A Fast Method to Estimate Kinetic Constants for Enzyme Inhibitors

We present a method to determine the reaction type and kinetic constants for enzyme inhibitors that decreases the number of experimental assays by at least a factor of five. It is based on a new theoretical formalism in terms of concentrations that dismisses the requirement of estimating initial velocities. Expressions for the time evolution of the concentrations of all the reactants are also given.     

A Fast Estimate for the Population Recombination Rate Based on Regression

Recombination is a fundamental evolutionary force. Therefore the population recombination rate ρ plays an important role in the analysis of population genetic data; however, it is notoriously difficult to estimate. This difficulty applies both to the accuracy of commonly used estimates and to the computational efforts required to obtain them. Some particularly popular methods are based on approximations to the likelihood. They require considerably less computational efforts than the full-likelihood method with not much less accuracy. Nevertheless, the computation of these approximate estimates can still be very time consuming, in particular when the sample size is large. Although auxiliary quantities for composite likelihood estimates can be computed in advance and stored in tables, these tables need to be recomputed if either the sample size or the mutation rate θ changes. Here we introduce a new method based on regression combined with boosting as a model selection technique. For large samples, it requires much less computational effort than other approximate methods, while providing similar levels of accuracy. Notably, for a sample of hundreds or thousands of individuals, the estimate of ρ using regression can be obtained on a single personal computer within a couple of minutes while other methods may need a couple of days or months (or even years). When the sample size is smaller (n ≤ 50), our new method remains computational efficient but produces biased estimates. We expect the new estimates to be helpful when analyzing large samples and/or many loci with possibly different mutation rates.     

Microorganisms in the Sediments of Lake Baikal,the Deepest and Oldest Lake in the World

Microbiology - The review summarizes the results of recent studies of microbial communities of the Lake Baikal sediments obtained using diverse techniques. In the sediments of the areas of stable...     

A Robust and Accurate Two-Step Auto-Labeling Conditional Iterative Closest Points (TACICP) Algorithm for Three-Dimensional Multi-Modal Carotid Image Registration

Atherosclerosis is among the leading causes of death and disability. Combining information from multi-modal vascular images is an effective and efficient way to diagnose and monitor atherosclerosis, in which image registration is a key technique. In this paper a feature-based registration algorithm, Two-step Auto-labeling Conditional Iterative Closed Points (TACICP) algorithm, is proposed to align three-dimensional carotid image datasets from ultrasound (US) and magnetic resonance (MR). Based on 2D segmented contours, a coarse-to-fine strategy is employed with two steps: rigid initialization step and non-rigid refinement step. Conditional Iterative Closest Points (CICP) algorithm is given in rigid initialization step to obtain the robust rigid transformation and label configurations. Then the labels and CICP algorithm with non-rigid thin-plate-spline (TPS) transformation model is introduced to solve non-rigid carotid deformation between different body positions. The results demonstrate that proposed TACICP algorithm has achieved an average registration error of less than 0.2mm with no failure case, which is superior to the state-of-the-art feature-based methods.     

Fast and Accurate Registration of the Proximal Femurs in Bilateral Hip Joint Images by Using the Random Sub-Sample Points

Background and ObjectiveAs is known, point clouds representing the objects are frequently used in object registration. Although the objects can be registered by using all the points in the corresponding point clouds of the objects, the registration process can also be achieved with a smaller number of the landmark points selected from the entire point clouds of the objects. This paper introduces a research study focusing on the fast and accurate rigid registration of the bilateral proximal femurs in bilateral hip joint images by using the random sub-sample points. For this purpose, Random Point Sub-sampling (RPS) was analyzed and the reduced point sets were used for an accurate registration of the bilateral proximal femurs in coronal hip joint magnetic resonance imaging (MRI) slices.MethodsIn registration, bilateral proximal femurs in MRI slices were registered rigidly by performing a process consisting of three main phases named as MR image preprocessing, proximal femur registration over the random sub-sample points and MR image postprocessing. In the stage of the MR image preprocessing, segmentation maps of the bilateral proximal femurs are obtained as region of interest (RoI) images from the entire MRI slices and then, the edge maps of the segmented proximal femurs are extracted. In the registration phase, the edge maps describing the proximal femur surfaces are represented as point clouds initially. Thereafter, the RPS is performed on the proximal femur point clouds and the number of points representing the proximal femurs is reduced at different ratios. For the registration of the point clouds, the Iterative Closest Point (ICP) algorithm is performed on the reduced sets of points. Finally, the registration procedures are completed by performing MR image postprocessing on the registered proximal femur images.ResultsIn performance evaluation tests performed on healthy and pathological proximal femurs in 13 bilateral coronal hip joint MRI slices of 13 Legg-Calve-Perthes disease (LCPD) patients, bilateral proximal femurs were successfully registered with very small error rates by using the reduced set of points obtained via the RPS and promising results were achieved. The minimum error rate was observed at RPS rate of 30% as the value of 0.41 (±0.31)% on all over the bilateral proximal femurs evaluated. When the range of RPS rate of 20-30% is considered as the reference, the elapsed time in registration can be reduced by almost 30-40% compared to the case where all the proximal femur points were included in registration. Additionally, it was observed that the RPS rate should be selected as at least 25% to achieve a successful registration with an error rate below 1%.ConclusionIt was concluded from the observed results that a more successful and faster registration can be accomplished by selecting fewer points randomly from the point sets of proximal femurs instead of using all the points describing the proximal femurs. Not only an accurate registration with low error rates was performed, but also a faster registration process was performed by means of the limited number of points that are sub-sampled randomly from the whole point sets.     

The Importance of Lake Morphometry for the Structureand Function of Lakes

This work demonstrates quantitatively and in a comprehensive way that the size and form of lakes regulate many general transport processes, such as sedimentation, resuspension, diffusion, mixing, burial and outflow, which in turn regulate many abiotic state variables, such as concentrations of phosphorus, suspended particulate matter, many water chemical variables and water clarity, which in turn regulate primary production, which regulate secondary production, for example of zooplankton and fish. Such relationships are discussed not qualitatively but quantitatively using a new generation of validated dynamic ecosystem models (LakeWeb and LakeMab) based on mechanistic principles. It has been shown by critical model tests (including blind tests using data covering wide limnological ranges) that these models give predictions that agree well with empirical data. This should lend credibility to the results presented in this work, which would have been very difficult to obtain using traditional methods with extensive field studies in a few lakes. Simulations have been carried out where the inflow of phosphorus is held constant and the consequences simulated for small, large, shallow and deep lakes. There are striking differences in total phosphorus (TP) concentrations and trophic state (from 10 to 100 µg TP/l) and hence also in changes in many variables characterizing lake structure and function, such as Secchi depth, suspended particulate matter, pH, water temperature, chlorophyll, algal volume, macrophyte cover; as well as production and biomasses of benthic algae, bacterioplankton, macrophytes, herbivorous zooplankton, predatory zooplankton, zoobenthos, prey fish and predatory fish. These changes have been quantified in a comprehensive manner in this work and the approach to calculate such changes are basic for an understanding of how different lakes react to changes in nutrient loading (eutrophication). (© 2005 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

The Potential for Restoration of Acid-Damaged Lake Trout Lakes

This review of long-term monitoring and experimental manipulation studies from Ontario, Canada, offers encouraging evidence that lake trout ( Salvelinus namaycush ) populations can be restored to acidified lakes once the problem of acid deposition is solved in North America. Predicting the rate of chemical and biological recovery remains difficult, because there are several limnological attributes (dissolved organic carbon, flushing rate) and biological attributes (acid sensitivity, species longevity, fish community composition) of lake trout ecosystems that can either enhance or delay the recovery process. Some options for managing recovering lakes are also reviewed, including increased pollution controls, liming, hatchery stocking, and harvest controls.     

Using Satellite Remote Sensing to Estimate the Colored Dissolved Organic Matter Absorption Coefficient in Lakes

Given the importance of colored dissolved organic matter (CDOM) for the structure and function of lake ecosystems, a method that could estimate the amount of CDOM in lake waters over large geographic areas would be highly desirable. Satellite remote sensing has the potential to resolve this problem. We carried out model simulations to evaluate the suitability of different satellite sensors (Landsat, IKONOS, and the Advanced land Imager [ALI]) to map the amount of CDOM in concentration ranges that occur in boreal lakes of the Nordic countries. The results showed that the 8-bit radiometric resolution of Landsat 7 is not adequate when absorption by CDOM at 420 nm is higher than 3 m⁻¹. On the other hand, the 16-bit radiometric resolution of ALI, a prototype of the next generation of Landsat, is suitable for mapping CDOM in a wider range of concentrations. An ALI image of southern Finland was acquired on 14, July 2002 and in situ measurements were carried out in 15 lakes (18 stations). The results showed that there is a high correlation (R² = 0.84) between the 565 nm/660 nm ALI band ratio and the CDOM absorption coefficient in lakes. Analysis of 245 lakes in the acquired satellite image showed a normal distribution of CDOM concentration among the lakes. However, the size distribution of lakes was highly skewed toward small lakes, resulting in the CDOM concentration per unit lake area being skewed toward high values. We showed that remote sensing enables synoptic monitoring of the CDOM concentration in a large number of lakes and thus enables scaling up to the level of large ecosystems and biomes.     

Projecting the Impact of Regional Land-Use Change and Water Management Policies on Lake Water Quality: An Application to Periurban Lakes and Reservoirs

As the human population grows, the demand for living space and supplies of resources also increases, which may induce rapid change in land-use/land-cover (LULC) and associated pressures exerted on aquatic habitats. We propose a new approach to forecast the impact of regional land cover change and water management policies (i.e., targets in nutrient loads reduction) on lake and reservoir water eutrophication status using a model that requires minimal parameterisation compared with alternative methods. This approach was applied to a set of 48 periurban lakes located in the Ile de France region (IDF, France) to simulate catchment-scale management scenarios. Model outputs were subsequently compared to governmental agencies’ 2030 forecasts. Our model indicated that the efforts made to reduce pressure in the catchment of seepage lakes might be expected to be proportional to the gain that might be obtained, whereas drainage lakes will display little improvement until a critical level of pressure reduction is reached. The model also indicated that remediation measures, as currently planned by governmental agencies, might only have a marginal impact on improving the eutrophication status of lakes and reservoirs within the IDF region. Despite the commitment to appropriately managing the water resources in many countries, prospective tools to evaluate the potential impacts of global change on freshwater ecosystems integrity at medium to large spatial scales are lacking. This study proposes a new approach to investigate the impact of region-scale human-driven changes on lake and reservoir ecological status and could be implemented elsewhere with limited parameterisation. Issues are discussed that relate to model uncertainty and to its relevance as a tool applied to decision-making.     

A Practical Approach Based on Analytic Deformable Algorithm for Scenic Image Registration

Background

Image registration is to produce an entire scene by aligning all the acquired image sequences. A registration algorithm is necessary to tolerance as much as possible for intensity and geometric variation among images. However, captured image views of real scene usually produce unexpected distortions. They are generally derived from the optic characteristics of image sensors or caused by the specific scenes and objects.

Methods and Findings

An analytic registration algorithm considering the deformation is proposed for scenic image applications in this study. After extracting important features by the wavelet-based edge correlation method, an analytic registration approach is then proposed to achieve deformable and accurate matching of point sets. Finally, the registration accuracy is further refined to obtain subpixel precision by a feature-based Levenberg-Marquardt (FLM) method. It converges evidently faster than most other methods because of its feature-based characteristic.

Conclusions

We validate the performance of proposed method by testing with synthetic and real image sequences acquired by a hand-held digital still camera (DSC) and in comparison with an optical flow-based motion technique in terms of the squared sum of intensity differences (SSD) and correlation coefficient (CC). The results indicate that the proposed method is satisfactory in the registration accuracy and quality of DSC images.     

The Impact of Nutrient State and Lake Depth on Top-down Control in the Pelagic Zone of Lakes: A Study of 466 Lakes from the Temperate Zone to the Arctic

Using empirical data from 466 temperate to arctic lakes covering a total phosphorus (TP) gradient of 2-1036 mg L-1, we describe how the relative contributions of resource supply, and predator control change along a nutrient gradient. We argue that (a) predator control on large-bodied zooplankton is unimodally related to TP and is highest in the most nutrient-rich and nutrient-poor lakes and generally higher in shallow than deep lakes, (b) the cascading effect of changes in predator control on phytoplankton decreases with increasing TP, and (c) these general patterns occur with significant variations--that is, the predation pressure can be low or high at all nutrient levels. A quantile regression revealed that the median share of the predator-sensitive Daphnia to the total cladoceran biomass was significantly related unimodally to TP, while the 10% and 90% percentiles approached 0 and 100%, respectively, at all TP levels. Moreover, deep lakes (more than 6 m) had a higher percentage of Daphnia than shallow (less than 6 m) lakes. The median percentage of Daphnia peaked at 0.15 mg L-1 in shallow lakes and 0.09 mg L-1 in deep lakes. The assumption that fish are responsible for the unimodality was supported by data on the abundance of potential planktivorous fish (catch net-1 night-1 gill nets with the different mesh sizes [CPUE]). To elucidate the potential cascading effect on phytoplankton, we examined the zooplankton phytoplankton biomass ratio. Even though this ratio was inversely related to CPUE at all TP levels, we found an overall higher ratio in oligotrophic lakes that declined toward low values (typically below 0.2) in hypertrophic lakes. These results suggest that planktivorous fish have a more limited effect on the grazing control of phytoplankton in oligotrophic lakes than in eutrophic lakes, despite similar predator control of large-bodied zooplankton. Accordingly, the phytoplankton yield, expressed as the chlorophyll a-TP ratio, did not relate to CPUE at low TP, but it increased significantly with CPUE at high TP. We conclude that the chances of implementing a successful restoration program using biomanipulation as a tool to reduce phytoplankton biomass increase progressively with increasing TP, but that success in the long term is most likely achieved at intermediate TP concentrations.     

FctClus: A Fast Clustering Algorithm for Heterogeneous Information Networks

It is important to cluster heterogeneous information networks. A fast clustering algorithm based on an approximate commute time embedding for heterogeneous information networks with a star network schema is proposed in this paper by utilizing the sparsity of heterogeneous information networks. First, a heterogeneous information network is transformed into multiple compatible bipartite graphs from the compatible point of view. Second, the approximate commute time embedding of each bipartite graph is computed using random mapping and a linear time solver. All of the indicator subsets in each embedding simultaneously determine the target dataset. Finally, a general model is formulated by these indicator subsets, and a fast algorithm is derived by simultaneously clustering all of the indicator subsets using the sum of the weighted distances for all indicators for an identical target object. The proposed fast algorithm, FctClus, is shown to be efficient and generalizable and exhibits high clustering accuracy and fast computation speed based on a theoretic analysis and experimental verification.     

A Fast EM Algorithm for BayesA-Like Prediction of Genomic Breeding Values

Prediction accuracies of estimated breeding values for economically important traits are expected to benefit from genomic information. Single nucleotide polymorphism (SNP) panels used in genomic prediction are increasing in density, but the Markov Chain Monte Carlo (MCMC) estimation of SNP effects can be quite time consuming or slow to converge when a large number of SNPs are fitted simultaneously in a linear mixed model. Here we present an EM algorithm (termed “fastBayesA”) without MCMC. This fastBayesA approach treats the variances of SNP effects as missing data and uses a joint posterior mode of effects compared to the commonly used BayesA which bases predictions on posterior means of effects. In each EM iteration, SNP effects are predicted as a linear combination of best linear unbiased predictions of breeding values from a mixed linear animal model that incorporates a weighted marker-based realized relationship matrix. Method fastBayesA converges after a few iterations to a joint posterior mode of SNP effects under the BayesA model. When applied to simulated quantitative traits with a range of genetic architectures, fastBayesA is shown to predict GEBV as accurately as BayesA but with less computing effort per SNP than BayesA. Method fastBayesA can be used as a computationally efficient substitute for BayesA, especially when an increasing number of markers bring unreasonable computational burden or slow convergence to MCMC approaches.     

A Model to Estimate Potential Submersed Aquatic Vegetation Habitat Based on Studies in Lake Pontchartrain, Louisiana

The depth distribution of submersed aquatic vegetation (SAV) was studied in Lake Pontchartrain, Louisiana, to develop a model to predict changes in SAV abundance from changes in environmental quality. We conducted annual line‐intercept surveys from 1997 through 2001 and monitored monthly photosynthetically active radiation at four sites with different shoreface slopes. The following relationships between SAV distribution and environmental factors were used as model parameters: (1) water clarity controls SAV colonization depth; (2) fluctuation in annual mean water level and wave mixing determines SAV minimum colonization depth; and (3) site differences in SAV areal coverage under the comparable water quality conditions are due to shoreface slope differences. These parameters expressed as mathematical components of the model are as follows: mean water clarity determines SAV colonization depth (Z_max= 2.3/K_d); mean water level and wave mixing controls SAV minimum depth (Z_min= 0.3 m); and shoreface slope angle (θ) determines the distance from Z_min to Z_max. The equation developed for the potential SAV habitat (PSAV) model is PSAV = (2.3 ? 0.3 ×K_d)/(sinθ×K_d). The model was validated by comparing empirical values from the dataset to values predicted by the model. Although the model was developed to predict the PSAV in Lake Pontchartrain, it can be applied to other coastal habitats if local SAV light requirements are substituted for Lake Pontchartrain values. This model is a useful tool in selecting potential restoration sites and in predicting the extent of SAV habitat gain after restoration.     

A Likelihood Approach to Estimate the Number of Co-Infections

The number of co-infections of a pathogen (multiplicity of infection or MOI) is a relevant parameter in epidemiology as it relates to transmission intensity. Notably, such quantities can be built into a metric in the context of disease control and prevention. Having applications to malaria in mind, we develop here a maximum-likelihood (ML) framework to estimate the quantities of interest at low computational and no additional costs to study designs or data collection. We show how the ML estimate for the quantities of interest and corresponding confidence-regions are obtained from multiple genetic loci. Assuming specifically that infections are rare and independent events, the number of infections per host follows a conditional Poisson distribution. Under this assumption, we show that a unique ML estimate for the parameter () describing MOI exists which is found by a simple recursion. Moreover, we provide explicit formulas for asymptotic confidence intervals, and show that profile-likelihood-based confidence intervals exist, which are found by a simple two-dimensional recursion. Based on the confidence intervals we provide alternative statistical tests for the MOI parameter. Finally, we illustrate the methods on three malaria data sets. The statistical framework however is not limited to malaria.     

A Rationale for the Subclassification of Biogenic Meromictic Lakes

An extension of Walker and Likens' (1974). scheme for the classification of meromictic lakes of the world has been based in part upon the mutual incompatibility of certain common chemical species and their generation within the monimolimnia of biogenic meromictic lakes.     

A Rapid Method to Estimate Population Variables for African Elephants

Abstract: We developed a noninvasive method to estimate reproductive and survival parameters for free-ranging African savannah elephants (Loxodonta africana africana) and used these to estimate finite population growth rates. We used published data from 2 populations with known growth rates and birth and survival histories to validate our technique. Based on body measurements, our method yielded estimates of age at first and last calving, calving interval, and age-specific survival rates that were similar to those determined during long-term studies at both Addo Elephant National Park and Amboseli National Park. Our technique generated population data required to estimate population growth rates. The method may be particularly useful where censuses yield imprecise estimates or where long-term population data are unavailable. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):822–829; 2008)     

A Fast and Scalable Kymograph Alignment Algorithm for Nanochannel-Based Optical DNA Mappings

Optical mapping by direct visualization of individual DNA molecules, stretched in nanochannels with sequence-specific fluorescent labeling, represents a promising tool for disease diagnostics and genomics. An important challenge for this technique is thermal motion of the DNA as it undergoes imaging; this blurs fluorescent patterns along the DNA and results in information loss. Correcting for this effect (a process referred to as kymograph alignment) is a common preprocessing step in nanochannel-based optical mapping workflows, and we present here a highly efficient algorithm to accomplish this via pattern recognition. We compare our method with the one previous approach, and we find that our method is orders of magnitude faster while producing data of similar quality. We demonstrate proof of principle of our approach on experimental data consisting of melt mapped bacteriophage DNA.     

Excessive Phosphorus Loading to Dal Lake,India: Implications for Managing Shallow Eutrophic Lakes in Urbanized Watersheds

Extensive watershed development has resulted in excessive total phosphorus (TP) loads to Dal Lake, a high altitude Himalayan lake known for its tourism and economic potential. External and internal TP loads of 5 and ∼1 g m^–2 yr^–1, respectively, were estimated for the lake. These loading rates are high in relation to the lake's critical tolerance range of 0.1–0.2 g m^–2 yr^–1, and, over time, have resulted in severe eutrophication in view of extremely high macrophyte biomass (average = 3.2 kg m^–2‐fresh weight) and bottom sediment enrichment (79 tons of TP reserves which contribute 88% of the annual TP budget). This study emphasizes the importance of external TP load reduction as a primary management objective to counteract internal TP loading and P storage within bottom sediments resulting from historic anthropogenic loads. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)