Miracle-dz/UniFAR-FTUs huggingface dataset:
Executive Functions in Insomnia Disorder: A Systematic Review and Exploratory Meta-Analysis

Background: Executive functions (EFs) are involved in the control of basic psychological processes such as attention and memory and also contribute to emotion regulation. Research on the presence of EFs impairments in insomnia yielded inconsistent results. Therefore, we performed a systematic review of the literature on three EFs: inhibitory control, working memory, and cognitive flexibility in adults with insomnia in order to investigate the presence and magnitude of insomnia-related EFs impairments. Methods: PubMed, Scopus, Medline, and PsycINFO were searched. Risk of bias assessment of included studies was performed by two independent researchers. Findings were summarised using both a narrative approach and meta-analysis. Cohen's d was calculated at 95% confidence interval (CI) as effect size of between groups differences. Results: Twenty-eight studies comparing adult individuals with a diagnosis of insomnia and healthy controls on neuropsychological measures of EFs were included. Narrative synthesis revealed substantial variability across study findings. Factors that were primarily hypothesised to account for this variability are: objective sleep impairments and test sensitivity. Exploratory meta-analysis showed impaired performance of small to moderate magnitude in individuals with insomnia as compared to controls in reaction times, but not accuracy rates, of inhibitory control (d = −0.32, 95% CI: −0.52 to −0.13) and cognitive flexibility tasks (d = −0.30, 95% CI: −0.59 to −0.01). Performance in working memory tasks was also significantly impacted (d = −0.19, 95% CI: −0.38 to −0.00). Effects sizes were larger when insomnia was associated with objective sleep impairments, rather than normal sleep. Conclusions: We gathered evidence supporting small to moderate deficits in EFs in individuals with insomnia. Due to the small sample size results should be considered preliminary and interpreted carefully.
Fracture Limits of Maxillary Fourth Premolar Teeth in Domestic Dogs Under Applied Forces

A cadaveric study was performed to investigate the external mechanical forces required to fracture maxillary fourth premolar teeth in domestic dogs and describe a clinically relevant model of chewing forces placed on functionally important teeth in which fracture patterns are consistent with those defined by the American Veterinary Dental College (AVDC). Twenty-four maxillary fourth premolar teeth were harvested from dog cadavers. Samples consisted of teeth with surrounding alveolar bone potted in polycarbonate cylinders filled with acrylic. The cylinders were held by an aluminum device at an angle of 60° with respect to the ground. An axial compression test was performed, creating a force upon the occluso-palatal aspects of the main cusps of the crowns of the teeth. The highest compressive force prior to failure was considered the maximum force sustained by the teeth. Results showed the mean maximum force (± SD) sustained by the tested teeth at the point of fracture was 1,281 N (± 403 N) at a mean impact angle (± SD) of 59.7° (± 5.2°). The most common fracture type that occurred among all samples was a complicated crown fracture (n = 12), followed by an uncomplicated crown fracture (n = 6), complicated crown-root fracture (n = 5), and uncomplicated crown-root fracture (n = 1). There was no statistically significant correlation between dog breed, age, weight, impact angle, crown height or crown diameter, and the maximum force applied at the point of fracture. The only independent variable that remained significantly associated with maximum force was the crown height to diameter ratio (p = 0.005), suggesting that a decreased ratio increases tooth fracture resistance. The methodology described herein has been successful in creating a pattern of fracture of maxillary fourth premolar teeth consistent with that defined by the AVDC under angled compression at forces within the maximum chewing capability of the average domestic dog.
Regulation of Type 2 Immunity in Myocardial Infarction

Type 2 immunity participates in the pathogeneses of helminth infection and allergic diseases. Emerging evidence indicates that the components of type 2 immunity are also involved in maintaining metabolic hemostasis and facilitating the healing process after tissue injury. Numerous preclinical studies have suggested regulation of type 2 immunity-related cytokines, such as interleukin-4, -13, and -33, and cell types, such as M2 macrophages, mast cells, and eosinophils, affects cardiac functions after myocardial infarction (MI), providing new insights into the importance of immune modulation in the infarcted heart. This review provides an overview of the functions of these cytokines and cells in the setting of MI as well as their potential to predict the severity and prognosis of MI.
Mycobacterial Aminoglycoside Acetyltransferases: A Little of Drug Resistance, and a Lot of Other Roles

Aminoglycoside acetyltransferases are important determinants of resistance to aminoglycoside antibiotics in most bacterial genera. In mycobacteria, however, aminoglycoside acetyltransferases contribute only partially to aminoglycoside susceptibility since they are related with low level resistance to these antibiotics (while high level aminoglycoside resistance is due to mutations in the ribosome). Instead, aminoglycoside acetyltransferases contribute to other bacterial functions, and this can explain its widespread presence along species of genus Mycobacterium. This review is focused on two mycobacterial aminoglycoside acetyltransferase enzymes. First, the aminoglycoside 2′-N-acetyltransferase [AAC(2′)], which was identified as a determinant of weak aminoglycoside resistance in M. fortuitum, and later found to be widespread in most mycobacterial species; AAC(2′) enzymes have been associated with resistance to cell wall degradative enzymes, and bactericidal mode of action of aminoglycosides. Second, the Eis aminoglycoside acetyltransferase, which was identified originally as a virulence determinant in M. tuberculosis (enhanced intracellular survival); Eis protein in fact controls production of pro-inflammatory cytokines and other pathways. The relation of Eis with aminoglycoside susceptibility was found after the years, and reaches clinical significance only in M. tuberculosis isolates resistant to the second-line drug kanamycin. Given the role of AAC(2′) and Eis proteins in mycobacterial biology, inhibitory molecules have been identified, more abundantly in case of Eis. In conclusion, AAC(2′) and Eis have evolved from a marginal role as potential drug resistance mechanisms into a promising future as drug targets.
Brown and Brite: The Fat Soldiers in the Anti-obesity Fight

Brown adipose tissue (BAT) is proposed to maintain thermal homeostasis through dissipation of chemical energy as heat by the uncoupling proteins (UCPs) present in their mitochondria. The recent demonstration of the presence of BAT in humans has invigorated research in this area. The research has provided many new insights into the biology and functioning of this tissue and the biological implications of its altered activities. Another finding of interest is browning of white adipose tissue (WAT) resulting in what is known as beige/brite cells, which have increased mitochondrial proteins and UCPs. In general, it has been observed that the activation of BAT is associated with various physiological improvements such as a reduction in blood glucose levels increased resting energy expenditure and reduced weight. Given the similar physiological functions of BAT and beige/ brite cells and the higher mass of WAT compared to BAT, it is likely that increasing the brite/beige cells in WATs may also lead to greater metabolic benefits. However, development of treatments targeting brown fat or WAT browning would require not only a substantial understanding of the biology of these tissues but also the effect of altering their activity levels on whole body metabolism and physiology. In this review, we present evidence from recent literature on the substrates utilized by BAT, regulation of BAT activity and browning by circulating molecules. We also present dietary and pharmacological activators of brown and beige/brite adipose tissue and the effect of physical exercise on BAT activity and browning.
Role of Soluble Epoxide Hydrolase in Metabolism of PUFAs in Psychiatric and Neurological Disorders

Inflammation plays a key role in the pathogenesis of a number of psychiatric and neurological disorders. Soluble epoxide hydrolases (sEH), enzymes present in all living organisms, metabolize epoxy fatty acids (EpFAs) to corresponding 1,2-diols by the addition of a molecule of water. Accumulating evidence suggests that sEH in the metabolism of polyunsaturated fatty acids (PUFAs) plays a key role in inflammation. Preclinical studies demonstrated that protein expression of sEH in the prefrontal cortex, striatum, and hippocampus from mice with depression-like phenotype was higher than control mice. Furthermore, protein expression of sEH in the parietal cortex from patients with major depressive disorder was higher than controls. Interestingly, Ephx2 knock-out (KO) mice exhibit stress resilience after chronic social defeat stress. Furthermore, the sEH inhibitors have antidepressant effects in animal models of depression. In addition, pharmacological inhibition or gene KO of sEH protected against dopaminergic neurotoxicity in the striatum after repeated administration of MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) in an animal model of Parkinson’s disease (PD). Protein expression of sEH in the striatum from MPTP-treated mice was higher than control mice. A number of studies using postmortem brain samples showed that the deposition of protein aggregates of α-synuclein, termed Lewy bodies, is evident in multiple brain regions of patients from PD and dementia with Lewy bodies (DLB). Moreover, the expression of the sEH protein in the striatum from patients with DLB was significantly higher compared with controls. Interestingly, there was a positive correlation between sEH expression and the ratio of phosphorylated α-synuclein to α-synuclein in the striatum. In the review, the author discusses the role of sEH in the metabolism of PUFAs in inflammation-related psychiatric and neurological disorders.
The Tenets of Teneurin: Conserved Mechanisms Regulate Diverse Developmental Processes in the Drosophila Nervous System

To successfully integrate a neuron into a circuit, a myriad of developmental events must occur correctly and in the correct order. Neurons must be born and grow out towards a destination, responding to guidance cues to direct their path. Once arrived, each neuron must segregate to the correct sub-region before sorting through a milieu of incorrect partners to identify the correct partner with which they can connect. Finally, the neuron must make a synaptic connection with their correct partner; a connection that needs to be broadly maintained throughout the life of the animal while remaining responsive to modes of plasticity and pruning. Though many intricate molecular mechanisms have been discovered to regulate each step, recent work showed that a single family of proteins, the Teneurins, regulates a host of these developmental steps in Drosophila – an example of biological adaptive reuse. Teneurins first influence axon guidance during early development. Once neurons arrive in their target regions, Teneurins enable partner matching and synapse formation in both the central and peripheral nervous systems. Despite these diverse processes and systems, the Teneurins use conserved mechanisms to achieve these goals, as defined by two tenets: 1) transsynaptic interactions with each other and 2) membrane stabilization via an interaction and regulation of the cytoskeleton. These processes are further distinguished by 1) the nature of the transsynaptic interaction – homophilic interactions (between the same Teneurins) to engage partner matching and heterophilic interactions (between different Teneurins) to enable synaptic connectivity and the proper apposition of pre- and postsynaptic sites and 2) the location of cytoskeletal regulation (presynaptic cytoskeletal regulation in the CNS and postsynaptic regulation of the cytoskeleton at the NMJ). Thus, both the roles and the mechanisms governing them are conserved across processes and synapses. In this review, we will highlight the contributions of Drosophila synaptic biology to our understanding of the Teneurins and discuss the mechanistic conservation that allows the Teneurins to achieve common neurodevelopmental goals. Finally, we will posit the next steps for understanding how this remarkably versatile family of proteins functions to control multiple distinct events in the creation of a nervous system.
Revised JNLPBA Corpus: A Revised Version of Biomedical NER Corpus for Relation Extraction Task

The advancement of biomedical named entity recognition (BNER) and biomedical relation extraction (BRE) researches promotes the development of text mining in biological domains. As a cornerstone of BRE, robust BNER system is required to identify the mentioned NEs in plain texts for further relation extraction stage. However, the current BNER corpora, which play important roles in these tasks, paid less attention to achieve the criteria for BRE task. In this study, we present Revised JNLPBA corpus, the revision of JNLPBA corpus, to broaden the applicability of a NER corpus from BNER to BRE task. We preserve the original entity types including protein, DNA, RNA, cell line and cell type while all the abstracts in JNLPBA corpus are manually curated by domain experts again basis on the new annotation guideline focusing on the specific NEs instead of general terms. Simultaneously, several imperfection issues in JNLPBA are pointed out and made up in the new corpus. To compare the adaptability of different NER systems in Revised JNLPBA and JNLPBA corpora, the F1-measure was measured in three open sources NER systems including BANNER, Gimli and NERSuite. In the same circumstance, all the systems perform average 10% better in Revised JNLPBA than in JNLPBA. Moreover, the cross-validation test is carried out which we train the NER systems on JNLPBA/Revised JNLPBA corpora and access the performance in both protein-protein interaction extraction (PPIE) and biomedical event extraction (BEE) corpora to confirm that the newly refined Revised JNLPBA is a competent NER corpus in biomedical relation application. The revised JNLPBA corpus is freely available at iasl-btm.iis. sinica.edu. tw/BNER/Content/Revised_JNLPBA. zip.
Learning for Multi-Model and Multi-Type Fitting

Multi-model fitting has been extensively studied from the random sampling and clustering perspectives. Most assume that only a single type/class of model is present and their generalizations to fitting multiple types of models/structures simultaneously are non-trivial. The inherent challenges include choice of types and numbers of models, sampling imbalance and parameter tuning, all of which render conventional approaches ineffective. In this work, we formulate the multi-model multi-type fitting problem as one of learning deep feature embedding that is clustering-friendly. In other words, points of the same clusters are embedded closer together through the network. For inference, we apply K-means to cluster the data in the embedded feature space and model selection is enabled by analyzing the K-means residuals. Experiments are carried out on both synthetic and real world multi-type fitting datasets, producing state-of-the-art results. Comparisons are also made on single-type multi-model fitting tasks with promising results as well.
Geometric Scattering for Graph Data Analysis

We explore the generalization of scattering transforms from traditional (e.g., image or audio) signals to graph data, analogous to the generalization of ConvNets in geometric deep learning, and the utility of extracted graph features in graph data analysis. In particular, we focus on the capacity of these features to retain informative variability and relations in the data (e.g., between individual graphs, or in aggregate), while relating our construction to previous theoretical results that establish the stability of similar transforms to families of graph deformations. We demonstrate the application the our geometric scattering features in graph classification of social network data, and in data exploration of biochemistry data.
A NOVEL METHOD FOR IDENTIFYING EXOPLANETARY RINGS

The discovery of rings around extrasolar planets (“exorings”) is one of the next breakthroughs in exoplanetary research. Previous studies have explored the feasibility of detecting exorings with present and future photometric sensitivities by seeking anomalous deviations in the residuals of a standard transit light curve fit, at the level of ≃ 100 ? > ppm for Kronian rings. In this work, we explore two much larger observational consequences of exorings: (1) the significant increase in transit depth that may lead to the misclassification of ringed planetary candidates as false-positives and/or the underestimation of planetary density; and (2) the so-called “photo-ring” effect, a new asterodensity profiling effect, revealed by a comparison of the light curve derived stellar density to that measured with independent methods (e.g., asteroseismology). While these methods do not provide an unambiguous detection of exorings, we show that the large amplitude of these effects, combined with their relatively simple analytic description, makes them highly suited to large-scale surveys to identify candidate ringed planets worthy of more detailed investigation. Moreover, these methods lend themselves to ensemble analyses seeking to uncover evidence of a population of ringed planets. We describe the method in detail, develop the basic underlying formalism, and test it in the parameter space of rings and transit configuration. We discuss the prospects of using this method for the first systematic search of exoplanetary rings in the Kepler database and provide a basic computational code for implementing it.
New Results on Fuzzy Synchronization for a Kind of Disturbed Memristive Chaotic System

This paper concerns the problem on the fuzzy synchronization for a kind of disturbed memristive chaotic system. First, based on fuzzy theory, the fuzzy model for a memristive chaotic system is presented; next, based on H-infinity technique, a multidimensional fuzzy controller and a single-dimensional fuzzy controller are designed to realize the synchronization of master-slave chaotic systems with disturbances. Finally, some typical examples are included to illuminate the correctness of the given control method.
Multi-hop Backscatter Tag-to-Tag Networks

We characterize the performance of a backscatter tag-to-tag (T2T) multi-hop network. For this, we developed a discrete component-based backscatter T2T transceiver and a communication protocol suite. The protocol composed of a novel (i) flooding-based link control tailored towards backscatter transmission, and (ii) low-power listening MAC. The MAC design is based on the new insight that backscatter reception is more energy costly than transmission. Our experiments show that multi-hopping extends the coverage of backscatter networks by enabling longer backward T2T links (tag far from the exciter sending to the tag close to the exciter). Four hops, for example, extend the communication range by a factor of two. Furthermore, we show that dead spots in multi-hop T2T networks are far less significant than those in the single-hop T2T networks.
Emergence of Hierarchy via Reinforcement Learning Using a Multiple Timescale Stochastic RNN

Although recurrent neural networks (RNNs) for reinforcement learning (RL) have addressed unique advantages in various aspects, e. g. , solving memory-dependent tasks and meta-learning, very few studies have demonstrated how RNNs can solve the problem of hierarchical RL by autonomously developing hierarchical control. In this paper, we propose a novel model-free RL framework called ReMASTER, which combines an off-policy actor-critic algorithm with a multiple timescale stochastic recurrent neural network for solving memory-dependent and hierarchical tasks. We performed experiments using a challenging continuous control task and showed that: (1) Internal representation necessary for achieving hierarchical control autonomously develops through exploratory learning. (2) Stochastic neurons in RNNs enable faster relearning when adapting to a new task which is a recomposition of sub-goals previously learned.
Stochastic Frank-Wolfe for Composite Convex Minimization

A broad class of convex optimization problems can be formulated as a semidefinite program (SDP), minimization of a convex function over the positive-semidefinite cone subject to some affine constraints. The majority of classical SDP solvers are designed for the deterministic setting where problem data is readily available. In this setting, generalized conditional gradient methods (aka Frank-Wolfe-type methods) provide scalable solutions by leveraging the so-called linear minimization oracle instead of the projection onto the semidefinite cone. Most problems in machine learning and modern engineering applications, however, contain some degree of stochasticity. In this work, we propose the first conditional-gradient-type method for solving stochastic optimization problems under affine constraints. Our method guarantees O(k−1/3) convergence rate in expectation on the objective residual and O(k−5/12) on the feasibility gap.
Attractors of Compactly Generated Semigroups of Regular Polynomial Mappings

We investigate the metric space of pluriregular sets as well as the contractions on that space induced by infinite compact families of proper polynomial mappings of several complex variables. The topological semigroups generated by such families, with composition as the semigroup operation, lead to the constructions of a variety of Julia-type pluriregular sets. The generating families can also be viewed as infinite iterated function systems with compact attractors. We show that such attractors can be approximated both deterministically and probabilistically in a manner of the classic chaos game.
Improving Adversarial Robustness of Ensembles with Diversity Training

Deep Neural Networks are vulnerable to adversarial attacks even in settings where the attacker has no direct access to the model being attacked. Such attacks usually rely on the principle of transferability, whereby an attack crafted on a surrogate model tends to transfer to the target model. We show that an ensemble of models with misaligned loss gradients can provide an effective defense against transfer-based attacks. Our key insight is that an adversarial example is less likely to fool multiple models in the ensemble if their loss functions do not increase in a correlated fashion. To this end, we propose Diversity Training, a novel method to train an ensemble of models with uncorrelated loss functions. We show that our method significantly improves the adversarial robustness of ensembles and can also be combined with existing methods to create a stronger defense.
A Framework for Understanding Unintended Consequences of Machine Learning

As machine learning increasingly affects people and society, it is important that we strive for a comprehensive and unified understanding of potential sources of unwanted consequences. For instance, downstream harms to particular groups are often blamed on "biased data," but this concept encompass too many issues to be useful in developing solutions. In this paper, we provide a framework that partitions sources of downstream harm in machine learning into six distinct categories spanning the data generation and machine learning pipeline. We describe how these issues arise, how they are relevant to particular applications, and how they motivate different solutions. In doing so, we aim to facilitate the development of solutions that stem from an understanding of application-specific populations and data generation processes, rather than relying on general statements about what may or may not be "fair. "