Effective Sampling Method Research Articles

Explainable and Interpretable Model for the Early Detection of Brain Stroke Using Optimized Boosting Algorithms

Stroke stands as a prominent global health issue, causing considerable mortality and debilitation. It arises when cerebral blood flow is compromised, leading to irreversible brain cell damage or death. Leveraging the power of machine learning, this paper presents a systematic approach to predict stroke-patient survival based on a comprehensive set of factors. These factors include demographic attributes, medical history, lifestyle elements, and physiological metrics. An effective random sampling method is proposed to handle the highly biased data on strokes. Stroke prediction using optimized boosting machine-learning algorithms is supported with explainable AI using LIME and SHAP. This enables the models to discern intricate data patterns and establish correlations between selected features and patient survival. Through this approach, the study seeks to uncover actionable insights to guide healthcare practitioners in devising personalized treatment strategies for stroke patients.

Using UAVs to collect filtered water samples for mineral exploration: Will it take off?

The advent of unmanned aerial vehicle (UAV) assisted surface water sampling and ongoing technological advances in sampling and data acquisition, offers many opportunities to conduct high-quality hydrogeochemical surveys with low cost, high efficiency, and reduced human interactions. Hydrogeochemical mineral exploration is one area that could greatly benefit from a UAV sampling revolution, with survey sites often located in highly remote areas with limited existing infrastructure. Currently, a lack of point source filtration and complicated physiochemical data acquisition hinder mainstream UAV deployment in the context of hydrogeochemical studies. The aim of this paper is to provide guidance on effective UAV sampling methods and physiochemical data collection for use in surface water hydrogeochemical mineral exploration. To date, case study surveys have utilized sampling systems where sampled waters are filtered after collection or analyzed for ‘total’ (unfiltered) concentrations. This paper details a methodology for point-source filtration of water samples using a UAV system to recover filter sample aliquots for the determination of ‘dissolved’ (<0.45 μm) trace element concentrations and compares UAV methods to conventional sampling strategies. This study systematically compares the quality of analytical data collected from lakes, ponds, and rivers in the Long Lake area of southern Ontario, using conventional manual sampling (from a boat or canoe) and a series of UAV-based sampling methodologies. The waters sampled within the study area are highly meteoric and show evidence of solute input from water-rock interaction with local country rocks. The results of this study show that in general, conventional sampling methodologies are statistically comparable to samples collected using UAVs. However, there is some evidence of element variation related to lake stratification, with dissolved Cu concentrations higher in samples collected at depth compared to those from the surface. Similarly, samples filtered after collection typically have lower concentrations of Fe and Mn, potentially resulting from precipitation before filtration. An enclosed sampling system offered from peristaltic pumping with in-line filtration removes the potential for contamination from the surrounding environment and from the UAV itself.

Upgrading Strategies for Managing Nematode Pests on Profitable Crops.

Plant-parasitic nematodes (PPNs) reduce the high profitability of many crops and degrade their quantitative and qualitative yields globally. Traditional nematicides and other nematode control methods are being used against PPNs. However, stakeholders are searching for more sustainable and effective alternatives with limited side effects on the environment and mankind to face increased food demand, unfavorable climate change, and using unhealthy nematicides. This review focuses on upgrading the pre-procedures of PPN control as well as novel measures for their effective and durable management strategies on economically important crops. Sound and effective sampling, extraction, identification, and counting methods of PPNs and their related microorganisms, in addition to perfecting designation of nematode-host susceptibility/resistance, form the bases for these strategies. Therefore, their related frontiers should be expanded to synthesize innovative integrated solutions for these strategies. The latter involve supplanting unsafe nematicides with a new generation of safe and reliable chemical nematicidal and bionematicidal alternatives. For better efficacy, nematicidal materials and techniques should be further developed via computer-aided nematicide design. Bioinformatics devices can reinforce the potential of safe and effective biocontrol agents (BCAs) and their active components. They can delineate the interactions of bionematicides with their targeted PPN species and tackle complex diseases. Also, the functional plan of nematicides based on a blueprint of the intended goals should be further explored. Such goals can currently engage succinate dehydrogenase, acetylcholinesterase, and chitin deacetylase. Nonetheless, other biochemical compounds as novel targets for nematicides should be earnestly sought. Commonly used nematicides should be further tested for synergistic or additive function and be optimized via novel sequential, dual-purpose, and co-application of agricultural inputs, especially in integrated pest management schemes. Future directions and research priorities should address this novelty. Meanwhile, emerging bioactivated nematicides that offer reliability and nematode selectivity should be advanced for their favorable large-scale synthesis. Recent technological means should intervene to prevail over nematicide-related limitations. Nanoencapsulation can challenge production costs, effectiveness, and manufacturing defects of some nematicides. Recent progress in studying molecular plant-nematode interaction mechanisms can be further exploited for novel PPN control given related topics such as interfering RNA techniques, RNA-Seq in BCA development, and targeted genome editing. A few recent materials/techniques for control of PPNs in durable agroecosystems via decision support tools and decision support systems are addressed. The capability and effectiveness of nematicide operation harmony should be optimized via employing proper cooperative mechanisms among all partners.

A novel customizing knowledge graph evaluation method for incorporating user needs

Knowledge graphs are now widely used in various domains, including Question-and-answer systems, intelligent search and recommendation systems, and intelligent decision-making systems. However, knowledge graphs inevitably contain inaccurate and incomplete knowledge during the creation process, which leads to a reduction in the usefulness of knowledge graphs. Therefore, to assess the usefulness of knowledge graphs based on specific application requirements, quality assessment is particularly important. Among them, accuracy assessment, as a necessary dimension, reflects the degree of correctness of the triples. However, in the actual assessment process, the existing assessment methods do not consider the user’s needs and do not implement the concept of “Fitness for Use”. Meanwhile, it takes a lot of labor cost to evaluate the accuracy of large-scale knowledge graphs. Therefore, to ensure the accuracy of the assessment in a cost-saving way while meeting the needs of users, we propose and implement a novel accuracy assessment method that focuses on the requirements of users by designing an effective sampling method to obtain accurate assessment results that are more practical and instructive for users. Finally, the performance of our proposed method is evaluated by comparing it with the real accuracy rate, and the experimental results show that the accuracy rate obtained by the proposed method is very close to the real accuracy rate, and the sample size is minimized.

Comparative assessment of eDNA metabarcoding and longline deployments for elasmobranch surveying across a large tropical marine park network

Abstract Medium‐ to large‐scale marine protected areas (MPAs) are playing an increasingly important role in global marine biodiversity conservation. A key question is “how do we collect relevant data on biodiversity and population trends to inform their design and measure success?” This question is particularly relevant for marine taxa that are difficult to survey, such as sharks and rays, and where populations may occur over vast areas. Environmental DNA (eDNA) metabarcoding has proven to be an effective sampling method and may provide a solution to these challenges; however, it remains unclear how its sampling effectiveness compares with traditionally used methods for elasmobranch surveying. Here, we directly compared the efficacy of eDNA metabarcoding and demersal longline deployments to survey elasmobranchs across 31 sites over a 55,000 km2 area of the Kimberley and Roebuck Australian Marine Parks in Australia's North‐west Marine Parks Network. In total, we documented 49 unique elasmobranch taxa, 36 of which were detected by eDNA and 32 (from a total of 815 captured sharks) by longline. A combined approach yielded over 34% more elasmobranch taxa than either method alone. Site community compositions varied between the two survey methods; notably eDNA was able to detect species from outside of the immediate sampling area, although this was still consistent with a detection radius of a few kilometres, highlighting a particular use in rugose reef habitats where it is difficult to deploy longlines. In investigating the quantitative use of eDNA data, we report that eDNA metabarcoding read abundance (count) data was poorly correlated with longline aggregate catch (count and biomass) data across raw, relative and rank abundance measures. However, we found that in multivariate analyses, both binary (presence–absence) and abundance (after square‐root transformation) datasets produced highly similar ordination patterns, largely segregating method (eDNA vs. longline), followed by latitude and depth. Lastly, we identified required levels of eDNA replication and longline deployments to maximize captured elasmobranch diversity. This study sets an integrated, georeferenced baseline and long‐term monitoring approach for the management and conservation of elasmobranch diversity within this unique marine park network.

TriSampler: A Better Negative Sampling Principle for Dense Retrieval

Negative sampling stands as a pivotal technique in dense retrieval, essential for training effective retrieval models and significantly impacting retrieval performance. While existing negative sampling methods have made commendable progress by leveraging hard negatives, a comprehensive guiding principle for constructing negative candidates and designing negative sampling distributions is still lacking. To bridge this gap, we embark on a theoretical analysis of negative sampling in dense retrieval. This exploration culminates in the unveiling of the quasi-triangular principle, a novel framework that elucidates the triangular-like interplay between query, positive document, and negative document. Fueled by this guiding principle, we introduce TriSampler, a straightforward yet highly effective negative sampling method. The keypoint of TriSampler lies in its ability to selectively sample more informative negatives within a prescribed constrained region. Experimental evaluation show that TriSampler consistently attains superior retrieval performance across a diverse of representative retrieval models.

Comparison of Three Preharvest Sampling Strategies to Monitor Pathogens in Cattle Lairage Areas

The objective of this study was to compare preharvest monitoring strategies by evaluating three different sampling methods in the lairage area to determine pathogen recovery for each sampling method and incoming pathogen prevalence from the cattle to inform in-plant decision making. Samples were gathered over a 5-month period, from February to June 2022, at a harvesting and processing facility located in Eastern Nebraska. Sampling methods included (i) fecal pats, (ii) boot swabs, and (iii) MicroTally swab. A total of 329 samples were collected over the study period (fecal pats: n = 105, boot swabs: n = 104, and MicroTally swabs: n = 120). Specific media combinations, an incubation temperature of 42°C, and incubation timepoints (18–24 h) were utilized for each matrix and the prevalence of Salmonella, Escherichia coli O157:H7, and six non-O157 Shiga-toxin producing E. coli (STEC) was evaluated using the BAX system Real-Time PCR assay. Overall, results from the study concluded that boot swabs were an effective sampling method for pathogen detection in the cattle lairage area. Boot swabs (97.1%) were statistically more likely to detect for Salmonella (p < 0.05) when compared to fecal pats (67.6%) and MicroTally swab (77.5%) methods. For E. coli O157:H7 and STEC – O26, O121, O45, and O103 prevalence, boot swabs were significantly better at detecting for these pathogens (p < 0.05) than MicroTally swabs (OR = 3.16 – 11.95) and a comparable sampling method to fecal pats (OR = 0.93 – 2.01, p > 0.05). Lastly, all three sampling methods detected a very low prevalence for E. coli O111 and O145; therefore, no further analysis was conducted. The boot swab sampling method was strongly favored because they require little training to implement, are inexpensive, and they do not require much sampling labor; therefore, would be a simple and effective sampling method to implement within the industry to evaluate pathogen prevalence preharvest.

Sampling methods based on expected traffic-volume information for long-term rotation-based bridge SHM in resource-constrained environments

Effective bridge structural health monitoring systems offer a route to assist in the safe and economic operation of bridges, but have seen limited adoption onto the short and medium-span bridges which represent the majority of our bridge stock. This lack of adoption is in part due to the practical challenges of deploying bridge SHM systems in resource-constrained environments, especially where long-term monitoring at transport network-scales is considered. This paper tackles the problem of implementing an effective sampling method to facilitate long-term, bridge rotation-based damage detection, particularly within the confines of a resource-constrained setting. We demonstrate that leveraging readily available expected traffic-volume information enables the creation of more effective bridge SHM sampling methods by capturing more vehicle crossings in the same time. Furthermore, we introduce an empirically tuned, temperature-capable bridge finite-element model for long-term simulations, incorporating a stochastic vehicle arrival model and a feature error model to allow for the presence of any residual noise/dynamics due to imperfect filtering or inaccuracies in the feature extraction stages. This allowed us to simulate 60 years worth of bridge rotation monitoring data with varying damage severities to evaluate the impact of environmental and operational variations on our proposed expected traffic volume based sampling methods. Our results show that sampling only the peak traffic hours was the most effective for the same reduced ‘on‘ time, i.e. for an 8x reduction in ‘on‘ time only a 1.5x increase in the minimum detectable damage was observed versus a 2x increase for naive random sampling.

Active surveillance for carbapenem-resistant Acinetobacter baumannii (CRAB) carriage.

Carbapenem-resistant Acinetobacter baumannii (CRAB) poses a significant threat in healthcare settings, necessitating robust infection control measures. To identify the most effective sampling method for CRAB carriage detection, we conducted a comparative analysis between skin sampling using a pre-moistened sponge, rectal swabs, and respiratory specimens. Our study encompassed both acute care and post-acute care hospitals. The results revealed that the skin sponge method, when plated on selective chromogenic media, demonstrated the highest sensitivity, exceeding 90% in both settings. Conversely, the commonly employed methods of rectal swabs and respiratory samples combined to exhibit sensitivity levels below 40%. Based on these findings, we recommend implementing the skin sponge method as the preferred approach for CRAB screening in healthcare facilities. This recommendation aims to optimize infection control strategies and mitigate the spread of CRAB in healthcare settings.IMPORTANCEOur study's results provide promising evidence for the incorporation of a high-sensitivity carbapenem-resistant Acinetobacter baumannii (CRAB) screening method in healthcare settings. Such an approach could prove beneficial in enhancing infection prevention and control measures, leading to improved patient outcomes and potentially alleviating the burden of CRAB in healthcare systems.

A rapid glove-based inoculum sampling technique to monitor Erysiphe necator in commercial vineyards.

Information on the presence and severity of grape powdery mildew (GPM), caused by Erysiphe necator, has long been used to guide management decisions. While recent advances in the available molecular diagnostic assays and particle samplers have made monitoring easier, there is still need for more efficient field collection of E. necator. The use of vineyard worker gloves worn during canopy manipulation as a sampler (glove swab) of E. necator was compared with samples identified by visual assessment with subsequent molecular confirmation (leaf swabs) and airborne spore samples collected by rotating-arm impaction traps (impaction traps). Samples from U.S. commercial vineyards in Oregon, Washington, and California were analyzed using two TaqMan qPCR assays targeting the internal transcribed spacer regions or cytochrome b gene of E. necator. Based on qPCR assays, visual disease assessments misidentified GPM up to 59% of the time with a higher frequency of misidentification occurring earlier in the growing season. Comparison of the aggregated leaf swab results for a row (n=915) to the row's corresponding glove swab had 60% agreement. The latent class analysis (LCA) indicated that glove swabs were more sensitive than leaf swabs in detecting E. necator presence. The impaction trap results had 77% agreement to glove swabs (n=206) taken from the same blocks. The LCAs estimated that the glove swabs and impaction trap samplers varied each year in which was more sensitive for detection. This likely indicates that these methods have similar levels of uncertainty and provide equivalent information. Additionally, all samplers, once E. necator was detected, were similarly sensitive and specific for detection of the A-143 resistance allele. Together, these results suggest that glove swabs are a viable sampling method for monitoring the presence of E. necator and, subsequently, the G143A amino acid substitution associated with resistance to quinone outside inhibitor fungicides in vineyards. Glove swabs could significantly reduce sampling costs due to the lack of need for specialized equipment, and time required for swab collection and processing.

Vacuum extraction: an effective larval sampling method for spotted-wing drosophila in small fruit crops.

Detecting and sampling the pest for pest management, either through enumerating their life stages or by quantifying the crop damage, is the cornerstone in deploying integrated pest management. Currently, for spotted-wing drosophila, Drosophila suzukii Matsumura, larval extraction from the fruit samples involves immersing the fruits in hot water, salt, or sugar solution. We are introducing a novel, fast, and effective larval sampling technique where D. suzukii larvae can be extracted from infested fruits by subjecting the fruit samples to vacuum pressure. We optimized the vacuum pressure and vacuum duration for larval extraction from blueberries by testing a range of vacuum pressures and durations. A vacuum pressure of -98 kPa for 60 min resulted in the maximum larval recovery of the small, medium, and large larvae from blueberries. A 30-min incubation at -98 kPa also yielded similar results. Larval extraction at -98 kPa for 60 min on average recovered 61, 70, and 83% of larvae from 2, 4, and 6-day incubated fruit samples, respectively. The fruit sample size (37, 149, and 298 g) did not affect the larval extraction efficacy. Additionally, comparing larval extraction efficacy at -98 kPa with the salt and sugar extraction, incubated for 10, 30, and 60 min, suggests that vacuum extraction is comparable to or more efficient than the salt and sugar methods in extracting larvae from the infested blueberries. Overall, our results indicate that vacuum sampling is a promising method for detecting D. suzukii larval infestation in small fruit crops.

Integrating satellite and street-level images for local climate zone mapping

Timely and accurate local climate zone (LCZ) classification maps are valuable for urban climate studies. The integration of remote sensing and street-level images is promising to produce high-quality LCZ maps, since the former can efficiently capture the information of landscapes on a large-scale while the latter include ground-level details. However, due to their significant differences in spatial distributions and capture views, as well as existing sampling issues of street-level images, how to fuse them effectively is challenging and remains an uncharted research area. To address these issues and fill the gap, this study proposes an effective method to integrate satellite and street-level images for LCZ mapping. Additionally, a simple yet effective street-level image sampling method is proposed. Extensive experiments have been performed and the results demonstrate the effectiveness of the proposed data fusion method and also confirm the usefulness of fusing street-level images with satellite images in enhancing the performance of LCZ mapping. Moreover, the proposed sampling method can increase data representativeness and avoid data redundancy, thus significantly reducing the number of required images while retaining high classification accuracy. To the best of our knowledge, this study is the first attempt to integrate cross-view satellite and street-level images for LCZ mapping. The study and proposed methods can contribute to the development of multi-source data fusion for LCZ map production and further benefit urban climatic research.

Pipelle Endometrial Sampling and Morphological Findings in Women with Abnormal Uterine Bleeding

Background: The abnormal uterine bleeding has many causes and consequences in women. The exact causes are described by histology of sample through D&amp;C. Pipple biopsy samples is considered one of the least invasive, OPD setting technique, comparable to D&amp;C. Objective: To see the adequacy of Pipelle endometrial sampling and morphological findings among women with abnormal uterine bleeding. Study Design: Cross-sectional descriptive study Place and Duration of Study: Department of Gynaecology &amp; Histopathology, Shaikh Zayed Hospital Lahore from 1st May to 31st December 2021. Methodology: Two hundred and thirty seven adult women with AUB recommended for D&amp;C were included. Those with endometrial thickness less than 4mm, having fibroids, with pelvic inflammatory disease, or clotting factor disorders were excluded. Pipelle endometrial sample was taken in Gyne OPD and were examined at Histopathology Department. Results: 80.0% were sampled adequately through Pipelle. Secondary endometrium was most common in 39.9% followed by proliferative endometrium in 36.2% and chronic endometritis 12.2%. There were 6 cases with carcinoma, 4 (2.1%) with adenocarcinoma and 2(1.1%) squamous cell carcinoma. Conclusion: Pipelle Endometrial sampling can be considered an effective methods for endometrial sampling. Keywords: Abnormal uterine bleeding, Pipelle endometrial biopsy, Sample adequacy, Endometrial pathology

Modeling Study of a Microbial Spray-Drying Process Based on Real-Time Sampling

The process of microbial spray-drying has inherent defects such as short time, complexity, and non-visualization of particle trajectory. However, there has been a lack of effective methods for real-time sampling, rehydration, and non-destructive storage of dried particles, as well as mathematical modeling of the drying process of yeast particles based on sampling and measurement data. Therefore, firstly, a real-time sampling system was developed which completed real-time sampling, rehydration, and non-destructive storage of spray-dried particles, and realized the real reproduction of the changes of yeast particles in the process of spray drying. The laws that the motion trajectory of microbial particles during spray drying are divided into the first cycle region and the reflux cycle region were concluded, and the partition mechanism was explored. Then, based on the sampling data and the law of heat and mass transfer, a mathematical model of porous media was established to predict the variation of moisture content and rehydration survival rate of dried microbial particles with drying time. Finally, the mathematical model was tested by a microbial spray drying experiment, and the maximum errors between the predicted value and the test value of moisture content and rehydration survival rate were Xmax1=0.027(d.b) and Qvmax1=1.06%, respectively, both were less than 5% which proved the correctness of the mathematical model of porous media and laid a foundation for the study of the damage mechanism of microbial spray drying.

A cross validation‐Voronoi and entropy based adaptive sampling strategy for uncertainty quantification through hybrid simulation

AbstractIntensive and complex engineering models drive the need for computational affordability and accuracy. To obtain more accurate prediction, an effective sampling method is proposed and evaluated in this study by integrating CV (cross validation)‐Voronoi (CV‐V) and entropy (EP) strategies. The new CVV (CV‐V) ‐DEP (Distance and EP) method uses CV‐V to divide the sample space into Voronoi units and selects the unit with largest error. The candidate sample point with the largest EP and distance values in the selected unit is then selected. To verify the efficacy of the proposed method, CVV‐DEP is first compared with traditional CV‐V and CVV‐EP (CV‐V and EP) strategies for six nonlinear functions. Computational simulations are further conducted using the proposed method for uncertainty quantification through hybrid simulation of two different nonlinear systems. The proposed method is demonstrated to provide great potential for uncertainty quantification of civil engineering infrastructures through hybrid simulation.

Bayesian neuroevolution using distributed swarm optimization and tempered MCMC

The major challenge of Bayesian neural networks has been in developing effective sampling methods that address deep neural networks and big data-related problems. As an alternative to gradient-based training methods, neuro-evolution features evolutionary algorithms that provide a black-box approach to learning in neural networks. Neuroevolution employs evolutionary and swarm optimization methods to provide an alternative where the training algorithm is not constrained to the architecture of the network and has the potential to reduce local-minima and vanishing gradient problems. Bayesian neural networks use variational inference and Markov chain Monte Carlo (MCMC) sampling methods. Tempered MCMC is a powerful MCMC method that can take advantage of a parallel computing environment and efficient proposal distributions. In this paper, we present a synergy of neuroevolution and Bayesian neural networks where operators in particle swarm optimization (PSO) are used for forming efficient proposals in tempered MCMC sampling. The results show that the proposed method provides better prediction accuracy when compared to random-walk proposal distribution in MCMC for both time-series and pattern classification problems. The results also show substantial computational time reduction compared to gradient-based proposals while generating comparable accuracy performance. The Bayesian neuroevolution framework can be further introduced to models that do not have gradient information.

Evaluating sampling strategies for enzootic Venezuelan equine encephalitis virus vectors in Florida and Panama.

Determining effective sampling methods for mosquitoes are among the first objectives in elucidating transmission cycles of vector-borne zoonotic disease, as the effectiveness of sampling methods can differ based on species, location, and physiological state. The Spissipes section of the subgenus Melanoconion of Culex represents an understudied group of mosquitoes which transmit Venezuelan equine encephalitis viruses (VEEV) in the Western Hemisphere. The objective of this study was to determine effective collection methods that target both blood-engorged and non-engorged females of the Spissipes section of Culex subgenus Melanoconion to test the hypothesis that favorable trapping methods differ between species and by physiological status within a species. Mosquitoes were collected using two commercially available traps, (CDC-light trap and BG-Sentinel trap), two novel passive traps (a novel mosquito drift fence and pop-up resting shelters), and two novel aspirators, (a small-diameter aspirator and a large-diameter aspirator) in Darién, Panama, and Florida, USA. The total number of female mosquitoes collected for each species was compared using rarefaction curves and diversity metrics. We also compared the utility of each trap for collecting total females and blood-engorged females of four Spissipes section mosquito species in Florida and Darién. In Darién, it was found that both blood-engorged and unfed females of Cx. pedroi were most effectively collected using the mosquito drift fence at 57.6% and 61.7% respectively. In contrast, the most unfed Cx. spissipes were collected using the mosquito drift fence (40.7%) while blood-engorged females were collected effectively by pop-up resting shelters (42.3%). In Florida, the best sampling technique for the collection of blood-engorged Cx. panocossa was the large diameter aspirator at 41.9%, while the best trap for collecting Cx. cedecei was the pop-up resting shelter at 45.9%. For unfed female Spissipes section mosquitoes in Florida, the CDC light trap with CO2 collected 84.5% and 98.3% of Cx. cedecei and Cx. panocossa respectively in Florida. Rarefaction analysis, and both the Shannon and Simpsons diversity indices all demonstrated that the mosquito drift fence was capable of collecting the greatest diversity of mosquito species regardless of location. The finding that the proportions of unfed and blood-engorged mosquitoes collected by traps differed both among and between species has implications for how studies of VEEV vectors will be carried out in future investigations. In Florida a combination of pop-up resting shelters and use of a large-diameter aspirator would be optimal for the collection of both VEEV vectors for host-use studies. Results demonstrate that traps can be constructed from common materials to collect mosquitoes for VEEV vector studies and could be assessed for their utilization in vectors of other systems as well. Unfortunately, no single method was effective for capturing all species and physiological states, highlighting a particular need for assessing trap utility for target species of a study.

Field sampling methods on seismic lines: a comparison between circular plots and belt transects

Seismic lines are linear clearings created for oil and gas exploration. Regeneration on seismic lines in Alberta's boreal forest is limited by slow natural recovery, making them persistent features on the landscape and prompting interest in line restoration through vegetation reestablishment. The goal of this study was to establish an effective woody vegetation sampling method for monitoring seismic line regeneration in boreal mixedwood forests. Data collected in belt transects and circular plots were similar, and doubling the area sampled did not impact the results. Based on ease of implementation, a 30 m × 2 m belt transect in the center of a seismic line is the recommended sampling method. However, if woody understory vegetation is also being measured, dividing the belt transect into smaller areas is recommended as a single belt transect yielded less accurate height data than circular plots. Plots were also measured along seismic lines' edge and inner areas to determine if vegetation recovery differs across the width of the line. While all tree responses did not differ between the plot locations, woody understory vegetation was more abundant when measured along the inner seismic line locations. Therefore, if woody understory recovery is the focus of the sampling program, using plots placed across the seismic line width is recommended to prevent overestimation. These recommendations are intended for measuring woody vegetation recovery on 5 m wide seismic lines in upland mixedwood forests in the boreal forest; implementation in other environments or for other response variables requires additional testing.

AtomNet PoseRanker: Enriching Ligand Pose Quality for Dynamic Proteins in Virtual High-Throughput Screens.

Structure-based, virtual High-Throughput Screening (vHTS) methods for predicting ligand activity in drug discovery are important when there are no or relatively few known compounds that interact with a therapeutic target of interest. State-of-the-art computational vHTS necessarily relies on effective methods for pose sampling and docking and generating an accurate affinity score from the docked poses. However, proteins are dynamic; invivo ligands bind to a conformational ensemble. In silico docking to the single conformation represented by a crystal structure can adversely affect the pose quality. Here, we introduce AtomNet PoseRanker (ANPR), a graph convolutional network trained to identify and rerank crystal-like ligand poses from a sampled ensemble of protein conformations and ligand poses. In contrast to conventional vHTS methods that incorporate receptor flexibility, a deep learning approach can internalize valid cognate and noncognate binding modes corresponding to distinct receptor conformations, thereby learning to infer and account for receptor flexibility even on single conformations. ANPR significantly enriched pose quality in docking to cognate and noncognate receptors of the PDBbind v2019 data set. Improved pose rankings that better represent experimentally observed ligand binding modes improve hit rates in vHTS campaigns and thereby advance computational drug discovery, especially for novel therapeutic targets or novel binding sites.

Quantifying HVAC electrical flexibility from building thermal mass: a case study of the DOE reference building

As a means to adjust the temperature of the thermal zones in buildings, building thermal mass is regarded as one of the essential sources of energy flexibility. It is still challenging to quantify the energy flexibility of passive thermal mass, making it oppugning to use thermal mass for buildings’ demand response (DR). A method to accurately quantify the energy flexibility from heating, ventilation, and air conditioning systems (HVAC) is important for buildings to participate in DR projects. This paper proposes a novel data-driven model to quantify HVAC’s electrical demand under dynamic global temperature adjustment. The Markov chain is first used to implement an effective sampling method to produce a global temperature resetting schedule representing different temperature resetting. Next, EnergyPlus evaluates the HVAC electrical demand under the various temperature reset scenarios. In the end, the LightGBM algorithm is used to develop the data-driven model. Having validated the proposed model, the case study was conducted in a DOE reference office building for EnergyPlus. Results demonstrate that the Markov chain outperforms the probabilistic method when sampling temperature setpoint schedules. In the future, the proposed data-driven model can be used to evaluate the flexibility capacity of an energy management system in grid-integrated buildings.