Worldwide Web: High Venom Potency and Ability to Optimize Venom Usage Make the Globally Invasive Noble False Widow Spider Steatoda nobilis (Thorell, 1875) (Theridiidae) Highly Competitive against Native European Spiders Sharing the Same Habitats

Venom compositions include complex mixtures of toxic proteins that evolved to immobilize/dissuade organisms by disrupting biological functions. Venom production is metabolically expensive, and parsimonious use is expected, as suggested by the venom optimisation hypothesis. The decision-making capacity to regulate venom usage has never been demonstrated for the globally invasive Noble false widow Steatoda nobilis (Thorell, 1875) (Theridiidae). Here, we investigated variations of venom quantities available in a wild population of S. nobilis and prey choice depending on venom availability. To partially determine their competitiveness, we compared their attack rate success, median effective dose (ED₅₀) and lethal dose (LD₅₀), with four sympatric synanthropic species: the lace webbed spider Amaurobius similis, the giant house spider Eratigena atrica, the missing sector orb-weaver Zygiella x-notata, and the cellar spider Pholcus phalangioides. We show that S. nobilis regulates its venom usage based on availability, and its venom is up to 230-fold (0.56 mg/kg) more potent than native spiders. The high potency of S. nobilis venom and its ability to optimize its usage make this species highly competitive against native European spiders sharing the same habitats.     

中国产8种甘草的理论分类及亲缘关系的理论判别

目的:根据广义生物遗传与变异信息理论方程推导的2个生物相似度常数Pg1=69％,Pg2=61％及生物亲缘关系判别函数Pg=1/lnNd对中国产8种甘草进行理论分类及亲缘关系判别,并提出了生物的理论品种概念.方法:基于8种甘草所含有的59种代表性黄酮类化合物,计算各种甘草之间的共有成分率及变异成分率,采用2个常数及判别函...     

Heat-Treated Meat Origin Tracing and Authenticity through a Practical Multiplex Polymerase Chain Reaction Approach

Meat adulteration have become a global issue, which has increasingly raised concerns due to not only economic losses and religious issues, but also public safety and its negative effects on human health. Using optimal primers for seven target species, a multiplex PCR method was developed for the molecular authentication of camel, cattle, dog, pig, chicken, sheep and duck in one tube reaction. Species-specific amplification from the premixed total DNA of seven species was corroborated by DNA sequencing. The limit of detection (LOD) is as low as 0.025 ng DNA for the simultaneous identification of seven species in both raw and heat-processed meat or target meat: as little as 0.1% (w/w) of the total meat weight. This method is strongly reproducible even while exposed to intensively heat-processed meat and meat mixtures, which renders it able to trace meat origins in real-world foodstuffs based on the authenticity assessment of commercial meat samples. Therefore, this method is a powerful tool for the inspection of meat adulterants and has broad application prospects.     

Environmental context dependency in species interactions

Ecological interactions are not uniform across time and can vary with environmental conditions. Yet, interactions among species are often measured with short-term controlled experiments whose outcomes can depend greatly on the particular environmental conditions under which they are performed. As an alternative, we use empirical dynamic modeling to estimate species interactions across a wide range of environmental conditions directly from existing long-term monitoring data. In our case study from a southern California kelp forest, we test whether interactions between multiple kelp and sea urchin species can be reliably reconstructed from time-series data and whether those interactions vary predictably in strength and direction across observed fluctuations in temperature, disturbance, and low-frequency oceanographic regimes. We show that environmental context greatly alters the strength and direction of species interactions. In particular, the state of the North Pacific Gyre Oscillation seems to drive the competitive balance between kelp species, asserting bottom-up control on kelp ecosystem dynamics. We show the importance of specifically studying variation in interaction strength, rather than mean interaction outcomes, when trying to understand the dynamics of complex ecosystems. The significant context dependency in species interactions found in this study argues for a greater utilization of long-term data and empirical dynamic modeling in studies of the dynamics of other ecosystems.

Interactions between species drive patterns of diversity, stability, resilience, and productivity in nature (1–4). In any ecosystem, the collection of species interactions determines community dynamics. Yet, since environmental conditions can influence these species interactions and environmental conditions can vary greatly over space or time (5–9), shifting interspecies dynamics can drive complex ecosystem changes. For example, the Stress Gradient Hypothesis (10–12) posits that interactions among species within a trophic level can shift from competitive to facilitative across large gradients of stress (e.g., thermal, nutrient, or water stress), with important implications for community dynamics. Similar hypotheses have been posited about shifts in other key species-interaction types, like parasitism and mutualism (13, 14), and consumer–prey interactions (15).Although ecologists have long recognized that many important species interactions may vary greatly over time and space, this context dependency remains very difficult to effectively measure and describe. Field experiments that measure interactions can generally be performed at only a few places over a relatively short window of time. They are therefore inevitably subject to only a subset of potential environmental contexts that may not encompass the full range of conditions experienced by that ecosystem over longer time scales or broader geographies (16). The resulting constraints increase the chance that the profound influence of environmental context on the outcome of species interactions ranging from keystone predation (8) to competition (6, 17, 18) to protective symbioses (19–21) will remain underappreciated. Since expanding the temporal and spatial scales of such experiments to rectify these challenges is a daunting task, we need additional tools.Moreover, even when context dependency of species interactions has been examined explicitly, studies commonly focus on estimating mean interaction strengths, rather than more comprehensive examinations of interaction variance and dynamics (9). This averaging approach may be appropriate for answering certain questions, but if species interactions are highly variable in both magnitude and direction—and therefore “weak” when averaged—key species interactions that are important drivers of community change may be dismissed as insignificant observational noise (4).Meeting these significant challenges requires placing interspecific interactions into their appropriate full environmental contexts. Controlled experiments can sort out the relative and interactive effects of a few orthogonal environmental drivers at a time; for example, examination of the effects of ocean warming and acidification on algal competition (22). But as species-interaction webs and lists of important environmental variables grow in size, fully factorial experimental designs quickly become unwieldy, if not impossible, to implement. One potential solution may lie in coupling long-term ecological observations that span a large range of environmental contexts with analytical methods that can directly estimate context-dependent species interactions from those time-series observations of changing abundances. Since long-term records of species abundances exist for a wide range of ecosystems, such an approach could help to characterize environmental contingencies in species interactions far more rapidly and could explicitly examine interaction variability in both strength and direction in a broader array of contexts.Here, we explore this alternative approach through a case study, by examining the effects of environmental context on species interactions using nonlinear time-series analyses applied to long-term monitoring data from a southern California kelp forest (23). Kelp forests are diverse and temporally dynamic ecosystems, in which many important species interactions are well-documented through decades of experimental and comparative studies (24–26). The study of kelp forests has been foundational to ecological theory, especially regarding the relative influence of top-down and bottom-up structuring forces in ecosystems (27–31). Recently, however, findings from long-term kelp-forest research programs have begun to challenge many long-held beliefs about the drivers of kelp-forest ecosystem dynamics (32). In particular, a longer-term perspective has led to growing hints about the critical importance of environmental context—such as the level of physical disturbance or the current state of El Niño conditions—for understanding kelp-forest processes (33–37).To explore the insights that can be gleaned from time-series data to determine patterns of variation in species interactions and their relationships to environmental drivers, we use empirical dynamic modeling [EDM (38)]. EDM uses information from single or multiple time series to empirically model relationships between variables through the reconstruction of dynamic attractors . The general modeling framework for all EDM methods is readily adaptable to many different sorts of time-series variables, including environmental variables manifesting at different scales (39–41). Because the methods are specifically designed for nonlinear dynamic systems, EDM—in theory—should be able to illuminate context-dependent patterns across diverse types of species interactions. Recently developed EDM methods exist for uncovering dynamic species interactions from time-series data (38), but these methods have, to date, been applied only to simulated and planktonic communities. Their utility to the study of other ecological systems remains untested. We use EDM to explore how a kelp-forest species-interaction network varies over time and to establish environmental context dependency in interaction strength and direction.     

PM2.5对BEAS-2B细胞脂质过氧化损伤作用

目的 用大气细颗粒物(PM_2.5)刺激人支气管上皮细胞(BEAS-2B细胞),探讨其脂质过氧化作用。方法 PM_2.5采自河南省郑州市;实验细胞为人支气管上皮细胞。用0、12.5、25 μg/mL的PM_2.5刺激BEAS-2B细胞4 h后,使用流式细胞仪检测其活性氧(ROS)水平;刺激BEAS-2B细胞8 h,用丙二醛试剂盒和超氧化物歧化酶活性测定试剂盒分别检测细胞裂解液中丙二醛(MDA)含量和超氧化物歧化酶(SOD)活性。结果 PM_2.5作用于BEAS-2B细胞4 h,12.5、25 μg/mL染毒组ROS水平分别为(6 074.69±41.65)、(7 338.58±168.34),均明显高于对照组的(5 816.66±114.69)(P<0.01);刺激BEAS-2B细胞8 h,12.5、25 μg/mL染毒组MDA含量分别为(195.44±35.58)、(334.11±26.75) μmol/mg,均明显高于对照组的(71.14±4.21) μmol/mg(P<0.01),同时染毒组SOD活力分别为(100.08±7.54)、(80.03±7.61) U/mg,均低于对照组的(159.91±10.59) U/mg(P<0.01)。结论 PM_2.5可以引起BEAS-2B细胞脂质过氧化损伤。     

2型糖尿病视网膜病的质量—数量Cox回归分析

采用质量-数量Cox回归分析对2型糖尿病视网膜病的影响因素进行探讨.结果表明:素食荤油、糖尿病家族史、高胆固醇和高甘油三酯水平以及高体重指数是2型糖尿病视网膜病的危险因素,RR值分别为2.009,1.665,1.213,1.208和1.056;多吃谷类或面粉类食物以及适当增加业余体力活动量是其保护因素,RR值分别为,0.542和0.659.     

无机纳米声敏剂在肿瘤声动力治疗中的应用进展

肿瘤声动力治疗（SDT）是利用超声波将声能聚集于深部组织,并激活声敏药物产生抗肿瘤效应。SDT能够克服目前肿瘤治疗中诸如穿透深度有限等弊端。无机纳米声敏剂具有体内稳定性高、物理化学性质可控以及易于实现多功能化等优点,在肿瘤治疗方面具有广阔的应用前景。综述无机纳米声敏剂在SDT中的应用进展,并对其未来可能的研究方向进行展望。

     

中药治疗糖尿病的研究进展

蒿属是菊科中分布最广的属之一,蒿属植物在我国民间被广泛使用。综述近年来蒿属植物降血糖的主要化学成分、降糖作用机制和提取方法等方面的研究进展,为蒿属植物降糖药效成分的进一步开发和利用提供参考。