Learning from sensory and reward prediction errors during motor adaptation

Voluntary motor commands produce two kinds of consequences. Initially, a sensory consequence is observed in terms of activity in our primary sensory organs (e.g., vision, proprioception). Subsequently, the brain evaluates the sensory feedback and produces a subjective measure of utility or usefulness of the motor commands (e.g., reward). As a result, comparisons between predicted and observed consequences of motor commands produce two forms of prediction error. How do these errors contribute to changes in motor commands? Here, we considered a reach adaptation protocol and found that when high quality sensory feedback was available, adaptation of motor commands was driven almost exclusively by sensory prediction errors. This form of learning had a distinct signature: as motor commands adapted, the subjects altered their predictions regarding sensory consequences of motor commands, and generalized this learning broadly to neighboring motor commands. In contrast, as the quality of the sensory feedback degraded, adaptation of motor commands became more dependent on reward prediction errors. Reward prediction errors produced comparable changes in the motor commands, but produced no change in the predicted sensory consequences of motor commands, and generalized only locally. Because we found that there was a within subject correlation between generalization patterns and sensory remapping, it is plausible that during adaptation an individual''s relative reliance on sensory vs. reward prediction errors could be inferred. We suggest that while motor commands change because of sensory and reward prediction errors, only sensory prediction errors produce a change in the neural system that predicts sensory consequences of motor commands.     

Perceptions of Speed and Risk: Experimental Studies of Road Crossing by Older People

Crossing a road safely is a complex task requiring good sensorimotor function and integration of information about traffic speed, distances and one’s own speed. Poor judgement through age-related sensorimotor or cognitive impairment or a predisposition to take risks could lead to errors with serious consequences. On a simulated road, 85 participants (age ≥70 years) were asked to cross in front of an approaching car with a clearance as small as considered safe in two conditions; (1) with nothing else to attend to (free crossing) and (2) with an additional ball-gathering task while waiting to cross (task crossing). Participants were categorised according to their crossing outcome (failed to cross, ‘hit’, exact, safe, cautious). Participants also performed two sub-studies; (1) the perception of the time-to-arrival of moving objects and (2) the perception of own gait speed. Physical and cognitive function and everyday risk-taking behaviour were also assessed. In free crossing, clearances varied but no participants were “hit” by the car. In task crossing, participants allowed smaller clearances and 10% of participants would have been hit while 13% missed the opportunity to cross altogether. Across a wide range of physical and cognitive measures, including perceived and actual gait speed, a consistent pattern was observed in the task crossing condition. The exact group performed best, the ‘hit’, safe and cautious groups performed less well while those who missed the opportunity (fail) performed worst. The exact group reported taking the greatest risks in everyday life whereas the remaining groups reported being cautious. In conclusion, we found older people with poorer perceptual, physical and cognitive function made inappropriate and risky decisions in a divided attention road-crossing task despite self-reports of cautious behaviour in everyday life.     

How Does Our Motor System Determine Its Learning Rate?

Motor learning is driven by movement errors. The speed of learning can be quantified by the learning rate, which is the proportion of an error that is corrected for in the planning of the next movement. Previous studies have shown that the learning rate depends on the reliability of the error signal and on the uncertainty of the motor system’s own state. These dependences are in agreement with the predictions of the Kalman filter, which is a state estimator that can be used to determine the optimal learning rate for each movement such that the expected movement error is minimized. Here we test whether not only the average behaviour is optimal, as the previous studies showed, but if the learning rate is chosen optimally in every individual movement. Subjects made repeated movements to visual targets with their unseen hand. They received visual feedback about their endpoint error immediately after each movement. The reliability of these error-signals was varied across three conditions. The results are inconsistent with the predictions of the Kalman filter because correction for large errors in the beginning of a series of movements to a fixed target was not as fast as predicted and the learning rates for the extent and the direction of the movements did not differ in the way predicted by the Kalman filter. Instead, a simpler model that uses the same learning rate for all movements with the same error-signal reliability can explain the data. We conclude that our brain does not apply state estimation to determine the optimal planning correction for every individual movement, but it employs a simpler strategy of using a fixed learning rate for all movements with the same level of error-signal reliability.     

Infants’ Somatotopic Neural Responses to Seeing Human Actions: I’ve Got You under My Skin

Human infants rapidly learn new skills and customs via imitation, but the neural linkages between action perception and production are not well understood. Neuroscience studies in adults suggest that a key component of imitation–identifying the corresponding body part used in the acts of self and other–has an organized neural signature. In adults, perceiving someone using a specific body part (e.g., hand vs. foot) is associated with activation of the corresponding area of the sensory and/or motor strip in the observer’s brain–a phenomenon called neural somatotopy. Here we examine whether preverbal infants also exhibit somatotopic neural responses during the observation of others’ actions. 14-month-old infants were randomly assigned to watch an adult reach towards and touch an object using either her hand or her foot. The scalp electroencephalogram (EEG) was recorded and event-related changes in the sensorimotor mu rhythm were analyzed. Mu rhythm desynchronization was greater over hand areas of sensorimotor cortex during observation of hand actions and was greater over the foot area for observation of foot actions. This provides the first evidence that infants’ observation of someone else using a particular body part activates the corresponding areas of sensorimotor cortex. We hypothesize that this somatotopic organization in the developing brain supports imitation and cultural learning. The findings connect developmental cognitive neuroscience, adult neuroscience, action representation, and behavioral imitation.     

Dynamic expression of cadherins regulates vocal development in a songbird

Background

Since, similarly to humans, songbirds learn their vocalization through imitation during their juvenile stage, they have often been used as model animals to study the mechanisms of human verbal learning. Numerous anatomical and physiological studies have suggested that songbirds have a neural network called ‘song system’ specialized for vocal learning and production in their brain. However, it still remains unknown what molecular mechanisms regulate their vocal development. It has been suggested that type-II cadherins are involved in synapse formation and function. Previously, we found that type-II cadherin expressions are switched in the robust nucleus of arcopallium from cadherin-7-positive to cadherin-6B-positive during the phase from sensory to sensorimotor learning stage in a songbird, the Bengalese finch. Furthermore, in vitro analysis using cultured rat hippocampal neurons revealed that cadherin-6B enhanced and cadherin-7 suppressed the frequency of miniature excitatory postsynaptic currents via regulating dendritic spine morphology.

Methodology/Principal Findings

To explore the role of cadherins in vocal development, we performed an in vivo behavioral analysis of cadherin function with lentiviral vectors. Overexpression of cadherin-7 in the juvenile and the adult stages resulted in severe defects in vocal production. In both cases, harmonic sounds typically seen in the adult Bengalese finch songs were particularly affected.

Conclusions/Significance

Our results suggest that cadherins control vocal production, particularly harmonic sounds, probably by modulating neuronal morphology of the RA nucleus. It appears that the switching of cadherin expressions from sensory to sensorimotor learning stage enhances vocal production ability to make various types of vocalization that is essential for sensorimotor learning in a trial and error manner.     

Predicting the Valence of a Scene from Observers’ Eye Movements

Multimedia analysis benefits from understanding the emotional content of a scene in a variety of tasks such as video genre classification and content-based image retrieval. Recently, there has been an increasing interest in applying human bio-signals, particularly eye movements, to recognize the emotional gist of a scene such as its valence. In order to determine the emotional category of images using eye movements, the existing methods often learn a classifier using several features that are extracted from eye movements. Although it has been shown that eye movement is potentially useful for recognition of scene valence, the contribution of each feature is not well-studied. To address the issue, we study the contribution of features extracted from eye movements in the classification of images into pleasant, neutral, and unpleasant categories. We assess ten features and their fusion. The features are histogram of saccade orientation, histogram of saccade slope, histogram of saccade length, histogram of saccade duration, histogram of saccade velocity, histogram of fixation duration, fixation histogram, top-ten salient coordinates, and saliency map. We utilize machine learning approach to analyze the performance of features by learning a support vector machine and exploiting various feature fusion schemes. The experiments reveal that ‘saliency map’, ‘fixation histogram’, ‘histogram of fixation duration’, and ‘histogram of saccade slope’ are the most contributing features. The selected features signify the influence of fixation information and angular behavior of eye movements in the recognition of the valence of images.     

Robot Guided ‘Pen Skill’ Training in Children with Motor Difficulties

Motor deficits are linked to a range of negative physical, social and academic consequences. Haptic robotic interventions, based on the principles of sensorimotor learning, have been shown previously to help children with motor problems learn new movements. We therefore examined whether the training benefits of a robotic system would generalise to a standardised test of ‘pen-skills’, assessed using objective kinematic measures [via the Clinical Kinematic Assessment Tool, CKAT]. A counterbalanced, cross-over design was used in a group of 51 children (37 male, aged 5–11 years) with manual control difficulties. Improved performance on a novel task using the robotic device could be attributed to the intervention but there was no evidence of generalisation to any of the CKAT tasks. The robotic system appears to have the potential to support motor learning, with the technology affording numerous advantages. However, the training regime may need to target particular manual skills (e.g. letter formation) in order to obtain clinically significant improvements in specific skills such as handwriting.     

A Lightweight,Headphones-based System for Manipulating Auditory Feedback in Songbirds

Experimental manipulations of sensory feedback during complex behavior have provided valuable insights into the computations underlying motor control and sensorimotor plasticity¹. Consistent sensory perturbations result in compensatory changes in motor output, reflecting changes in feedforward motor control that reduce the experienced feedback error. By quantifying how different sensory feedback errors affect human behavior, prior studies have explored how visual signals are used to recalibrate arm movements^2,3 and auditory feedback is used to modify speech production^4-7. The strength of this approach rests on the ability to mimic naturalistic errors in behavior, allowing the experimenter to observe how experienced errors in production are used to recalibrate motor output.Songbirds provide an excellent animal model for investigating the neural basis of sensorimotor control and plasticity^8,9. The songbird brain provides a well-defined circuit in which the areas necessary for song learning are spatially separated from those required for song production, and neural recording and lesion studies have made significant advances in understanding how different brain areas contribute to vocal behavior^9-12. However, the lack of a naturalistic error-correction paradigm - in which a known acoustic parameter is perturbed by the experimenter and then corrected by the songbird - has made it difficult to understand the computations underlying vocal learning or how different elements of the neural circuit contribute to the correction of vocal errors¹³.The technique described here gives the experimenter precise control over auditory feedback errors in singing birds, allowing the introduction of arbitrary sensory errors that can be used to drive vocal learning. Online sound-processing equipment is used to introduce a known perturbation to the acoustics of song, and a miniaturized headphones apparatus is used to replace a songbird''s natural auditory feedback with the perturbed signal in real time. We have used this paradigm to perturb the fundamental frequency (pitch) of auditory feedback in adult songbirds, providing the first demonstration that adult birds maintain vocal performance using error correction¹⁴. The present protocol can be used to implement a wide range of sensory feedback perturbations (including but not limited to pitch shifts) to investigate the computational and neurophysiological basis of vocal learning.     

The cerebellum updates predictions about the visual consequences of one's behavior

Each action has sensory consequences that need to be distinguished from sensations arising from the environment. This is accomplished by the comparing of internal predictions about these consequences with the actual afference, thereby isolating the afferent component that is self-produced. Because the sensory consequences of actions vary as a result of changes of the effector's efficacy, internal predictions need to be updated continuously and on a short time scale. Here, we tested the hypothesis that this updating of predictions about the sensory consequences of actions is mediated by the cerebellum, a notion that parallels the cerebellum's role in motor learning. Patients with cerebellar lesions and their matched controls were equally able to detect experimental modifications of visual feedback about their pointing movements. When such feedback was constantly rotated, both groups instantly attributed the visual feedback to their own actions. However, in interleaved trials without actual feedback, patients did no longer account for this feedback rotation--neither perceptually nor with respect to motor performance. Both deficits can be explained by an impaired updating of internal predictions about the sensory consequences of actions caused by cerebellar pathology. Thus, the cerebellum guarantees both precise performance and veridical perceptual interpretation of actions.     

Distinct brain oscillatory responses for the perception and identification of one’s own body from other’s body

The body recognition process includes complex visual processing, the sensation, perception, and distinction stages of the stimulus. This study examined this process by using the time–frequency analysis of EEG signals and analyzed the obtained data by using the event-related oscillations method. This study aimed to examine the oscillatory brain responses and distinguish one’s own body from other’s body. In the present study, 17 young adults were included and the EEGs were recorded with 32 electrodes placed in different locations. Event-related power spectrum and phase-locking analyzes were performed. ITC and ERSP data were analyzed using 2 (condition) × 11 (location) × 2 (hemisphere) ANOVA Design. As we observed a prolonged response in the theta band in the grand averages, we included the time variable in the overall model. As a result, we found that the phase-locking and the event-related power spectrum of the theta response in recognizing one’s own body were higher when compared to the phase-locking and the event-related power spectrum of the theta response in recognizing others’ body (p < 0.05). When the time variable was included, the early theta response was more phase-locked and had a higher power spectrum compared to the late theta response (p < 0.05). As a result of the power spectrum analysis, the condition × hemisphere interaction effect in the beta band was higher in the left hemisphere regarding increased responses in recognizing one’s own body (p < 0.05). As a result of ITC, the main effect of the condition was higher in the recognition of the stimulus of one’s own body (p < 0.05). Finally, the theta oscillator response stood out in distinguishing one’s own body from other’s body. Similarly, the power spectrum in the beta response was higher in the left hemisphere, and this finding is consistent with the literature.     

Aberration-corrected ultrafine analysis of miRNA reads at single-base resolution: a k-mer lattice approach

Raw sequencing reads of miRNAs contain machine-made substitution errors, or even insertions and deletions (indels). Although the error rate can be low at 0.1%, precise rectification of these errors is critically important because isoform variation analysis at single-base resolution such as novel isomiR discovery, editing events understanding, differential expression analysis, or tissue-specific isoform identification is very sensitive to base positions and copy counts of the reads. Existing error correction methods do not work for miRNA sequencing data attributed to miRNAs’ length and per-read-coverage properties distinct from DNA or mRNA sequencing reads. We present a novel lattice structure combining kmers, (k – 1)mers and (k + 1)mers to address this problem. The method is particularly effective for the correction of indel errors. Extensive tests on datasets having known ground truth of errors demonstrate that the method is able to remove almost all of the errors, without introducing any new error, to improve the data quality from every-50-reads containing one error to every-1300-reads containing one error. Studies on experimental miRNA sequencing datasets show that the errors are often rectified at the 5′ ends and the seed regions of the reads, and that there are remarkable changes after the correction in miRNA isoform abundance, volume of singleton reads, overall entropy, isomiR families, tissue-specific miRNAs, and rare-miRNA quantities.     

Sensorimotor Learning of Acupuncture Needle Manipulation Using Visual Feedback

Objective

Humans can acquire a wide variety of motor skills using sensory feedback pertaining to discrepancies between intended and actual movements. Acupuncture needle manipulation involves sophisticated hand movements and represents a fundamental skill for acupuncturists. We investigated whether untrained students could improve their motor performance during acupuncture needle manipulation using visual feedback (VF).

Methods

Twenty-one untrained medical students were included, randomly divided into concurrent (n = 10) and post-trial (n = 11) VF groups. Both groups were trained in simple lift/thrusting techniques during session 1, and in complicated lift/thrusting techniques in session 2 (eight training trials per session). We compared the motion patterns and error magnitudes of pre- and post-training tests.

Results

During motion pattern analysis, both the concurrent and post-trial VF groups exhibited greater improvements in motion patterns during the complicated lifting/thrusting session. In the magnitude error analysis, both groups also exhibited reduced error magnitudes during the simple lifting/thrusting session. For the training period, the concurrent VF group exhibited reduced error magnitudes across all training trials, whereas the post-trial VF group was characterized by greater error magnitudes during initial trials, which gradually reduced during later trials.

Conclusions

Our findings suggest that novices can improve the sophisticated hand movements required for acupuncture needle manipulation using sensorimotor learning with VF. Use of two types of VF can be beneficial for untrained students in terms of learning how to manipulate acupuncture needles, using either automatic or cognitive processes.     

galign: A Tool for Rapid Genome Polymorphism Discovery

Background

Highly parallel sequencing technologies have become important tools in the analysis of sequence polymorphisms on a genomic scale. However, the development of customized software to analyze data produced by these methods has lagged behind.

Methods/Principal Findings

Here I describe a tool, ‘galign’, designed to identify polymorphisms between sequence reads obtained using Illumina/Solexa technology and a reference genome. The ‘galign’ alignment tool does not use Smith-Waterman matrices for sequence comparisons. Instead, a simple algorithm comparing parsed sequence reads to parsed reference genome sequences is used. ‘galign’ output is geared towards immediate user application, displaying polymorphism locations, nucleotide changes, and relevant predicted amino-acid changes for ease of information processing. To do so, ‘galign’ requires several accessory files easily derived from an annotated reference genome. Direct sequencing as well as in silico studies demonstrate that ‘galign’ provides lesion predictions comparable in accuracy to available prediction programs, accompanied by greater processing speed and more user-friendly output. We demonstrate the use of ‘galign’ to identify mutations leading to phenotypic consequences in C. elegans.

Conclusion/Significance

Our studies suggest that ‘galign’ is a useful tool for polymorphism discovery, and is of immediate utility for sequence mining in C. elegans.     

Assessment from Functional Perspectives: Using Sensorimotor Control in the Hand as an Outcome Indicator in the Surgical Treatment of Carpal Tunnel Syndrome

To investigate whether sensorimotor control of the hand could be an outcome indicator after carpal tunnel release (CTR), this work examined changes in the results of patients’ manual tactile test (MTT), pinch-holding-up activity (PHUA), two-point discrimination (2PD) and Semmes-Weinstein monofilament (SWM) tests. Participants included 30 predominantly sensory neuropathy CTS patients, as confirmed by a nerve conduction study. The MTT, precision pinch performance in PHUA and traditional sensibility (2PD and SWM) tests were used to examine different aspects of sensory status at the time-points of two weeks before operation and one month post-operation, with a single-blind design. The results showed significant improvements in the sensory function as detected by the 2PD and SWM tests (p<0.001) and sensorimotor function as detected by the MTT (p<0.001) and PHUA test (p<0.05) for patients receiving CTR. The responsiveness of the SWM, MTT and PHUA tests (effect size>0.5, p<0.01) are better than that of two-point discrimination test (effect size<0.5, p<0.001). However, pinch strength saw a decline compared to baseline with a moderate effect sizes (effect size = 0.7, p<0.001). This cohort study found that the MTT and PHUA test can both meet all the statistical criteria with regard to assessing treatment outcomes for patients with CTS. In addition, the results of this work provide clinicians with the information that the sensorimotor functions of the hands, as assessed by MTT and PHUA, are responsive to clinical changes due to CTR.     

Multivariate Statistical Assessment of Predictors of Firefighters’ Muscular and Aerobic Work Capacity

Physical capacity has previously been deemed important for firefighters physical work capacity, and aerobic fitness, muscular strength, and muscular endurance are the most frequently investigated parameters of importance. Traditionally, bivariate and multivariate linear regression statistics have been used to study relationships between physical capacities and work capacities among firefighters. An alternative way to handle datasets consisting of numerous correlated variables is to use multivariate projection analyses, such as Orthogonal Projection to Latent Structures. The first aim of the present study was to evaluate the prediction and predictive power of field and laboratory tests, respectively, on firefighters’ physical work capacity on selected work tasks. Also, to study if valid predictions could be achieved without anthropometric data. The second aim was to externally validate selected models. The third aim was to validate selected models on firefighters’ and on civilians’. A total of 38 (26 men and 12 women) + 90 (38 men and 52 women) subjects were included in the models and the external validation, respectively. The best prediction (R²) and predictive power (Q²) of Stairs, Pulling, Demolition, Terrain, and Rescue work capacities included field tests (R² = 0.73 to 0.84, Q² = 0.68 to 0.82). The best external validation was for Stairs work capacity (R² = 0.80) and worst for Demolition work capacity (R² = 0.40). In conclusion, field and laboratory tests could equally well predict physical work capacities for firefighting work tasks, and models excluding anthropometric data were valid. The predictive power was satisfactory for all included work tasks except Demolition.     

Active inference unifies intentional and conflict-resolution imperatives of motor control

The field of motor control has long focused on the achievement of external goals through action (e.g., reaching and grasping objects). However, recent studies in conditions of multisensory conflict, such as when a subject experiences the rubber hand illusion or embodies an avatar in virtual reality, reveal the presence of unconscious movements that are not goal-directed, but rather aim at resolving multisensory conflicts; for example, by aligning the position of a person’s arm with that of an embodied avatar. This second, conflict-resolution imperative of movement control did not emerge in classical studies of motor adaptation and online corrections, which did not allow movements to reduce the conflicts; and has been largely ignored so far in formal theories. Here, we propose a model of movement control grounded in the theory of active inference that integrates intentional and conflict-resolution imperatives. We present three simulations showing that the active inference model is able to characterize movements guided by the intention to achieve an external goal, by the necessity to resolve multisensory conflict, or both. Furthermore, our simulations reveal a fundamental difference between the (active) inference underlying intentional and conflict-resolution imperatives by showing that it is driven by two different (model and sensory) kinds of prediction errors. Finally, our simulations show that when movement is only guided by conflict resolution, the model incorrectly infers that is velocity is zero, as if it was not moving. This result suggests a novel speculative explanation for the fact that people are unaware of their subtle compensatory movements to avoid multisensory conflict. Furthermore, it can potentially help shed light on deficits of motor awareness that arise in psychopathological conditions.     

Influence of Precision of Emission Characteristic Parameters on Model Prediction Error of VOCs/Formaldehyde from Dry Building Material

Mass transfer models are useful in predicting the emissions of volatile organic compounds (VOCs) and formaldehyde from building materials in indoor environments. They are also useful for human exposure evaluation and in sustainable building design. The measurement errors in the emission characteristic parameters in these mass transfer models, i.e., the initial emittable concentration (C ₀), the diffusion coefficient (D), and the partition coefficient (K), can result in errors in predicting indoor VOC and formaldehyde concentrations. These errors have not yet been quantitatively well analyzed in the literature. This paper addresses this by using modelling to assess these errors for some typical building conditions. The error in C ₀, as measured in environmental chambers and applied to a reference living room in Beijing, has the largest influence on the model prediction error in indoor VOC and formaldehyde concentration, while the error in K has the least effect. A correlation between the errors in D, K, and C ₀ and the error in the indoor VOC and formaldehyde concentration prediction is then derived for engineering applications. In addition, the influence of temperature on the model prediction of emissions is investigated. It shows the impact of temperature fluctuations on the prediction errors in indoor VOC and formaldehyde concentrations to be less than 7% at 23±0.5°C and less than 30% at 23±2°C.     

No Longer Confidential: Estimating the Confidence of Individual Regression Predictions

Quantitative predictions in computational life sciences are often based on regression models. The advent of machine learning has led to highly accurate regression models that have gained widespread acceptance. While there are statistical methods available to estimate the global performance of regression models on a test or training dataset, it is often not clear how well this performance transfers to other datasets or how reliable an individual prediction is–a fact that often reduces a user’s trust into a computational method. In analogy to the concept of an experimental error, we sketch how estimators for individual prediction errors can be used to provide confidence intervals for individual predictions. Two novel statistical methods, named CONFINE and CONFIVE, can estimate the reliability of an individual prediction based on the local properties of nearby training data. The methods can be applied equally to linear and non-linear regression methods with very little computational overhead. We compare our confidence estimators with other existing confidence and applicability domain estimators on two biologically relevant problems (MHC–peptide binding prediction and quantitative structure-activity relationship (QSAR)). Our results suggest that the proposed confidence estimators perform comparable to or better than previously proposed estimation methods. Given a sufficient amount of training data, the estimators exhibit error estimates of high quality. In addition, we observed that the quality of estimated confidence intervals is predictable. We discuss how confidence estimation is influenced by noise, the number of features, and the dataset size. Estimating the confidence in individual prediction in terms of error intervals represents an important step from plain, non-informative predictions towards transparent and interpretable predictions that will help to improve the acceptance of computational methods in the biological community.     

Foster thy young: enhanced prediction of orphan genes in assembled genomes

Proteins encoded by newly-emerged genes (‘orphan genes’) share no sequence similarity with proteins in any other species. They provide organisms with a reservoir of genetic elements to quickly respond to changing selection pressures. Here, we systematically assess the ability of five gene prediction pipelines to accurately predict genes in genomes according to phylostratal origin. BRAKER and MAKER are existing, popular ab initio tools that infer gene structures by machine learning. Direct Inference is an evidence-based pipeline we developed to predict gene structures from alignments of RNA-Seq data. The BIND pipeline integrates ab initio predictions of BRAKER and Direct inference; MIND combines Direct Inference and MAKER predictions. We use highly-curated Arabidopsis and yeast annotations as gold-standard benchmarks, and cross-validate in rice. Each pipeline under-predicts orphan genes (as few as 11 percent, under one prediction scenario). Increasing RNA-Seq diversity greatly improves prediction efficacy. The combined methods (BIND and MIND) yield best predictions overall, BIND identifying 68% of annotated orphan genes, 99% of ancient genes, and give the highest sensitivity score regardless dataset in Arabidopsis. We provide a light weight, flexible, reproducible, and well-documented solution to improve gene prediction.     

Role of Abca7 in Mouse Behaviours Relevant to Neurodegenerative Diseases

ATP-binding cassette transporters of the subfamily A (ABCA) are responsible for the translocation of lipids including cholesterol, which is crucial for neurological function. Recent studies suggest that the ABC transporter ABCA7 may play a role in the development of brain disorders such as schizophrenia and Alzheimer’s disease. However, Abca7’s role in cognition and other behaviours has not been investigated. Therefore, we characterised homozygous Abca7 knockout mice in a battery of tests for baseline behaviours (i.e. physical exam, baseline locomotion and anxiety) and behaviours relevant to schizophrenia (i.e. prepulse inhibition and locomotor response to psychotropic drugs) and Alzheimer’s disease (i.e. cognitive domains). Knockout mice had normal motor functions and sensory abilities and performed the same as wild type-like animals in anxiety tasks. Short-term spatial memory and fear-associated learning was also intact in Abca7 knockout mice. However, male knockout mice exhibited significantly impaired novel object recognition memory. Task acquisition was unaffected in the cheeseboard task. Female mice exhibited impaired spatial reference memory. This phenomenon was more pronounced in female Abca7 null mice. Acoustic startle response, sensorimotor gating and baseline locomotion was unaltered in Abca7 knockout mice. Female knockouts showed a moderately increased motor response to MK-801 than control mice. In conclusion, Abca7 appears to play only a minor role in behavioural domains with a subtle sex-specific impact on particular cognitive domains.