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Semantic segmentation is a key technique involved in automatic interpretation of high-resolution remote sensing (HRS) imagery and has drawn much attention in the remote sensing community. Deep convolutional neural networks (DCNNs) have been successfully applied to the HRS imagery semantic segmentation task due to their hierarchical representation ability. However, the heavy dependency on a large number of training data with dense annotation and the sensitiveness to the variation of data distribution severely restrict the potential application of DCNNs for the semantic segmentation of HRS imagery. This study proposes a novel unsupervised domain adaptation semantic segmentation network (MemoryAdaptNet) for the semantic segmentation of HRS imagery. MemoryAdaptNet constructs an output space adversarial learning scheme to bridge the domain distribution discrepancy between source domain and target domain and to narrow the influence of domain shift. Specifically, we embed an invariant feature memory module to store invariant domain-level context information because the features obtained from adversarial learning only tend to represent the variant feature of current limited inputs. This module is integrated by a category attention-driven invariant domain-level context aggregation module to current pseudo invariant feature for further augmenting the pixel representations. An entropy-based pseudo label filtering strategy is used to update the memory module with high-confident pseudo invariant feature of current target images. Extensive experiments under three cross-domain tasks indicate that our proposed MemoryAdaptNet is remarkably superior to the state-of-the-art methods., Comment: 17 pages, 12 figures and 8 tables

Hepatocyte growth factor receptor (HGFR or Met) upregulation has been proven to play important roles in non-small cell lung carcinoma (NSCLC). Interestingly, chemoresistance against epidermal growth factor receptor (EGFR) inhibitors including erlotinib and gefitinib was also related to Met. Targeting bromodomain and extra terminal domain (BET) proteins, especially BRD4, has shown inhibitory effects on lung cancer, but the mechanism is unclear. Herein, we found that JQ1 (BET inhibitor) suppressed NSCLC cell growth, reduced the Met expression, and contributed to inactivation of PI3K/Akt and MAPK/ERK pathways. Moreover, another BET protein inhibitor I-BET151, or BRD4 depletion, also inhibited NSCLC cell growth and downregulated Met. JQ1 inhibited HGF-induced cell growth and Met/PI3K/Akt activation, also inhibited A549 tumor growth in xenograft mouse models, in parallel with Met downregulation. Moreover, JQ1 inhibited the growth of paired erlotinib-sensitive and resistant HCC827 cells in parallel with Met downregulation and PI3K/Akt signaling inactivation. JQ1 also exerted inhibitory influences on the growth of erlotinib-sensitive and resistant HCC827 tumors in xenograft mouse models. These results suggested that targeting BET proteins inhibited NSCLC via downregulating Met and inactivating PI3K/AKT pathway. Our findings reveal a novel mechanism of BET proteins implicated in NSCLC progression with Met taken into consideration.

With the development of IoT and 5G technologies, more and more online resources are presented in trendy multimodal data forms over the Internet. Hence, effectively processing multimodal information is significant to the development of various online applications, including e-Iearning and digital health, to just name a few. However, most AI-driven systems or models can only handle limited forms of information. In this study, we investigate the correlation between natural language processing (NLP) and pattern recognition, trying to apply the mainstream approaches and models used in the computer vision (CV) to the task of NLP. Based on two different Twitter datasets, we propose a convolutional neural network based model to interpret the content of short text with different goals and application backgrounds. The experiments have demonstrated that our proposed model shows fairly competitive performance compared to the mainstream recurrent neural network based NLP models such as bidirectional long short-term memory (Bi-LSTM) and bidirectional gate recurrent unit (Bi-GRU). Moreover, the experimental results also demonstrate that the proposed model can precisely locate the key information in the given text.

While the application of manure to improve soil quality has attracted attention, the effect of pig manure application rates on soil fertility and acidity remains poorly understood. This study analyzed the changes in and correlations between soil acidity, pH buffer capacity (pHBC), soil chemical properties and crop yields after 8 years of pig manure application at different rates (i.e., 0, 7.5, 15, 30, and 45 t·ha-1) in red soil. Crop yield, soil nutrients, pH, pHBC, iron activation, and exchangeable base cations increased with increased pig manure application rates. Soil fertility and pHBC were significantly enhanced when the application rate was larger than 15 t·ha-1. At 30 and 45 t·ha-1, pH values increased significantly while exchangeable acid decreased (41.3% and 52.8%) compared to no pig manure input. Crop yields were positively related to soil pH, but negatively correlated with soil exchangeable hydrogen and exchangeable aluminum. Redundancy analysis revealed pH and pHBC to be positively correlated with exchangeable base cations, total phosphorus, total nitrogen, soil organic carbon, and amorphous iron oxide. In conclusion, crop yield could be improved by adjusting soil acidity characteristics, and the application of pig manure reduced soil acidity, with an optimal application rate of 15–30 t·ha-[1]. [1]Hunan Soil and Fertilizer Institute, Changsha, 410125, China. 2Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences/ Key Laboratory of Agricultural Environment, Ministry of Agriculture and Rural Affairs of P.R.China, 100081, Beijing, China. 3Hunan Agricultural University, Changsha, 410128, China. *email: longzd313@hunaas.cn; sungeng@hunaas.cn.

Smart agriculture which integrates the agriculture with Internet of Things (IoT) has attracted attention since it contributes to increase the productivity and quality of crops, reduce energy consumption and facilitate the farmers. Wireless sensor networks (WSNs) and unmanned aerial vehicles (UAVs) are two most commonly deployed devices that are used for enabling the smart agriculture. In this paper, we design a collaborative WSN-UAV system, wherein different clusters of sensor nodes form different sensor-based virtual antenna arrays (SVAAs) to transmit the collected data towards different receiver UAVs via adopting collaborative beamforming (CB), then the receiver UAVs will take the collected data back to the ground control station (GCS). We formulate a transmission rate and battery energy bi-objective optimization problem (TRBEBOP) to simultaneously maximize the total transmission rate of the sensor-based CB clusters and the total remaining battery energy of the selected sensor nodes, by selecting appropriate sensor nodes in each cluster that can form a predominant SVAA, determining suitable receiver UAVs and optimizing the excitation current weights of the selected sensor nodes. To handle the formulated TRBEBOP that is demonstrated to be non-convex and NP-hard, an enhanced non-dominated sorting genetic algorithm II (ENSGA-II) with several specific designs is presented. Simulation results validate the effectiveness of the proposed ENSGA-II for solving the formulated TRBEBOP. Moreover, compared with other benchmark algorithms, the superiority of the proposed ENSGA-II is demonstrated. In addition, the impacts of several fortuitous circumstances on the system are estimated, and the results illustrate the robustness of the proposed scheme. Finally, the discussion about several mechanisms to deal with the interference induced by the sidelobe levels and the impact of UAV movement on receiving rate are provided.

Many herbs and herbal formulae have been proven to offer safe and effective treatment for hyperlipidemia. However, the molecular mechanisms of their treatment have not been fully elucidated. Here, a total of 103 herbal formulae, including 146 individual herbs used to treat hyperlipidemia, were screened. Next, 40 common targets of core herbs and hyperlipidemia were extracted, representing potential targets of the herbal medicines for the treatment of hyperlipidemia. Further analysis revealed that the seven proteins most relevant to hyperlipidemia treatment were IL6, AKT1, IL1B, PTGS2, VEGFA, PPARG, and NOS3. Surprisingly, KEGG pathway enrichment analysis suggested that the herbs may treat hyperlipidemia by influencing the lipid and atherosclerosis pathway and the pathway in cancer. Molecular docking revealed significant affinity between bioactive compounds such as physciondiglucoside and baicalin with proteins involved in cancer pathways. Three active compounds, including 3-beta-hydroxymethyllenetanshiquinone, dan-shexinkum d, and physciondiglucoside have good binding ability with AKT1, PTGS2, and PPARG. The anti-hyperlipidemia activity of herbal medicines may be mediated via regulating proteins with roles in cancer. This study provides further evidence supporting the potential pharmacotherapeutic candidates for the combined treatment of hyperlipidemia and cancer in the future.

Supplementary Figure S2 shows SUR1 regulated the growth of NSCLCs, and the expression levels of phosphorylated p70S6K and KLF4.

Supplementary Figure S5 shows the proteomics analysis of p70S6K binding proteins in HEK293T cells.

One of several challenges in design of clinical chemoprevention trials is the selection of the dose, formulation, and dose schedule of the intervention agent. Therefore, a cross-over clinical trial was undertaken to compare the bioavailability and tolerability of sulforaphane from two of broccoli sprout–derived beverages: one glucoraphanin-rich (GRR) and the other sulforaphane-rich (SFR). Sulforaphane was generated from glucoraphanin contained in GRR by gut microflora or formed by treatment of GRR with myrosinase from daikon (Raphanus sativus) sprouts to provide SFR. Fifty healthy, eligible participants were requested to refrain from crucifer consumption and randomized into two treatment arms. The study design was as follows: 5-day run-in period, 7-day administration of beverages, 5-day washout period, and 7-day administration of the opposite intervention. Isotope dilution mass spectrometry was used to measure levels of glucoraphanin, sulforaphane, and sulforaphane thiol conjugates in urine samples collected daily throughout the study. Bioavailability, as measured by urinary excretion of sulforaphane and its metabolites (in approximately 12-hour collections after dosing), was substantially greater with the SFR (mean = 70%) than with GRR (mean = 5%) beverages. Interindividual variability in excretion was considerably lower with SFR than with GRR beverage. Elimination rates were considerably slower with GRR, allowing for achievement of steady-state dosing as opposed to bolus dosing with SFR. Optimal dosing formulations in future studies should consider blends of sulforaphane and glucoraphanin as SFR and GRR mixtures to achieve peak concentrations for activation of some targets and prolonged inhibition of others implicated in the protective actions of sulforaphane. Cancer Prev Res; 4(3); 384–95. ©2011 AACR.

Supplementary Table S1 shows the clinicopathological characteristics of non-small cell lung carcinoma patients in Fig. 1C.

Supplementary Figure S6 shows knockdown of KLF4 expression promoted cell growth, EMT, and migration.

Supplementary Table S2 shows the gene ontology biological process analysis of glibenclamide regulated genes.

Supplementary Figure S3 shows glibenclamide downregulated cyclin D1 expression and p70S6K signaling pathway in NSCLCs.

Supplementary Figure S4 shows KATP channels openers inhibited the growth of NSCLCs.

Supplementary Tables from Bioavailability of Sulforaphane from Two Broccoli Sprout Beverages: Results of a Short-term, Cross-over Clinical Trial in Qidong, China

Supplementary Figure S8 shows photos and histological sections of xenografts and livers.

Sulfonylurea receptor 1 (SUR1) is the regulatory subunit of ATP-sensitive potassium channels (KATP channels) and the receptor of antidiabetic drugs, such as glibenclamide, which induce insulin secretion in pancreatic β cells. However, the expression and role of SUR1 in cancer are unknown. In this study, we found that SUR1 expression was elevated in human non–small cell lung carcinoma (NSCLC) tissues and cell lines. SUR1 silencing suppressed the growth of NSCLC cells, while SUR1 overexpression promoted cell growth. Targeting SUR1 with glibenclamide suppressed cell growth, cell-cycle progression, epithelial–mesenchymal transition (EMT), and cell migration. Moreover, SUR1 directly interacted with p70S6K and upregulated p70S6K phosphorylation and activity. In addition, glibenclamide inhibited p70S6K, and overexpression of p70S6K partially reversed the growth-inhibiting effect of glibenclamide. Furthermore, glibenclamide upregulated the expression of the tumor suppressor Krüppel-like factor 4 (KLF4), and silencing KLF4 partially reversed the inhibitory effect of glibenclamide on cell growth, EMT, and migration. We found that SUR1 targeted p70S6K to downregulate KLF4 expression by enhancing DNA-methyltransferase 1–mediated methylation of the KLF4 promoter. Finally, in xenograft mouse models, SUR1 expression silencing or glibenclamide treatment inhibited the growth of A549 tumors, downregulated p70S6K activity, and upregulated KLF4 expression. These findings suggested that SUR1 expression was elevated in some NSCLC tissues and functioned as a tumor enhancer. Targeting SUR1 with glibenclamide inhibited NSCLC through downregulation of p70S6K activity and subsequent upregulation of the expression of the tumor suppressor gene KLF4. SUR1 can be developed as a new target for cancer therapy and glibenclamide has potential anticancer effects.

Supplementary Figure S1 shows the expression and survival correlation of Kir6.1 and Kir6.2 in NSCLC.

Supplementary Figure from Bioavailability of Sulforaphane from Two Broccoli Sprout Beverages: Results of a Short-term, Cross-over Clinical Trial in Qidong, China

Supplementary Figure S7 shows histone acetylation was not involved in p70S6K induced downregulation of KLF4.

A high-quality built environment is important for human health and well-being. Assessing the quality of the urban built environment can provide planners and managers with decision-making for urban renewal to improve resident satisfaction. Many studies evaluate the built environment from the perspective of street scenes, but it is difficult for street-view data to cover every area of the built environment and its update frequency is low, which cannot meet the requirement of built-environment assessment under rapid urban development. Earth-observation data have the advantages of wide coverage, high update frequency, and good availability. This paper proposes an intelligent evaluation method for urban built environments based on scene understanding of high-resolution remote-sensing images. It contributes not only the assessment criteria for the built environment in remote-sensing images from the perspective of visual cognition but also an image-caption dataset applicable to urban-built-environment assessment. The results show that the proposed deep-learning-driven method can provide a feasible paradigm for representing high-resolution remote-sensing image scenes and large-scale urban-built-area assessment.

BackgroundWhole genome sequencing (WGS) is becoming increasingly prevalent for molecular diagnosis, staging and prognosis because of its declining costs and the ability to detect nearly all genes associated with a patient’s disease. The currently widely accepted variant calling pipeline, GATK, is limited in terms of its computational speed and efficiency, which cannot meet the growing analysis needs.MethodsIn this study, we propose a fast and accurate DNASeq variant calling workflow that is purely composed of tools from LUSH toolkit. The LUSH pipeline is highly optimized for the WGS pipeline based on SOAPnuke, BWA and GATK which can be deployed on any general-purpose CPU-based computing system. We validated the accuracy, speed and scalability of the LUSH pipeline on several standard WGS datasets.ResultsOur test results show that the LUSH pipeline and the GATK pipeline are highly consistent in terms of accuracy, achieving over 99% precision and recall on NA12878. For speed, the LUSH pipeline completes 30x WGS data in 1.6 hours, which is about 17x faster than the GATK pipeline. From BAM to VCF, LUSH_HC even takes only 12 minutes, about 76x faster than GATK. Moreover, the LUSH pipeline shows favorable scalability in terms of thread and sequencing depth.ConclusionThe LUSH pipeline provides far superior computational speed to GATK while maintaining a high level of accuracy comparable to that of GATK, which greatly facilitates bedside analysis of acute patients, large-scale cohort data analysis, and variant calling in crop breeding programs.

Purpose This study aims to clarify the evolution law of stress field and fracture field during the mining process of inclined coal seam, to prevent the occurrence of roof burst water and impact ground pressure accident during the advancing process of working face. Design/methodology/approach The evolution law of stress-fracture field under different mining conditions of inclined coal seam was studied by using discrete element method and similar material simulation method. Findings The overburden stress at the lower end of the coal seam was mainly transmitted to the deep rock mass on the left side, and the overburden stress at the upper end was mainly transmitted to the floor direction. With the increase of the inclined length of the mining coal seam, the development of the fracture zone gradually evolves from the “irregular arch” form to the “transversely developed trapezoid” form. The development range of the fracture zone was always in the internal area of the stress concentration shell. Originality/value An original element of this paper is based on the condition that the dip angle of coal seam is 35°, and the evolution law of overburden stress-fracture field during the excavation of coal seam with different lengths was analyzed by UDEC numerical simulation software. The coupling relationship between stress shell and fracture field was proposed, and the development range of fracture zone was determined by stress. The value of this paper is to provide technical support and practical basis for the safety production of a mine working face.

A high-resolution remote sensing image (HRSI) scene typically contains multiple geo-objects, and geospatial relations among these geo-objects are obvious. As the important information conveyed by HRSI, the intelligent expression of geospatial relation is helpful in understanding HRSI scenes. Previous HRSI semantic understanding was mainly based on image captions that only generate one sentence to describe image content, thereby resulting in insufficient understanding of the scene. Thus, the present letter proposes an approach to represent geospatial relations in an HRSI scene with structured form of langlesubject, geospatial relation, object⟩. A geospatial relation triplet representation data set that contains visual and semantic information, such as category, location, and geospatial relations of the geo-objects, is constructed first. An ``object-relation'' message-passing mechanism is adopted to enhance the information exchange between the geo-objects and geospatial relations to predict triplets accurately. The experimental results show that the proposed method can effectively predict the geospatial relation in a HRSI scene.

Viruses often establish their own infection by altering host metabolism. How viruses co-opt plant metabolism to support their successful infection remains an open question. Here, we used untargeted metabolomics to reveal that lactate accumulates immediately before and after robust sugarcane mosaic virus (SCMV) infection. Induction of lactate-involved anaerobic glycolysis is beneficial to SCMV infection. The enzyme activity and transcriptional levels of lactate dehydrogenase (LDH) were up-regulated by SCMV infection, and LDH is essential for robust SCMV infection. Moreover, LDH relocates in viral replicase complexes (VRCs) by interacting with SCMV-encoded 6K2 protein, a key protein responsible for inducing VRCs. Additionally, lactate could promote SCMV infection by suppressing plant defense responses. Taken together, we have revealed a viral strategy to manipulate host metabolism to support replication compartment but also depress the defense response during the process of infection.