Multi-dimensional variables and feature parameter selection for aboveground biomass estimation of potato based on UAV multispectral imagery

Aboveground biomass (AGB) is an essential assessment of plant development and guiding agricultural production management in the field. Therefore, efficient and accurate access to crop AGB information can provide a timely and precise yield estimation, which is strong evidence for securing food supply and trade. In this study, the spectral, texture, geometric, and frequency-domain variables were extracted through multispectral imagery of drones, and each variable importance for different dimensional parameter combinations was computed by three feature parameter selection methods. The selected variables from the different combinations were used to perform potato AGB estimation. The results showed that compared with no feature parameter selection, the accuracy and robustness of the AGB prediction models were significantly improved after parameter selection. The random forest based on out-of-bag (RF-OOB) method was proved to be the most effective feature selection method, and in combination with RF regression, the coefficient of determination (R2) of the AGB validation model could reach 0.90, with root mean square error (RMSE), mean absolute error (MAE), and normalized RMSE (nRMSE) of 71.68 g/m2, 51.27 g/m2, and 11.56%, respectively. Meanwhile, the regression models of the RF-OOB method provided a good solution to the problem that high AGB values were underestimated with the variables of four dimensions. Moreover, the precision of AGB estimates was improved as the dimensionality of parameters increased. This present work can contribute to a rapid, efficient, and non-destructive means of obtaining AGB information for crops as well as provide technical support for high-throughput plant phenotypes screening

Optimizing the Optimal Planting Period for Potato Based on Different Water-Temperature Year Types in the Agro-Pastoral Ecotone of North China

Potato is the fourth staple crop in China after wheat, maize and rice. The agro-pastoral ecotone (APE) in North China is a main region for potato production. However, potato yield has been seriously constrained by water shortages because of low precipitation and highly variable precipitation patterns during the growing season in this area. In this study, the Agricultural Production Systems Simulator (APSIM) model was used to simulate potato water-limited yield and historical years were divided into different water-temperature year types to optimize the optimal planting period (OPP) and cultivar of potato. The results showed that the potato yield varied in different water-temperature year types. Fast-developing cultivar Favorita could obtain the highest yield in most places and water-temperature year types due to its relatively short length of tuber formation stage. In this study, we suggest changing the planting date according to the water-temperature year type, which offers a new way to adapt to a highly variable climate. However, our method should be adopted carefully because we only considered climate factors; other agronomic management practices (adjusting planting density, plastic film mulch, conservation tillage etc.) also have a great effect on planting date and cultivar selection, which should be further investigated in the future

多种环境条件下的亲本效应均惠及后代：基于动植物数据的元分析研究

在生物界，当上代生存环境恶劣时，是否一定会带给后代一个消极、负面的影响?环境与生态学院李庆顺教授课题组就此展开了大数据元分析研究，综合分析了从上世纪90年代至今的大量相关研究数据,从关键词索引得到的1000余篇论文中，筛选出139篇研究论文，这些论文涉及112个物种，包括不同的亲代环境处理、不同的子世代等。结果发现，对那些世代周期短、活动能力受限的一年生植物和无脊椎动物（如昆虫），无论上一代经历的是优质环境还是恶劣环境，这些经历总能使子代受益。但是，对于脊椎动物如老鼠和人等，只有上代经历优质环境才能使其子代受益。这一“源头”上的机制发现或为今后快速改良农作物，使其更有效应对干旱、升温、虫害等不良影响提供一种思路和方向。The adaptive value of transgenerational effects (the ancestor environmental effects on offspring) in changing environments has received much attention in recent years, but the related empirical evidence remains equivocal. Here, we conducted a meta‐analysis summarising 139 experimental studies in plants and animals with 1170 effect sizes to investigate the generality of transgenerational effects across taxa, traits, and environmental contexts. It was found that transgenerational effects generally enhanced offspring performance in response to both stressful and benign conditions. The strongest effects are in annual plants and invertebrates, whereas vertebrates appear to benefit mostly under benign conditions, and perennial plants show hardly any transgenerational responses at all. These differences among taxonomic/life‐history groups possibly reflect that vertebrates can avoid stressful conditions through their mobility, and longer‐lived plants have alternative strategies. In addition to environmental contexts and taxonomic/life‐history groups, transgenerational effects also varied among traits and developmental stages of ancestors and offspring, but the effects were similarly strong across three generations of offspring. By way of a more comprehensive data set and a different effect size, our results differ from those of a recent meta‐analysis, suggesting that transgenerational effects are widespread, strong and persistent and can substantially impact the responses of plants and animals to changing environments.This study is supported by the National Natural Science Foundation of China (grant No. 31600291 to Y‐YZ) and the Fundamental Research Funds for the Central Universities in China (grant No. 20720170074 to Y‐YZ)

Venous resection increases risk of chyle leak after total pancreatectomy for pancreatic tumors

Abstract Background Existing research on chyle leak (CL) after pancreatic surgery is mostly focused on pancreaticoduodenectomy and lacks investigation on total pancreatectomy (TP). This study aimed to explore potential risk factors of CL and develop a predictive model for patients with pancreatic tumor undergoing TP. Methods This retrospective study enrolled 90 consecutive patients undergoing TP from January 2015 to December 2023 at Peking Union Medical College Hospital. According to the inclusion criteria, 79 patients were finally included in the following analysis. The LASSO regression and multivariate logistic regression analysis were performed to identify risk factors associated with CL and construct a predictive nomogram. Then, the ROC analysis, calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were performed to assess its discrimination, accuracy, and efficacy. Due to the small sample size, we adopted the bootstrap resampling method with 500 repetitions for validation. Lastly, we plotted and analyzed the trend of postoperative drainage volume in CL patients. Results We revealed that venous resection (OR = 4.352, 95%CI 1.404–14.04, P = 0.011) was an independent risk factor for CL after TP. Prolonged operation time (OR = 1.473, 95%CI 1.015–2.237, P = 0.052) was also associated with an increased incidence of CL. We included these two factors in our prediction model. The area under the curve (AUC) was 0.752 (95%CI 0.622–0.874) after bootstrap. The calibration curve, DCA and CIC showed great accuracy and clinical benefit of our nomogram. In patients with CL, the mean drainage volume was significantly higher in venous resection group and grade B CL group. Conclusion Venous resection was an independent risk factor for chyle leak after TP. Patients undergoing vascular resection during TP should be alert for the occurrence of CL after surgery. We then constructed a nomogram consisted of venous resection and operation time to predict the odds of CL in patients undergoing TP

Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation

Abstract Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain in male mice. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation