172,716 research outputs found
Tatari, tautoko, tauawhi - Hei awhina tamariki ki te panui pukapuka: Some preliminary findings
No full textThe Tatari, Tautoko, Tauawhi reading tutoring procedures have been adapted from the procedures known as Pause, Prompt, Praise, first developed in Mangere in 1977. The first author offered the procedures as a koha at a Special Education Service hui at Poho o Rawiri in 1991. The second author took up the koha and obtained the support of kaumatua and kuia at Hairini marae Tauranga Moana, and the support of senior Maori staff of the Special Education Service National Office to produce a Maori language video and training booklet. This began an important bicultural journey through the processes of producing instructional materials and trailing and evaluating them in ways that are biculturally appropriate. This paper reports on that journey and presents some preliminary data on the implementation of Tatari, Tautoko, Tauawhi by seven tuakana - teina pairs in a bi-lingual classroom
Text-based and Signal-based Prediction of Break Indices and Pause Durations
No full textThe relation between symbolic and signal features of prosodic
boundaries is experimentally studied using prediction methods.
Text-based break index prediction turns out to be fairly good,
but signal-based prediction and pause duration prediction perform worse. A possible reason is that random signal feature
variations, as usually produced by humans, are hard to predict
c-Myc regulates transcriptional pause release
No full textRecruitment of the RNA polymerase II (Pol II) transcription initiation apparatus to promoters by specific DNA-binding transcription factors is well recognized as a key regulatory step in gene expression. We report here that promoter-proximal pausing is a general feature of transcription by Pol II in mammalian cells and thus an additional step where regulation of gene expression occurs. This suggests that some transcription factors recruit the transcription apparatus to promoters, whereas others effect promoter-proximal pause release. Indeed, we find that the transcription factor c-Myc, a key regulator of cellular proliferation, plays a major role in Pol II pause release rather than Pol II recruitment at its target genes. We discuss the implications of these results for the role of c-Myc amplification in human cancer.National Institutes of Health (U.S.) (Grant number RO1-HG002668)National Institutes of Health (U.S.) (Grant number RO1-GM34277)National Institutes of Health (U.S.) (Grant number RO1-CA133404)National Cancer Institute (U.S.) (Grant Number PO1- CA42063)National Cancer Institute (U.S.) Cancer Center Support Grant (Grant Number P30-CA14051)National Institutes of Health (U.S.) Postdoctoral Fellowship (5-F32-HD051190
Pause beginning type synchronization mediate more accurate time-locking than pause overlapping synchronization.
No full text<p>In all panels red color stands for pause beginning condition, blue for pause overlapping condition. (*) represents comparisons in variability of latency between pause beginning and pause overlapping type synchronization that are significant (p<0.05) and (^) represents insignificant comparisons (p> = 0.05). (A&E) Population spike-timing histogram of all PNs projecting onto the CN neuron. (B&F) Variability of latency calculated from 100 trials for pause beginning and pause overlapping type synchronization and low gain condition. (C&G) Same for medium gain condition. (D&H) Same for high gain condition.</p
Anatomy of a Pause
No full text<p>An ensemble of average system outcome analyses, with (solid lines) and without (dotted lines) simulated Brownian motion of the bacterium (see discussion on the relationship between applied Brownian simulation force and numerical time-step in the model description). These averages were compiled from pauses with duration greater than 10 ms, using 10,365 pauses with, and 4,358 pauses without, simulated Brownian motion (there are fewer, longer pauses without Brownian motion of the bacterium). Only sufficiently time-separated pauses contributed to these averages, so that the 10 ms preceding the pause start and the 10 ms following the pause stop are guaranteed not to include the effects of any adjacent pause. The Brownian simulation force trends can be read from the total force curve, which is only slightly offset by the link and collision forces in the case where Brownian movement of the bacterium is simulated (solid lines). Here, initiation of a pause follows a large forward-directed Brownian simulation force on the bacterium (segment A), which increases link turnover and produces a large population of synchronously strained links. Backward path-directed Brownian simulation forces (segment B) maintains the pause until, aided again by forward-directed Brownian simulation forces (segment C), the bacterium transitions back into a run. The Brownian simulation force trends can be read from the total force curve, which is only slightly offset by the link and collision forces. Without simulated Brownian motion of the bacterium (dotted lines), a pause is initiated and maintained when a population of ActA–actin filament links can resist the essentially constant total filament collision force. A pause terminates in this case when these links break en masse. Any individual pause in the averaged set of pauses might not demonstrate all of these response features.</p
Individual behaviour after and within a pause.
No full text<p>(<b>A</b>) showing our calculation of locust turning behaviour within moves or pauses, or after a pause. We define a turn as a change from CW to ACW movement or vice versa. Arrows indicate the time steps for which the switch between CW to ACW was considered. Within a move or pause only consecutive time steps were examined (dotted arrows). For turning after a pause, the time steps immediately before and after the pause were considered (solid arrow). (<b>B</b>) shows the mean probability of changing direction after a pause for observed pause lengths (s), using log-binned averages. The left and right dashed lines show 6 s and 100 s, respectively. (<b>C</b>) shows the mean probability of changing direction after a pause for pause lengths of up to 20 s on a normal scale. (<b>D</b>) shows the mean probability of turning within a pause for different pause lengths. We have presented pause lengths up to 6 s as pause lengths greater than 6 s show a probability of one. For (<b>B–D</b>) error bars show 95% confidence intervals of the mean. (<b>E</b>) shows the relationship between the mean proportion of turns within a pause and the probability of changing direction after a pause for pause lengths of: less than 6 s (blue squares); between 6 s and 100 s (red triangles); and greater than 100 s (black circles). Each data point is a mean calculated from data within logged bin classes for pause length.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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