Prion Protein Is Necessary for Latent Learning and Long-Term Memory Retention

1. The cellular prion protein, designated PrP^c, is a key molecule in the prion diseases but its physiological function remains unknown. To elucidate whether PrP^c plays some role in the central nervous system, we established a line of mice in which the PrP gene had been disrupted and subsequently conducted long-term observations.2. Performance in latent learning and passive avoidance was evaluated using water-finding and step-through tests, respectively.3. PrP ^–/– mice showed impaired performance in the water-finding test, indicating a disturbance in latent learning, at 23 weeks of age. In the step-through test, although the PrP ^–/– mice showed normal learning ability and short-term memory retention, they evidenced a significant disturbance in long-term memory retention.4. These results indicate that PrP^c is needed for certain types of learning and memory and that the loss of function of this protein may contribute to the pathogenesis of prion diseases.     

Risk-Based Learning Games Improve Long-Term Retention of Information among School Pupils

Risk heightens motivation and, if used appropriately, may have the potential to improve engagement in the classroom. We have developed a risk-based learning game for school pupils in order to test whether such learning games can improve later recall of information. The study was performed during a series of public engagement workshops delivered by undergraduate students. Undergraduate neuroscience students delivered 90-minute science workshops to 9–10 year old school pupils (n = 448) that were divided into ‘Risk’, ‘No risk’ and ‘Control’ classes. ‘Risk’ classes received periodic multiple-choice questions (MCQs) during the workshops which required small teams of pupils to assign tokens to the answer(s) they believed to be correct. Tokens assigned to the correct answer were returned to the group and an equal number given back as a prize; tokens assigned to incorrect answers were lost. Participation was incentivised by the promise of a brain-related prize to the team with the most tokens at the end of the workshop. ‘No risk’ classes received MCQs without the risk component whilst the ‘Control’ classes received no MCQs. When presented with a neuroscience quiz based on workshop content at the end of the workshop, pupils in the ‘Risk’ classes exhibited significantly greater recall of information one week later. Quiz scores were higher than scores from the day of the workshop which suggested pupils may have discussed the workshop content outside of the classroom, thereby increasing knowledge over and above what was learned during the workshop. This is supported by feedback from pupils in ‘Risk’ classes which indicated that ‘Risk’ workshops were more interesting than ‘No risk’ and ‘Control’ workshops. These data suggest that there is a role for risk in the classroom but further investigations are required to elucidate the causal mechanisms of improved retention of information.     

Long-Term Retention of a Relocated Population of Gopher Tortoises

Abstract: Relocations of gopher tortoises (Gopherus polyphemus) in Florida, USA, are frequently employed as mitigation tools when tortoises occupy land desired for development. Here we present information about retention and health of a relocated population of gopher tortoises 17 years after relocation. We combine our 17-year postrelocation data with earlier surveys 1 year and 2 years postrelocation to examine whether retention rates change over time. We also evaluate whether retention rates vary by age and gender. Of 74 gopher tortoises relocated in 1985, 31 were present in 2002. We found a 1-year retention rate of 42%, with retention rates of 100% each year thereafter, when we used the percentage of relocated individuals captured at each survey. We found a 1-year retention rate of 73%, a retention rate of 92% from year 1 to year 2, and an overall retention rate of 98.5% from year 2 through year 17, based on the assumption that all individuals present in later surveys were present in earlier surveys. We found no significant difference in retention rate over the 17-year period for adults and juveniles and for adult males and females. Relocated gopher tortoises showed natural growth patterns, indicating good health, but 35% of these gopher tortoises had ≥1 symptom of upper respiratory tract disease, a disease associated elsewhere with population declines of tortoises. Thus, retention rates of relocated gopher tortoises change over time, with relatively low retention during the first year postrelocation but nearly 100% retention in subsequent years. In general, our study shows that relocations can successfully lead to long-term retention of gopher tortoises, but we predict that without management (e.g., fire management and predator control) this relocated population is not viable.     

Learning Auditory Space: Generalization and Long-Term Effects

Background

Previous findings have shown that humans can learn to localize with altered auditory space cues. Here we analyze such learning processes and their effects up to one month on both localization accuracy and sound externalization. Subjects were trained and retested, focusing on the effects of stimulus type in learning, stimulus type in localization, stimulus position, previous experience, externalization levels, and time.

Method

We trained listeners in azimuth and elevation discrimination in two experiments. Half participated in the azimuth experiment first and half in the elevation first. In each experiment, half were trained in speech sounds and half in white noise. Retests were performed at several time intervals: just after training and one hour, one day, one week and one month later. In a control condition, we tested the effect of systematic retesting over time with post-tests only after training and either one day, one week, or one month later.

Results

With training all participants lowered their localization errors. This benefit was still present one month after training. Participants were more accurate in the second training phase, revealing an effect of previous experience on a different task. Training with white noise led to better results than training with speech sounds. Moreover, the training benefit generalized to untrained stimulus-position pairs. Throughout the post-tests externalization levels increased. In the control condition the long-term localization improvement was not lower without additional contact with the trained sounds, but externalization levels were lower.

Conclusion

Our findings suggest that humans adapt easily to altered auditory space cues and that such adaptation spreads to untrained positions and sound types. We propose that such learning depends on all available cues, but each cue type might be learned and retrieved differently. The process of localization learning is global, not limited to stimulus-position pairs, and it differs from externalization processes.     

Alzheimer's Disease Risk Assessment Using Large-Scale Machine Learning Methods

The goal of this work is to introduce new metrics to assess risk of Alzheimer''s disease (AD) which we call AD Pattern Similarity (AD-PS) scores. These metrics are the conditional probabilities modeled by large-scale regularized logistic regression. The AD-PS scores derived from structural MRI and cognitive test data were tested across different situations using data from the Alzheimer''s Disease Neuroimaging Initiative (ADNI) study. The scores were computed across groups of participants stratified by cognitive status, age and functional status. Cox proportional hazards regression was used to evaluate associations with the distribution of conversion times from mild cognitive impairment to AD. The performances of classifiers developed using data from different types of brain tissue were systematically characterized across cognitive status groups. We also explored the performance of anatomical and cognitive-anatomical composite scores generated by combining the outputs of classifiers developed using different types of data. In addition, we provide the AD-PS scores performance relative to other metrics used in the field including the Spatial Pattern of Abnormalities for Recognition of Early AD (SPARE-AD) index and total hippocampal volume for the variables examined.     

Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory

Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.     

Metrical Presentation Boosts Implicit Learning of Artificial Grammar

The present study investigated whether a temporal hierarchical structure favors implicit learning. An artificial pitch grammar implemented with a set of tones was presented in two different temporal contexts, notably with either a strongly metrical structure or an isochronous structure. According to the Dynamic Attending Theory, external temporal regularities can entrain internal oscillators that guide attention over time, allowing for temporal expectations that influence perception of future events. Based on this framework, it was hypothesized that the metrical structure provides a benefit for artificial grammar learning in comparison to an isochronous presentation. Our study combined behavioral and event-related potential measurements. Behavioral results demonstrated similar learning in both participant groups. By contrast, analyses of event-related potentials showed a larger P300 component and an earlier N2 component for the strongly metrical group during the exposure phase and the test phase, respectively. These findings suggests that the temporal expectations in the strongly metrical condition helped listeners to better process the pitch dimension, leading to improved learning of the artificial grammar.     

Retention of Habitat Complexity Minimizes Disassembly of Reef Fish Communities following Disturbance: A Large-Scale Natural Experiment

High biodiversity ecosystems are commonly associated with complex habitats. Coral reefs are highly diverse ecosystems, but are under increasing pressure from numerous stressors, many of which reduce live coral cover and habitat complexity with concomitant effects on other organisms such as reef fishes. While previous studies have highlighted the importance of habitat complexity in structuring reef fish communities, they employed gradient or meta-analyses which lacked a controlled experimental design over broad spatial scales to explicitly separate the influence of live coral cover from overall habitat complexity. Here a natural experiment using a long term (20 year), spatially extensive (∼115,000 kms²) dataset from the Great Barrier Reef revealed the fundamental importance of overall habitat complexity for reef fishes. Reductions of both live coral cover and habitat complexity had substantial impacts on fish communities compared to relatively minor impacts after major reductions in coral cover but not habitat complexity. Where habitat complexity was substantially reduced, species abundances broadly declined and a far greater number of fish species were locally extirpated, including economically important fishes. This resulted in decreased species richness and a loss of diversity within functional groups. Our results suggest that the retention of habitat complexity following disturbances can ameliorate the impacts of coral declines on reef fishes, so preserving their capacity to perform important functional roles essential to reef resilience. These results add to a growing body of evidence about the importance of habitat complexity for reef fishes, and represent the first large-scale examination of this question on the Great Barrier Reef.     

Learning Retention Mechanisms and Evolutionary Parameters of Duplicate Genes from Their Expression Data

Learning about the roles that duplicate genes play in the origins of novel phenotypes requires an understanding of how their functions evolve. A previous method for achieving this goal, CDROM, employs gene expression distances as proxies for functional divergence and then classifies the evolutionary mechanisms retaining duplicate genes from comparisons of these distances in a decision tree framework. However, CDROM does not account for stochastic shifts in gene expression or leverage advances in contemporary statistical learning for performing classification, nor is it capable of predicting the parameters driving duplicate gene evolution. Thus, here we develop CLOUD, a multi-layer neural network built on a model of gene expression evolution that can both classify duplicate gene retention mechanisms and predict their underlying evolutionary parameters. We show that not only is the CLOUD classifier substantially more powerful and accurate than CDROM, but that it also yields accurate parameter predictions, enabling a better understanding of the specific forces driving the evolution and long-term retention of duplicate genes. Further, application of the CLOUD classifier and predictor to empirical data from Drosophila recapitulates many previous findings about gene duplication in this lineage, showing that new functions often emerge rapidly and asymmetrically in younger duplicate gene copies, and that functional divergence is driven by strong natural selection. Hence, CLOUD represents a major advancement in classifying retention mechanisms and predicting evolutionary parameters of duplicate genes, thereby highlighting the utility of incorporating sophisticated statistical learning techniques to address long-standing questions about evolution after gene duplication.     

Insights into the Effects of Long-Term Artificial Selection on Seed Size in Maize

Grain produced from cereal crops is a primary source of human food and animal feed worldwide. To understand the genetic basis of seed-size variation, a grain yield component, we conducted a genome-wide scan to detect evidence of selection in the maize Krug Yellow Dent long-term divergent seed-size selection experiment. Previous studies have documented significant phenotypic divergence between the populations. Allele frequency estimates for ∼3 million single nucleotide polymorphisms (SNPs) in the base population and selected populations were estimated from pooled whole-genome resequencing of 48 individuals per population. Using F_ST values across sliding windows, 94 divergent regions with a median of six genes per region were identified. Additionally, 2729 SNPs that reached fixation in both selected populations with opposing fixed alleles were identified, many of which clustered in two regions of the genome. Copy-number variation was highly prevalent between the selected populations, with 532 total regions identified on the basis of read-depth variation and comparative genome hybridization. Regions important for seed weight in natural variation were identified in the maize nested association mapping population. However, the number of regions that overlapped with the long-term selection experiment did not exceed that expected by chance, possibly indicating unique sources of variation between the two populations. The results of this study provide insights into the genetic elements underlying seed-size variation in maize and could also have applications for other cereal crops.     

Early Exposure to Volatile Anesthetics Impairs Long-Term Associative Learning and Recognition Memory

Background

Anesthetic exposure early in life affects neural development and long-term cognitive function, but our understanding of the types of memory that are altered is incomplete. Specific cognitive tests in rodents that isolate different memory processes provide a useful approach for gaining insight into this issue.

Methods

Postnatal day 7 (P7) rats were exposed to either desflurane or isoflurane at 1 Minimum Alveolar Concentration for 4 h. Acute neuronal death was assessed 12 h later in the thalamus, CA1-3 regions of hippocampus, and dentate gyrus. In separate behavioral experiments, beginning at P48, subjects were evaluated in a series of object recognition tests relying on associative learning, as well as social recognition.

Results

Exposure to either anesthetic led to a significant increase in neuroapoptosis in each brain region. The extent of neuronal death did not differ between groups. Subjects were unaffected in simple tasks of novel object and object-location recognition. However, anesthetized animals from both groups were impaired in allocentric object-location memory and a more complex task requiring subjects to associate an object with its location and contextual setting. Isoflurane exposure led to additional impairment in object-context association and social memory.

Conclusion

Isoflurane and desflurane exposure during development result in deficits in tasks relying on associative learning and recognition memory. Isoflurane may potentially cause worse impairment than desflurane.     

Retention of Learning through Metamorphosis in the Grain Beetle Tenebrio molitor)

Two groups of 15 larve were trained to go consistently to oneside of a T-maze, using escape from light as a reinforcer forcorrect performance. Two yoked control groups received non-responsecontingent reinforcement on trials when individuals in the experimentalgroups were reinforced. A third control group received non-responsecontingent reinforcement on every trial, and a fourth controlgroup was left untrained. After metamorphosis, one experimentalgroup was trained to turn in the same direction as in originallearning (relearning), while the other experimental group wasnamed to turn in the opposite direction (rexersal learning).All the control groups were also trained as adults. In the experimentalgroups, relearning occurred significanty faster and reversallearning occurred significantly slower than learning in anyof the control conditions. These findings are interpreted asprovinding evidence of retention of leirning through metamorphosis.     

Associative Learning during Early Adulthood Enhances Later Memory Retention in Honeybees

Background

Cognitive experiences during the early stages of life play an important role in shaping the future behavior in mammals but also in insects, in which precocious learning can directly modify behaviors later in life depending on both the timing and the rearing environment. However, whether olfactory associative learning acquired early in the adult stage of insects affect memorizing of new learning events has not been studied yet.

Methodology

Groups of adult honeybee workers that experienced an odor paired with a sucrose solution 5 to 8 days or 9 to 12 days after emergence were previously exposed to (i) a rewarded experience through the offering of scented food, or (ii) a non-rewarded experience with a pure volatile compound in the rearing environment.

Principal Findings

Early rewarded experiences (either at 1–4 or 5–8 days of adult age) enhanced retention performance in 9–12-day-conditioned bees when they were tested at 17 days of age. The highest retention levels at this age, which could not be improved with prior rewarded experiences, were found for memories established at 5–8 days of adult age. Associative memories acquired at 9–12 days of age showed a weak effect on retention for some pure pre-exposed volatile compounds; whereas the sole exposure of an odor at any younger age did not promote long-term effects on learning performance.

Conclusions

The associative learning events that occurred a few days after adult emergence improved memorizing in middle-aged bees. In addition, both the timing and the nature of early sensory inputs interact to enhance retention of new learning events acquired later in life, an important matter in the social life of honeybees.     

Feeding Fitness and Quality of Domesticated and Feral Predators: Effects of Long-Term Rearing on Artificial Diet

Predatory geocorids, Geocoris punctipes Say (Geocoridae: Hemiptera), that had been reared (domesticated) for over 6 years (60 continuous generations) on an artificial diet were compared with feral (F₁) counterparts to determine possible domestication-associated losses in predatory capabilities. Using adult female predators provided with either tobacco budworm larvae, Heliothis virescens F., or pea aphids, Acyrthosiphon pisum Harris, as prey, I measured predator weights, handling time with a single prey, amount extracted, consumption rate, and feeding (gut) capacity. Domesticated females were significantly smaller than ferals, weighing 4.53 mg versus 5.09 mg, respectively. Domestication did not significantly influence handling times, which averaged 131 (domesticated) and 122 min (feral) for predators feeding on H. virescens larvae and 106 (domesticated) and 94 min (feral) for G. punctipes feeding on A. pisum. Although there were significant differences in the weights of the two kinds of prey (H. virescens larvae being about twice as heavy as the A. pisum), both prey species exceeded the ingestion capacity of the predators. Amounts extracted by predators were 1.12 to 1.20 mg and were not significantly influenced by rearing background, prey biomass, or prey type. Consumption rates of 11.86 and 12.91 μg/min were nearly identical for both domesticated and feral predators regardless of prey species.     

Comparison of Eleven Methods for Genomic DNA Extraction Suitable for Large-Scale Whole-Genome Genotyping and Long-Term DNA Banking Using Blood Samples

Over the recent years, next generation sequencing and microarray technologies have revolutionized scientific research with their applications to high-throughput analysis of biological systems. Isolation of high quantities of pure, intact, double stranded, highly concentrated, not contaminated genomic DNA is prerequisite for successful and reliable large scale genotyping analysis. High quantities of pure DNA are also required for the creation of DNA-banks. In the present study, eleven different DNA extraction procedures, including phenol-chloroform, silica and magnetic beads based extractions, were examined to ascertain their relative effectiveness for extracting DNA from ovine blood samples. The quality and quantity of the differentially extracted DNA was subsequently assessed by spectrophotometric measurements, Qubit measurements, real-time PCR amplifications and gel electrophoresis. Processing time, intensity of labor and cost for each method were also evaluated. Results revealed significant differences among the eleven procedures and only four of the methods yielded satisfactory outputs. These four methods, comprising three modified silica based commercial kits (Modified Blood, Modified Tissue, Modified Dx kits) and an in-house developed magnetic beads based protocol, were most appropriate for extracting high quality and quantity DNA suitable for large-scale microarray genotyping and also for long-term DNA storage as demonstrated by their successful application to 600 individuals.     

Long-Term Inactivation Study of Three Enteroviruses in Artificial Surface and Groundwaters, Using PCR and Cell Culture

Since the transmission of pathogenic viruses via water is indistinguishable from the transmission via other routes and since the levels in drinking water, although significant for health, may be too low for detection, quantitative viral risk assessment is a useful tool for assessing disease risk due to consumption of drinking water. Quantitative viral risk assessment requires information concerning the ability of viruses detected in drinking water to infect their host. To obtain insight into the infectivity of viruses in relation to the presence of virus genomes, inactivation of three different enteroviruses in artificial ground and surface waters under different controlled pH, temperature, and salt conditions was studied by using both PCR and cell culture over time. In salt-peptone medium, the estimated ratio of RNA genomes to infectious poliovirus 1 in freshly prepared suspensions was about 10⁰. At 4°C this ratio was 10³ after 600 days, and at 22°C it was 10⁴ after 200 days. For poliovirus 1 and 2 the RNA/infectious virus ratio was higher in artificial groundwater than in artificial surface water, but this was not the case for coxsackievirus B4. When molecular detection is used for virus enumeration, it is important that the fraction of infectious virus (based on all virus genomes detected) decays with time, especially at temperatures near 22°C.     

Artificial Grammar Learning of Melody Is Constrained by Melodic Inconsistency: Narmour's Principles Affect Melodic Learning

Considerable evidence suggests that people acquire artificial grammars incidentally and implicitly, an indispensable capacity for the acquisition of music or language. However, less research has been devoted to exploring constraints affecting incidental learning. Within the domain of music, the extent to which Narmour''s (1990) melodic principles affect implicit learning of melodic structure was experimentally explored. Extending previous research (Rohrmeier, Rebuschat & Cross, 2011), the identical finite-state grammar is employed having terminals (the alphabet) manipulated so that melodies generated systematically violated Narmour''s principles. Results indicate that Narmour-inconsistent melodic materials impede implicit learning. This further constitutes a case in which artificial grammar learning is affected by prior knowledge or processing constraints.     

Trainable High Resolution Melt Curve Machine Learning Classifier for Large-Scale Reliable Genotyping of Sequence Variants

High resolution melt (HRM) is gaining considerable popularity as a simple and robust method for genotyping sequence variants. However, accurate genotyping of an unknown sample for which a large number of possible variants may exist will require an automated HRM curve identification method capable of comparing unknowns against a large cohort of known sequence variants. Herein, we describe a new method for automated HRM curve classification based on machine learning methods and learned tolerance for reaction condition deviations. We tested this method in silico through multiple cross-validations using curves generated from 9 different simulated experimental conditions to classify 92 known serotypes of Streptococcus pneumoniae and demonstrated over 99% accuracy with 8 training curves per serotype. In vitro verification of the algorithm was tested using sequence variants of a cancer-related gene and demonstrated 100% accuracy with 3 training curves per sequence variant. The machine learning algorithm enabled reliable, scalable, and automated HRM genotyping analysis with broad potential clinical and epidemiological applications.     

A Model for Improving the Learning Curves of Artificial Neural Networks

In this article, the performance of a hybrid artificial neural network (i.e. scale-free and small-world) was analyzed and its learning curve compared to three other topologies: random, scale-free and small-world, as well as to the chemotaxis neural network of the nematode Caenorhabditis Elegans. One hundred equivalent networks (same number of vertices and average degree) for each topology were generated and each was trained for one thousand epochs. After comparing the mean learning curves of each network topology with the C. elegans neural network, we found that the networks that exhibited preferential attachment exhibited the best learning curves.