Molecular cloning and functional analysis of two calcium associated cuticular protein genes in Neocaridina denticulata sinensis

The body surface of crustaceans is covered with a sturdy shell.The growth and development of crustaceans are realized through molting.Neocaridina denticulata sinensis is a suitable candidate for crustacean scientific research.Two calcium-associated cuticular protein genes,named NdCAP-1 and NdCAP-2,were obtained from N.denticulata sinensis.Molecular docking simulated the binding effect of both proteins and calcium ions.Semi-quantitative reverse transcription PCR results show that NdCAP-1 is expressed in D_(2-4) stage,NdCAP-2 expressed in D_(2-4) and A-B stages,and both were significantly expressed in the cephalothorax cuticle and pereiopods.Then,it was revealed that NdCAP-1 and NdCAP-2 are regulated by NdEcR-mediated 20 E signaling pathways.Knockdown of NdCAP-1 and NdCAP-2 was observed to cause surface defects.The recombinant proteins(rNdCAP-1 and rNdCAP-2),obtained by prokaryotic expression,had calcium-binding and chitin-binding ability,inhibited formation of calcium carbonate precipitate.These results show that calcium-associated cuticular proteins play important roles in cuticle formation and calcification.

Return direction forecasting:a conditional autoregressive shape model with beta density

This paper derives a new decomposition of stock returns using price extremes and proposes a conditional autoregressive shape(CARS)model with beta density to predict the direction of stock returns.The CARS model is continuously valued,which makes it different from binary classification models.An empirical study is performed on the US stock market,and the results show that the predicting power of the CARS model is not only statistically significant but also economically valuable.We also compare the CARS model with the probit model,and the results demonstrate that the proposed CARS model outperforms the probit model for return direction forecasting.The CARS model provides a new framework for return direction forecasting.

The burgeoning importance of PIWI-interacting RNAs in cancer progression

PIWI-interacting RNAs(pi RNAs)are a class of small noncoding RNA molecules that specifically bind to piwi protein family members to exert regulatory functions in germ cells.Recent studies have found that pi RNAs,as tissue-specific molecules,both play oncogenic and tumor suppressive roles in cancer progression,including cancer cell proliferation,metastasis,chemoresistance and stemness.Additionally,the atypical manifestation of pi RNAs and PIWI proteins in various malignancies presents a promising strategy for the identification of novel biomarkers and therapeutic targets in the diagnosis and management of tumors.Nonetheless,the precise functions of pi RNAs in cancer progression and their underlying mechanisms have yet to be fully comprehended.This review aims to examine current research on the biogenesis and functions of pi RNA and its burgeoning importance in cancer progression,thereby offering novel perspectives on the potential utilization of pi RNAs and piwi proteins in the management and treatment of advanced cancer.

Quantifying the Temporal and Spatial Spread of COVID-19:Empirical Evidence from Shanghai

The worldwide spread of COVID-19 has caused a grave threat to human life, health, and socio-economic development. It is of great significance to study the transmission mechanism of COVID-19 and evaluate the effect of epidemic prevention policies. This paper employs a spatial dynamic panel data(SDPD) model to analyze the temporal and spatial spread of COVID-19, incorporating the time-varying features of epidemic transmission and the impact of geographic interconnections.Empirical studies on the COVID-19 outbreak in Shanghai during early 2022 show that the intra-regional transmission of COVID-19 dominated the cross-regional one. Additionally, strict policies are found to effectively reduce the transmission risk of COVID-19 and curb the spillover effect of the epidemic in Shanghai on other regions. Based on these results, we provide three policy suggestions. Furthermore,this research methodology can be extended to investigate other infectious diseases, thereby providing a scientific framework and theoretical basis for evaluating the spread risk of pandemics and formulating appropriate strategies.

Secondary solar heat gain modelling of spectral-selective glazing based on dynamic solar radiation spectrum

The secondary solar heat gain,defined as the heat flows from glazing to indoor environment through longwave radiation and convection,grows with the increasing of glazing absorption.With the rapid development and application of spectrally selective glazing,the secondary solar heat gain becomes the main way of glazing heat transfer and biggest proportion,and indicates it should not be simplified calculated conventionally.Therefore,a dynamic secondary solar heat gain model is developed with electrochromic glazing system in this study,taking into account with three key aspects,namely,optical model,heat transfer model,and outdoor radiation spectrum.Compared with the traditional K-Sc model,this new model is verified by on-site experimental measurements with dynamic outdoor spectrum and temperature.The verification results show that the root mean square errors of the interior and exterior glass surface temperature are 3.25°C and 3.33°C,respectively,and the relative error is less than 10.37%.The root mean square error of the secondary heat gain is 13.15 W/m2,and the dynamic maximum relative error is only 13.2%.The simulated and measured results have a good agreement.In addition,the new model is further extended to reveal the variation characteristics of secondary solar heat gain under different application conditions(including orientations,locations,EC film thicknesses and weather conditions).In summary,based on the outdoor spectrum and window spectral characteristics,the new model can accurately calculate the increasing secondary solar heat gain in real time,caused by spectrally selective windows,and will provide a computational basis for the evaluation and development of spectrally selective glazing materials.

Molecular assembly of recombinant chicken type II collagen in the yeast Pichia pastoris

Effective treatment of rheumatoid arthritis can be mediated by native chicken type II collagen(n CCII), recombinant peptide containing n CCII tolerogenic epitopes(CTEs), or a therapeutic DNA vaccine encoding the full-length CCOL2 A1 c DNA. As recombinant CCII(r CCII) might avoid potential pathogenic virus contamination during n CCII preparation or chromosomal integration and oncogene activation associated with DNA vaccines, here we evaluated the importance of propeptide and telopeptide domains on r CCII triple helix molecular assembly. We constructed p C-and p N-procollagen(without N-or Cpropeptides, respectively) as well as CTEs located in the triple helical domain lacking both propeptides and telopeptides, and expressed these in yeast Pichia pastoris host strain GS115(his4, Mut+) simultaneously with recombinant chicken prolyl-4-hydroxylase α and β subunits. Both p C-and p N-procollagen monomers accumulated inside P. pastoris cells, whereas CTE was assembled into homotrimers with stable conformation and secreted into the supernatants, suggesting that the large molecular weight p C-or p N-procollagens were retained within the endoplasmic reticulum whereas the smaller CTEs proceeded through the secretory pathway. Furthermore, resulting recombinant chicken type II collagen p Cα1(II) can induce collagen-induced arthritis(CIA) rat model, which seems to be as effective as the current standard n CCII. Notably, protease digestion assays showed that r CCII could assemble in the absence of C-and N-propeptides or telopeptides. These findings provide new insights into the minimal structural requirements for r CCII expression and folding.

Out-of-sample forecasts of China's economic growth and inflation using rolling weighted least squares

Macroeconomic forecasting in China is essential for the government to take proper policy decisions on government expenditure and money supply,among other matters.The existing literature on forecasting Chinas macroeconomic variables is unclear on the crucial issue of how to choose an optimal window to estimate parameters with rolling out-of-sample forecasts.This study fills this gap in forecasting economic growth and inflation in China,by using the rolling weighted least squares(WLS)with the practically feasible cross-validation(CV)procedure of Hong et al.(2018)to choose an optimal estimation window.We undertake an empirical analysis of monthly data on up to 30 candidate indicators(mainly asset prices)for a span of 17 years(2000-2017).It is documented that the forecasting performance of rolling estimation is sensitive to the selection of rolling windows.The empirical analysis shows that the rolling WLS with the CV-based rolling window outperforms other rolling methods on univariate regressions in most cases.One possible explanation for this is that these macroeconomic variables often suffer from structural changes due to changes in institutional reforms,policies,crises,and other factors.Furthermore,we find that,in most cases,asset prices are key variables for forecasting macroeconomic variables,especially output growth rate.

Distribution characteristics of photoinitiators and their flux estimation from the Pearl River Delta to the coastal waters of the South China Sea

Photoinitiators(PIs)are widely used in industrial polymerization processes.It has been reported that PIs are ubiquitous in indoor environments and that humans are exposed to PIs,but the occurrence of PIs in natural environments are rarely known.In the present study,25 PIs,including 9 benzophenones(BZPs),8 amine co-initiators(ACIs),4 thioxanthones(TXs)and 4 phosphine oxides(POs),were analyzed in water and sediment samples collected from eight riverine outlets of the Pearl River Delta(PRD).Eighteen,14,and 14 of the 25 target PIs were detected in water,suspended particulatematter(SPM)and sediment samples,respectively.The total concentrations of PIs in water,SPM,and sediment were in the ranges of 2.88–96.1 ng/L,9.25–923 ng/g dry weight(dw),and 3.79–56.9 ng/g dw,with geometric mean concentration(GM)of 10.8 ng/L,48.6 ng/g dw,and 17.1 ng/g dw,respectively.A significant linear regression was observed between the log partitioning coefficients(Kd)values of PIs and their log octanol water partition coefficient(Kow)values(R^(2)=0.535,p<0.05).The annual riverine input of PIs to the coastal waters of the South China Sea via eightmain outlets of the PRD was estimated to be 4.12×10^(3)kg/year,and the∑BZPs,∑ACIs,∑TXs and∑POs contributed to 1.96×10^(3),1.24×10^(3),89.6 and 830 kg/year,respectively.This is the first report of a systematic description of the occurrence characteristics of PIs exposure in water,SPM,and sediment.The environmental fate and risks of PIs in aquatic environments need further investigations.

Recombination and mutation shape variations in the major histocompatibility complex

The major histocompatibility complex(MHC)is closely associated with numerous diseases,but its high degree of polymorphism complicates the discovery of disease-associated variants.In principle,recombination and de novo mutations are two critical factors responsible for MHC polymorphisms.However,direct evidence for this hypothesis is lacking.Here,we report the generation of fine-scale MHC recombination and de novo mutation maps of~5 Mb by deep sequencing(>100×)of the MHC genome for 17 MHC recombination and 30 non-recombination Han Chinese families(a total of 190 individuals).Recombination hotspots and Han-specific breakpoints are located in close proximity at haplotype block boundaries.The average MHC de novo mutation rate is higher than the genome-wide de novo mutation rate,particularly in MHC recombinant individuals.Notably,mutation and recombination generated polymorphisms are located within and outside linkage disequilibrium regions of the MHC,respectively,and evolution of the MHC locus was mainly controlled by positive selection.These findings provide insights on the evolutionary causes of the MHC diversity and may facilitate the identification of disease-associated genetic variants.

An Event Analysis of Bitcoin Based on a Novel DRE Methods

Since Bitcoin came into the world,modelling and analyzing the underlying characteristics of Bitcoin has attracted increasing attention.This paper uses a framework including decomposition,reconstruction and extraction method(DRE)to analyze price fluctuations based on ultra-high-frequency data from Dec.1,2019,to Nov.30,2021.First,the ensemble mode decomposition(EMD)is employed to decompose the Bitcoin hourly spot price into 13 intrinsic mode functions(IMF)plus a residual.Second,the IMFs are reconstructed into high-frequency components,low-frequency components and a trend based on fine-to-coarse reconstruction.Furthermore,the intraday volatility analysis based on LM test is applied on 15-minutes frequency data to detect discontinuous jump arrivals and extract jump from realized quadratic variation.Empirical results show that three components of reconstruction can be identified as short term fluctuations process caused by microstructure noise,the shocks affected by major events,and a long-term trend based on inelastic supply and rigid demand.We find that approximately 40%of jumps can be matched with the news from the public news database(Factiva),and the jump sizes are larger than that of stock markets.This finding indicates that the Bitcoin market has more irregularly noise and unforeseen shocks from unscheduled events.