Pattern formation in oscillatory complex networks consisting of excitable nodes

Oscillatory dynamics of complex networks has recently attracted great attention. In this paper we study pattern formation in oscillatory complex networks consisting of excitable nodes. We find that there exist a few center nodes and small skeletons for most oscillations. Complicated and seemingly random oscillatory patterns can be viewed as well-organized target waves propagating from center nodes along the shortest paths, and the shortest loops passing through both the center nodes and their driver nodes play the role of oscillation sources. Analyzing simple skeletons we are able to understand and predict various essential properties of the oscillations and effectively modulate the oscillations. These methods and results will give insights into pattern formation in complex networks, and provide suggestive ideas for studying and controlling oscillations in neural networks.Comment: 15 pages, 7 figures, to appear in Phys. Rev.

A brain-inspired computational model for spatio-temporal information processing

Spatio-temporal information processing is fundamental in both brain functions and AI applications. Current strategies for spatio-temporal pattern recognition usually involve explicit feature extraction followed by feature aggregation, which requires a large amount of labeled data. In the present study, motivated by the subcortical visual pathway and early stages of the auditory pathway for motion and sound processing, we propose a novel brain-inspired computational model for generic spatio-temporal pattern recognition. The model consists of two modules, a reservoir module and a decision-making module. The former projects complex spatio-temporal patterns into spatially separated neural representations via its recurrent dynamics, the latter reads out neural representations via integrating information over time, and the two modules are linked together using known examples. Using synthetic data, we demonstrate that the model can extract the frequency and order information of temporal inputs. We apply the model to reproduce the looming pattern discrimination behavior as observed in experiments successfully. Furthermore, we apply the model to the gait recognition task, and demonstrate that our model accomplishes the recognition in an event-based manner and outperforms deep learning counterparts when training data is limited

Changes in shooting accuracy among basketball players under fatigue: a systematic review and meta-analysis

ObjectiveTo investigate the influence of physical and mental fatigue of different intensities (mild, moderate or severe) on basketball shooting accuracy, with the aim of informing more effective training protocols and competition strategies.MethodsLiterature searches were conducted on Web of Science, PubMed, and EBSCO databases up to 25 June 2024. Inclusion and exclusion criteria were specified, and data extraction sheets were prepared. Study quality was assessed by using the Cochrane Risk of Bias Tool in Review Manager 5.4, and Stata18.0 software was used for heterogeneity analysis, subgroup analysis, forest plots, stratification analysis, and bias assessment.ResultsModerate physical fatigue affected two-point shooting accuracy (P < 0.01),severe physical fatigue affected both two-point (P = 0.02) and three-point shooting accuracy (p < 0.01),with severe physical fatigue showing a greater detrimental impact on three-point shooting accuracy, while two-point shooting accuracy may vary under specific conditions. Additionally, adolescent athletes were less affected by severe physical fatigue compared to adult athletes or those with longer training experience. Moderate mental fatigue also significantly reduced free-throw accuracy (p < 0.01).ConclusionThe shooting accuracy of basketball players was significantly affected by moderate and severe physical fatigue. Severe physical fatigue notably adversely affected the accuracy of three-point shooting relative to moderate fatigue; Additionally, moderate mental fatigue significantly reduced free-throw accuracy, which may be attributed to a decline in cognitive executive functions, highlighting the importance of fatigue management in sports training.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/#myprospero, identifier CRD4202453955

Long-Term Conditioning to Elevated pCO2 and Warming Influences the Fatty and Amino Acid Composition of the Diatom Cylindrotheca fusiformis

The unabated rise in anthropogenic CO2 emissions is predicted to strongly influence the ocean's environment, increasing the mean sea-surface temperature by 4°C and causing a pH decline of 0.3 units by the year 2100. These changes are likely to affect the nutritional value of marine food sources since temperature and CO2 can influence the fatty (FA) and amino acid (AA) composition of marine primary producers. Here, essential amino (EA) and polyunsaturated fatty (PUFA) acids are of particular importance due to their nutritional value to higher trophic levels. In order to determine the interactive effects of CO2 and temperature on the nutritional quality of a primary producer, we analyzed the relative PUFA and EA composition of the diatom Cylindrotheca fusiformis cultured under a factorial matrix of 2 temperatures (14 and 19°C) and 3 partial pressures of CO2 (180, 380, 750 μatm) for >250 generations. Our results show a decay of ∼3% and ∼6% in PUFA and EA content in algae kept at a pCO2 of 750 μatm (high) compared to the 380 μatm (intermediate) CO2 treatments at 14°C. Cultures kept at 19°C displayed a ∼3% lower PUFA content under high compared to intermediate pCO2, while EA did not show differences between treatments. Algae grown at a pCO2 of 180 μatm (low) had a lower PUFA and AA content in relation to those at intermediate and high CO2 levels at 14°C, but there were no differences in EA at 19°C for any CO2 treatment. This study is the first to report adverse effects of warming and acidification on the EA of a primary producer, and corroborates previous observations of negative effects of these stressors on PUFA. Considering that only ∼20% of essential biomolecules such as PUFA (and possibly EA) are incorporated into new biomass at the next trophic level, thepotential impacts of adverse effects of ocean warming and acidification at the base of the food web may be amplified towards higher trophic levels, which rely on them as source of essential biomolecules