Number Of Samples Research Articles

Gambaran Stres Mahasiswa Tingkat Akhir dalam Penyusunan Skripsi di FKM Universitas Nusa Cendana Kupang

Stress is an unpleasant or uncomfortable situation experienced by individuals that can interfere with thoughts, emotions, actions or behaviors in daily life. The purpose of this study is to find out the description of the stress of final year students in the preparation of a thesis at the Faculty of Public Health, Nusa Cendana University, Kupang. The population in this study is 118 students of the class of 2018 and the number of samples is 118 people. This type of research is a descriptive research using a sampling technique in the form of total sampling. The instrument used was the DASS-42 questionnaire. Based on the results of the study, it was obtained that the characteristics of the respondents were more female, which was 83.1% and had a residential status at a boarding house, which was 45.8%. In the stress level category, it was found that FKM students of Nusa Cendana University Kupang, especially the class of 2018, were in the mild category, namely 28.8%. For educational institutions, the results of this study are expected to be useful as a source of information, add knowledge about stress and as input or comparison for future researchers regarding the description of stress in final year students at the Faculty of Medicine, Nusa Cendana University, Kupang.

Dual-path Frequency Discriminators for few-shot anomaly detection

Few-shot anomaly detection (FSAD) plays a crucial role in industrial manufacturing. However, existing FSAD methods encounter difficulties leveraging a limited number of normal samples, frequently failing to detect and locate inconspicuous anomalies in the spatial domain. We have further discovered that these subtle anomalies would be more noticeable in the frequency domain. In this paper, we propose a Dual-Path Frequency Discriminators (DFD) network from a frequency perspective to tackle these issues. The original spatial images are transformed into multi-frequency images, making them more conducive to the tailored discriminators in detecting anomalies. Additionally, the discriminators learn a joint representation with forms of pseudo-anomalies. Extensive experiments conducted on MVTec AD and VisA benchmarks demonstrate that our DFD surpasses current state-of-the-art methods. The code is available at https://github.com/yuhbai/DFD.

Maximizing efficiency in sedimentary ancient DNA analysis: a novel extract pooling approach

In the last few decades, the field of ancient DNA has taken a new direction towards using sedimentary ancient DNA (sedaDNA) for studying human and mammalian population dynamics as well as past ecosystems. However, the screening of numerous sediment samples from archaeological sites remains a time-consuming and costly endeavor, particularly when targeting hominin DNA. Here, we present a novel high-throughput method that facilitates the fast and efficient analysis of sediment samples by applying a pooled testing approach. This method combines multiple extracts, enabling early parallelization of laboratory procedures and effective aDNA screening. Pooled samples with detectable aDNA signals undergo detailed analysis, while empty pools are discarded. We have successfully applied our method to multiple sediment samples from Middle and Upper Paleolithic sites in Europe, Asia, and Africa. Notably, our results reveal that an aDNA signal remains discernible even when pooled with four negative samples. We also demonstrate that the DNA yield of double-stranded libraries increases significantly when reducing the extract input, potentially mitigating the effects of inhibition. By embracing this innovative approach, researchers can analyze large numbers of sediment samples for aDNA preservation, achieving significant cost reductions of up to 70% and reducing hands-on laboratory time to one-fifth.

Optimising eDNA analysis for urban otter monitoring: seasonal patterns, detection strategies and prey availability

Significant advancements in environmental DNA (eDNA) analysis technology have led to its widespread adoption for species monitoring. However, the low DNA concentration in eDNA often raises concerns about PCR bias, which is a primary issue in enhancing the reliability of eDNA survey results. This study aims to identify the suitable eDNA analysis method and detection strategy for obtaining the presence data of Eurasian otters in an urban stream and to investigate the seasonal distribution patterns of Eurasian otters and their potential prey species. The eDNA survey revealed the presence of six out of the 13 mammal species recorded in fieldwork and literature reviews, including Canis lupus, Felis silvestris and Mustela sibirica. Notably, Eurasian otters were not detected in the eDNA metabarcoding conducted in April and October, despite traditional surveys confirming their presence. In contrast, qPCR assays successfully amplified Eurasian otters from the same samples that were analysed using metabarcoding. When evaluating detection criteria, based on the number of positive samples in repetitions, Eurasian otters were detected at 42.9% to 85.7% of the sampling sites in April and at all sampling sites in October. This suggests a higher detection probability of Eurasian otters in October, indicating a potential expansion of their home range during that season compared to April. Metabarcoding results revealed similar findings regarding fish species as traditional surveys, with Cyprinidae accounting for the largest proportion of fish species at the family level (April, 54.57%; October, 43.58%), followed by Gobiidae (April, 16.90%; October, 22.88%). At the species level, P. parva was the dominant fish species in Saetgang, constituting 5.68% and 6.35% of relative abundance in April and October, respectively. This implies that Eurasian otters as opportunistic predators, may increasingly take advantage of the availability of species within the family Cyprinidae, notably Pseudorasbora parva, as a food source within the study area during the months of April and October. This study highlights that qPCR is the more effective approach in urban areas to offer insights into otters’ distribution patterns, while metabarcoding is useful to provide the properties of the biological environment. Furthermore, this study indicated that it is necessary to determine the suitable eDNA analysis methods depending on the research purpose to obtain detection results effectively.

PENGARUH PENGHARGAAN FINANSIAL, PERTIMBANGAN PASAR KERJA DAN MOTIVASI DIRI TERHADAP PEMILIHAN KARIER SEBAGAI AKUNTAN PADA MAHASISWA AKUNTANSI DI UNIVERSITAS BATAM, UNIVERSITAS RIAU KEPULAUAN DAN UNIVERSITAS UNIVERSAL

Career is a person's process of building a job or working their way up work in stages to get maximum results or that expected of that person within a certain period of time. Career selection appropriate and in accordance with a person's interests and talents is a stageearly in career formation. So that someone can choose a suitable career with what is expected he must consider the influencing factors career choice. The accounting profession currently has an important role for entities business, government and society because they are considered as one of the parties able to make a major contribution in solving their problems face The aim of this research is to obtain empirical evidence of influence financial reward, consideration Job market and self-motivation in choosing a career as an accountant. This research was conducted at the Faculty of Economics and Business, Batam University, Riau Islands University and Universal University. The sampling method used is the purposive sampling method, while the data collection method was carried out using a questionnaire. The number of research samples used was 87 respondents. Analysis techniques The data used is a multiple linear regression analysis technique. Research result This shows that Financial Rewards, Job Market Considerations and Self-Motivation have a positive influence on choosing a career as an accountant.

Performance evaluation of semi-supervised learning frameworks for multi-class weed detection.

Precision weed management (PWM), driven by machine vision and deep learning (DL) advancements, not only enhances agricultural product quality and optimizes crop yield but also provides a sustainable alternative to herbicide use. However, existing DL-based algorithms on weed detection are mainly developed based on supervised learning approaches, typically demanding large-scale datasets with manual-labeled annotations, which can be time-consuming and labor-intensive. As such, label-efficient learning methods, especially semi-supervised learning, have gained increased attention in the broader domain of computer vision and have demonstrated promising performance. These methods aim to utilize a small number of labeled data samples along with a great number of unlabeled samples to develop high-performing models comparable to the supervised learning counterpart trained on a large amount of labeled data samples. In this study, we assess the effectiveness of a semi-supervised learning framework for multi-class weed detection, employing two well-known object detection frameworks, namely FCOS (Fully Convolutional One-Stage Object Detection) and Faster-RCNN (Faster Region-based Convolutional Networks). Specifically, we evaluate a generalized student-teacher framework with an improved pseudo-label generation module to produce reliable pseudo-labels for the unlabeled data. To enhance generalization, an ensemble student network is employed to facilitate the training process. Experimental results show that the proposed approach is able to achieve approximately 76% and 96% detection accuracy as the supervised methods with only 10% of labeled data in CottonWeedDet3 and CottonWeedDet12, respectively. We offer access to the source code (https://github.com/JiajiaLi04/SemiWeeds), contributing a valuable resource for ongoing semi-supervised learning research in weed detection and beyond.

A New Look at Diffusion in Vapor Intrusion Assessments; Passive Adsorptive Diffusion Samplers

AbstractVapor intrusion of toxic volatile organic compounds (VOCs) from subsurface soil vapor, through a building slab/floor, and into the indoor air is an important environmental contaminant transport mechanism. It is widely believed that advective flow, driven by the pressure differential between the subslab and indoor air, is the primary mechanism of subslab soil vapor entry into buildings. This paper explores the hypothesis that molecular diffusion through the slab may potentially play a larger role in vapor intrusion than previously believed and may even be the predominant vapor intrusion mechanism when the subslab vapor source strength is sufficiently high or the pressure differential is relatively low. A novel sampling device, referred to as a Passive Adsorptive Diffusion Sampler (PADS), is presented for the purpose of directly measuring the diffusion of VOCs through a building slab. A vacant warehouse was identified as a case study site where historical sampling had determined that vapor intrusion of trichloroethene (TCE) was adversely impacting the indoor air. Calculations using Fick's First Law of Diffusion are presented which demonstrate that diffusion alone can theoretically account for all the TCE observed in the indoor air at this building based on an effective diffusion coefficient for concrete that was calculated from the Johnson and Ettinger Model. Two groups of nine replicate PADS were deployed at two areas on the slab and used to measure the flux and effective diffusion coefficient at each of the 18 total points, which showed an order of magnitude variability within each area and over two orders of magnitude variability overall. These results indicate that diffusion through concrete is inherently variable when measured at a sub‐meter scale. However, when combined over both areas, the overall average approached that calculated from the Johnson and Ettinger Model. An additional 12 PADS were deployed across the building slab (for a total of 30) to quantify the overall building‐wide diffusive flux. This area‐weighted average diffusive flux was consistent with the predicted diffusive flux as calculated from Fick's First Law and the vapor intrusion mass input required to achieve the observed indoor air TCE concentration. The results of this study show that PADS provides a simple way to measure diffusive flux directly without having to drill through the slab. However, significant variability in the measured flux should be expected and will need to be accounted for by the inclusion of a relatively large number of samples including replicates. When using PADs at a new site, the collection of traditional subslab vapors at a select number of locations is recommended for the verification of a building‐specific effective diffusion coefficient, which may not necessarily be the same as for this building.

Characterization of bovine vaginal microbiota using 16S rRNA sequencing: associations with host fertility, longevity, health, and production

Due to their potential impact on the host’s phenotype, organ-specific microbiotas are receiving increasing attention in several animal species, including cattle. Specifically, the vaginal microbiota of ruminants is attracting growing interest, due to its predicted critical role on cows’ reproductive functions in livestock contexts. Notably, fertility disorders represent a leading cause for culling, and additional research would help to fill relevant knowledge gaps. In the present study, we aimed to characterize the vaginal microbiota of a large cohort of 1171 female dairy cattle from 19 commercial herds in Northern France. Vaginal samples were collected using a swab and the composition of the microbiota was determined through 16S rRNA sequencing targeting the V3–V4 hypervariable regions. Initial analyses allowed us to define the core bacterial vaginal microbiota, comprising all the taxa observed in more than 90% of the animals. Consequently, four phyla, 16 families, 14 genera and a single amplicon sequence variant (ASV) met the criteria, suggesting a high diversity of bacterial vaginal microbiota within the studied population. This variability was partially attributed to various environmental factors such as the herd, sampling season, parity, and lactation stage. Next, we identified numerous significant associations between the diversity and composition of the vaginal microbiota and several traits related to host’s production and reproduction performance, as well as reproductive tract health. Specifically, 169 genera were associated with at least one trait, with 69% of them significantly associated with multiple traits. Among these, the abundances of Negativibacillus and Ruminobacter were positively correlated with the cows’ performances (i.e., longevity, production performances). Other genera showed mixed relationships with the phenotypes, such as Leptotrichia being overabundant in cows with improved fertility records and reproductive tract health, but also in cows with lower production levels. Overall, the numerous associations underscored the complex interactions between the vaginal microbiota and its host. Given the large number of samples collected from commercial farms and the diversity of the phenotypes considered, this study marks an initial step towards a better understanding of the intimate relationship between the vaginal microbiota and the dairy cow’s phenotypes.

PCA, PC-CVA, and Random Forest of GCIB-SIMS Data for the Elucidation of Bacterial Envelope Differences in Antibiotic Resistance Research.

Antibiotic resistance can rapidly spread through bacterial populations via bacterial conjugation. The bacterial membrane has an important role in facilitating conjugation, thus investigating the effects on the bacterial membrane caused by conjugative plasmids, antibiotic resistance, and genes involved in conjugation is of interest. Analysis of bacterial membranes was conducted using gas cluster ion beam-secondary ion mass spectrometry (GCIB-SIMS). The complexity of the data means that data analysis is important for the identification of changes in the membrane composition. Preprocessing of data and several analytical methods for identification of changes in bacterial membranes have been investigated. GCIB-SIMS data from Escherichia coli samples were subjected to principal components analysis (PCA), principal components-canonical variate analysis (PC-CVA), and Random Forests (RF) data analysis with the aim of extracting the maximum biological information. The influence of increasing replicate data was assessed, and the effect of diminishing biological variation was studied. Optimized m/z region-specific scaling provided improved clustering, with an increase in biologically significant peaks contributing to the loadings. PC-CVA improved clustering, provided clearer loadings, and benefited from larger data sets collected over several months. RF required larger sample numbers and while showing overlap with the PC-CVA, produced additional peaks of interest. The combination of PC-CVA and RF allowed very subtle differences between bacterial strains and growth conditions to be elucidated for the first time. Specifically, comparative analysis of an E. coli strain with and without the F-plasmid revealed changes in cyclopropanation of fatty acids, where the addition of the F-plasmid led to a reduction in cyclopropanation.

The Relationship Between Junk Food with the Incidence of Early Menarche Among Female Students Aged 9 – 12 Years Old at Al-Iman Private Elemantary School South Tangerang City

Background : Early menarche is tihe first menstruation experienced by fertile woman under the age of 12 years. The condition of early menarche is because it gets more production of the hormone estrogen than other woman in general. One of the factors in occurrence of early menarche is caused by nutritional status as junk food as a food that is high in calories and fat but low in dietary fiber and if excessive consumption will stimulate the acceleration of reproductive organ maturity. Objective : To determine the relationship between junk food consumption with the incidence of early menarche among female students agen 9-12 years old at Al-Iman Private Elemantary School. Methods : This research is oobservational analytic with a cross sectional approach. The number of samples was 30 with the Quota Sampling technique. The instrument used was a questionnaire. Data were analyzed using the chi square test. Result : This study shows that there is a relationship between junk food consumption with the incidence of early menarche with the result of chi suare test with the alternative Fisher`s Exact Test obtained a value of p = 0,007 which means p < α = 0,05 and 95% CI 2,167 – 128,176. Conclusion : There is a relationship between junk food consumption with the incidence of early menarche among female students at Al-Iman Private Elemantary School. Suggestion: For further researchers to examine other variable that have not been studied with larger sample.

Creating Job Satisfaction through Improving the Quality of Transformational Leadership and Organizational Culture in Blister City Government, Indonesia

Civil Servants (PNS). As elements of the state apparatus, state servants bear a hefty burden in carrying out their duties and functions, considering the various phenomena of dynamic changes in the strategic environment in the current era of globalization. Based on these problems, this research aims To determine the description of transformational leadership style, organizational culture, and job satisfaction in the Blitar City Government. To analyze the influence of transformational leadership, organizational culture and motivation on job satisfaction in the Blitar City Government Environment. The population in this study consisted of civil servants within the Blitar City government. Based on data from Regional Personnel Agency Blitar City, in 2022, the number of employees will be 886. Sampling from the research population was done using a proportional random sampling technique. The number of samples in this study used the Slovin formula with an error rate of 10% of 274. This research concludes that Transformational leadership style and organizational culture affect employee job satisfaction at the Blitar City Government. The results of this research are expected to significantly contribute to the development of knowledge relevant to research problems. They are expected to strengthen the body of theories and for further research in human resource management.

Long-Term Monitoring of Microplastics in a German Municipal Wastewater Treatment Plant

Wastewater treatment plants (WWTPs) have been identified as important point sources for microplastics (MPs) in the environment; monitoring MP emissions in the WWTP effluent is therefore essential for contamination control. The aim of this study is to acquire a large number of samples (320) over a period of two years and three months to determine the temporal variations in microplastic contamination in the outlet of the municipal WWTP Landau-Mörlheim. The effluent of the third cleaning stage is sampled with a 10 µm filter cartridge, processed in the laboratory using a hydrogen peroxide treatment, and MPs are then detected by fluorescence staining. The results show high temporal variations in the microplastic concentrations in the effluent of the WWTP. This indicates that high numbers of samples are necessary to obtain a representative assessment of the microplastic emissions; single samples are not representative. The average microplastic concentration in the effluent was 27.8 ± 29.8 MP/L, ranging from 0.6 MP/L to 194.0 MP/L. This leads to a yearly emission of 1.5 × 1011 MP for the WWTP Landau-Mörlheim, corresponding to an emission of 2.8 × 106 MP/inhabitant and year. Statistically significant seasonal variations could not be observed, although there is a trend towards lower MP concentrations in summer. Further, no correlations with other wastewater or weather parameters could be found.

The Influence of Product Quality and Brand Image with the Moderation of Customer Satisfaction on Customer Retention of iPhone Smartphone Users (Case Study in Sukabumi Regency)

This study aims to analyze the influence of product quality and brand image with customer satisfaction moderation on customer retention of iPhone smartphone users in Sukabumi Regency. The background of this study is based on the importance of understanding the factors that influence customer loyalty in the highly competitive smartphone industry. This study uses a quantitative research method. The population in this study is iPhone smartphone users who live in Sukabumi Regency. The number of samples taken was 155 people, with non-probability sampling used to determine the sample. The data used in this study is primary data, namely by distributing questionnaires. The measurement scale used is the Likert scale. The statistical method used is Partial Least Square (PLS) and the data analysis technique uses the SmartPLS version 3.0 program. The results of the study show that product quality and brand image have a positive and significant effect on customer retention. However, customer satisfaction does not positively moderate the relationship between product quality and brand image with customer retention. Further research is recommended to explore other factors that can moderate this relationship.

A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis.

Recent advancements in biomedical technologies and the proliferation of high-dimensional Next Generation Sequencing (NGS) datasets have led to significant growth in the bulk and density of data. The NGS high-dimensional data, characterized by a large number of genomics, transcriptomics, proteomics, and metagenomics features relative to the number of biological samples, presents significant challenges for reducing feature dimensionality. The high dimensionality of NGS data poses significant challenges for data analysis, including increased computational burden, potential overfitting, and difficulty in interpreting results. Feature selection and feature extraction are two pivotal techniques employed to address these challenges by reducing the dimensionality of the data, thereby enhancing model performance, interpretability, and computational efficiency. Feature selection and feature extraction can be categorized into statistical and machine learning methods. The present study conducts a comprehensive and comparative review of various statistical, machine learning, and deep learning-based feature selection and extraction techniques specifically tailored for NGS and microarray data interpretation of humankind. A thorough literature search was performed to gather information on these techniques, focusing on array-based and NGS data analysis. Various techniques, including deep learning architectures, machine learning algorithms, and statistical methods, have been explored for microarray, bulk RNA-Seq, and single-cell, single-cell RNA-Seq (scRNA-Seq) technology-based datasets surveyed here. The study provides an overview of these techniques, highlighting their applications, advantages, and limitations in the context of high-dimensional NGS data. This review provides better insights for readers to apply feature selection and feature extraction techniques to enhance the performance of predictive models, uncover underlying biological patterns, and gain deeper insights into massive and complex NGS and microarray data.

Study on Dynamic Modulus Prediction Model of In-Service Asphalt Pavement

The dynamic modulus of in-service asphalt pavements serves as a critical parameter for the computation of residual life and the design of overlays. However, its acquisition is currently limited to laboratory dynamic modulus testing using a limited number of core samples, necessitating a reassessment of its representativeness. To facilitate the prediction of dynamic modulus design parameters through Falling Weight Deflectometer (FWD) back-calculated modulus data, an integrated approach encompassing FWD testing, modulus back-calculation, core sample dynamic modulus testing, and asphalt DSR testing was employed to concurrently acquire dynamic modulus at identical locations under varying temperatures and frequencies. Dynamic modulus prediction models for in-service asphalt pavements were developed utilizing fundamental model deduction and gene expression programming (GEP) techniques. The findings indicate that GEP exhibits superior efficacy in the development of dynamic modulus prediction models. The dynamic modulus prediction model developed can enhance both the precision and representativeness of asphalt pavement’s dynamic modulus design parameters, as well as refine the accuracy of residual life estimations for in-service asphalt pavements. Concurrently, the modulus derived from FWD back-calculation can be transmuted into the dynamic modulus adhering to a uniform standard criterion, facilitating the identification of problematic segments within the asphalt structural layer. This is of paramount importance for the maintenance or reconstruction of in-service asphalt pavements.

Small-Sample Fault Diagnosis of Axial Piston Pumps across Working Conditions, Based on 1D-SENet Model Migration

Hydraulic pumps are the core components that provide power for hydraulic transmission systems, which are widely used in aerospace, marine engineering, and mechanical engineering, and their failure affects the normal operation of the entire system. This paper takes a single axial piston pump as the research object and proposes a small-sample fault diagnosis method based on the model migration strategy for the situation in which only a small number of training samples are available for axial piston pump fault diagnosis. To achieve end-to-end fault diagnosis, a 1D Squeeze-and-Excitation Networks (1D-SENets) model was constructed based on a one-dimensional convolutional neural network and combined with the channel domain attention mechanism. The model was first pre-trained with sufficient labeled fault data from the source conditions, and then, based on the model migration strategy, some of the underlying network parameters were fixed, and a small amount of labeled fault data from the target conditions was used to fine-tune the rest of the parameters of the pre-trained model. In this paper, the proposed method was validated using an axial piston pump fault dataset, and the experimental results show that the method can effectively improve the overfitting problem in the small sample fault diagnosis of axial piston pumps and improve the recognition accuracy.

Deeper Exploration of Gut Microbiome: Profile of Resistome, Virome and Viral Auxiliary Metabolic Genes of Three Ethnic Indian Groups.

The current study explored the resistomes and viromes of three Indian ethnic populations: Jaisalmer, Khargone, and Ladakh. These three groups had different dietary habits and antibiotic consumption rates. A resistome analysis indicated that compared to the Jaisalmer (n = 10) group, the burden of antibiotic resistance genes in the gut microbiome was higher in the Khargone (n = 12) and Ladakh (n = 9) groups. However, correlational analysis factoring in food habits, healthcare, and economic status was not statistically significant due to the limited number of samples. A considerable number of antibiotic resistance genes (ARGs) were present in well-known gut commensals such as Bifidobacteriaceae, Acidomonococcaceae, etc., as retrieved directly by mapping to the Resfinder database using the Groot tool. Further, the raw reads were assembled using MEGAHIT, and putative bacteriophages were retrieved using the VIBRANT tool. Many of the classified bacteriophages of the virome revealed that bacteria belonging to the families Bifidobacteriaceae and Enterocococcaceae were their hosts. The prophages identified in these groups primarily contained auxiliary metabolic genes (AMGs) for primary amino acid metabolism. However, there were significantly fewer AMGs in the Ladakh group than in the Jaisalmer group (p < 0.05). None of the classified bacteriophages or prophages contained ARGs. This indicates that phages do not normally carry antibiotic resistance genes.

Using established biorepositories for emerging research questions: a feasibility study

BackgroundProteomics and metabolomics offer substantial potential for advancing kidney transplant research by providing versatile opportunities for gaining insights into the biomolecular processes occurring in donors, recipients, and grafts. To achieve this, adequate quality and numbers of biological samples are required. Whilst access to donor samples is facilitated by initiatives such as the QUOD biobank, an adequately powered biobank allowing exploration of recipient-related aspects in long-term transplant outcomes is missing. Rich, yet unverified resources of recipient material are the serum repositories present in the immunological laboratories of kidney transplant centers that prospectively collect recipient sera for immunological monitoring. However, it is yet unsure whether these samples are also suitable for -omics applications, since such clinical samples are collected and stored by individual centers using non-uniform protocols and undergo an undocumented number of freeze–thaw cycles. Whilst these handling and storage aspects may affect individual proteins and metabolites, it was reasoned that incidental handling/storage artifacts will have a limited effect on a theoretical network (pathway) analysis. To test the potential of such long-term stored clinical serum samples for pathway profiling, we submitted these samples to discovery proteomics and metabolomics.MethodsA mass spectrometry-based shotgun discovery approach was used to obtain an overview of proteins and metabolites in clinical serum samples from the immunological laboratories of the Dutch PROCARE consortium. Parallel analyses were performed with material from the strictly protocolized QUOD biobank.ResultsFollowing metabolomics, more than 800 compounds could be identified in both sample groups, of which 163 endogenous metabolites were found in samples from both biorepositories. Proteomics yielded more than 600 proteins in both groups. Despite the higher prevalence of fragments in the clinical, non-uniformly collected samples compared to the biobanked ones (42.5% vs 26.5% of their proteomes, respectively), these fragments could still be connected to their parent proteins. Next, the proteomic and metabolomic profiles were successfully mapped onto theoretical pathways through integrated pathway analysis, which showed significant enrichment of 79 pathways.ConclusionsThis feasibility study demonstrated that long-term stored serum samples from clinical biorepositories can be used for qualitative proteomic and metabolomic pathway analysis, a notion with far-reaching implications for all biomedical, long-term outcome-dependent research questions and studies focusing on rare events.

Aquatic invasive species exhibit contrasting seasonal detectability patterns based on environmental DNA: Implications for monitoring

Abstract Aquatic invasive species (AIS) are a global threat to freshwater biodiversity and ecosystem services. Documenting AIS prevalence at broad spatial scales is critical to effective management and early detection. However, conventional monitoring for AIS is costly and is rarely applied at the resolution and scale required for effective management. Monitoring of AIS using environmental DNA (eDNA) has the potential to enable broadscale surveillance at a fraction of the cost of conventional methods, but key questions must first be addressed related to how eDNA detection probability varies among environments, seasons, and multiple species with different life histories. To quantify spatiotemporal variation in the detection probability of AIS using eDNA sampling, we surveyed 20 lakes with known populations of four aquatic invasive species: common carp (Cyprinus carpio), rusty crayfish (Faxonius rusticus), spiny waterflea (Bythotrephes longimanus), and zebra mussels (Dreissena polymorpha). We collected water samples at 10 locations per lake, five times throughout the open water season resulting in a total of 1,000 water samples. Quantitative polymerase chain reaction was used with species‐specific assays to determine the presence of each species' eDNA in water samples. With Bayesian occupancy models, we quantified the effects of lake and site characteristics and Julian date on eDNA detection probability. The probability of eDNA detection varied seasonally, and the seasonal variation was species‐specific and related to species life histories. Zebra mussel eDNA was generally the most detectable among the species we targeted, and detection probability peaked in midsummer when only six water samples were required to achieve a 95% probability of detection (80% Bayesian credible interval: 3–12 samples). Spiny waterflea eDNA detections also peaked in mid to late summer, but were overall the most difficult species to detect, requiring 160 samples for a 95% probability of detection (80% Bayesian credible interval: 67–1,616 samples). Common carp eDNA was most detectable in the spring and rusty crayfish eDNA was most detectable in the early autumn, corresponding to key life history events. Sampling for eDNA during the optimal time of the year for each species decreased the number of samples required to reach a 95% probability of detection by an order of magnitude or more. Our results are relevant for decision makers interested in using eDNA as a multi‐species monitoring tool and highlight the importance of life history in the efficacy of eDNA monitoring.

Pengaruh Pertumbuhan Perusahaan, Leverage, Komisaris Independen, dan Modal Bersih Terhadap Kinerja Keuangan

This research aims to provide empirical evidence regarding the influence of company growth, debt levels, independent commissioners, net working capital on financial performance. This research method uses quantitative research methods. The research object is food and beverage companies listed on the Indonesian stock exchange during 2020-2022. The data source in this research uses secondary data, namely data contained on the official IDX website, namely www.idx.co.id and the official website of the related company. The population used in this research is 30 food and beverage manufacturing companies listed on the Indonesian stock exchange for the 2020-2022 period. The number of samples taken was 30 companies, so the data obtained was 90 data. The research results show that company growth has a significant positive effect on the financial performance of consumer goods sub-sector manufacturing companies listed on the IDX in 2020-2022. Debt levels have a significant negative effect on the financial performance of Consumer Goods Sub-Sector Manufacturing Companies Listed on the IDX in 2020-2022. Commissioners have a significant positive influence on the financial performance of Consumer Goods Sub-Sector Manufacturing Companies Listed on the IDX in 2020-2022. Net working capital has a significant positive effect on the financial performance of Consumer Goods Sub-Sector Manufacturing Companies Listed on the IDX in 2020-2022. Simultaneously, company growth, debt levels, commissioners, dividend policy and net working capital have a significant positive effect on the financial performance of Consumer Goods Sub-Sector Manufacturing Companies Listed on the IDX in 2020-2022.