Landfill intermediate cover soil microbiomes and their potential for mitigating greenhouse gas emissions revealed through metagenomics

Landfills are a major source of anthropogenic methane emissions and have been found to produce nitrous oxide, an even more potent greenhouse gas than methane. Intermediate cover soil (ICS) plays a key role in reducing methane emissions but may also result in nitrous oxide production. To assess the potential for microbial methane oxidation and nitrous oxide production, long sequencing reads were generated from ICS microbiome DNA and reads were functionally annotated for 24 samples across ICS at a large landfill in New York. Further, incubation experiments were performed to assess methane consumption and nitrous oxide production with varying amounts of ammonia supplemented. Methane was readily consumed by microbes in the composite ICS and all incubations with methane produced small amounts of nitrous oxide even when ammonia was not supplemented. Incubations without methane produced significantly less nitrous oxide than those incubated with methane. In incubations with methane added, the observed specific rate of methane consumption was 0.776 +/- 0.055 μg CH4 g dry weight (DW) soil-1 h-1 and the specific rate of nitrous oxide production was 3.64 × 10-5 +/- 1.30 × 10-5 μg N2O g DW soil-1 h-1. The methanotrophs Methylobacter and an unclassified genus within the family Methlyococcaceae were present in the original ICS samples and the incubation samples, and their abundance increased during incubations with methane. Genes encoding particulate methane monooxygenase/ ammonia monooxygenase (pMMO) were much more abundant than genes encoding soluble methane monooxygenase (sMMO) across the landfill ICS. Genes encoding proteins that convert hydroxylamine to nitrous oxide were not highly abundant in the ICS or incubation metagenomes. In total, these results suggest that although ammonia oxidation via methanotrophs may result in low levels of nitrous oxide production, ICS microbial communities have the potential to greatly reduce the overall global warming potential of landfill emissions.

Microbiome assembly and stability during start-up of a full-scale, two-phase anaerobic digester fed cow manure and mixed organic feedstocks

Carbon transformations during anaerobic digestion are mediated by complex microbiomes, but their assembly is poorly understood, especially in full-scale digesters. Gene-centric metagenomics combining functional and taxonomic classification was performed for an on-farm digester during start-up. Cow manure and organic waste pre-treated in a hydrolysis tank were fed to the methane-producing digester and the volatile solids loading rate was slowly increased from 0 to 3.5 kg volatile solids m-3 d-1 over one year. The microbial community in the anaerobic digester exhibited a high ratio of archaea, which were dominated by hydrogenotrophic methanogens. Bacteria in the anaerobic digester had a high abundance of genes for ferredoxin cycling, H2 generation, and more metabolically complex fermentations than in the hydrolysis tank. In total, the results show that a functionally stable microbiome was achieved quickly during start-up and that the microbiome created in the low-pH hydrolysis tank did not persist in the downstream anaerobic digester.

An Acoustical and Lexical Machine-Learning Pipeline to Identify Connectional Silences

Context: Developing scalable methods for conversation analytics is essential for health care communication science and quality improvement. Purpose: To assess the feasibility of automating the identification of a conversational feature, Connectional Silence, which is associated with important patient outcomes. Methods: Using audio recordings from the Palliative Care Communication Research Initiative cohort study, we develop and test an automated measurement pipeline comprising three machine-learning (ML) tools-a random forest algorithm and a custom convolutional neural network that operate in parallel on audio recordings, and subsequently a natural language processing algorithm that uses brief excerpts of automated speech-to-text transcripts. Results: Our ML pipeline identified Connectional Silence with an overall sensitivity of 84% and specificity of 92%. For Emotional and Invitational subtypes, we observed sensitivities of 68% and 67%, and specificities of 95% and 97%, respectively. Conclusion: These findings support the capacity for coordinated and complementary ML methods to fully automate the identification of Connectional Silence in natural hospital-based clinical conversations.

Developing a Direct Observation Measure of Therapeutic Connection in Psilocybin-Assisted Therapy: A Feasibility Study

Context: Measuring therapeutic connection during psilocybin-assisted therapy is essential to understand underlying mechanisms, inform training, and guide quality improvement. Purpose: To evaluate the feasibility of directly observing indicators of therapeutic connection during psilocybin administration encounters. Methods: We evaluated audio and video data from a recent clinical trial for observable expressions of therapeutic connection as defined in proposed best-practice competencies (i.e., empathic abiding presence and interpersonal grounding). We selected the first four 8-hour encounters involving unique participants, therapists, and gender pairs. Each video was independently coded by three members of an interprofessional six-person team. Using a structured checklist, coders recorded start-stop times, the audible (i.e., speech prosody or words) and visible (i.e., body movements, eye gaze, and touch) cues marking the event, and the qualities of the interaction (e.g., expression of awe, trust, distress, and calmness). We assessed feasibility by observing the frequency, distribution, and overlap of cues and qualities coders used to identify and define moments of therapeutic connection. Results: Among the 2074 minutes of video, coders recorded 372 moments of therapeutic connection. Eighty-three percent were identified by at least two coders and 41% by all three. Coders used a combination of audible and visual cues to identify therapeutic connection in 51% of observed events (190/372). Both the cues and qualities of therapeutic connection expressions varied over the course of psilocybin temporal effects on states of consciousness. Conclusion: Direct observation of therapeutic human connection is feasible, sensitive to changes in states of consciousness and requires evaluation of audible and visual data.

Somtimes: self organizing maps for time series clustering and its application to serious illness conversations

There is demand for scalable algorithms capable of clustering and analyzing large time series data. The Kohonen self-organizing map (SOM) is an unsupervised artificial neural network for clustering, visualizing, and reducing the dimensionality of complex data. Like all clustering methods, it requires a measure of similarity between input data (in this work time series). Dynamic time warping (DTW) is one such measure, and a top performer that accommodates distortions when aligning time series. Despite its popularity in clustering, DTW is limited in practice because the runtime complexity is quadratic with the length of the time series. To address this, we present a new a self-organizing map for clustering TIME Series, called SOMTimeS, which uses DTW as the distance measure. The method has similar accuracy compared with other DTW-based clustering algorithms, yet scales better and runs faster. The computational performance stems from the pruning of unnecessary DTW computations during the SOM's training phase. For comparison, we implement a similar pruning strategy for K-means, and call the latter K-TimeS. SOMTimeS and K-TimeS pruned 43% and 50% of the total DTW computations, respectively. Pruning effectiveness, accuracy, execution time and scalability are evaluated using 112 benchmark time series datasets from the UC Riverside classification archive, and show that for similar accuracy, a 1.8× speed-up on average for SOMTimeS and K-TimeS, respectively with that rates vary between 1× and 18× depending on the dataset. We also apply SOMTimeS to a healthcare study of patient-clinician serious illness conversations to demonstrate the algorithm's utility with complex, temporally sequenced natural language.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10618-023-00979-9.

Toward Digital Phenotypes of Early Childhood Mental Health via Unsupervised and Supervised Machine Learning

Childhood mental health disorders such as anxiety, depression, and ADHD are commonly-occurring and often go undetected into adolescence or adulthood. This can lead to detrimental impacts on long-term wellbeing and quality of life. Current parent-report assessments for pre-school aged children are often biased, and thus increase the need for objective mental health screening tools. Leveraging digital tools to identify the behavioral signature of childhood mental disorders may enable increased intervention at the time with the highest chance of long-term impact. We present data from 84 participants (4-8 years old, 50% diagnosed with anxiety, depression, and/or ADHD) collected during a battery of mood induction tasks using the ChAMP System. Unsupervised Kohonen Self-Organizing Maps (SOM) constructed from movement and audio features indicate that age did not tend to explain clusters as consistently as gender within task-specific and cross-task SOMs. Symptom prevalence and diagnostic status also showed some evidence of clustering. Case studies suggest that high impairment (>80th percentile symptom counts) and diagnostic subtypes (ADHD-Combined) may account for most behaviorally distinct children. Based on this same dataset, we also present results from supervised modeling for the binary classification of diagnoses. Our top performing models yield moderate but promising results (ROC AUC .6-.82, TPR .36-.71, Accuracy .62-.86) on par with our previous efforts for isolated behavioral tasks. Enhancing features, tuning model parameters, and incorporating additional wearable sensor data will continue to enable the rapid progression towards the discovery of digital phenotypes of childhood mental health.Clinical Relevance- This work advances the use of wearables for detecting childhood mental health disorders.

The StoryListening Project: Feasibility and Acceptability of a Remotely Delivered Intervention to Alleviate Grief during the COVID-19 Pandemic

Background: The events surrounding the COVID-19 pandemic have created heightened challenges to coping with loss and grief for family and friends of deceased individuals, as well as clinicians who experience loss of their patients. There is an urgent need for remotely delivered interventions to support those experiencing grief, particularly due to growing numbers of bereaved individuals during the COVID-19 pandemic. Objective: To determine the feasibility and acceptability of the brief, remotely delivered StoryListening storytelling intervention for individuals experiencing grief during the COVID pandemic. Setting/Subjects: A single-arm pilot study was conducted in the United States. Participants included adult English-speaking family members, friends, or clinicians of individuals who died during the COVID-19 pandemic. All participants engaged in a televideo StoryListening session with a trained StoryListening doula. Measurements: Participants completed a brief follow-up telephone interview two weeks after the StoryListening session. We describe enrollment and retention data to assess feasibility and conducted a deductive thematic analysis of the follow-up interview data to assess acceptability. Results: Sixteen clinicians and 48 friends/family members enrolled in the study (n = 64; 75% enrollment), 62 completed a StoryListening session; 60 completed the follow-up interview. Participants reported that the intervention was useful and offered a valuable opportunity to process their grief experience. Conclusions: The StoryListening intervention is feasible and acceptable for friends/family members and clinicians who have experienced grief during COVID. Our intervention may offer an accessible first-line option to address the increasing wave of bereavement-related distress and clinician burnout in the United States.

Balancing multiple stakeholder objectives for floodplain reconnection and wetland restoration

Floodplain reconnection and wetland restoration projects are increasingly implemented to enhance flood resiliency, and these nature-based solutions can also achieve co-benefits of nutrient storage and improved habitats. Considering the multiple and sometimes incompatible objectives of stakeholders for uses of riverside lands, a decision-support tool linked to a hydraulic model would enable planners to simulate floodplain restoration scenarios while also quantifying and assessing the trade-offs between the stakeholder objectives to arrive at optimal restoration designs. We illustrate a simple ranking approach using an n-dimensional objective function to represent key stakeholders engaged in restoration. We applied our approach in a watershed in central Vermont (USA) that has been identified by regional and state-level stakeholders as an important location to mitigate flooding damages but also to improve water quality - all within a context of increasing development pressures on riparian lands and limited financial resources to accomplish restoration. Eleven different floodplain reconnection and wetland restoration modifications were combined in six scenarios and simulated with 2D Hydrologic Engineering Center's River Analysis System (2D HEC-RAS), along with a baseline (no-action) scenario. Only modest attenuation of peak flows for 2-, 25-, 50- and 100-year design storms was achieved by the floodplain restoration scenarios due to the steep setting, and flashy nature of the watershed. Yet, several scenarios of floodplain reconnection projects more than met the necessary annual phosphorus load reductions targeted under a Total Maximum Daily Load implementation plan. Our approach provided planners with a ranking of restoration scenarios that best met multiple stakeholder objectives and allowed effectiveness of alternate design scenarios to be quantified, justified, and visualized to promote consensus decision-making.

A Formative Mixed-Methods Study of Emotional Responsiveness in Telepalliative Care

Background: It is unknown whether telemedicine-delivered palliative care (tele-PC) supports emotionally responsive patient-clinician interactions. Objectives: We conducted a mixed-methods formative study at two academic medical centers in rural U.S. states to explore the acceptability, feasibility, and emotional responsiveness of tele-PC. Design: We assessed clinicians' emotional responsiveness through questionnaires, qualitative interviews, and video coding. Results: We completed 11 tele-PC consultations. Mean age was 71 years, 30% did not complete high school, 55% experienced at least moderate financial insecurity, and 2/3 rated their overall health poorly. All patients rated tele-PC as equal to, or better than, in-person PC at providing emotional support. There was a tendency toward higher positive and lower negative emotions following the consultation. Video coding identified 114 instances of patients expressing emotions, and clinicians detected and responded to 98% of these events. Conclusion: Tele-PC appears to support emotionally responsive patient-clinician interactions. A mixed-methods approach to evaluating tele-PC yields useful, complementary insights.

Epidemiology of Connectional Silence in specialist serious illness conversations

CONTEXT

Human connection can reduce suffering and facilitate meaningful decision-making amid the often terrifying experience of hospitalization for advanced cancer. Some conversational pauses indicate human connection, but we know little about their prevalence, distribution or association with outcomes.

PURPOSE

To describe the epidemiology of Connectional Silence during serious illness conversations in advanced cancer.

METHODS

We audio-recorded 226 inpatient palliative care consultations at two academic centers. We identified pauses lasting 2+ seconds and distinguished Connectional Silences from other pauses, sub-categorized as either Invitational (ICS) or Emotional (ECS). We identified treatment decisional status pre-consultation from medical records and post-consultation via clinicians. Patients self-reported quality-of-life before and one day after consultation.

RESULTS

Among all 6769 two-second silences, we observed 328 (4.8%) ECS and 240 (3.5%) ICS. ECS prevalence was associated with decisions favoring fewer disease-focused treatments (OR_adj: 2.12; 95% CI: 1.12, 4.06). Earlier conversational ECS was associated with improved quality-of-life (p = 0.01). ICS prevalence was associated with clinicians' prognosis expectations.

CONCLUSIONS

Connectional Silences during specialist serious illness conversations are associated with decision-making and improved patient quality-of-life. Further work is necessary to evaluate potential causal relationships.

PRACTICE IMPLICATIONS

Pauses offer important opportunities to advance the science of human connection in serious illness decision-making.

Modeling the sensitivity of cyanobacteria blooms to plausible changes in precipitation and air temperature variability

Many recent studies have attributed the observed variability of cyanobacteria blooms to meteorological drivers and have projected blooms with worsening societal and ecological impacts under future climate scenarios. Nonetheless, few studies have jointly examined their sensitivity to projected changes in both precipitation and temperature variability. Using an Integrated Assessment Model (IAM) of Lake Champlain's eutrophic Missisquoi Bay, we demonstrate a factorial design approach for evaluating the sensitivity of concentrations of chlorophyll a (chl-a), a cyanobacteria surrogate, to global climate model-informed changes in the central tendency and variability of daily precipitation and air temperature. An Analysis of Variance (ANOVA) and multivariate contour plots highlight synergistic effects of these climatic changes on exceedances of the World Health Organization's moderate 50 μg/L concentration threshold for recreational contact. Although increased precipitation produces greater riverine total phosphorus loads, warmer and drier scenarios produce the most severe blooms due to the greater mobilization and cyanobacteria uptake of legacy phosphorus under these conditions. Increases in daily precipitation variability aggravate blooms most under warmer and wetter scenarios. Greater temperature variability raises exceedances under current air temperatures but reduces them under more severe warming when water temperatures exceed optimal values for cyanobacteria growth more often. Our experiments, controlled for wind-induced changes to lake water quality, signal the importance of larger summer runoff events for curtailing bloom growth through reductions of water temperature, sunlight penetration and stratification. Finally, the importance of sequences of wet and dry periods in generating cyanobacteria blooms motivates future research on bloom responses to changes in interannual climate persistence.

Conversational stories & self organizing maps: Innovations for the scalable study of uncertainty in healthcare communication

BACKGROUND

Understanding uncertainty in participatory decision-making requires scientific attention to interaction between what actually happens when patients, families and clinicians engage one another in conversation and the multi-level contexts in which these occur. Achieving this understanding will require conceptually grounded and scalable methods for use in large samples of people representing diversity in cultures, speaking and decision-making norms, and clinical situations.

DISCUSSION

Here, we focus on serious illness and describe Conversational Stories as a scalable and conceptually grounded framework for characterizing uncertainty expression in these clinical contexts. Using actual conversations from a large direct-observation cohort study, we demonstrate how natural language processing and unsupervised machine learning methods can reveal underlying types of uncertainty stories in serious illness conversations.

CONCLUSIONS

Conversational Storytelling offers a meaningful analytic framework for scalable computational methods to study uncertainty in healthcare conversations.

The Power Law in Operating Room Management

The Acute Care Surgery model has been implemented by many hospitals in the United States. As complex adaptive systems, healthcare systems are composed of many interacting elements that respond to intrinsic and extrinsic inputs. Systems level analysis may reveal the underlying organizational structure of tactical block allocations like the Acute Care Surgery model. The purpose of this study is to demonstrate one method to identify a key characteristic of complex adaptive systems in the perioperative services. Start and end times for all surgeries performed at the University of Vermont Medical Center OR1 were extracted for two years prior to the transition to an Acute Care Surgery service and two years following the transition. Histograms were plotted for the inter-event times calculated from the difference between surgical cases. A power law distribution was fit to the post-transition histogram. The Kolmogorov-Smirnov test for goodness-of-fit at 95% level of significance shows the histogram plotted from post-transition inter-event times follows a power law distribution (K-S = 0.088, p = 0.068), indicating a Complex Adaptive System. Our analysis demonstrates that the strategic decision to create an Acute Care Surgery service has direct implications on tactical and operational processes in the perioperative services. Elements of complex adaptive systems can be represented by a power law distributions and similar methods may be applied to identify other processes that operate as complex adaptive systems in perioperative care. To make sustained improvements in the perioperative services, focus on manufacturing-based interventions such as Lean Six Sigma should instead be shifted towards the complex interventions that modify system-specific behaviors described by complex adaptive system principles when power law relationships are present.

Quantifying the social benefits and costs of reducing phosphorus pollution under climate change

Excess phosphorus loading to waterbodies has led to increasing frequency and severity of harmful algal blooms, negatively impacting economic activity and human health. While interventions to improve water quality can create large societal benefits, these investments are costly and the value of benefits is often unknown. Understanding the social and economic impacts of reduced phosphorus loading is critical for developing effective land use policies and for generating public and political support for these initiatives. Here, we quantify the social benefits and costs of improving water quality in Lake Champlain under a range of phosphorus reduction and climate change scenarios between 2016 and 2050. We use statistical models to link water quality outputs from an established integrated assessment model with three categories of benefits: tourism expenditures, property sales, and avoided human health impacts. We estimate the costs of reducing phosphorus loading using data reported by the State of Vermont. We find that under the most aggressive phosphorus reduction scenario, the total benefits of improved water quality are $55 to $60 million between 2016 and 2050. Over this 35 year time horizon, the combined benefits do not outweigh the costs under any scenario. If the time horizon is extended to 2100 or beyond, however, the benefits may exceed the costs if the applied discount rate is less than 3%. Importantly, we almost certainly underestimate the value of clean water, due to the omission of other types of benefits. Despite this uncertainty, our study provides a tractable framework for disentangling the complex relationships between water quality and human well-being, and illuminates the value of reductions in phosphorus loading to society.

A general model of conversational dynamics and an example application in serious illness communication

Conversation has been a primary means for the exchange of information since ancient times. Understanding patterns of information flow in conversations is a critical step in assessing and improving communication quality. In this paper, we describe COnversational DYnamics Model (CODYM) analysis, a novel approach for studying patterns of information flow in conversations. CODYMs are Markov Models that capture sequential dependencies in the lengths of speaker turns. The proposed method is automated and scalable, and preserves the privacy of the conversational participants. The primary function of CODYM analysis is to quantify and visualize patterns of information flow, concisely summarized over sequential turns from one or more conversations. Our approach is general and complements existing methods, providing a new tool for use in the analysis of any type of conversation. As an important first application, we demonstrate the model on transcribed conversations between palliative care clinicians and seriously ill patients. These conversations are dynamic and complex, taking place amidst heavy emotions, and include difficult topics such as end-of-life preferences and patient values. We use CODYMs to identify normative patterns of information flow in serious illness conversations, show how these normative patterns change over the course of the conversations, and show how they differ in conversations where the patient does or doesn’t audibly express anger or fear. Potential applications of CODYMs range from assessment and training of effective healthcare communication to comparing conversational dynamics across languages, cultures, and contexts with the prospect of identifying universal similarities and unique “fingerprints” of information flow.

Epidemiology of Fear, Sadness, and Anger Expression in Palliative Care Conversations

CONTEXT

Advancing the science of serious illness communication requires methods for measuring characteristics of conversations in large studies. Understanding which characteristics predict clinically important outcomes can help prioritize attention to scalable measure development.

OBJECTIVES

To understand whether audibly recognizable expressions of distressing emotion during palliative care serious illness conversations are associated with ratings of patient experience or six-month enrollment in hospice.

METHODS

We audiorecorded initial palliative care consultations involving 231 hospitalized people with advanced cancer at two large academic medical centers. We coded conversations for expressions of fear, anger, and sadness. We examined the distribution of these expressions and their association with pre/post ratings of feeling heard and understood and six-month hospice enrollment after the consultation.

RESULTS

Nearly six in 10 conversations included at least one audible expression of distressing emotion (59%; 137 of 231). Among conversations with such an expression, fear was the most prevalent (72%; 98 of 137) followed by sadness (50%; 69 of 137) and anger (45%; 62 of 137). Anger expression was associated with more disease-focused end-of-life treatment preferences, pre/post consultation improvement in feeling heard and understood and lower six-month hospice enrollment. Fear was strongly associated with preconsultation patient ratings of shorter survival expectations. Sadness did not exhibit strong association with patient descriptors or outcomes.

CONCLUSION

Fear, anger, and sadness are commonly expressed in hospital-based palliative care consultations with people who have advanced cancer. Anger is an epidemiologically useful predictor of important clinical outcomes.

Social-psychological determinants of farmer intention to adopt nutrient best management practices: Implications for resilient adaptation to climate change

Successful adaptation to global climate change and enhancement of agricultural watersheds' resilience requires widespread use of Nutrient Best Management Practices (NBMPs) by farms of all sizes. In the US, adoption of many NBMP practices is voluntary and insufficient to achieve local and downstream conservation objectives. Despite evidence that both social-psychological factors and socio-economic factors influence farmer decision-making, very few studies of farmers' decision-making related to NBMP adoption combine these two factor groups in a theoretically rigorous way. To better understand farmers' management decisions, we test the social-psychological Theory of Planned Behavior (TPB) to determine the relative influence of attitudes, perceived social norms, and perceived behavioral control on adoption of nine NBMPs. A survey was designed by the research team and implemented by the U.S. Department of Agriculture-National Agricultural Statistics Service (USDA-NASS) in 2013, and replicated in 2016, on a stratified sample of 129 farmers (including panel data on 56 farmers). Farmers were located in the Missisquoi, and Lamoille River watersheds of the Lake Champlain Basin in the Northeast region of the United States. Survey responses revealed variation in past adoption of NBMPs was sensitive to practice type and farm size. We developed nine weighted structural equation models to test endogenous (social-psychological) and exogenous (policy, economic and demographic) predictors of farmer intention to adopt NBMPs. We found that perceived behavioral control had the largest effect size and strongest statistical significance on the farmers' expressed intentions to adopt NBMPs in the future. For a subset of NBMPs, perceived social norms and farmer attitudes toward these NBMPs were each also significant drivers of intention to adopt individual practices. Among the exogenous variables, we found that large farm size, college education, and having a conservation easement all had a positive influence on farmers' intention to adopt NBMPs. This study suggests that for widespread adoption of NBMPs, environmental managers, policy makers, and program developers should be attentive to farmers' perceived behavioral control, and support the design and execution of outreach and technical assistance programs that build on drivers of farmers' decision making.

Novel Evolutionary Algorithm Identifies Interactions Driving Infestation of Triatoma dimidiata, a Chagas Disease Vector

Chagas disease is a lethal, neglected tropical disease. Unfortunately, aggressive insecticide-spraying campaigns have not been able to eliminate domestic infestation of Triatoma dimidiata, the native vector in Guatemala. To target interventions toward houses most at risk of infestation, comprehensive socioeconomic and entomologic surveys were conducted in two towns in Jutiapa, Guatemala. Given the exhaustively large search space associated with combinations of risk factors, traditional statistics are limited in their ability to discover risk factor interactions. Two recently developed statistical evolutionary algorithms, specifically designed to accommodate risk factor interactions and heterogeneity, were applied to this large combinatorial search space and used in tandem to identify sets of risk factor combinations associated with infestation. The optimal model includes 10 risk factors in what is known as a third-order disjunctive normal form (i.e., infested households have chicken coops AND deteriorated bedroom walls OR an accumulation of objects AND dirt floors AND total number of occupants ≥ 5 AND years of electricity ≥ 5 OR poor hygienic condition ratings AND adobe walls AND deteriorated walls AND dogs). Houses with dirt floors and deteriorated walls have been reported previously as risk factors and align well with factors currently targeted by Ecohealth interventions to minimize infestation. However, the tandem evolutionary algorithms also identified two new socioeconomic risk factors (i.e., households having many occupants and years of electricity ≥ 5). Identifying key risk factors may help with the development of new Ecohealth interventions and/or reduce the survey time needed to identify houses most at risk.

Story Arcs in Serious Illness: Natural Language Processing features of Palliative Care Conversations

OBJECTIVE

Serious illness conversations are complex clinical narratives that remain poorly understood. Natural Language Processing (NLP) offers new approaches for identifying hidden patterns within the lexicon of stories that may reveal insights about the taxonomy of serious illness conversations.

METHODS

We analyzed verbatim transcripts from 354 consultations involving 231 patients and 45 palliative care clinicians from the Palliative Care Communication Research Initiative. We stratified each conversation into deciles of "narrative time" based on word counts. We used standard NLP analyses to examine the frequency and distribution of words and phrases indicating temporal reference, illness terminology, sentiment and modal verbs (indicating possibility/desirability).

RESULTS

Temporal references shifted steadily from talking about the past to talking about the future over deciles of narrative time. Conversations progressed incrementally from "sadder" to "happier" lexicon; reduction in illness terminology accounted substantially for this pattern. We observed the following sequence in peak frequency over narrative time: symptom terms, treatment terms, prognosis terms and modal verbs indicating possibility.

CONCLUSIONS

NLP methods can identify narrative arcs in serious illness conversations.

PRACTICE IMPLICATIONS

Fully automating NLP methods will allow for efficient, large scale and real time measurement of serious illness conversations for research, education and system re-design.

Simulating hydraulic interdependence between bridges along a river corridor under transient flood conditions

Structural alterations to bridges may result in unintended consequences up- or downstream, such as changes in discharge, velocity, stream power, and water levels. This work presents a framework and methodology to analyze interconnected bridge-stream interactions under flood conditions. Such analysis may help prioritize limited resources available for bridge and river rehabilitations, facilitate holistic design of bridges and better-informed cost/benefit analyses, and address stakeholder concerns raised in response to planned bridge and infrastructure alterations. A two-dimensional unsteady HEC-RAS hydraulic model of the Otter Creek between Rutland and Middlebury, VT is used to simulate the impact of individual structures on the bridge-stream network, as well as potential sensitivity to those impacts, during extreme flood events. The presence of a bridge and approach roadway may induce measurable changes in peak discharge throughout the entire 46 miles of modeled river. These may be by as much as 10% at adjacent structures, down to 1% at structures as far as six miles upstream and nine miles downstream. Depending on their characteristics, bridges and roadways may either attenuate or amplify peak flood flows up- and downstream, or have little to no effect at all, suggesting that there is no easily predictable impact, and that hydraulic modeling is necessary for such analysis on rivers. Alterations to structures that develop substantial backwaters may result in the most dramatic impacts to the network, which can be both positive and negative. Affected bridges may or may not be sensitive to these changes in discharge; structures that do not experience relief (e.g., roadway overtopping) may be most sensitive to any distant perturbations.

Applying Performance Frontiers in Operating Room Management: A Tutorial Using Data From an Academic Medical Center

Although the primary goal of operating room (OR) management is to minimize inefficiencies, it may be difficult for OR managers to track metrics when one extrapolates possible scenarios across every OR on a daily basis. With the ability to visualize the statistical relationships to help simplify the analysis of large datasets, a more elaborate efficiency framework can be established using Pareto optimality (or performance frontiers), a multicriteria framework that includes variables that serve as proxies for a variety of outcomes. Applied to OR management, performance frontiers allow for the evaluation of common and well-understood issues of under- and over-utilized time.

A Tandem Evolutionary Algorithm for Identifying Causal Rules from Complex Data

We propose a new evolutionary approach for discovering causal rules in complex classification problems from batch data. Key aspects include (a) the use of a hypergeometric probability mass function as a principled statistic for assessing fitness that quantifies the probability that the observed association between a given clause and target class is due to chance, taking into account the size of the dataset, the amount of missing data, and the distribution of outcome categories, (b) tandem age-layered evolutionary algorithms for evolving parsimonious archives of conjunctive clauses, and disjunctions of these conjunctions, each of which have probabilistically significant associations with outcome classes, and (c) separate archive bins for clauses of different orders, with dynamically adjusted order-specific thresholds. The method is validated on majority-on and multiplexer benchmark problems exhibiting various combinations of heterogeneity, epistasis, overlap, noise in class associations, missing data, extraneous features, and imbalanced classes. We also validate on a more realistic synthetic genome dataset with heterogeneity, epistasis, extraneous features, and noise. In all synthetic epistatic benchmarks, we consistently recover the true causal rule sets used to generate the data. Finally, we discuss an application to a complex real-world survey dataset designed to inform possible ecohealth interventions for Chagas disease.

Identifying Connectional Silence in Palliative Care Consultations: A Tandem Machine-Learning and Human Coding Method

Background: Systematic measurement of conversational features in the natural clinical setting is essential to better understand, disseminate, and incentivize high quality serious illness communication. Advances in machine-learning (ML) classification of human speech offer exceptional opportunity to complement human coding (HC) methods for measurement in large scale studies. Objectives: To test the reliability, efficiency, and sensitivity of a tandem ML-HC method for identifying one feature of clinical importance in serious illness conversations: Connectional Silence. Design: This was a cross-sectional analysis of 354 audio-recorded inpatient palliative care consultations from the Palliative Care Communication Research Initiative multisite cohort study. Setting/Subjects: Hospitalized people with advanced cancer. Measurements: We created 1000 brief audio "clips" of randomly selected moments predicted by a screening ML algorithm to be two-second or longer pauses in conversation. Each clip included 10 seconds of speaking before and 5 seconds after each pause. Two HCs independently evaluated each clip for Connectional Silence as operationalized from conceptual taxonomies of silence in serious illness conversations. HCs also evaluated 100 minutes from 10 additional conversations having unique speakers to identify how frequently the ML screening algorithm missed episodes of Connectional Silence. Results: Connectional Silences were rare (5.5%) among all two-second or longer pauses in palliative care conversations. Tandem ML-HC demonstrated strong reliability (kappa 0.62; 95% confidence interval: 0.47-0.76). HC alone required 61% more time than the Tandem ML-HC method. No Connectional Silences were missed by the ML screening algorithm. Conclusions: Tandem ML-HC methods are reliable, efficient, and sensitive for identifying Connectional Silence in serious illness conversations.

Uncovering vector, parasite, blood meal and microbiome patterns from mixed-DNA specimens of the Chagas disease vector Triatoma dimidiata

Chagas disease, considered a neglected disease by the World Health Organization, is caused by the protozoan parasite Trypanosoma cruzi, and transmitted by >140 triatomine species across the Americas. In Central America, the main vector is Triatoma dimidiata, an opportunistic blood meal feeder inhabiting both domestic and sylvatic ecotopes. Given the diversity of interacting biological agents involved in the epidemiology of Chagas disease, having simultaneous information on the dynamics of the parasite, vector, the gut microbiome of the vector, and the blood meal source would facilitate identifying key biotic factors associated with the risk of T. cruzi transmission. In this study, we developed a RADseq-based analysis pipeline to study mixed-species DNA extracted from T. dimidiata abdomens. To evaluate the efficacy of the method across spatial scales, we used a nested spatial sampling design that spanned from individual villages within Guatemala to major biogeographic regions of Central America. Information from each biotic source was distinguished with bioinformatics tools and used to evaluate the prevalence of T. cruzi infection and predominant Discrete Typing Units (DTUs) in the region, the population genetic structure of T. dimidiata, gut microbial diversity, and the blood meal history. An average of 3.25 million reads per specimen were obtained, with approximately 1% assigned to the parasite, 20% to the vector, 11% to bacteria, and 4% to putative blood meals. Using a total of 6,405 T. cruzi SNPs, we detected nine infected vectors harboring two distinct DTUs: TcI and a second unidentified strain, possibly TcIV. Vector specimens were sufficiently variable for population genomic analyses, with a total of 25,710 T. dimidiata SNPs across all samples that were sufficient to detect geographic genetic structure at both local and regional scales. We observed a diverse microbiotic community, with significantly higher bacterial species richness in infected T. dimidiata abdomens than those that were not infected. Unifrac analysis suggests a common assemblage of bacteria associated with infection, which co-occurs with the typical gut microbial community derived from the local environment. We identified vertebrate blood meals from five T. dimidiata abdomens, including chicken, dog, duck and human; however, additional detection methods would be necessary to confidently identify blood meal sources from most specimens. Overall, our study shows this method is effective for simultaneously generating genetic data on vectors and their associated parasites, along with ecological information on feeding patterns and microbial interactions that may be followed up with complementary approaches such as PCR-based parasite detection, 18S eukaryotic and 16S bacterial barcoding.

Automated Detection of Conversational Pauses from Audio Recordings of Serious Illness Conversations in Natural Hospital Settings

OBJECTIVE

Automating conversation analysis in the natural clinical setting is essential to scale serious illness communication research to samples that are large enough for traditional epidemiological studies. Our objective is to automate the identification of pauses in conversations because these are important linguistic targets for evaluating dynamics of speaker involvement and turn-taking, listening and human connection, or distraction and disengagement.

DESIGN

We used 354 audio recordings of serious illness conversations from the multisite Palliative Care Communication Research Initiative cohort study.

SETTING/SUBJECTS

Hospitalized people with advanced cancer seen by the palliative care team.

MEASUREMENTS

We developed a Random Forest machine learning (ML) algorithm to detect Conversational Pauses of two seconds or longer. We triple-coded 261 minutes of audio with human coders to establish a gold standard for evaluating ML performance characteristics.

RESULTS

ML automatically identified Conversational Pauses with a sensitivity of 90.5 and a specificity of 94.5.

CONCLUSIONS

ML is a valid method for automatically identifying Conversational Pauses in the natural acoustic setting of inpatient serious illness conversations.

Minimal East Antarctic Ice Sheet retreat onto land during the past eight million years

The East Antarctic Ice Sheet (EAIS) is the largest potential contributor to sea-level rise. However, efforts to predict the future evolution of the EAIS are hindered by uncertainty in how it responded to past warm periods, for example, during the Pliocene epoch (5.3 to 2.6 million years ago), when atmospheric carbon dioxide concentrations were last higher than 400 parts per million. Geological evidence indicates that some marine-based portions of the EAIS and the West Antarctic Ice Sheet retreated during parts of the Pliocene^1,2, but it remains unclear whether ice grounded above sea level also experienced retreat. This uncertainty persists because global sea-level estimates for the Pliocene have large uncertainties and cannot be used to rule out substantial terrestrial ice loss ³ , and also because direct geological evidence bearing on past ice retreat on land is lacking. Here we show that land-based sectors of the EAIS that drain into the Ross Sea have been stable throughout the past eight million years. We base this conclusion on the extremely low concentrations of cosmogenic ¹⁰Be and ²⁶Al isotopes found in quartz sand extracted from a land-proximal marine sediment core. This sediment had been eroded from the continent, and its low levels of cosmogenic nuclides indicate that it experienced only minimal exposure to cosmic radiation, suggesting that the sediment source regions were covered in ice. These findings indicate that atmospheric warming during the past eight million years was insufficient to cause widespread or long-lasting meltback of the EAIS margin onto land. We suggest that variations in Antarctic ice volume in response to the range of global temperatures experienced over this period-up to 2-3 degrees Celsius above preindustrial temperatures ⁴ , corresponding to future scenarios involving carbon dioxide concentrations of between 400 and 500 parts per million-were instead driven mostly by the retreat of marine ice margins, in agreement with the latest models^5,6.

Geospatial and Temporal Analysis of Thyroid Cancer Incidence in a Rural Population

BACKGROUND

The increasing incidence of thyroid cancer has resulted in the rate tripling over the past 30 years. Reasons for this increase have not been established. Geostatistics and geographic information system (GIS) tools have emerged as powerful geospatial technologies to identify disease clusters, map patterns and trends, and assess the impact of ecological and socioeconomic factors (SES) on the spatial distribution of diseases. In this study, these tools were used to analyze thyroid cancer incidence in a rural population.

METHODS

Thyroid cancer incidence and socio-demographic factors in Vermont (VT), United States, between 1994 and 2007 were analyzed by logistic regression and geospatial and temporal analyses.

RESULTS

The thyroid cancer age-adjusted incidence in Vermont (8.0 per 100,000) was comparable to the national level (8.4 per 100,000), as were the ratio of the incidence of females to males (3.1:1) and the mortality rate (0.5 per 100,000). However, the estimated annual percentage change was higher (8.3 VT; 5.7 U.S.). Incidence among females peaked at 30-59 years of age, reflecting a significant rise from 1994 to 2007, while incidence trends for males did not vary significantly by age. For both females and males, the distribution of tumors by size did not vary over time; ≤1.0 cm, 1.1-2.0 cm, and >2.0 cm represented 38%, 22%, and 40%, respectively. In females, papillary thyroid cancer (PTC) accounted for 89% of cases, follicular (FTC) 8%, medullary (MTC) 2%, and anaplastic (ATC) 0.6%, while in males PTC accounted for 77% of cases, FTC 15%, MTC 1%, and ATC 3%. Geospatial analysis revealed locations and spatial patterns that, when combined with multivariate incidence analyses, indicated that factors other than increased surveillance and access to healthcare (physician density or insurance) contributed to the increased thyroid cancer incidence. Nine thyroid cancer incidence hot spots, areas with very high normalized incidence, were identified based on zip code data. Those locations did not correlate with urban areas or healthcare centers.

CONCLUSIONS

These data provide evidence of increased thyroid cancer incidence in a rural population likely due to environmental drivers and SES. Geospatial modeling can provide an important framework for evaluation of additional associative risk factors.

Sources of blood meals of sylvatic Triatoma guasayana near Zurima, Bolivia, assayed with qPCR and 12S cloning

Background

In this study we compared the utility of two molecular biology techniques, cloning of the mitochondrial 12S ribosomal RNA gene and hydrolysis probe-based qPCR, to identify blood meal sources of sylvatic Chagas disease insect vectors collected with live-bait mouse traps (also known as Noireau traps). Fourteen T. guasayana were collected from six georeferenced trap locations in the Andean highlands of the department of Chuquisaca, Bolivia.

Methodology/Principal Findings

We detected four blood meals sources with the cloning assay: seven samples were positive for human (Homo sapiens), five for chicken (Gallus gallus) and unicolored blackbird (Agelasticus cyanopus), and one for opossum (Monodelphis domestica). Using the qPCR assay we detected chicken (13 vectors), and human (14 vectors) blood meals as well as an additional blood meal source, Canis sp. (4 vectors).

Conclusions/Significance

We show that cloning of 12S PCR products, which avoids bias associated with developing primers based on a priori knowledge, detected blood meal sources not previously considered and that species-specific qPCR is more sensitive. All samples identified as positive for a specific blood meal source by the cloning assay were also positive by qPCR. However, not all samples positive by qPCR were positive by cloning. We show the power of combining the cloning assay with the highly sensitive hydrolysis probe-based qPCR assay provides a more complete picture of blood meal sources for insect disease vectors.

The World Health Organization (WHO) estimates that 7 to 8 million people are currently infected with Trypanosoma cruzi, the parasite that causes Chagas disease. The WHO recommends insect vector control as the primary prevention method; and insecticide spraying is the most commonly used intervention technique. Sylvatic insect vectors are a special concern because they are a source of reinfestation after insecticides have been applied to living quarters (domestic) and immediate surroundings (peridomestic). To better understand sylvatic insect vector movement, we used two molecular biology techniques to detect the blood meal sources of sylvatic insect vectors. The first technique, cloning of 12S PCR products, allows us to cast a wide net and detect blood meal sources with no previous knowledge of vertebrates or mammals in the study site. After acquiring knowledge of vertebrates in the study site (either through the aforementioned cloning technique, literature review or survey of the area), the second technique, the species-specific hydrolysis probe-based qPCR provides a highly sensitive assay for particular taxa.

First Report of the NA2 Lineage of Phytophthora ramorum from an Ornamental Rhododendron in the Interior of California

In July 2012, we collected a rhododendron var. Trilby with twig dieback symptoms in the lower canopy, consistent with the disease "ramorum blight" caused by Phytophthora ramorum. The symptomatic plant had been planted a year earlier to replace a dead rhododendron in a landscape setting in Placer County, California (Lat: 39.036216°; Long: -120.999274°), Sierra Nevada foothills at ~600 m elevation. Isolations yielded a culture with a fast growth rate and overall morphology resembling the P. ramorum NA2 lineage described by Ivors et al. (4). DNA was extracted from the culture as described previously (4) and six SSR loci: MS18, MS39, MS43, MS45, MS64, MS145, were amplified (2,4). Allelic patterns were compared with those of three testers from each of the three lineages NA1, NA2, and EU1 known to be present in ornamental plants in North America, and they unambiguously confirmed the isolate belongs to the NA2 lineage of the pathogen. Although the symptomatic plant was confined to a landscape setting, it had been planted in that location for a year, providing a possible source of inoculum for the surrounding area. This is the first report of P. ramorum from the Sierra Nevada eco-region in the interior of California. It is also the first report of a NA2 isolate from a plant outside of commercial nurseries in California. The mating type of the isolate was not determined, but NA2 isolates are normally A2, the same mating type of NA1 isolates. The only other report of a NA2 isolate found outside of a nursery is from Washington State (1). Although there is no evidence the pathogen may have infected other plants, the infected rhododendron was found at a location situated over 100 km east of the closest known infestation (www.sodmap.org). Additionally, this is the first report of the pathogen outside the coast mountain range of California. Because the three lineages are genetically and phenotypically distinct (3), the escape of NA2 or EU1 isolates, both still absent from plants in natural settings, could have significant implications for California ecosystems. This finding highlights that introductions of P. ramorum via ornamental plants are still possible, in spite of current regulations. References: (1) G. Chastagner et al. Phytopathology 101:S32, 2011. (2) P. P. Croucher et al. Biol. Invasions 15:2281, 2013. (3) N. J. Grünwald et al. Trends Microbiol. 20:131, 2012. (4) K. Ivors et al. Mol. Ecol. 15:1493, 2006.

Unraveling associations between cyanobacteria blooms and in-lake environmental conditions in Missisquoi Bay, Lake Champlain, USA, using a modified self-organizing map

Exploratory data analysis on physical, chemical, and biological data from sediments and water in Lake Champlain reveals a strong relationship between cyanobacteria, sediment anoxia, and the ratio of dissolved nitrogen to soluble reactive phosphorus. Physical, chemical, and biological parameters of lake sediment and water were measured between 2007 and 2009. Cluster analysis using a self-organizing artificial neural network, expert opinion, and discriminant analysis separated the data set into no-bloom and bloom groups. Clustering was based on similarities in water and sediment chemistry and non-cyanobacteria phytoplankton abundance. Our analysis focused on the contribution of individual parameters to discriminate between no-bloom and bloom groupings. Application to a second, more spatially diverse data set, revealed similar no-bloom and bloom discrimination, yet a few samples possess all the physicochemical characteristics of a bloom without the high cyanobacteria cell counts, suggesting that while specific environmental conditions can support a bloom, another environmental trigger may be required to initiate the bloom. Results highlight the conditions coincident with cyanobacteria blooms in Missisquoi Bay of Lake Champlain and indicate additional data are needed to identify possible ecological contributors to bloom initiation.

Exploratory Analysis in Time-Varying Data Sets: a Healthcare Network Application

We introduce a new method for exploratory analysis of large data sets with time-varying features, where the aim is to automatically discover novel relationships between features (over some time period) that are predictive of any of a number of time-varying outcomes (over some other time period). Using a genetic algorithm, we co-evolve (i) a subset of predictive features, (ii) which attribute will be predicted (iii) the time period over which to assess the predictive features, and (iv) the time period over which to assess the predicted attribute. After validating the method on 15 synthetic test problems, we used the approach for exploratory analysis of a large healthcare network data set. We discovered a strong association, with 100% sensitivity, between hospital participation in multi-institutional quality improvement collaboratives during or before 2002, and changes in the risk-adjusted rates of mortality and morbidity observed after a 1-2 year lag. The proposed approach is a potentially powerful and general tool for exploratory analysis of a wide range of time-series data sets.

Household model of Chagas disease vectors (Hemiptera: Reduviidae) considering domestic, peridomestic, and sylvatic vector populations

ABSTRACT Disease transmission is difficult to model because most vectors and hosts have different generation times. Chagas disease is such a situation, where insect vectors have 1-2 generations annually and mammalian hosts, including humans, can live for decades. The hemataphagous triatominae vectors (Hemiptera: Reduviidae) of the causative parasite Trypanosoma cruzi (Kinetoplastida: Trypanosomatidae) usually feed on sleeping hosts, making vector infestation of houses, peridomestic areas, and wild animal burrows a central factor in transmission. Because of difficulties with different generation times, we developed a model considering the dwelling as the unit of infection, changing the dynamics from an indirect to a direct transmission model. In some regions, vectors only infest houses; in others, they infest corrals; and in some regions, they also infest wild animal burrows. We examined the effect of sylvatic and peridomestic vector populations on household infestation rates. Both sylvatic and peridomestic vectors increase house infestation rates, sylvatic much more than peridomestic, as measured by the reproductive number R0. The efficacy of manipulating parameters in the model to control vector populations was examined. When R0 > 1, the number of infested houses increases. The presence of sylvatic vectors increases R0 by at least an order of magnitude. When there are no sylvatic vectors, spraying rate is the most influential parameter. Spraying rate is relatively unimportant when there are sylvatic vectors; in this case, community size, especially the ratio of houses to sylvatic burrows, is most important. The application of this modeling approach to other parasites and enhancements of the model are discussed.

Ecohealth interventions limit triatomine reinfestation following insecticide spraying in La Brea, Guatemala

In this study, we evaluate the effect of participatory Ecohealth interventions on domestic reinfestation of the Chagas disease vector Triatoma dimidiata after village-wide suppression of the vector population using a residual insecticide. The study was conducted in the rural community of La Brea, Guatemala between 2002 and 2009 where vector infestation was analyzed within a spatial data framework based on entomological and socio-economic surveys of homesteads within the village. Participatory interventions focused on community awareness and low-cost home improvements using local materials to limit areas of refuge and alternative blood meals for the vector within the home, and potential shelter for the vector outside the home. As a result, domestic infestation was maintained at ≤ 3% and peridomestic infestation at ≤ 2% for 5 years beyond the last insecticide spraying, in sharp contrast to the rapid reinfestation experienced in earlier insecticide only interventions.

Phellinus coronadensis: a new species from southern Arizona, USA

Phellinus coronadensis is characterized and described morphologically as a new species from southern Arizona, USA. This fungus was previously reported as P. torulosus based on morphological similarities of the basidiomes and type of wood decay. However, P. coronadensis is restricted to two mountain ranges in southern Arizona and found almost exclusively on living southwestern white pine (Pinus strobiformis). Phellinus torulosus is found primarily in Europe and parts of Asia and is primarily associated with hardwood hosts. Based on sequence analysis of small subunit mitochondrial ribosomal DNA (mt-SSU), we determined that P. coronadensis is in a different lineage from P. torulosus and apparently more closely related to the P. pini complex. The taxon associated with southwestern white pine, being distinct and not yet having been validly named, is proposed as a new species here.

Data-driven cluster reinforcement and visualization in sparsely-matched self-organizing maps

A self-organizing map (SOM) is a self-organized projection of high-dimensional data onto a typically 2-dimensional (2-D) feature map, wherein vector similarity is implicitly translated into topological closeness in the 2-D projection. However, when there are more neurons than input patterns, it can be challenging to interpret the results, due to diffuse cluster boundaries and limitations of current methods for displaying interneuron distances. In this brief, we introduce a new cluster reinforcement (CR) phase for sparsely-matched SOMs. The CR phase amplifies within-cluster similarity in an unsupervised, data-driven manner. Discontinuities in the resulting map correspond to between-cluster distances and are stored in a boundary (B) matrix. We describe a new hierarchical visualization of cluster boundaries displayed directly on feature maps, which requires no further clustering beyond what was implicitly accomplished during self-organization in SOM training. We use a synthetic benchmark problem and previously published microbial community profile data to demonstrate the benefits of the proposed methods.

Root Infections May Challenge Management of Invasive Phytophthora spp. in U.K. Woodlands

Because sporulation of Phytophthora ramorum and P. kernoviae on Rhododendron ponticum, an invasive plant, serves as primary inoculum for trunk infections on trees, R. ponticum clearance from pathogen-infested woodlands is pivotal to inoculum management. The efficacy of clearance for long-term disease management is unknown, in part due to lack of knowledge of pathogen persistence in roots and emerging seedlings. The main objectives of this work were to (i) investigate whether both pathogens infect R. ponticum roots, (ii) determine the potential for residual inoculum of P. kernoviae to infect R. ponticum seedlings in cleared woodlands, and (iii) assess potential for R. ponticum roots to support survival and transmission of P. kernoviae. Roots of R. ponticum were collected from both unmanaged and cleared woodlands and assessed for pathogen recovery. Both P. ramorum and P. kernoviae were recovered from asymptomatic roots of R. ponticum in unmanaged woodlands, and P. kernoviae was recovered from asymptomatic roots from seedlings in cleared woodland. Oospore production of P. kernoviae was observed in naturally infected R. ponticum foliage and in inoculated roots. Roots also supported P. kernoviae sporangia production. The results of this study suggest that post-clearance management of R. ponticum regrowth is necessary for long-term inoculum management in invaded woodlands.

Evaluating the efficiency and temporal variation of pilot-scale constructed wetlands and steel slag phosphorus removing filters for treating dairy wastewater

The performance and temporal variation of three hybrid and three integrated, saturated flow, pilot-scale constructed wetlands (CWs) were tested for treating dairy farm effluent. The three hybrid systems each consisted of two CWs in-series, with horizontal and vertical flow. Integrated systems consisted of a CW (horizontal and vertical flow) followed by a steel slag filter for removing phosphorus. Time series temporal semivariogram analyses of measured water parameters illustrated different treatment efficiencies existed over the course of one season. As a result, data were then divided into separate time period groups and CW systems were compared using ANOVA for parameter measurements within each distinct time period group. Both hybrid and integrated CWs were efficient in removing organics; however, hybrid systems had significantly higher performance (p<0.05) during peak vegetation growth. Compared to hybrid CWs, integrated CWs achieved significantly higher DRP reduction (p<0.05) throughout the period of investigation and higher ammonia reduction (p<0.05) in integrated CWs was observed in late summer. Geochemical modeling demonstrates hydroxyapatite and vivianite minerals forming on steel slag likely control the fate of phosphate ions given the reducing conditions prevalent in the system. The model also demonstrates how the wastewater:slag ratio can be adjusted to maximize phosphorus removal while staying at a near-neutral pH.

Phytophthora ramorum does not cause physiologically significant systemic injury to California bay laurel, its primary reservoir host

California bay laurel trees (Umbellularia californica) play a crucial role in the reproduction and survival of Phytophthora ramorum in coastal California forests by supporting sporulation during the rainy season and by providing a means for the pathogen to survive the dry, Mediterranean summer. While bay laurel is thus critical to the epidemiology of sudden oak death and other P. ramorum diseases in California, the relatively minor symptoms observed on this reservoir host suggest that it may not sustain ecologically significant injury itself. The long-term role that P. ramorum will play in California forests will depend in part on the extent to which this pathogen decreases the ecological fitness of bay laurel. Despite the importance of this question, no study has yet investigated in detail the physiological impact that ramorum blight imposes on bay laurel. This experimental study quantifies the impact that P. ramorum has on artificially inoculated bay laurel seedlings with measurements that integrate the full injury that infection with an oomycete may cause: photosynthetic efficiency, total photosynthetic area, and growth. Leaf area and leaf mass were not impacted significantly by infection of P. ramorum. Photosynthetic efficiency was mildly depressed in symptomatic, but not asymptomatic leaves, despite unnaturally high levels of necrosis that were imposed on the seedlings. These results demonstrate that bay laurel trees suffer only minor injury from ramorum blight beyond visible necrotic symptoms. Consequently, it is highly likely that bay laurel will continue to be widely available as a host for P. ramorum in California forests, which has long-term implications for the composition of these forests.

Survival, dispersal, and potential soil-mediated suppression of Phytophthora ramorum in a California redwood-tanoak forest

Because the role of soil inoculum of Phytophthora ramorum in the sudden oak death disease cycle is not well understood, this work addresses survival, chlamydospore production, pathogen suppression, and splash dispersal of the pathogen in infested forest soils. Colonized rhododendron and bay laurel leaf disks were placed in mesh sachets before transfer to the field in January 2005 and 2006. Sachets were placed under tanoak, bay laurel, and redwood at three vertical locations: leaf litter surface, litter-soil interface, and below the soil surface. Sachets were retrieved after 4, 8, 20, and 49 weeks. Pathogen survival was higher in rhododendron leaf tissue than in bay tissue, with >80% survival observed in rhododendron tissue after 49 weeks in the field. Chlamydospore production was determined by clearing infected tissue in KOH. Moist redwood-associated soils suppressed chlamydospore production. Rain events splashed inoculum as high as 30 cm from the soil surface, inciting aerial infection of bay laurel and tanoak. Leaf litter may provide an incomplete barrier to splash dispersal. This 2-year study illustrates annual P. ramorum survival in soil and the suppressive nature of redwood-associated soils to chlamydospore production. Infested soil may serve as primary inoculum for foliar infections by splash dispersal during rain events.

Sources of inoculum for Phytophthora ramorum in a redwood forest

ABSTRACT Sources of inoculum were investigated for dominant hosts of Phytophthora ramorum in a redwood forest. Infected trunks, twigs, and/or leaves of bay laurel (Umbellularia californica), tanoak (Lithocarpus densiflorus), and redwood (Sequoia sempervirens) were tested in the laboratory for sporangia production. Sporangia occurred on all plant tissues with the highest percentage on bay laurel leaves and tanoak twigs. To further compare these two species, field measurements of inoculum production and infection were conducted during the rainy seasons of 2003-04 and 2004-05. Inoculum levels in throughfall rainwater and from individual infections were significantly higher for bay laurel as opposed to tanoak for both seasons. Both measurements of inoculum production from bay laurel were significantly greater during 2004-05 when rainfall extended longer into the spring, while inoculum quantities for tanoak were not significantly different between the 2 years. Tanoak twigs were more likely to be infected than bay laurel leaves in 2003-04, and equally likely to be infected in 2004-05. These results indicate that the majority of P. ramorum inoculum in redwood forest is produced from infections on bay laurel leaves. Years with extended rains pose an elevated risk for tanoak because inoculum levels are higher and infectious periods continue into late spring.

Detection, Distribution, Sporulation, and Survival of Phytophthora ramorum in a California Redwood-Tanoak Forest Soil

ABSTRACT Recovery of Phytophthora ramorum from soils throughout sudden oak death-affected regions of California illustrates that soil may serve as an inoculum reservoir, but the role of soil inoculum in the disease cycle is unknown. This study addresses the efficacy of soil baiting, seasonal pathogen distribution under several epidemiologically important host species, summer survival and chlamydospore production in soil, and the impact of soil drying on pathogen survival. The efficacy of rhododendron leaves and pears as baits for detection of soilborne propagules were compared. Natural inoculum associated with bay laurel (Umbellularia californica), tanoak (Lithocarpus densiflorus), and redwood (Sequoia sempervirens) were determined by monthly baiting. Summer survival and chlamydospore production were assessed in infected rhododendron leaf disks incubated under bay laurel, tanoak, and redwood at either the surface, the litter/soil interface, or in soil. Rhododendron leaf baits were superior to pear baits for sporangia detection, but neither bait detected chlamydospores. Most inoculum was associated with bay laurel and recovery was higher in soil than litter. Soil-incubated inoculum exhibited over 60% survival at the end of summer and also supported elevated chlamydospore production. P. ramorum survives and produces chlamydospores in forest soils over summer, providing a possible inoculum reservoir at the onset of the fall disease cycle.

Dynamic morphometric characterization of local connective tissue network structure in humans using ultrasound

Background

In humans, connective tissue forms a complex, interconnected network throughout the body that may have mechanosensory, regulatory and signaling functions. Understanding these potentially important phenomena requires non-invasive measurements of collagen network structure that can be performed in live animals or humans. The goal of this study was to show that ultrasound can be used to quantify dynamic changes in local connective tissue structure in vivo. We first performed combined ultrasound and histology examinations of the same tissue in two subjects undergoing surgery: in one subject, we examined the relationship of ultrasound to histological images in three dimensions; in the other, we examined the effect of a localized tissue perturbation using a previously developed robotic acupuncture needling technique. In ten additional non-surgical subjects, we quantified changes in tissue spatial organization over time during needle rotation vs. no rotation using ultrasound and semi-variogram analyses.

Results

3-D renditions of ultrasound images showed longitudinal echogenic sheets that matched with collagenous sheets seen in histological preparations. Rank correlations between serial 2-D ultrasound and corresponding histology images resulted in high positive correlations for semi-variogram ranges computed parallel (r = 0.79, p < 0.001) and perpendicular (r = 0.63, p < 0.001) to the surface of the skin, indicating concordance in spatial structure between the two data sets. Needle rotation caused tissue displacement in the area surrounding the needle that was mapped spatially with ultrasound elastography and corresponded to collagen bundles winding around the needle on histological sections. In semi-variograms computed for each ultrasound frame, there was a greater change in the area under the semi-variogram curve across successive frames during needle rotation compared with no rotation. The direction of this change was heterogeneous across subjects. The frame-to-frame variability was 10-fold (p < 0.001) greater with rotation than with no rotation indicating changes in tissue structure during rotation.

Conclusion

The combination of ultrasound and semi-variogram analyses allows quantitative assessment of dynamic changes in the structure of human connective tissue in vivo.

A multivariate statistical approach to spatial representation of groundwater contamination using hydrochemistry and microbial community profiles

Managers of landfill sites are faced with enormous challenges when attempting to detect and delineate leachate plumes with a limited number of monitoring wells, assess spatial and temporal trends for hundreds of contaminants, and design long-term monitoring (LTM) strategies. Subsurface microbial ecology is a unique source of data that has been historically underutilized in LTM groundwater designs. This paper provides a methodology for utilizing qualitative and quantitative information (specifically, multiple water quality measurements and genome-based data) from a landfill leachate contaminated aquifer in Banisveld, The Netherlands, to improve the estimation of parameters of concern. We used a principal component analysis (PCA) to reduce nonindependent hydrochemistry data, Bacteria and Archaea community profiles from 16S rDNA denaturing gradient gel electrophoresis (DGGE), into six statistically independent variables, representing the majority of the original dataset variances. The PCA scores grouped samples based on the degree or class of contamination and were similar over considerable horizontal distances. Incorporation of the principal component scores with traditional subsurface information using cokriging improved the understanding of the contaminated area by reducing error variances and increasing detection efficiency. Combining these multiple types of data (e.g., genome-based information, hydrochemistry, borings) may be extremely useful at landfill or other LTM sites for designing cost-effective strategies to detect and monitor contaminants.

Phylogenetic divergence in a local population of the ectomycorrhizal fungus Cenococcum geophilum

Cenococcum geophilum is a widely distributed mycorrhizal species associated with diverse gymnosperm and angiosperm hosts. In previous studies, a significant amount of genetic and genotypic diversity has been detected in this species, despite the fact that C. geophilum is not thought to reproduce by meiotic or mitotic spores. We conducted a phylogenetic analysis of 103 C. geophilum isolates from a California oak woodland and seven non-California isolates using a glyceraldehyde 3-phosphate dehydrogenase gene. In addition, a subset of isolates was analyzed using sequences from ITS-rDNA, a Group I intron located in the 3' end of the SSU-rDNA and a portion of the mitochondrial SSU-rDNA. Phylogenetically distinct lineages, or cryptic species, of C. geophilum were detected at the scale of a single soil sample within our field site. As much genetic diversity was found within a soil sample as was found for isolates collected across the USA. Our results help explain the large amount of physiological, phenotypic, and genetic differences reported among isolates of C. geophilum from similar as well as diverse geographic regions. The ecological role that these sympatric cryptic species play remains to be determined.