Yeast Npl3 regulates replicative senescence outside of TERRA R-loop resolution and co-transcriptional processing.

Eukaryotic cells without telomerase experience progressively shorter telomeres with each round of cell division until cell cycle arrest is initiated, leading to replicative senescence. When yeast TLC1, which encodes the RNA template of telomerase, is deleted, senescence is accompanied by increased expression of TERRA (non-coding telomere repeat-containing RNA). Deletion of Npl3, an RNA-processing protein with telomere maintenance functions, accelerates senescence in tlc1Δ cells and significantly increases TERRA levels. Using genetic approaches, we set out to determine how Npl3 is involved in regulating TERRA expression and maintaining telomere homeostasis. Even though Npl3 regulates hyperrecombination, we found that Npl3 does not help resolve RNA:DNA hybrids formed during TERRA synthesis in the same way as RNase H1 and H2. Furthermore, Rad52 is still required for cells to escape senescence by telomere recombination in the absence of Npl3. Npl3 also works separately from the THO/TREX pathway for processing nascent RNA for nuclear export. However, deleting Dot1, a histone methyltransferase involved in tethering telomeres to the nuclear periphery, rescued the accelerated senescence phenotype of npl3Δ cells. Thus, our study suggests that Npl3 plays an additional role in regulating cellular senescence outside of RNA:DNA hybrid resolution and co-transcriptional processing.

Detecting goals of care conversations in clinical notes with active learning.

OBJECTIVE: Goals of care (GOC) discussions are an increasingly used quality metric in serious illness care and research. Wide variation in documentation practices within the Electronic Health Record (EHR) presents challenges for reliable measurement of GOC discussions. Novel natural language processing approaches are needed to capture GOC discussions documented in real-world samples of seriously ill hospitalized patients' EHR notes, a corpus with a very low event prevalence. METHODS: To automatically detect sentences documenting GOC discussions outside of dedicated GOC note types, we proposed an ensemble of classifiers aggregating the predictions of rule-based, feature-based, and three transformers-based classifiers. We trained our classifier on 600 manually annotated EHR notes among patients with serious illnesses. Our corpus exhibited an extremely imbalanced ratio between sentences discussing GOC and sentences that do not. This ratio challenges standard supervision methods to train a classifier. Therefore, we trained our classifier with active learning. RESULTS: Using active learning, we reduced the annotation cost to fine-tune our ensemble by 70% while improving its performance in our test set of 176 EHR notes, with 0.557 F1-score for sentence classification and 0.629 for note classification. CONCLUSION: When classifying notes, with a true positive rate of 72% (13/18) and false positive rate of 8% (13/158), our performance may be sufficient for deploying our classifier in the EHR to facilitate bedside clinicians' access to GOC conversations documented outside of dedicated notes types, without overburdening clinicians with false positives. Improvements are needed before using it to enrich trial populations or as an outcome measure.

Genome Sequences and Characteristics of Six Cluster B1 Mycobacteriophages Discovered at Saint Joseph's University.

We report on six new siphoviruses infecting Mycobacterium smegmatis that were isolated from soil samples collected on the campus of Saint Joseph's University, on the western edge of Philadelphia, Pennsylvania. All phages have circularly permuted genomes that are 68,721 to 68,929 bp long, with an average G+C content of 66.4%.