Harmonising the bioassessment of large rivers in the absence of near‐natural reference conditions – a case study of the Danube River

1. International river catchments pose challenges for effective water resource management. Catchment‐wide strategies are often complicated by differences in national bioassessment and quality classification. Intercalibration efforts aim to harmonise these differences, but rely on the consistent delineation of near‐natural reference sites that are almost unavailable in today’s landscape, especially for large rivers. 2. We introduce the concept of alternative benchmarking that is based on the notion of aquatic communities at similar (low) levels of impairment associated with least‐disturbed conditions (LDC) as defined by abiotic criteria. Using data acquired during the second Joint Danube Survey, we defined LDC sites based on a multivariate gradient of anthropogenic pressures, mostly related to morphological deterioration, that spans the entire navigable Danube. 3. The river was subdivided into four stretches, each featuring homogeneous biological assemblages. Indirect gradient analysis revealed relationships between the pressure gradient and selected features of the macroinvertebrate and macrophyte community but not for diatoms or phytoplankton. 4. We identified biological metrics suitable for the quality classification of individual stretches or the entire river. Impoundment is the major hydromorphological alteration on the Danube but various metrics still responded significantly to differences in the morphological condition of sites not affected by impoundment. 5. A comparison of macroinvertebrate sampling techniques (airlift versus kick‐and‐sweep) revealed differences in how the acquired data reflect the effects of anthropogenic pressure. Biological metrics based only on kick‐and‐sweep sample data were insensitive to habitat deterioration in the heavily modified Upper Danube. 6. This study exemplifies the empirical approach of alternative benchmarking in intercalibration and offers practical solutions to some of the challenges of large river bioassessment.     

Morphological evolution through integration: a quantitative study of cranial integration in Homo, Pan, Gorilla and Pongo

Morphological integration refers to coordinated variation among traits that are closely related in development and/or function. Patterns of integration can offer important insight into the structural relationship between phenotypic units, providing a framework to address questions about phenotypic evolvability and constraints. Integrative features of the primate cranium have recently become a popular subject of study. However, an important question that still remains under-investigated is: what is the pattern of cranial shape integration among closely related hominoids? To address this question, we conducted a Procrustes-based geometric morphometrics study to quantify and analyze shape covariation patterns between different cranial regions in Homo, Pan, Gorilla and Pongo. A total of fifty-six 3D landmarks were collected on 407 adult individuals. We then sub-divided the landmarks corresponding to cranial units as outlined in the ‘functional matrix hypothesis.’ Sub-dividing the cranium in this manner allowed us to explore patterns of covariation between the face, basicranium and cranial vault, using the two-block partial least squares approach. Our results suggest that integrated shape changes in the hominoid cranium are complex, but that the overall pattern of integration is similar among human and non-human apes. Thus, despite having very distinct morphologies the way in which the face, basicranium and cranial vault covary is shared among these taxa. These results imply that the pattern of cranial integration among hominoids is conserved.     

A new analytical approach to landscape genetic modelling: least-cost transect analysis and linear mixed models

Landscape genetics aims to assess the effect of the landscape on intraspecific genetic structure. To quantify interdeme landscape structure, landscape genetics primarily uses landscape resistance surfaces (RSs) and least-cost paths or straight-line transects. However, both approaches have drawbacks. Parameterization of RSs is a subjective process, and least-cost paths represent a single migration route. A transect-based approach might oversimplify migration patterns by assuming rectilinear migration. To overcome these limitations, we combined these two methods in a new landscape genetic approach: least-cost transect analysis (LCTA). Habitat-matrix RSs were used to create least-cost paths, which were subsequently buffered to form transects in which the abundance of several landscape elements was quantified. To maintain objectivity, this analysis was repeated so that each landscape element was in turn regarded as migration habitat. The relationship between explanatory variables and genetic distances was then assessed following a mixed modelling approach to account for the nonindependence of values in distance matrices. Subsequently, the best fitting model was selected using the statistic. We applied LCTA and the mixed modelling approach to an empirical genetic dataset on the endangered damselfly, Coenagrion mercuriale. We compared the results to those obtained from traditional least-cost, effective and resistance distance analysis. We showed that LCTA is an objective approach that identifies both the most probable migration habitat and landscape elements that either inhibit or facilitate gene flow. Although we believe the statistical approach to be an improvement for the analysis of distance matrices in landscape genetics, more stringent testing is needed.     

Hybrid modeling framework for process analytical technology: application to Bordetella pertussis cultures

In the process analytical technology (PAT) initiative, the application of sensors technology and modeling methods is promoted. The emphasis is on Quality by Design, online monitoring, and closed-loop control with the general aim of building in product quality into manufacturing operations. As a result, online high-throughput process analyzers find increasing application and therewith high amounts of highly correlated data become available online. In this study, an hybrid chemometric/mathematical modeling method is adopted for data analysis, which is shown to be advantageous over the commonly used chemometric techniques in PAT applications. This methodology was applied to the analysis of process data of Bordetella pertussis cultivations, namely online data of near-infrared, (NIR), pH, temperature and dissolved oxygen, and off-line data of biomass, glutamate, and lactate concentrations. The hybrid model structure consisted of macroscopic material balance equations in which the specific reactions rates are modeled by nonlinear partial least square (PLS). This methodology revealed a significant higher statistical confidence in comparison to PLSs, translated in a reduction of mean squared prediction errors (e.g., individual root mean squared prediction errors calibration/validation obtained through the hybrid model for the concentrations of lactate: 0.8699/0.7190 mmol/L; glutamate: 0.6057/0.2917 mmol/L; and biomass: 0.0520/0.0283 OD; and obtained through the PLS model for the concentrations of lactate: 1.3549/1.0087 mmol/L; glutamate: 0.7628/0.3504 mmol/L; and biomass: 0.0949/0.0412 OD). Moreover, the analysis of loadings and scores in the hybrid approach revealed that process features can, as for PLS, be extracted by the hybrid method.     

近红外光谱技术预测猪肉的pH、嫩度和蒸煮损失

以冷却猪肉为研究对象,评价近红外光谱（NIR）技术用于肉类物理特性预测的可行性以及不同的光谱处理方法和建模方法对预测准确性的影响。试样取自排酸24h的同一批猪胴体的小里脊肉,采集4000—10000cm-1的光谱。经外部验证的偏最小二乘（PLS）模型在预测pH时表现出良好的相关性（Rc^2=0．88,Rp^2=0．80,SEC=0．08,SEP=0．084）,嫩度与蒸煮损失模型的相关性分别是Rc^2=0．50和0．57,R;=0．34和0．50。在各种光谱预处理方法中,平滑处理结合多元散射校正（MSC）或标准正态变量变换（SNV）的效果最好。     

Influence of DNA end structure on the mechanism of initiation of DNA unwinding by the Escherichia coli RecBCD and RecBC helicases

Escherichiacoli RecBCD is a bipolar DNA helicase possessing two motor subunits (RecB, a 3′-to-5′ translocase, and RecD, a 5′-to-3′ translocase) that is involved in the major pathway of recombinational repair. Previous studies indicated that the minimal kinetic mechanism needed to describe the ATP-dependent unwinding of blunt-ended DNA by RecBCD in vitro is a sequential n-step mechanism with two to three additional kinetic steps prior to initiating DNA unwinding. Since RecBCD can “melt out” ∼ 6 bp upon binding to the end of a blunt-ended DNA duplex in a Mg²⁺-dependent but ATP-independent reaction, we investigated the effects of noncomplementary single-stranded (ss) DNA tails [3′-(dT)₆ and 5′-(dT)₆ or 5′-(dT)₁₀] on the mechanism of RecBCD and RecBC unwinding of duplex DNA using rapid kinetic methods. As with blunt-ended DNA, RecBCD unwinding of DNA possessing 3′-(dT)₆ and 5′-(dT)₆ noncomplementary ssDNA tails is well described by a sequential n-step mechanism with the same unwinding rate (mk_U = 774 ± 16 bp s^− 1) and kinetic step size (m = 3.3 ± 1.3 bp), yet two to three additional kinetic steps are still required prior to initiation of DNA unwinding (k_C = 45 ± 2 s^− 1). However, when the noncomplementary 5′ ssDNA tail is extended to 10 nt [5′-(dT)₁₀ and 3′-(dT)₆], the DNA end structure for which RecBCD displays optimal binding affinity, the additional kinetic steps are no longer needed, although a slightly slower unwinding rate (mk_U = 538 ± 24 bp s^− 1) is observed with a similar kinetic step size (m = 3.9 ± 0.5 bp). The RecBC DNA helicase (without the RecD subunit) does not initiate unwinding efficiently from a blunt DNA end. However, RecBC does initiate well from a DNA end possessing noncomplementary twin 5′-(dT)₆ and 3′-(dT)₆ tails, and unwinding can be described by a simple uniform n-step sequential scheme, without the need for the additional k_C initiation steps, with a similar kinetic step size (m = 4.4 ± 1.7 bp) and unwinding rate (mk_obs = 396 ± 15 bp s^− 1). These results suggest that the additional kinetic steps with rate constant k_C required for RecBCD to initiate unwinding of blunt-ended and twin (dT)₆-tailed DNA reflect processes needed to engage the RecD motor with the 5′ ssDNA.     

Natural selection drives patterns of lake-stream divergence in stickleback foraging morphology

To what extent are patterns of biological diversification determined by natural selection? We addressed this question by exploring divergence in foraging morphology of threespine stickleback fish inhabiting lake and stream habitats within eight independent watersheds. We found that lake fish generally displayed more developed gill structures and had more streamlined bodies than did stream fish. Diet analysis revealed that these morphological differences were associated with limnetic vs. benthic foraging modes, and that the extent of morphological divergence within watersheds reflected differences in prey resources utilized by lake and stream fish. We also found that patterns of divergence were unrelated to patterns of phenotypic trait (co)variance within populations (i.e. the ‘line of least resistance’). Instead, phenotypic (co)variances were more likely to have been shaped by adaptation to lake vs. stream habitats. Our study thus implicates natural selection as a strong deterministic force driving morphological diversification in lake–stream stickleback. The strength of this inference was obtained by complementing a standard analysis of parallel divergence in means between discrete habitat categories (lake vs. stream) with quantitative estimates of selective forces and information on trait (co)variances.     

Airborne hyperspectral imaging for estimating acorn yield based on the PLS B-matrix calibration technique

Alternate bearing of acorn is a well-marked yield variability phenomenon in forest production. In Japan, this phenomenon is also related to wildlife management (e.g. of animals such as wild pigs, that rely on acorn as their major feed source). Effective management of animals dependent on acorn will require accurate estimation of acorn yield at an early stage. In this paper, we proposed a way to estimate acorn yield from the canopy reflectance values of individual trees. Using an Airborne Imaging Spectrometer for Application (AISA) Eagle System, hyperspectral images in 72 visible and near-infrared wavelengths (407–898 nm) were acquired over an acorn forest in Japan 10 times over three consecutive years (2003–2005) during the early acorn growing season. The canopy spectral reflectance values for individual trees at each wavelength were extracted from the images, and important wavelengths were determined as estimating factors by the B-matrix technique based on partial least squares (PLS) analysis. Yield-estimating models were then developed by multiple linear regression (MLR). Three models obtained from images acquired on June 27 in 2003, July 13 in 2004 and June 21 in 2005 estimated acorn yield well in comparison with ground truth, indicating that the procedure has considerable potential. The study also demonstrated the B-matrix technique based on PLS analysis to be reliable and efficient in identifying important wavelengths for determining suitable estimating factors that best contribute to the estimation model.     

Sliced inverse regression with regularizations

Summary .   In high-dimensional data analysis, sliced inverse regression (SIR) has proven to be an effective dimension reduction tool and has enjoyed wide applications. The usual SIR, however, cannot work with problems where the number of predictors, p , exceeds the sample size, n , and can suffer when there is high collinearity among the predictors. In addition, the reduced dimensional space consists of linear combinations of all the original predictors and no variable selection is achieved. In this article, we propose a regularized SIR approach based on the least-squares formulation of SIR. The L ₂ regularization is introduced, and an alternating least-squares algorithm is developed, to enable SIR to work with   n < p   and highly correlated predictors. The L ₁ regularization is further introduced to achieve simultaneous reduction estimation and predictor selection. Both simulations and the analysis of a microarray expression data set demonstrate the usefulness of the proposed method.     

On the asymptotics of marginal regression splines with longitudinal data

There have been studies on how the asymptotic efficiency ofa nonparametric function estimator depends on the handling ofthe within-cluster correlation when nonparametric regressionmodels are used on longitudinal or cluster data. In particular,methods based on smoothing splines and local polynomial kernelsexhibit different behaviour. We show that the generalized estimationequations based on weighted least squares regression splinesfor the nonparametric function have an interesting property:the asymptotic bias of the estimator does not depend on theworking correlation matrix, but the asymptotic variance, andtherefore the mean squared error, is minimized when the truecorrelation structure is specified. This property of the asymptoticbias distinguishes regression splines from smoothing splines.