Deep learning based decomposition for visual navigation in industrial platforms

In the heavy asset industry, such as oil & gas, offshore personnel need to locate various equipment on the installation on a daily basis for inspection and maintenance purposes. However, locating equipment in such GPS denied environments is very time consuming due to the complexity of the environment and the large amount of equipment. To address this challenge we investigate an alternative approach to study the navigation problem based on visual imagery data instead of current ad-hoc methods where engineering drawings or large CAD models are used to find equipment. In particular, this paper investigates the combination of deep learning and decomposition for the image retrieval problem which is central for visual navigation. A convolutional neural network is first used to extract relevant features from the image database. The database is then decomposed into clusters of visually similar images, where several algorithms have been explored in order to make the clusters as independent as possible. The Bag-of-Words (BoW) approach is then applied on each cluster to build a vocabulary forest. During the searching process the vocabulary forest is exploited to find the most relevant images to the query image. To validate the usefulness of the proposed framework, intensive experiments have been carried out using both standard datasets and images from industrial environments. We show that the suggested approach outperforms the BoW-based image retrieval solutions, both in terms of computing time and accuracy. We also show the applicability of this approach on real industrial scenarios by applying the model on imagery data from offshore oil platforms.publishedVersio

Corticothalamic feedback sculpts visual spatial integration in mouse thalamus

En route from retina to cortex, visual information travels through the dorsolateral geniculate nucleus of the thalamus (dLGN), where extensive cortico-thalamic (CT) feedback has been suggested to modulate spatial processing. How this modulation arises from direct excitatory and indirect inhibitory CT feedback components remains enigmatic. We show that in awake mice topographically organized cortical feedback modulates spatial integration in dLGN by sharpening receptive fields (RFs) and increasing surround suppression. Guided by a network model revealing wide-scale inhibitory CT feedback necessary to reproduce these effects, we targeted the visual sector of the thalamic reticular nucleus (visTRN) for recordings. We found that visTRN neurons have large receptive fields, show little surround suppression, and have strong feedback-dependent responses to large stimuli, making them an ideal candidate for mediating feedback-enhanced surround suppression in dLGN. We conclude that cortical feedback sculpts spatial integration in dLGN, likely via recruitment of neurons in visTRN

Rigosertib elicits potent anti-tumor responses in colorectal cancer by inhibiting Ras signaling pathway

Background: The therapeutic potency of Rigosertib (RGS) in the treatment of the myelodysplastic syndrome has been investigated previously, but little is known about its mechanisms of action. Methods: The present study integrates systems and molecular biology approaches to investigate the mechanisms of the anti-tumor effects of RGS, either alone or in combination with 5-FU in cellular and animal models of colorectal cancer (CRC). Results: The effects of RGS were more pronounced in dedifferentiated CRC cell types, compared to cell types that were epithelial-like. RGS inhibited cell proliferation and cell cycle progression in a cell-type specific manner, and that was dependent on the presence of mutations in KRAS, or its down-stream effectors. RGS increased both early and late apoptosis, by regulating the expression of p53, BAX and MDM2 in tumor model. We also found that RGS induced cell senescence in tumor tissues by increasing ROS generation, and impairing oxidant/anti-oxidant balance. RGS also inhibited angiogenesis and metastatic behavior of CRC cells, by regulating the expression of CD31, E-cadherin, and matrix metalloproteinases-2 and 9. Conclusion: Our findings support the therapeutic potential of this potent RAS signaling inhibitor either alone or in combination with standard regimens for the management of patients with CRC.Peer reviewe

Ab Initio Molecular Dynamics: A Virtual Laboratory

In this thesis, we perform ab initio molecular dynamics (MD) simulations at the Hartree-Fock level, where the forces are computed on-the-fly using the Born-Oppenheimer approximation. The theory behind the Hartree-Fock method is discussed in detail and an implementation of this method based on Gaussian basis functions is explained. We also demonstrate how to calculate the analytic energy derivatives needed for obtaining the forces acting on the nuclei. Hartree-Fock calculations on the ground state energy, dipole moment, ionization potential and population analysis are done for H₂, N₂, FH, CO, NH₃, H₂O, and CH₄. These results are in perfect agreement with the literature. Ab initio MD calculations with different Gaussian basis sets, are performed on the diatomic systems H₂, N₂, F₂, FH, and CO, for equilibrium bond length and vibration frequency analysis. Finally, a study on the reaction dynamics of the nucleophilic substitution reaction H⁻ + CH₄ → CH₄ + H⁻ is done, illustrating the importance of the initial vibrational energy of the methane molecule for the reaction to occur

Biophysical network modeling of the dLGN circuit: Effects of cortical feedback on spatial response properties of relay cells

publishedVersio

Firing-rate based network modeling of the dLGN circuit: Effects of cortical feedback on spatiotemporal response properties of relay cells

Visually evoked signals in the retina pass through the dorsal geniculate nucleus (dLGN) on the way to the visual cortex. This is however not a simple feedforward flow of information: there is a significant feedback from cortical cells back to both relay cells and interneurons in the dLGN. Despite four decades of experimental and theoretical studies, the functional role of this feedback is still debated. Here we use a firing-rate model, the extended difference-of-Gaussians (eDOG) model, to explore cortical feedback effects on visual responses of dLGN relay cells. For this model the responses are found by direct evaluation of two- or three-dimensional integrals allowing for fast and comprehensive studies of putative effects of different candidate organizations of the cortical feedback. Our analysis identifies a special mixed configuration of excitatory and inhibitory cortical feedback which seems to best account for available experimental data. This configuration consists of (i) a slow (long-delay) and spatially widespread inhibitory feedback, combined with (ii) a fast (short-delayed) and spatially narrow excitatory feedback, where (iii) the excitatory/inhibitory ON-ON connections are accompanied respectively by inhibitory/excitatory OFF-ON connections, i.e. following a phase-reversed arrangement. The recent development of optogenetic and pharmacogenetic methods has provided new tools for more precise manipulation and investigation of the thalamocortical circuit, in particular for mice. Such data will expectedly allow the eDOG model to be better constrained by data from specific animal model systems than has been possible until now for cat. We have therefore made the Python tool pyLGN which allows for easy adaptation of the eDOG model to new situations

Deep learning based decomposition for visual navigation in industrial platforms

In the heavy asset industry, such as oil & gas, offshore personnel need to locate various equipment on the installation on a daily basis for inspection and maintenance purposes. However, locating equipment in such GPS denied environments is very time consuming due to the complexity of the environment and the large amount of equipment. To address this challenge we investigate an alternative approach to study the navigation problem based on visual imagery data instead of current ad-hoc methods where engineering drawings or large CAD models are used to find equipment. In particular, this paper investigates the combination of deep learning and decomposition for the image retrieval problem which is central for visual navigation. A convolutional neural network is first used to extract relevant features from the image database. The database is then decomposed into clusters of visually similar images, where several algorithms have been explored in order to make the clusters as independent as possible. The Bag-of-Words (BoW) approach is then applied on each cluster to build a vocabulary forest. During the searching process the vocabulary forest is exploited to find the most relevant images to the query image. To validate the usefulness of the proposed framework, intensive experiments have been carried out using both standard datasets and images from industrial environments. We show that the suggested approach outperforms the BoW-based image retrieval solutions, both in terms of computing time and accuracy. We also show the applicability of this approach on real industrial scenarios by applying the model on imagery data from offshore oil platforms

Deep learning based decomposition for visual navigation in industrial platforms

In the heavy asset industry, such as oil & gas, offshore personnel need to locate various equipment on the installation on a daily basis for inspection and maintenance purposes. However, locating equipment in such GPS denied environments is very time consuming due to the complexity of the environment and the large amount of equipment. To address this challenge we investigate an alternative approach to study the navigation problem based on visual imagery data instead of current ad-hoc methods where engineering drawings or large CAD models are used to find equipment. In particular, this paper investigates the combination of deep learning and decomposition for the image retrieval problem which is central for visual navigation. A convolutional neural network is first used to extract relevant features from the image database. The database is then decomposed into clusters of visually similar images, where several algorithms have been explored in order to make the clusters as independent as possible. The Bag-of-Words (BoW) approach is then applied on each cluster to build a vocabulary forest. During the searching process the vocabulary forest is exploited to find the most relevant images to the query image. To validate the usefulness of the proposed framework, intensive experiments have been carried out using both standard datasets and images from industrial environments. We show that the suggested approach outperforms the BoW-based image retrieval solutions, both in terms of computing time and accuracy. We also show the applicability of this approach on real industrial scenarios by applying the model on imagery data from offshore oil platforms

Experimental Directory Structure (Exdir): An Alternative to HDF5 Without Introducing a New File Format

Natural sciences generate an increasing amount of data in a wide range of formats developed by different research groups and commercial companies. At the same time there is a growing desire to share data along with publications in order to enable reproducible research. Open formats have publicly available specifications which facilitate data sharing and reproducible research. Hierarchical Data Format 5 (HDF5) is a popular open format widely used in neuroscience, often as a foundation for other, more specialized formats. However, drawbacks related to HDF5's complex specification have initiated a discussion for an improved replacement. We propose a novel alternative, the Experimental Directory Structure (Exdir), an open specification for data storage in experimental pipelines which amends drawbacks associated with HDF5 while retaining its advantages. HDF5 stores data and metadata in a hierarchy within a complex binary file which, among other things, is not human-readable, not optimal for version control systems, and lacks support for easy access to raw data from external applications. Exdir, on the other hand, uses file system directories to represent the hierarchy, with metadata stored in human-readable YAML files, datasets stored in binary NumPy files, and raw data stored directly in subdirectories. Furthermore, storing data in multiple files makes it easier to track for version control systems. Exdir is not a file format in itself, but a specification for organizing files in a directory structure. Exdir uses the same abstractions as HDF5 and is compatible with the HDF5 Abstract Data Model. Several research groups are already using data stored in a directory hierarchy as an alternative to HDF5, but no common standard exists. This complicates and limits the opportunity for data sharing and development of common tools for reading, writing, and analyzing data. Exdir facilitates improved data storage, data sharing, reproducible research, and novel insight from interdisciplinary collaboration. With the publication of Exdir, we invite the scientific community to join the development to create an open specification that will serve as many needs as possible and as a foundation for open access to and exchange of data