Estimating moose population parameters from aerial surveys

Successful moose management depends on knowledge of population dynamics. The principal parameters required are size, rate of change, recruitment, sex composition, and mortality. Moose management in Alaska has been severely hampered by the lack of good estimates of these parameters, and unfortunately, this lack contributed to the decline of many Alaskan moose populations during the 1970s (e.g., Gasaway et al. 1983). The problems were: (1) population size not adequately estimated, (2) rapid rates of decline not acknowledged until populations were low, (3) meaningful recruitment rates were not available in the absence of good population estimates, and (4) calf and adult mortality rates were grossly underestimated. Frustration of moose managers working with inadequate data led to development of aerial survey procedures that yield minimally biased, sufficiently precise estimates of population parameters for most Alaskan moose management and research. This manual describes these procedures. Development of these procedures would have been impossible without the inspiration, support, advice, and criticism of many colleagues. We thank these colleagues for their contributions. Dale Haggstrom and Dave Kelleyhouse helped develop flight patterns, tested and improved early sampling designs, and as moose managers, put these procedures into routine use. Pilots Bill Lentsch and Pete Haggland were instrumental in developing and testing aerial surveying techniques. Their interest and dedication to improving moose management made them valuable allies. Statisticians Dana Thomas of the University of Alaska and W. Scott Overton of Oregon State University provided advice on variance approximations for the population estimator. Warren Ballard, Sterling Miller, SuzAnne Miller, Doug Larsen, and Wayne Kale tested procedures and provided valuable criticisms and suggestions. Jim Raymond initially programmed a portable calculator to make lengthy calculation simple, fast, and error-free. Angie Babcock, Lisa Ingalls, Vicky Leffingwell, and Laura McManus patiently typed several versions of this manual. John Coady and Oliver Burris provided continuous moral and financial support for a 3-year project that lasted 6 years. Joan Barnett, Rodney Boetje, Steven Peterson, and Wayne Regelin of the Alaska Department of Fish and Game provided helpful editorial suggestions in previous drafts. Finally, we thank referees David Anderson of the Utah Cooperative Wildlife Research Unit, Vincent Schultz of Washington State University, and James Peek, E. "Oz" Garton, and Mike Samuel of the University of Idaho whose comments and suggestions improved this manual. This project was funded by the Alaska Department of Fish and Game through Federal Aid in Wildlife Restoration Projects W-17-9 through W-22-1

MPF: A portable message passing facility for shared memory multiprocessors

The design, implementation, and performance evaluation of a message passing facility (MPF) for shared memory multiprocessors are presented. The MPF is based on a message passing model conceptually similar to conversations. Participants (parallel processors) can enter or leave a conversation at any time. The message passing primitives for this model are implemented as a portable library of C function calls. The MPF is currently operational on a Sequent Balance 21000, and several parallel applications were developed and tested. Several simple benchmark programs are presented to establish interprocess communication performance for common patterns of interprocess communication. Finally, performance figures are presented for two parallel applications, linear systems solution, and iterative solution of partial differential equations

A linear-time algorithm for finding a complete graph minor in a dense graph

Let g(t) be the minimum number such that every graph G with average degree d(G) \geq g(t) contains a K_{t}-minor. Such a function is known to exist, as originally shown by Mader. Kostochka and Thomason independently proved that g(t) \in \Theta(t*sqrt{log t}). This article shows that for all fixed \epsilon > 0 and fixed sufficiently large t \geq t(\epsilon), if d(G) \geq (2+\epsilon)g(t) then we can find this K_{t}-minor in linear time. This improves a previous result by Reed and Wood who gave a linear-time algorithm when d(G) \geq 2^{t-2}.Comment: 6 pages, 0 figures; Clarification added in several places, no change to arguments or result

Interactions between sea urchin grazing and prey diversity on temperate rocky reef communities

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116948/1/ecy20139471636.pd

Keep It Simple: CNN Model Complexity Studies for Interference Classification Tasks

The growing number of devices using the wireless spectrum makes it important to find ways to minimize interference and optimize the use of the spectrum. Deep learning models, such as convolutional neural networks (CNNs), have been widely utilized to identify, classify, or mitigate interference due to their ability to learn from the data directly. However, there have been limited research on the complexity of such deep learning models. The major focus of deep learning-based wireless classification literature has been on improving classification accuracy, often at the expense of model complexity. This may not be practical for many wireless devices, such as, internet of things (IoT) devices, which usually have very limited computational resources and cannot handle very complex models. Thus, it becomes important to account for model complexity when designing deep learning-based models for interference classification. To address this, we conduct an analysis of CNN based wireless classification that explores the trade-off amongst dataset size, CNN model complexity, and classification accuracy under various levels of classification difficulty: namely, interference classification, heterogeneous transmitter classification, and homogeneous transmitter classification. Our study, based on three wireless datasets, shows that a simpler CNN model with fewer parameters can perform just as well as a more complex model, providing important insights into the use of CNNs in computationally constrained applications.Comment: 6 pages, 7 figures, 3 table

Dielectronic recombination for boronlike ions

We have calculated the total dielectronic recombination (DR) coefficients for the 2P1/2 and 2P3/2 states in B-like Ti17+, Fe21+, and Mo37+ ions for electron temperatures 0.1<~T<~10000 eV. The calculations are carried out using the multiconfiguration Dirac-Fock method in intermediate coupling with a configuration interaction. We find that accurate Coster-Kronig energies are critical for a successful determination of low-temperature DR coefficients. We also find that the DR involving fine-structure excitations can be as important as the 2s-2p excitation channels in the low-temperature regime for some ions

A Preliminary Investigation towards the Risk Stratification of Allogeneic Stem Cell Recipients with Respect to the Potential for Development of GVHD via Their Pre-Transplant Plasma Lipid and Metabolic Signature

The clinical outcome of allogeneic hematopoietic stem cell transplantation (SCT) may be influenced by the metabolic status of the recipient following conditioning, which in turn may enable risk stratification with respect to the development of transplant-associated complications such as graft vs. host disease (GVHD). To better understand the impact of the metabolic profile of transplant recipients on post-transplant alloreactivity, we investigated the metabolic signature of 14 patients undergoing myeloablative conditioning followed by either human leukocyte antigen (HLA)-matched related or unrelated donor SCT, or autologous SCT. Blood samples were taken following conditioning and prior to transplant on day 0 and the plasma was comprehensively characterized with respect to its lipidome and metabolome via liquid chromatography/mass spectrometry (LCMS) and gas chromatography/mass spectrometry (GCMS). A pro-inflammatory metabolic profile was observed in patients who eventually developed GVHD. Five potential pre-transplant biomarkers, 2-aminobutyric acid, 1-monopalmitin, diacylglycerols (DG 38:5, DG 38:6), and fatty acid FA 20:1 demonstrated high sensitivity and specificity towards predicting post-transplant GVHD. The resulting predictive model demonstrated an estimated predictive accuracy of risk stratification of 100%, with area under the curve of the ROC of 0.995. The likelihood ratio of 1-monopalmitin (infinity), DG 38:5 (6.0), and DG 38:6 (6.0) also demonstrated that a patient with a positive test result for these biomarkers following conditioning and prior to transplant will be at risk of developing GVHD. Collectively, the data suggest the possibility that pre-transplant metabolic signature may be used for risk stratification of SCT recipients with respect to development of alloreactivity

Pain Coping Skills Training for Patients Who Catastrophize About Pain Prior to Knee Arthroplasty: A Multisite Randomized Clinical Trial

BACKGROUND: Pain catastrophizing has been identified as a prognostic indicator of poor outcome following knee arthroplasty. Interventions to address pain catastrophizing, to our knowledge, have not been tested in patients undergoing knee arthroplasty. The purpose of this study was to determine whether pain coping skills training in persons with moderate to high pain catastrophizing undergoing knee arthroplasty improves outcomes 12 months postoperatively compared with usual care or arthritis education. METHODS: A multicenter, 3-arm, single-blinded, randomized comparative effectiveness trial was performed involving 5 university-based medical centers in the United States. There were 402 randomized participants. The primary outcome was the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) Pain Scale, measured at baseline, 2 months, 6 months, and 12 months following the surgical procedure. RESULTS: Participants were recruited from January 2013 to June 2016. In 402 participants, 66% were women and the mean age of the participants (and standard deviation) was 63.2 ± 8.0 years. Three hundred and forty-six participants (90% of those who underwent a surgical procedure) completed a 12-month follow-up. All 3 treatment groups had large improvements in 12-month WOMAC pain scores with no significant differences (p > 0.05) among the 3 treatment arms. No differences were found between WOMAC pain scores at 12 months for the pain coping skills and arthritis education groups (adjusted mean difference, 0.3 [95% confidence interval (CI), -0.9 to 1.5]) or between the pain coping and usual-care groups (adjusted mean difference, 0.4 [95% CI, -0.7 to 1.5]). Secondary outcomes also showed no significant differences (p > 0.05) among the 3 groups. CONCLUSIONS: Among adults with pain catastrophizing undergoing knee arthroplasty, cognitive behaviorally based pain coping skills training did not confer pain or functional benefit beyond the large improvements achieved with usual surgical and postoperative care. Future research should develop interventions for the approximately 20% of patients undergoing knee arthroplasty who experience persistent function-limiting pain. LEVEL OF EVIDENCE: Therapeutic Level I. See Instructions for Authors for a complete description of levels of evidence