Implementing long‐term baselines into primate tool‐use studies

Studies on primate tool‐use often involve the use of baseline conditions, as they allow for the examination of any differences in the subjects' behavior before and after the introduction of a tool‐use task. While these baseline conditions can be powerful for identifying the relative contributions of individual and social learning for the acquisition of tool‐use behaviors in naïve (usually captive) subjects, many have criticized them for being too short, and not allowing enough time for the behavior to develop spontaneously. Furthermore, some wild tool‐use behaviors such as chimpanzee nut‐cracking require animals to manipulate and familiarize themselves with the materials of the behavior within a “sensitive learning period” before it develops later on in life. One solution to this problem is to implement long‐term baselines, in which, with collaboration with zoological institutions, the materials of the behavior are left in the enclosure for an extended period. The keepers would then be asked not to demonstrate or train the animals in the target behavior, but to report back to the researchers if they observe the behavior emerge during this extended period. Alongside keeper reports, video cameras could be installed in the enclosure to minimize the chance of false negatives and to allow for coding and inter‐rater reliability to be carried out on the videos. These long‐term baselines therefore provide extended enrichment opportunities for the animals, alongside allowing the zoological institution to publicize their involvement with the study and guests to observe animals interacting with different testing apparatuses and tools. Finally, long‐term baselines can provide invaluable insight on the individual and social learning abilities of primates as well as the potential development stages and sensitive learning periods required for specific behaviors.     

α8‐Integrins are required for hippocampal long‐term potentiation but not for hippocampal‐dependent learning

Integrins are heterodimeric transmembrane cell adhesion receptors that are essential for a wide range of biological functions via cell–matrix and cell–cell interactions. Recent studies have provided evidence that some of the subunits in the integrin family are involved in synaptic and behavioral plasticity. To further understand the role of integrins in the mammalian central nervous system, we generated a postnatal forebrain and excitatory neuron‐specific knockout of α8‐integrin in the mouse. Behavioral studies showed that the mutant mice are normal in multiple hippocampal‐dependent learning tasks, including a T‐maze, non‐match‐to‐place working memory task for which other integrin subunits like α3‐ and β1‐integrin are required. In contrast, mice mutant for α8‐integrin exhibited a specific impairment of long‐term potentiation (LTP) at Schaffer collateral–CA1 synapses, whereas basal synaptic transmission, paired‐pulse facilitation and long‐term depression (LTD) remained unaffected. Because LTP is also impaired in the absence of α3‐integrin, our results indicate that multiple integrin molecules are required for the normal expression of LTP, and different integrins display distinct roles in behavioral and neurophysiological processes like synaptic plasticity.     

MUFOLD‐SS: New deep inception‐inside‐inception networks for protein secondary structure prediction

Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accuracy. In this article, a new deep neural network architecture, named the Deep inception‐inside‐inception (Deep3I) network, is proposed for protein secondary structure prediction and implemented as a software tool MUFOLD‐SS. The input to MUFOLD‐SS is a carefully designed feature matrix corresponding to the primary amino acid sequence of a protein, which consists of a rich set of information derived from individual amino acid, as well as the context of the protein sequence. Specifically, the feature matrix is a composition of physio‐chemical properties of amino acids, PSI‐BLAST profile, and HHBlits profile. MUFOLD‐SS is composed of a sequence of nested inception modules and maps the input matrix to either eight states or three states of secondary structures. The architecture of MUFOLD‐SS enables effective processing of local and global interactions between amino acids in making accurate prediction. In extensive experiments on multiple datasets, MUFOLD‐SS outperformed the best existing methods and other deep neural networks significantly. MUFold‐SS can be downloaded from http://dslsrv8.cs.missouri.edu/~cf797/MUFoldSS/download.html .     

A deep‐learning‐based approach for noise reduction in high‐speed optical coherence Doppler tomography

Optical coherence Doppler tomography (ODT) increasingly attracts attention because of its unprecedented advantages with respect to high contrast, capillary‐level resolution and flow speed quantification. However, the trade‐off between the signal‐to‐noise ratio of ODT images and A‐scan sampling density significantly slows down the imaging speed, constraining its clinical applications. To accelerate ODT imaging, a deep‐learning‐based approach is proposed to suppress the overwhelming phase noise from low‐sampling density. To handle the issue of limited paired training datasets, a generative adversarial network is performed to implicitly learn the distribution underlying Doppler phase noise and to generate the synthetic data. Then a 3D based convolutional neural network is trained and applied for the image denoising. We demonstrate this approach outperforms traditional denoise methods in noise reduction and image details preservation, enabling high speed ODT imaging with low A‐scan sampling density.     

Label‐free diagnosis for colorectal cancer through coffee ring‐assisted surface‐enhanced Raman spectroscopy on blood serum

Surface‐enhanced Raman spectroscopy (SERS) is garnering considerable attention for the swift diagnosis of pathogens and abnormal biological status, that is, cancers. In this work, a simple, fast and inexpensive optical sensing platform is developed by the design of SERS sampling and data analysis. The pretreatment of spectral measurement employed gold nanoparticle colloid mixing with the serum from patients with colorectal cancer (CRC). The droplet of particle‐serum mixture formed coffee‐ring‐like region at the rim, providing strong and stable SERS profiles. The obtained spectra from cancer patients and healthy volunteers were analyzed by unsupervised principal component analysis (PCA) and supervised machine learning model, such as support‐vector machine (SVM), respectively. The results demonstrate that the SVM model provides the superior performance in the classification of CRC diagnosis compared with PCA. In addition, the values of carcinoembryonic antigen from the blood samples were compiled with the corresponding SERS spectra for SVM calculation, yielding improved prediction results.     

Effects of prenatal exposure to a 50‐Hz magnetic field on one‐trial passive avoidance learning in 1‐day‐old chicks

We investigated memory impairment in newly hatched chicks following in ovo exposure to a 50‐Hz magnetic field (MF) of 2 mT (60 min/day) on embryonic days 12–18. Isolated and paired chicks were used to test the effect of stress during training, and memory retention was tested at 10, 30, and 120 min, following exposure to a bitter‐tasting bead (100% methylanthranilate). Results showed that memory was intact at 10 min in both isolated and paired chicks with or without MF exposure. However, while isolated chicks had good memory retention levels at 30 and 120 min, those exposed to MF did not. The results suggest a potential disruption of memory formation following in ovo exposure to MF, with this effect only evident in the more stressed, isolated chicks. Bioelectromagnetics 31:150–155, 2010. © 2009 Wiley‐Liss, Inc.     

EVOLUTION OF THE SOCIAL‐LEARNER‐EXPLORER STRATEGY IN AN ENVIRONMENTALLY HETEROGENEOUS TWO‐ISLAND MODEL

Social‐learner‐explorer (SE) is a learning strategy that combines accurate social learning with exploratory individual learning in that order. Arguably, it is one of the few plausible learning strategies that can support cumulative culture. We investigate numerically the factors that affect the evolution of SE in an environmentally heterogeneous two‐island model. Conditions favorable to the evolution of SE include a small exogenous cost of social learning, the occurrence of migration after social learning but before individual learning, the ability to adaptively modify the behavioral phenotype in the postmigration environment (asymmetrical individual learning), and a relatively high migration rate. The implications of our model for the evolution of SE in humans are discussed. Of particular interest is the prediction that behaviors affecting fitness would have to be socially learned in the natal environment and then subsequently modified by individual learning in the postmigration environment, suggesting a life‐cycle stage dependent reliance on the two types of learning.     

Attenuated stress‐evoked anxiety,increased sucrose preference and delayed spatial learning in glucocorticoid‐induced receptor‐deficient mice

The glucocorticoid‐induced receptor (GIR) is a stress‐responsive gene that is abundantly expressed in forebrain limbic regions. Glucocorticoid‐induced receptor has been classified as a Neuropeptide Y‐like receptor, however, physiological attributes have not been investigated. In this study, mice lacking GIR (?/?) were screened in various paradigms related to stress, anxiety, activity, memory, fear and reward. GIR ?/? mice elicited behavioral insensitivity to the anxiogenic effects of restraint stress. However, hypothalamic pituitary adrenal axis response to stress was not impacted by GIR deficiency. Increased preference for sucrose was observed in GIR ?/? mice suggestive of modulation of reward‐associated behaviors by the receptor. A delayed acquisition of spatial learning was also observed in GIR ?/? mice. There were no effects of genotype on the modulation of anxiety‐like behavior, activity, fear‐conditioning and extinction. Our data extend previous studies on GIR regulation by glucocorticoids and provide novel evidence for a role of GIR in reward, learning and the behavioral outcomes of stress .     

Isoform‐specific effects of apolipoprotein E on cognitive performance in targeted‐replacement mice overexpressing human APP

The ε4 allele of apolipoprotein E (apoE4) is the predominant genetic risk factor for late‐onset Alzheimer's disease (AD) and is also implicated in cognitive deficits associated with normal aging. The biological mechanisms by which APOE genotype affects cognitive processes or AD pathogenesis remain unclear, but interactions of apoE with amyloid β peptide (Aβ) are thought to play an important role in mediating apoE's isoform‐specific effects on brain function. Here, we investigated the potential isoform‐dependent effects of apoE on behavioral and cognitive performance in human apoE3 and apoE4 targeted‐replacement (TR) mice that also overexpress the human amyloid precursor protein (APP). Beginning at 6–7 months of age, female APP‐Yac/apoE3‐TR (‘poE3’) and APP‐Yac/apoE4‐TR (‘poE4’) mice were tested on a battery of tests to evaluate basic sensorimotor functioning, spatial working memory, spatial recognition, episodic‐like memory and attentional processing. Compared with apoE3 mice, a generalized reduction in locomotor activity was observed in apoE4 mice. Moderate, but significant, cognitive impairments were also detected in apoE4 mice in the novel object‐location preference task, the contextual fear conditioning test, and a two‐choice visual discrimination/detection test, however spontaneous alternation performance in the Y‐maze was spared. These results offer additional support for the negative impact of apoE4 on both memory and attention and further suggest that APP‐Yac/apoE‐TR mice provide a novel and useful model for investigating the role of apoE in mediating susceptibility to cognitive decline.     

DNABind: A hybrid algorithm for structure‐based prediction of DNA‐binding residues by combining machine learning‐ and template‐based approaches

Accurate prediction of DNA‐binding residues has become a problem of increasing importance in structural bioinformatics. Here, we presented DNABind, a novel hybrid algorithm for identifying these crucial residues by exploiting the complementarity between machine learning‐ and template‐based methods. Our machine learning‐based method was based on the probabilistic combination of a structure‐based and a sequence‐based predictor, both of which were implemented using support vector machines algorithms. The former included our well‐designed structural features, such as solvent accessibility, local geometry, topological features, and relative positions, which can effectively quantify the difference between DNA‐binding and nonbinding residues. The latter combined evolutionary conservation features with three other sequence attributes. Our template‐based method depended on structural alignment and utilized the template structure from known protein–DNA complexes to infer DNA‐binding residues. We showed that the template method had excellent performance when reliable templates were found for the query proteins but tended to be strongly influenced by the template quality as well as the conformational changes upon DNA binding. In contrast, the machine learning approach yielded better performance when high‐quality templates were not available (about 1/3 cases in our dataset) or the query protein was subject to intensive transformation changes upon DNA binding. Our extensive experiments indicated that the hybrid approach can distinctly improve the performance of the individual methods for both bound and unbound structures. DNABind also significantly outperformed the state‐of‐art algorithms by around 10% in terms of Matthews's correlation coefficient. The proposed methodology could also have wide application in various protein functional site annotations. DNABind is freely available at http://mleg.cse.sc.edu/DNABind/ . Proteins 2013; 81:1885–1899. © 2013 Wiley Periodicals, Inc.     

RBRDetector: Improved prediction of binding residues on RNA‐binding protein structures using complementary feature‐ and template‐based strategies

Computational prediction of RNA‐binding residues is helpful in uncovering the mechanisms underlying protein‐RNA interactions. Traditional algorithms individually applied feature‐ or template‐based prediction strategy to recognize these crucial residues, which could restrict their predictive power. To improve RNA‐binding residue prediction, herein we propose the first integrative algorithm termed RBRDetector (RNA‐Binding Residue Detector) by combining these two strategies. We developed a feature‐based approach that is an ensemble learning predictor comprising multiple structure‐based classifiers, in which well‐defined evolutionary and structural features in conjunction with sequential or structural microenvironment were used as the inputs of support vector machines. Meanwhile, we constructed a template‐based predictor to recognize the putative RNA‐binding regions by structurally aligning the query protein to the RNA‐binding proteins with known structures. The final RBRDetector algorithm is an ingenious fusion of our feature‐ and template‐based approaches based on a piecewise function. By validating our predictors with diverse types of structural data, including bound and unbound structures, native and simulated structures, and protein structures binding to different RNA functional groups, we consistently demonstrated that RBRDetector not only had clear advantages over its component methods, but also significantly outperformed the current state‐of‐the‐art algorithms. Nevertheless, the major limitation of our algorithm is that it performed relatively well on DNA‐binding proteins and thus incorrectly predicted the DNA‐binding regions as RNA‐binding interfaces. Finally, we implemented the RBRDetector algorithm as a user‐friendly web server, which is freely accessible at http://ibi.hzau.edu.cn/rbrdetector . Proteins 2014; 82:2455–2471. © 2014 Wiley Periodicals, Inc.     

Fur seal mothers memorize subsequent versions of developing pups’ calls: adaptation to long‐term recognition or evolutionary by‐product?

In pinnipeds and especially in otariids, mothers and pups develop the capacity to recognize each other's voices. Pups become able to discriminate their mother's voice a few days after birth. For females, this discrimination seems to occur earlier, probably during the few hours after parturition. However, during lactation, mothers are confronted with a major problem: the change of the characteristics of their pup's calls. To investigate this problem, we first performed an acoustic analysis of pups' calls from birth to weaning to identity the successive different versions of these calls. Secondly, we performed playback experiments just before weaning to test if females retain these different versions over a long time period. The acoustic analysis of pups' calls reveals that several characteristics of their vocalizations change with age. Playback experiments demonstrate that females still recognize all the successive immature and mature versions of their pup's calls. In our opinion, this long-term memorization seems to be a by-product of the permanent pups' voice learning from birth to weaning since no apparent adaptive benefit seems to arise from this capacity.  © 2003 The Linnean Society of London, Biological Journal of the Linnean Society , 2003, 80 , 305–312.     

Plasticity‐related genes in brain development and amygdala‐dependent learning

Learning about motivationally important stimuli involves plasticity in the amygdala, a temporal lobe structure. Amygdala‐dependent learning involves a growing number of plasticity‐related signaling pathways also implicated in brain development, suggesting that learning‐related signaling in juveniles may simultaneously influence development. Here, we review the pleiotropic functions in nervous system development and amygdala‐dependent learning of a signaling pathway that includes brain‐derived neurotrophic factor (BDNF), extracellular signaling‐related kinases (ERKs) and cyclic AMP‐response element binding protein (CREB). Using these canonical, plasticity‐related genes as an example, we discuss the intersection of learning‐related and developmental plasticity in the immature amygdala, when aversive and appetitive learning may influence the developmental trajectory of amygdala function. We propose that learning‐dependent activation of BDNF, ERK and CREB signaling in the immature amygdala exaggerates and accelerates neural development, promoting amygdala excitability and environmental sensitivity later in life.     

PlasmoSEP: Predicting surface‐exposed proteins on the malaria parasite using semisupervised self‐training and expert‐annotated data

Accurate and comprehensive identification of surface‐exposed proteins (SEPs) in parasites is a key step in developing novel subunit vaccines. However, the reliability of MS‐based high‐throughput methods for proteome‐wide mapping of SEPs continues to be limited due to high rates of false positives (i.e., proteins mistakenly identified as surface exposed) as well as false negatives (i.e., SEPs not detected due to low expression or other technical limitations). We propose a framework called PlasmoSEP for the reliable identification of SEPs using a novel semisupervised learning algorithm that combines SEPs identified by high‐throughput experiments and expert annotation of high‐throughput data to augment labeled data for training a predictive model. Our experiments using high‐throughput data from the Plasmodium falciparum surface‐exposed proteome provide several novel high‐confidence predictions of SEPs in P. falciparum and also confirm expert annotations for several others. Furthermore, PlasmoSEP predicts that 25 of 37 experimentally identified SEPs in Plasmodium yoelii salivary gland sporozoites are likely to be SEPs. Finally, PlasmoSEP predicts several novel SEPs in P. yoelii and Plasmodium vivax malaria parasites that can be validated for further vaccine studies. Our computational framework can be easily adapted to improve the interpretation of data from high‐throughput studies.     

Indirect cues in selecting a hunting site in a sit‐and‐wait predator

Sit‐and‐wait predators use relatively simple rules for their decisions to choose and leave a patch, such as using the direct presence of prey to select a hunting site. However, the direct presence of prey can only be used when there is a highly visited patch in the proximity of the predator. Therefore, it is plausible that sit‐and‐wait predators also exploit indirect cues of prey presence and, consequently, use associative learning to select a hunting site. The present study tests for the role of associative learning in a sit‐and‐wait predator species for which the ecology is well understood: Misumena vatia Clerck crab spiders. An ecologically relevant scenario is used by selecting flower colour as the conditioned stimulus and prey presence as the unconditioned stimulus. The results provide no evidence that M. vatia crab spiders use the association between flower colour and food presence for selecting a hunting site. After a training phase of being exposed to a colourful artificial flower highly visited by bees, spiders select a hunting site independently of its colour during the testing phase. Investigations of similar scope and ecological relevance are required with other sit‐and‐wait predators to identify the conditions promoting the use of associative learning for foraging site selection when animals face an unpredictable food supply.