Recent advances and emerging trends in antiviral defense networking in rice

Plants and viruses coexist in the natural ecosystem for extended periods of time,interacting with each other and even coevolving,maintaining a dynamic balance between plant disease resistance and virus pathogenicity.During virus–host interactions,plants often exhibit abnormal growth and development.However,plants do not passively withstand virus attacks but have evolved sophisticated and effective defense mechanisms to resist,limit,or undermine virus infections.It is widely believed that the initial stage of infection features the most intense interactions between the virus and the host and the greatest variety of activated signal transduction pathways.This review describes the most recent findings in rice antiviral research and discusses a variety of rice antiviral molecular mechanisms,including those based on R genes and recessive resistance,RNA silencing,phytohormone signaling,autophagy and WUSmediated antiviral immunity.Finally,we discuss the challenges and future prospects of breeding rice for enhanced virus resistance.

A Novel Method for the Prenatal Diagnosis of Cleft Palate Based on Amniotic Fluid Metabolites

Background and purpose The prenatal diagnosis of cleft palate is an important component of sequential therapy,but the relevant diagnostic methods are still limited.We aimed here,to explore the possibility of an early prenatal diagnosis of cleft palate by assessing metabolites in pregnant mice.Methods Twenty-four inseminated females were randomly divided into retinoic acid(RA)-treated(treated with retinoic acid at 10.5 gestation days)and control groups.The metabolites of the embryonic palatal tissue,maternal amniotic fluid,and serum were characterized using 9.4T magnetic resonance spectroscopy in vitro.Then,a predictive model was established through the principal component analysis(PCA),and the correlations between the metabolites of amniotic fluid and palatal tissue were explored using orthogonal-2 partial least squares(O2-PLS).Results The incidences of cleft palate were 100%and 0%in the RA-treated and control groups,respectively.A predictive PCA model with a high specificity and sensitivity was established for the early prenatal diagnosis of isolated cleft palate using amniotic fluid metabolic data.Between RA-treated and control mice,we found that two metabolites in the amniotic fluid and palatal tissue were correlated.Creatinine showed the same trend in the palatal tissue and amniotic fluid,while choline showed opposite trends in the two tissues.However,the data for serum metabolites could not be used to establish a prediction model.Conclusion This study indicates that assessing the metabolites of amniotic fluid is a potential approach for the prenatal diagnosis of isolated cleft palate.

CO_2 seasonal variation and global change: Test global warming from another point of view

CO2 and temperature records at Mauna Loa, Hawaii, and other observation stations show that the correlation between CO2 and temperature is not significant. These stations are located away from big cities, and in various latitudes and hemi-spheres. But the correlation is significant in global mean data. Over the last five decades, CO2 has grown at an accelerating rate with no corresponding rise in temperature in the stations. This discrepancy indicates that CO2 probably is not the driving force of temperature change globally but only locally （mainly in big cities）. We suggest that the Earth＇s atmospheric concentration of CO2 is too low to drive global temperature change. Our empirical perception of the global warming record is due to the urban heat island effect： temperature rises in areas with rising population density and rising industrial activity. This effect mainly occurs in the areas with high population and intense human activities, and is not representative of global warming. Regions far from cities, such as the Mauna Loa highland, show no evident warming trend. The global monthly mean temperature calculated by record data, widely used by academic researchers, shows R2=0.765, a high degree of correlation with CO2 . However, the R2 shows much less significance （mean R2=0.024） if calculated by each record for 188 selected stations over the world. This test suggests that the inflated high correlation between CO2 and temperature （mean R2=0.765-0.024=0.741） used in reports from the Intergovernmental Panel on Climate Change （IPCC） was very likely produced during data correction and processing. This untrue global monthly mean temperature has created a picture： human emission drives global warming.

Benggang segmentation via deep exchanging of digital orthophoto map and digital surface model features

Benggang is a typical fragmented erosional landscape in southern and southeastern China,posing sig-nificant risk to the local residents and economic development.Therefore,an efficient and accurate fine-grained segmentation method is crucial for monitoring the Benggang areas.In this paper,we propose a deep learning-based automatic segmentation method for Benggang by integrating high-resolution Digital Orthophoto Map(DOM)and Digital Surface Model(DSM)data.The DSM data is used to extract slope maps,aiming to capture primary morphological features.The proposed method consists of a dual-stream convolutional encoder-decoder network in which multiple cascaded convolutional layers and a skip connection scheme are used to extract morphological and visual features of the Benggang areas.The rich discriminative information in the DOM and slope data is fused by a channel exchanging mechanism that dynamically exchanges the most discriminative features from either the DOM or DSM stream ac-cording to their importance at the channel level.Evaluation experiments were conducted on a chal-lenging dataset collected from Guangdong Province,China,and the results show that the proposed channel exchanging network based deep fusion method achieves 84.62%IoU in Benggang segmentation,outperforming several existing unimodal or multimodal baselines.The proposed multimodal segmen-tation method greatly improves the efficiency of large-scale discovery of Benggang,and thus is important for the management and restoration of Benggang in southern and southeastern China,as well as the monitoring of other similar erosional landscapes.