The ecology of Atlantic white cedar wetlands: a community profile

This monograph on the ecology of Atlantic white cedar wetlands is one of a series of U.S. Fish and Wildlife Service profiles of important freshwater wetland ecosystems of the United States. The purpose of the profile is to describe the extent, components, functioning, history, and treatment of these wetlands. It is intended to provide a useful reference to relevant scientific information and a synthesis of the available literature. The world range of Atlantic white cedar (Chamaecyparis thyoides) is limited to a ribbon of freshwater wetlands within 200 km of the Atlantic and Gulf coasts of the United States, extending from mid-Maine to mid-Florida and Mississippi. Often in inaccessible sites and difficult to traverse, cedar wetlands contain distinctive suites of plant species. Highly valued as commercial timber since the early days of European colonization of the continent, the cedar and its habitat are rapidly disappearing. This profile describes the Atlantic white cedar and the bogs and swamps it dominates or codominates throughout its range, discussing interrelationships with other habitats, putative origins and migration patterns, substrate biogeochemistry, associated plant and animal species (with attention to those that are rare, endangered, or threatened regionally or nationally), and impacts of both natural and anthropogenic disturbance. Research needs for each area are outlined. Chapters are devoted to the practices and problems of harvest and management, and to an examination of a large preserve recently acquired by the USFWS, the Alligator River National Wildlife Refuge in North Carolina

Levy Random Bridges and the Modelling of Financial Information

The information-based asset-pricing framework of Brody, Hughston and Macrina (BHM) is extended to include a wider class of models for market information. In the BHM framework, each asset is associated with a collection of random cash flows. The price of the asset is the sum of the discounted conditional expectations of the cash flows. The conditional expectations are taken with respect to a filtration generated by a set of "information processes". The information processes carry imperfect information about the cash flows. To model the flow of information, we introduce in this paper a class of processes which we term Levy random bridges (LRBs). This class generalises the Brownian bridge and gamma bridge information processes considered by BHM. An LRB is defined over a finite time horizon. Conditioned on its terminal value, an LRB is identical in law to a Levy bridge. We consider in detail the case where the asset generates a single cash flow $X_T$ occurring at a fixed date $T$. The flow of market information about $X_T$ is modelled by an LRB terminating at the date $T$ with the property that the (random) terminal value of the LRB is equal to $X_T$. An explicit expression for the price process of such an asset is found by working out the discounted conditional expectation of $X_T$ with respect to the natural filtration of the LRB. The prices of European options on such an asset are calculated

Unlocking biomarker discovery: Large scale application of aptamer proteomic technology for early detection of lung cancer

Lung cancer is the leading cause of cancer deaths, because ~84% of cases are diagnosed at an advanced stage. Worldwide in 2008, ~1.5 million people were diagnosed and ~1.3 million died – a survival rate unchanged since 1960. However, patients diagnosed at an early stage and have surgery experience an 86% overall 5-year survival. New diagnostics are therefore needed to identify lung cancer at this stage. Here we present the first large scale clinical use of aptamers to discover blood protein biomarkers in disease with our breakthrough proteomic technology. This multi-center case-control study was conducted in archived samples from 1,326 subjects from four independent studies of non-small cell lung cancer (NSCLC) in long-term tobacco-exposed populations. We measured >800 proteins in 15uL of serum, identified 44 candidate biomarkers, and developed a 12-protein panel that distinguished NSCLC from controls with 91% sensitivity and 84% specificity in a training set and 89% sensitivity and 83% specificity in a blinded, independent verification set. Performance was similar for early and late stage NSCLC. This is a significant advance in proteomics in an area of high clinical need

Structure and function of a dual antagonist of the human growth hormone and prolactin receptors with site-specific PEG conjugates

Human growth hormone (hGH) is a pituitary-derived endocrine protein that regulates several critical postnatal physiologic processes including growth, organ development, and metabolism. Following adulthood, GH is also a regulator of multiple pathologies like fibrosis, cancer, and diabetes. Therefore, there is a significant pharmaceutical interest in developing antagonists of hGH action. Currently, there is a single FDA-approved antagonist of the hGH receptor (hGHR) prescribed for treating patients with acromegaly and discovered in our laboratory almost 3 decades ago. Here, we present the first data on the structure and function of a new set of protein antagonists with the full range of hGH actions—dual antagonists of hGH binding to the GHR as well as that of hGH binding to the prolactin receptor. We describe the site-specific PEG conjugation, purification, and subsequent characterization using MALDI-TOF, size-exclusion chromatography, thermostability, and biochemical activity in terms of ELISA-based binding affinities with GHR and prolactin receptor. Moreover, these novel hGHR antagonists display distinct antagonism of GH-induced GHR intracellular signaling in vitro and marked reduction in hepatic insulin-like growth factor 1 output in vivo. Lastly, we observed potent anticancer biological efficacies of these novel hGHR antagonists against human cancer cell lines. In conclusion, we propose that these new GHR antagonists have potential for development towards multiple clinical applications related to GH-associated pathologies.</p

Aptamer-based multiplexed proteomic technology for biomarker discovery

Interrogation of the human proteome in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology. We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 [mu]L of serum or plasma). Our current assay allows us to measure ~800 proteins with very low limits of detection (1 pM average), 7 logs of overall dynamic range, and 5% average coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding DNA aptamer concentration signature, which is then quantified with a DNA microarray. In essence, our assay takes advantage of the dual nature of aptamers as both folded binding entities with defined shapes and unique sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to discover unique protein signatures characteristic of various disease states. More generally, we describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine

Large-scale serum protein biomarker discovery in Duchenne muscular dystrophy.

Serum biomarkers in Duchenne muscular dystrophy (DMD) may provide deeper insights into disease pathogenesis, suggest new therapeutic approaches, serve as acute read-outs of drug effects, and be useful as surrogate outcome measures to predict later clinical benefit. In this study a large-scale biomarker discovery was performed on serum samples from patients with DMD and age-matched healthy volunteers using a modified aptamer-based proteomics technology. Levels of 1,125 proteins were quantified in serum samples from two independent DMD cohorts: cohort 1 (The Parent Project Muscular Dystrophy-Cincinnati Children's Hospital Medical Center), 42 patients with DMD and 28 age-matched normal volunteers; and cohort 2 (The Cooperative International Neuromuscular Research Group, Duchenne Natural History Study), 51 patients with DMD and 17 age-matched normal volunteers. Forty-four proteins showed significant differences that were consistent in both cohorts when comparing DMD patients and healthy volunteers at a 1% false-discovery rate, a large number of significant protein changes for such a small study. These biomarkers can be classified by known cellular processes and by age-dependent changes in protein concentration. Our findings demonstrate both the utility of this unbiased biomarker discovery approach and suggest potential new diagnostic and therapeutic avenues for ameliorating the burden of DMD and, we hope, other rare and devastating diseases

A 680,000-person megastudy of nudges to encourage vaccination in pharmacies

Encouraging vaccination is a pressing policy problem. To assess whether text-based reminders can encourage pharmacy vaccination and what kinds of messages work best, we conducted a megastudy. We randomly assigned 689,693 Walmart pharmacy patients to receive one of 22 different text reminders using a variety of different behavioral science principles to nudge flu vaccination or to a business-as-usual control condition that received no messages. We found that the reminder texts that we tested increased pharmacy vaccination rates by an average of 2.0 percentage points, or 6.8%, over a 3-mo follow-up period. The most-effective messages reminded patients that a flu shot was waiting for them and delivered reminders on multiple days. The top-performing intervention included two texts delivered 3 d apart and communicated to patients that a vaccine was “waiting for you.” Neither experts nor lay people anticipated that this would be the best-performing treatment, underscoring the value of simultaneously testing many different nudges in a highly powered megastudy