Systematic sample subdividing strategy for training landslide susceptibility models

© 2019 Elsevier B.V. Current practice in choosing training samples for landslide susceptibility modelling (LSM) is to randomly subdivide inventory information into training and testing samples. Where inventory data differ in distribution, the selection of training samples by a random process may cause inefficient training of machine learning (ML)/statistical models. A systematic technique may, however, produce efficient training samples that well represent the entire inventory data. This is particularly true when inventory information is scarce. This research proposed a systemic strategy to deal with this problem based on the fundamental distribution of probabilities (i.e. Hellinger) and a novel graphical representation of information contained in inventory data (i.e. inventory information curve, IIC). This graphical representation illustrates the relative increase in available information with the growth of the training sample size. Experiments on a selected dataset over the Cameron Highlands, Malaysia were conducted to validate the proposed methods. The dataset contained 104 landslide inventories and 7 landslide-conditioning factors (i.e. altitude, slope, aspect, land use, distance from the stream, distance from the road and distance from lineament) derived from a LiDAR-based digital elevation model and thematic maps acquired from government authorities. In addition, three ML/statistical models, namely, k-nearest neighbour (KNN), support vector machine (SVM) and decision tree (DT), were utilised to assess the proposed sampling strategy for LSM. The impacts of model's hyperparameters, noise and outliers on the performance of the models and the shape of IICs were also investigated and discussed. To evaluate the proposed method further, it was compared with other standard methods such as random sampling (RS), stratified RS (SRS) and cross-validation (CV). The evaluations were based on the area under the receiving characteristic curves. The results show that IICs are useful in explaining the information content in the training subset and their differences from the original inventory datasets. The quantitative evaluation with KNN, SVM and DT shows that the proposed method outperforms the RS and SRS in all the models and the CV method in KNN and DT models. The proposed sampling strategy enables new applications in landslide modelling, such as measuring inventory data content and complexity and selecting effective training samples to improve the predictive capability of landslide susceptibility models

Optimizing street mobility through a netlogo simulation environment

The routes and streets make it possible to drive and travel through the cities, but unfortunately traffic and particularly congestion leads to drivers losing time while traveling from one place to another, because of the time it takes to transit on the roads, in addition to waiting times by traffic lights. This research introduces the extension of an agent-oriented system aimed at reducing driver waiting times at a street intersection. The simulation environment was implemented in NetLogo, which allowed comparison of the impact of Smart traffic light use versus a fixed-time traffic light

Comparison of four kernel functions used in support vector machines for landslide susceptibility mapping: a case study at Suichuan area (China)

Suichuan is a mountainous area at the Jiangxi province in Central China, where rainfall-induced landslides occur frequently. The purpose of this study is to assess landslide susceptibility of this region using support vector machine (SVM) with four kernel functions: polynomial (PL), radial basis function (RBF), sigmoid (SIG), and linear (LN). A total of 178 landslides were used to accomplish this approach, of which, 125 (70%) landslides were randomly selected for training the landslide susceptibility models, whereas the remaining 53 (30%) were used for the model validation. Fifteen landslide conditioning factors were considered including slope-angle, altitude, slope-aspect, topographic wetness index (TWI), sediment transport index (STI), stream power index (SPI), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, precipitation, landuse, normalized difference vegetation index (NDVI), and lithology. Using the training dataset, nine landslide susceptibility models for the Suichuan area were constructed with the four kernel functions. To evaluate the performance of these models, the receiver-operating characteristic curve (ROC) and area under the curve (AUC) were used. Using the training dataset, AUC values for the SVM-PL models with six degrees PL function (1–6) are 0.715, 0.801, 0.856, 0.891, 0.919, 0.953, respectively, and for the SVM-RBF model, the SVM-SIG model, and the SVM-LN model are 0.716, 0.741, and 0.740, respectively. Using the validation dataset, AUC values for the SVM-PL models with six degrees PL function (1–6) are 0.738, 0.730, 0.683, 0.648, 0.608, and 0.598, respectively, and for the SVM-RBF model, the SVM-SIG model, and the SVM-LN model are 0.716, 0.741, and 0.740, respectively. Our results suggested that the SVM-RBF model is the most suitable for landslide susceptibility assessment for the study area

A combined computational and experimental investigation of the [2Fe–2S] cluster in biotin synthase

Biotin synthase was the first example of what is now regarded as a distinctive enzyme class within the radical S-adenosylmethionine superfamily, the members of which use Fe/S clusters as the sulphur source in radical sulphur insertion reactions. The crystal structure showed that this enzyme contains a [2Fe–2S] cluster with a highly unusual arginine ligand, besides three normal cysteine ligands. However, the crystal structure is at such a low resolution that neither the exact coordination mode nor the role of this exceptional ligand has been elucidated yet, although it has been shown that it is not essential for enzyme activity. We have used quantum refinement of the crystal structure and combined quantum mechanical and molecular mechanical calculations to explore possible coordination modes and their influences on cluster properties. The investigations show that the protonation state of the arginine ligand has little influence on cluster geometry, so even a positively charged guanidinium moiety would be in close proximity to the iron atom. Nevertheless, the crystallised enzyme most probably contains a deprotonated (neutral) arginine coordinating via the NH group. Furthermore, the Fe···Fe distance seems to be independent of the coordination mode and is in perfect agreement with distances in other structurally characterised [2Fe–2S] clusters. The exceptionally large Fe···Fe distance found in the crystal structure could not be reproduced

Extracting causal relations on HIV drug resistance from literature

<p>Abstract</p> <p>Background</p> <p>In HIV treatment it is critical to have up-to-date resistance data of applicable drugs since HIV has a very high rate of mutation. These data are made available through scientific publications and must be extracted manually by experts in order to be used by virologists and medical doctors. Therefore there is an urgent need for a tool that partially automates this process and is able to retrieve relations between drugs and virus mutations from literature.</p> <p>Results</p> <p>In this work we present a novel method to extract and combine relationships between HIV drugs and mutations in viral genomes. Our extraction method is based on natural language processing (NLP) which produces grammatical relations and applies a set of rules to these relations. We applied our method to a relevant set of PubMed abstracts and obtained 2,434 extracted relations with an estimated performance of 84% for F-score. We then combined the extracted relations using logistic regression to generate resistance values for each <drug, mutation> pair. The results of this relation combination show more than 85% agreement with the Stanford HIVDB for the ten most frequently occurring mutations. The system is used in 5 hospitals from the Virolab project <url>http://www.virolab.org</url> to preselect the most relevant novel resistance data from literature and present those to virologists and medical doctors for further evaluation.</p> <p>Conclusions</p> <p>The proposed relation extraction and combination method has a good performance on extracting HIV drug resistance data. It can be used in large-scale relation extraction experiments. The developed methods can also be applied to extract other type of relations such as gene-protein, gene-disease, and disease-mutation.</p

Phase II trial of Modified Vaccinia Ankara (MVA) virus expressing 5T4 and high dose Interleukin-2 (IL-2) in patients with metastatic renal cell carcinoma

<p>Abstract</p> <p>Background</p> <p>Interleukin-2 (IL-2) induces durable objective responses in a small cohort of patients with metastatic renal cell carcinoma (RCC) but the antigen(s) responsible for tumor rejection are not known. 5T4 is a non-secreted membrane glycoprotein expressed on clear cell and papillary RCCs. A modified vaccinia virus Ankara (MVA) encoding 5T4 was tested in combination with high-dose IL-2 to determine the safety, objective response rate and effect on humoral and cell-mediated immunity.</p> <p>Methods</p> <p>25 patients with metastatic RCC who qualified for IL-2 were eligible and received three immunizations every three weeks followed by IL-2 (600,000 IU/kg) after the second and third vaccinations. Blood was collected for analysis of humoral, effector and regulatory T cell responses.</p> <p>Results</p> <p>There were no serious vaccine-related adverse events. While no objective responses were observed, three patients (12%) were rendered disease-free after nephrectomy or resection of residual metastatic disease. Twelve patients (48%) had stable disease which was associated with improved median overall survival compared to patients with progressive disease (not reached vs. 28 months, p = 0.0261). All patients developed 5T4-specific antibody responses and 13 patients had an increase in 5T4-specific T cell responses. Although the baseline frequency of Tregs was elevated in all patients, those with stable disease showed a trend toward increased effector CD8+ T cells and a decrease in Tregs.</p> <p>Conclusion</p> <p><b>V</b>accination with MVA-5T4 did not improve objective response rates of IL-2 therapy but did result in stable disease associated with an increase in the ratio of 5T4-specific effector to regulatory T cells in selected patients.</p> <p>Trial registration number</p> <p>ISRCTN83977250</p

Dietary tuna hydrolysate modulates growth performance, immune response, intestinal morphology and resistance to Streptococcus iniae in juvenile barramundi, Lates calcarifer

This study investigated the effects of tuna hydrolysate (TH) inclusion in fishmeal (FM) based diets on the growth performance, innate immune response, intestinal health and resistance to Streptococcus iniae infection in juvenile barramundi, Lates calcarifer. Five isonitrogenous and isoenergetic experimental diets were prepared with TH, replacing FM at levels of 0% (control) 5%, 10%, 15% and 20%, and fed fish to apparent satiation three times daily for 8 weeks. The results showed that fish fed diets containing 5% and 10% TH had significantly higher final body weight and specific growth rate than the control. A significant reduction in blood glucose was found in fish fed 10%, 15% and 20% TH compared to those in the control whereas none of the other measured blood and serum indices were influenced by TH inclusion. Histological observation revealed a significant enhancement in goblet cell numbers in distal intestine of fish fed 5 to 10% TH in the diet. Moreover, fish fed 10% TH exhibited the highest resistance against Streptococcus iniae infection during a bacterial challenge trial. These findings therefore demonstrate that the replacement of 5 to 10% FM with TH improves growth, immune response, intestinal health and disease resistance in juvenile barramundi

Clique-width : harnessing the power of atoms.

Many NP-complete graph problems are polynomial-time solvable on graph classes of bounded clique-width. Several of these problems are polynomial-time solvable on a hereditary graph class G if they are so on the atoms (graphs with no clique cut-set) of G . Hence, we initiate a systematic study into boundedness of clique-width of atoms of hereditary graph classes. A graph G is H-free if H is not an induced subgraph of G, and it is (H1,H2) -free if it is both H1 -free and H2 -free. A class of H-free graphs has bounded clique-width if and only if its atoms have this property. This is no longer true for (H1,H2) -free graphs, as evidenced by one known example. We prove the existence of another such pair (H1,H2) and classify the boundedness of clique-width on (H1,H2) -free atoms for all but 18 cases

Development of Immune-Specific Interaction Potentials and Their Application in the Multi-Agent-System VaccImm

Peptide vaccination in cancer therapy is a promising alternative to conventional methods. However, the parameters for this personalized treatment are difficult to access experimentally. In this respect, in silico models can help to narrow down the parameter space or to explain certain phenomena at a systems level. Herein, we develop two empirical interaction potentials specific to B-cell and T-cell receptor complexes and validate their applicability in comparison to a more general potential. The interaction potentials are applied to the model VaccImm which simulates the immune response against solid tumors under peptide vaccination therapy. This multi-agent system is derived from another immune system simulator (C-ImmSim) and now includes a module that enables the amino acid sequence of immune receptors and their ligands to be taken into account. The multi-agent approach is combined with approved methods for prediction of major histocompatibility complex (MHC)-binding peptides and the newly developed interaction potentials. In the analysis, we critically assess the impact of the different modules on the simulation with VaccImm and how they influence each other. In addition, we explore the reasons for failures in inducing an immune response by examining the activation states of the immune cell populations in detail