Low-Cost Motility Tracking System (LOCOMOTIS) for time-lapse microscopy applications and cell visualisation

This article has been made available through the Brunel Open Access Publishing Fund.Direct visualisation of cells for the purpose of studying their motility has typically required expensive microscopy equipment. However, recent advances in digital sensors mean that it is now possible to image cells for a fraction of the price of a standard microscope. Along with low-cost imaging there has also been a large increase in the availability of high quality, open-source analysis programs. In this study we describe the development and performance of an expandable cell motility system employing inexpensive, commercially available digital USB microscopes to image various cell types using time-lapse and perform tracking assays in proof-of-concept experiments. With this system we were able to measure and record three separate assays simultaneously on one personal computer using identical microscopes, and obtained tracking results comparable in quality to those from other studies that used standard, more expensive, equipment. The microscopes used in our system were capable of a maximum magnification of 413.6x. Although resolution was lower than that of a standard inverted microscope we found this difference to be indistinguishable at the magnification chosen for cell tracking experiments (206.8x). In preliminary cell culture experiments using our system, velocities (mean mm/min ± SE) of 0.81±0.01 (Biomphalaria glabrata hemocytes on uncoated plates), 1.17±0.004 (MDA-MB-231 breast cancer cells), 1.24±0.006 (SC5 mouse Sertoli cells) and 2.21±0.01 (B. glabrata hemocytes on Poly-L-Lysine coated plates), were measured and are consistent with previous reports. We believe that this system, coupled with open-source analysis software, demonstrates that higher throughput time-lapse imaging of cells for the purpose of studying motility can be an affordable option for all researchers. © 2014 Lynch et al

Financing sustainable energy for all: pay-as-you-go vs. traditional solar finance approaches in Kenya

This paper focuses on finance for Solar Home Systems (SHSs) in Kenya and asks to what extent emerging new finance approaches are likely to address the shortcomings of past approaches. Drawing on the STEPS Pathways Approach we adopt a framing that understands finance within a broader socio-technical context as a necessary but not sufficient component of achieving alternative pathways to sustainable energy access. The paper contributes in four ways. Firstly, it presents a comprehensive overview of past and new emerging approaches to financing SHSs in Kenya and their relative strengths and weaknesses. Secondly, it represents one of the first attempts in the literature to analyse the potential of new, real time monitoring technologies and pay as you go finance models to overcome the barriers faced by conventional consumer finance models for off-grid renewable energy technologies (RETs). Thirdly, by applying for the first time we are aware of a socio-technical approach, via the application of Strategic Niche Management (SNM) theory, to analyse the finance of RETs in developing countries, the analysis considers finance in the context of the social practices poor people seek to fulfil via access to the energy services that off-grid RETs provide, and the ways in which people previously paid for these services (e.g. via kerosene for lighting). This also situates the analysis within the understanding of SHSs as a niche that has to compete with the established regime of energy service provision and its attendant social and political institutional support. The paper therefore also contributes to the small but expanding body of literature that seeks to operationalise socio-technical transitions thinking and SNM within a developing country context

Beyond technology and finance: pay-as-you-go sustainable energy access and theories of social change

Two-thirds of people in sub-Saharan Africa lack access to electricity, a precursor of poverty reduction and development. The international community has ambitious commitments in this regard, e.g. the UN's Sustainable Energy for All by 2030. But scholarship has not kept up with policy ambitions. This paper operationalises a sociotechnical transitions perspective to analyse for the first time the potential of new, mobileenabled, pay-as-you-go approaches to financing sustainable energy access, focussing on a case study of pay-as-you-go approaches to financing solar home systems in Kenya. The analysis calls into question the adequacy of the dominant, two-dimensional treatment of sustainable energy access in the literature as a purely financial/technology, economics/ engineering problem (which ignores sociocultural and political considerations) and demonstrates the value of a new research agenda that explicitly attends to theories of social change – even when, as in this paper, the focus is purely on finance. The paper demonstrates that sociocultural considerations cut across the literature's traditional two-dimensional analytic categories (technology and finance) and are material to the likely success of any technological or financial intervention. It also demonstrates that the alignment of new payas- you-go finance approaches with existing sociocultural practices of paying for energy can explain their early success and likely longevity relative to traditional finance approaches

Real Time Monitoring Technologies for Pro-Poor Access to Electricity

Existing literature strongly and consistently reports the high upfront cost of energy technology hardware as one of the main demand-side barriers to increased use of modern energy services by the poor. Existing literature also shows that lack of control over monthly bills and unawareness of consumption levels lead to inefficient and sometimes insufficient electricity consumption patterns by the poor. Innovative technologies drawing from existing power metering and mobile payment technologies are now targeting the barriers of affordability and financial sustainability of electricity provision to the poor by allowing fee-for-services and rent-to-buy schemes for the sale of electricity, tariffs related to actual consumption, consumers’ control of their electricity bills and suppliers’ more efficient collection of payments. Real time monitoring (RTM) of on-grid electricity consumption has a long history, with prepaid meters being used in several developed and developing countries. However, new mobile technologies are enabling their use in off-grid systems, including both mini-grids and mobile household systems.DFI

An Exploration of Deep-Learning Based Phenotypic Analysis to Detect Spike Regions in Field Conditions for UK Bread Wheat

Wheat is one of the major crops in the world, with a global demand expected to reach 850 million tons by 2050 that is clearly outpacing current supply. The continual pressure to sustain wheat yield due to the world’s growing population under fluctuating climate conditions requires breeders to increase yield and yield stability across environments. We are working to integrate deep learning into field-based phenotypic analysis to assist breeders in this endeavour. We have utilised wheat images collected by distributed CropQuant phenotyping workstations deployed for multiyear field experiments of UK bread wheat varieties. Based on these image series, we have developed a deep-learning based analysis pipeline to segment spike regions from complicated backgrounds. As a first step towards robust measurement of key yield traits in the field, we present a promising approach that employ Fully Convolutional Network (FCN) to perform semantic segmentation of images to segment wheat spike regions. We also demonstrate the benefits of transfer learning through the use of parameters obtained from other image datasets. We found that the FCN architecture had achieved a Mean classification Accuracy (MA) >82% on validation data and >76% on test data and Mean Intersection over Union value (MIoU) >73% on validation data and and >64% on test datasets. Through this phenomics research, we trust our attempt is likely to form a sound foundation for extracting key yield-related traits such as spikes per unit area and spikelet number per spike, which can be used to assist yield-focused wheat breeding objectives in near future