Using Mindfulness Practices to Increase Self-Regulation in Pre-Kindergarten and Kindergarten-Aged Children

BACKGROUND: Self-regulation is identified in the literature as an early predictor of later life success and an important skill that develops over the course of a lifetime beginning in early childhood (Flook et al., 2015; Montroy et al., 2016; Murray et al., 2017). OBJECTIVE: The purpose of this research study was to assess whether direct instruction of Mindfulness Practices, such as guided meditation and yoga poses (Lee et al., 2020; Poehlmann-Tynan et al., 2016; Zelazo et al., 2012) would increase self-regulatory behaviors, such as impulse control, emotion regulation, and problem-solving in pre-kindergarten and kindergarten aged children. METHOD: Target children were chosen based on teacher nomination of children who displayed a lack of self-regulatory behaviors in combination with the results of the Ages and Stages Questionnaire (Squires & Bricker, 2009). A Mindfulness Practices Intervention, consisting of yoga poses and guided mediation was implemented using a multiple baseline design across classrooms. Data were collected using interval recording for a 10-minute observation daily over a six to nine-week period using an iPhone. Child’s self-regulatory behaviors were recorded using behavior definitions modified from the Regulation-Related Skills Measure (RRSM) (McCoy et al., 2017). RESULTS: All three targeted children displayed increases in self-regulatory behaviors after the Mindfulness Practices were introduced. CONCLUSION: Teacher should consider integrating Mindfulness Practices within their daily classroom schedule, as these practices can positively impact students’ self-regulatory behaviors

Beating the news using social media: the case study of American Idol

We present a contribution to the debate on the predictability of social events using big data analytics. We focus on the elimination of contestants in the American Idol TV shows as an example of a well defined electoral phenomenon that each week draws millions of votes in the USA. This event can be considered as basic test in a simplified environment to assess the predictive power of Twitter signals. We provide evidence that Twitter activity during the time span defined by the TV show airing and the voting period following it correlates with the contestants ranking and allows the anticipation of the voting outcome. Twitter data from the show and the voting period of the season finale have been analyzed to attempt the winner prediction ahead of the airing of the official result. We also show that the fraction of tweets that contain geolocation information allows us to map the fanbase of each contestant, both within the US and abroad, showing that strong regional polarizations occur. The geolocalized data are crucial for the correct prediction of the final outcome of the show, pointing out the importance of considering information beyond the aggregated Twitter signal. Although American Idol voting is just a minimal and simplified version of complex societal phenomena such as political elections, this work shows that the volume of information available in online systems permits the real time gathering of quantitative indicators that may be able to anticipate the future unfolding of opinion formation events

Evolution by Any Other Name: Antibiotic Resistance and Avoidance of the E-Word

The word "evolution" is rarely used in papers from medical journals describing antimicrobial resistance, which may directly impact public perception of the importance of evolutionary biology in our everyday lives

Cytosolic Superoxide Dismutase (SOD1) Is Critical for Tolerating the Oxidative Stress of Zinc Deficiency in Yeast

Zinc deficiency causes oxidative stress in many organisms including the yeast Saccharomyces cerevisiae. Previous studies of this yeast indicated that the Tsa1 peroxiredoxin is required for optimal growth in low zinc because of its role in degrading H2O2. In this report, we assessed the importance of other antioxidant genes to zinc-limited growth. Our results indicated that the cytosolic superoxide dismutase Sod1 is also critical for growth under zinc-limiting conditions. We also found that Ccs1, the copper-delivering chaperone required for Sod1 activity is essential for optimal zinc-limited growth. To our knowledge, this is the first demonstration of the important roles these proteins play under this condition. It has been proposed previously that a loss of Sod1 activity due to inefficient metallation is one source of reactive oxygen species (ROS) under zinc-limiting conditions. Consistent with this hypothesis, we found that both the level and activity of Sod1 is diminished in zinc-deficient cells. However, under conditions in which Sod1 was overexpressed in zinc-limited cells and activity was restored, we observed no decrease in ROS levels. Thus, these data indicate that while Sod1 activity is critical for low zinc growth, diminished Sod1 activity is not a major source of the elevated ROS observed under these conditions

The yeast P5 type ATPase, Spf1, regulates manganese transport into the endoplasmic reticulum

The endoplasmic reticulum (ER) is a large, multifunctional and essential organelle. Despite intense research, the function of more than a third of ER proteins remains unknown even in the well-studied model organism Saccharomyces cerevisiae. One such protein is Spf1, which is a highly conserved, ER localized, putative P-type ATPase. Deletion of SPF1 causes a wide variety of phenotypes including severe ER stress suggesting that this protein is essential for the normal function of the ER. The closest homologue of Spf1 is the vacuolar P-type ATPase Ypk9 that influences Mn2+ homeostasis. However in vitro reconstitution assays with Spf1 have not yielded insight into its transport specificity. Here we took an in vivo approach to detect the direct and indirect effects of deleting SPF1. We found a specific reduction in the luminal concentration of Mn2+ in ∆spf1 cells and an increase following it’s overexpression. In agreement with the observed loss of luminal Mn2+ we could observe concurrent reduction in many Mn2+-related process in the ER lumen. Conversely, cytosolic Mn2+-dependent processes were increased. Together, these data support a role for Spf1p in Mn2+ transport in the cell. We also demonstrate that the human sequence homologue, ATP13A1, is a functionally conserved orthologue. Since ATP13A1 is highly expressed in developing neuronal tissues and in the brain, this should help in the study of Mn2+-dependent neurological disorders

How useful is Active Learning for Image-based Plant Phenotyping?

Deep learning models have been successfully deployed for a diverse array of image-based plant phenotyping applications including disease detection and classification. However, successful deployment of supervised deep learning models requires large amount of labeled data, which is a significant challenge in plant science (and most biological) domains due to the inherent complexity. Specifically, data annotation is costly, laborious, time consuming and needs domain expertise for phenotyping tasks, especially for diseases. To overcome this challenge, active learning algorithms have been proposed that reduce the amount of labeling needed by deep learning models to achieve good predictive performance. Active learning methods adaptively select samples to annotate using an acquisition function to achieve maximum (classification) performance under a fixed labeling budget. We report the performance of four different active learning methods, (1) Deep Bayesian Active Learning (DBAL), (2) Entropy, (3) Least Confidence, and (4) Coreset, with conventional random sampling-based annotation for two different image-based classification datasets. The first image dataset consists of soybean [Glycine max L. (Merr.)] leaves belonging to eight different soybean stresses and a healthy class, and the second consists of nine different weed species from the field. For a fixed labeling budget, we observed that the classification performance of deep learning models with active learning-based acquisition strategies is better than random sampling-based acquisition for both datasets. The integration of active learning strategies for data annotation can help mitigate labelling challenges in the plant sciences applications particularly where deep domain knowledge is required

Use of near-infrared light to reduce symptoms associated with restless legs syndrome in a woman: a case report

<p>Abstract</p> <p>Introduction</p> <p>We describe a potential new treatment option for patients suffering from restless legs syndrome. Contemporary treatment for restless legs syndrome consists mostly of dopaminergic drugs that leave some patients feeling nauseated and dizzy. A non-invasive, drug-free option would open new doors for patients suffering from restless legs syndrome.</p> <p>Case presentation</p> <p>A 69-year-old Caucasian woman met International Restless Legs Syndrome Study Group criteria for the diagnosis of restless legs syndrome. She had been afflicted with restless legs syndrome for over 30 years and tried many of the available pharmaceutical remedies without success. For this study she received 30-minute treatment sessions with near-infrared light, three times a week for four weeks. The restless legs syndrome rating scale was used to track symptom changes; at baseline she scored "27" on the 0 to 40 point scale, which is considered to be "severe". Our patient was almost symptom free at week two, indicated by a score of "2" on the rating scale. By week four she was completely symptom free. The symptoms slowly returned during week three post treatment.</p> <p>Conclusions</p> <p>The findings suggest that near-infrared light may be a feasible method for treating patients suffering from restless legs syndrome. Undesirable side-effects from medication are non-existent. This study might revive the neglected vascular mechanism theory behind restless legs syndrome and encourage further research into this area.</p

Anti-AMPA GluA3 antibodies in Frontotemporal dementia: A new molecular target

Frontotemporal Dementia (FTD) is a neurodegenerative disorder mainly characterised by Tau or TDP43 inclusions. A co-autoimmune aetiology has been hypothesised. In this study, we aimed at defining the pathogenetic role of anti-AMPA GluA3 antibodies in FTD. Serum and cerebrospinal fluid (CSF) anti-GluA3 antibody dosage was carried out and the effect of CSF with and without anti-GluA3 antibodies was tested in rat hippocampal neuronal primary cultures and in differentiated neurons from human induced pluripotent stem cells (hiPSCs). TDP43 and Tau expression in hiPSCs exposed to CSF was assayed. Forty-one out of 175 screened FTD sera were positive for the presence of anti-GluA3 antibodies (23.4%). FTD patients with anti-GluA3 antibodies more often presented presenile onset, behavioural variant FTD with bitemporal atrophy. Incubation of rat hippocampal neuronal primary cultures with CSF with anti-GluA3 antibodies led to a decrease of GluA3 subunit synaptic localization of the AMPA receptor (AMPAR) and loss of dendritic spines. These results were confirmed in differentiated neurons from hiPSCs, with a significant reduction of the GluA3 subunit in the postsynaptic fraction along with increased levels of neuronal Tau. In conclusion, autoimmune mechanism might represent a new potentially treatable target in FTD and might open new lights in the disease underpinnings

Unifying generative and discriminative learning principles

<p>Abstract</p> <p>Background</p> <p>The recognition of functional binding sites in genomic DNA remains one of the fundamental challenges of genome research. During the last decades, a plethora of different and well-adapted models has been developed, but only little attention has been payed to the development of different and similarly well-adapted learning principles. Only recently it was noticed that discriminative learning principles can be superior over generative ones in diverse bioinformatics applications, too.</p> <p>Results</p> <p>Here, we propose a generalization of generative and discriminative learning principles containing the maximum likelihood, maximum a posteriori, maximum conditional likelihood, maximum supervised posterior, generative-discriminative trade-off, and penalized generative-discriminative trade-off learning principles as special cases, and we illustrate its efficacy for the recognition of vertebrate transcription factor binding sites.</p> <p>Conclusions</p> <p>We find that the proposed learning principle helps to improve the recognition of transcription factor binding sites, enabling better computational approaches for extracting as much information as possible from valuable wet-lab data. We make all implementations available in the open-source library Jstacs so that this learning principle can be easily applied to other classification problems in the field of genome and epigenome analysis.</p