In many machine learning applications, labeling every instance of data is burdensome. Multiple Instance Learning (MIL), in which training data is provided in the form of labeled bags rather than labeled instances, is one approach for a more relaxed form of supervised learning. Though much progress has been made in analyzing MIL problems, existing work considers bags that have a finite number of instances. In this paper we argue that in many applications of MIL (e.g. image, audio, etc.) the bags are better modeled as low dimensional manifolds in high dimensional feature space. We show that the geometric structure of such manifold bags affects PAC-learnability. We discuss how a learning algorithm that is designed for finite sized bags can be adapted to learn from manifold bags. Furthermore, we propose a simple heuristic that reduces the memory requirements of such algorithms. Our experiments on real-world data validate our analysis and show that our approach works well.

Inhomogeneous optical properties of biological samples make it difficult to obtain diffraction-limited resolution in depth. Correcting the sample-induced optical aberrations needs adaptive optics (AO). However, the direct wavefront-sensing approach commonly used in astronomy is not suitable for most biological samples due to their strong scattering of light. We developed an image-based AO approach that is insensitive to sample scattering. By comparing images of the sample taken with different segments of the pupil illuminated, local tilt in the wavefront is measured from image shift. The aberrated wavefront is then obtained either by measuring the local phase directly using interference or with phase reconstruction algorithms similar to those used in astronomical AO. We implemented this pupil-segmentation-based approach in a two-photon fluorescence microscope and demonstrated that diffraction-limited resolution can be recovered from nonbiological and biological samples.

The Rfam database aims to catalogue non-coding RNAs through the use of sequence alignments and statistical profile models known as covariance models. In this contribution, we discuss the pros and cons of using the online encyclopedia, Wikipedia, as a source of community-derived annotation. We discuss the addition of groupings of related RNA families into clans and new developments to the website. Rfam is available on the Web at http://rfam.sanger.ac.uk.

The neural underpinnings of sensorimotor integration are best studied in the context of well-characterized behavior. A rich trove of Drosophila behavioral genetics research offers a variety of well-studied behaviors and candidate brain regions that can form the bases of such studies. The development of tools to perform in vivo physiology from the Drosophila brain has made it possible to monitor activity in defined neurons in response to sensory stimuli. More recently still, it has become possible to perform recordings from identified neurons in the brain of head-fixed flies during walking or flight behaviors. In this chapter, we discuss how experiments that simultaneously monitor behavior and physiology in Drosophila can be combined with other techniques to produce testable models of sensorimotor circuit function.

We address the problem of labeling individual datapoints given some knowledge about (small) subsets or groups of them. The knowledge we have for a group is the likelihood value for each group member to satisfy a certain model. This problem is equivalent to hypergraph labeling problem where each datapoint corresponds to a node and the each subset correspond to a hyperedge with likelihood value as its weight. We propose a novel method to model the label dependence using an Undirected Graphical Model and reduce the problem of hypergraph labeling into an inference problem. This paper describes the structure and necessary components of such model and proposes useful cost functions. We discuss the behavior of proposed algorithm with different forms of the cost functions, identify suitable algorithms for inference and analyze required properties when it is theoretically guaranteed to have exact solution. Examples of several real world problems are shown as applications of the proposed method.

Phenolic fluorophores such as fluorescein, Tokyo Green, resorufin, and their derivatives are workhorses of biological science. Acylating the phenolic hydroxyl group(s) in these fluorophores masks their fluorescence. The ensuing ester is a substrate for cellular esterases, which can restore fluorescence. These esters are, however, notoriously unstable to hydrolysis, severely compromising their utility. The acetoxymethyl (AM) group is an esterase-sensitive motif that can mask polar functionalities in small molecules. Here, we report on the use of AM ether groups to mask phenolic fluorophores. The resulting profluorophores have a desirable combination of low background fluorescence, high chemical stability, and high enzymatic reactivity, both in vitro and in cellulo. These simple phenyl ether-based profluorophores could supplement or supplant the use of phenyl esters for imaging biochemical and biological systems.

Expression of an individual gene can vary considerably among genetically identical cells because of stochastic fluctuations in transcription. However, proteins comprising essential complexes or pathways have similar abundances and lower variability. It is not known whether coordination in the expression of subunits of essential complexes occurs at the level of transcription, mRNA abundance or protein expression. To directly measure the level of coordination in the expression of genes, we used highly sensitive fluorescence in situ hybridization (FISH) to count individual mRNAs of functionally related and unrelated genes within single Saccharomyces cerevisiae cells. Our results revealed that transcript levels of temporally induced genes are highly correlated in individual cells. In contrast, transcription of constitutive genes encoding essential subunits of complexes is not coordinated because of stochastic fluctuations. The coordination of these functional complexes therefore must occur post-transcriptionally, and likely post-translationally.

The innate sexual behaviors of Drosophila melanogaster males are an attractive system for elucidating how complex behavior patterns are generated. The potential for male sexual behavior in D. melanogaster is specified by the fruitless (fru) and doublesex (dsx) sex regulatory genes. We used the temperature-sensitive activator dTRPA1 to probe the roles of fru(M)- and dsx-expressing neurons in male courtship behaviors. Almost all steps of courtship, from courtship song to ejaculation, can be induced at very high levels through activation of either all fru(M) or all dsx neurons in solitary males. Detailed characterizations reveal different roles for fru(M) and dsx in male courtship. Surprisingly, the system for mate discrimination still works well when all dsx neurons are activated, but is impaired when all fru(M) neurons are activated. Most strikingly, we provide evidence for a fru(M)-independent courtship pathway that is primarily vision dependent.