PLANTdataHUB: a collaborative platform for continuous FAIR data sharing in plant research.

In modern reproducible, hypothesis-driven plant research, scientists are increasingly relying on research data management (RDM) services and infrastructures to streamline the processes of collecting, processing, sharing, and archiving research data. FAIR (i.e., findable, accessible, interoperable, and reusable) research data play a pivotal role in enabling the integration of interdisciplinary knowledge and facilitating the comparison and synthesis of a wide range of analytical findings. The PLANTdataHUB offers a solution that realizes RDM of scientific (meta)data as evolving collections of files in a directory - yielding FAIR digital objects called ARCs - with tools that enable scientists to plan, communicate, collaborate, publish, and reuse data on the same platform while gaining continuous quality control insights. The centralized platform is scalable from personal use to global communities and provides advanced federation capabilities for institutions that prefer to host their own satellite instances. This approach borrows many concepts from software development and adapts them to fit the challenges of the field of modern plant science undergoing digital transformation. The PLANTdataHUB supports researchers in each stage of a scientific project with adaptable continuous quality control insights, from the early planning phase to data publication. The central live instance of PLANTdataHUB is accessible at (https://git.nfdi4plants.org), and it will continue to evolve as a community-driven and dynamic resource that serves the needs of contemporary plant science.

DeepSTABp: A Deep Learning Approach for the Prediction of Thermal Protein Stability.

Proteins are essential macromolecules that carry out a plethora of biological functions. The thermal stability of proteins is an important property that affects their function and determines their suitability for various applications. However, current experimental approaches, primarily thermal proteome profiling, are expensive, labor-intensive, and have limited proteome and species coverage. To close the gap between available experimental data and sequence information, a novel protein thermal stability predictor called DeepSTABp has been developed. DeepSTABp uses a transformer-based protein language model for sequence embedding and state-of-the-art feature extraction in combination with other deep learning techniques for end-to-end protein melting temperature prediction. DeepSTABp can predict the thermal stability of a wide range of proteins, making it a powerful and efficient tool for large-scale prediction. The model captures the structural and biological properties that impact protein stability, and it allows for the identification of the structural features that contribute to protein stability. DeepSTABp is available to the public via a user-friendly web interface, making it accessible to researchers in various fields.

COVID-19 Presenting as Severe Rhabdomyolysis With Normal Renal Function.

Coronavirus disease 2019 (COVID-19) continues to increase morbidity and mortality. Early recognition of symptoms, along with prompt intervention, is required to improve patient outcomes. COVID-19 can have a multifaceted presentation, which can be a diagnostic challenge. Here, we report the first case of COVID-19 presenting as severe rhabdomyolysis with creatine kinase > 500,000 U/L with normal renal function in a young adult.

Suitability of Thiel embalmed tendons for biomechanical investigation.

The standard post-mortem storage method for biomechanical testing is freezing. Freezing minimally alters the biomechanical characteristics of tendons but only suspends the process of decay. Chemical fixation arrests decay and overcomes risk of infection, but alters the biomechanical properties of tendons. On the other hand, Thiel preservation has been reported to maintain soft tissue consistency similar to that of living tissue. The current study investigates the effects of Thiel embalming on human digitorum profundus tendons (FDP) from fresh-frozen and Thiel embalmed cadavers. Cross-sectional area was measured at pre-load, samples were preconditioned and then ramped at a constant strain-rate to failure. Thiel preserved tendons had statistically lower failure stress with median of 38MPa compared to fresh frozen samples with median of 60MPa (p-value=0.048) and trended to a decreased tangential modulus. To overcome limited donor number and masking factors of age, gender, and time embalmed, we also performed experiments in rat tail tendon fascicle. Similar quasi-static ramp to failure tests were performed with control and Thiel treated sample pairs. Similar differences were observed to those found as in human FDP, however these trends were statistically significant. In both tendons, Thiel preserved samples demonstrated altered failure characteristics, indicating a different collagen fiber/collagen network failure mechanism most likely due to partial denaturing by boric acid in Thiel solution. In conclusion, Thiel embalmed tendons did not faithfully represent the biomechanical characteristics of fresh frozen tendons.

Prevalence of the erm(T) gene in clinical isolates of erythromycin-resistant group D Streptococcus and Enterococcus.

Among 48 erythromycin-resistant group D streptococci (GDS), 36 had the erm(T) resistance gene. erm(T) was also found in 4 of 31 erythromycin-resistant Enterococcus faecium isolates. This is the first report of the erm(T) gene in U.S. GDS isolates and the first report of the erm(T) gene in enterococci.