EGFR Regulation of Epidermal Barrier Function

Keratinocyte terminal differentiation is the process that ultimately forms the epidermal barrier that is essential for mammals to survive in the ex utero environment. This process is tightly controlled by the expression of many well-characterized genes. Although a few of these genes are known to be regulated by the epidermal growth factor receptor (EGFR), an important regulator of multiple epidermal functions, neither the genome-wide scale of EGFR-mediated regulation nor the mechanisms by which EGFR signaling controls keratinocyte differentiation are well understood. Using microarray analysis we identified 2,676 genes that are regulated by EGF, a ligand of the EGFR. We further discovered, and separately confirmed by functional assays, that EGFR activation abrogates all essential metabolic processes of keratinocyte differentiation by (1) decreasing the expression of lipid matrix biosynthetic enzymes, (2) regulating numerous genes forming the cornified envelope, and (3) suppressing the expression of tight junction proteins. In organotypic cultures of skin, the collective effect of EGF impaired epidermal barrier integrity, evidenced by increased transepidermal water loss. As defective epidermal differentiation and disruption of the epidermal barrier are primary features of many human skin diseases, we used bioinformatics analysis to identify genes that are known to be associated with human skin diseases. In comparison to non-EGF-regulated genes, the EGF-regulated gene list was significantly enriched for disease genes. Further validation of the expression profiles of many of the 114 identified skin disease genes included the transcription factors GATA binding protein 3 (GATA3) and Kruppel-like factor 4 (KLF4), both required for establishing the barrier function of the skin in developing mice. These results provide a new systems level understanding of the actions of EGFR signaling to inhibit keratinocyte differentiation. As the overall effect of this inhibition is to impair epidermal barrier integrity, this study clarifies how dysregulation of the EGFR and its ligands may contribute to diseases of the skin

Gene Expression Signature in Adipose Tissue of Acromegaly Patients.

To study the effect of chronic excess growth hormone on adipose tissue, we performed RNA sequencing in adipose tissue biopsies from patients with acromegaly (n = 7) or non-functioning pituitary adenomas (n = 11). The patients underwent clinical and metabolic profiling including assessment of HOMA-IR. Explants of adipose tissue were assayed ex vivo for lipolysis and ceramide levels. Patients with acromegaly had higher glucose, higher insulin levels and higher HOMA-IR score. We observed several previously reported transcriptional changes (IGF1, IGFBP3, CISH, SOCS2) that are known to be induced by GH/IGF-1 in liver but are also induced in adipose tissue. We also identified several novel transcriptional changes, some of which may be important for GH/IGF responses (PTPN3 and PTPN4) and the effects of acromegaly on growth and proliferation. Several differentially expressed transcripts may be important in GH/IGF-1-induced metabolic changes. Specifically, induction of LPL, ABHD5, and NRIP1 can contribute to enhanced lipolysis and may explain the elevated adipose tissue lipolysis in acromegalic patients. Higher expression of TCF7L2 and the fatty acid desaturases FADS1, FADS2 and SCD could contribute to insulin resistance. Ceramides were not different between the two groups. In summary, we have identified the acromegaly gene expression signature in human adipose tissue. The significance of altered expression of specific transcripts will enhance our understanding of the metabolic and proliferative changes associated with acromegaly

Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks

Federated Learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet-of-Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning efficiency, reduce communication overheads and enhance privacy for cyberattack detection systems. Challenges in implementation of FL in such systems include unavailability of labeled data and dissimilarity of data features in different IoT networks. In this paper, we propose a novel collaborative learning framework that leverages Transfer Learning (TL) to overcome these challenges. Particularly, we develop a novel collaborative learning approach that enables a target network with unlabeled data to effectively and quickly learn knowledge from a source network that possesses abundant labeled data. It is important that the state-of-the-art studies require the participated datasets of networks to have the same features, thus limiting the efficiency, flexibility as well as scalability of intrusion detection systems. However, our proposed framework can address these problems by exchanging the learning knowledge among various deep learning models, even when their datasets have different features. Extensive experiments on recent real-world cybersecurity datasets show that the proposed framework can improve more than 40% as compared to the state-of-the-art deep learning based approaches.Comment: 12 page

Интеллектуальная радиосеть с нечеткой конфигурацией

В статье обсуждаются возможности применения одноранговой радиосети стандарта IEEE 802.15.4 (ZigBee) диапазона 2,4 ГГц для работы системы, состоящей из группы малогабаритных мобильных роботов и одного командного пункта. Основная задача группы роботов – проведение разведки во время спасательных операций после техногенных и природных катастроф и аварий. Для сохранения управляемости отдельными роботами и системой в целом предлагается повысить «интеллект» системы связи за счет гибкой маршрутизации каналов между командным пунктом и конкретным мобильным роботом с тем, чтобы иметь систему с автоматическим, интеллектуальным восстановлением канала обмена данных.У статті обговорюються можливості застосування однорангової радіомережі стандарту ІЕЕ 802.15.4 (ZigBee) діапазону 2,4 Ггу для роботи системи, що складається з групи малогабаритних мобільних роботів та одного командного пункту. Основна задача групи роботів – проведення розвідки під час рятувальних операцій після техногенних та природних катастроф і аварій. Для збереження керованості окремими ротами та системою в цілому пропонується підвищити інтелект системи зв’язку за рахунок гнучкої маршрутитизації каналів між командним пунктом та конкретним мобільним роботом з тим, щоб мати систему з автоматичним, інтелектуальним відновлюванням каналу обміну даних.In the article the possibilities of application peer-to-peer radio networks of standard IEEE 802.15.4 (ZigBee) a range of 2,4 GHz for work of the system consisting of small-sized mobile robots group and one command point are discussed. The primary goal of group of robots – is carrying out of investigation during rescue operations after technogenic and natural accidents and failures. For controllability preservation by separate robots and system as a whole, it is offered to raise “intelligence” of a communication system at the expense of flexible routeing of channels between command point and the concrete mobile robot to have system with automatic, intellectual restoration of the channel of data exchange

Time-resolved characterization of the mechanisms of toxicity induced by silica and amino-modified polystyrene on alveolar-like macrophages

Macrophages play a major role in the removal of foreign materials, including nano-sized materials, such as nanomedicines and other nanoparticles, which they accumulate very efficiently. Because of this, it is recognized that for a safe development of nanotechnologies and nanomedicine, it is essential to investigate potential effects induced by nano-sized materials on macrophages. To this aim, in this work, a recently established model of primary murine alveolar-like macrophages was used to investigate macrophage responses to two well-known nanoparticle models: 50 nm amino-modified polystyrene, known to induce cell death via lysosomal damage and apoptosis in different cell types, and 50 nm silica nanoparticles, which are generally considered non-toxic. Then, a time-resolved study was performed to characterize in detail the response of the macrophages following exposure to the two nanoparticles. As expected, exposure to the amino-modified polystyrene led to cell death, but surprisingly no lysosomal swelling or apoptosis were detected. On the contrary, a peculiar mitochondrial membrane hyperpolarization was observed, accompanied by endoplasmic reticulum stress (ER stress), increased cellular reactive oxygen species (ROS) and changes of metabolic activity, ultimately leading to cell death. Strong toxic responses were observed also after exposure to silica, which included mitochondrial ROS production, mitochondrial depolarization and cell death by apoptosis. Overall, these results showed that exposure to the two nanoparticles led to a very different series of intracellular events, suggesting that the macrophages responded differently to the two nanoparticle models. Similar time-resolved studies are required to characterize the response of macrophages to nanoparticles, as a key parameter in nanosafety assessment

The recruitment experience of a randomized clinical trial to aid young adult smokers to stop smoking without weight gain with interactive technology

AbstractMultiple recruitment strategies are often needed to recruit an adequate number of participants, especially hard to reach groups. Technology-based recruitment methods hold promise as a more robust form of reaching and enrolling historically hard to reach young adults. The TARGIT study is a randomized two-arm clinical trial in young adults using interactive technology testing an efficacious proactive telephone Quitline versus the Quitline plus a behavioral weight management intervention focusing on smoking cessation and weight change. All randomized participants in the TARGIT study were required to be a young adult smoker (18–35 years), who reported smoking at least 10 cigarettes per day, had a BMI < 40 kg/m2, and were willing to stop smoking and not gain weight. Traditional recruitment methods were compared to technology-based strategies using standard descriptive statistics based on counts and proportions to describe the recruitment process from initial pre-screening (PS) to randomization into TARGIT. Participants at PS were majority Black (59.80%), female (52.66%), normal or over weight (combined 62.42%), 29.5 years old, and smoked 18.4 cigarettes per day. There were differences in men and women with respect to reasons for ineligibility during PS (p < 0.001; ignoring gender specific pregnancy-related ineligibility). TARGIT experienced a disproportionate loss of minorities during recruitment as well as a prolonged recruitment period due to either study ineligibility or not completing screening activities. Recruitment into longer term behavioral change intervention trials can be challenging and multiple methods are often required to recruit hard to reach groups.ClinicalTrials.gov Identifier NCT01199185The NHLBI funded TARGIT as part of a U01 Cooperative Agreement and as such the study design was approved. They did not have input into the data collection, analysis, or the interpretation of the data or in the writing of this report

Detection of turning freeze in Parkinson's disease based on S-transform decomposition of EEG signals

© 2017 IEEE. Freezing of Gait (FOG) is a highly debilitating and poorly understood symptom of Parkinson's disease (PD), causing severe immobility and decreased quality of life. Turning Freezing (TF) is known as the most common sub-type of FOG, also causing the highest rate of falls in PD patients. During a TF, the feet of PD patients appear to become stuck whilst making a turn. This paper presents an electroencephalography (EEG) based classification method for detecting turning freezing episodes in six PD patients during Timed Up and Go Task experiments. Since EEG signals have a time-variant nature, time-frequency Stockwell Transform (S-Transform) techniques were used for feature extraction. The EEG sources were separated by means of independent component analysis using entropy bound minimization (ICA-EBM). The distinctive frequency-based features of selected independent components of EEG were extracted and classified using Bayesian Neural Networks. The classification demonstrated a high sensitivity of 84.2%, a specificity of 88.0% and an accuracy of 86.2% for detecting TF. These promising results pave the way for the development of a real-time device for detecting different sub-types of FOG during ambulation

Pharmacologic activation of estrogen receptor α increases mitochondrial function, energy expenditure, and brown adipose tissue

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154285/1/fsb2fj201600787rr.pd

Significant transcriptional changes in 15q duplication but not Angelman syndrome deletion stem cell-derived neurons

Abstract Background The inability to analyze gene expression in living neurons from Angelman (AS) and Duplication 15q (Dup15q) syndrome subjects has limited our understanding of these disorders at the molecular level. Method Here, we use dental pulp stem cells (DPSC) from AS deletion, 15q Duplication, and neurotypical control subjects for whole transcriptome analysis. We identified 20 genes unique to AS neurons, 120 genes unique to 15q duplication, and 3 shared transcripts that were differentially expressed in DPSC neurons vs controls. Results Copy number correlated with gene expression for most genes across the 15q11.2-q13.1 critical region. Two thirds of the genes differentially expressed in 15q duplication neurons were downregulated compared to controls including several transcription factors, while in AS differential expression was restricted primarily to the 15q region. Here, we show significant downregulation of the transcription factors FOXO1 and HAND2 in neurons from 15q duplication, but not AS deletion subjects suggesting that disruptions in transcriptional regulation may be a driving factor in the autism phenotype in Dup15q syndrome. Downstream analysis revealed downregulation of the ASD associated genes EHPB2 and RORA, both genes with FOXO1 binding sites. Genes upregulated in either Dup15q cortex or idiopathic ASD cortex both overlapped significantly with the most upregulated genes in Dup15q DPSC-derived neurons. Conclusions Finding a significant increase in both HERC2 and UBE3A in Dup15q neurons and significant decrease in these two genes in AS deletion neurons may explain differences between AS deletion class and UBE3A specific classes of AS mutation where HERC2 is expressed at normal levels. Also, we identified an enrichment for FOXO1-regulated transcripts in Dup15q neurons including ASD-associated genes EHPB2 and RORA indicating a possible connection between this syndromic form of ASD and idiopathic cases.https://deepblue.lib.umich.edu/bitstream/2027.42/140784/1/13229_2018_Article_191.pd