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Abstract Navigating mobile robots in crowded environments poses a significant challenge and is essential for the coexistence of robots and humans in future intelligent societies. As a pragmatic data-driven approach, deep reinforcement learning (DRL) holds promise for addressing this challenge. However, current DRL-based navigation methods have possible improvements in understanding agent interactions, feedback mechanism design, and decision foresight in dynamic environments. This paper introduces the model inductive bias enhanced deep reinforcement learning (MIBE-DRL) method, drawing inspiration from a fusion of data-driven and model-driven techniques. MIBE-DRL extensively incorporates model inductive bias into the deep reinforcement learning framework, enhancing the efficiency and safety of robot navigation. The proposed approach entails a multi-interaction network featuring three modules designed to comprehensively understand potential agent interactions in dynamic environments. The pedestrian interaction module can model interactions among humans, while the temporal and spatial interaction modules consider agent interactions in both temporal and spatial dimensions. Additionally, the paper constructs a reward system that fully accounts for the robot’s direction and position factors. This system's directional and positional reward functions are built based on artificial potential fields (APF) and navigation rules, respectively, which can provide reasoned evaluations for the robot's motion direction and position during training, enabling it to receive comprehensive feedback. Furthermore, the incorporation of Monte-Carlo tree search (MCTS) facilitates the development of a foresighted action strategy, enabling robots to execute actions with long-term planning considerations. Experimental results demonstrate that integrating model inductive bias significantly enhances the navigation performance of MIBE-DRL. Compared to state-of-the-art methods, MIBE-DRL achieves the highest success rate in crowded environments and demonstrates advantages in navigation time and maintaining a safe social distance from humans.

In this study, a microcosm experiment was conducted to investigate the effects of Na2S2O8 preoxidation combined with biostimulation on petroleum-contaminated soil remediation. The response of microbial community during this process was explored using BIOLOG ECO microplate carbon utilization method and 16 s rDNA high-throughput sequencing. The results showed that use of 10 mg/g Na2S2O8 removed 19.8 % of the petroleum hydrocarbons, reduced soil biotoxicity and did not affect soil microbial activity compared to other concentrations. Therefore, sodium persulfate of ca. 10 mg/g was used to oxidize petroleum in soil before the biostimulation experiment with organic and inorganic fertilizers. Our finding showed that the content of total petroleum hydrocarbons (TPHs) in soil was reduced by 43.3 % in inorganic fertilizer treatment after 60 days. The results of BIOLOG ECO microplate carbon utilization analysis and 16 S rDNA high-throughput sequencing further confirmed that biostimulation quickly restored the microbial activities in oxidant treated soil. The main marker bacteria in chemical oxidation combined with biostimulation remediation were Arthrobacter and Paenarthrobacter, and their relative abundances were both significantly negatively correlated with the content of petroleum hydrocarbons in soil.

Abstract Intermittent hypoxia (IH) is a hallmark of obstructive sleep apnea (OSA), which is related to tumorigenesis and progression. We explored the possible mechanisms by which OSA may promote the development of non‐small cell lung cancer (NSCLC). In this study, NSCLC cells with and without miR‐106a‐5p inhibition were exposed to IH or room air (RA), and subsequently, exosomes were extracted and identified. Macrophages were incubated with these exosomes to detect the expression of the STAT3 signaling pathway and M2‐type macrophage markers, as well as the effect of the macrophages on the malignancy of NSCLC cells. A nude mouse tumorigenesis model was constructed to detect the effects of exosomal miR‐106a‐5p on M2 macrophage polarization and NSCLC cell malignancy. Our results showed that IH exosomes promoted the polarization of M2 macrophages, thereby promoting the proliferation, invasion, and metastasis of NSCLC cells. Further, Based on microarray analysis of RA and IH exosomes, we discovered that miR‐106a‐5p, transferred to the macrophages through exosomes, participated in this mechanism by promoting M2 macrophage polarization via down‐regulating PTEN and activating the STAT3 signaling pathway in vitro and in vivo. For patients with NSCLC and OSA, exosomal miR‐106a‐5p levels showed a positive relation to AHI. Exosomal miR‐106a‐5p represents a potential therapeutic target among patients with concomitant cancer and NSCLC.

Cadmium (Cd) pollution has been rapidly increasing due to the global rise in industries. Cd not only harms the ecological environment but also endangers human health through the food chain and drinking water. Therefore, the remediation of Cd-polluted soil is an imminent issue. In this work, ryegrass and a strain of Cd-tolerant bacterium were used to investigate the impact of inoculated bacteria on the physiology and biochemistry of ryegrass and the Cd enrichment of ryegrass in soil contaminated with different concentrations of Cd (4 and 20 mg/kg). The results showed that chlorophyll content increased by 24.7% and 41.0%, while peroxidase activity decreased by 56.7% and 3.9%. In addition, ascorbic acid content increased by 16.7% and 6.3%, whereas glutathione content decreased by 54.2% and 6.9%. The total Cd concentration in ryegrass increased by 21.5% and 10.3%, and the soil’s residual Cd decreased by 86.0% and 44.1%. Thus, the inoculation of Cd-tolerant bacteria can improve the antioxidant stress ability of ryegrass in Cd-contaminated soil and change the soil’s Cd form. As a result, the Cd enrichment in under-ground and above-ground parts of ryegrass, as well as the biomass of ryegrass, is increased, and the ability of ryegrass to remediate Cd-contaminated soil is significantly improved.

The perennial ryegrass Lolium perenne can be used in conjunction with cadmium (Cd)-tolerant bacteria such as Cdq4–2 (Enterococcus spp.) for bioremediation of Cd-contaminated soil. In this study, a theoretical basis was provided to increase the efficiency of L. perenne remediation of Cd-contaminated soil using microorganisms to maintain the stability of the soil microbiome. The experimental design involved three treatment groups: CK (soil without Cd addition) as the control, 20 mg·kg–1 Cd-contaminated soil, and 20 mg·kg–1 Cd-contaminated soil + Cdq4–2, all planted with L. perenne. The soil was collected on day 60 to determine the soil microbial activity and bacterial community structure and to analyze the correlation between soil variables, the bacterial community, available Cd content in the soil, Cd accumulation, and L. perenne growth. The soil microbial activity and bacterial community diversity decreased under Cd stress, and the soil microbial community composition was changed; while inoculation with Cdq4–2 significantly increased soil basal respiration and the activities of urease, invertase, and fluorescein diacetate (FDA) hydrolase by 83.65%, 79.72%, 19.88%, and 96.15% respectively; and the stability of the community structure was also enhanced. The Actinobacteriota biomass, the amount of available Cd, and the above- and belowground Cd content of L. perenne were significantly negatively correlated with the total phosphorus, total potassium, and pH. The activity of urease, invertase, and FDA hydrolase were significantly positively correlated with the biomasses of Acidobacteriota and L. perenne and significantly negatively correlated with the Chloroflexi biomass. Further, the available soil Cd content and the above- and belowground Cd levels of L. perenne were significantly positively correlated with the Actinobacteriota biomass and significantly negatively correlated with the Gemmatimonadetes biomass. Overall, inoculating Cd-tolerant bacteria improved the microbial activity, diversity, and abundance, and changed the microbial community composition, facilitating the remediation of Cd-contaminated soil by L. perenne.

Abstract Background Radiotherapy resistance is the main cause of low tumor regression for locally advanced rectum adenocarcinoma (READ). The biomarkers correlated to radiotherapy sensitivity and potential molecular mechanisms have not been completely elucidated. Methods A mRNA expression profile and a gene expression dataset of READ (GSE35452) were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differentially expressed genes (DEGs) between radiotherapy responder and non-responder of READ were screened out. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for DEGs were performed. Random survival forest analysis was used to identified hub genes by randomForestSRC package. Based on CIBERSORT algorithm, Genomics of Drug Sensitivity in Cancer (GDSC) database, Gene set variation analysis (GSVA), enrichment analysis (GSEA), nomogram, motif enrichment and non-coding RNA network analyses, the associations between hub genes and immune cell infiltration, drug sensitivity, specific signaling pathways, prognosis prediction and TF – miRNA regulatory and ceRNA network were investigated. The expressions of hub genes in clinical samples were displayed with the online Human Protein Atlas (HPA). Results In total, 544 up-regulated and 575 down-regulated DEGs in READ were enrolled. Among that, three hubs including PLAGL2, ZNF337 and ALG10 were identified. These three hub genes were significantly associated with tumor immune infiltration, different immune-related genes and sensitivity of chemotherapeutic drugs. Also, they were correlated with the expression of various disease-related genes. In addition, GSVA and GSEA analysis revealed that different expression levels of PLAGL2, ZNF337 and ALG10 affected various signaling pathways related to disease progression. A nomogram and calibration curves based on three hub genes showed excellent prognosis predictive performance. And then, a regulatory network of transcription factor (ZBTB6) - mRNA (PLAGL2) and a ceRNA network of miRNA (has-miR-133b) - lncRNA were established. Finally, the results from HPA online database demonstrated the protein expression levels of PLAGL2, ZNF337 and ALG10 varied widely in READ patients. Conclusion These findings indicated that up-regulation of PLAGL2, ZNF337 and ALG10 in READ associated with radiotherapy response and involved in multiple process of cellular biology in tumor. They might be potential predictive biomarkers for radiotherapy sensitivity and prognosis for READ.

As a gas signal molecule, hydrogen sulfide (H2S) can participate in many physiological and biochemical processes such as seed germination and photosynthesis regulation. In order to explore the regulatory effect of H2S on the growth of Miscanthus sacchariflorus under Cd stress and to provide sufficient theoretical basis for the complex action of H2S and energy plants to remediate soil pollution. In this experiment, the effects of different concentrations of H2S (10, 25, 50, 100, 300, 400, 500 μmol·L−1 (μM)) pretreatment on the growth index, lipid peroxidation degree, chlorophyll (Chl) content, osmoregulation substance content, antioxidant enzyme activity and non-enzymatic antioxidant content of M. sacchariflorus under Cd stress (50 μM) were studied. The results showed that under Cd stress, the reactive oxygen species (ROS) content in the body of M. sacchariflorus was unbalanced, and the growth were severely inhibited, the activities of antioxidant enzymes, such as catalase (CAT) and superoxide dismutase (SOD) significantly decreased, and the content of osmoregulation substance, ascorbic acid (AsA) and glutathione (GSH) significantly increased. With the increase of H2S concentration, its effect on resisting Cd stress can be shown as ''low concentration promotes, high concentration inhibits''. When the concentration of H2S ≤ 300 μM, although there was no significant difference in Cd content compared with Cd treatment alone, it can regulate the activities of peroxidase (POD), SOD, glutathione reductase (GR) and monodehydroascorbate reductase (MDHAR), increase the content of osmoregulation substances, oxidized glutathione (GSSG), and the transformation rate of AsA and dehydroascorbic acid (DHA) to reduce the oxidative damage and improve the growth and photosynthetic indicators of plants; when the concentration of H2S ≥ 400 μM, Cd content in the ground and root decreased significantly, but the transport factor increased significantly, the growth status of M. sacchariflorus were more severely inhibited by the combined stress of H2S and Cd. In this experiment, it was found that the concentration of H2S pretreatment ≤ 300 μM could regulate the growth of M. sacchariflorus under Cd stress to normal level, and when the treatment concentration was 50 μM, the effect was the best. It will provide a new idea for the treatment of contaminated soil by energy plants.

Biological activated carbon filter backwash water (BAC FBW) removes pollutants from BAC filters in drinking water treatment plants. Characterizing the dissolved organic matter (DOM) in BAC FBW is of great significance for the recycling and treatment of backwash water. DOM pollution was monitored over a year using three-dimensional excitation–emission matrix fluorescence spectra. The dissolved organic carbon (DOC) concentration and fluorescence results showed that the DOC concentration reached the lowest value of 1.95 mg/L in the summer. The relative abundances of fluorescence components had a direct relationship with temperature variations. The soluble microbial products were enhanced greatly during cold seasons, while fulvic-acid-like and humic-acid-like compounds remained constant. Disinfection by-products (DBPs) formation potential and cytotoxicity were evaluated. The total haloacetonitriles (HANs), trihalomethanes (THMs), halonitromethanes (HNMs), and haloacetaldehyde (HALs) formation potentials ranged from 35.5 to 47.3 μg/L, 95.1 to 126 μg/L, 4.95 to 8.06 μg/L, and 43.4 to 53.0 μg/L in BAC FBW, respectively. Among all DBPs included in the calculation, the order of contribution to cytotoxicity was HANs > HALs > THMs > HNMs, respectively. Especially, HANs were much higher than the other DBPs, which should be controlled to avoid the biological risk of effluent quality. HIGHLIGHTS DOC concentration of backwash water reached its lowest value in the summer.; Seasonal variations impacted the composition of DOM due to microbial activity.; THMs were the main DBPs in the backwash water.; The order of contribution to cytotoxicity was HANs > HALS > THMs > HNMs in backwash water.;

Gene flow is crucial for maintaining population genetic diversity and increasing the adaptive potential of species. Therefore, identifying factors affecting gene flow is important for conservation planning, especially for threatened mammals in complex mountain system. In this study, we conducted a microgeographic-scale landscape genetic analysis for the endangered and ecologically isolated Alashan red deer (Cervus elaphus alxaicus) for the first time. Using data from 329 individuals for 11 microsatellite loci, we assessed how the environmental factors in the Chinese Helan Mountains affect the gene flow of this species. Three genetic clusters were identified by both Bayesian (STRUCTURE and GENELAND) and non-Bayesian (DAPC) methods, revealing clear spatial genetic structure within a limited geographic range. Landscape genetic analysis based on causal modelling and linear mixed-effect modelling revealed that gene flow was primarily restricted by topographic factors, including topographic complexity, aspect, altitude and slope, rather than geographical distance or anthropogenic barriers. Based on these results, we identified two important corridors that can potentially facilitate gene flow within the complex landscape of the Helan Mountains. Our results highlight the role of natural landscape features on Alashan red deer's population genetic structure and functional connectivity. This differs from findings regarding red deer populations in other regions, indicating that landscape genetic patterns are not homogenous across the range of a species, but rather differ among specific landscapes.

Wetlands provide important ecosystem services, such as water conservation, biodiversity protection, and carbon sequestration. The Zoige alpine wetland is the largest high-altitude swamp in the world and plays a critical role in regional ecological balance and climate change. However, little is known about the fate of its soil organic carbon (SOC) storage. In this study, we estimated the degradation status of the wetland over the past 35 years and used machine learning to investigate the dynamics and driving factors of SOC at different soil depths of the Zoige wetland in 1985, 2000, and 2020. We also simulated the future SOC balance under different scenarios. The results showed that the area of Zoige wetland has degraded by 378.71 km2 in the past 35 years. Increased precipitation and solar radiation offset the adverse effects of global warming, making the soil act as a carbon sink in the past 35 years. The total SOC storage of the wetland soils in 1985, 2000, and 2020 was estimated to be 2.03 Pg, 2.05 Pg, and 2.21 Pg, respectively, with 46.95% of SOC distributed in the subsoil layers. Climate change was the most important driving factor controlling the SOC storage of the Zoige wetland, explaining 51.33% of the SOC changes in the soil. Temperature change was always the most important factor controlling wetland SOC, and precipitation had a greater impact on the topsoil. Under the temperature control targets of 1.5 °C and 2 °C, the SOC pool of the Zoige wetland will decrease by 60.21 Tg C and 69.19 Tg C, respectively. Under scenarios of a 10% and 20% increase in precipitation, the wetland soil will accumulate an additional 46.53 Tg C and 118.89 Tg C, respectively. The study results provide important references for the sustainable management of the Zoige wetland under the background of global climate change.

Rapid economic development and industrialization may include environmentally harmful human activities that cause heavy-metal accumulation in soils, ultimately threatening the quality of the soil environment and human health. Therefore, accurate identification of pollution sources is an important weapon in efforts to control and prevent pollution. The self-organizing map (SOM) method is widely used in pollution source identification because of its capacity for visualization of high-dimensional data. The SOM ignores the graph structure relationship among chemical elements in soils; the SOM analysis of pollution sources has high uncertainty. Here, we propose a new analysis method, i.e., the graph convolutional self-organizing map (GCSOM), which uses a graph convolutional network (GCN) to extract the graph structure relationship among the chemical elements in soils, then performs data visualization using an SOM. We compared the performances of GCSOM and SOM, then assessed the pollution source characteristics of trace metal(loid)s (TMs, mostly heavy metals) in Jiangmen City using the GCSOM. Our experimental results showed that the GCSOM is superior to the SOM for identification of TM sources, while the TMs in the soil of Jiangmen originate from three main sources: agricultural activities (mainly in Taishan City, Jiangmen), traffic emissions (mainly in Xinhui and Pengjiang Districts), and industrial activities (mainly in Xinhui District). The risk assessment indicated that the risk of all TMs was within threshold.

Accurate time series forecasting is of great importance in real-world scenarios such as health care, transportation, and finance. Because of the tendency, temporal variations, and periodicity of the time series data, there are complex and dynamic dependencies among its underlying features. In time series forecasting tasks, the features learned by a specific task at the current time step (such as predicting mortality) are related to the features of historical timesteps and the features of adjacent timesteps of related tasks (such as predicting fever). Therefore, capturing dynamic dependencies in data is a challenging problem for learning accurate future prediction behavior. To address this challenge, we propose a cross-timestep feature-sharing multi-task time series forecasting model that can capture global and local dynamic dependencies in time series data. Initially, the global dynamic dependencies of features within each task are captured through a self-attention mechanism. Furthermore, an adaptive sparse graph structure is employed to capture the local dynamic dependencies inherent in the data, which can explicitly depict the correlation between features across timesteps and tasks. Lastly, the cross-timestep feature sharing between tasks is achieved through a graph attention mechanism, which strengthens the learning of shared features that are strongly correlated with a single task. It is beneficial for improving the generalization performance of the model. Our experimental results demonstrate that our method is significantly competitive compared to baseline methods.

Mild cognitive impairment (MCI) is a nervous system disease, and its clinical status can be used as an early warning of Alzheimer's disease (AD). Subtle and slow changes in brain structure between patients with MCI and normal controls (NCs) deprive them of effective diagnostic methods. Therefore, the identification of MCI is a challenging task. The current functional brain network (FBN) analysis to predict human brain tissue structure is a new method emerging in recent years, which provides sensitive and effective medical biomarkers for the diagnosis of neurological diseases. Therefore, to address this challenge, we propose a novel Deep Spatiotemporal Attention Network (DSTAN) framework for MCI recognition based on brain functional networks. Specifically, we first extract spatiotemporal features between brain functional signals and FBNs by designing a spatiotemporal convolution strategy (ST-CONV). Then, on this basis, we introduce a learned attention mechanism to further capture brain nodes strongly correlated with MCI. Finally, we fuse spatiotemporal features for MCI recognition. The entire network is trained in an end-to-end fashion. Extensive experiments show that our proposed method significantly outperforms current baselines and state-of-the-art methods, with a classification accuracy of 84.21%.

Bacterivorous nematodes are abundant in petroleum-contaminated soils. However, the ecological functions of bacterivorous nematodes and their impacts together with the addition of organic materials on the activity and diversity of microorganisms in petroleum-contaminated soils remain unknown. To assess such effects, six treatments were established in this study, including uncontaminated nematodes-free soil (Control), petroleum-contaminated soil (PC), petroleum-contaminated soil + 5 nematodes per gram dry soil (PCN), and petroleum-contaminated soil + 5 nematodes per gram dry soil + 1% wheat straw (PCNW), or + 1% rapeseed cake (PCNR), or + 1% biochar (PCNB). Results showed that the enzyme activities in the six treatments generally increased firstly and then decreased during the incubation period. Compared with Control, the invertase activity in PCNW, PCNR, and PCNB increased by 80.6%, 313.5%, and 12.4%, respectively, whereas the urease activity in PC, PCN, PCNW, PCNR, and PCNW increased by 1.2%, 25.5%, 124.3%, 105.3%, and 25.5%, respectively. Petroleum pollution, inoculation of bacterivorous nematodes, and the addition of organic materials all significantly boosted the concentrations of phospholipid fatty acids (PLFAs) of soil bacteria, actinobacteria, and total microorganisms, and increased the concentrations of both G+ and G− bacteria PLFAs and the ratio of G−/G+. The concentration of fungi PLFAs and the ratio of fungi to bacteria were significantly higher in PCNW and PCNR than those in other treatments. Overall, adding bacterivorous nematodes and organic materials to the petroleum-contaminated soil significantly improved soil microbial activity and community structure, suggesting that bacterivorous nematodes could be used for the bioremediation in petroleum contaminated soils.

Circular RNAs (circRNAs) function as efficient microRNA (miRNA) sponges that regulate gene expression in the pathogenesis of many human malignancies. However, their roles in cervical adenocarcinoma remain largely unknown. In this study, we aimed to seek novel circRNAs that regulate cervical adenocarcinoma carcinogenesis and to explore their regulatory mechanisms as well as clinical significance. We identified that 24 circRNAs were differentially expressed in cervical adenocarcinoma tissues by RNA sequencing. Among them, circEYA1 was the most significantly downregulated circRNA in cervical adenocarcinoma. In cervical adenocarcinoma cells, circEYA1 overexpression led to suppression of cell viability and colony formation, promotion of apoptosis, and a decrease of the xenograft tumor growth. The mechanism underlying these observations is that circEYA1 functioned as a sponge of miR-582-3p and abrogated its suppression of CXCL14 expression. Consistently, miR-582-3p inhibition phenocopied the biological effects of circEYA1 overexpression in cervical adenocarcinoma cells. Moreover, miR-582-3p overexpression reversed the suppressive behaviors of circEYA1 in vitro and in vivo. In addition, the expression, correlation, and clinical diagnostic value of circEYA1/miR-582-3p/CXCL14 were confirmed in 198 clinical cervical tissue samples. In summary, our findings highlight a novel tumor suppressive role of circEYA1 in cervical adenocarcinoma tumorigenesis and may provide a potential diagnostic marker and therapeutic target for patients with cervical adenocarcinoma.

Hepatitis B virus X protein (HBx), encoded by the hepatitis B virus (HBV) genome, plays a pivotal in mediating pathogenicity in liver diseases. However, the underlying mechanisms are still needed to be elucidated. Receptor-interacting protein 1 (RIP1) has been identified as a serine/threonine kinase with various biological functions in cell fate, proliferation, and death. In the current study, we found that overexpression of HBx in LO2 human normal hepatocytes increased the expression of RIP1. Importantly, our results indicate that blockage of RIP1 using its specific antagonist necrostatin-1 (Nec-1) ameliorated HBx-induced oxidative stress by mitigating the production of ROS and Nox-4 expression. Also, the presence of Nec-1 improved HBx-induced mitochondrial dysfunction by increasing MMP. Importantly, we found that Nec-1 could inhibit the production of pro-inflammatory cytokines IL-6, IL-8, and CXCL2 as well as the secretion of HMGB1. Mechanistically, we found that Nec-1 treatment suppressed the activation of the JNK/AP-1 and NF-κB signalling pathways. Our findings implicated a novel biological function of RIP1 in HBx-induced cytotoxicity and inflammation in human normal hepatocytes. Antagonism of RIP1 might be a potential therapeutic approach for the treatment of HBV-associated liver diseases.

Plants are affected by changes in light and adaptation mechanisms can affect secondary metabolite synthesis. In this study, the physiological response and regulation of the coumarin biosynthetic pathway of Angelica dahurica to different light intensities (natural light (CK), shade rate 50% (L1), shade rate 70% (L2), and shade rate 90% (L3)) were examined. The chlorophyll content, level of the enzymes of the antioxidant system, extent of lipid peroxidation, and concentrations of the osmoregulatory solute levels were determined in potted plants. Root transcriptome under different light intensities was sequenced using high-throughput technology, and differentially expressed genes (DEGs) related to coumarin biosynthesis were analyzed by quantitative real-time PCR (qRT-PCR). With increasing shade, Chl a, Chl b, Chl a + b, and Chl a/b content increased, while the Chl a/b ratio decreased. The antioxidant enzyme system activity and extent of membrane lipid peroxidation increased. The soluble protein (SP) and proline (Pro) content decreased with the reduction in the light intensity, and soluble sugar (SS) content was found to be highest at 50% shade. The RNA-seq analysis showed that 9388 genes were differentially expressed in the L3 group (7561 were upregulated and 1827 were downregulated). In both the L1 and L2 groups, DEGs were significantly enriched in “Ribosome biosynthesis”; meanwhile, in the L3 group, the DEGs were significantly enriched in “Amino and ribonucleotide sugar metabolism” in KEGG metabolic pathway analysis. Additionally, 4CL (TRINITY_DN40230_c0_g2) and COMT (TRINITY_DN21272_c0_g1) of the phenylpropanoid metabolic pathway were significantly downregulated in the L3 group. In conclusion, A. dahurica grew best under 50% shade and the secondary-metabolite coumarin biosynthetic pathway was inhibited by 90% shade, affecting the yield and quality of medicinal compounds.

In recent years, with the development of geological prospecting from shallow ore to deep and hidden ore, the difficulty of prospecting is increasing day by day, so the application of computer technology and new methods of geological and mineral exploration is paid more and more attention. The mining and prediction of geological prospecting information based on deep learning have become the frontier field of earth science. However, as a deep artificial intelligence algorithm, deep learning still has many problems to be solved in the big data mining and prediction of geological prospecting, such as the small number of training samples of geological and mineral images, the difficulty of building deep learning network models, and the universal applicability of deep learning models. In this paper, the training samples and convolutional neural network models suitable for geochemical element data mining are constructed to solve the above problems, and the model is successfully applied to the prediction research of gold, silver, lead and zinc polymetallic metallogenic areas in South China. Taking the Pangxidong research area in the west of Guangdong Province as an example, this paper carries out prospecting target prediction research based on a 1:50000 stream sediment survey original data. Firstly, the support vector machine (SVM) model and statistical method were used to determine the ore-related geochemical element assemblage. Secondly, the experimental data of geochemical elements were augmented and a dataset was established. Finally, ResNet-50 neural network model is used for data training and prediction research. The experimental results show that the areas numbered 9, 29, 38, 40, 95, 111, 114, 124, 144 have great metallogenic potential, and this method would be a promising tool for metallogenic prediction. By applying the ResNet-50 neural network in metallogenic prediction, it can provide a new idea for the future exploration of mineral resources. In order to verify the generality of the research method in this paper, we conducted experimental tests on the geochemical dataset of B area, another deposit research area in South China. The results show that 100% of the prediction area obtained by using the proposed method covers the known ore deposit area. This model also provides method support for further delineating the prospecting target area in study area B.

Precipitation estimate is important for earth science studies and applications, and it is one of the most difficult meteorological quantities to estimate accurately. For regions such as Peru, reliable gridded precipitation products are lacking due to complex terrains and large portions of remote lands that limit the accuracy of satellite precipitation estimation and in situ measurement density. This study evaluates and cross-examines two high-resolution satellite-based precipitation products, a global rain-gauge interpolated precipitation product, and a Weather Research and Forecast (WRF) model that simulated precipitation for a ten-year period from 2010 to 2019 in the Peruvian Andes region across the Pacific coast, Andes, and in the Amazon. The precipitation estimates examined in this study are the Integrated Multi-SatellitE Retrievals for GPM (IMERG), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Precipitation Climatology Center product (GPCC), and a 3 km grid spacing WRF-based regional climate model (RCM) simulation. The evaluation and cross-examination were performed at sub-daily (6 h), daily, and monthly time scales, and at various spatial resolutions. The results show that the WRF simulation performs as well as, if not better than, GPM IMERG in the low precipitation and dry regions but becomes inaccurate in wet regions. GPM IMERG is more suitable for higher precipitation and wet regions, and MSWEP shows a systematic overestimation over the study area. It is therefore important to choose the most suitable precipitation product based on research needs and climate condition of the study for the challenging Peruvian Andes region.

This work describes an eco-friendly approach for in situ immobilization of Cu nanoparticles on the surface of lignin modified Fe3O4 nanoparticles, without using any toxic reducing and capping agents. The structure, morphology, and physicochemical properties were characterized by various analytical techniques such as Fourier transformed infrared spectroscopy (FT-IR), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), energy dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), inductively coupled plasma (ICP) and vibrating sample magnetometer (VSM). The Fe3O4/Lignin/Cu NPs was proven to be highly efficient nanocatalyst for reduction of nitroarenes. The reaction was performed in water medium and excellent yields of the products were achieved. The nanocatalyst was easily magnetically recovered and recycled 8 times without any significant loss of catalytic activity. While studying the biological activity, cell viability of Fe3O4/Lignin/Cu NPs was very low against A549 lung cancer cells without any cytotoxicity on the normal cell line. The viability of lung cancer cell line reduced dose-dependently in the presence of Fe3O4/Lignin/Cu nanocomposite. The IC50 of Fe3O4/Lignin/Cu nanocomposite was32 µg/mL against A549 cell line.

Background: Long non-coding RNA (lncRNA) distal-less homeobox 6 antisense 1 (DLX6-AS1) was reported to be dysregulated in lung cancer. However, detailed roles of DLX6-AS1 in the pathogenesis of non-small cell lung cancer (NSCLC) were largely unknown. Methods: The expression of DLX6-AS1 was measured in NSCLC tissues and cells by quantitative real-time PCR (qRT-PCR). The abundance of proline rich 11 (PRR11) were detected by qRT-PCR and western blot, respectively. The effects of DLX6-AS1 and PRR11 on cell proliferation, migration, invasion and apoptosis were explored by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), transwell and flow cytometry analysis, respectively. Luciferase reporter assay, qRT-PCR and western blot were performed to confirm the interaction between miR-144 and DLX6-AS1 or PRR11. Tumor xenograft assay was performed to verify the role of DLX6-AS1 in NSCLC in vivo. Results: DLX6-AS1 and PRR11 were elevated in NSCLC tissues and cells. DLX6-AS1 was positively correlated with PRR11 mRNA expression in NSCLC tissues. Knockdown of DLX6-AS1 and PRR11 significantly suppressed cell proliferation, migration and invasion and induced apoptosis in NSCLC cells, which was reversed by PRR11 overexpression. In addition, DLX6-AS1 and PRR11 were demonstrated to interact with microRNA-144 (miR-144) and DLX6-AS1 upregulated PRR11 expression by acting as a competing endogenous RNA (ceRNA) of miR-144 in NSCLC cells. Furthermore, DLX6-AS1 knockdown suppressed tumor growth in NSCLC in vivo by upregulating miR-144 and downregulating PRR11. Conclusion: Knockdown of DLX6-AS1 inhibited cell proliferation, migration, invasion and promoted apoptosis by downregulating PRR11 expression and upregulating miR-144 in NSCLC.

In recent years, with the integration and development of artificial intelligence technology and geology, traditional geological prospecting has begun to change to intelligent prospecting. Intelligent prospecting mainly uses machine learning technology to predict the prospecting target area by mining the correlation between geological variables and metallogenic characteristics, which usually requires a large amount of data for training. However, there are some problems in the actual research, such as fewer geological sample data and irregular mining features, which affect the accuracy and reliability of intelligent prospecting prediction. Taking the Pangxidong study area in Guangdong Province as an example, this paper proposes a deep learning framework (SKT) for prospecting target prediction based on selective knowledge transfer and carries out intelligent prospecting target prediction research based on geochemical data in Pangxidong. The irregular features of different scales in the mining area are captured by dilation convolution, and the weight parameters of the source network are selectively transferred to different target networks for training, so as to increase the generalization performance of the model. A large number of experimental results show that this method has obvious advantages over other state-of-the-art methods in the prediction of prospecting target areas, and the prediction effect in the samples with mines is greatly improved, which can effectively alleviate the problems of a small number of geological samples and irregular features of mining areas in prospecting prediction.

Bolted joints are commonly used for assembling carbon fiber/resin composite structures. Since drilling may generate defects at hole edges which affect mechanical properties, it is of great engineering significance to develop proper repair methods to restore the mechanical properties of the defective parts. However, there are few studies on hole edge defects and their repair methods. Therefore, a novelty short fiber filling repair method was proposed to repair defective holes in this study. The mechanical properties of intact, defective and repaired countersunk composite joints were compared and investigated. Experimental tensile tests showed that defective joints had lower initial stiffness and failure loads compared to intact joints, while the mechanical properties were effectively restored after repair. Three-dimensional finite element models were also established to analyze the damage process of the joints. Results of numerical modelling were consistent with the experimental results. The simulations showed that changes in contact behaviors and local deformations caused by hole edge defects led to the low initial stiffness and stiffness transition point of the joint, while this phenomenon was reduced after repair. Additionally, despite different joint types, laminate failure mainly occurred around the hole and countersink.

Rebar corrosion-induced durability issue is a major concern for bridges. The ECC cover was employed to prevent the intrusion of the corrosive agent. This paper studied the flexural behavior of ECC-concrete composite beam. The effects of bonding at the interface and fiber mesh reinforcement on the flexural properties and cracking pattern were investigated. The strain distribution and midspan deflection were evaluated. Test results show that the bonded composite beam had a higher loading capacity. But the unbonded composite beam showed better postcrack energy absorption capacity with higher midspan deflection. The fiber mesh reinforcement could further improve the flexural properties regardless of the bonding condition. The strain at the bottom of the unbonded beam was much smaller than that of the bonded beam. The penetrated cracks were observed at the ECC layer of the bonded composited beam.

The objective of this study was to establish the effects of cold expansion, chamfering, bolt clamping, and their combinations on the fatigue life of an aluminum–lithium alloy single plate. Fatigue tests were conducted to quantify the anti-fatigue effects of the different techniques. A scanning electron microscope was used to perform fracture analyses of the used specimens, and the residual stresses were measured using an X-ray diffraction device. In addition, three-dimensional finite element models of the specimens were established and used to characterize their stress states, and the Smith–Watson–Topper method was used to predict the fatigue lives of the specimens. The fatigue test results showed that all the considered processes improved the fatigue life of the pristine specimen. The most effective was a combination of 3.2% cold expansion, 1-mm chamfering, and bolt clamping using a 6.4-N m torque, which improved the fatigue life of the pristine specimen by a factor of 15.5. The finite element method results also revealed that this combination decreased the maximum stress and confirmed its superiority in relation to the other fatigue-life enhancement techniques in terms of the anti-fatigue effect. The Smith–Watson–Topper method underestimated the specimen fatigue life, but the accuracy satisfied engineering requirements.

Cervical cancer is the most common cause of cancer-related deaths in women from developing countries. Identification of novel prognostic predictors or therapeutic targets may improve patient prognosis. In the current study, we demonstrated by real-time PCR that miR-224 expression was significantly upregulated (1.82-fold, P = 0.0025) in cervical cancer tissues (n = 126) compared with in normal cervical tissues (n = 64). Higher expression of miR-224 was significantly associated with poorer prognostic factors, including advanced FIGO stage, nodal metastasis, larger tumor size, vascular involvement and deep stromal invasion (all P < 0.05). Enforced expression of miR-224 promoted cell proliferation, migration and invasion in SiHa and CaSki cancer cell lines. Bioinformatic analysis indicated that RASSF8 (RAS-association domain family 8) was a potential target of miR-224. Western blot analysis and luciferase reporter assay showed that overexpressed miR-224 inhibited RASSF8 protein expression and decreased the activity of a luciferase reporter containing the 3' untranslated region (UTR) of RASSF8, respectively. Further, RASSF8 knockdown by specific RNAi showed similar effects in cervical cancer cells transfected with miR-224 mimic. Our findings suggest that miR-224 directly targets RASSF8 and thereby acts as a tumor promoter in cervical cancer progression.

Using the observations from the two-dimensional video disdrometer and polarimetric radar, a detailed process-based evaluation of five bulk microphysics schemes in the simulation of an extreme rainfall event over the mountainous coast of South China is performed. Most schemes reproduce one of the heavy rainfall areas, and the National Severe Storms Laboratory (NSSL) scheme successfully simulates both heavy rainfall areas in this event. However, our analysis reveals that even the NSSL simulation still cannot accurately represent the rain microphysics for this event. Observational analysis shows that abundant small- and medium-sized (1-4 mm) raindrops are the main contributors to the extreme rainfall. All the simulations tend to underpredict raindrops for diameter around 3 mm. The Lin, WSM6, and Morrison simulations agree better with the observed drop size distribution (DSD) for diameters between 1 and 2 mm for higher rain rates. The Thompson simulation shows a relatively narrow distribution with overpredicted small-sized (1-2 mm) raindrops. The NSSL simulation has a broad distribution with more large (>4 mm) raindrops probably related to its efficient rain self-collection process at the low levels, which is conducive to producing extreme rainfall. Proper rain evaporation rate is important in generating cold pools with favorable strength for the maintenance of a convective system in this event. Similar results are obtained in the simulations of two additional extreme rainfall cases, in which the NSSL simulation also overpredicts large raindrops while the Thompson simulation produces more small raindrops. This study indicates that more efforts are needed to improve the representation of rain self-collection/breakup, rain evaporation processes, and DSD for extreme rainfall over South China. It also highlights the importance in careful consideration of rain DSD in addition to radar reflectivity and surface precipitation when analyzing simulations of extreme rainfall in order to avoid "wrong" interpretation of "right" results. [ABSTRACT FROM AUTHOR]