工作记忆对雌性锯腿原指树蛙配偶选择的影响

大多数无尾两栖类的配偶选择依赖声音通讯。为吸引雌性,雄性可通过增加音节数量或鸣声复杂性的方式提升鸣声吸引力。工作记忆是指在进行复杂认知活动时对过去短时间内接收到的信息进行处理和储存的一种记忆程序。目前,大多数无尾两栖类鸣声通讯研究侧重于揭示鸣声信号的功能,但关于工作记忆对雌性配偶选择的影响及其在复杂求偶信号进化过程中的作用的研究十分匮乏。本研究以锯腿原指树蛙(Kurixalus odontotarsus)为实验对象,利用趋声性实验测试雌性对不同复杂程度鸣声信号的工作记忆。雄性锯腿原指树蛙的鸣声主要包含A音节("呱"音)和B音节("啾"音),两类音节可以组成不同复杂程度的鸣声,如简单的广告鸣叫5A、复杂的组合鸣叫5A2B和5A5B。实验过程中为雌蛙播放不同复杂程度的鸣声刺激对(5A vs. 5A2B及5A vs. 5A5B),然后进行不同时长安静处理(0 s、5 s、10 s、15 s和30 s)。若安静处理后大部分雌蛙仍选择之前播放复杂鸣声的音箱,则认为此次安静处理时长在雌性对复杂鸣声的工作记忆范围内。实验数据通过广义估计方程(GEE)和精确二项分布检验进行统计分析。研究结果表明,相较于5A,雌性对组合鸣叫5A2B的工作记忆大约有15 s,对5A5B的工作记忆大约有10 s;而组间比较结果表明,雌性对于5A2B和5A5B的工作记忆没有显著性差异。因此,本研究认为复杂鸣声信号会通过工作记忆影响雌性的行为决策,且工作记忆对复杂鸣声信号进化的影响可能具有物种特异性。     

Environmentally induced phenotypic plasticity and DNA methylation changes in a wild potato growing in two contrasting Andean experimental gardens

DNA methylation can be environmentally modulated and plays a role in phenotypic plasticity. To understand the role of environmentally induced epigenetic variation and its dynamics in natural populations and ecosystems, it is relevant to place studies in a real-world context. Our experimental model is the wild potato Solanum kurtzianum, a close relative of the cultivated potato S. tuberosum. It was evaluated in its natural habitat, an arid Andean region in Argentina characterised by spatial and temporal environmental fluctuations. The dynamics of phenotypic and epigenetic variability (with Methyl Sensitive Amplified Polymorphism markers, MSAP) were assayed in three genotypes across three growing seasons. These genotypes were cultivated permanently and also reciprocally transplanted between experimental gardens (EG) differing in ca. 1000 m of altitude. In two seasons, the genotypes presented differential methylation patterns associated to the EG. In the reciprocal transplants, a rapid epigenomic remodelling occurred according to the growing season. Phenotypic plasticity, both spatial (between EGs within season) and temporal (between seasons), was detected. The epigenetic and phenotypic variability was positively correlated. The lack of an evident mitotic epigenetic memory would be a common response to short-term environmental fluctuations. Thus, the environmentally induced phenotypic and epigenetic variation could contribute to populations persistence through time. These results have implications for understanding the great ecological diversity of wild potatoes.     

原发性醛固酮增多症大鼠自主活动和学习记忆行为的影响

摘要 目的：探讨原发性醛固酮增多症(primary aldosteronism,PA)大鼠其自主活动和对学习记忆行为的影响。方法：8周龄健康雄SD(Sprague-Dawley)大鼠(n=30)随机分为对照组与模型组各15只。两组都皮下埋置微量渗透泵,模型组泵内灌注醛固酮,对照组泵内灌注等量的生理盐水,记录大鼠自主活动和学习记忆行为的变化情况。结果：所有大鼠均存活,模型组都造模成功,切口愈合良好。模型组造模后的收缩压高于对照组(P<0.05),也高于造模前(P<0.05),两组造模前后心率对比差异无统计学意义(P>0.05)。模型组造模后的逃避潜伏期与穿台次数少于对照组(P<0.05),也少于造模前(P<0.05)。模型组造模后的自主活动次数高于对照组(P<0.05),也高于造模前(P<0.05)。造模后模型组的鼠双微基因2(Mouse Double Microgene 2,MDM2)蛋白相对表达水平高于对照组(P<0.05)。造模后模型组的血清醛固酮含量都高对照组(P<0.05),血清钾离子、钠离子、肾素活性低于对照组(P<0.05)。结论：原发性醛固酮增多症大鼠伴随有血清钾离子、钠离子含量降低与MDM2蛋白的高表达,从而导致大鼠出现自主活动和学习记忆行为障碍。     

Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach

Deep learning techniques have recently made considerable advances in the field of artificial intelligence. These methodologies can assist psychologists in early diagnosis of mental disorders and preventing severe trauma. Major Depression Disorder (MDD) is a common and serious medical condition whose exact manifestations are not fully understood. So, early discovery of MDD patients helps to cure or limit the adverse effects. Electroencephalogram (EEG) is prominently used to study brain diseases such as MDD due to having high temporal resolution information, and being a noninvasive, inexpensive and portable method. This paper has proposed an EEG-based deep learning framework that automatically discriminates MDD patients from healthy controls. First, the relationships among EEG channels in the form of effective brain connectivity analysis are extracted by Generalized Partial Directed Coherence (GPDC) and Direct directed transfer function (dDTF) methods. A novel combination of sixteen connectivity methods (GPDC and dDTF in eight frequency bands) was used to construct an image for each individual. Finally, the constructed images of EEG signals are applied to the five different deep learning architectures. The first and second algorithms were based on one and two-dimensional convolutional neural network (1DCNN–2DCNN). The third method is based on long short-term memory (LSTM) model, while the fourth and fifth algorithms utilized a combination of CNN with LSTM model namely, 1DCNN-LSTM and 2DCNN-LSTM. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals. The efficiency of the proposed algorithms is evaluated on resting state EEG data obtained from 30 healthy subjects and 34 MDD patients. The experiments show that the 1DCNN-LSTM applied on constructed image of effective connectivity achieves best results with accuracy of 99.24% due to specific architecture which captures the presence of spatial and temporal relations in the brain connectivity. The proposed method as a diagnostic tool is able to help clinicians for diagnosing the MDD patients for early diagnosis and treatment.