Wildlife management by habitat units: A preliminary plan of action

Procedures for yielding vegetation type maps were developed using LANDSAT data and a computer assisted classification analysis (LARSYS) to assist in managing populations of wildlife species by defined area units. Ground cover in Travis County, Texas was classified on two occasions using a modified version of the unsupervised approach to classification. The first classification produced a total of 17 classes. Examination revealed that further grouping was justified. A second analysis produced 10 classes which were displayed on printouts which were later color-coded. The final classification was 82 percent accurate. While the classification map appeared to satisfactorily depict the existing vegetation, two classes were determined to contain significant error. The major sources of error could have been eliminated by stratifying cluster sites more closely among previously mapped soil associations that are identified with particular plant associations and by precisely defining class nomenclature using established criteria early in the analysis

JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics

In applications of machine learning to particle physics, a persistent challenge is how to go beyond discrimination to learn about the underlying physics. To this end, a powerful tool would be a framework for unsupervised learning, where the machine learns the intricate high-dimensional contours of the data upon which it is trained, without reference to pre-established labels. In order to approach such a complex task, an unsupervised network must be structured intelligently, based on a qualitative understanding of the data. In this paper, we scaffold the neural network's architecture around a leading-order model of the physics underlying the data. In addition to making unsupervised learning tractable, this design actually alleviates existing tensions between performance and interpretability. We call the framework JUNIPR: "Jets from UNsupervised Interpretable PRobabilistic models". In this approach, the set of particle momenta composing a jet are clustered into a binary tree that the neural network examines sequentially. Training is unsupervised and unrestricted: the network could decide that the data bears little correspondence to the chosen tree structure. However, when there is a correspondence, the network's output along the tree has a direct physical interpretation. JUNIPR models can perform discrimination tasks, through the statistically optimal likelihood-ratio test, and they permit visualizations of discrimination power at each branching in a jet's tree. Additionally, JUNIPR models provide a probability distribution from which events can be drawn, providing a data-driven Monte Carlo generator. As a third application, JUNIPR models can reweight events from one (e.g. simulated) data set to agree with distributions from another (e.g. experimental) data set.Comment: 37 pages, 24 figure

ESTIMATING RETURNS TO AGRICULTURAL RESEARCH, EXTENSION, AND TEACHING AT THE STATE LEVEL

The majority of decisions concerning investment and allocation of public funds for agricultural research, extension, and teaching (RET) are made at the state-level, while most of the quantitative RET evaluations are made on a national basis. This paper illustrates an approach for conducting a disaggregated state-level evaluation of agricultural research, extension, and teaching. Ridge regression is employed to handle multicollinearity problems.Teaching/Communication/Extension/Profession,

First-in-man evaluation of 124I-PGN650: A PET tracer for detecting phosphatidylserine as a biomarker of the solid tumor microenvironment

Purpose: PGN650 is a F(ab′) 2 antibody fragment that targets phosphatidylserine (PS), a marker normally absent that becomes exposed on tumor cells and tumor vasculature in response to oxidative stress and increases in response to therapy. PGN650 was labeled with 124 I to create a positron emission tomography (PET) agent as an in vivo biomarker for tumor microenvironment and response to therapy. In this phase 0 study, we evaluated the pharmacokinetics, safety, radiation dosimetry, and tumor targeting of this tracer in a cohort of patients with cancer. Methods: Eleven patients with known solid tumors received approximately 140 MBq (3.8 mCi) 124 I-PGN650 intravenously and underwent positron emission tomography–computed tomography (PET/CT) approximately 1 hour, 3 hours, and either 24 hours or 48 hours later to establish tracer kinetics for the purpose of calculating radiation dosimetry (from integration of the organ time-activity curves and OLINDA/EXM using the adult male and female models). Results: Known tumor foci demonstrated mildly increased uptake, with the highest activity at the latest imaging time. There were no unexpected adverse events. The liver was the organ receiving the highest radiation dose (0.77 mGy/MBq); the effective dose was 0.41 mSv/MBq. Conclusion: Although 124 I-PGN650 is safe for human PET imaging, the tumor targeting with this agent in patients was less than previously observed in animal studies