Multicellularity evolved independently multiple times in eukaryotes. Two distinct mechanisms underpin multicellularity: clonality (serial cell division without sister-cell separation) and aggregation (whereby independent cells assemble into a multicellular entity). Clonal and aggregative multicellularity are traditionally considered to be mutually exclusive, with rare exceptions, and evolutionary hypotheses have addressed why multicellularity might diverge towards one or the other extreme. Both animals and their sister group, the choanoflagellates, are currently known to acquire multicellularity only clonally. 

Here we show that the choanoflagellate Choanoeca flexa forms motile and contractile cell monolayers (sheets) through multiple mechanisms—C. flexa sheets can form purely clonally, purely aggregatively or through a combination of both processes. We characterize the life history of C. flexa in its natural environment—ephemeral splash pools on the island of Curaçao—and show that C. flexa undergoes reversible transitions between unicellularity and multicellularity during evaporation–refilling cycles. Different splash pools house genetically distinct strains of C. flexa and kin recognition constrains aggregation between them. 

We show that clonal-aggregative multicellularity is a versatile strategy for the robust establishment of multicellularity in this variable and fast-fluctuating environment. Our findings challenge former generalizations about choanoflagellates and expand the option space of choanozoan multicellularity.

This paper describes a LLM-supported assistive dialog system and a proposed evaluation methodology for this system, developed for Air Force intelligence analysts in intelligence reporting scenarios. Part of this technology has previously been described in other work which developed the core technology of the assistive dialog function.

Wave Kinetic Equations (WKEs) are often used to describe the evolution of ensemble averaged wave amplitudes for nonlinear wave systems. In the present manuscript we describe a new approach to direct numerical simulation of solutions to WKEs. This new method relies on a piecewise polynomial approximation of the resonant manifold, followed by numerical quadrature of the collision integral. The approach is general in nature, and is discussed in detail here for a particular nonlinear Schrödinger model in 2 spatial dimensions. Detailed convergence studies demonstrate 2nd-order accuracy for model collision integrals, and self-convergence studies for the WKE show near 2nd-order rates. Furthermore, comparison of the WKE approximation to ensemble averages of the NLS illustrate the efficacy of the method and the validity of the WKE, for both isotropic and an-isotropic solutions.

Preterm infants have pulmonary ventilation and perfusion abnormalities, yet few imaging modalities can inform clinicians about this ventilation/perfusion (V/Q) relationship. Electrical impedance tomography (EIT) is an imaging technique with V/Q imaging capabilities that has not been well described in infants with BPD. 

EIT was performed every 4 weeks in preterm infants for a maximum of 5 visits per infant. Term infants with healthy lungs had one EIT imaging visit as controls. Data were collected from a total of 51 EIT visits. Novel V/Q maps were generated from each visit. 

Ventilation heterogeneity (measured by the global inhomogeneity index) and V/Q heterogeneity (measured by coefficient of variation of V/Q maps) were significantly higher in preterm infants at the visit closest to 36 weeks post-menstrual age than controls (p = 0.002 and p = 0.039, respectively). Pulmonary ventilation, perfusion, and V/Q relationship can be quantified by EIT, and may be indicators of chronic lung disease. 

Finding an appropriate residential setting for individuals with autism spectrum disorder and co-occurring medical conditions is challenging due to the heterogeneous needs of the individuals, but also because residential programs utilize different approaches. As such, the decision making process of which individual is a good fit for a particular residential setting is both important and challenging. The decision to accept an individual’s placement in a particular program is generally made based upon the medical history, an intake medical examination, and prior experience of the program’s staff with residents with similar needs. As such, the decision is strongly influenced by the experience of the staff. 

This work builds an artificial intelligence model to support this decision making process for screening potential residents for one particular program, The Center for Discovery (TCFD). The model is based upon a nonlinear classifier and trained with data from a cohort of current and past residents to predict if an individual will respond well to the type of care offered by TCFD. 

The classifier is able to predict a successful placement with over 80% balanced accuracy when using information about past medical diagnoses of a resident and sleep data from a resident’s baseline after admission. While this work focuses on one particular residential program, it demonstrates the validity of utilizing AI models to support admissions decisions in these settings and the approach can be generalized to other settings, assuming data from successful and unsuccessful placements of past residents is available.