Hard Datasets Research Articles

BENTHIC PREDATORS AND NORTHERN QUAHOG (=HARD CLAM) (MERCENARIA MERCENARIA LINNAEUS, 1758) POPULATIONS

Increased numbers of benthic predators, especially crabs, have been proposed as a factor contributing to the decline of hard clams (Mercenaria mercenaria Linnaeus, 1758) in Great South Bay, NY. The long-term trend in benthic predators in this system was examined using observations on the distribution and abundance of predators that have been collected by the Town of Islip, NY as part of an annual survey of hard clam populations. The survey began in 1978 and extends to the present and provides concurrent observations of habitat (sediment type, and presence/absence of eelgrass), and hard clam size-frequency distribution and abundance. Predator type and abundance were reported from 1978 to 1981 and 1991 to 2003, which represents one of the most comprehensive benthic predator data sets currently available for any estuarine system. The annual averages of predator abundance in the survey area primarily show interannual fluctuations in abundance. Xanthid crabs (mud crabs, primarily Dispanopeus sayi Smith, 1869) were the numerically dominant predator in the system; blue crabs (Callinectes sapidus Rathbun, 1895) appeared in the late 1990s. Hard clam abundance has declined by 44% since the early 1990s. An Empirical Orthogonal Function (EOF) Analysis of the predator and hard clam data sets showed that fluctuations in predator abundance are: 1) mostly in phase over the survey region and 2) dominated by year-to-year fluctuations in abundance. The EOF results for the hard clams show that hard clam abundance fluctuations are: 1) in phase over the survey region and 2) dominated by a decreasing trend in abundance over the time series. The primary EOF modes essentially were uncoupled, which implies no strong predator- prey interactions between the predators and hard clams. By inference, increasing predator abundance does not appear to be a primary factor producing the long-term decline in hard clam populations. Predation pressure per recruit may still have increased because of declining hard clam population abundance and the concomitant decline in recruitment.

Optimum cut-based clustering

This paper presents a new method for solving clustering problem. We treat clustering as a graph-partitioning problem and propose a new global criterion, the optimum cut, for segmenting the graph. An important feature is that optimizing the optimum cut criterion can ensure that the intra-cluster similarity is maximized while the inter-cluster similarity is minimized. We show that an efficient computational technique based on an eigenvalue problem can be used to optimize this criterion. The experimental results on a number of hard artificial and real-world data sets show the effectiveness of the approach.

MUSTANG: A multiple structural alignment algorithm

Multiple structural alignment is a fundamental problem in structural genomics. In this article, we define a reliable and robust algorithm, MUSTANG (MUltiple STructural AligNment AlGorithm), for the alignment of multiple protein structures. Given a set of protein structures, the program constructs a multiple alignment using the spatial information of the C(alpha) atoms in the set. Broadly based on the progressive pairwise heuristic, this algorithm gains accuracy through novel and effective refinement phases. MUSTANG reports the multiple sequence alignment and the corresponding superposition of structures. Alignments generated by MUSTANG are compared with several handcurated alignments in the literature as well as with the benchmark alignments of 1033 alignment families from the HOMSTRAD database. The performance of MUSTANG was compared with DALI at a pairwise level, and with other multiple structural alignment tools such as POSA, CE-MC, MALECON, and MultiProt. MUSTANG performs comparably to popular pairwise and multiple structural alignment tools for closely related proteins, and performs more reliably than other multiple structural alignment methods on hard data sets containing distantly related proteins or proteins that show conformational changes.

Weaving Single Photon Imaging into New Drug Development

The specific aim of this review is to assess the potential contribution of single photon emitting radiopharmaceutical technologies to new drug development. For each phase of therapeutic drug development, published literature was sought that shows single photon emitters can add value by quantifying pharmacokinetics, visualizing mechanisms of drug action, estimating therapeutic safety indices, or measuring dose-dependent pharmacodynamic effects. Not any published reports were found that describe using nuclear medicine techniques to help manage the progress of a new drug development program. As a consequence, most of the case in favor of weaving single photon imaging into the process had to be built on extrapolations from studies that showed feasibility post hoc. The strongest evidence of potential value was found for drug candidates that hope to influence diseases characterized by cell proliferation or cell death, particularly in the fields of oncology, cardiology, nephrology, and inflammation. Receptor occupancy studies were observed to occasionally offer unique advantages over analogous studies with positron emission tomography (PET). Enough hard data sets were found to justify the costs of using single photon imaging in a variety of new drug development paradigms.

Diffusion tensor imaging for the assessment of upper motor neuron integrity in ALS

High angular resolution diffusion tensor imaging (HARD) is an MRI technique that exploits the mobility of water molecules to yield maps of structural order and directionality of white matter tracts with greater precision than six-direction diffusion tensor imaging (DTI) schemes. To assess whether HARD is more sensitive than conventional MRI or neurologic assessment in detecting the upper motor neuron (UMN) pathology of patients with ALS. Twenty-five patients with definite UMN clinical signs and 23 healthy volunteers underwent conventional MRI. HARD datasets were collected from a subset of these participants plus four patients with isolated lower motor neuron (LMN) signs. ALS symptom severity was assessed by a neurologist, the conventional MR images were reviewed by neuroradiologists, and the DTI maps were subject to quantitative region of interest analysis. Motor cortex hypointensity on T2-weighted images and corona radiata hyperintensity on proton density-weighted images distinguished patients with UMN involvement from volunteers with 100% specificity, but only 20% sensitivity. Fractional anisotropy (FA) was reduced in the posterior limb of the internal capsule in patients with UMN involvement compared to volunteers. A FA threshold value with a sensitivity of 95% to detect patients with ALS (including those with isolated LMN signs) had a specificity of 71%. High angular resolution diffusion tensor imaging may be more sensitive than conventional MRI or neurologic assessment to the upper motor neuron (UMN) pathology of ALS, but it lacks the specificity required of a diagnostic marker. Instead, it is potentially useful as a quantitative tool for monitoring the progression of UMN pathology.

Application of the BME approach to soil texture mapping

In order to derive accurate space/time maps of soil properties, soil scientists need tools that combine the usually scarce hard data sets with the more easily accessible soft data sets. In the field of modern geostatistics, the Bayesian maximum entropy (BME) approach provides new and powerful means for incorporating various forms of physical knowledge (including hard and soft data, soil classification charts, land cover data from satellite pictures, and digital elevation models) into the space/time mapping process. BME produces the complete probability distribution at each estimation point, thus allowing the calculation of elaborate statistics (even when the distribution is not Gaussian). It also offers a more rigorous and systematic method than kriging for integrating uncertain information into space/time mapping. In this work, BME is used to estimate the three textural fractions involved in a texture map. The first case study focuses on the estimation of the clay fraction, whereas the second one considers the three textural fractions (sand, silt and clay) simultaneously. The BME maps obtained are informative (important soil characteristics are identified, natural variations are well reproduced, etc.). Furthermore, in both case studies, the estimates obtained by BME were more accurate than the simple kriging (SK) estimates, thus offering a better picture of soil reality. In the multivariate case, classification error rate analysis in terms of BME performs considerably better than in terms of kriging. Analysis in terms of BME can offer valuable information to be used in sampling design, in optimizing the hard to soft data ratio, etc.

Cladistical Analysis of G-Banded Chromosomes of Nectar Feeding Bats (Glossophaginae: Phyllostomidae)

Cladistical Analysis of G-Banded Chromosomes of Nectar Feeding Bats (Glossophaginae: Phyllostomidae)