Improvement,identification, and target prediction for miRNAs in the porcine genome by using massive,public high-throughput sequencing data

Despite the broad variety of available microRNA (miRNA) research tools and methods, their application to the identification, annotation, and target prediction of miRNAs in nonmodel organisms is still limited. In this study, we collected nearly all public sRNA-seq data to improve the annotation for known miRNAs and identify novel miRNAs that have not been annotated in pigs (Sus scrofa). We newly annotated 210 mature sequences in known miRNAs and found that 43 of the known miRNA precursors were problematic due to redundant/missing annotations or incorrect sequences. We also predicted 811 novel miRNAs with high confidence, which was twice the current number of known miRNAs for pigs in miRBase. In addition, we proposed a correlation-based strategy to predict target genes for miRNAs by using a large amount of sRNA-seq and RNA-seq data. We found that the correlation-based strategy provided additional evidence of expression compared with traditional target prediction methods. The correlation-based strategy also identified the regulatory pairs that were controlled by nonbinding sites with a particular pattern, which provided abundant complementarity for studying the mechanism of miRNAs that regulate gene expression. In summary, our study improved the annotation of known miRNAs, identified a large number of novel miRNAs, and predicted target genes for all pig miRNAs by using massive public data. This large data-based strategy is also applicable for other nonmodel organisms with incomplete annotation information.     

Assessing the efficacy and impact of management of an invasive species in a protected area: Poa annua on sub‐Antarctic Macquarie Island

Plant eradication is difficult, particularly in remote, protected areas. The Southern Ocean Islands are very isolated and highly protected, but the flora contains many alien plants. Small restricted populations have been eradicated, but eradication of established species has proven difficult. A better understanding of the efficacy of control methods at sub‐Antarctic temperatures and their off‐target impacts may increase eradication success. With interest in controlling non‐native Poa annua in the region, we aimed to determine if physical and chemical methods can control P. annua (the sub‐Antarctic biotype) in sub‐Antarctic conditions and examined their impact on native plants. We quantified the effectiveness of physical control methods on P. annua in situ on sub‐Antarctic Macquarie Island through field‐based experiments and assessed their selectivity on P. annua compared with native grasses. We also quantified the effectiveness of several herbicides on P. annua at sub‐Antarctic temperatures and assessed their selectivity on native grasses. Of the four physical disturbance methods tested, none effectively reduced P. annua cover as one‐off treatments. Of the herbicide treatments, glyphosate and trifloxysulfuron sodium were effective and were less damaging to native grass species, indicating potential selectivity. Physical control was of limited effectiveness, but did not affect native species richness. An integrated weed management programme utilising the strategic use of selective herbicides with follow‐up chemical and physical intervention may balance control and biodiversity outcomes. This research highlights the importance of site‐specific testing of control methods and understanding off‐target impacts of control when managing alien plant species in protected areas.     

基于级联卷积神经网络的番茄花期识别检测方法

对作物花期状态的准确识别是温室作物执行授粉的前提。为提高花期状态识别的准确度，该研究以温室番茄为例提出了一种基于级联卷积神经网络的番茄花朵花期识别方法。首先采用改进的端到端的特征金字塔网络FS-FPN实现番茄花束的分割，然后采用Prim最小生成树对分割后的花束图片进行花期识别优先级排序，最后将已排序的分割花束图片输入改进的Yolov3网络，实现番茄花朵花期状态的精准识别。在由1600幅包含花蕾期、全开期、谢花期、初果期4类花期状态的番茄花束图像构成的数据集上，所提方法对番茄花朵花期平均检测时间为12.54 ms，平均检测精度分别比Mask R-CNN和SPP-Net提高了3.67%和2.39%，识别错误率比改进前的Yolov3网络降低了1.25%。最终将该方法部署到番茄授粉机器人上，并在大型玻璃温室内进行验证，结果表明，所提方法对番茄花朵各花期的检测精度分别为花蕾期85.71%、全开期95.46%、谢花期62.66%、初果期88.34%，该研究结果可为智能授粉机器人的精准作业提供重要依据。     

H9N2禽流感病毒全基因组密码子使用偏好性及影响因素分析

【目的】探究H9N2禽流感病毒(AIV)全基因组的密码子使用偏好性及影响因素。【方法】选取2010—2018年国内H9N2 AIV流行毒株的全基因组为研究对象,分析其碱基组成特性、最优密码子、密码子使用偏好性的影响因素以及病毒对宿主密码子使用模式的适应性。【结果】H9N2 AIV的全基因组中AU含量高于GC。大部分最优密码子以A或U结尾,有效密码子数(ENC)平均值为52.86,提示存在密码子使用偏好性但偏好性较低。密码子使用偏好性主要受到突变压力和自然选择的共同作用,其中自然选择(所占比例为61.79%～76.15%)作用大于突变压力(所占比例为23.85%～38.21%)。H9N2 AIV对人Homo sapiens的密码子适应指数平均值为0.739～0.741,提示H9N2AIV禽流感病毒可能已适应人类的密码子使用模式。【结论】本研究为H9N2 AIV的基因进化分析、已有疫苗的密码子优化和新型疫苗(密码子去优化疫苗)研制提供了理论依据。     

设施农田土壤重金属污染评价及分区阈值研究

设施农田高强度的耕作模式使土壤重金属累积日益加重，因地制宜地确定其土壤重金属目标值与筛选值对设施农田可持续生产意义重大。本文以石家庄、衡水与唐山为研究区，基于土壤重金属污染风险和生态风险评价，用熵权法得出综合风险等级并确定重金属污染分区阈值。结果表明土壤Cd、Cu、Zn污染最严重，高风险区占比达40.57%。研究区设施农田土壤Cd、Cu、Pb、Zn全量与有效态的目标值分别为0.12、40.17、19.23、116.03 mg·kg^-1与0.02、4.19、0.59、8.80 mg·kg^-1；筛选值分别为0.40、90.27、38.33、170.68 mg·kg^-1与0.11、11.77、1.55、14.24 mg·kg^-1。基于综合评价结果反推分区阈值的方法简单便捷，重金属全量与有效态的目标值及筛选值的确定为研究区设施农田重金属污染的预防提供了多方位的指导。     

利用氦氖激光诱变提高枯草芽孢杆菌纤溶酶活力的研究

通过诱变筛选纤溶酶活性高的枯草芽孢杆菌菌株,得到突变株HDBF-N7HN5。使用氦氖激光诱变,设置不同的诱变时间,通过不同的蛋白平板处理,筛选高产突变株并检测纤溶酶活力。实验结果表明,在氦氖激光诱变处理45 min后,最高产突变株纤溶酶活力达到429.89±5.74 IU/mL。突变株经过10次传代培养后,保持稳定的高产纤溶酶特性。纤溶酶粗酶液处理血凝块的绝对溶解率可达到57.74±0.72%,10倍稀释液处理血凝块的绝对溶解率也能达到26.89±0.68%,菌株产生的纤溶酶具有良好的体外溶栓效果。本研究结果既提高了微生物纤溶酶活性,也为该菌株产纤溶酶的产业化提供了依据。     

烤烟EMS诱变后代高香气突变体筛选

为筛选高香气且品质较优的烤烟后代,以中烟100为材料,采用EMS诱变技术对中烟100种子进行处理,筛选出2个叶面腺毛密度大的突变体材料,通过田间试验,对其叶面腺毛密度、叶面化学成分、烤后化学成分、香气物质含量和感官质量评吸等指标进行评估,并与正常中烟100后代和NC89进行对照。研究结果表明,EMS-2诱变株系腺毛密度最大,为65.55个/mm ²,分别是中烟100（37.19个/mm ²）和NC89（56.06个/mm ²）的1.8和1.2倍;叶面化学成分分析结果表明,EMS-1诱变株系西柏烷类和烷烃类含量最多,分别为57.18、5.16mg/m ²,EMS-2突变株系蔗糖酯类物质分泌较旺盛,含量最多,为0.33mg/m ²;烤后样化学成分分析结果表明,EMS-2突变株系化学成分含量较协调,且香气成分含量最多,达到890.32μg/g,分别比中烟100和NC89增加34.5%和29.0%;结合对诱变后代的感官评价,得出结论：诱变株系综合品质较好,可提高烟叶质量,EMS-2综合品质最好,可以作为待选株系进行下一步品种选育。