COSMOS: a platform for real-time morphology-based, label-free cell sorting using deep learning.

Cells are the singular building blocks of life, and a comprehensive understanding of morphology, among other properties, is crucial to the assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without the need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in the ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.

The marmoset as a model system for studying voluntary motor control.

The common marmoset has recently gained interest as an animal model for systems and behavioral neuroscience. This is due in part to the advent of transgenic marmosets, which affords the possibility of combining genetic manipulations with physiological recording and behavioral monitoring to study neural systems. In this review, they will argue that the marmoset provides a unique opportunity to study the neural basis of voluntary motor control from an integrative perspective. First, as an intermediate animal model, the marmoset represents an important bridge in motor system function between other primates, including humans, and rodents. Second, due to the marmoset's small brain size and lissencephalic cortex, novel electrophysiological and optical recording technologies will allow an integrative study of cortical function at multiple spatial scales beyond that afforded by other non-human primate models. Finally, as a primate expressing an ancestral state of corticospinal organization, the marmoset offers the possibility of understanding the integrative function of cortical and spinal interneuron circuitry in isolation of more recent corticomotoneuronal elaborations. If the potential of the marmoset as a model species is to be realized, they will need to learn to work with their natural behavioral repertoire. They have concluded by considering practical aspects of studying motor systems with marmosets. © 2016 Wiley Periodicals, Inc. Develop Neurobiol 77: 273-285, 2017.

How Universal Is the Relationship between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global Assessment.

Leaf Area Index (LAI) is a key variable that bridges remote sensing observations to the quantification of agroecosystem processes. In this study, we assessed the universality of the relationships between crop LAI and remotely sensed Vegetation Indices (VIs). We first compiled a global dataset of 1459 in situ quality-controlled crop LAI measurements and collected Landsat satellite images to derive five different VIs including Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), two versions of the Enhanced Vegetation Index (EVI and EVI2), and Green Chlorophyll Index (CIGreen). Based on this dataset, we developed global LAI-VI relationships for each crop type and VI using symbolic regression and Theil-Sen (TS) robust estimator. Results suggest that the global LAI-VI relationships are statistically significant, crop-specific, and mostly non-linear. These relationships explain more than half of the total variance in ground LAI observations (R2 >0.5), and provide LAI estimates with RMSE below 1.2 m2/m2. Among the five VIs, EVI/EVI2 are the most effective, and the crop-specific LAI-EVI and LAI-EVI2 relationships constructed by TS, are robust when tested by three independent validation datasets of varied spatial scales. While the heterogeneity of agricultural landscapes leads to a diverse set of local LAI-VI relationships, the relationships provided here represent global universality on an average basis, allowing the generation of large-scale spatial-explicit LAI maps. This study contributes to the operationalization of large-area crop modeling and, by extension, has relevance to both fundamental and applied agroecosystem research.

Development of ultralow energy (1-10 eV) ion scattering spectrometry coupled with reflection absorption infrared spectroscopy and temperature programmed desorption for the investigation of molecular solids.

Extremely surface specific information, limited to the first atomic layer of molecular surfaces, is essential to understand the chemistry and physics in upper atmospheric and interstellar environments. Ultra low energy ion scattering in the 1-10 eV window with mass selected ions can reveal extremely surface specific information which when coupled with reflection absorption infrared (RAIR) and temperature programmed desorption (TPD) spectroscopies, diverse chemical and physical properties of molecular species at surfaces could be derived. These experiments have to be performed at cryogenic temperatures and at ultra high vacuum conditions without the possibility of collisions of neutrals and background deposition in view of the poor ion intensities and consequent need for longer exposure times. Here we combine a highly optimized low energy ion optical system designed for such studies coupled with RAIR and TPD and its initial characterization. Despite the ultralow collision energies and long ion path lengths employed, the ion intensities at 1 eV have been significant to collect a scattered ion spectrum of 1000 counts/s for mass selected CH2(+).

Life's simple measures: unlocking the proteome.

Using modified nucleotides and selecting for slow off-rates in the SELEX procedure, we have evolved a special class of aptamers, called SOMAmers (slow off-rate modified aptamers), which bind tightly and specifically to proteins in body fluids. We use these in a novel assay that yields 1:1 complexes of the SOMAmers with their cognate proteins in body fluids. Measuring the SOMAmer concentrations of the resultant complexes reflects the concentration of the proteins in the fluids. This is simply done by hybridization to complementary sequences on solid supports, but it can also be done by any other DNA quantification technology (including NexGen sequencing). We use measurements of over 1000 proteins in under 100 µL of serum or plasma to answer important medical questions, two of which are reviewed here. A number of bioinformatics methods have guided our discoveries, including principal component analysis. We use various methods to evaluate sample handling procedures in our clinical samples and can identify many parameters that corrupt proteomics analysis.