Don’t skip a beat : studying cardiac rhythm in human pluripotent stem cell-derived cardiomyocytes

Cardiovascular disease is responsible for 17 million deaths globally each year. Research aimed at understanding the form and function of this vital organ will be key to improving patient care. Although animal models such as rat, rabbit and dog have proven to be valuable to study heart physiology, there are a number of important species-specific differences, for e.g. heart rate, when compared with the human heart. In the recent years, research on cardiomyocytes(CMs) derived from human pluripotent stem cells has demonstrated that they resemble native human CMs and hence, make excellent models to study heart development and disease in vitro.In this dissertation,we present studies that describe the use of hPSC-CMs for pharamacological testing and for modeling inherited arrhythmogenic disorders.Combined with other novel technologies in the fields of genetic medicine, tissue engineering,and genome editing, hPSC-CM models will be valuable for 1) understanding lineage decisions determining CM specification, 2) unraveling molecular basis of disease, 3) translational applications such as target/drug discovery, diagnostic medicine and developing effective treatment strategies

Nile red-dye based analysis of synthetic fibres for forensic applications.

Forensic evaluation of crime scenes normally involves examination of textile fibers, to find out the association between an individual and a crime scene, or between a suspect and a victim. The forensic samples normally include a mix of various types, sizes (micro to nano - scale) and shapes of natural and synthetic fibers, which are very difficult to differentiate/identify. Various sophisticated analytical instruments are being used to carry out the examination of these fibers. They involve various microscopy and spectroscopy based techniques, most of which are very complex and highly sensitive. Further, they may require a series of sample preparation steps to get high selectivity and are highly time consuming. Here we report a fluorescence microscopy based synthetic (plastic) fiber detection method using Nile Red (NR) dye, which provides high selectivity for synthetic fibers. The methodology involves the use of NR dye which selectively stains the fibers collected on filter papers following separation from samples/soils and water. The selectivity of NR towards the fibers is due to their non-polar property. Binding with NR makes the fibers fluoresce when viewed under a fluorescence microscope. This selectivity of NR for fibers makes the identification of fibers lot easier and less timeconsuming in forensic samples when compared to the more commonly used optical microscopy (where the presence of naturally-occurring substances of similar size can result in more errors). The paper will discuss optimisation of various parameters and method validation for detection of synthetic fibers and microplastics from soil samples. As an example, our method has shown to provide distinct clarity for the analysis of microfibers. The potential for the application of the method for faster forensics analysis will be discussed

Group membership in asynchronous distributed environments using logically ordered views

A group membership protocol ensures agreement and consistent commit actions among group members to maintain a sequence of identical group views in spite of continuous changes, either voluntary or otherwise, in processors' membership status. In asynchronous distributed environments, such consistency among group views must be guaranteed using messages over a network which does not bound message delivery times. Assuming a network that provides a reliable, FIFO channel between any pair of processors, one approach to designing such a protocol is to centralize the responsibility to detect changes, ensure agreement, and commit them consistently in a single manager process. This approach is complicated by the fact that a protocol to elect a new manager with a consistent membership proposal must be executed when the manager itself fails. In this report, we present a membership protocol based on ordering of group members in a logical ring that eliminates the need for such centralized responsibility. Agreement and commit actions are token-based and the protocol ensures that no tokens are lost or duplicated due to changes in membership. The cost of committing a change is 2n point-to-point messages over FIFO channels where n is the group size. The protocol correctness has been proven formally. Agreement, Asynchronous, Commit, Distributed, Failure, Group Membership, Logical Ring, Reliable Multicast, TokenMonterey, California. Naval Postgraduate Schoolhttp://archive.org/details/groupmembershipi00shukMonterey, California. Naval Postgraduate SchoolNAApproved for public release; distribution is unlimited

Shared Control of Mobile Robots Using Model Predictive Control

With the world constantly driving towards attaining complete autonomy, there is still a major question of safety when it comes to trusting a machine completely. Autonomous systems of today also do not have the ability to perform flawlessly in an environment that is cluttered and unstructured. This calls for the need of having a human operate the machine at all times either remotely via tele-operation methods or by being physically present alongside the machine. With tele-operation of remote systems, the cognitive load required from the human operator is high, while also the perception of the remote systems environment is low. This can cause many undesirable human errors causing damage to machinery. For example, tele-operating a forestry machine in a forest can be a very daunting task as there will be many trees and not all trees around the machine can be seen by the operator during remote tele-operation. With this in context, a few industries and sectors have now largely started research with using shared control methodologies to aid their machine in tele-operation tasks. This thesis proposes a shared control methodology to provide a certain level of autonomy to the machine while still allowing the human operator to always be in control. The proposed methodology uses a Model predictive controller as the base controller to control the robot and perform obstacle avoidance tasks. The robot considered for implementation is a differential drive mobile robot, in specific the MiR 100 from Mobile Industrial Robots. The key motivation behind the thesis is to evaluate the performance of the shared control approach against a manual tele-operation task, to better understand the advantages and possible disadvantages of using a shared control strategy. The proposed strategy is implemented using the CasADi optimization toolbox on Matlab and tested through user testings. The results obtained from the user test prove that shared control can largely help in improving the safety of the system, but not so much with performance, at least not with the proposed methodology

Potential for Nile red dye-based analysis of microplastics from oceanic samples.

The Global production of plastics has exceeded over 300 million metric tons. Billions of tons of plastic waste that is generated gets accumulated as plastic debris in soil, sediments, oceans and surface water with no obvious strategy to tackle them. The plastics disposed in the water and land ultimately disintegrate to microplastics, widely accepted as particles [less than]5 mm size. These microplastics are ubiquitous contaminants prevalent in the environment and pose great ecological hazard. Ensuring sustainability of coastal marine areas worldwide and reducing biodiversity loss has long been identified as a global challenge. However, dearth of scientific strategies and standardized protocols for fast and accurate detection of microplastics is a matter of concern and needs immediate attention. Therefore, robust, reliable and high through-put detection method for microplastics in oceanic environment is highly sought after. Quite a few studies have explored the potential of Nile red a solvatochromic dye in detection of microplastics. However, often Nile red alone cannot be used in quantifying microplastic due to false positives generated by staining of organic matter. In this work, we have adopted a method based on costaining of microplastics using Nile red dye and Methylene blue by fluorescence microscopy. We have observed that Nile red overestimated microplastic particles and this study serves as foundation to our future work

A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head

Purpose: To develop a deep learning approach to de-noise optical coherence tomography (OCT) B-scans of the optic nerve head (ONH). Methods: Volume scans consisting of 97 horizontal B-scans were acquired through the center of the ONH using a commercial OCT device (Spectralis) for both eyes of 20 subjects. For each eye, single-frame (without signal averaging), and multi-frame (75x signal averaging) volume scans were obtained. A custom deep learning network was then designed and trained with 2,328 "clean B-scans" (multi-frame B-scans), and their corresponding "noisy B-scans" (clean B-scans + gaussian noise) to de-noise the single-frame B-scans. The performance of the de-noising algorithm was assessed qualitatively, and quantitatively on 1,552 B-scans using the signal to noise ratio (SNR), contrast to noise ratio (CNR), and mean structural similarity index metrics (MSSIM). Results: The proposed algorithm successfully denoised unseen single-frame OCT B-scans. The denoised B-scans were qualitatively similar to their corresponding multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean SNR increased from $4.02 \pm 0.68$ dB (single-frame) to $8.14 \pm 1.03$ dB (denoised). For all the ONH tissues, the mean CNR increased from $3.50 \pm 0.56$ (single-frame) to $7.63 \pm 1.81$ (denoised). The MSSIM increased from $0.13 \pm 0.02$ (single frame) to $0.65 \pm 0.03$ (denoised) when compared with the corresponding multi-frame B-scans. Conclusions: Our deep learning algorithm can denoise a single-frame OCT B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior quality OCT B-scans with reduced scanning times and minimal patient discomfort

Freisetzung kinetischer Energie und Hammond-Postulat bei der intramolekularen aromatischen Substitution in 2-Stilbazol-Ionen

Schubert R, Ramana DV, Grützmacher H-F. Freisetzung kinetischer Energie und Hammond-Postulat bei der intramolekularen aromatischen Substitution in 2-Stilbazol-Ionen. Chemische Berichte. 1980;113(12):3758-3774

Deep learning algorithms to isolate and quantify the structures of the anterior segment in optical coherence tomography images

Background/Aims Accurate isolation and quantification of intraocular dimensions in the anterior segment (AS) of the eye using optical coherence tomography (OCT) images is important in the diagnosis and treatment of many eye diseases, especially angle-closure glaucoma. Method In this study, we developed a deep convolutional neural network (DCNN) for the localisation of the scleral spur; moreover, we introduced an information-rich segmentation approach for this localisation problem. An ensemble of DCNNs for the segmentation of AS structures (iris, corneosclera shell adn anterior chamber) was developed. Based on the results of two previous processes, an algorithm to automatically quantify clinically important measurements were created. 200 images from 58 patients (100 eyes) were used for testing. Results With limited training data, the DCNN was able to detect the scleral spur on unseen anterior segment optical coherence tomography (ASOCT) images as accurately as an experienced ophthalmologist on the given test dataset and simultaneously isolated the AS structures with a Dice coefficient of 95.7%. We then automatically extracted eight clinically relevant ASOCT measurements and proposed an automated quality check process that asserts the reliability of these measurements. When combined with an OCT machine capable of imaging multiple radial sections, the algorithms can provide a more complete objective assessment. The total segmentation and measurement time for a single scan is less than 2 s. Conclusion This is an essential step towards providing a robust automated framework for reliable quantification of ASOCT scans, for applications in the diagnosis and management of angle-closure glaucoma