Herbivore-induced shifts in carbon and nitrogen allocation in red oak seedlings

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65993/1/j.1469-8137.2008.02420.x.pd

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics

Two years follow-up study of the pain-relieving effect of gold bead implantation in dogs with hip-joint arthritis

Seventy-eight dogs with pain from hip dysplasia participated in a six-month placebo-controlled, double-blinded clinical trial of gold bead implantation. In the present, non-blinded study, 73 of these dogs were followed for an additional 18 months to evaluate the long-term pain-relieving effect of gold bead implantation. The recently-published results of the six month period revealed that 30 of the 36 dogs (83%) in the gold implantation group showed significant improvement (p = 0.02), included improved mobility and reduction in the signs of pain, compared to the placebo group (60% improvement). In the long-term two-year follow-up study, 66 of the 73 dogs had gold implantation and seven dogs continued as a control group. The 32 dogs in the original placebo group had gold beads implanted and were followed for a further 18 months. A certified veterinary acupuncturist used the same procedure to insert the gold beads as in the blinded study, and the owners completed the same type of detailed questionnaires. As in the blinded study, one investigator was responsible for all the assessments of each dog. The present study revealed that the pain-relieving effect of gold bead implantation observed in the blinded study continued throughout the two-year follow-up period

Learning deterministic probabilistic automata from a model checking perspective

Probabilistic automata models play an important role in the formal design and analysis of hard- and software systems. In this area of applications, one is often interested in formal model-checking procedures for verifying critical system properties. Since adequate system models are often difficult to design manually, we are interested in learning models from observed system behaviors. To this end we adopt techniques for learning finite probabilistic automata, notably the Alergia algorithm. In this paper we show how to extend the basic algorithm to also learn automata models for both reactive and timed systems. A key question of our investigation is to what extent one can expect a learned model to be a good approximation for the kind of probabilistic properties one wants to verify by model checking. We establish theoretical convergence properties for the learning algorithm as well as for probability estimates of system properties expressed in linear time temporal logic and linear continuous stochastic logic. We empirically compare the learning algorithm with statistical model checking and demonstrate the feasibility of the approach for practical system verification

Isotopic Investigation of Contemporary and Historic Changes in Penguin Trophic Niches and Carrying Capacity of the Southern Indian Ocean

A temperature-defined regime shift occurred in the 1970s in the southern Indian Ocean, with simultaneous severe decreases in many predator populations. We tested a possible biological link between the regime shift and predator declines by measuring historic and contemporary feather isotopic signatures of seven penguin species with contrasted foraging strategies and inhabiting a large latitudinal range. We first showed that contemporary penguin isotopic variations and chlorophyll a concentration were positively correlated, suggesting the usefulness of predator δ13C values to track temporal changes in the ecosystem carrying capacity and its associated coupling to consumers. Having controlled for the Suess effect and for increase CO2 in seawater, δ13C values of Antarctic penguins and of king penguins did not change over time, while δ13C of other subantarctic and subtropical species were lower in the 1970s. The data therefore suggest a decrease in ecosystem carrying capacity of the southern Indian Ocean during the temperature regime-shift in subtropical and subantarctic waters but not in the vicinity of the Polar Front and in southward high-Antarctic waters. The resulting lower secondary productivity could be the main driving force explaining the decline of subtropical and subantarctic (but not Antarctic) penguins that occurred in the 1970s. Feather δ15N values did not show a consistent temporal trend among species, suggesting no major change in penguins’ diet. This study highlights the usefulness of developing long-term tissue sampling and data bases on isotopic signature of key marine organisms to track potential changes in their isotopic niches and in the carrying capacity of the environment

Rituximab in B-Cell Hematologic Malignancies: A Review of 20 Years of Clinical Experience

Rituximab is a human/murine, chimeric anti-CD20 monoclonal antibody with established efficacy, and a favorable and well-defined safety profile in patients with various CD20-expressing lymphoid malignancies, including indolent and aggressive forms of B-cell non-Hodgkin lymphoma. Since its first approval 20 years ago, intravenously administered rituximab has revolutionized the treatment of B-cell malignancies and has become a standard component of care for follicular lymphoma, diffuse large B-cell lymphoma, chronic lymphocytic leukemia, and mantle cell lymphoma. For all of these diseases, clinical trials have demonstrated that rituximab not only prolongs the time to disease progression but also extends overall survival. Efficacy benefits have also been shown in patients with marginal zone lymphoma and in more aggressive diseases such as Burkitt lymphoma. Although the proven clinical efficacy and success of rituximab has led to the development of other anti-CD20 monoclonal antibodies in recent years (e.g., obinutuzumab, ofatumumab, veltuzumab, and ocrelizumab), rituximab is likely to maintain a position within the therapeutic armamentarium because it is well established with a long history of successful clinical use. Furthermore, a subcutaneous formulation of the drug has been approved both in the EU and in the USA for the treatment of B-cell malignancies. Using the wealth of data published on rituximab during the last two decades, we review the preclinical development of rituximab and the clinical experience gained in the treatment of hematologic B-cell malignancies, with a focus on the well-established intravenous route of administration. This article is a companion paper to A. Davies, et al., which is also published in this issue

Inhaled ambient-level traffic-derived particulates decrease cardiac vagal influence and baroreflexes and increase arrhythmia in a rat model of metabolic syndrome

Background Epidemiological studies have linked exposures to ambient fine particulate matter (PM2.5) and traffic with autonomic nervous system imbalance (ANS) and cardiac pathophysiology, especially in individuals with preexisting disease. It is unclear whether metabolic syndrome (MetS) increases susceptibility to the effects of PM2.5. We hypothesized that exposure to traffic-derived primary and secondary organic aerosols (P + SOA) at ambient levels would cause autonomic and cardiovascular dysfunction in rats exhibiting features of MetS. Male Sprague Dawley (SD) rats were fed a high-fructose diet (HFrD) to induce MetS, and exposed to P + SOA (20.4 ± 0.9 μg/m3) for 12 days with time-matched comparison to filtered-air (FA) exposed MetS rats; normal diet (ND) SD rats were separately exposed to FA or P + SOA (56.3 ± 1.2 μg/m3). Results In MetS rats, P + SOA exposure decreased HRV, QTc, PR, and expiratory time overall (mean effect across the entirety of exposure), increased breathing rate overall, decreased baroreflex sensitivity (BRS) on three exposure days, and increased spontaneous atrioventricular (AV) block Mobitz Type II arrhythmia on exposure day 4 relative to FA-exposed animals receiving the same diet. Among ND rats, P + SOA decreased HRV only on day 1 and did not significantly alter BRS despite overall hypertensive responses relative to FA. Correlations between HRV, ECG, BRS, and breathing parameters suggested a role for autonomic imbalance in the pathophysiologic effects of P + SOA among MetS rats. Autonomic cardiovascular responses to P + SOA at ambient PM2.5 levels were pronounced among MetS rats and indicated blunted vagal influence over cardiovascular physiology. Conclusions Results support epidemiologic findings that MetS increases susceptibility to the adverse cardiac effects of ambient-level PM2.5, potentially through ANS imbalance

Computational Models of Timing Mechanisms in the Cerebellar Granular Layer

A long-standing question in neuroscience is how the brain controls movement that requires precisely timed muscle activations. Studies using Pavlovian delay eyeblink conditioning provide good insight into this question. In delay eyeblink conditioning, which is believed to involve the cerebellum, a subject learns an interstimulus interval (ISI) between the onsets of a conditioned stimulus (CS) such as a tone and an unconditioned stimulus such as an airpuff to the eye. After a conditioning phase, the subject’s eyes automatically close or blink when the ISI time has passed after CS onset. This timing information is thought to be represented in some way in the cerebellum. Several computational models of the cerebellum have been proposed to explain the mechanisms of time representation, and they commonly point to the granular layer network. This article will review these computational models and discuss the possible computational power of the cerebellum

Synaptic Responses Evoked by Tactile Stimuli in Purkinje Cells in Mouse Cerebellar Cortex Crus II In Vivo

Sensory stimuli evoke responses in cerebellar Purkinje cells (PCs) via the mossy fiber-granule cell pathway. However, the properties of synaptic responses evoked by tactile stimulation in cerebellar PCs are unknown. The present study investigated the synaptic responses of PCs in response to an air-puff stimulation on the ipsilateral whisker pad in urethane-anesthetized mice.Thirty-three PCs were recorded from 48 urethane-anesthetized adult (6-8-week-old) HA/ICR mice by somatic or dendritic patch-clamp recording and pharmacological methods. Tactile stimulation to the ipsilateral whisker pad was delivered by an air-puff through a 12-gauge stainless steel tube connected with a pressurized injection system. Under current-clamp conditions (I = 0), the air-puff stimulation evoked strong inhibitory postsynaptic potentials (IPSPs) in the somata of PCs. Application of SR95531, a specific GABA(A) receptor antagonist, blocked IPSPs and revealed stimulation-evoked simple spike firing. Under voltage-clamp conditions, tactile stimulation evoked a sequence of transient inward currents followed by strong outward currents in the somata and dendrites in PCs. Application of SR95531 blocked outward currents and revealed excitatory postsynaptic currents (EPSCs) in somata and a temporal summation of parallel fiber EPSCs in PC dendrites. We also demonstrated that PCs respond to both the onset and offset of the air-puff stimulation.These findings indicated that tactile stimulation induced asynchronous parallel fiber excitatory inputs onto the dendrites of PCs, and failed to evoke strong EPSCs and spike firing in PCs, but induced the rapid activation of strong GABA(A) receptor-mediated inhibitory postsynaptic currents in the somata and dendrites of PCs in the cerebellar cortex Crus II in urethane-anesthetized mice