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GENOME WIDE DNA METHYLATION PROFILING IS PREDICTIVE OF OUTCOME IN JUVENILE MYELOMONOCYTIC LEUKEMIA
The value of migration information for conservation prioritization of sea turtles in the Mediterranean
Aim: Conservation plans often struggle to account for connectivity in spatial prioritization approaches for the protection of migratory species. Protection of such species is challenging because their movements may be uncertain and variable, span vast distances, cross international borders and traverse land and sea habitats. Often we are faced with small samples of information from various sources and the collection of additional data can be costly and time-consuming. Therefore it is important to evaluate what degree of spatial information provides sufficient results for directing management actions. Here we develop and evaluate an approach that incorporates habitat and movement information to advance the conservation of migratory species. We test our approach using information on threatened loggerhead sea turtles (Caretta caretta) in the Mediterranean. Location: The Mediterranean Sea. Methods: We use Marxan, a spatially explicit decision support tool, to select priority conservation areas. Four approaches with increasing amounts of information about the loggerhead sea turtle are compared, ranging from (1) the broad distribution, (2) multiple habitat types that represent foraging, nesting and inter-nesting habitats, (3) mark-recapture movement information to (4) telemetry-derived migration tracks. Results: We find that spatial priorities for sea turtle conservation are sensitive to the information used in the prioritization process. Setting conservation targets for migration tracks altered the location of conservation priorities, indicating that conservation plans designed without such data would miss important sea turtle habitat. We discover that even a small number of tracks make a significant contribution to a spatial conservation plan if those tracks are substantially different. Main conclusions: This study presents a novel approach to improving spatial prioritization for conserving migratory species. We propose that future telemetry studies tailor their efforts towards conservation prioritization needs, meaning that spatially dispersed samples rather than just large numbers should be obtained. This work highlights the valuable information that telemetry research contributes to the conservation of migratory species
Cross-boundary collaboration: Key to the conservation puzzle
Conservation science is advancing rapidly, yet the majority of research overlooks a key factor that can play a major role in shaping the outcomes of conservation initiatives: collaboration. Here, we review the importance, benefits and limitations of incorporating collaboration into conservation and specifically into systematic conservation planning, providing a general framework for considering collaboration in conservation planning. Recent work shows that cross-boundary collaboration can have both positive and negative impacts on the outcomes of conservation and management efforts for protected areas, ecosystems, threatened and invasive species. The feasibility of collaboration, its likely effects and associated trade-offs should therefore be explicitly incorporated into conservation science and planning. This will ensure that conservation decisions avoid wasted funding when collaboration is infeasible, promoting collaboration when the benefits outweigh the costs
Hidrogenionic potential (pH) of the attractant, trap density and control threshold for Ceratitis capitata (Diptera: tephritidae) on Hamlin oranges in São Paulo central region, Brazil
This study evaluated the effect of initial pH values of 4.5, 6.5 and 8.5 of the attractant
(protein bait) Milhocina® and borax (sodium borate) in the feld, on the capture of fruit flies in McPhail
traps, using 1, 2, 4 and 8 traps per hectare, in order to estimate control thresholds in a Hamlin orange grove
in the central region of the state of São Paulo. The most abundant fruit fly species was Ceratitis capitata,
comprising almost 99% of the fruit flies captured, of which 80% were females. The largest captures of C.
capitata were found in traps baited with Milhocina® and borax at pH 8.5. Captures per trap for the four
densities were similar, indicating that the population can be estimated with one trap per hectare in areas
with high populations. It was found positive relationships between captures of C. capitata and the number
of Hamlin oranges damaged, 2 and 3 weeks after capture. It was obtained equations that correlate captures
and damage levels which can be used to estimate control thresholds. The average loss caused in Hamlin
orange fruits by C. capitata was 2.5 tons per hectare or 7.5% of production.Esta pesquisa teve como objetivos: avaliar o efeito do pH inicial, 4.5; 6.5 e 8.5, do atrativo
proteico Milhocina® e bórax (tetraborato de sódio) na captura de moscas-das-frutas em armadilhas McPhail;
estudar densidades de armadilhas, 1; 2; 4 e 8 por hectare, para estimar níveis de controle em laranja cv.
Hamlin, na região central de São Paulo. A espécie predominante, com 99% das moscas-das-frutas capturadas,
foi Ceratitis capitata, sendo 80% de fêmeas. As maiores capturas de C. capitata ocorreram nas armadilhas
com Milhocina® e bórax em pH 8.5. As capturas, nas 4 densidades, foram semelhantes, indicando que a
população pode ser estimada com uma armadilha por hectare em áreas de altas populações. Houve relações
positivas entre capturas de C. capitata e o número de frutos danificados, 2 e 3 semanas após a captura. Assim,
foram obtidas equações que relacionam a captura e o dano, possibilitando estimar níveis de controle desse
inseto. As perdas médias causadas por C. capitata em laranja cv. Hamlin chegaram a 2,5 toneladas de frutos
por hectare ou 7,5% da produção.info:eu-repo/semantics/publishedVersio
Disease burden and conditioning regimens in ASCT1221, a randomized phase II trial in children with juvenile myelomonocytic leukemia: A Children's Oncology Group study
Background: Most patients with juvenile myelomonocytic leukemia (JMML) are curable only with allogeneic hematopoietic cell transplantation (HCT). However, the current standard conditioning regimen, busulfan-cyclophosphamide-melphalan (Bu-Cy-Mel), may be associated with higher risks of morbidity and mortality. ASCT1221 was designed to test whether the potentially less-toxic myeloablative conditioning regimen containing busulfan-fludarabine (Bu-Flu) would be associated with equivalent outcomes. Procedure: Twenty-seven patients were enrolled on ASCT1221 from 2013 to 2015. Pre- and post-HCT (starting Day +30) mutant allele burden was measured in all and pre-HCT therapy was administered according to physician discretion. Results: Fifteen patients were randomized (six to Bu-Cy-Mel and nine to Bu-Flu) after meeting diagnostic criteria for JMML. Pre-HCT low-dose chemotherapy did not appear to reduce pre-HCT disease burden. Two patients, however, received aggressive chemotherapy pre-HCT and achieved low disease-burden state; both are long-term survivors. All four patients with detectable mutant allele burden at Day +30 post-HCT eventually progressed compared to two of nine patients with unmeasurable allele burden (P = 0.04). The 18-month event-free survival of the entire cohort was 47% (95% CI, 21–69%), and was 83% (95% CI, 27–97%) and 22% (95% CI, 03–51%) for Bu-Cy-Mel and Bu-Flu, respectively (P = 0.04). ASCT1221 was terminated early due to concerns that the Bu-Flu arm had inferior outcomes. Conclusions: The regimen of Bu-Flu is inadequate to provide disease control in patients with JMML who present to HCT with large burdens of disease. Advances in molecular testing may allow better characterization of biologic risk, pre-HCT responses to chemotherapy, and post-HCT management
Can we identify non-stationary dynamics of trial-to-trial variability?"
Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings
Stochastic Growth Equations and Reparametrization Invariance
It is shown that, by imposing reparametrization invariance, one may derive a
variety of stochastic equations describing the dynamics of surface growth and
identify the physical processes responsible for the various terms. This
approach provides a particularly transparent way to obtain continuum growth
equations for interfaces. It is straightforward to derive equations which
describe the coarse grained evolution of discrete lattice models and analyze
their small gradient expansion. In this way, the authors identify the basic
mechanisms which lead to the most commonly used growth equations. The
advantages of this formulation of growth processes is that it allows one to go
beyond the frequently used no-overhang approximation. The reparametrization
invariant form also displays explicitly the conservation laws for the specific
process and all the symmetries with respect to space-time transformations which
are usually lost in the small gradient expansion. Finally, it is observed, that
the knowledge of the full equation of motion, beyond the lowest order gradient
expansion, might be relevant in problems where the usual perturbative
renormalization methods fail.Comment: 42 pages, Revtex, no figures. To appear in Rev. of Mod. Phy
Learning Shapes Spontaneous Activity Itinerating over Memorized States
Learning is a process that helps create neural dynamical systems so that an appropriate output pattern is generated for a given input. Often, such a memory is considered to be included in one of the attractors in neural dynamical systems, depending on the initial neural state specified by an input. Neither neural activities observed in the absence of inputs nor changes caused in the neural activity when an input is provided were studied extensively in the past. However, recent experimental studies have reported existence of structured spontaneous neural activity and its changes when an input is provided. With this background, we propose that memory recall occurs when the spontaneous neural activity changes to an appropriate output activity upon the application of an input, and this phenomenon is known as bifurcation in the dynamical systems theory. We introduce a reinforcement-learning-based layered neural network model with two synaptic time scales; in this network, I/O relations are successively memorized when the difference between the time scales is appropriate. After the learning process is complete, the neural dynamics are shaped so that it changes appropriately with each input. As the number of memorized patterns is increased, the generated spontaneous neural activity after learning shows itineration over the previously learned output patterns. This theoretical finding also shows remarkable agreement with recent experimental reports, where spontaneous neural activity in the visual cortex without stimuli itinerate over evoked patterns by previously applied signals. Our results suggest that itinerant spontaneous activity can be a natural outcome of successive learning of several patterns, and it facilitates bifurcation of the network when an input is provided
Redo renal denervation using a multi-electrode radiofrequency system in patients with persistent therapy-resistant hypertension
Techniques for temporal detection of neural sensitivity to external stimulation
We propose a simple measure of neural sensitivity for characterizing stimulus coding. Sensitivity is defined as the fraction of neurons that show positive responses to n stimuli out of a total of N. To determine a positive response, we propose two methods: Fisherian statistical testing and a data-driven Bayesian approach to determine the response probability of a neuron. The latter is non-parametric, data-driven, and captures a lower bound for the probability of neural responses to sensory stimulation. Both methods are compared with a standard test that assumes normal probability distributions. We applied the sensitivity estimation based on the proposed method to experimental data recorded from the mushroom body (MB) of locusts. We show that there is a broad range of sensitivity that the MB response sweeps during odor stimulation. The neurons are initially tuned to specific odors, but tend to demonstrate a generalist behavior towards the end of the stimulus period, meaning that the emphasis shifts from discrimination to feature learning
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