Machine learning approaches in MALDI-MSI: clinical applications

Introduction: Despite the unquestionable advantages of Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging in visualizing the spatial distribution and the relative abundance of biomolecules directly on-tissue, the yielded data is complex and high dimensional. Therefore, analysis and interpretation of this huge amount of information is mathematically, statistically and computationally challenging.

Areas covered: This article reviews some of the challenges in data elaboration with particular emphasis on machine learning techniques employed in clinical applications, and can be useful in general as an entry point for those who want to study the computational aspects. Several characteristics of data processing are described, enlightening advantages and disadvantages. Different approaches for data elaboration focused on clinical applications are also provided. Practical tutorial based upon Orange Canvas and Weka software is included, helping familiarization with the data processing.

Expert commentary: Recently, MALDI-MSI has gained considerable attention and has been employed for research and diagnostic purposes, with successful results. Data dimensionality constitutes an important issue and statistical methods for information-preserving data reduction represent one of the most challenging aspects. The most common data reduction methods are characterized by collecting independent observations into a single table. However, the incorporation of relational information can improve the discriminatory capability of the data.     

Effective aerial monitoring of cyanobacterial harmful algal blooms is dependent on understanding cellular migration

Cyanobacterial harmful algal blooms (CHABs) degrade water quality and may produce toxins. The distribution of CHABs can change rapidly due to variations in population dynamics and environmental conditions. Biological and ecological aspects of CHABs were studied in order to better understand CHABs dynamics. Field experiments were conducted near Hartington, Ontario, Canada in ponds dominated by Microcystis aeruginosa and CHABs floating experiments were conducted at Lake Taihu during the summers of 2015 and 2016. Single colonies composed of hundreds to thousands of cells with an average median of 0.2–0.5 mm in diameter were the basic form assumed by the Microcystis, and this remained the same throughout the growing season. Thorough mixing of the water column followed by calm conditions resulted in over 90% of the cyanobacteria floating after 1 h. Multiple colonies floated on the water surface in four types of assemblages: aggregates, ribbons, patches, and mats. It is the mats that are conventionally considered the blooming stage of cyanobacteria.Presence of CHABs on open water surfaces also depends on environmental influences such as direct and indirect wind effects. For example, field tests revealed that the surface coverage of CHABs can be reduced to half within an hour at wind speeds of 0.5 m/s.Because our findings indicated that blooming involves surface display of cyanobacteria essentially presenting as a two-dimensional plane under defined conditions, the use of surface imagery to quantify CHABs was justified. This is particularly important in light of the fact that traditional detection methods do not provide accurate distribution information. Nor do they portray CHABs events in a real-time manner due to limitations in on-demand surveillance and delays between sample time and analyzed results. Therefore, a new CHAB detection method using small unmanned aerial systems with consumer-grade cameras was developed at a maximum detection altitude of 80 m. When cyanobacteria were floating on the surface, CHABs detection through RGB band cameras and spectral enhancement techniques was efficient and accurate. Small unmanned aerial systems were capable of providing coverage up to 1 km² per mission and the short intervals between sampling and results (approx. 2 h) allowed for the rapid analysis of data and for implementing follow-up monitoring or treatments. This method is very cost-effective at an estimate of as low as $100 CAD per mission with an average cyanobacterial detection accuracy of 86%. Thus, it is a good candidate method to fill the urgent need for CHABs detection, providing cost effective, rapid, and accurate information to improve risk management at a local level as well as to help quickly allocate resources for purposes of mitigation.     

Walsh Hadamard Transform for Simple Linear Iterative Clustering (SLIC) Superpixel Based Spectral Clustering of Multimodal MRI Brain Tumor Segmentation

The automated brain tumor segmentation methods are challenging due to the diverse nature of tumors. Recently, the graph based spectral clustering method is utilized for brain tumor segmentation to make high-quality segmentation output. In this paper, a new Walsh Hadamard Transform (WHT) texture for superpixel based spectral clustering is proposed for segmentation of a brain tumor from multimodal MRI images. First, the selected kernels of WHT are utilized for creating texture saliency maps and it becomes the input for the Simple Linear Iterative Clustering (SLIC) algorithm, to generate more precise texture based superpixels. Then the texture superpixels become nodes in the graph of spectral clustering for segmenting brain tumors of MRI images. Finally, the original members of superpixels are recovered to represent Complete Tumor (CT), Tumor Core (TC) and Enhancing Tumor (ET) tissues. The observational results are taken out on BRATS 2015 datasets and evaluated using the Dice Score (DS), Hausdorff Distance (HD) and Volumetric Difference (VD) metrics. The proposed method produces competitive results than other existing clustering methods.     

Influence of Stimuli Spatial Proximity on a SSVEP-Based BCI Performance

Objective: Steady-State Visual Evoked Potentials based Brain-Computer Interfaces (SSVEP-based BCIs) systems have been shown as promising technology due to their short response time and ease of use. SSVEP-based BCIs use brain responses to a flickering visual stimulus as an input command to an external application or device, and it can be influenced by stimulus properties, signal recording, and signal processing. We aim to investigate the system performance varying the stimuli spatial proximity (a stimulus property).Material and methods: We performed a comparative analysis of two visual interface designs (named cross and square) for an SSVEP-based BCI. The power spectrum density (PSD) was used as feature extraction and the Support Machine Vector (SVM) as classification method. We also analyzed the effects of five flickering frequencies (6.67, 8.57, 10, 12 e 15 Hz) between and within interfaces.Results: We found higher accuracy rates for the flickering frequencies of 10, 12, and 15 Hz. The stimulus of 10 Hz presented the highest SSVEP amplitude response for both interfaces. The system presented the best performance (highest classification accuracy and information transfer rate) using the cross interface (lower visual angle).Conclusion: Our findings suggest that the system has the highest performance in the spatial proximity range from 4° to 13° (visual angle). In addition, we conclude that as the stimulus spatial proximity increases, the interference from other stimuli reduces, and the SSVEP amplitude response decreases, which reduces system accuracy. The inter-stimulus distance is a visual interface parameter that must be chosen carefully to increase the efficiency of an SSVEP-based BCI.     

Developmental increase in the amount of rapsyn per acetylcholine receptor promotes postsynaptic receptor packing and stability

Neuromuscular synaptic transmission depends upon tight packing of acetylcholine receptors (AChRs) into postsynaptic AChR aggregates, but not all postsynaptic AChRs are aggregated. Here we describe a new confocal Fluorescence Resonance Energy Transfer (FRET) assay for semi-quantitative comparison of the degree to which AChRs are aggregated at synapses. During the first month of postnatal life the mouse tibialis anterior muscle showed increases both in the number of postsynaptic AChRs and the efficiency with which AChR was aggregated (by FRET). There was a concurrent two-fold increase in immunofluorescent labeling for the AChR-associated cytoplasmic protein, rapsyn. When 1-month old muscle was denervated, postsynaptic rapsyn immunostaining was reduced, as was the efficiency of AChR aggregation. In vivo electroporation of rapsyn-EGFP into muscle fibers increased postsynaptic rapsyn levels. Those synapses with higher ratios of rapsyn-EGFP to AChR displayed a slower metabolic turnover of AChR. Conversely, the reduction of postsynaptic rapsyn after denervation was accompanied by an acceleration of AChR turnover. Thus, a developmental increase in the amount of rapsyn targeted to the postsynaptic membrane may drive enhanced postsynaptic AChRs aggregation and AChR stability within the postsynaptic membrane.     

Beyond the epithelium: cadherin function in fibrous connective tissues

In fibrous connective tissues, fibroblasts are organized into syncytia, cellular networks that enable matrix remodeling and that are interconnected by intercellular adherens junctions (AJs). The AJs of fibroblasts are mediated by N-cadherin, a broadly expressed classical cadherin that is critically involved in developmental processes, wound healing and several diseases of mesenchymal tissues. In contrast to E-cadherin-dependent junctions of epithelia, the formation of AJs in fibrous connective tissues is relatively uncharacterized. Work over the last several years has documented an expanding list of molecules which function to regulate N-cadherin mediated junctions such as: Fer, PTP1B, cortactin, calcium, gelsolin, PIP5KIgamma, PIP2, and the Rho family of GTPases. We present an overview on the regulation of N-cadherin-mediated junction formation that highlights recent molecular advances in the field and rationalizes the roles of N-cadherin in connective tissue function.     

Applications of a new subspace clustering algorithm (COSA) in medical systems biology

A novel clustering approach named Clustering Objects on Subsets of Attributes (COSA) has been proposed (Friedman and Meulman, (2004). Clustering objects on subsets of attributes. J. R. Statist. Soc. B 66, 1–25.) for unsupervised analysis of complex data sets. We demonstrate its usefulness in medical systems biology studies. Examples of metabolomics analyses are described as well as the unsupervised clustering based on the study of disease pathology and intervention effects in rats and humans. In comparison to principal components analysis and hierarchical clustering based on Euclidean distance, COSA shows an enhanced capability to trace partial similarities in groups of objects enabling a new discovery approach in systems biology as well as offering a unique approach to reveal common denominators of complex multi-factorial diseases in animal and human studies. Doris Damian, Matej Orešič, and Elwin Verheij contributed equally to this work.     

Influence of the physical environment and conspecific aggression on the spatial arrangement of breeding grey seals

Understanding the habitat requirements of a species for breeding is essential for its conservation, particularly if the availability of suitable habitat is a limiting factor for population growth. This is postulated to be the case for grey seals, one of the more abundant marine apex predators in northern European waters. In common with similar studies that have investigated the habitat preferences of breeding grey seals, we use abiotic (topographical, climatological) attributes but, unlike previous work, we also incorporate behavioural variables, particularly the occurrence of aggressive interactions between females and the presence of neighbouring seals. We used two Generalized Additive Models (GAM) in a sequential and iterative fashion. The first model links the occurrence of aggression at particular points in the colony to local topography derived from a Geographical Information System (GIS), presence of neighbouring seal pups and the day of the breeding season. The output of this GAM is used as one of the explanatory variables in a GAM of daily variation in the spatial distribution of births. Although proximity of a birth site to a water source and the presence of neighbouring seal pups both had significant influences on the distribution of newborn pups over time and space, at the scale of the study site it was found that simple rules could predict pup distribution more efficiently than a complex individual-based simulation model.     

Automated SNP Genotype Clustering Algorithm to Improve Data Completeness in High-Throughput SNP Genotyping Datasets from Custom Arrays

High-throughput SNP genotyping platforms use automated genotype calling algo- rithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been opti- mized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be ad- visable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.