West Nile virus (WNV) is a mosquito-borne flavivirus maintained in bird-mosquito cycles, with occasional spillover events causing neuroinvasive disease in humans and animals. This study describes two fatal cases of WNV lineage 2 infection in captive harbor seals housed in a zoological park in southern Italy, together with genomic characterization and phylogenetic analysis of the detected strains.

RNA structure is a key determinant of RNA function and regulation. The coupling of chemical probing technologies, such as SHAPE, with deep sequencing has enabled large-scale experimental characterization of RNA structures in complex samples and under diverse conditions. Furthermore, probing data are often used to guide thermodynamics-based secondary structure prediction algorithms and have been shown to improve their accuracy. However, current algorithms treat these single-nucleotide measurements as statistically independent signals, inherently overlooking short-range dependencies in the data. Here, we show that discretized SHAPE data display context dependence within loop regions and within stem regions and we use Markov models to formally capture such dependencies. We then leverage Markov modeling in the classification of small structure motifs from their discretized SHAPE data signatures and subsequently integrate the classifying feature into the dynamic programming recursions that underlie computational RNA folding. Compared to state-of-the-art SHAPE-guided structure prediction methods, our Markov- informed framework improves prediction performance. Furthermore, we identify SHAPE signatures characteristic of highly stable hairpins, such as GAAA, GCAA, and UUCG tetraloops, and integrate these insights into the folding recursions to further improve predictions. Overall, the proposed framework provides a foundation for context-aware statistical modeling of SHAPE data, particularly in loop regions, where signal characterization has proven challenging due to high variance. This work further demonstrates that finer modeling of SHAPE data has the potential to push the limits of data-guided secondary structure prediction.

Within-person variation in serum uric acid (SUA) may reflect clinical instability, but its association with outcomes in chronic kidney disease (CKD) is uncertain.

Tinnitus is one of the most common hearing disorders affecting one-third of Americans and is defined as the buzzing or ringing sound one perceives in one or both ears in the absence of an acoustic stimulus. One objective method for tinnitus screening in rodents is gap prepulse inhibition of the acoustic startle reflex (GPIAS), a reduction in the abrupt motor response elicited by an intense auditory stimulus following a silent gap in noise. Reduced inhibition by gaps embedded in narrow-band noise is hypothesized to reflect the primary tinnitus pitch, as the tinnitus "fills-in" the gap. However, Lobarinas et al. (2013) identified a critical limitation: after acoustic trauma or hearing loss, rodents often exhibit markedly diminished acoustic startle reflexes, creating a "floor effect" where further suppression becomes undetectable even if gap perception remains intact, leading to false-positive tinnitus screening results. To address this limitation, we utilized the CBA/CaJ mouse model to assess tactile airpuff modification efficacy and employed a machine learning algorithm to classify startle responses, achieving 98% accuracy in differentiating startles from non-startles. We induced unilateral conductive hearing loss via ear plugging and found enhanced gap detection ability, contrasting with false-positive tinnitus indicators observed in rats by Lobarinas and colleagues. We also pharmacologically induced tinnitus via sodium salicylate, revealing frequency-specific alterations in gap detection patterns. Our findings suggest that differences in data analysis methodology, specifically using a machine learning algorithm to filter non-startle responses, may explain species-specific differences between mouse and rat models and significantly improve GPIAS validity as a tinnitus screening assessment tool.