Wolf (Canis lupus) Winter Density and Territory Size in a Low Biomass Moose (Alces alces) System

We investigated the winter density and territory size of wolves (Canis lupus) on the Yukon Flats, Alaska, where moose (Alces alces) was the sole ungulate prey, occurring at a low density and representing a biomass of ungulate food lower than previously studied in North America. Using locations (GPS coordinates) from collars deployed on seven wolves, we estimated territory sizes with adaptive kernel and minimum convex polygon methods. We then estimated wolf density from a population area defined by these territory sizes and counts of wolves in five marked packs. From November 2009 to April 2010, we obtained 6263 GPS locations. Pack size ranged from two to 10 wolves, with average size of 5.0 in November 2009 and 4.8 in March 2010. Average winter territory size for five packs was 1433 km2 with the 95% adaptive kernel method and 1608 km2 with the minimum convex polygon method. Density (wolves/1000 km2) was 3.6 in November and 3.4 in March with the 95% adaptive kernel method and 3.4 in both November and March with the minimum convex polygon method. Territories were large and estimates produced by the two methods differed by 11%. Densities were low, and the two analysis methods yielded densities that differed from each other by 3% to 6%. Low wolf density corresponded with low biomass of ungulate food, suggesting that moose availability on the Yukon Flats likely limited wolf density.Nous avons étudié la densité hivernale et la taille du territoire du loup (Canis lupus) aux Yukon Flats, en Alaska, où l’orignal (Alces alces) était la seule proie ongulée. Il s’y trouvait en faible densité et représentait une biomasse de nourriture ongulée inférieure à celle étudiée ailleurs en Amérique du Nord. Grâce aux positions (coordonnées de GPS) prélevées à partir de colliers posés sur sept loups, nous avons estimé la taille des territoires au moyen de la méthode d’estimation adaptative à noyaux et de la méthode du polygone convexe minimal. Ensuite, nous avons estimé la densité du loup à partir d’une zone de population définie par la taille de ces territoires et par les dénombrements de loups de cinq meutes marquées. De novembre 2009 à avril 2010, nous avons obtenu 6 263 positions GPS. La taille des meutes variait de deux à dix loups, pour une taille moyenne de 5,0 loups en novembre 2009 et de 4,8 en mars 2010. La taille moyenne du territoire hivernal de cinq meutes était de 1 433 km2 dans le cas de la méthode adaptative à noyaux de 95 % et de 1 608 km2 dans le cas de la méthode du polygone convexe minimal. La densité (loups/1000 km2) était de 3,6 en novembre et de 3,4 en mars avec la méthode adaptative à noyaux de 95 % et de 3,4 en novembre et en mars avec la méthode du polygone convexe minimal. Les territoires étaient vastes et les estimations obtenues à l’aide des deux méthodes différaient de 11 %. Les densités étaient faibles, et les deux méthodes d’analyse ont donné des densités qui différaient l’une de l’autre dans une mesure 3 % à 6 %. La faible densité des loups correspondait à la faible biomasse de nourriture ongulée, ce qui laisse supposer que la disponibilité de l’orignal aux Yukon Flats limitait vraisemblablement la densité du loup

Cytologic scoring of equine exercise-induced pulmonary hemorrhage: Performance of human experts and a deep learning-based algorithm

Exercise-induced pulmonary hemorrhage (EIPH) is a relevant respiratory disease in sport horses, which can be diagnosed by examination of bronchoalveolar lavage fluid (BALF) cells using the total hemosiderin score (THS). The aim of this study was to evaluate the diagnostic accuracy and reproducibility of annotators and to validate a deep learning-based algorithm for the THS. Digitized cytological specimens stained for iron were prepared from 52 equine BALF samples. Ten annotators produced a THS for each slide according to published methods. The reference methods for comparing annotator’s and algorithmic performance included a ground truth dataset, the mean annotators’ THSs, and chemical iron measurements. Results of the study showed that annotators had marked interobserver variability of the THS, which was mostly due to a systematic error between annotators in grading the intracytoplasmatic hemosiderin content of individual macrophages. Regarding overall measurement error between the annotators, 87.7% of the variance could be reduced by using standardized grades based on the ground truth. The algorithm was highly consistent with the ground truth in assigning hemosiderin grades. Compared with the ground truth THS, annotators had an accuracy of diagnosing EIPH (THS of < or ≥ 75) of 75.7%, whereas, the algorithm had an accuracy of 92.3% with no relevant differences in correlation with chemical iron measurements. The results show that deep learning-based algorithms are useful for improving reproducibility and routine applicability of the THS. For THS by experts, a diagnostic uncertainty interval of 40 to 110 is proposed. THSs within this interval have insufficient reproducibility regarding the EIPH diagnosis

Variation in use of procurement biopsies and its implications for discard of deceased donor kidneys recovered for transplantation

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150616/1/ajt15325.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150616/2/ajt15325_am.pd

Regional Similarities and NOx‐Related Increases in Biogenic Secondary Organic Aerosol in Summertime Southeastern United States

During the 2013 Southern Oxidant and Aerosol Study, Fourier transform infrared spectroscopy (FTIR) and aerosol mass spectrometer (AMS) measurements of submicron mass were collected at Look Rock (LRK), Tennessee, and Centreville (CTR), Alabama. Carbon monoxide and submicron sulfate and organic mass concentrations were 15–60% higher at CTR than at LRK, but their time series had moderate correlations (r ~ 0.5). However, NOx had no correlation (r = 0.08) between the two sites with nighttime‐to‐early‐morning peaks 3–10 times higher at CTR than at LRK. Organic mass (OM) sources identified by FTIR Positive Matrix Factorization (PMF) had three very similar factors at both sites: fossil fuel combustion‐related organic aerosols, mixed organic aerosols, and biogenic organic aerosols (BOA). The BOA spectrum from FTIR is similar (cosine similarity > 0.6) to that of lab‐generated particle mass from the photochemical oxidation of both isoprene and monoterpenes under high NOx conditions from chamber experiments. The BOA mass fraction was highest during the night at CTR but in the afternoon at LRK. AMS PMF resulted in two similar pairs of factors at both sites and a third nighttime NOx‐related factor (33% of OM) at CTR but a daytime nitrate‐related factor (28% of OM) at LRK. NOx was correlated with BOA and LO‐OOA for NOx concentrations higher than 1 ppb at both sites, producing 0.5 ± 0.1 μg/m3 for CTR‐LO‐OOA and 1.0 ± 0.3 μg/m3 for CTR‐BOA additional biogenic OM for each 1 ppb increase of NOx.Key PointsAerosol concentration and composition are largely similar at two different forested sites during summertime in the southeastern United StatesFTIR of ambient biogenic SOA factors are similar to isoprene and monoterpene chamber experiment, supporting NOx‐related oxidation pathwaysNOx increases biogenic SOA by 0.5 ± 0.1 μg/m3 for CTR‐LO‐OOA and 1.0 ± 0.3 μg/m3 for CTR‐BOA for each ppb NOx above 1 ppb at Centreville but not at Look Rock (where NOx was usually below 1 ppb)Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146465/1/jgrd54860-sup-0001-SI.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146465/2/jgrd54860.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146465/3/jgrd54860_am.pd

What Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in Genetic Programming

This paper addresses the issue of what makes a problem genetic programming (GP)-hard by considering the binomial-3 problem. In the process, we discuss the efficacy of the metaphor of an adaptive fitness landscape to explain what is GP-hard. We indicate that, at least for this problem, the metaphor is misleading.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45613/1/10710_2004_Article_335714.pd

Novel EDGE encoding method enhances ability to identify genetic interactions

Assumptions are made about the genetic model of single nucleotide polymorphisms (SNPs) when choosing a traditional genetic encoding: additive, dominant, and recessive. Furthermore, SNPs across the genome are unlikely to demonstrate identical genetic models. However, running SNP-SNP interaction analyses with every combination of encodings raises the multiple testing burden. Here, we present a novel and flexible encoding for genetic interactions, the elastic data-driven genetic encoding (EDGE), in which SNPs are assigned a heterozygous value based on the genetic model they demonstrate in a dataset prior to interaction testing. We assessed the power of EDGE to detect genetic interactions using 29 combinations of simulated genetic models and found it outperformed the traditional encoding methods across 10%, 30%, and 50% minor allele frequencies (MAFs). Further, EDGE maintained a low false-positive rate, while additive and dominant encodings demonstrated inflation. We evaluated EDGE and the traditional encodings with genetic data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes: age-related macular degeneration (AMD), age-related cataract, glaucoma, type 2 diabetes (T2D), and resistant hypertension. A multi-encoding genome-wide association study (GWAS) for each phenotype was performed using the traditional encodings, and the top results of the multi-encoding GWAS were considered for SNP-SNP interaction using the traditional encodings and EDGE. EDGE identified a novel SNP-SNP interaction for age-related cataract that no other method identified: rs7787286 (MAF: 0.041;intergenic region of chromosome 7)-rs4695885 (MAF: 0.34;intergenic region of chromosome 4) with a Bonferroni LRT p of 0.018. A SNP-SNP interaction was found in data from the UK Biobank within 25 kb of these SNPs using the recessive encoding: rs60374751 (MAF: 0.030) and rs6843594 (MAF: 0.34) (Bonferroni LRT p: 0.026). We recommend using EDGE to flexibly detect interactions between SNPs exhibiting diverse action. Author summary Although traditional genetic encodings are widely implemented in genetics research, including in genome-wide association studies (GWAS) and epistasis, each method makes assumptions that may not reflect the underlying etiology. Here, we introduce a novel encoding method that estimates and assigns an individualized data-driven encoding for each single nucleotide polymorphism (SNP): the elastic data-driven genetic encoding (EDGE). With simulations, we demonstrate that this novel method is more accurate and robust than traditional encoding methods in estimating heterozygous genotype values, reducing the type I error, and detecting SNP-SNP interactions. We further applied the traditional encodings and EDGE to biomedical data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes, and EDGE identified a novel interaction for age-related cataract not detected by traditional methods, which replicated in data from the UK Biobank. EDGE provides an alternative approach to understanding and modeling diverse SNP models and is recommended for studying complex genetics in common human phenotypes

ERK inhibitor LY3214996-based treatment strategies for RAS-driven lung cancer

RAS gene mutations are the most frequent oncogenic event in lung cancer. They activate multiple RAS-centric signaling networks among them the MAPK, PI3K and RB pathways. Within the MAPK pathway ERK1/2 proteins exert a bottleneck function for transmitting mitogenic signals and activating cytoplasmic and nuclear targets. In view of disappointing anti-tumor activity and toxicity of continuously applied MEK inhibitors in patients with KRAS mutant lung cancer, research has recently focused on ERK1/2 proteins as therapeutic targets and on ERK inhibitors for their ability to prevent bypass and feedback pathway activation. Here we show that intermittent application of the novel and selective ATP-competitive ERK1/2 inhibitor LY3214996 exerts single-agent activity in patient-derived xenograft (PDX) models of RAS mutant lung cancer. Combination treatments were well tolerated and resulted in synergistic (ERKi plus PI3K/mTORi LY3023414) and additive (ERKi plus CDK4/6i abemaciclib) tumor growth inhibition in PDX models. Future clinical trials are required to investigate if intermittent ERK inhibitor-based treatment schedules can overcome toxicities observed with continuous MEK inhibition and - equally important - to identify biomarkers for patient stratification