Pixel and superpixel classifiers have become essential tools for EM segmentation algorithms. Training these classifiers remains a major bottleneck primarily due to the requirement of completely annotating the dataset which is tedious, error-prone and costly. In this paper, we propose an interactive learning scheme for the superpixel classifier for EM segmentation. Our algorithm is "active semi-supervised" because it requests the labels of a small number of examples from user and applies label propagation technique to generate these queries. Using only a small set (<20%) of all datapoints, the proposed algorithm consistently generates a classifier almost as accurate as that estimated from a complete groundtruth. We provide segmentation results on multiple datasets to show the strength of these classifiers.

The basal ganglia plays a significant role in transforming activity in the cerebral cortex into directed behavior, involving motor learning, habit formation and the selection of actions based on desirable outcomes, and the organization of the basal ganglia is intimately linked to that of the cerebral cortex. In this chapter, we focus primarily on the neocortical part of the basal ganglia. A general canonical organizational plan of the neocortical-related basal ganglia is described. An understanding of the canonical organization of the neostriatal part of the basal ganglia, provides a framework for determining the general organizational principles of the parts of the basal ganglia connected with allocortical areas and the amygdala, and this is discussed. While it has been proposed that the basal ganglia provide interactions between disparate functional circuits, another approach might be that there are parallel functional circuits, in which distinct functions are for the most part maintained, or segregated, one from the other. This chapter, however, is biased toward the view that there is maintenance of functional parallel circuits in the organization of the basal ganglia, but that the circuit contains neuroanatomical features that provide for considerable interaction between adjacent circuits.

MicroED uses very small three-dimensional protein crystals and electron diffraction for structure determination. We present an improved data collection protocol for MicroED called 'continuous rotation'. Microcrystals are continuously rotated during data collection, yielding more accurate data. The method enables data processing with the crystallographic software tool MOSFLM, which resulted in improved resolution for the model protein lysozyme. These improvements are paving the way for the broad implementation and application of MicroED in structural biology.

The fruit fly is an excellent model system for investigating the sequence of epithelial tissue invaginations constituting the process of gastrulation. By combining recent advancements in light sheet fluorescence microscopy (LSFM) and image processing, the three-dimensional fly embryo morphology and relevant gene expression patterns can be accurately recorded throughout the entire process of embryogenesis. LSFM provides exceptionally high imaging speed, high signal-to-noise ratio, low level of photoinduced damage, and good optical penetration depth. This powerful combination of capabilities makes LSFM particularly suitable for live imaging of the fly embryo.The resulting high-information-content image data are subsequently processed to obtain the outlines of cells and cell nuclei, as well as the geometry of the whole embryo tissue by image segmentation. Furthermore, morphodynamics information is extracted by computationally tracking objects in the image. Towards that goal we describe the successful implementation of a fast fitting strategy of Gaussian mixture models.The data obtained by image processing is well-suited for hypothesis testing of the detailed biomechanics of the gastrulating embryo. Typically this involves constructing computational mechanics models that consist of an objective function providing an estimate of strain energy for a given morphological configuration of the tissue, and a numerical minimization mechanism of this energy, achieved by varying morphological parameters.In this chapter, we provide an overview of in vivo imaging of fruit fly embryos using LSFM, computational tools suitable for processing the resulting images, and examples of computational biomechanical simulations of fly embryo gastrulation.

We describe an adaptive optics method that modulates the intensity or phase of light rays at multiple pupil segments in parallel to determine the sample-induced aberration. Applicable to fluorescent protein-labeled structures of arbitrary complexity, it allowed us to obtain diffraction-limited resolution in various samples in vivo. For the strongly scattering mouse brain, a single aberration correction improved structural and functional imaging of fine neuronal processes over a large imaging volume.

Chromatin immunoprecipitation (ChIP) is a technique that reveals in vivo location of a protein bound to DNA. ChIP coupled with DNA microarrays (ChIP-chip) or next-generation sequencing (ChIP-seq) allows for identification of binding sites of transcription factors on a global scale. Here we describe a protocol for ChIP to identify binding of the Ultrabithorax (Ubx) Hox transcription factors from imaginal discs of Drosophila larvae. The protocol can be extended to other model organisms and transcription factors.

The rules governing the formation of spatial maps in the hippocampus have not been determined. We investigated the large-scale structure of place field activity by recording hippocampal neurons in rats exploring a previously unencountered 48-meter-long track. Single-cell and population activities were well described by a two-parameter stochastic model. Individual neurons had their own characteristic propensity for forming fields randomly along the track, with some cells expressing many fields and many exhibiting few or none. Because of the particular distribution of propensities across cells, the number of neurons with fields scaled logarithmically with track length over a wide, ethological range. These features constrain hippocampal memory mechanisms, may allow efficient encoding of environments and experiences of vastly different extents and durations, and could reflect general principles of population coding.

Bacteroidetes are a phylum of Gram-negative bacteria abundant in mammalian-associated polymicrobial communities, where they impact digestion, immunity, and resistance to infection. Despite the extensive competition at high cell density that occurs in these settings, cell contact-dependent mechanisms of interbacterial antagonism, such as the type VI secretion system (T6SS), have not been defined in this group of organisms. Herein we report the bioinformatic and functional characterization of a T6SS-like pathway in diverse Bacteroidetes. Using prominent human gut commensal and soil-associated species, we demonstrate that these systems localize dynamically within the cell, export antibacterial proteins, and target competitor bacteria. The Bacteroidetes system is a distinct pathway with marked differences in gene content and high evolutionary divergence from the canonical T6S pathway. Our findings offer a potential molecular explanation for the abundance of Bacteroidetes in polymicrobial environments, the observed stability of Bacteroidetes in healthy humans, and the barrier presented by the microbiota against pathogens.

We report the larval CNS expression patterns for 6,650 GAL4 lines based on cis-regulatory regions (CRMs) from the Drosophila genome. Adult CNS expression patterns were previously reported for this collection, thereby providing a unique resource for determining the origins of adult cells. An illustrative example reveals the origin of the astrocyte-like glia of the ventral CNS. Besides larval neurons and glia, the larval CNS contains scattered lineages of immature, adult-specific neurons. Comparison of lineage expression within this large collection of CRMs provides insight into the codes used for designating neuronal types. The CRMs encode both dense and sparse patterns of lineage expression. There is little correlation between brain and thoracic lineages in patterns of sparse expression, but expression in the two regions is highly correlated in the dense mode. The optic lobes, by comparison, appear to use a different set of genetic instructions in their development.