Disambiguating the species of biomedical named entities using natural language parsers

Motivation: Text mining technologies have been shown to reduce the laborious work involved in organizing the vast amount of information hidden in the literature. One challenge in text mining is linking ambiguous word forms to unambiguous biological concepts. This article reports on a comprehensive study on resolving the ambiguity in mentions of biomedical named entities with respect to model organisms and presents an array of approaches, with focus on methods utilizing natural language parsers

FAM222A encodes a protein which accumulates in plaques in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by amyloid plaques and progressive cerebral atrophy. Here, we report FAM222A as a putative brain atrophy susceptibility gene. Our cross-phenotype association analysis of imaging genetics indicates a potential link between FAM222A and AD-related regional brain atrophy. The protein encoded by FAM222A is predominantly expressed in the CNS and is increased in brains of patients with AD and in an AD mouse model. It accumulates within amyloid deposits, physically interacts with amyloid-β (Aβ) via its N-terminal Aβ binding domain, and facilitates Aβ aggregation. Intracerebroventricular infusion or forced expression of this protein exacerbates neuroinflammation and cognitive dysfunction in an AD mouse model whereas ablation of this protein suppresses the formation of amyloid deposits, neuroinflammation and cognitive deficits in the AD mouse model. Our data support the pathological relevance of protein encoded by FAM222A in AD

Learning the Species of Biomedical Named Entities from Annotated Corpora

In biomedical articles, terms with the same surface forms are often used to refer to different entities across a number of model organisms, in which case determining the species becomes crucial to term identification systems that ground terms to specific database identifiers. This paper describes a rule-based system that extracts ‘species indicating words’, such as human or murine, which can be used to decide the species of the nearby entity terms, and a machine-learning species disambiguation system that was developed on manually speciesannotated corpora. Performance of both systems were evaluated on gold-standard datasets, where the machine-learning system yielded better overall results

Phylloxera infestation and the uptake and distribution of 13C and 15N tracers in grape vines

In order to study the reason phylloxera (Daktulosphaira vitifolia Fitch) feeding on roots leads to decreased plant productivity, the uptake and distribution of 13C photosynthates and 15N in the grape vine 'Wuhe 8612' in response to phylloxera infestation were investigated. Phylloxera and grapevines cocultivated in pots were treated with 13CO2 and 15N-urea. The plant weight, nitrogen concentration and accumulation, 15N utilization efficiency, Nitrogen derived from fertilizer (Ndff%), and carbon isotope ratio (δ13C) of different organs were measured. Phylloxera infestation significantly reduced grape weight, shoot length, and N concentration and accumulation in different organs, whereas it increased the ratio between N content of the of roots and above-ground organs. Phylloxera infestation reduced leaf and root nitrogen 15N utilization efficiency, by 24 % and 14 %, respectively compared to controls. Labeled leaves of infested plants took up rather more 13C and 15N and exported a substantial amount of these nutrients to roots. Labeled roots took up rather more 15N and exported a small amount of these nutrients to upper leaves. This study found that phylloxera infestation reduced 13C and 15N uptake in leaves and roots, but increased N and photosynthates, which were mostly distributed to the roots, but also to the upper leaves. These factors together led to weak grape vine growth.

TDP-43 Liquid-Liquid Phase Separation (LLPS) Deficiency Attenuates Amyloid Beta Deposition in the 5XFAD Transgenic Mouse Model of Alzheimer\u27s Disease

Background: TAR DNA-binding protein 43 (TDP-43) can be found within the cell nucleus in most tissues and is a fundamental component to protein production, as it works to slice and reconfigure mRNA molecules. Recently, TDP-43 inclusions have been identified as a prevalent proteinopathy in the brains of individuals diagnosed with Alzheimer\u27s Disease (AD). However, despite the growing body of evidence demonstrating the important role of TDP-43 in AD pathogenesis, whether and how TDP-43 proteinopathy and other AD pathological hallmarks interact remain largely unknown. Furthermore, TDP-43 has a high propensity to undergo liquid-liquid phase separation (LLPS), a biological process necessary for the condensation of proteins, nucleic acids, and other biomolecules. Purpose of Research: The correlation between TDP-43 LLPS and AD deposition is an intriguing, yet currently unexplored area of interest. The purpose of this study is to investigate whether and how TDP-43 and its phase separation are involved in amyloid deposition in APP transgenic mice for Alzheimer\u27s Disease. Methods: We crossed our recently generated mice expressing endogenous LLPS-deficient murine TDP-43 with the widely used 5XFAD transgenic mouse model. Different approaches were then performed to assess amyloid deposition and associated neuroinflammation. Results: When compared to 5XFAD mice, 5XFAD mice expressing LLPS-deficient TDP-43 showed significantly reduced amyloid deposition throughout the brain. Neuroinflammation, as evaluated by GFAP and Iba1 expression was also alleviated by LLPS-deficient TDP-43. Conclusion: For the first time, our study demonstrates the likely role TDP-43 LLPS plays in amyloid deposition. And, targeting TDP-43 LLPS may serve as a novel therapeutic approach to Alzheimer\u27s Disease treatment.https://digitalcommons.unmc.edu/surp2021/1036/thumbnail.jp