ALBERT

Description: The majority of advanced breast cancers have genetic alterations that are potentially targetable with drugs. Through initiatives such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), data can be mined to provide context for next-generation sequencing (NGS) results in the landscape of advanced breast cancer. Therapies for targets other than estrogen receptor alpha (ER) and HER2, such as cyclin-dependent kinases CDK4 and CDK6, were recently approved based on efficacy in patient subpopulations, but no predictive biomarkers have been found, leaving clinicians to continue a trial-and-error approach with each patient. Next-generation sequencing identifies potentially actionable alterations in genes thought to be drivers in the cancerous process including phosphatidylinositol 3-kinase (PI3K), AKT, fibroblast growth factor receptors (FGFRs), and mutant HER2. Epigenetically-directed and immunologic therapies have also shown promise for the treatment of breast cancer via histone deacetylases (HDAC) 1 and 3, programmed T cell death 1 (PD-1), and programmed T cell death ligand 1 (PD-L1). Identifying biomarkers to predict primary resistance in breast cancer will ultimately affect clinical decisions regarding adjuvant therapy in the first-line setting. However, the bulk of medical decision-making is currently made in the secondary resistance setting. Herein, we review the clinical potential of PI3K, AKT, FGFRs, mutant HER2, HDAC1/3, PD-1, and PD-L1 as therapeutic targets in breast cancer, focusing on the rationale for therapeutic development and the status of clinical testing. This article is protected by copyright. All rights reserved

Description: Adverse conditions may be the norm rather than the exception in natural populations. Many populations experience poor nutrition on a seasonal basis. Further, brief interludes of inbreeding can be common as population density fluctuates and because of habitat fragmentation. Here, we investigated the effects of poor nutrition and inbreeding on traits that can be very important to reproductive success and fitness in males: testes mass, sperm concentration, and sperm viability. Our study species was Narnia femorata, a species introduced to north-central Florida in the 1950s. This species encounters regular, seasonal changes in diet that can have profound phenotypic effects on morphology and behavior. We generated inbred and outbred individuals through a single generation of full-sibling mating or outcrossing, respectively. All juveniles were provided a natural, high-quality diet of Opuntia humifusa cactus cladode with fruit until they reached adulthood. New adult males were put on a high- or low-quality diet for at least 21 days before measurements were taken. As expected, the low-quality diet led to significantly decreased testes mass in both inbred and outbred males, although there were surprisingly no detectable effects on sperm traits. We did not find evidence that inbreeding affected testes mass, sperm concentration, and sperm viability. Our results highlight the immediate and overwhelming effects of nutrition on testes mass, while suggesting that a single generation of inbreeding might not be detrimental for primary sexual traits in this particular population. Lessening the quality of adult male leaf-footed cactus bugs’ natural diet resulted in decreased testes mass without significantly affecting sperm concentration or sperm viability. Low inbreeding did not affect the quantity or quality of primary sexual traits.

Description: Regional monitoring strategies frequently employ a nested sampling design where a finite set of study areas from throughout a region are selected and intensive sampling occurs within a subset of sites within the individual study areas. This sampling protocol naturally lends itself to a hierarchical analysis to account for dependence among subsamples. Implementing such an analysis using a classic likelihood framework is computationally challenging when accounting for detection errors in species occurrence models. Bayesian methods offer an alternative approach for fitting models that readily allows for spatial structure to be incorporated. We demonstrate a general approach for estimating occupancy when data come from a nested sampling design. We analyzed data from a regional monitoring program of wood frogs ( Lithobates sylvaticus ) and spotted salamanders ( Ambystoma maculatum ) in vernal pools using static and dynamic occupancy models. We analyzed observations from 2004 to 2013 that were collected within 14 protected areas located throughout the northeast United States. We use the data set to estimate trends in occupancy at both the regional and individual protected area levels. We show that occupancy at the regional level was relatively stable for both species. However, substantial variation occurred among study areas, with some populations declining and some increasing for both species. In addition, When the hierarchical study design is not accounted for, one would conclude stronger support for latitudinal gradient in trends than when using our approach that accounts for the nested design. In contrast to the model that does not account for nesting, the nested model did not include an effect of latitude in the 95% credible interval. These results shed light on the range-level population status of these pond-breeding amphibians, and our approach provides a framework that can be used to examine drivers of local and regional occurrence dynamics. We demonstrate analysis of regional and protected area specific occupancy of two amphibians in the northeast. We show how hierarchical Bayesian models can be used to control for the nested nature of many sampling designs and improve inferences across a network of monitoring sites.

Description: The size of weapons and testes can be central to male reproductive success. Yet, the expression of these traits is often extremely variable. Studies are needed that take a more complete organism perspective, investigating the sources of variation in both traits simultaneously and using developmental conditions that mimic those in nature. In this study, we investigated the components of variation in weapon and testis sizes using the leaf-footed cactus bug, Narnia femorata (Hemiptera: Coreidae) on three natural developmental diets. We show that the developmental diet has profound effects on both weapon and testis expression and scaling. Intriguingly, males in the medium-quality diet express large weapons but have relatively tiny testes, suggesting complex allocation decisions. We also find that heritability, evolvability, and additive genetic variation are highest in the high-quality diet for testis and body mass. This result suggests that these traits may have an enhanced ability to respond to selection during a small window of time each year when this diet is available. Taken together, these results illustrate that normal, seasonal fluctuations in the nutritional environment may play a large role in the expression of sexually selected traits and the ability of these traits to respond to selection. We found that the expression, heritability, and evolvability of weapons and testes of a cactus bug, Narnia femorata, vary with seasonal changes to their nutritional environment. These results suggest that the ability of these traits to respond to selection may change predictably across seasons.

Description: Abstract Advances in species distribution modeling continue to be driven by a need to predict species responses to environmental change coupled with increasing data availability. Recent work has focused on development of methods that integrate multiple streams of data to model species distributions. Combining sources of information increases spatial coverage and can improve accuracy in estimates of species distributions. However, when fusing multiple streams of data, the temporal and spatial resolutions of data sources may be mismatched. This occurs when data sources have fluctuating geographic coverage, varying spatial scales and resolutions, and differing sources of bias and sparsity. It is well documented in the spatial statistics literature that ignoring the misalignment of different data sources will result in bias in both the point estimates and uncertainty. This will ultimately lead to inaccurate predictions of species distributions. Here, we examine the issue of misaligned data as it relates specifically to integrated species distribution models. We then provide a general solution that builds off work in the statistical literature for the change‐of‐support problem. Specifically, we leverage spatial correlation and repeat observations at multiple scales to make statistically valid predictions at the ecologically relevant scale of inference. An added feature of the approach is that addressing differences in spatial resolution between data sets can allow for the evaluation and calibration of lesser‐quality sources in many instances. Using both simulations and data examples, we highlight the utility of this modeling approach and the consequences of not reconciling misaligned spatial data. We conclude with a brief discussion of the upcoming challenges and obstacles for species distribution modeling via data fusion.

Description: Abstract In September 2017, Hurricane Maria severely defoliated Puerto Rico's landscape, coinciding with a series of persistent hydrological consequences involving the atmospheric, terrestrial, and marine components of the water cycle. During the defoliated period, the atmosphere's thermodynamic structure more strongly explained daily cloud activity (R2PRE = 0.02; R2POST = 0.40) and precipitation (R2PRE = 0.19; R2POST = 0.33) than before landfall, indicating that post‐Maria land‐atmosphere interactions were comparatively muted, with similar precipitation patterns also found following Hurricanes Hugo (1989) and Georges (1998). Meanwhile, modeled post‐Maria runoff exceeded statistical expectations given the magnitude of contemporaneous precipitation. Enhanced runoff also coincided with greater sediment loads in nearshore waters, increasing sediment content greater than twofold. This study offers a holistic narrative of hydrospheric disturbance and recovery, whereby the instantaneous, large‐scale removal of vegetation is accompanied by hydrologic changes “upstream” in the atmosphere and “downstream” in rivers and estuaries.

Description: [1]  Particle emissions from open burning of southwestern (SW) and southeastern (SE) U.S. fuel types during 77 controlled laboratory burns are presented. The fuels include SW vegetation types: ceanothus, chamise/scrub oak, coastal sage scrub, California sagebrush, manzanita, maritime chaparral, masticated mesquite, oak savanna, and oak woodland as well as SE vegetation types: 1-year, 2-year rough, pocosin, chipped understory, understory hardwood, and pine litter. The SW fuels burned at a higher Modified Combustion Efficiency (MCE) than the SE fuels resulting in lower particulate matter (PM) mass emission factor (EF). Particle mass distributions for six fuels and particle number emission for all fuels are reported. Excellent mass closure (slope = 1.00, r 2  = 0.94) between ions, metals, and carbon with total weight was obtained. Organic carbon emission factors inversely correlated ( R 2 = 0.72) with average MCE, while elemental carbon (EC) had little correlation with average MCE ( R 2 =0.10). The EC/total carbon (TC) ratio sharply increased with MCE for MCEs exceeding 0.94. The average levoglucosan and total polycyclicaromatic hydrocarbon (PAH) emissions factors ranged from 25–1272 mg/kg fuel and 1.8-11.3 mg/kg fuel, respectively. No correlation between average MCE and emissions of PAHs/levoglucosan was found. Additionally, PAH diagnostic ratios were observed to be poor indicators of biomass burning. Large fuel-type and regional dependency was observed in the emission rates of ammonium, nitrate, chloride, sodium, and potassium.

Description: Many factors may affect daily nest survival. We present a novel multi-state, multi-stage model to estimate daily survival for each nest stage, daily hatching probability and probability that a failed nest died during a specific stage when stage of failure is unknown. The model does not require that hatching date be known. We used data from a large citizen science dataset to demonstrate the application of this approach, exploring the impact of laying dates, weather conditions, conserved soil moisture, soil carbon, habitat type and urbanisation on failure rates of common blackbird ( Turdus merula ) nests. Models selected and estimates of nest success were similar to those of the simpler logistic exposure method, but accounted for additional uncertainty. Simulations suggest the multi-state approach performs better when incubation mortality is affected by nest age, but not when incubation mortality is assumed constant. Both approaches worked best when date of incubation initiation was known for all nests first visited during the incubation stage. Daily blackbird survival probabilities were higher in human rural habitat than in urban or countryside habitats supporting the hypothesis that these intermediate habitats offer a better balance between low food availability in urban areas and high predation rates in the wider countryside. Nest success was influenced more by recent precipitation in urban habitats, but by a longer-term measure of water availability, soil moisture, in non-human dominated habitats, indicating that climatic change is likely to alter relationships between habitat and breeding success (and their temporal scale) by influencing the trade-off between food availability and predation rates. The multi-state, multi-stage model developed here may be helpful to other researchers modelling ecological processes in which transition probabilities between multiple stages are of interest. This article is protected by copyright. All rights reserved.

Description: Species’ distributions will respond to climate change based on the relationship between local demographic processes and climate and how this relationship varies based on range position. A rarely tested demographic prediction is that populations at the extremes of a species’ climate envelope (e.g., populations in areas with the highest mean annual temperature) will be most sensitive to local shifts in climate (i.e., warming). We tested this prediction using a dynamic species distribution model linking demographic rates to variation in temperature and precipitation for wood frogs ( Lithobates sylvaticus ) in North America. Using long-term monitoring data from 746 populations in 27 study areas, we determined how climatic variation affected population growth rates and how these relationships varied with respect to long-term climate. Some models supported the predicted pattern, with negative effects of extreme summer temperatures in hotter areas and positive effects on recruitment for summer water availability in drier areas. We also found evidence of interacting temperature and precipitation influencing population size, such as extreme heat having less of a negative effect in wetter areas. Other results were contrary to predictions, such as positive effects of summer water availability in wetter parts of the range and positive responses to winter warming especially in milder areas. In general, we found wood frogs were more sensitive to changes in temperature or temperature interacting with precipitation than to changes in precipitation alone. Our results suggest that sensitivity to changes in climate cannot be predicted simply by knowing locations within the species’ climate envelope. Many climate processes did not affect population growth rates in the predicted direction based on range position. Processes such as species-interactions, local adaptation, and interactions with the physical landscape likely affect the responses we observed. Our work highlights the need to measure demographic responses to changing climate. Demographic processes and climate interact and vary across a species’ range to determine how species’ distributions will respond to climate change. We predicted that populations at the extremes of a species’ climate envelope are most sensitive to climate shifts. We tested this using a dynamic species distribution model linking demographic rates to variation in climate for wood frogs ( Lithobates sylvaticus ) in North America. Sensitivity to changes in climate cannot be predicted simply by knowing locations within the species’ climate envelope.

Description: Eastern Puerto Rico and the surrounding Caribbean experienced a severe drought in 2015 that resulted in record low reservoir and river levels. Rainfall deficits in April and May, which represent the period when the drought began, were more severe in 2015 than recent droughts of record. While El Niño has been associated with drought in the Caribbean, onset of the 2015 drought was strongly associated with lower-than-average values of a recently developed tool used by weather forecasters in San Juan, the Gálvez-Davison Index (GDI), which is used to measure the potential for thunderstorm development and rainfall. Persistently low GDI values indicate strong and frequent intrusions of hot, dry air in the low- to mid-troposphere, suppressing convection, both locally and in development regions for tropical waves that impact Puerto Rico. The Saharan Air Layer (SAL) is largely responsible for this anomalously hot, dry air, which produced thermodynamically stable conditions and limited thunderstorms and rainfall. Moreover, higher-than-normal aerosol concentrations, typically associated with SAL intrusion over the Caribbean, were recorded in April and May 2015. A comparison to AVHRR aerosol optical thickness demonstrate that higher Caribbean aerosols in the early rainfall season, particularly June, are associated with decreased rainfall in eastern Puerto Rico. Results here demonstrate a direct link between the early and more pronounced SAL intrusions into the Caribbean and the suppression of the early rainfall season. More broadly, a reduction in the GDI and increase in the trade wind inversion was associated with reduced early season rainfall in the eastern Caribbean.