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Machine learning (ML) has increasingly been used to assist and expand current practices in neuropathology. However, generating large imaging datasets with quality labels is challenging in fields which demand high levels of expertise. Further complicating matters is the often seen disagreement between experts in neuropathology-related tasks, both at the case level and at a more granular level. Neurofibrillary tangles (NFTs) are a hallmark pathological feature of Alzheimer disease, and are associated with disease progression which warrants further investigation and granular quantification at a scale not currently accessible in routine human assessment. In this work, we first provide a baseline of annotator/rater agreement for the tasks of Braak NFT staging between experts and NFT detection using both experts and novices in neuropathology. We use a whole-slide-image (WSI) cohort of neuropathology cases from Emory University Hospital immunohistochemically stained for Tau. We develop a workflow for gathering annotations of the early stage formation of NFTs (Pre-NFTs) and mature intracellular (iNFTs) and show ML models can be trained to learn annotator nuances for the task of NFT detection in WSIs. We utilize a model-assisted-labeling approach and demonstrate ML models can be used to aid in labeling large datasets efficiently. We also show these models can be used to extract case-level features, which predict Braak NFT stages comparable to expert human raters, and do so at scale. This study provides a generalizable workflow for various pathology and related fields, and also provides a technique for accomplishing a high-level neuropathology task with limited human annotations.

The mechanical properties of crystalline materials are crucial knowledge for their screening, design, and exploitation. Density functional theory (DFT), remains one of the most effective computational tools for quantitatively predicting and rationalising the mechanical response of these materials. DFT predictions have been shown to quantitatively correlate to a number of experimental techniques, such as nanoindentation, high-pressure X-ray crystallography, impedance spectroscopy, and spectroscopic ellipsometry. Not only can bulk mechanical properties be derived from DFT calculations, this computational methodology allows for a full understanding of the elastic anisotropy in complex crystalline systems. Here we introduce the concepts behind DFT, and highlight a number of case studies and methodologies for predicting the elastic constants of materials that span ice, biomolecular crystals, polymer crystals, and metal–organic frameworks (MOFs). Key parameters that should be considered for theorists are discussed, including exchange–correlation functionals and dispersion corrections. The broad range of software packages and post-analysis tools are also brought to the attention of current and future DFT users. It is envisioned that the accuracy of DFT predictions of elastic constants will continue to improve with advances in high-performance computing power, as well as the incorporation of many-body interactions with quasi-harmonic approximations to overcome the negative effects of calculations carried out at absolute zero. [ABSTRACT FROM AUTHOR]

Objectives Grooming has important utilitarian and social functions in primates but little is known about grooming and its functional analogues in traditional human societies. We compare human grooming to typical primate patterns to test its hygienic and social functions. Materials and Methods Bayesian phylogenetic analyses were used to derive expected human grooming time given the potential associations between grooming, group size, body size, terrestriality, and several climatic variables across 69 primate species. This was compared against observed times dedicated to grooming, other hygienic behavior, and conversation among the Maya, Pumé, Sanöma, Tsimane', Yanomamö, and Ye'kwana (mean number of behavioral scans = 23,514). Results Expected grooming time for humans was 4% (95% Credible Interval = 0.07%-14%), similar to values observed in primates, based largely on terrestriality and phylogenetic signal (mean λ = 0.56). No other covariates strongly associated with grooming across primates. Observed grooming time across societies was 0.8%, lower than 89% of the expected values. However, the observed times dedicated to any hygienic behavior (3.0%) or 'vocal grooming,' that is conversation (7.3%), fell within the expected range. Conclusions We found (i) that human grooming may be a (recent) phylogenetic outlier when defined narrowly as parasite removal but not defined broadly as personal hygiene, (ii) there was no support for thermoregulatory functions of grooming, and (iii) no support for the 'vocal grooming' hypothesis of language having evolved as a less time-consuming means of bonding. Thus, human grooming reflects decreased hygienic needs, but similar social needs compared to primate grooming. [ABSTRACT FROM AUTHOR]

The notion of a two-hit or multi-hit model of carcinogenesis dates to at least the 1970's and work done by Alfred Knudson. This concept was considered in the design and execution of a previous FLP/FRT screen in Drosophila melanogaster for conditional growth suppressors. During the course of this work, the lethal allele E7.25D.7 was identified as being of phenotypic interest. Here we report the genetic mapping of E7.25D.7 , an allele of the sterile-20 kinase misshapen ( msn )., (Copyright: © 2021 by the authors.)