Assembly archetypes in ecological communities.

An instrumental discovery in comparative and developmental biology is the existence of assembly archetypes that synthesize the vast diversity of organisms' body plans-from legs and wings to human arms-into simple, interpretable and general design principles. Here, we combine a novel mathematical formalism based on category theory with experimental data to show that similar 'assembly archetypes' exist at the larger organization scale of ecological communities when assembling a species pool across diverse environmental contexts, particularly when species interactions are highly structured. We applied our formalism to clinical data discovering two assembly archetypes that differentiate between healthy and unhealthy human gut microbiota.

Identifying keystone species in microbial communities using deep learning.

Previous studies suggested that microbial communities can harbour keystone species whose removal can cause a dramatic shift in microbiome structure and functioning. Yet, an efficient method to systematically identify keystone species in microbial communities is still lacking. Here we propose a data-driven keystone species identification (DKI) framework based on deep learning to resolve this challenge.

Identifying keystone species in microbial communities using deep learning.

Previous studies suggested that microbial communities harbor keystone species whose removal can cause a dramatic shift in microbiome structure and functioning. Yet, an efficient method to systematically identify keystone species in microbial communities is still lacking. This is mainly due to our limited knowledge of microbial dynamics and the experimental and ethical difficulties of manipulating microbial communities.

Predicting microbiome compositions from species assemblages through deep learning.

Microbes can form complex communities that perform critical functions in maintaining the integrity of their environment or their hosts' well-being. Rationally managing these microbial communities requires improving our ability to predict how different species assemblages affect the final species composition of the community. However, making such a prediction remains challenging because of our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics.

Structure-based identification of sensor species for anticipating critical transitions.

Ecological systems can undergo sudden, catastrophic changes known as critical transitions. Anticipating these critical transitions remains challenging in systems with many species because the associated early warning signals can be weakly present or even absent in some species, depending on the system dynamics. Therefore, our limited knowledge of ecological dynamics may suggest that it is hard to identify those species in the system that display early warning signals.

Generalizing game-changing species across microbial communities.

Microbes form multispecies communities that play essential roles in our environment and health. Not surprisingly, there is an increasing need for understanding if certain invader species will modify a given microbial community, producing either a desired or undesired change in the observed collection of resident species. However, the complex interactions that species can establish between each other and the diverse external factors underlying their dynamics have made constructing such understanding context-specific.

Coexistence holes characterize the assembly and disassembly of multispecies systems.

A central goal of ecological research has been to understand the limits on the maximum number of species that can coexist under given constraints. However, we know little about the assembly and disassembly processes under which a community can reach such a maximum number, or whether this number is in fact attainable in practice. This limitation is partly due to the challenge of performing experimental work and partly due to the lack of a formalism under which one can systematically study such processes.

Supporting "Bleeders" and "Billers": How Safety-Net Administrators Mitigate Provider Burnout During the COVID-19 Pandemic and Beyond.

Organizational factors impacting burnout have been underexplored among providers in low-income, minority-serving, safety-net settings. Our team interviewed 14 health care administrators, serving as key decision makers in Federally Qualified Health Center primary care clinics. Using a semistructured interview guide, we explored burnout mitigation strategies and elements of organizational culture and practice.

A simple criterion to design optimal non-pharmaceutical interventions for mitigating epidemic outbreaks.

For mitigating the COVID-19 pandemic, much emphasis is made on implementing non-pharmaceutical interventions to keep the reproduction number below one. However, using that objective ignores that some of these interventions, like bans of public events or lockdowns, must be transitory and as short as possible because of their significant economic and societal costs. Here, we derive a simple and mathematically rigorous criterion for designing optimal transitory non-pharmaceutical interventions for mitigating epidemic outbreaks.

An ecological framework to understand the efficacy of fecal microbiota transplantation.

Human gut microbiota plays critical roles in physiology and disease. Our understanding of ecological principles that govern the dynamics and resilience of this highly complex ecosystem remains rudimentary. This knowledge gap becomes more problematic as new approaches to modifying this ecosystem, such as fecal microbiota transplantation (FMT), are being developed as therapeutic interventions.

Revealing Complex Ecological Dynamics via Symbolic Regression.

Understanding the dynamics of complex ecosystems is a necessary step to maintain and control them. Yet, reverse-engineering ecological dynamics remains challenging largely due to the very broad class of dynamics that ecosystems may take. Here, this challenge is tackled through symbolic regression, a machine learning method that automatically reverse-engineers both the model structure and parameters from temporal data.

A theoretical framework for controlling complex microbial communities.

Microbes form complex communities that perform critical roles for the integrity of their environment or the well-being of their hosts. Controlling these microbial communities can help us restore natural ecosystems and maintain healthy human microbiota. However, the lack of an efficient and systematic control framework has limited our ability to manipulate these microbial communities.

Robust qualitative estimation of time-varying contact rates in uncertain epidemics.

We will inevitably face new epidemics where the lack of long time-series data and the uncertainty about the outbreak dynamics make difficult to obtain quantitative predictions. Here we present an algorithm to qualitatively infer time-varying contact rates from short time-series data, letting us predict the start, relative magnitude and decline of epidemic outbreaks. Using real time-series data of measles, dengue, and the current zika outbreak, we demonstrate our algorithm can outperform existing algorithms based on estimating reproductive numbers.

Mapping the ecological networks of microbial communities.

Mapping the ecological networks of microbial communities is a necessary step toward understanding their assembly rules and predicting their temporal behavior. However, existing methods require assuming a particular population dynamics model, which is not known a priori. Moreover, those methods require fitting longitudinal abundance data, which are often not informative enough for reliable inference.

"It Feels Good to Know That Someone Cares".

Introduction: Diabetes is the leading cause of death in Hispanic communities. Self-management is an important part of diabetes care, and diabetes self-management education (DSME) aims to teach the skills necessary for preventing and delaying complication. However, DSME is underutilized.

Fundamental limitations of network reconstruction from temporal data.

Inferring properties of the interaction matrix that characterizes how nodes in a networked system directly interact with each other is a well-known network reconstruction problem. Despite a decade of extensive studies, network reconstruction remains an outstanding challenge. The fundamental limitations governing which properties of the interaction matrix (e.

Evaluating the Validity and Reliability of the Beliefs About Medicines Questionnaire in Low-Income, Spanish-Speaking Patients With Diabetes in the United States.

Purpose The purpose of this study was to examine the reliability and validity of a Spanish version of the Beliefs about Medicines Questionnaire (BMQ) as a measure to evaluate beliefs about medications and to differentiate adherent from nonadherent patients among low-income Latino patients with diabetes in the United States. Methods Seventy-three patients were administered the BMQ and surveyed for evidence of medication nonadherence. Internal consistency of the BMQ was assessed by Cronbach's alpha along with performing a confirmatory factor analysis.

A clinic-based pilot intervention to enhance diabetes management for elderly Hispanic patients.

Background And Objectives: Successful diabetes self-management requires behavioral and lifestyle changes. However, low-income patients may face challenges related to poverty that make it difficult to engage in lifestyle changes. We piloted an intervention designed to help older, low-income, Hispanic, patients with diabetes access free or low-cost community resources to enhance diabetes self-management.

Effectiveness and feasibility of a software tool to help patients communicate with doctors about problems they face with their medication regimen (EMPATHy): study protocol for a randomized controlled trial.

Background: Low-income, Mexican-American patients with diabetes exhibit high rates of medication nonadherence, poor blood sugar control and serious complications, and often have difficulty communicating their concerns about the medication regimen to physicians. Interventions led by community health workers, non-professional community members who are trained to work with patients to improve engagement and communication during the medical visit, have had mixed success in improving outcomes. The primary objective of this project is to pilot test a prototype software toolkit called "EMPATHy" that a community health worker can administer to help patients identify the most important barriers to adherence that they face and discuss these barriers with their doctor.

A novel enrichment program using cascading mentorship to increase diversity in the health care professions.

The authors describe an innovative summer enrichment program based on a cascading mentorship model to transfer knowledge and skills from faculty to medical students to undergraduate students and finally to high school students. The program was designed to give high school students a glimpse of life in medical school and enhance the teaching and leadership skills of underrepresented undergraduate and medical students. Started in 2010 with 30 high school students and 9 college and medical student coaches, the University of California, Irvine, School of Medicine Summer Premed Program expanded rapidly over the next two summers and enrolled a total of 253 high school students, 48 college students, and 12 medical students.