<Emphasis Type="Italic">In vitro</Emphasis> Enhancement of Lactate Esters on the Percutaneous Penetration of Drugs with Different Lipophilicity

Lactate esters are widely used as food additives, perfume materials, medicine additives, and personal care products. The objective of this work was to investigate the effect of a series of lactate esters as penetration enhancers on the in vitro skin permeation of four drugs with different physicochemical properties, including ibuprofen, salicylic acid, dexamethasone and 5-fluorouracil. The saturated donor solutions of the evaluated drugs in propylene glycol were used in order to keep a constant driving force with maximum thermodynamic activity. The permeability coefficient (K _p), skin concentration of drugs (SC), and lag time (T), as well as the enhancement ratios for K _p and SC were recorded. All results indicated that lactate esters can exert a significant influence on the transdermal delivery of the model drugs and there is a structure-activity relationship between the tested lactate esters and their enhancement effects. The results also suggested that the lactate esters with the chain length of fatty alcohol moieties of 10–12 are more effective enhancers. Furthermore, the enhancement effect of lactate esters increases with a decrease of the drug lipophilicity, which suggests that they may be more efficient at enhancing the penetration of hydrophilic drugs than lipophilic drugs. The influence of the concentration of lactate esters was evaluated and the optimal concentration is in the range of 5∼10 wt.%. In sum, lactate esters as a penetration enhancer for some drugs are of interest for transdermal administration when the safety of penetration enhancers is a prime consideration.     

不同磷水平对柱花草形态学指标的影响

在不同磷素水平培养条件下,探讨磷胁迫对柱花草形态指标及生物量的影响,评价柱花草对磷浓度的耐受性.该文以12个不同基因型柱花草品种为材料,设置7个磷肥梯度,分别为0.2、0.1、0.075、0.050、0.035、0.02 g·kg-1,以不施磷肥为对照,采用盆栽方法进行施肥试验,比较不同柱花草品种形态指标对不同磷浓度的...     

基于人工掩蔽物法的若尔盖湿地中华蟾蜍种群生态研究

近几十年来,全球两栖动物种群衰减显著,两栖动物的生存现状引起了越来越多的生态学家和保护生物学家的关注。若尔盖湿地不仅是世界上最大的一块高原泥炭沼泽湿地,也是我国生物多样性保护的热点地区之一。该地区分布有3种两栖类:高原林蛙(Rana kukunoris)、倭蛙(Narorana pleskei)和中华蟾蜍岷山亚种(Bufo gargarizans minshanica)。已有研究发现该3种两栖动物种群数量均有不同程度的下降。采用人工掩蔽物法,在2011—2014年对该地区中华蟾蜍种群生态做了连续追踪。结果表明:该区域中华蟾蜍种群数量逐年波动较大,年龄结构数据显示该种群处于增长期;中华蟾蜍为聚集分布,且发现率与水体距离呈显著的线形关系(P0.01),在样地范围内离水体越远,发现的个体越多;线形样方比方形样方捕获动物的效率更高(P=0.018);中华蟾蜍亚成体的肥满度季节间无明显差异;中华蟾蜍具有较强的迁徙能力,可能沿着固定的线路周而复始的迁徙。对于家域范围大,迁徙距离远的中华蟾蜍这类物种,应加强最适栖息地的保护并防止栖息地破碎化。     

镉污染对烟草叶片超微结构及部分元素含量的影响

采用水培试验,利用电感耦合等离子体、透射电镜、扫描电镜等技术研究了镉污染对烟草(N icotiana tabacum)叶绿素含量及叶绿素a/b值、叶下表皮气孔器密度、腺毛密度、叶片细胞超微结构和P、K、C a等元素含量的影响,以及叶片细胞、腺毛对镉污染的反应。结果表明随着营养液中镉浓度的增高,烟草叶绿素含量及叶绿素a/b值降低;叶下表皮气孔器的密度及腺毛的密度增加;叶绿体中组成基粒的类囊体层数减少、分布不均、或粘连成索状,叶绿体膜系统崩溃,内外膜均解体,类囊体消失;但烟草可通过将镉隔离于细胞壁中或排出表皮细胞或通过腺毛分泌作用来减少其毒害。随着营养液中镉浓度的增高,腺毛分泌物中S i、K、A l、C a、M g、F e的含量增加;浓度为3m g/L的镉污染可增加叶片中P、C a、M g、F e、Cu、Zn、A l元素的含量,但浓度为30m g/L的镉污染造成叶片中P、C a、M g、F e、Cu、Zn、A l元素含量的减少;镉污染可引起叶片中N a含量增加,且随着营养液中镉浓度的增高N a含量增加;镉污染造成K、M n在叶片中的含量下降,而且随着营养液中镉浓度的增高,K、M n含量下降幅度增加。     

芽孢杆菌B3的鉴定及其对常见污染真菌的拮抗作用

从平菇废弃培养料中分离获得一株细菌B3,通过对其进行形态学、培养特征比较研究和16SrDNA序列分析,鉴定为解淀粉芽孢杆菌(Bacillus amyloliquefaciens)。在与常见的三种食用菌污染真菌脉孢菌(Neurospora sp.)、木霉(Trichoderma sp.)及鬼伞(Coprinus sp.)分别进行对峙培养时,都有明显的抑菌作用,形成的抑菌带分别为0.38cm、0.57cm及0.56cm,而与平菇(Pleurotus ostreatus)、鸡腿菇(Coprinus comatus)及草菇(Volvariella volvacea)分别进行对峙培养时,除对草菇有明显的抑制作用外,对平菇和鸡腿菇的抑制作用很小。在混接B3菌株麸皮菌剂栽培料培养基上,B3菌株对三种污染真菌的抑制作用都很明显;可抑制木霉分生孢子形成、脉孢菌分生孢子萌发及鬼伞菌丝扩展;但不抑制平菇菌丝的生长。     

土壤理化性质驱动烤烟根际细菌群落的组配及其共现性网络互作

【目的】解析土壤微生物在植物根际的组配机制对于认识和维护农田生态系统的稳定性至关重要。【方法】通过Illumina高通量测序和生物信息学分析方法明确了我国主要种植烟草生态区烤烟根际土壤细菌群落与土壤理化性质的互作关系。【结果】烤烟根际细菌类群主要为放线菌纲(Actinobacteria)、α-变形菌纲(Alphaproteobacteria)、γ-变形菌纲(Gammaproteobacteria)和嗜热油菌纲(Thermoleophilia)。细菌群落组成按生态区聚类,且样本空间距离和细菌群落相似度显著负相关。共现性网络分析表明,烤烟根际细菌群落间协同作用大于拮抗作用,武陵秦巴生态区、黄淮平原生态区、南岭丘陵生态区和沂蒙丘陵生态区细菌群落高度模块化,小单胞菌属(Micromonospora)为南岭丘陵生态区和黄淮生态区细菌共现性网络的网络中心,Bryobacter和气单胞菌属(Arenimonas)为南岭丘陵生态区细菌网络的模块核心,其菌群特性而非相对丰度决定了其在稳定细菌网络中的重要作用。冗余分析结果证实pH、有效铁、交换性镁和有效锰能显著影响烤烟根际细菌群落结构。【结论】烤烟根际细...     

Cross-scale drivers of plant trait distributions in a fragmented forest landscape

During community assembly, plant functional traits are under selective pressure from processes operating at multiple spatial scales. However, in fragmented landscapes, there is little understanding of the relative importance of local-, patch- and landscape-scale processes in shaping trait distributions. Here, we investigate cross-scale influences of landscape change on traits that dictate plant life history strategies in re-assembling plant communities in a fragmented landscape in eastern China. Using forest dynamics plots (FDPs) on 29 land-bridge islands in which all woody plants have been georeferenced and identified to species, we characterized and derived two composite measures of trait variation, representing variation across the leaf economics spectrum and plant size. We then tested for trait shifts in response to local-, patch- and landscape-scale factors, and their potential cross-scale interactions. We found substantial community-wide trait changes along local-scale gradients (i.e. forest edge to interior): more acquisitive leaf economic traits and larger sized species occurred at edges, with a significant increase in trait means and trait range. Moreover, there were significant cross-scale interaction effects of patch and landscape variables on local-scale edge effects. Altered spatial arrangement of habitat in the surrounding landscape (i.e. declining habitat amount and increasing patch density), as well as decreasing area at the patch level, exacerbated edge effects on traits distributions. We suggest that synergistic interactions of landscape- and patch-scale processes, such as dispersal limitation, on local-scale environmental filtering at edges, together shape the spatial distributions of plant life history strategies in fragmented plant communities.     

Winter low temperature disturbance in the southern subtropics of China promotes the competitiveness of an invasive plant

Winter low temperature disturbance in the southern subtropics has important effects on the weed community structure, but the role of uniquely low temperatures in biological invasions is unclear. Here, we examined the competitive effects of an invasive plant, Bidens pilosa L., and its native congener, Bidens biternata (Lour.) Merr. et Sherff, during high and low temperature seasons to determine whether low temperatures promote the competitiveness of B. pilosa in the southern subtropics of China. The growth and physiological responses of the two Bidens species to low (10/5 °C) and optimum (30/25 °C) temperatures were examined to determine how the invasive B. pilosa responds to low temperature stress. Our results showed that the competitive balance index values of B. pilosa in low temperature seasons were significantly higher than those in high temperature seasons, which implied that low temperatures may be more beneficial to the competitiveness of B. pilosa than high temperatures in the southern subtropics. The smaller decline in the relative growth rate and the photosynthetic ability of B. pilosa compared with B. biternata under low temperature stress indicated that the former was less negatively affected by low temperature than the latter. A higher DPPH^· (1.1-diphenyl-2-picrylhy-drazyl) scavenging rate and greater heat-stable protein content in B. pilosa under low temperatures might help the invasive plant to maintain more effective physiological functions and thus a higher growth rate. Overall, the uniquely low temperature in the southern subtropics of China is expected to promote the invasiveness of the exotic B. pilosa.

     

ProDCoNN: Protein design using a convolutional neural network

Designing protein sequences that fold to a given three-dimensional (3D) structure has long been a challenging problem in computational structural biology with significant theoretical and practical implications. In this study, we first formulated this problem as predicting the residue type given the 3D structural environment around the C _α atom of a residue, which is repeated for each residue of a protein. We designed a nine-layer 3D deep convolutional neural network (CNN) that takes as input a gridded box with the atomic coordinates and types around a residue. Several CNN layers were designed to capture structure information at different scales, such as bond lengths, bond angles, torsion angles, and secondary structures. Trained on a very large number of protein structures, the method, called ProDCoNN (protein design with CNN), achieved state-of-the-art performance when tested on large numbers of test proteins and benchmark datasets.