Memory blocking: Facts,problems, and models

The tip-of-the-tongue state, or memory blocking, is considered with regard to the feasibility of its neural network modeling. The results of psycholinguistic and neurobiological studies on memory blocking are reviewed, and basic problems that need be solved to comprehend this phenomenon are formulated. One such point is the dramatic discrepancy between the subjective assurance that an image is familiar and the inability to recollect it fully. To explain this discrepancy, we propose a biologically plausible neural network model of recognition, demonstrating cardinal superiority in the capacity of image recognition over its recollection.     

Expression of Sox1 during Xenopus early embryogenesis

Sox B1 group genes, Sox1, Sox2, and Sox3 (Sox1-3), are involved in neurogenesis in various species. Here, we identified the Xenopus homolog of Sox1, and investigated its expression patterns and neural inducing activity. Sox1 was initially expressed in the anterior neural plate of Xenopus embryos, with expression restricted to the brain and optic vesicle by the tailbud stage. Expression subsequently decreased in the eye region by the tadpole stage. Sox1 expression in animal cap explants was induced by inhibition of BMP signaling in the same manner as Sox2, Sox3, and SoxD. In addition, overexpression of Sox1 induced neural markers in ventral ectoderm and in animal caps. These results implicate Xenopus Sox1 in neurogenesis, especially brain and eye development.     

Banknote recognition: investigating processing and cognition framework using competitive neural network

Humans are apt at recognizing patterns and discovering even abstract features which are sometimes embedded therein. Our ability to use the banknotes in circulation for business transactions lies in the effortlessness with which we can recognize the different banknote denominations after seeing them over a period of time. More significant is that we can usually recognize these banknote denominations irrespective of what parts of the banknotes are exposed to us visually. Furthermore, our recognition ability is largely unaffected even when these banknotes are partially occluded. In a similar analogy, the robustness of intelligent systems to perform the task of banknote recognition should not collapse under some minimum level of partial occlusion. Artificial neural networks are intelligent systems which from inception have taken many important cues related to structure and learning rules from the human nervous/cognition processing system. Likewise, it has been shown that advances in artificial neural network simulations can help us understand the human nervous/cognition system even furthermore. In this paper, we investigate three cognition hypothetical frameworks to vision-based recognition of banknote denominations using competitive neural networks. In order to make the task more challenging and stress-test the investigated hypotheses, we also consider the recognition of occluded banknotes. The implemented hypothetical systems are tasked to perform fast recognition of banknotes with up to 75 % occlusion. The investigated hypothetical systems are trained on Nigeria’s Naira banknotes and several experiments are performed to demonstrate the findings presented within this work.     

Agarose Microchambers for Long-term Calcium Imaging of Caenorhabditis elegans

Behavior is controlled by the nervous system. Calcium imaging is a straightforward method in the transparent nematode Caenorhabditis elegans to measure the activity of neurons during various behaviors. To correlate neural activity with behavior, the animal should not be immobilized but should be able to move. Many behavioral changes occur during long time scales and require recording over many hours of behavior. This also makes it necessary to culture the worms in the presence of food. How can worms be cultured and their neural activity imaged over long time scales? Agarose Microchamber Imaging (AMI) was previously developed to culture and observe small larvae and has now been adapted to study all life stages from early L1 until the adult stage of C. elegans. AMI can be performed on various life stages of C. elegans. Long-term calcium imaging is achieved without immobilizing the animals by using short externally triggered exposures combined with an electron multiplying charge-coupled device (EMCCD) camera recording. Zooming out or scanning can scale up this method to image up to 40 worms in parallel. Thus, a method is described to image behavior and neural activity over long time scales in all life stages of C. elegans.     

A mannan binding lectin is involved in cell-cell attachment in a toxic strain of Microcystis aeruginosa

Microcystin, a hepatotoxin that represents a serious health risk for humans and livestock, is produced by the bloom-forming cyanobacterium Microcystis aeruginosa in freshwater bodies worldwide. Here we describe the discovery of a lectin, microvirin (MVN), in M. aeruginosa PCC7806 that shares 33% identity with the potent anti-HIV protein cyanovirin-N from Nostoc ellipsosporum. Carbohydrate microarrays were employed to demonstrate the high specificity of the protein for high-mannose structures containing alpha(1-->2) linked mannose residues. Lectin binding analyses and phenotypic characterizations of MVN-deficient mutants suggest that MVN is involved in cell-cell recognition and cell-cell attachment of Microcystis. A binding partner of MVN was identified in the lipopolysaccharide fraction of M. aeruginosa PCC7806. MVN is differentially expressed in mutants lacking the hepatotoxin microcystin. Additionally, MVN-deficient mutants contain much lower amounts of microcystin than the wild-type cells. We discuss a possible functional correlation between microcystin and the lectin and possible implications on Microcystis morphotype formation. This study provides the first experimental evidence that microcystins may have an impact on Microcystis colony formation that is highly important for the competitive advantage of Microcystis over other phytoplankton species.     

Solution structure of the monovalent lectin microvirin in complex with Man(alpha)(1-2)Man provides a basis for anti-HIV activity with low toxicity

Lectins that bind surface envelope glycoprotein gp120 of HIV with high avidity can potently inhibit viral entry. Yet properties such as multivalency that facilitate strong interactions can also cause nonspecific binding and toxicity. The cyanobacterial lectin microvirin (MVN) is unusual as it potently inhibits HIV-1 with negligible toxicity compared with cyanovirin-N (CVN), its well studied antiviral homolog. To understand the structural and mechanistic basis for these differences, we solved the solution structure of MVN free and in complex with its ligand Manα(1-2)Man, and we compared specificity and time windows of inhibition with CVN and Manα(1-2)Man-specific mAb 2G12. We show by NMR and analytical ultracentrifugation that MVN is monomeric in solution, and we demonstrate by NMR that Manα(1-2)Man-terminating carbohydrates interact with a single carbohydrate-binding site. Synchronized infectivity assays show that 2G12, MVN, and CVN inhibit entry with distinct kinetics. Despite shared specificity for Manα(1-2)Man termini, combinations of the inhibitors are synergistic suggesting they recognize discrete glycans and/or dynamic glycan conformations on gp120. Entry assays employing amphotropic viruses show that MVN is inactive, whereas CVN potently inhibits both. In addition to demonstrating that HIV-1 can be inhibited through monovalent interactions, given the similarity of the carbohydrate-binding site common to MVN and CVN, these data suggest that gp120 behaves as a clustered glycan epitope and that multivalent-protein interactions achievable with CVN but not MVN are required for inhibition of some viruses.     

Eya1 acts upstream of Tbx1, Neurogenin 1, NeuroD and the neurotrophins BDNF and NT-3 during inner ear development

Cell fate specification during inner ear development is dependent upon regional gene expression within the otic vesicle. One of the earliest cell fate determination steps in this system is the specification of neural precursors, and regulators of this process include the Atonal-related basic helix-loop-helix genes, Ngn1 and NeuroD and the T-box gene, Tbx1. In this study we demonstrate that Eya1 signaling is critical to the normal expression patterns of Tbx1, Ngn1, and NeuroD in the developing mouse otocyst. We discuss a potential mechanism for the absence of neural precursors in the Eya1-/- inner ears and the primary and secondary mechanisms for the loss of cochleovestibular ganglion cells in the Eya1bor/bor hypomorphic mutant.     

Medium-Throughput Processing of Whole Mount In Situ Hybridisation Experiments into Gene Expression Domains

Understanding the function and evolution of developmental regulatory networks requires the characterisation and quantification of spatio-temporal gene expression patterns across a range of systems and species. However, most high-throughput methods to measure the dynamics of gene expression do not preserve the detailed spatial information needed in this context. For this reason, quantification methods based on image bioinformatics have become increasingly important over the past few years. Most available approaches in this field either focus on the detailed and accurate quantification of a small set of gene expression patterns, or attempt high-throughput analysis of spatial expression through binary pattern extraction and large-scale analysis of the resulting datasets. Here we present a robust, “medium-throughput” pipeline to process in situ hybridisation patterns from embryos of different species of flies. It bridges the gap between high-resolution, and high-throughput image processing methods, enabling us to quantify graded expression patterns along the antero-posterior axis of the embryo in an efficient and straightforward manner. Our method is based on a robust enzymatic (colorimetric) in situ hybridisation protocol and rapid data acquisition through wide-field microscopy. Data processing consists of image segmentation, profile extraction, and determination of expression domain boundary positions using a spline approximation. It results in sets of measured boundaries sorted by gene and developmental time point, which are analysed in terms of expression variability or spatio-temporal dynamics. Our method yields integrated time series of spatial gene expression, which can be used to reverse-engineer developmental gene regulatory networks across species. It is easily adaptable to other processes and species, enabling the in silico reconstitution of gene regulatory networks in a wide range of developmental contexts.     

Changes in Histamine Receptors (H1, H2, and H3) Expression in Rat Medial Vestibular Nucleus and Flocculus after Unilateral Labyrinthectomy: Histamine Receptors in Vestibular Compensation

Vestibular compensation is the process of behavioral recovery following peripheral vestibular lesion. In clinics, the histaminergic medicine is the most widely prescribed for the treatment of vertigo and motion sickness, however, the molecular mechanisms by which histamine modulates vestibular function remain unclear. During recovery from the lesion, the modulation of histamine receptors in the medial vestibular nucleus (MVN) and the flocculus may play an important role. Here with the means of quantitative real-time PCR, western blotting and immunohistochemistry, we studied the expression of histamine receptors (H1, H2, and H3) in the bilateral MVN and the flocculus of rats on the 1st, 3rd, and 7th day following unilateral labyrinthectomy (UL). Our results have shown that on the ipsi-lesional flocculus the H1, H2 and H3 receptors mRNA and the protein increased significantly on the 1st and 3rd day, with compare of sham controls and as well the contralateral side of UL. However, on the 7th day after UL, this expression returned to basal levels. Furthermore, elevated mRNA and protein levels of H1, H2 and H3 receptors were observed in the ipsi-lesional MVN on the 1st day after UL compared with sham controls and as well the contralateral side of UL. However, this asymmetric expression was absent by the 3rd post-UL. Our findings suggest that the upregulation of histamine receptors in the MVN and the flocculus may contribute to rebalancing the spontaneous discharge in bilateral MVN neurons during vestibular compensation.     

基于神经网络的复介电常数重建算法

提出将一种神经网络的方法应用于微波断层成象,重建复的介电常数图象。微波断层成象发展了二十多年,仍然处于实验阶段,这是因为必须求解逆散射问题,它具有非适应性和非线性。为了改善其非适应性及非线性,引入适当的正则化过程并且应用神经网络的方法来求解这个问题。数值模拟实例证明了此方法的有效性。     

Boosting accuracy of automated classification of fluorescence microscope images for location proteomics

Background

Detailed knowledge of the subcellular location of each expressed protein is critical to a full understanding of its function. Fluorescence microscopy, in combination with methods for fluorescent tagging, is the most suitable current method for proteome-wide determination of subcellular location. Previous work has shown that neural network classifiers can distinguish all major protein subcellular location patterns in both 2D and 3D fluorescence microscope images. Building on these results, we evaluate here new classifiers and features to improve the recognition of protein subcellular location patterns in both 2D and 3D fluorescence microscope images.

Results

We report here a thorough comparison of the performance on this problem of eight different state-of-the-art classification methods, including neural networks, support vector machines with linear, polynomial, radial basis, and exponential radial basis kernel functions, and ensemble methods such as AdaBoost, Bagging, and Mixtures-of-Experts. Ten-fold cross validation was used to evaluate each classifier with various parameters on different Subcellular Location Feature sets representing both 2D and 3D fluorescence microscope images, including new feature sets incorporating features derived from Gabor and Daubechies wavelet transforms. After optimal parameters were chosen for each of the eight classifiers, optimal majority-voting ensemble classifiers were formed for each feature set. Comparison of results for each image for all eight classifiers permits estimation of the lower bound classification error rate for each subcellular pattern, which we interpret to reflect the fraction of cells whose patterns are distorted by mitosis, cell death or acquisition errors. Overall, we obtained statistically significant improvements in classification accuracy over the best previously published results, with the overall error rate being reduced by one-third to one-half and with the average accuracy for single 2D images being higher than 90% for the first time. In particular, the classification accuracy for the easily confused endomembrane compartments (endoplasmic reticulum, Golgi, endosomes, lysosomes) was improved by 5–15%. We achieved further improvements when classification was conducted on image sets rather than on individual cell images.

Conclusions

The availability of accurate, fast, automated classification systems for protein location patterns in conjunction with high throughput fluorescence microscope imaging techniques enables a new subfield of proteomics, location proteomics. The accuracy and sensitivity of this approach represents an important alternative to low-resolution assignments by curation or sequence-based prediction.

     

Identification of genes driving lineage divergence from single-cell gene expression data in C. elegans

The nematode Caenorhabditis elegans (C. elegans) is an ideal model organism to study the cell fate specification mechanisms during embryogenesis. It is generally believed that cell fate specification in C. elegans is mainly mediated by lineage-based mechanisms, where the specification paths are driven forward by a succession of asymmetric cell divisions. However, little is known about how each binary decision is made by gene regulatory programs. In this study, we endeavor to obtain a global understanding of cell lineage/fate divergence processes during the early embryogenesis of C. elegans. We reanalyzed the EPIC data set, which traced the expression level of reporter genes at single-cell resolution on a nearly continuous time scale up to the 350-cell stage in C. elegans embryos. We examined the expression patterns for a total of 131 genes from 287 embryos with high quality image recordings, among which 86 genes have replicate embryos. Our results reveal that during early embryogenesis, divergence between sister lineages could be largely explained by a few genes. We predicted genes driving lineage divergence and explored their expression patterns in sister lineages. Moreover, we found that divisions leading to fate divergence are associated with a large number of genes being differentially expressed between sister lineages. Interestingly, we found that the developmental paths of lineages could be differentiated by a small set of genes. Therefore, our results support the notion that the cell fate patterns in C. elegans are achieved through stepwise binary decisions punctuated by cell divisions. Our predicted genes driving lineage divergence provide good starting points for future detailed characterization of their roles in the embryogenesis in this important model organism.     

Face recognition from a single image per person using deep architecture neural networks

Implementing an accurate face recognition system requires images in different variations, and if our database is large, we suffer from problems such as storing cost and low speed in recognition algorithms. On the other hand, in some applications there is only one image available per person for training recognition model. In this article, we propose a neural network model inspired of bidirectional analysis and synthesis brain network which can learn nonlinear mapping between image space and components space. Using a deep neural network model, we have tried to separate pose components from person ones. After setting apart these components, we can use them to synthesis virtual images of test data in different pose and lighting conditions. These virtual images are used to train neural network classifier. The results showed that training neural classifier with virtual images gives better performance than training classifier with frontal view images.