Drug screening in human physiologic medium identifies uric acid as an inhibitor of rigosertib efficacy.

The nonphysiological nutrient levels found in traditional culture media have been shown to affect numerous aspects of cancer cell physiology, including how cells respond to certain therapeutic agents. Here, we comprehensively evaluated how physiological nutrient levels affect therapeutic response by performing drug screening in human plasma-like medium. We observed dramatic nutrient-dependent changes in sensitivity to a variety of FDA-approved and clinically trialed compounds, including rigosertib, an experimental cancer therapeutic that recently failed in phase III clinical trials. Mechanistically, we found that the ability of rigosertib to destabilize microtubules is strongly inhibited by the purine metabolism end product uric acid, which is uniquely abundant in humans relative to traditional in vitro and in vivo cancer models. These results demonstrate the broad and dramatic effects nutrient levels can have on drug response and how incorporation of human-specific physiological nutrient medium might help identify compounds whose efficacy could be influenced in humans.

A cytological F1 RNAi screen for defects in Drosophila melanogaster female meiosis.

Genetic screens for recessive alleles induce mutations, make the mutated chromosomes homozygous, and then assay those homozygotes for the phenotype of interest. When screening for genes required for female meiosis, the phenotype of interest has typically been nondisjunction from chromosome segregation errors. As this requires that mutant females be viable and fertile, any mutants that are lethal or sterile when homozygous cannot be recovered by this approach. To overcome these limitations, we have screened the VALIUM22 collection of RNAi constructs that target germline-expressing genes in a vector optimized for germline expression by driving RNAi with GAL4 under control of a germline-specific promoter (nanos or mat-alpha4). This allowed us to test genes that would be lethal if knocked down in all cells, and by examining unfertilized metaphase-arrested mature oocytes, we could identify defects in sterile females. After screening >1,450 lines of the collection for two different defects (chromosome congression and the hypoxic sequestration of Mps1-GFP to ooplasmic filaments), we obtained multiple hits for both phenotypes, identified novel meiotic phenotypes for genes that had been previously characterized in other processes, and identified the first phenotypes to be associated with several previously uncharacterized genes.

A Cytological F1 RNAi Screen for Defects in Drosophila melanogaster Female Meiosis.

Genetic screens for recessive alleles induce mutations, make the mutated chromosomes homozygous, and then assay those homozygotes for the phenotype of interest. When screening for genes required for female meiosis, the phenotype of interest has typically been nondisjunction from chromosome segregation errors. As this requires that mutant females be viable and fertile, any mutants that are lethal or sterile when homozygous cannot be recovered by this approach. To overcome these limitations, our lab has screened the VALIUM22 collection produced by the Harvard TRiP Project, which contains RNAi constructs targeting genes known to be expressed in the germline in a vector optimized for germline expression. By driving RNAi with GAL4 under control of a germline-specific promoter (nanos or mat-alpha4), we can test genes that would be lethal if knocked down in all cells, and by examining unfertilized metaphase-arrested mature oocytes, we can identify defects associated with genes whose knockdown results in sterility or causes other errors besides nondisjunction. We screened this collection to identify genes that disrupt either of two phenotypes when knocked down: the ability of meiotic chromosomes to congress to a single mass at the end of prometaphase, and the sequestration of Mps1-GFP to ooplasmic filaments in response to hypoxia. After screening >1450 lines of the collection, we obtained multiple hits for both phenotypes, identified novel meiotic phenotypes for genes that had been previously characterized in other processes, and identified the first phenotypes to be associated with several previously uncharacterized genes.

ASCT2 is the primary serine transporter in cancer cells.

The non-essential amino acid serine is a critical nutrient for cancer cells due to its diverse biosynthetic functions. While some tumors can synthesize serine de novo, others are auxotrophic for serine and therefore reliant on the uptake of exogenous serine. Importantly, however, the transporter(s) that mediate serine uptake in cancer cells are not known. Here, we characterize the amino acid transporter ASCT2 (coded for by the gene SLC1A5) as the primary serine transporter in cancer cells. ASCT2 is well-known as a glutamine transporter in cancer, and our work demonstrates that serine and glutamine compete for uptake through ASCT2. We further show that ASCT2-mediated serine uptake is essential for purine nucleotide biosynthesis and that ERα promotes serine uptake by directly activating SLC1A5 transcription. Together, our work defines an additional important role for ASCT2 as a serine transporter in cancer and evaluates ASCT2 as a potential therapeutic target in serine metabolism.

Drug screening in human physiologic medium identifies uric acid as an inhibitor of rigosertib efficacy.

The non-physiological nutrient levels found in traditional culture media have been shown to affect numerous aspects of cancer cell physiology, including how cells respond to certain therapeutic agents. Here, we comprehensively evaluated how physiological nutrient levels impact therapeutic response by performing drug screening in human plasma-like medium (HPLM). We observed dramatic nutrient-dependent changes in sensitivity to a variety of FDA-approved and clinically trialed compounds, including rigosertib, an experimental cancer therapeutic that has recently failed in phase 3 clinical trials. Mechanistically, we found that the ability of rigosertib to destabilize microtubules is strongly inhibited by the purine metabolism waste product uric acid, which is uniquely abundant in humans relative to traditional in vitro and in vivo cancer models. Structural modelling studies suggest that uric acid interacts with the tubulin-rigosertib complex and may act as an uncompetitive inhibitor of rigosertib. These results offer a possible explanation for the failure of rigosertib in clinical trials and demonstrate the utility of physiological media to achieve in vitro results that better represent human therapeutic responses.

Observed and projected functional reorganization of riverine fish assemblages from global change.

Climate and land-use/land-cover change ("global change") are restructuring biodiversity, globally. Broadly, environmental conditions are expected to become warmer, potentially drier (particularly in arid regions), and more anthropogenically developed in the future, with spatiotemporally complex effects on ecological communities. We used functional traits to inform Chesapeake Bay Watershed fish responses to future climate and land-use scenarios (2030, 2060, and 2090). We modeled the future habitat suitability of focal species representative of key trait axes (substrate, flow, temperature, reproduction, and trophic) and used functional and phylogenetic metrics to assess variable assemblage responses across physiographic regions and habitat sizes (headwaters through large rivers). Our focal species analysis projected future habitat suitability gains for carnivorous species with preferences for warm water, pool habitats, and fine or vegetated substrates. At the assemblage level, models projected decreasing habitat suitability for cold-water, rheophilic, and lithophilic individuals but increasing suitability for carnivores in the future across all regions. Projected responses of functional and phylogenetic diversity and redundancy differed among regions. Lowland regions were projected to become less functionally and phylogenetically diverse and more redundant while upland regions (and smaller habitat sizes) were projected to become more diverse and less redundant. Next, we assessed how these model-projected assemblage changes 2005-2030 related to observed time-series trends (1999-2016). Halfway through the initial projecting period (2005-2030), we found observed trends broadly followed modeled patterns of increasing proportions of carnivorous and lithophilic individuals in lowland regions but showed opposing patterns for functional and phylogenetic metrics. Leveraging observed and predicted analyses simultaneously helps elucidate the instances and causes of discrepancies between model predictions and ongoing observed changes. Collectively, results highlight the complexity of global change impacts across broad landscapes that likely relate to differences in assemblages' intrinsic sensitivities and external exposure to stressors.

Conceptualising the Episodic Nature of Disability among Adults Living with Long COVID: A Qualitative Study

ObjectivesTo describe episodic nature of disability among adults living with Long COVID. MethodsWe conducted a community-engaged qualitative descriptive study involving online semi-structured interviews and participant visual illustrations. We recruited participants via collaborator community organizations in Canada, Ireland, United Kingdom, and United States. ParticipantsAdults who self-identified as living with Long COVID. We purposively recruited for diversity in age, gender, race/ethnicity, sexual orientation, and duration since initial COVID-19 infection. Main Outcome Measure(s)We used a semi-structured interview guide to explore experiences of disability living with Long COVID, specifically health-related challenges and how they were experienced over time. We asked participants to draw their health trajectory and conducted a group-based content analysis. ResultsAmong the 40 participants, the median age was 39 years (interquartile range: 32, 49); majority were women (63%), white (73%), heterosexual (75%), and living with Long COVID for [≥]1 year (83%). Participants described their disability experiences as episodic in nature, characterized by fluctuations in presence and severity of health-related challenges (disability) that may occur both within a day and over the long-term living with Long COVID. They described living with ups and downs, flare-ups, and peaks followed by crashes, troughs, and valleys, likened to a yo-yo rolling hills, and rollercoaster ride with relapsing/remitting, waxing/waning, fluctuations in health. Drawn illustrations demonstrated variety of trajectories across health dimensions, some more episodic than others. Uncertainty intersected with the episodic nature of disability, characterized as unpredictability of episodes, their length, severity and triggers, and process of long-term trajectory, which had implications on broader health. ConclusionsAmong this sample of adults living with Long COVID, experiences of disability were described as episodic, characterized by fluctuating health challenges, which may be unpredictable in nature. Results help to better understand experiences of disability among adults living with Long COVID to inform healthcare and rehabilitation. KEY MESSAGESO_LIWhat is already known on this topic: Globally, a growing number of individuals are living with persistent and prolonged signs and symptoms following infection consistent with COVID-19, referred to as Long COVID, Post COVID-19 Condition (PCC) or Post-acute sequelae of SARS-CoV2 (PASC). Individuals living with Long COVID are experiencing a range of symptoms and impairments that impact their ability to carry out day to day activities or engage in social and community life roles. C_LIO_LIWhat this study adds: Disability living with Long COVID was described as episodic, characterized by fluctuations in presence and severity of health related challenges, which may be unpredictable in nature, occurring both within the day, and over the long-term of months and years living with Long COVID. C_LIO_LIHow this study might affect research, practice or policy: Results will help researchers, healthcare providers, policymakers, employers, and community members to better understand experiences of disability among adults living with Long COVID, to inform future disability measurement, health and rehabilitation care and service delivery, programs and policies for insurance, return to work, and workplace accommodations. C_LI

Explainable machine learning improves interpretability in the predictive modeling of biological stream conditions in the Chesapeake Bay Watershed, USA.

Anthropogenic alterations have resulted in widespread degradation of stream conditions. To aid in stream restoration and management, baseline estimates of conditions and improved explanation of factors driving their degradation are needed. We used random forests to model biological conditions using a benthic macroinvertebrate index of biotic integrity for small, non-tidal streams (upstream area ≤200 km2) in the Chesapeake Bay watershed (CBW) of the mid-Atlantic coast of North America. We utilized several global and local model interpretation tools to improve average and site-specific model inferences, respectively. The model was used to predict condition for 95,867 individual catchments for eight periods (2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019). Predicted conditions were classified as Poor, FairGood, or Uncertain to align with management needs and individual reach lengths and catchment areas were summed by condition class for the CBW for each period. Global permutation and local Shapley importance values indicated percent of forest, development, and agriculture in upstream catchments had strong impacts on predictions. Development and agriculture negatively influenced stream condition for model average (partial dependence [PD] and accumulated local effect [ALE] plots) and local (individual condition expectation and Shapley value plots) levels. Friedman's H-statistic indicated large overall interactions for these three land covers, and bivariate global plots (PD and ALE) supported interactions among agriculture and development. Total stream length and catchment area predicted in FairGood conditions decreased then increased over the 19-years (length/area: 66.6/65.4% in 2001, 66.3/65.2% in 2011, and 66.6/65.4% in 2019). Examination of individual catchment predictions between 2001 and 2019 showed those predicted to have the largest decreases in condition had large increases in development; whereas catchments predicted to exhibit the largest increases in condition showed moderate increases in forest cover. Use of global and local interpretative methods together with watershed-wide and individual catchment predictions support conservation practitioners that need to identify widespread and localized patterns, especially acknowledging that management actions typically take place at individual-reach scales.

Lineage-specific silencing of PSAT1 induces serine auxotrophy and sensitivity to dietary serine starvation in luminal breast tumors.

A major challenge of targeting metabolism for cancer therapy is pathway redundancy, in which multiple sources of critical nutrients can limit the effectiveness of some metabolism-targeted therapies. Here, we analyze lineage-dependent gene expression in human breast tumors to identify differences in metabolic gene expression that may limit pathway redundancy and create therapeutic vulnerabilities. We find that the serine synthesis pathway gene PSAT1 is the most depleted metabolic gene in luminal breast tumors relative to basal tumors. Low PSAT1 prevents de novo serine biosynthesis and sensitizes luminal breast cancer cells to serine and glycine starvation in vitro and in vivo. This PSAT1 expression disparity preexists in the putative cells of origin of basal and luminal tumors and is due to luminal-specific hypermethylation of the PSAT1 gene. Our data demonstrate that luminal breast tumors are auxotrophic for serine and may be uniquely sensitive to therapies targeting serine availability.

Transnasal Humidified Rapid-Insufflation Ventilatory Exchange for Difficult Airway Management in Adults With Recessive Dystrophic Epidermolysis Bullosa: A Case Series.

Airway management of adult patients with recessive dystrophic epidermolysis bullosa presents significant challenges associated with tissue fragility and distortion of airway anatomy. This retrospective case series describes 11 adult patients with recessive dystrophic epidermolysis bullosa and difficult airways undergoing 24 general anesthetics in which transnasal humidified rapid-insufflation ventilatory exchange was used for preoxygenation and apneic oxygenation. Despite an average time to intubation of over 6 minutes, transnasal humidified rapid-insufflation ventilatory exchange provided oxygenation before endotracheal intubation without the need for bag-mask ventilation or supraglottic airway ventilation, facilitating smooth and atraumatic flexible scope intubation. There were no major adverse events.

Time marches on, but do the causal pathways driving instream habitat and biology remain consistent?

Stream ecosystems are complex networks of interacting terrestrial and aquatic drivers. To untangle these ecological networks, efforts evaluating the direct and indirect effects of landscape, climate, and instream predictors on biological condition through time are needed. We used structural equation modeling and leveraged a stream survey program to identify and compare important predictors driving condition of benthic macroinvertebrate and fish assemblages. We used data resampled 14 years apart at 252 locations across Maryland, USA. Sample locations covered a wide range of conditions that varied spatiotemporally. Overall, the relationship directions were consistent between sample periods, but their relative strength varied temporally. For benthic macroinvertebrates, we found that the total effect of natural landscape (e.g., elevation, longitude, latitude, geology) and land use (i.e., forest, development, agriculture) predictors was 1.4 and 1.5 times greater in the late 2010s compared to the 2000s. Moreover, the total effect of water quality (e.g., total nitrogen and conductivity) and habitat (e.g., embeddedness, riffle quality) was 1.2 and 4.8 times lower in the 2010s, respectively. For fish assemblage condition, the total effect of land use-land cover predictors was 2.3 times greater in the 2010s compared to the 2000s, while the total effect of local habitat was 1.4 times lower in the 2010s, respectively. As expected, we found biological assemblages in catchments with more agriculture and urban development were generally comprised of tolerant, generalist species, while assemblages in catchments with greater forest cover had more-specialized, less-tolerant species (e.g., Ephemeroptera, Plecoptera, and Trichoptera taxa, clingers, benthic and lithophilic spawning fishes). Changes in the relative importance of landscape and land-use predictors suggest other correlated, yet unmeasured, proximal factors became more important over time. By untangling these ecological networks, stakeholders can gain a better understanding of the spatiotemporal relationships driving biological condition to implement management practices aimed at improving stream condition.

Seasonality of distinct respiratory viruses in a tropical city: implications for prophylaxis.

OBJECTIVE: The frequency and seasonality of viruses in tropical regions are scarcely reported. We estimated the frequency of seven respiratory viruses and assessed seasonality of respiratory syncytial virus (RSV) and influenza viruses in a tropical city. METHODS: Children (age ≤ 18 years) with acute respiratory infection were investigated in Salvador, Brazil, between July 2014 and June 2017. Respiratory viruses were searched by direct immunofluorescence and real-time polymerase chain reaction for detection of RSV, influenza A virus, influenza B virus, adenovirus (ADV) and parainfluenza viruses (PIV) 1, 2 and 3. Seasonal distribution was evaluated by Prais-Winsten regression. Due to similar distribution, influenza A and influenza B viruses were grouped to analyse seasonality. RESULTS: The study group comprised 387 cases whose median (IQR) age was 26.4 (10.5-50.1) months. Respiratory viruses were detected in 106 (27.4%) cases. RSV (n = 76; 19.6%), influenza A virus (n = 11; 2.8%), influenza B virus (n = 7; 1.8%), ADV (n = 5; 1.3%), PIV 1 (n = 5; 1.3%), PIV 3 (n = 3; 0.8%) and PIV 2 (n = 1; 0.3%) were identified. Monthly count of RSV cases demonstrated seasonal distribution (b3 = 0.626; P = 0.003). More than half (42/76 [55.3%]) of all RSV cases were detected from April to June. Monthly count of influenza cases also showed seasonal distribution (b3 = -0.264; P = 0.032). Influenza cases peaked from November to January with 44.4% (8/18) of all influenza cases. CONCLUSIONS: RSV was the most frequently detected virus. RSV and influenza viruses showed seasonal distribution. These data may be useful to plan the best time to carry out prophylaxis and to increase the number of hospital beds.

Using expert knowledge to support Endangered Species Act decision-making for data-deficient species.

Many questions relevant to conservation decision-making are characterized by extreme uncertainty due to lack of empirical data and complexity of the underlying ecologic processes, leading to a rapid increase in the use of structured protocols to elicit expert knowledge. Published ecologic applications often employ a modified Delphi method, where experts provide judgments anonymously and mathematical aggregation techniques are used to combine judgments. The Sheffield elicitation framework (SHELF) differs in its behavioral approach to synthesizing individual judgments into a fully specified probability distribution for an unknown quantity. We used the SHELF protocol remotely to assess extinction risk of three subterranean aquatic species that are being considered for listing under the U.S. Endangered Species Act. We provided experts an empirical threat assessment for each known locality over a video conference and recorded judgments on the probability of population persistence over four generations with online submission forms and R-shiny apps available through the SHELF package. Despite large uncertainty for all populations, there were key differences between species' risk of extirpation based on spatial variation in dominant threats, local land use and management practices, and species' microhabitat. The resulting probability distributions provided decision makers with a full picture of uncertainty that was consistent with the probabilistic nature of risk assessments. Discussion among experts during SHELF's behavioral aggregation stage clearly documented dominant threats (e.g., development, timber harvest, animal agriculture, and cave visitation) and their interactions with local cave geology and species' habitat. Our virtual implementation of the SHELF protocol demonstrated the flexibility of the approach for conservation applications operating on budgets and time lines that can limit in-person meetings of geographically dispersed experts.

Uso del Conocimiento Experto para Respaldar la Toma de Decisiones del Acta de Especies en Peligro para Especies con Información Deficiente Resumen Muchas preguntas relevantes para la toma de decisiones de conservación se caracterizan por una incertidumbre extrema causada por la falta de información empírica y por la complejidad de los procesos ecológicos subyacentes. Esto lleva a un rápido incremento en el uso de protocolos estructurados para obtener conocimiento de los expertos en el tema. Las aplicaciones ecológicas publicadas con frecuencia emplean un método Delphi modificado, en el cual los expertos proporcionan dictámenes anónimamente y luego se usan técnicas de agregación matemática para combinar estos dictámenes. El marco de trabajo de obtención Sheffield (SHELF) difiere en su enfoque conductual para sintetizar los dictámenes individuales en una distribución de probabilidad completamente especificada para una cantidad desconocida. Usamos el protocolo SHELF remotamente para evaluar el riesgo de extinción de tres especies acuáticas subterráneas que están siendo consideradas para ser incluidas en el Acta de Especies en Peligro de los E.U.A. Les proporcionamos a los expertos una evaluación empírica de la amenaza para cada localidad conocida durante una videoconferencia y registramos los dictámenes sobre la probabilidad de la persistencia poblacional durante cuatro generaciones por medio de formularios enviados en línea y las apps R-shiny disponibles a través del paquete SHELF. A pesar de la gran incertidumbre para todas las poblaciones, hubo diferencias importantes entre el riesgo de extirpación de las especies con base en la variación espacial en las amenazas dominantes, el uso del suelo local y las prácticas de manejo, y el microhábitat de las especies. Las distribuciones resultantes de la probabilidad proporcionaron al órgano decisorio un cuadro completo de la incertidumbre que fue consistente con la naturaleza probabilística de las evaluaciones de riesgo. Las discusiones entre los expertos durante la fase de agregación conductual de SHELF documentaron claramente las amenazas dominantes (p. ej.: desarrollo, extracción de madera, agricultura animal y visitas a las cuevas) y sus interacciones con la geología de las cuevas locales y el hábitat de la especie. Nuestra implementación virtual del protocolo SHELF demostró la flexibilidad del enfoque para las aplicaciones de la conservación que operan con presupuestos y líneas de tiempo que pueden limitar las reuniones en persona de expertos dispersados geográficamente.

Techniques to improve ecological interpretability of black-box machine learning models.

Statistical modeling of ecological data is often faced with a large number of variables as well as possible nonlinear relationships and higher-order interaction effects. Gradient boosted trees (GBT) have been successful in addressing these issues and have shown a good predictive performance in modeling nonlinear relationships, in particular in classification settings with a categorical response variable. They also tend to be robust against outliers. However, their black-box nature makes it difficult to interpret these models. We introduce several recently developed statistical tools to the environmental research community in order to advance interpretation of these black-box models. To analyze the properties of the tools, we applied gradient boosted trees to investigate biological health of streams within the contiguous U.S., as measured by a benthic macroinvertebrate biotic index. Based on these data and a simulation study, we demonstrate the advantages and limitations of partial dependence plots (PDP), individual conditional expectation (ICE) curves and accumulated local effects (ALE) in their ability to identify covariate-response relationships. Additionally interaction effects were quantified according to interaction strength (IAS) and Friedman's H2 statistic. Interpretable machine learning techniques are useful tools to open the black-box of gradient boosted trees in the environmental sciences. This finding is supported by our case study on the effect of impervious surface on the benthic condition, which agrees with previous results in the literature. Overall the most important variables were ecoregion, bed stability, watershed area, riparian vegetation and catchment slope. These variables were also present in most identified interaction effects. In conclusion, graphical tools (PDP, ICE, ALE) enable visualization and easier interpretation of GBT but should be supported by analytical statistical measures. Future methodological research is needed to investigate the properties of interaction tests.

Medical adhesive-related skin injury.

The skin's main function is to act as a physical barrier against harmful substances. Medical adhesive-related skin injury (MARSI) is a prevalent and under-reported condition that compromises the skin's integrity. Repeated applications and removal of appliances can increase the likelihood of MARSI occurring. Prevention and treatment are key to ensure appropriate skin preparation, product appliance and removal. The use of structured approaches is imperative and there is a need to increase the awareness of MARSI among patients and health professionals to ensure that informed decisions are made.

'Everybody in Nigeria is a doctor ': a qualitative study of stakeholder perspectives on lay diagnosis of malaria and pneumonia in Nigeria.

BACKGROUND: Lay diagnosis is a widely used diagnostic approach for home management of common illnesses in Nigeria. This study aimed to explore the perspectives of caregivers and healthcare professionals on lay diagnosis of childhood malaria and pneumonia. Aligned to this, the study sought to explore the feasibility of training caregivers in the Integrated Management of Childhood Illness (IMCI) guidelines for improved recognition and treatment of these diseases. METHODS: A qualitative study using individual face-to-face semi-structured interviews was conducted in Benin City, Nigeria. Participants included 13 caregivers with children under 5 years and 17 healthcare professionals (HPs). An inductive approach to thematic analysis was used to generate themes and analyses. RESULTS: Caregivers relied on lay diagnosis but recognised its limitations. The perceived severity of malaria and pneumonia significantly influenced caregivers' preference for reliance on lay diagnosis practices, health-seeking behaviour and willingness to undertake training in IMCI guidelines for home management of diseases. Safety and potential unintended misuse of medications were recognised by caregivers and HPs as the main challenges. CONCLUSIONS: The high level of acceptance among caregivers to receive IMCI training could help improve effective management of childhood malaria and pneumonia at the community level through early recognition and prompt treatment.

Shale gas development has limited effects on stream biology and geochemistry in a gradient-based, multiparameter study in Pennsylvania.

The number of horizontally drilled shale oil and gas wells in the United States has increased from nearly 28,000 in 2007 to nearly 127,000 in 2017, and research has suggested the potential for the development of shale resources to affect nearby stream ecosystems. However, the ability to generalize current studies is limited by the small geographic scope as well as limited breadth and integration of measured chemical and biological indicators parameters. This study tested the hypothesis that a quantifiable, significant relationship exists between the density of oil and gas (OG) development, increasing stream water concentrations of known geochemical tracers of OG extraction, and the composition of benthic macroinvertebrate and microbial communities. Twenty-five headwater streams that drain lands across a gradient of shale gas development intensity were sampled. Our strategy included comprehensive measurements across multiple seasons of sampling to account for temporal variability of geochemical parameters, including known shale OG geochemical tracers, and microbial and benthic macroinvertebrate communities. No significant relationships were found between the intensity of OG development, shale OG geochemical tracers, or benthic macroinvertebrate or microbial community composition, whereas significant seasonal differences in stream chemistry were observed. These results highlight the importance of considering spatial and temporal variability in stream chemistry and biota and not only the presence of anthropogenic activities in a watershed. This comprehensive, integrated study of geochemical and biological variability of headwater streams in watersheds undergoing OG development provides a robust framework for examining the effects of energy development at a regional scale.

Disentangling the potential effects of land-use and climate change on stream conditions.

Land-use and climate change are significantly affecting stream ecosystems, yet understanding of their long-term impacts is hindered by the few studies that have simultaneously investigated their interaction and high variability among future projections. We modeled possible effects of a suite of 2030, 2060, and 2090 land-use and climate scenarios on the condition of 70,772 small streams in the Chesapeake Bay watershed, United States. The Chesapeake Basin-wide Index of Biotic Integrity, a benthic macroinvertebrate multimetric index, was used to represent stream condition. Land-use scenarios included four Special Report on Emissions Scenarios (A1B, A2, B1, and B2) representing a range of potential landscape futures. Future climate scenarios included quartiles of future climate changes from downscaled Coupled Model Intercomparison Project - Phase 5 (CMIP5) and a watershed-wide uniform scenario (Lynch2016). We employed random forests analysis to model individual and combined effects of land-use and climate change on stream conditions. Individual scenarios suggest that by 2090, watershed-wide conditions may exhibit anywhere from large degradations (e.g., scenarios A1B, A2, and the CMIP5 25th percentile) to small degradations (e.g., scenarios B1, B2, and Lynch2016). Combined land-use and climate change scenarios highlighted their interaction and predicted, by 2090, watershed-wide degradation in 16.2% (A2 CMIP5 25th percentile) to 1.0% (B2 Lynch2016) of stream kilometers. A goal for the Chesapeake Bay watershed is to restore 10% of stream kilometers over a 2008 baseline; our results suggest meeting and sustaining this goal until 2090 may require improvement in 11.0%-26.2% of stream kilometers, dependent on land-use and climate scenario. These results highlight inherent variability among scenarios and the resultant uncertainty of predicted conditions, which reinforces the need to incorporate multiple scenarios of both land-use (e.g., development, agriculture, etc.) and climate change in future studies to encapsulate the range of potential future conditions.

A Random Forest Approach for Bounded Outcome Variables.

Random forests have become an established tool for classification and regression, in particular in high-dimensional settings and in the presence of non-additive predictor-response relationships. For bounded outcome variables restricted to the unit interval, however, classical modeling approaches based on mean squared error loss may severely suffer as they do not account for heteroscedasticity in the data. To address this issue, we propose a random forest approach for relating a beta dis-tributed outcome to a set of explanatory variables. Our approach explicitly makes use of the likelihood function of the beta distribution for the selection of splits dur-ing the tree-building procedure. In each iteration of the tree-building algorithm it chooses one explanatory variable in combination with a split point that maximizes the log-likelihood function of the beta distribution with the parameter estimates de-rived from the nodes of the currently built tree. Results of several simulation studies and an application using data from the U.S.A. National Lakes Assessment Survey demonstrate the properties and usefulness of the method, in particular when compared to random forest approaches based on mean squared error loss and parametric regression models.