Phenotype Classification of Zebrafish Embryos by Supervised Learning

Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.     

Can Newts Cope with the Heat? Disparate Thermoregulatory Strategies of Two Sympatric Species in Water

Many ectotherms effectively reduce their exposure to low or high environmental temperatures using behavioral thermoregulation. In terrestrial ectotherms, thermoregulatory strategies range from accurate thermoregulation to thermoconformity according to the costs and limits of thermoregulation, while in aquatic taxa the quantification of behavioral thermoregulation have received limited attention. We examined thermoregulation in two sympatric newt species, Ichthyosaura alpestris and Lissotriton vulgaris, exposed to elevated water temperatures under semi-natural conditions. According to a recent theory, we predicted that species for which elevated water temperatures pose a lower thermal quality habitat, would thermoregulate more effectively than species in thermally benign conditions. In the laboratory thermal gradient, L. vulgaris maintained higher body temperatures than I. alpestris. Semi-natural thermal conditions provided better thermal quality of habitat for L. vulgaris than for I. alpestris. Thermoregulatory indices indicated that I. alpestris actively thermoregulated its body temperature, whereas L. vulgaris remained passive to the thermal heterogeneity of aquatic environment. In the face of elevated water temperatures, sympatric newt species employed disparate thermoregulatory strategies according to the species-specific quality of the thermal habitat. Both strategies reduced newt exposure to suboptimal water temperatures with the same accuracy but with or without the costs of thermoregulation. The quantification of behavioral thermoregulation proves to be an important conceptual and methodological tool for thermal ecology studies not only in terrestrial but also in aquatic ectotherms.     

Metabolites in Blood for Prediction of Bacteremic Sepsis in the Emergency Room

A metabolomics approach for prediction of bacteremic sepsis in patients in the emergency room (ER) was investigated. In a prospective study, whole blood samples from 65 patients with bacteremic sepsis and 49 ER controls were compared. The blood samples were analyzed using gas chromatography coupled to time-of-flight mass spectrometry. Multivariate and logistic regression modeling using metabolites identified by chromatography or using conventional laboratory parameters and clinical scores of infection were employed. A predictive model of bacteremic sepsis with 107 metabolites was developed and validated. The number of metabolites was reduced stepwise until identifying a set of 6 predictive metabolites. A 6-metabolite predictive logistic regression model showed a sensitivity of 0.91(95% CI 0.69–0.99) and a specificity 0.84 (95% CI 0.58–0.94) with an AUC of 0.93 (95% CI 0.89–1.01). Myristic acid was the single most predictive metabolite, with a sensitivity of 1.00 (95% CI 0.85–1.00) and specificity of 0.95 (95% CI 0.74–0.99), and performed better than various combinations of conventional laboratory and clinical parameters. We found that a metabolomics approach for analysis of acute blood samples was useful for identification of patients with bacteremic sepsis. Metabolomics should be further evaluated as a new tool for infection diagnostics.     

Structural Insight of a Trimodular Halophilic Cellulase with a Family 46 Carbohydrate-Binding Module

Cellulases are the key enzymes used in the biofuel industry. A typical cellulase contains a catalytic domain connected to a carbohydrate-binding module (CBM) through a flexible linker. Here we report the structure of an atypical trimodular cellulase which harbors a catalytic domain, a CBM46 domain and a rigid CBM_X domain between them. The catalytic domain shows the features of GH5 family, while the CBM46 domain has a sandwich-like structure. The catalytic domain and the CBM46 domain form an extended substrate binding cleft, within which several tryptophan residues are well exposed. Mutagenesis assays indicate that these residues are essential for the enzymatic activities. Gel affinity electrophoresis shows that these tryptophan residues are involved in the polysaccharide substrate binding. Also, electrostatic potential analysis indicates that almost the entire solvent accessible surface of CelB is negatively charged, which is consistent with the halophilic nature of this enzyme.     

Solving the N-Queens problem with a binary Hopfield-type network

The application of a discrete Hopfield-type neural network to solving the NP-Hard optimization problem — the N-Queens Problem (NQP) — is presented. The applied network is binary, and at every moment each neuron potential is equal to either 0 or 1. The network can be implemented in the asynchronous mode as well as in the synchronous one with n parallel running processors. In both cases the convergence rate is up to 100%, and the experimental estimate of the average computational complexity is polynomial. Based on the computer simulation results and the theoretical analysis, the proper network parameters are established. The behaviour of the network is explained.     

Flavonoids enantiomer distribution in different parts of goldenrod (Solidago virgaurea L.), lucerne (Medicago sativa L.) and phacelia (Phacelia tanacetifolia Benth.)

Plant material is a rich source of valuable compounds such as flavanones. Their different forms influence bioavailability and biological activity, causing problems with the selection of plant material for specific purposes. The purpose of this research was to determine selected flavanone (eriodictyol, naringenin, liquiritigenin, and hesperetin) enantiomer contents in free form and bonded to glycosides by an RP‐UHPLC‐ESI‐MS/MS method. Different parts (stems, leaves, and flowers) of goldenrod (Solidago virgaurea L.), lucerne (Medicago sativa L.), and phacelia (Phacelia tanacetifolia Benth.) were used. The highest content of eriodictyol was found in goldenrod flowers (13.1 μg/g), where it occurred mainly as the (S)‐enantiomer, and the greatest proportion of the total amount was bonded to glycosides. The richest source of naringenin was found to be lucerne leaves (4.7 μg/g), where it was mainly bonded to glycosides and with the (S)‐enantiomer as the dominant form. Liquiritigenin was determined only in lucerne, where the flowers contained the highest amount (1.2 μg/g), with the (R)‐enantiomer as dominant aglycone form and the (S)‐enantiomer as the dominant glycosylated form. The highest hesperetin content was determined in phacelia leaves (0.38 μg/g), where it was present in the form of a glycoside and only as the (S)‐enantiomer. A comparison of the different analyte forms occurring in different plant parts was performed for the first time.