Complete Genomic Sequence of Bacteriophage B3, a Mu-Like Phage of \u3ci\u3ePseudomonas aeruginosa\u3c/i\u3e

Bacteriophage B3 is a transposable phage of Pseudomonas aeruginosa. In this report, we present the complete DNA sequence and annotation of the B3 genome. DNA sequence analysis revealed that the B3 genome is 38,439 bp long with a G+C content of 63.3%. The genome contains 59 proposed open reading frames (ORFs) organized into at least three operons. Of these ORFs, the predicted proteins from 41 ORFs (68%) display significant similarity to other phage or bacterial proteins. Many of the predicted B3 proteins are homologous to those encoded by the early genes and head genes of Mu and Mu-like prophages found in sequenced bacterial genomes. Only two of the predicted B3 tail proteins are homologous to other well-characterized phage tail proteins; however, several Mu-like prophages and transposable phage D3112 encode approximately 10 highly similar proteins in their predicted tail gene regions. Comparison of the B3 genomic organization with that of Mu revealed evidence of multiple genetic rearrangements, the most notable being the inversion of the proposed B3 immunity/early gene region, the loss of Mu-like tail genes, and an extreme leftward shift of the B3 DNA modification gene cluster. These differences illustrate and support the widely held view that tailed phages are genetic mosaics arising by the exchange of functional modules within a diverse genetic pool

Selective Caries Removal in Permanent Teeth (SCRiPT) for the treatment of deep carious lesions:a randomised controlled clinical trial in primary care

Background Dental caries is one of the most prevalent non-communicable disease globally and can have serious health sequelae impacting negatively on quality of life. In the UK most adults experience dental caries during their lifetime and the 2009 Adult Dental Health Survey reported that 85% of adults have at least one dental restoration. Conservative removal of tooth tissue for both primary and secondary caries reduces the risk of failure due to tooth-restoration, complex fracture as well as remaining tooth surfaces being less vulnerable to further caries. However, despite its prevalence there is no consensus on how much caries to remove prior to placing a restoration to achieve optimal outcomes. Evidence for selective compared to complete or near-complete caries removal suggests there may be benefits for selective removal in sustaining tooth vitality, therefore avoiding abscess formation and pain, so eliminating the need for more complex and costly treatment or eventual tooth loss. However, the evidence is of low scientific quality and mainly gleaned from studies in primary teeth. Method This is a pragmatic, multi-centre, two-arm patient randomised controlled clinical trial including an internal pilot set in primary dental care in Scotland and England. Dental health professionals will recruit 623 participants over 12-years of age with deep carious lesions in their permanent posterior teeth. Participants will have a single tooth randomised to either the selective caries removal or complete caries removal treatment arm. Baseline measures and outcome data (during the 3-year follow-up period) will be assessed through clinical examination, patient questionnaires and NHS databases. A mixed-method process evaluation will complement the clinical and economic outcome evaluation and examine implementation, mechanisms of impact and context. The primary outcome at three years is sustained tooth vitality. The primary economic outcome is net benefit modelled over a lifetime horizon. Clinical secondary outcomes include pulp exposure, progession of caries, restoration failure; as well as patient-centred and economic outcomes. Discussion SCRiPT will provide evidence for the most clinically effective and cost-beneficial approach to managing deep carious lesions in permanent posterior teeth in primary care. This will support general dental practitioners, patients and policy makers in decision making. Trial Registration Trial registry: ISRCTN. Trial registration number: ISRCTN76503940. Date of Registration: 30.10.2019

Direct nanoscale mapping of open circuit voltages at local back surface fields for PERC solar cells

The open circuit voltage (VOC) is a critical and common indicator of solar cell performance as well as degradation, for panel down to lab-scale photovoltaics. Detecting VOC at the nanoscale is much more challenging, however, due to experimental limitations on spatial resolution, voltage resolution, and/or measurement times. Accordingly, an approach based on Conductive Atomic Force Microscopy is implemented to directly detect the local VOC, notably for monocrystalline Passivated Emitter Rear Contact (PERC) cells which are the most common industrial-scale solar panel technology in production worldwide. This is demonstrated with cross-sectioned monocrystalline PERC cells around the entire circumference of a poly-aluminum-silicide via through the rear emitter. The VOC maps reveal a local back surface field extending * 2 lm into the underlying p-type Si absorber due to Al in-diffusion as designed. Such high spatial resolution methods for photovoltaic performance mapping are especially promising for directly visualizing the effects of processing parameters, as well as identifying signatures of degradation for silicon and other solar cell technologies

Aging and Early Life Stress: Telomerase Dynamics and The Consequences for Telomeres in a Wild Bird

Aging is an underlying risk factor for many major diseases including cancer, cardiovascular disease, and neurodegeneration. Yet we still do not know the full extent of how our bodies age and what determines our lifespan. One mechanism that may play an important role are telomeres, which are protective caps at the end of chromosomes. Telomeres are directly linked to longevity and can be lengthened by the enzyme telomerase. Early life telomere length is critical for lifespan, but we do not know how telomerase performs during this period. Whether variation in telomerase levels can influence telomere length and loss during development with consequences to longevity is still unknown. This thesis focuses on the role of telomerase during post-natal development and its response to stressors and activators with effects on telomeres. Taken together this research enhances our understanding of how telomerase acts and influences telomere during post-natal development

Image Analysis Method for Quantifying Snow Losses on PV Systems

Modeling and predicting snow-related power loss is important to economic calculations, load management and system optimization for all scales of photovoltaic (PV) power plants. This paper describes a new method for measuring snow shedding from fielded modules and also describes the application of this method to a commercial scale PV power plant in Vermont with two subsystems, one with modules in portrait orientation and the other in landscape. The method relies on time-series images taken at 5 minute intervals to capture the dynamics of module-level snow accumulation and shedding. Module-level images extracted from the full-field view are binarized into snow and clear areas, allowing for the quantification of percentage snow coverage, estimation of resulting module power output, and temporal changes in snow coverage. Preliminary data from the Vermont case study suggests that framed modules in portrait orientation outperform their framed counterparts in landscape orientation by as much as 24% energy yield during a single shedding event. While these data reflect a single event, and do not capture snow shedding behavior across diverse temperature and other climatic conditions, the study nonetheless demonstrates that 1) module orientation and position in the array influence shedding patterns; 2) the start of power production and bypass diode activation differ for portrait and landscape module orientations at similar percentages and orientations of snow coverage; and 3) system design is an important factor in snow mitigation and increased system efficiency in snowy climates

Training & Testing EL Image Dataset for Machine Learning

As discussed in A. M. Karimi, J. S. Fada, M. A. Hossain, S. Yang, T. J. Peshek, J. L. Braid, R. H. French, Automated Pipeline for Photovoltaic Module Electroluminescence Image Processing and Degradation Feature Classification, IEEE Journal of Photovoltaics. (2019) 1–12. https://doi.org/10.1109/JPHOTOV.2019.2920732

Physics-Based Method for Generating Fully Synthetic IV Curve Training Datasets for Machine Learning Classification of PV Failures

Classification machine learning models require high-quality labeled datasets for training. Among the most useful datasets for photovoltaic array fault detection and diagnosis are module or string current-voltage (IV) curves. Unfortunately, such datasets are rarely collected due to the cost of high fidelity monitoring, and the data that is available is generally not ideal, often consisting of unbalanced classes, noisy data due to environmental conditions, and few samples. In this paper, we propose an alternate approach that utilizes physics-based simulations of string-level IV curves as a fully synthetic training corpus that is independent of the test dataset. In our example, the training corpus consists of baseline (no fault), partial soiling, and cell crack system modes. The training corpus is used to train a 1D convolutional neural network (CNN) for failure classification. The approach is validated by comparing the model’s ability to classify failures detected on a real, measured IV curve testing corpus obtained from laboratory and field experiments. Results obtained using a fully synthetic training dataset achieve identical accuracy to those obtained with use of a measured training dataset. When evaluating the measured data’s test split, a 100% accuracy was found both when using simulations or measured data as the training corpus. When evaluating all of the measured data, a 96% accuracy was found when using a fully synthetic training dataset. The use of physics-based modeling results as a training corpus for failure detection and classification has many advantages for implementation as each PV system is configured differently, and it would be nearly impossible to train using labeled measured data

Data-Driven I–V Feature Extraction for Photovoltaic Modules

In research on photovoltaic (PV) device degradation, current-voltage (I-V ) datasets carry a large amount of information in addition to the maximum power point. Performance parameters such as short-circuit current, open-circuit voltage, shunt resistance, series resistance, and fill factor are essential for diagnosing the performance and degradation of solar cells and modules. To enable the scaling of I-V studies to millions of I-V curves, we have developed a data-driven method to extract I-V curve parameters and distributed this method as an open-source package in R. In contrast with the traditional practice of fitting the diode equation to I-V curves individually, which requires solving a transcendental equation, this data-driven method can be applied to large volumes of I-V data in a short time. Our data-driven feature extraction technique is tested on I-V curves generated with the single-diode model and applied to I-V curves with different data point densities collected from three different sources. This method has a high repeatability for extracting I-V features, without requiring knowledge of the device or expected parameters to be input by the researcher. We also demonstrate how this method can be applied to large datasets and accommodates nonstandard I-V curves including those showing artifacts of connection problems or shading where bypass diode activation produces multiple “steps.” These features together make the data-driven I-V feature extraction method ideal for evaluating time-series I-V data and analyzing power degradation mechanisms in PV modules through cross comparisons of the extracted parameters