Developmental changes in spinal neuronal properties, motor network configuration, and neuromodulation at free-swimming stages of Xenopus tadpoles

We describe a novel preparation of the isolated brainstem and spinal cord from pro-metamorphic tadpole stages of the South African clawed frog (Xenopus laevis) that permits whole cell patch-clamp recordings from neurons in the ventral spinal cord. Previous research on earlier stages of the same species has provided one of the most detailed understandings of the design and operation of a CPG circuit. Here we have addressed how development sculpts complexity from this more basic circuit. The preparation generates bouts of fictive31 swimming activity either spontaneously or in response to electrical stimulation of the optic tectum, allowing an investigation into how the neuronal properties, activity patterns and neuromodulation of locomotor rhythm generation change during development. We describe an increased repertoire of cellular responses compared to younger larval stages and investigate the cellular level effects of nitrergic neuromodulation as well as the development of a sodium pump-mediated ultra-slow afterhyperpolarisation (usAHP) in these free-swimming larval animals.PostprintPeer reviewe

Patient-specific Conditional Joint Models of Shape, Image Features and Clinical Indicators

We propose and demonstrate a joint model of anatomical shapes, image features and clinical indicators for statistical shape modeling and medical image analysis. The key idea is to employ a copula model to separate the joint dependency structure from the marginal distributions of variables of interest. This separation provides flexibility on the assumptions made during the modeling process. The proposed method can handle binary, discrete, ordinal and continuous variables. We demonstrate a simple and efficient way to include binary, discrete and ordinal variables into the modeling. We build Bayesian conditional models based on observed partial clinical indicators, features or shape based on Gaussian processes capturing the dependency structure. We apply the proposed method on a stroke dataset to jointly model the shape of the lateral ventricles, the spatial distribution of the white matter hyperintensity associated with periventricular white matter disease, and clinical indicators. The proposed method yields interpretable joint models for data exploration and patient-specific statistical shape models for medical image analysis.Comment: Supplementary material: https://www.youtube.com/watch?v=gPoHP_iFQI

Questionnaire study to gain an insight into the manufacturing and fitting process of artificial eyes in children: an ocularist perspective

Purpose To gain an insight into the manufacturing and fitting of artificial eyes in children and potential improvements to the process. Method An online qualitative survey was distributed to 39 ocularists/prosthetists in Europe and Canada. Participants were recruited through purposive sampling, specifically maximum variation sampling from the researcher’s contacts and an online search. Results The findings highlighted the current impression technique as being the most difficult yet most important part of the current process for both the ocularist and child patient. Negatively affecting obtaining a good impression, the child patients distress can be reduced by their parents by providing encouragement, reassurance, practicing the insertion and removal of the artificial eye and being matter of fact. Whilst improvements to the current process provided mixed views, the incorporation of current technology was perceived as not being able to meet the requirements to produce aesthetically pleasing artificial eyes. Conclusion The current artificial eye process can be seen as an interaction with its success being dependent on the child patient’s acceptance and adjustment which is dependent on the factors associated to the process. Investigation into the needs of the patient and whether technology can improve the process are the next steps in its advancement

Environmental Factors Influence Language Development in Children with Autism Spectrum Disorders

International audienc

Hedgehog pathway mutations drive oncogenic transformation in high-risk T-cell acute lymphoblastic leukemia.

The role of Hedgehog signaling in normal and malignant T-cell development is controversial. Recently, Hedgehog pathway mutations have been described in T-ALL, but whether mutational activation of Hedgehog signaling drives T-cell transformation is unknown, hindering the rationale for therapeutic intervention. Here, we show that Hedgehog pathway mutations predict chemotherapy resistance in human T-ALL, and drive oncogenic transformation in a zebrafish model of the disease. We found Hedgehog pathway mutations in 16% of 109 childhood T-ALL cases, most commonly affecting its negative regulator PTCH1. Hedgehog mutations were associated with resistance to induction chemotherapy (P = 0.009). Transduction of wild-type PTCH1 into PTCH1-mutant T-ALL cells induced apoptosis (P = 0.005), a phenotype that was reversed by downstream Hedgehog pathway activation (P = 0.007). Transduction of most mutant PTCH1, SUFU, and GLI alleles into mammalian cells induced aberrant regulation of Hedgehog signaling, indicating that these mutations are pathogenic. Using a CRISPR/Cas9 system for lineage-restricted gene disruption in transgenic zebrafish, we found that ptch1 mutations accelerated the onset of notch1-induced T-ALL (P = 0.0001), and pharmacologic Hedgehog pathway inhibition had therapeutic activity. Thus, Hedgehog-activating mutations are driver oncogenic alterations in high-risk T-ALL, providing a molecular rationale for targeted therapy in this disease

Copula Eigenfaces with Attributes: Semiparametric Principal Component Analysis for a Combined Color, Shape and Attribute Model

Principal component analysis is a ubiquitous method in parametric appearance modeling for describing dependency and variance in datasets. The method requires the observed data to be Gaussian-distributed. We show that this requirement is not fulfilled in the context of analysis and synthesis of facial appearance. The model mismatch leads to unnatural artifacts which are severe to human perception. As a remedy, we use a semiparametric Gaussian copula model, where dependency and variance are modeled separately. This model enables us to use arbitrary Gaussian and non-Gaussian marginal distributions. Moreover, facial color, shape and continuous or categorical attributes can be analyzed in an unified way. Accounting for the joint dependency between all modalities leads to a more specific face model. In practice, the proposed model can enhance performance of principal component analysis in existing pipelines: The steps for analysis and synthesis can be implemented as convenient pre- and post-processing steps

The interplay of microscopic and mesoscopic structure in complex networks

Not all nodes in a network are created equal. Differences and similarities exist at both individual node and group levels. Disentangling single node from group properties is crucial for network modeling and structural inference. Based on unbiased generative probabilistic exponential random graph models and employing distributive message passing techniques, we present an efficient algorithm that allows one to separate the contributions of individual nodes and groups of nodes to the network structure. This leads to improved detection accuracy of latent class structure in real world data sets compared to models that focus on group structure alone. Furthermore, the inclusion of hitherto neglected group specific effects in models used to assess the statistical significance of small subgraph (motif) distributions in networks may be sufficient to explain most of the observed statistics. We show the predictive power of such generative models in forecasting putative gene-disease associations in the Online Mendelian Inheritance in Man (OMIM) database. The approach is suitable for both directed and undirected uni-partite as well as for bipartite networks

SCI

Persistent homology is a powerful tool in Topological Data Analysis (TDA) to capture the topological properties of data succinctly at different spatial resolutions. For graphical data, the shape, and structure of the neighborhood of individual data items (nodes) are an essential means of characterizing their properties. We propose the use of persistent homology methods to capture structural and topological properties of graphs and use it to address the problem of link prediction. We achieve encouraging results on nine different real-world datasets that attest to the potential of persistent homology-based methods for network analysis

A behaviorally related developmental switch in nitrergic modulation of locomotor rhythmogenesis in larval Xenopus tadpoles

Supported by PICS (Projet International de Coopération Scientifique) of the French CNRS and a LabEx BRAIN Visiting Professorship to KTS. SPC was a BBSRC research student. NWS was an MPhil student supported in part by the E & RS Research Fund of the University of St Andrews.Locomotor control requires functional flexibility to support an animal's full behavioral repertoire. This flexibility is partly endowed by neuromodulators, allowing neural networks to generate a range of motor output configurations. In hatchling Xenopus tadpoles, before the onset of free-swimming behavior, the gaseous modulator nitric oxide (NO) inhibits locomotor output, shortening swim episodes and decreasing swim cycle frequency. While populations of nitrergic neurons are already present in the tadpole's brain stem at hatching, neurons positive for the NO-synthetic enzyme, NO synthase, subsequently appear in the spinal cord, suggesting additional as yet unidentified roles for NO during larval development. Here, we first describe the expression of locomotor behavior during the animal's change from an early sessile to a later free-swimming lifestyle and then compare the effects of NO throughout tadpole development. We identify a discrete switch in nitrergic modulation from net inhibition to overall excitation, coincident with the transition to free-swimming locomotion. Additionally, we show in isolated brain stem-spinal cord preparations of older larvae that NO's excitatory effects are manifested as an increase in the probability of spontaneous swim episode occurrence, as found previously for the neurotransmitter dopamine, but that these effects are mediated within the brain stem. Moreover, while the effects of NO and dopamine are similar, the two modulators act in parallel rather than NO operating serially by modulating dopaminergic signaling. Finally, NO's activation of neurons in the brain stem also leads to the release of NO in the spinal cord that subsequently contributes to NO's facilitation of swimming.Publisher PDFPeer reviewe