The Effect of Soil Non-linearity on Mixed Traffic Railway Lines: Passenger vs Freight Loads

To add additional capacity to railway networks, freight services might be added to lines that have previously only be used for passenger services. Existing ballasted lines may have mixed subgrade conditions and thus the effect of increased axle loads on track behavior is unclear. Typically, such cases will result in elevated track deflections in comparison to passenger vehicles. As a result, the supporting subgrade experiences higher strain levels, which can fall into the large strain range. The related non-linear subgrade behavior plays an important role in track response but is challenging to model. As a solution, this paper presents a new semi-analytical numerical model, where the track is simulated analytically and allows for 1D wave propagation. The ground is modelled using a non-linear equivalent thin-layer finite element formulation. This allows for the subgrade stiffness to be updated in an iterative manner with minimal computational effort. A case study is presented to show that modest increases in axle load can have a marked effect on track deflections

Effect of concrete slats, three mat types and out-wintering pads on performance and welfare of finishing beef steers

peer-reviewedBackground The objective was to investigate the effect of placing mats on concrete slatted floors on performance, behaviour, hoof condition, dirt scores, physiological and immunological variables of beef steers, and to compare responses with animals on out-wintering pads. Continental crossbred beef steers [n = 360; mean (±SD) initial live weight 539 kg (42.2)] were blocked by breed and live weight and randomly assigned to one of five treatments; (1) Concrete slats alone, (2) Mat 1 (Natural Rubber structure) (Durapak Rubber Products), (3) Mat 2 (Natural rubber structure) (EasyFix), (4) Mat 3 (modified ethylene vinyl acetate (EVA) foam structure) and (5) Out-wintering pads (OWP’s). Results Animals on the OWPs had a greater (P  0.05) as the other treatments. Animals on the OWPs had reduced lying percentage time compared with all the other treatments. Dry matter (DM) intake was greater for animals on the OWPs compared with all the other treatments. Carcass weight, kill out proportion, carcass fat score, carcass composition score, FCR and physiological responses were similar (P > 0.05) among treatments. No incidence of laminitis was observed among treatments. The number of hoof lesions was greater on all mat types (P < 0.05) compared with concrete slats and OWP treatments. Dirt scores were greater (P < 0.05) for animals on OWPs when measured on days 42, 84, 105, 126 and 150 compared with animals on slats. Conclusions Under the conditions adopted for the present study, there was no evidence to suggest that animals housed on bare concrete slats were disadvantaged in respect of animal welfare compared with animals housed on other floor types. It is concluded that the welfare of steers was not adversely affected by slats compared with different mat types or OWPs

Prediction of DNA-binding propensity of proteins by the ball-histogram method using automatic template search

We contribute a novel, ball-histogram approach to DNA-binding propensity prediction of proteins. Unlike state-of-the-art methods based on constructing an ad-hoc set of features describing physicochemical properties of the proteins, the ball-histogram technique enables a systematic, Monte-Carlo exploration of the spatial distribution of amino acids complying with automatically selected properties. This exploration yields a model for the prediction of DNA binding propensity. We validate our method in prediction experiments, improving on state-of-the-art accuracies. Moreover, our method also provides interpretable features involving spatial distributions of selected amino acids

ANGLOR: A Composite Machine-Learning Algorithm for Protein Backbone Torsion Angle Prediction

We developed a composite machine-learning based algorithm, called ANGLOR, to predict real-value protein backbone torsion angles from amino acid sequences. The input features of ANGLOR include sequence profiles, predicted secondary structure and solvent accessibility. In a large-scale benchmarking test, the mean absolute error (MAE) of the phi/psi prediction is 28°/46°, which is ∼10% lower than that generated by software in literature. The prediction is statistically different from a random predictor (or a purely secondary-structure-based predictor) with p-value <1.0×10−300 (or <1.0×10−148) by Wilcoxon signed rank test. For some residues (ILE, LEU, PRO and VAL) and especially the residues in helix and buried regions, the MAE of phi angles is much smaller (10–20°) than that in other environments. Thus, although the average accuracy of the ANGLOR prediction is still low, the portion of the accurately predicted dihedral angles may be useful in assisting protein fold recognition and ab initio 3D structure modeling

Glycosylation site prediction using ensembles of Support Vector Machine classifiers

Article discussing the performance of different computational methods for prediction of glycosylation sites from amino acid sequences

A structurally distinct TGF-β mimic from an intestinal helminth parasite potently induces regulatory T cells.

Helminth parasites defy immune exclusion through sophisticated evasion mechanisms, including activation of host immunosuppressive regulatory T (Treg) cells. The mouse parasite Heligmosomoides polygyrus can expand the host Treg population by secreting products that activate TGF-β signalling, but the identity of the active molecule is unknown. Here we identify an H. polygyrus TGF-β mimic (Hp-TGM) that replicates the biological and functional properties of TGF-β, including binding to mammalian TGF-β receptors and inducing mouse and human Foxp3+ Treg cells. Hp-TGM has no homology with mammalian TGF-β or other members of the TGF-β family, but is a member of the complement control protein superfamily. Thus, our data indicate that through convergent evolution, the parasite has acquired a protein with cytokine-like function that is able to exploit an endogenous pathway of immunoregulation in the host

Macrophage origin limits functional plasticity in helminth-bacterial co-infection

Rapid reprogramming of the macrophage activation phenotype is considered important in the defense against consecutive infection with diverse infectious agents. However, in the setting of persistent, chronic infection the functional importance of macrophage-intrinsic adaptation to changing environments vs. recruitment of new macrophages remains unclear. Here we show that resident peritoneal macrophages expanded by infection with the nematode Heligmosomoides polygyrus bakeri altered their activation phenotype in response to infection with Salmonella enterica ser. Typhimurium in vitro and in vivo. The nematode-expanded resident F4/80high macrophages efficiently upregulated bacterial induced effector molecules (e.g. MHC-II, NOS2) similarly to newly recruited monocyte-derived macrophages. Nonetheless, recruitment of blood monocyte-derived macrophages to Salmonella infection occurred with equal magnitude in co-infected animals and caused displacement of the nematode-expanded, tissue resident-derived macrophages from the peritoneal cavity. Global gene expression analysis revealed that although nematode-expanded resident F4/80high macrophages made an anti-bacterial response, this was muted as compared to newly recruited F4/80low macrophages. However, the F4/80high macrophages adopted unique functional characteristics that included enhanced neutrophil-stimulating chemokine production. Thus, our data provide important evidence that plastic adaptation of MΦ activation does occur in vivo, but that cellular plasticity is outweighed by functional capabilities specific to the tissue origin of the cell

Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors

A state-of-the-art review of curve squeal noise: Phenomena, mechanisms, modelling and mitigation

[EN] Curve squeal is an intense tonal noise occurring when a rail vehicle negotiates a sharp curve. The phenomenon can be considered to be chaotic, with a widely differing likelihood of occurrence on different days or even times of day. The term curve squeal may include several different phenomena with a wide range of dominant frequencies and potentially different excitation mechanisms. This review addresses the different squeal phenomena and the approaches used to model squeal noise; both time-domain and frequency-domain approaches are discussed and compared. Supporting measurements using test rigs and field tests are also summarised. A particular aspect that is addressed is the excitation mechanism. Two mechanisms have mainly been considered in previous publications. In many early papers the squeal was supposed to be generated by the so-called falling friction characteristic in which the friction coefficient reduces with increasing sliding velocity. 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Linking Employee Stakeholders to Environmental Performance: The Role of Proactive Environmental Strategies and Shared Vision

Drawing on the natural-resource-based view (NRBV), we propose that employee stakeholder integration is linked to environmental performance through firms’ proactive environmental strategies, and that this link is contingent on shared vision. We tested our model with a cross-country and multi-industry sample. In support of our theory, results revealed that firms’ proactive environmental strategies translated employee stakeholder integration into environmental performance. This relationship was pronounced for high levels of shared vision. Our findings demonstrate that shared vision represents a key condition for advancing the corporate greening agenda through proactive environmental strategies. We discuss implications for the CSR and the environmental management literatures, with a particular focus on the NRBV and stakeholder integration debates