| 1. |
- Wilhelm, Matthias, et al.
(författare)
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Early Repolarization, Left Ventricular Diastolic Function, and Left Atrial Size in Professional Soccer Players
- 2010
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Ingår i: American Journal of Cardiology. - : Elsevier. - 0002-9149 .- 1879-1913. ; 106:4, s. 569-574
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Tidskriftsartikel (refereegranskat)abstract
- Recent data have suggested a relation among long-term endurance sport practice, left atrial remodeling, and atrial fibrillation. We investigated the influence of an increased vagal tone, represented by the early repolarization (ER) pattern, on diastolic function and left atrial size in professional soccer players. Fifty-four consecutive athletes underwent electrocardiography, echocardiography, and exercise testing as part of their preparticipation screening. Athletes were divided into 2 groups according to presence or absence of an ER pattern, defined as a ST-segment elevation at the J-point (STE) ≥0.1 mm in 2 leads. For linear comparisons average STE was calculated. Mean age was 24 ± 4 years. Twenty-five athletes (46%) showed an ER pattern. Athletes with an ER pattern had a significant lower heart rate (54 ± 9 vs 62 ± 11 beats/min, p = 0.024), an increased E/e′ ratio (6.1 ± 1.2 vs 5.1 ± 1.0, p = 0.002), and larger volumes of the left atrium (25.6 ± 7.3 vs 21.8 ± 5.0 ml/m2, p = 0.031) compared to athletes without an ER pattern. There were no significant differences concerning maximum workload, left ventricular dimensions, and systolic function. Univariate regression analysis revealed significant correlations among age, STE, and left atrial volume. In a stepwise multivariate regression analysis age, STE and e′ contributed independently to left atrial size (r = 0.659, p <0.001). In conclusion, athletes with an ER pattern had an increased E/e′ ratio, reflecting a higher left atrial filling pressure, contributing to left atrial remodeling over time.
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| 2. |
- Breitfeld, H. T., et al.
(författare)
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The onshore West Baram Delta deposits: Provenance and drainage in the Middle Miocene to Pliocene in NW Borneo and comparison to the Champion Delta
- 2023
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Ingår i: Marine and Petroleum Geology. - 0264-8172. ; 158
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Tidskriftsartikel (refereegranskat)abstract
- The Baram Delta province in NW Borneo forms a major hydrocarbon reservoir offshore northern Sarawak and Brunei. The delta sequence is thereby subdivided into the West Baram delta to the south and the Champion delta to the north. Onshore are the remains of the Neogene delta deposits exposed and provide the possibility to study the equivalent offshore successions in outcrop. This study focuses on the Neogene West Baram delta successions which were studied for sedimentological facies and provenance characteristics. The successions consist of the Lambir, Miri, Tukau, and the enigmatic southern Lambir/Belait-Sarawak formations. Deposition took place in various mixed-energy delta environments between the Langhian and early Pliocene. The sediments are all quartzrich and heavy minerals are dominated by ultra-stable zircon, rutile and tourmaline. Dominant detrital zircon age clusters are in the Early Cretaceous and Permian-Triassic. Based on light mineral petrography, heavy mineral assemblages, and detrital zircon U-Pb geochronology, all formations are interpreted as derived from multirecycled sources, likely the underlying Paleogene Rajang Group turbidites and the Oligocene to Lower Miocene Nyalau-Tatau delta deposits. Additionally, literature data of the Champion Delta and one sample from Labuan analysed for provenance in this study are used to demonstrate that the Champion Delta can be distinguished from the West Baram Delta by having higher chrome spinel and garnet contents and slightly different detrital zircon age populations. The Champion Delta deposits are interpreted as sourced by recycling of the Crocker Formation and older turbidites (e.g., Sapulut Formation) with potentially input from ultra-mafic basement rocks of Sabah.
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| 3. |
- Hilgen, Gerrit, et al.
(författare)
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Unsupervised Spike Sorting for Large-Scale, High-Density Multielectrode Arrays
- 2017
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Ingår i: Cell Reports. - : CELL PRESS. - 2211-1247. ; 18:10, s. 2521-2532
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Tidskriftsartikel (refereegranskat)abstract
- We present amethod for automated spike sorting for recordings with high-density, large-scale multielectrode arrays. Exploiting the dense sampling of single neurons by multiple electrodes, an efficient, low-dimensional representation of detected spikes consisting of estimated spatial spike locations and dominant spike shape features is exploited for fast and reliable clustering into single units. Millions of events can be sorted in minutes, and the method is parallelized and scales better than quadratically with the number of detected spikes. Performance is demonstrated using recordings with a 4,096-channel array and validated using anatomical imaging, optogenetic stimulation, and model-based quality control. A comparison with semi-automated, shape-based spike sorting exposes significant limitations of conventional methods. Our approach demonstrates that it is feasible to reliably isolate the activity of up to thousands of neurons and that dense, multi-channel probes substantially aid reliable spike sorting.
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| 4. |
- Knief, Ulrich, et al.
(författare)
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Highly pathogenic avian influenza causes mass mortality in Sandwich Tern Thalasseus sandvicensis breeding colonies across north-western Europe
- 2024
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Ingår i: Bird conservation international. - : Cambridge University Press. - 0959-2709 .- 1474-0001. ; 34
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Tidskriftsartikel (refereegranskat)abstract
- In 2022, highly pathogenic avian influenza (HPAI) A(H5N1) virus clade 2.3.4.4b became enzootic and caused mass mortality in Sandwich TernThalasseus sandvicensis and other seabird species across north-western Europe. We present data on the characteristics of the spread of the virus between and within breeding colonies and the number of dead adult Sandwich Terns recorded at breeding sites throughout north-western Europe. Within two months of the first reported mortalities, 20,531 adult Sandwich Terns were found dead, which is >17% of the total north-western European breeding population. This is probably an under-representation of total mortality, as many carcasses are likely to have gone unnoticed and unreported. Within affected colonies, almost all chicks died. After the peak of the outbreak, in a colony established by late breeders, 25.7% of tested adults showed immunity to HPAI subtype H5. Removal of carcasses was associated with lower levels of mortality at affected colonies. More research on the sources and modes of transmission, incubation times, effective containment, and immunity is urgently needed to combat this major threat for colonial seabirds.
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| 5. |
- Martinez Mayorquin, Ramon Heberto
(författare)
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Sequence learning in the Bayesian Confidence Propagation Neural Network
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis examines sequence learning in the Bayesian Confidence PropagationNeural Network (BCPNN). The methodology utilized throughout this work is com-putational and analytical in nature and the contributions here presented can beunderstood along the following four major themes: 1) this work starts by revisitingthe properties of the BCPNN as an attractor neural network and then provides anovel formalization of some of those properties. First, a bayesian theoretical frame-work for the lower bounds in the BCPNN. Second, a differential formulation ofthe BCPNN plasticity rule that highlights its relationship to similar rules in thelearning literature. Third, closed form analytical results for the BCPNN trainingprocess. 2) After that, this work describes how the addition of an adaptation processto the BCPNN enables its sequence recall capabilities. The specific mechanisms ofsequence learning are then studied in detail as well as the properties of sequencerecall such as the persistence time (how long does the network last in a specific stateduring sequence recall) and its robustness to noise. 3) This work also shows howthe BCPNN can be enhanced with memory traces of the activity (z-traces) to pro-vide the network with disambiguation capabilities. 4) Finally, this works provides acomputational study to quantify the number of the sequences that the BCPNN canstore successfully. Alongside these central themes, results concerning robustness,stability and the relationship between the learned patterns and the input statisticsare presented in either computational or analytical form. The thesis concludes witha discussion of the sequence learning capabilities of the BCPNN in the context of thewider literature and describes both his advantages and disadvantages with respectto other attractor neural networks.
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| 6. |
- Sweeney, Yann, et al.
(författare)
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A Diffusive Homeostatic Signal Maintains Neural Heterogeneity and Responsiveness in Cortical Networks
- 2015
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Ingår i: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 11:7
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Tidskriftsartikel (refereegranskat)abstract
- Gaseous neurotransmitters such as nitric oxide (NO) provide a unique and often overlooked mechanism for neurons to communicate through diffusion within a network, independent of synaptic connectivity. NO provides homeostatic control of intrinsic excitability. Here we conduct a theoretical investigation of the distinguishing roles of NO-mediated diffusive homeo-stasis in comparison with canonical non-diffusive homeostasis in cortical networks. We find that both forms of homeostasis provide a robust mechanism for maintaining stable activity following perturbations. However, the resulting networks differ, with diffusive homeostasis maintaining substantial heterogeneity in activity levels of individual neurons, a feature disrupted in networks with non-diffusive homeostasis. This results in networks capable of representing input heterogeneity, and linearly responding over a broader range of inputs than those undergoing non-diffusive homeostasis. We further show that these properties are preserved when homeostatic and Hebbian plasticity are combined. These results suggest a mechanism for dynamically maintaining neural heterogeneity, and expose computational advantages of non-local homeostatic processes.
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| 7. |
- Sweeney, Yann
(författare)
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Functional Relevance of Homeostatic Intrinsic Plasticity in Neurons and Networks
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Maintaining the intrinsic excitability of neurons is crucial for stable brain activity. This can be achieved by the homeostatic regulation of membrane ion channel conductances, although it is not well understood how these processes influence broader aspects of neuron and network function. One of the many mechanisms which contribute towards this task is the modulation of potassium channel conductances by activity-dependent nitric oxide signalling. Here, we first investigate this mechanism in a conductance-based neuron model. By fitting the model to experimental data we find that nitric oxide signalling improves synaptic transmission fidelity at high firing rates, but that there is an increase in the metabolic cost of action potentials associated with this improvement. Although the improvement in function had been observed previously in experiment, the metabolic constraint was unknown. This additional constraint provides a plausible explanation for the selective activation of nitric oxide signalling only at high firing rates. In addition to mediating homeostatic control of intrinsic excitability, nitric oxide can diffuse freely across cell membranes, providing a unique mechanism for neurons to communicate within a network, independent of synaptic connectivity. We next conduct a theoretical investigation of the distinguishing roles of diffusive homeostasis mediated by nitric oxide in comparison with canonical non-diffusive homeostasis in cortical networks. We find that both forms of homeostasis robustly maintain stable activity. However, the resulting networks differ, with diffusive homeostasis maintaining substantial heterogeneity in activity levels of individual neurons, a feature disrupted in networks with non-diffusive homeostasis. This results in networks capable of representing input heterogeneity, and linearly responding over a broader range of inputs than those undergoing non-diffusive homeostasis. We further show that diffusive homeostasis interferes less than non-diffusive homeostasis in the synaptic weight dynamics of networks undergoing Hebbian plasticity. Overall, these results suggest a novel homeostatic mechanism for maintaining stable network activity while simultaneously minimising metabolic cost and conserving network functionality.
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| 8. |
- Trpevski, Daniel
(författare)
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Models of Corticostriatal Synaptic Plasticity and Plateau Potentials in Striatal Projection Neurons
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In this thesis we studied synaptic plasticity and neuronal computation in single striatal projection neurons (SPNs), which have a major role in goal-directed learning. Goal-directed or reward learning means to learn, based on sensory information from the body and the environment, to select actions out of all the behavioral repertoire that lead to obtaining a goal or reward (such as food or water). In mammals, all the behavioral motor repertoire is under constant, tonic inhibition, and the direct-pathway SPNs (dSPNs) select (disinhibit) goal-obtaining actions. The learning process is guided by the neuromodulator dopamine which signals the positive or negative out-come of an action. The synapses from cortical neurons on to the dSPNs, called corticostriatal synapses, are responsive to dopamine signals, and can strengthen and weaken based on the (positive or negative) action outcome. This promotes or discourages future actions in the same or similar sensory context.Within a collaborative computational modeling effort, we studied the biochemical circuitry in the corticostriatal synapses with multiscale modeling and simulations. This circuitry in the corticostriatal synapses responds to neuromodulatory signals and controls the expression of synaptic plasticity. Multiscale modeling and simulations enable studying a system at multiple temporal and spatial scales, and integrating the results across the different scales. Based on molecular dynamics simulations of the enzyme which transduces extracellular neuromodulatory signals into an intracellular second messenger molecule, and Brownian dynamics simulations of regulator molecules binding to the enzyme, we constructed a kinetic model ofthe enzyme-based signal transduction network. The kinetic model showed that two co-occuring neuromodulatory signals, a dopamine peak and an acetylcholine pause, are required to produce the second messenger and thus enable strengthening of corticostriatal synapses onto dSPNs, and that only the dopamine signal is not enough.Next, we developed a local, calcium- and reward-dependent learning rule based on what is known about the biochemical circuitry of corticostriatalsynapses onto dSPNs. We show that with this biologically-based learning rule, single SPNs can learn to solve the nonlinear feature binding problem(NFBP), a computationally hard problem representing the class of linearly nonseparable tasks. This result suggests that different, unrelated or partially related stimuli that require executing the same action to obtain a goal, canuse the same SPNs responsible for selecting that action, and that a single SPN can reliably distinguish between similar stimuli.The solution of the NFBP with the aforementioned learning rule relieson supralinear dendritic voltage elevations called plateau potentials. Experimentally, plateau potentials are all-or-none events, a property crucial for performing nonlinear computations required to solve the NFBP. However, computational models of plateau potentials often produce graded voltage elevations. We analyzed and compared existing plateau potential models, and found that long-lasting glutamate spillover in the extrasynaptic space robustly produces all-or-none plateau potentials by activating extrasynaptic N-methyl-D-aspartate (NMDA) glutamate receptors. This suggests that glutamate spillover may be a mechanism for generating all-or-none plateau potentials in vivo, as well.In summary, the findings presented in this thesis advance our understanding of the role of single dSPNs in goal-directed learning, the biophysical mechanisms involved in performing their nonlinear computations, and the neuromodulatory signals necessary to produce synaptic strengthening and thus implement goal-directed learning.
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| 9. |
- Tully, Philip J., et al.
(författare)
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Spike-Based Bayesian-Hebbian Learning of Temporal Sequences
- 2016
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Ingår i: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 12:5
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Tidskriftsartikel (refereegranskat)abstract
- Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison.
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| 10. |
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