Temporal Activity Patterns Research Articles

Stride-to-stride fluctuations and temporal patterns of muscle activity exhibit similar responses during walking to variable visual cues

Incorporating variability within gait retraining approaches has been proposed and shown to lead to positive changes. Specifically, submitting the individuals to walk in synchrony to cues that are temporally organized with a fractal-like patterns, promotes changes at the stride-to-stride fluctuations closer to those typically find in young adults. However, there is still a need to understand the underlying neuromuscular mechanisms associated to such improvement. Thus, this study aimed to investigate whether changes in the temporal structure of the variability in gait patterns are accompanied by changes in muscle activity patterns. Fourteen young individuals walked synchronized to one uncued (UNC) and three cued conditions: isochronous (ISO), fractal (FRC) and random (RND). Inter-stride intervals were determined from an accelerometer placed on the lateral malleoli. Inter-muscle peak intervals were obtained from the electromyographic signal from the gastrocnemius muscle. Fractal scaling, obtained through detrended fluctuation analysis, and coefficient of variation were calculated. Repeated measures ANOVAs were used to identify differences between conditions. Significant main effect was observed for both fractal scaling and coefficient of variation. Both shown no differences between UNC and FRC conditions, while ISO and RND were significantly lower compared to UNC and FRC conditions. In addition, a Pearson’s Correlation was used to test the correlation between variables. A strong correlation was found the temporal structure of gait and muscle activity patterns. These findings strengthen the current literature regarding the incorporation of variability within cued approaches. Specifically, it shows that such an approach allows the modification of the neuromuscular processes underlying the stride-to-stride fluctuations.

Temporal mismatches in flight activity patterns between Pipistrellus kuhlii and Prays oleae in olive farms: Implications for biocontrol services potential

Abstract Biocontrol services are widely recognized to provide key incentives for bat conservation. However, we have virtually no information on whether and how disruptions in bat‐mediated biocontrol services are driven by mismatches between the temporal activity patterns of insectivorous bats and insect pests. We investigated the temporal relationship between the nightly activity patterns of the common pipistrelle bat (Pipistrellus kuhlii) and the olive fruit moth (Prays oleae). Temporal mismatches between species pairs were estimated as the time difference (expressed as a percentage of the night) at which P. kuhlii and P. oleae reached 50% of their abundance. The study was carried out during spring, summer, and fall between 2017 and 2019 in 60 olive farms representing increasing levels of structural simplification (as a surrogate of agricultural intensification). Olive farms were classified as exhibiting high (i.e. HIGH olive farms; n = 27), intermediate (MID; n = 18), and low (LOW; n = 15) structural complexity. Temporal mismatches between the activity levels of P. kuhlii and P. oleae varied between seasons and types of olive farms, being comparatively lower in summer than in spring and fall. Furthermore, summer was the only season in which temporal mismatches between species pairs differed between types of olive farms, with higher temporal mismatches found in LOW than in HIGH and MID olive farms. Overall, our work demonstrates the existence of temporal mismatches between the nightly activity patterns of P. kuhlii and P. oleae. Furthermore, it demonstrates that the structural simplification of olive farms increases temporal mismatches between species pairs, particularly in summer when bat‐mediated biocontrol services are most needed. Synthesis and applications. Future research should consider mismatches between the temporal activity patterns of insectivorous bats and insect pests. Otherwise, the actual impact of agricultural intensification on bat‐mediated biocontrol services as well as the economic impact of their loss on the agriculture industry might be underestimated. To enhance biocontrol services, we propose increasing the availability of suitable roosting and foraging sites as well as conserving areas of remnant native woodland and scattered hollow‐bearing trees.

Aquatic Soundscape Recordings Reveal Diverse Vocalizations and Nocturnal Activity of an Endangered Frog.

AbstractAutonomous sensors provide opportunities to observe organisms across spatial and temporal scales that humans cannot directly observe. By processing large data streams from autonomous sensors with deep learning methods, researchers can make novel and important natural history discoveries. In this study, we combine automated acoustic monitoring with deep learning models to observe breeding-associated activity in the endangered Sierra Nevada yellow-legged frog (Rana sierrae), a behavior that current surveys do not measure. By deploying inexpensive hydrophones and developing a deep learning model to recognize breeding-associated vocalizations, we discover three undocumented R. sierrae vocalization types and find an unexpected temporal pattern of nocturnal breeding-associated vocal activity. This study exemplifies how the combination of autonomous sensor data and deep learning can shed new light on species' natural history, especially during times or in locations where human observation is limited or impossible.

Spatio-temporal interactions between the red fox and the wolf in two contrasting European landscapes

Relationships among carnivore species are complex, potentially switching from competition to facilitation on a context-dependent basis. Negative associations are predicted to increase with latitude, due to limited resources emphasising competition and/or intra-guild predation. Accordingly, a stronger negative correlation between large- and meso-carnivore abundances should be expected at higher latitudes, with a substantial spatio-temporal partitioning favouring interspecific coexistence. Human presence may influence spatio-temporal relationships between (meso)carnivore species, as it can be perceived as a risk factor, but anthropogenic food can also provide an important additional food resource. Using camera-trap data, we studied the spatio-temporal associations between two of the most widespread carnivores in Europe, i.e., the red fox and wolf. We compared their monthly/daily spatio-temporal partitioning between two different landscapes: Białowieża Primeval Forest (Poland) and the Mediterranean Maremma Regional Park (Italy). We predicted a stronger interspecific partitioning, as well as more attraction of red foxes to humans in the northern site (Poland). Temporal activity patterns of the two carnivores overlapped in both sites, and their detection rates were positively associated, even though in weaker way in Poland. We observed a positive spatial association of red foxes with human activity in Białowieża, but not in Maremma. This association occurred only at a monthly temporal scale and disappeared at a daily scale, suggesting some disturbance in the shorter term. Our results provided partial support to our predictions and suggest that, despite the ecological differences between our study areas, only weak differences in wolf-fox relations were observed, suggesting that red fox responses to wolves may be relatively comparable over large spatial scales.

Seasonal Distribution of Gravity Waves Near the Stratopause in 2019–2022

AbstractThe cloud imaging and particle size (CIPS) instrument onboard the Aeronomy of Ice in the Mesosphere satellite provides images of gravity waves (GWs) near the stratopause and lowermost mesosphere (altitudes of 50–55 km). GW identification is based on Rayleigh Albedo Anomaly (RAA) variances, which are derived from GW‐induced fluctuations in Rayleigh scattering at 265 nm. Based on 3 years of CIPS RAA variance data from 2019 to 2022, we report for the first time the seasonal distribution of GWs entering the mesosphere with high (7.5 km) horizontal resolution on a near‐global scale. Seasonally averaged GW variances clearly show spatial and temporal patterns of GW activity, mainly due to the seasonal variation of primary GW sources such as convection, the polar vortices and flow over mountains. Measurements of stratospheric GWs derived from Atmospheric InfraRed Sounder (AIRS) observations of 4.3 μm brightness temperature perturbations within the same 3‐year time range are compared to the CIPS results. The comparisons show that locations of GW hotspots are similar in the CIPS and AIRS observations. Variability in GW variances and the monthly changes in background zonal wind suggest a strong GW‐wind correlation. This study demonstrates the utility of the CIPS GW variance data set for statistical investigations of GWs in the lowermost mesosphere, as well as provides a reference for location/time selection for GW case studies.

High-resolution chemical imaging to understand Cd activation in rice rhizosphere of karstic soils

Cadmium (Cd) activation, especially at a high spatial resolution, in paddy soils with a high geogenic Cd background is yet to be understood. To investigate the temporal and spatial patterns of Cd activation in rice rhizosphere, pot and rhizotron experiments were conducted using four paddy soils with high geogenic Cd (0.11–3.70 mg kg−1) from Guangxi, southwestern China. The pot experiment results showed that porewater Cd concentrations initially decreased and then increased over the complete rice growth period, reaching its lowest value during the late-tillering and early-filling stages. Besides, correlation analysis identified organic matter and root manganese (Mn) content as the main factors affecting rice Cd uptake, with Mn having a negative effect and organic matter having a positive effect. Sub-millimeter two-dimensional chemical imaging revealed that the distribution of labile Cd in the rhizosphere (by diffusive gradients in thin-films, or DGT) was influenced by the root system and soil properties, such as pH (by planar optode) and acid phosphatase activity (by soil zymography). Soil acid phosphatase activity increased under Cd stress. The overall pH at rice rhizosphere decreased. Moreover, a close relationship was found between the spatial distributions of soil labile Mn and Cd at the rhizosphere, with higher Mn being associated with lower Cd lability. This study highlights Mn as a key element in regulating rice Cd uptake and enlightens future Mn-based strategies for addressing Cd pollution in rice paddy soils, especially in karst areas with high geochemical background.

Artificial Consciousness: Misconception(s) of a Self-Fulfilling Prophecy Nobody Wants

The rise of Artificial Intelligence (AI) has produced prophets and prophecies announcing that the age of artificial consciousness is near. Not only does the mere idea that any machine could ever possess the full potential of human consciousness suggest that AI could replace the role of God in the future, it also puts into question the fundamental human right to freedom and dignity. This position paper takes the stand that, in the light of all we currently know about brain evolution and the never-stopping formation of adaptive neural circuitry for learning, memory, decision making and, ultimately, fully conscious reasoning and creativity in the human species, the idea of an artificial consciousness appears misconceived. The paper highlights some of the major reasons why. While awareness to external stimuli for processes such as perception, recognition, and operational problem solving is under the direct control of functionally specific brain networks associated with sensory and cognitive functions across animal species, consciousness is a unique property of the human mind. Potentiated by brain evolution, consciousness has come to be when humans became able to represent, and reflect on, the Self in relation to past, present and future, and to project these representations into possible worlds by drawing and other forms of conceptual and creative expression. Epigenetically determined, shaped by experience, capable of representing real and non-real world states, consciousness is enabled by context-dependent adaptive brain circuits that have evolved on the grounds of self-organizing functional interactions at different levels of integration in a from-local-to global functional brain design. The evolution of the latter being continuous, the limits of consciousness are unpredictable. If cracking the computational code to human consciousness were possible, the resulting algorithms would have to be able to generate temporal activity patterns simulating long-distance signal reverberation across the brain and the de-correlation of spatial signal contents from their temporal signatures. In the light of scientific evidence for complex interactions between implicit (non-conscious) and explicit (conscious) representations in learning, memory, and the construction of conscious representation, the code would have to be capable of making all implicit processing explicit. Algorithms would have to be capable of a progressive, less and less arbitrary selection of temporal activity patterns in a continuously developing neural network structure akin to that of the human brain, from synapses to higher cognitive functional integration. The code would have to possess the self-organizing capacities that generate the brain signatures of phenomenal consciousness. In the biological brain, consolidation or extinction of these temporal brain signatures is driven by external event probabilities according to the principles of Hebbian learning. Consciousness is constantly fed by such learning, capable of generating stable representations despite an incommensurable amount of variability in input data, across time and across individuals, for life-long integration of experience data. Artificial consciousness would require probabilistic adaptive computations capable of emulating all the dynamics of human learning and memory that enable human intelligence and creativity. No AI is likely to ever have such potential.

A review of skin immune processes in acne.

Acne vulgaris is one of the most prevalent skin conditions, affecting almost all teenagers worldwide. Multiple factors, including the excessive production of sebum, dysbiosis of the skin microbiome, disruption of keratinization within hair follicles, and local inflammation, are believed to trigger or aggravate acne. Immune activity plays a crucial role in the pathogenesis of acne. Recent research has improved our understanding of the immunostimulatory functions of microorganisms, lipid mediators, and neuropeptides. Additionally, significant advances have been made in elucidating the intricate mechanisms through which cutaneous innate and adaptive immune cells perceive and transmit stimulatory signals and initiate immune responses. However, our understanding of precise temporal and spatial patterns of immune activity throughout various stages of acne development remains limited. This review provides a comprehensive overview of the current knowledge concerning the immune processes involved in the initiation and progression of acne. Furthermore, we highlight the significance of detailed spatiotemporal analyses, including analyses of temporal dynamics of immune cell populations as well as single-cell and spatial RNA sequencing, for the development of targeted therapeutic and prevention strategies.

A time-resolved meta-analysis of consensus gene expression profiles during human T-cell activation

BackgroundThe coordinated transcriptional regulation of activated T-cells is based on a complex dynamic behavior of signaling networks. Given an external stimulus, T-cell gene expression is characterized by impulse and sustained patterns over the course. Here, we analyze the temporal pattern of activation across different T-cell populations to develop consensus gene signatures for T-cell activation.ResultsHere, we identify and verify general biomarker signatures robustly evaluating T-cell activation in a time-resolved manner. We identify time-resolved gene expression profiles comprising 521 genes of up to 10 disjunct time points during activation and different polarization conditions. The gene signatures include central transcriptional regulators of T-cell activation, representing successive waves as well as sustained patterns of induction. They cover sustained repressed, intermediate, and late response expression rates across multiple T-cell populations, thus defining consensus biomarker signatures for T-cell activation. In addition, intermediate and late response activation signatures in CAR T-cell infusion products are correlated to immune effector cell-associated neurotoxicity syndrome.ConclusionThis study is the first to describe temporally resolved gene expression patterns across T-cell populations. These biomarker signatures are a valuable source, e.g., monitoring transcriptional changes during T-cell activation with a reasonable number of genes, annotating T-cell states in single-cell transcriptome studies, or assessing dysregulated functions of human T-cell immunity.

Activation of neurons in the insular cortex and lateral hypothalamus during food anticipatory period caused by food restriction in mice

Mice fed a single meal daily at a fixed time display food anticipatory activity (FAA). It has been reported that the insular cortex (IC) plays an essential role in food anticipation, and lateral hypothalamus (LH) regulates the expression of FAA. However, how these areas contribute to FAA production is still unclear. Thus, we examined the temporal and spatial activation pattern of neurons in the IC and LH during the food anticipation period to determine their role in FAA establishment. We observed an increase of c-Fos-positive neurons in the IC and LH, including orexin neurons of male adult C57BL/6 mice. These neurons were gradually activated from the 1st day to 15th day of restricted feeding. The activation of these brain regions, however, peaked at a distinct point in the food restriction procedure. These results suggest that the IC and LH are differently involved in the neural network for FAA production.

Fibromyalgia is associated with hypersensitivity but not with abnormal pain modulation: evidence from QST trials and spinal fMRI.

Widespread pain and hyperalgesia are characteristics of chronic musculoskeletal pain conditions, including fibromyalgia syndrome (FM). Despite mixed evidence, there is increasing consensus that these characteristics depend on abnormal pain augmentation and dysfunctional pain inhibition. Our recent investigations of pain modulation with individually adjusted nociceptive stimuli have confirmed the mechanical and thermal hyperalgesia of FM patients but failed to detect abnormalities of pain summation or descending pain inhibition. Furthermore, our functional magnetic resonance imaging evaluations of spinal and brainstem pain processing during application of sensitivity-adjusted heat stimuli demonstrated similar temporal patterns of spinal cord activation in FM and HC participants. However, detailed modeling of brainstem activation showed that BOLD activity during "pain summation" was increased in FM subjects, suggesting differences in brain stem modulation of nociceptive stimuli compared to HC. Whereas these differences in brain stem activation are likely related to the hypersensitivity of FM patients, the overall central pain modulation of FM showed no significant abnormalities. These findings suggest that FM patients are hyperalgesic but modulate nociceptive input as effectively as HC.

Temporal Patterns of Diet and Physical Activity and of Diet Alone Have More Numerous Relationships With Health and Disease Status Indicators Compared to Temporal Patterns of Physical Activity Alone.

Daily temporal patterns of energy intake (temporal dietary patterns [TDPs]) and physical activity (temporal physical activity patterns [TPAPs]) have been independently and jointly (temporal dietary and physical activity patterns [TDPAPs]) associated with health and disease status indicators. The aim of this study was to compare the number and strength of association between clusters of daily TDPs, TPAPs, and TDPAPs and multiple health and disease status indicators. This cross-sectional study used 1 reliable weekday dietary recall and 1 random weekday of accelerometer data to partition to create clusters of participants representing the 3 temporal patterns. Four clusters were created via kernel-k means clustering algorithm of the same constrained dynamic time warping distance computed over the time series for each temporal pattern. From the National Health and Nutrition Examination Survey (2003-2006), 1,836 US adults aged 20 through 65 years who were not pregnant and had valid diet, physical activity, sociodemographic, anthropometric, questionnaire, and health and disease status indicator data were included. Health status indicators used as outcome measures were body mass index, waist circumference, fasting plasma glucose, hemoglobin A1c, triglycerides, high-density lipoprotein cholesterol, total cholesterol, and systolic and diastolic blood pressure; disease status indicators included obesity, type 2 diabetes mellitus, and metabolic syndrome. Multivariate regression models determined associations between the clusters representing each pattern and health and disease status indicators, controlling for confounders and adjusting for multiple comparisons. The number of significant differences among clusters and adjusted R2 and Akaike information criterion compared the strength of associations between clusters of patterns and continuous and categorical health and disease status indicators. TDPAPs showed 21 significant associations with health and disease status indicators, including body mass index, waist circumference, obesity, and type 2 diabetes; TDPs showed 19 significant associations; and TPAPs showed 8 significant associations. TDPAPs and TDPs had stronger and more numerous associations with health and disease status indicators compared with TPAPs. Patterns representing the integration of daily dietary habits hold promise for early detection of obesity.

A guide to ERK dynamics, part 1: mechanisms and models.

Extracellular signal-regulated kinase (ERK) has long been studied as a key driver of both essential cellular processes and disease. A persistent question has been how this single pathway is able to direct multiple cell behaviors, including growth, proliferation, and death. Modern biosensor studies have revealed that the temporal pattern of ERK activity is highly variable and heterogeneous, and critically, that these dynamic differences modulate cell fate. This two-part review discusses the current understanding of dynamic activity in the ERK pathway, how it regulates cellular decisions, and how these cell fates lead to tissue regulation and pathology. In part 1, we cover the optogenetic and live-cell imaging technologies that first revealed the dynamic nature of ERK, as well as current challenges in biosensor data analysis. We also discuss advances in mathematical models for the mechanisms of ERK dynamics, including receptor-level regulation, negative feedback, cooperativity, and paracrine signaling. While hurdles still remain, it is clear that higher temporal and spatial resolution provide mechanistic insights into pathway circuitry. Exciting new algorithms and advanced computational tools enable quantitative measurements of single-cell ERK activation, which in turn inform better models of pathway behavior. However, the fact that current models still cannot fully recapitulate the diversity of ERK responses calls for a deeper understanding of network structure and signal transduction in general.

Artificial Consciousness: Misconception(s) of a Self-Fulfilling Prophecy Nobody Wants

The rise of Artificial Intelligence (AI) has produced prophets and prophecies announcing that the age of artificial consciousness is near. Not only does the mere idea that any machine could ever possess the full potential of human consciousness suggest that AI could replace the role of God in the future, it also puts into question the fundamental human right to freedom and dignity. Yet, in the light of all we currently know about brain evolution and the adaptive neural dynamics underlying human consciousness, the idea of an artificial consciousness appears misconceived. This article highlights some of the major reasons why the prophecy of a successful emulation of human consciousness by AI ignores most of the data about adaptive processes of learning and memory as the developmental origins of consciousness. The analysis provided leads to conclude that human consciousness is epigenetically determined as a unique property of the mind, shaped by experience, capable of representing real and non-real world states and creatively projecting these representations into the future. The development of the adaptive brain circuitry that enables this expression of consciousness is highly context-dependent, shaped by multiple self-organizing functional interactions at different levels of integration displaying a from-local-to global functional organization. Human consciousness is subject to changes in time that are essentially unpredictable. If cracking the computational code to human consciousness were possible, the resulting algorithms would have to be able to generate temporal activity patterns simulating long-distance signal reverberation in the brain, and the de-correlation of spatial signal contents from their temporal signatures in the brain. In the light of scientific evidence for complex interactions between implicit (non-conscious) and explicit (conscious) representations in learning, memory, and the construction of conscious representations such a code would have to be capable of making all implicit processing explicit. Algorithms would have to be capable of a progressive and less and less arbitrary selection of temporal activity patterns in a continuously developing neural network structure that is functionally identical to that of the human brain, from synapses to higher cognitive functional integration. The code would have to possess the self-organizing capacities of the brain that generate the temporal signatures of a conscious experience. The consolidation or extinction of these temporal brain signatures are driven by external event probabilities according to the principles of Hebbian learning. Human consciousness is constantly fed by such learning, capable of generating stable representations despite an incommensurable amount of variability in input data, across time and across individuals, for a life-long integration of experience data. Artificial consciousness would require probabilistic adaptive computations capable of emulating all the dynamics of individual human learning, memory and experience. No AI is likely to ever have such potential.

Constructing temporal networks with bursty activity patterns

Human social interactions tend to vary in intensity over time, whether they are in person or online. Variable rates of interaction in structured populations can be described by networks with the time-varying activity of links and nodes. One of the key statistics to summarize temporal patterns is the inter-event time, namely the duration between successive pairwise interactions. Empirical studies have found inter-event time distributions that are heavy-tailed, for both physical and digital interactions. But it is difficult to construct theoretical models of time-varying activity on a network that reproduce the burstiness seen in empirical data. Here we develop a spanning-tree method to construct temporal networks and activity patterns with bursty behavior. Our method ensures any desired target inter-event time distributions for individual nodes and links, provided the distributions fulfill a consistency condition, regardless of whether the underlying topology is static or time-varying. We show that this model can reproduce burstiness found in empirical datasets, and so it may serve as a basis for studying dynamic processes in real-world bursty interactions.

Impact of water level change on shifts in subsistence regimes revealed by anthropogenic fire in the lower Yangtze basin during the Mid-Holocene

The lower Yangtze is one of the regions of origin of rice agriculture but it is unclear if environmental change drove the transition from hunting-gathering to agriculture. Long-term and continuous lacustrine records of anthropogenic fire are a more effective means of addressing this problem than the fragmentary terrestrial records available for the lower Yangtze region. We constructed a Holocene fire history revealed by records of black carbon content (BCC) and grain size of the sediments of Lake Nanyi in the lower Yangtze. Our results show the following: 1) Peaks in fire activity occurred during ∼8400–6500 (fire peak P1) and ∼ 1100–0 cal BP (fire peak P2), while weaker fire activity occurred during ∼6500–1100 cal BP (S2). 2) This temporal pattern of fire activity, with a double peak (P1-S2-P2), was asynchronous from east to west across the lower Yangtze. The warm and humid climate would not have caused intense fires and the reduction of forest during ∼8400–6500 and ∼ 1100–0 cal BP. Therefore, we suggest that anthropogenic ignitions, corresponding to pronounced population fluctuations after 8400 cal BP, dominated the regional fire activity. Combined with archaeological evidence, the inverse relationship between the agricultural and population levels and fire intensity during the mid-Holocene suggests that fire intensity at this time was the result of the diversity of landscape types associated with prehistoric subsistence patterns, rather than the magnitude of anthropogenic activity. Combined with sea level, rainfall and archaeological evidence, we propose a mechanism whereby asynchronous water-level rise during ∼8400–6500 cal BP forced the migration of humans to the foothills where they often used fire to modify the landscape as part of a broad-spectrum subsistence pattern. Whereas, water-level fall after 6500 cal BP expanded the area of open ground for human settlement, and in this less diverse landscape, less use was made of fire in agricultural subsistence. Overall, our study reveals how regional water-level, driven by sea-level rise and extreme rainfall, delayed the transition from hunting-gathering to agriculture in the lower Yangtze region.

Pesticide Exposure and Effects on Non-Apis Bees.

Bees are essential pollinators of many crops and wild plants, and pesticide exposure is one of the key environmental stressors affecting their health in anthropogenically modified landscapes. Until recently, almost all information on routes and impacts of pesticide exposure came from honey bees, at least partially because they were the only model species required for environmental risk assessments (ERAs) for insect pollinators. Recently, there has been a surge in research activity focusing on pesticide exposure and effects for non-Apis bees, including other social bees (bumble bees and stingless bees) and solitary bees. These taxa vary substantially from honey bees and one another in several important ecological traits, including spatial and temporal activity patterns, foraging and nesting requirements, and degree of sociality. In this article, we review the current evidence base about pesticide exposure pathways and the consequences of exposure for non-Apis bees. We find that the insights into non-Apis bee pesticide exposure and resulting impacts across biological organizations, landscapes, mixtures, and multiple stressors are still in their infancy. The good news is that there are many promising approaches that could be used to advance our understanding, with priority given to informing exposure pathways, extrapolating effects, and determining how well our current insights (limited to very few species and mostly neonicotinoid insecticides under unrealistic conditions) can be generalized to the diversity of species and lifestyles in the global bee community. We conclude that future research to expand our knowledge would also be beneficial for ERAs and wider policy decisions concerning pollinator conservation and pesticide regulation.

Effects of food-based enrichment on enclosure use and behavioral patterns in captive mammalian predators: a case study from an Austrian wildlife park.

Combining naturalistic enclosure design and animal welfare with visitor interests and education can be challenging for zoos and wildlife parks. To accomplish both purposes, different types of enrichment (food-based or non-food-based items, such as environmental, sensory, cognitive, social) can be used. The aim of the present study is to investigate the effect of food-based and olfactory enrichments on enclosure use, behavior, and visibility of captive brown bears (Ursus arctos), pine martens (Martes martes), domestic ferrets (Mustela putorius furo), and golden jackals (Canis aureus). We used observational approaches to measure enclosure use, behavior, and visibility during three different experimental phases: (1) pre-enrichment (baseline, no experience with the enrichment yet), (2) during enrichment (enrichment was provided at low frequented locations in the enclosures that are easily visible to visitors), and (3) post-enrichment (enrichment was removed from the enclosures). We found that enrichment led to a uniform use of the enclosure and enhanced visibility in brown bears, increased activity budgets in pine martens, and observed high object interaction in both species. No effects of enrichment were detected in domestic ferrets. Golden jackals did not leave their burrows during daytime during the entire observation period; thus, observations were not possible at all. Our results suggest different effects of food-based enrichment, e.g., enclosure use, temporal activity patterns, and animal visibility. However, further studies should control for the specific role of the factors involved. Our study represents one of the first explorations of food-based enrichment in rather understudied species.

Antithrombin activity and central venous catheter-associated thrombosis in critically ill children at high risk of bleeding

BackgroundNormalization of antithrombin activity may prevent catheter-associated thrombosis in critically ill children at high risk of bleeding. ObjectivesTo characterize the temporal pattern of antithrombin activity, assess its association with catheter-associated thrombosis and clinically relevant bleeding, and evaluate its relationship with thrombin generation in these children. MethodsIn this prospective cohort study, critically ill children <18 years old at high risk of bleeding with central venous catheter were eligible. Antithrombin activity and thrombin generation were measured from platelet-poor plasma and after in vitro antithrombin supplementation. Systematic surveillance ultrasound was performed to diagnose thrombosis. Children were followed for bleeding. ResultsWe enrolled 8 infants (median age: 0.2 years, IQR: 0.2, 0.3 years) and 72 older children (median age: 14.3 years, IQR: 9.1, 16.1 years). Mean antithrombin on the day of catheter insertion was 64 IU/dL (SD: 32 IU/dL) in infants and 83 IU/dL (SD: 35 IU/dL) in older children. Antithrombin normalized by the day of catheter removal.Thrombosis developed in 27 children, while 31 children bled. Thrombosis (regression coefficient: 0.008, 95% CI: -0.01, 0.03) and bleeding (regression coefficient: -0.0007, 95% CI: -0.02, 0.02) were not associated with antithrombin. Antithrombin was not correlated with in vivo change in endogenous thrombin potential (correlation coefficient: -0.07, 95% CI: -0.21, 0.08). In vitro supplementation reduced endogenous thrombin potential (correlation coefficient: -0.78; 95% CI: -0.95, -0.23). ConclusionThese findings may not support normalization of antithrombin activity to prevent catheter-associated thrombosis in critically ill children at high risk of bleeding.

A long short-term memory modeling-based compensation method for muscle synergy.

Muscle synergy containing temporal and spatial patterns of muscle activity has been frequently used in prediction of kinematic characteristics. However, there is often some discrepancy between the predicted results based on muscle synergy and the actual movement performance. This study aims to propose a new method for compensating muscle synergy that allows the compensated synergy signal to predict kinematic characteristics more accurately. The study used the change of direction in running as background. Non-negative matrix factorisation was used to extract the muscle synergy during the change of direction at different angles. A non-linear association between synergy and the height of pelvic mass centre was established using long and short-term memory neural networks. Based on this model, the height fluctuations of the pelvic centre of mass are used as input and predict the fluctuations of the synergy which were used to compensate for the original synergy in different ways. The accuracy of the synergies compensated in different ways in predicting pelvic centre of mass movement was then assessed by back propagation neural networks. It was found that the compensated synergy significantly improves accuracy in predicting pelvic centre of mass displacement (R2, p<0.05). The predicted results of all-compensation are significantly different from actual performance in the end-swing (p<0.05). The predicted results of half-compensation do not differ significantly from the actual performance, and its damage to the original synergy is smaller and does not increase with angle compared to all-compensation. The all-compensation may be affected by individual variability and lead to increased errors. The half-compensation can improve the predictive accuracy of the synergy while reducing the adjustment to the original synergy.