Spatial-temporal patterns and influencing factors for hemorrhagic fever with renal syndrome: A 16-year national surveillance analysis in China.

Background: China is confronted with the significant menace posed by hemorrhagic fever with renal syndrome (HFRS). Nevertheless, the long-term spatial-temporal variations, regional prevalence patterns, and fundamental determinants' mechanisms for HFRS remain inadequately elucidated. Methods: Newly diagnosed cases of HFRS from January 2004 to December 2019 were acquired from the China Public Health Science Data repository. We used Age-period-cohort and Bayesian Spacetime Hierarchy models to identify high-risk populations and regions in mainland China. Additionally, the Geographical Detector model was employed to quantify the determinant powers of significant driver factors to the disease. Results: A total of 199,799 cases of HFRS were reported in mainland China during 2004-2019. The incidence of HFRS declined from 1.93 per 100,000 in 2004 to 0.69 per 100,000 in 2019. The incidence demonstrated an inverted U-shaped trend with advancing age, peaking in the 50-54 age group, with higher incidences observed among individuals aged 20-74 years. Hyperendemic areas were mainly concentrated in the northeastern regions of China, while some western provinces exhibited a potential upward trend. Geographical detector model identified that the spatial variations of HFRS were significantly associated with the relative humidity (Q = 0.36), forest cover (Q = 0.26), rainfall (Q = 0.18), temperature (Q = 0.16), and the surface water resources (Q = 0.14). Conclusions: This study offered comprehensive examinations of epidemic patterns, identified high-risk areas quantitatively, and analyzed factors influencing HFRS transmission in China. The findings may contribute to the necessary implementations for the effective prevention and control of HFRS.

Construct validity and test-retest reliability of a chrononutrition questionnaire for shift work and non-shift work populations.

The irregular eating patterns of both shift workers and evening chronotypes adversely affect cardiometabolic health. A tool that conveniently captures temporal patterns of eating alongside an indicator of circadian rhythm such as chronotype will enable researchers to explore relationships with diverse health outcome measures. We aimed to investigate the test-retest reliability and convergent validity of a Chrononutrition Questionnaire (CNQ) that captures temporal patterns of eating and chronotype in the general population (non-shift workers, university students, retirees, unemployed individuals) and shift work population. Participants attended two face-to-face/virtual sessions and completed the CNQ and food/sleep/work diaries. Outcomes included subjective chronotype, wake/sleep/mid-sleep time, sleep duration, meal/snack regularity, meal/snack/total frequency, times of first/last/largest eating occasions (EO), main meal (MM) 1/2/3, and duration of eating window (DEW). 116 participants enrolled (44.5 ± 16.5 years, BMI: 27.3 ± 5.8 kg/m2, 73% female, 52% general population); 105 completed the study. Reliability was acceptable for chronotype, sleep, and all temporal eating patterns except on night shifts. Convergent validity was good for chronotype and sleep except for certain shift/shift-free days. Generally, meal/snack regularity and frequency, and times of first/last EO showed good validity for the general population but not shift workers. Validity was good for DEW (except work-free days and afternoon shifts) and times of MM 1/2/3 (except afternoon and night shifts), while time of largest EO had poor validity. The CNQ has good test-retest reliability and acceptable convergent validity for the general and shift work population, although it will benefit from further validation, especially regarding regularity, frequency, and times of first and last eating occasions across more days amongst a larger sample size of shift workers. Use of the CNQ by researchers will expand our current understanding of chrononutrition as relationships between timing of food intake and the multitude of health outcomes are examined.

Feature-based 3D+t descriptors of hyperactivated human sperm beat patterns.

The flagellar movement of the mammalian sperm plays a crucial role in fertilization. In the female reproductive tract, human spermatozoa undergo a process called capacitation which promotes changes in their motility. Only capacitated spermatozoa may be hyperactivated and only those that transition to hyperactivated motility are capable of fertilizing the egg. Hyperactivated motility is characterized by asymmetric flagellar bends of greater amplitude and lower frequency. Historically, clinical fertilization studies have used two-dimensional analysis to classify sperm motility, despite the inherently three-dimensional (3D) nature of sperm motion. Recent research has described several 3D beating features of sperm flagella. However, the 3D motility pattern of hyperactivated spermatozoa has not yet been characterized. One of the main challenges in classifying these patterns in 3D is the lack of a ground-truth reference, as it can be difficult to visually assess differences in flagellar beat patterns. Additionally, it is worth noting that only a relatively small proportion, approximately 10-20% of sperm incubated under capacitating conditions exhibit hyperactivated motility. In this work, we used a multifocal image acquisition system that can acquire, segment, and track sperm flagella in 3D+t. We developed a feature-based vector that describes the spatio-temporal flagellar sperm motility patterns by an envelope of ellipses. The classification results obtained using our 3D feature-based descriptors can serve as potential label for future work involving deep neural networks. By using the classification results as labels, it will be possible to train a deep neural network to automatically classify spermatozoa based on their 3D flagellar beating patterns. We demonstrated the effectiveness of the descriptors by applying them to a dataset of human sperm cells and showing that they can accurately differentiate between non-hyperactivated and hyperactivated 3D motility patterns of the sperm cells. This work contributes to the understanding of 3D flagellar hyperactive motility patterns and provides a framework for research in the fields of human and animal fertility.

Spatial and temporal analysis of haemorrhagic septicaemia outbreaks in India over three decades (1987-2016).

Haemorrhagic septicaemia (HS) is an economically important disease affecting cattle and buffaloes and the livelihoods of small-holder farmers that depend upon them. The disease is caused by Gram-negative bacterium, Pasteurella multocida, and is considered to be endemic in many states of India with more than 25,000 outbreaks in the past three decades. Currently, there is no national policy for control of HS in India. In this study, we analysed thirty year (1987-2016) monthly data on HS outbreaks using different statistical and mathematical methods to identify spatial variability and temporal patterns (seasonality, periodicity). There was zonal variation in the trend and seasonality of HS outbreaks. Overall, South zone reported maximum proportion of the outbreaks (70.2%), followed by East zone (7.2%), Central zone (6.4%), North zone (5.6%), West zone (5.5%) and North-East zone (4.9%). Annual state level analysis indicated that the reporting of HS outbreaks started at different years independently and there was no apparent transmission between the states. The results of the current study are useful for the policy makers to design national control programme on HS in India and implement state specific strategies. Further, our study and strategies could aid in implementation of similar approaches in HS endemic tropical countries around the world.

RPCA-based techniques for pattern extraction, hotspot identification and signal correction using data from a dense network of low-cost NO2 sensors in London.

High-density low-cost air quality sensor networks are a promising technology to monitor air quality at high temporal and spatial resolution. However the collected data is high-dimensional and it is not always clear how to best leverage this information, particularly given the lower data quality coming from the sensors. Here we report on the use of robust Principal Component Analysis (RPCA) using nitrogen dioxide data obtained from a recently deployed dense network of 225 air pollution monitoring nodes based on low-cost sensors in the Borough of Camden in London. RPCA addresses the brittleness of singular value decomposition towards outliers by using a decomposition of the data into low-rank and sparse contributions, with the latter containing outliers. The modal decomposition enabled by RPCA identifies major periodic patterns including spatial and temporal bias, dominant spatial variance, and north-south bias. The five most descriptive components capture 98 % of the data's variance, achieving a compression by a factor of 1500. We present a new technique that uses the sparse part of the data to identify hotspots. The data indicates that at the locations of the top 15 % most susceptible nodes in the network, the model identifies 23 % more hotspots than in all other locations combined. Moreover, the median hotspot event at these at-risk locations exceeds the mean NO2concentration by 33µg/m3. We show the potential of RPCA for signal correction; it corrects random errors yielding a reference signal with R2>0.8. Moreover, RPCA successfully reconstructs missing data from a sensor with R2=0.72 from the rest of the sensor network, an improvement upon PCA of around 50 %, allowing air quality estimations even if a sensor is out of use temporarily.

Temporal Patterns in Youth Suicide Deaths Reported in the National Violent Death Reporting System.

PURPOSE: Suicide is a leading cause of death in children and adolescents. Prevention efforts may benefit from examining temporal patterns and precipitating circumstances of youth suicide deaths. The purpose of this study was to investigate if there are distinct temporal patterns of suicide death for youth. METHODS: We examined the distributions of suicide deaths across months of the year and days of the week in youth ages 10-18 using data from the National Violent Death Reporting System. RESULTS: We found that youth suicides peaked during March, April, and October on Monday, Tuesday, and Wednesday. When disaggregated by separate years of age, we discovered unique patterns in 18-year-olds and 10-year-olds. Suicides peaked during July for 18-year-olds and on Saturday for 10-year-olds. DISCUSSION: These findings suggest a need to examine temporal patterns by separate years of age, as grouping ages together may mask unique trends.

Seasonality and longer-term development generate temporal dynamics in the Populus microbiome.

Temporal variation in community composition is central to our understanding of the assembly and functioning of microbial communities, yet the controls over temporal dynamics for microbiomes of long-lived plants, such as trees, remain unclear. Temporal variation in tree microbiomes could arise primarily from seasonal (i.e., intra-annual) fluctuations in community composition or from longer-term changes across years as host plants age. To test these alternatives, we experimentally isolated temporal variation in plant microbiome composition using a common garden and clonally propagated plants, and we used amplicon sequencing to characterize bacterial/archaeal and fungal communities in the leaf endosphere, root endosphere, and rhizosphere of two Populus spp. over four seasons across two consecutive years. Microbial community composition differed among seasons and years (which accounted for up to 21% of the variation in microbial community composition) and was correlated with seasonal dissimilarity in climatic conditions. However, microbial community dissimilarity was also positively correlated with time, reflecting longer-term compositional shifts as host trees aged. Together, our findings demonstrate that temporal patterns in tree microbiomes arise from both seasonal fluctuations and longer-term changes, which interact to generate unique seasonal patterns each year. In addition to shedding light on two important controls over the assembly of plant microbiomes, our results also suggest future studies of tree microbiomes should account for background temporal dynamics when testing the drivers of spatial patterns in microbial community composition and temporal responses of plant microbiomes to environmental change.IMPORTANCEMicrobiomes are integral to the health of host plants, but we have a limited understanding of the factors that control how the composition of plant microbiomes changes over time. Especially little is known about the microbiome of long-lived trees, relative to annual and non-woody plants. We tested how tree microbiomes changed between seasons and years in poplar (genus Populus), which are widespread and ecologically important tree species that also serve as important biofuel feedstocks. We found the composition of bacterial, archaeal, and fungal communities differed among seasons, but these seasonal differences depended on year. This dependence was driven by longer-term changes in microbial composition as host trees developed across consecutive years. Our findings suggest that temporal variation in tree microbiomes is driven by both seasonal fluctuations and longer-term (i.e., multiyear) development.

Differences in Game Dynamics between High-Level Volleyball and Beach Volleyball Matches.

The aim of this research is to verify whether there is a difference in the average duration of the active and passive phases of the game between volleyball and beach volleyball. A total of 2392 active and passive phases were measured during high-level volleyball and beach volleyball matches for males. Matches were played by four teams (53 players) in indoor volleyball (age 29 ± 4.94 years) and five teams (10 players) in beach volleyball (age 28.27 ± 6.64 years). Possible differences were evaluated using the Mann-Whitney U test. The average duration of the active phase in volleyball is 5.55 s ± 4.38 s. In beach volleyball, the active phase lasts marginally longer, at 6.00 s ± 3.44 s. The average duration of the passive phase in volleyball is 35.27 s ± 25.96 s and it lasts longer than the average passive phase in beach volleyball at 33.82 s ± 22.98 s. The Mann-Whitney U test showed a statistically significant difference (p = 0.00) between the active phases in volleyball (Md = 3.53, n = 727) and beach volleyball (Md = 3.43, n = 484), U = 140770.00, z = -5.90 with small effect according to Cohen's criterion (r = 0.14). The Mann-Whitney U test (U = 160773.00, z = -1.10) showed no statistically significant difference in the average duration of the passive phases at volleyball and beach volleyball. This research determined that there is a statistically significant difference in the average duration of the active phase between volleyball and beach volleyball. The new insights gained in this research can support a more precise programming of training intensity in high-level volleyball and beach volleyball.

Temporal patterns and driving factors of sediment carbon, nitrogen, and phosphorus stoichiometry in a eutrophication plateau lake.

Stoichiometry determines the key characteristics of organisms and ecosystems on a global scale and provides strong instructions on the fate of sediment carbon, nitrogen, and phosphorus (C-N-P) during the sedimentation process, contributing to the Earth's C-N-P balance. However, the mechanisms underlying C-N-P stoichiometry in response to intensive human activity and organic matter sources remain underexplored, especially in freshwater ecosystems. This study identifies the temporal patterns of C-N-P stoichiometry, reveals the inner driving factors, and clarifies its impact path, especially in eutrophication (the late 1970s). The results revealed that sediment RCP and RNP increased significantly and were controlled by TCAR and TNAR, respectively, indicating the direct impact of burial rate on C-N-P stoichiometry. Based on redundancy analysis and the STM model, autochthonous origin, GDP, and population had positive effects on sediment TCAR, TNAR, and TPAR, which, in turn, affected RCN, RCP, and RNP. Organic matter sources and human activities have a significant influence on RCN, RCP, and RNP, possibly regulated by the variation of TCAR and TNAR. Autochthonous origin had an indirect positive impact on RCN and RCP through the mediating effect of TCAR. Similarly, through the mediating effect of TNAR, it had an indirect negative impact on RCN and an indirect positive impact on RNP. This study showed that TCAR, TNAR, TPAR, GDP, autochthonous, allochthonous and population better explained the changes in RCN, RCP, and RNP over a-hundred-year deposition, highlighting an in-depth understanding of the dynamic change mechanism of sediment C-N-P stoichiometry during the lake deposition process.

Modeling default mode network patterns via a universal spatio-temporal brain attention skip network.

Designing a comprehensive four-dimensional resting-state functional magnetic resonance imaging (4D Rs-fMRI) based default mode network (DMN) modeling methodology to reveal the spatio-temporal patterns of individual DMN, is crucial for understanding the cognitive mechanisms of the brain and the pathogenesis of psychiatric disorders. However, there are still two limitations of existing approaches for DMN modeling. The approaches either (1) simply split the spatio-temporal components and ignore the overall character of the spatio-temporal patterns or (2) are biased in the process of feature extraction for DMN modeling, and their spatio-temporal accuracy is thus not warranted. To this end, we propose a novel Spatio-Temporal Brain Attention Skip Network (STBAS-Net) to model the personalized spatio-temporal patterns of the DMN. STBAS-Net consists of spatial and temporal components, where the multi-head attention skip connection block in the spatial component achieves detailed feature extraction and enhancement in the shallow stage. Under the guidance of spatial information, we technically fuse multiple spatio-temporal information in the temporal component, which dexterously exploits the overall spatio-temporal features and achieves mutual constraints of spatio-temporal patterns to characterize the spatio-temporal patterns of the DMN. We verify the proposed STBAS-Net on a publicly released 4D Rs-fMRI dataset and an EMCI dataset. The experimental results show that compared with existing advanced methods, the proposed network can more accurately model the personalized spatio-temporal patterns of the human brain DMN and successfully identify abnormal spatio-temporal patterns in EMCI patients. This study provides a potential tool for revealing the spatio-temporal patterns of the human brain DMN and is expected to provide an effective methodological framework for future exploration of abnormal brain spatio-temporal patterns and modeling of other functional brain networks.

Wastewater surveillance for antibiotics and resistance genes in a river catchment: Spatiotemporal variations and the main drivers.

Wastewater-based epidemiology (WBE) is used for mining information about public health such as antibiotics resistance. This study investigated the distribution profiles of six types of antibiotic resistance genes (ARGs) in wastewater and rivers in Wuhu City, China. The levels of ARGs found in the Qingyijiang River were significantly higher than other rivers, and were comparable to effluent levels. Among the ARGs, sulfonamides ARGs and intI1 were the predominant in both wastewaters and rivers. Additionally, the concentrations of ARGs were higher on weekends as opposed to weekdays. Their distribution patterns remained consistent inter-week and inter-season using linear regression analysis (p < 0.001). Interestingly, the occurrence levels of ARGs in wastewaters during spring were significantly higher than in autumn, although insignificant in rivers. The apparent removal rate of ARGs in domestic wastewater sources ranged from 61.52-99.29%, except for qepA (-1.91% to 81.09%), whereas the removal rates in mixed domestic and industrial wastewaters showed a marked decrease (-92.94% to 76.67%). A correlation network analysis revealed that azithromycin and erythromycin were key antibiotics, while blaNDM-1, tetM, tetB, and ermB were identified as key ARGs. Sulfonamide and fluoroquinolone antibiotics, and tetracycline and macrolide ARGs were the primary contributors. Linear mixed models demonstrated that socio-economic variables positively impacted the occurrence levels of ARGs, whereas wastewater flow and river runoff were the negative drivers for their concentrations in wastewaters and surface waters, respectively. Overall, this WBE study contributes to the understanding of spatiotemporal profiles and main drivers of the occurrence of ARGs in wastewater and receiving water.

Spatiotemporal patterns and drivers of the urban air pollution island effect for 2273 cities in China.

Air pollution levels tend to be higher in urban areas than in surrounding rural areas, and this air pollution has a negative effect on human health. However, the spatiotemporal patterns of urban-rural air pollution differences and the determinants of these differences remain unclear. Here, we calculate the Urban Air Pollution Island (UAPI) intensity for PM2.5 and PM10 on a monthly, seasonal, and annual scale for 2273 cities in China from 2000 to 2020. Subsequently, we analyze the influence of urban characteristics using a combined approach of a two-way fixed effects model and a spatial Durbin model. Results show a strong downward trend in the UAPI intensity since 2013, with reductions ranging from 42 % to 61 % until 2020, for both pollutants and in summer as well as winter. Consistently, the proportion of the cities experiencing the UAPI phenomenon decreased from 94.5 % to 77.3 % for both PM2.5 and PM10. We find a significant influence of urban morphology on UAPI. Specifically, urban sprawl, polycentric development, and an increase in urban green spaces are associated with a reduction in UAPI, while dense urban areas intensify it. Our study also reveals a robust inverted U-shaped relationship between stages of economic development and UAPI. Moreover, economic development and air pollution itself show spillover effects that oppose their direct impacts. These results suggest that urban and regional planning and more ambitious climate change mitigation policies could be more effective strategies for mitigating air pollution in cities than end-of-pipe control.

Factors associated with change in moderate or severe symptoms of anxiety and depression in community-living adults and older adults during the COVID-19 pandemic.

OBJECTIVES: Few are the longitudinal studies on the changes in moderate or severe symptoms of anxiety or depression (MSS-ANXDEP) from before to during the COVID-19 pandemic in Canada. The aim was to study the change in MSS-ANXDEP and associated sociodemographic, economic, psychosocial, health behaviour and lifestyle, and clinical factors. METHODS: The current sample includes 59,997 adults aged ≥ 35 years participating in the 2018 and 2020 health surveys of the 5 established cohorts of the Canadian Partnership for Tomorrow's Health (CanPath). MSS-ANXDEP was based on a cutoff score ≥ 10 on the 7-item Generalized Anxiety Disorder Scale and Patient Health Questionnaire (PHQ-8). Change in MSS-ANXDEP was categorized as follows: no MSS-ANXDEP, remitted, incident, and persistent. Multinomial regressions were used to study MSS-ANXDEP as a function of sociodemographic, economic, psychosocial, health behaviours and lifestyle, and clinical factors. RESULTS: Sociodemographic and economic (i.e. age, gender, cohort, race/ethnicity, lower income, decreased in income, work status, being an essential worker), lifestyle and health behaviours (i.e. smoking, cannabis and alcohol use, drinking more alcohol), psychosocial (i.e. provide help to others, information and instrumental support, and change in relationships with friends, family, and partner) and clinical factors (i.e. lifetime mental disorder and multimorbidity) were associated with remitted, incident, and persistent MSS-ANXDEP. CONCLUSION: Health and socio-economic factors were associated with changes in symptoms of anxiety and depression during the pandemic, further increasing inequities in mental health needs. Public health campaigns on the importance of healthy behaviours should continue and health policies should reduce economic and social barriers to integrated substance use and mental health care.

RéSUMé: OBJECTIFS: Les études longitudinales sur l'évolution des symptômes modérés ou sévères d'anxiété ou de dépression (SMS-ANXDEP) avant et pendant la pandémie de COVID-19 au Canada sont rares. L'objectif était d'étudier l'association entre l'évolution des SMS-ANXDEP et les facteurs sociodémographiques, économiques, psychosociaux, cliniques et liés aux comportements et au mode de vie, avant et pendant la pandémie. MéTHODES: Ce grand échantillon comprend 59 997 adultes âgés de ≥ 35 ans qui ont participé aux enquêtes de santé 2018 et 2020 des 5 cohortes établies du Partenariat canadien pour la santé de demain (CanPath). La présence de SMS-ANXDEP a été définie par un résultat ≥ 10 sur les échelles Generalized Anxiety Disorder Scale à 7 items (GAD-7) et Patient Health Questionnaire (PHQ-8). Les changements dans les SMS-ANXDEP ont été catégorisés selon les patrons temporels suivants : absence, rémission, incidence et persistance de SMS-ANXDEP. Des régressions multinomiales multivariées ont été utilisées pour étudier les patrons temporels de SMS-ANXDEP en fonction, des facteurs socio-démographiques, économiques, associés au style de vie et aux comportements de santé, psychosociaux et cliniques. RéSULTATS: Les facteurs socio-démographiques et économiques (âge, genre, cohorte, race/ethnie, revenu inférieur, diminution du revenu, statut d'emploi, être un travailleur essentiel), associés au style de vie et aux comportements de santé (tabagisme, consommation de cannabis et d'alcool, consommation accrue d'alcool), psychosociaux (offrir de l'aide pendant la pandémie, soutien en information et instrumental, changement dans les relations avec les amis, la famille et la personne partenaire) et cliniques (trouble mental au cours de la vie, multimorbidité) étaient associés à la présence de SMS-ANXDEP en rémission, incident et persistant. CONCLUSION: Les patrons temporels des SMS-ANXDEP pendant la pandémie étaient associés aux facteurs socio-économiques et de santé, suggérant des inégalités accrues en matière de besoins de santé mentale. Les campagnes de santé publique sur l'importance d'adopter des comportements sains devraient continuer et les politiques de santé devraient réduire les barrières économiques et sociales aux soins intégrés de santé mentale et de toxicomanie.

Impact of spiking neurons leakages and network recurrences on event-based spatio-temporal pattern recognition.

Spiking neural networks coupled with neuromorphic hardware and event-based sensors are getting increased interest for low-latency and low-power inference at the edge. However, multiple spiking neuron models have been proposed in the literature with different levels of biological plausibility and different computational features and complexities. Consequently, there is a need to define the right level of abstraction from biology in order to get the best performance in accurate, efficient and fast inference in neuromorphic hardware. In this context, we explore the impact of synaptic and membrane leakages in spiking neurons. We confront three neural models with different computational complexities using feedforward and recurrent topologies for event-based visual and auditory pattern recognition. Our results showed that, in terms of accuracy, leakages are important when there are both temporal information in the data and explicit recurrence in the network. Additionally, leakages do not necessarily increase the sparsity of spikes flowing in the network. We also investigated the impact of heterogeneity in the time constant of leakages. The results showed a slight improvement in accuracy when using data with a rich temporal structure, thereby validating similar findings obtained in previous studies. These results advance our understanding of the computational role of the neural leakages and network recurrences, and provide valuable insights for the design of compact and energy-efficient neuromorphic hardware for embedded systems.

Differential response of fire on the community dynamics of five insect taxa in a tropical mountaintop forest archipelago.

The Earth's most diverse group of organisms is facing an imminent crisis, as recent investigations suggest a remarkable decline in insect diversity. Within this context, altimontane forest islands might emerge as important refuges holding an invaluable diversity of species that would be doomed to disappear. Here, we aimed to examine the impact of fire on the temporal variation of ant, bee, butterfly, dung beetle, and wasp communities in natural and highly threatened altimontane forest islands. We predicted that fire incidence would increase the natural variation in the structure of these insects' communities over time. Furthermore, we predicted that each taxon would respond accordingly to their ability to move between forest islands (i.e., vagility). We sampled these five bioindicator taxa in the rainy seasons of 2014, 2015, 2018, and 2020 within 14 forest islands in southeast Brazil. We assessed the incidence (presence/absence) of fire occurrence on each forest island toward the end of the dry season in each sampling year. We found an influence of fire incidence on the species composition changes over time (temporal ß-diversity) in the less vagile insect groups: ants, and dung beetles. Nevertheless, we found no influence of fire incidence on shifts in species composition of highly vagile insects: bees, butterflies, and wasps. Importantly, species turnover was the primary component of temporal ß-diversity driving the interannual variation of all insect taxa examined in this study. Our results highlight the distinct responses of more-or-less vagile insect groups to fire in forested ecosystems and shed light on the drivers of vulnerability and resilience of these groups to this critical anthropogenic pressure. By understanding and elucidating the intricate responses of distinct insect communities to global stressors, we can strengthen our capacity to predict future trends in biodiversity decline and provide valuable insights for conservation efforts and environmental management strategies.

Effects of the New COVID-19-Induced Rule on Substitutions and Performance in Italian Elite Soccer.

COVID-19 has resulted in widespread changes, including within the realm of sports. Professional soccer has adapted by allowing more substitutions, leading to tactical adjustments and potential physical benefits. Accordingly, this study analyzed the impact of the new rule in Italian top-level soccer, focusing on substitution patterns and performance differences between the pre-COVID (2017-2018, 2018-2019 seasons) and post COVID (2020-2021, 2021-2022 seasons) eras. As such, publicly available data from 1520 matches (760 matches per era) were recorded. The sample included matches played from 40 Italian top division teams in both the pre- and post-COVID eras. Analyses confirmed substitutions follow a consistent temporal pattern throughout the match in both eras, highlighting a slight difference in second-half management, and showed the new rule is still not used to its full potential, thus raising concerns about teams' financial strength, as not all managers possess "deep benches" (i.e., a large number of top-level players available to play). Further analyses revealed a statistically significant increment (p = 0.002) in the quantity of collectively produced sprints in the post-COVID era compared to the pre-COVID one. The results from this study emphasize the need to carefully address sprint preparation and repeated sprint abilities, also considering factors such as the number of substitutes and their skill level.

Same place, different time, head up: Multiple antipredator responses to a recolonizing apex predator.

Prey adjust their antipredator behavioral tactics to minimize the risk of an encounter with predators. Spatiotemporal responses of prey to predators have been reported, but the nature of antipredator response is not ubiquitous and it is the object of increasing interest, especially considering the recent recovery of large carnivores in Europe, and the potential for behavioral antipredator responses to elicit consequences at the ecosystem level. We have tested multiple antipredator responses by fallow deer Dama dama to wolf Canis lupus in a Mediterranean protected area recently recolonized by this apex predator. Through intensive camera trapping, we tested for temporal and spatial association between predator and prey, and we have also studied deer vigilance in forest habitats where focal observations are usually impossible. Wolf detection rates were spatially associated with those of fallow deer. Accordingly, no evidence was found for fallow deer avoiding sites with higher predator detection rates. Temporal activity patterns were significantly different between the 2 species, with the wolf being mainly nocturnal whereas fallow deer was active especially during daylight. A comparison with a preliminary study strongly suggests an increase in the diurnal activity of fallow deer along with the stabilization of wolf presence in the area. Both the rate and the duration of vigilance of female fallow deer increased with the local frequency of wolf activity. We suggest an antipredator response based on temporal-rather than spatial-avoidance, as well as increased vigilance.

Integrating health geography and behavioral economic principles to strengthen context-specific behavior change interventions.

The long-term economic viability of modern health care systems is uncertain, in part due to costs of health care at the end of life and increasing health care utilization associated with an increasing population prevalence of multiple chronic diseases. Control of health care spending and sustaining delivery of health care services will require strategic investments in prevention to reduce the risk of disease and its complications over an individual's life course. Behavior change interventions aimed at reducing a range of harmful and risky health-related behaviors including smoking, physical inactivity, excess alcohol consumption, and excess weight, are one approach that has proven effective at reducing risk and preventing chronic disease. However, large-scale efforts to reduce population-level chronic diseases are challenging and have not been very successful at reducing the burden of chronic diseases. A new approach is required to identify when, where, and how to intervene to disrupt patterns of behavior associated with high-risk factors using context-specific interventions that can be scaled. This paper introduces the need to integrate theoretical and methodological principles of health geography and behavioral economics as opportunities to strengthen behavior change interventions for the prevention of chronic diseases. We discuss how health geography and behavioral economics can be applied to expand existing behavior change frameworks and how behavior change interventions can be strengthened by characterizing contexts of time and activity space.

Behavior change interventions are challenged by lack of information about the contexts influencing decisions patients make as part of their daily routine such as when, where, and how health behaviors occur. A new approach is required to strengthen behavior change interventions by integrating contexts of time and activity space so that strategies can be scaled across populations to influence how individuals make decisions about improving their health behaviors. Incorporating ideas from health geography and behavioral economics into the design of behavior change interventions provides an opportunity to collect and investigate individual-level health information characterizing contexts of individuals' activities across space, connections to place, time management, and patterns in behavior over time. By visualizing and characterizing key spatiotemporal contexts about an individual's day-to-day routine, insight can be gained about where and for how long activities occur and what opportunities exist for adapting day-to-day routines. This paper will discuss how theory from health geography could be applied to understand contexts influencing behaviors and how spatiotemporal information could be applied for the purpose of tailoring behavioral economic strategies to strengthen the design of behavior change interventions.

Hybrid office work in women and men: do directly measured physical behaviors differ between days working from home and days working at the office?

OBJECTIVE: We investigated and compared temporal sitting patterns among male and female hybrid office workers when working at the office (WAO), working from home (WFH), and for non-working days (NWD). METHODS: We analyzed data collected in 2020 among 165 hybrid office workers, carrying thigh-worn accelerometers for 938 days in total. Day type (WAO, WFH, or NWD) and time in bed were identified using diaries. Time awake was exhaustively classified as non-sitting time and time sitting in short, moderate, and long bouts. Effects of day type and gender on the 24-h compositions of physical behaviors were analyzed using multilevel linear mixed models. RESULTS: During workdays (both WAO and WFH), workers spent less time in bed and more time sitting, particularly in moderate and long bouts, than during NWD. Time in bed was longer when working from home than when working at the office, and more of the awake time was spent sitting. Differences between WAO and WFH in ratios between short, moderate, and long bouts of sitting were small and inconsistent. Men spent more time sitting than women, and more time in moderate and long sitting bouts relative to short bouts. CONCLUSIONS: When working from home, hybrid office workers sat more during their hours awake compared to when working at the office. Sitting time was larger during working days than during non-working days and was higher in men than in women. These results may contribute to support organizational policies for hybrid work.

Seasonal Abundance of Aedes Sollicitans and Aedes Taeniorhynchus Related to Temperature, Rainfall and Tidal Levels in Northeastern Florida.

The Anastasia Mosquito Control District, which manages mosquitoes in St. Johns County in northeastern Florida, has observed that the maximum numbers of the salt marsh mosquitoes, Aedes taeniorhynchus and Ae. sollicitan appeared to shift or change relative to each other, as evidenced by the Centers for Disease Control and Prevention (CDC) light trap data in the past 17 years. The aim of this study was to analyze environmental data to identify and explore these changes. Data from CDC light traps, temperature, rainfall, and tidal levels were analyzed using ANOVA. Analyses showed the 2 species had maximum abundance at different temperatures, which translated into seasonal differences with peaks of Ae. taeniorhynchus in the summer and, to a lesser extent, later in the year, and Ae. sollicitans with a peak in the autumn. This seasonal pattern was reflected in rainfall (more rain in autumn than in summer) and also, in the general area, in tidal levels (mean highest tide levels at the recording station were in autumn). The research demonstrated that simplifying the mosquito data, initially using only very high trap numbers (Mean ± 2 SD) that are important for control, identified, and made the seasonal pattern very obvious. The pattern was also observed using all the data but, although significant, was not as clear. Having identified tide as a potential driving variable, further research needs to detail spatial tidal patterns to identify areas and timing of flooding and explore the relationship between salinity and mosquito species and abundance. This is important as sea levels rise and climate changes, both potentially changing the mosquito situation and affecting control actions.