A support architecture for reliable distributed computing systems

The Clouds kernel design was through several design phases and is nearly complete. The object manager, the process manager, the storage manager, the communications manager, and the actions manager are examined

Phase chirality and stereo-selective swelling of cholesteric elastomers

Cholesteric elastomers possess a macroscopic ``phase chirality'' as the director n rotates in a helical fashion along an optical axis $z$ and can be described by a chiral order parameter. This parameter can be tuned by changing the helix pitch p and/or the elastic properties of the network. The cholesterics also possess a local nematic order, changing with temperature or during solvent swelling. In this paper, by measuring the power of optical rotation, we discover how these two parameters vary as functions of temperature or solvent adsorbed by the network. The main result is a finding of pronounced stereo-selectivity of cholesteric elastomers, demonstrating itself in the retention of the ``correct'' chirality component of a racemic solvent. It has been possible to quantify the amount of such stereo-separation, as the basic dynamics of the effect

Automated phenotyping of mosquito larvae enables high-throughput screening for novel larvicides and offers potential for smartphone-based detection of larval insecticide resistance

Pyrethroid-impregnated nets have contributed significantly to halving the burden of malaria but resistance threatens their future efficacy and the pipeline of new insecticides is short. Here we report that an invertebrate automated phenotyping platform (INVAPP), combined with the algorithm Paragon, provides a robust system for measuring larval motility in Anopheles gambiae (and An. coluzzi) as well as Aedes aegypti with the capacity for high-throughput screening for new larvicides. By this means, we reliably quantified both time- and concentration-dependent actions of chemical insecticides faster than using the WHO standard larval assay. We illustrate the effectiveness of the system using an established larvicide (temephos) and demonstrate its capacity for library-scale chemical screening using the Medicines for Malaria Venture (MMV) Pathogen Box library. As a proof-of-principle, this library screen identified a compound, subsequently confirmed to be tolfenpyrad, as an effective larvicide. We have also used the INVAPP / Paragon system to compare responses in larvae derived from WHO classified deltamethrin resistant and sensitive mosquitoes. We show how this approach to monitoring larval response to insecticides can be adapted for use with a smartphone camera application and therefore has potential for further development as a simple portable field-assay with associated real-time, geo-located information to identify hotspots

Winter wheat, 2000

"July 2000.""Publication costs paid by Missouri Seed Improvement Association."The objective of the Missouri Winter Wheat Performance Tests is to provide wheat growers in Missouri with a reliable, unbiased, up-to-date source of information that will permit valid comparisons among improved wheat varieties. This information should help Missouri wheat growers select varieties best suited to their particular area and growing conditions. This report summarizes soft red winter wheat variety trials conducted throughout Missouri during the 1999-00 cropping season. No hard red winter wheat test was grown in 2000

Clinical Validation of a Rapid Variant-Proof RT-RPA Assay for the Detection of SARS-CoV-2

The COVID-19 pandemic has unveiled a pressing need to expand the diagnostic landscape to permit high-volume testing in peak demand. Rapid nucleic acid testing based on isothermal amplification is a viable alternative to real-time reverse transcription polymerase chain reaction (RT-PCR) and can help close this gap. With the emergence of SARS-CoV-2 variants of concern, clinical validation of rapid molecular tests needs to demonstrate their ability to detect known variants, an essential requirement for a robust pan-SARS-CoV-2 assay. To date, there has been no clinical validation of reverse transcription recombinase polymerase amplification (RT-RPA) assays for SARS-CoV-2 variants. We performed a clinical validation of a one-pot multi-gene RT-RPA assay with the E and RdRP genes of SARS-CoV-2 as targets. The assay was validated with 91 nasopharyngeal samples, with a full range of viral loads, collected at University College London Hospitals. Moreover, the assay was tested with previously sequenced clinical samples, including eleven lineages of SARS-CoV-2. The rapid (20 min) RT-RPA assay showed high sensitivity and specificity, equal to 96% and 97%, respectively, compared to gold standard real-time RT-PCR. The assay did not show cross-reactivity with the panel of respiratory pathogens tested. We also report on a semi-quantitative analysis of the RT-RPA results with correlation to viral load equivalents. Furthermore, the assay could detect all eleven SARS-CoV-2 lineages tested, including four variants of concern (Alpha, Beta, Delta, and Omicron). This variant-proof SARS-CoV-2 assay offers a significantly faster and simpler alternative to RT-PCR, delivering sensitive and specific results with clinical samples

High Bandwidth GaN-Based Micro-LEDs for Multi-Gb/s Visible Light Communications

Gallium-nitride (GaN) based light-emitting diodes (LEDs) are highly-efficient sources for general purpose illumination. Furthermore, being semiconductor-based light sources, they are readily compatible with intelligent drive electronics. Visible light communications (VLC) is a technology which leverages these advantageous properties of GaN LEDs and can supplement existing wireless communications by offering a large, licence-free spectral bandwidth. Here we report on progress in the development of micro-scale GaN LEDs (micro-LEDs), optimised for VLC. These blue-emitting micro-LEDs are shown to have very high electrical-to-optical modulation bandwidths, exceeding 800~MHz. The data transmission capabilities of the micro-LEDs are illustrated by demonstrations using on-off-keying (OOK), pulse-amplitude modulation (PAM) and orthogonal frequency division multiplexing (OFDM) modulation schemes to transmit data over free space at rates of 1.7, 3.4 and 5 Gb/s, respectively

Forming reproducible non-lithographic nanocontacts to assess the effect of contact compressive strain in nanomaterials

The application of electrical nanoprobes to measure and characterize nanomaterials has become widely spread. However, the formation of quality electrical contacts using metallic probes on nanostructures has not been directly assessed. We investigate here the electrical behaviour of non-lithographically formed contacts to ZnO nanowires (NWs) and develop a method to reproducibly form Ohmic contacts for accurate electrical measurement of the nanostructures. The contacting method used in this work relies on an electrical feedback mechanism to determine the point of contact to the individual NWs, ensuring minimal compressive strain at the contact. This developed method is compared with the standard tip deflection contacting technique and shows a significant improvement in reproducibility. The effect of excessive compressive strain at the contact was investigated, with a change from rectifying to ohmic I–V behaviour observed as compressive strain at the contact was increased, leading to irreversible changes to the electrical properties of the NW. This work provides an ideal method for forming reproducible non-lithographic nanocontacts to a multitude of nanomaterials

Tracking COVID-19 using online search

Previous research has demonstrated that various properties of infectious diseases can be inferred from online search behaviour. In this work we use time series of online search query frequencies to gain insights about the prevalence of COVID-19 in multiple countries. We first develop unsupervised modelling techniques based on associated symptom categories identified by the United Kingdom's National Health Service and Public Health England. We then attempt to minimise an expected bias in these signals caused by public interest -- as opposed to infections -- using the proportion of news media coverage devoted to COVID-19 as a proxy indicator. Our analysis indicates that models based on online searches precede the reported confirmed cases and deaths by 16.7 (10.2 - 23.2) and 22.1 (17.4 - 26.9) days, respectively. We also investigate transfer learning techniques for mapping supervised models from countries where the spread of disease has progressed extensively to countries that are in earlier phases of their respective epidemic curves. Furthermore, we compare time series of online search activity against confirmed COVID-19 cases or deaths jointly across multiple countries, uncovering interesting querying patterns, including the finding that rarer symptoms are better predictors than common ones. Finally, we show that web searches improve the short-term forecasting accuracy of autoregressive models for COVID-19 deaths. Our work provides evidence that online search data can be used to develop complementary public health surveillance methods to help inform the COVID-19 response in conjunction with more established approaches.Comment: Published in Nature Digital Medicine. Please note that the published version differs from this preprin

Deep learning of HIV field-based rapid tests

Although deep learning algorithms show increasing promise for disease diagnosis, their use with rapid diagnostic tests performed in the field has not been extensively tested. Here we use deep learning to classify images of rapid human immunodeficiency virus (HIV) tests acquired in rural South Africa. Using newly developed image capture protocols with the Samsung SM-P585 tablet, 60 fieldworkers routinely collected images of HIV lateral flow tests. From a library of 11,374 images, deep learning algorithms were trained to classify tests as positive or negative. A pilot field study of the algorithms deployed as a mobile application demonstrated high levels of sensitivity (97.8%) and specificity (100%) compared with traditional visual interpretation by humans-experienced nurses and newly trained community health worker staff-and reduced the number of false positives and false negatives. Our findings lay the foundations for a new paradigm of deep learning-enabled diagnostics in low- and middle-income countries, termed REASSURED diagnostics1, an acronym for real-time connectivity, ease of specimen collection, affordable, sensitive, specific, user-friendly, rapid, equipment-free and deliverable. Such diagnostics have the potential to provide a platform for workforce training, quality assurance, decision support and mobile connectivity to inform disease control strategies, strengthen healthcare system efficiency and improve patient outcomes and outbreak management in emerging infections