The Effects of Incubation Length and Temperature on the Specificity and Sensitivity of the BANA (N‐Benzoyl‐DL‐Arginine‐Naphthylamide) Test

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141244/1/jper0848.pd

Machine learned daily life history classification using low frequency tracking data and automated modelling pipelines: application to North American waterfowl

Background: Identifying animal behaviors, life history states, and movement patterns is a prerequisite for many animal behavior analyses and effective management of wildlife and habitats. Most approaches classify short-term movement patterns with high frequency location or accelerometry data. However, patterns reflecting life history across longer time scales can have greater relevance to species biology or management needs, especially when available in near real-time. Given limitations in collecting and using such data to accurately classify complex behaviors in the long-term, we used hourly GPS data from 5 waterfowl species to produce daily activity classifications with machine-learned models using “automated modelling pipelines”. Methods: Automated pipelines are computer-generated code that complete many tasks including feature engineering, multi-framework model development, training, validation, and hyperparameter tuning to produce daily classifications from eight activity patterns reflecting waterfowl life history or movement states. We developed several input features for modeling grouped into three broad categories, hereafter “feature sets”: GPS locations, habitat information, and movement history. Each feature set used different data sources or data collected across different time intervals to develop the “features” (independent variables) used in models. Results: Automated modelling pipelines rapidly developed easily reproducible data preprocessing and analysis steps, identification and optimization of the best performing model and provided outputs for interpreting feature importance. Unequal expression of life history states caused unbalanced classes, so we evaluated feature set importance using a weighted F1-score to balance model recall and precision among individual classes. Although the best model using the least restrictive feature set (only 24 hourly relocations in a day) produced effective classifications (weighted F1 = 0.887), models using all feature sets performed substantially better (weighted F1 = 0.95), particularly for rarer but demographically more impactful life history states (i.e., nesting). Conclusions: Automated pipelines generated models producing highly accurate classifications of complex daily activity patterns using relatively low frequency GPS and incorporating more classes than previous GPS studies. Near real-time classification is possible which is ideal for time-sensitive needs such as identifying reproduction. Including habitat and longer sequences of spatial information produced more accurate classifications but incurred slight delays in processing

Adsorption of para-Hydrogen on Krypton pre-plated graphite

Adsorption of para-Hydrogen on the surface of graphite pre-plated with a single layer of atomic krypton is studied thoretically by means of Path Integral Ground State Monte Carlo simulations. We compute energetics and density profiles of para-hydrogen, and determine the structure of the adsorbed film for various coverages. Results show that there are two thermodynamically stable monolayer phases of para-hydrogen, both solid. One is commensurate with the krypton layer, the other is incommensurate. No evidence is seen of a thermodynamically stable liquid phase, at zero temperature. These results are qualitatively similar to what is seen for for para-hydrogen on bare graphite. Quantum exchanges of hydrogen molecules are suppressed in this system.Comment: 12 pages, 6 figures, to appear in the proceedings of "Advances in Computational Many-Body Physics", Banff, Alberta (Canada), January 13-16 200

The IceCube Neutrino Observatory: Instrumentation and Online Systems

The IceCube Neutrino Observatory is a cubic-kilometer-scale high-energy neutrino detector built into the ice at the South Pole. Construction of IceCube, the largest neutrino detector built to date, was completed in 2011 and enabled the discovery of high-energy astrophysical neutrinos. We describe here the design, production, and calibration of the IceCube digital optical module (DOM), the cable systems, computing hardware, and our methodology for drilling and deployment. We also describe the online triggering and data filtering systems that select candidate neutrino and cosmic ray events for analysis. Due to a rigorous pre-deployment protocol, 98.4% of the DOMs in the deep ice are operating and collecting data. IceCube routinely achieves a detector uptime of 99% by emphasizing software stability and monitoring. Detector operations have been stable since construction was completed, and the detector is expected to operate at least until the end of the next decade.Comment: 83 pages, 50 figures; updated with minor changes from journal review and proofin

Impaired Chromatin Remodelling at STAT1-Regulated Promoters Leads to Global Unresponsiveness of Toxoplasma gondii-Infected Macrophages to IFN-γ

Intracellular pathogens including the apicomplexan and opportunistic parasite Toxoplasma gondii profoundly modify their host cells in order to establish infection. We have shown previously that intracellular T. gondii inhibit up-regulation of regulatory and effector functions in murine macrophages (MΦ) stimulated with interferon (IFN)-γ, which is the cytokine crucial for controlling the parasites' replication. Using genome-wide transcriptome analysis we show herein that infection with T. gondii leads to global unresponsiveness of murine macrophages to IFN-γ. More than 61% and 89% of the transcripts, which were induced or repressed by IFN-γ in non-infected MΦ, respectively, were not altered after stimulation of T. gondii-infected cells with IFN-γ. These genes are involved in a variety of biological processes, which are mostly but not exclusively related to immune responses. Analyses of the underlying mechanisms revealed that IFN-γ-triggered nuclear translocation of STAT1 still occurred in Toxoplasma-infected MΦ. However, STAT1 bound aberrantly to oligonucleotides containing the IFN-γ-responsive gamma-activated site (GAS) consensus sequence. Conversely, IFN-γ did not induce formation of active GAS-STAT1 complexes in nuclear extracts from infected MΦ. Mass spectrometry of protein complexes bound to GAS oligonucleotides showed that T. gondii-infected MΦ are unable to recruit non-muscle actin to IFN-γ-responsive DNA sequences, which appeared to be independent of stimulation with IFN-γ and of STAT1 binding. IFN-γ-induced recruitment of BRG-1 and acetylation of core histones at the IFN-γ-regulated CIITA promoter IV, but not β-actin was diminished by >90% in Toxoplasma-infected MΦ as compared to non-infected control cells. Remarkably, treatment with histone deacetylase inhibitors restored the ability of infected macrophages to express the IFN-γ regulated genes H2-A/E and CIITA. Taken together, these results indicate that Toxoplasma-infected MΦ are unable to respond to IFN-γ due to disturbed chromatin remodelling, but can be rescued using histone deacetylase inhibitors

The Influence of Diversity and Experience on the Effects of Crowd Size

One advantage of crowds over traditional teams is that crowds enable the assembling of a large number of individuals to address problems. The literature is unclear, however, about the relationship between the size of crowds and its impact on outcomes. To better understand the effects of crowd size we conducted a study on the retention and performance based on 4,317 articles in the WikiProject Film community. Our results suggest that crowds benefit from their size when they are diverse, experienced, and have low retention rates.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136213/1/Robert and Romero 2017 (JAIST).pd