated collective classiﬁcation algorithms. We validate our arguments by performing experiments on synthetic and real datasets.
GLOBAL STAR FORMATION REVISITED

A general treatment of disk star formation is developed from a dissipative multiphase model, with the dominant dissipation due to cloud collisions. The Schmidt–Kennicutt (SK) law emerges naturally for star-forming disks and starbursts. We predict that there should be an inverse correlation between Tully–Fisher law and SK law residuals. The model is extended to include a multiphase treatment of supernova feedback that leads to a turbulent pressure-regulated generalization of the star formation law and is applicable to gas-rich starbursts. Enhanced pressure, as expected in merger-induced star formation, enhances star formation efficiency. An upper limit is derived for the disk star formation rate in starbursts that depends on the ratio of global ISM to cloud pressures. We extend these considerations to the case where the interstellar gas pressure in the inner galaxy is dominated by outflows from a central active galactic nucleus (AGN). During massive spheroid formation, AGN-driven winds trigger star formation, resulting in enhanced supernova feedback and outflows. The outflows are comparable to the AGN-boosted star formation rate and saturate in the super-Eddington limit. Downsizing of both SMBH and spheroids is a consequence of AGN-driven positive feedback. Bondi accretion feeds the central black hole with a specific accretion rate that is proportional to the black hole mass. AGN-enhanced star formation is mediated by turbulent pressure and relates spheroid star formation rate to black hole accretion rate. The relation between black hole mass and spheroid velocity dispersion has a coefficient (Salpeter time to gas consumption time ratio) that provides an arrow of time. Highly efficient, AGN-boosted star formation can occur at high redshift.
Evaluating Non-Cognitive Skills among Students Switching Into and Out of Charter Schools in North Carolina

ABSTRACT Despite several charter schools specifically targeting improving students’ noncognitive skills, the majority of charter school literature focuses on cognitive learning outcomes. Using behavioral measures as proxies for noncognitive outcomes, I find that North Carolina charter schools have no effect on tardiness or unexcused absences but are slightly improving total absences, suspensions, and disciplinary infractions. These positive results were driven primarily by charter schools that had been operating for at least five years. My results suggest that charter schools should be evaluated on a wider selection of outcomes than just standardized test scores.
Expression of Glutamate and GABA during the Process of Rat Retinal Synaptic Plasticity Induced by Acute High Intraocular Pressure

Acute high intraocular pressure (HIOP) can induce plastic changes of retinal synapses during which the expression of the presynaptic marker synaptophysin (SYN) has a distinct spatiotemporal pattern from the inner plexiform layer to the outer plexiform layer. We identified the types of neurotransmitters in the retina that participated in this process and determined the response of these neurotransmitters to HIOP induction. The model of acute HIOP was established by injecting normal saline into the anterior chamber of the rat eye. We found that the number of glutamate-positive cells increased successively from the inner part to the outer part of the retina (from the ganglion cell layer to the inner nuclear layer to the outer nuclear layer) after HIOP, which was similar to the spatiotemporal pattern of SYN expression (internally to externally) following HIOP. However, the distribution and intensity of GABA immunoreactivity in the retina did not change significantly at different survival time post injury and had no direct correlation with SYN expression. Our results suggested that the excitatory neurotransmitter glutamate might participate in the plastic process of retinal synapses following acute HIOP, but no evidence was found for the role of the inhibitory neurotransmitter GABA.
Three-stage prediction of protein ?-sheets by neural networks, alignments and graph algorithms

MOTIVATION Protein beta-sheets play a fundamental role in protein structure, function, evolution and bioengineering. Accurate prediction and assembly of protein beta-sheets, however, remains challenging because protein beta-sheets require formation of hydrogen bonds between linearly distant residues. Previous approaches for predicting beta-sheet topological features, such as beta-strand alignments, in general have not exploited the global covariation and constraints characteristic of beta-sheet architectures. RESULTS We propose a modular approach to the problem of predicting/assembling protein beta-sheets in a chain by integrating both local and global constraints in three steps. The first step uses recursive neural networks to predict pairing probabilities for all pairs of interstrand beta-residues from profile, secondary structure and solvent accessibility information. The second step applies dynamic programming techniques to these probabilities to derive binding pseudoenergies and optimal alignments between all pairs of beta-strands. Finally, the third step uses graph matching algorithms to predict the beta-sheet architecture of the protein by optimizing the global pseudoenergy while enforcing strong global beta-strand pairing constraints. The approach is evaluated using cross-validation methods on a large non-homologous dataset and yields significant improvements over previous methods. AVAILABILITY http://www.igb. uci.edu/servers/psss.html.