Fish ectoparasite detection, collection and curation.

Fish parasitology is a dynamic and internationally important discipline with numerous biological, ecological and practical applications. We reviewed optimal fish and parasite sampling methods for key ectoparasite phyla (i.e. Ciliophora, Platyhelminthes, Annelida and Arthropoda) as well as recent advances in molecular detection of ectoparasites in aquatic environments. Ideally, fish capture and anaesthesia as well as parasite recovery methods should be validated to eliminate potential sampling bias and inaccuracy in determining ectoparasite population parameters. There are considerable advantages to working with fresh samples and live parasites, when combined with appropriate fixation methods, as sampling using dead or decaying materials can lead to rapid decomposition of soft-bodied parasites and subsequent challenges for identification. Sampling methods differ between target phyla, and sometimes genera, with optimum techniques largely associated with identification of parasite microhabitat and the method of attachment. International advances in fish parasitology can be achieved through the accession of whole specimens and/or molecular voucher specimens (i.e. hologenophores) in curated collections for further study. This approach is now critical for data quality because of the increased application of environmental DNA (eDNA) for the detection and surveillance of parasites in aquatic environments where the whole organism may be unavailable. Optimal fish parasite sampling methods are emphasised to aid repeatability and reliability of parasitological studies that require accurate biodiversity and impact assessments, as well as precise surveillance and diagnostics.

An immunosensor for the near real-time and site of inflammation detections of multiple proinflammatory cytokines.

Diseases mediated by cytokine storms are often characterized by an overexuberant pace of pathogenesis accompanied by significant morbidity and mortality. Thus, near real-time (NRT) detections via a site-of-inflammation (SOI) sampling of proinflammatory cytokines are essential to ensure a timely and effective treatment of acute inflammations, which up to now, has not been fully possible. In this work, we proposed a novel NRT and SOI immunosensor using ZIF-8 signal amplification together with an off-on strategy. To achieve NRT detections via a SOI sampling, the body fluid of choice is the dermal interstitial fluid (ISF). The significant merits of ISF over blood are the quality, quantity and diversity of ISF-based biomarkers; the fluid is non-coagulating, making it feasible to perform multiple or continuous samplings and the sampling is minimally invasive. Our immunosensor requires only 5 µL of ISF to achieve a simultaneous detection of five highly potent proinflammatory cytokines: IL-6, IFN-γ, IL-1ß, TNF-α, IP-10. We employed a microneedle array patch (MAP) together with a trifurcated nozzle pump to extract a mean volume of between 30 and 60 µL of ISF in 20 min. Under optimal conditions, the biosensor is capable of high-quality performance that exhibits a lower limit of detection (LOD) of 5.761 pg/mL over a wide linear range of 5.761-3 ‒ 20.00 ng/mL. We believe our immunosensor for NRT detections via a SOI sampling of ISF-biomarkers offers new theranostic opportunities that may not be possible with blood-based biomarkers.

A Comparison of 06:00 AM Versus 09:00 AM Serum Cortisol as a Basal Morning Cortisol in Guwahati, Assam: A Pilot Study.

Introduction: Cortisol secretion is regulated by circadian rhythm, which is influenced by zeitgebers like light. In India, the entire country operates under a single time zone, Indian Standard Time, which may not align with the local sunrise timing across different regions. Aims: This study aimed to compare the basal serum cortisol levels between 06:00 AM and 09:00 AM in Guwahati, Assam, where sunrise occurs earlier compared with the western part of the country. A cross-sectional pilot study was conducted from December 2022 to June 2023 in a tertiary care hospital in Guwahati. Methods: Serum cortisol samples were collected at 06:00 AM and 09:00 AM from 25 healthy adult participants once in winter and again in summer. Descriptive statistics and paired Student's t-tests were used. Results: The mean serum cortisol levels at 06:00 AM in winter, summer and overall were 13.2, 13.4 and 13.3 µg/dL, respectively. At 09:00 AM, the mean serum cortisol levels in winter, summer and overall were 8.2, 7.7 and 8.0 µg/dL, respectively. Significant differences were observed between the 06:00 AM and 09:00 AM cortisol levels in both winter and summer (P <0.001). Conclusion: This study highlights the importance of considering the influence of earlier sunrise on circadian rhythm, cortisol secretion and sampling protocols. Recognising the impact of earlier sunrise on cortisol secretion and adapting sampling protocols accordingly to align with the local sunrise can provide a more accurate assessment of basal cortisol levels and help avoid potential misinterpretation and diagnostic challenges associated with low values.

Prediction of Deleterious Single Amino Acid Polymorphisms with a Consensus Holdout Sampler.

Background: Single Amino Acid Polymorphisms (SAPs) or nonsynonymous Single Nucleotide Variants (nsSNVs) are the most common genetic variations. They result from missense mutations where a single base pair substitution changes the genetic code in such a way that the triplet of bases (codon) at a given position is coding a different amino acid. Since genetic mutations sometimes cause genetic diseases, it is important to comprehend and foresee which variations are harmful and which ones are neutral (not causing changes in the phenotype). This can be posed as a classification problem. Methods: Computational methods using machine intelligence are gradually replacing repetitive and exceedingly overpriced mutagenic tests. By and large, uneven quality, deficiencies, and irregularities of nsSNVs datasets debase the convenience of artificial intelligence-based methods. Subsequently, strong and more exact approaches are needed to address these problems. In the present work paper, we show a consensus classifier built on the holdout sampler, which appears strong and precise and outflanks all other popular methods. Results: We produced 100 holdouts to test the structures and diverse classification variables of diverse classifiers during the training phase. The finest performing holdouts were chosen to develop a consensus classifier and tested using a k-fold (1 ≤ k ≤5) cross-validation method. We also examined which protein properties have the biggest impact on the precise prediction of the effects of nsSNVs. Conclusion: Our Consensus Holdout Sampler outflanks other popular algorithms, and gives excellent results, highly accurate with low standard deviation. The advantage of our method emerges from using a tree of holdouts, where diverse LM/AI-based programs are sampled in diverse ways.

Generation of cutting heat and simulation of core tube wall temperature during coring in primary structure coal seam.

When the drilling core method is used to determine the coalbed gas content, the cutting heat generated by the core bit cutting coal will increase the core tube temperature, and the excessively high core tube temperature will have an heating effect on the coal core, which will accelerate the coal core gas desorption rate and increase the gas loss amount. The generation of cutting heat of core bit and the measurement of core tube temperature are the basis for grasping the gas desorption law of coal core and projecting the amount of gas loss. Firstly, the self-developed core tube temperature measurement device was used to conduct on-site core temperature measurement experiments at different cutting speeds. Then, the cutting temperature of core bit was solved by establishing thermodynamic model for cutting coal and heat transfer model of cutting edge. Finally, based on the thermal conductivity characteristics of the core tube, the core tube temperature at different cutting speeds was simulated, and the simulated temperature was compared with the on-site measured temperature to verify the reliability of the model. The results show that when coring in primary structural coal, the temperature change trend of core tube wall temperature measurement point at different cutting speeds is basically consistent, the temperature measurement point at the front end of the core tube mainly goes through a relatively stable period in the drilling process, a sharp rising period in the cutting process, a slow rise and cooling period in the withdrawal process. However, the temperature measurement point at the back end of the core tube wall mainly goes through a relatively stable phase and a slowly increasing phase. The temperature rise of the core bit and the core tube wall are significantly positively correlated with the cutting speed. When coring in hard coal seam and the core depth is not large, the cutting heat generated by the core bit and the coal body is the dominant factor for the temperature rise of the core tube. The core tube wall temperature calculated using the model matches well with the field measured temperature, and the error is small, which fully shows that the coring thermodynamic model is feasible. This study provides a basis for further research on the dynamic distribution characteristic of coal core temperature during coring, which is of profound significance to calculate the gas loss and coalbed gas content.

A user-friendly method for estimating discrete choice land-use model in a panel data setting.

Land-use modeling stands as a pivotal tool in shaping sustainable development policies. With the rapid advancement of remote-sensing technology and the widespread adoption of satellite imagery-based land cover products, these datasets have emerged as primary sources for understanding land-use dynamics due to their high spatial and temporal resolutions. Yet, it remains challenging to effectively integrate such rich panel data into nonlinear econometric land-use models. This paper introduces a method to seamlessly incorporate land cover panel data into econometric models, enabling comprehensive utilization of temporal information within a single framework.-By capturing dynamic land-use patterns, the method enhances prediction accuracy while mitigating issues such as autocorrelated error terms commonly encountered in panel data analysis.-The method is straightforward to implement and applicable to many nonlinear models, making it particularly suitable for datasets with large sample sizes.

Enhanced direct and synthetic estimators for domain mean with simulation and applications.

This article considers the issue of domain mean estimation utilizing bivariate auxiliary information based enhanced direct and synthetic logarithmic type estimators under simple random sampling (SRS). The expressions of mean square error (MSE) of the proposed estimators are provided to the 1 s t order approximation. The efficiency criteria are derived to exhibit the dominance of the proposed estimators. To exemplify the theoretical results, a simulation study is conducted on a hypothetically drawn trivariate normal population from R programming language. Some applications of the suggested methods are also provided by analyzing the actual data from the municipalities of Sweden and acreage of paddy crop in the Mohanlal Ganj tehsil of the Indian state of Uttar Pradesh. The findings of the simulation and real data application exhibit that the proposed direct and synthetic logarithmic estimators dominate the conventional direct and synthetic mean, ratio, and logarithmic estimators in terms of least MSE and highest percent relative efficiency.

Optimizing Soil Sampling for Accurately Prediction of the Potential Remediation-Effective Area in a Contaminated Agricultural Land.

To achieve food security in a contaminated agricultural land, the remediation areas usually need more samples to obtain accurate contamination information and implement appropriate measures. In this study, we propose an optimal encryption sampling design to instead of the detailed survey, which is determined by the variation of heavy metals and the technology capability of remediation, to guide soil sampling for accurately remediation in the potential remediation-effective areas (PRA). The coefficient of screening variation threshold (CSVT), considering spatial variation, technology capacity and acceptable error of sampling, together with the spatial cyclic statistics method of neighbourhood analysis, is introduced to identify and delineate the PRA. Both of the hypothetical analysis and application case studies are conducted to illustrate the advantages and disadvantages of the optimization. The results show that, compared with the detailed survey, the optimal design shows a lower overall accuracy due to its sparsely sampling at the clean area, but it exhibits a similar effect of accurately prediction in boundary delineation and further classification in the PRA in both simulation and application studies. This work provides an effective method for subsequent accurate remediation at the investigation stage and valuable insights into application combination of technology capacity and contaminated agricultural land investigation.

LUCMT: Learnable under-sampling and reconstructed network with cross multi-head attention transformer for accelerating MR image reconstruction.

BACKGROUND AND OBJECTIVE: As a widely used technique for Magnetic Resonance Image (MRI) acceleration, compressed sensing MRI involves two main issues: designing an effective sampling strategy and reconstructing the image from significantly under-sampled K-space data. In this paper, an innovative approach is proposed to address these two challenges simultaneously. METHODS: A novel MRI reconstruction method, termed as LUCMT, is implemented by integrating a learnable under-sampling strategy with a reconstruction network based on the Cross Multi-head Attention Transformer. In contrast to conventional static sampling methods, the proposed adaptive sampling scheme is processed optimally by learning the optimal sampling technique, which involves binarizing the sampling pattern by a sigmoid function and computing gradients by backpropagation. And the reconstruction network is designed by using CS-MRI depth unfolding network that incorporates a Cross Multi-head Attention (CMA) module with inertial and gradient descent terms. RESULTS: T1 brain MR images from the FastMRI dataset are used to validate the performance of the proposed method. A series of experiments are conducted to validate the superior performance of our proposed network in terms of quantitative metrics and visual quality. Compared with other state-of-the-art reconstruction methods, LUCMT achieves better reconstruction performances with more accurate details. Specifically, LUCMT achieves PSNR and SSIM results of 41.87/0.9749, 46.64/0.9868, 50.41/0.9924, and 53.51/0.9955 at sampling rates of 10 %, 20 %, 30 %, and 40 %, respectively. CONCLUSIONS: The proposed LUCMT method can provide a promising way for generating optimal under-sampling mask and accelerating MRI reconstruction accurately.

PFAS distribution in cascade derived foam at wastewater treatment plants: The role of non-linear drainage, collapse induced enrichment, and implications for removal.

Per- and polyfluorinated alkyl substances (PFAS) enrichment in foam was investigated for the first time at a wastewater treatment plant cascade. A novel sampling device was utilized to allow spatial and temporal heterogeneity in PFAS concentrations and liquid content to be characterized. Concentrations of 8 PFAS compounds were normalized to liquid content and fit to a power law model revealing strong correlation (R2 = 0.91) between drainage induced enrichment and PFAS molar volume. Short chain PFAS such as perfluorobutanoate (PFBA) exhibited minor to no enrichment factors in foam (0.24-5.9) compared to effluent concentrations across the range of foam liquid contents (0.28-6.24 %), while long chain compounds such as perfluorooctane sulfonate (PFOS) became highly enriched with factors of 295-143,000. A conceptual model is proposed to explain higher than expected enrichment of more surface-active PFAS relative to liquid content, which combines continuous partitioning of PFAS to air bubbles during foam formation with additional partitioning during non-linear drainage and foam collapse, both controlled by their affinity for the air-water interface. Scoping calculations suggest the majority of PFOS and other long chain PFAS may be removed if foam is continuously collected with potential to reduce waste volume under economic barriers for current destructive technologies.

Assessment of fine and coarse tyre wear particles along a highway stormwater system and in receiving waters: Occurrence and transport.

Tyre wear has been identified as a major road-related pollutant source, with road runoff transporting tyre wear particles (TWP) to adjacent soil, watercourses, or further through stormwater systems. The aim of this study was to investigate the occurrence and transport of TWP along a stormwater system. Water and sediment have been sampled at selected points (road runoff, gully pots, wells, outlet to a ditch, and stream) through a stormwater system situated along a highway in Sweden during November and December 2022, and March 2023. As there is limited data on the size distribution of TWP in different environmental media, especially in the size fraction <20 µm, the samples were fractioned into a fine (1.6-20 µm) and a coarse (1.6-500 µm) size fraction. The samples were analysed using a combination of marker compounds (benzene, α-methylstyrene, ethylstyrene, and butadiene trimer) for styrene-butadiene rubbers with PYR-GC/MS from which TWP concentration was calculated. Suspended solids were analysed in the water samples, and organic content was analysed in the sediment samples. TWP was found at nearly all locations, with concentrations up to 17 mg/L in the water samples and up to 40 mg/g in the sediment samples. In the sediment samples, TWP in the size fraction 1.6-20 µm represented a significant proportion (20-60%). Correlations were found between TWP concentration and suspended solids in the water samples (r = 0.87) and organic content in the sediment samples (r = 0.72). The results presented in this study demonstrate that TWP can be transported to the surrounding environment through road runoff, with limited retention in the studied stormwater system.

The fungal diversity in the lungs of children with cystic fibrosis captured by sputum-induction and bronchoalveolar lavage.

BACKGROUND: The prevalence of fungi in cystic fibrosis (CF) lung infections is poorly understood and studies have focused on adult patients. We investigated the fungal diversity in children with CF using bronchoalveolar lavage (BAL) and induced sputum (IS) samples to capture multiple lung niches. METHODS: Sequencing of the fungal ITS2 region and molecular mycobiota diversity analysis was performed on 25 matched sets of BAL-IS samples from 23 children collected as part of the CF-SpIT study (UKCRN14615; ISRCTNR12473810). RESULTS: Aspergillus and Candida were detected in all samples and were the most abundant and prevalent genera, followed by Dipodascus, Lecanicillium and Simplicillium. The presumptive CF pathogens Exophiala, Lomentospora and Scedosporium were identified at variable abundances in 100 %, 64 %, and 24 % of sample sets, respectively. Fungal pathogens observed at high relative abundance (≥40 %) were not accurately diagnosed by routine culture microbiology in over 50 % of the cohort. The fungal communities captured by BAL and IS samples were similar in diversity and composition, with exception to C. albicans being significantly increased in IS samples. The respiratory mycobiota varied greatly between individuals, with only 13 of 25 sample sets containing a dominant fungal taxon. In 11/25 BAL sample sets, airway compartmentalisation was observed with diverse mycobiota detected from different lobes of the lung. CONCLUSIONS: The paediatric mycobiota is diverse, complex and inadequately diagnosed by conventional microbiology. Overlapping fungal communities were identified in BAL and IS samples, showing that IS can capture fungal genera associated with the lower airway. Compartmentalisation of the lower airway presents difficulties for consistent mycobiota sampling.

A health decision analytical model to evaluate the cost-effectiveness of female genital schistosomiasis screening strategies: The female genital schistosomiasis SCREEN framework.

Female genital schistosomiasis is a chronic gynaecological disease caused by the waterborne parasite Schistosoma (S.) haematobium. It affects an estimated 30-56 million girls and women globally, mostly in sub-Saharan Africa where it is endemic, and negatively impacts their sexual and reproductive life. Recent studies found evidence of an association between female genital schistosomiasis and increased prevalence of HIV and cervical precancer lesions. Despite the large population at risk, the burden and impact of female genital schistosomiasis are scarcely documented, resulting in neglect and insufficient resource allocation. There is currently no standardised method for individual or population-based female genital schistosomiasis screening and diagnosis which hinders accurate assessment of disease burden in endemic countries. To optimise financial allocations for female genital schistosomiasis screening, it is necessary to explore the cost-effectiveness of different strategies by combining cost and impact estimates. Yet, no economic evaluation has explored the value for money of alternative screening methods. This paper describes a novel application of health decision analytical modelling to evaluate the cost-effectiveness of different female genital schistosomiasis screening strategies across endemic settings. The model combines a decision tree for female genital schistosomiasis screening strategies, and a Markov model for the natural history of cervical cancer to estimate the cost per disability-adjusted life-years averted for different screening strategies, stratified by HIV status. It is a starting point for discussion and for supporting priority setting in a data-sparse environment.

HPV self-sampling in organized cervical cancer screening program: A randomized pilot study in Estonia in 2021.

BACKGROUND: Cervical cancer incidence in Estonia ranks among the highest in Europe, but screening attendance has remained low. This randomized study aimed to evaluate the impact of opt-in and opt-out human papillomavirus (HPV) self-sampling options on participation in organized screening. METHODS: A random sample of 25,591 women were drawn from the cervical cancer screening target population who were due to receive a reminder in autumn 2021 and thereafter randomly allocated to two equally sized intervention arms (opt-out and opt-in) receiving a choice between HPV self-sampling or clinician sampling. In the opt-out arm, a self-sampler was sent to home address by regular mail; the opt-in arm received an e-mail containing a link to order a self-sampler online. The remaining 30,102 women in the control group received a standard reminder for conventional screening. Participation by intervention arm, age and region of residence was calculated; a questionnaire was used to assess self-sampling user experience. RESULTS: A significant difference in participation was seen between opt-out (41.7%) (19.8% chose self-sampling and 21.9% clinician sampling), opt-in (34.1%) (7.9% self-sampling, 26.2% clinician sampling) and control group (29.0%, clinician sampling only). All age groups and regions in the intervention arms showed higher participation compared to the control group, but the size of the effect varied. Among self-sampling users, 99% agreed that the device was easy to use and only 3.5% preferred future testing at the clinic. CONCLUSION: Providing women with a choice between self-sampling and clinician sampling significantly increased participation in cervical cancer screening. Opt-in and opt-out options had a different effect across age groups, suggesting the need to adapt strategies.

A review on over-sampling techniques in classification of multi-class imbalanced datasets: insights for medical problems.

There has been growing attention to multi-class classification problems, particularly those challenges of imbalanced class distributions. To address these challenges, various strategies, including data-level re-sampling treatment and ensemble methods, have been introduced to bolster the performance of predictive models and Artificial Intelligence (AI) algorithms in scenarios where excessive level of imbalance is present. While most research and algorithm development have been focused on binary classification problems, in health informatics there is an increased interest in the field to address the problem of multi-class classification in imbalanced datasets. Multi-class imbalance problems bring forth more complex challenges, as a delicate approach is required to generate synthetic data and simultaneously maintain the relationship between the multiple classes. The aim of this review paper is to examine over-sampling methods tailored for medical and other datasets with multi-class imbalance. Out of 2,076 peer-reviewed papers identified through searches, 197 eligible papers were chosen and thoroughly reviewed for inclusion, narrowing to 37 studies being selected for in-depth analysis. These studies are categorised into four categories: metric, adaptive, structure-based, and hybrid approaches. The most significant finding is the emerging trend toward hybrid resampling methods that combine the strengths of various techniques to effectively address the problem of imbalanced data. This paper provides an extensive analysis of each selected study, discusses their findings, and outlines directions for future research.

Investigating Best Practices for Ecological Momentary Assessment: Nationwide Factorial Experiment.

BACKGROUND: Ecological momentary assessment (EMA) is a measurement methodology that involves the repeated collection of real-time data on participants' behavior and experience in their natural environment. While EMA allows researchers to gain valuable insights into dynamic behavioral processes, the need for frequent self-reporting can be burdensome and disruptive. Compliance with EMA protocols is important for accurate, unbiased sampling; yet, there is no "gold standard" for EMA study design to promote compliance. OBJECTIVE: The purpose of this study was to use a factorial design to identify optimal study design factors, or combinations of factors, for achieving the highest completion rates for smartphone-based EMAs. METHODS: Participants recruited from across the United States were randomized to 1 of 2 levels on each of 5 design factors in a 2×2×2×2×2 design (32 conditions): factor 1-number of questions per EMA survey (15 vs 25); factor 2-number of EMAs per day (2 vs 4); factor 3-EMA prompting schedule (random vs fixed times); factor 4-payment type (US $1 paid per EMA vs payment based on the percentage of EMAs completed); and factor 5-EMA response scale type (ie, slider-type response scale vs Likert-type response scale; this is the only within-person factor; each participant was randomized to complete slider- or Likert-type questions for the first 14 days or second 14 days of the study period). All participants were asked to complete prompted EMAs for 28 days. The effect of each factor on EMA completion was examined, as well as the effects of factor interactions on EMA completion. Finally, relations between demographic and socioenvironmental factors and EMA completion were examined. RESULTS: Participants (N=411) were aged 48.4 (SD 12.1) years; 75.7% (311/411) were female, 72.5% (298/411) were White, 18.0% (74/411) were Black or African American, 2.7% (11/411) were Asian, 1.5% (6/411) were American Indian or Alaska Native, 5.4% (22/411) belonged to more than one race, and 9.6% (38/396) were Hispanic/Latino. On average, participants completed 83.8% (28,948/34,552) of scheduled EMAs, and 96.6% (397/411) of participants completed the follow-up survey. Results indicated that there were no significant main effects of the design factors on compliance and no significant interactions. Analyses also indicated that older adults, those without a history of substance use problems, and those without current depression tended to complete more EMAs than their counterparts. No other demographic or socioenvironmental factors were related to EMA completion rates. Finally, the app was well liked (ie, system usability scale score=82.7), and there was a statistically significant positive association between liking the app and EMA compliance. CONCLUSIONS: Study results have broad implications for developing best practices guidelines for future studies that use EMA methodologies. TRIAL REGISTRATION: ClinicalTrials.gov number NCT05194228; https://clinicaltrials.gov/study/NCT05194228.

Precision assessment of a hypertension prevalence survey.

BACKGROUND: Population surveys are crucial for public policy planning and provide valuable representative data. In the health sector studies to identify and assess the prevalence of Arterial Hypertension (AH), a chronic non-communicable disease (NCD), along with its associated risk factors have been conducted. OBJECTIVES: This study aims to assess the effectiveness of a population health survey in estimating the prevalence of arterial hypertension (AH) in the Sorocaba municipality between August 2021 and June 2023. METHODS: The analyzed performance indicator is the precision (design effect - deff) of AH prevalence in adults (≥ 18 years) and their exposure to primary risk factors. The total sample included 1,080 individuals from the urban area, deemed sufficient to estimate a deff of 1.5. This cluster-based study utilized census sectors as clusters, with data collected through household interviews, standardized questionnaires, and measurements of blood pressure and biometric parameters. The deff calculation formula used was weighted variance / raw variance. The Research Ethics Committee approved this study, with registration CAAE 30538520-1-0000-5373. RESULTS: The deff values ranged from 0.44 for chronic obstructive pulmonary disease to 1.63 for asthma, with a deff of 1.00 for AH prevalence. CONCLUSION: The study demonstrated good precision in its results, with high receptivity and cooperation from participants. The cost-effectiveness of the research deemed appropriate. The technique of selecting households within clusters (census sectors) based on detailed mapping and demographic data from the Instituto Brasileiro de Geografia e Estatística (IBGE) proved to be practical and efficient, suitable for replication in other municipalities and for studying other NCDs.

A protocol for assessing bias and robustness of social network metrics using GPS based radio-telemetry data.

BACKGROUND: Social network analysis of animal societies allows scientists to test hypotheses about social evolution, behaviour, and dynamic processes. However, the accuracy of estimated metrics depends on data characteristics like sample proportion, sample size, and frequency. A protocol is needed to assess for bias and robustness of social network metrics estimated for the animal populations especially when a limited number of individuals are monitored. METHODS: We used GPS telemetry datasets of five ungulate species to combine known social network approaches with novel ones into a comprehensive five-step protocol. To quantify the bias and uncertainty in the network metrics obtained from a partial population, we presented novel statistical methods which are particularly suited for autocorrelated data, such as telemetry relocations. The protocol was validated using a sixth species, the fallow deer, with a known population size where ∼ 85 % of the individuals have been directly monitored. RESULTS: Through the protocol, we demonstrated how pre-network data permutations allow researchers to assess non-random aspects of interactions within a population. The protocol assesses bias in global network metrics, obtains confidence intervals, and quantifies uncertainty of global and node-level network metrics based on the number of nodes in the network. We found that global network metrics like density remained robust even with a lowered sample size, while local network metrics like eigenvector centrality were unreliable for four of the species. The fallow deer network showed low uncertainty and bias even at lower sampling proportions, indicating the importance of a thoroughly sampled population while demonstrating the accuracy of our evaluation methods for smaller samples. CONCLUSIONS: The protocol allows researchers to analyse GPS-based radio-telemetry or other data to determine the reliability of social network metrics. The estimates enable the statistical comparison of networks under different conditions, such as analysing daily and seasonal changes in the density of a network. The methods can also guide methodological decisions in animal social network research, such as sampling design and allow more accurate ecological inferences from the available data. The R package aniSNA enables researchers to implement this workflow on their dataset, generating reliable inferences and guiding methodological decisions.

Epidemiological inference at the threshold of data availability: an influenza A(H1N2)v spillover event in the United Kingdom.

Viruses that infect animals regularly spill over into the human population, but individual events may lead to anything from a single case to a novel pandemic. Rapidly gaining an understanding of a spillover event is critical to calibrating a public health response. We here propose a novel method, using likelihood-free rejection sampling, to evaluate the properties of an outbreak of swine-origin influenza A(H1N2)v in the United Kingdom, detected in November 2023. From the limited data available, we generate historical estimates of the probability that the outbreak had died out in the days following the detection of the first case. Our method suggests that the outbreak could have been said to be over with 95% certainty between 19 and 29 days after the first case was detected, depending upon the probability of a case being detected. We further estimate the number of undetected cases conditional upon the outbreak still being live, the epidemiological parameter R 0, and the date on which the spillover event itself occurred. Our method requires minimal data to be effective. While our calculations were performed after the event, the real-time application of our method has potential value for public health responses to cases of emerging viral infection.

Modelling passive sampling of hydrophilic compounds under time-variable aqueous concentrations.

Passive sampling is a crucial method for evaluating concentrations of hydrophilic organic compounds in the aquatic environment, but it is insufficiently understood to what extent passive samplers capture the intermittent emissions that frequently occur for this group of compounds. In the present study, silicone sheets and styrene-divinyl benzene-reversed phase sulfonated extraction disks with and without a polyethersulfone membrane were exposed under semi-field conditions in a 31 m3 flume at three different flow velocities. Natural processes and spiking/dilution measures caused aqueous concentrations to vary strongly with time. The data were analyzed using two analytical models that account for these time-variable concentrations: a sampling rate model and a diffusion model. The diffusion model generally gave a better fit of the data than the sampling rate model, but the difference in residual errors was quite small (median errors of 19 vs. 25% for silicone and 22 vs. 25% for SDB-RPS samplers). The sampling rate model was therefore adequate enough to evaluate the time-integrative capabilities of the samplers. Sampler performance was best for SDB-RPS samplers with a polyethersulfone membrane, despite the occurrence of lag times for some compounds (0.1 to 0.4 days). Sampling rates for this design also spanned a narrower range (80 to 110 mL/day) than SDB-RPS samplers without a membrane (100 to 660 mL/day). The effect of biofouling was similar for all compounds and was consistent with a biofouling layer thickness of 150 µm.