Characteristics of the Private Nuisance Wildlife Control Industry in New York

The nuisance wildlife control industry is rapidly expanding in New York State. To gain additional insight about this industry and the number of animals handled, we reviewed the 1989-90 annual logs submitted by Nuisance Wildlife Control Operators (NWCOs) to the New York State Department of Environmental Conservatlon (DEC). The specific objectives of this study were to determine: (1) the number and species of different wildlife responsible for damage incidents, (2) the cause of damage complaints, (3) the disposition of animals handled, (4) the location of damage events (i.e., urban, suburban, rural), and (5) an estimate of the economic impact of the nuisance wildlife industry in Upstate New York. The Nuisance Wildlife Logs (NWLs) were examined for 7 urban and 7 rural counties (25.5% of Upstate counties), and these data were used to estimate total NWCO activity in DEC Regions 3 through 9 (excludes Long Island). Approximately 75% of NWCOs licensed by DEC were active during 1989-90, and nearly 2,800 complaints were handled in the 14 counties sampled. More than 90% of complaints came from urban counties, and we estimated that NWCOs responded to more than 11,000 calls in Upstate New York. At a conservative estimate of $35/call, revenue generated by this industry exceeded$385,000 annually . Six wildlife species accounted for 85% of the nuisance complaints in urban and rural counties. During 1986 to 1993, the number of NWCOs licensed by DEC nearly quadrupled, and there is no indication that this trend will change in the near future

Pilot Medical Certification Period Health State Forecasts

DTFAWA-10-C-00080The Federal Aviation Administration (FAA) Office of Aerospace Medicine supports research to use available healthcare data to inform policies regarding pilots' medical certifications. The MITRE Corporation's Center for Advanced System Development (MITRE CAASD) was asked to examine methods in advanced data analytics and machine learning (ML) to inform such risk-based decision-making. As an initial step in assessing the potential predictive value of commercially available healthcare data, the FAA provided MITRE CAASD the IBM MarketScan dataset\u2014a large set of commercial healthcare claims records. Using this dataset, we developed methods for identifying health status and changes in health status across conditions; for measuring changes in health status among enrollees with diabetes mellitus (DM); and for measuring the onset of new cases of DM, traumatic brain injury, sleep apnea, and chronic obstructive pulmonary disease. We developed a repeatable workflow and modeled these conditions using a wide range of ML methods. We conclude that ML-based predictive modeling of health conditions from IBM MarketScan data is feasible and informative. However, additional clinical information from commercially available electronic health records would likely improve accuracy and more closely align with future FAA needs

Mesenchymal Cancer Cell-Stroma Crosstalk Promotes Niche Activation, Epithelial Reversion, and Metastatic Colonization

SummaryDuring metastatic colonization, tumor cells must establish a favorable microenvironment or niche that will sustain their growth. However, both the temporal and molecular details of this process remain poorly understood. Here, we found that metastatic initiating cells (MICs) exhibit a high capacity for lung fibroblast activation as a result of Thrombospondin 2 (THBS2) expression. Importantly, inhibiting the mesenchymal phenotype of MICs by blocking the epithelial-to-mesenchymal transition (EMT)-associated kinase AXL reduces THBS2 secretion, niche-activating ability, and, consequently, metastatic competence. Subsequently, disseminated metastatic cells revert to an AXL-negative, more epithelial phenotype to proliferate and decrease the phosphorylation levels of TGF-β-dependent SMAD2-3 in favor of BMP/SMAD1-5 signaling. Remarkably, newly activated fibroblasts promote this transition. In summary, our data reveal a crosstalk between cancer cells and their microenvironment whereby the EMT status initially triggers and then is regulated by niche activation during metastatic colonization

The Mayo/MITRE system for discovery of obesity and its comorbidities

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Epithelial to Mesenchymal Transition Is Mechanistically Linked with Stem Cell Signatures in Prostate Cancer Cells

Current management of patients diagnosed with prostate cancer (PCa) is very effective; however, tumor recurrence with Castrate Resistant Prostate Cancer (CRPC) and subsequent metastasis lead to poor survival outcome, suggesting that there is a dire need for novel mechanistic understanding of tumor recurrence, which would be critical for designing novel therapies. The recurrence and the metastasis of PCa are tightly linked with the biology of prostate cancer stem cells or cancer-initiating cells that is reminiscent of the acquisition of Epithelial to Mesenchymal Transition (EMT) phenotype. Increasing evidence suggests that EMT-type cells share many biological characteristics with cancer stem-like cells.In this study, we found that PCa cells with EMT phenotype displayed stem-like cell features characterized by increased expression of Sox2, Nanog, Oct4, Lin28B and/or Notch1, consistent with enhanced clonogenic and sphere (prostasphere)-forming ability and tumorigenecity in mice, which was associated with decreased expression of miR-200 and/or let-7 family. Reversal of EMT by re-expression of miR-200 inhibited prostasphere-forming ability of EMT-type cells and reduced the expression of Notch1 and Lin28B. Down-regulation of Lin28B increased let-7 expression, which was consistent with repressed self-renewal capability.These results suggest that miR-200 played a pivotal role in linking the characteristics of cancer stem-like cells with EMT-like cell signatures in PCa. Selective elimination of cancer stem-like cells by reversing the EMT phenotype to Mesenchymal-Epithelial Transition (MET) phenotype using novel agents would be useful for the prevention of tumor recurrence especially by eliminating those cells that are the "Root Cause" of tumor development and recurrence

Epithelial-mesenchymal transition and cancer stem cells: a dangerously dynamic duo in breast cancer progression

Aberrant activation of a latent embryonic program - known as the epithelial-mesenchymal transition (EMT) - can endow cancer cells with the migratory and invasive capabilities associated with metastatic competence. The induction of EMT entails the loss of epithelial characteristics and the de novo acquisition of a mesenchymal phenotype. In breast cancer, the EMT state has been associated with cancer stem cell properties including expression of the stem cell-associated CD44+/CD24-/low antigenic profile, self-renewal capabilities and resistance to conventional therapies. Intriguingly, EMT features are also associated with stem cells isolated from the normal mouse mammary gland and human breast reduction tissues as well as the highly aggressive metaplastic and claudin-low breast tumor subtypes. This has implications for the origin of these breast tumors as it remains unclear whether they derive from cells that have undergone EMT or whether they represent an expansion of a pre-existing stem cell population that expresses EMT-associated markers to begin with. In the present review, we consider the current evidence connecting EMT and stem cell attributes and discuss the ramifications of these newly recognized links for our understanding of the emergence of distinct breast cancer subtypes and breast cancer progression

Toward Conditional Models of Identity Uncertainty with Application to Proper Noun Coreference

Coreference analysis, also known as record linkage or identity uncertainty, is a difficult and important problem in natural language processing, databases, citation matching and many other tasks. This paper introduces several discriminative, conditionalprobability models for coreference analysis, all examples of undirected graphical models. Unlike many historical approaches to coreference, the models presented here are relational—they do not assume that pairwise coreference decisions should be made independently from each other. Unlike other relational models of coreference that are generative, the conditional model here can incorporate a great variety of features of the input without having to be concerned about their dependencies— paralleling the advantages of conditional random fields over hidden Markov models. We present experiments on proper noun coreference in two text data sets, showing results in which we reduce error by nearly 28% or more over traditional thresholded record-linkage, and by up to 33% over an alternative coreference technique previously used in natural language processing

Weakly Supervised Learning Methods for Improving the Quality of Gene Name Normalization Data

A pervasive problem facing many biomedical text mining applications is that of correctly associating mentions of entities in the literature with corresponding concepts in a database or ontology. Attempts to build systems for automating this process have shown promise as demonstrated by the recent BioCreAtIvE Task 1B evaluation. A significant obstacle to improved performance for this task, however, is a lack of high quality training data. In this work, we explore methods for improving the quality of (noisy) Task 1B training data using variants of weakly supervised learning methods. We present positive results demonstrating that these methods result in an improvement in training data quality as measured by improved system performance over the same system using the originally labeled data