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Antillean manatees produce vocalizations reported to be important for communication, but their vocal behavior throughout their geographic range is poorly understood. A SoundTrap recorder (sample rates: 288/576 kHz) was deployed in Belize to record vocalizations of wild manatees in a seagrass channel and of a young rehabilitated and released manatee in a shallow lagoon. Spectral analysis revealed broadband vocalizations with frequencies up to 150 kHz and a high proportion of calls with ultrasonic components. Ultrasonic frequency components appear prevalent in their vocal repertoire and may be important to manatee communication.

Epithelial remodeling determines the structure of many organs in the body through changes in cell shape, polarity and behavior and is a major area of study in developmental biology. Accurate and high-throughput methods are necessary to systematically analyze epithelial organization and dynamics at single-cell resolution. We developed SEGGA, an easy-to-use software for automated image segmentation, cell tracking and quantitative analysis of cell shape, polarity and behavior in epithelial tissues. SEGGA is free, open source, and provides a full suite of tools that allow users with no prior computational expertise to independently perform all steps of automated image segmentation, semi-automated user-guided error correction, and data analysis. Here we use SEGGA to analyze changes in cell shape, cell interactions and planar polarity during convergent extension in the Drosophila embryo. These studies demonstrate that planar polarity is rapidly established in a spatiotemporally regulated pattern that is dynamically remodeled in response to changes in cell orientation. These findings reveal an unexpected plasticity that maintains coordinated planar polarity in actively moving populations through the continual realignment of cell polarity with the tissue axes.

Relative to individually distinctivesignature whistles, little is known about the “non-signature” calls – particularly the non-signature whistles – of the common Atlantic bottlenose dolphin,Tursiops truncatus. While such calls are suspected to serve social function, tracking their exchange among conspecifics and correlating their usage with non-acoustic behavior has proven challenging, given both their relative scarcity in the dolphin repertoire and their characteristic shared use among dolphins, which precludes the unique identification of callers on the basis of whistle properties alone. Towards the goal of robustly identifying the callers of non-signature whistles (equivalently, attributing non-signature whistles to callers), we present a new, long-term audiovisual monitoring system designed for and tested at the Dolphin Discovery exhibit of the National Aquarium in Baltimore, Maryland. In this paper, we confirm the system’s ability to spatially localize impulse-like sounds using traditional signal processing approaches that have already been used to localize dolphin echolocation clicks. We go on to provide the first rigorous experimental evaluation of the component time-difference-of-arrival-(TDOA) extraction methods on whistle-like tonal sounds in a (reverberant) aquatic environment, showing that they are generally not suited to sound localization. Nevertheless, we find that TDOA extraction under these circumstances is performed significantly better using a Generalized Cross-Correlation with Phase Transform (GCC-PHAT) method than a standard circular cross-correlation method, a potentially important result.

The initial synapses of the auditory system, which connect hair cells to afferent nerve fibers, display two unusual features. First, synaptic transmission occurs in a multiquantal fashion: the contents of multiple synaptic vesicles are discharged simultaneously. Second, synaptic transmission may be tuned to specific frequencies of stimulation. We developed a minimal theoretical model to explore the possibility that hair-cell synapses achieve both multiquantal release and frequency selectivity through a cooperative mechanism for the exocytotic release of neurotransmitter. We first characterized vesicle release as a four-step cycle at each release site, then generalized the result to an arbitrary number of steps. The cyclic process itself induces some degree of resonance, and may display a stable, underdamped fixed point of the release dynamics associated with a pair of complex eigenvalues. Cooperativity greatly enhances the frequency selectivity by moving the eigenvalues toward the imaginary axis; spontaneously oscillatory release can arise beyond a Hopf bifurcation. These phenomena occur both in the macroscopic limit, when the number of release sites involved is very large, and in the more realistic stochastic regime, when only a limited number of release sites participate at each synapse. It is thus possible to connect multiquantal release with frequency selectivity through the mechanism of cooperativity.

Effector CD4+ T cells rapidly activate high-level cytokine expression following TCR stimulation. Consistent with accelerated protein production in these cells, global mRNA profiles revealed that, after cytokines, the most impressive cluster of activated genes encode rRNA-maturation factors. Activation of these genes was ERK-MAPK dependent, accompanied by increased rRNA transcription and faster maturation kinetics, and much greater in effector CD4+ T cells than in naive cells. Ribosomal protein subunit (RPS) synthesis was also ERK-MAPK dependent and increased to match rRNA production, but without evident increase in RPS mRNA. Instead, stimulation promoted polysome loading of RPS mRNA via cis-acting, 5′-terminal oligopyrimidines. These results demonstrate how, in response to extracellular signals, effector CD4+ T cells coordinately increase multiple ribosomal components to accommodate burgeoning cytokine production.

Although mRNA decay rates are a key determinant of the steady-state concentration for any given mRNA species, relatively little is known, on a population level, about what factors influence turnover rates and how these rates are integrated into cellular decisions. We decided to measure mRNA decay rates in two human cell lines with high-density oligonucleotide arrays that enable the measurement of decay rates simultaneously for thousands of mRNA species. Using existing annotation and the Gene Ontology hierarchy of biological processes, we assign mRNAs to functional classes at various levels of resolution and compare the decay rate statistics between these classes. The results show statistically significant organizational principles in the variation of decay rates among functional classes. In particular, transcription factor mRNAs have increased average decay rates compared with other transcripts and are enriched in “fast-decaying” mRNAs with half-lives Saccharomyces cerevisiae mRNA decay shows the same functional organization of decay rates, implying that it is a general organizational scheme for eukaryotes. Additionally, we investigated the dependence of decay rates on sequence composition, that is, the presence or absence of short mRNA motifs in various regions of the mRNA transcript. Our analysis recovers the positive correlation of mRNA decay with known AU-rich mRNA motifs, but we also uncover further short mRNA motifs that show statistically significant correlation with decay. However, we also note that none of these motifs are strong predictors of mRNA decay rate, indicating that the regulation of mRNA decay is more complex and may involve the cooperative binding of several RNA-binding proteins at different sites.

Oligonucleotide microarrays are based on the hybridization of labeled mRNA molecules to short length oligonucleotide probes on a glass surface. Two effects have been shown to affect the raw data: the sequence dependence of the probe hybridization properties and the chemical saturation resulting from surface adsorption processes. We address both issues simultaneously using a physically motivated hybridization model. Based on publicly available calibration data sets, we show that Langmuir adsorption accurately describes GeneChip hybridization, with model parameters that we predict from the sequence composition of the probes. Because these parameters have physical units, we are able to estimate absolute mRNA concentrations in picomolar. Additionally, by accounting for chemical saturation, we substantially reduce the compressive bias of differential expression estimates that normally occurs toward high concentrations.

We describe here an automated system that accurately maps tissue sections stained by immunocytochemistry for an inducible nuclear protein. The sections are scanned with a computer-controlled microscope setup hooked to a CCD camera. Raw images captured at high resolution are filtered using highly selective criteria for the recognition of labeled cell nuclei. The total population of recognized labeled nuclei is then divided into separate bins, according to their labeling intensities. Finally, information about both the position and labeling intensity of labeled nuclei is represented in average density maps. The system was optimized for the quantitative mapping of neuronal cells expressing the inducible gene ZENK in the brain of songbirds, in response to stimulation with song, but should be of general applicability for the mapping of inducible nuclear proteins.

Time-reversal symmetry breaking is a key feature of many classes of natural sounds, originating in the physics of sound production. While attention has been paid to the response of the auditory system to "natural stimuli," very few psychophysical tests have been performed. We conduct psychophysical measurements of time-frequency acuity for stylized representations of "natural"-like notes (sharp attack, long decay) and the time-reversed versions of these notes (long attack, sharp decay). Our results demonstrate significantly greater precision, arising from enhanced temporal acuity, for such sounds over their time-reversed versions, without a corresponding decrease in frequency acuity. These data inveigh against models of auditory processing that include tradeoffs between temporal and frequency acuity, at least in the range of notes tested and suggest the existence of statistical priors for notes with a sharp-attack and a long-decay. We are additionally able to calculate a minimal theoretical bound on the sophistication of the nonlinearities in auditory processing. We find that among the best studied classes of nonlinear time-frequency representations, only matching pursuit, spectral derivatives, and reassigned spectrograms are able to satisfy this criterion.

Records of excitatory postsynaptic currents (EPSCs) are often complex, with overlapping signals that display a large range of amplitudes. Statistical analysis of the kinetics and amplitudes of such complex EPSCs is nonetheless essential to the understanding of transmitter release. We therefore developed a maximum-likelihood blind deconvolution algorithm to detect exocytotic events in complex EPSC records. The algorithm is capable of characterizing the kinetics of the prototypical EPSC as well as delineating individual release events at higher temporal resolution than other extant methods. The approach also accommodates data with low signal-to-noise ratios and those with substantial overlaps between events. We demonstrated the algorithm’s efficacy on paired whole-cell electrode recordings and synthetic data of high complexity. Using the algorithm to align EPSCs, we characterized their kinetics in a parameter-free way. Combining this approach with maximum-entropy deconvolution, we were able to identify independent release events in complex records at a temporal resolution of less than 250 µs. We determined that the increase in total postsynaptic current associated with depolarization of the presynaptic cell stems primarily from an increase in the rate of EPSCs rather than an increase in their amplitude. Finally, we found that fluctuations owing to postsynaptic receptor kinetics and experimental noise, as well as the model dependence of the deconvolution process, explain our inability to observe quantized peaks in histograms of EPSC amplitudes from physiological recordings.

Biology presents many examples of planar distribution and structural networks having dense sets of closed loops. An archetype of this form of network organization is the vasculature of dicotyledonous leaves, which showcases a hierarchically-nested architecture containing closed loops at many different levels. Although a number of methods have been proposed to measure aspects of the structure of such networks, a robust metric to quantify their hierarchical organization is still lacking. We present an algorithmic framework, the hierarchical loop decomposition, that allows mapping loopy networks to binary trees, preserving in the connectivity of the trees the architecture of the original graph. We apply this framework to investigate computer generated graphs, such as artificial models and optimal distribution networks, as well as natural graphs extracted from digitized images of dicotyledonous leaves and vasculature of rat cerebral neocortex. We calculate various metrics based on the Asymmetry, the cumulative size distribution and the Strahler bifurcation ratios of the corresponding trees and discuss the relationship of these quantities to the architectural organization of the original graphs. This algorithmic framework decouples the geometric information (exact location of edges and nodes) from the metric topology (connectivity and edge weight) and it ultimately allows us to perform a quantitative statistical comparison between predictions of theoretical models and naturally occurring loopy graphs., Comment: 17 pages, 8 figures. During preparation of this manuscript the authors became aware of the work of Mileyko at al., concurrently submitted for publication

Plutarch and Heraclitus believed a certain passage in the 20th book of the Odyssey ("Theoclymenus's prophecy") to be a poetic description of a total solar eclipse. In the late 1920s, Schoch and Neugebauer computed that the solar eclipse of 16 April 1178 B.C.E. was total over the Ionian Islands and was the only suitable eclipse in more than a century to agree with classical estimates of the decade-earlier sack of Troy around 1192-1184 B.C.E. However, much skepticism remains about whether the verses refer to this, or any, eclipse. To contribute to the issue independently of the disputed eclipse reference, we analyze other astronomical references in the Epic, without assuming the existence of an eclipse, and search for dates matching the astronomical phenomena we believe they describe. We use three overt astronomical references in the epic: to Boötes and the Pleiades, Venus, and the New Moon; we supplement them with a conjectural identification of Hermes's trip to Ogygia as relating to the motion of planet Mercury. Performing an exhaustive search of all possible dates in the span 1250-1115 B.C., we looked to match these phenomena in the order and manner that the text describes. In that period, a single date closely matches our references: 16 April 1178 B.C.E. We speculate that these references, plus the disputed eclipse reference, may refer to that specific eclipse., Facultad de Ciencias Astronómicas y Geofísicas

Background Microarray studies of the same phenomenon in different labs often appear at variance because the published lists of regulated transcripts have disproportionately small intersections. We demonstrate that comparing studies by intersecting lists in this manner is methodologically flawed by reanalyzing three studies of the molecular signature of "stemness" in human embryonic stem cells. There are only 7 genes common to all three published lists, suggesting disagreement. Results Carefully reanalyzing all three together from the raw data we detect 111 genes upregulated and 95 downregulated in all three studies. The upregulated list was subject to rtRTPCR analysis and 75% of the genes were confirmed. Conclusion Our findings show that the three studies have a substantial core of common genes, which is missed if only the published lists are examined. Combined analysis of multiple experiments can be a powerful way to distil coherent conclusions.

Neural stem cells and neurogenesis persist in the adult mammalian brain subventricular zone (SVZ). Cells born in the rodent SVZ migrate to the olfactory bulb (Ob) where they differentiate into interneurons. To determine the gene expression and functional profile of SVZ neurogenesis, we performed three complementary sets of transcriptional analysis experiments using Affymetrix GeneChips: (1) comparison of adult mouse SVZ and Ob gene expression profiles with those of the striatum, cerebral cortex, and hippocampus; (2) profiling of SVZ stem cells and ependyma isolated by fluorescent-activated cell sorting (FACS); and (3) analysis of gene expression changes during in vivo SVZ regeneration after anti-mitotic treatment. Gene Ontology (GO) analysis of data from these three separate approaches showed that in adult SVZ neurogenesis, RNA splicing and chromatin remodeling are biological processes as statistically significant as cell proliferation, transcription, and neurogenesis. In non-neurogenic brain regions, RNA splicing and chromatin remodeling were not prominent processes. Fourteen mRNA splicing factors including Sf3b1, Sfrs2, Lsm4, and Khdrbs1/Sam68 were detected along with 9 chromatin remodeling genes including Mll, Bmi1, Smarcad1, Baf53a, and Hat1. We validated the transcriptional profile data with Northern blot analysis and in situ hybridization. The data greatly expand the catalogue of cell cycle components, transcription factors, and migration genes for adult SVZ neurogenesis and reveal RNA splicing and chromatin remodeling as prominent biological processes for these germinal cells.

Experimental results in recent years have shown that adult neurogenesis is a significant phenomenon in the mammalian brain. Little is known, however, about the functional role played by the generation and destruction of neurons in the context of and adult brain. Here we propose two models where new projection neurons are incorporated. We show that in both models, using incorporation and removal of neurons as a computational tool, it is possible to achieve a higher computational efficiency that in purely static, synapse-learning driven networks. We also discuss the implication for understanding the role of adult neurogenesis in specific brain areas., Comment: To appear Physica A, 7 pages

The eukaryotic nucleus is surrounded by a protective nuclear envelope, which is perforated by trafficking machines termed nuclear pore complexes (NPCs). The NPCs are the sole mediators of exchange between the nucleus and the cytoplasm. Small molecules pass through the NPCs unchallenged; however, large macromolecules are excluded unless chaperoned across by transport factors. Here, we suggest a model, termed 'virtual gating', to explain the mechanism of this rapid and selective macromolecular trafficking.

Evolution is based on a complex and intertwined interplay between generation of new variants and their selection by competition in the environment. Much research has established that the generation of evolutionary diversity is not a passive process that just “occurs” to organisms: rather, a fluid and active set of strategies is in place so that living beings control the ways in which diversity is generated. We describe some of the consequences for our current views of evolu- tionary theory and survey how the understanding of the field is changing.

The enzymes that pass DNA through DNA so as to remove entanglements, adenosine-triphosphate-hydrolyzing type-II topoisomerases, are able to suppress the probability of self-entanglements (knots) and mutual entanglements (links) between approximately 10 kb plasmids, well below the levels expected, given the assumption that the topoisomerases pass DNA segments at random by thermal motion. This implies that a 10-nm type-II topoisomerase can somehow sense the topology of a large DNA. We previously introduced a "kinetic proofreading" model which supposes the enzyme to require two successive collisions in order to allow exchange of DNA segments, and we showed how it could quantitatively explain the reduction in knotting and linking complexity. Here we show how the same model quantitatively explains the reduced variance of the double-helix linking number (supercoiling) distribution observed experimentally.

We present a model of sound production in a songbird’s vocal organ and find that much of the complexity of the song of the canary (Serinus canaria) can be produced from simple time variations in forcing functions. The starts, stops, and pauses between syllables, as well as variation in pitch and timbre are inherent in the mechanics and can often be expressed through smooth and simple variations in the frequency and relative phase of two driving parameters Human language and the song of many bird species are both learned by juveniles through experience. The ontogeny of learned song begins with early food-begging calls, continues through more complex bablings known as subsong, to eventually reach an adult form of song which is first plastic, and later stereotyped [1]. This development depends on auditory experience, for abnormal forms of song develop if a juvenile is deafened and unable to hear its own sound production or is isolated from the experience of any adult song model to copy. The time course of development and the dependence on auditory experience are similar to aspects of human speech, suggesting there may be common principles of learning and memory under

Cells must remove all entanglements between their replicated chromosomal DNAs to segregate them during cell division. Entanglement removal is done by ATP-driven enzymes that pass DNA strands through one another, called type II topoisomerases. In vitro, some type II topoisomerases can reduce entanglements much more than expected, given the assumption that they pass DNA segments through one another in a random way1. These type II topoisomerases (of less than 10 nm in diameter) thus use ATP hydrolysis to sense and remove entanglements spread along flexible DNA strands of up to 3,000 nm long. Here we propose a mechanism for this, based on the higher rate of collisions along entangled DNA strands, relative to collision rates on disentangled DNA strands. We show theoretically that if a type II topoisomerase requires an initial ‘activating’ collision before a second strand-passing collision, the probability of entanglement may be reduced to experimentally observed levels. This proposed two-collision reaction is similar to ‘kinetic proofreading’ models of molecular recognition2,3.

Amplification of auditory stimuli by hair cells augments the sensitivity of the vertebrate inner ear. Cell-body contractions of outer hair cells are thought to mediate amplification in the mammalian cochlea. In vertebrates that lack these cells, and perhaps in mammals as well, active movements of hair bundles may underlie amplification. We have evaluated a mathematical model in which amplification stems from the activity of mechanoelectrical-transduction channels. The intracellular binding of Ca 2+ to channels is posited to promote their closure, which increases the tension in gating springs and exerts a negative force on the hair bundle. By enhancing bundle motion, this force partially compensates for viscous damping by cochlear fluids. Linear stability analysis of a six-state kinetic model reveals Hopf bifurcations for parameter values in the physiological range. These bifurcations signal conditions under which the system’s behavior changes from a damped oscillatory response to spontaneous limit-cycle oscillation. By varying the number of stereocilia in a bundle and the rate constant for Ca 2+ binding, we calculate bifurcation frequencies spanning the observed range of auditory sensitivity for a representative receptor organ, the chicken’s cochlea. Simulations using prebifurcation parameter values demonstrate frequency-selective amplification with a striking compressive nonlinearity. Because transduction channels occur universally in hair cells, this active-channel model describes a mechanism of auditory amplification potentially applicable across species and hair-cell types.

A number of studies have suggested that many properties of brain activity can be understood in terms of critical systems. However it is still not known how the long-range susceptibilities characteristic of criticality arise in the living brain from its local connectivity structures. Here we prove that a dynamically critically-poised model of cortex acquires an infinitely-long ranged susceptibility in the absence of input. When an input is presented, the susceptibility attenuates exponentially as a function of distance, with an increasing spatial attenuation constant (i.e., decreasing range) the larger the input. This is in direct agreement with recent results that show that waves of local field potential activity evoked by single spikes in primary visual cortex of cat and macaque attenuate with a characteristic length that also increases with decreasing contrast of the visual stimulus. A susceptibility that changes spatial range with input strength can be thought to implement an input-dependent spatial integration: when the input is large, no additional evidence is needed in addition to the local input; when the input is weak, evidence needs to be integrated over a larger spatial domain to achieve a decision. Such input-strength-dependent strategies have been demonstrated in visual processing. Our results suggest that input-strength dependent spatial integration may be a natural feature of a critically-balanced cortical network.

High-density oligonucleotide microarrays provide a powerful tool for assessing differential mRNA expression levels. Characterizing the noise resulting from the enzymatic and hybridization steps, called type I noise, is essential for attributing significance measures to the differential expression scores. We introduce scoring functions for expression ratios, and associated quality measures. Both the PM (Perfect Match) probes and PM-MM differentials (MM is the single MisMatch) are considered as raw intensities. We then characterize the log-ratio noise structure using robust estimates of their intensity dependent variance. We show the relationships between the obtained ratios and their quality measures. The complementarity of PM and PM-MM methods is emphasized by the probe sets signal to noise measures. Using a large set of replicate experiments, we demonstrate that the noise structure in the log-ratios very closely follows a local log-normal distribution for both the PM and PM-MM cases. Therefore, significance relative to the type I noise can be quantified reliably using the local STD. We discuss the intensity dependence of the STD and show that ratio scores >1.25 are significant in the mid- to high-intensity range. The ratio noise structure inherent to high-density oligonucleotide arrays can be well described in terms of local log-normal ratio distributions with characteristic intensity dependence. Therefore, robust estimates of the local STD of these distributions provide a simple and powerful way for assessing significance (relative to type I noise) in differential gene expression. This approach will be helpful for improving the reliability of predictions from hybridization experiments in general.

Background The processing of images acquired through microscopy is a challenging task due to the large size of datasets (several gigabytes) and the fast turnaround time required. If the throughput of the image processing stage is significantly increased, it can have a major impact in microscopy applications. Results We present a high performance computing (HPC) solution to this problem. This involves decomposing the spatial 3D image into segments that are assigned to unique processors, and matched to the 3D torus architecture of the IBM Blue Gene/L machine. Communication between segments is restricted to the nearest neighbors. When running on a 2 Ghz Intel CPU, the task of 3D median filtering on a typical 256 megabyte dataset takes two and a half hours, whereas by using 1024 nodes of Blue Gene, this task can be performed in 18.8 seconds, a 478× speedup. Conclusion Our parallel solution dramatically improves the performance of image processing, feature extraction and 3D reconstruction tasks. This increased throughput permits biologists to conduct unprecedented large scale experiments with massive datasets.