Systematic Datasets Research Articles

Spatiotemporal variability of ocean chemistry in the early Cambrian, South China

Following the Ediacaran metazoan radiation, the “Cambrian Explosion” set up the major framework of todays’ animal phyla as well as modern marine ecosystem. Here, we present a preliminary investigation on the temporal and spatial (from shallow to deep waters) variations of the early Cambrian ocean chemistry in South China through analyzing a Fe-S-C systematic dataset integrated from literature. Our investigation indicates that the early Cambrian deep ocean in South China was still anoxic and Fe2+-enriched (i.e., ferruginous) although its surface was oxic, and in between a metastable euxinic (anoxic and sulfidic) water zone may have dynamically developed in anoxic shelf waters with an increasing weathering sulfate supply. Furthermore, accompanying marine transgression and regression cycles in the early Cambrian, such a “sandwich” structure in ocean redox chemistry demonstrates five evolutional stages, which can be well correlated to the spatiotemporal patterns of fossil records in South China. The good correlation between metazoan fossil occurrences and water chemistry in South China suggests that early animals possibly possessed ability to inhabit anoxic but generally not euxinic environments as free H2S was fatal to most eukaryotes. This view can well explain why those small shell fauna and sponges disappeared from shelf to slope areas where sulfidic Ni-Mo-rich shales were widely deposited. Thus, we conclude that the spatiotemporal variations of ocean chemistry and its biological effects probably played a key role in the phased animal radiations and “extinctions” in the early Cambrian.

A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening

We present an integrated approach that predicts and validates novel anti-cancer drug targets. We first built a classifier that integrates a variety of genomic and systematic datasets to prioritize drug targets specific for breast, pancreatic and ovarian cancer. We then devised strategies to inhibit these anti-cancer drug targets and selected a set of targets that are amenable to inhibition by small molecules, antibodies and synthetic peptides. We validated the predicted drug targets by showing strong anti-proliferative effects of both synthetic peptide and small molecule inhibitors against our predicted targets.

One Moon, many measurements 1: Radiance values

Several modern optical instruments orbited the Moon during 2008 and 2009 onboard the SELENE and Chandrayaan-1 spacecraft and provided a welcomed feast of spectroscopic data to be used for scientific analyses. The different spatial and spectral resolutions of these sensors along with diverse illumination geometry during data acquisition make each set of data unique, and each instrument contributes special value to integrated science analyses. In order to provide the maximum science benefit, we have undertaken a careful cross-validation of radiance data among these orbital instruments and also a set of systematic data acquired using Earth-based telescopes. Most radiance values at 750nm fall between 0 and 100W/(m2μmsr), but a small important fraction can be up to ×2 to ×3 that value, with the largest values occurring at the highest spatial resolution. All instruments are in agreement about overall spectral properties of lunar materials, but small systematic differences are documented between instruments. Lunar radiance values measured with remote sensors for landing sites are all not as high as that estimated from laboratory measurements of returned soil. This is largely because laboratory measurements of lunar soils cannot retain or duplicate the fine structure of lunar regolith found in the natural space environment.

A model for remote estimation of ultraviolet absorption by chromophoric dissolved organic matter based on the global distribution of spectral slope

Absorption of ultraviolet radiation (UV, 280–400nm) by chromophoric dissolved organic matter (CDOM) precedes a host of light-sensitized surface ocean processes relevant to global climate. These include photo- and biogeochemical cycling of organic material, release of sulfur and carbon-containing gases to the atmosphere, and the photoprotection of marine microorganisms. Synoptic CDOM absorption data in the UV is highly desired yet difficult to estimate by satellite methods as the atmosphere interferes with direct detection of water-leaving UV radiance. The absorption spectrum of CDOM is typically modeled as an exponential function in which a spectral slope parameter, S, describes the rate of decrease in absorption with increase in wavelength. Significant functional relationships are observed in aquatic environments between S and the CDOM absorption coefficient at 443nm, aCDOM(443). In this paper, we use a large, systematic dataset of spectroscopic CDOM measurements from the U.S. CO2/CLIVAR Repeat Hydrography Survey to examine the relationship between S and aCDOM(443) as a means to model aCDOM(λ) in the UV from ocean color. Our resultant model predicts aCDOM(λ) at wavelengths from 325 to 412nm from the absorption coefficient of colored dissolved and detrital materials (CDM) at 443nm, aCDM(443), retrieved by an existing semi-analytical ocean color algorithm. Expected agreement (near 1:1) with the training dataset was achieved (r2=0.71–0.85, p=0, n=127). Considering inherent satellite data uncertainties as well as the model's limitations in regions with potential terrestrial influence, good correspondence between modeled and in situ values was observed during independent validation with open ocean CDOM data, such as from BIOSOPE (r2=0.77–0.85, p<0.05, n=29). The model has immediate application in global scale assessments of photochemical rate processes and CDOM cycling in the open ocean due to its simplicity and optimization using a large base of field data (>7500 samples) from diverse Case I waters.

Proteomanalyse des Myelins, der Isolierschicht der Nerven

Rapid signal propagation along vertebrate axons is facilitated by their insulation with myelin, a plasma membrane specialization of glial cells. The recent application of proteomic approaches to biochemically purified myelin established that its protein composition is much more complex than previously thought. Bioinformatic integration of the myelin proteome with other systematic datasets such as mRNA abundance profiles of myelinating glial cells provides valuable resources for the molecular dissection of myelin biology and pathology.

Systematic data generation and test design for solution algorithms on the example of SALBPGen for assembly line balancing

Recently, the importance of correctly designed computational experiments for testing algorithms has been a subject of extended discussions. Whenever real-world data is lacking, generated data sets provide a substantive methodological tool for experiments. Focused research questions need to base on specialized, randomized and sufficiently large data sets, which are sampled from the population of interest. We integrate the generation of data sets into the process of scientific testing.Until now, no appropriate generators or systematic data sets have been available for the assembly line balancing problem (ALBP). Computational experiments were mostly based on very limited data sets unsystematically collected from the literature and from some real-world cases. As a consequence, former performance analyses often come to contradictory conclusions and lack on statistical evidence.We introduce SALBPGen, a new instance generator for the simple ALBP which can be applied and extended to any generalized ALBP, too. Unlike most generators, SALBPGen takes into account usual properties of precedence graphs in manufacturing. It is very flexible and able to create instances with very diverse structures under full control of the experiment’s designer. We also propose new challenging data sets, as shown with the new direct measure of instance’s hardness called trickiness. By two exemplary computational experiments, we illustrate how important insights can be gained with the help of the systematically generated data sets.

Why Transcription Factor Binding Sites Are Ten Nucleotides Long

Gene expression is controlled primarily by transcription factors, whose DNA binding sites are typically 10 nt long. We develop a population-genetic model to understand how the length and information content of such binding sites evolve. Our analysis is based on an inherent trade-off between specificity, which is greater in long binding sites, and robustness to mutation, which is greater in short binding sites. The evolutionary stable distribution of binding site lengths predicted by the model agrees with the empirical distribution (5-31 nt, with mean 9.9 nt for eukaryotes), and it is remarkably robust to variation in the underlying parameters of population size, mutation rate, number of transcription factor targets, and strength of selection for proper binding and selection against improper binding. In a systematic data set of eukaryotic and prokaryotic transcription factors we also uncover strong relationships between the length of a binding site and its information content per nucleotide, as well as between the number of targets a transcription factor regulates and the information content in its binding sites. Our analysis explains these features as well as the remarkable conservation of binding site characteristics across diverse taxa.

Analysis of cyanobacteria bloom in the Waihai part of Dianchi Lake, China

Blue-green algae (BGA) bloom is a typical phenomenon in eutrophied lakes. However, up to now, no environmental mechanism has been commonly accepted. Systematic and complete data sets of BGA blooms and environmental factors without any missing data are rare, which seriously affected previous studies. In this study, a bootstrapping based multiple imputation algorithm (EMB) was first applied to reconstruct a complete data set from the available data set with missing data, hence forming a basis for quantitatively relating BGA bloom to contributing factors. Then, the probability of BGA bloom outbreak was simulated using a binomial (or binary) logistic regression model, which is an effective tool for recognizing key contributing factors. The results suggest that 1) the outbreak frequency or probability of BGA bloom tends to first increase and then decrease with a turning point between June and September each year; 2) air temperature, relative humidity, and precipitation were significant positive factors correlated with outbreak frequency, whereas wind speed and the number of sunshine hours were negative factors; 3) water temperature had a strong positive effect on the probability of BGA bloom outbreak, whereas other water quality factors, such as concentrations of organics and nutrients, were not so significant. However, water quality factors, such as NO3–N, SD, pH, NH4–N, COD and DO, still need to be concerned, which had a potential to aggravate the outbreak of BGA bloom in Dianchi Lake, if they were out of control.

Validation of a Citizen Science-Based Model of Site Occupancy for Eastern Screech Owls with Systematic Data in Suburban New York and Connecticut

We characterized the landscape-level habitat use of Megascops asio (Eastern Screech Owl) in a suburban/urban region of New York and Connecticut using citizen- science methodologies and GIS-based land-use information. Volunteers sampled their properties using call-playback surveys in the summers of 2009 and 2010. We modeled detection and occupancy as functions of distance to forest and two coarse measures of development. AICc-supported models were validated with an independent dataset collected by trained professionals. Validated models indicated a negative association between occupancy and percent forest cover or, similarly, a positive association with percent impervious cover. When compared against the systematic dataset, models that used forest cover as a predictor had the highest accuracy (kappa = 0.73 ± 0.18) in predict- ing the occupancy observations in the systematic survey. After accounting for detection, both datasets support similar owl-habitat patterns of predicting occupancy in developed areas compared to highly rural. While there is likely a minimum amount of forest cover and/or maximum level of urbanization that Screech Owls can tolerate, such limits appear to be beyond the ranges sampled in this study. Future research that seeks to determine this development limit should focus on very urbanized areas. The high accuracy of the citizen-science models in predicting the systematic dataset indicates that volunteer-based efforts can provide reliable data for wildlife studies.

Linear Solvation Energy Relationship (LSER) Analysis of Liquid–Liquid Distribution Constants of 8-Hydroxyquinoline and Its Derivatives

The linear solvation energy relationship (LSER) analysis of liquid–liquid distribution constants of 8-hydroxyquinoline and its derivatives has been performed in terms of hydrophobicity of solutes, property parameters of diluents in the organic phase, and ionic strength of the aqueous phase. The effect of temperature has been also discussed. The obtained correlations permit the evaluation of unknown values of distribution constants of 8-hydroxyquinoline and/or its derivatives in the given extraction system. The set of available systematic experimental data reflects only the effect of temperature on the distribution of 8-hydroxyquinoline and its 2-methyl derivative; however, the distribution constants of the other 8-hydroxyquinolinols were determined sporadically at the different ionic strengths of the aqueous phase and lower or higher temperatures than 298 K. The correlation describing the effect of temperature can be used for the raw estimation of unknown values of distribution constants of 8-hydroxyquino...

Flow and Heat Transfer Characteristics of Liquid–Solid Circulating Fluidized Beds

A series of experiments was conducted to investigate the flow and heat transfer characteristics of liquid–solid circulating fluidized beds. The experimental apparatus consisted of a single riser and a downcomer. Water at ambient conditions was used as the fluidizing fluid. Six kinds of particles were tested. First, particle holdup was measured, and a set of systematic data was acquired. By analyzing the experimental data, a simple predicting correlation was derived for the particle holdup. Next, pressure drop measurement was performed, and a predicting correlation was derived. Then heat transfer coefficient was measured, where two regions were identified, i.e., the “heat transfer enhanced region” and the “liquid single-phase heat transfer region.” On the basis of the experimental data, a predicting correlation was derived for each region, and a correlation was proposed for the entire region. Lastly, making use of the already-derived correlations, an analogy was investigated between the frictional pressure drop and the heat transfer coefficient.

Systematic Data Set for Structure−Property Investigations: Solubility and Solid-State Structure of Alkaline Earth Metal Salts of Benzoates

A new resource for studying structure property relationships is presented, namely a systematic database of 36 organic salt structures together with phase specific aqueous solubility data. The salts are derived from four M2+ cations (Mg2+, Ca2+, Sr2+, Ba2+) and nine substituted benzoate anions. The intrinsic solubility of the free acid is found to have a major contribution to make to salt solubility, but despite previous literature assertions, there appears to be little correlation of solubility with the polarity of the organic ions, with cation size, or with hydration state. Importantly, we also show that consideration of the array structure rather than just molecular considerations improves prediction of rank orders of solubility. Thus, three-dimensional intermolecular networks (here formed with hydrogen bonding, M−O−M and M−N−M interactions, and halide interactions) are found to have lower aqueous solubilities than lower dimensional networks.

Regional avian species declines estimated from volunteer‐collected long‐term data using List Length Analysis

Long-term systematic population monitoring data sets are rare but are essential in identifying changes in species abundance. In contrast, community groups and natural history organizations have collected many species lists. These represent a large, untapped source of information on changes in abundance but are generally considered of little value. The major problem with using species lists to detect population changes is that the amount of effort used to obtain the list is often uncontrolled and usually unknown. It has been suggested that using the number of species on the list, the "list length," can be a measure of effort. This paper significantly extends the utility of Franklin's approach using Bayesian logistic regression. We demonstrate the value of List Length Analysis to model changes in species prevalence (i.e., the proportion of lists on which the species occurs) using bird lists collected by a local bird club over 40 years around Brisbane, southeast Queensland, Australia. We estimate the magnitude and certainty of change for 269 bird species and calculate the probabilities that there have been declines and increases of given magnitudes. List Length Analysis confirmed suspected species declines and increases. This method is an important complement to systematically designed intensive monitoring schemes and provides a means of utilizing data that may otherwise be deemed useless. The results of List Length Analysis can be used for targeting species of conservation concern for listing purposes or for more intensive monitoring. While Bayesian methods are not essential for List Length Analysis, they can offer more flexibility in interrogating the data and are able to provide a range of parameters that are easy to interpret and can facilitate conservation listing and prioritization.

A study of the gas-star formation relation over cosmic time★

We use the first systematic data sets of CO molecular line emission in z~1-3 normal star forming galaxies for a comparison of the dependence of galaxy-averaged star formation rates on molecular gas masses at low and high redshifts, and in different galactic environments. Although the current high-z samples are still small and biased toward the luminous and massive tail of the actively star-forming 'main-sequence', a fairly clear picture is emerging. Independent of whether galaxy integrated quantities or surface densities are considered, low- and high-z SFG galaxy populations appear to follow similar molecular gas-star formation relations with slopes 1.1 to 1.2. The gas-depletion time scale in these SFGs grows from 0.5 Gyrs at z~2 to 1.5 Gyrs at z~0. Because star formation depletion times are significantly smaller than the Hubble time at all redshifts sampled, star formation rates and gas fractions are set by the balance between gas accretion from the halo and stellar feedback. In contrast, very luminous gas rich major mergers at both low-z and high-z produce on average 4 to10 times more far-infrared luminosity per unit gas mass. Only some fraction of this difference can be explained by uncertainties in gas-mass or luminosity estimators; much of it must be intrinsic. The most likely interpretation is that the star formation relation is driven by global dynamical effects. For a given mass, the more compact merger systems produce stars more rapidly because their gas clouds are more compressed with shorter dynamical times, so that they churn more quickly through the available gas reservoir than the typical normal disk galaxies. When the dependence on galactic dynamical time scale is explicitly included, disk galaxies and mergers appear to follow similar gas to star-formation relations. The mergers may be forming stars at slightly higher efficiencies than the disks.

Is evolutionary history repeatedly rewritten in light of new fossil discoveries?

Mass media and popular science journals commonly report that new fossil discoveries have 'rewritten evolutionary history'. Is this merely journalistic hyperbole or is our sampling of systematic diversity so limited that attempts to derive evolutionary history from these datasets are premature? We use two exemplars-catarrhine primates (Old World monkeys and apes) and non-avian dinosaurs-to investigate how the maturity of datasets can be assessed. Both groups have been intensively studied over the past 200 years and so should represent pinnacles in our knowledge of vertebrate systematic diversity. We test the maturity of these datasets by assessing the completeness of their fossil records, their susceptibility to changes in macroevolutionary hypotheses and the balance of their phylogenies through study time. Catarrhines have shown prolonged stability, with discoveries of new species being evenly distributed across the phylogeny, and thus have had little impact on our understanding of their fossil record, diversification and evolution. The reverse is true for dinosaurs, where the addition of new species has been non-random and, consequentially, their fossil record, tree shape and our understanding of their diversification is rapidly changing. The conclusions derived from these analyses are relevant more generally: the maturity of systematic datasets can and should be assessed before they are exploited to derive grand macroevolutionary hypotheses.

Flood frequency analysis using historical data: accounting for random and systematic errors

Flood frequency analysis based on a set of systematic data and a set of historical floods is applied to several Mediterranean catchments. After identification and collection of data on historical floods, several hydraulic models were constructed to account for geomorphological changes. Recent and historical rating curves were constructed and applied to reconstruct flood discharge series, together with their uncertainty. This uncertainty stems from two types of error: (a) random errors related to the water-level readings; and (b) systematic errors related to over- or under-estimation of the rating curve. A Bayesian frequency analysis is performed to take both sources of uncertainty into account. It is shown that the uncertainty affecting discharges should be carefully evaluated and taken into account in the flood frequency analysis, as it can increase the quantiles confidence interval. The quantiles are found to be consistent with those obtained with empirical methods, for two out of four of the catchments. Citation Neppel, L., Renard, B., Lang, M., Ayral, P.-A., Coeur, D., Gaume, E., Jacob, N., Payrastre, O., Pobanz, K. & Vinet, F. (2010) Flood frequency analysis using historical data: accounting for random and systematic errors. Hydrol. Sci. J. 55(2), 192–208.

Regional avian species declines estimated from volunteer-collected long-term data using List Length Analysis

Long-term systematic population monitoring data sets are rare but are essential in identifying changes in species abundance. In contrast, community groups and natural history organizations have collected many species lists. These represent a large, untapped source of information on changes in abundance but are generally considered of little value. The major problem with using species lists to detect population changes is that the amount of effort used to obtain the list is often uncontrolled and usually unknown. It has been suggested that using the number of species on the list, the “list length,” can be a measure of effort. This paper significantly extends the utility of Franklin's approach using Bayesian logistic regression. We demonstrate the value of List Length Analysis to model changes in species prevalence (i.e., the proportion of lists on which the species occurs) using bird lists collected by a local bird club over 40 years around Brisbane, southeast Queensland, Australia. We estimate the magnitude and certainty of change for 269 bird species and calculate the probabilities that there have been declines and increases of given magnitudes. List Length Analysis confirmed suspected species declines and increases. This method is an important complement to systematically designed intensive monitoring schemes and provides a means of utilizing data that may otherwise be deemed useless. The results of List Length Analysis can be used for targeting species of conservation concern for listing purposes or for more intensive monitoring. While Bayesian methods are not essential for List Length Analysis, they can offer more flexibility in interrogating the data and are able to provide a range of parameters that are easy to interpret and can facilitate conservation listing and prioritization.

The Need for More Individual-Level Judicial Election Data

After the 2000 election cycle, the Brennan Center for Justice and the National Institute on Money in State Politics issued a report on that year’s state supreme court elections. According to the report, a “new politics of judicial elections” had materialized in which judicial elections were getting more competitive, negative, and costly (Goldberg, Holman, and Sanchez, 2002). Around the same time, a new wave of judicial elections research emerged beginning largely with the work of Hall (2001). Although scholars had studied judicial elections for decades (e.g., Dubois, 1980), in many ways this new research was more methodologically advanced and employed more systematic data sets. In Hall’s (2001) integral article, she examined contestation and competition in judicial elections using an impressive set of data from 1980 to 1995.1 Her findings challenged the conventional wisdom that judicial elections are usually uncompetitive. From there, Hall, both by herself and with Bonneau (e.g., Hall, 2001, 2007a; Hall and Bonneau, 2006, 2008; Bonneau and Hall, 2003), examined numerous questions relating to such issues as challenger emergence and political participation in judicial elections. Bonneau (2004; 2005a, b; 2007a, b) also conducted extensive research on campaign spending in judicial races. These studies added a tremendous amount to our understanding of judicial elections at the supreme court level. More recently, Streb and Frederick (e.g., Streb and Frederick, 2009; Frederick and Streb, 2008; Streb, Frederick, and LaFrance, 2009), have replicated several of the works of Hall and Bonneau at the intermediate appellate court (IAC) level to get a better understanding of the ways in which the two appellate court levels’ elections are similar or different. One shortcoming of all of these analyses, however, is that both Hall and Bonneau and Streb and Frederick had to rely on aggregate-level election results to analyze the dynamics of judicial elections. Although these aggregate-level data sets were comprehensive, the lack of systematic individual-level data raises as many questions about judicial elections as the aforementioned studies answered. There is simply much we cannot glean by relying on aggregate election returns alone. Just as studies of other elections, such as those for president or Congress, have been enriched by aggregate and individual-level data, the judicial elections subfield would benefit greatly from more complete individual-level data sets, both of voters and candidates. This is not to imply that no work on judicial elections has used individual-level data or that the studies that have employed individual-level data have some-

Fuzzy Logic Analysis of Kinase Pathway Crosstalk in TNF/EGF/Insulin-Induced Signaling

When modeling cell signaling networks, a balance must be struck between mechanistic detail and ease of interpretation. In this paper we apply a fuzzy logic framework to the analysis of a large, systematic dataset describing the dynamics of cell signaling downstream of TNF, EGF, and insulin receptors in human colon carcinoma cells. Simulations based on fuzzy logic recapitulate most features of the data and generate several predictions involving pathway crosstalk and regulation. We uncover a relationship between MK2 and ERK pathways that might account for the previously identified pro-survival influence of MK2. We also find unexpected inhibition of IKK following EGF treatment, possibly due to down-regulation of autocrine signaling. More generally, fuzzy logic models are flexible, able to incorporate qualitative and noisy data, and powerful enough to produce quantitative predictions and new biological insights about the operation of signaling networks.

Daily snow cover estimation from Advanced Very High Resolution Radiometer Polar Pathfinder data over Northern Hemisphere land surfaces during 1982–2004

The Global Climate Observing System has identified the need for systematic global daily snow cover data sets over land. Current in situ snow cover data sets have limited spatial coverage while satellite‐based snow cover records have either limited historical extent or limited temporal and spatial resolution because of cloud cover or specific sensor availability. NOAA Advanced Very High Resolution Radiometer (AVHRR) data offers nearly complete daily global coverage of the Northern Hemisphere, extending back to the early 1980s with successors slated to continue into the next decade. In this paper, we apply a new algorithm, Snowcover, to estimate daily snow cover, including periods of cloudy conditions, from AVHRR Polar Pathfinder (APP) data over Northern Hemisphere land surfaces. This new snow cover product is compared to snow cover estimates derived from long‐term in situ snow depth measurements over Canada and the northern Eurasia. The APP snow cover maps showed an 80% agreement rate or better at 95% of the in situ sites. This performance was comparable to the agreement of MODIS 0.05 degree snow cover products over the same sites; although the MODIS product was only retrieved ∼20% of the time corresponding to clear sky conditions in contrast to over 95% of the time with the APP snow product. The almost continuously temporal and spatial coverage for the past 23 years from 1982 to 2004 over Northern Hemisphere makes the new daily snow cover product especially suitable for analysis of large‐scale patterns of spring snowmelt in association with variability of circumpolar climate and ecological parameters.