Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.     

Pollen productivity estimates of key European plant taxa for quantitative reconstruction of past vegetation: a review

Information on the spatial distribution of past vegetation on local, regional and global scales is increasingly used within climate modelling, nature conservancy and archaeology. It is possible to obtain such information from fossil pollen records in lakes and bogs using the landscape reconstruction algorithm (LRA) and its two models, REVEALS and LOVE. These models assume that reliable pollen productivity estimates (PPEs) are available for the plant taxa involved in the quantitative reconstructions of past vegetation, and that PPEs are constant through time. This paper presents and discusses the PPEs for 15 tree and 18 herb taxa obtained in nine study areas of Europe. Observed differences in PPEs between regions may be explained by methodological issues and environmental variables, of which climate and related factors such as reproduction strategies and growth forms appear to be the most important. An evaluation of the PPEs at hand so far suggests that they can be used in modelling applications and quantitative reconstructions of past vegetation, provided that consideration of past environmental variability within the region is used to inform selection of PPEs, and bearing in mind that PPEs might have changed through time as a response to climate change. Application of a range of possible PPEs will allow a better evaluation of the results.     

A click chemistry approach for the synthesis of cyclic ureido tethered coumarinyl and 1-aza coumarinyl 1,2,3-triazoles as inhibitors of Mycobacterium tuberculosis H37Rv and their in silico studies

Nucleoside bases like uracil, pharmacophoric triazoles and benzimidazolones have been used during the present study to design molecular matrices for antitubercular activity, employing Click Chemistry. Click triazoles 4/7/10 have been obtained by the reaction of 4-(Azidomethyl)-2H-chromen-2-ones/quinolin-2(1H)-ones 3 and propargyl ethers 2/6/9 derived from theophylline/6-methyl uracil/2-benzimidazolone respectively. In addition to spectral data structures have been confirmed by single crystal X-ray diffraction studies in case of uracil bis alkyne (6) and theophylline mono triazole (4c). Theophylline linked mono triazoles, 4(a-d) and 6-methyl uracil linked bis triazoles, 7(a-e) have been found to inhibit Mycobacterium tuberculosis H37Rv with MIC values in the range 55.62–115.62 μM. Benzimidazolone bis triazoles, 10(a-n) showed better activity with MIC in the range 2.33–18.34 μM. Molecular modeling studies using Surflex-Dock algorithm supported our results.     

De novo inference of protein function from coarse‐grained dynamics

Inference of molecular function of proteins is the fundamental task in the quest for understanding cellular processes. The task is getting increasingly difficult with thousands of new proteins discovered each day. The difficulty arises primarily due to lack of high‐throughput experimental technique for assessing protein molecular function, a lacunae that computational approaches are trying hard to fill. The latter too faces a major bottleneck in absence of clear evidence based on evolutionary information. Here we propose a de novo approach to annotate protein molecular function through structural dynamics match for a pair of segments from two dissimilar proteins, which may share even <10% sequence identity. To screen these matches, corresponding 1 µs coarse‐grained (CG) molecular dynamics trajectories were used to compute normalized root‐mean‐square‐fluctuation graphs and select mobile segments, which were, thereafter, matched for all pairs using unweighted three‐dimensional autocorrelation vectors. Our in‐house custom‐built forcefield (FF), extensively validated against dynamics information obtained from experimental nuclear magnetic resonance data, was specifically used to generate the CG dynamics trajectories. The test for correspondence of dynamics‐signature of protein segments and function revealed 87% true positive rate and 93.5% true negative rate, on a dataset of 60 experimentally validated proteins, including moonlighting proteins and those with novel functional motifs. A random test against 315 unique fold/function proteins for a negative test gave >99% true recall. A blind prediction on a novel protein appears consistent with additional evidences retrieved therein. This is the first proof‐of‐principle of generalized use of structural dynamics for inferring protein molecular function leveraging our custom‐made CG FF, useful to all. Proteins 2014; 82:2443–2454. © 2014 Wiley Periodicals, Inc.     

An enhanced topologically significant directed random walk in cancer classification using gene expression datasets

Microarray technology has become one of the elementary tools for researchers to study the genome of organisms. As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analysis, cancerous classification is an emerging important trend. Significant directed random walk is proposed as one of the cancerous classification approach which have higher sensitivity of risk gene prediction and higher accuracy of cancer classification. In this paper, the methodology and material used for the experiment are presented. Tuning parameter selection method and weight as parameter are applied in proposed approach. Gene expression dataset is used as the input datasets while pathway dataset is used to build a directed graph, as reference datasets, to complete the bias process in random walk approach. In addition, we demonstrate that our approach can improve sensitive predictions with higher accuracy and biological meaningful classification result. Comparison result takes place between significant directed random walk and directed random walk to show the improvement in term of sensitivity of prediction and accuracy of cancer classification.     

Evaluation of mesh- and binary-based contour propagation methods in 4D thoracic radiotherapy treatments using patient 4D CT images

Based on four dimensional (4D) computed tomography (CT) images, mesh- and binary-based contour propagation algorithms for 4D thoracic radiotherapy treatments were evaluated. Gross tumor volumes (GTVs), lungs, hearts and spinal cords on the CT images at the end-exhale and end-inhale phases for six patients were delineated by the physician. All volumes of interest (VOIs) were automatically propagated from the end-exhale phase to the end-inhale phase using two propagation methods. The propagated VOIs were quantitatively compared with the VOIs contoured at the end-inhale phase by the physician using Dice Similarity Coefficient (DSC), Mean Slicewise Hausdorff Distance (MSHD), Center Of Mass (COM) displacement and volume difference. A two-sided Student’s t test was implemented to examine the significance of the differences between the results obtained from the two algorithms. For GTVs, statistically significant differences between the two algorithms were not observed. For all the other VOIs, the mesh-based method showed higher mean DSCs for the heart, left lung, right lung and spinal cord, lower mean MSHD for the spinal cord, lower mean COM displacement for the heart, and lower mean volume differences for the left lung, right lung and spinal cord with statistically significant differences than the binary-based method. The running time for propagation was approximately 3 s and 3 min for the mesh- and binary-based methods, respectively. Collectively, the mesh-based algorithm provides superiorities in running time and reliability for contour propagation in 4D radiotherapy.     

A methodological basis for description and analysis of systems with complex switch-like interactions

 A wide range of complex systems appear to have switch-like interactions, i.e. below (or above) a certain threshold x has no or little influence on y, while above (or below) this threshold the effect of x on y saturates rapidly to a constant level. Switching functions are frequently described by sigmoid functions or combinations of these. Within the context of ordinary differential equations we present a very general methodological basis for designing and analysing models involving complicated switching functions together with any other non-linearities. A procedure to determine position and stability properties of all stationary points lying close to a threshold for one or several variables, so-called singular stationary points, is developed. Such points may represent homeostatic states in models, and are therefore of considerable interest. The analysis provides a profound insight into the generic effects of steep sigmoid interactions on the dynamics around homeostatic points. It leads to qualitative as well as quantitative predictions without using advanced mathematical methods. Thus, it may have an important heuristic function in connection with numerical simulations aimed at unfolding the predictive potential of realistic models. Received 25 January 1996; received in revised form 29 June 1997     

Models and Algorithms for Hub and Spoke Locations for Emergency Service Facilities in Response to Serious Emergency Incidents

This article examines the location-allocation of emergency service facilities as a research subject. The research presents the setup of the single allocation set covering location-allocation models for emergency service facilities under strong time constraints, in view of the shortage of hub & spoke network bypass. The article also presents an extension to the single allocation set covering location-allocation model (SASCP) and the SASCP model with bypass constraints (γ-SASCP) for emergency service facilities under large-scale emergency requirements. For the two models, an improved genetic algorithm was designed and the two models were respectively solved, with the effectiveness of the algorithm verified by a specific example. The impacts of change of parameters such as time discount rate, maximum time constraints, and bypass ratio on the model's results are compared and analyzed, based on solved results by the specific example.