Risk assessment of broflanilide resistance in Panonychus citri (McGregor): Cross-resistance, inheritance and relative fitness.

Panonychus citri (McGregor) is an important economic pest in the orange orchard of the world, which has developed varying degrees of resistance to many acaricides. Broflanilide is a novel γ-aminobutyric acid (GABA) receptor allosteric modulator with high insecticidal activity against a broad spectrum of insects. However, the risk of resistance to broflanilide in P.

Sublethal and transgenerational effects of broflanilide on the citrus red mite, Panonychus citri.

Background: The citrus red mite, Panonychus citri is a serious pest of the citrus industry and has developed resistance to many acaricides. Broflanilide is a novel meta-diamide insecticide that binds to a new site on the γ -aminobutyric acid receptor with high potency against pests. However, little information has been reported about its effect on the citrus red mite.

Loneliness shapes disparities in healthy life expectancy: a multi-state analysis from China.

Objectives: To assess the influence of loneliness on the healthy life expectancy of older adults in China and its gender disparities across different health indicators, in order to provide insights for enhancing the health status and subjective well-being of the older population.

Method: We conducted a cohort analysis using four waves of weighted samples (2008, 2011, 2014, and 2018) from the Chinese Longitudinal Healthy Longevity Survey, encompassing 15,507 respondents aged 65-99. Physical and subjective health were assessed through activity of daily living (ADL) and self-rated health (SRH), respectively.

Lethal and sublethal effects of fluralaner on the citrus red mite, Panonychus citri (McGregor).

Background: The citrus red mite, Panonychus citri (McGregor) is a globally distributed agricultural pest. Of late, resistance to common acaricides has raised concerns that chemical control of P. citri is an inefficient means of control.

Analysis of Corn Yield Prediction Potential at Various Growth Phases Using a Process-Based Model and Deep Learning.

Early and accurate prediction of grain yield is of great significance for ensuring food security and formulating food policy. The exploration of key growth phases and features is beneficial to improving the efficiency and accuracy of yield prediction. In this study, a hybrid approach using the WOFOST model and deep learning was developed to forecast corn yield, which analysed yield prediction potential at different growth phases and features.