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Solar radiation is one of the most important factors affecting climate and environment, and its long-term variation is of much concern in climate change studies. In the light of the limited number of radiation stations with reliable long-term time series observations, this paper presents a new evaluation of the long-term variation of surface solar radiation over China by combining quality-controlled observed data and two radiation models. One is the ANN-based (Artificial Neutral Network) model and the other is a physical model. The two models produce radiation trends comparable to the observed ones at a few validation stations possessing reliable and continuous data. Then, the trend estimate is extended by the ANN-based model to all 96 radiation stations and furthermore extended by the physical model to all 716 China Meteorological Administration (CMA) routine stations. The new trend estimate is different from previous ones in two aspects. First, the magnitude of solar radiation over China decreased by about −0.23 W m−2 yr−1 between 1961 and 2000, which is greatly less in magnitude than trend slopes estimated in previous studies (ranging over −0.41 ~ −0.52 W m−2 yr−1). Second, the \"From Dimming to Brightening\" transition in China during the late 1980s ~ the early 1990s was addressed in previous studies, but this study indicates the solar radiation reached a stable level since the 1990s and the transition is not noticeable. These differences indicate the importance of data-quality control and analysis approaches. Finally, an obvious transition from brightening to dimming around 1978 is found over the Tibetan Plateau, where aerosol loads are very low, indicating that the importance of cloud changes in altering solar radiation may be comparable to that of the aerosol changes.

Deep learning has seen increased application in the data-driven fault diagnosis of manufacturing system components such as rolling bearing. However, deep learning methods often require a large amount of training data. This is a major barrier in particular for bearing datasets whose sizes are generally limited due to the high costs of data acquisition especially for fault scenarios. When small datasets are employed, over-fitting may occur for a deep learning network with many parameters. To tackle this challenge, in this research, we propose a new methodology of parallel convolutional neural network (P-CNN) for bearing fault identification that is capable of feature fusion. Raw vibration signals in the time domain are divided into non-overlapping training data slices, and two different convolutional neural network (CNN) branches are built in parallel to extract features in the time domain and in the time-frequency domain, respectively. Subsequently, in the merged layer, the time-frequency features extracted by continuous wavelet transform (CWT) are fused together with the time-domain features as inputs to the final classifier, thereby enriching feature information and improving network performance. By incorporating empirical feature extraction such as CWT, this proposed method can effectively enable deep learning even with dataset size limitation in practical bearing diagnosis. The algorithm is validated through case studies on publicly accessible experimental rolling bearing datasets. A wide range of dataset sizes is tested with cross-validation, and influencing factors on network performance are discussed. Compared with existing methods, the proposed approach not only possesses higher accuracy but also exhibits better stability and robustness as training dataset sizes and load conditions vary. The concept of feature fusion through P-CNN can be extended to other fault diagnosis applications in manufacturing systems.

In the tumor microenvironment, chemokine system has a critical role in tumorigenesis and metastasis. The acquisition of stem-like properties by cancer cells is involved in metastasis and drug resistance, which are pivotal problems that result in poor outcomes in patients with lung cancer. Patients with advanced lung cancer present high plasma levels of transforming growth factor-β1 (TGFβ1), which correlate with poor prognostic features. Therefore, TGFβ1 may be important in the tumor microenvironment, where chemokines are widely expressed. However, the role of chemokines in TGFβ1-induced tumor progression still remains unclear. In our study, TGFβ1 upregulated CXC chemokine receptor expression, migration, invasion, epithelial–mesenchymal transition (EMT) and cancer stem cell (CSC) formation in lung adenocarcinoma. We found that CXCR7 was the most upregulated chemokine receptor induced by TGFβ1. CXCR7 knockdown resulted in reduction of migration, invasion and EMT induced by TGFβ1, whereas CXCR4 knockdown did not reverse TGFβ1-promoted EMT. CXCR7 silencing significantly decreased cancer sphere-forming capacity, stem-like properties, chemoresistance and TGFβ1-induced CSC tumor initiation in vivo . In clinical samples, high TGFβ1 and CXCR7 expression was significantly associated with the late stages of lung adenocarcinoma. Moreover, TGFβ1 and CXCR7 coexpression was positively correlated with the CSC marker, CD44, which is associated with lymph node metastasis. Besides, patients with high expression of both CXCR7 and TGFβ1 presented a significantly worse survival rate. These results suggest that the TGFβ1-CXCR7 axis may be a prognostic marker and may provide novel targets for combinational therapies to be used in the treatment of advanced lung cancer in the future.

Multi-objective optimization allows satisfying multiple decision criteria concurrently, and generally yields multiple solutions. It has the potential to be applied to structural damage identification applications which are oftentimes under-determined. How to achieve high-quality solutions in terms of accuracy, diversity, and completeness is a challenging research subject. The solution techniques and parametric selections are believed to be problem specific. In this research, we formulate a reinforcement learning hyper-heuristic scheme to work coherently with the single-point search algorithm MOSA/R (Multi-Objective Simulated Annealing Algorithm based on Re-seed). The four low-level heuristics proposed can meet various optimization requirements adaptively and autonomously using the domination amount, crowding distance, and hypervolume calculations. The new approach exhibits improved and more robust performance than AMOSA, NSGA-II, and MOEA/D when applied to benchmark test cases. It is then applied to an active damage interrogation scheme for structural damage identification where solution diversity/completeness and accuracy are critically important. Results show that this approach can successfully include the true damage scenario in the solution set identified. The outcome of this research can potentially be extended to a variety of applications.

Soils are a crucial component of the Earth system; they comprise a large portion of terrestrial carbon stocks, mediate the supply and demand of nutrients, and influence the overall response of terrestrial ecosystems to perturbations. In this paper, we develop a new soil biogeochemistry model for the Community Land Model, version 4 (CLM4). The new model includes a vertical dimension to carbon (C) and nitrogen (N) pools and transformations, a more realistic treatment of mineral N pools, flexible treatment of the dynamics of decomposing carbon, and a radiocarbon (14C) tracer. We describe the model structure, compare it with site-level and global observations, and discuss the overall effect of the revised soil model on Community Land Model (CLM) carbon dynamics. Site-level comparisons to radiocarbon and bulk soil C observations support the idea that soil C turnover is reduced at depth beyond what is expected from environmental controls for temperature, moisture, and oxygen that are considered in the model. In better agreement with observations, the revised soil model predicts substantially more and older soil C, particularly at high latitudes, where it resolves a permafrost soil C pool. In addition, the 20th century-C dynamics of the model are more realistic than those of the baseline model, with more terrestrial C uptake over the 20th century due to reduced N downregulation and longer turnover times for decomposing C.

Carbon nanotubes-reinforced aluminum matrix (CNTs/Al) composites possess wide application prospects in many fields, and how to achieve a high performance is always a research hot spot. In this study, a novel high-performance Al matrix nanocomposite reinforced with short CNTs and in situ Al4C3 nanorods was fabricated by combining ball milling and hot extrusion, and they exhibit excellent comprehensive mechanical properties. The 2 wt% CNTs/Al composite reached a tensile strength of 312 MP and an elongation of 15.8%, showing an 102% strength improvement compared with the pure aluminum prepared by the same process. The remarkable improvement of the mechanical properties originates from the synergistical enhancement of fine-grained strengthening and dispersion strengthening of in situ Al4C3 nanorods and short CNTs.

A number of patient-specific and leukemia-associated factors are related to the poor outcome in older patients with acute myeloid leukemia (AML). However, comprehensive studies regarding the impact of genetic alterations in this group of patients are limited. In this study, we compared relevant mutations in 21 genes between AML patients aged 60 years or older and those younger and exposed their prognostic implications. Compared with the younger patients, the elderly had significantly higher incidences of PTPN11 , NPM1 , RUNX1 , ASXL1 , TET2 , DNMT3A and TP53 mutations but a lower frequency of WT1 mutations. The older patients more frequently harbored one or more adverse genetic alterations. Multivariate analysis showed that DNMT3A and TP53 mutations were independent poor prognostic factors among the elderly, while NPM1 mutation in the absence of FLT3 /ITD was an independent favorable prognostic factor. Furthermore, the status of mutations could well stratify older patients with intermediate-risk cytogenetics into three risk groups. In conclusion, older AML patients showed distinct genetic alterations from the younger group. Integration of cytogenetics and molecular mutations can better risk-stratify older AML patients. Development of novel therapies is needed to improve the outcome of older patients with poor prognosis under current treatment modalities.

MicroRNAs (miRs) are small noncoding RNAs that negatively regulate gene expression by binding to the three untranslated regions of their target mRNAs. Deregulations of miRs were shown to play pivotal roles in tumorigenesis and progression. Recent research efforts have been devoted to translating these basic discoveries into applications that could improve the therapeutic outcome of patients with cancer. MiR-34a is a highly conserved miR throughout many different species. In humans, there are three homologs ( hsa-miR34a, hsa-miR-34b and hsa-miR-34c ). Early studies have shown that miR-34a acts as a tumor-suppressor gene by targeting many oncogenes related to proliferation, apoptosis and invasion. In this review, we provide a complex overview of miR-34a , including regulating its expression, its known functions in cancer and future challenges as a potential therapeutic target in human cancers.

Metabolic models can estimate intrinsic product yields for microbial factories, but such frameworks struggle to predict cell performance (including product titer or rate) under suboptimal metabolism and complex bioprocess conditions. On the other hand, machine learning, complementary to metabolic modeling necessitates large amounts of data. Building such a database for metabolic engineering designs requires significant manpower and is prone to human errors and bias. We propose an approach to integrate data-driven methods with genome scale metabolic model for assessment of microbial bio-production (yield, titer and rate). Using engineered E. coli as an example, we manually extracted and curated a data set comprising about 1200 experimentally realized cell factories from ~100 papers. We furthermore augmented the key design features (e.g., genetic modifications and bioprocess variables) extracted from literature with additional features derived from running the genome-scale metabolic model iML1515 simulations with constraints that match the experimental data. Then, data augmentation and ensemble learning (e.g., support vector machines, gradient boosted trees, and neural networks in a stacked regressor model) are employed to alleviate the challenges of sparse, non-standardized, and incomplete data sets, while multiple correspondence analysis/principal component analysis are used to rank influential factors on bio-production. The hybrid framework demonstrates a reasonably high cross-validation accuracy for prediction of E.coli factory performance metrics under presumed bioprocess and pathway conditions (Pearson correlation coefficients between 0.8 and 0.93 on new data not seen by the model).