plication of environmentally costly inputs. Moreover, analysis of satellite data shows that a large proportion of the coffee farms surveyed retain a level of forest cover and quality approaching primary (undisturbed) forest, and that the coffee production area at Yayu has not experienced any significant deforestation (since 2000). We recommend that coffee premiums linked to environmental benefit should demonstrate clearly defined and appropriate metrics, as we have demonstrated here for forest (canopy) quality and coverage (area). In China, double-seed (DS) sowing (i.e., sowing two seeds per hole) has been conventionally performed towards the erect-plant-type peanuts to increase the low germination rate due to poor seed preservation conditions. However, the corresponding within-hole plant competition usually limits the subsequent plant growth and the final yield. We developed a high-yield cultivation system of single-seed (SS) precision sowing to solve this paradox, saving 20% of seeds and increasing yields by more than 10% relative to the conventional DS sowing. To explore the mechanisms of these two different cropping patterns in peanut yields, we conducted transcriptomic and physiological comparisons in the seeding plant leaf and root tissues between SS precision sowing and standard DS sowing treatments. After assembly, each library contained an average of 43 million reads and generated a total of 523,800, 338 clean reads. After GO and Kyoto Encyclopedia of Genes and Genomes pathway analysis, we found the key genes for biotic and abiotic stress showed higher expression in roots of plants grown under the SS precision sowing treatment, including genes encoding disease resistance, oxidation-reduction, hormone related, and stress response transcription factors and signaling regulation proteins. In particular, the resveratrol synthesis genes related to stress and disease resistance appeared induced in roots under the SS sowing treatment. These data indicated that resistance and stress tolerance in roots under SS precision sowing were enhanced compared with roots under the DS sowing treatment. This work benefits the development of underground pods and thus increasing peanut yields.These data indicated that Aspergillus flavus resistance and stress tolerance in roots under SS precision sowing were enhanced compared with roots under the DS sowing treatment. This work benefits the development of underground pods and thus increasing peanut yields. Coronavirus Disease 2019 (COVID-19) has been surging globally. Risk strata in medical attention are of dynamic significance for apposite assessment and supply distribution. Presently, no known cultured contrivance is available to fill this gap of this pandemic. The aim of this study is to develop a predictive model based on vector autoregressive moving average (VARMA) model of various orders for gender based daily COVID-19 incidence in Nigeria. This study also aims to proffer empirical evidence that compares incidence between male and female for COVID-19 risk factors. Wilcoxon signed-rank test is employed to investigate the significance of the difference in the gender distributions of the daily incidence. A VARMA model of various orders is formulated for the gender based daily COVID-19 incidence in Nigeria. The optimal VARMA model is identified using Bayesian information criterion. Also, a predictive model based on univariate autoregressive moving average model is formulated for the daily death cases in Nes is identified to be ARIMA (0,1,1). There is no evidence of massive underreporting and under-testing of COVID-19 cases in Nigeria. Comparison of the observed incidence with fitted data by gender shows that the optimal VARMA and ARIMA models fit the data well. Findings highlight the significant roles of gender on daily COVID-19 incidence in Nigeria.Comparison of the observed incidence with fitted data by gender shows that the optimal VARMA and ARIMA models fit the data well. Findings highlight the significant roles of gender on daily COVID-19 incidence in Nigeria.Wheat stem rust, caused by Puccinia graminis f. sp. tritici, (Pgt) is a devastating disease in wheat production. The disease has been effectively controlled since the 1970s due to the widespread use of the Sr31 resistance gene. However, Sr31 has lost its effectiveness following the emergence and spread of the Ug99 race variants. selleck products Therefore, there is an urgent global effort to identify new germplasm resources effective against those races. In this study, the resistance to Pgt of 95 wheat advance lines from Heilongjiang Province was evaluated using three predominant races of Pgt, 21C3CTTTM, 34C0MKGSM, and 34C3MTGQM, in China at the seedling and adult plant stage. The presence of 6 Sr genes (Sr2, Sr24, Sr25, Sr26, Sr31, and Sr38) was evaluated using linked molecular markers. The results showed that 86 (90.5%) wheat lines had plant stage resistance to all three races. Molecular marker analysis showed that 24 wheat lines likely carried Sr38, 15 wheat lines likely carried Sr2, 11 wheat lines likely carried Sr31, while none of the wheat lines carried Sr24, Sr25, or Sr26. Furthermore, six out of the 95 wheat lines tested carried both Sr2 and Sr38, three contained both Sr31 and Sr38, and two wheat lines contained both Sr2 and Sr31. Wheat lines with known Sr genes may be used as donor parents for further breeding programs to provide resistance to stem rust. Gastric cancer (GC) is one of the most common carcinomas of the digestive tract, and the prognosis for these patients may be poor. There is evidence that some long non-coding RNAs(lncRNAs) can predict the prognosis of patients with GC. However, few lncRNA signatures have been used to predict prognosis. Herein, we aimed to construct a risk score model based on the expression of five lncRNAs to predict the prognosis of patients with GC and provide new potential therapeutic targets. We performed differentially expressed and survival analyses to identify differentially expressed survival-ralated lncRNAs by using GC patient expression profile data from The Cancer Genome Atlas (TCGA) database. We then established a formula including five lncRNAs to predict the prognosis of patients with GC. In addition, to verify the prognostic value of this risk score model, two independent Gene Expression Omnibus (GEO) datasets, GSE62254 ( = 300) and GSE15459 ( = 200), were employed as validation groups. Based on the characteristics of five lncRNAs, patients with GC were divided into high or low risk subgroups.