A microRNA Imparts Robustness against Environmental Fluctuation during Development

The microRNA miR-7 is perfectly conserved from annelids to humans, and yet some of the genes that it regulates in Drosophila are not regulated in mammals. We have explored the role of lineage restricted targets, using Drosophila , in order to better understand the evolutionary significance of microRNA-target relationships. From studies of two well characterized developmental regulatory networks, we find that miR-7 functions in several interlocking feedback and feedforward loops, and propose that its role in these networks is to buffer them against perturbation. To directly demonstrate this function for miR-7, we subjected the networks to temperature fluctuation and found that miR-7 is essential for the maintenance of regulatory stability under conditions of environmental flux. We suggest that some conserved microRNAs like miR-7 may enter into novel genetic relationships to buffer developmental programs against variation and impart robustness to diverse regulatory networks

Revisiting LFSMs

Linear Finite State Machines (LFSMs) are particular primitives widely used in information theory, coding theory and cryptography. Among those linear automata, a particular case of study is Linear Feedback Shift Registers (LFSRs) used in many cryptographic applications such as design of stream ciphers or pseudo-random generation. LFSRs could be seen as particular LFSMs without inputs. In this paper, we first recall the description of LFSMs using traditional matrices representation. Then, we introduce a new matrices representation with polynomial fractional coefficients. This new representation leads to sparse representations and implementations. As direct applications, we focus our work on the Windmill LFSRs case, used for example in the E0 stream cipher and on other general applications that use this new representation. In a second part, a new design criterion called diffusion delay for LFSRs is introduced and well compared with existing related notions. This criterion represents the diffusion capacity of an LFSR. Thus, using the matrices representation, we present a new algorithm to randomly pick LFSRs with good properties (including the new one) and sparse descriptions dedicated to hardware and software designs. We present some examples of LFSRs generated using our algorithm to show the relevance of our approach.Comment: Submitted to IEEE-I

Early pioneers to reversible computation

Reversible computing is one of the most intensively developing research areas nowadays. We present a survey of less known or forgotten papers to show that a transfer of ideas between different disciplines is possible

Read alignment using deep neural networks

2019 Spring.Includes bibliographical references.Read alignment is the process of mapping short DNA sequences into the reference genome. With the advent of consecutively evolving "next generation" sequencing technologies, the need for sequence alignment tools appeared. Many scientific communities and the companies marketing the sequencing technologies developed a whole spectrum of read aligners/mappers for different error profiles and read length characteristics. Among the most recent successfully marketed sequencing technologies are Oxford Nanopore and PacBio SMRT sequencing, which are considered top players because of their extremely long reads and low cost. However, the reads may contain error up to 20% that are not generally uniformly distributed. To deal with that level of error rate and read length, proximity preserving hashing techniques, such as Minhash and Minimizers, were utilized to quickly map a read to the target region of the reference sequence. Subsequently, a variant of global or local alignment dynamic programming is then used to give the final alignment. In this research work, we train a Deep Neural Network (DNN) to yield a hashing scheme for the highly erroneous long reads, which is deemed superior to Minhash for mapping the reads. We implemented that idea to build a read alignment tool: DNNAligner. We evaluated the performance of our aligner against the popular read aligners in the bioinformatics community currently — minimap2, bwa-mem and graphmap. Our results show that the performance of DNNAligner is comparable to other tools without any code optimization or integration of other advanced features. Moreover, DNN exhibits superior performance in comparison with Minhashon neighborhood classification

Maximum likelihood sequence detection with closed-form metrics in OOK optical systems impaired by GVD and PMD

This paper thoroughly investigates the maximum-likelihood sequence detection (MLSD) receiver for the optical ON-OFF keying (OOK) channel in the presence of both polarization mode dispersion and group velocity dispersion (GVD). A reliable method is provided for computing the relevant performance for any possible value of the system parameters, with no constraint on the sampling rate. With one sample per bit time, a practically exact expression of the statistics of the received samples is found, and therefore the performance of a synchronous MLSD receiver is evaluated and compared with that of other electronic techniques such as combined feedforward and decision-feedback equalizers (FFE and DFE). It is also shown that the ultimate performance of electronic processing can be obtained by sampling the received signal at twice the bit rate. An approximate accurate closed-form expression of the receiver metrics is also identified, allowing for the implementation of a practically optimal MLSD receiver

A study of neural-related microRNAs in the developing amphioxus

<p>Abstract</p> <p>Background</p> <p>MicroRNAs are small noncoding RNAs regulating expression of protein coding genes at post-transcriptional level and controlling several biological processes. At present microRNAs have been identified in various metazoans and seem also to be involved in brain development, neuronal differentiation and subtypes specification. An approach to better understand the role of microRNAs in animal gene expression is to determine temporal and tissue-specific expression patterns of microRNAs in different model organisms. Therefore, we have investigated the expression of six neural related microRNAs in amphioxus, an organism having an important phylogenetic position in terms of understanding the origin and evolution of chordates.</p> <p>Results</p> <p>In amphioxus, all the microRNAs we examined are expressed in specific regions of the CNS, and some of them are correlated with specific cell types. In addition, miR-7, miR-137 and miR-184 are also expressed in endodermal and mesodermal tissues. Several potential targets expressed in the nervous system of amphioxus have been identified by computational prediction and some of them are coexpressed with one or more miRNAs.</p> <p>Conclusion</p> <p>We identified six miRNAs that are expressed in the nervous system of amphioxus in a variety of patterns. miR-124 is found in both differentiating and mature neurons, miR-9 in differentiated neurons, miR-7, miR-137 and miR-184 in restricted CNS regions, and miR-183 in cells of sensory organs. Therefore, such amphioxus miRNAs may play important roles in regional patterning and/or specification of neuronal cell types.</p

Introducing deep learning -based methods into the variant calling analysis pipeline

Biological interpretation of the genetic variation enhances our understanding of normal and pathological phenotypes, and may lead to the development of new therapeutics. However, it is heavily dependent on the genomic data analysis, which might be inaccurate due to the various sequencing errors and inconsistencies caused by these errors. Modern analysis pipelines already utilize heuristic and statistical techniques, but the rate of falsely identified mutations remains high and variable, particular sequencing technology, settings and variant type. Recently, several tools based on deep neural networks have been published. The neural networks are supposed to find motifs in the data that were not previously seen. The performance of these novel tools is assessed in terms of precision and recall, as well as computational efficiency. Following the established best practices in both variant detection and benchmarking, the discussed tools demonstrate accuracy metrics and computational efficiency that spur further discussion

An investigation of factual and counterfactual feedback information in early visual cortex

Primary visual cortex receives approximately 90% of the input to the retina, however this only accounts for around 5% of the input to V1 (Muckli, 2010). The majority of the input to V1 is in fact from other cortical and sub-cortical parts of the brain that arrive there via lateral and feedback pathways. It is therefore critical to our knowledge of visual perception to understand how these feedback responses influence visual processing. The aim of this thesis is to investigate different sources of non-visual feedback to early visual cortex. To do this we use a combination of an occlusion paradigm, derived from F. W. Smith and Muckli (2010), and functional magnetic resonance imagining. Occlusion offers us a method to inhibit the feedforward flow of information to the retina from a specific part of the visual field. By inhibiting the feedforward information we exploit the highly precise retinotopic organisation of visual cortex by rendering a corresponding patch of cortex free of feedforward input. From this isolated patch of cortex we can ask questions about the information content of purely feedback information. In Chapter 3 we investigated whether or not information about valance was present in non-stimulated early visual cortex. We constructed a 900 image set that contained an equal number of images for neutral, positive and negative valance across animal, food and plant categories. We used an m-sequence design to allow us to present image set within a standard period of time for fMRI. We were concerned about low-level image properties being a potential confound, so a large image set would allow us to average out these low-level properties. We occluded the lower-right quadrant of each image and presented each image only once to our subjects. The image set was rated for valance and arousal after fMRI so that individual subjectivity could be accounted for. We used multivariate pattern analysis (MVPA) to decode pairs of neutral, positive and negative valance. We found that in both stimulated and non-stimulated V1, V2 and V3, and the amygdala and pulvinar only information about negative valance could be decoded. In a second analysis we again used MVPA to cross-decode between pairs of valance and category. By training the classifier on pairs of valance that each contained two categories, we could ask the question of whether the classifier generalises to the left out category for the same pair of valance. We found that valance does generalise across category in both stimulated and non-stimulated cortex, and in the amygdala and pulvinar. These results demonstrate that information about valance, particularly negative valance, is represented in low level visual areas and is generalisable across animal, food and plant categories. In Chapter 4 we explored the retinotopic organisation of object and scene sound responses in non-stimulated early visual cortex. We embedded a repeating object sound (axe chopping or motor starting) in to a scene sound (blizzard wind or forest) and used MVPA to read out object or scene information from non-stimulated early visual cortex. We found that object sounds were decodable in the fovea and scene sounds were decodable in the periphery. This finding demonstrates that auditory feedback to visual cortex has an eccentricity bias corresponding to the functional role involved. We suggest that object information feeds back to the fovea for fine-scaled discrimination whereas abstract information feeds back to the periphery to provide a modulatory contextual template for vision. In a second experiment in Chapter 4 we further explored the similarity between categorical representations between sound and video stimuli in non-stimulated early visual cortex. We use video stimuli and separate the audio and visual parts in to unimodal stimuli. We occlude the bottom right quadrant of the videos and use MVPA to cross-decode between sounds and videos (and vice-versa) from responses in occluded cortex. We find that a classifier trained on one modality can decode the other in occluded cortex. This finding tells us that there is an overlap in the neural representation of aural and visual stimuli in early visual cortex. In Chapter 5 we probe the internal thought processes of subjects after occluding a short video sequence. We use a priming sequence to generate predictions as subjects are asked to imagine how events from a video unfold during occlusion. We then probe these predictions with a series of test frames corresponding to points in time, either close in time to the offset of the video, just before the video would be expected to reappear, the matching frame from when the video would be expected to reappear or a frame from the very distant future. In an adaption paradigm we find that predictions best match the test frames around the point in time that subjects expect the video to reappear. The test frame from a point close in time to the offset of the video was rarely a match. This tells us that the predictions that subjects make are not related to the offset of the priming sequence but represent a future state of the world that they have not seen. In a second control experiment we show that these predictions are absent when the priming sequence is randomised, and that predictions take between 600ms and 1200ms to fully develop. These findings demonstrate the dynamic flexibility of internal models, that information about these predictions can be read out in early visual cortex and that stronger representations form if given additional time. In Chapter 6 we again probe at internal dynamic predictions by using virtual navigation paradigm. We use virtual reality to train subjects in a new environment where they can build strong representations of four categorical rooms (kitchen, bedroom, office and game room). Later in fMRI we provide subjects with a direction cue and a starting room and ask them to predict the upcoming room by combining the information. The starting room is shown as a short video clip with the bottom right quadrant occluded. During the video sequence of the starting room, we find that we can read out information about the future room from non-stimulated early visual cortex. In a second control experiment, when we remove the direction cue information about the future room can no longer be decoded. This finding demonstrates that dynamic predictions about the immediate future are present in early visual cortex during simultaneous visual stimulation and that we can read out these predictions with 3T fMRI. These findings increase our knowledge about the types of non-visual information available to early visual cortical areas and provide insight in to the influence they have on vision. These results lend support to the idea that early visual areas may act as a blackboard for read and write operations for communication around the brain (Muckli et al., 2015; Mumford, 1991; Murray et al., 2016; Roelfsema & de Lange, 2016; Williams et al., 2008). Current models of predictive coding will need to be updated to account for the brains ability to switch between two different processing streams, one that is factual and related to an external stimulus and one that is stimulus independent and internal