Detailed flow patterns in the nasal cavity.

The human nasal cavity filters and conditions inspired air while providing olfactory function. Detailed experimental study of nasal airflow patterns has been limited because of the complex geometry of the nasal cavity. In this work, particle image velocimetry was used to determine two-dimensional instantaneous velocity vector fields in parallel planes throughout a model of the nasal cavity that was subjected to a nonoscillatory flow rate of 125 ml/s. The model, which was fabricated from 26 computed tomography scans by using rapid prototyping techniques, is a scaled replica of a human right nasal cavity. The resulting vector plots show that the flow is laminar and regions of highest velocity are in the nasal valve and in the inferior airway. The relatively low flow in the olfactory region appears to protect the olfactory bulb from particulate pollutants. Low flows were also observed in the nasal meatuses, whose primary function has been the subject of debate. Comparison of sequentially recorded data suggests a steady flow.     

Divergence time estimates of mammals from molecular clocks and fossils: Relevance of new fossil finds from India

This paper presents a brief review of recent advances in the classification of mammals at higher levels using fossils and molecular clocks. It also discusses latest fossil discoveries from the Cretaceous — Eocene (66–55 m.y.) rocks of India and their relevance to our current understanding of placental mammal origins and diversifications.     

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.