Distribution Of Class Research Articles

Distribution of Class 1 and 2 Integrons Among Salmonella Enterica Serovars Isolated from Iranian Patients

Background:Salmonella entericahas become increasingly resistant to antimicrobial agents, partly as a result of genes carried by integrons.Objective:The aim of the present study was to investigate the prevalence of class 1 and 2 integrons and resistance to antimicrobial agents in clinical isolates ofS. enterica.Methods:This study included allSalmonellaisolates, recovered from patients with salmonellosis, admitted to Medical Children Hospital, Tehran, Iran during 2015-2016. Bacterial isolates were identified using standard biochemical and agglutination tests. Antimicrobial susceptibility testing was performed according to the Clinical and Laboratory Standards Institute guidelines. The presence of class 1 and 2 integrons was investigated by Polymerase Chain Reaction (PCR) assay, using specific primers.Results:A total of 138Salmonellastrains were isolated and included in this study. Integrons were detected in 45 (32%) isolates. Class 1 and 2 integrons were detected in 24 (17.3%) and 21 (15.2%) isolates, respectively. All integron-positive isolates showed multidrug-resistant phenotypes. Resistance to more than three antimicrobial agents was observed in integron-positive isolates.Conclusions:Our findings showed that integrons were widely distributed amongS. entericaisolates in Tehran. Class 1 integrons are more prevalent than class 2 integrons inSalmonellaisolates, and there is an association with MDR patterns. Therefore, these integrons are more likely to be involved in the distribution of resistant phenotypes inSalmonellastrains.

Distribution of TLR4 and MHC class II molecules of the spleen in broiler chicks treated with and without LPS in the first 2 weeks of the post-hatch period

ABSTRACT1. The purpose of this study was to investigate the distribution of Toll-like receptor-4 (TLR4) and major histocompatibility complex (MHC) class II molecules of the spleen in chicks treated with lipopolysaccharide (LPS) during the first 2 weeks of their life.2. A total of 225 Ross-308 commercial broiler chicks were used. Within the 2-week experimental period, chicks were divided into 5 main groups according to the days of decapitation which were 1, 4, 7, 10 and 14 d after hatch. Each main group had 45 chicks. The main groups were further divided into three subgroups (15 chicks each), which included control chicks (no injection), and phosphate-buffered saline (PBS) and LPS-injected chicks. Spleen samples were collected 1-, 3-, 6-, 12- and 24-h after the PBS or LPS administrations. Tissue sections were stained using streptavidin–biotin–peroxidase complex staining method.3. From 1 d of age, TLR4 positivity was found in the spleen in diffuse granular form. The cells showing intense TLR4 positivity were observed in periellipsoidal lymphoid tissue in 4-d-old chicks. The same cells were determined in the germinal centre of the spleen in 7-d-old chicks. LPS stimulation led to an increase in the intensity of TLR4 positivity in 14-d-old chicks.4. From 1 d of age, MHC class II positivity was found in both white pulp and red pulp. This was higher in 14-d-old chicks injected with LPS than in the controls and the chicks injected with PBS.5. The findings indicate that, from 1 d of age in chicks, the spleen has both non-specific defence elements and the molecules having the information to induce adaptive immunity. In addition, at the end of the 2-week experimental period, it was determined that the spleen had the capacity to recognise antigens.

Intelligent diagnosis of cardiac valve calcification in ESRD patients with peritoneal dialysis based on improved Takagi-Sugeno-Kang fuzzy system

Without a B-ultrasound result, if a doctor diagnoses a suspected patient using only the basic clinical features, such as age, gender, serum calcium, and urea clearance index (KT/V), the diagnostic accuracy will be very low (even less than 50%). To solve this problem, a machine learning technology is proposed to intelligently diagnose cardiac valve calcification in end-stage renal disease (ESRD) patients with peritoneal dialysis. Compared with classical classification technologies, the proposed method aims to develop a model that has both medical interpretability and high recognition performance. In terms of interpretability, the Takagi-Sugeno-Kang fuzzy system is considered a basic model due to its built-in interpretable ability. In addition, because the distribution of the positive class (cardiac valve calcification is positive) and negative class (cardiac valve calcification is negative) in the peritoneal dialysis patient dataset is unbalanced, a novel unbalanced TSK (Takagi-Sugeno-Kang) fuzzy system (B-TSK-FS) is developed using a novel unbalanced fuzzy learning mechanism. The corresponding results reveal that the B-TSK-FS method obtains promising results (the max testing accuracy is over 98%) compared with classical machine learning methods for intelligently diagnosing cardiac valve calcification in ESRD patients with peritoneal dialysis.

WGAN-Based Synthetic Minority Over-Sampling Technique: Improving Semantic Fine-Grained Classification for Lung Nodules in CT Images

Data imbalance issue generally exists in most medical image analysis problems and maybe getting important with the popularization of data-hungry deep learning paradigms. We explore the cutting-edge Wasserstein generative adversarial networks (WGANs) to address the data imbalance problem with oversampling on the minority classes. The WGAN can estimate the underlying distribution of a minority class to synthesize more plausible and helpful samples for the classification model. In this paper, the WGAN-based over-sampling technique is applied to augment the data to balance for the fine-grained classification of seven semantic attributes of lung nodules in computed tomography images. The fine-grained classification is carried out with a normal convolutional neural network (CNN). To further illustrate the efficacy of the WGAN-based over-sampling technique, the conventional data augmentation method commonly used in many deep learning works, the generative adversarial networks (GANs), and the deep convolutional generative adversarial networks (DCGANs) are implemented for comparison. The whole schemes of the minority oversampling and fine-grained classification are tested with the public lung imaging database consortium dataset. The experimental results suggest that the WGAN-based oversampling technique can synthesize helpful samples for the minority classes to assist the training of the CNN model and to boost the fine-grained classification performance better than the conventional data augmentation method and the two schemes of the GAN and DCGAN techniques do. It may thus suggest that the WGAN technique offers an alternative methodological option for the further deep learning on imbalanced classification studies.

Spatial Distribution and Tree Cover of Hillside and Ravine Forests in Uruguay: The Challenges of Mapping Patchy Ecosystems

The mapping and monitoring of forest ecosystems on a national scale is key to their management and conservation. Native forests in Uruguay are considered given their importance for biodiversity and ecosystem services. Here we evaluate the spatial distribution of the land cover class ‘hillside and ravine forest’ -a subclass of native forest characterized by patches and transition zones with native grasslands- using Landsat images (30 x 30 m) from 2014 and 2015 and high-resolution images from Google Earth. To evaluate spatial heterogeneity within hillside forests, we then used high-resolution spot images of 1 km2 from 1998-2012 to evaluate differences in the normalized difference vegetation index (ndvi) among canopy coverage categories. The hillside and ravine forest class were characterized as a composite cover class with an average canopy coverage of 69 ± 23%, variability of wich was reflected in ndvi values. The total area of this class in 2015 was estimated as 334,480 ha, somewhat less than an earlier 2008 estimate (384,240 ha). Among the potential errors in delineating hillside forests using Landsat images, there was the classification of «forest» in areas characterized by grassland and a tree canopy cover <25 %. This potential error in delimitation at broader scales led to the overestimation of hillside and ravine forest area in southeastern Uruguay, but an underestimation in northern Uruguay. Our study highlights the large discrepancies in the estimation of the distribution of hillside and ravine forest at different spatial scales, and also indicates the potential of ndvi to evaluate the heterogeneity of this forest within the same cover class.

Further results on certain type of nonlinear difference polynomials

In this paper we continue the study of the value distribution of certain class of nonlinear difference polynomials of the form $$f\left( z\right) \Delta _{c}^{n}f\left( z\right) -q\left( z\right) $$ where f is an entire function of finite order.

A Probabilistic Approach to Cross-Region Matching-Based Image Retrieval.

With deep convolutional features, cross-region matching (CRM) has recently shown superior performance on image retrieval. It evaluates image similarity by comparing image regions at different locations and scales, and is, therefore, more robust to geometric variance of objects. This paper first scrutinizes CRM-based image retrieval to provide a rigorous probabilistic interpretation by following the probability ranking principle. In addition to manifesting the assumptions implicitly taken by CRM, our interpretation highlights a fundamental issue hindering the performance of CRM-when comparing two image regions, CRM ignores modeling the distribution of the visual concept class associated with an image region, making the similarity comparison less precise. Taking advantage of the unprecedented representation capability of deep convolutional features, this paper proposes one approach to tackle that issue. It treats locally clustered image regions as a pseudo-labeled class sharing the same visual concept and utilizes them to model the distribution of the visual concept class associated with an image region. Both non-parametric and parametric methods are developed for this purpose, with careful probabilistic justification. Extensive experimental study on multiple benchmark data sets demonstrates the superior performance of the proposed pseudo-label approach to CRM and other comparable methods, with the maximum improvement of more than 10 percentage points over CRM.

Data Synthesize for Semantic Fine-Grained Prediction of Lung Nodule in CT Images

Background/AimSemantic terms of lung nodules such as lobulation, spiculation, malignancy, etc. can be commonly found in radiology reports to describe the semantic characteristics for diagnostic reference. The prevalence of malignancy among pulmonary nodules ranges between 0.2% and 50%. The main purpose of fine-grained prediction is to differentiate subordinate categories of the same semantic base classes and to provide an imaging biomarker with richer quantitative assessments of nodules for better prevalence estimation and diagnostic decision.ChallengesSince some subtypes or stages of one disease are rare but important, significant data imbalance might present and render the prediction problem very arduous. Data imbalance could bias the learning frameworks and is essential in the popularization of data-hungry deep learning paradigms. However, this problem was not elaborated in previous works of lung nodules.MethodsWe explored the cutting-edge Wasserstein Generative Adversarial Networks (WGAN) to address the data imbalance issue with over-sampling on the minority classes and tested this method on the public Lung Imaging Database Consortium (LIDC) dataset. The WGAN can estimate the underlying distribution of a minority class to synthesize more plausible and helpful samples for the prediction model.ResultsThe experiment shows that the proposed method improves F-measure by an average of 9.53% for the smallest minority classes compared to conventional method. It suggests that the WGAN technique can synthesize helpful samples for minority classes to assist the training of the Convolutional Neural Network (CNN) model and to boost fine-grained prediction performance.ConclusionMore synthesized samples obtained by considering the underlying distribution of minority classes will benifit the prediction. Future work will explore the relation among semantic terms for the data synthesis to simplify the oversampling process as well as to boost the synthetic quality even further.

Almost periodic functions and hyperbolic counting

We prove the existence of asymptotic moments and an estimate on the tails of the limiting distribution for a specific class of almost periodic functions. Then we introduce the hyperbolic circle problem, proving an estimate on the asymptotic variance of the remainder that improves a result of Chamizo. Applying the results of the first part we prove the existence of limiting distribution and asymptotic moments for three functions that are integrated versions of the remainder, and were considered originally (with due adaptations to our settings) by Wolfe, Phillips and Rudnick, and Hill and Parnovski.

Explaining Spatial Distribution of the Middle Class: A Multiple Indicator Approach with Multiple Explanatory Dimensions

This study attempts to investigate the pattern of the middle class in the Seoul Metropolitan Region and to explain why such a pattern occurs from spatial perspectives. For this purpose, the middle class are first defined based on four indicators: income, education, occupation and housing. Then, the spatial concentration of the middle class are explored by each indicator for 2010. The explanation of the pattern is pursued based on four dimensions often considered as critical factors in residential location decisions of the middle class: neighborhood, employment, consumption and educational environment. The findings suggest that the spatial distribution of the middle class generally shows a combination of concentric and sector models as we go farther from Seoul. The results of the global and the local regression analyses suggest that employment (producer service jobs) and educational environment (private after-school educational institutes) are important dimensions in explaining the residential location of the middle class in the Seoul Metropolitan Region.

Autism & Gluten: The Proof By Regression!

Investigators from different clinical and research centers from Paris, France studied the polymorphisms of the HLA class II loci in an autistic population. Through a case control design, they looked for the distribution of HLA class II alleles, genotypes and haplotypes in Autism Spectrum Disorders (ASD) patients meeting DSM-IV TR criteria versus healthy controls (HC).

GENETIC AND INFECTIOUS MARKERS OF INFERTILITY

The report presents the results of own research of the most important factors in the genesis of various forms of sterility. A married couples (n = 50) was examined with regard to possible etiological structure of these forms of pathology — infections of the family herpes (Herpes simplex types 1, 2, Cytomegalovirus hominis (CMV), Epstein–Barr virus (EBV) and genetic distribution for the second class of histocompatibilit (HLA II class): HLA-DRB1, HLA- DQA1 и HLA-DQB1. The authors present own data on genetic typing of the couples for the second class of histocompatibility, which allow to correlate these data with clinical and laboratory associations with infectious agents (infections of the herpes family). The genetic characteristics that determine the reproductive ability of the couple are associated with susceptibility to certain infectious markers. It is shown that the dominant majority of couples determined the HLA- DRB1 gene, which is more frequently associated with infections of the herpes family. Despite the fact that the level of positive PCR results in the group of couples with primary infertility was higher than in couples with secondary sterility (100 and 92%, respectively), maximum titers of specific antibodies to infections of the herpes family, including the frequency of markers of exacerbation, definition of IgM was higher than in the group of couples with primary infertility, and significantly higher than in couples with normal fertility. So, this antigen histocompatibility (HLA-DRB1) is associated with significant frequency of sterility, especially the secondary one, and infectious contamination infections of the family of herpes — HSV 1, 2 types, CMV, EBV in the study group — couples with secondary infertility. Marker HLA-DRB1, the most frequent in coup les with infertility, including idiopathic, associated in this group not only with significant contamination of infectious of the herpes family, but the presence of a chronic infectious process at the stage of reactivation. Conducted clinical and immunological studies clearly demonstrate the need for a comprehensive survey of women with disabilities in the system of immunogenesis with the mandatory assessment of the level of infectious (viral) contamination. Analysis of the results of their clinical and immunological studies allows us to propose an algorithm of complex examination in functional disorders in the system of immunogenesis, including the study of viral load as a necessary component.

Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest

As the risk of diseases diabetes and hypertension increases, machine learning algorithms are being utilized to improve early stage diagnosis. This study proposes a Hybrid Prediction Model (HPM), which can provide early prediction of type 2 diabetes (T2D) and hypertension based on input risk-factors from individuals. The proposed HPM consists of Density-based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection to remove the outlier data, Synthetic Minority Over-Sampling Technique (SMOTE) to balance the distribution of class, and Random Forest (RF) to classify the diseases. Three benchmark datasets were utilized to predict the risk of diabetes and hypertension at the initial stage. The result showed that by integrating DBSCAN-based outlier detection, SMOTE, and RF, diabetes and hypertension could be successfully predicted. The proposed HPM provided the best performance result as compared to other models for predicting diabetes as well as hypertension. Furthermore, our study has demonstrated that the proposed HPM can be applied in real cases in the IoT-based Health-care Monitoring System, so that the input risk-factors from end-user android application can be stored and analyzed in a secure remote server. The prediction result from the proposed HPM can be accessed by users through an Android application; thus, it is expected to provide an effective way to find the risk of diabetes and hypertension at the initial stage.

Conserved phylogenetic distribution and limited antibiotic resistance of class 1 integrons revealed by assessing the bacterial genome and plasmid collection

BackgroundIntegrons, especially the class 1 integrons, are major contributors to the acquisition and dissemination of antibiotic resistance genes (ARGs). However, comprehensive knowledge of the types, content, and distribution of integrons in bacterial taxa is lacking to evaluate their contribution.ResultsWe have constructed a new integrase database and developed a pipeline that provides comprehensive recovery of class 1 integrons. Previous PCR-based techniques might only detect one fourth of the integron-integrases and integrons recovered in this study. By exploring the class 1 integrons in over 73,000 currently available complete and draft bacterial genomes, the contribution of class 1 integrons in spreading and acquiring ARGs was evaluated. Firstly, the host species of class 1 integrons are highly conserved within (96%) in class Gammaproteobacteria, dominated by four pathogenic species of “ESKAPE.” Secondly, more than half of class 1 integrons are embedded in chromosomes with less potential for horizontal gene transfer. Finally, ARGs that have been acquired by these integrons only cover 11% of all the ARG genotypes detected in bacterial genomes.ConclusionsThe above observations indicated that there are both biological and ecological limitations to class 1 integrons in acquiring and spreading ARGs across different classes of the domain Bacteria.

A study of virulence and antimicrobial resistance pattern in diarrhoeagenic Escherichia coli isolated from diarrhoeal stool specimens from children and adults in a tertiary hospital, Puducherry, India

BackgroundEmergence of atypical enteropathogenic Escherichia coli (EPEC) and hybrid E. coli (harboring genes of more than one DEC pathotypes) strains have complicated the issue of growing antibiotic resistance in diarrhoeagenic Escherichia coli (DEC). This ongoing evolution occurs in nature predominantly via horizontal gene transfers involving the mobile genetic elements like integrons notably class 1 integron. This study was undertaken to determine the virulence pattern and antibiotic resistance among the circulating DEC strains in a tertiary care center in south of India.MethodsDiarrhoeal stool specimens were obtained from 120 children (< 5 years) and 100 adults (> 18 years), subjected to culture and isolation of diarrhoeal pathogens. Conventional PCR was performed to detect 10 virulence and 27 antimicrobial resistance (AMR) genes among the E. coli isolated.ResultsDEC infection was observed in 45 (37.5%) children and 18 (18%) adults, among which [18 (40%), 10 (10%)] atypical EPEC was most commonly detected followed by [6 (13.3%), 4 (4%)] ETEC, [5 (11.1%) 2 (2%)] EAEC, [(3 (6.6%), 0 (0%)] EIEC, [3 (6.6%), 0 (0%] typical EPEC, and [4 (8.8%), 1 (1%)] STEC, and no NTEC and CDEC was detected. DEC co-infection in 3 (6.6%) children, and 1(1%) adult and sole hybrid DEC infection in 3 (6.6%) children was detected. The distribution of sulphonamide resistance genes (sulI, sulII, and sulIII were 83.3 and 21%, 60.41 and 42.1%, and 12.5 and 26.3%, respectively) and class 1 integron (int1) genes (41.6 and 26.31%) was higher in DEC strains isolated from children and adults, respectively. Other AMR genes detected were qnrS, qnrB, aac(6’)Ib-cr, dhfr1, aadB, aac(3)-IV, tetA, tetB, tetD, catI, blaCTX, blaSHV, and blaTEM. None harbored qnrA, qnrC, qepA, tetE, tetC, tetY, ermA, mcr1, int2, and int3 genes.ConclusionsAtypical EPEC was a primary etiological agent of diarrhea in children and adults among the DEC pathotypes. Detection of high numbers of AMR genes and class 1 integron genes indicate the importance of mobile genetic elements in spreading of multidrug resistance genes among these strains.

Phytoplankton size class in the East China Sea derived from MODIS satellite data

Abstract. The distribution and variation of phytoplankton size class (PSC) are key to understanding ocean biogeochemical processes and ecosystems. Remote sensing of the PSC in the East China Sea (ECS) remains a challenge, although many algorithms have been developed to estimate PSC. Here based on a local dataset from the ECS, a regional model was tuned to estimate the PSC from the spectral features of normalized phytoplankton absorption (aph) using a principal component analysis approach. Before applying the refined PSC model to MODIS (Moderate Resolution Imaging Spectroradiometer) data, reconstructing satellite remote sensing reflectance (Rrs) at 412 and 443 nm was critical through modeling them from Rrs between 469 and 555 nm using multiple regression analysis. Satellite-derived PSC results compared well with those derived from pigment composition, which demonstrated the potential of satellite ocean color data to estimate PSC distributions in the ECS from space. Application of the refined PSC model to the reconstructed MODIS data from 2003 to 2016 yielded the seasonal distributions of the PSC in the ECS, suggesting that the PSC distributions were heterogeneous in both temporal and spatial scales. Micro-phytoplankton were dominant in coastal waters throughout the year, especially in the Changjiang estuary. For the middle shelf region, the seasonal shifts from the dominance of micro- and nano-phytoplankton in the winter and spring to the dominance of nano- and pico-phytoplankton in the summer and autumn were observed. Pico-phytoplankton were especially dominant in the Kuroshio region in the spring, summer, and autumn. The seasonal variations of the PSC in the ECS were probably affected by a combination of the water column stability, upwelling, sea surface temperature, and the Kuroshio. Additionally, human activity and riverine discharge might also influence the PSC distribution in the ECS, especially in the coastal region.

Social and Health Inequalities in Rich and Developed Countries: The Role of Sociology and Suggestion of Future Sociological Agendas

In the early 1840s, Friedrich Engels and Rudolph Virchow argued that the “mode of social organization” was a lens through which the social patterning of disease could be understood (Scambler, 2012: 131). Virchow was reluctant to attribute disease to individual behaviour, and asserted that illness belonged to the discipline of social science (de Maio, 2010: 20). In the late 20th century Richard G. Wilkinson, a British social epidemiologist and a professor at University of Nottingham, echoed Engels and Virchow, postulating that Emile Durkheim, if he remained alive, would analyse suicide based on the modern burden in developed societies (Wilkinson, 1996: 15). This article addresses the discourse on how the imbalanced distribution of class, income and social capital contributes to the poorer physical and psychosocial health conditions among socially disadvantaged cohorts, based on the arugments made by key scholars from the field of sociology and health. This article also examines the future sociological agendeas to better examine social and health inequalities, in accordance with ideas suggested by a variety of scholars, especially Graham Scambler. The article is review-based, prompting the understandings of the contemporary debates about social and health inequalities, and what roles should sociology play in such debates.

The anti-staphylococcal lipolanthines are ribosomally synthesized lipopeptides.

The potent antibacterial lanthipeptide microvionin, isolated from a culture of Microbacterium arborescens, exhibits a new triamino-dicarboxylic acid moiety, termed avionin, and an unprecedented N-terminal guanidino fatty acid. We identified the corresponding biosynthetic gene cluster and reconstituted central steps of avionin biosynthesis in vitro. Genome mining and isolation of nocavionin from Nocardia terpenica revealed a widespread distribution of this lanthipeptide class, termed lipolanthines, which may be useful as future antimicrobial drugs.

Asymptotic normality for plug-in estimators of diversity indices on countable alphabets

ABSTRACTThe plug-in estimator is one of the most popular approaches to the estimation of diversity indices. In this paper, we study its asymptotic distribution for a large class of diversity indices on countable alphabets. In particular, we give conditions for the plug-in estimator to be asymptotically normal, and in the case of uniform distributions, where asymptotic normality fails, we give conditions for the asymptotic distribution to be chi-squared. Our results cover some of the most commonly used indices, including Simpson's index, Reńyi's entropy and Shannon's entropy.

The feasibility and stability of large complex biological networks: a random matrix approach

In the 70’s, Robert May demonstrated that complexity creates instability in generic models of ecological networks having random interaction matrices A. Similar random matrix models have since been applied in many disciplines. Central to assessing stability is the “circular law” since it describes the eigenvalue distribution for an important class of random matrices A. However, despite widespread adoption, the “circular law” does not apply for ecological systems in which density-dependence operates (i.e., where a species growth is determined by its density). Instead one needs to study the far more complicated eigenvalue distribution of the community matrix S = DA, where D is a diagonal matrix of population equilibrium values. Here we obtain this eigenvalue distribution. We show that if the random matrix A is locally stable, the community matrix S = DA will also be locally stable, providing the system is feasible (i.e., all species have positive equilibria D > 0). This helps explain why, unusually, nearly all feasible systems studied here are locally stable. Large complex systems may thus be even more fragile than May predicted, given the difficulty of assembling a feasible system. It was also found that the degree of stability, or resilience of a system, depended on the minimum equilibrium population.