Association of candidate genes with flowering time and water-soluble carbohydrate content in Lolium perenne (L.)

Sk?t, L., Humphreys, J., Humphreys, M. O., Thorogood, D., Gallagher, J. A., Sanderson, R., Armstead, I. P., Thomas, I. D. (2007). Association of candidate genes with flowering time and water-soluble carbohydrate content in Lolium perenne (L.). Genetics, 177 (1), 535-547. Sponsorship: BBSRC RAE2008

Ethical Dimension of Time in Plato’s Apology of Socrates

The aim of the present article is to analyse the Apology in its aspect of time. When defending himself against the charges, Socrates appeals to the past, the present and the future. Furthermore, the philosopher stresses the meaning of the duration of time. Thus, the seems to suggest that all really important activities demand a long time to benefit, since they are almost invariably connected with greater efforts. While the dialogue proves thereby to be an ethical one, the various time expressions also gain an ethical dimension.

Form, fi gure, and the experience of time in seven southern composers of the 1950s-60s

After the 1980s it is diffi cult, following stylistic criteria, to draw a map of contemporary academic music. All styles are compossible, and all are practiced. In this context, the geographical entity “South of Italy” does not stand out for a musical identity with special technical-stylistic features. Rather, at a socio-cultural level, the South remains today – in music no less than in all areas where there is a gap between top development and stagnation – a land of emigrants: six out of the seven composers treated (Ivan Fedele, Giuseppe Colardo, Rosario Mirigliano, Giuseppe Soccio, Nicola Cisternino, Biagio Putignano, Paolo Aralla) live in the North of Italy. The positive aspect of this is the affi nity of the South with the transnational and superstructural community of contemporary music, which from European and Western has now become almost global. The composers under consideration belong to the generation of the ‘50s, rooted in the serial and post-serial movements (from which Franco Donatoni, Luciano Berio, Luigi Nono, Salvatore Sciarrino, Giacinto Scelsi, are the principals models, to mention only the Italians), dipped in the general phenomenon of timbrism (particularly spectralism), and acquainted with electronics. They draw from these sources various instruments of compositional technique and aspects of their poetics. In particular these composers, active from the ‘80s, develop new ways of construction of the temporal form of music. They share the goal to establish a new continuity, different from the tonal one but at the same time transcending the serial and post-serial disintegration and fragmentation. The primary means to this end is a new enhancement of the category of fi gure, as a clear and distinct, recognizable aggregate of pitches, intervals, register, durations, timbre, articulation, dynamics, and texture. Each composer elaborates the atonal fi gural material in different ways, emphasizing one aspect or another. For example, Fedele (1953) is a master in the management of form per se, Colardo (1953) in the activation of disturbed harmonic effects, Mirigliano (1950) in the creation of a slight tension from the smallest vibrations of sound, Soccio (1950) in the set up of movement by means of accumulations and discharges of energy, Cisternino (1957) in a Cagean-Scelsian emphasis on sound as such, Putignano (1960) in the suspension of time through the succession and transformation of images, Aralla (1960) in the foundation of form from below, from the concreteness of sound.

Nonlinear Acoustics in Underwater and Biomedical Applications: Array Performance Degradation and Time Reversal Invariance

This dissertation describes a model for acoustic propagation in inhomogeneous flu- ids, and explores the focusing by arrays onto targets under various conditions. The work explores the use of arrays, in particular the time reversal array, for underwater and biomedical applications. Aspects of propagation and phasing which can lead to reduced focusing effectiveness are described. An acoustic wave equation was derived for the propagation of finite-amplitude waves in lossy time-varying inhomogeneous fluid media. The equation was solved numerically in both Cartesian and cylindrical geometries using the finite-difference time-domain (FDTD) method. It was found that time reversal arrays are sensitive to several debilitating factors. Focusing ability was determined to be adequate in the presence of temporal jitter in the time reversed signal only up to about one-sixth of a period. Thermoviscous absorption also had a debilitating effect on focal pressure for both linear and nonlinear propagation. It was also found that nonlinearity leads to degradation of focal pressure through amplification of the received signal at the array, and enhanced absorption in the shocked waveforms. This dissertation also examined the heating effects of focused ultrasound in a tissue-like medium. The application considered is therapeutic heating for hyperther- mia. The acoustic model and a thermal model for tissue were coupled to solve for transient and steady temperature profiles in tissue-like media. The Pennes bioheat equation was solved using the FDTD method to calculate the temperature fields in tissue-like media from focused acoustic sources. It was found that the temperature-dependence of the medium's background prop- erties can play an important role in the temperature predictions. Finite-amplitude effects contributed excess heat when source conditions were provided for nonlinear ef- fects to manifest themselves. The effect of medium heterogeneity was also found to be important in redistributing the acoustic and temperature fields, creating regions with hotter and colder temperatures than the mean by local scattering and lensing action. These temperature excursions from the mean were found to increase monotonically with increasing contrast in the medium's properties.

Speculative Concurrency Control for Real-Time Databases

In this paper, we propose a new class of Concurrency Control Algorithms that is especially suited for real-time database applications. Our approach relies on the use of (potentially) redundant computations to ensure that serializable schedules are found and executed as early as possible, thus, increasing the chances of a timely commitment of transactions with strict timing constraints. Due to its nature, we term our concurrency control algorithms Speculative. The aforementioned description encompasses many algorithms that we call collectively Speculative Concurrency Control (SCC) algorithms. SCC algorithms combine the advantages of both Pessimistic and Optimistic Concurrency Control (PCC and OCC) algorithms, while avoiding their disadvantages. On the one hand, SCC resembles PCC in that conflicts are detected as early as possible, thus making alternative schedules available in a timely fashion in case they are needed. On the other hand, SCC resembles OCC in that it allows conflicting transactions to proceed concurrently, thus avoiding unnecessary delays that may jeopardize their timely commitment.

Skin Color-Based Video Segmentation under Time-Varying Illumination

A novel approach for real-time skin segmentation in video sequences is described. The approach enables reliable skin segmentation despite wide variation in illumination during tracking. An explicit second order Markov model is used to predict evolution of the skin-color (HSV) histogram over time. Histograms are dynamically updated based on feedback from the current segmentation and predictions of the Markov model. The evolution of the skin-color distribution at each frame is parameterized by translation, scaling and rotation in color space. Consequent changes in geometric parameterization of the distribution are propagated by warping and resampling the histogram. The parameters of the discrete-time dynamic Markov model are estimated using Maximum Likelihood Estimation, and also evolve over time. The accuracy of the new dynamic skin color segmentation algorithm is compared to that obtained via a static color model. Segmentation accuracy is evaluated using labeled ground-truth video sequences taken from staged experiments and popular movies. An overall increase in segmentation accuracy of up to 24% is observed in 17 out of 21 test sequences. In all but one case the skin-color classification rates for our system were higher, with background classification rates comparable to those of the static segmentation.

An Admission Control Paradigm for Real-time Databases

We propose and evaluate an admission control paradigm for RTDBS, in which a transaction is submitted to the system as a pair of processes: a primary task, and a recovery block. The execution requirements of the primary task are not known a priori, whereas those of the recovery block are known a priori. Upon the submission of a transaction, an Admission Control Mechanism is employed to decide whether to admit or reject that transaction. Once admitted, a transaction is guaranteed to finish executing before its deadline. A transaction is considered to have finished executing if exactly one of two things occur: Either its primary task is completed (successful commitment), or its recovery block is completed (safe termination). Committed transactions bring a profit to the system, whereas a terminated transaction brings no profit. The goal of the admission control and scheduling protocols (e.g., concurrency control, I/O scheduling, memory management) employed in the system is to maximize system profit. We describe a number of admission control strategies and contrast (through simulations) their relative performance.

Ergodicity and Mixing Rate of One-Dimensional Cellular Automata

One-and two-dimensional cellular automata which are known to be fault-tolerant are very complex. On the other hand, only very simple cellular automata have actually been proven to lack fault-tolerance, i.e., to be mixing. The latter either have large noise probability ε or belong to the small family of two-state nearest-neighbor monotonic rules which includes local majority voting. For a certain simple automaton L called the soldiers rule, this problem has intrigued researchers for the last two decades since L is clearly more robust than local voting: in the absence of noise, L eliminates any finite island of perturbation from an initial configuration of all 0's or all 1's. The same holds for a 4-state monotonic variant of L, K, called two-line voting. We will prove that the probabilistic cellular automata Kε and Lε asymptotically lose all information about their initial state when subject to small, strongly biased noise. The mixing property trivially implies that the systems are ergodic. The finite-time information-retaining quality of a mixing system can be represented by its relaxation time Relax(⋅), which measures the time before the onset of significant information loss. This is known to grow as (1/ε)^c for noisy local voting. The impressive error-correction ability of L has prompted some researchers to conjecture that Relax(Lε) = 2^(c/ε). We prove the tight bound 2^(c1log^21/ε) ＜ Relax(Lε) ＜ 2^(c2log^21/ε) for a biased error model. The same holds for Kε. Moreover, the lower bound is independent of the bias assumption. The strong bias assumption makes it possible to apply sparsity/renormalization techniques, the main tools of our investigation, used earlier in the opposite context of proving fault-tolerance.

ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network

This article introduces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This supervised learning system is built up from a pair of Adaptive Resonance Theory modules (ARTa and ARTb) that are capable of self-organizing stable recognition categories in response to arbitrary sequences of input patterns. During training trials, the ARTa module receives a stream {a^(p)} of input patterns, and ARTb receives a stream {b^(p)} of input patterns, where b^(p) is the correct prediction given a^(p). These ART modules are linked by an associative learning network and an internal controller that ensures autonomous system operation in real time. During test trials, the remaining patterns a^(p) are presented without b^(p), and their predictions at ARTb are compared with b^(p). Tested on a benchmark machine learning database in both on-line and off-line simulations, the ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms, and achieves 100% accuracy after training on less than half the input patterns in the database. It achieves these properties by using an internal controller that conjointly maximizes predictive generalization and minimizes predictive error by linking predictive success to category size on a trial-by-trial basis, using only local operations. This computation increases the vigilance parameter ρa of ARTa by the minimal amount needed to correct a predictive error at ARTb· Parameter ρa calibrates the minimum confidence that ARTa must have in a category, or hypothesis, activated by an input a^(p) in order for ARTa to accept that category, rather than search for a better one through an automatically controlled process of hypothesis testing. Parameter ρa is compared with the degree of match between a^(p) and the top-down learned expectation, or prototype, that is read-out subsequent to activation of an ARTa category. Search occurs if the degree of match is less than ρa. ARTMAP is hereby a type of self-organizing expert system that calibrates the selectivity of its hypotheses based upon predictive success. As a result, rare but important events can be quickly and sharply distinguished even if they are similar to frequent events with different consequences. Between input trials ρa relaxes to a baseline vigilance pa When ρa is large, the system runs in a conservative mode, wherein predictions are made only if the system is confident of the outcome. Very few false-alarm errors then occur at any stage of learning, yet the system reaches asymptote with no loss of speed. Because ARTMAP learning is self stabilizing, it can continue learning one or more databases, without degrading its corpus of memories, until its full memory capacity is utilized.

Typical delay determines waiting time on periodic-food schedules: Static and dynamic tests.

Pigeons and other animals soon learn to wait (pause) after food delivery on periodic-food schedules before resuming the food-rewarded response. Under most conditions the steady-state duration of the average waiting time, t, is a linear function of the typical interfood interval. We describe three experiments designed to explore the limits of this process. In all experiments, t was associated with one key color and the subsequent food delay, T, with another. In the first experiment, we compared the relation between t (waiting time) and T (food delay) under two conditions: when T was held constant, and when T was an inverse function of t. The pigeons could maximize the rate of food delivery under the first condition by setting t to a consistently short value; optimal behavior under the second condition required a linear relation with unit slope between t and T. Despite this difference in optimal policy, the pigeons in both cases showed the same linear relation, with slope less than one, between t and T. This result was confirmed in a second parametric experiment that added a third condition, in which T + t was held constant. Linear waiting appears to be an obligatory rule for pigeons. In a third experiment we arranged for a multiplicative relation between t and T (positive feedback), and produced either very short or very long waiting times as predicted by a quasi-dynamic model in which waiting time is strongly determined by the just-preceding food delay.

Comparison of pattern detection methods in microarray time series of the segmentation clock.

While genome-wide gene expression data are generated at an increasing rate, the repertoire of approaches for pattern discovery in these data is still limited. Identifying subtle patterns of interest in large amounts of data (tens of thousands of profiles) associated with a certain level of noise remains a challenge. A microarray time series was recently generated to study the transcriptional program of the mouse segmentation clock, a biological oscillator associated with the periodic formation of the segments of the body axis. A method related to Fourier analysis, the Lomb-Scargle periodogram, was used to detect periodic profiles in the dataset, leading to the identification of a novel set of cyclic genes associated with the segmentation clock. Here, we applied to the same microarray time series dataset four distinct mathematical methods to identify significant patterns in gene expression profiles. These methods are called: Phase consistency, Address reduction, Cyclohedron test and Stable persistence, and are based on different conceptual frameworks that are either hypothesis- or data-driven. Some of the methods, unlike Fourier transforms, are not dependent on the assumption of periodicity of the pattern of interest. Remarkably, these methods identified blindly the expression profiles of known cyclic genes as the most significant patterns in the dataset. Many candidate genes predicted by more than one approach appeared to be true positive cyclic genes and will be of particular interest for future research. In addition, these methods predicted novel candidate cyclic genes that were consistent with previous biological knowledge and experimental validation in mouse embryos. Our results demonstrate the utility of these novel pattern detection strategies, notably for detection of periodic profiles, and suggest that combining several distinct mathematical approaches to analyze microarray datasets is a valuable strategy for identifying genes that exhibit novel, interesting transcriptional patterns.

Measuring fast gene dynamics in single cells with time-lapse luminescence microscopy.

Time-lapse fluorescence microscopy is an important tool for measuring in vivo gene dynamics in single cells. However, fluorescent proteins are limited by slow chromophore maturation times and the cellular autofluorescence or phototoxicity that arises from light excitation. An alternative is luciferase, an enzyme that emits photons and is active upon folding. The photon flux per luciferase is significantly lower than that for fluorescent proteins. Thus time-lapse luminescence microscopy has been successfully used to track gene dynamics only in larger organisms and for slower processes, for which more total photons can be collected in one exposure. Here we tested green, yellow, and red beetle luciferases and optimized substrate conditions for in vivo luminescence. By combining time-lapse luminescence microscopy with a microfluidic device, we tracked the dynamics of cell cycle genes in single yeast with subminute exposure times over many generations. Our method was faster and in cells with much smaller volumes than previous work. Fluorescence of an optimized reporter (Venus) lagged luminescence by 15-20 min, which is consistent with its known rate of chromophore maturation in yeast. Our work demonstrates that luciferases are better than fluorescent proteins at faithfully tracking the underlying gene expression.

One hundred years of forgetting: A quantitative description of retention

A sample of 210 published data sets were assembled that (a) plotted amount remembered versus time, (b) had 5 or more points, and (c) were smooth enough to fit at least 1 of the functions tested with a correlation coefficient of .90 or greater. Each was fit to 105 different 2-parameter functions. The best fits were to the logarithmic function, the power function, the exponential in the square root of time, and the hyperbola in the square root of time. It is difficult to distinguish among these 4 functions with the available data, but the same set of 4 functions fit most data sets, with autobiographical memory being the exception. Theoretical motivations for the best fitting functions are offered. The methodological problems of evaluating functions and the advantages of searching existing data for regularities before formulating theories are considered.

Neuronal correlates of visual time perception at brief timescales.

Successful interaction with the world depends on accurate perception of the timing of external events. Neurons at early stages of the primate visual system represent time-varying stimuli with high precision. However, it is unknown whether this temporal fidelity is maintained in the prefrontal cortex, where changes in neuronal activity generally correlate with changes in perception. One reason to suspect that it is not maintained is that humans experience surprisingly large fluctuations in the perception of time. To investigate the neuronal correlates of time perception, we recorded from neurons in the prefrontal cortex and midbrain of monkeys performing a temporal-discrimination task. Visual time intervals were presented at a timescale relevant to natural behavior (<500 ms). At this brief timescale, neuronal adaptation--time-dependent changes in the size of successive responses--occurs. We found that visual activity fluctuated with timing judgments in the prefrontal cortex but not in comparable midbrain areas. Surprisingly, only response strength, not timing, predicted task performance. Intervals perceived as longer were associated with larger visual responses and shorter intervals with smaller responses, matching the dynamics of adaptation. These results suggest that the magnitude of prefrontal activity may be read out to provide temporal information that contributes to judging the passage of time.

Biogeography in deep time - What do phylogenetics, geology, and paleoclimate tell us about early platyrrhine evolution?

Molecular data have converged on a consensus about the genus-level phylogeny of extant platyrrhine monkeys, but for most extinct taxa and certainly for those older than the Pleistocene we must rely upon morphological evidence from fossils. This raises the question as to how well anatomical data mirror molecular phylogenies and how best to deal with discrepancies between the molecular and morphological data as we seek to extend our phylogenies to the placement of fossil taxa. Here I present parsimony-based phylogenetic analyses of extant and fossil platyrrhines based on an anatomical dataset of 399 dental characters and osteological features of the cranium and postcranium. I sample 16 extant taxa (one from each platyrrhine genus) and 20 extinct taxa of platyrrhines. The tree structure is constrained with a "molecular scaffold" of extant species as implemented in maximum parsimony using PAUP with the molecular-based 'backbone' approach. The data set encompasses most of the known extinct species of platyrrhines, ranging in age from latest Oligocene (∼26 Ma) to the Recent. The tree is rooted with extant catarrhines, and Late Eocene and Early Oligocene African anthropoids. Among the more interesting patterns to emerge are: (1) known early platyrrhines from the Late Oligocene through Early Miocene (26-16.5Ma) represent only stem platyrrhine taxa; (2) representatives of the three living platyrrhine families first occur between 15.7 Ma and 13.5 Ma; and (3) recently extinct primates from the Greater Antilles (Cuba, Jamaica, Hispaniola) are sister to the clade of extant platyrrhines and may have diverged in the Early Miocene. It is probable that the crown platyrrhine clade did not originate before about 20-24 Ma, a conclusion consistent with the phylogenetic analysis of fossil taxa presented here and with recent molecular clock estimates. The following biogeographic scenario is consistent with the phylogenetic findings and climatic and geologic evidence: Tropical South America has been a center for platyrrhine diversification since platyrrhines arrived on the continent in the middle Cenozoic. Platyrrhines dispersed from tropical South America to Patagonia at ∼25-24 Ma via a "Paraná Portal" through eastern South America across a retreating Paranense Sea. Phylogenetic bracketing suggests Antillean primates arrived via a sweepstakes route or island chain from northern South America in the Early Miocene, not via a proposed land bridge or island chain (GAARlandia) in the Early Oligocene (∼34 Ma). Patagonian and Antillean platyrrhines went extinct without leaving living descendants, the former at the end of the Early Miocene and the latter within the past six thousand years. Molecular evidence suggests crown platyrrhines arrived in Central America by crossing an intermittent connection through the Isthmus of Panama at or after 3.5Ma. Any more ancient Central American primates, should they be discovered, are unlikely to have given rise to the extant Central American taxa in situ.