Maternal corticotropin-releasing hormone is associated with LEP DNA methylation at birth and in childhood: an epigenome-wide study in Project Viva

BackgroundCorticotropin-releasing hormone (CRH) plays a central role in regulating the secretion of cortisol which controls a wide range of biological processes. Fetuses overexposed to cortisol have increased risks of disease in later life. DNA methylation may be the underlying association between prenatal cortisol exposure and health effects. We investigated associations between maternal CRH levels and epigenome-wide DNA methylation of cord blood in offsprings and evaluated whether these associations persisted into mid-childhood.MethodsWe investigated mother-child pairs enrolled in the prospective Project Viva pre-birth cohort. We measured DNA methylation in 257 umbilical cord blood samples using the HumanMethylation450 Bead Chip. We tested associations of maternal CRH concentration with cord blood cells DNA methylation, adjusting the model for maternal age at enrollment, education, maternal race/ethnicity, maternal smoking status, pre-pregnancy body mass index, parity, gestational age at delivery, child sex, and cell-type composition in cord blood. We further examined the persistence of associations between maternal CRH levels and DNA methylation in children's blood cells collected at mid-childhood (n = 239, age: 6.7-10.3 years) additionally adjusting for the children's age at blood drawn.ResultsMaternal CRH levels are associated with DNA methylation variability in cord blood cells at 96 individual CpG sites (False Discovery Rate <0.05). Among the 96 CpG sites, we identified 3 CpGs located near the LEP gene. Regional analyses confirmed the association between maternal CRH and DNA methylation near LEP. Moreover, higher maternal CRH levels were associated with higher blood-cell DNA methylation of the promoter region of LEP in mid-childhood (P < 0.05, β = 0.64, SE = 0.30).ConclusionIn our cohort, maternal CRH was associated with DNA methylation levels in newborns at multiple loci, notably in the LEP gene promoter. The association between maternal CRH and LEP DNA methylation levels persisted into mid-childhood

The 2021 battery technology roadmap

Sun, wind and tides have huge potential in providing us electricity in an environmental-friendly way. However, its intermittency and non-dispatchability are major reasons preventing full-scale adoption of renewable energy generation. Energy storage will enable this adoption by enabling a constant and high-quality electricity supply from these systems. But which storage technology should be considered is one of important issues. Nowadays, great effort has been focused on various kinds of batteries to store energy, lithium-related batteries, sodium-related batteries, zinc-related batteries, aluminum-related batteries and so on. Some cathodes can be used for these batteries, such as sulfur, oxygen, layered compounds. In addition, the construction of these batteries can be changed into flexible, flow or solid-state types. There are many challenges in electrode materials, electrolytes and construction of these batteries and research related to the battery systems for energy storage is extremely active. With the myriad of technologies and their associated technological challenges, we were motivated to assemble this 2020 battery technology roadmap

Photochromic and Electrochemical characteristics of Bi-Diarylethene Molecules with Tetrathiafulvalene as Bridge Unit

Abstract not Available.</jats:p

The Influence of College Students’ Innovation and Entrepreneurship Intention in the Art Field of Art Film and Television Appreciation by Deep Learning Under Entrepreneurial Psychology

There are many films and televisions (FATs) on the Internet, but the quality is uneven. This study explores the ability of college students to screen good films and resist bad films in television works in such a large environment. In the deep learning model of FAT, the ability of college students to think about the ideas expressed and the degree of influence on college students’ values are analyzed. Based on this conceptual basis, a questionnaire is designed for the intention and influencing factors of college students’ FAT innovation and entrepreneurship. It reflects the influence of concentration on FAT learning, the cognitive level of deep learning, the ability to process deep learning ideas, the feeling of the teaching process, and the process of self-learning, which all positively impact college students’ FAT entrepreneurial intentions. The importance of innovative deep learning is highlighted, which proves that a good deep learning course guidance method can improve students’ interest and ability and provide a reference for relevant colleges and universities to cultivate pertinent talents of the field of FAT.</jats:p

IoT-Based Airport Noise Perception and Monitoring: Multi-Source Data Fusion, Spatial Distribution Modeling, and Analysis

With the acceleration of global urbanization, airport noise pollution has emerged as a significant environmental concern that demands attention. Traditional airport noise monitoring systems are fraught with limitations, including restricted spatial coverage, inadequate real-time data acquisition capabilities, poor data correlation, and suboptimal cost-effectiveness. To address these challenges, this paper proposes an innovative airport noise perception and monitoring approach leveraging Internet of Things (IoT) technology. This method integrates multiple data streams, encompassing noise, meteorological, and ADS&ndash;B data, to achieve precise noise event tracing and deep multi-source data fusion. Furthermore, this study employs Kriging interpolation and Inverse Distance Weighting (IDW) techniques to perform spatial interpolation on data from sparse monitoring sites, thereby constructing a spatial distribution model of airport noise. The results of the practical application demonstrate that the proposed airport noise monitoring method can accurately reflect the spatiotemporal distribution patterns of airport noise and effectively correlate noise events, thereby providing robust data support for the development of airport noise control policies

Integrating Multi-Source Data for Aviation Noise Prediction: A Hybrid CNN–BiLSTM–Attention Model Approach

Driven by the increasing global population and rapid urbanization, aircraft noise pollution has emerged as a significant environmental challenge, impeding the sustainable development of the aviation industry. Traditional noise prediction methods are limited by incomplete datasets, insufficient spatiotemporal consistency, and poor adaptability to complex meteorological conditions, making it difficult to achieve precise noise management. To address these limitations, this study proposes a novel noise prediction framework based on a hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory–Attention (CNN–BiLSTM–Attention) model. By integrating multi-source data, including meteorological parameters (e.g., temperature, humidity, wind speed) and aircraft trajectory data (e.g., altitude, longitude, latitude), the framework achieves high-precision prediction of aircraft noise. The Haversine formula and inverse distance weighting (IDW) interpolation are employed to effectively supplement missing data, while spatiotemporal alignment techniques ensure data consistency. The CNN–BiLSTM–Attention model leverages the spatial feature extraction capabilities of CNNs, the bidirectional temporal sequence processing capabilities of BiLSTMs, and the context-enhancing properties of the attention mechanism to capture the spatiotemporal characteristics of noise. The experimental results indicate that the model’s predicted mean value of 68.66 closely approximates the actual value of 68.16, with a minimal difference of 0.5 and a mean absolute error of 0.89%. Notably, the error remained below 2% in 91.4% of the prediction rounds. Furthermore, ablation studies revealed that the complete CNN–BiLSTM–AM model significantly outperformed single-structure models. The incorporation of the attention mechanism was found to markedly enhance both the accuracy and generalization capability of the model. These findings highlight the model’s robust performance and reliability in predicting aviation noise. This study provides a scientific basis for effective aviation noise management and offers an innovative solution for addressing noise prediction problems under data-scarce conditions

Two-dimensional materials based two-transistor-two-resistor synaptic kernel for efficient neuromorphic computing

Abstract Neuromorphic computing based on two-dimensional materials represents a promising hardware approach for data-intensive applications. Central to this new paradigm are memristive devices, which serve as the essential components in synaptic kernels. However, large-scale implementation of synaptic matrix using two-dimensional materials is hindered by challenges related to random component variation and array-level integration. Here, we develop a 16 × 16 computing kernel based on two-transistor-two-resistor unit with three-dimensional heterogeneous integration compatibility to boost energy efficiency and computing performance. We demonstrate the 4-bit weight characteristics of artificial synapses with low stochasticity. The synaptic array demonstration validates the practicality of utilizing emerging two-dimensional materials for monolithic three-dimensional heterogeneous integration. Additionally, we introduce the Gaussian noise quantization weight-training scheme alongside the ConvMixer convolution architecture to achieve image dataset identification with high accuracy. Our findings indicate that the synaptic kernel can significantly improve detection accuracy and inference performance on the CIFAR-10 dataset

Solid-state integrated micro-supercapacitor array construction with low-cost porous biochar

Solid-state micro-supercapacitor arrays made from porous biochar are integrated on flexible substrates by screen printing and writing, providing new insights into exploring low cost, eco-friendly, large scale integrated micro energy storage devices.</p

Consistently low prevalence of syphilis among female sex workers in Jinan, China: findings from two consecutive respondent driven sampling surveys.

BACKGROUND: Routine surveillance using convenient sampling found low prevalence of HIV and syphilis among female sex workers in China. Two consecutive surveys using respondent driven sampling were conducted in 2008 and 2009 to examine the prevalence of HIV and syphilis among female sex workers in Jinan, China. METHODS: A face-to-face interview was conducted to collect demographic, behavioral and service utilization information using a structured questionnaire. Blood samples were drawn for serological tests of HIV-1 antibody and syphilis antibody. Respondent Driven Sampling Analysis Tool was used to generate population level estimates. RESULTS: In 2008 and in 2009, 363 and 432 subjects were recruited and surveyed respectively. Prevalence of syphilis was 2.8% in 2008 and 2.2% in 2009, while no HIV case was found in both years. Results are comparable to those from routine sentinel surveillance system in the city. Only 60.8% subjects in 2008 and 48.3% in 2009 reported a consistent condom use with clients during the past month. Over 50% subjects had not been covered by any HIV-related services in the past year, with only 15.6% subjects in 2008 and 13.1% in 2009 ever tested for HIV. CONCLUSIONS: Despite the low prevalence of syphilis and HIV, risk behaviors are common. Targeted interventions to promote the safe sex and utilization of existing intervention services are still needed to keep the epidemic from growing