Classification-based prediction of effective connectivity between timeseries with a realistic cortical network model

Effective connectivity measures the pattern of causal interactions between brain regions. Traditionally, these patterns of causality are inferred from brain recordings using either non-parametric, i.e., model-free, or parametric, i.e., model-based, approaches. The latter approaches, when based on biophysically plausible models, have the advantage that they may facilitate the interpretation of causality in terms of underlying neural mechanisms. Recent biophysically plausible neural network models of recurrent microcircuits have shown the ability to reproduce well the characteristics of real neural activity and can be applied to model interacting cortical circuits. Unfortunately, however, it is challenging to invert these models in order to estimate effective connectivity from observed data. Here, we propose to use a classification-based method to approximate the result of such complex model inversion. The classifier predicts the pattern of causal interactions given a multivariate timeseries as input. The classifier is trained on a large number of pairs of multivariate timeseries and the respective pattern of causal interactions, which are generated by simulation from the neural network model. In simulated experiments, we show that the proposed method is much more accurate in detecting the causal structure of timeseries than current best practice methods. Additionally, we present further results to characterize the validity of the neural network model and the ability of the classifier to adapt to the generative model of the data

Evaluation and rational design of guide RNAs for efficient CRISPR/Cas9-mediated mutagenesis in Ciona

The CRISPR/Cas9 system has emerged as an important tool for various genome engineering applications. A current obstacle to high throughput applications of CRISPR/Cas9 is the imprecise prediction of highly active single guide RNAs (sgRNAs). We previously implemented the CRISPR/Cas9 system to induce tissue-specific mutations in the tunicate Ciona. In the present study, we designed and tested 83 single guide RNA (sgRNA) vectors targeting 23 genes expressed in the cardiopharyngeal progenitors and surrounding tissues of Ciona embryo. Using high-throughput sequencing of mutagenized alleles, we identified guide sequences that correlate with sgRNA mutagenesis activity and used this information for the rational design of all possible sgRNAs targeting the Ciona transcriptome. We also describe a one-step cloning-free protocol for the assembly of sgRNA expression cassettes. These cassettes can be directly electroporated as unpurified PCR products into Ciona embryos for sgRNA expression in vivo, resulting in high frequency of CRISPR/Cas9-mediated mutagenesis in somatic cells of electroporated embryos. We found a strong correlation between the frequency of an Ebf loss-of-function phenotype and the mutagenesis efficacies of individual Ebf-targeting sgRNAs tested using this method. We anticipate that our approach can be scaled up to systematically design and deliver highly efficient sgRNAs for the tissue-specific investigation of gene functions in Ciona

Rational design and whole-genome predictions of single guide RNAs for efficient CRISPR/Cas9-mediated genome editing in Ciona

The CRISPR/Cas9 system has emerged as an important tool for a wide variety of genome engineering applications, including reverse genetic screens. Previously, we described the implementation of the CRISPR/Cas9 system to induce tissue-specific mutations at targeted locations in the genome of the sea squirt Ciona (STOLFI et al. 2014). In the present study, we designed 83 single guide RNA (sgRNA) vectors targeting 23 genes expressed in the cardiopharyngeal progenitors and surrounding tissues in the Ciona embryo and measured their mutagenesis efficacy rates by massively parallel indel detection at the targeted loci using highthroughput sequencing. We show that the combined activity of two highly active sgRNAs allows us to generate large (>3 kbp) deletions of intervening genomic DNA in somatic cells of electroporated embryos, permitting tissue-specific gene knockouts. Additionally, we employed L1-regularized regression modeling to develop an optimal sgRNA design algorithm (TuniCUT), based on correlations between target sequence features and mutagenesis rates. Using this algorithm, we have predicted mutagenesis rates for sgRNAs targeting all 4,853,589 sites in the Ciona genome, which we have compiled into a "CRISPR/Cas9-induced Ciona Knock-Out" (Ci2KO) sgRNA sequence library. Finally, we describe a new method for the assembly of sgRNA expression cassettes using a simple one-step overlap PCR (OSO-PCR) protocol. These cassettes can be electroporated directly into Ciona embryos as unpurified PCR products to drive sgRNA expression, bypassing the need for time-consuming cloning and plasmid DNA preparations. We anticipate that this method will be used in combination with genome-wide sgRNA predictions to systematically investigate tissue-specific gene functions in Ciona

Image-based Skin Disease Detection and Classification through Bioinspired Machine Learning Approaches

A self-learning disease detection model will be useful for identifying skin infections in suspected individuals using skin images of infected patients. To detect skin diseases, some AI-based bioinspired models employ skin images. Skin infection is a common problem that is currently faced due to various reasons, such as food, water, environmental factors, and many others. Skin infections such as psoriasis, skin cancer, monkeypox, and tomato flu, among others, have a lower death rate but a significant impact on quality of life. Neural Networks (NNs) and Swarm intelligence (SI) based approaches are employed for skin disease diagnosis and classification through image processing. In this paper, the convolutional neural networks-based Cuckoo search algorithm (CNN-CS) is trained using the well-known multi-objective optimization technique cuckoo search. The performance of the suggested CNN-CS model is evaluated by comparing it with three commonly used metaheuristic-based classifiers: CNN-GA, CNN-BAT, and CNN-PSO. This comparison was based on various measures, including accuracy, precision, recall, and F1-score. These measures are calculated using the confusion matrices from the testing phase. The results of the experiments revealed that the proposed model has outperformed the others, achieving an accuracy of 97.72%

Distributed functions of prefrontal and parietal cortices during sequential categorical decisions

Comparing sequential stimuli is crucial for guiding complex behaviors. To understand mechanisms underlying sequential decisions, we compared neuronal responses in the prefrontal cortex (PFC), the lateral intraparietal (LIP), and medial intraparietal (MIP) areas in monkeys trained to decide whether sequentially presented stimuli were from matching (M) or nonmatching (NM) categories. We found that PFC leads M/NM decisions, whereas LIP and MIP appear more involved in stimulus evaluation and motor planning, respectively. Compared to LIP, PFC showed greater nonlinear integration of currently visible and remembered stimuli, which correlated with the monkeys’ M/NM decisions. Furthermore, multi-module recurrent networks trained on the same task exhibited key features of PFC and LIP encoding, including nonlinear integration in the PFC-like module, which was causally involved in the networks’ decisions. Network analysis found that nonlinear units have stronger and more widespread connections with input, output, and within-area units, indicating putative circuit-level mechanisms for sequential decisions

Rational design and whole-genome predictions of single guide RNAs for efficient CRISPR/Cas9-mediated genome editing in Ciona

The CRISPR/Cas9 system has emerged as an important tool for a wide variety of genome engineering applications, including reverse genetic screens. Previously, we described the implementation of the CRISPR/Cas9 system to induce tissue-specific mutations at targeted locations in the genome of the sea squirt Ciona (STOLFI et al. 2014). In the present study, we designed 83 single guide RNA (sgRNA) vectors targeting 23 genes expressed in the cardiopharyngeal progenitors and surrounding tissues in the Ciona embryo and measured their mutagenesis efficacy rates by massively parallel indel detection at the targeted loci using highthroughput sequencing. We show that the combined activity of two highly active sgRNAs allows us to generate large (>3 kbp) deletions of intervening genomic DNA in somatic cells of electroporated embryos, permitting tissue-specific gene knockouts. Additionally, we employed L1-regularized regression modeling to develop an optimal sgRNA design algorithm (TuniCUT), based on correlations between target sequence features and mutagenesis rates. Using this algorithm, we have predicted mutagenesis rates for sgRNAs targeting all 4,853,589 sites in the Ciona genome, which we have compiled into a "CRISPR/Cas9-induced Ciona Knock-Out" (Ci2KO) sgRNA sequence library. Finally, we describe a new method for the assembly of sgRNA expression cassettes using a simple one-step overlap PCR (OSO-PCR) protocol. These cassettes can be electroporated directly into Ciona embryos as unpurified PCR products to drive sgRNA expression, bypassing the need for time-consuming cloning and plasmid DNA preparations. We anticipate that this method will be used in combination with genome-wide sgRNA predictions to systematically investigate tissue-specific gene functions in Ciona

Single Biological Neurons as Temporally Precise Spatio-Temporal Pattern Recognizers

This PhD thesis is focused on the central idea that single neurons in the brain should be regarded as temporally precise and highly complex spatio-temporal pattern recognizers. This is opposed to the prevalent view of biological neurons as simple and mainly spatial pattern recognizers by most neuroscientists today. In this thesis, I will attempt to demonstrate that this is an important distinction, predominantly because the above-mentioned computational properties of single neurons have far-reaching implications with respect to the various brain circuits that neurons compose, and on how information is encoded by neuronal activity in the brain. Namely, that these particular "low-level" details at the single neuron level have substantial system-wide ramifications. In the introduction we will highlight the main components that comprise a neural microcircuit that can perform useful computations and illustrate the inter-dependence of these components from a system perspective. In chapter 1 we discuss the great complexity of the spatio-temporal input-output relationship of cortical neurons that are the result of morphological structure and biophysical properties of the neuron. In chapter 2 we demonstrate that single neurons can generate temporally precise output patterns in response to specific spatio-temporal input patterns with a very simple biologically plausible learning rule. In chapter 3, we use the differentiable deep network analog of a realistic cortical neuron as a tool to approximate the gradient of the output of the neuron with respect to its input and use this capability in an attempt to teach the neuron to perform nonlinear XOR operation. In chapter 4 we expand chapter 3 to describe extension of our ideas to neuronal networks composed of many realistic biological spiking neurons that represent either small microcircuits or entire brain regions

The performance of insolvency prediction and credit risk models in the UK: A comparative study, development and wider application.

Contingent claims models have recently been applied to the field of corporate insolvency prediction in an attempt to provide the art with a theoretical methodology that has been lacking in the past. Limited studies have been carried out in order to empirically compare the performance of these “market” models with that of their accounting number-based counterparts. This thesis contributes to the literature in several ways: The thesis traces the evolution of the art of corporate insolvency prediction from its inception through to the present day, combining key developments and methodologies into a single document of reference. I use receiver operating characteristic curves and tests of economic value to assess the efficacy of sixteen models, carefully selected to represent key moments in the evolution of the art, and tested upon, for the first time, post-IFRS UK data. The variability of model efficacy is also measured for the first time, using Monte Carlo simulation upon 10,000 randomly generated training and validation samples from a dataset consisting of over 12,000 firmyear observations. The results provide insights into the distribution of model accuracy as a result of sample selection, which is something which has not appeared in the literature prior to this study. I find overall that the efficacy of the models is generally less than that reported in the prior literature; but that the theoretically driven, market-based models outperform models which use accounting numbers; the latter showing a relatively larger efficacy distribution. Furthermore, I obtain the counter-intuitive finding that predictions based on a single ratio can be as efficient as those which are based on models which are far more complicated – in terms of variable variety and mathematical construction. Finally, I develop and test a naïve version of the down-and-out-call barrier option model for insolvency prediction and find that, despite its simple formulation, it performs favourably compared alongside other market-based models