EpCAM Is an Endoderm-Specific Wnt Derepressor that Licenses Hepatic Development

SummaryMechanisms underlying cell-type-specific response to morphogens or signaling molecules during embryonic development are poorly understood. To learn how response to the liver-inductive Wnt2bb signal is achieved, we identify an endoderm-enriched, single transmembrane protein, epithelial-cell-adhesion-molecule (EpCAM), as an endoderm-specific Wnt derepressor in zebrafish. hi2151/epcam mutants exhibit defective liver development similar to prt/wnt2bb mutants. EpCAM directly binds to Kremen1 and disrupts the Kremen1-Dickkopf2 (Dkk2) interaction, which prevents Kremen1-Dkk2-mediated removal of Lipoprotein-receptor-related protein 6 (Lrp6) from the cell surface. These data lead to a model in which EpCAM derepresses Lrp6 and cooperates with Wnt ligand to activate Wnt signaling through stabilizing membrane Lrp6 and allowing Lrp6 clustering into active signalosomes. Thus, EpCAM cell autonomously licenses and cooperatively activates Wnt2bb signaling in endodermal cells. Our results identify EpCAM as the key molecule and its functional mechanism to confer endodermal cells the competence to respond to the liver-inductive Wnt2bb signal

LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression

In long context scenarios, large language models (LLMs) face three main challenges: higher computational/financial cost, longer latency, and inferior performance. Some studies reveal that the performance of LLMs depends on both the density and the position of the key information (question relevant) in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt compression towards improving LLMs' perception of the key information to simultaneously address the three challenges. We conduct evaluation on a wide range of long context scenarios including single-/multi-document QA, few-shot learning, summarization, synthetic tasks, and code completion. The experimental results show that LongLLMLingua compressed prompt can derive higher performance with much less cost. The latency of the end-to-end system is also reduced. For example, on NaturalQuestions benchmark, LongLLMLingua gains a performance boost of up to 17.1% over the original prompt with ~4x fewer tokens as input to GPT-3.5-Turbo. It can derive cost savings of \$28.5 and \$27.4 per 1,000 samples from the LongBench and ZeroScrolls benchmark, respectively. Additionally, when compressing prompts of ~10k tokens at a compression rate of 2x-10x, LongLLMLingua can speed up the end-to-end latency by 1.4x-3.8x. Our code is available at https://aka.ms/LLMLingua

Individual Tree Detection in Coal Mine Afforestation Area Based on Improved Faster RCNN in UAV RGB Images

Forests are the most important part of terrestrial ecosystems. In the context of China’s industrialization and urbanization, mining activities have caused huge damage to the forest ecology. In the Ulan Mulun River Basin (Ordos, China), afforestation is standard method for reclamation of coal mine degraded land. In order to understand, manage and utilize forests, it is necessary to collect local mining area’s tree information. This paper proposed an improved Faster R-CNN model to identify individual trees. There were three major improved parts in this model. First, the model applied supervised multi-policy data augmentation (DA) to address the unmanned aerial vehicle (UAV) sample label size imbalance phenomenon. Second, we proposed Dense Enhance Feature Pyramid Network (DE-FPN) to improve the detection accuracy of small sample. Third, we modified the state-of-the-art Alpha Intersection over Union (Alpha-IoU) loss function. In the regression stage, this part effectively improved the bounding box accuracy. Compared with the original model, the improved model had the faster effect and higher accuracy. The result shows that the data augmentation strategy increased AP by 1.26%, DE-FPN increased AP by 2.82%, and the improved Alpha-IoU increased AP by 2.60%. Compared with popular target detection algorithms, our improved Faster R-CNN algorithm had the highest accuracy for tree detection in mining areas. AP was 89.89%. It also had a good generalization, and it can accurately identify trees in a complex background. Our algorithm detected correct trees accounted for 91.61%. In the surrounding area of coal mines, the higher the stand density is, the smaller the remote sensing index value is. Remote sensing indices included Green Leaf Index (GLI), Red Green Blue Vegetation Index (RGBVI), Visible Atmospheric Resistance Index (VARI), and Normalized Green Red Difference Index (NGRDI). In the drone zone, the western area of Bulianta Coal Mine (Area A) had the highest stand density, which was 203.95 trees ha−1. GLI mean value was 0.09, RGBVI mean value was 0.17, VARI mean value was 0.04, and NGRDI mean value was 0.04. The southern area of Bulianta Coal Mine (Area D) was 105.09 trees ha−1 of stand density. Four remote sensing indices were all the highest. GLI mean value was 0.15, RGBVI mean value was 0.43, VARI mean value was 0.12, and NGRDI mean value was 0.09. This study provided a sustainable development theoretical guidance for the Ulan Mulun River Basin. It is crucial information for local ecological environment and economic development

Individual Tree Detection in Coal Mine Afforestation Area Based on Improved Faster RCNN in UAV RGB Images

Forests are the most important part of terrestrial ecosystems. In the context of China’s industrialization and urbanization, mining activities have caused huge damage to the forest ecology. In the Ulan Mulun River Basin (Ordos, China), afforestation is standard method for reclamation of coal mine degraded land. In order to understand, manage and utilize forests, it is necessary to collect local mining area’s tree information. This paper proposed an improved Faster R-CNN model to identify individual trees. There were three major improved parts in this model. First, the model applied supervised multi-policy data augmentation (DA) to address the unmanned aerial vehicle (UAV) sample label size imbalance phenomenon. Second, we proposed Dense Enhance Feature Pyramid Network (DE-FPN) to improve the detection accuracy of small sample. Third, we modified the state-of-the-art Alpha Intersection over Union (Alpha-IoU) loss function. In the regression stage, this part effectively improved the bounding box accuracy. Compared with the original model, the improved model had the faster effect and higher accuracy. The result shows that the data augmentation strategy increased AP by 1.26%, DE-FPN increased AP by 2.82%, and the improved Alpha-IoU increased AP by 2.60%. Compared with popular target detection algorithms, our improved Faster R-CNN algorithm had the highest accuracy for tree detection in mining areas. AP was 89.89%. It also had a good generalization, and it can accurately identify trees in a complex background. Our algorithm detected correct trees accounted for 91.61%. In the surrounding area of coal mines, the higher the stand density is, the smaller the remote sensing index value is. Remote sensing indices included Green Leaf Index (GLI), Red Green Blue Vegetation Index (RGBVI), Visible Atmospheric Resistance Index (VARI), and Normalized Green Red Difference Index (NGRDI). In the drone zone, the western area of Bulianta Coal Mine (Area A) had the highest stand density, which was 203.95 trees ha−1. GLI mean value was 0.09, RGBVI mean value was 0.17, VARI mean value was 0.04, and NGRDI mean value was 0.04. The southern area of Bulianta Coal Mine (Area D) was 105.09 trees ha−1 of stand density. Four remote sensing indices were all the highest. GLI mean value was 0.15, RGBVI mean value was 0.43, VARI mean value was 0.12, and NGRDI mean value was 0.09. This study provided a sustainable development theoretical guidance for the Ulan Mulun River Basin. It is crucial information for local ecological environment and economic development

Radiation Protection of Polydatin Against Radon Exposure Injury of Epithelial Cells and Mice

Radon exposure is significantly associated with lung cancer. Radon concentration is currently reduced mainly by physical methods, but there is a lack of protective drugs or biochemical reagents for radon damage. This study aimed to explore the protective effect of polydatin (PD) on the radon-exposed injury. The results showed that PD can significantly reduce ROS level, raise SOD activity, weaken the migration ability, increase E-cad, and decrease mesenchymal cell surface markers (FN1, Vimentin, N-cad, α-SMA, and Snail) in radon-exposed epithelial cells. In vivo, PD increased the mice weight, promoted SOD activity, and decreased MDA content, the number of bullae, pulmonary septum thickness, lung collagenous fibers, and mesenchymal cell surface markers. Furthermore, PD inhibited p-PI3K, p-AKT, and p-mTOR expression. Compared with directly adding PD on radon-exposed cells, adding PD before and after radon exposure could more obviously improve the adhesion of radon-exposed cells, significantly alleviate the migration ability, and more significantly reduce mesenchyme markers and p-AKT and p-mTOR. These results indicate that PD can reduce oxidative stress, weaken epithelial-mesenchymal transition (EMT) and lung fibrosis in radon-exposed cells/mice, and have good radiation protection against radon injury. The mechanism is related to the inhibition of the PI3K/AKT/mTOR pathway

Utilization of a new Gemini surfactant as the collector for the reverse froth flotation of phosphate ore in sustainable production of phosphate fertilizer

An effective collector plays a vital role in the reverse flotation of phosphate ore as well as sustainable production of phosphate fertilizer. Phosphate fertilizer is used to achieve the sustainable increase in the production of food. Phosphate ore is concentrated by reverse froth flotation and used as a raw material to produce phosphate fertilizer in industry. However, all the surfactants used in the phosphate ore reverse flotation process are conventional monomeric surfactants contain a single similar hydrophobic group in the molecule, which results in a low production efficiency and severe environmental contamination. In this work, a novel Gemini surfactant, N,N′-bis(dodecyldimethyl)-1,4-butane diammonium dibromide (BDBD) was prepared, and originally recommended as a collector for reverse froth flotation separation of silicoide from phosphate ore. The performance of BDBD was compared with the results acquired using its conventional monomeric surfactant dodecylamine hydrochloride (DAH). The bench-scale flotation results showed that BDBD had excellent collecting power for silicoide and significant selectivity against apatite at pH 9. Moreover, BDBD presented stronger desilication efficiency than DAH. Achieving the superior flotation performance (P O recovery increased by 2.41%, P O content increased by 0.26%, and SiO content reduced by 2.52%), the dosage of Gemini type BDBD collector (100 g/t) is four times less than that of monomeric DAH collector (400 g/t). Therefore, this work provides a promising approach to the sustainable phosphate fertilizer production and to address global food security concerns

Management and control mode of underground coal mining based on medium and high-level intelligent technology

Coal is the guarantee energy in China, and its dominant position in energy will not change for a period of time in the future. Coal intelligent mining will show a rapid development trend and enter into a new stage of development. Through induction and analysis, the great significance, development law, contradiction, construction method and future development direction of intelligent construction of coal mine are summarized in this paper. This paper systematically expounds the reform process of coal mining technology and management mode in China, and summarizes the five stages of manual mining, ordinary mining, comprehensive mechanized mining, automatic mining, primary intelligent mining, as well as the corresponding technical modes. Breaking through the intelligent research ideas such as traditional coal roadheader mining, this paper puts forward 10 characteristics of medium and high-level intelligent mining technology of coal, namely, complete transparency of coal mine, comprehensive intelligence of perception, high-end intelligence of equipment, real-time reliability of network, integrated platform control, intelligent analysis of data, group collaboration intelligence, professional team employees, dynamic decision-making intelligence, disaster prevention and control matching, and gives the definition, characteristics, content and function of each characteristic. Based on the premise of medium and high-level intelligent technology, the corresponding ‘1+1’ management and control mode of coal mine mining is conceived, that is, one level is set in one coal mine, and a new flat platform management and control mode of ‘one command and control center+one professional team’ is constructed. Therefore, the mode can promote the reform of production mode, match the requirements of corresponding mining mode in the future intelligent rapid development and realize the real meaning of reducing personnel, increasing safety and improving efficiency. Finally, the further development of intelligent mining technology and mode in coal mine is prospected

13-Methyltetradecanoic Acid Exhibits Anti-Tumor Activity on T-Cell Lymphomas <i>In Vitro</i> and <i>In Vivo</i> by Down-Regulating p-AKT and Activating Caspase-3

<div><p>13-Methyltetradecanoic acid (13-MTD), a saturated branched-chain fatty acid purified from soy fermentation products, induces apoptosis in human cancer cells. We investigated the inhibitory effects and mechanism of action of 13-MTD on T-cell non-Hodgkin’s lymphoma (T-NHL) cell lines both <i>in vitro</i> and <i>in vivo</i>. Growth inhibition in response to 13-MTD was evaluated by the cell counting kit-8 (CCK-8) assay in three T-NHL cell lines (Jurkat, Hut78, EL4 cells). Flow cytometry analyses were used to monitor the cell cycle and apoptosis. Proteins involved in 13-MTD-induced apoptosis were examined in Jurkat cells by western blotting. We found that 13-MTD inhibited proliferation and induced the apoptosis of T-NHL cell lines. 13-MTD treatment also induced a concentration-dependent arrest of Jurkat cells in the G₁-phase. During 13-MTD-induced apoptosis in Jurkat cells, the cleavage of caspase-3 and poly ADP-ribose polymerase (PARP, a caspase enzymolysis product) were detected after incubation for 2 h, and increased after extending the incubation time. However, there was no change in the expression of Bcl-2 or c-myc proteins. The appearance of apoptotic Jurkat cells was accompanied by the inhibition of AKT and nuclear factor-kappa B (NF-κB) phosphorylation. In addition, 13-MTD could also effectively inhibit the growth of T-NHL tumors <i>in vivo</i> in a xenograft model. The tumor inhibition rate in the experimental group was 40%. These data indicate that 13-MTD inhibits proliferation and induces apoptosis through the down-regulation of AKT phosphorylation followed by caspase activation, which may provide a new approach for treating T-cell lymphomas.</p></div