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Abstract Protein Disulfide-Isomerase A2 (PDIA2) is a gene that encodes proteins, responsible for protein folding and modification within cells. The development and course of many disorders are intimately linked to the aberrant expression of PDIA2. Nevertheless, more research is necessary to fully understand PDIA2’s biological significance in pan-cancer, notably in prostate cancer (PCa). PDIA2 expression is elevated in various tumors and closely related to patient prognosis. Patients with prostate cancer who express PDIA2 high in particular have a bad prognosis in terms of progression-free survival. In addition, the upregulation of PDIA2 expression in prostate cancer patients is accompanied by higher Gleason scores, advanced tumor staging, lymph node metastasis, and elevated PSA levels. Detailed experiments further demonstrate that PDIA2 is a carcinogenic gene affecting prostate cancer cells’ response to dasatinib therapy. For patients with prostate cancer, there is a clear positive connection between the expression level of PDIA2 and a bad prognosis. The prostate cancer treatment efficacy of dasatinib is hampered by PDIA2, which is intimately linked to the growth, invasion, and metastasis of PCa cells. In summary, our research highlights the potential of PDIA2 as a biomarker for the diagnosis and management of PCa.

Abstract. Background:. Although some well-established oncogenes are involved in cancer initiation and progression such as prostate cancer (PCa), the long tail of cancer genes remains to be defined. Goosecoid (GSC) has been implicated in cancer development. However, the comprehensive biological role of GSC in pan-cancer, specifically in PCa, remains unexplored. The aim of this study was to investigate the role of GSC in PCa development. Methods:. We performed a systematic bioinformatics exploration of GSC using datasets from The Cancer Genome Atlas, Genotype-Tissue Expression, Gene Expression Omnibus, German Cancer Research Center, and our in-house cohorts. First, we evaluated the expression of GSC and its association with patient prognosis, and identified GSC-relevant genetic alterations in cancers. Further, we focused on the clinical characterization and prognostic analysis of GSC in PCa. To understand the transcriptional regulation of GSC by E2F transcription factor 1 (E2F1), we performed chromatin immunoprecipitation quantitative polymerase chain reaction (qPCR). Functional experiments were conducted to validate the effect of GSC on the tumor cellular phenotype and sensitivity to trametinib. Results:. GSC expression was elevated in various tumors and significantly correlated with patient prognosis. The alterations of GSC contribute to the progression of various tumors especially in PCa. Patients with PCa and high GSC expression exhibited worse progression-free survival and biochemical recurrence outcomes. Further, GSC upregulation in patients with PCa was mostly accompanied with higher Gleason score, advanced tumor stage, lymph node metastasis, and elevated prostate-specific antigen (PSA) levels. Mechanistically, the transcription factor, E2F1, stimulates GSC by binding to its promoter region. Detailed experiments further demonstrated that GSC acted as an oncogene and influenced the response of PCa cells to trametinib treatment. Conclusions:. GSC was highly overexpressed and strongly correlated with patient prognosis in PCa. We found that GSC, regulated by E2F1, acted as an oncogene and impeded the therapeutic efficacy of trametinib in PCa.

The risk of typhoon disasters is high in the Zhejiang coastal areas; however, it has been difficult to characterize waves generated by these typhoons, particularly in shallow waters. In this study, the characteristics of nearshore typhoon waves were analyzed using data from 14 typhoon processes from a wave station on the central coast of Zhejiang Province for three consecutive years from 2016 to 2018. According to their paths, these typhoons were divided into landing and non-landing steering types. Wave parameters were evaluated using statistical analyses and linear regression. Variations in wave parameters and spectra are discussed. We obtained several key results: (1) The dominant wave direction was E-SE and the strong wave direction was SE. (2) There were strong correlations between characteristic wave heights and between characteristic periods. (3) Single-peak spectra were dominant, with a frequency of 92.33%. The minimum peak frequency was 0.06–0.1 Hz and the maximum peak period was 17.7 s. The typhoon waves were mainly composed of swells with a frequency of 94.54%. (4) Affected by Typhoon Maria (No. 201808), a maximum wave height of 8.20 m was observed at 04:00 on July 11, 2018. This study provides an important reference for the design of offshore structures and disaster prevention and mitigation.

Abstract Background Epithelial–mesenchymal transition (EMT) is associated with early recurrence and a poor prognosis in clear cell renal cell carcinoma (ccRCC). Studies have shown that EMT‐related genes play an important regulatory role in tumor invasion, metastasis, and drug resistance, but the biological functions of EMT‐related genes in ccRCC have not been specifically described. Methods The mRNA and clinicopathological data of 532 ccRCC and 72 normal samples were downloaded from The Cancer Genome Atlas as a training set. The gene expression matrix and survival data of 91 and 101 ccRCC samples were obtained from the International Cancer Genome Consortium and the ArrayExpress databases as validation sets, respectively. Univariate Cox analysis was used to identify and cluster prognostic genes, and multivariate Cox was performed to construct a prognostic signature. Moreover, CIBERSORT and CellMiner were used to assess immune cell infiltration and prognostic gene‐drug sensitivity of the signature, respectively. Most importantly, we performed detailed experiments to verify the oncogenic function of a significant gene, OLFML2B, in vitro and in vivo. Results We constructed a prognostic signature including seven genes and divided patients into high‐risk and low‐risk groups. The prognosis of the high‐risk group was significantly worse than that of the low‐risk group through Kaplan–Meier survival analysis. Interestingly, significant differences were observed in clinical characteristics and immune cell infiltration between the two groups. In addition, a significant correlation was found between the expression of prognostic genes and the sensitivity of tumor cells to chemotherapeutics. Most importantly, OLFML2B was proved to contribute to the proliferation and metastasis of ccRCC through detailed functional experiments in vitro and in vivo, and its prognostic efficacy for ccRCC patients was affirmed. Conclusion We identified the prognostic signature of seven genes based on EMT‐related genes as prognostic biomarkers for ccRCC. Besides, OLFML2B was validated as a potential diagnostic and therapeutic target for ccRCC by our detailed experiments.

As the relationship between climate change and agricultural production increasingly gains attention, the FAO recommends the adoption of climate-smart agriculture practices (CSAPs) to ensure the stable development of agriculture amidst changing climatic conditions. However, the adoption rate of CSAPs remains low and the effects of livelihood capitals have received little attention. Based on the survey data for 916 farmers in the Jianghan Plain of China, this paper adopts a multivariate Probit model to examine the impact of farmers’ livelihood capitals which are measured by an entropy-TOPSIS approach on their adoption of CSAPs. Our results demonstrate that different livelihood capitals exert various influence on the adoption of CSAPs. Specifically, human, financial, physical, and social capital have positive relationships with pesticide-oriented CSAPs such as integrated pest management (IPM). Natural capital has a positive relationship with seed- and water- oriented CSAPs like tolerant rice varieties (TRV). Natural capital positively relates to soil-oriented CPSPs including rice straw mulching (RSM) while physical capital has a negative effect. Natural and physical capitals have positive relationships with fertilizer-oriented CSAPs like deep placement of fertilizer (DPF). Social and natural capitals have positive relationships with soil-oriented CSAPs such as no-tillage direct seeding (NTDS) while financial capital has a negative effect. Climate factors are also important in the adoption of CSAPs such as TRV and RSM. Finally, policy recommendations are suggested to enhance household livelihood capitals to promote the adoption of each type of CSAP.

ABSTRACTFarmers’ adoption of green agricultural technologies (GATs) could reduce the negative environmental impacts of traditional agriculture in China. Despite the benefits of GATs, their adoption rate has not been high. While previous studies have examined the information and communication technologies that influence the adoption of GATs, most have selected only one of the GATs and have not distinguished between public and private channels of knowledge acquisition. In this paper, based on a sample of 732 rural households from Hubei Province, we used a negative binomial model to examine the role of knowledge acquisition channels on GATs adoption. Overall, we found that radio and the government (through its face-to-face channels) are positively associated with farmers’ adoption of GATs; however, farmers adopted more GATs when they had acquired knowledge from the government than from radio. A possible explanation and a policy implication is that direct communication with farmers and practical demonstration of the benefits of GATs are preferred to passive reception of information.

Abstract The Warburg effect‐related metabolic dysfunction of the tricarboxylic acid (TCA) cycle has emerged as a hallmark of various solid tumors, particularly renal cell carcinoma (RCC). RCC is characterized by high immune infiltration and thus recommended for immunotherapeutic interventions at an advanced stage in clinical guidelines. Nevertheless, limited benefits of immunotherapy have prompted investigations into underlying mechanisms, leading to the proposal of metabolic dysregulation‐induced immunoevasion as a crucial contributor. In this study, a significant decrease is found in the abundance of alpha‐ketoglutarate (αKG), a crucial intermediate metabolite in the TCA cycle, which is correlated with higher grades and a worse prognosis in clinical RCC samples. Elevated levels of αKG promote major histocompatibility complex‐I (MHC‐I) antigen processing and presentation, as well as the expression of β2‐microglobulin (B2M). While αKG modulates broad‐spectrum demethylation activities of histone, the transcriptional upregulation of B2M is dependent on the demethylation of H3K4me1 in its promoter region. Furthermore, the combination of αKG supplementation and PD‐1 blockade leads to improved therapeutic efficacy and prolongs survival in murine models when compared to monotherapy. Overall, the findings elucidate the mechanisms of immune evasion in anti‐tumor immunotherapies and suggest a potential combinatorial treatment strategy in RCC.

The literature examining the role played by family life cycle in farm households’ decisions to adopt sustainable agricultural technology (SAT) remains scant. To bridge this gap, we evaluate the impact of family life cycle on farm households’ adoption of SATs by using multivariate probit regression analyses of survey data from 902 farm households in Hubei Province, China. The results show that most farm households belong to the middle stages of the family life cycle. Straw returning is the most frequently adopted SAT among farm households. Farm households’ SAT adoption decisions are closely related to family life cycle. SAT adoption by farm households differs at each family life cycle stage. Based on this finding, when stimulating SAT adoption, the government should consider the different capital endowments and needs of farm households at different family life cycle stages as well as conduct differential and targeted technology promotion measures.

Abstract The high speed and high manoeuvrability of a hypersonic gliding vehicle (HGV) pose a severe challenge to the existing early warning detection system. Optimising the search resources of a phased array radar to improve the HGV detecting efficiency has become a practical problem. In this study, a HGV search method based on a hybrid optimisation algorithm is proposed with the early warning information guidance as a priori condition. Firstly, the HGV priority judgement model is established, and the priority quantification equations for height, velocity and distance are designed. Secondly, the search parameters of the radar are optimised with the maximum cumulative detection probability, the shortest average discovery time and the highest priority level as the objective functions, and a radar search model is established. Finally, to overcome the problem that particle swarm optimisation (PSO) is easy to fall into local optimal solution, a hybrid optimisation algorithm based on differential evolution (DE) and PSO is proposed. The performance of the proposed method is verified in two simulation scenarios, and the results show that the proposed method outperforms existing mainstream search methods and can reasonably allocate search resources according to the priority of HGV.

Abstract Background Residue-based biogas is considered as a renewable energy that should be used to improve energy security and household livelihoods in rural areas. Observational learning and word-of-mouth learning are critical in the dissemination of knowledge about agricultural technologies. Yet, scholars have little understanding of the impacts of these learning methods on farmers’ use of residue-based biogas. Using survey data from rural areas of Hubei China, this study estimates the impacts of observational learning and word-of-mouth learning from different subjects (i.e., relatives, neighbors, cadres, cooperative members, and technical instructors) on the use of residue-based biogas by farmers. Additionally, the moderating role of interpersonal trust in these relationships is explored. Results Results from logistic regression models show that observational learning from technical instructors significantly increases farmers’ use of biogas. Furthermore, interpersonal trust significantly and positively influences the impact of observational learning on farmers’ decisions to use biogas. Similarly, interpersonal trust significantly and positively moderates the influence of positive word-of-mouth learning on farmers’ decision to use biogas. In contrast, a negative moderating role exists in the relationship between negative word-of-mouth learning and farmers’ decision to use biogas. These impacts are further affirmed by robustness checks. Conclusions The results presented here show that enhancing farmers’ interpersonal trust promotes the use of residue-based biogas by farmers. One important implication is that the government might promote the use of residue-based biogas by organizing technology demonstration activities, providing communication platforms, and enhancing mutual trust between farmers and relevant groups.

Clear cell renal cell carcinoma (ccRCC) is considered to be related to the worse prognosis, which might in part be attributed to the early recurrence and metastasis, compared with other type of kidney cancer. Oxidative stress refers to an imbalance between production of oxidants and antioxidant defense. Accumulative studies have indicated that oxidative stress genes contribute to the tumor invasion, metastasis and drug sensitivity. However, the biological functions of oxidative stress genes in ccRCC remain largely unknown. In this study, we identified 1,399 oxidative stress genes from GeneCards with a relevance score ≥7. Data for analysis were accessed from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) database, and were utilized as training set and validation set respectively. Univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox were employed to construct a prognostic signature in ccRCC. Finally, a prognostic signature including four different oxidative stress genes was constructed from 1,399 genes, and its predictive performance was verified through Kaplan-Meier survival analysis and the receiver operating characteristic (ROC) curve. Interestingly, we found that there was significant correlation between the expression of oxidative stress genes and the immune infiltration and the sensitivity of tumor cells to chemotherapeutics. Moreover, the highest hazard ratio gene urocortin (UCN) was chosen for further study; some necessary vitro experiments proved that the UCN could promote the ability of ccRCC proliferation and migration and contribute to the degree of oxidative stress. In conclusion, it was promising to predict the prognosis of ccRCC through the four oxidative stress genes signature. UCN played oncogenic roles in ccRCC by influencing proliferation and oxidative stress pathway, which was expected to be the novel therapeutic target for ccRCC.

Over-the-horizon radar (OTHR) is an important equipment for the ultralong-range early warning in the military, but the use of constant false-alarm rate (CFAR), which is a traditional detection method, makes it difficult in multi-aircraft formation recognition. To solve this problem, a multi-aircraft formation recognition method based on deep transfer learning in OTHR is proposed. First, the range-Doppler images of aircraft formation in OTHR are simulated, which are composed of four categories of samples. Secondly, a recognition model based on Convolutional Neural Network (CNN) and CFAR detection technology is constructed, whose training method is designed as a two-step transfer. Finally, the trained model can well distinguish the spectral characteristics of aircraft formation, and then recognize the aircraft number of a formation. Experiments show that the proposed method is better than the traditional CFAR detection method, and can detect the number of aircraft more accurately in the formation with the same false alarm rate.

Hypersonic glide vehicle (HGV) has brought severe challenges to the existing defense system due to its characteristics of high maneuverability, high speed and high precision. Simultaneously, these characteristics also bring great difficulties to trajectory prediction. In this paper, a method for HGV motion state recognition and trajectory prediction based on deep learning is proposed. The proposed method consists of two modules, namely the motion state recognition module and the trajectory prediction module. The motion state recognition module can identify the HGV’s motion state according to state information, and divide it into eight categories. The softmax function is added to the state recognition module to calculate the probability of each motion state. The trajectory prediction module comprises a nonlinear prediction part and a linear prediction part. According to the result of motion state recognition, the appropriate prediction scheme is adopted to better extract the linear and nonlinear characteristics of HGV trajectory, which improves the robustness and prediction accuracy of the proposed method. The experimental results of HGV trajectory prediction show that the proposed method can maintain good stability when the HGV maneuver state changes, and has higher accuracy than the four benchmark methods.

As a typical maneuver model, the second-order Markov model, which is based on acceleration periodic autocorrelation, is an effective way used for tracking near space hypersonic vehicle(NSHV) target tracking. It is found that, however, the model responds slowly to the target maneuvering and has weak maneuverability. To solve this problem, the adaptive non-zero mean damped oscillation model (ANM-DO), which based on the idea of mean compensation, is proposed. Then the difference of the mean compensation method between the first order Markov model and the two order Markov model is analyzed, and the physical essence of adaptive nonzero mean is discussed from time domain and frequency domain. Furthermore, to further investigate the performance of the ANM-DO model, we deduced the systematic dynamic errors of ANM-DO taking Kalman Filter (KF) as filtering algorithm. On this basis, the superior performance of ANM-DO model is verified in terms of maneuverability. Finally, simulation experiments in different scenarios show that the ANM-DO model shows lower filtering errors tracking near space hypersonic jump gliding targets, and verified the adaptability of the model proposed in this paper.

In order to practice the concept of‘lucid waters and lush mountains are invaluable assets’ and promote the green development of agriculture, it is necessary to improve the efficiency of agricultural energy utilization. Based on the panel data of 28 provinces from 1995 to 2018, this paper calculated China’s agricultural energy input from two categories of direct energy and indirect energy, and used EBM (Epsilon-based Measure) mixed distance function model to measure the energy efficiency of agriculture in China. The nuclear density function and spatial autocorrelation were used to analyze the dynamic evolution of agricultural energy efficiency, and the dynamic panel model was used to analyze the influencing factors of agricultural energy efficiency. The results showed that: ① From 1995 to 2018, the total agricultural energy input had increased year by year in China, with an average annual growth rate of 2% . Energy input structure changed from indirect energy-based to direct energy-based. Agricultural energy efficiency showed an evolutionary trend of ‘rising-stagnating-rising rapidly’in China. The agricultural energy efficiency was generally low in China, and there was a large space for improvement in agricultural energy efficiency. ② From 1995 to 2018, the average annual growth rate of agricultural energy efficiency in the eastern, central and western regions was 2. 7%, 1. 9% and 1. 4% respectively. In 2018, the agricultural energy efficiency in the eastern, central and western regions was 0. 81, 0. 71 and 0. 59 respectively. The gap between regions was expanding rapidly, and the agricultural energy efficiency in the central and western regions needed to be improved. ③ From 1995 to 2018, the agricultural energy efficiency of each province was polarized and the absolute gap was widened. There was obvious improvement in agricultural energy efficiency in Guangdong, Shandong, Jiangxi, Jiangsu, Liaoning and Tianjin, while the agricultural energy efficiency of Xinjiang, Guizhou, Zhejiang, Shanghai, and Inner Mongolia deteriorated. ④ From 1995 to 2018, there was no global spatial correlation of China’s agricultural energy efficiency. However, local ‘high-high’concentration gradually appeared in the eastern region since 2010. ⑤ The first lag of energy efficiency had a significant positive impact on agricultural energy efficiency, and agricultural energy efficiency improvement had a time lag. The level of human capital, per capita net income of farmers and the level of urbanizaton had a significant positive impact on agricultural energy efficiency. The disaster rate, the level of development of secondary and tertiary industries, and the level of opening up had a significant negative impact on agricultural energy efficiency. In the implementation of the strategy of rural revitalization, we should focus on the central and western regions, take the cultivation of professional farmers as the key, focus on improving agricultural production conditions, enhance the level of cooperation between regions, exert the leading role of the secondary and tertiary industries, and enhance the ability of agricultural disaster prevention and mitigation.

To better understand the hydrodynamic characteristics of Sansha Bay and how they are changed by the addition of a wharf, this study takes the design of the wharf on Changyao Island as an example. It uses the MIKE21 model to study the variation of tidal current velocity, tide direction and silt buildup before and after the construction of the wharf. The research results show that:1. The design solution of the wharf is feasible. 2. The included angle between the main axis of the wharf and the tides’ direction is chosen to be less than 10º. 3. After the construction of the wharf, the peak tidal flow velocity that passes through the pile foundation of the wharf and the dike behind the wharf decreases by 10% to 15% and 30% to 50%, respectively. The range of the influence of buildings on the tidal current velocity is 900 m around the buildings. 4. The final siltation the upstream and downstream of the main axis of the wharf is 0.30 to 0.70 m, that on the dike at the rear edge of the wharf is 0.60 to 1.40 m from the wharf, and the final scour depth on the waterway of wharf apron is 0.03 to 0.05m.

This paper aims to share findings on nonclassical biomanipulation from a field study on eutrophic West Yangcheng (WY) Lake. In the forty years before 2009, cyanobacteria frequently bloomed in WY Lake, an event which has not occurred since 2009. The stocking density of filter-feeding carp was estimated to be more than 50 g/m3 in July after 2009, approximately double the amount observed before 2009, and the ratio of silver carp to bighead carp (SBR) increased from 0.8 to more than 1.6. Compared with the water quality between 2004 and 2007, the total nitrogen (TN) and the chlorophyll-a (Chl-a) concentration decreased while total phosphorus (TP) and transparency (SD) increased significantly after 2009 (P 0.05). A significant negative correlation was observed between fish yield and SD (P

Despite increased research and policy focus on the adoption and impact of climate-smart agricultural practices (CSAPs) on small household farmers’ welfare, synergies between adaptive and mitigatory CSAPs are not well understood. This study responds to this knowledge gap by examines the determinants of adoption of adaptive and mitigatory CSAPs in isolation and when combined, and their impacts on rice yield and rice net income in Hubei, China. Using three-year household-level pooled cross-sectional data and addressing selection biases from both observable and unobservable heterogeneity were managed by applying a multinomial endogenous switching regression framework. Our findings indicate that adopting both adaptive and mitigatory CSAPs increases rice yield and rice net income by 15.879% and 19.288%, respectively. Besides, the strong complementarities exist between adaptive and mitigatory CSAPs to address climate change in improving productivity and income when experiencing extreme weather events (e.g., EI Niño event). These findings highlight the positive role of integrating adaptive and mitigatory CSAPs in coping with climate change challenges, and increasing household farmers’ economic welfare in China, and potentially elsewhere.

Ten strains of endophytic diazotroph were isolated and identified from the plants collected from three different agricultural crop species, wheat, rice and maize, using the nitrogen-free selective isolation conditions. The nitrogen-fixing ability of endophytic diazotroph was verified by the nifH-PCR assay that showed positive nitrogen fixation ability. These identified strains were classified by 879F-RAPD and 16S rRNA sequence analysis. RAPD analyses revealed that the 10 strains were clustered into seven 879F-RAPD groups, suggesting a clonal origin. 16S rRNA sequencing analyses allowed the assignment of the 10 strains to known groups of nitrogen-fixing bacteria, including organisms from the genera Paenibacillus, Enterobacter, Klebsiella and Pantoea. These representative genus are not endophytic diazotrophs in the conventional sense. They may have obtained nitrogen fixation ability through lateral gene transfer, however, the evolutionary forces of lateral gene transfer are not well known. Molecular identification results from 16S rRNA analyses were also confirmed by morphological and biochemical data. The test strains SH6A and MZB showed positive effect on the growth of plants.

This paper examines the interval forecasting of carbon futures prices in one of the most important carbon futures market. Specifically, the purpose of this study is to present a novel hybrid approach, which is composed of multioutput support vector regression (MSVR) and particle swarm optimization (PSO), in the task of forecasting the highest and lowest prices of carbon futures on the next trading day. Furthermore, we set out to investigate if considering some potential predictors, which have strong influence on carbon futures prices, in modeling process is useful for achieving better prediction performance. Aiming at testing its effectiveness, we benchmark the forecasting performance of our approach against four competitors. The daily interval prices of carbon futures contracts traded in the Intercontinental Futures Exchange from August 12, 2010, to November 13, 2014, are used as the experiment dataset. The statistical significance of the interval forecasts is examined. The proposed hybrid approach is found to demonstrate the higher forecasting performance relative to all other competitors. Our application offers practitioners a promising set of results with interval forecasting in carbon futures market.