Number Of Samples Research Articles

Can pre-analytical procedures improve microbiological culture yield in patients with periprosthetic infections?

BackgroundThe detection of causative pathogens plays a crucial role in the diagnosis and targeted treatment of periprosthetic joint infections (PJI). While there have been improvements in analytic methods in the past, pre-analytical procedures have not yet been sufficiently investigated. The objective of this study was to compare the culture yield of four different pre-analytical procedures.MethodsPatients with perioperative diagnosis of PJI were included in a single center cross-sectional study (2021–2022). Tissue samples (n = 20) of each patient were randomly and equally distributed to each of the four study arms. Tissue samples were either send to the laboratory without culture medium (group A) or were transported in thioglycolate medium immediately after sampling at three different temperatures (room temperature, 4 °C, 37° for 24 h; group B-D). Culture media were investigated for growth on days 1, 3, 7, 12, 14. All organisms, the number of positive samples and the time to positivity were recorded and compared between the study arms. Single positive cultures were considered as contamination.ResultsIn total, 71 patients were included. The proportions of culture negative samples (10–15%) and polymicrobial infections (51–54%) were comparable between the four arms. Seven patients (10%) were culture-negative in group A, but showed growth in thioglycolate media (group B-D). Furthermore, 13% of patients showed growth in all groups, but additional organisms were cultured in thioglycolate. There was growth beyond day 7 of culturing only in thioglycolate, but not in group A. A storage temperature of 4 °C showed a longer time to positivity compared to the other groups (p < 0.001).ConclusionsPre-analytical storage of tissue samples in thioglycolate broth did not improve the culture yield and did not detect additional cases of infection compared to the standard (pre-analytical storage in sterile containers). However, including a thioglycolate medium to the sampling algorithm reduced the rate of culture-negative infections and helped to identify additional organisms.

The Effect of Tax Awareness, Tax Understanding, and Tax Morals on Taxpayer Compliance from the Gender Perspective of MSME Entrepreneurs in the Tanah Abang Market

Aims: This study aims to determine the effect of tax awareness, tax understanding, and tax morals on taxpayer compliance from the gender point of view of MSME entrepreneurs. Study Design: The research design used is a survey by distributing questionnaires directly to respondents or MSE taxpayers in the Tanah Abang Market. The sample collection technique used in this study was simple random sampling. Place and Duration of Study: The sample in this study were MSME taxpayers in the Tanah Abang Market as many as 154 male respondents and 116 female respondents. Methodology: Researchers used the Hair et al. formula [1] to determine the number of research samples. The type of data used in this study is quantitative data in the form of values or scores for the answers given by MSME respondents in the Jakarta Tanah Abang Market to the questions in the questionnaire. The data obtained was transferred into data tabulation and analyzed using PLS-SEM (Partial Least Squares Structural Equation Modeling) software version 4.0 to generate a hypothesis of a causal relationship between the independent variables (tax awareness, tax understanding, and tax morale) and the dependent variable (taxpayer compliance). Results: The results of the study are tax awareness, tax understanding, and tax morale have a significant influence on the compliance of male and female taxpayers. Conclusion: From this study, it was found that in combination gender, tax awareness, tax understanding, and tax morale had an effect on taxpayer compliance. However, based on the significance value, it is known that tax awareness and tax understanding of females have a more significant effect on tax compliance than males. This is different from tax morale, based on the significance value, it is known that male tax morale has a more significant effect than female. This is in line with Gender theory which explains the differentiation of roles, positions, responsibilities, and division of labor between men and women determined by society based on the nature of women and men who are considered appropriate in accordance with the norms, customs, beliefs or habits of society [2].

Priority screening of contaminants of emerging concern (CECs) in surface water: Comparing cell-based bioassays and exposure-activity ratios (EARs)

In this study, we compared a wide range of cell-based bioassays to the use of chemical analysis followed by exposure-activity ratio (EAR) and Toxicological Prioritization index (ToxPi) for prioritizing chemicals, sites, and hazard concerns in water samples. Surface water samples were collected from nine sites in three Central Pennsylvania streams and analyzed for a forty-six contaminants of emerging concern (CECs), including pesticides, personal care products, and pharmaceuticals. Cell-based reporter assays evaluated human and zebrafish molecular initiating events (MIEs) in endocrine and metabolic disruption, altered lipid metabolism, and oxidative stress. Bioassays showed that 12 out of 40 assays had at least one site with activity over the effect-based trigger (EBT) values. The receptors that exhibited the highest number of samples above the EBT that would be expected to cause toxicity were Aryl hydrocarbon receptor (AhR, human and zebrafish), Pregnane X Receptor (PXR), Estrogen Receptor-beta (ERB), and Androgen Receptor (AR). Characterizing the collection sites by their bioactivity aligned closely with the stream in which samples were collected. The sum of all EARs for each chemical indicated that the pharmaceutical Carbamazepine and the pesticides Carbaryl and Atrazine posed the greatest concern. However, predicted activity and site prioritization based on individual chemical analysis and calculated EAR were different than those measured by bioassay, indicating that biologically active chemicals are present in the samples that were not included in the targeted analytes. Taken together, these data show that chemical analysis and EAR analysis are beneficial for prioritization of chemicals, whereas mechanism-based bioassays are more inclusive of known as well as unknown chemical contaminants and thus of more use for overall water quality analysis and site prioritization.

SUB-immunogold-SEM reveals nanoscale distribution of submembranous epitopes

Electron microscopy paired with immunogold labeling is the most precise tool for protein localization. However, these methods are either cumbersome, resulting in small sample numbers and restricted quantification, or limited to identifying protein epitopes external to the membrane. Here, we introduce SUB-immunogold-SEM, a scanning electron microscopy technique that detects intracellular protein epitopes proximal to the membrane. We identify four critical sample preparation factors contributing to the method’s sensitivity. We validate its efficacy through precise localization and high-powered quantification of cytoskeletal and transmembrane protein distribution. We evaluate the capabilities of SUB-immunogold-SEM on cells with highly differentiated apical surfaces: (i) auditory hair cells, revealing the presence of nanoscale MYO15A-L rings at the tip of stereocilia; and (ii) respiratory multiciliate cells, mapping the distribution of the SARS-CoV-2 receptor ACE2 along the motile cilia. SUB-immunogold-SEM extends the application of SEM-based nanoscale protein localization to the detection of intracellular epitopes on the exposed surfaces of any cell.

Pengaruh Kedisiplinan, Lingkungan Kerja, dan Motivasi terhadap Kinerja Pegawai pada Kantor Pelayanan Pajak Pratama Bandung Tegallega

This study aims to find out and analyze how much the influence of discipline has a significant effect on employee performance at the Bandung Tegallega Primary Tax Service Office. The population of this study is 75 employees of the Bandung Tegallega Primary Tax Service Office. To meet the number of samples, the sampling method in the field uses random sampling techniques. The researcher determined a sample of 75 respondents who were employees of the Bandung Pratama Tax Service Office. As a result, the work motivation of employees is still in the sufficient category, so it needs to be improved so that the performance of employees who are also in the sufficient category can increase. Discipline and Work Environment are classified as good and need to be maintained. Discipline, Work Environment, and Work Motivation each have a significant positive effect on Employee Performance, with Discipline and Work Environment making a major contribution to improving Performance. Overall, these three variables affect Employee Performance by 82%, while the remaining 18% is influenced by other factors that are not studied.

Detection and subtyping of influenza A virus in porcine clinical samples from Spain in 2020

A total of 1019 samples collected on 726 Spanish swine farms suffering from outbreaks of respiratory disease were screened for influenza A viruses (IAVs) using a RT-qPCR method. A subset of positive samples was further analyzed using a subtype-specific RT-qPCR method (n: 142) and Sanger sequencing (n: 64). A total of 19.4% samples from 23% farms tested positive, with infection being most common in suckling (53.6%) and weaning pigs (30.2%). Viruses belonging to four HA subtypes (H1av, H1hu, H1pdm, H3) were detected, with subtypes H1avN2, H1huN2 and H1avN1 accounting for over half of the specimens. An optimized protocol with newly designed primers allowed the detection of H3 viruses in a significant number of samples (21%). A comparison of antigenic positions revealed that circulating strains exhibited mutations with vaccine strains in a significant percentage of amino acid residues, both in the NA protein (27.8–43.3%) and particularly in the HA protein (51–75.3%).

Construction of Prediction Models of Mass Ablation Rate for Silicone Rubber-Based Flexible Ablative Composites Based on a Small Dataset

The prediction of the ablation rate of silicone rubber-based composites is of great significance to accelerate the development of flexible thermal protection materials. Herein, a method which combines uniform design experimentation, active learning, and virtual sample generation was proposed to establish a prediction model of the mass ablation rate based on a small dataset. Briefly, a small number of sample points were collected using uniform design experimentation, which were marked to construct the initial dataset and primitive model. Then, data points were acquired from the sample pool and iterated using various integrated algorithms through active learning to update the above dataset and model. Finally, a large number of virtual samples were generated based on the optimal model, and a further optimized prediction model was achieved. The results showed that after introducing 300 virtual samples, the average percentage error of the gradient boosting decision tree (GBDT) prediction model on the test set decreased to 3.1%, which demonstrates the effectiveness of the proposed method in building prediction models based on a small dataset.

Analisis Gambaran Durasi Kerja dan Masa Kerja dengan Kelelahan Kerja pada Driver Perusahaan Distributor Gas di Gresik

Fatigue can be caused by personal stress factors. Based on observations and interviews, it is known that 5 drivers at the Gas Distributor Company in Gresik experienced work fatigue. The drivers complained that working hours that exceeded capacity limits and the pressure to continue making deliveries caused fatigue and increased turnover rates in the company. This study aims to analyze the description of work duration and work period with work fatigue in drivers of the Gas Distributor Company in Gresik. This study uses a quantitative descriptive method. The number of samples in this study was determined by a total sampling of 30 drivers. The method of obtaining research data was carried out by filling out a questionnaire. Based on the results of the analysis of the description of work period with work fatigue, 21 people with a work period of ≥5 years were obtained 5 people had high work fatigue and 4 people had very high work fatigue and 21 people &lt;5 years were obtained 20 people had high work fatigue and 1 person had very high work fatigue. Analysis of the description of work duration with work fatigue showed that 6 people who worked &lt;8 hours were obtained 3 people had high work fatigue and 3 people had very high work fatigue. Then from 24 people who worked ≥8 hours, 22 people had high work fatigue and 2 people had very high work fatigue. Based on the results of the work period analysis, most drivers worked &lt;5 years as many as 20 drivers and in the results of the work duration analysis, most drivers worked ≥8 hours as many as 22 drivers.

Less is more: A closer look at semantic-based few-shot learning

Few-shot Learning (FSL) aims to learn and distinguish new categories from a scant number of available samples, presenting a significant challenge in the realm of deep learning. Recent researchers have sought to leverage the additional semantic or linguistic information of scarce categories with a pre-trained language model to facilitate learning, thus partially alleviating the problem of insufficient supervision signals. Nonetheless, the full potential of the semantic information and pre-trained language model have been underestimated in the few-shot learning till now, resulting in limited performance enhancements. To address this, we propose a straightforward and efficacious framework for few-shot learning tasks, specifically designed to exploit the semantic information and language model. Specifically, we explicitly harness the zero-shot capability of the pre-trained language model with learnable prompts. And we directly add the visual feature with the textual feature for inference without the intricate designed fusion modules as in prior studies. Additionally, we apply the self-ensemble and distillation to further enhance performance. Extensive experiments conducted across four widely used few-shot datasets demonstrate that our simple framework achieves impressive results. Particularly noteworthy is its outstanding performance in the 1-shot learning task, surpassing the current state-of-the-art by an average of 3.3% in classification accuracy. Our code will be available at https://github.com/zhouchunpong/SimpleFewShot.

Target localization and defect detection of distribution insulators based on ECA‐SqueezeNet and CVAE‐GAN

AbstractInsulators, as typical equipment for distribution networks, provide good electrical insulation between live conductors and earth. Timely and accurate detection is essential for insulator detection issues. However, as the complexity of neural networks increases, the detection efficiency is often lower. Therefore, this paper proposes a fast insulator positioning and defect detection method. Firstly, for insulator target localization, the SqueezeNet network is improved using ECA attention mechanism. In addition, to address the issue of low defect detection accuracy, a joint algorithm has been proposed. The integration of convolutional variational autoencoder (CVAE) and generative adversarial network (GAN) solve their own shortcomings due to different image focus angles. The target localization accuracy reaches 94.30%, and the defect detection accuracy reaches 89.60%. It solves the problems of difficulty in locating small targets in a large field of view and inaccurate detection due to a small number of abnormal samples. This method has been tried and tested in practical distribution network systems.

PENGARUH SIKAP KEUANGAN, PERILAKU KEUANGAN, DAN LITERASI KEUANGAN TERHADAP MINAT MENABUNG ANGGOTA KPN SUSILA BHAKTI

This research aims to analyze the influence of financial attitudes, financial behavior and financial literacy on the interest in saving of members of the Susila Bhakti Civil Service Cooperative. This research uses a quantitative approach with data collected through distributing questionnaires. Questionnaire measurements use a Likert scale with a scale from 1-5. The selection of respondents used a non-probability sampling method, namely sampling used by researchers accidentally or who happened to meet the researcher at that time. Determining the number of samples used the Slovin formula with the number of respondents used being 70 respondents. The data analysis technique used to assist this research uses the SPSS version 18 model. The results of this research are that all the variables studied including financial attitudes, financial behavior and financial literacy have a positive and significant influence on interest in saving, and financial literacy is able to moderate financial attitudes. on the interest in saving of members of the Susila Bhakti Civil Servant Cooperative. Members of the Civil Servant Cooperative are expected to always be wise in responding and behaving with their finances and can increase their financial literacy in order to stimulate interest in saving. Abstrak berbahasa inggris terjemahan dari abstrak berbahasa indonesia

Domain adaptation hyperspectral image fusion based on spatial-spectral domain separation

ABSTRACT Hyperspectral datasets captured by airborne are one of the important data sources in the field of hyperspectral image fusion. However, the limited number of data samples often hinders the optimal performance of deep learning-based methods on these datasets. Domain adaptation is expected to alleviate the issue of data scarcity. However, there are two issues that need to be addressed in applying domain adaptation to hyperspectral image fusion, which are the heterogeneity and spatial degradation diversity of hyperspectral data. In this paper, we propose a domain adaptation hyperspectral image fusion network based on spatial-spectral domain separation. The model, constructed with three-dimensional convolutional layers, effectively addresses the challenge of hyperspectral data heterogeneity. Compared with 2D convolution, 3D convolution is able to consider both spatial and spectral dimensions and extract spatial-spectral features efficiently. Based on this, we design a domain-separation architecture to extract domain-invariant and domain-private features in the spatial-spectral domain. The architecture achieves domain adaptation by extracting domain-invariant features from both datasets to obtain the prior knowledge in the source dataset and apply it to the target dataset. Additionally, the spatial degradation of different datasets varies due to distinct shooting conditions during the acquisition. Therefore, the fusion algorithms should be robust to various degradation scenarios. To tackle this challenge, we design the Observation Module to predict degradation information and the Degradation Information Modulation Module to apply it to the input, thereby enhancing the network’s robustness. Experiments on various datasets demonstrate that our method is qualitatively and quantitatively superior to state-of-the-art methods.

Addressing the multiplicity of optimal solutions to the Clonal Deconvolution and Evolution Problem

The Clonal Deconvolution and Evolution Problem consists on unraveling the clonal structure and phylogeny of a tumor using estimated mutation frequency values obtained from multiple biopsies containing mixtures of tumor clones. In this article, we tackle the problem from an optimization perspective and we explore the number of optimal solutions for a given instance. Even in ideal scenarios without noise, we demonstrate that the Clonal Deconvolution and Evolution Problem is highly under-determined, leading to multiple solutions. Through a comprehensive analysis, we examine the factors contributing to the multiplicity of solutions. We find that as the number of samples increases, the number of optimal solutions decreases. Additionally, we explore how this phenomenon operates across various tumor topology scenarios. To address the issue of the existence of multiple solutions, we present sufficient conditions under which the problem can have a unique solution, and we propose a linear programming-based algorithm that leverages mutation orderings to generate instances with a single solution for a given topology. This algorithm encounters numerical challenges when applied to large instance sizes so, to overcome this, we propose a heuristic adaptation that enables the algorithm’s use for instances of any size.

Microblog Negative Comments Data Analysis Model Based on Multi-scale Convolutional Neural Network and Weighted Naive Bayes Algorithm

As a form of public supervision, Microblog’s negative reviews allow people to share their opinions and experiences and express dissatisfaction with unfair and unreasonable phenomena. This form of supervision has the potential to promote social fairness, drive governments, businesses, and individuals to correct mistakes and enhance transparency. To characterize the sentiment trend and determine the influence of Microblog negative reviews, we propose a multi-scale convolutional neural network and weighted naive bayes algorithm (MCNN–WNB). We define the feature vector characterization index for Microblog negative review data and preprocess the data accordingly. We quantify the relationship between attributes and categories using the weighted Naive Bayes method and use the quantification value as the weighting coefficient for the attributes, addressing the issue of decreased classification performance in traditional methods. We introduce a sentiment classification model based on word vector representation and a multi-scale convolutional neural networks to filter out Microblog negative review data. We conduct simulation experiments using real data, analyzing key influencing parameters such as convergence time, training set sample size, and number of categories. By comparing with K-means, Naive Bayes algorithm, Spectral Clustering algorithm and Autoencoder algorithm, we validate the effectiveness of our proposed method. We discover that the convergence time of the MCNN–WNB algorithm increases as the number of categories increases. The average classification accuracy of the algorithm remains relatively stable with varying test iterations. The algorithm’s precision increases with the number of training set samples and eventually stabilizes.

A best practices framework for spatial biology studies in drug discovery and development: enabling successful cohort studies using digital spatial profiling

ABSTRACT The discovery of biomarkers, essential for successful drug development, is often hindered by the limited availability of tissue samples, typically obtained through core needle biopsies. Standard ‘omics platforms can consume significant amounts of tissue, forcing scientist to trade off spatial context for high-plex assays, such as genome-wide assays. While bulk gene expression approaches and standard single-cell transcriptomics have been valuable in defining various molecular and cellular mechanisms, they do not retain spatial context. As such, they have limited power in resolving tissue heterogeneity and cell–cell interactions. Current spatial transcriptomics platforms offer limited transcriptome coverage and have low throughput, restricting the number of samples that can be analyzed daily or even weekly. While the Digital Spatial Profiling (DSP) method does not provide single-cell resolution, it presents a significant advancement by enabling scalable whole transcriptome and ultrahigh-plex protein analysis from distinct tissue compartments and structures using a single tissue slide. These capabilities overcome significant constraints in biomarker analysis in solid tissue specimens. These advancements in tissue profiling play a crucial role in deepening our understanding of disease biology and in identifying potential therapeutic targets and biomarkers. To enhance the use of spatial biology tools in drug discovery and development, the DSP Scientific Consortium has created best practices guidelines. These guidelines, built on digital spatial profiling data and expertise, offer a practical framework for designing spatial studies and using current and future spatial biology platforms. The aim is to improve tissue analysis in all research areas supporting drug discovery and development.

Debt is Not Always Bad Even in Difficult Economic Conditions

The problem faced by the company is the use of debt that is not according to plan. The purpose of this study is to determine whether debt policy affects company performance. This study was conducted on manufacturing sector companies listed on the Indonesia Stock Exchange in 2018–2023 with a population of 296 companies, the sample used was 73 companies for six years. Thus, the number of samples used in this study was 438. The company performance variable is proxied by (ROE) while the independent variables in this study are debt policy proxied by (DER and DER*KE), investment decisions (KI), managerial ownership (MNJR), and economic conditions (KE). The data analysis method uses multiple linear regression methods with SPSS software version 26. The results of the study show that the debt policy variable (DER) has a negative and significant effect on company performance (ROE) and the debt policy variable*economic conditions (DER*KE) has a positive and significant effect on company performance (ROE). This means that when economic conditions are supportive, companies that use debt to finance operational activities or business expansion tend to experience increased performance. Funds obtained from debt can be used for investment, new product development, or market expansion. Proper use of debt can increase a company's return on equity (ROE), as long as the rate of return on the investment financed by debt is higher than the cost of debt. Timing the debt decision is crucial. In improving economic conditions, companies can take advantage of low interest rates and increase production capacity.

Did Covid-19 Pandemic Raised Tax Incomes in Bad Economic Situations?

The fact that COVID-19 pandemic affected economic situation is known widely, but many companies actually can use ratios for measuring financial situation. If we look deeper, financial ratios can give actual fact about the financial performance periodically. The aim of this research is to find out whether liquidity ratios, Solvability ratios, activity ratios, profitability ratios, and tax ratios have an effect on financial performance. This research was conducted on companies listed on the Indonesia Stock Exchange in 2019 - 2023 with a sample of 117 companies for five years. Thus, the number of samples used in this research was 585. The independent variables in this research were liquidity ratios, Solvability ratios, activity ratios, profitability ratios, and tax ratios, while the dependent variable was financial performance. The data analysis method uses the difference test analyze method with SPSS version 26 software. The results of different tests for liquidity ratios show that there is no significant difference between the years before the COVID-19 pandemic, during the COVID-19 pandemic and after the COVID-19 pandemic. The results of different tests for the Solvability ratios, activity ratios, profitability ratios, and tax ratios showed that there was no significant difference between the years before the COVID-19 pandemic, during the COVID-19 pandemic and after the COVID-19 pandemic.

Remote and Proximal Sensors Data Fusion: Digital Twins in Irrigation Management Zoning.

The scientific field of precision agriculture employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impact. However, obtaining a high number of soil samples is challenging in order to make precision agriculture viable. There is a trade-off between the amount of data needed and the time and resources spent to obtain these data compared to the accuracy of the maps produced with more or fewer points. In the present study, the research was based on an exhaustive dataset of apparent electrical conductivity (aEC) containing 3906 points distributed along 26 transects with spacing between each of up to 40 m, measured by the proximal soil sensor EM38-MK2, for a grain-producing area of 72 ha in São Paulo, Brazil. A second sparse dataset was simulated, showing only four transects with a 400 m distance and, in the end, only 162 aEC points. The aEC map via ordinary kriging (OK) from the grid with 26 transects was considered the reference, and two other mapping approaches were used to map aEC via sparse grid: kriging with external drift (KED) and geographically weighted regression (GWR). These last two methods allow the increment of auxiliary variables, such as those obtained by remote sensors that present spatial resolution compatible with the pivot scale, such as data from the Landsat-8, Aster, and Sentinel-2 satellites, as well as ten terrain covariates derived from the Alos Palsar digital elevation model. The KED method, when used with the sparse dataset, showed a relatively good fit to the aEC data (R2 = 0.78), with moderate prediction accuracy (MAE = 1.26, RMSE = 1.62) and reasonable predictability (RPD = 1.76), outperforming the GWR method, which had the weakest performance (R2 = 0.57, MAE = 1.78, RMSE = 2.30, RPD = 0.81). The reference aEC map using the exhaustive dataset and OK showed the highest accuracy with an R2 of 0.97, no systematic bias (ME = 0), and excellent precision (RMSE = 0.56, RPD = 5.86). Management zones (MZs) derived from these maps were validated using soil texture data from clay samples measured at 0-10 cm depth in a grid of 72 points. The KED method demonstrated the highest potential for accurately defining MZs for irrigation, producing a map that closely resembled the reference MZ map, thereby providing reliable guidance for irrigation management.

Molecular epidemiology of brucellosis in Asia: insights from genotyping analyses.

Brucellosis infects humans and animals worldwide but is particularly prevalent in Asia. In many Asian countries, molecular diagnostic tools for accurate molecular diagnostics and molecular epidemiology are lacking. Nonetheless, some countries have conducted in-depth molecular epidemiological studies. The objective of this study was to reveal the genetic relationships, geographic origins, and distributions of Brucella strains across Asia for two primary species, B. abortus and B. melitensis. For this, we systematically searched genotyping data from published studies on the molecular epidemiology of Brucella species for both humans and livestock in Asia. We used data from multilocus sequence typing (MLST), multiple-locus variable-number tandem repeat analysis (MLVA), and whole genome sequencing analysis of Brucella strains. We also analyzed the MLVA genotypes of 129 B. abortus isolates and 242 B. melitensis isolates with known origins in Asia from an online MLVA database using MLVA-11 data in minimum spanning trees and MLVA-16 data in neighbor-joining trees. We found that the B. melitensis East Mediterranean lineage is predominant across the continent, with only a small number of samples from the Africa and Americas lineages, and none from the West Mediterranean lineage. The "abortus C" genotype was the most common group of B. abortus in Asia, with limited genetic variation for this species. Several studies also reported that Near Eastern countries frequently encounter human brucellosis cases of B. abortus from genotypes 42 and 43. Our study highlights the inconsistent collection of genetic data for Brucella species across Asia and a need for more extensive sampling in most countries. Finally, a consistent nomenclature is necessary to define various groupings of strains within a lineage (i.e., clade) so uniform terminology should denote particular genetic groups that are understood by all researchers.

Evaluation of a self-monitoring protocol for assessing soot and polycyclic aromatic hydrocarbon exposure among chimney sweeps.

Traditional methods for measuring chemical exposure have challenges in terms of obtaining sufficient data; therefore, improved methods for better assessing occupational exposure are needed. One possible approach to mitigate these challenges is to use self-monitoring methods such as sensors, diaries, or biomarkers. In the present study, a self-monitored method for measuring soot exposure, which included real-time air monitoring, a work diary, and the collection of urine samples, was evaluated. To validate the method, exposure measurements during the workday and diary entries were compared with velocities calculated from GPS tracking and the expected polycyclic aromatic hydrocarbon (PAH) metabolite patterns in urine. The method was applied with chimney sweeps, an occupational group at a high risk of many severe health outcomes and for whom effective control measures for reducing exposure are needed. In the study, 20 chimney sweeps followed a self-monitoring protocol for 8 consecutive workdays. Personal exposure to soot was measured as black carbon (BC) using micro-aethalometers. A diary was used to record the work tasks performed, and urine samples were collected and analysed for PAH metabolites. From the expected 160 full day measurements, 146 (91%) BC measurements and 149 (93%) diaries were collected. From the expected 320 urine samples, 304 (95%) were collected. The tasks noted in the diaries overlapped with information obtained from the GPS tracking of the chimney sweeps, which covered 96% of the measurement time. The PAH metabolites in urine increased during the work week. Factors believed to have positively influenced the sample collection and task documentation were the highly motivated participants and the continuous presence of trained occupational hygiene professionals during the planning of the study and throughout the measurement stage, during which they were available to inform, instruct, and address questions. In conclusion, the self-monitored protocol used in this study with chimney sweeps is a valuable and valid method that can be used to collect larger numbers of samples. This is especially valuable for occupations in which the employees are working independently and the exposure is difficult to monitor with traditional occupational hygiene methods.