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.     

Effects of experimental warming on survival,phenology, and morphology of an aquatic insect (Odonata)

1. Organisms can respond to changing climatic conditions in multiple ways including changes in phenology, body size or morphology, and range shifts. Understanding how developmental temperatures affect insect life‐history timing and morphology is crucial because body size and morphology affect multiple aspects of life history, including dispersal ability, whereas phenology can shape population performance and community interactions. 2. It was experimentally assessed how developmental temperatures experienced by aquatic larvae affected survival, phenology, and adult morphology of dragonflies [Pachydiplax longipennis (Burmeister)]. Larvae were reared under three environmental temperatures: ambient, +2.5, and +5 °C, corresponding to temperature projections for our study area 50 and 100 years in the future, respectively. Experimental temperature treatments tracked naturally‐occurring variation. 3. Clear effects of temperature were found in the rearing environment on survival and phenology: dragonflies reared at the highest temperatures had the lowest survival rates and emerged from the larval stage approximately 3 weeks earlier than animals reared at ambient temperatures. There was no effect of rearing temperature on overall body size. Although neither the relative wing nor thorax size was affected by warming, a non‐significant trend towards an interaction between sex and warming in relative thorax size suggests that males may be more sensitive to warming than females, a pattern that should be investigated further. 4. Warming strongly affected survival in the larval stage and the phenology of adult emergence. Understanding how warming in the developmental environment affects later life‐history stages is critical to interpreting the consequences of warming for organismal performance.     

Leaf phenology mediates provenance differences in herbivore populations on valley oaks in a common garden

1. Plants from different populations often display a variation in herbivore resistance. However, it is rarely understood what plant traits mediate such differences. 2. It was tested how leaf phenology affects herbivore populations in a 15‐year‐old common garden of valley oaks (Quercus lobata Née) with different populations and maternal parents from throughout the Q. lobata range. 3. The abundance of leaf miners (Stigmella sp. Shrank) and leaf phenology of oaks in the common garden was measured. 4. Leaf miner abundance varied among provenance locations (population), but not among maternal parents within populations. Leaf phenology varied by provenance location and maternal parent, and trees that leafed out earlier accrued higher leaf‐miner abundance. Path analysis indicated that leaf phenology was the likely driver of provenance and parental differences in resistance to leaf miners. 5. Understanding population differences is particularly important when considering transport of genotypes for ornamental or restoration purposes. The present study suggests that similarity in leaf phenology may be one factor that could be used to find genotypes with a similar herbivore resistance to local genotypes.