Leishmania tarentolae: taxonomic classification and its application as a promising biotechnological expression host

In this review, we summarize the current knowledge concerning the eukaryotic protozoan parasite Leishmania tarentolae, with a main focus on its potential for biotechnological applications. We will also discuss the genus, subgenus, and species-level classification of this parasite, its life cycle and geographical distribution, and similarities and differences to human-pathogenic species, as these aspects are relevant for the evaluation of biosafety aspects of L. tarentolae as host for recombinant DNA/protein applications. Studies indicate that strain LEM-125 but not strain TARII/UC of L. tarentolae might also be capable of infecting mammals, at least transiently. This could raise the question of whether the current biosafety level of this strain should be reevaluated. In addition, we will summarize the current state of biotechnological research involving L. tarentolae and explain why this eukaryotic parasite is an advantageous and promising human recombinant protein expression host. This summary includes overall biotechnological applications, insights into its protein expression machinery (especially on glycoprotein and antibody fragment expression), available expression vectors, cell culture conditions, and its potential as an immunotherapy agent for human leishmaniasis treatment. Furthermore, we will highlight useful online tools and, finally, discuss possible future applications such as the humanization of the glycosylation profile of L. tarentolae or the expression of mammalian recombinant proteins in amastigotelike cells of this species or in amastigotes of avirulent human-pathogenic Leishmania species

New and emerging technologies for the treatment of inherited retinal diseases: a horizon scanning review.

The horizon scanning review aimed to identify new and emerging technologies in development that have the potential to slow or stop disease progression and/or reverse sight loss in people with inherited retinal diseases (IRDs). Potential treatments were identified using recognized horizon scanning methods. These included a combination of online searches using predetermined search terms, suggestions from clinical experts and patient and carer focus groups, and contact with commercial developers. Twenty-nine relevant technologies were identified. These included 9 gene therapeutic approaches, 10 medical devices, 5 pharmacological agents, and 5 regenerative and cell therapies. A further 11 technologies were identified in very early phases of development (typically phase I or pre-clinical) and were included in the final report to give a complete picture of developments 'on the horizon'. Clinical experts and patient and carer focus groups provided helpful information and insights, such as the availability of specialised services for patients, the potential impacts of individual technologies on people with IRDs and their families, and helped to identify additional relevant technologies. This engagement ensured that important areas of innovation were not missed. Most of the health technologies identified are still at an early stage of development and it is difficult to estimate when treatments might be available. Further, well designed trials that generate data on efficacy, applicability, acceptability, and costs of the technologies, as well as the long-term impacts for various conditions are required before these can be considered for adoption into routine clinical practice

A novel application of deep learning with image cropping: a smart city use case for flood monitoring

© 2020, The Author(s). Event monitoring is an essential application of Smart City platforms. Real-time monitoring of gully and drainage blockage is an important part of flood monitoring applications. Building viable IoT sensors for detecting blockage is a complex task due to the limitations of deploying such sensors in situ. Image classification with deep learning is a potential alternative solution. However, there are no image datasets of gullies and drainages. We were faced with such challenges as part of developing a flood monitoring application in a European Union-funded project. To address these issues, we propose a novel image classification approach based on deep learning with an IoT-enabled camera to monitor gullies and drainages. This approach utilises deep learning to develop an effective image classification model to classify blockage images into different class labels based on the severity. In order to handle the complexity of video-based images, and subsequent poor classification accuracy of the model, we have carried out experiments with the removal of image edges by applying image cropping. The process of cropping in our proposed experimentation is aimed to concentrate only on the regions of interest within images, hence leaving out some proportion of image edges. An image dataset from crowd-sourced publicly accessible images has been curated to train and test the proposed model. For validation, model accuracies were compared considering model with and without image cropping. The cropping-based image classification showed improvement in the classification accuracy. This paper outlines the lessons from our experimentation that have a wider impact on many similar use cases involving IoT-based cameras as part of smart city event monitoring platforms

A digital technique to compensate for time- base error in magnetic tape recording

Digital technique for compensation of time base error in magnetic tape recorder dat

Waveform distortion in an FM/FM telemetry system

Waveform distortion in FM/FM telemetry syste

Prevalence and clinical characteristics of left ventricular dysfunction among elderly patients in general practice setting: cross sectional survey

Objective: To assess the prevalence and clinical characteristics of left ventricular dysfunction among elderly patients in the general practice setting by echocardiographic assessment of ventricular function. Design: Cross sectional survey. Setting: Four centre general practice in Poole, Dorset. Subjects: 817 elderly patients aged 70-84 years. Main outcomes: Echocardiographic assessment of left ventricular systolic function including measurement of ejection fraction by biplane summation method where possible, clinical symptoms, and signs of left ventricular dysfunction. Results: The overall prevalence of left ventricular systolic dysfunction was 7.5% (95% confidence interval 5.8% to 9.5%); mild dysfunction (5.0%) was considerably more prevalent than moderate (1.6%) or severe dysfunction (0.7%). Measurement of ejection fraction was possible in 82% of patients (n=667): in patients categorised as having mild, moderate, or severe dysfunction, the mean ejection fraction was 48% (SD 12.0), 38% (8.1), and 26% (7.9) respectively. At all ages the prevalence was much higher in men than in women (odds ratio 5.1, 95% confidence interval 2.6 to 10.1). No clinical symptom or sign was both sensitive and specific. In around half the patients with ventricular dysfunction (52%, 32/61) heart failure had not been previously diagnosed. Conclusions: Unrecognised left ventricular dysfunction is a common problem in elderly patients in the general practice setting. Appropriate treatment with angiotensin converting enzyme inhibitors has the potential to reduce hospitalisation and mortality in these patients, but diagnosis should not be based on clinical history and examination alone. Screening is feasible in general practice, but it should not be implemented until the optimum method of identifying left ventricular dysfunction is clarified, and the cost effectiveness of screening has been shown

An optically actuated surface scanning probe

We demonstrate the use of an extended, optically trapped probe that is capable of imaging surface topography with nanometre precision, whilst applying ultra-low, femto-Newton sized forces. This degree of precision and sensitivity is acquired through three distinct strategies. First, the probe itself is shaped in such a way as to soften the trap along the sensing axis and stiffen it in transverse directions. Next, these characteristics are enhanced by selectively position clamping independent motions of the probe. Finally, force clamping is used to refine the surface contact response. Detailed analyses are presented for each of these mechanisms. To test our sensor, we scan it laterally over a calibration sample consisting of a series of graduated steps, and demonstrate a height resolution of ∼ 11 nm. Using equipartition theory, we estimate that an average force of only ∼ 140 fN is exerted on the sample during the scan, making this technique ideal for the investigation of delicate biological samples