Before zygotic genome activation (ZGA), the quiescent genome undergoes reprogramming to transition into the transcriptionally active state. However, the mechanisms underlying euchromatin ...establishment during early embryogenesis remain poorly understood. Here, we show that histone H4 lysine 16 acetylation (H4K16ac) is maintained from oocytes to fertilized embryos in Drosophila and mammals. H4K16ac forms large domains that control nucleosome accessibility of promoters prior to ZGA in flies. Maternal depletion of MOF acetyltransferase leading to H4K16ac loss causes aberrant RNA Pol II recruitment, compromises the 3D organization of the active genomic compartments during ZGA, and causes downregulation of post-zygotically expressed genes. Germline depletion of histone deacetylases revealed that other acetyl marks cannot compensate for H4K16ac loss in the oocyte. Moreover, zygotic re-expression of MOF was neither able to restore embryonic viability nor onset of X chromosome dosage compensation. Thus, maternal H4K16ac provides an instructive function to the offspring, priming future gene activation.
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•Evolutionarily conserved H4K16ac transmission from the maternal germline•Maternal MOF and H4K16ac prime gene activation in a sex-independent manner•Zygotic MOF fails to rescue embryonic lethality caused by maternal MOF loss•X chromosome dosage compensation onset in males relies on maternal H4K16ac
The developmental events following gamete fusion are guided by maternally provided mRNAs and proteins prior to zygotic genome activation. Samata et al. show that H4K16ac transmission from the maternal germline poises genes for future activation.
Viroids are plant infecting, non - coding RNA molecules of economic importance. Potato spindle tuber viroid (PSTVd), the type species of Pospiviroidae family, has been shown to be affected by ...specific RNA silencing pathways. Dicer like 1 (DCL1), a key player in micro RNA (miRNA) pathway has been previously linked with PSTVd infectivity. In this report we aim to further dissect the interaction between the miRNA pathway and Pospiviroid virulence. We mainly focused on the Zinc-finger protein SERRATE (SE) a co-factor of DCL1 and core component of miRNA pathway. We generated Nicotiana tabacum and Nicotiana benthamiana SE knock-down plants exhibiting considerable miRNA reduction and strong phenotypic abnormalities. PSTVd infection of SE suppressed plants resulted in a significant viroid reduction, especially at the initial infection stages. This positive correlation between SE levels and viroid infectivity underlines its role in PSTVd life cycle and reveals the importance of the miRNA pathway upon viroid infection.
Hypoglycaemia is one of the most common complications in diabetes, which can be life threatening if not managed appropriately. So far, research on hypoglycaemia prediction has been scarce, focusing ...on small cohorts linked to specific geographical regions, thus limiting the generalizability of the findings. In this paper, we developed and validated different machine learning models for next-day hypoglycaemia prediction in type 2 diabetes. We used a large international cohort comprising 669 participants, who had been regular users (for over a couple of years) of a mobile app for diabetes self-management and used common portable commercial devices for measuring their blood glucose and blood pressure levels, collecting in total 96121 observations. Random Forests (RF), Support Vector Machines, Adaptive Boosting and Feed-Forward Artificial Neural Networks were employed to train predictive models based on 10-day temporal sequences with blood glucose and blood pressure measurements towards estimating next day hypoglycaemic episodes. We used a leave-one-subject-out (LOSO) approach for model validation, and found that RF achieved the best accuracy (0.814) and F1-score (0.812) with sensitivity (0.805) and specificity (0.824) for next-day hypoglycaemia prediction. The results of this study provide an expedient and reliable app-based approach to accurately predict hypoglycaemia in day-to-day life, thereby facilitating patient and care provider awareness and potentially preventing other serious complications.
•Social robots can promote healthy behaviors in child healthcare.•Social robots should be considered in the design of psychological interventions.•Rigorous studies with social robot-based ...interventions are needed.
We present a systematic review of the literature on social robot interventions for child healthcare. The primary features and outcomes of studies using social robots are illustrated, to advance our comprehension towards the development of useful and effective social robot-based health interventions for the children.
We conducted a literature search in the bibliographic databases of PubMed and Scopus in order to find studies incorporating social robot interventions targeting child healthcare. The studies were synthesized according to the intervention's target, main robot features, study design, target age of children, number of participants, follow-up duration, and primary outcomes.
Our review reveals that most studies involved only a single session of interactions with the robot, and conducted with a limited number of participants. The interventions targeted alleviation of distress in children with cancer, exercise coaching, improvement of playfulness in children with intellectual disabilities, improvement of mental health, reduction of pain and distress in pediatric inpatient or outpatient settings, e.g., during needle insertions or vaccination, improvement of nutritional knowledge, and achievement of instant therapeutic or education goals in children with physical disabilities. The majority of the studies (85%) reported significant outcomes in technology acceptance, feasibility, enjoyment, engagement, achievement of therapeutic/education goals, pain, and mental health outcomes.
Significant outcomes in the mental state of children, suggest that social robots should be considered in the design of psychological interventions for children. More rigorous research in the area of evaluation of social robot interventions for child healthcare is warranted.
Major chronic diseases such as cardiovascular disease (CVD), diabetes, and cancer impose a significant burden on people and health care systems around the globe. Recently, deep learning (DL) has ...shown great potential for the development of intelligent mobile health (mHealth) interventions for chronic diseases that could revolutionize the delivery of health care anytime, anywhere.
The aim of this study is to present a systematic review of studies that have used DL based on mHealth data for the diagnosis, prognosis, management, and treatment of major chronic diseases and advance our understanding of the progress made in this rapidly developing field.
A search was conducted on the bibliographic databases Scopus and PubMed to identify papers with a focus on the deployment of DL algorithms that used data captured from mobile devices (eg, smartphones, smartwatches, and other wearable devices) targeting CVD, diabetes, or cancer. The identified studies were synthesized according to the target disease, the number of enrolled participants and their age, and the study period as well as the DL algorithm used, the main DL outcome, the data set used, the features selected, and the achieved performance.
In total, 20 studies were included in the review. A total of 35% (7/20) of DL studies targeted CVD, 45% (9/20) of studies targeted diabetes, and 20% (4/20) of studies targeted cancer. The most common DL outcome was the diagnosis of the patient's condition for the CVD studies, prediction of blood glucose levels for the studies in diabetes, and early detection of cancer. Most of the DL algorithms used were convolutional neural networks in studies on CVD and cancer and recurrent neural networks in studies on diabetes. The performance of DL was found overall to be satisfactory, reaching >84% accuracy in most studies. In comparison with classic machine learning approaches, DL was found to achieve better performance in almost all studies that reported such comparison outcomes. Most of the studies did not provide details on the explainability of DL outcomes.
The use of DL can facilitate the diagnosis, management, and treatment of major chronic diseases by harnessing mHealth data. Prospective studies are now required to demonstrate the value of applied DL in real-life mHealth tools and interventions.
Automated meeting scheduling is the task of reaching an agreement on a time slot to schedule a new meeting, taking into account the participants’ preferences over various aspects of the problem. ...Such a negotiation is commonly performed in a non-automated manner, that is, the users decide whether they can reschedule existing individual activities and, in some cases, already scheduled meetings in order to accommodate the new meeting request in a particular time slot, by inspecting their schedules. In this work, we take advantage of SelfPlanner, an automated system that employs greedy stochastic optimization algorithms to schedule individual activities under a rich model of preferences and constraints, and we extend that work to accommodate meetings. For each new meeting request, participants decide whether they can accommodate the meeting in a particular time slot by employing SelfPlanner’s underlying algorithms to automatically reschedule existing individual activities. Time slots are prioritized in terms of the number of users that need to reschedule existing activities. An agreement is reached as soon as all agents can schedule the meeting at a particular time slot, without anyone of them experiencing an overall utility loss, that is, taking into account also the utility gain from the meeting. This dynamic multi-agent meeting scheduling approach has been tested on a variety of test problems with very promising results.
Objective: Long term behavioural disturbances and interventions in healthy habits (mainly eating and physical activity) are the primary cause of childhood obesity. Current approaches for obesity ...prevention based on health information extraction lack the integration of multi-modal datasets and the provision of a dedicated Decision Support System (DSS) for health behaviour assessment and coaching of children. Methods: Continuous co-creation process has been applied in the frame of the Design Thinking Methodology, involving children, educators and healthcare professional in the whole process. Such considerations were used to derive the user needs and the technical requirements needed for the conception of the Internet of Things (IoT) platform based on microservices. Results: To promote the adoption of healthy habits and the prevention of the obesity onset for children (9-12 years old), the proposed solution empowers children -including families and educators- in taking control of their health by collecting and following-up real-time information about nutrition, physical activity data coming from IoT devices, and interconnecting healthcare professionals to provide a personalised coaching solution. The validation has two phases involving +400 children (control/intervention group), on four schools in three countries: Spain, Greece and Brazil. The prevalence of obesity decreased in 75.5% from baseline levels in the intervention group. The proposed solution created a positive impression and satisfaction from the technology acceptance perspective. Conclusions: Main findings confirm that this ecosystem can assess behaviours of children, motivating and guiding them towards achieving personal goals.
The conserved 3′–5′ RNA exonuclease ERI1 is implicated in RNA interference inhibition, 5.8S rRNA maturation and histone mRNA maturation and turnover. The single ERI1 homologue in Drosophila ...melanogaster Snipper (Snp) is a 3′–5′ exonuclease, but its in vivo function remains elusive. Here, we report Snp requirement for normal Drosophila development, since its perturbation leads to larval arrest and tissue‐specific downregulation results in abnormal tissue development. Additionally, Snp directly interacts with histone mRNA, and its depletion results in drastic reduction in histone transcript levels. We propose that Snp protects the 3′‐ends of histone mRNAs and upon its absence, histone transcripts are readily degraded. This in turn may lead to cell cycle delay or arrest, causing growth arrest and developmental perturbations.
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•Automated collection of health-related data and triggering of notifications were the main reasons for using smartwatches in interventional studies.•Smartwatch interventions have ...shown positive health outcomes for patients in most studies.•Challenges include: Charging frequency, Internet connection, data quality.•The evaluation of the implementation strategy in smartwatch research studies is needed.•The methodological quality of smartwatch studies should be improved.
The use of smartwatches has attracted considerable interest in developing smart digital health interventions and improving health and well-being during the past few years. This work presents a systematic review of the literature on smartwatch interventions in healthcare. The main characteristics and individual health-related outcomes of smartwatch interventions within research studies are illustrated, in order to acquire evidence of their benefit and value in patient care.
A literature search in the bibliographic databases of PubMed and Scopus was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, in order to identify research studies incorporating smartwatch interventions. The studies were grouped according to the intervention’s target disease, main smartwatch features, study design, target age and number of participants, follow-up duration, and outcome measures.
The literature search identified 13 interventions incorporating smartwatches within research studies with people of middle and older age. The interventions targeted different conditions: cardiovascular diseases, diabetes, depression, stress and anxiety, metastatic gastrointestinal cancer and breast cancer, knee arthroplasty, chronic stroke, and allergic rhinitis. The majority of the studies (76%) were randomized controlled trials. The most used smartwatch was the Apple Watch utilized in 4 interventions (31%). Positive outcomes for smartwatch interventions concerned foot ulcer recurrence, severity of symptoms of depression, utilization of healthcare resources, lifestyle changes, functional assessment and shoulder range of motion, medication adherence, unplanned hospital readmissions, atrial fibrillation diagnosis, adherence to self-monitoring, and goal attainment for emotion regulation. Challenges in using smartwatches included frequency of charging, availability of Internet and synchronization with a mobile app, the burden of using a smartphone in addition to a patient’s regular phone, and data quality.
The results of this review indicate the potential of smartwatches to bring positive health-related outcomes for patients. Considering the low number of studies identified in this review along with their moderate quality, we implore the research community to carry out additional studies in intervention settings to show the utility of smartwatches in clinical contexts.
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