Daily Patterns Research Articles

Temporal niche partitioning among sympatric wild and domestic ungulates between warm and cold seasons

The coexistence of sympatric species with similar ecological niches has been a central issue in ecology. Clarifying the daily activity patterns of sympatric wild ungulates can help understand their temporal niche differentiation and the mechanisms of coexistence, providing information for their conservation. The Baotianman National Nature Reserve in northern China is rich in wild ungulates, but little is known about the daily activity patterns of wild ungulates in the area, making it difficult to develop effective conservation strategies. We studied five representative wild ungulates (i.e. forest musk deer, Chinese goral, Reeve’s muntjac, Siberian roe deer, and wild boar) of the region using camera-trapping data, focusing on the seasonal daily activity patterns and effects of seasonal grazing of domestic sheep, to reveal their coexistence based on temporal ecological niche differentiation. Comparative analyses of the seasonal daily activity showed that forest musk deer exhibited a single-peak activity in the warm season. Other ungulates exhibited multipeak activity. All five ungulates differed significantly in daily activity patterns. Notably, wild boar and Reeve’s muntjac showed high overlap coefficients between the cold and warm seasons. In both cold and warm seasons, the five wild ungulates and domestic sheep displayed low overlap in their daily activity rhythms potentially indicating temporal ecological niche differentiation. The results suggest that temporal isolation might be a strategy for wild ungulates to avoid domestic sheep and reduce interspecific competition, and that temporal ecological niche differentiation potentially promoted the coexistence among the studied sympatric ungulates. This understanding may provide new insights for the development of targeted conservation strategies.

Seasonal to interannual variations of daily growth rate of a Tridacna shell from Palau Island, western Pacific, and paleoclimatic implications

Seasonal-interannual climate change is an important component of Earth's climate system and has a significant impact on ecosystems and social systems. However, the short-run of modern observational data limits our understanding of the seasonal-interannual climates on long-term timescales. The natural archives can provide information on climate change before the industrial period, but most of their temporal resolution is too low to capture the seasonal-interannual climate signals. Tridacna spp. is the largest marine bivalve, and its shell has the potential to trace climatic/environmental changes on a daily to interannual scale. In this study, we investigated the daily growth pattern of a modern Tridacna gigas shell (PL-1) from Palau based on laser scanning confocal microscopy images. The results showed that the growth pattern of PL-1 was affected by effective solar radiation primarily, followed by nutrients on the seasonal timescale. On the interannual timescale, the growth rate of the shell was modulated by ENSO, which affected the effective solar radiation and nutrients accepted by Tridacna. The growth rate of Tridacna PL-1 was accelerated during the ENSO positive phase, and vice versa. However, due to a lack of situ observations, the effects of nutrients on Tridacna and the symbiotic zooxanthella in detail need to be further investigated. This study indicates that the daily growth rate of Tridacna shells in equatorial regions may have the potential to reconstruct seasonal-interannual climate/environment variations.

PSI-2 Daily thermoregulatory performance patterns of Duroc boars with or without electronically controlled floor cooling pads during the peak summer heat stress period

Abstract The objective of the study was to evaluate the daily thermoregulatory pattern of Duroc boars with and without electronically controlled floor cooling pads (ECP) during the summer season in a boar stud at Scottsville, Kentucky. Thirty boars were placed in two rows of pens (2.06 x 0.66 m) of a 350 head tunnel ventilated barn and were randomly assigned to either CONTROL (n = 15; no ECP) or PAD (n = 15; 4.25 L cold water flushed when sensors reached 27°C or every 4 min, whichever occurred first, through a group of 5 pads connected to each other) treatments. Boars experienced natural seasonal heat stress (daily mean temperature = 25.7°C; hourly high temperature = 28.8°C, corresponding relative humidity = 85.4%, corresponding dew point = 26°C) during the period of 4 d wherein the ECP remained on, and flushing based on the setpoints mentioned above. Rectal temperature (RT) and respiration rate (RR) were taken at 600, 830, 1100 1330, 1600, 1830 and 2100 h daily for all 4 d. Two statistical analyses were conducted. The first was a mixed model including the fixed effects of ECP, day, time of measurement and their interactions with boar included as a random effect. Secondly, mixed model periodic regressions were fitted to the RR and RT data. Both RT and RR were impacted by ECP, day, time, and all two and three factor interactions (P < 0.001). The final periodic regression model for RR included the random effect of boar, main effect of ECP, interaction of ECP and day, single phase sine/cosine and 2-phase cosine variables, the interaction with the single 2 and 3 phase sine/cosine variables with day (P < 0.001). The periodic regression results indicate that the response of the ECP to reduce RR were consistent relative to the time of the day (mean of 57.5 breaths/min versus 18.9 breaths/min, P < 0.001) but the overall response in RR relative to time of day was variable for the four days. The final model for RT using periodic regression included the random effect of boar, ECP, single-phase sine and cosine and three-phase sine variables (P < 0.001) and interaction of single-phase sine by ECP (P = 0.001) and interaction of the three-phase sine with day. The interaction of the ECP by single phase sine interaction was due to an increase in temperature from 600 to 1330 for control boars (0.21°C) versus no change for boars on the ECP. The ECP reduced RT at all measurement times, a lesser extent from 600 to 1100 (0.31°C to 0.37°C) and greater extent from 1330 (0.59°C) and 1600 h (0.63°C) and 2100 (0.67°C). The impact of the ECP to reduce RR and RT varied from day to day with greater effects on the days with higher barn temperatures.

102 Circadian patterns of behavior, respiration, rectal temperature, saliva melatonin, cytokine concentrations, and milk immune cells are altered by cooling heat-stressed lactating sows

Abstract Lactating sows are particularly vulnerable to heat stress (HS) due to the increased metabolism from milk production. A greater understanding of HS response could assist in mitigating its detrimental effects on animal production. Two core circadian clock genes were pinpointed in a recent genome study as potential mediators of HS response, indicating the possible alteration of circadian clocks by HS exposure. This study aimed to assess how cooling of lactating sows under natural heat stress conditions in the summer influenced circadian patterns of respiration rate (RR), rectal temperature (RT), behaviors, salivary concentration of cortisol and melatonin, milk cell populations and relative abundance of multiple cytokines in saliva. Lactating sows of mixed parities were divided into two treatment groups: treated with electronic cooling pad (C; n=9) and heat-stressed control (H; n=9). Experiments were conducted during two 48 h periods of increased environmental temperatures from summer heatwaves. During the initial 48 h period, rectal temperature (RT) was monitored every 60 min, rectal temperature (RT) every 30 min, and behaviors of eating, standing, sitting, laying, sleeping, drinking, and nursing every 5 min. Morning milk samples were collected following the completion of the first phase and utilized to categorize and count somatic cells. The second trial was approximately 1 wk later, and during this period saliva samples were collected every 4 h over the 48-h period. Results were analyzed for significant effect of treatments and interaction using periodic regression while circadian patterns were further detected by incorporating a Fourier series model utilizing sine-cosine variables that denote a fit and/or shift in circadian pattern. The analysis revealed that cooling decreased RT and RR while also modifying circadian patterns (P < 0.05). Cooling had no impact on the percentage of time spent in any activity over the 48-h period (P > 0.05). Nevertheless, the daily patterns of eating, standing, and sitting were different between the treatments (P < 0.05). A shift in eating time to later in the day in H sows relative to C sows related to a second rise in RT during the dark phase of the light-dark cycle (P < 0.05). Saliva melatonin concentrations were increased in C sows and circadian pattern altered relative to H (P < 0.05), but there was no effect on salivary cortisol concentrations (P > 0.05). Cooling reduced milk lymphocytes (P < 0.05) and influenced diurnal pattern of saliva cytokines including interleukins (IL) IL-1 alpha, IL-1 beta, IL-10, and IL-15 and transforming growth factor-beta 1. In conclusion, the cooling of heat-stressed sows altered the circadian patterns of physiology, behavior and melatonin, suggesting the need for additional research to investigate whether circadian disruption contributes to reduced production efficiency observed in HS animals.

Daily glucose pattern and physical activity levels in cancer survivors: preliminary results from remote monitoring

Background: Cancer survivors have elevated risk for type 2 diabetes. Increasing physical activity improves glucose control. However, this evidence is majorly based on blood glucose metrics (e.g., glucose or hemoglobin A1c) at a single point in time. Little is known about the effect of physical activity on blood glucose changes throughout a day in cancer survivors. Thus, we tested the hypothesis that higher physical activity levels is associated with a more favorable daily glucose pattern in cancer survivors in free-living settings. Methods: This is a preliminary analysis of an ongoing randomized controlled trial (Project KNOWN) to examine the effect of physical activity on daily glucose pattern via continuous remote monitoring. A total of 11 cancer survivors were included in this analysis (90% female, 90% white, average age 62.5 years [range 44-76 yr], 67% obese, and 75% hypertension). Participants wore a wrist-worn accelerometer (for daily steps) and a blinded continuous glucose monitor (CGM; for daily glucose averages [interstitial glucose was measured at every 15 minutes] and daily frequency of glucose spikes [defined as glucose level >140 mg/dL]) for two weeks. Multilevel regression model was used to account for multiple observations within each individual and to separate the within-person (WP) and between-person (BP) effects. The WP effect represents deviation from one’s own mean on any given day, and the BP effect represents the individual mean deviation from the study sample mean. To explore moderating effect on the relationship between physical activity and daily glucose, inflammatory biomarkers, blood lipids, blood pressure and body fatness (DEXA scan) were measured. Results: At the WP level, participants with elevated (>4 mg/L) systemic C-reactive protein (marker of inflammatory status), on the day when they had higher-than-usual step count, their glucose average was also lower than their usual level ( p=0.04). At the BP level, daily steps were negatively associated with daily frequency of glucose spikes in participants with less severe hypertension, lower % gynoid fat, and higher android/gynoid ratio than others in the study sample (all p<0.05). Further, daily steps were negatively associated with daily glucose average in participants with higher trunk-to-total fat ratio than others (p<0.05). Discussion: This preliminary study demonstrated the use of data from wearable sensors to analyze intra- and inter-person variability in daily glucose patterns in cancer survivors. Results suggested the impact of physical activity on daily glucose patterns might differ by a person’s physiological status. Future research can further characterize meaningful daily glucose dynamics from CGM data in cancer survivors to better understand how physical activity may have downstream effect on their health outcomes. American Institute for Cancer Research. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Revealing Daily Mobility Pattern Disparities of Monomodal and Multimodal Travelers through a Multi-Layer Cluster Analysis: Insights from a Combined Big Dataset

Revealing Daily Mobility Pattern Disparities of Monomodal and Multimodal Travelers through a Multi-Layer Cluster Analysis: Insights from a Combined Big Dataset

Associations between day-level affect profiles and same-day substance use among young adults.

Emotions play a critical role in health risk behaviors, including substance use. However, current research often focuses exclusively on average levels of positive and negative affect, neglecting the complexity of daily emotional patterns. By capturing multiple dimensions of affect, including arousal and discrete states, we can improve our understanding of proximal predictors of substance use. The present study demonstrates the utility of a novel methodological approach for assessing affect patterns in daily life in relation to alcohol and cannabis use. Daily diary data from N = 154 young adults who reported recent heavy episodic drinking and simultaneous use of alcohol and cannabis were analyzed using a mixed-indicator latent profile analysis to identify and describe day-level affective patterns and outcomes. Results revealed five distinct day-level profiles of affect: undifferentiated negative affect days, undifferentiated positive affect days, high-arousal positive affect days, mixed affect days, and low reactivity days. Undifferentiated positive affect days, high-arousal positive affect days, and low reactivity days were associated with significantly greater odds of same-day alcohol use compared to days characterized by undifferentiated negative affect (χ² = 10.55, p = .032). Findings suggest that daily affect patterns differentially impact alcohol use and can inform the development of interventions for problematic substance use. Additionally, the innovative methodological approach employed herein could be applicable for investigating the role of emotion in other health behaviors. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

AgRP Neurons Encode Circadian Feeding Time

Food intake follows a predictable daily pattern and synchronizes metabolic rhythms. Neurons expressing Agouti-related protein (AgRP) read out physiological energetic state and elicit feeding, but the regulation of these neurons across daily timescales is poorly understood. Using a combination of neuron-dynamics measurements and timed optogenetic activation in mice, we show that daily AgRP-neuron activity was not fully consistent with existing models of homeostatic regulation. Instead of operating as deprivation counter, AgRP-neuron activity primarily followed the circadian rest-activity cycle through a process that required an intact suprachiasmatic nucleus (SCN) and synchronization by light. Imposing novel feeding patterns through time-restricted food access or periodic AgRP-neuron stimulation was suffcient to resynchronize the daily AgRP-neuron activity rhythm and drive anticipatory-like behavior through a process that required DMHPDYN neurons. These results indicate that AgRP-neurons integrate time-of-day information of past feeding experience with current metabolic needs to predict circadian feeding time. This work is supported by NIH to D.A. R01DK126740. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Exploring the Application of Force Intelligent Wearable Devices in Quantitative Management of Daily Physical Activity

This study looks into how force-intelligent wearable devices can be used to quantitatively manage daily physical activity levels. The study intends to investigate the effectiveness of these devices in measuring various physical activity metrics such as step count, distance walked, calories burned, and exercise intensity. A thorough methodology is used, which includes device selection, participant recruiting, data collecting, and analysis methods. Participants wear the selected wearable devices continuously for a set amount of time, enabling longitudinal tracking of daily physical activity patterns. The acquired data is evaluated using statistical analysis approaches such as descriptive statistics, correlation analysis, and cross-demographic group comparisons. The findings provide insights into participants' activity levels, device accuracy, user engagement, and relationships between various physical activity measures. The study's findings help to better understand the usefulness of force-intelligent wearable devices in promoting physical activity and improving health outcomes. Further research in this area could help to inform programs aimed at increasing physical activity and general well-being.

Automated extraction of Tridacna shell growth patterns via machine learning for enhanced paleoclimate/paleoweather research

Tridacna spp. are valuable archives for paleoclimate and paleoweather research due to their distinct daily growth patterns and the sensitivity of the daily growth patterns to environment changes. However, manually identifying daily growth lines and measuring the daily growth increment width (DGIW) of Tridacna shells from Laser Scanning Confocal Microscopy (LSCM) images is a tedious task that has become a significant barrier to Tridacna studies. This paper addresses this challenge by integrating machine learning into Tridacna research for the first time to automate the calculation of the number of daily growth lines and DGIW of Tridacna shells. Specifically, we propose an unsupervised generative adversarial attention network called TriGAN to automatically recognize distinct daily growth lines of Tridacna shells from LSCM images. Utilizing modern Tridacna specimens collected from the South China Sea, our experimental results demonstrate that TriGAN can effectively reconstruct the ambiguous and blurred regions in LSCM images and produce higher quality images of daily growth patterns compared to existing image generation networks. Furthermore, the daily growth line number and DGIW of Tridacna shells can be counted automatically from the images recognized by TriGAN, which are in good agreement with the statistical results obtained manually from the original LSCM images (R = 0.7, p < 0.01 for the DGIW profile of T. gigas specimen MD1 and R = 0.6, p < 0.01 for T. derasa specimen XB10). This automated method provides an efficient solution for researching the laminar chronology of Tridacna.

Identifying daily water consumption patterns based on K-means Clustering, Agglomerative Hierarchical Clustering, and Spectral Clustering algorithms

ABSTRACT Understanding daily water consumption patterns is crucial for efficient management and distribution of water resources, as well as for promoting energy conservation and achieving carbon peaking and neutrality targets. It compares performance of three clustering algorithms, K-means Clustering (KC), Agglomerative Hierarchical Clustering (AHC), and Spectral Clustering (SC), using Silhouette Coefficient Index (SCI) and Calinski–Harabasz Index (CHI) as evaluation metrics. We conducted a case study using original hourly flow series of a water distribution division. It aims to identify typical daily water consumption patterns and explore factors that influence them. Findings are as follows: (1) among the three algorithms, KC demonstrates the best, with SCI of 0.6315, 0.5922, and 0.6272, and CHI of 305.9207, 274.1120, and 302.4738 for KC, AHC, and SC, respectively. (2) KC successfully identifies three distinct typical daily water consumption patterns. (3) Results indicate a significant impact of seasons on daily water consumption patterns. (4) Conversely, weekdays and holidays have minimal effect on daily water consumption patterns. It highlights the importance of comprehending daily water consumption patterns and underscores the effectiveness of KC in identifying such patterns. Furthermore, it emphasizes the significant influence of seasons while revealing limited impact of weekdays and holidays on daily water consumption patterns.

Spatial distribution, activity patterns, and influence of roads on mammals in the Qinling Mountains of China

Abstract Species distribution and activity patterns vary across regions, and these patterns reveal key information about the biology of a given species. However, such data in Niubeiliang National Nature Reserve located in the Qinling Mountains, Shaanxi Province, China, are inadequate due to limited monitoring studies. From September 2017 to December 2019, 57 infrared cameras were installed to monitor species distribution relative to distance from National Highway 210, and spatial and temporal activity patterns in Niubeiliang National Nature Reserve (elevation range 1,100 to 2,802 m). Sixteen species of mammals were recorded in our survey. The 4 most frequently detected species were Reeves’s Muntjac (Muntiacus reevesi), Wild Boar (Sus scrofa), Pere David’s Rock Squirrel (Sciurotamias davidianus), and Golden Takin (Budorcas taxicolor bedfordi). Daily patterns of these species indicated that Reeves’s muntjacs and golden takins were more active at dawn and dusk, whereas wild boars and Pere David’s rock squirrels were more active during the day. The relative abundance index of these mammals varied seasonally. Wild boars and Pere David’s rock squirrels showed no obvious preference in elevation distribution, Reeves’s muntjacs preferred low-elevation habitats (1,300 to 1,600 m), and golden takins mainly inhabited high-elevation areas (1,900 to 2,100 m). Golden takins showed obvious avoidance of roads, with avoidance distance from the road of &amp;gt;300 m. In contrast, Reeves’s muntjacs were remarkably abundant within 200 m of the road. For both wild boars and Pere David’s rock squirrels, there was no significant difference in RAI among 5 highway ranges. These findings help describe the distribution and activity patterns of these species in Qinling, to monitor their population dynamics, and to develop tailored conservation strategies for the 4 species and sympatric wildlife.

Influence of the demand side management on the daily performance of microgrids in smart environments using grey wolf optimizer

PurposeThis study aims to investigate the daily performance of the proposed microgrid (MG) that comprises photovoltaic, wind turbines and is connected to the main grid. The load demand is a residential area that includes 20 houses.Design/methodology/approachThe daily operational strategy of the proposed MG allows to vend and procure utterly between the main grid and MG. The smart metre of every consumer provides the supplier with the daily consumption pattern which is amended by demand side management (DSM). The daily operational cost (DOC) CO2 emission and other measures are utilized to evaluate the system performance. A grey wolf optimizer was employed to minimize DOC including the cost of procuring energy from the main grid, the emission cost and the revenue of sold energy to the main grid.FindingsThe obtained results of winter and summer days revealed that DSM significantly improved the system performance from the economic and environmental perspectives. With DSM, DOC on winter day was −26.93 ($/kWh) and on summer day, DOC was 10.59 ($/kWh). While without considering DSM, DOC on winter day was −25.42 ($/kWh) and on summer day DOC was 14.95 ($/kWh).Originality/valueAs opposed to previous research that predominantly addressed the long-term operation, the value of the proposed research is to investigate the short-term operation (24-hour) of MG that copes with vital contingencies associated with selling and procuring energy with the main grid considering the environmental cost. Outstandingly, the proposed research engaged the consumers by smart meters to apply demand-sideDSM, while the previous studies largely focused on supply side management.

W4-Groups: Modeling the Who, What, When and Where of Group Behavior via Mobility Sensing

Human social interactions occur in group settings of varying sizes and locations, depending on the type of social activity. The ability to distinguish group formations based on their purposes transforms how group detection mechanisms function. Not only should such tools support the effective detection of serendipitous encounters, but they can derive categories of relation types among users. Determining who is involved, what activity is performed, and when and where the activity occurs are critical to understanding group processes in greater depth, including supporting goal-oriented applications (e.g., performance, productivity, and mental health) that require sensing social factors. In this work, we propose W4-Groups that captures the functional perspective of variability and repeatability when automatically constructing short-term and long-term groups via multiple data sources (e.g., WiFi and location check-in data). We design and implement W4-Groups to detect and extract all four group features who-what-when-where from the user's daily mobility patterns. We empirically evaluate the framework using two real-world WiFi datasets and a location check-in dataset, yielding an average of 92% overall accuracy, 96% precision, and 94% recall. Further, we supplement two case studies to demonstrate the application of W4-Groups for next-group activity prediction and analyzing changes in group behavior at a longitudinal scale, exemplifying short-term and long-term occurrences.

Hippocampal neuroimmune response in mice undergoing serial daily torpor induced by calorie restriction.

Hibernating animals demonstrate a remarkable ability to withstand extreme physiological brain changes without triggering adverse neuroinflammatory responses. While hibernators may offer valuable insights into the neuroprotective mechanisms inherent to hibernation, studies using such species are constrained by the limited availability of molecular tools. Laboratory mice may serve as an alternative, entering states of hypometabolism and hypothermia similar to the torpor observed in hibernation when faced with energy shortage. Notably, prolonged calorie restriction (CR) induces serial daily torpor patterns in mice, comparable to species that utilize daily hibernation. Here, we examined the neuroinflammatory response in the hippocampus of male C57BL/6 mice undergoing serial daily torpor induced by a 30% CR for 4 weeks. During daily torpor episodes, CR mice exhibited transient increases in TNF-α mRNA expression, which normalized upon arousal. Concurrently, the CA1 region of the hippocampus showed persistent morphological changes in microglia, characterized by reduced cell branching, decreased cell complexity and altered shape. Importantly, these morphological changes were not accompanied by evident signs of astrogliosis or oxidative stress, typically associated with detrimental neuroinflammation. Collectively, the adaptive nature of the brain's inflammatory response to CR-induced torpor in mice parallels observations in hibernators, highlighting its value for studying the mechanisms of brain resilience during torpor. Such insights could pave the way for novel therapeutic interventions in stroke and neurodegenerative disorders in humans.

Female delayed mating has a limited impact on the reproductive output of Cerambyx welensii, a synovigenic longhorn beetle

AbstractIncreased female age at mating is considered a detrimental factor on reproductive output and fitness in insects, even if the impact is rather species specific. The effect of delayed mating on reproductive output has been widely studied in pest species controlled with mating disruption, as if the method is not fully effective in suppressing matings, it could still delay them, limiting female fitness and pest damage. Female mating delay, however, may also occur in natural habitats without invoking mating disruption. We studied the effect of female delayed mating in Cerambyx welensii (Küster) (Coleoptera: Cerambycidae), an oak‐living sapro‐xylophagous longhorn beetle considered a critical factor in oak decline. Several life history and ecological traits may potentially delay mating, including low abundance, (re)colonisation processes, sexual communication channel, host‐produced kairomones, operational sex ratio, reproductive interference and adverse weather conditions. We conducted laboratory tests to assess the impact of mating delays (0‐, 10‐, 20‐ and 30‐day post‐emergence) on reproductive output. Data showed that mating delay until at least 20 days of age had a limited effect on mating success, lifetime fecundity, longevity and fertility. The daily fecundity pattern depended on mating delay, and virgin females showed ovarian retention. We conclude that C. welensii females have evolved physiological adaptations to overcome mating delays and optimise fitness. We discuss our results from an evolutionary perspective, considering specifically the risk of early predation and egg‐laying time limitation. We hypothesise that unpredictable recurrent stochastic variation in male availability could act as an additional driver selecting for synovigeny in this longhorn species.

Camera trap surveys of Atlantic Forest mammals: A data set for analyses considering imperfect detection (2004-2020).

Camera traps became the main observational method of a myriad of species over large areas. Data sets from camera traps can be used to describe the patterns and monitor the occupancy, abundance, and richness of wildlife, essential information for conservation in times of rapid climate and land-cover changes. Habitat loss and poaching are responsible for historical population losses of mammals in the Atlantic Forest biodiversity hotspot, especially for medium to large-sized species. Here we present a data set from camera trap surveys of medium to large-sized native mammals (>1 kg) across the Atlantic Forest. We compiled data from 5380 ground-level camera trap deployments in 3046 locations, from 2004 to 2020, resulting in 43,068 records of 58 species. These data add to existing data sets of mammals in the Atlantic Forest by including dates of camera operation needed for analyses dealing with imperfect detection. We also included, when available, information on important predictors of detection, namely the camera brand and model, use of bait, and obstruction of camera viewshed that can be measured from example pictures at each camera location. Besides its application in studies on the patterns and mechanisms behind occupancy, relative abundance, richness, and detection, the data set presented here can be used to study species' daily activity patterns, activity levels, and spatiotemporal interactions between species. Moreover, data can be used combined with other data sources in the multiple and expanding uses of integrated population modeling. An R script is available to view summaries of the data set. We expect that this data set will be used to advance the knowledge of mammal assemblages and to inform evidence-based solutions for the conservation of the Atlantic Forest. The data are not copyright restricted; please cite this paper when using the data.

Quantifying Apathy in Late-Life Depression: Unraveling Neurobehavioral Links Through Daily Activity Patterns and Brain Connectivity Analysis

BackgroundBetter understanding apathy in late-life depression would help improve prediction of poor prognosis of diseases such as dementia. Actimetry provides an objective and ecological measure of apathy from patients’ daily motor activity. We aimed to determine whether patterns of motor activity were associated with apathy and brain connectivity in networks that underlie goal-directed behaviors. MethodsResting-state functional magnetic resonance imaging and diffusion magnetic resonance imaging were collected from 38 nondemented participants with late-life depression. Apathy was evaluated using the diagnostic criteria for apathy, Apathy Evaluation Scale, and Apathy Motivation Index. Functional principal components (fPCs) of motor activity were derived from actimetry recordings taken for 72 hours. Associations between fPCs and apathy were estimated by linear regression. Subnetworks whose connectivity was significantly associated with fPCs were identified via threshold-free network-based statistics. The relationship between apathy and microstructure metrics was estimated along fibers by diffusion tensor imaging and a multicompartment model called neurite orientation dispersion and density imaging via tractometry. ResultsWe found 2 fPCs associated with apathy: mean diurnal activity, negatively associated with Apathy Evaluation Scale scores, and an early chronotype, negatively associated with Apathy Motivation Index scores. Mean diurnal activity was associated with increased connectivity in the default mode, cingulo-opercular, and frontoparietal networks, while chronotype was associated with a more heterogeneous connectivity pattern in the same networks. We did not find significant associations between microstructural metrics and fPCs. ConclusionsOur findings suggest that mean diurnal activity and chronotype could provide indirect ambulatory measures of apathy in late-life depression, associated with modified functional connectivity of brain networks that underlie goal-directed behaviors.

ANALYSIS OF DAILY ENERGY USAGE BY WORKERS IN A TOBACCO PRODUCTION FACILITY

This study analyzes the daily energy consumption patterns of workers in a tobacco production enterprise. Data was collected over a specified period to determine the average energy usage and identify any significant fluctuations or trends. The results provide valuable insights into energy management practices, highlighting areas for potential optimization and efficiency improvements within the enterprise. The findings aim to inform strategic decision-making and promote sustainable energy practices in the tobacco production sector.

Ground-based MAX-DOAS observations of formaldehyde and glyoxal in Xishuangbanna, China

Multi-axial differential optical absorption spectroscopy (MAX-DOAS) measurements were conducted in Xishuangbanna, Yunnan, China, between November 1, 2021 and June 30, 2022 to obtain vertical distributions of formaldehyde (HCHO) and glyoxal (CHOCHO). The observations show an increase in vertical column densities (VCDs) and volume mixing ratios (VMRs) for both HCHO and CHOCHO concentrations during periods of biomass combustion. The VCDs of HCHO and CHOCHO from TROPOMI are in good agreement with the MAX-DOAS observations. (R2HCHO = 0.71; R2CHOCHO = 0.70). Regarding seasonal variations, HCHO predominantly occupies the upper layer (400-800 m) during the biomass burning, possibly attributed to the formation of secondary HCHO as the plume ascends during combustion. CHOCHO is primarily found in the lower layer (0-200 m), suggesting a longer lifespan for HCHO compared to CHOCHO, preventing the latter from diffusing to higher altitudes. Concerning the daily variation patterns, both HCHO and CHOCHO VMRs exhibited peaks at 9:00 and 13:00, which were attributed to the nighttime accumulation and midday oxidation. Furthermore, we also investigated the sources of volatile organic compounds (VOCs) using the CHOCHO to HCHO ratio (RGF). During the period of biomass burning, there are minimal differences in the daily RGF across layers, indicating that biomass burning is the predominant source. During the non-biomass burning period, the daily RGF shows significant differences among layers, indicating that emissions from biological and anthropogenic sources primarily contribute during the period.