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Population aging has indeed emerged as a significant challenge for China. Using data from four waves of the China Family Panel Studies, this paper explores the impact of population aging on household carbon emissions and its underlying mechanisms. Results show that population aging significantly decreases per capita household carbon emissions, and this conclusion is robust with respect to sample selection, measurement of primary independent variable, and data selection. Population aging influences household carbon emissions through negative consumption effects and positive composition effects, with the former playing a dominant role. Notably, population aging increases per capita household carbon emissions in items such as medicine, heating, and healthcare, while decreasing emissions in most other items. Furthermore, empirical evidence suggests impact heterogeneity across hukou, income level, and the health status of household head. The insights presented in this paper are crucial for advancing our understanding of sustainable development. • Population aging decreases per capita household carbon emissions. • It is achieved through negative consumption effects and positive composition effects. • The consumption effects play a dominant role. • Evidence indicates impact heterogeneity across, hukou, income level, and the health status of household head. [ABSTRACT FROM AUTHOR]

Abstract Source apportionment studies have been performed on online receptor datasets in recent years (called online source apportionment), including mass spectra and online chemical dataset. Single particle aerosol mass spectrometry (SPAMS), an important online technique, has the ability to analyze mass spectrum (MS) and particular size information of a single particle in real time. Clustering methods have been widely applied to MS dataset to investigate the sources of particles, although the receptor models are the common tools to probe the particle sources based on the receptor dataset. This work developed a new method (SPAMS-RM) that employed the receptor model (RM) on an MS dataset from SPAMS to identify particle sources. Particles were measured by SPAMS from July 14 to August 15, 2015, at an urban site in Tianjin, China. Multilinear Engine-2 (ME2) and adaptive resonance theory-based neural networks-2a (ART-2a) were separately used to analyze the single particle MS dataset. This work also evaluated the performance of SPAMS-RM method. Concentrations of chemical components of PM 2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) and gaseous pollutants were measured by independent online instruments (1 h resolution). Source apportionment was separately conducted using two receptor models, Positive Matrix Factorization (PMF) and ME2, based on the 1 h resolution chemical dataset. This method was called online chemical source apportionment (OCSA-RM). ART-2a obtained 19 clusters that merged into five major classes: carbon species, rich-K, sea salt, crustal dust, and industrial metals. SPAMS-RM identified eight sources by interpreting the MS characteristic of factors and investigating the relationship of the temporal trends of factor contributions, chemical species, gaseous pollutants, and particle clusters. OCSA-ME2 and OCSA-PMF both identified seven factors. Source apportionment results between SPAMS-RM and OCSA-ME2/PMF were compared. Each method identified coal combustion, biomass burning, sea salt, nitrate source, sulfate source, vehicle emission, and crustal dust. The SPAMS-RM results showed that nitrate source was the most significant contributor (34%) to the PM followed by sulfate source (17%), coal combustion (14%), crustal dust (11%), vehicle emission (10%), biomass burning-OCEC (7%), and industrial activities & sea salt (4%). Some differences between SPAMS-RM and OCSA-ME2/PMF results existed and might be due to chemical analysis methods and sampling methods. ME2 was used for the first time to identify the PM sources based on the MS dataset from SPAMS and demonstrated its capability when coupled with MS dataset from SPAMS to apportion the source of PM. Highlights • A new method that employed ME2 on SPAMS dataset was developed to infer PM sources. • PMF and ME2 were employed on the 1 h resolution chemical dataset. • ART-2a was employed on the SPAMS dataset to qualitatively identify the PM sources. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is one of the most common cancers in the world. A small proportion of CRCs can be attributed to recognizable hereditary germline variants of known CRC susceptibility genes. To better understand cancer risk, it is necessary to explore the prevalence of hereditary CRC and pathogenic variants of multiple cancer-predisposing genes in non-European populations. We analyzed the coding regions of 27 cancer-predisposing genes in 12,503 unselected Japanese CRC patients and 23,705 controls by target sequencing and genome-wide SNP chip. Their clinical significance was assessed using ClinVar and the guidelines by ACMG/AMP. We identified 4,804 variants in the 27 genes and annotated them as pathogenic in 397 and benign variants in 941, of which 43.6% were novel. In total, 3.3% of the unselected CRC patients and 1.5% of the controls had a pathogenic variant. The pathogenic variants of MSH2 (odds ratio (OR) = 18.1), MLH1 (OR = 8.6), MSH6 (OR = 4.9), APC (OR = 49.4), BRIP1 (OR=3.6), BRCA1 (OR = 2.6), BRCA2 (OR = 1.9), and TP53 (OR = 1.7) were significantly associated with CRC development in the Japanese population (P -values<0.01, FDR<0.05). These pathogenic variants were significantly associated with diagnosis age and personal/family history of cancer. In total, at least 3.5% of the Japanese CRC population had a pathogenic variant or CNV of the 27 cancer-predisposing genes, indicating hereditary cancers. This largest study of CRC heredity in Asia can contribute to the development of guidelines for genetic testing and variant interpretation for heritable CRCs. [Display omitted] [ABSTRACT FROM AUTHOR]

In current scenario, crop productivity is being challenged by decreasing soil fertility. To cope up with this problem, different beneficial microbes are explored to increase the crop productivity with value additions. In this study, Brassica napus L., an important agricultural economic oilseed crop with rich source of nutritive qualities, was interacted with Piriformospora indica , a unique root colonizing fungus with wide host range and multifunctional aspects. The fungus-treated plants showed a significant increase in agronomic parameters with plant biomass, lodging-resistance, early bolting and flowering, oil yield and quality. Nutritional analysis revealed that plants treated by P. indica had reduced erucic acid and glucosinolates contents, and increased the accumulation of N, Ca, Mg, P, K, S, B, Fe and Zn elements. Low erucic acid and glucosinolates contents are important parameters for high quality oil, because oils high in erucic acid and glucosinolates are considered undesirable for human nutrition. Furthermore, the expression profiles of two encoding enzyme genes, Bn-FAE1 and BnECR , which are responsible for regulating erucic acid biosynthesis, were down-regulated at mid- and late- life stages during seeds development in colonized plants. These results demonstrated that P. indica played an important role in enhancing plant growth, rapeseed yield and quality improvement of B. napus. [ABSTRACT FROM AUTHOR]

Abstract: Nanoporous composite oxides Sr2Fe1.5Mo0.5O6–Sm0.2Ce0.8O1.9 (SFM–SDC) have been prepared by a facile one-step method as cathode for intermediate-temperature solid oxide fuel cells (IT-SOFCs). The SFM–SDC composite materials have been characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), scanning transmission electron microscopy (STEM) and electrochemical impedance spectroscopy (EIS). The EIS results exhibit that SFM–SDC40 (wt% 60:40) cathode has encouraging electrochemical performance with low polarization resistance (R p) on YSZ (Y2O3-stabilized ZrO2) electrolyte. Subsequently, bi-layer cathodes SDC/SFM–SDC are fabricated, and excellent electrochemical performance of such composite cathodes are observed. We demonstrate that the SDC interlayer significantly decreases the R p of cathode and accelerates the charge transfer process. As a result, the R p of the SDC/SFM–SDC40 bi-layer cathodes is almost 50% less than that of SFM–SDC40 cathode on YSZ electrolyte at 800 °C, and R p is only 0.11 Ω cm2. Compared with single cells without an interlayer, the anode-supported single cells with SDC interlayer exhibit enhancement in overall power performance. [Copyright &y& Elsevier]

Highlights: [•] Co2+ substituted lithium ferrite. [•] Structure changes from order to disorder. [•] Coercivity increased with Co2+ substitution. [•] Blocking temperature increased whereas Curie temperature decreased with Co2+ substitution. [Copyright &y& Elsevier]

Abstract: The Cu x Mg0.5−x Zn0.5Fe2O4 (x =0, 0.1, 0.2, 0.3, 0.4 and 0.5) ferrites were prepared by sol–gel method. The effect of copper content on the structural and magnetic properties was investigated by X-ray diffraction (XRD), Mössbauer spectroscopy (MS), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), vibrating sample magnetometer (VSM) and magnetic susceptometer. Results show that single spinel ferrite particles were prepared by controlling the type and amount of additive. Copper content has significant influence on magnetic properties of prepared samples. It was found that with increasing copper in Cu x Mg0.5−x Zn0.5Fe2O4, saturation magnetizations along with grain size were increased while the values of coercivity were decreased. The variation of effective magnetic susceptibility exhibits almost linear trend with magnetic field. A frequency-dependence peak was observed in ac magnetic susceptibility versus temperature for fine particles which is well fitted by Vogel–Fulcher model. It was proved that there is strong magnetic interaction among prepared nanoparticles. [ABSTRACT FROM AUTHOR]