Untreated lyme disease can lead to neurological, cardiac, and dermatological complications. Rapid diagnosis of the erythema migrans (EM) rash, a characteristic symptom of Lyme disease, is therefore crucial to early diagnosis and treatment. In this study, we aim to utilize deep learning frameworks including Tensorflow and Keras to create deep convolutional neural networks (DCNN) to detect images of acute Lyme Disease from images of erythema migrans. This study uses a custom database of erythema migrans images of varying quality to train a DCNN capable of classifying images of EM rashes vs non-EM rashes. Images from publicly available sources were mined to create an initial database. Machine based removal of duplicate images was then performed, followed by a thorough examination of all images by a clinician. The resulting database was combined with images of confounding rashes and regular skin, resulting in a total of 683 images. This database was then used to create a DCNN with an accuracy of 93% when classifying images of rashes as EM vs non EM. Finally, this model was converted into a web and mobile application to allow for rapid diagnosis of EM rashes by both patients and clinicians. This tool could be used for patient prescreening prior to treatment and lead to a lower mortality rate from Lyme disease.

Background: Many COVID-19 survivors experience persistent COVID-19 related cardiac abnormalities weeks to months after recovery from acute SARS-CoV-2 infection. Non-invasive cardiac magnetic resonance (CMR) imaging is an important tool of choice for clinical diagnosis of cardiac dysfunctions. In this systematic review, we analyzed the CMR findings and biomarkers of COVID-19 related cardiac sequela after SARS-CoV-2 infection. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA), we conducted a systematic review of studies that assessed COVID-19 related cardiac abnormalities using cardiovascular magnetic resonance imaging. A total of 21 cross-sectional, case-control, and cohort studies were included in the analyses. Results: Ten studies reported CMR results <3 months after SARS-CoV-2 infection and 11 studies >3 months after SARS-CoV-2 infection. Abnormal T1, abnormal T2, elevated extracellular volume, late gadolinium enhancement and myocarditis was reported less frequently in the >3-month studies. Eight studies reported an association between biomarkers and CMR findings. Elevated troponin was associated with CMR pathology in 5/6 studies, C-reactive protein in 3/5 studies, N-terminal pro-brain natriuretic peptide in 1/2 studies, and lactate dehydrogenase and D-dimer in a single study. The rate of myocarditis via CMR was 18% (154/868) across all studies. Most SARS-CoV-2 associated CMR abnormalities resolved over time. Conclusions: There were CMR abnormalities associated with SARS-CoV-2 infection and most abnormalities resolved over time. A panel of cardiac injury and inflammatory biomarkers could be useful in identifying patients who are likely to present with abnormal CMR pathology after COVID-19. Multiple mechanisms are likely responsible for COVID-19 induced cardiac abnormalities.

Conformational dynamics play essential roles in RNA function. However, detailed structural characterization of excited states of RNA remains challenging. Here, we apply high hydrostatic pressure (HP) to populate excited conformational states of tRNA Lys3 , and structurally characterize them using a combination of HP 2D-NMR, HP-SAXS (HP-small-angle X-ray scattering), and computational modeling. HP-NMR revealed that pressure disrupts the interactions of the imino protons of the uridine and guanosine U–A and G–C base pairs of tRNA Lys3 . HP-SAXS profiles showed a change in shape, but no change in overall extension of the transfer RNA (tRNA) at HP. Configurations extracted from computational ensemble modeling of HP-SAXS profiles were consistent with the NMR results, exhibiting significant disruptions to the acceptor stem, the anticodon stem, and the D-stem regions at HP. We propose that initiation of reverse transcription of HIV RNA could make use of one or more of these excited states.

Novel methods of food production are required to feed an ever-growing world population. The emergence of Internet of Things (IoT) technology has had an impact on a wide variety of industries. The use of IoT devices in agriculture, known as smart farming, is a potential solution to the growing food crisis. This technology has been shown to greatly increase farm yield while simultaneously reducing the number of farm-related injuries in agricultural workers. However, a major drawback of IoT systems is their vulnerability to cyberattacks. Man in the Middle attacks, Denial of Service attacks, and Phishing attacks among others have all been shown to be effective avenues to attack IoT systems. This paper will provide an overview of smart farming, IoT devices used on smart farms, and potential vulnerabilities present in these systems. In addition, it will also provide mitigation techniques to prevent cyberattacks on smart farms. More targeted research and penetration testing is needed to identify approaches to improving the cybersecurity of smart farming and associated technologies.