Positive Neutrino Masses with DESI DR2 via Matter Conversion to Dark Energy.

The Dark Energy Spectroscopic Instrument (DESI) is a massively parallel spectroscopic survey on the Mayall telescope at Kitt Peak, which has released measurements of baryon acoustic oscillations determined from over 14 million extragalactic targets. We combine DESI Data Release 2 with CMB datasets to search for evidence of matter conversion to dark energy (DE), focusing on a scenario mediated by stellar collapse to cosmologically coupled black holes (CCBHs). In this physical model, which has the same number of free parameters as ΛCDM, DE production is determined by the cosmic star formation rate density (SFRD), allowing for distinct early- and late-time cosmologies.

Endocrine disrupting effects of contaminants on communication and behaviors of insects: from molecular effect to ecological consequences.

An increasing number of studies have highlighted the insidious effects of endocrine disruptors on insect endocrinology, development, and behavior, prompting concerns regarding the ecological consequences of these pollutants. This review explores the effects of endocrine-disrupting chemicals (EDCs) on insect communication and behavior, focusing on the molecular mechanisms and ecological consequences. This study underscores the importance of addressing EDC risks in maintaining biodiversity and ecosystem functions.

Stimuli presented on the fringe of awareness do not cause proactive interference.

A key question in consciousness studies is what is distinct about a conscious as opposed to a sub-conscious percept. The experiments presented here provide evidence for the hypothesis that only conscious percepts enable the formation of episodic memories. We do this by assessing proactive interference (PI) in rapid serial visual presentation (RSVP) streams.

Phenotypic plasticity in maize grain yield: Genetic and environmental insights of response to environmental gradients.

Understanding genotype-by-environment (G × E) interactions that underlie phenotypic variation, when observed for complex traits in multi-environment trials, is important for biological discovery and for crop improvement. The regression-on-the-mean model is an approach to observe G × E trends for complex traits across a gradient of environmental inputs. Biologically relevant environmental index values can be utilized to quantify phenotypic plasticity of individuals by correlating environmental means and environmental parameters within specific time windows.