The extraction of consistent and identifiable features from an image of the human iris is known as iris recognition. Identifying which pixels belong to the iris, known as segmentation, is the first stage of iris recognition. Errors in segmentation propagate to later stages. Current segmentation approaches are tuned to specific environments. We propose using a convolution neural network for iris segmentation. Our algorithm is accurate when trained in a single environment and tested in multiple environments. Our network builds on the Mask R-CNN framework (He et al., ICCV 2017). Our approach segments faster than previous approaches including the Mask R-CNN network. Our network is accurate when trained on a single environment and tested with a different sensors (either visible light or near-infrared). Its accuracy degrades when trained with a visible light sensor and tested with a near-infrared sensor (and vice versa). A small amount of retraining of the visible light model (using a few samples from a near-infrared dataset) yields a tuned network accurate in both settings. For training and testing, this work uses the Casia v4 Interval, Notre Dame 0405, Ubiris v2, and IITD datasets.

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A molecular pathway primarily refers to a chain of chemical reactions within a cell and their analysis plays a vital role in developing effective drugs for various human infectious diseases, such as Cancer and Malaria. However, the existing cell pathway analysis techniques, such as paper-andpencil proof methods, graph theory and Petri Nets, are either incapable of assuring accurate results or only deal with certain biological systems, which limits the usage of these techniques in the safety-critical field of human medicine. In this paper, we propose to use higher-order-logic theorem proving to accurately deduce results of biological reactions in a pathway. The proposed framework is primarily based on Z-Syntax, which is a formal language to model molecular reactions and is based on three logical operators and four inference rules. As a first step towards this goal, we formalize these operators and inference rules in higher-order logic and provide automated reasoning support for verifying molecular reactions using the HOL4 theorem prover. For illustration purposes, we present the formal verification of a reaction involving TP53 degradation.

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Abstract. Recent progress in nanotechnology and optical imaging offers promising features to develop effective drugs to cure chronic diseases, such as cancer and malaria. However, qualitative characterization of biological organisms (such as molecules) is the foremost requirement to identify key drug targets. One of the most widely used approaches in this domain is molecular pathways, which offers a systematic way to represent and analyse complex biological systems. Traditionally, such pathways are analysed using paper-and-pencil based proofs and simulations. However, these methods cannot ascertain accurate analysis, which is a serious drawback given the safety-critical nature of the applications of molecular pathways. To overcome these limitations, we recently proposed to formally reason about molecular pathways within the sound core of a theorem prover. As a first step towards this direction, we formally expressed three logical operators and four inference rules of Zsyntax, which is a deduction language for molecular pathways. In the current paper, we extend this formalization by verifying a couple of behavioural properties of Zsyntax based deduction using the HOL4 theorem prover and developing a biologist friendly graphical user interface. Moreover, we demonstrate the utilization of our work by presenting the formal analysis of cancer related molecular pathway, i.e., TP53 degradation and metabolic pathway known as Glycolysis.Key words: Molecular biology, logical deduction AMS subject classifications. 68Q25, 92C40, 03D051. Introduction. Molecular biology is extensively used to construct models of biological processes in the form of networks or pathways, such as protein-protein interaction networks and signalling pathways. The analysis of these biological networks, usually referred to as biological regulatory networks (BRNs) or gene regulatory networks (GRNs) [10], is based on the principles of molecular biology to understand the dynamics of complex living organisms. Moreover, the analysis of molecular pathways plays a vital role in investigating the treatment of various human infectious diseases and future drug design targets. For example, the analysis of BRNs has been recently used to predict treatment decisions for sepsis patients [15].Traditionally, the molecular biology based analysis is carried out by biologists in the form of wet-lab experiments (e.g. [7,13]). These experiments, despite being very slow and expensive, do not ensure accurate results due to the inability to accurately characterize the complex biological processes in an experimental setting. Other alternatives for deducing molecular reactions include paper-and-pencil proof methods (e.g. using Boolean modelling [27] or kinetic logic [28]) or computer-based techniques (e.g. [29]) for analysing molecular biology problems. The manual proofs become quite tedious for large systems, where the calculation of unknown parameters takes several hundred proof steps, and are thus prone to human errors. The computer-based methods consist of graph-theoretic...

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