Enhanced heat transfer is dependent on thickness of graphene films: the heat dissipation during boiling

Boiling heat transfer (BHT) is a particularly efficient heat transport method because of the latent heat associated with the process. However, the efficiency of BHT decreases significantly with increasing wall temperature when the critical heat flux (CHF) is reached. Graphene has received much recent research attention for applications in thermal engineering due to its large thermal conductivity. In this study, graphene films of various thicknesses were deposited on a heated surface, and enhancements of BHT and CHF were investigated via pool-boiling experiments. In contrast to the well-known surface effects, including improved wettability and liquid spreading due to micron-and nanometer-scale structures, nanometer-scale folded edges of graphene films provided a clue of BHT improvement and only the thermal conductivity of the graphene layer could explain the dependence of the CHF on the thickness. The large thermal conductivity of the graphene films inhibited the formation of hot spots, thereby increasing the CHF. Finally, the provided empirical model could be suitable for prediction of CHF.open111522Nsciescopu

ATAD5 restricts R-loop formation through PCNA unloading and RNA helicase maintenance at the replication fork

R-loops are formed when replicative forks collide with the transcriptional machinery and can cause genomic instability. However, it is unclear how R-loops are regulated at transcription-replication conflict (TRC) sites and how replisome proteins are regulated to prevent R-loop formation or mediate R-loop tolerance. Here, we report that ATAD5, a PCNA unloader, plays dual functions to reduce R-loops both under normal and replication stress conditions. ATAD5 interacts with RNA helicases such as DDX1, DDX5, DDX21 and DHX9 and increases the abundance of these helicases at replication forks to facilitate R-loop resolution. Depletion of ATAD5 or ATAD5-interacting RNA helicases consistently increases R-loops during the S phase and reduces the replication rate, both of which are enhanced by replication stress. In addition to R-loop resolution, ATAD5 prevents the generation of new R-loops behind the replication forks by unloading PCNA which, otherwise, accumulates and persists on DNA, causing a collision with the transcription machinery. Depletion of ATAD5 reduces transcription rates due to PCNA accumulation. Consistent with the role of ATAD5 and RNA helicases in maintaining genomic integrity by regulating R-loops, the corresponding genes were mutated or downregulated in several human tumors

Cellular plasticity and immune microenvironment of malignant pleural effusion are associated with EGFR-TKI resistance in non-small-cell lung carcinoma

Malignant pleural effusion (MPE) is a complication of lung cancer that can be used as an alternative method for tissue sampling because it is generally simple and minimally invasive. Our study evaluated the diagnostic potential of non-small-cell lung carcinoma (NSCLC)-associated MPE in terms of understanding tumor heterogeneity and identifying response factors for EGFR tyrosine kinase inhibitor (TKI) therapy. We performed a single-cell RNA sequencing analysis of 31,743 cells isolated from the MPEs of 9 patients with NSCLC (5 resistant and 4 sensitive to EGFR TKI) with EGFR mutations. Interestingly, lung epithelial precursor-like cells with upregulated GNB2L1 and CAV1 expression were enriched in the EGFR TKI-resistant group. Moreover, GZMK upregulated transitional effector T cells, and plasmacytoid dendritic cells were significantly enriched in the EGFR TKI-resistant patients. Our results suggest that cellular plasticity and immunosuppressive microenvironment in MPEs are potentially associated with the TKI response of patients with EGFR-mutated NSCLC

Crop Growth Monitoring System in Vertical Farms Based on Region-of-Interest Prediction

Vertical farms are to be considered the future of agriculture given that they not only use space and resources efficiently but can also consistently produce large yields. Recently, artificial intelligence has been introduced for use in vertical farms to boost crop yields, and crop growth monitoring is an essential example of the type of automation necessary to manage a vertical farm system. Region of interest predictions are generally used to find crop regions from the color images captured by a camera for the monitoring of growth. However, most deep learning-based prediction approaches are associated with performance degradation issues in the event of high crop densities or when different types of crops are grown together. To address this problem, we introduce a novel method, termed pseudo crop mixing, a model training strategy that targets vertical farms. With a small amount of labeled crop data, the proposed method can achieve optimal performance. This is particularly advantageous for crops with a long growth period, and it also reduces the cost of constructing a dataset that must be frequently updated to support the various crops in existing systems. Additionally, the proposed method demonstrates robustness with new data that were not introduced during the learning process. This advantage can be used for vertical farms that can be efficiently installed and operated in a variety of environments, and because no transfer learning was required, the construction time for container-type vertical farms can be reduced. In experiments, we show that the proposed model achieved a performance of 76.9%, which is 12.5% better than the existing method with a dataset obtained from a container-type indoor vertical farm. Our codes and dataset will be available publicly

Crop Growth Monitoring System in Vertical Farms Based on Region-of-Interest Prediction

Vertical farms are to be considered the future of agriculture given that they not only use space and resources efficiently but can also consistently produce large yields. Recently, artificial intelligence has been introduced for use in vertical farms to boost crop yields, and crop growth monitoring is an essential example of the type of automation necessary to manage a vertical farm system. Region of interest predictions are generally used to find crop regions from the color images captured by a camera for the monitoring of growth. However, most deep learning-based prediction approaches are associated with performance degradation issues in the event of high crop densities or when different types of crops are grown together. To address this problem, we introduce a novel method, termed pseudo crop mixing, a model training strategy that targets vertical farms. With a small amount of labeled crop data, the proposed method can achieve optimal performance. This is particularly advantageous for crops with a long growth period, and it also reduces the cost of constructing a dataset that must be frequently updated to support the various crops in existing systems. Additionally, the proposed method demonstrates robustness with new data that were not introduced during the learning process. This advantage can be used for vertical farms that can be efficiently installed and operated in a variety of environments, and because no transfer learning was required, the construction time for container-type vertical farms can be reduced. In experiments, we show that the proposed model achieved a performance of 76.9%, which is 12.5% better than the existing method with a dataset obtained from a container-type indoor vertical farm. Our codes and dataset will be available publicly

Experimental systems for the analysis of mutational signatures: no 'one-size-fits-all' solution

Cells constantly accumulate mutations, which are caused by replication errors, as well as through the action of endogenous and exogenous DNA-damaging agents. Mutational patterns reflect the status of DNA repair machinery and the history of genotoxin exposure of a given cellular clone. Computationally derived mutational signatures can shed light on the origins of cancer. However, to understand the etiology of cancer signatures, they need to be compared with experimental signatures, which are obtained from the isogenic cell lines or organisms under controlled conditions. Experimental mutational patterns were instrumental in understanding the nature of signatures caused by mismatch repair and BRCA deficiencies. Here, we describe how different cell lines and model organisms were used in recent years to decipher mutational signatures observed in cancer genomes and provide examples of how data from different experimental systems complement and support each other.11Nsciescopu

Multispectral Benchmark Dataset and Baseline for Forklift Collision Avoidance

In this paper, multispectral pedestrian detection is mainly discussed, which can contribute to assigning human-aware properties to automated forklifts to prevent accidents, such as collisions, at an early stage. Since there was no multispectral pedestrian detection dataset in an intralogistics domain, we collected a dataset; the dataset employs a method that aligns image pairs with different domains, i.e. RGB and thermal, without the use of a cumbersome device such as a beam splitter, but rather by exploiting the disparity between RGB sensors and camera geometry. In addition, we propose a multispectral pedestrian detector called SSD 2.5D that can not only detect pedestrians but also estimate the distance between an automated forklift and workers. In extensive experiments, the performance of detection and centroid localization is validated with respect to evaluation metrics used in the driving car domain but with distinct categories, such as hazardous zone and warning zone, to make it more applicable to the intralogistics domain

Boiling characteristics on the reduced graphene oxide films

The graphene has been interested world-widely for the superb mechanical and electrical properties. Among them, the thermal conductivity of graphene was often reported as the 500-5000 W/mK. We tried to apply the graphene to the thermal application of boiling heat transfer through the graphene coating. The graphene was used for the reduced graphene oxide flakes in water (RGO colloid). The RGO colloid boiling on silicon-dioxide heater (substrate) showed both the boiling heat transfer and the critical heat flux enhancement as 65% and 70%, respectively. After RGO colloid boiling, the base graphene layer (BGL) with 10-100 nm thickness below the self-assembled foam-like graphene (SFG) was observed. In order to confirm the effect of BGL on the enhanced boiling performance, the only water boiling on an artificial graphene multilayers (RGO film) coated heater was conducted, and shows the similar result with the RGO colloid boiling. (C) 2014 Elsevier Inc. All rights reserved.X1187Nsciescopu

Single-cell RNA Sequencing Reveals Novel Cellular Factors for Response to Immunosuppressive Therapy in Aplastic Anemia

Aplastic anemia (AA) is a lethal hematological disorder; however, its pathogenesis is not fully understood. Although immunosuppressive therapy (IST) is a major treatment option for AA, one-third of patients do not respond to IST and its resistance mechanism remains elusive. To understand AA pathogenesis and IST resistance, we performed single-cell RNA sequencing (scRNA-seq) of bone marrow (BM) from healthy controls and patients with AA at diagnosis. We found that CD34(+) early-stage erythroid precursor cells and PROM1(+) hematopoietic stem cells were significantly depleted in AA, which suggests that the depletion of CD34(+) early-stage erythroid precursor cells and PROM1(+) hematopoietic stem cells might be one of the major mechanisms for AA pathogenesis related with BM-cell hypoplasia. More importantly, we observed the significant enrichment of CD8(+) T cells and T cell-activating intercellular interactions in IST responders, indicating the association between the expansion and activation of T cells and the positive response of IST in AA. Taken together, our findings represent a valuable resource offering novel insights into the cellular heterogeneity in the BM of AA and reveal potential biomarkers for IST, building the foundation for future precision therapies in AA