The [PSI+] prion phenotype in yeast manifests as a white, pink, or red color pigment. Experimental manipulations destabilize prion phenotypes, and allow colonies to exhibit [psi-] (red) sectored phenotypes within otherwise completely white colonies. Further investigation of the size and frequency of sectors that emerge as a result of experimental manipulation is capable of providing critical information on mechanisms of prion curing, but we lack a way to reliably extract this information. Images of experimental colonies exhibiting sectored phenotypes offer an abundance of data to help uncover molecular mechanisms of sectoring, yet the structure of sectored colonies is ignored in traditional biological pipelines. In this study, we present [PSI]-CIC, the first computational pipeline designed to identify and characterize features of sectored yeast colonies. To overcome the barrier of a lack of manually annotated data of colonies, we develop a neural network architecture that we train on synthetic images of colonies and apply to real images of [PSI+] , [psi-] , and sectored colonies. In hand-annotated experimental images, our pipeline correctly predicts the state of approximately 95% of colonies detected and frequency of sectors in approximately 89.5% of colonies detected. The scope of our pipeline could be extended to categorizing colonies grown under different experimental conditions, allowing for more meaningful and detailed comparisons between experiments. Our approach streamlines the analysis of sectored yeast colonies providing a rich set of quantitative metrics and provides insight into mechanisms driving the curing of prion phenotypes.
Candida parapsilosis is known to cause severe and persistent outbreaks in clinical settings. Patients infected with multidrug-resistant C. parapsilosis (MDR Cp) isolates were identified in a large Turkish hospital from 2017-2020. We subsequently identified three additional patients infected with MDR Cp isolates in 2022 from the same hospital and two echinocandin-resistant (ECR) isolates from a single patient in another hospital. The increasing number of MDR and ECR isolates contradicts the general principle that the severe fitness cost associated with these phenotypes could prevent their dominance in clinical settings. Here, we employed a multidimensional approach to systematically assess the fitness costs of MDR and ECR C. parapsilosis isolates. Whole-genome sequencing revealed a novel MDR genotype infecting two patients in 2022. Despite severe in vitro defects, the levels and tolerances of the biofilms of our ECR and MDR isolates were generally comparable to those of susceptible wild-type isolates. Surprisingly, the MDR and ECR isolates showed major alterations in their cell wall components, and some of the MDR isolates consistently displayed increased tolerance to the fungicidal activities of primary human neutrophils and were more immunoevasive during exposure to primary human macrophages. Our systemic infection mouse model showed that MDR and ECR C. parapsilosis isolates had comparable fungal burden in most organs relative to susceptible isolates. Overall, we observed a notable increase in the genotypic diversity and frequency of MDR isolates and identified MDR and ECR isolates potentially capable of causing persistent outbreaks in the future.
Shared clinical decision-making (SCDM) about HPV vaccination has been recommended for U.S. mid-adults aged 27-45 since 2019. To explore barriers and facilitators to HPV vaccination in this population, we conducted 14 virtual focus groups with 86 unvaccinated mid-adults (34 men and 52 women) in Californias medically underserved Inland Empire between September 2020 and January 2021. We systematically analyzed the focus group data using the rigorous and accelerated data reduction (RADaR) technique to identify key themes. Identified barriers included: lack of awareness, vaccine hesitancy, and perceived unaffordability (cited in 14 groups); lack of healthcare provider communication and insufficient time (13 groups); fear of moral judgment (12 groups); lack of motivation and information needs (10 groups); and lack of reliable transportation and foregone care during the COVID-19 pandemic (3 groups). Proposed facilitators included: tailored HPV vaccine information for mid-adults, cost mitigation, and improved vaccine accessibility (12 groups); healthcare provider-initiated conversations (6 groups); and vaccine reminders (4 groups). These findings highlight challenges to HPV vaccination among U.S. mid-adults eligible for SCDM and point to actionable strategies for improvement. Specifically, tailored educational interventions, decision-making tools for pharmacists, and integrating HPV vaccination into other healthcare encounters may enhance vaccination efforts in areas with limited primary care resources.
Dynamin 1 (Dyn1) GTPase, a principal driver of membrane fission during synaptic endocytosis, self-assembles into short mechanoactive helices cleaving the necks of endocytic vesicles. While structural information about Dyn1 helix is abundant, little is known about the nanoscale dynamics of the helical scaffolding at the moment of fission, complicating mechanistic understanding of Dyn1 action. To address the role of the helix dynamics in fission, we used High-Speed Atomic Force Microscopy (HS-AFM) and fluorescence microscopy to track and compare the spatiotemporal characteristics of the helices formed by wild-type Dyn1 and its K44A mutant impaired in GTP hydrolysis on minimal lipid membrane templates. In the absence of nucleotide, membrane-bound WTDyn1 and K44ADyn1 self-assembled into tubular protein scaffolding of similar diameter encaging the lipid bilayer. In both cases, the GTP addition caused scaffold constriction coupled with formation of 20 to 30 nm nanogaps in the protein coverage. While both proteins reached scaffold diameters characteristic for membrane superconstriction causing fission, the fission was detected only with WTDyn1. We associated the fission activity with the dynamic evolution of the nanogaps: K44ADyn1 gaps were static, while WTDyn1 gaps actively evolved via repetitive nonaxisymmetric constriction-bending deformations caused by localized GTP hydrolysis. Modeling of the deformations implicated filament twist as an additional deformation mode which combines with superconstriction to facilitate membrane fission. Our results thus show that the dynamics of the Dyn1 helical scaffold goes beyond radial constriction and involves nonaxisymmetric deformations, where filament twist emerges as a critical driver of membrane fission.
OBJECTIVE: We outline the development of a narrative intervention guided by the Common-Sense Model of Self-Regulation (CSM) to promote Human Papillomavirus (HPV) vaccination in a diverse college population. METHODS: We adapted the Obesity-Related Behavioral Intervention Trials (ORBIT) model to guide the development, evaluation, and refinement of a CSM-guided narrative video. First, content experts developed a video script containing information on HPV, HPV vaccines, and HPV-related cancers. The script and video contents were evaluated and refined, in succession, utilizing the think-aloud method, open-ended questions, and a brief survey during one-on-one interviews with university students. RESULTS: Script and video content analyses led to significant revisions that enhanced quality, informativeness, and relevance to the participants. We highlight the critical issues that were revealed and revised in the iterative process. CONCLUSIONS: We developed and refined a CSM guided narrative video for diverse university students. This framework serves as a guide for developing health communication interventions for other populations and health behaviors. INNOVATION: This project is the first to apply the ORBIT framework to HPV vaccination and describe a process to develop, evaluate, and refine comparable CSM guided narrative interventions that are tailored to specific audiences.
Recently, we reported the discovery of a novel endoglucanase of the glycoside hydrolase family 12 (GH12), designated IfCelS12A, from the haloalkaliphilic anaerobic bacterium Iocasia fonsfrigidae strain SP3-1, which was isolated from a hypersaline pond in the Samut Sakhon province of Thailand (ca. 2017). IfCelS12A exhibits high substrate specificity on carboxymethyl cellulose and amorphous cellulose but low substrate specificity on b-1,3;1,4-glucan. Unlike some endoglucanases of the GH12 family, IfCelS12A does not exhibit hydrolytic activity on crystalline cellulose (i.e., Avicel™). High-Pressure Liquid Chromatography (HPLC) and Thin Layer Chromatography (TLC) analyses of products resulting from IfCelS12-mediated hydrolysis indicate mode of action for this enzyme. Notably, IfCelS12A preferentially hydrolyzes cellotetraoses, cellopentaoses, and cellohexaoses with negligible activity on cellobiose or cellotriose. Kinetic analysis with cellopentaose and barely b-D-glucan as cellulosic substrates were conducted. On cellopentaose, IfCelS12A demonstrates a 16-fold increase in activity (KM = 0.27 mM; kcat = 0.36 s-1; kcat/KM = 1.34 mM-1 s-1) compared to the enzymatic hydrolysis of barley b-D-glucan (KM: 0.04 mM, kcat: 0.51 s-1, kcat/KM = 0.08 mM-1 s-1). Moreover, IfCelS12A enzymatic efficacy is stable in hypersaline sodium chlorids (NaCl) solutions (up to 10% NaCl). Specifically, IfCel12A retains notable activity after 24 h at 2M NaCl (10% saline solution). IfCelS12A used as a cocktail component with other cellulolytic enzymes and in conjunction with mobile sequestration platform technology offers additional options for deconstruction of ionic liquid-pretreated cellulosic feedstock. KEY POINTS: • IfCelS12A from an anaerobic alkaliphile Iocasia fronsfrigidae shows salt tolerance • IfCelS12A in cocktails with other enzymes efficiently degrades cellulosic biomass • IfCelS12A used with mobile enzyme sequestration platforms enhances hydrolysis.
Machine learning (ML) applications in soil science have significantly increased over the past two decades, reflecting a growing trend towards data-driven research addressing soil security. This extensive application has mainly focused on enhancing predictions of soil properties, particularly soil organic carbon, and improving the accuracy of digital soil mapping (DSM). Despite these advancements, the application of ML in soil science faces challenges related to data scarcity and the interpretability of ML models. There is a need for a shift towards Soil Science-Informed ML (SoilML) models that use the power of ML but also incorporate soil science knowledge in the training process to make predictions more reliable and generalisable. This paper proposes methodologies for embedding ML models with soil science knowledge to overcome current limitations. Incorporating soil science knowledge into ML models involves using observational priors to enhance training datasets, designing model structures which reflect soil science principles, and supervising model training with soil science-informed loss functions. The informed loss functions include observational constraints, coherency rules such as regularisation to avoid overfitting, and prior or soil-knowledge constraints that incorporate existing information about the parameters or outputs. By way of illustration, we present examples from four fields: digital soil mapping, soil spectroscopy, pedotransfer functions, and dynamic soil property models. We discuss the potential to integrate process-based models for improved prediction, the use of physics-informed neural networks, limitations, and the issue of overparametrisation. These approaches improve the relevance of ML predictions in soil science and enhance the models’ ability to generalise across different scenarios while maintaining soil science principles, transparency and reliability.
Abstract: Time-dependent Hartree–Fock (TDHF) is one of the fundamental post-Hartree–Fock (HF) methods to describe excited states. In its Tamm-Dancoff form, equivalent to Configuration Interaction Singles, it is still widely used and particularly applicable to big molecules where more accurate methods may be unfeasibly expensive. However, it is rarely implemented in real space, mostly because of the expensive nature of the exact-exchange potential in real space. Compared to widely used Gaussian-type orbitals (GTO) basis sets, real space often offers easier implementation of equations and more systematic convergence of Rydberg states, as well as favorable scaling, effective domain parallelization, flexible boundary conditions, and ability to treat model systems. We implemented TDHF in the Octopus real-space code as a step toward linear-response hybrid time-dependent density-functional theory (TDDFT), other post-HF methods, and ensemble density-functional theory methods involving exact exchange. Calculation of HF’s non-local exact exchange is very expensive in real space. We overcome this limitation with Octopus’ implementation of Adaptively Compressed Exchange, and find the appropriate mixing scheme and starting point to complete the ground-state calculation in a practical amount of time, and thus enable TDHF. We compared our results to those from GTOs on a set of small molecules and confirmed close agreement of results, though with larger deviations than in the case of semi-local TDDFT. We find that convergence of TDHF demands a finer real-space grid than semi-local TDDFT. We also present the subtleties in benchmarking a real-space calculation against GTOs, relating to Rydberg and vacuum states.
Fungi are increasingly recognized as key players in various extreme environments. Here we present an analysis of publicly-sourced metagenomes from global extreme environments, focusing on fungal taxonomy and function. The majority of 855 selected metagenomes contained scaffolds assigned to fungi. Relative abundance of fungi was as high as 10% of protein-coding genes with taxonomic annotation, with up to 289 fungal genera per sample. Despite taxonomic clustering by environment, fungal communities were more dissimilar than archaeal and bacterial communities, both for within- and between-environment comparisons. Relatively abundant fungal classes in extreme environments included Dothideomycetes, Eurotiomycetes, Leotiomycetes, Pezizomycetes, Saccharomycetes, and Sordariomycetes. Broad generalists and prolific aerial spore formers were the most relatively abundant fungal genera detected in most of the extreme environments, bringing up the question of whether they are actively growing in those environments or just surviving as spores. More specialized fungi were common in some environments, such as zoosporic taxa in cryosphere water and hot springs. Relative abundances of genes involved in adaptation to general, thermal, oxidative, and osmotic stress were greatest in soda lake, acid mine drainage, and cryosphere water samples.
