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Purpose: This study aims to investigate the association of serum TSH with BMD in Chinese adults with normal thyroid function. Methods: These participants were divided into tertiles based on serum TSH levels. Linear regression model and multinomial logistic regression models were used to analyze the associations of continuous BMD and categorical BMD with serum TSH, respectively. Results: In women younger than 60 years, BMD decreased with the increase of TSH at normal level, while in women older than 60 years, BMD increased with the increase of TSH at normal level; besides, the BMD of women younger than 60 years old was significantly higher than that of women over 60 years old (156.05 ± 39.34 mg/cm3 vs. 86.95 ± 29.51 mg/cm3, P < 0.001). Linear regression results showed negative associations of BMD and normal TSH level in women with age younger than 60 years (β=-4.34, P < 0.001), but this inverse trend was observed in women over 60 years old (β = 2.04, P = 0.041). Both in men younger than 60 years and over 60 years old, BMD decreased with the increase of TSH at normal levels; besides, the BMD of men younger than 60 years was significantly higher than those over 60 years old (143.08 ± 32.76 mg/cm3 vs. 108.13 ± 31.99 mg/cm3, P < 0.001). Conclusions: The results demonstrated an opposite trend in BMD at normal TSH levels in younger and elder females, that is, in females younger than 60 years, BMD decreased with the increase of TSH, which indicated that TSH might play a different role in younger and elder females. However, this trend was not significant in males. Summary: This study demonstrated an opposite trend in BMD at normal TSH levels in younger and elder females, that is, in females younger than 60 years, BMD decreased with the increase of TSH, which indicated that TSH might play a different role in younger and elder females. [ABSTRACT FROM AUTHOR]

Projections of commodity prices have long been a significant source of dependence for investors and the government. This study investigates the challenging topic of forecasting the daily regional steel price index in the northeast Chinese market from January 1, 2010, to April 15, 2021. The projection of this significant commodity price indication has not received enough attention in the literature. The forecasting model that is used is Gaussian process regressions, which are trained using a mix of cross-validation and Bayesian optimizations. The models that were built precisely predicted the price indices between January 8, 2019, and April 15, 2021, with an out-of-sample relative root mean square error of 0.5432%. Investors and government officials can use the established models to study pricing and make judgments. Forecasting results can help create comparable commodity price indices when reference data on the price trends suggested by these models are used. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate dynamic relations among office property price indices of 10 major cities in China for the years 2005–2021. Design/methodology/approach: Using monthly data, the authors adopt vector error correction modeling and the directed acyclic graph for the characterization of contemporaneous causality among the 10 indices. Findings: The PC algorithm identifies the causal pattern, and the linear non-Gaussian acyclic model algorithm further determines the causal path from which we perform innovation accounting analysis. Sophisticated price dynamics are found in price adjustment processes following price shocks, which are generally dominated by the top tier of cities. Originality/value: This suggests that policies on office property prices, in the long run, might need to be planned with particular attention paid to the top tier of cities. [ABSTRACT FROM AUTHOR]

In recent years, underground logistics systems have attracted more and more attention from scholars and are considered to be a promising new green and intelligent transportation mode. This paper proposes a yard design problem considering an underground container logistics system. The structure and workflow of the underground container logistics system are analyzed, and key features are recognized for the yard design problem, such as the container block layout direction, the lane configuration in the yard, and the number of container blocks. We formulate the problem into mathematical models under different scenarios of the key features with the comprehensive objective of maximizing the total throughput and minimizing the total operation cost simultaneously. An improved tabu search algorithm is designed to solve the problem. Experimental results show that the proposed algorithm can generate a satisfactory layout design solution for a real-size instance. Our research studies different container yard design options for introducing the underground logistics system into port terminals, which provides an important scientific foundation for promoting the application of underground container logistics systems. [ABSTRACT FROM AUTHOR]

For market players and policy officials, commodity price forecasts are crucial problems that are challenging to address due to the complexity of price time series. Given its strategic importance, corn crops are hardly an exception. The current paper evaluates the forecasting issue for China's weekly wholesale price index for yellow corn from January 1, 2010 to January 10, 2020. We develop a Gaussian process regression model using cross validation and Bayesian optimizations over various kernels and basis functions that could effectively handle this sophisticated commodity price forecast problem. The model provides precise out-of-sample forecasts from January 4, 2019 to January 10, 2020, with a relative root mean square error, root mean square error, and mean absolute error of 1.245%, 1.605, and 0.936, respectively. The models developed here might be used by market players for market evaluations and decision-making as well as by policymakers for policy creation and execution. [ABSTRACT FROM AUTHOR]

Tobacco mosaic virus (TMV; genus Tobamovirus) is one of the most prevailing pathogens that seriously affects the quality and yield of tobacco (Nicotiana tabacum) leaves. Cross-protection using mild strains is a potential strategy for the biological prevention of plant viral diseases. Complementary mutations in attenuated strains may cause attenuated ones to suddenly evolve into virulent strains, which limits the application of cross-protection in practice. To data there has been no study on engineering the complementary mutation sites to generate stable attenuated mutants for cross-protection. In this study, we found that the substitution of the conserved arginine at position 88 (R88) in p126 protein with alanine (A) abolished the cell-to-cell movement and reduced the replication of TMV. However, a spontaneous complementary mutation of serine at position 114 (S114) to lysine (K) in p126 restored TMV virulence. Substitution of S114 with R in p126 restored the systemic infection but not the virulence of TMV, therefore, the mutant TMV-R88A/S114R was an attenuated one. Furthermore, our results showed that TMV-R88A/S114R was a stable attenuated mutant, and could effectively protect tobacco plants against the wild-type TMV infection. This study reports a promising TMV mild mutant for cross-protection in tobacco plants by modifying the complementary mutation site in p126. [ABSTRACT FROM AUTHOR]

Purpose: The Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021. Design/methodology/approach: The goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios. Findings: The authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases. Originality/value: Results here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis. [ABSTRACT FROM AUTHOR]

Due to significant implications for resource and food sectors that directly influence social well-being, commodity price comovements represent an important issue in agricultural economics. In this study, we approach this issue by concentrating on daily prices of the corn futures market and 496 cash markets from 16 states in the United States for the period of July 2006 – February 2011 through correlation based hierarchical analysis and synchronization analysis, which allow for determining interactions and interdependence among these prices, heterogeneities in price synchronization, and their changing patterns over time. As the first study of the issue focusing on prices of the futures and hundreds of spatially dispersed cash markets for a commodity of indubitable economic significance, empirical findings show that the degree of comovements is generally higher after March 2008 but no persistent increase is observed. Different groups of cash markets are identified, each of which has its members exhibit relatively stable price synchronization over time that is generally at a higher level than the synchronization among the futures and all of the 496 cash markets. The futures is not found to show stable price synchronization with any cash market. Certain cash markets have potential of serving as cash price leaders. Results here benefit resource and food policy analysis and design for economic welfare. The empirical framework has potential of being adapted to network analysis of prices of different commodities. [ABSTRACT FROM AUTHOR]

Purpose: Understandings of house prices and their interrelationships have undoubtedly drawn a great amount of attention from various market participants. This study aims to investigate the monthly newly-built residential house price indices of seventy Chinese cities during a 10-year period spanning January 2011–December 2020 for understandings of issues related to their interdependence and synchronizations. Design/methodology/approach: Analysis here is facilitated through network analysis together with topological and hierarchical characterizations of price comovements. Findings: This study determines eight sectoral groups of cities whose house price indices are directly connected and the price synchronization within each group is higher than that at the national level, although each shows rather idiosyncratic patterns. Degrees of house price comovements are generally lower starting from 2018 at the national level and for the eight sectoral groups. Similarly, this study finds that the synchronization intensity associated with the house price index of each city generally switches to a lower level starting from early 2019. Originality/value: Results here should be of use to policy design and analysis aiming at housing market evaluations and monitoring. [ABSTRACT FROM AUTHOR]

Purpose: Triple-negative breast cancer (TNBC) has a poor prognosis due to the absence of effective therapeutic targets. Vascular endothelial growth factor (VEGF) family are expressed in 30–60% of TNBC, therefore providing potential therapeutic targets for TNBC. Aflibercept (Abe), a humanized recombinant fusion protein specifically bound to VEGF-A, B and placental growth factor (PIGF), has proven to be effective in the treatment in some cancers. Therefore, 89Zr/177Lu-labeled Abe was investigated for its theranostic role in TNBC. Methods: Abe was radiolabeled with 89Zr and 177Lu via the conjugation of chelators. Flow cytometry and cell immunofluorescent staining were performed to evaluate the binding affinity of Abe. Sequential PET imaging and fluorescent imaging were conducted in TNBC tumor bearing mice following the injection of 89Zr-labeled Abe and Cy5.5-labeled Abe. Treatment study was performed after the administration of 177Lu-labeled Abe. Tumor volume and survival were monitored and SPECT imaging and biodistribution studies were conducted. Safety evaluation was performed including body weight, blood cell measurement, and hematoxylin–eosin (H&E) staining of major organs. Expression of VEGF and CD31 was tested by immunohistochemical staining. Dosimetry was estimated using the OLINDA software. Results: FITC-labeled Abe showed a strong binding affinity to VEGF in TNBC 4T1 cells and HUVECs by flow cytometry and cell immunofluorescence. Tumor uptake of 89Zr-labeled Abe peaked at 120 h (SUVmax = 3.2 ± 0.64) and persisted before 168 h (SUVmax = 2.54 ± 0.42). The fluorescence intensity of the Cy5.5-labeled Abe group surpassed that of the Cy5.5-labeled IgG group, implying that Cy5.5-labeled Abe is a viable candidate monitoring in vivo tumor targeting and localization. 177Lu-labeled Abe (11.1 MBq) served well as the therapeutic component to suppress tumor growth with standardized tumor volume at 16 days, significantly smaller than PBS group (about 815.66 ± 3.58% vs 3646.52 ± 11.10%, n = 5, P < 0.01). Moreover, SPECT images confirmed high contrast between tumors and normal organs, indicating selective tumor uptake of 177Lu-labeled Abe. No discernible abnormalities in blood cells, and no evident histopathological abnormality observed in liver, spleen, and kidney. Immunohistochemical staining showed that 177Lu-labeled Abe effectively inhibited the expression of VEGF and CD31 of tumor, suggesting that angiogenesis may be suppressed by 177Lu-labeled Abe. The whole-body effective dose for an adult human was estimated to be 0.16 mSv/MBq. Conclusion: 89Zr/177Lu-labeled Abe could be a TNBC-specific marker with diagnostic value and provide insights into targeted therapy in the treatment of TNBC. Further clinical evaluation and translation may be of high significance for TNBC. [ABSTRACT FROM AUTHOR]

We synthesize supramolecular poly(disulfide) (CPS) containing covalently attached cucurbit[7]uril (CB[7]), which is exploited not only as a carrier to deliver plasmid DNA encoding destabilized Cas9 (dsCas9), but also as a host to include trimethoprim (TMP) by CB[7] moieties through the supramolecular complexation to form TMP@CPS/dsCas9. Once the plasmid is transfected into tumor cells by CPS, the presence of polyamines can competitively trigger the decomplexation of TMP@CPS, thereby displacing and releasing TMP from CB[7] to stabilize dsCas9 that can target and edit the genomic locus of PLK1 to inhibit the growth of tumor cells. Following the systemic administration of TMP@CPS/dsCas9 decorated with hyaluronic acid (HA), tumor‐specific editing of PLK1 is detected due to the elevated polyamines in tumor microenvironment, greatly minimizing off‐target editing in healthy tissues and non‐targeted organs. As the metabolism of polyamines is dysregulated in a wide range of disorders, this study offers a supramolecular approach to precisely control CRISPR/Cas9 functions under particular pathological contexts. [ABSTRACT FROM AUTHOR]

We synthesize supramolecular poly(disulfide) (CPS) containing covalently attached cucurbit[7]uril (CB[7]), which is exploited not only as a carrier to deliver plasmid DNA encoding destabilized Cas9 (dsCas9), but also as a host to include trimethoprim (TMP) by CB[7] moieties through the supramolecular complexation to form TMP@CPS/dsCas9. Once the plasmid is transfected into tumor cells by CPS, the presence of polyamines can competitively trigger the decomplexation of TMP@CPS, thereby displacing and releasing TMP from CB[7] to stabilize dsCas9 that can target and edit the genomic locus of PLK1 to inhibit the growth of tumor cells. Following the systemic administration of TMP@CPS/dsCas9 decorated with hyaluronic acid (HA), tumor‐specific editing of PLK1 is detected due to the elevated polyamines in tumor microenvironment, greatly minimizing off‐target editing in healthy tissues and non‐targeted organs. As the metabolism of polyamines is dysregulated in a wide range of disorders, this study offers a supramolecular approach to precisely control CRISPR/Cas9 functions under particular pathological contexts. [ABSTRACT FROM AUTHOR]

The bioinertness, poor antibacterial properties, and weak osseointegration of polyether‐etherketone (PEEK) limit its clinical application as oral implants. In this study, it is developed a personalized and customizable PEEK implant with multifunctional capabilities through sequential 3D printing, surface sulfonation (SPEEK), and modification using a drug‐loaded photocured hydrogel composed of acrylic acid‐acrylamide (AA‐AM) containing simvastatin‐cefepime (SIM‐CFP). The modified implant, referred to as SCHSPEEK, mimics the structure of biomimetic periodontal ligament (PDL) fibers. The chemical composition, surface characteristics, and biological properties of SCHSPEEK both in vitro and in vivo are systematically investigated. The results demonstrate that hydrogel modification not only enhances hydrophilicity and reduces surface roughness and friction coefficient, but also improves biocompatibility, drug release, and antibacterial efficacy. Importantly, SCHSPEEK exhibits a significant improvement in osseointegration capacity in vivo. These findings highlight the potential of SCHSPEEK implants to serve as promising orthopedic and dental implants in clinical settings. [ABSTRACT FROM AUTHOR]

In this study, we aim at investigating dynamic relations among steel price indices of all seven regional markets in China, spanning the period of 2010M1–2021M4. With daily data, we apply techniques of vector error correction modeling and the directed acyclic graph to analyze the issue of contemporaneous causality among the seven indices, the latter of which is facilitated through the LiNGAM algorithm. We find via vector error correction modeling that each of the seven indices is part of cointegration relations and responses to long-run equilibrium disturbances. We arrive at causal paths via the LiNGAM algorithm for enabling innovation accounting analysis. With this analysis, we find sophisticated dynamics in price adjustment processes following shocks, for which east and northeast regions have the most significant impacts on other regions, followed by south and north regions, followed by the southwest region. Central south and northwest regions are not found to have significant impacts on others. These results suggest that policies on regional steel prices in the long run might be designed with most attention paid to five of the seven regions, particularly east and northeast regions. [ABSTRACT FROM AUTHOR]

Purpose: The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021. Design/methodology/approach: The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios. Findings: The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases. Originality/value: The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis. [ABSTRACT FROM AUTHOR]

Food price forecasts in the agricultural sector have always been a vital matter to a wide variety of market participants. In this work, we approach this forecast problem for the weekly wholesale food price index in the Chinese market during a 10-year period of January 1, 2010–January 3, 2020. To facilitate the analysis, we propose the use of the nonlinear auto-regressive neural network. Technically, we investigate forecast performance, based upon the relative root mean square error (RRMSE) as the evaluation metrics, corresponding to one hundred and twenty settings that cover different algorithms for model estimations, numbers of hidden neurons and delays, and ratios for splitting the data. Our experimental result suggests the construction of the neural network with three delays and 10 hidden neurons, which is trained through the Levenberg–Marquardt algorithm, as the forecast model. It leads to high accuracy and stabilities with the RRMSEs of 1.93% for the training phase, 2.16% for the validation phase, and 1.95% for the testing phase. Comparisons of forecast accuracy between the proposed model and some other machine learning models, as well as traditional time-series econometric models, suggest that our proposed model leads to statistically significant better performance. Our results could benefit different forecast users, such as policymakers and various market participants, in policy analysis and market assessments. [ABSTRACT FROM AUTHOR]

Forecasting commodity prices is a vital issue to a wide spectrum of market participants and policy makers in the metal sector. In this work, the forecast problem is investigated by focusing on the Chinese market, with the daily steel price indices of the composite index, long products, flat products, and rolled products spanning a 10-year period from June 15, 2011, to April 15, 2021. The non-linear auto-regressive neural network is employed as the forecasting model and model performance corresponding to a variety of settings is explored over algorithms for model estimations, numbers of hidden neurons and delays, and ratios for splitting the data for each of the four price indices. Models that are relatively simple and generate forecasts of high accuracy and stabilities are arrived at. Particularly, relative root mean square errors (RRMSEs) of 0.49%/0.49%/0.52%/0.55%, 0.51%/0.44%/0.51%/0.49%, and 0.52%/0.53%/0.56%/0.53%, respectively, for training, validation, and testing, and the overall RRMSE of 0.50%/0.49%/0.53%/0.54% are reached for the composite index/long products/rolled products/flat products. The results could, on the one hand, serve as standalone technical price forecasts. They could, on the other hand, be combined with other (fundamental) forecast results for forming perspectives of price trends and carrying out policy analysis. [ABSTRACT FROM AUTHOR]

Although 2D MoS2 alone shows excellent gas-sensing performance, it is prone to stacking when used as the sensitive layer, resulting in insufficient contact with the target gas and lower sensitivity. To solve this, a 2D-MoS2/1D-CuPc heterojunction was prepared with different weight ratios of MoS2 nanosheets to CuPc micro-nanowires, and its room-temperature gas-sensing properties were studied. The response of the 2D-MoS2/1D-CuPc heterojunction to a target gas was related to the weight ratio of MoS2 to CuPc. When the weight ratio of MoS2 to CuPc was 20:7 (7-CM), the gas sensitivity of MoS2/CuPc composites was the best. Compared with the pure MoS2 sensor, the responses of 7-CM to 1000 ppm formaldehyde (CH2O), acetone (C3H6O), ethanol (C2H6O), and 98% RH increased by 122.7, 734.6, 1639.8, and 440.5, respectively. The response of the heterojunction toward C2H6O was twice that of C3H6O and 13 times that of CH2O. In addition, the response time of all sensors was less than 60 s, and the recovery time was less than 10 s. These results provide an experimental reference for the development of high-performance MoS2-based gas sensors. [ABSTRACT FROM AUTHOR]

Purpose: With the rapid-growing house market in the past decade, the purpose of this paper is to study the important issue of house price information flows among 12 major cities in China, including Shanghai, Beijing, Xiamen, Shenzhen, Guangzhou, Hangzhou, Ningbo, Nanjing, Zhuhai, Fuzhou, Suzhou and Dongguan, during the period of June 2010 to May 2019. Design/methodology/approach: The authors approach this issue in both time and frequency domains, latter of which is facilitated through wavelet analysis and by exploring both linear and nonlinear causality under the vector autoregressive framework. Findings: The main findings are threefold. First, in the long run of the time domain and for timescales beyond 16 months of the frequency domain, house prices of all cities significantly affect each other. For timescales up to 16 months, linear causality is weaker and is most often identified for the scale of four to eight months. Second, while nonlinear causality is seldom determined in the time domain and is never found for timescales up to four months, it is identified for scales beyond four months and particularly for those beyond 32 months. Third, nonlinear causality found in the frequency domain is partly explained by the volatility spillover effect. Originality/value: Results here should be of use to policymakers in certain policy analysis. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the association of serum TSH with BMD in Chinese adults with normal thyroid function. Methods: These participants were divided into tertiles based on serum TSH levels. Linear regression model and multinomial logistic regression models were used to analyze the associations of continuous BMD and categorical BMD with serum TSH, respectively. Results: In women younger than 60 years, BMD decreased with the increase of TSH at normal level, while in women older than 60 years, BMD increased with the increase of TSH at normal level; besides, the BMD of women younger than 60 years old was significantly higher than that of women over 60 years old (156.05 ± 39.34 mg/cm3 vs. 86.95 ± 29.51 mg/cm3, P < 0.001). Linear regression results showed negative associations of BMD and normal TSH level in women with age younger than 60 years (β=-4.34, P < 0.001), but this inverse trend was observed in women over 60 years old (β = 2.04, P = 0.041). Both in men younger than 60 years and over 60 years old, BMD decreased with the increase of TSH at normal levels; besides, the BMD of men younger than 60 years was significantly higher than those over 60 years old (143.08 ± 32.76 mg/cm3 vs. 108.13 ± 31.99 mg/cm3, P < 0.001). Conclusions: The results demonstrated an opposite trend in BMD at normal TSH levels in younger and elder females, that is, in females younger than 60 years, BMD decreased with the increase of TSH, which indicated that TSH might play a different role in younger and elder females. However, this trend was not significant in males. Summary: This study demonstrated an opposite trend in BMD at normal TSH levels in younger and elder females, that is, in females younger than 60 years, BMD decreased with the increase of TSH, which indicated that TSH might play a different role in younger and elder females. [ABSTRACT FROM AUTHOR]

For policy makers and a diverse spectrum of market participants, understandings of commodity price forecasts are considered as an important matter. In this work, we focus on the metal business sector and examine forecast problems for seven daily regional steel prices in the Chinese market over an eleven-year period spanning 2010–2021. Specifically, our analysis covers steel prices of east, south, north, central south, northeast, southwest, and northwest China through the use of non-linear auto-regressive neural networks. For each price series, we test forecast accuracy based upon one hundred and twenty settings over training algorithms for model estimations, numbers of hidden neurons, numbers of delays, and ratios used for segmenting the data into training, validation, and testing phases. Our analysis leads to the construction of a rather simple model that generates high forecast accuracy and stabilities for each of the seven price series. Correspondingly, forecast accuracy measured by the relative root mean square error is lower than 0.60% for all considered series based upon the overall sample. Forecast results here could be utilized on a standalone basis or combined with forecasts from other models by different forecast users as part of evaluating price trends and performing policy analysis. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate dynamic relationships among residential housing price indices of ten major Chinese cities for the years 2005–2021. Design/methodology/approach: Using monthly data, this study uses vector error correction modeling and the directed acyclic graph for characterization of contemporaneous causality among the ten indices. Findings: The PC algorithm identifies the causal pattern and the Linear Non-Gaussian Acyclic Model algorithm further determines the causal path, from which this study conducts innovation accounting analysis. Sophisticated price dynamics are found in price adjustment processes following price shocks, which are generally dominated by the top tiers of cities. Originality/value: This study suggests that policies on residential housing prices in the long run might need to be planned with particular attention paid to these top tiers of cities. [ABSTRACT FROM AUTHOR]

Predictions of financial index trading volumes represent an essential issue to market participants and policy makers. We investigate this problem for the high-frequency one-minute CSI300 first distant futures trading volume from the launch of the futures index to two years after all its constituent stocks becoming shortable, a period with generally continuously expanding trading magnitude. We utilize the neural network to model this complex and irregular trading volume series and attempt to answer the following questions: can the trading volume be predicted by its own lags, and if so, how far to predict and how well; can the nearby futures or spot trading volume series help predictions, and if so, by how much; how complex does the model need to be and how robust can it be? We find that the trading volume could be predicted using one to thirty minutes ahead data through a relatively low complex model with five hidden neurons based on its own lags, resulting in generally accurate and stable performance based on the relative root mean square error. Incorporating the nearby futures or spot trading volume series generally does not help improve predictions. Results here should be of interest and use to design trading platforms, monitor system risk, and form index price predictions. [ABSTRACT FROM AUTHOR]

Coking coal price forecasting is a significant issue for investors and policy makers. This study explores usefulness of the nonlinear autoregressive neural network for this forecasting problem in a dataset of daily closing prices of the coking coal futures traded in China Dalian Commodity Exchange during January 4, 2016–December 31, 2020. Through examining various model settings across the algorithm, delay, hidden neuron, and data splitting ratio, the model leading to generally accurate and stable performance is reached. Particularly, the model's inputs are the lagged coking coal futures prices and output is the 1-day ahead price forecast. The model is based on the two-layer feedforward network with six delays and two hidden neurons, which is trained through the Levenberg-Marquardt algorithm, and leads to relative root mean square errors of 1.84%, 1.85%, and 1.84% for the training, validation, and testing phases, respectively. Usefulness of the machine learning technique for the price forecasting problem of the coking coal price is illustrated. Results here might be used on a standalone basis as technical forecasts or combined with fundamental forecasts to form perspectives of price trends and perform policy analysis. [ABSTRACT FROM AUTHOR]

The throttling flowmeter used in utility boilers has been used as a measuring instrument for a long time. However, its safety performance, such as welding, lacks enough attention. The manufacturing welds of the throttling flowmeter are welded with austenitic stainless steel and pearlitic heat-resistant steel. Cracks and other defects are generally found in the inspection and detection of the manufacturing welds of throttling flowmeters in service, which have serious potential accidents. To find out the relationship between weldingdissimilar steels and defects, the microhardness indentation method was used to measure the residual stress of welding. Combined with the self-developed calculation software of microscopic indentation residual stress distribution, we used the difference between the color of the indentation area and the surrounding area to quickly measure the indentation strain collected by the microscope using the computer image recognition algorithm, which greatly improved the accuracy and speed of the microscopic indentation residual stress test. The results show that the residual stress at the weld of dissimilar steel is relatively large, and the mechanical property test is generally unqualified. The microindentation residual stress analysis method based on computer image recognition is used to quickly, intuitively, and accurately reveal the direct relationship between the combination of dissimilar steel welding materials and the generation of cracks and other defects. [ABSTRACT FROM AUTHOR]

Non-alcoholic fatty liver disease (NAFLD), characterized by excessive lipid accumulation in hepatocytes, is an increasing global healthcare burden. Sirtuin 2 (SIRT2) functions as a preventive molecule for NAFLD with incompletely clarified regulatory mechanisms. Metabolic changes and gut microbiota imbalance are critical to the pathogenesis of NAFLD. However, their association with SIRT2 in NAFLD progression is still unknown. Here, we report that SIRT2 knockout (KO) mice are susceptible to HFCS (high-fat/high-cholesterol/high-sucrose)-induced obesity and hepatic steatosis accompanied with an aggravated metabolic profile, which indicates SIRT2 deficiency promotes NAFLD-NASH (nonalcoholic steatohepatitis) progression. Under palmitic acid (PA), cholesterol (CHO), and high glucose (Glu) conditions, SIRT2 deficiency promotes lipid deposition and inflammation in cultured cells. Mechanically, SIRT2 deficiency induces serum metabolites alteration including upregulation of L-proline and downregulation of phosphatidylcholines (PC), lysophosphatidylcholine (LPC), and epinephrine. Furthermore, SIRT2 deficiency promotes gut microbiota dysbiosis. The microbiota composition clustered distinctly in SIRT2 KO mice with decreased Bacteroides and Eubacterium, and increased Acetatifactor. In clinical patients, SIRT2 is downregulated in the NALFD patients compared with healthy controls, and is associated with exacerbated progression of normal liver status to NAFLD to NASH in clinical patients. In conclusion, SIRT2 deficiency accelerates HFCS-induced NAFLD-NASH progression by inducing alteration of gut microbiota and changes of metabolites. [ABSTRACT FROM AUTHOR]

Artemisia annua residue (ARR) is a pharmaceutical by-product produced after the extraction of artemisinin; it is rich in protein, crude fat, vitamins, trace elements, and bioactive compounds and contains negligible anti-nutritional factors. The present study aimed to optimize the fermentation conditions of ARR, evaluate the compound and microbial compositions of fermented AAR, and explore its effects on the production performance of laying hens. A total of 288 Xinyang black-feather laying hens were randomly allocated into four treatments for 30 days, including a control group (basal diet) and a basal diet supplemented with 1%, 2%, and 4% fermented AAR, respectively. The results showed that the optimized fermentation conditions of AAR were 80% moisture content, 3% inoculation quantity, 34 °C fermentation for 6 days, initial pH at 8, and 60 mesh (sieving). The compounds of 2-furyl-5-methyl furan, deoxyartemisinin, phytol, n-hexadecanoic acid, aromandendrene, and calarene had higher contents (average 6.86%) in the fermented AAR. The bacteria of Proteobacteria and Firmicutes (average 45.18%) were the most abundant phyla, and Acinetobacter, Bacillus, and Brevundimonas (average 15.87%) were the most abundant genera in the fermented AAR. The fungi of Phragmoplastophyta, Vertebrata, and Ascomycota (average 30.13%) were the most abundant phyla, and Magnoliophyta, Mammalia, Wickerhamomyces-Candida_clade, and Aspergillus were the most abundant genera (average 21.12%) in the fermented AAR. Furthermore, dietary supplementation of fermented AAR increased the average daily feed intake (ADFI), egg weight, and albumen height. Dietary supplementation of 2% and 4% fermented AAR increased the laying rate, while 2% fermented AAR increased the Haugh unit and decreased the feed-to-egg ratio. Collectively, it is concluded that fermented AAR has the potential to become a phytogenic feed additive, and dietary supplementation of 2% fermented AAR had better effects on the production performance of laying hens. [ABSTRACT FROM AUTHOR]

Housing price comovements are an important issue in economics. This study focuses on monthly housing prices of 99 major cities in China for the years 2010–2019 by using correlation-based hierarchical analysis and synchronisation analysis, through which one could determine interactions and interdependence among the prices, heterogeneous patterns in price synchronisations and their changing paths over time. Empirical results show that the degree of comovements is slightly lower after March 2017 but no persistent drop is found. Several groups of cities are identified, each of which has its members showing relatively strong but volatile price synchronisations. Certain cities show potential of serving as price leaders within a group. Results here could be useful to policy analysis regarding housing price comovements. [ABSTRACT FROM AUTHOR]

Objective: Inhibitors of cyclin‐dependent kinase 4 and 6 (CDK4/6) are targeted therapeutic drugs for breast cancer treatment. The mechanism of resistance to these inhibitors requires further investigation. Methods: We used bioinformatics to screen differentially expressed genes between cells that were susceptible and resistant to CDK4/6 inhibitors. Quantitative real‐time PCR (qRT‐PCR) was used to identify gene expressions in different cell lines. Cell viability, colony formation, cell cycle, and apoptosis assays were used to evaluate the effect of carboxypeptidase vitellogenic like (CPVL) on breast cancer cells under the condition of CDK4/6 inhibitors. Gene set enrichment analysis (GSEA) suggested the potential regulatory pathway of CPVL in breast cancer. Xenograft formation assay was conducted in nude mice to study the role of CPVL in vivo. Results: Based on bioinformatics analysis and qRT‐PCR, CPVL was identified more abundantly in cells that were resistant than sensitive to CDK4/6 inhibitors. Overexpressed or knocked down CPVL regulated the effects of CDK4/6 inhibitors in resistant cell lines. GSEA showed that resistance might be induced by CPVL through altered phosphatase and tensin homolog (PTEN)‐related pathways. Our findings showed that CPVL negatively regulates PTEN to impact the anticancer effects of CDK4/6 inhibitors in vitro and in vivo. Conclusion: CPVL might be a key factor in regulating breast cancer resistance to CDK4/6 inhibitors. [ABSTRACT FROM AUTHOR]

Most existing studies model interdependent networks as simple network systems consisting of two or more undirected subnets, and the interdependent edges between the networks are undirected. However, many real-world interdependent networks are coupled by a directed subnet and an undirected subnet, such as supply chain networks coupled with cyber networks, and cyber manufacturing networks coupled with service networks. Therefore, in this work, we focus on a ubiquitous type of interdependent network—the directed–undirected interdependent network—and research the cascading failures of directed–undirected interdependent networks with different coupling patterns. Owing to the diversity of coupling patterns to realistic interdependent network systems, we introduce two types of interdependent edges (i.e., directed-to-undirected and undirected-to-directed interdependent edges). On this basis, we generated different types of directed–undirected interdependent networks with varying coupling patterns (i.e., one-to-one, one-to-many, and many-to-one) and investigated the cascading failure robustness of these types of networks. Finally, we explored the cascading robustness of directed–undirected interdependent networks under two different attack strategies (single-node attack and multi-node attack). Through extensive experiments, we have obtained some meaningful findings: (1) the cascading robustness of directed–undirected interdependent networks is positively related to the overload tolerance coefficient and load exponential coefficient; (2) high-degree nodes and high-in-degree nodes should be protected to improve the cascading robustness of directed–undirected interdependent networks; (3) the cascading robustness of one-to-many interdependent networks can be improved by adding directed-to-undirected interdependent edges; and the cascading robustness of many-to-one interdependent networks can be improved by adding undirected-to-directed interdependent edges. [ABSTRACT FROM AUTHOR]

This study investigates cointegration between monthly housing prices from one hundred Chinese cities for years 2010–2019, utilising both time-invariant and time-varying approaches. Their wavelet transformations are further explored for cointegration at the scale of greater than five years. Also studied is quantitative importance of housing price information of one city to another. Empirical results show different price relationships across different pairs of cities. While cointegrating patterns are heterogeneous across tested pairs, we present many relatively stable cointegrating relationships, which tend to be aggregated results of price relationships of different scales. Housing price information of certain cities could be reflected in that of other cities at a relatively large magnitude. The results here should be of use to investors and policymakers in the housing market. [ABSTRACT FROM AUTHOR]

Rapid growth has been seen in the Chinese housing market during the past decade, and housing price forecasts have become more significant to investors and policy makers. In this study, we explore neural networks for retail property price index forecasts from ten major Chinese cities for July 2005–April 2021. Our goal is to build simple and accurate neural networks to contribute to purely technical forecasts of the Chinese retail property market. To facilitate the analysis, we examine different model settings based on algorithms, delays, hidden neurons, and data splitting ratios and construct a simple neural network with five delays and two hidden neurons. This leads to a rather stable performance of about 1.6% average relative root mean square error across the ten cities for the training, validation, and testing phases. Results here can be used on a stand-alone basis or combined with fundamental forecasts to form perspectives of retail property price trends and conduct policy analyses. [ABSTRACT FROM AUTHOR]

Grinding-assisted liquid-phase exfoliation is a widely used method for the preparation of two-dimensional nanomaterials. In this study, N-methylpyrrolidone and acetonitrile, two common grinding solvents, were used during the liquid-phase exfoliation for the preparation of MoS2 nanosheets. The morphology and structure of MoS2 nanosheets were analyzed via scanning electron microscopy, X-ray diffraction, and Raman spectroscopy. The effects of grinding solvents on the gas-sensing performance of the MoS2 nanosheets were investigated for the first time. The results show that the sensitivities of MoS2 nanosheet exfoliation with N-methylpyrrolidone were 2.4-, 1.4-, 1.9-, and 2.7-fold higher than exfoliation with acetonitrile in the presence of formaldehyde, acetone, and ethanol and 98% relative humidity, respectively. MoS2 nanosheet exfoliation with N-methylpyrrolidone also has fast response and recovery characteristics to 50–1000 ppm of CH2O. Accordingly, although N-methylpyrrolidone cannot be removed completely from the surface of MoS2, it has good gas sensitivity compared with other samples. Therefore, N-methylpyrrolidone is preferred for the preparation of gas-sensitive MoS2 nanosheets in grinding-assisted liquid-phase exfoliation. The results provide an experimental basis for the preparation of two-dimensional materials and their application in gas sensors. [ABSTRACT FROM AUTHOR]

Purpose: Chinese housing market has been growing fast during the past decade, and price-related forecasting has turned to be an important issue to various market participants, including the people, investors and policy makers. Here, the authors approach this issue by researching neural networks for rent index forecasting from 10 major cities for March 2012 to May 2020. The authors aim at building simple and accurate neural networks to contribute to pure technical forecasting of the Chinese rental housing market. Design/methodology/approach: To facilitate the analysis, the authors examine different model settings over the algorithm, delay, hidden neuron and data spitting ratio. Findings: The authors reach a rather simple neural network with six delays and two hidden neurons, which leads to stable performance of 1.4% average relative root mean square error across the ten cities for the training, validation and testing phases. Originality/value: The results might be used on a standalone basis or combined with fundamental forecasting to form perspectives of rent price trends and conduct policy analysis. [ABSTRACT FROM AUTHOR]

F‐doped TiO2 is developed through simple solid‐phase synthesis and innovatively employed to support IrOx nanoparticles towards acidic oxygen evolution reaction. Benefiting from the high electrical conductivity, improved electronic interaction, and well dispersing/anchoring effects of F‐TiO2, the IrOx/F‐TiO2 electrocatalyst requires only 272 mV overpotential to achieve 10 mA cm−2, accompanied with the mass activity of 320.2 A gIr−1@1.55 V. Moreover, the single cell voltages are only 1.662 V@1 A cm−2 and 1.749 V@2 A cm−2 when employing IrOx/F‐TiO2 as the anode catalyst, and operates steadily for at least 240 h. Hence, low overpotential, enhanced stability and prominent catalyst utilization are simultaneously realized. [ABSTRACT FROM AUTHOR]

Transparent conducting materials (TCMs) are essential components for a variety of optoelectronic devices, such as photovoltaics, displays and touch screens. In recent years, extensive efforts have been made to develop TCMs with both high electrical conductivity and optical transmittance. Based on material types, they can be mainly categorized into the following classes: metal oxides, metal nanowire networks, carbon-material-based TCMs (graphene and carbon nanotube networks) and conjugated conductive polymers (PEDOT:PSS). This review will discuss the fundamental electrical and optical properties, typical fabrication methods and the applications in solar cells for each class of TCMs and highlight the current challenges and potential future research directions. [ABSTRACT FROM AUTHOR]

In this article, the development of a compact 10–14.5 GHz quadrature hybrid with digitally reconfigurable I/Q phase in 130 nm SiGe BiCMOS process is presented. Thanks to the switched capacitance loaded on I/Q path of the quadrature hybrid, the I/Q phase difference can be optimized and digitally reconfigured. The equivalent model is analyzed with even/odd mode theory, and the ABCD matrix is used for the circuit derivation. In order to obtain high coupling coefficient, the broadside coupled line sections are utilized, and compact hybrid size can be realized accordingly. Measured results show that the compact quadrature hybrid has optimized phase difference of 90 ± 1.0° and amplitude difference less than ±0.5 dB for 10–14.5 GHz, with an ultra-compact size of 460 µm × 151 µm, or 0.031λ0 × 0.011λ0. Meanwhile, with the seven reconfigurable phase states, the quadrature hybrid I/Q phase can be digitally reconfigured for a range of 3 degrees to compensate the I/Q phase imbalance in the quadrature system, without DC power consumption. [ABSTRACT FROM AUTHOR]

We present in this work a theoretical study of the triple differential cross sections for electron-impact single ionization of 12a′ and 9b orbitals of a tetrahydrofuran molecule. The calculations are performed by the multicenter distorted-wave (MCDW) method in coplanar asymmetric kinematics with an incident electron energy of 250 eV, where previous (e, 2e) experiments and theories are available for comparison. The present calculations reproduce the experimental measurements satisfactorily, indicating that the MCDW method can be easily extended to study biomolecules with sufficiently high calculation efficiency. [ABSTRACT FROM AUTHOR]

Objective: Incisors tubed prosthesis with bilateral anterior elevation (BAE) relation had been reported to stimulate the proliferative response in the mandibular condylar cartilage of mice, thus the prosthetic occlusion elevation had been proposed to treat cartilage degeneration. Currently, we aimed to detect the long‐term effect of BAE on temporomandibular joints (TMJs). Materials and Methods: Twelve 6‐week‐old female mice were assigned to age‐matched control and BAE groups (n = 6). Micro‐CT images and the macro‐ and micro‐morphology of the mandibular condyles were analyzed at 29 weeks. Results: Compared with the age‐matched controls, in BAE group, there were loss of subchondral cortical bone and heavy loss of the subchondral trabecular bone at the superior sites of the TMJ condyles, but hyperostosis at the inferior sites as revealed by micro‐CT images and histological slices. In BAE group, cartilage thickness and matrix area were increased with upregulated expression of type II, type X collagen, and Ki67, but the expression of cleaved caspase‐3 was downregulated (all, p < 0.05). Conclusion: In addition to cartilage thickening, long‐term BAE induces loss of the subchondral cortical bone and heavy loss of the underneath subchondral trabecular bone, but hyperostosis further underneath. Using BAE as a treatment remains double‐edged. [ABSTRACT FROM AUTHOR]

This study explores dynamic relationships among Chinese housing prices for the years 2010–2019. With monthly data from 99 major cities in China, we use the vector error correction model and directed acyclic graph to characterize contemporaneous causality among housing prices from different tiers of cities. The PC algorithm identifies the causal pattern and the LiNGAM algorithm further identifies the causal path, from which we perform innovation accounting analysis. Complex housing price dynamics are found in the price adjustment process following price shocks, which is not only dominated by the top tiers of cities. This suggests that policies on housing prices in the long run might need to be planned from a national perspective. [ABSTRACT FROM AUTHOR]

As major elements of the basement membrane, laminins play a significant role in angiogenesis, migration, and adhesion of various cells. Sebastes schlegelii is a marine viviparous teleost of commercial importance. Previous research has reported abundant blood vessels and connective tissue in the ovary during gestation. In this study, 14 laminin genes of the α, β, and γ subfamilies from genomic data were identified based on zebrafish and human laminins, distributed on 9 chromosomes in S. schlegelii. Analysis of structural domains showed that coiled-coil regions and EGF domains existed in all laminin genes. Moreover, via qPCR, we found that the expression of laminin genes, including lama4, lama5, lamb4, lamc1, and lamc3, gradually increased from the phase III ovary stage and peaked in the early stage of gestation, especially lama4 and lama5 which showed dramatically increased expression at the blastula stage. Accordingly, in situ hybridization of lama4 was conducted. The results revealed that signals became stronger following the phase IV ovary stage, and the strongest signals were located on the follicular pseudoplacenta at the blastula stage. These results suggest that the high expression of laminin genes, especially lama4 after fertilization, may drive cell proliferation, migration, and tissue expansion in the S. schlegelii ovary and ultimately promote follicular pseudoplacenta formation. [ABSTRACT FROM AUTHOR]

Machine learning approaches can help facilitate the optimization of machining processes. Model performance, including accuracy, stability, and robustness, are major criteria to choose among different methods. Besides, the applicability, ease of implementations, and cost-effectiveness should be considered for industrial applications. In this study, we develop the Gaussian process regression (GPR) models to predict springback radii and angles for air bending of high-strength sheet steel from the sheet geometry, tool design, and metal properties. The models are simple and accurate and stable for the data considered, which might contribute to fast springback radius and angle estimations. By combining the optimization results from the Taguchi method and GPR approach, it might be expected that more quantitative data can be extracted from fewer experimental trials at the same time. [ABSTRACT FROM AUTHOR]

During the past decade, the housing market in China has witnessed rapid growth and the significance of forecasting related to housing prices has undoubtedly elevated, which has become an important issue to the people in investment and policymakers in regulations. In this study, we explore neural networks for residential housing price index forecasts from ten major Chinese cities for July 2005–April 2021. We aim at constructing simple and accurate neural networks as a contribution to pure technical forecasts of the Chinese residential housing market. To facilitate the analysis, we investigate different model settings across algorithms, delays, hidden neurons, and data spitting ratios, and arrive at a simple neural network with three delays and three hidden neurons, which produces stable performance of about 0.75% average relative root mean square error across the ten cities for the training, validation, and testing phases. Our results can be used on a standalone basis or combined with fundamental forecasts to form perspectives of residential housing price trends and carry out policy analysis. [ABSTRACT FROM AUTHOR]

Purpose: Here, the authors use step angles, stage ratios, feed rates and spindle speeds as predictors to develop a Gaussian process regression for predicting thrust force during composite laminates drilling with step drills. Design/methodology/approach: Use of machine learning methods could benefit machining process optimizations. Accurate, stable and robust performance is one of major criteria in choosing among different models. For industrial applications, it is also important to consider model applicability, ease of implementations and cost effectiveness. Findings: This model turns out to be simple, accurate and stable, which helps fast estimates of thrust force. Through combining the Taguchi method's optimization results and the Gaussian process regression, more data could be expected to be extracted through fewer experiments. Originality/value: Through combining the Taguchi method's optimization results and the Gaussian process regression, more data could be expected to be extracted through fewer experiments. [ABSTRACT FROM AUTHOR]

The house market in China has been growing rapidly for the past decade and price forecasting has become a significant issue to the people and policymakers. We approach this problem by examining neural networks for second-hand house price index forecasting from 10 major cities for March 2012–May 2020. Our purpose is to build simple and accurate neural networks to contribute to pure technical house price forecasting of the Chinese market. We explore various model settings across the algorithm, delay, hidden neuron, and data spitting ratio, and arrive at a rather simple neural network with three delays and eight hidden neurons, which leads to stable performance of 0.8% average relative root-mean-square error across the 10 cities for the training, validation, and testing phases. Results here can be used on a standalone basis or combined with fundamental forecasting in forming perspectives of house price trends and conducting policy analysis. [ABSTRACT FROM AUTHOR]