Miracle-dz/UniFAR-FTUs huggingface dataset:
Maternal Parenting Stress, Disengagement Strategies for Managing Parenting Stress, and Hazardous Alcohol Use among Mothers of Adolescents

Abstract Objective: According to the reward- stress dysregulation model of addiction and parenting, parenting stress may play a critical role in the etiology and maintenance of substance misuse. To date, limited work has examined how parenting stress may relate to hazardous alcohol use among mothers of adolescents. The present study examined if parenting stress interacted with maternal stress responses (i.e., disengagement coping, involuntary disengagement stress responses) in relation to maternal hazardous alcohol use. Method: Two hundred and fifty-three mothers (n = 147 hazardous alcohol users; 58% of the sample) of adolescents between the ages of 12 and 16 years participated in the current study. Mothers were recruited using Qualtrics panel services. Mothers reported on their parenting stress, strategies for managing parenting stress, and use of alcohol. Mothers also reported basic demographic information (Mage = 41.10 years, 86.6% identified as white, 81.1% of mothers were married). Interaction analyses were used to test hypotheses. Results: Maternal parenting stress interacted with maternal involuntary disengagement stress responses in relation to maternal hazardous alcohol use, such that at average and high levels of disengagement stress responses, parenting stress was associated with hazardous alcohol use. These results were robust to covariates (i.e., family income, maternal anxiety symptoms, maternal depressive symptoms, general levels of maternal stress, maternal cigarette use). Maternal disengagement coping strategies were not related to maternal hazardous alcohol use. Conclusion: Results suggest that parenting stress and involuntary disengagement stress responses may be associated with hazardous alcohol use among mothers of adolescents.
Automatic skin lesions detection from images through microscopic hybrid features set and machine learning classifiers

Skin cancer occurrences increase exponentially worldwide due to the lack of awareness of significant populations and skin specialists. Medical imaging can help with early detection and more accurate diagnosis of skin cancer. The physicians usually follow the manual diagnosis method in their clinics but nonprofessional dermatologists sometimes affect the accuracy of the results. Thus, the automated system is required to assist physicians in diagnosing skin cancer at early stage precisely to decrease the mortality rate. This article presents an automatic skin lesions detection through a microscopic hybrid feature set and machine learning‐based classification. The employment of deep features through AlexNet architecture with local optimal‐oriented pattern can accurately predict skin lesions. The proposed model is tested on two open‐access datasets PAD‐UFES‐20 and MED‐NODE comprising melanoma and nevus images. Experimental results on both datasets exhibit the efficacy of hybrid features with the help of machine learning. Finally, the proposed model achieved 94.7% accuracy using an ensemble classifier.
Epigenetics in drug disposition & drug therapy: symposium report of the 24th North American meeting of the International Society for the Study of Xenobiotics (ISSX)

Abstract The 24th North American International Society for the Study of Xenobiotics (ISSX) meeting, held virtually from September 13 to 17, 2021, embraced the theme of “Broadening Our Horizons. ” This reinforces a key mission of ISSX: striving to share innovative science related to drug discovery and development. Session speakers and the ISSX New Investigators Group, which supports the scientific and professional development of student and early career ISSX members, elected to highlight the scientific content presented during the captivating session titled, “Epigenetics in Drug Disposition & Drug Therapy. ” The impact genetic variation has on drug response is well established; however, this session underscored the importance of investigating the role of epigenetics in drug disposition and drug discovery. Session speakers, Drs. Ning, McClay, and Lazarus, detailed mechanisms by which epigenetic players including long non-coding RNA (lncRNAs), microRNA (miRNAs), DNA methylation, and histone acetylation can alter the expression of genes involved in pharmacokinetics, pharmacodynamics, and toxicity. Dr. Ning detailed current knowledge about miRNAs and lncRNAs and the mechanisms by which they can affect the expression of drug metabolizing enzymes (DMEs) and nuclear receptors. Dr. Lazarus discussed the potential role of miRNAs on UDP-glucuronosyltransferase (UGT) expression and activity. Dr. McClay provided evidence that aging alters methylation and acetylation of DMEs in the liver, affecting gene expression and activity. These topics, compiled by the symposium organizers, presenters, and the ISSX New Investigators Group, are herein discussed, along with exciting future perspectives for epigenetics in drug disposition and drug discovery research.
Mid‐ and Long‐Term Comparison Analysis of Two Approaches for the Treatment of Level III or Higher Lenke–Silva Adult Degenerative Scoliosis: Radical or Limited Surgery?

As the population in general is living longer, less invasive adult degenerative scoliosis (ADS) surgery that balances risks and benefits requires long‐term clinical outcomes to determine its strengths and weaknesses. We design a retrospective study to compare the postoperative mid‐ and long‐term outcomes in terms of efficacy, surgical complications, and reoperation rate of patients with ADS treated with two different surgical approaches (long‐segment complete reconstruction or short‐segment limited intervention).
Autism-spectrum traits in neurotypicals predict the embodiment of manipulation knowledge about object concepts: Evidence from eyetracking

Sensorimotor-based theories of cognition predict that even subtle developmental motor differences, such as those characterizing autism spectrum disorder (ASD), impact how we represent the meaning of manipulable objects (e.g., faucet). Here, we test 85 neurotypical participants, who varied widely on the Adult Autism Spectrum Quotient (AQ), a measure intended to capture variability in ASD characteristics in the general adult population (participant scores were all below the clinical threshold for autism). Participants completed a visual world eyetracking task designed to assess the activation of conceptual representations of manipulable objects. Participants heard words referring to manually manipulable objects (e.g., faucet) while we recorded their eye movements to arrays of four objects: the named object, a related object typically manipulated similarly (e.g., jar), and two unrelated objects. Consistent with prior work, we observed more looks to the related object than to the unrelated ones (i.e., a manipulation-relatedness effect). This effect likely reflects the overlapping conceptual representations of objects sharing manipulation characteristics (e.g., faucet and jar), due to embodied sensorimotor properties being part of their representations. Critically, we observed—among typically developed young adults—that as AQ scores increased, manipulation-relatedness effects decreased. In contrast, in a visual control condition, in which a target object was paired with related objects of a similar shape (e.g., snake and rope), relatedness effects increased with AQ scores. The results show that AQ scores can predict variation in how object-concept representations are activated for typically developed individuals. More speculatively, they are consistent with the hypothesis that in individuals with ASD, differences in object-concept representations emerge at least in part via differences in sensorimotor experience.
Clostridium Tract Innate Immunity against Misoprostol Impairs Female Reproductive

Fatal cases of acute shock complicating Clostridium sordellii endometritis following medical abortion with mifepristone (also known as RU-486) used with misoprostol were reported. The pathogenesis of this unexpected complication remains enigmatic. Misoprostol is a pharmacomimetic of PGE 2 , an endogenous suppressor of innate immunity. Clinical C. sordellii infections were associated with intravaginal misoprostol administration, suggesting that high misoprostol concentrations within the uterus impair immune responses against C. sordellii . We modeled C. sordellii endometritis in rats to test this hypothesis. The intrauterine but not the intragastric delivery of misoprostol signiﬁcantly worsened mortality from C. sordellii uterine infection, and impaired bacterial clearance in vivo. Misoprostol also reduced TNF- (cid:1) production within the uterus during infection. The intrauterine injection of misoprostol did not enhance mortality from infection by the vaginal commensal bacterium Lactobacillus crispatus . In vitro, misoprostol suppressed macrophage TNF- (cid:1) and chemokine generation following C. sordellii or peptidoglycan challenge, impaired leukocyte phagocytosis of C. sordellii , and inhibited uterine epithelial cell human (cid:2) -defensin expression. These immunosuppressive effects of misoprostol, which were not shared by mifepristone, correlated with the activation of the G s protein-coupled E prostanoid (EP) receptors EP2 and EP4 (macrophages) or EP4 alone (uterine epithelial cells). provide a novel explanation for sepsis leading to death and also suggest that 2 , in which production is exaggerated within the reproductive tract might be an important causal determinant in the pathogenesis of more common infections of the gravid uterus.
Merging automatic differentiation and the adjoint method for photonic inverse design

Optimizing shapes and topology of physical devices is crucial for both scientific and technological advancements, given its wide-ranging implications across numerous industries and research areas. Innovations in shape and topology optimization have been seen across a wide range of fields, notably structural mechanics, fluid mechanics, and photonics. Gradient-based inverse design techniques have been particularly successful for photonic and optical problems, resulting in integrated, miniaturized hardware that has set new standards in device performance. To calculate the gradients, there are typically two approaches: implementing specialized solvers using automatic differentiation or deriving analytical solutions for gradient calculation and adjoint sources by hand. In this work, we propose a middle ground and present a hybrid approach that leverages and enables the benefits of automatic differentiation and machine learning frameworks for handling gradient derivation while using existing, proven solvers for numerical solutions. Utilizing the adjoint method, we turn existing numerical solvers differentiable and seamlessly integrate them into an automatic differentiation framework. Further, this enables users to integrate the optimization environment with machine learning applications which could lead to better photonic design workflows. We illustrate the approach through two distinct examples: optimizing the Purcell factor of a magnetic dipole in the vicinity of an optical nanocavity and enhancing the light extraction efficiency of a {\textmu}LED.
Bayesian Methodologies with pyhf

bayesian_pyhf is a Python package that allows for the parallel Bayesian and frequentist evaluation of multi-channel binned statistical models. The Python library pyhf is used to build such models according to the HistFactory framework and already includes many frequentist inference methodologies. The pyhf-built models are then used as data-generating model for Bayesian inference and evaluated with the Python library PyMC. Based on Monte Carlo Chain Methods, PyMC allows for Bayesian modelling and together with the arviz library offers a wide range of Bayesian analysis tools.
HP Windows Mixed Reality vs Meta 2: Investigating Differences in HP Windows Mixed Reality vs Meta 2: Investigating Differences in Workload and Usability for a Ball-sorting Task Workload and Usability for a Ball-sorting Task

:ABSTRACT: Perceived workload and usability are crucial components of human-computer interactions. Currently, there is a gap in research comparing Augmented Reality (AR) and Virtual Reality (VR) systems for workload and usability. This study attempts to bridge that gap through the comparison of the HP Windows Mixed Reality system and the Meta 2 system for a ball-sorting task. Subjective questionnaires on workload and usability were implemented as comparative measures for three game scenarios of increasing difficulty. Forty-one participants were recruited from the University of Central Florida and its surrounding communities. Results showed significantly lower cumulative total workload and greater usability (for the subscale of ease of use) for the HP Windows Mixed Reality system when compared to the Meta 2 system. There were no statistically significant differences reported for the other usability subscales between the two systems. Also, there were no statistically significant differences in total workload within the three scenarios for both systems. The findings could be attributed to differences in control schemes (i.e., native handheld controllers versus hand gestures), user experience with AR and VR systems, and difficulty of task scenarios.
Accumulation in

Citrus is a complex source of carotenoids with the largest number of carotenoids found in any fruit. Carotenoid concentration and composition vary greatly among citrus varieties. Satsuma mandarin ( Citrus unshiu Marc.) predominantly accumulates β -cryptoxanthin in the juice sacs. Valencia orange ( Citrus sinensis Osbeck) predominantly accumulates violaxanthin isomers in the juice sacs. Lisbon lemon ( Citrus limon Burm.f.) accumulates low level of carotenoids in the juice sacs. To elucidate the carotenoid accumulation in citrus fruit maturation, the expression of genes related to carotenoid biosynthesis and catabolism was investigated in the three citrus varieties exhibited different carotenoid profile. The results showed that the carotenoid accumulation during citrus fruit maturation is highly regulated by the coordination of the expression for the genes related to carotenoid biosynthesis and catabolism in both flavedo and juice sacs. ‘Tamami’ is a hybrid between ‘Kiyomi’ tangor ( Citrus unshiu Marc. × Citrus sinensis Osbeck) and ‘Wilking’ mandarin ( Citrus nobilis Lour. × Citrus deliciosa Ten.). To elucidate the mechanism of the accumulation of β -cryptoxanthin in ‘Tamami’, a variety accumulating higher β -cryptoxanthin than Satsuma mandarin, the expression of genes related to carotenoid biosynthesis and catabolism was investigated in the juice sacs of ‘Tamami’. The results showed that the mechanism of β -cryptoxanthin accumulation in ‘Tamami’ was similar to that in Satsuma mandarin. Furthermore, in the recent studies, possible factors, which regulate carotenoid concentration and composition in citrus juice sacs were investigated in vitro .
A 3-DoF robotic platform for the rehabilitation and assessment of reaction time and balance skills of MS patients

The central nervous system (CNS) exploits anticipatory (APAs) and compensatory (CPAs) postural adjustments to maintain the balance. The postural adjustments comprising stability of the center of mass (CoM) and the pressure distribution of the body influence each other if there is a lack of performance in either of them. Any predictable or sudden perturbation may pave the way for the divergence of CoM from equilibrium and inhomogeneous pressure distribution of the body. Such a situation is often observed in the daily lives of Multiple Sclerosis (MS) patients due to their poor APAs and CPAs and induces their falls. The way of minimizing the risk of falls in neurological patients is by utilizing perturbation-based rehabilitation, as it is efficient in the recovery of the balance disorder. In light of the findings, we present the design, implementation, and experimental evaluation of a novel 3 DoF parallel manipulator to treat the balance disorder of MS. The robotic platform allows angular motion of the ankle based on its anthropomorphic freedom. Moreover, the end-effector endowed with upper and lower platforms is designed to evaluate both the pressure distribution of each foot and the CoM of the body, respectively. Data gathered from the platforms are utilized to both evaluate the performance of the patients and used in high-level control of the robotic platform to regulate the difficulty level of tasks. In this study, kinematic and dynamic analyses of the robot are derived and validated in the simulation environment. Low-level control of the first prototype is also successfully implemented through the PID controller. The capacity of each platform is evaluated with a set of experiments considering the assessment of pressure distribution and CoM of the foot-like objects on the end-effector. The experimental results indicate that such a system well-address the need for balance skill training and assessment through the APAs and CPAs.
Diagrammatics for the Inverse Problem in Spin Systems and Simple Liquids

Modeling complex systems, like neural networks, simple liquids or flocks of birds, often works in reverse to textbook approaches: given data for which averages and correlations are known, we try to find the parameters of a given model consistent with it. In general, no exact calculation directly from the model is available and we are left with expensive numerical approaches. A particular situation is that of a perturbed Gaussian model with polynomial corrections for continuous degrees of freedom. Indeed perturbation expansions for this case have been implemented in the last 60 years. However, there are models for which the exactly solvable part is non-Gaussian, such as independent Ising spins in a field, or an ideal gas of particles. We implement a diagrammatic perturbative scheme in weak correlations around a non-Gaussian yet solvable probability weight. This applies in particular to spin models (Ising, Potts, Heisenberg) with weak couplings, or to a simple liquid with a weak interaction potential. Our method casts systems with discrete degrees of freedom and those with continuous ones within the same theoretical framework. When the core theory is Gaussian it reduces to the well-known Feynman diagrammatics.
Searching for the extra-tidal stars of globular clusters using high-dimensional analysis and a core particle spray code

Three-body interactions can eject stars from the core of a globular cluster, causing them to enter the Galactic halo as extra-tidal stars. While finding extra-tidal stars is imperative for understanding cluster evolution, connecting isolated extra-tidal field stars back to their birth cluster is extremely difficult. In this work, we present a new methodology consisting of high-dimensional data analysis and a particle spray code to identify extra-tidal stars of any Galactic globular cluster using M3 as a case study. Using the t-Stochastic Neighbour Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) machine learning dimensionality reduction algorithms, we first identify a set of 103 extra-tidal candidates in the APOGEE DR17 data catalogue with chemical abundances similar to M3 stars. To confirm each candidate’s extra-tidal nature, we introduce corespray - a new Python-based three-body particle spray code that simulates extra-tidal stars for any Galactic globular cluster. Using Gaia EDR3 proper motions and APOGEE DR17 radial velocities, we apply multivariate Gaussian modelling and an extreme deconvolution to identify the extra-tidal candidates that are more likely to be associated with a distribution of corespray-simulated M3 extra-tidal stars than the field. Through these methods, we identify 10 new high-probability extra-tidal stars produced via three-body interactions in M3. We also explore whether any of our extra-tidal candidates are consistent with being ejected from M3 through different dynamical processes. Future applications of corespray will yield better understandings of core dynamics, star formation histories and binary fractions in globular clusters.
PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training

Deep learning based semi-supervised learning (SSL) methods have achieved strong performance in medical image segmentation, which can alleviate doctors' expensive annotation by utilizing a large amount of unlabeled data. Unlike most existing semi-supervised learning methods, adversarial training based methods distinguish samples from different sources by learning the data distribution of the segmentation map, leading the segmenter to generate more accurate predictions. We argue that the current performance restrictions for such approaches are the problems of feature extraction and learning preference. In this paper, we propose a new semi-supervised adversarial method called Patch Confidence Adversarial Training (PCA) for medical image segmentation. Rather than single scalar classification results or pixel-level confidence maps, our proposed discriminator creates patch confidence maps and classifies them at the scale of the patches. The prediction of unlabeled data learns the pixel structure and context information in each patch to get enough gradient feedback, which aids the discriminator in convergent to an optimal state and improves semi-supervised segmentation performance. Furthermore, at the discriminator's input, we supplement semantic information constraints on images, making it simpler for unlabeled data to fit the expected data distribution. Extensive experiments on the Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset and the Brain Tumor Segmentation (BraTS) 2019 challenge dataset show that our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation.
Towards An Optimal Solution to Place Bistatic Radars for Belt Barrier Coverage with Minimum Cost

With the rapid growth of threats, sophistication and diversity in the manner of intrusion, traditional belt barrier systems are now faced with a major challenge of providing high and concrete coverage quality to expand the guarding service market. Recent efforts aim at constructing a belt barrier by deploying bistatic radar(s) on a specific line regardless of the limitation on deployment locations, to keep the width of the barrier from going below a specific threshold and the total bistatic radar placement cost is minimized, referred to as the Minimum Cost Linear Placement (MCLP) problem. The existing solutions are heuristic, and their validity is tightly bound by the barrier width parameter that these solutions only work for a fixed barrier width value. In this work, we propose an optimal solution, referred to as the Opt_MCLP, for the"open MCLP problem"that works for full range of the barrier width. Through rigorous theoretical analysis and experimentation, we demonstrate that the proposed algorithms perform well in terms of placement cost reduction and barrier coverage guarantee.
Personalised Robot Behaviour Modelling for Robot-Assisted Therapy in the Context of Autism Spectrum Disorder

In robot-assisted therapy for individuals with Autism Spectrum Disorder, the workload of therapists during a therapeutic session is increased if they have to control the robot manually. To allow therapists to focus on the interaction with the person instead, the robot should be more autonomous, namely it should be able to interpret the person's state and continuously adapt its actions according to their behaviour. In this paper, we develop a personalised robot behaviour model that can be used in the robot decision-making process during an activity; this behaviour model is trained with the help of a user model that has been learned from real interaction data. We use Q-learning for this task, such that the results demonstrate that the policy requires about 10,000 iterations to converge. We thus investigate policy transfer for improving the convergence speed; we show that this is a feasible solution, but an inappropriate initial policy can lead to a suboptimal final return.