The effect of manganese-induced hypercholesterolemia on learning in rats

Since the exact mechanism of manganese (Mn)-induced learning disability is not known, we investigated the role of elevated cholesterol in rats exposed daily to 357 and 714 μg Mn/kg for 30 d. Significant Mn accumulation was accompanied by increased cholesterol content in the hippocampal region of Mn-treated rats. The learning, which is based on the time needed to reach food placed at the exit of a T-maze after a 1-d training period, was significantly slower in exposed rats than in unexposed rats. The rats receiving 357 and 714 μg Mn/kg reached the food in 104.5±13.8 and 113.3±25.7 s, respectively, on d 30, whereas their untreated counterparts reached the food in 28.7±11.4 s. This delay was completely corrected to 29.3±7.8 and 30.7±6.0 s in rats with coadministration of an inhibitor of cholesterol biosynthesis with 357 and 714 μg/kg of Mn. The correction of impaired learning was associated with the normalization of hippocampal cholesterol, but the Mn level in this region of the brain was not influenced in rats treated with a drug that inhibits cholesterol biosynthesis. These results suggested that Mn-induced hypercholesterolemia is involved in Mn-dependent learning disability.     

CryoRes: Local Resolution Estimation of Cryo-EM Density Maps by Deep Learning

Recent progress in cryo-EM research has ignited a revolution in biological macromolecule structure determination. Resolution is an essential parameter for quality assessment of a cryo-EM density map, and it is known that resolution varies in different regions of a map. Currently available methods for local resolution estimation require manual adjustment of parameters and in some cases necessitate acquisition or de novo generation of so-called “half maps”. Here, we developed CryoRes, a deep-learning algorithm to estimate local resolution directly from a single final cryo-EM density map, specifically by learning resolution-aware patterns of density map voxels through supervised training on a large dataset comprising 1,174 experimental cryo-EM density maps. CryoRes significantly outperforms all of the state-of-the-art competing resolution estimation methods, achieving an average RMSE of 2.26 Å for local resolution estimation relative to the currently most reliable FSC-based method blocres, yet requiring only the single final map as input. Further, CryoRes is able to generate a molecular mask for each map, with accuracy 12.12% higher than the masks generated by ResMap. CryoRes is ultra-fast, fully automatic, parameter-free, applicable to cryo-EM subtomogram data, and freely available at https://cryores.zhanglab.net.     

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.     

Regulation of the utilization of 4-hydroxybenzoate and vanillate in batch and continuous cultures of Pseudomonas acidovorans

In Pseudomonas acidovorans, the pathways of 4-hydroxybenzoate and vanillate metabolism converge on the early intermediate, protocatechuate, which undergoes meta-cleavage. The methoxyl group of vanillate is almost completely oxidized, as shown by an experiment with (¹⁴C-methoxyl) vanillate. In batch cultures, 4-hydroxybenzoate and vanillate are simultaneously oxidized. Simultaneous oxidation was explained above all by the fact that both substrates mutually repress the ability of the cells to utilize the partner substrate.If P. acidovorans is growing in a turbidostat on one of the two substrates and is suddenly exposed to an equimolar mixture of both substrates, the respiration rates for the two substrates reciprocate, the for the substrate utilized first passing through a transient minimum, that for the added substrate passing through a transient maximum. Finally, a balance appears to be established, the for 4-hydroxybenzoate being slightly above that for vanillate. Transient phenomena also occur if a chemostat culture with both substrates is suddenly operated as a turbidostat culture or if cells not adapted to either substrate are suddenly exposed to a mixture of both substrates in the turbidostat.If a chemostat culture of P. acidovorans, growing at the expense of an equimolar mixture of 4-hydroxybenzoate and vanillate, is operated under conditions of increasing oxygen deficiency, the utilization ratio of the two substrates increases in favour of 4-hydroxybenzoate. However, if the culture is operated under conditions of increasing nitrogen deficiency, the utilization ratio increases in favour of vanillate.Abbreviations 4HB 4-hydroxybenzoate - VA vanillate - OD optical density     

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。     

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.