Long-term drought sensitivity of trees in second-growth forests in a humid region

Classical field methods of reconstructing drought using tree rings in humid, temperate regions typically target old trees from drought-prone sites. This approach limits investigators to a handful of species and excludes large amounts of data that might be useful, especially for coverage gaps in large-scale networks. By sampling in more “typical” forests, network density and species diversity would increase in ways that could potentially improve reconstructions. Ten nonclassical tree-ring chronologies derived from randomly selected trees, trees from logged forests, or both were compared to more classical chronologies and an independent regional drought reconstruction to determine their usefulness for dendrohydroclimatic research. We find that nonclassical chronologies are significantly correlated to classical chronologies and reconstructed drought over the last 2–3 centuries. While nonclassical chronologies have spectral properties similar to those from classical dendroclimatic collections, they do lack spectral power at lower frequencies that are present in the drought reconstruction. Importantly, our results show that tree growth is strongly dependent on moisture availability, even for small, randomly selected trees in cut forests. These results indicate that there could be more data available in areas with few current tree-ring collections for studying climate history and that drought plays an important role in humid forests.

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Spatiotemporal dynamics of maximal and minimal EEG spectral power

10.1101/2021.01.15.426906 ◽

2021 ◽

Author(s):

Melisa Menceloglu ◽

Marcia Grabowecky ◽

Satoru Suzuki

Keyword(s):

Large Scale ◽

Stochastic Dynamics ◽

Spectral Power ◽

Spatiotemporal Dynamics ◽

Small Scale ◽

Neural Activities ◽

Eeg Spectral Power ◽

Neural Communication ◽

Scale Network ◽

Large Scale Networks

AbstractOscillatory neural activities are prevalent in the brain with their phase realignment contributing to the coordination of neural communication. Phase realignments would have especially strong (or weak) impact when neural activities are strongly synchronized (or desynchronized) within the interacting sub-populations. We report that the spatiotemporal dynamics of strong regional synchronization (reflected in maximal EEG spectral power)—activation—and strong regional desynchronization (reflected in minimal EEG spectral power)—suppression—are characterized by the spatial segregation of isolated small-scale networks and highly cooperative large-scale networks. Specifically, small-scale spectral-power activations and suppressions involving only 2%–7% of EEG scalp sites were prolonged (relative to stochastic dynamics) and consistently co-localized in a frequency specific manner. For example, the small-scale networks for θ, α, β1, and β2 bands (4–30 Hz) consistently included frontal sites when the eyes were closed, whereas the small-scale network for γ band (31–55 Hz) consistently clustered in medial-central-posterior sites whether the eyes were open or closed. Large-scale activations and suppressions involving over 30% of EEG sites were also prolonged and generally clustered in regions complementary to where small-scale activations and suppressions clustered. In contrast, intermediate-scale activations and suppressions tended to follow stochastic dynamics and were less consistently localized. These results suggest that strong synchronizations and desynchronizations occur in small-scale and large-scale networks that are spatially segregated and frequency specific. These synchronization networks may broadly segregate the relatively independent and highly cooperative oscillatory processes while phase realignments fine-tune the network configurations based on behavioral demands.

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Spatiotemporal dynamics of maximal and minimal EEG spectral power

PLoS ONE ◽

10.1371/journal.pone.0253813 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0253813

Author(s):

Melisa Menceloglu ◽

Marcia Grabowecky ◽

Satoru Suzuki

Keyword(s):

Large Scale ◽

Stochastic Dynamics ◽

Spectral Power ◽

Spatiotemporal Dynamics ◽

Small Scale ◽

Neural Activities ◽

Eeg Spectral Power ◽

Neural Communication ◽

Scale Network ◽

Large Scale Networks

Oscillatory neural activities are prevalent in the brain with their phase realignment contributing to the coordination of neural communication. Phase realignments may have especially strong (or weak) impact when neural activities are strongly synchronized (or desynchronized) within the interacting populations. We report that the spatiotemporal dynamics of strong regional synchronization measured as maximal EEG spectral power—referred to as activation—and strong regional desynchronization measured as minimal EEG spectral power—referred to as suppression—are characterized by the spatial segregation of small-scale and large-scale networks. Specifically, small-scale spectral-power activations and suppressions involving only 2–7% (1–4 of 60) of EEG scalp sites were prolonged (relative to stochastic dynamics) and consistently co-localized in a frequency specific manner. For example, the small-scale networks for θ, α, β1, and β2 bands (4–30 Hz) consistently included frontal sites when the eyes were closed, whereas the small-scale network for γ band (31–55 Hz) consistently clustered in medial-central-posterior sites whether the eyes were open or closed. Large-scale activations and suppressions involving over 17–30% (10–18 of 60) of EEG sites were also prolonged and generally clustered in regions complementary to where small-scale activations and suppressions clustered. In contrast, intermediate-scale activations and suppressions (involving 7–17% of EEG sites) tended to follow stochastic dynamics and were less consistently localized. These results suggest that strong synchronizations and desynchronizations tend to occur in small-scale and large-scale networks that are spatially segregated and frequency specific. These synchronization networks may broadly segregate the relatively independent and highly cooperative oscillatory processes while phase realignments fine-tune the network configurations based on behavioral demands.

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of Brain Cells & Circuits “Camillo Golgi”: The Neural Bases of Action – from cellular microcircuits to large-scale networks and modelling

School of Brain Cells & Circuits “Camillo Golgi”: The Neural Bases of Action – from cellular microcircuits to large-scale networks and modelling ◽

10.3389/978-2-88963-087-5 ◽

2019 ◽

Keyword(s):

Large Scale ◽

Brain Cells ◽

Neural Bases ◽

Large Scale Networks ◽

Camillo Golgi

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Contribution of Ionotropic Glutamatergic Receptors to Excitability and Attentional Signals in Macaque Frontal Eye Field

Cerebral Cortex ◽

10.1093/cercor/bhab007 ◽

2021 ◽

Author(s):

Miguel Dasilva ◽

Christian Brandt ◽

Marc Alwin Gieselmann ◽

Claudia Distler ◽

Alexander Thiele

Keyword(s):

Attentional Control ◽

Large Scale ◽

Neuronal Excitability ◽

Cell Types ◽

Receptor Activation ◽

Frontal Eye Field ◽

Control Signals ◽

Cognitive Operations ◽

Eye Field ◽

Large Scale Networks

Abstract Top-down attention, controlled by frontal cortical areas, is a key component of cognitive operations. How different neurotransmitters and neuromodulators flexibly change the cellular and network interactions with attention demands remains poorly understood. While acetylcholine and dopamine are critically involved, glutamatergic receptors have been proposed to play important roles. To understand their contribution to attentional signals, we investigated how ionotropic glutamatergic receptors in the frontal eye field (FEF) of male macaques contribute to neuronal excitability and attentional control signals in different cell types. Broad-spiking and narrow-spiking cells both required N-methyl-D-aspartic acid and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor activation for normal excitability, thereby affecting ongoing or stimulus-driven activity. However, attentional control signals were not dependent on either glutamatergic receptor type in broad- or narrow-spiking cells. A further subdivision of cell types into different functional types using cluster-analysis based on spike waveforms and spiking characteristics did not change the conclusions. This can be explained by a model where local blockade of specific ionotropic receptors is compensated by cell embedding in large-scale networks. It sets the glutamatergic system apart from the cholinergic system in FEF and demonstrates that a reduction in excitability is not sufficient to induce a reduction in attentional control signals.

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Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15 ◽

10.1145/2783258.2783385 ◽

2015 ◽

Cited By ~ 26

Author(s):

Michael Mitzenmacher ◽

Jakub Pachocki ◽

Richard Peng ◽

Charalampos Tsourakakis ◽

Shen Chen Xu

Keyword(s):

Large Scale ◽

Large Scale Networks

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How does the position of firms in the supply chain affect their performance? An empirical study

Applied Network Science ◽

10.1007/s41109-021-00364-9 ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Siddharth Arora ◽

Alexandra Brintrup

Keyword(s):

Supply Chain ◽

Empirical Data ◽

Large Scale ◽

Individual Performance ◽

Supply Network ◽

Supply Chain Networks ◽

Focal Firm ◽

Cooperative Competition ◽

Large Scale Networks ◽

The Relationship

AbstractThe relationship between a firm and its supply chain has been well studied, however, the association between the position of firms in complex supply chain networks and their performance has not been adequately investigated. This is primarily due to insufficient availability of empirical data on large-scale networks. To addresses this gap in the literature, we investigate the relationship between embeddedness patterns of individual firms in a supply network and their performance using empirical data from the automotive industry. In this study, we devise three measures that characterize the embeddedness of individual firms in a supply network. These are namely: centrality, tier position, and triads. Our findings caution us that centrality impacts individual performance through a diminishing returns relationship. The second measure, tier position, allows us to investigate the concept of tiers in supply networks because we find that as networks emerge, the boundaries between tiers become unclear. Performance of suppliers degrade as they move away from the focal firm (i.e., Toyota). The final measure, triads, investigates the effect of buying and selling to firms that supply the same customer, portraying the level of competition and cooperation in a supplier’s network. We find that increased coopetition (i.e., cooperative competition) is a performance enhancer, however, excessive complexity resulting from being involved in both upstream and downstream coopetition results in diminishing performance. These original insights help understand the drivers of firm performance from a network perspective and provide a basis for further research.

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EEG Data Quality: Determinants and Impact in a Multicenter Study of Children, Adolescents, and Adults with Attention-Deficit/Hyperactivity Disorder (ADHD)

Brain Sciences ◽

10.3390/brainsci11020214 ◽

2021 ◽

Vol 11 (2) ◽

pp. 214

Author(s):

Anna Kaiser ◽

Pascal-M. Aggensteiner ◽

Martin Holtmann ◽

Andreas Fallgatter ◽

Marcel Romanos ◽

...

Keyword(s):

Data Quality ◽

Large Scale ◽

Linear Models ◽

Spectral Power ◽

Control Group ◽

Continuous Performance Task ◽

Related Data ◽

Eyes Closed ◽

Eeg Data ◽

Eyes Open

Electroencephalography (EEG) represents a widely established method for assessing altered and typically developing brain function. However, systematic studies on EEG data quality, its correlates, and consequences are scarce. To address this research gap, the current study focused on the percentage of artifact-free segments after standard EEG pre-processing as a data quality index. We analyzed participant-related and methodological influences, and validity by replicating landmark EEG effects. Further, effects of data quality on spectral power analyses beyond participant-related characteristics were explored. EEG data from a multicenter ADHD-cohort (age range 6 to 45 years), and a non-ADHD school-age control group were analyzed (ntotal = 305). Resting-state data during eyes open, and eyes closed conditions, and task-related data during a cued Continuous Performance Task (CPT) were collected. After pre-processing, general linear models, and stepwise regression models were fitted to the data. We found that EEG data quality was strongly related to demographic characteristics, but not to methodological factors. We were able to replicate maturational, task, and ADHD effects reported in the EEG literature, establishing a link with EEG-landmark effects. Furthermore, we showed that poor data quality significantly increases spectral power beyond effects of maturation and symptom severity. Taken together, the current results indicate that with a careful design and systematic quality control, informative large-scale multicenter trials characterizing neurophysiological mechanisms in neurodevelopmental disorders across the lifespan are feasible. Nevertheless, results are restricted to the limitations reported. Future work will clarify predictive value.

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