One challenge in avian embryology is establishing a standard developmental timetable, primarily because eggs incubated for identical durations can vary in developmental progress, even within the same species. For remedy, avian development is classified into distinct stages based on the formation of key morphological structures. Developmental stages exist for a few galliform species, but the literature is lacking a description of normal stages for California valley quail (Callipepla californica). Thus, the objective of this study was to stage and document the morphological and structural development of California valley quail. Over two laying seasons, 390 eggs were incubated at 37.8֯ C in 60% RH for ≤23 days. Eggs were opened every ≤6 hours to document embryonic development, including, blastoderm diameter, anterior angle of nostril to beak tip, and lengths of wing, tarsus, third toe, total beak, total foot, and embryo. California valley quail embryos were staged and compared to domestic chicken (Gallus gallus domesticus), the staging standard for galliformes, as well as Japanese quail (Coturnix japonica), blue-breasted quail (Synoicus chinensis) and northern bobwhite quail (Colinus virginianus). This study produced the first description of the 43 normal stages of development for California valley quail. Compared with other galliformes, the California valley quail has a different number of stages and displays developmental heterochrony in stages 1-24, and morphological and developmental differences in stages 25-hatch. The observed differences emphasize the importance of staging individual avian species instead of relying on poultry animal models or close relatives for developmental reference. This is extremely important in species-specific embryological studies that evaluate critical windows of development or evaluate the impacts of environmental change on avian development. This study also suggests that staging frequencies of ≤6 hours and egg transport protocols should be standardized for future staging studies.

Grain production is vital to the national economy and people's livelihood, and improving grain production efficiency is of great significance to the sustainable development of China's economy and society. From the perspective of financial support, using the DEA global Malmquist productivity index model and based on the data of 13 main grain producing areas in China from 2001 to 2017, this paper discusses the evolution characteristics and regional distribution differences of the total factor productivity index of grain production in China's main grain producing areas. The results show that from 2001 to 2017, the total factor productivity index of grain production in China's main grain producing areas showed an overall fluctuation trend of gradual decline, with an average annual decline of 7.3%. From the perspective of spatial analysis, the grain production efficiency in China's main grain producing areas is characterized by uneven spatial development, which is generally manifested as the decreasing trend from the central region to the eastern and western regions. Meanwhile, it can be seen from the decomposition index that the change of total factor productivity of grain production in China's main grain producing areas mainly depends on the change of technical efficiency.

Literature data have shown that the consumption of dietary proteins may cause modulatory effects on the host immune system, process denominated oral tolerance by bystander suppression. It has been shown that the bystander suppression induced by dietary proteins can improve inflammatory diseases such as experimental arthritis. Here, we evaluated the effects of oral tolerance induced by ingestion of ovalbumin (OVA) on TNBS-induced colitis in mice, an experimental model for human Crohn's disease.

Deer products from sika deer (Cervus nippon) and red deer (C. elaphus) are considered genuine and used for Traditional Chinese Medicine (TCM) materials in China. Deer has a very high economic and ornamental value, resulting in the formation of a characteristic deer industry in the prescription preparation of traditional Chinese medicine, health food, cosmetics, and other areas of development and utilization. Due to the high demand for deer products, the products are expensive and have limited production, but the legal use of deer is limited to only two species of sika deer and red deer; other wild deer are prohibited from hunting, so there are numerous cases of mixing and adulteration of counterfeit products and so on. There have been many reports that other animal (pig, cow, sheep, etc.) tissues or organs are often used for adulteration and confusion, resulting in poor efficacy of deer traditional medicine and trade fraud in deer products. To authenticate the deer products in a rapid and effective manner, the analysis used 22 deer products (antler, meat, bone, fetus, penis, tail, skin, and wool) that were in the form of blind samples. Total DNA extraction using a modified protocol successfully yielded DNA from the blind samples that was useful for PCR. Three candidate DNA barcoding loci, cox1, Cyt b, and rrn12, were evaluated for their discrimination strength through BLAST and phylogenetic clustering analyses. For the BLAST analysis, the 22 blind samples obtained 100% match identity across the three gene loci tested. It was revealed that 12 blind samples were correctly labeled for their species of origin, while three blind samples that were thought to originate from red deer were identified as C. nippon, and seven blind samples that were thought to originate from sika deer were identified as C. elaphus, Dama dama, and Rangifer tarandus. DNA barcoding analysis showed that all three gene loci were able to distinguish the two Cervus species and to identify the presence of adulterant species. The DNA barcoding technique was able to provide a useful and sensitive approach in identifying the species of origin in deer products.

Temperature extremes alter development, growth, hatching, and survival of eggs of ground-nesting birds, particularly during pre-incubation (egg laying) when eggs are left unattended and exposed to the environment for days or weeks before parental incubation begins. The northern bobwhite quail is a ground-nesting bird whose eggs experience high temperatures (≥45° C) during pre-incubation. It is known that chronic high temperatures during pre-incubation alter development and reduce hatching and survival of bobwhite eggs, but it is not known if acute doses of high temperatures during pre-incubation have the same effect. In this study, the 12-d pre-incubation period was divided into thirds. Fresh bobwhite eggs were exposed to either a commercial holding temperature for all 12 d (serving as a control), or a high oscillating temperature regimen for 4 d (one third of pre-incubation) either in the early, middle, or late third of pre-incubation, with a low oscillating temperature regimen during the remaining 8 d. The timing of acute exposure to high oscillating temperatures significantly affected bobwhite development. Eggs exposed in the first 2/3 of pre-incubation developed twice as much as eggs exposed late in pre-incubation, even though all eggs received the same amount of heating degree-hours. Thus, a critical window of thermal susceptibility exists for developing northern bobwhites. Acute exposure to high oscillating temperatures resulted in reduced hatchling mass, hatching success, survival, and compromised hatching synchrony. Thus, acute hyperthermic nest temperatures during pre-incubation could result in the observed reductions in the percentage of juveniles in natural populations during hot and droughty years.

Market timing is an investment technique that tries to continuously switch investment into assets forecast to have better returns. What is the likelihood of having a successful market timing strategy? With an emphasis on modeling simplicity, I calculate the feasible set of market timing portfolios using index mutual fund data for perfectly timed (by hindsight) all or nothing quarterly switching between two asset classes, US stocks and bonds over the time period 1993-2017. The historical optimal timing path of switches is shown to be indistinguishable from a random sequence. The key result is that the probability distribution function of market timing returns is asymmetric, that the highest probability outcome for market timing is a below median return. Put another way, simple math says market timing is more likely to lose than to win-even before accounting for costs. The median of the market timing return probability distribution can be directly calculated as a weighted average of the returns of the model assets with the weights given by the fraction of time each asset has a higher return than the other. For the time period of the data the median return was close to, but not identical with, the return of a static 60:40 stock:bond portfolio. These results are illustrated through Monte Carlo sampling of timing paths within the feasible set and by the observed return paths of several market timing mutual funds.

The aim of this study was to elucidate the molecular mechanisms by which food-derived casein glycomacropeptide (CGMP) maintains internal homeostasis in the intestinal mucosa and to investigate the effects of CGMP on the intestinal mucosal immunological barrier and related signal transduction pathways.

In this study, high-throughput pyrosequencing was applied on the analysis of the microbial community of activated sludge and biofilm in a lab-scale UV/O3- anaerobic/aerobic (A/O) integrated process for the treatment of petrochemical nanofiltration concentrate (NFC) wastewater. NFC is a type of saline wastewater with low biodegradability. From the anaerobic activated sludge (Sample A) and aerobic biofilm (Sample O), 59,748 and 51,231 valid sequence reads were obtained, respectively. The dominant phylotypes related to the metabolism of organic compounds, polycyclic aromatic hydrocarbon (PAH) biodegradation, assimilation of carbon from benzene, and the biodegradation of nitrogenous organic compounds were detected as genus Clostridium, genera Pseudomonas and Stenotrophomonas, class Betaproteobacteria, and genus Hyphomicrobium. Furthermore, the nitrite-oxidising bacteria Nitrospira, nitrite-reducing and sulphate-oxidising bacteria (NR-SRB) Thioalkalivibrio were also detected. In the last twenty operational days, the total Chemical Oxygen Demand (COD) and Total Organic Carbon (TOC) removal efficiencies on average were 64.93% and 62.06%, respectively. The removal efficiencies of ammonia nitrogen and Total Nitrogen (TN) on average were 90.51% and 75.11% during the entire treatment process.

Alkaline-surfactant-polymer (ASP) flooding technology is used to maximize crude oil recovery. However, the extensive use of alkaline materials makes it difficult to treat the water used. Here, an improved multi-zone anaerobic baffled reactor (ABR) using FeSO4 as electron acceptor was employed to treat the wastewater from ASP flooding technology, and the effects on major pollutants (hydrolyzed polyacrylamide, petroleum substances, surfactants suspended solids) and associated parameters (chemical oxygen demand, viscosity) were evaluated. Gas chromatography-mass spectrometry (GC-MS) was used to follow the degradation and evolution of organic compounds while high-throughput DNA sequencing was used to determine the bacterial diversity in the ABR. The results obtained after 90 d of operation showed decreases in all parameters measured and the highest mean removal rates were obtained for petroleum substances (98.8%) and suspended solids (77.0%). Amounts of petroleum substances in the ABR effluent could meet the requirements of a national standard for oilfield reinjection water. GC-MS analysis showed that a wide range of chemicals (e.g. aromatic hydrocarbons, esters, alcohols, ketones) could be sequentially removed from the influent by each zone of ABR. The high-throughput DNA sequencing showed that the bacteria Micropruina, Saccharibacteria and Synergistaceae were involved in the degradation of pollutants in the anaerobic and anoxic reaction zones, while Rhodobacteraceae and Aliihoeflea were the main functional microorganisms in the aerobic reaction zones. The results demonstrated that the improved ABR reactor had the potential for the treatment of wastewater from ASP flooding technology.

Most mitochondrial mRNAs in trypanosomes undergo uridine insertion/deletion editing that is catalyzed by approximately 20S editosomes. The editosome component KREPA3 is essential for editosome structural integrity and its two zinc finger (ZF) motifs are essential for editing in vivo but not in vitro. KREPA3 function was further explored by examining the consequence of mutation of its N- and C-terminal ZFs (ZF1 and ZF2, respectively). Exclusively expressed myc-tagged KREPA3 with ZF2 mutation resulted in lower KREPA3 abundance and a relative increase in KREPA2 and KREL1 proteins. Detailed analysis of edited RNA products revealed the accumulation of partially edited mRNAs with less insertion editing compared to the partially edited mRNAs found in the cells with wild type KREPA3 expression. Mutation of ZF1 in TAP-tagged KREPA3 also resulted in accumulation of partially edited mRNAs that were shorter and only edited in the 3'-terminal editing region. Mutation of both ZFs essentially eliminated partially edited mRNA. The mutations did not affect gRNA abundance. These data indicate that both ZFs are essential for the progression of editing and perhaps its accuracy, which suggests that KREPA3 plays roles in the editing process via its ZFs interaction with editosome proteins and/or RNA substrates.

There are numerous reports on poly-β-hydroxybutyrate (PHB) depolymerases produced by various microorganisms isolated from various habitats, however, reports on PHB depolymerase production by an isolate from plastic rich sites scares. Although PHB has attracted commercial significance, the inefficient production and recovery methods, inefficient purification of PHB depolymerase and lack of ample knowledge on PHB degradation by PHB depolymerase have hampered its large scale commercialization. Therefore, to ensure the biodegradability of biopolymers, it becomes imperative to study the purification of the biodegrading enzyme system. We report the production, purification, and characterization of extracellular PHB depolymerase from Stenotrophomonas sp. RZS7 isolated from a dumping yard rich in plastic waste. The isolate produced extracellular PHB depolymerase in the mineral salt medium (MSM) at 30°C during 4 days of incubation under shaking. The enzyme was purified by three methods namely ammonium salt precipitation, column chromatography, and solvent purification. Among these purification methods, the enzyme was best purified by column chromatography on the Octyl-Sepharose CL-4B column giving optimum yield (0.7993 Umg-1mL-1). The molecular weight of purified PHB depolymerase was 40 kDa. Studies on the assessment of biodegradation of PHB in liquid culture medium and under natural soil conditions confirmed PHB biodegradation potential of Stenotrophomonas sp. RZS7. The results obtained in Fourier-Transform Infrared (FTIR) analysis, High-Performance Liquid Chromatography (HPLC) study and Gas Chromatography Mass-Spectrometry (GC-MS) analysis confirmed the biodegradation of PHB in liquid medium by Stenotrophomonas sp. RZS7. Changes in surface morphology of PHB film in soil burial as observed in Field Emission Scanning Electron Microscopy (FESEM) analysis confirmed the biodegradation of PHB under natural soil environment. The isolate was capable of degrading PHB and it resulted in 87.74% biodegradation. A higher rate of degradation under the natural soil condition is the result of the activity of soil microbes that complemented the biodegradation of PHB by Stenotrophomonas sp. RZS7.

Online shopping behavior has the characteristics of rich granularity dimension and data sparsity and presents a challenging task in e-commerce. Previous studies on user behavior prediction did not seriously discuss feature selection and ensemble design, which are important to improving the performance of machine learning algorithms. In this paper, we proposed an SE-stacking model based on information fusion and ensemble learning for user purchase behavior prediction. After successfully using the ensemble feature selection method to screen purchase-related factors, we used the stacking algorithm for user purchase behavior prediction. In our efforts to avoid the deviation of the prediction results, we optimized the model by selecting ten different types of models as base learners and modifying the relevant parameters specifically for them. Experiments conducted on a publicly available dataset show that the SE-stacking model can achieve a 98.40% F1 score, approximately 0.09% higher than the optimal base models. The SE-stacking model not only has a good application in the prediction of user purchase behavior but also has practical value when combined with the actual e-commerce scene. At the same time, this model has important significance in academic research and the development of this field.

The association of socioeconomic status and lifestyle behaviours on mental health appears well-established in the literature, as several studies report that better socioeconomic status such as higher levels of disposable income and employment as well as practising healthy lifestyles can enhance mental well-being. However, the reliance on cross-sectional correlations and lack of adequate statistical controls are possible limitations. This study aims to add the evidence of longitudinal association to the literature by using Japanese representative longitudinal household panel data. We employed panel data analytical techniques such as the random-effects conditional logistic regression (RE-CLR) and the fixed-effects conditional logistic regression (FE-CLR) models with possible time variant confounders being controlled. Our sample was comprised of 14,717 observations of 3,501 individuals aged 22-59 years for five waves of the Japanese Household Panel Survey. We confirmed many of the factors associated with mental health reported in existing studies by analysing cross-sectional data. These significant associations are also longitudinal (within) associations estimated by the FE-CLR models. Such factors include unemployment, low household income, short nightly sleeping duration, and lack of exercise. However, we also found that several factors such as disposable income, living alone, and drinking habits are not significantly associated with mental health in the FE-CRL models. The results imply the reverse causality that poor mental health conditions cause lower disposal income, possibly due to the inability to exhibit higher productivity, but an increase in disposal income would not necessarily improve mental health conditions. In this case, aggressive policy interventions to increase the disposal income of people of lower socioeconomic backgrounds would not necessarily be effective to minimize health inequalities.

This study aims to investigate whether combining forecasts generated from different models can improve forecast accuracy rather than individual models using the hierarchical time series. Various approaches of hierarchical forecasting have been considered; a bottom-up, top-down, and an optimal combination approach. Autoregressive moving averages (ARIMA) and exponential smoothing (ETS) were used as forecasting models in creating forecasting for all levels in the hierarchy to show the effect of different forecasting methods for each hierarchical model. The results indicated that the Minimum Trace Sample estimator (MinT-Sample) and the bottom-up approaches with the ARIMA model have good predictive performance than other approaches. Moreover, the forecasts from the MinT-Sample and bottom-up approaches were combined using five different combining methods. The experimental results showed that the (AC) method is superior to all other combining methods and more accurate than other individual models at level zero (international total trade in Egypt) and level one (total exports, and total imports). So, combining forecasts generated from different models by hierarchical time series leads to more accurate forecasting of the value of imports and exports which will improve the overall international trade performance, and that is through using the forecasting values of imports and exports to plan for improving the trade balance and drawing up a more efficient production policy. Finally, the study recommends using hierarchical forecasting methods in the areas of international trade, and the Ministry of Commerce and Industry could adopt the results of this study to produce precise forecasts for international trade. Moreover, the results of this study are to be a guide for the researchers to apply these approaches in other fields to improve the performance of forecasting.

The significantly related diseases of sequences could play an important role in understanding the functions of these sequences. In this paper, we introduced BLAT2DOLite, an online system for annotating human genes and diseases and identifying the significant relationships between sequences and diseases. Currently, BLAT2DOLite integrates Entrez Gene database and Disease Ontology Lite (DOLite), which contain loci of gene and relationships between genes and diseases. It utilizes hypergeometric test to calculate P-values between genes and diseases of DOLite. The system can be accessed from: http://123.59.132.21:8080/BLAT2DOLite. The corresponding web service is described in: http://123.59.132.21:8080/BLAT2DOLite/BLAT2DOLiteIDMappingPort?wsdl.

Environmental noise may affect hearing and a variety of non-auditory disease processes. There is some evidence that, like other environmental hazards, noise may be differentially distributed across communities based on socioeconomic status. We aimed to a) predict daytime noise pollution levels and b) assess disparities in daytime noise exposure in Chicago, Illinois. We measured 5-minute daytime noise levels (Leq, 5-min) at 75 randomly selected sites in Chicago in March, 2019. Geographically-based variables thought to be associated with noise were obtained, and used to fit a noise land-use regression model to estimate the daytime environmental noise level at the centroid of the census blocks. Demographic and socioeconomic data were obtained from the City of Chicago for the 77 community areas, and associations with daytime noise levels were assessed using spatial autoregressive models. Mean sampled noise level (Leq, 5-min) was 60.6 dBA. The adjusted R2 and root mean square error of the noise land use regression model and the validation model were 0.60 and 4.67 dBA and 0.51 and 5.90 dBA, respectively. Nearly 75% of city blocks and 85% of city communities have predicted daytime noise level higher than 55 dBA. Of the socioeconomic variables explored, only community per capita income was associated with mean community predicted noise levels, and was highest for communities with incomes in the 2nd quartile. Both the noise measurements and land-use regression modeling demonstrate that Chicago has levels of environmental noise likely contributing to the total burden of environmental stressors. Noise is not uniformly distributed across Chicago; it is associated with proximity to roads and public transportation, and is higher among communities with mid-to-low incomes per capita, which highlights how socially and economically disadvantaged communities may be disproportionately impacted by this environmental exposure.

Poly-β-hydroxybutyrate (PHB) depolymerase is known to decompose PHB, biodegradable polymers and therefore has great commercial significance in the bioplastic sector. However, reports on PHB depolymerases from isolates obtained from plastic-contaminated sites that reflect the potential of the source organism is scarce. In this study, we evaluated the production of extracellular PHB depolymerase from Microbacterium paraoxydans RZS6 isolated from the plastic-contaminated site in the municipal area of Shahada, Maharashtra, India, for the first time. The isolate was identified using 16S rRNA gene sequencing, gas chromatographic analysis of fatty acid methyl esters (GC-FAME), and BIOLOG method. Ithydrolyzed PHB on minimal salt medium (MSM) containing PHB as the only source of carbon. The isolate produced PHB depolymerase at 45°C during 48 h of incubation. The enzyme was purified most efficiently using octyl-sepharose CL-4B column, with the highest purification yield of 6.675 Umg-1mL-1. The activity of the enzyme was enhanced in the presence of Ca2+ and Mg2+ ions but inhibited by Fe2+ (1 mM) ions and mercaptoethanol (1000 rpm). the nzyme kinetic analysis revealed that the enzyme was a metalloenzyme; requiring Mg2+ ions, that showed optimum enzyme activity at 30°C (mesophilic) and under neutrophilic (pH 7) conditions. Scale-up from the shake-flask level to a laboratory-scale bioreactor further enhanced the enzyme yield by 0.809 UmL-1. The molecular weight of the enzyme (40 kDa), as estimated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis, closely resembled the PHB depolymerase of Aureobacterium saperdae. Our findings highlighted the applicability of M. paraoxydans as a producer of extracellular PHB depolymerase having potential of degrading PHB under diverse conditions.

Today's consumer goods markets are rapidly evolving with significant growth in the number of information media as well as the number of competitive products. In this environment, obtaining a quantitative grasp of heterogeneous interactions of firms and customers, which have attracted interest of management scientists and economists, requires the analysis of extremely high-dimensional data. Existing approaches in quantitative research could not handle such data without any reliable prior knowledge nor strong assumptions. Alternatively, we propose a novel method called complex Hilbert principal component analysis (CHPCA) and construct a synchronization network using Hodge decomposition. CHPCA enables us to extract significant comovements with a time lead/delay in the data, and Hodge decomposition is useful for identifying the time-structure of correlations. We apply this method to the Japanese beer market data and reveal comovement of variables related to the consumer choice process across multiple products. Furthermore, we find remarkable customer heterogeneity by calculating the coordinates of each customer in the space derived from the results of CHPCA. Lastly, we discuss the policy and managerial implications, limitations, and further development of the proposed method.

Common walnut (Juglans regia L) is an economically important species cultivated worldwide for its wood and nuts. It is generally accepted that J. regia survived and grew spontaneously in almost completely isolated stands in its Asian native range after the Last Glacial Maximum. Despite its natural geographic isolation, J. regia evolved over many centuries under the influence of human management and exploitation. We evaluated the hypothesis that the current distribution of natural genetic resources of common walnut in Asia is, at least in part, the product of ancient anthropogenic dispersal, human cultural interactions, and afforestation. Genetic analysis combined with ethno-linguistic and historical data indicated that ancient trade routes such as the Persian Royal Road and Silk Road enabled long-distance dispersal of J. regia from Iran and Trans-Caucasus to Central Asia, and from Western to Eastern China. Ancient commerce also disrupted the local spatial genetic structure of autochthonous walnut populations between Tashkent and Samarkand (Central-Eastern Uzbekistan), where the northern and central routes of the Northern Silk Road converged. A significant association between ancient language phyla and the genetic structure of walnut populations is reported even after adjustment for geographic distances that could have affected both walnut gene flow and human commerce over the centuries. Beyond the economic importance of common walnut, our study delineates an alternative approach for understanding how the genetic resources of long-lived perennial tree species may be affected by the interaction of geography and human history.

Given that an excellent performance of any parametric functional form for the Lorenz curve that is based on a single country case study and a limited range of distribution must be treated with great caution, this study investigates the performance of a single-parameter functional form proposed by Paul and Shankar (2020) who use income data of Australia to show that their functional form is superior to the other existing widely used functional forms considered in their study. By using both mathematical proof and empirical data of 40 countries around the world, this study demonstrates that Paul and Shankar (2020)'s functional form not only fails to fit the actual observations well but also is generally outperformed by the other popular functional forms considered in their study. Moreover, to overcome the limitation of the performance of a single-parameter functional form on the criterion of the estimated Gini index, this study employs a functional form that has more than one parameter in order to show that, by and large, it performs better than all popular single-parameter functional forms considered in Paul and Shankar (2020)'s study. Thus, before applying any functional form to estimate the Lorenz curve, policymakers should check if it could describe the shape of income distributions of different countries through the changes in parameter values and yield the values of the estimated Gini index that are close to their observed data. Using a functional form that does not fit the actual observations could adversely affect inequality measures and income distribution policies.