Culturally-transmitted feeding behaviour in primates: Evidence for accelerating learning rates

Cultural transmission implies the rapid spread of behavioural innovations when initially naïve individuals copy more informed ones. Mathematical models of transmission feature accelerating (and in most cases, logistic) rates of learning as animals that acquire an innovation provide ever increasing numbers of informers for potential learners. Conversely, non-accelerating rates have been proposed as a null hypothesis for apparent cases of cultural transmission that can best be explained by simpler mechanisms such as trial-and-error learning. Using the AIC technique for comparing models with different numbers of parameters, this paper examines the 21 cases in the primate literature where quantifiable data are available on learning rates for presumed culturally-transmitted feeding innovations. In each case, cumulative distributions over time of the frequency or proportion of individuals that acquire an innovation are compared with three accelerating functions (logistic, positive exponential, and hyperbolic sine) and two non-accelerating ones (linear and logarithmic). In 16 cases, the best fit is given by an accelerating function: nine of these support the logistic, four support the positive exponential and three, the reverse S-shaped hyperbolic sine. Individual cases often show small differences between alternative functions, but overall trends support the cultural assumption of accelerating learning rates.     

Met-enkephalin effects on associations formed in utero

Rat fetuses exposed to an odor stimulus and an aversive stimulus in utero showed an aversion to the odor when tested 16 days postnatally. Fetuses that also received 80 μg/kg Met-enkephalin showed a greater aversion to the odor stimulus than those subjects that did not receive the peptide. The difference between these groups was marginally significant. Control subjects did not show an aversion. But, subjects exposed to the odor and Met-enkephalin without the aversive stimulus, when tested, showed a significant preference for the odor over other control groups. These data show that associative learning in rat fetuses at 20 days of gestation may be enhanced by administration of Met-enkephalin.     

DLGCN:基于图卷积网络的药物-lncRNA[]关联预测

为实现高通量识别新的药物-长链非编码RNA(Long non-coding RNA, lncRNA)关联,本文提出了一种基于图卷积网络模型来识别潜在药物-lncRNA关联的方法DLGCN(Drug-LncRNA graph convolution network)。首先,基于药物的结构信息和lncRNA的序列信息分别构建了药物-药物和lncRNA-lncRNA相似性网络,并整合实验证实的药物-lncRNA关联构建了药物-lncRNA异质性网络。然后,将注意力机制和图卷积运算应用于该网络中,学习药物和lncRNA的低维特征,基于整合的低维特征预测新的药物-lncRNA关联。通过效能评估,DLGCN的受试者工作特性曲线下面积(Area under receiver operating characteristic, AUROC)达到0.843 1,优于经典的机器学习方法和常见的深度学习方法。此外,DLGCN预测到姜黄素能够调控lncRNA MALAT1的表达,已被最近的研究证实。DLGCN能够有效预测药物-lncRNA关联,为肿瘤治疗新靶点的识别和抗癌药物的筛选提供了重要参考。     

Experience acquisition by Trichogramma australicum Girault (Hymenoptera: Trichogrammatidae)

We investigated experience acquisition (alpha-conditioning) by females of the parasitoid Trichogramma australicum using host eggs of the noctuid moth Helicoverpa armigera (Hübner). We compared the acceptance of a host egg by females with different levels of ovipositional experience. The level of experience was designated using the standard oviposition sequence: (1) host contact (C); (2) host examination (E); (3) drilling (D); (4) full insertion (FI); and (5) oviposition (O). Each treatment consisted of a single experience level, but together these consituted a qualitative behavioural continuum of oviposition experience from naive (N) to experienced wasps. We found that host experience by adult T. australicum females can modify their behaviour. Mean duration of host finding was: N = C > E = D > FI = O. Mean duration of host examination and full insertion were: N = C > E = D = FI = O. Drilling was constant for all experience levels. Experience in drilling of the chorion during the previous host-exposure process represents a critical experience for a female and results in efficient handling and more ready acceptance of a subsequently encountered host egg.     

鲍肽素对血管性痴呆大鼠学习与记忆能力的干预及机制研究

目的：观察鲍肤索对血管性痴呆大鼠学习与记忆能力的干预及机制。方法：制备生物鲍肤索,分剂量喂饲血管性痴呆大鼠,测试学习与记忆能力、红细胞和血红蛋白。结果：鲍肤素提高大鼠Y型迷宫测试的分值和红细胞、血红蛋白水平。结论：鲍肤素能提高血管性痴呆大鼠的学习与记忆能力和红细胞、血红蛋白水平。     

国际基因工程机器大赛对本科生科研教育的启示

近年来,国际基因工程机器大赛(International genetically engineered machine,iGEM,简称iGEM大赛)在全球迅猛发展。仅2017年iGEM大赛全球注册队伍就达到了史无前例的313支,中国地区有98支iGEM团队报名参赛并取得了优异成绩。与国内已有的诸多大学生创新项目、科研培养项目不同,iGEM的组织模式是以学生为主体的研究型学习。该模式取得了丰富的教育效果,体现了新的教育理念,对于我国高校组织本科生课外科研训练有较大的借鉴意义。文中以北京大学参加iGEM大赛为线索,介绍国际基因工程机器大赛(iGEM)的背景和基本情况并以一个参赛周期为序再现北京大学iGEM团队组织和参赛的主要过程。通过与其他本科生科研训练的组织模式进行比较,探讨iGEM对本科生科研训练意义,并总结iGEM的组织经验和对本科生科研能力培养以及组织本科生科研学术竞赛的启示,希望能为国内高校的iGEM活动组织以及本科教育改革提供借鉴。     

Distinguishing enzyme structures from non-enzymes without alignments

The ability to predict protein function from structure is becoming increasingly important as the number of structures resolved is growing more rapidly than our capacity to study function. Current methods for predicting protein function are mostly reliant on identifying a similar protein of known function. For proteins that are highly dissimilar or are only similar to proteins also lacking functional annotations, these methods fail. Here, we show that protein function can be predicted as enzymatic or not without resorting to alignments. We describe 1178 high-resolution proteins in a structurally non-redundant subset of the Protein Data Bank using simple features such as secondary-structure content, amino acid propensities, surface properties and ligands. The subset is split into two functional groupings, enzymes and non-enzymes. We use the support vector machine-learning algorithm to develop models that are capable of assigning the protein class. Validation of the method shows that the function can be predicted to an accuracy of 77% using 52 features to describe each protein. An adaptive search of possible subsets of features produces a simplified model based on 36 features that predicts at an accuracy of 80%. We compare the method to sequence-based methods that also avoid calculating alignments and predict a recently released set of unrelated proteins. The most useful features for distinguishing enzymes from non-enzymes are secondary-structure content, amino acid frequencies, number of disulphide bonds and size of the largest cleft. This method is applicable to any structure as it does not require the identification of sequence or structural similarity to a protein of known function.