The NSL complex is required for piRNA production from telomeric clusters.

The NSL complex is a transcriptional activator. Germline-specific knockdown of NSL complex subunits NSL1, NSL2, and NSL3 results in reduced piRNA production from a subset of bidirectional piRNA clusters, accompanied by widespread transposon derepression. The piRNAs most transcriptionally affected by NSL2 and NSL1 RNAi map to telomeric piRNA clusters. At the chromatin level, these piRNA clusters also show decreased levels of H3K9me3, HP1a, and Rhino after NSL2 depletion. Using NSL2 ChIP-seq in ovaries, we found that this protein specifically binds promoters of telomeric transposons HeT-A, TAHRE, and TART Germline-specific depletion of NSL2 also led to a reduction in nuclear Piwi in nurse cells. Our findings thereby support a role for the NSL complex in promoting the transcription of piRNA precursors from telomeric piRNA clusters and in regulating Piwi levels in the Drosophila female germline.

Promoting Obesity Prevention and Healthy Habits in Childhood: The OCARIoT Experience.

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. Clinical and Translational Impact Statement-This study presents Early Research on the adoption of a smart childhood obesity caring solution adopting a multidisciplinary approach; it involves researchers from biomedical engineering, medicine, computer science, ethics and education. The solution has the potential to decrease the obesity rates in children aiming to impact to get a better global health.

A social robot-based platform for health behavior change toward prevention of childhood obesity.

Childhood obesity is a major public health challenge which is linked with the occurrence of diseases such as diabetes and cancer. The COVID-19 pandemic has forced changes to the lifestyle behaviors of children, thereby making the risk of developing obesity even greater. Novel preventive tools and approaches are required to fight childhood obesity. We present a social robot-based platform which utilizes an interactive motivational strategy in communication with children, collects self-reports through the touch of tangible objects, and processes behavioral data, aiming to: (a) screen and assess the behaviors of children in the dimensions of physical activity, diet, and education, and (b) recommend individualized goals for health behavior change. The platform was integrated through a microservice architecture within a multi-component system targeting childhood obesity prevention. The platform was evaluated in an experimental study with 30 children aged 9-12 years in a real-life school setting, showing children's acceptance to use it, and an 80% success rate in achieving weekly personal health goals recommended by the social robot-based platform. The results provide preliminary evidence on the implementation feasibility and potential of the social robot-based platform toward the betterment of children's health behaviors in the context of childhood obesity prevention. Further rigorous longer-term studies are required.

Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review.

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Detection of Mild Cognitive Impairment in an At-Risk Group of Older Adults: Can a Novel Self-Administered Serious Game-Based Screening Test Improve Diagnostic Accuracy?

BACKGROUND: Literature supports the use of serious games and virtual environments to assess cognitive functions and detect cognitive decline. This promising assessment method, however, has not yet been translated into self-administered screening instruments for pre-clinical dementia. OBJECTIVE: The aim of this study is to assess the performance of a novel self-administered serious game-based test, namely the Virtual Supermarket Test (VST), in detecting mild cognitive impairment (MCI) in a sample of older adults with subjective memory complaints (SMC), in comparison with two well-established screening instruments, the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE). METHODS: Two groups, one of healthy older adults with SMC (N = 48) and one of MCI patients (N = 47) were recruited from day centers for cognitive disorders and administered the VST, the MoCA, the MMSE, and an extended pencil and paper neuropsychological test battery. RESULTS: The VST displayed a correct classification rate (CCR) of 81.91% when differentiating between MCI patients and older adults with SMC, while the MoCA displayed of CCR of 72.04% and the MMSE displayed a CCR of 64.89%. CONCLUSION: The three instruments assessed in this study displayed significantly different performances in differentiating between healthy older adults with SMC and MCI patients. The VST displayed a good CCR, while the MoCA displayed an average CCR and the MMSE displayed a poor CCR. The VST appears to be a robust tool for detecting MCI in a population of older adults with SMC.

Intergenerationally Maintained Histone H4 Lysine 16 Acetylation Is Instructive for Future Gene Activation.

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.

Computerized decision support and machine learning applications for the prevention and treatment of childhood obesity: A systematic review of the literature.

BACKGROUND: Digital health interventions based on tools for Computerized Decision Support (CDS) and Machine Learning (ML), which take advantage of new information, sensing and communication technologies, can play a key role in childhood obesity prevention and treatment. OBJECTIVES: We present a systematic literature review of CDS and ML applications for the prevention and treatment of childhood obesity. The main characteristics and outcomes of studies using CDS and ML are demonstrated, to advance our understanding towards the development of smart and effective interventions for childhood obesity care. METHODS: A search in the bibliographic databases of PubMed and Scopus was performed to identify childhood obesity studies incorporating either CDS interventions, or advanced data analytics through ML algorithms. Ongoing, case, and qualitative studies, along with those not providing specific quantitative outcomes were excluded. The studies incorporating CDS were synthesized according to the intervention's main technology (e.g., mobile app), design type (e.g., randomized controlled trial), number of enrolled participants, target age of children, participants' follow-up duration, primary outcome (e.g., Body Mass Index (BMI)), and main CDS feature(s) and their outcomes (e.g., alerts for caregivers when BMI is high). The studies incorporating ML were synthesized according to the number of subjects included and their age, the ML algorithm(s) used (e.g., logistic regression), as well as their main outcome (e.g., prediction of obesity). RESULTS: The literature search identified 8 studies incorporating CDS interventions and 9 studies utilizing ML algorithms, which met our eligibility criteria. All studies reported statistically significant interventional or ML model outcomes (e.g., in terms of accuracy). More than half of the interventional studies (n = 5, 63 %) were designed as randomized controlled trials. Half of the interventional studies (n = 4, 50 %) utilized Electronic Health Records (EHRs) and alerts for BMI as means of CDS. From the 9 studies using ML, the highest percentage targeted at the prognosis of obesity (n = 4, 44 %). In the studies incorporating more than one ML algorithms and reporting accuracy, it was shown that decision trees and artificial neural networks can accurately predict childhood obesity. CONCLUSIONS: This review has found that CDS tools can be useful for the self-management or remote medical management of childhood obesity, whereas ML algorithms such as decision trees and artificial neural networks can be helpful for prediction purposes. Further rigorous studies in the area of CDS and ML for childhood obesity care are needed, considering the low number of studies identified in this review, their methodological limitations, and the scarcity of interventional studies incorporating ML algorithms in CDS tools.

SERRATE, a miRNA biogenesis factor, affects viroid infection in Nicotiana benthamiana and Nicotiana tabacum.

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.

Snipper, an Eri1 homologue, affects histone mRNA abundance and is crucial for normal Drosophila melanogaster development.

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.

Essential roles of Da transactivation domains in neurogenesis and in E(spl)-mediated repression.

E proteins are a special class of basic helix-loop-helix (bHLH) proteins that heterodimerize with many bHLH activators to regulate developmental decisions, such as myogenesis and neurogenesis. Daughterless (Da) is the sole E protein in Drosophila and is ubiquitously expressed. We have characterized two transcription activation domains (TADs) in Da, called activation domain 1 (AD1) and loop-helix (LH), and have evaluated their roles in promoting peripheral neurogenesis. In this context, Da heterodimerizes with proneural proteins, such as Scute (Sc), which is dynamically expressed and also contributes a TAD. We found that either one of the Da TADs in the Da/Sc complex is sufficient to promote neurogenesis, whereas the Sc TAD is incapable of doing so. Besides its transcriptional activation role, the Da AD1 domain serves as an interaction platform for E(spl) proteins, bHLH-Orange family repressors which antagonize Da/Sc function. We show that the E(spl) Orange domain is needed for this interaction and strongly contributes to the antiproneural activity of E(spl) proteins. We present a mechanistic model on the interplay of these bHLH factors in the context of neural fate assignment.