Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy.

Taxonomic resolution is a major challenge in palynology, largely limiting the ecological and evolutionary interpretations possible with deep-time fossil pollen data. We present an approach for fossil pollen analysis that uses optical superresolution microscopy and machine learning to create a quantitative and higher throughput workflow for producing palynological identifications and hypotheses of biological affinity. We developed three convolutional neural network (CNN) classification models: maximum projection (MPM), multislice (MSM), and fused (FM). We trained the models on the pollen of 16 genera of the legume tribe Amherstieae, and then used these models to constrain the biological classifications of 48 fossil Striatopollis specimens from the Paleocene, Eocene, and Miocene of western Africa and northern South America. All models achieved average accuracies of 83 to 90% in the classification of the extant genera, and the majority of fossil identifications (86%) showed consensus among at least two of the three models. Our fossil identifications support the paleobiogeographic hypothesis that Amherstieae originated in Paleocene Africa and dispersed to South America during the Paleocene-Eocene Thermal Maximum (56 Ma). They also raise the possibility that at least three Amherstieae genera (Crudia, Berlinia, and Anthonotha) may have diverged earlier in the Cenozoic than predicted by molecular phylogenies.

Long-term Monocular Deprivation during Juvenile Critical Period Disrupts Binocular Integration in Mouse Visual Thalamus.

Study of the neural deficits caused by mismatched binocular vision in early childhood has predominantly focused on circuits in the primary visual cortex (V1). Recent evidence has revealed that neurons in mouse dorsolateral geniculate nucleus (dLGN) can undergo rapid ocular dominance plasticity following monocular deprivation (MD). It remains unclear, however, whether the long-lasting deficits attributed to MD during the critical period originate in the thalamus. Using in vivo two-photon Ca2+ imaging of dLGN afferents in superficial layers of V1 in female and male mice, we demonstrate that 14 d MD during the critical period leads to a chronic loss of binocular dLGN inputs while sparing response strength and spatial acuity. Importantly, MD leads to profoundly mismatched visual tuning properties in remaining binocular dLGN afferents. Furthermore, MD impairs binocular modulation, reducing facilitation of responses of both binocular and monocular dLGN inputs during binocular viewing. As predicted by our findings in thalamic inputs, Ca2+ imaging from V1 neurons revealed spared spatial acuity but impaired binocularity in L4 neurons. V1 L2/3 neurons in contrast displayed deficits in both binocularity and spatial acuity. Our data demonstrate that critical-period MD produces long-lasting disruptions in binocular integration beginning in early binocular circuits in dLGN, whereas spatial acuity deficits first arise from circuits further downstream in V1. Our findings indicate that the development of normal binocular vision and spatial acuity depend upon experience-dependent refinement of distinct stages in the mammalian visual system.SIGNIFICANCE STATEMENT Abnormal binocular vision and reduced acuity are hallmarks of amblyopia, a disorder that affects 2%-5% of the population. It is widely thought that the neural deficits underlying amblyopia begin in the circuits of primary visual cortex. Using in vivo two-photon calcium imaging of thalamocortical axons in mice, we show that depriving one eye of input during a critical period in development chronically impairs binocular integration in thalamic inputs to primary visual cortex. In contrast, visual acuity is spared in thalamic inputs. These findings shed new light on the role for developmental mechanisms in the thalamus in establishing binocular vision and may have critical implications for amblyopia.

Visual Recognition Software for Binary Classification and Its Application to Spruce Pollen Identification.

Discriminating between black and white spruce (Picea mariana and Picea glauca) is a difficult palynological classification problem that, if solved, would provide valuable data for paleoclimate reconstructions. We developed an open-source visual recognition software (ARLO, Automated Recognition with Layered Optimization) capable of differentiating between these two species at an accuracy on par with human experts. The system applies pattern recognition and machine learning to the analysis of pollen images and discovers general-purpose image features, defined by simple features of lines and grids of pixels taken at different dimensions, size, spacing, and resolution. It adapts to a given problem by searching for the most effective combination of both feature representation and learning strategy. This results in a powerful and flexible framework for image classification. We worked with images acquired using an automated slide scanner. We first applied a hash-based "pollen spotting" model to segment pollen grains from the slide background. We next tested ARLO's ability to reconstruct black to white spruce pollen ratios using artificially constructed slides of known ratios. We then developed a more scalable hash-based method of image analysis that was able to distinguish between the pollen of black and white spruce with an estimated accuracy of 83.61%, comparable to human expert performance. Our results demonstrate the capability of machine learning systems to automate challenging taxonomic classifications in pollen analysis, and our success with simple image representations suggests that our approach is generalizable to many other object recognition problems.

Classification of grass pollen through the quantitative analysis of surface ornamentation and texture.

Taxonomic identification of pollen and spores uses inherently qualitative descriptions of morphology. Consequently, identifications are restricted to categories that can be reliably classified by multiple analysts, resulting in the coarse taxonomic resolution of the pollen and spore record. Grass pollen represents an archetypal example; it is not routinely identified below family level. To address this issue, we developed quantitative morphometric methods to characterize surface ornamentation and classify grass pollen grains. This produces a means of quantifying morphological features that are traditionally described qualitatively. We used scanning electron microscopy to image 240 specimens of pollen from 12 species within the grass family (Poaceae). We classified these species by developing algorithmic features that quantify the size and density of sculptural elements on the pollen surface, and measure the complexity of the ornamentation they form. These features yielded a classification accuracy of 77.5%. In comparison, a texture descriptor based on modelling the statistical distribution of brightness values in image patches yielded a classification accuracy of 85.8%, and seven human subjects achieved accuracies between 68.33 and 81.67%. The algorithmic features we developed directly relate to biologically meaningful features of grass pollen morphology, and could facilitate direct interpretation of unsupervised classification results from fossil material.