Occupancy Estimation Research Articles

Estimating occupancy and detection probability of the Amazonian manatee (Trichechus inunguis), in Central Amazon, Brazil

Understanding the distribution and abundance of threatened species is crucial to elaborate effective management plans for wild populations; however, elusive species prove difficult to detect. To support conservation strategies for the Vulnerable Amazonian manatee (Trichechus inunguis), the only freshwater sirenian, we analyzed presence/absence data with hierarchical models based on imperfect detection to assess T. inunguis occupancy in a Sustainable Development Reserve, Brazilian Central Amazon. In parallel, we compared the effectiveness of direct and indirect sampling methods to provide occupancy (ψ) and detection (p) estimates. Combining both sampling methods’ presence datasets provided higher accuracy estimates. The Amazonian manatee’s detection probability had never been estimated before: surprisingly, it was high (p = 0.50, SD = 0.05) and positively related with macrophyte coverage. Results suggest that the studied communities resident impact is not affecting the manatee occupancy, with greatest probabilities closer to human settlements. The final occupancy estimate obtained (ψ = 0.85, SD = 0.12) can be a baseline to Amazonian manatee long-term monitoring studies, and provide support for decision makers and local communities to establish effective protection zones for the species. Our approach highlights the potential of hierarchical models to understand the distribution not only of T. inunguis in different habitats, but also of other threatened Amazonian aquatic mammals.

MC-DeF

Executing complex scientific applications on Coarse-Grain Reconfigurable Arrays ( CGRAs ) promises improvements in execution time and/or energy consumption compared to optimized software implementations or even fully customized hardware solutions. Typical CGRA architectures contain of multiple instances of the same compute module that consist of simple and general hardware units such as ALUs, simple processors. However, generality in the cell contents, while convenient for serving a wide variety of applications, penalizes performance and energy efficiency. To that end, a few proposed CGRAs use custom logic tailored to a particular application’s specific characteristics in the compute module. This approach, while much more efficient, restricts the versatility of the array. To date, versatility at hardware speeds is only supported with Field programmable gate arrays (FPGAs), that are reconfigurable at a very fine grain. This work proposes MC-DeF, a novel Mixed-CGRA Definition Framework targeting a Mixed-CGRA architecture that leverages the advantages of CGRAs by utilizing a customized cell array, and those of FPGAs by incorporating a separate LUT array used for adaptability. The framework presented aims to develop a complete CGRA architecture. First, a cell structure and functionality definition phase creates highly customized application/domain specific CGRA cells. Then, mapping and routing phases define the CGRA connectivity and cell-LUT array transactions. Finally, an energy and area estimation phase presents the user with area occupancy and energy consumption estimations of the final design. MC-DeF uses novel algorithms and cost functions driven by user defined metrics, threshold values, and area/energy restrictions. The benefits of our framework, besides creating fast and efficient CGRA designs, include design space exploration capabilities offered to the user. The validity of the presented framework is demonstrated by evaluating and creating CGRA designs of nine applications. Additionally, we provide comparisons of MC-DeF with state-of-the-art related works, and show that MC-DeF offers competitive performance (in terms of internal bandwidth and processing throughput) even compared against much larger designs, and requires fewer physical resources to achieve this level of performance. Finally, MC-DeF is able to better utilize the underlying FPGA fabric and achieves the best efficiency (measured in LUT/GOPs).

Learning Occupancy Priors of Human Motion From Semantic Maps of Urban Environments

Understanding and anticipating human activity is an important capability for intelligent systems in mobile robotics, autonomous driving, and video surveillance. While learning from demonstrations with on-site collected trajectory data is a powerful approach to discover recurrent motion patterns, generalization to new environments, where sufficient motion data are not readily available, remains a challenge. In many cases, however, semantic information about the environment is a highly informative cue for the prediction of pedestrian motion or the estimation of collision risks. In this work, we infer occupancy priors of human motion using only semantic environment information as input. To this end, we apply and discuss a traditional Inverse Optimal Control approach, and propose a novel approach based on Convolutional Neural Networks (CNN) to predict future occupancy maps. Our CNN method produces flexible context-aware occupancy estimations for semantically uniform map regions and generalizes well already with small amounts of training data. Evaluated on synthetic and real-world data, it shows superior results compared to several baselines, marking a qualitative step-up in semantic environment assessment.

Moving through the mosaic: identifying critical linkage zones for large herbivores across a multiple‐use African landscape

Reduced connectivity across grassland ecosystems can impair their functional heterogeneity and negatively impact large herbivore populations. Maintaining landscape connectivity across human-dominated rangelands is therefore a key conservation priority. Integrate data on large herbivore occurrence and species richness with analyses of functional landscape connectivity to identify important areas for maintaining or restoring connectivity for large herbivores. The study was conducted on a landscape with a mosaic of multiple land uses in Laikipia County, Kenya. We used occupancy estimates for four herbivore species [African elephant (Loxodonta africana), reticulated giraffe (Giraffa reticulata), plains zebra (Equus quagga), and Grevy’s zebra (Equus grevyi)] and species richness estimates derived from aerial surveys to create resistance surfaces to movement for single species and a multi-species assemblage, respectively. We validated single-species resistance surfaces using telemetry data. We used circuit theory and least cost-path analyses to model linkage zones across the landscape and prioritize areas for connectivity restoration. Resistance layers approximated the movements of our focal species. Results for single-species and multi-species connectivity models were highly correlated (rp > 0.9), indicating similar spatial patterns of functional connectivity between individual species and the larger herbivore assemblage. We identified critical linkage zones that may improve permeability to large-herbivore movements. Our analysis highlights the utility of aerial surveys in modeling landscape connectivity and informing conservation management when animal movement data are scarce. Our results can guide management decisions, providing valuable information to evaluate the trade-offs between improving landscape connectivity and safeguarding livelihoods with electrified fences across rangelands.

Nesting ecology and reuse of nest burrows by Bank Swallow (<i>Riparia riparia</i>) in southern Yukon

Bank Swallow (<i>Riparia riparia</i>) is a declining insectivorous bird that nests colonially in near-vertical surfaces, including natural banks along waterways as well as those created by industrial excavation. Several threats are likely contributing to the population decline, conservation measures have been recommended, and monitoring methods have been developed. However, little is known of this species in the extensive boreal portion of its breeding range. To assess whether recommendations developed in southern areas are likely to be effective in a more northerly region, we investigated aspects of the nesting ecology of Bank Swallow in southern Yukon during 2013–2017. Nesting activity occurred between 20 May and 21 August. We found an exceptional abundance of nest burrows in natural riverbanks along 46 km of the Yukon River near Whitehorse (326 burrows/km), but relatively low percent burrow occupancy in both natural and artificial habitats compared to studies from other regions. Year-to-year persistence of nest burrows and rates of reuse of burrows were high compared to other studies. We highlight the potential importance of the boreal region for recovery of Bank Swallow in Canada, and the importance of using region-specific estimates of percent occupancy when monitoring Bank Swallow using burrow counts. Further study is needed to determine whether unoccupied burrows contribute to nesting success, and whether there are situations in which Bank Swallow burrows should be protected year-round instead of only during nesting.

Unsupervised Learning Using Variational Inference on Finite Inverted Dirichlet Mixture Models with Component Splitting

Unsupervised learning has been one of the essentials of pattern recognition and data mining. The role of Dirichlet family of mixture models in this field is inevitable. In this article, we propose a finite Inverted Dirichlet mixture model for unsupervised learning using variational inference. In particular, we develop an incremental algorithm with a component splitting approach for local model selection, which makes the clustering algorithm more efficient. We illustrate our model and learning algorithm with synthetic data and some real applications for occupancy estimation in smart homes and topic learning in images and videos. Extensive comparisons with comparable recent approaches have shown the merits of our proposed model.

Probabilities of detecting submersed aquatic vegetation species using a rake method may vary with biomass

Levels of submersed aquatic vegetation (SAV) are commonly assessed using a modified garden rake. However, the utility of the rake sampling method relative to methods that are typically viewed as more definitive (and expensive) such as snorkeling and coring remains a matter of debate. This study explores whether probabilities of species detections for four SAV species varied among sampling units in a rake-biomass study and, if so, whether such variation reflected variation in species abundance. Variation in detection probabilities, when unaddressed, may yield biased estimators of percent frequency of occurrence (“occupancy”) and of occurrence-habitat associations. Biomass-driven variation in detection probabilities is important because such variation may not be explainable using covariates typically measured when sampling using the rake method. This study found substantial among-unit variation in detection probabilities, with majorities of that variation on the logit or modeling scale being associated with biomass but not with the non-biomass covariates substrate type, water depth and day of study. The study closes by exploring sampling protocols and modeling methods that may yield improved SAV occupancy estimates.

FISHER OCCUPANCY TWENTY-FIVE YEARS AFTER TRANSLOCATION IN THE ROCKY MOUNTAINS OF MONTANA

The historical distribution of Fishers throughout Montana and the northern Rocky Mountains of the United States is poorly understood, and most Fishers in Montana appear to be descendants of translocated animals originating from the midwestern United States and British Columbia; however, a genotype that is unique and native to the Northern Rockies of Idaho and Montana exists in west-central Montana. Predictions based on Idaho models depict potential suitable habitat for Fishers throughout the Cabinet Mountains of northwestern Montana, yet distribution, occupancy, and population status is currently unknown for these Fishers. We conducted the 1st comprehensive monitoring of Fishers in the Cabinet Mountains of Montana using baited camera/DNA stations. We detected Fishers at 7 out of 21 cells, which resulted in a 0.43 probability that Fishers occupied a grid cell. Detection probability was low. Genetic analysis revealed a minimum population count of at least 4 individual Fishers in the study area, but all individuals successfully identified were males and of midwestern genetic origin. The low number of Fisher detections may indeed reflect low abundance of Fisher. We recommend future multi-season monitoring over a longer period to increase precision of occupancy estimates, and additional research is needed to evaluate the lack of female detections. We also recommend maintaining a closed trapping season on Fishers, until data exist to document a population large enough to sustain harvest.

Real-time occupancy detection with physics-informed pattern-recognition machines based on limited CO2 and temperature sensors

An indoor occupancy estimator is proposed for the purpose of identifying the room’s binary occupancy state based on limited carbon dioxide (CO2) and temperature measurements from only two available sensors. The proposed approach includes a physics-informed pattern-recognition machine (PI-PRM). Invariant features, including CO2 level, CO2 rate of change and HVAC state, are extracted by the first PI-PRM submodule based on Savitzky-Golay (S-G) filtering and other physics-informed transformations. Optimal parameterization of the feature extraction transformations is also provided based on Stein’s unbiased risk estimator (SURE) and a physics-informed transformation for the HVAC operation mode. The proposed PI-PRM method is available for real-time estimation within few minutes from an occupancy change. Experimental results in an average size room, demonstrate the efficacy of our method with an accuracy of 97%. Among other applications, the proposed work can potentially be leveraged in energy efficiency, air quality improvement and emergency evacuation.

Multi-species occupancy modeling provides novel insights into amphibian metacommunity structure and wetland restoration.

A fundamental goal of community ecology is to understand species-habitat relationships and how they shape metacommunity structure. Recent advances in occupancy modeling enable habitat relationships to be assessed for both common and rare species within metacommunities using multi-species occupancy models (MSOM). These models account for imperfect species detection and offer considerable advantages over other analytical tools commonly used for community analyses under the elements of metacommunity structure (EMS) framework. Here, we demonstrate that MSOM can be used to infer habitat relationships and test metacommunity theory, using amphibians. Repeated frog surveys were undertaken at 55 wetland sites in southeastern Australia. We detected 11 frog species from three families (Limnodynastidae, Myobatrachidae, and Pelodryadidae). The rarest species was detected at only one site whereas the most common species was detected at 42 sites (naive occupancy rate 0.02-0.76). Two models were assessed representing two competing hypotheses; the best-supported model included the covariates distance to the nearest site (connectivity), wetland area, presence of the non-native eastern mosquitofish (Gambusia holbrooki), proportion cover of emergent vegetation, an interaction term between Gambusia and emergent vegetation cover, and the proportion canopy cover over a site. Hydroperiod played no detectable role in metacommunity structure. We found species-habitat relationships that fit with current metacommunity theory: occupancy increased with wetland area and connectivity. There was a strong negative relationship between occupancy and the presence of predatory Gambusia, and a positive interaction between Gambusia and emergent vegetation. The presence of canopy cover strongly increased occupancy for several tree frog species, highlighting the importance of terrestrial habitat for amphibian community structure. We demonstrated how responses by amphibians to environmental covariates at the species level can be linked to occupancy patterns at the metacommunity scale. Our results have clear management implications: wetland restoration projects for amphibians and likely other taxa should maximize wetland area and connectivity, establish partial canopy cover, and eradicate Gambusia or provide aquatic vegetation to mitigate the impact of this non-native fish. We strongly advocate the use of MSOM to elucidate the habitat drivers behind animal occupancy patterns and to derive unbiased occupancy estimates for monitoring programs.

Estimating abundance based on time‐to‐detection data

AbstractMany studies in ecology and management aim at quantifying absolute abundance based on counts at a set of surveyed sites. As time for data collection is typically limited, methods for reliable estimation of occupancy or abundance from low‐cost data are desirable. Time‐to‐detection (TTD) models have shown promise for the estimation of occupancy. However, they remain heavily underutilized, and restricted to inference about occupancy, rather than abundance.We developed a binomial N‐mixture model for species‐level TTD protocols that allows estimation of abundance with multiple‐ or single‐visit data. An extension of the multi‐visit version allows estimating availability per visit, given temporary emigration is random. We provide JAGS code and a new function (nmixTTD) in the R packageunmarkedfor fitting a variety of such models.Simulations showed accurate parameter estimation from single‐visit species‐level TTD data if individual detection probability is high (≥~0.7) and the number of visited sites is in the hundreds (≥~300). Additional visits improved the accuracy of estimates considerably. A comparison with the Royle‐Nichols‐ and the classic binomial N‐mixture‐model revealed that the performance of our model is between these two, but requires data that are less expensive and less error‐prone than count data required for binomial N‐mixture‐models. In a case study, we found similar results when analysing data with the Royle‐Nichols‐, the binomial N‐mixture‐model or the multi‐visit version of our TTD model. Analysing single‐visit data with our model yielded lower abundance and higher detectability estimates. Presumably these differences are due to temporary emigration, as the single‐visit method estimates the abundance of individuals available at one sampling occasion, whereas the multi‐visit methods refer to the superpopulation, that is, the number of individuals present over the study period.Our new TTD‐N‐mixture model shows promise because it enables estimation of abundance, corrected for imperfect detection, for single‐ and multiple‐visit data, based on data that are less expensive and that will be available in large quantities in the near future thanks to technical advances like autonomous recording units. The effects of unmodelled heterogeneity in detection rate and imperfect availability require further study.

Forest patch characteristics affect reptile occurrence in north‐western Madagascar

AbstractCharacterising species–landscape associations is relevant for conservation planning and management in response to environmental transformation. The occurrence of reptiles is linked to landscape structure that allows for thermoregulation, shelter and feeding, but the relative importance of these factors is unknown for many tropical and subtropical species. We used community‐level occupancy modelling and remote sensing to investigate the effects of vegetation structure and anthropogenic development on reptile occurrence in a Malagasy dry forest during the dry season (June and July). The occupancy estimates for some species revealed that species occupancy increased for more densely vegetated sites situated closer to the forest edge. The proximity of anthropogenic transformation did not significantly affect the occurrence of species except for Uroplatus guentheri which showed preference for sites away from settlements. Because most species and those most sensitive to habitat transformation were associated with edges of intact forest patches with denser green vegetation, these areas should form the focus of efforts to conserve reptiles in tropical dry forests. We also demonstrate how setting and scale can affect how habitat‐use patterns are interpreted and the importance of local, species‐specific studies.

Environmental Factors and Spatial Heterogeneity Affect Occupancy Estimates of Waterbirds in Peninsular Malaysia

In Malaysia, multiple land use by humans has caused substantial losses of wetland ecosystems, and shrinkage of the populations, habitat and food bases of avian species. However, studies of avian populations, especially of waterbirds, is important, allowing us to understand the complexity of the wetland ecosystem structure, and also develop appropriate management techniques with robust monitoring tools to ensure the ecological sustainability of wetlands. This study aimed to determine the eco-climatic factors influencing the occurrence of waterbirds and to develop habitat suitability models for thePaya Indah (PIW) and Putrajaya wetlands (PW), Malaysia. A distance sampling point count technique using stratified random design was employed to survey the wetlands from November 2016 to January 2019. A total of 57 sampling points at 14 lakes at PIW and 54 sampling points at 24 lakes at PW were chosen. An automatic linear modelling algorithm and geographic information systems were employed to compute the importance ratios of 17 eco-climatic factors (hydrology 9; climate 5; waterscape 1 and landscape 2). The results revealed that all individual and estimated indices for observed waterbirds were significant. The automatic linear modelling algorithm results for PIW waterbirds also showed that the maximum and minimum weights of the factors were land cover and water dissolved oxygen, while in PW they were atmospheric pressure and Normalized Difference Water Index (NDWI). The maximum and minimum weights of the factors for waterbirds in PIW were water turbidity and electrical conductivity, while at PW they were atmospheric pressure and six water parameters. Large areas of Putrajaya Wetland were classified as more suitable for waterbirds than Paya Indah Wetland due to the favourable water pH, atmospheric pressure and land cover (forage availability). Thus, the models' adoption as a management tool can help in the maintenance of the wetlands' habitat quality and management effectiveness of waterbird species.

Occupancy rate and observations of Baird’s tapir (Tapirella bairdii) near waterholes in the Maya forest corridor, Belize

It is estimated that 25 % of the mammal species in the world are at risk of extinction due to habitat loss, fragmentation, farming, and overexploitation. Baird’s tapir (Tapirella bairdii), an ungulate with part of its distribution within the Maya Forest, is at risk of extinction. Waterholes, a primary source for water and an essential element for the survival of tapirs, were the target of this study design. We used a camera-trap survey to determine tapir occupancy and detection rate probability models at seven survey stations at Runaway Creek Nature Reserve which is located within the Maya Forest Corridor in central Belize. The survey was carried out from March to September 2015 and from January 2017 to October 2019 with a total sampling effort of 8,932 camera-trap nights. Our results indicated a cumulative naïve occupancy estimate of 85.7 %, a rate probability of occupancy of 0.97 +/- 0.15 (SE), and a probability of detection of 0.14 +/- 0.01 for all sites and years. The results of occupancy models with human infrastructure covariate showed that the distance to roads model had the highest influence on tapir occurrence (β = -0.95 +/- 0.87), followed by the village covariate model (β = 0.77 +/- 0.99). The topographic covariate of river model (β = 0.47 +/- 0.85) had minimal support. This study highlights the critical importance of waterholes for survival of tapirs, and the influence of roads on tapir occurrence at Runaway Creek Nature Reserve.

Practical server-side WiFi-based indoor localization: Addressing cardinality & outlier challenges for improved occupancy estimation

Server-side WiFi-based indoor localization offers a compelling approach for passive occupancy estimation (i.e., without requiring active participation by client devices, such as smartphones carried by visitors), but is known to suffer from median error of 6–8 meters. By analyzing the characteristics of an operationally-deployed, WiFi-based passive indoor location system, based on the classical RADAR algorithm, we identify and tackle 2 practical challenges for accurate individual device localization. The first challenge is the low-cardinality issue, whereby only the associated AP generates sufficiently frequent RSSI reports, causing a client to experience large localization error due to the absence of sufficient measurements from all nearby APs. The second is the outlier resolution issue, whereby clients physically located outside the fingerprinted region but attached to the WiFi network are localized erroneously to a fingerprinted landmark. To tackle the low-cardinality challenge, we identify the stationary-period of devices, and augment the device’s live AP-reported RSSI readings, during such stationary periods, with the useful but ‘stale’ values reported by neighboring APs. To eliminate extraneous outlier devices, we apply a threshold-based filtering strategy, where the RSSI thresholds for all interior points are derived using a combination of a weighted path-loss propagation model and the Voronoi tessellation of the fingerprinting map. In addition, to overcome intermittent false positives/negatives in localization or subsequent occupancy estimation, we apply two additional techniques: (a) temporal smoothing of location estimates over a time period, and (b) identification and removal of static devices. We experimentally evaluate these combined set of techniques on 3 different indoor work, collaboration & residential spaces, and show how these techniques improve the robustness of location tracking, which subsequently translates into an approx. 80+% reduction in the overall occupancy estimation error.

Joint species distribution models of Everglades wading birds to inform restoration planning.

Restoration of the Florida Everglades, a substantial wetland ecosystem within the United States, is one of the largest ongoing restoration projects in the world. Decision-makers and managers within the Everglades ecosystem rely on ecological models forecasting indicator wildlife response to changes in the management of water flows within the system. One such indicator of ecosystem health, the presence of wading bird communities on the landscape, is currently assessed using three species distribution models that assume perfect detection and report output on different scales that are challenging to compare against one another. We sought to use current advancements in species distribution modeling to improve models of Everglades wading bird distribution. Using a joint species distribution model that accounted for imperfect detection, we modeled the presence of nine species of wading bird simultaneously in response to annual hydrologic conditions and landscape characteristics within the Everglades system. Our resulting model improved upon the previous model in three key ways: 1) the model predicts probability of occupancy for the nine species on a scale of 0–1, making the output more intuitive and easily comparable for managers and decision-makers that must consider the responses of several species simultaneously; 2) through joint species modeling, we were able to consider rarer species within the modeling that otherwise are detected in too few numbers to fit as individual models; and 3) the model explicitly allows detection probability of species to be less than 1 which can reduce bias in the site occupancy estimates. These improvements are essential as Everglades restoration continues and managers require models that consider the impacts of water management on key indicator wildlife such as the wading bird community.

Sparing land for secondary forest regeneration protects more tropical biodiversity than land sharing in cattle farming landscapes

Effectively managing farming to meet food demand is vital for the future of biodiversity.1,2 Increasing yields on existing farmland can allow the abandonment (sparing) of low-yielding areas that subsequently recover as secondary forest.2-5 A key question is whether such "secondary sparing" conserves biodiversity more effectively than retaining wildlife-friendly habitat within farmland ("land sharing"). Focusing on the Colombian Choco-Andes, a global hotspot of threatened biodiversity,6 and on cattle farming, we examined the outcomes of secondary sparing and land sharing via simulated scenarios that maintained constant landscape-wide production and equal within-pasture yield: (1) for species and functional diversity of dung beetles and birds; (2) for avian phylogenetic diversity; and (3) across different stages of secondary forest regeneration, relative to spared primary forests. Sparing older secondary forests (15-30 years recovery) promotes substantial species, functional, and phylogenetic (birds only) diversity benefits for birds and dung beetles compared to land sharing. Species of conservation concern had higher occupancy estimates under land-sparing compared to land-sharing scenarios. Spared secondary forests accumulated equivalent diversity to primary forests for dung beetles within 15 years and within 15-30 years for birds, highlighting the need for longer term protection to maximize the biodiversity gains of secondary sparing. Promoting the recovery and protection of large expanses of secondary forests under the land-sparing model provides a critical mechanism for protecting tropical biodiversity, with important implications for concurrently assisting in the delivery of global targets to restore 350 million hectares of forested landscapes.7,8.

Generalized model-based solutions to false-positive error in species detection/nondetection data.

Detection/nondetection data are widely collected by ecologists interested in estimating species distributions, abundances, and phenology, and are often imperfect. Recent model development has focused on accounting for both false-positive and false-negative errors given evidence that misclassification is common across many sampling protocols. To date, however, model-based solutions to false-positive error have largely addressed occupancy estimation. We describe a generalized model structure that allows investigators to account for false-positive error in detection/nondetection data across a broad range of ecological parameters and model classes, and demonstrate that previously developed model-based solutions are special cases of the generalized model. Simulation results demonstrate that estimators for abundance and migratory arrival time ignoring false-positive error exhibit severe (20-70%) relative bias even when only 5-10% of detections are false positives. Bias increased when false-positive detections were more likely to occur at sites or within occasions in which true positive detections were unlikely to occur. Models accounting for false-positive error following the site-confirmation or observation-confirmation designs generally reduced bias substantially, even when few detections were confirmed as true or false positives or when the process model for false-positive error was misspecified. Results from an empirical example focusing on gray fox (Urocyon cinereoargenteus) abundance in Wisconsin, USA reinforce concerns that biases induced by false-positive error can also distort spatial predictions often used to guide decision making. Model sensitivity to false-positive error extends well beyond occupancy estimation, but encouragingly, model-based solutions developed for occupancy estimators are generalizable and effective across a range of models widely used in ecological research.

PET Imaging Estimates of Regional Acetylcholine Concentration Variation in Living Human Brain.

Acetylcholine (ACh) has distinct functional roles in striatum compared with cortex, and imbalance between these systems may contribute to neuropsychiatric disease. Preclinical studies indicate markedly higher ACh concentrations in the striatum. The goal of this work was to leverage positron emission tomography (PET) imaging estimates of drug occupancy at cholinergic receptors to explore ACh variation across the human brain, because these measures can be influenced by competition with endogenous neurotransmitter. PET scans were analyzed from healthy human volunteers (n = 4) and nonhuman primates (n = 2) scanned with the M1-selective radiotracer [11C]LSN3172176 in the presence of muscarinic antagonist scopolamine, and human volunteers (n = 10) scanned with the α4β2* nicotinic ligand (-)-[18F]flubatine during nicotine challenge. In all cases, occupancy estimates within striatal regions were consistently lower (M1/scopolamine human scans, 31 ± 3.4% occupancy in striatum, 43 ± 2.9% in extrastriatal regions, p = 0.0094; nonhuman primate scans, 42 ± 26% vs. 69 ± 28%, p < 0.0001; α4β2*/nicotine scans, 67 ± 15% vs. 74 ± 16%, p = 0.0065), indicating higher striatal ACh concentration. Subject-level measures of these concentration differences were estimated, and whole-brain images of regional ACh concentration gradients were generated. These results constitute the first in vivo estimates of regional variation in ACh concentration in the living brain and offer a novel experimental method to assess potential ACh imbalances in clinical populations.

Real-time ventilation control based on a Bayesian estimation of occupancy

Demand-controlled ventilation (DCV) is commonly implemented to provide variable amounts of outdoor air according to an internal ventilation demand. The objective of the present study is to investigate the applicability and the performance of occupancy-based DCV schemes in comparison with time-based and CO2-based DCV schemes. To do this, we apply the occupancy estimation method by the Bayes theorem to control the ventilation rate of an office building in real-time. We investigated six cases in total (two cases for each control scheme). Experiments were conducted in a small office room with controllable ventilation equipment and relevant sensors. The observed results indicated that the occupancy-based schemes relying on Bayes theorem could be applied successfully to perform continuous control of ventilation rates without causing recursive problems. Additionally, we discussed the time delays associated with the control procedure, including dispersion time, sensor-response time, and data processing time. Finally, we compared the performance of the proposed approach in six DCV cases in terms of a resultant indoor CO2 level and the total ventilation-air volume. We concluded that DCV control based on both occupancy and floor area provided the best conformity to the ASHRAE standard among the analyzed schemes.