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.     

Multiattribute evaluation of regional cotton variety trials

Summary The Australian Cotton Cultivar Trials (ACCT) are designed to investigate various cotton [Gossypium hirsutum (L.)] lines in several locations in New South Wales and Queensland each year. If these lines are to be assessed by the simultaneous use of yield and lint quality data, then a multivariate technique applicable to three-way data is desirable. Two such techniques, the mixture maximum likelihood method of clustering and three-mode principal component analysis, are described and used to analyze these data. Applied together, the methods enhance each other's usefulness in interpreting the information on the line response patterns across the locations. The methods provide a good integration of the responses across environments of the entries for the different attributes in the trials. For instance, using yield as the sole criterion, the excellence of the namcala and coker group for quality is overlooked. The analyses point to a decision in favor of either high yields of moderate to good quality lint or moderate yield but superior lint quality. The decisions indicated by the methods confirmed the selections made by the plant breeders. The procedures provide a less subjective, relatively easy to apply and interpret analytical method of describing the patterns of performance and associations in complex multiattribute and multilocation trials. This should lead to more efficient selection among lines in such trials.     

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.     

Ordination and classification of operational geographic units in Southwest Sweden

The vascular plant distributions of Dalsland and northern Bohuslän (Southwest Sweden) were subjected to multivariate analyses in order to delimit geographically coherent floristic zones. 271 squares of 5×5 km were the Operational Geographic Units; the data matrix comprises presence/absence species records for each OGU. Different ordination and classification methods were tested and detailed results are presented for detrended correspondence analysis (DCA), UPGMA and ordination space partitioning (OSP). A weighting procedure, neighbour-weighting, which gives pseudo-frequency scores along the nominal scale 0–9 depending on the species' distribution patterns, is introduced. The superior method for delimiting geographically coherent floristic zones was judged to be ordination space partitioning, using DCA and neighbour-weighted species scores.Abbreviations DCA Detrended Correspondence Analysis - OGU Operational Geographical Unit - OSP Ordination Space Partitioning - UPGMA Unweighted Pair-Group Method using Arithmetic Averages     

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.     

Brain-derived neurotrophic factor promotes gephyrin protein expression and GABAA receptor clustering in immature cultured hippocampal cells

Fast synaptic inhibition in the adult brain is largely mediated by GABA_A receptors (GABA_AR). GABA_AR are anchored to synaptic sites by gephyrin, a scaffolding protein that appears to be assembled as a hexagonal lattice beneath the plasma membrane. Brain derived neurotrophic factor (BDNF) alters the clustering and synaptic distribution of GABA_AR but mechanisms behind this regulation are just starting to emerge. The current study was aimed to examine if BDNF alters the protein levels and/or clustering of gephyrin and to investigate whether the modulation of gephyrin is accompanied by changes in the distribution and/or clustering of GABA_AR. Exogenous application of BDNF to immature neuronal cultures from rat hippocampus increased the protein levels and clustering of gephyrin. BDNF also augmented the association of gephyrin with GABA_AR and promoted the formation of GABA_AR clusters. Together, these observations indicate that BDNF might regulate the assembly of GABAergic synapses by promoting the association of GABA_AR with gephyrin.     

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.