Learning-Induced Expression of Meningeal Ependymin mRNA and Demonstration of Ependymin in Neurons and Glial Cells

Abstract: The turnover of a CNS-specific cell adhesion glycoprotein, ependymin, has earlier been found to increase during periods of neuronal plasticity. Here, ependymin mRNA expression was analyzed by semiquantitative in situ hybridization in goldfish. Learning of an active avoidance response resulted in a significant increase in ependymin mRNA expression 20 min to 4 h after acquisition of the task. In contrast, yoked control animals that were exposed to the same numbers of conditioned and unconditioned stimuli in a random, unpaired manner exhibited a strong down-regulation of ependymin mRNA. Hybridization signals were also increased by injection of anti-ependymin antiserum into brain ventricles. Ependymin mRNA was exclusively localized to reticular-shaped fibroblasts of the inner endomeningeal cell layer. Immunoelectron microscopic investigation, however, revealed ependymin also in distinct neuronal and glial cell populations in which no ependymin mRNA had been detected. Uptake of meningeal protein factors into glial and neuronal cells may therefore be of functional importance for plastic adaptations of the CNS.     

Computational approaches to motor control

New concepts and computational models that integrate behavioral and neurophysiological observations have addressed several of the most fundamental long-standing problems in motor control. These problems include the selection of particular trajectories among the large number of possibilities, the solution of inverse kinematics and dynamics problems, motor adaptation and the learning of sequential behaviors.     

Increased sampled volume improves Microcystis aeruginosa complex (MAC) colonies detection and prediction using Random Forests

Limited predictability of cyanobacteria and algal harmful blooms (CyanoHABs) impairs the development of adequate water management programs. The Microcystis aeruginosa complex (MAC) is ubiquitous worldwide. Their large colony size and relatively low numerical abundance imply that MAC abundance and presence are usually underestimated in traditional phytoplankton quantifications, which are based on samples of small volume. The objective of this work was twofold: (a) evaluate four sampling strategies of increasing sampling size to detect MAC organisms and (b) asses the predictability of MAC presence using easy-to-measure environmental variables. Sampling strategies were (I) 5–25 mL sedimented water samples inspected under inverted light microscope; (II) 20 L of water samples inspected by on-board naked-eye; (III) samples collected by towing a 25 μm pore size net inspected under light microscope; (IV) naked-eye inspection of 1000–7000 L concentrated water samples collected using a 115 μm-pore plankton net. We evaluated these objectives in a large environmental gradient (800 km) from freshwater to marine water (salinity range = 0–33) covering a wide range of temperatures (10–33 °C), underwater turbidity (0–158 NTU) and wind intensity (0–8 ms⁻¹). Classification Random Forest models (presence/absence of MAC organisms) were constructed and evaluated for each strategy by randomly partitioning data into training (2/3) and test (1/3) sets. A systematic increase in average accuracy (from 51 to 90%) and sensitivity (from 45 to 94%) towards methods with larger sampling size was found (i.e. I–IV). The best obtained model showed a high accuracy (90%) and sensitivity (94%) to detect MAC presence. These results suggest that the presence of MAC organisms can be accurately predicted using easy-to-measure environmental variables once sampling size is adequate. The proposed methodology demands very low costs and could be readily incorporated in most water monitoring plans to provide early warning of MAC occurrence, even when there is a low biomass of organisms.     

An intelligent system for estimating frog community calling activity and species richness

Over the past decade, dramatic declines in frog populations have been noticed worldwide. To examine this decline, monitoring frogs is becoming increasingly important. Compared to traditional field survey methods, recent advances in acoustic sensor technology have greatly extended spatial and temporal scales for monitoring animal populations. In this paper, we examine the problem of monitoring frog populations by analysing acoustic sensor data, where the population is reflected by community calling activity and species richness. Specifically, a novel acoustic event detection (AED) algorithm is first proposed to filter out those recordings without frog calls. Then, multi-label learning is used to classify each individual recording with six acoustic features: linear predictive coding coefficients, Mel-frequency cepstral coefficients, linear-frequency cepstral coefficients, acoustic complexity index, acoustic diversity index, and acoustic evenness index. Next, frog community calling activity and species richness are estimated by accumulating the results of AED and multi-label learning, respectively. Finally, ordinary least squares regression (OLS) is conducted to reveal the relationship between frog populations (frog calling activity and species richness) and weather variables (maximum temperature and rainfall). Experimental results demonstrate that our proposed intelligent system can significantly facilitate the effort to estimate frog community calling activity and species richness with comparable accuracies. The statistical results of OLS indicate that rainfall pattern has a lagged impact on frog community calling activity (significant in the first day after rainy day) and species richness (significant in the fourth day after rainy day). Temperature is shown to affect species richness but is less likely to change calling activity.     

Understanding and Application of Learning Theory in UK-based Equestrians

Learning in equines occurs through a predictable chain of stimulus–response–consequence processing. Whether the behavior persists will depend on the nature and timing of the consequence, whether it punishes or reinforces the response behavior. Knowledge and application of learning theory in UK-based equestrians was assessed by an online survey which probed three aspects of knowledge and understanding. Fifty-eight UK individuals (91% female) took part in the study and had an average of 12.4 years’ equestrian experience. Almost one-third (31%) described themselves as professional equestrians. A questionnaire was used to assess participants’ ability to identify and describe terminology associated with learning theory; self-report on their own knowledge and training methodology; and to express how they would respond in different training situations. Human-focused educational strategies were tested to assess their effectiveness and suitability for increasing understanding of learning theory. Professional equestrians expressed higher levels of subjective knowledge than amateurs (t₍₂₇₎ = –2.752, p = 0.018), with a moderate positive correlation between subjective knowledge and observer-scored knowledge (r = 0.443, p < 0.001). Professionals scored higher than amateurs on all questions asked, however a deficit of knowledge was observed across the board; in particular, in defining positive punishment (professional 38.8%, amateur 12.5% successful) and negative reinforcement (professional 33%, amateur 12.5% successful). Application of both an educational video and a leaflet increased respondents’ ability to correctly define terminology (Video t₍₈₎ = –4.07, p = 0.004: Leaflet t₍₁₂₎ = –5.02, p < 0.001). Improving understanding and application of learning theory in amateurs and professionals alike has the potential to improve equine welfare and training outcomes for both leisure and performance horses through reducing wastage attributed to behavioral problems or poor training.     

A historical review of research on the weaver ant Oecophylla in biological control

1 Although the weaver ant Oecophylla is the first written record of biological control, dating from 304 ad , there have been fewer than 70 scientific publications on this predator as a biological control agent in Asia, from the early 1970s onwards, and fewer than 25 in Africa. 2 Apart from crop‐specific ecological and perceptual factors, a historical review shows that political and market forces have also determined the extent to which Oecophylla was incorporated into research and development programmes. 3 In Africa, research on weaver ants in biological control concentrated on export crops, such as coconut and cocoa, whereas, in Asia and Australia, research focused on fruit and nut crops, primarily destined for domestic markets. 4 Increased evidence of pesticide inefficiency under tropical smallholder conditions, changing paradigm shifts in participatory research and a growing scientific interest in local knowledge in the early 1990s opened up new avenues for research on conservation biological control. 5 Lobbying and advocacy have been needed to ensure that Oecophylla was recognized as an effective biological control agent. 6 With an increased market demand for organic produce, holistic approaches such as conservation biological control, particularly the use of Oecophylla, are increasing in importance. 7 Multi‐stakeholder strategies for collaborative learning are proposed for a better control of major fruit, nut and timber tree pests in Africa, Asia and Australia.     

Proteins in Tumor-Derived Plasma Extracellular Vesicles Indicate Tumor Origin

Cancer-derived extracellular vesicles (EVs) promote tumorigenesis, premetastatic niche formation, and metastasis via their protein cargo. However, the proteins packaged by patient tumors into EVs cannot be determined in vivo because of the presence of EVs derived from other tissues. We therefore developed a cross-species proteomic method to quantify the human tumor-derived proteome of plasma EVs produced by patient-derived xenografts of four cancer types. Proteomic profiling revealed individualized packaging of novel protein cargo, and machine learning accurately classified the type of the underlying tumor.     

免疫细胞浸润在非小细胞肺癌诊断与预后中的应用

免疫细胞浸润对癌症的诊断与预后有着重要意义。文中收集TCGA数据库已收录的非小细胞肺癌肿瘤与正常组织基因表达数据,利用CIBERSORT工具得到22种免疫细胞占比来评估免疫细胞浸润情况。以22种免疫细胞占比为特征,用机器学习方法构建了非小细胞肺癌肿瘤与正常组织的分类模型,其中随机森林方法构建的模型分类效果AUC=0.987、敏感性0.98及特异性0.84。并且用随机森林方法构建的肺腺癌和肺鳞癌肿瘤组织分类模型效果AUC=0.827、敏感性0.75及特异性0.77。用LASSO回归筛选22种免疫细胞特征,保留8种强相关特征组成的免疫细胞评分结合临床特征构建了非小细胞肺癌预后模型。经评估及验证,预后模型C-index=0.71并且3年和5年的校准曲线拟合良好,可以对预后风险度进行准确预测。本研究基于免疫细胞浸润所构建的分类模型与预后模型,旨在对非小细胞肺癌的诊断与预后研究提供新的策略。     

An iterative learning strategy for the auto-tuning of the feedforward and feedback controller in type-1 diabetes

This paper proposes a scheme for the control of the blood glucose in subjects with type-1 diabetes mellitus based on the subcutaneous (s.c.) glucose measurement and s.c. insulin administration. The tuning of the controller is based on an iterative learning strategy that exploits the repetitiveness of the daily feeding habit of a patient. The control consists of a mixed feedback and feedforward contribution whose parameters are tuned through an iterative learning process that is based on the day-by-day automated analysis of the glucose response to the infusion of exogenous insulin. The scheme does not require any a priori information on the patient insulin/glucose response, on the meal times and on the amount of ingested carbohydrates (CHOs). Thanks to the learning mechanism the scheme is able to improve its performance over time. A specific logic is also introduced for the detection and prevention of possible hypoglycaemia events. The effectiveness of the methodology has been validated using long-term simulation studies applied to a set of nine in silico patients considering realistic uncertainties on the meal times and on the quantities of ingested CHOs.     

Diploma Training for Technologists in Tissue Banking

There is a great demand for a formal training programme for tissue bank technologists not only for the Asia Pacific Region but also for technologists in other regions including Latin America and Africa. To meet this need, National University Hospital (NUH) Tissue Bank was established as the International Atomic Energy Agency (IAEA)/National University of Singapore (NUS) Regional Training Centre for training tissue bank operators in the Asia Pacific Region (Regional co-operative Agreement, RCA) in November 1997. The training centre conducts a one-year distance learning Diploma in Tissue Banking offered by the NUS. The syllabus for the Diploma Course included the multi-media IAEA curriculum on tissue banking. The first Diploma Course has been successfully completed in October 1998. Twelve students convocated, 4 with Distinction, 5 with Credit and 3 with Pass. Sixteen candidates from the Asia Pacific Region registered for the Second Diploma Course in April 1999. This second batch will be due to sit for their Diploma Examination in April 2000. With the increasing popularity of this Diploma Course, the third batch of students which will be registered in April 2000, will include technologists not only from Asia Pacific Region but also from other regions including Africa.