Impact of Home Field Advantage: Analyzed Across Three Professional Sports

We examined the impact of home-field advantage in the NFL, NBA, and MLB. We defined home-field advantage as winning more than 50% of the home games. Additionally, we took into consideration how season length could act as a moderator and influence the impact of home-field advantage. We collected data from the 2015 NBA and MLB seasons and the 2015 and 2016 NFL seasons to determine statistical significance. In total, we got data from 4,141 games to analyze. We found that there is statistical significance that the home team has a better chance of winning than the away team across the NFL, NBA, and MLB. We also found that season length has a significant impact on home team winning percentage

Interview with Marjorie Risser, Class of 1944

Oral history interview with Illinois State Normal University alumnus Marjorie Munns Risser, Class of 1944. The interview was conducted on November 3, 1979, by student Denise Cook of ISU Alumni Services. They discuss changes in the student body size, extracurricular activities, and memorable faculty.https://ir.library.illinoisstate.edu/aoh/1010/thumbnail.jp

Local likelihood estimation for covariance functions with spatially-varying parameters: the convoSPAT package for R

In spite of the interest in and appeal of convolution-based approaches for nonstationary spatial modeling, off-the-shelf software for model fitting does not as of yet exist. Convolution-based models are highly flexible yet notoriously difficult to fit, even with relatively small data sets. The general lack of pre-packaged options for model fitting makes it difficult to compare new methodology in nonstationary modeling with other existing methods, and as a result most new models are simply compared to stationary models. Using a convolution-based approach, we present a new nonstationary covariance function for spatial Gaussian process models that allows for efficient computing in two ways: first, by representing the spatially-varying parameters via a discrete mixture or "mixture component" model, and second, by estimating the mixture component parameters through a local likelihood approach. In order to make computation for a convolution-based nonstationary spatial model readily available, this paper also presents and describes the convoSPAT package for R. The nonstationary model is fit to both a synthetic data set and a real data application involving annual precipitation to demonstrate the capabilities of the package

Quantifying the effect of interannual ocean variability on the attribution of extreme climate events to human influence

In recent years, the climate change research community has become highly interested in describing the anthropogenic influence on extreme weather events, commonly termed "event attribution." Limitations in the observational record and in computational resources motivate the use of uncoupled, atmosphere/land-only climate models with prescribed ocean conditions run over a short period, leading up to and including an event of interest. In this approach, large ensembles of high-resolution simulations can be generated under factual observed conditions and counterfactual conditions that might have been observed in the absence of human interference; these can be used to estimate the change in probability of the given event due to anthropogenic influence. However, using a prescribed ocean state ignores the possibility that estimates of attributable risk might be a function of the ocean state. Thus, the uncertainty in attributable risk is likely underestimated, implying an over-confidence in anthropogenic influence. In this work, we estimate the year-to-year variability in calculations of the anthropogenic contribution to extreme weather based on large ensembles of atmospheric model simulations. Our results both quantify the magnitude of year-to-year variability and categorize the degree to which conclusions of attributable risk are qualitatively affected. The methodology is illustrated by exploring extreme temperature and precipitation events for the northwest coast of South America and northern-central Siberia; we also provides results for regions around the globe. While it remains preferable to perform a full multi-year analysis, the results presented here can serve as an indication of where and when attribution researchers should be concerned about the use of atmosphere-only simulations

Adhesive Virulence Factors of Staphylococcus aureus Resist Digestion by Coagulation Proteases Thrombin and Plasmin

Staphylococcus aureus (S. aureus) is an invasive and life-threatening pathogen that has undergone extensive coevolution with its mammalian hosts. Its molecular adaptations include elaborate mechanisms for immune escape and hijacking of the coagulation and fibrinolytic pathways. These capabilities are enacted by virulence factors including microbial surface components recognizing adhesive matrix molecules (MSCRAMMs) and the plasminogen-activating enzyme staphylokinase (SAK). Despite the ability of S. aureus to modulate coagulation, until now the sensitivity of S. aureus virulence factors to digestion by proteases of the coagulation system was unknown. Here, we used protein engineering, biophysical assays, and mass spectrometry to study the susceptibility of S. aureus MSCRAMMs to proteolytic digestion by human thrombin, plasmin, and plasmin/SAK complexes. We found that MSCRAMMs were highly resistant to proteolysis, and that SAK binding to plasmin enhanced this resistance. We mapped thrombin, plasmin, and plasmin/SAK cleavage sites of nine MSCRAMMs and performed biophysical, bioinformatic, and stability analysis to understand structural and sequence features common to protease-susceptible sites. Overall, our study offers comprehensive digestion patterns of S. aureus MSCRAMMs by thrombin, plasmin, and plasmin/SAK complexes and paves the way for new studies into this resistance and virulence mechanism

Interpreting forest and grassland biome productivity utilizing nested scales of image resolution and biogeographical analysis

Several hardware, software, and data collection problems encountered were conquered. The Geographic Information System (GIS) data from other systems were converted to ERDAS format for incorporation with the image data. Statistical analysis of the relationship between spectral values and productivity is being pursued. Several project sites, including Jackson, Pope, Boulder, Smokies, and Huntington Forest are evolving as the most intensively studied areas, primarily due to availability of data and time. Progress with data acquisition and quality checking, more details on experimental sites, and brief summarizations of research results and future plans are discussed. Material on personnel, collaborators, facilities, site background, and meetings and publications of the investigators are included

Heterocyst placement strategies to maximize growth of cyanobacterial filaments

Under conditions of limited fixed-nitrogen, some filamentous cyanobacteria develop a regular pattern of heterocyst cells that fix nitrogen for the remaining vegetative cells. We examine three different heterocyst placement strategies by quantitatively modelling filament growth while varying both external fixed-nitrogen and leakage from the filament. We find that there is an optimum heterocyst frequency which maximizes the growth rate of the filament; the optimum frequency decreases as the external fixed-nitrogen concentration increases but increases as the leakage increases. In the presence of leakage, filaments implementing a local heterocyst placement strategy grow significantly faster than filaments implementing random heterocyst placement strategies. With no extracellular fixed-nitrogen, consistent with recent experimental studies of Anabaena sp. PCC 7120, the modelled heterocyst spacing distribution using our local heterocyst placement strategy is qualitatively similar to experimentally observed patterns. As external fixed-nitrogen is increased, the spacing distribution for our local placement strategy retains the same shape while the average spacing between heterocysts continuously increases.Comment: This is an author-created, un-copyedited version of an article accepted for publication in Physical Biology. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher-authenticated version will be available onlin

Explaining the unexplainable: leveraging extremal dependence to characterize the 2021 Pacific Northwest heatwave

In late June, 2021, a devastating heatwave affected the US Pacific Northwest and western Canada, breaking numerous all-time temperature records by large margins and directly causing hundreds of fatalities. The observed 2021 daily maximum temperature across much of the U.S. Pacific Northwest exceeded upper bound estimates obtained from single-station temperature records even after accounting for anthropogenic climate change, meaning that the event could not have been predicted under standard univariate extreme value analysis assumptions. In this work, we utilize a flexible spatial extremes model that considers all stations across the Pacific Northwest domain and accounts for the fact that many stations simultaneously experience extreme temperatures. Our analysis incorporates the effects of anthropogenic forcing and natural climate variability in order to better characterize time-varying changes in the distribution of daily temperature extremes. We show that greenhouse gas forcing, drought conditions and large-scale atmospheric modes of variability all have significant impact on summertime maximum temperatures in this region. Our model represents a significant improvement over corresponding single-station analysis, and our posterior medians of the upper bounds are able to anticipate more than 96% of the observed 2021 high station temperatures after properly accounting for extremal dependence.Comment: 19 pages, 4 figures and 2 table

Robotic intra-row weed hoeing in maize and sugar beet

A prototype of robotic intra-row weed hoeing in maize and sugar beet is presented in this study. Weeds in the crop rows were identified using a bi-spectral image analysis system and shape analysis. Positions of weeds in the images were recorded. Selective weed control in the row was performed with a modified finger weeder driven by electrical motors. Speed of the finger weeder was increased at positions where only weeds were classified. The system was triggered by an encoder and controlled by a micro-controller. Roboter-gesteuerte Unkrauthacke in der Reihe von Mais und ZuckerrübenEin Prototyp einer roboter-gesteuerten Hacke zur Unkrautbekämpfung in den Reihen von Mais und Zuckerrübe wird in dieser Studie vorgestellt. Unkräuter und Kulturpflanzen wurden mit einer bi-spektralen Kamera und Formenanalyse erkannt. Die Positionen der Unkräuter im Bild wurden bestimmt. Die selektive Unkrautbekämpfung in der Reihe geschah mit einer modifizierten Fingerhacke, die über Elektromotoren angetrieben werden. Die Fingerhacke wurde beschleunigt, wenn nur Unkräuter in der Reihe klassifiziert wurden. Das System wurde mit einem Inkrementalgeber getriggert und über einen Micro-Controller gesteuert