The consequences of replicating in the wrong orientation: Bacterial chromosome duplication without an active replication origin

Chromosome replication is regulated in all organisms at the assembly stage of the replication machinery at specific origins. In Escherichia coli the DnaA initiator protein regulates the assembly of replication forks at oriC. This regulation can be undermined by defects in nucleic acid meta¬bolism. In cells lacking RNase HI replication initiates indepen¬dently of DnaA and oriC, presumably at persisting R-loops. A similar mechanism was assumed for origin-independent synthesis in cells lacking RecG. However, recently we suggested that this synthesis initiates at intermediates resulting from replication fork fusions. Here we present data suggesting that in cells lacking RecG or RNase HI origin-independent synthesis arises by different mechanisms, indicative of these two proteins having different roles in vivo. Our data support the idea that RNase HI processes R-loops, while RecG is required to process replication fork fusion intermediates. However, regardless of how origin-independent synthesis is initiated, a fraction of forks will proceed in an orientation opposite to normal. We show that the resulting head-on encounters with transcription threaten cell viability, especially if taking place in highly-transcribed areas. Thus, despite their different functions, RecG and RNase HI are both important factors for maintaining replication control and orientation. Their absence causes severe replication problems, highlighting the advantages of the normal chromosome arrangement, which exploits a single origin to control the number of forks and their orientation relative to transcription, and a defined termination area to contain fork fusions. Any changes to this arrangement endanger cell cycle control, chromosome dynamics and, ultimately, cell viability.This work was supported by the Royal Society (RG110414 to C.J.R.) and The Biotechnology and Biological Sciences Research Council (BB/K015729/1 to C.J.R.)

HyperProv: Decentralized Resilient Data Provenance at the Edge with Blockchains

Data provenance and lineage are critical for ensuring integrity and reproducibility of information in research and application. This is particularly challenging for distributed scenarios, where data may be originating from decentralized sources without any central control by a single trusted entity. We present HyperProv, a general framework for data provenance based on the permissioned blockchain Hyperledger Fabric (HLF), and to the best of our knowledge, the first system that is ported to ARM based devices such as Raspberry Pi (RPi). HyperProv tracks the metadata, operation history and data lineage through a set of built-in queries using smart contracts, enabling lightweight retrieval of provenance data. HyperProv provides convenient integration through a NodeJS client library, and also includes off-chain storage through the SSH file system. We evaluate HyperProv's performance, throughput, resource consumption, and energy efficiency on x86-64 machines, as well as on RPi devices for IoT use cases at the edge

Demo abstract: Towards IoT service deployments on edge community network microclouds

Internet of Things (IoT) services for personal devices and smart homes provided by commercial solutions are typically proprietary and closed. These services provide little control to the end users, for instance to take ownership of their data and enabling services, which hinders these solutions' wider acceptance. In this demo paper, we argue for an approach to deploy professional IoT services on user-controlled infrastructure at the network edge. The users would benefit from the ability to choose the most suitable service from different IoT service offerings, like the one which satisfies their privacy requirements, and third-party service providers could offer more tailored IoT services at customer premises. We conduct the demonstration on microclouds, which have been built with the Cloudy platform in the Guifi.net community network. The demonstration is conducted from the perspective of end users, who wish to deploy professional IoT data management and analytics services in volunteer microclouds.Peer ReviewedPostprint (author's final draft

AnonFACES: Anonymizing Faces Adjusted to Constraints on Efficacy and Security

Image data analysis techniques such as facial recognition can threaten individuals’ privacy. Whereas privacy risks often can be reduced by adding noise to the data, this approach reduces the utility of the images. For this reason, image de-identification techniques typically replace directly identifying features (e.g., faces, car number plates) present in the data with synthesized features, while still preserving other non-identifying features. As of today, existing techniques mostly focus on improving the naturalness of the generated synthesized images, without quantifying their impact on privacy. In this paper, we propose the first methodology and system design to quantify, improve, and tune the privacy-utility trade-off, while simultaneously also improving the naturalness of the generated images. The system design is broken down into three components that address separate but complementing challenges. This includes a two-step cluster analysis component to extract low-dimensional feature vectors representing the images (embedding) and to cluster the images into fixed-sized clusters. While the importance of good clustering mostly has been neglected in previous work, we find that our novel approach of using low-dimensional feature vectors can improve the privacy-utility trade-off by better clustering similar images. The use of these embeddings has been found particularly useful when wanting to ensure high naturalness and utility of the synthetically generated images. By combining improved clustering and incorporating StyleGAN, a state-of-the-art Generative Neural Network, into our solution, we produce more realistic synthesized faces than prior works, while also better preserving properties such as age, gender, skin tone, or even emotional expressions. Finally, our iterative tuning method exploits non-linear relations between privacy and utility to identify good privacy-utility trade-offs. We note that an example benefit of these improvements is that our solution allows car manufacturers to train their autonomous vehicles while complying with privacy laws

Effects of Epistasis and Pleiotropy on Fitness Landscapes

The factors that influence genetic architecture shape the structure of the fitness landscape, and therefore play a large role in the evolutionary dynamics. Here the NK model is used to investigate how epistasis and pleiotropy -- key components of genetic architecture -- affect the structure of the fitness landscape, and how they affect the ability of evolving populations to adapt despite the difficulty of crossing valleys present in rugged landscapes. Populations are seen to make use of epistatic interactions and pleiotropy to attain higher fitness, and are not inhibited by the fact that valleys have to be crossed to reach peaks of higher fitness.Comment: 10 pages, 6 figures. To appear in "Origin of Life and Evolutionary Mechanisms" (P. Pontarotti, ed.). Evolutionary Biology: 16th Meeting 2012, Springer-Verla

Machine Learning based Energy Management Model for Smart Grid and Renewable Energy Districts

The combination of renewable energy sources and prosumer-based smart grid is a sustainable solution to cater to the problem of energy demand management. A pressing need is to develop an efficient Energy Management Model (EMM) that integrates renewable energy sources with smart grids. However, the variable scenarios and constraints make this a complex problem. Machine Learning (ML) methods can often model complex and non-linear data better than the statistical models. Therefore, developing an ML algorithm for the EMM is a suitable option as it reduces the complexity of the EMM by developing a single trained model to predict the performance parameters of EMM for multiple scenarios. However, understanding latent correlations and developing trust in highly complex ML models for designing EMM within the stochastic prosumer-based smart grid is still a challenging task. Therefore, this paper integrates ML and Gaussian Process Regression (GPR) in the EMM. At the first stage, an optimization model for Prosumer Energy Surplus (PES), Prosumer Energy Cost (PEC), and Grid Revenue (GR) is formulated to calculate base performance parameters (PES, PEC, and GR) for the training of the ML-based GPR model. In the second stage, stochasticity of renewable energy sources, load, and energy price, same as provided by the Genetic Algorithm (GA) based optimization model for PES, PEC, and GR, and base performance parameters act as input covariates to produce a GPR model that predicts PES, PEC, and GR. Seasonal variations of PES, PEC, and GR are incorporated to remove hitches from seasonal dynamics of prosumers energy generation and prosumers energy consumption. The proposed adaptive Service Level Agreement (SLA) between energy prosumers and the grid benefits both these entities. The results of the proposed model are rigorously compared with conventional optimization (GA and PSO) based EMM to prove the validity of the proposed model

Assessment of endophytic fungi cultural filtrate on soybean seed germination

Soybean seeds have high amount of isoflavones but its germination is often confronted with a variety of environmental problems resulting in low germination rate and growth. To overcome this in eco-friendly manner, we investigated the influence of cultural filtrate (CF) of gibberellins-producing endophytic fungi on soybean seed germination. Three endophytic fungi namely: Chrysosporium pseudomerdarium, Aspergillus fumigatus and Paecilomyces sp. were previously isolated from the roots of soybean plants. The culture filtrate application of the three endophyte resulted in significantly higher rate of soybean seed germination, germination percentage, relative seed germination percentage, peak value, germination value, shoot and root length, germination index and vigour index. Among the endophytes, A. fumigatus significantly increased the rate of germination, shoot and root length and vigour index. Same trend was noted in germination percentage and relative seed germination percentage for all the endophytic fungi. However, C. pseudomerdarium was the only one that enhanced germination index. The enhanced soybean seed germination by endophytes can be used for seed priming and hence improved crop plant growth under extreme environmental conditions.Key words: Chrysosporium pseudomerdarium, Aspergillus fumigatus, Paecilomyces sp., soybean, seed germination