Genome-Wide Histone H3K27 Acetylation Profiling Identified Genes Correlated With Prognosis in Papillary Thyroid Carcinoma

Thyroid carcinoma (TC) is the most common endocrine malignancy, and papillary TC (PTC) is the most frequent subtype of TC, accounting for 85–90% of all the cases. Aberrant histone acetylation contributes to carcinogenesis by inducing the dysregulation of certain cancer-related genes. However, the histone acetylation landscape in PTC remains elusive. Here, we interrogated the epigenomes of PTC and benign thyroid nodule (BTN) tissues by applying H3K27ac chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) along with RNA-sequencing. By comparing the epigenomic features between PTC and BTN, we detected changes in H3K27ac levels at active regulatory regions, identified PTC-specific super-enhancer-associated genes involving immune-response and cancer-related pathways, and uncovered several genes that associated with disease-free survival of PTC. In summary, our data provided a genome-wide landscape of histone modification in PTC and demonstrated the role of enhancers in transcriptional regulations associated with prognosis of PTC

Pollen morphology of Clerodendrum L. (Lamiaceae) from China and its systematic implications

Pollen morphology of 26 taxa of Clerodendrum, as well as one species of Volkameria from China, was investigated through a scanning electron microscope (SEM). Pollen grains of Clerodendrum are monads, radiosymmetric and tricolpate, with medium or large size. The equatorial view of the pollen grains is spheroidal or subprolate and the polar view is (sub) circular or rounded triangular. The colpus membrane of the investigated taxa is sunken (rarely even). Five varying pollen types are delimited on the basis of exine sculpturing: (1) spine-tectum perforatum; (2) spine-tectum imperforatum; (3) microspine-tectum perforatum; (4) microspine-tectum imperforatum; and (5) obtuser spine. The results indicate that Clerodendrum is closely related to several genera in Lamiaceae, including Aegiphila, Amasonia, Kalaharia, Tetraclea, Volkameria, Oxera, Faradaya, and Hosea, as supported by previous phylogenic studies. Additionally, the conventional infrageneric classification of Clerodendrum based on inflorescence and leaf characters is not supported by the results. However, the palynological data can be used to identify some closely related species with similar external characteristics. In conclusion, the investigation of pollen morphology not only contributes novel data from palynology for Clerodendrum but also provides a basis for future comprehensive classification of this genus

Experiment-based simulation of a cross-flow large-scale SOFC model

The multi-physical field full-coupling simulation of solid oxide fuel cell (SOFC) stack requires huge computational resources. Repeated iteration of highly non-linear calculation is easy to cause oscillation and lead to solution failure. At present, the simulation of SOFC stack models mainly focuses on the co-flow condition and counter-flow condition models. Most of them are simplified models that simplify the stack scale or physical field. In this paper, a SOFC decoupling model based on machine learning is established, and the full three-dimensional and multi-physical fields of the cross-flow large-scale SOFC stack are simulated. The model is divided into three parts for calculation, unit cell model, alternative mapping model, and cross-flow large-scale SOFC stack model. The alternative mapping model obtained by the BP neural network algorithm replaces the nonlinear multi-physics equations in the traditional model. Compared with the traditional method, the decoupling model can greatly reduce the computing resources and improve the stability of computing. In this paper, the experimental data of the single cell and the 30-layer stack are used to calibrate and verify the simulation results of stack. Studying the performance of the SOFC stack under different parameter conditions. Temperature, flow uniformity, gas mole fraction, and voltage distribution in the SOFC stack under different inlet flow rates and stack currents are obtained. Obtaining the output power and fuel utilization rate of the stack under different working conditions

Optimization of SOFC stack gas distribution structure based on BP Neural network and CFD

The flow field distribution of solid oxide fuel cells significantly affects the performance of the stack. The flow uniformity can be improved and the power generation efficiency can be improved by optimizing the gas distribution structure of the stack. Based on the simplified 6kW stack model, the stack gas distribution structure with two-stage buffer cavity was designed, and the stack model was numerically simulated by ANSYS Fluent software. The BP neural network model, which can predict the uniformity of the outlet of the integrated stack, is established successfully. The parameters of the gas distribution structure are analyzed and optimized by using the orthogonal test and BP neural network. The results show that at the same time considering pile distribution structure under the condition of surface area and uniformity, when the first stage inlet buffer chamber depth is 40 mm, the channel width is 40 mm, the secondary inlet buffer chamber depth is 80 mm, can effectively reduce the electric pile distribution structure, surface area, to reduce heat loss, at the same time guarantee the integrated electric reactor outlet flow uniformity of more than 96%, greatly improves the efficiency of power generation

Spatial-Temporal Data Collection with Compressive Sensing in Mobile Sensor Networks

Compressive sensing (CS) provides an energy-efficient paradigm for data gathering in wireless sensor networks (WSNs). However, the existing work on spatial-temporal data gathering using compressive sensing only considers either multi-hop relaying based or multiple random walks based approaches. In this paper, we exploit the mobility pattern for spatial-temporal data collection and propose a novel mobile data gathering scheme by employing the Metropolis-Hastings algorithm with delayed acceptance, an improved random walk algorithm for a mobile collector to collect data from a sensing field. The proposed scheme exploits Kronecker compressive sensing (KCS) for spatial-temporal correlation of sensory data by allowing the mobile collector to gather temporal compressive measurements from a small subset of randomly selected nodes along a random routing path. More importantly, from the theoretical perspective we prove that the equivalent sensing matrix constructed from the proposed scheme for spatial-temporal compressible signal can satisfy the property of KCS models. The simulation results demonstrate that the proposed scheme can not only significantly reduce communication cost but also improve recovery accuracy for mobile data gathering compared to the other existing schemes. In particular, we also show that the proposed scheme is robust in unreliable wireless environment under various packet losses. All this indicates that the proposed scheme can be an efficient alternative for data gathering application in WSNs