A recurrent loss-of-function variant in DRC1 causes non-syndromic severe asthenozoospermia with favorable intracytoplasmic sperm injection and pregnancy outcomes.

Background: Asthenozoospermia, characterized by reduced sperm motility, is a common cause of male infertility. Multiple morphological abnormalities of the sperm flagella (MMAF) represent a severe and genetically heterogeneous form of asthenozoospermia. Over 50 genes have been associated, but approximately half of MMAF cases remain unexplained.

Receipt of Family Planning Services in the United States: 2022-2023.

Introduction: This report uses data from the 2022‒2023 National Survey of Family Growth (NSFG) to estimate receipt of family planning services by selected characteristics among females ages 15‒49 in the United States.

Methods: NSFG data were collected through a multi-mode design (in-person and web interviews) with a nationally representative samples of males and females ages 15-49 in the household population of the United States. This report uses data from 5,586 females who completed the survey between January 2022 and December 2023.

A signaling inspired synthetic toolkit for efficient production of tyrosine phosphorylated proteins.

Tyrosine phosphorylation is an important post-translational modification that regulates many biochemical signaling networks in multicellular organisms. To date, 46,000 tyrosines have been observed in human proteins, but relatively little is known about the function and regulation of most of these sites. A major challenge has been producing recombinant phospho-proteins in order to test the effects of phosphorylation.

Moving beyond word frequency based on tally counting: AI-generated familiarity estimates of words and phrases are an interesting additional index of language knowledge.

This study investigates the potential of large language models (LLMs) to estimate the familiarity of words and multi-word expressions (MWEs). We validated LLM estimates for isolated words using existing human familiarity ratings and found strong correlations. LLM familiarity estimates performed even better in predicting lexical decision and naming performance in megastudies than the best available word frequency measures.