A Conserved DNA Repeat Promotes Selection of a Diverse Repertoire of Trypanosoma brucei Surface Antigens from the Genomic Archive.

African trypanosomes are mammalian pathogens that must regularly change their protein coat to survive in the host bloodstream. Chronic trypanosome infections are potentiated by their ability to access a deep genomic repertoire of Variant Surface Glycoprotein (VSG) genes and switch from the expression of one VSG to another. Switching VSG expression is largely based in DNA recombination events that result in chromosome translocations between an acceptor site, which houses the actively transcribed VSG, and a donor gene, drawn from an archive of more than 2,000 silent VSGs. One element implicated in these duplicative gene conversion events is a DNA repeat of approximately 70 bp that is found in long regions within each BES and short iterations proximal to VSGs within the silent archive. Early observations showing that 70-bp repeats can be recombination boundaries during VSG switching led to the prediction that VSG-proximal 70-bp repeats provide recombinatorial homology. Yet, this long held assumption had not been tested and no specific function for the conserved 70-bp repeats had been demonstrated. In the present study, the 70-bp repeats were genetically manipulated under conditions that induce gene conversion. In this manner, we demonstrated that 70-bp repeats promote access to archival VSGs. Synthetic repeat DNA sequences were then employed to identify the length, sequence, and directionality of repeat regions required for this activity. In addition, manipulation of the 70-bp repeats allowed us to observe a link between VSG switching and the cell cycle that had not been appreciated. Together these data provide definitive support for the long-standing hypothesis that 70-bp repeats provide recombinatorial homology during switching. Yet, the fact that silent archival VSGs are selected under these conditions suggests the 70-bp repeats also direct DNA pairing and recombination machinery away from the closest homologs (silent BESs) and toward the rest of the archive

Laminins promote postsynaptic maturation by an autocrine mechanism at the neuromuscular junction

A prominent feature of synaptic maturation at the neuromuscular junction (NMJ) is the topological transformation of the acetylcholine receptor (AChR)-rich postsynaptic membrane from an ovoid plaque into a complex array of branches. We show here that laminins play an autocrine role in promoting this transformation. Laminins containing the α4, α5, and β2 subunits are synthesized by muscle fibers and concentrated in the small portion of the basal lamina that passes through the synaptic cleft at the NMJ. Topological maturation of AChR clusters was delayed in targeted mutant mice lacking laminin α5 and arrested in mutants lacking both α4 and α5. Analysis of chimeric laminins in vivo and of mutant myotubes cultured aneurally demonstrated that the laminins act directly on muscle cells to promote postsynaptic maturation. Immunohistochemical studies in vivo and in vitro along with analysis of targeted mutants provide evidence that laminin-dependent aggregation of dystroglycan in the postsynaptic membrane is a key step in synaptic maturation. Another synaptically concentrated laminin receptor, Bcam, is dispensable. Together with previous studies implicating laminins as organizers of presynaptic differentiation, these results show that laminins coordinate post- with presynaptic maturation

A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes

Background: Most cardiovascular (CV)/stroke risk calculators using the integration of carotid ultrasound image-based phenotypes (CUSIP) with conventional risk factors (CRF) have shown improved risk stratification compared with either method. However such approaches have not yet leveraged the potential of machine learning (ML). Most intelligent ML strategies use follow-ups for the endpoints but are costly and time-intensive. We introduce an integrated ML system using stenosis as an endpoint for training and determine whether such a system can lead to superior performance compared with the conventional ML system.Methods: The ML-based algorithm consists of an offline and online system. The offline system extracts 47 features which comprised of 13 CRF and 34 CUSIP. Principal component analysis (PCA) was used to select the most significant features. These offline features were then trained using the event-equivalent gold standard (consisting of percentage stenosis) using a random forest (RF) classifier framework to generate training coefficients. The online system then transforms the PCA-based test features using offline trained coefficients to predict the risk labels on test subjects. The above ML system determines the area under the curve (AUC) using a 10-fold cross-validation paradigm. The above system so-called "AtheroRisk-Integrated" was compared against "AtheroRisk-Conventional", where only 13 CRF were considered in a feature set.Results: Left and right common carotid arteries of 202 Japanese patients (Toho University, Japan) were retrospectively examined to obtain 395 ultrasound scans. AtheroRisk-Integrated system [AUC=0.80, P<0.0001, 95% confidence interval (CI): 0.77 to 0.84] showed an improvement of similar to 18% against AtheroRisk-Conventional ML (AUC=0.68, P<0.0001, 95% CI: 0.64 to 0.72).Conclusions: ML-based integrated model with the event-equivalent gold standard as percentage stenosis is powerful and offers low cost and high performance CV/stroke risk assessment

Effects of dietary microalgae on growth, survival and fatty acid composition of sea urchin Paracentrotus lividus throughout larval development

This study investigated the growth, survival and fatty acid composition of sea urchin Paracentrotus lividus larvae fed four microalgal diets: Cricosphaera elongata, Pleurochrysis carterae, Tetraselmis suecica and Dunaliella tertiolecta (control). Larvae were successfully raised to competence for metamorphosis when fed C. elongata, P. carterae and D. tertiolecta diets but significant differences were found in survival rate and development. Larvae fed C. elongata showed 3 times higher survival and 20 % faster development than larvae fed the other two microalgae diets that supported development. In contrast, T. suecica failed to fully support development and larvae stalled at the four arms stage for more than 30 days. The urchin larvae could accumulate long-chain polyunsaturated fatty acids (LC-PUFA) such as docosahexaenoate (DHA; 22:6n-3), eicosapentaenoate (EPA; 20:5n-3) and arachidonate (ARA; 20:4n-6), either by assimilation and retention of dietary fatty acids, and/or synthesis from α-linolenic acid 18:3n-3 and linoleic acid 18:2n-6. Moreover, an accumulation of n-3 LC-PUFA and higher EPA/DHA and EPA/ARA ratios appeared to be associated with improved larval performance. The results indicate that live microalgae species, with appropriate fatty acid profiles are able to improve P. lividus larval performance, ultimately increasing hatchery profitability

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients

Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review.

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment

Compressed representation of a partially defined integer function over multiple arguments

In OLAP (OnLine Analitical Processing) data are analysed in an n-dimensional cube. The cube may be represented as a partially defined function over n arguments. Considering that often the function is not defined everywhere, we ask: is there a known way of representing the function or the points in which it is defined, in a more compact manner than the trivial one

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors