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Diagnosis of Breakthrough Fungal Infections in the Clinical Mycology Laboratory: An ECMM Consensus Statement.
No full textBreakthrough invasive fungal infections (bIFI) cause significant morbidity and mortality. Their diagnosis can be challenging due to reduced sensitivity to conventional culture techniques, serologic tests, and PCR-based assays in patients undergoing antifungal therapy, and their diagnosis can be delayed contributing to poor patient outcomes. In this review, we provide consensus recommendations on behalf of the European Confederation for Medical Mycology (ECMM) for the diagnosis of bIFI caused by invasive yeasts, molds, and endemic mycoses, to guide diagnostic efforts in patients receiving antifungals and support the design of future clinical trials in the field of clinical mycology. The cornerstone of lab-based diagnosis of breakthrough infections for yeast and endemic mycoses remain conventional culture, to accurately identify the causative pathogen and allow for antifungal susceptibility testing. The impact of non-culture-based methods are not well-studied for the definite diagnosis of breakthrough invasive yeast infections. Non-culture-based methods have an important role for the diagnosis of breakthrough invasive mold infections, in particular invasive aspergillosis, and a combination of testing involving conventional culture, antigen-based assays, and PCR-based assays should be considered. Multiple diagnostic modalities, including histopathology, culture, antibody, and/or antigen tests and occasionally PCR-based assays may be required to diagnose breakthrough endemic mycoses. A need exists for diagnostic tests that are effective, simple, cheap, and rapid to enable the diagnosis of bIFI in patients taking antifungals
The Most Important Election of Our Lifetime
No full textResearchers have dedicated substantial effort to investigating important non-material motivations for people to get involved in politics, such as duty, emotions, and identities. Less attention, however, has been paid to the expectations people develop for what governments and politicians will deliver. This dissertation is about what people think elections will do for them, where those expectations come from, and their political consequences.The first substantive chapter explores the policy changes people expect from elections, and how those expectations influence the decision to vote. There I study voters' beliefs about what candidates would actually do if given political power. I first find that public respondents likely underestimate the impediments that the separation of power poses to policy change. Just before the 2020 election, these general population respondents expected much more legislation than political scientists completing an identical survey. Second, among the general public, there was a 16 percentage point difference between voters and non-voters in expectations for policy change resulting from the election. Most importantly, these high expectations predicted validated voter turnout better than education, identifying as a Democrat or as a Republican (as well as partisan strength and ideology), having voted in 2016, and political interest. These results support explanations for the decision to turnout which center on the benefits, whether individual or social, that people believe their preferred candidate will deliver. Next, Chapter 3 argues that a psychological bias called focalism contributes to an overestimation of the differences between political candidates, which in turn increases participation and polarization. Focalism causes people to confuse the allocation of attention to things with the importance of those things. Because attention to politics typically centers on conflict, the result is an exaggeration of differences across the partisan divide. I test this intuition using an experimental design that provides all respondents with all of the information they need to estimate how much Joe Biden and Donald Trump objectively disagreed on policy positions just before the 2020 election. I find that shifting attention – towards either those positions the candidates agreed or disagreed with each other on – influences beliefs about the differences between candidates. The effect exceeds that of identifying as a Democrat or as a Republican. Beyond those perceptions, focalism increases turnout intentions, perceptions of election importance, negative feelings towards the out-candidate, and affective polarization.Finally, Chapter 4 attempts to moderate people's expectations using a series of real-world experiments. That final essay asks: would learning about coverage biases as people learn about the news soften people's beliefs about how different Democrats and Republicans are? To test this question, I use two experiments, one of which recruited participants to consume news covering the full population of partisan and non-partisan sources and the second of which randomized coverage among a sample predisposed to change their minds. I find that giving people the tools to understand media bias does give people the opportunity to choose to consume centrist news. Exploring app-use data, I show that people who explicitly choose to engage with stories favored by these moderate sources stories while avoiding stories favored by partisan sources feel less polarized. </p
Stable Variable Selection for Sparse Linear Regression in a Non-uniqueness Regime
No full textThis thesis presents a comprehensive investigation of the LASSO method in a non-uniqueness regime and its ability of stable variable selection. We characterize when LASSO may have non-unique solutions through a sufficient and necessary condition, and how these non-unique solutions behave geometrically: all solutions must lie in the same simplex within the same orthant, and they form a polytope structure in which each corner represents the most parsimonious collection of features that does not contain other corners. Leveraging this geometric structure, this work then explores what to do to practically obtain these estimators, by proposing an efficient sampling algorithm that returns uniformly distributed points on the polytope.We present a non-asymptotic analysis of the l_2 coefficient error and feature selection consistency for the non-unique LASSO, by restricting necessary conditions (the eigenvalue condition and mutual incoherence condition) to a certain direction or a subset of features. Our theoretical results show that, under strong assumptions, the non-unique LASSO is as theoretically efficient as the original LASSO. Moreover, when dealing with linearly combined features in a dataset, numerical experiments demonstrate the superior stable variable selection performance of our proposed non-unique LASSO over other existing algorithms, particularly if the proper tuning parameters can be selected.</p
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.
No full textShort-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks
Periostin facilitates ovarian cancer recurrence by enhancing cancer stemness.
No full textThe lethality of epithelial ovarian cancer (OC) is largely due to a high rate of recurrence and development of chemoresistance, which requires synergy between cancer cells and the tumor microenvironment (TME) and is thought to involve cancer stem cells. Our analysis of gene expression microarray data from paired primary and recurrent OC tissues revealed significantly elevated expression of the gene encoding periostin (POSTN) in recurrent OC compared to matched primary tumors (p = 0.015). Secreted POSTN plays a role in the extracellular matrix, facilitating epithelial cell migration and tissue regeneration. We therefore examined how elevated extracellular POSTN, as we found is present in recurrent OC, impacts OC cell functions and phenotypes, including stemness. OC cells cultured with conditioned media with high levels of periostin (CMPOSTNhigh) exhibited faster migration (p = 0.0044), enhanced invasiveness (p = 0.006), increased chemoresistance (p CTL). Further, CMPOSTNhigh-cultured OC cells exhibited an elevated stem cell side population (p = 0.027) along with increased expression of cancer stem cell marker CD133 relative to CMCTL-cultured cells. POSTN-transfected 3T3-L1 cells that were used to generate CMPOSTNhigh had visibly enhanced intracellular and extracellular lipids, which was also linked to increased OC cell expression of fatty acid synthetase (FASN) that functions as a central regulator of lipid metabolism and plays a critical role in the growth and survival of tumors. Additionally, POSTN functions in the TME were linked to AKT pathway activities. The mean tumor volume in mice injected with CMPOSTNhigh-cultured OC cells was larger than that in mice injected with CMCTL-cultured OC cells (p = 0.0023). Taken together, these results show that elevated POSTN in the extracellular environment leads to more aggressive OC cell behavior and an increase in cancer stemness, suggesting that increased levels of stromal POSTN during OC recurrence contribute to more rapid disease progression and may be a novel therapeutic target. Furthermore, they also demonstrate the utility of having matched primary-recurrent OC tissues for analysis and support the need for better understanding of the molecular changes that occur with OC recurrence to develop ways to undermine those processes
Robust Information Storage and Consolidation in Attractor Neural Networks
No full textLong-term memory is believed to be stored in the human brain by changing synapses in a neuronal activity-dependent way. This idea has been implemented in the attractor neural network models, where the connectivity strength between neurons is determined by Hebbian synaptic plasticity rules. Classical studies of memory modeling synapses as continuous variables in networks of binary neurons have shown that such networks can have large storage capacities. However, a rising number of evidence suggests that synapses in brain structures involved in memory, such as the hippocampus and neocortex, are more digital than analog. Understanding how a large amount of information can be robustly stored with discrete-like synapses in the brain remains an open question in the field of computational neuroscience.In this study, we explored a series of synaptic plasticity rules for discrete-like synapses and investigated how their application in attractor neural networks will affect the memory function of the system. We built mean-field equations to calculate the storage capacity of the network. We studied a network with a binarized Hebbian learning rule, showing that such networks can provide a near-optimal storage capacity, in the space of all possible binary connectivity matrices. We investigated a model with double-well synapses, where each synapse is described by a continuous variable that evolves in a potential with multiple minima. We showed that this model could interpolate between models with discrete synapses and models with continuous synapses by varying the shape of the potential. Our results indicated that discrete-like synapses could benefit neural networks by increasing their robustness with respect to noise. Furthermore, we incorporated the double-well synapses model with the memory consolidation mechanism. Our result showed that memory consolidation could significantly enhance the storage capacity of the network, leading to a power law decay of the memory forgetting curve as observed in psychological experiments.</p
High early death rates, treatment resistance, and short survival of Black adolescents and young adults with AML.
No full textSurvival of patients with acute myeloid leukemia (AML) is inversely associated with age, but the impact of race on outcomes of adolescent and young adult (AYA; range, 18-39 years) patients is unknown. We compared survival of 89 non-Hispanic Black and 566 non-Hispanic White AYA patients with AML treated on frontline Cancer and Leukemia Group B/Alliance for Clinical Trials in Oncology protocols. Samples of 327 patients (50 Black and 277 White) were analyzed via targeted sequencing. Integrated genomic profiling was performed on select longitudinal samples. Black patients had worse outcomes, especially those aged 18 to 29 years, who had a higher early death rate (16% vs 3%; P=.002), lower complete remission rate (66% vs 83%; P=.01), and decreased overall survival (OS; 5-year rates: 22% vs 51%; P<.001) compared with White patients. Survival disparities persisted across cytogenetic groups: Black patients aged 18 to 29 years with non-core-binding factor (CBF)-AML had worse OS than White patients (5-year rates: 12% vs 44%; P<.001), including patients with cytogenetically normal AML (13% vs 50%; P<.003). Genetic features differed, including lower frequencies of normal karyotypes and NPM1 and biallelic CEBPA mutations, and higher frequencies of CBF rearrangements and ASXL1, BCOR, and KRAS mutations in Black patients. Integrated genomic analysis identified both known and novel somatic variants, and relative clonal stability at relapse. Reduced response rates to induction chemotherapy and leukemic clone persistence suggest a need for different treatment intensities and/or modalities in Black AYA patients with AML. Higher early death rates suggest a delay in diagnosis and treatment, calling for systematic changes to patient care
Evaluating Contributions of Small-Scale Fisheries on Food Security via Fisheries Indicators, Economic Inequalities, and Gender
No full textThe contributions of small-scale fisheries (SSF) to food security are underappreciated globally. This issue is further exacerbated in the Galapagos Archipelago, where the majority of food sources are imported from mainland Ecuador. In collaboration with the Charles Darwin Foundation (CDF), our report underscores the contributions of SSF in the Galapagos through the lens of food security, economic inequalities, and gender. Using the Food and Agriculture Organization’s (FAO) four pillars of food security – availability, use, access, and stability – we identified catch, price of fish, access based on income, consumption patterns, and nutrition to be the most significant indicators of SSF contributions in the Galapagos. Furthermore, our report includes a toolkit that measures the contributions of SSF, geospatial figures, and policy recommendations to CDF. Our recommendations seek to promote the health of permanent residents through direct access to fresh seafood and to promote sustainable fisheries practices through legislation
Toxicity of an Urban Creek: Effects of developmental exposure to water from Ellerbe Creek Watershed on zebrafish (Danio rerio) swimming behavior
No full textUrban changes in land use, such as increasing impervious surface cover and the building of stormwater pipes, result in anthropogenically manipulated water drainage into local watersheds. Along with changes in drainage patterns, urbanization introduces new chemicals into the watershed or changes existing chemical concentrations. Therefore, contaminant sources in urban watersheds, such as stormwater runoff and municipal wastewater discharge, lead to complex mixtures of chemicals entering aquatic ecosystems. Urbanization is projected to increase across the world, resulting in more changes in hydrology and more chemicals entering urban watersheds. While it is known that urban infrastructure and pollution changes the chemical and physical properties of an urban watershed, there is little known about how these changes impact the developmental health of aquatic organisms that call the watershed home. It is important to understand developmental toxicity because changes in development can impact the adult fitness of organisms, ultimately impacting the population and potentially the entire ecosystem.
In Durham, North Carolina Ellerbe Creek Watershed is a highly developed watershed with 22% impervious surface by area. Ellerbe Creek (EC) cuts through Downtown Durham and has various urban pollution sources such as industrial, residential, and recreational development. Due to the exceedance of water quality standards, multiple segments of EC are considered impaired under the Clean Water Act.
EC is home to many species of fish. Understanding the influence of chemical contamination on fish populations is important for estimating the broader ecosystem health. To examine the effects that EC chemical constituents may have on fish development, we conducted a series of behavioral toxicity studies following treatment with EC water using zebrafish (Danio rerio). Specifically, we raised zebrafish in urban watershed samples collected during four seasons across sixteen points along EC. Sites were selected based on their location along the main branch or tributaries of EC. Main branch sites were selected based on their spatial relationship (upstream versus downstream) to a wastewater treatment plant. Tributary sites differed in their local land cover, resulting in highly varying chemical profiles at each site. Sampling seasons roughly corresponded with seasonal variance in water quality parameters. At five days post fertilization, we assessed the swimming behavior of the zebrafish. This allowed us to determine if developing in differing urban water samples had an adverse effect on zebrafish behavior, which can ultimately have an impact on survivability and competitiveness of fish larvae.
When analyzing our data, we wanted to answer four leading questions. The first was whether any effect on swimming behavior was seen between fish raised in EC samples compared to fish raised in control water. We then wanted to know if the wastewater treatment site’s effluent impacted the swimming behavior of zebrafish since we knew that the majority of water in sites downstream of the wastewater treatment plant was comprised of wastewater effluent. Similarly, we asked if fish raised in water samples from different tributaries showed differing swimming behavior. Tributaries act as a snapshot of the water that is feeding into them and the water chemistry of one does not change the water chemistry of another – allowing us to better understand the conditions that may result in urban water toxicity. Finally, we aimed to evaluate how the collection season of the water samples may influence swimming behavior.
Results show spatial and temporal heterogeneity in the impacts of water samples on zebrafish larval behavior. Fish raised in EC water samples had varying responses in their swimming behavior. Among fish raised in select EC water samples, there was a change in behavior compared to fish raised in control water. This change appeared to be both spatial, varying based on collection site, and temporal, varying based on collection month. All changes in fish swimming behavior were hypoactive compared to the controls. We did not see a difference in swimming behavior between fish raised in water samples from upstream of the wastewater effluent site compared to fish raised in water samples from downstream of the wastewater effluent site. There was, however, a difference in swimming behavior among fish raised in different tributary water samples. Significant site-specific heterogeneity among tributaries appeared to be driven by one to two collection sites and varied by collection month.
This study aimed to better understand how water from EC impacts the health and development of organisms living in the creek. The effects seen on swimming behavior of zebrafish raised in EC water samples suggest that the urban watershed has an adverse effect on the development of the zebrafish. Different sites and seasons had different chemical and physical properties that may have resulted in these changes and future research focused on identifying the drivers of this behavioral change is imperative for understanding and ameliorating urban watershed ecosystem health. Broadly, the impacts of urban development on watershed chemical constituents and their toxicity and on ultimate ecosystem-level consequences are an important consideration when planning future development. To this end, whole organismal toxicity assays can serve to improve ecological risk assessments