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.
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.
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.
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.
Lightning is a major source of wildfire ignition in the western United States (WUS). We build and train convolutional neural networks (CNNs) to predict the occurrence of cloud-to-ground (CG) lightning across the WUS during June-September from the spatial patterns of seven large-scale meteorological variables from reanalysis (1995-2022). Individually trained CNN models at each 1° × 1° grid cell (n = 285 CNNs) show high skill at predicting CG lightning days across the WUS (median AUC = 0.8) and perform best in parts of the interior Southwest where summertime CG lightning is most common. Further, interannual correlation between observed and predicted CG lightning days is high (median r = 0.87), demonstrating that locally trained CNNs realistically capture year-to-year variation in CG lightning activity across the WUS. We then use layer-wise relevance propagation (LRP) to investigate the relevance of predictor variables to successful CG lightning prediction in each grid cell. Using maximum LRP values, our results show that two thermodynamic variables-ratio of surface moist static energy to free-tropospheric saturation moist static energy, and the 700-500 hPa lapse rate-are the most relevant CG lightning predictors for 93%-96% of CNNs depending on the LRP variant used. As lightning is not directly simulated by global climate models, these CNNs could be used to parameterize CG lightning in climate models to assess changes in future CG lightning occurrence with projected climate change. Understanding changes in CG lightning risk and consequently lightning-caused wildfire risk across the WUS could inform fire management, planning, and disaster preparedness.
Compared to ground-state electronic structure optimizations, accurate simulations of molecular real-time electron dynamics are usually much more difficult to perform. To simulate electron dynamics, the time-dependent density matrix renormalization group (TDDMRG) has been shown to offer an attractive compromise between accuracy and cost. However, many simulation parameters significantly affect the quality and efficiency of a TDDMRG simulation. So far, it is unclear whether common wisdom from ground-state DMRG carries over to the TDDMRG, and a guideline on how to choose these parameters is missing. Here, in order to establish such a guideline, we investigate the convergence behavior of the main TDDMRG simulation parameters, such as time integrator, the choice of orbitals, and the choice of matrix-product-state representation for complex-valued nonsinglet states. In addition, we propose a method to select orbitals that are tailored to optimize the dynamics. Lastly, we showcase the TDDMRG by applying it to charge migration ionization dynamics in furfural, where we reveal a rapid conversion from an ionized state with a σ character to one with a π character within less than a femtosecond.
