Small Number Of Positive Samples Research Articles

Vaginal carriage of Haemophilus influenzae in a non-pregnant reproductive-age population

BackgroundHaemophilus influenzae (Hi) is an emerging cause of early onset neonatal sepsis, but mechanisms of transmission are not well understood. We aimed to determine the prevalence of vaginal carriage of Hi in reproductive age women and to examine behavioral and demographic characteristics associated with its carriage.MethodsWe performed a secondary analysis of stored vaginal lavage specimens from a prospective cohort study of nonpregnant reproductive-age women. After extraction of bacterial genomic DNA, samples were tested for the presence of the gene encoding Haemophilus protein d (hpd) by quantitative real-time polymerase chain reaction (PCR) using validated primers and probe. PCR for the V3-V4 region of the 16 S rRNA gene (positive control) assessed sample quality. Samples with cycle threshold (CT) value < 35 were defined as positive. Sanger sequencing confirmed the presence of hpd. Behavioral and demographic characteristics associated with vaginal carriage of Hi were examined.Results415 samples were available. 315 (75.9%) had sufficient bacterial DNA and were included. 14 (4.4%) were positive for hpd. There were no demographic or behavioral differences between the women with Hi vaginal carriage and those without. There was no difference in history of bacterial vaginosis, vaginal microbiome community state type, or presence of Group B Streptococcus in women with and without vaginal carriage of Hi.ConclusionHi was present in vaginal lavage specimens of 4.4% of this cohort. Hi presence was unrelated to clinical or demographic characteristics, though the relatively small number of positive samples may have limited power to detect such differences.

Improving Non-Negative Positive-Unlabeled Learning for News Headline Classification

With the development of Internet technology, network platforms have gradually become a tool for people to obtain hot news. How to filter out the current hot news from a large number of news collections and push them to users has important application value. In supervised learning scenarios, each piece of news needs to be labeled manually, which takes a lot of time and effort. From the perspective of semi-supervised learning, on the basis of the non-negative Positive-Unlabeled (nnPU) learning, this paper proposes a novel algorithm, called ’Enhanced nnPU with Focal Loss’ (FLPU), for news headline classification, which uses the Focal Loss to replace the way the classical nnPU calculates the empirical risk of positive and negative samples. Then, by introducing the Virtual Adversarial Training (VAT) of the Adversarial training for large neural LangUage Models (ALUM) into FLPU, another (and better) algorithm, called ’FLPU+ALUM’, is proposed for the same purpose, aiming to label only a small number of positive samples. The superiority of both algorithms to state-of-the-art PU algorithms considered is demonstrated by means of experiments, conducted on two datasets for performance comparison. Moreover, through another set of experiments, it is shown that, if enriched by the proposed algorithms, the RoBERTa-wwm-ext model can achieve better classification performance than the state-of-the-art binary classification models included in the comparison. In addition, a ’Ratio Batch’ method is elaborated and proposed as more stable for use in scenarios involving only a small number of labeled positive samples, which is also experimentally demonstrated.

An ensemble contrastive classification framework for imbalanced learning with sample-neighbors pair construction

While existing imbalanced classification methods have made great progress, there are still some challenges in the current imbalanced learning field: (1) How to achieve the balance between classes without introducing noise and losing information; (2) How to mine the differences between classes from datasets with a relatively small number of positive samples; (3) How to learn the distribution differences in overlapping areas efficiently. To address these problems, this paper proposes an ensemble contrastive classification framework with sample-neighbors pair construction. The traditional pointwise imbalanced classification is redefined as a pairwise label matching task between the sample to be classified (Target Sample, TS) and a group of neighbor samples (Contrastive Sample Group, CSG). For any TS, we can obtain multiple CSGs containing same/different class with the TS, and combine the TS with different CSGs into sample-neighbors pairs as positive/negative samples in the new task. In this way, the balance of classes and multiplied increase of sample scale are achieved without introducing any noise. Based on the obtained rich data, a robust classifier can be trained to mine the distribution differences in overlapping areas through the contrastive learning between TS and its CSGs. For a given test sample, we can obtain abundant sample-neighbors pairs and their corresponding classification results. Its label can be obtained by result integration and reverse reasoning. Extensive experimental evaluations on 48 KEEL and UCI public datasets show that the proposed method outperforms the existing state-of-the-art imbalanced classification methods in terms of F1-measure and G-mean.

Principle, method and application of relationship inference based on biological networks

In the era of big biomedical data, systematically discovering key elements, including diseasecausinggenes and/or drug targets, and understanding the micro-level nature of macro-level phenotypes ina holistic fashion have remained a common challenge for information science, Western medicine, and traditional Chinese medicine (TCM). The key to overcoming thechallenge is how to solve the problems of multi-scale informationfusion and high-dimensionality, high-noise, and small-scale samples that exist in biomedical data, throughthe in-depth understanding of the “relationship" nature of complex biological systems (CBSs), as biology is a typicalcomplex system. Biological networks, as the basis of CBSs, reflect the interrelationshipsof various biological molecules such as genes and gene products in the human body, as well as those betweenbiological molecules and diseases and drugs at different levels. Biological networks have been widely used inbiomedical sciences analysis of data. We started the research on the relationship between Chinese and Westernmedicines and complex biological networks (CBNs) more than 20 years ago, and took the lead in proposing the hypothesis of a “network target",and proceeded with the method construction and application. In principle, this article uncovers a novel relationshipnamed as “multilevel modular relationship", between macro-level phenotypes and micro-level molecules based onCBNs, and discusses CBN-based “relationship inference". It reveals that the macro-level emergence haslocal modularity at the micro-level, and the more similar the macro-level phenotypes, the stronger the modularrelationships among micro-level molecules (disease-causing genes or drug targets). Methodologically, we furtherestablish a general CBN-based computational framework for the relationship inference to infer key elements frombig biomedical data with a small number of positive samples, from a global perspective. It consists of three parts:(1) relationship network construction, (2) relationship representation and modeling, and (3) unknown relationshipsinferring, with the aim of the substantialization, mathematicization and integration of relationships. At the applicationlevel, the relationship inference framework has shown good performance in predicting disease-causing genesand drug targets, identifying disease-related markers, and uncovering molecular mechanisms related toTCM. Thus, this framework has provided a systematic solution for comprehensively understanding the micro-levelnature of complex diseases and TCM. It has also provided important theoretical and methodological supports forsome emerging disciplines, including network pharmacology.

Molecular Epidemiology of SARS-CoV-2 in Tunisia (North Africa) through Several Successive Waves of COVID-19.

Documenting the circulation dynamics of SARS-CoV-2 variants in different regions of the world is crucial for monitoring virus transmission worldwide and contributing to global efforts towards combating the pandemic. Tunisia has experienced several waves of COVID-19 with a significant number of infections and deaths. The present study provides genetic information on the different lineages of SARS-CoV-2 that circulated in Tunisia over 17 months. Lineages were assigned for 1359 samples using whole-genome sequencing, partial S gene sequencing and variant-specific real-time RT-PCR tests. Forty-eight different lineages of SARS-CoV-2 were identified, including variants of concern (VOCs), variants of interest (VOIs) and variants under monitoring (VUMs), particularly Alpha, Beta, Delta, A.27, Zeta and Eta. The first wave, limited to imported and import-related cases, was characterized by a small number of positive samples and lineages. During the second wave, a large number of lineages were detected; the third wave was marked by the predominance of the Alpha VOC, and the fourth wave was characterized by the predominance of the Delta VOC. This study adds new genomic data to the global context of COVID-19, particularly from the North African region, and highlights the importance of the timely molecular characterization of circulating strains.

Multi-View Auxiliary Diagnosis Algorithm for Lung Nodules

Lung is an important organ of human body. More and more people are suffering from lung diseases due to air pollution. These diseases are usually highly infectious. Such as lung tuberculosis, novel coronavirus COVID-19, etc. Lung nodule is a kind of high-density globular lesion in the lung. Physicians need to spend a lot of time and energy to observe the computed tomography image sequences to make a diagnosis, which is inefficient. For this reason, the use of computer-assisted diagnosis of lung nodules has become the current main trend. In the process of computer-aided diagnosis, how to reduce the false positive rate while ensuring a low missed detection rate is a difficulty and focus of current research. To solve this problem, we propose a three-dimensional optimization model to achieve the extraction of suspected regions, improve the traditional deep belief network, and to modify the dispersion matrix between classes. We construct a multi-view model, fuse local three-dimensional information into two-dimensional images, and thereby to reduce the complexity of the algorithm. And alleviate the problem of unbalanced training caused by only a small number of positive samples. Experiments show that the false positive rate of the algorithm proposed in this paper is as low as 12%, which is in line with clinical application standards.

613 Vaginal carriage of haemophilus influenzae in a non-pregnant reproductive-age population

Haemophilus influenzae (Hi) is an emerging cause of early onset neonatal sepsis, but mechanisms of transmission are not well understood. We aimed to evaluate the prevalence of vaginal carriage of Hi in reproductive age women as a first step in assessing the role of peripartum vertical transmission in neonatal Hi disease. We performed a secondary analysis of stored vaginal lavage specimens from nonpregnant reproductive-age women. After extraction of bacterial genomic DNA, samples were tested for the presence of the gene encoding Haemophilus protein d (hpd) by quantitative real-time polymerase chain reaction (PCR) using validated primers and probe. PCR for the V3-V4 region of the 16S rRNA gene was performed as a positive control to assess sample quality. Samples with a cycle threshold (CT) value < 35 were defined as positive. Sanger sequencing confirmed the presence of hpd. We compared demographic and behavioral characteristics of women with and without vaginal carriage of Hi. Chi-square, Fisher’s exact, Mann-Whitney and t-test were used with p<0.05 set as the level of statistical significance. 415 samples were available for analysis. 315 (75.9%) had sufficient bacterial DNA present and were included in the analysis. Of these, 14 (4.4%) were positive for hpd. There were no demographic or behavioral differences between the women with Hi vaginal carriage and those without (Table 1). There was no difference in history of bacterial vaginosis, vaginal microbiome community state type, or presence of Group B Streptococcus (GBS) in women with and without vaginal carriage of Hi (Table 2). Haemophilus influenzae was present in vaginal lavage specimens of 4.4% of this cohort. Hi presence was unrelated to clinical or demographic characteristics, though the relatively small number of positive samples may have limited power to detect such differences. Further studies should assess for the presence of this possible perinatal pathogen in pregnant women and the rate of transmission to neonates.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Genetic characterization and zoonotic potential of Blastocystis from wild animals in Sichuan Wolong National Natural Reserve, Southwest China.

Blastocystis is a prevalent eukaryotic parasite that has been identified in a wide range of hosts. Several species are considered potential sources of Blastocystis infection in humans, but little is known about the prevalence of Blastocystis in wild animals. In this study, the prevalence and subtypes of Blastocystis were investigated to assess the zoonotic potential of wild animals in Sichuan Wolong National Natural Reserve. A total of 300 fecal samples were collected from 27 wildlife species in three areas of the Reserve. The subtype (ST), genetic characteristics, and prevalence of Blastocystis were determined by PCR amplification of part (~600 bp) of the SSU rRNA gene. Thirty fecal samples (10.0%) were Blastocystis-positive. The highest prevalence of Blastocystis was found in Yinchanggou (18.3%), with significantly less found in Niutoushan (7.5%) and Genda (5.5%) (p < 0.05). No significant differences were associated with different orders of animals in prevalence, which may be because of the small number of positive samples obtained. Sequence analysis showed five subtypes (ST1, ST3, ST5, ST13, and ST14), with ST13 and ST14 being predominant (33% each), followed by ST1 (20%). This is the first molecular investigation of Blastocystis infection in the wild animals of southwestern China. Subtypes ST1, ST3, ST5, and ST14 have previously been identified in humans, suggesting that wild animals may be potential reservoirs of Blastocystis for humans.

A positive and unlabeled learning algorithm for mineral prospectivity mapping

Application of supervised machine learning algorithms for mineral prospectivity mapping (MPM) requires positive and negative training samples. Typically, known mineral deposits are considered as positive training samples. However, the selection of negative training samples in the process of MPM is challenging. The one-class classification methods require positive and unlabeled samples or only positive samples; while without requiring negative training samples. In this study, the positive and unlabeled learning (PUL) algorithm was employed to produce a potential map for Fe polymetallic mineralization in southwestern Fujian province, China. This study first examined the sensitivity of the PUL algorithm to different training sets of labeled and unlabeled locations. The predictive results on 10 random unlabeled datasets confirm that PUL modeling with different training sets is reproducible and stable. In addition, the trained model provides a strong spatial correlation between the predictive variables and the locations of known mineral deposits. Finally, the performance of PUL1algorithm is compared to one-class support vector machine (OCSVM), artificial neural networks (ANN), and logistic regression (LR). Comparative results indicate that the PUL model can achieve a better performance in terms of both fitting-rate, prediction-rate, and AUC value compared with OCSVM, ANN and LR. The labelling efforts can be significantly reduced because the PUL algorithm requires only a small number of positive samples and utilizes unlabeled data in training.

Genetic Analysis of Zoonotic Gastrointestinal Protozoa and Microsporidia in Shelter Cats in South Korea.

Feral cats that are roaming outside can serve as reservoirs for zoonotic pathogens, negatively impacting public health. They may experience high levels of parasitic infection. Some gastrointestinal protozoa and microsporidia possessing zoonotic potential in cats include Cryptosporidium spp., Giardia duodenalis, Blastocystis sp., Enterocytozoon bieneusi, and Toxoplasma gondii. Here, we show the infection rates and risk factors of intestinal protozoa and microsporidia detected from shelter cats on Jeju Island in South Korea in 2020. Among 158 cats, we detected genes for five internal protozoa and microsporidia, namely, Cryptosporidium felis (0.6%), G. duodenalis (3.8%), Blastocystis sp. (0.6%), E. bieneusi (3.8%), and T. gondii (1.3%). Furthermore, 16 cats (10.1%) were PCR-positive for at least one protozoan or microsporidium. To our knowledge, this study is the first to describe the existence of C. felis, G. duodenalis assemblage F, Blastocystis sp. ST4 subtype, and E. bieneusi genotype Peru11 in cats in South Korea. Despite the small number of positive samples, this study expands our understanding of the incidence of zoonotic gastrointestinal protozoa and microsporidia in shelter cats and genetically characterizes the isolates found in the infected animals. Moreover, these findings emphasize the need for a better control strategy on protozoa and microsporidia in cats, tailored to their individual needs, to protect animal and public health.

Fake Review Detection Based on PU Learning and Behavior Density

Today, app stores offer ranking lists to help users to find quality apps that meet their needs. In order to prevent people from spreading fake reviews which can be used to defame certain apps or manipulate the ranking lists of the app store, we propose a method based on Positive and Unlabeled (PU) learning and behavior density to detect fake reviews. To identify the trusted negative samples, the classifier is trained by the Biased-SVM algorithm. Then, the preliminary screening results of the classifier are combined with user behavior density to identify fake reviews. The traditional fully supervised detection method relies on manually labeled data, the quality of which directly affects the trained classifier. Our proposed method can overcome such a deficiency, and achieve effective learning when there are only a small number of positive samples and a large number of unlabeled samples. Through experiments and case analysis, we demonstrate that our method has high detection accuracy.

Extracting Vehicle Trajectories Using Unmanned Aerial Vehicles in Congested Traffic Conditions

Obtaining the trajectories of all vehicles in congested traffic is essential for analyzing traffic dynamics. To conduct an effective analysis using trajectory data, a framework is needed to efficiently and accurately extract the data. Unfortunately, obtaining accurate trajectories in congested traffic is challenging due to false detections and tracking errors caused by factors in the road environment, such as adjacent vehicles, shadows, road signs, and road facilities. Unmanned aerial vehicles (UAVs), with incorporating machine learning and image processing, can mitigate these difficulties by their ability to hover above the traffic. However, research is lacking regarding the extraction and evaluation of vehicle trajectories in congested traffic. In this study, we propose and compare two learning-based frameworks for detecting vehicles: the aggregated channel feature (ACF), which is based on human-made features, and the faster region-based convolutional neural network (Faster R-CNN), which is based on data-driven features. We extend the detection results to extract vehicle trajectories in congested traffic conditions from UAV images. To remove the errors associated with tracking vehicles, we also develop a postprocessing method based on motion constraints. Then, we conduct detailed performance analyses to confirm the feasibility of the proposed framework on a congested expressway in Korea. The results show that Faster R-CNN outperforms the ACF in images with large objects and in those with small objects if sufficient data are provided. This framework extracts the vehicle trajectories with high precision, making them available for analyzing traffic dynamics based on the training of just a small number of positive samples. The results of this study provide a practical guideline for building a framework to extract vehicles trajectories based on given conditions.

Antimicrobial effectiveness of polyhexamethylene biguanide on Enterococcus faecalis, Staphylococcus epidermidis and Candida albicans

Aim To evaluate the antimicrobial activity of 0.2% polyhexamethilene biguanide (PHMB) in root canal models infected with Enterococcus faecalis, Candida albicans and Staphylococcus epidermidis. PHMB was compared in these tests with 2.5% NaOCl and 0.2% CHX. Methods Prepared models of 50 human root canals (n=50) were immerged in mixed, four- weeks old culture that consisted of E .faecalis, S. epidermidis and C. albicans. Roots were randomly divided into three groups: one with 30 (n=30) and two with 10 (n=10) samples. Samples were treated with polyhexamethylene biguanide (PHMB) (0.2%), sodium hypochlorite (NaOCl) (2.5%) and chlorhexidine (CHX) (0.2%), respectively. Root dentin was sampled before and after the tretment with these solutions. Colony- forming units (CFU) were counted to asses the antimicrobial effects of three solutions on viability of selected microrganisms in specimens before and after the treatment.T-test was used for comparison of results between specimens before and after the treatment, while Newman-Keuls test was used for pairwise comparison at p=0.05. Results The PHMB was significantly more efficient in reducing the number of all three tested microorganisms. NaOCl and CHX made only statistically significant (p<0.05) difference in case of E. faecalis and S. epidermidis. In the case of C. albicans, this difference was not statistically significant due to the small number of positive samples and high initial dispersion of results. Conclusion Both solutions PHMB and NaOCl were successful in eliminating E. faecalis and S. epidermidis from the mature dentin biofilm, CHX was not successful enough.

A prospective study to assess the diagnostic performance of the Sofia® Immunoassay for Influenza and RSV detection.

BackgroundRespiratory viruses RSV and influenza A and B viruses are responsible for important disease outbreaks during the winter season in temperate climate regions. Rapid diagnostic tests (RDTs) are assays designed to yield a rapid diagnosis, which facilitates patient management. The Sofia Influenza A+B Fluorescence Immunoassay and Sofia RSV Fluorescence Immunoassay are RDTs for Influenza and RSV detection that employ a new technology to enhance their sensitivity. ObjectivesSensitivity, specificity and positive and negative predictive values of the assays were calculated compared with the reference diagnostic method: real-time RT-PCR. Study designA prospective evaluation was carried out on 1065 respiratory samples for Sofia Influenza A+B FIA and on 261 samples for Sofia RSV FIA from November 2013 to April 2014. ResultsThe sensitivities of the Sofia Influenza A+B FIA for influenza A and influenza B detection were, respectively, 75.3% (244/324) and 50.0% (8/16). The sensitivity of the Sofia RSV FIA was 92.1% (128/139). There were no differences in Sofia FIA performance depending on the virus subtype. ConclusionsThe results showed high sensitivity and specificity values for influenza A and RSV detection, but values were lower for influenza B. More information is needed regarding the performance for influenza B given the small number of positive samples assessed.

FRECUENCIA DE Toxoplasma gondii Y Neospora caninum EN VICUÑAS (Vicugna vicugna) DE LA SIERRA CENTRAL PERUANA MEDIANTE LAS TÉCNICAS DE INMUNOFLUORESCENCIA INDIRECTA Y ELISA INDIRECTA.

The objective of this study was to estimate the frequency of antibodies againstToxoplasma gondii and Neospora caninum in vicuñas in the Yauli province, Junín, Peru,and to determine the degree of concordance between the indirect immunofluorescence(IFA) and indirect ELISA test. A total of 79 blood samples were obtained during a «Chaku»(rodeo). The results showed a frequency of antibodies against T. gondii of 5.1 and 3.1%for indirect ELISA and IFA respectively, whereas none of the samples were positive to N. caninum. The kappa value was 0.85, but the small number of positive samples does notallowed inferring that the degree of concordance between the two test was good.

Lead and Cadmium Levels in Liver, Kidney and Muscle of Harvested Wild Springbok (Antidorcus marsupialis) Under Extensive Management in Southern and Southeastern Namibia

The aims of the study were to assess heavy metals (lead and cadmium) values in meat and offal from harvested springbok. For cadmium (Cd), mean concentrations in liver and kidney were 0.10 ± 0.04 mg/kg, 0.33 ± 0.05 mg/kg and 1.04 ± 0.15 mg/kg, 0.91 ± 0.12 mg/kg for lead (Pb). None of the samples had a Cd level above the limits set for the liver (0.5 mg/kg) and 1.0 mg/kg set for the kidney while two of the samples had a Pb level above the limits for the liver (0.5 mg/kg) and 12 samples had Pb values above the limits for the kidney (0.5 mg/kg). The mean Pb and Cd concentrations in muscles were below the detection limit. In the current study, interpretation of the results was limited by the small number of positive samples; however, values were within the range previously reported in literature for wildlife species elsewhere.

Polymerase Chain Reaction Detection of Cytauxzoon felis From Field-Collected Ticks and Sequence Analysis of the Small Subunit and Internal Transcribed Spacer 1 Region of the Ribosomal RNA Gene

Cytauxzoon felis produces a disease in domestic cats in the Midwest (U.S.A.), which often leads to a fatal outcome. Although the clinical disease process is well described, there are still many unanswered questions about this organism. For example, it is unknown whether species of ticks other than Dermacentor variabilis can serve as vectors for transmission. With recent reports of surviving cats from limited geographic areas, another relevant question is the potential for genetically less virulent organism strains. This study evaluated 352 individual or pooled tick samples (1,362 total ticks) for the presence of C. felis small subunit ribosomal RNA and internal transcribed spacer 1 (ITS-1) region genes using a polymerase chain reaction (PCR). These ticks were collected from dogs and cats in several Missouri counties, including 10 from cats diagnosed with cytauxzoonosis. Only 3 positive C. felis samples were identified in Amblyomma americanum nymphs, and there was very limited genetic variation noted in both genes. The small number of positive samples did not allow the study to determine which PCR analysis was more sensitive. This is the first known report of ITS-1 gene identification and sequencing for C. felis. It is also the first published investigation of genetic variation in C. felis.

Nematode control practices and anthelmintic resistance in dairy calves in the south west of England

A postal survey of worming practices on West Country dairy farms was undertaken and farmers were requested to send faecal samples for nematode egg counts. The majority of the farmers...

Incidence and Recovery of Salmonellae and Other Bacteria from Commercially Processed Poultry Carcasses at Selected Pre- and Post-Evisceration Steps

This study was undertaken to determine whether bacteria are already attached to poultry skin when birds arrive at the processing plant. Multiple rinses were performed on breast skin and whole carcasses taken from five processing points in a commercial plant: Before scalding, after scalding, after picking, after the final washer, and from the exit end of the chiller. Aerobic bacteria and Enterobacteriaceae were recovered from carcasses in up to 40 consecutive whole carcass rinses with a difference of only about one log for Enterobacteriaceae, and 1 to 2 logs for aerobes from the first to the last rinse of carcasses taken from the beginning and the end of the processing line. Data from rinses prior to scalding indicated that bacteria were firmly attached to poultry carcasses when they first arrived in the plant. Not all bacteria were removed during processing; however, there were fewer aerobes and Enterobacteriaceae at progressive sampling points. Attached salmonellae were not always recovered in the first whole carcass rinse, but were sometimes recovered in 3rd, 5th, and 10th rinses. These data show that a single whole carcass rinse can result in false negative test results for salmonellae. Because of the small number of positive samples in this study, the probability of recovering salmonellae with a single whole carcass rinse could not be estimated accurately.