We describe two new species of sucking lice in the genus Hoplopleura Enderlein, 1904 (Psocodea Phthiraptera Hoplopleuridae) from Australia Hoplopleura gracilicaudatusa n. sp. from the eastern chestnut mouse Pseudomys gracilicaudatus (Gould) (Rodentia Muridae), and Hoplopleura nanusa n. sp. from the western chestnut mouse Pseudomys nanus (Gould) (Rodentia Muridae). Pseudomys Gray is the most speciose genus of rodents endemic to Australia with 24 species; however, only two Pseudomys species have been reported previously to be hosts of sucking lice. The description of the new species in the present study doubles the number of sucking louse species known to parasitize Pseudomys mice and increases the total number of sucking louse species known from endemic Australian rodents from 21 to 23. Pseudomys gracilicaudatus and P. nanus are closely related murines that diverged ~1 MYA with distinct and widely separated extant geographic distributions. The two new Hoplopleura species described in the present study share some morphological characters and likely co-evolved and co-speciated with their chestnut mouse hosts.
  To understand how medical scribes' work may contribute to alleviating clinician burnout attributable directly or indirectly to the use of health IT.
 
  Qualitative analysis of semistructured interviews with 32 participants who had scribing experience in a variety of clinical settings.
 
  We identified 7 categories of clinical tasks that clinicians commonly choose to offload to medical scribes, many of which involve delegated use of health IT. These range from notes-taking and computerized data entry to foraging, assembling, and tracking information scattered across multiple clinical information systems. Some common characteristics shared among these tasks include (1) time-consuming to perform; (2) difficult to remember or keep track of; (3) disruptive to clinical workflow, clinicians' cognitive processes, or patient-provider interactions; (4) perceived to be low-skill "clerical" work; and (5) deemed as adding no value to direct patient care.
 
  The fact that clinicians opt to "outsource" certain clinical tasks to medical scribes is a strong indication that performing these tasks is not perceived to be the best use of their time. Given that a vast majority of healthcare practices in the US do not have the luxury of affording medical scribes, the burden would inevitably fall onto clinicians' shoulders, which could be a major source for clinician burnout.
 
  Medical scribes help to offload a substantial amount of burden from clinicians-particularly with tasks that involve onerous interactions with health IT. Developing a better understanding of medical scribes' work provides useful insights into the sources of clinician burnout and potential solutions to it.
 Medical scribes help to offload a substantial amount of burden from clinicians-particularly with tasks that involve onerous interactions with health IT. Developing a better understanding of medical scribes' work provides useful insights into the sources of clinician burnout and potential solutions to it.
  Hi-C is the most widely used assay for investigating genome-wide 3D organization of chromatin. When working with Hi-C data, it is often useful to calculate the similarity between contact matrices in order to asses experimental reproducibility or to quantify relationships among Hi-C data from related samples. The HiCRep algorithm has been widely adopted for this task, but the existing R implementation suffers from run time limitations on high resolution Hi-C data or on large single-cell Hi-C datasets.
 
  We introduce a Python implementation of HiCRep and demonstrate that it is much faster and consume much less memory than the existing R implementation. Furthermore, we give examples of HiCRep's ability to accurately distinguish replicates from non-replicates and to reveal cell type structure among collections of Hi-C data.
 
  HiCRep.py and its documentation are available with a GPL license at https//github.com/Noble-Lab/hicrep.  https://www.selleckchem.com/products/ml792.html  The software may be installed automatically using the pip package installer.
 
  Supplementary methods and results are included in an appendix at Bioinformatics online.
 Supplementary methods and results are included in an appendix at Bioinformatics online.
  The ESC/EACTS guidelines propose criteria that determine the likelihood of true-severe aortic stenosis (AS). We aimed to investigate the impact of the guideline-based criteria of the likelihood of true-severe AS in patients with low-flow low-gradient (LFLG) AS with preserved ejection fraction (pEF) on outcomes following transcatheter aortic valve replacement (TAVR).
 
  In a prospective TAVR registry, LFLG-AS patients with pEF were retrospectively categorized into high (criteria ≥6) and intermediate (criteria <6) likelihood of true-severe AS. Hemodynamic, functional and clinical outcomes were compared with high-gradient AS patients with pEF. Among 632 eligible patients, 202 fulfilled diagnostic criteria for LFLG-AS. Significant hemodynamic improvement after TAVR was observed in LFLG-AS patients, irrespective of the likelihood. Although >70% of LFLG-AS patients had functional improvement, impaired functional status (NYHA III/IV) persisted more frequently at 1 year in LFLG-AS than in high-gradient AS patients (7.8%), irrespective of the likelihood (high 17.4%, p = 0.006; intermediate 21.1%, p < 0.001). All-cause death at 1 year occurred in 6.6% of high-gradient AS patients, 10.9% of LFLG-AS patients with high likelihood (HRadj 1.43, 95%CI 0.68-3.02), and in 7.2% of those with intermediate likelihood (HRadj 0.92, 95%CI 0.39-2.18). Among the criteria, only the absence of AVA ≤0.8 cm2 emerged as an independent predictor of treatment futility, a combined endpoint of all-cause death or NYHA III/IV at 1 year (OR 2.70, 95%CI 1.14-6.25).
 
  Patients with LFLG-AS with pEF had comparable survival but worse functional status at 1 year than high-gradient AS with pEF, irrespective of the likelihood of true-severe AS.
 
  https//www.clinicaltrials.gov. NCT01368250.
 https//www.clinicaltrials.gov. NCT01368250.