scholarly journals Fine Tuning Data Mining Algorithm for an Efficient Classification of E-Coli

2019 ◽  
Vol 9 (1) ◽  
pp. 109-113
Keyword(s):  
Treatment Strategies ◽  
Fine Tuning ◽  
Clinical Implications ◽  
Data Mining Algorithm ◽  
Classification Approach ◽  
E Coli ◽  
Biofilm Production ◽  
Diagnostic Potential ◽  
Typical Epec

E. coli is the first organisms to be sequenced as genome and the classification within the DEC pathotypes has epidemiologic and clinical implications for managing diarrheal diseases. In many developing countries E.coli leads to cause of diarrhea in children. where the mode of transmissions takes place via food and water. based on their pathogenic phenotype and diseases they cause it can be classified into 6 groups. consequently, our awareness of the spectrum of diseases and syndromes that they cause is quite limited. Also, because we cannot readily identify infected patients, there are many complexities in defining the modes of attainment, prevention and treatment strategies, and estimating the burden of infectious squealed. These infections create many challenges, and no progress will take place until the diagnostic potential for these agents got improved. Identifying E. coli isolate co-express LA reiterates the difficulty of assigning bacteria to groups on the basis of their adherence phenotype or genotype. Therefore the analysis of E -coli with molecular methods demonstrates that strains carry will represent more characteristics of typical EPEC and also the lack of AggR regulon, we propose a novel classification approach for classifying E-coli therefore to recognize pathogens. In addition, the ability to simultaneously induce attaching effacing lesions and biofilm production may enhance the potential of the strains to cause diarrhea and prolong bacterial residence in the intestines, thus worsening malnutrition in the patients.

scholarly journals In-Vitro Biofilm Detection among Uropathogens and their Antibiogram Profile

2018 ◽  
Vol 5 ◽  
pp. 57-62
Author(s):  
Bijayata Shrestha ◽  
Basudha Shrestha ◽  
Asia Poudel ◽  
Binod Lekhak ◽  
Milan Kumar Upreti
Keyword(s):  
Treatment Strategies ◽  
Multidrug Resistant ◽  
Diffusion Method ◽  
P Value ◽  
E Coli ◽  
Microdilution Method ◽  
Biofilm Production ◽  
Microbiological Techniques ◽  
Agar Dilution

Objectives: The study was carried out in Kathmandu Model Hospital, Kathmandu with the aim of in- vitro biofilm detection among uropathogens and its correlation with antibiotic resistance. Methods: Uropathogens (n=234) were isolated, and identified with standard microbiological techniques and further subjected to Modified Congo Red Agar Method for the biofilm detection in-vitro; antimicrobial susceptibility testing (10 antibiotics) was performed by Modified Kirby Bauer disc diffusion method. The MIC and MBEC values of Levofloxacin were determined by agar dilution for planktonic forms and by microdilution method for biofilm phase respectively. Results: Among 234 urine isolates, 134(57%) were positive for in-vitro biofilm production and 88(37.6%) were multidrug resistant (MDR). E. coli was the predominant biofilm forming uropathogens. The incidence of biofilm producers was found to be independent of age-wise, gender wise and indoor-outdoor distribution of patients. The association between biofilm production and multidrug resistance among uropathogens was found statistically non-significant (p-value>0.05). The MBEC values of biofilm phase of growth were found to be greater than the MIC values for their planktonic counterparts. The MBEC values ranged from 4 to more than1024 μg/ml whereas the MIC values ranged from 0.003-16 μg/ml. Conclusion: The results of the present study suggest that biofilm detection is a critical step to fight against biofilm-involved infections. However, further studies are needed for the development of effective preventive and treatment strategies of biofilm associated UTIs to avoid recurrence and persistence.  

scholarly journals Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Zhe Yang ◽  
Dejan Gjorgjevikj ◽  
Jianyu Long ◽  
Yanyang Zi ◽  
Shaohui Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Supervised Classification ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Reconstruction Error ◽  
Fine Tuning ◽  
Fault Diagnostics ◽  
Sparse Autoencoder

AbstractSupervised fault diagnosis typically assumes that all the types of machinery failures are known. However, in practice unknown types of defect, i.e., novelties, may occur, whose detection is a challenging task. In this paper, a novel fault diagnostic method is developed for both diagnostics and detection of novelties. To this end, a sparse autoencoder-based multi-head Deep Neural Network (DNN) is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data. The detection of novelties is based on the reconstruction error. Moreover, the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function, instead of performing the pre-training and fine-tuning phases required for classical DNNs. The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer. The results show that its performance is satisfactory both in detection of novelties and fault diagnosis, outperforming other state-of-the-art methods. This research proposes a novel fault diagnostics method which can not only diagnose the known type of defect, but also detect unknown types of defects.

scholarly journals Smart Design Nano-Hybrid Formulations by Machine Learning

Proceedings ◽  
2020 ◽  
Vol 78 (1) ◽  
pp. 5
Author(s):  
Raquel de Melo Barbosa ◽  
Fabio Fonseca de Oliveira ◽  
Gabriel Bezerra Motta Câmara ◽  
Tulio Flavio Accioly de Lima e Moura ◽  
Fernanda Nervo Raffin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Water Solubility ◽  
Inorganic Materials ◽  
Fine Tuning ◽  
Support Vector ◽  
Drug Solubility ◽  
Physical Behavior ◽  
Best Fit

Nano-hybrid formulations combine organic and inorganic materials in self-assembled platforms for drug delivery. Laponite is a synthetic clay, biocompatible, and a guest of compounds. Poloxamines are amphiphilic four-armed compounds and have pH-sensitive and thermosensitive properties. The association of Laponite and Poloxamine can be used to improve attachment to drugs and to increase the solubility of β-Lapachone (β-Lap). β-Lap has antiviral, antiparasitic, antitumor, and anti-inflammatory properties. However, the low water solubility of β-Lap limits its clinical and medical applications. All samples were prepared by mixing Tetronic 1304 and LAP in a range of 1–20% (w/w) and 0–3% (w/w), respectively. The β-Lap solubility was analyzed by UV-vis spectrophotometry, and physical behavior was evaluated across a range of temperatures. The analysis of data consisted of response surface methodology (RMS), and two kinds of machine learning (ML): multilayer perceptron (MLP) and support vector machine (SVM). The ML techniques, generated from a training process based on experimental data, obtained the best correlation coefficient adjustment for drug solubility and adequate physical classifications of the systems. The SVM method presented the best fit results of β-Lap solubilization. In silico tools promoted fine-tuning, and near-experimental data show β-Lap solubility and classification of physical behavior to be an excellent strategy for use in developing new nano-hybrid platforms.

scholarly journals Refactoring of a synthetic raspberry ketone pathway with EcoFlex

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Simon J. Moore ◽  
Yonek B. Hleba ◽  
Sarah Bischoff ◽  
David Bell ◽  
Karen M. Polizzi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Synthetic Biology ◽  
Combinatorial Libraries ◽  
Value Added ◽  
Microtiter Plate ◽  
Fine Tuning ◽  
Golden Gate ◽  
E Coli ◽  
Fine Chemical ◽  
Raspberry Ketone

Abstract Background  A key focus of synthetic biology is to develop microbial or cell-free based biobased routes to value-added chemicals such as fragrances. Originally, we developed the EcoFlex system, a Golden Gate toolkit, to study genes/pathways flexibly using Escherichia coli heterologous expression. In this current work, we sought to use EcoFlex to optimise a synthetic raspberry ketone biosynthetic pathway. Raspberry ketone is a high-value (~ £20,000 kg−1) fine chemical farmed from raspberry (Rubeus rubrum) fruit. Results  By applying a synthetic biology led design-build-test-learn cycle approach, we refactor the raspberry ketone pathway from a low level of productivity (0.2 mg/L), to achieve a 65-fold (12.9 mg/L) improvement in production. We perform this optimisation at the prototype level (using microtiter plate cultures) with E. coli DH10β, as a routine cloning host. The use of E. coli DH10β facilitates the Golden Gate cloning process for the screening of combinatorial libraries. In addition, we also newly establish a novel colour-based phenotypic screen to identify productive clones quickly from solid/liquid culture. Conclusions  Our findings provide a stable raspberry ketone pathway that relies upon a natural feedstock (L-tyrosine) and uses only constitutive promoters to control gene expression. In conclusion we demonstrate the capability of EcoFlex for fine-tuning a model fine chemical pathway and provide a range of newly characterised promoter tools gene expression in E. coli.

scholarly journals Intelligent radar software defect classification approach based on the latent Dirichlet allocation topic model

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xi Liu ◽  
Yongfeng Yin ◽  
Haifeng Li ◽  
Jiabin Chen ◽  
Chang Liu ◽  
...  
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Recall Rate ◽  
Defect Classification ◽  
Software Defects ◽  
Classification Approach ◽  
Software Defect ◽  
Model Combining ◽  
Dirichlet Allocation

AbstractExisting software intelligent defect classification approaches do not consider radar characters and prior statistics information. Thus, when applying these appaoraches into radar software testing and validation, the precision rate and recall rate of defect classification are poor and have effect on the reuse effectiveness of software defects. To solve this problem, a new intelligent defect classification approach based on the latent Dirichlet allocation (LDA) topic model is proposed for radar software in this paper. The proposed approach includes the defect text segmentation algorithm based on the dictionary of radar domain, the modified LDA model combining radar software requirement, and the top acquisition and classification approach of radar software defect based on the modified LDA model. The proposed approach is applied on the typical radar software defects to validate the effectiveness and applicability. The application results illustrate that the prediction precison rate and recall rate of the poposed approach are improved up to 15 ~ 20% compared with the other defect classification approaches. Thus, the proposed approach can be applied in the segmentation and classification of radar software defects effectively to improve the identifying adequacy of the defects in radar software.

Erratum to “Characterization and diagnostic potential of hepatitis B virus nucleocapsid expressed in E. coli and P. pastoris”

2002 ◽  
Vol 102 (1-2) ◽  
pp. 173
Author(s):  
Bénédicte Watelet ◽  
Martine Quibriac ◽  
Dominique Rolland ◽  
Gaspard Gervasi ◽  
Marie Gauthier ◽  
...  
Keyword(s):  
Hepatitis B Virus ◽  
Hepatitis B ◽  
E Coli ◽  
Diagnostic Potential ◽  
B Virus

Erratum to “Characterization and diagnostic potential of hepatitis B virus nucleocapsid expressed in E. coli and P. pastoris”

2002 ◽  
Vol 102 (1-2) ◽  
pp. 175-190 ◽  
Author(s):  
Bénédicte Watelet ◽  
Martine Quibriac ◽  
Dominique Rolland ◽  
Gaspard Gervasi ◽  
Marie Gauthier ◽  
...  
Keyword(s):  
Hepatitis B Virus ◽  
Hepatitis B ◽  
E Coli ◽  
Diagnostic Potential ◽  
B Virus

Diverse Dimensions of the Cotard’s Syndrome - an Approach Towards a Clear Classification of the Phenomenon

2009 ◽  
Vol 24 (S1) ◽  
pp. 1-1
Author(s):  
A. Nagy ◽  
V. Voros ◽  
T. Tenyi
Keyword(s):  
Clinical Practice ◽  
Diagnostic Category ◽  
Treatment Strategies ◽  
Differential Diagnostics ◽  
Dimensional System ◽  
Central Feature ◽  
Scientific Databases ◽  
Search Terms ◽  
The One

Aim:The authors present the Cotard's syndrome, a rare psychiatric condition, pointing out the latest results in terms of psychoneurology and classification of the phenomenon. The central feature of the syndrome is a nihilistic delusion, in which the patient denies his or her own existence and that of the external world.Method:We searched electronic scientific databases using the appropriate search terms; relevant articles were carefully reviewed. We also present three cases from our clinical practice.Results:After the overview of the latest biological and neuropsychological findings, the terminology, the nosology, the classification and the differential diagnostics are discussed. To sum up with useful information for the clinical practice, the possible treatment strategies, the course and the prognosis of the disease are also presented.Conclusions:The reported cases together with the reviewed literature suggest that a dimensional system of classifying Cotard's syndrome is preferable. At the one end of the spectrum is the presence of the pure nihilistic delusions, appearing as a symptom of an underlying psychiatric or neurological condition. The full-blown, classical syndrome as a diagnostic category forms the other end of the spectrum. The presented theoretical and practical aspects give a lead on deeper understanding, easier recognition and more adequate therapy of the Cotard's syndrome.

scholarly journals RGD-Binding Integrins Revisited: How Recently Discovered Functions and Novel Synthetic Ligands (Re-)Shape an Ever-Evolving Field

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1711
Author(s):  
Beatrice S. Ludwig ◽  
Horst Kessler ◽  
Susanne Kossatz ◽  
Ute Reuning
Keyword(s):  
Molecular Imaging ◽  
Cancer Progression ◽  
Viral Infections ◽  
Treatment Strategies ◽  
Fine Tuning ◽  
Treatment Protocols ◽  
High Expectations ◽  
Elevated Expression ◽  
Substantial Progress ◽  
Integrin Ligands

Integrins have been extensively investigated as therapeutic targets over the last decades, which has been inspired by their multiple functions in cancer progression, metastasis, and angiogenesis as well as a continuously expanding number of other diseases, e.g., sepsis, fibrosis, and viral infections, possibly also Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2). Although integrin-targeted (cancer) therapy trials did not meet the high expectations yet, integrins are still valid and promising targets due to their elevated expression and surface accessibility on diseased cells. Thus, for the future successful clinical translation of integrin-targeted compounds, revisited and innovative treatment strategies have to be explored based on accumulated knowledge of integrin biology. For this, refined approaches are demanded aiming at alternative and improved preclinical models, optimized selectivity and pharmacological properties of integrin ligands, as well as more sophisticated treatment protocols considering dose fine-tuning of compounds. Moreover, integrin ligands exert high accuracy in disease monitoring as diagnostic molecular imaging tools, enabling patient selection for individualized integrin-targeted therapy. The present review comprehensively analyzes the state-of-the-art knowledge on the roles of RGD-binding integrin subtypes in cancer and non-cancerous diseases and outlines the latest achievements in the design and development of synthetic ligands and their application in biomedical, translational, and molecular imaging approaches. Indeed, substantial progress has already been made, including advanced ligand designs, numerous elaborated pre-clinical and first-in-human studies, while the discovery of novel applications for integrin ligands remains to be explored.

Studies of Virulence Factors of Avian Pathogenic Escherichia coli in Avian Colibacillosis by In vitro Method and PCR

2021 ◽  
Author(s):  
Ayushi Singh ◽  
Daljeet Chhabra ◽  
Rakhi Gangil ◽  
Rakesh Sharda ◽  
Ravi Sikrodia ◽  
...  
Keyword(s):  
Virulence Factors ◽  
Molecular Detection ◽  
Bacterial Disease ◽  
Avian Pathogenic Escherichia Coli ◽  
E Coli ◽  
Pathological Conditions ◽  
Biofilm Production ◽  
Pathogenic Escherichia Coli ◽  
Stx1 Gene

Background: Avian colibacillosis is considered as major cause of morbidity and mortality in poultry. It is a common bacterial disease of poultry and many virulence factors of E. coli are associated with the disease. The current study was aimed to investigate the presence of some virulence factors of E. coli isolated from the cases of colibacillosis.Methods: In present study, total 150 samples (liver, heart, lungs, air sacs and feaces) of chicken exhibiting pathological conditions of colibacillosis were collected from various poultry farms (organized and backyard) situated in and around Mhow and Indore cities. E.coli was isolated and identified from the samples on the basis of cultural characteristics and biochemical test. All E. coli isolates were further subjected to evaluate the presence of virulence factors such as biofilm production, haemolysis, invasiveness and molecular detection of fimH and stx1 gene.Result: Out of these 51.33% of incidence of E. coli was recorded. E. coli O84 and O149 serotypes were found most prevalent. Out of 77 isolates, 46 (59.7%) and 45 (58.4%) were positive for biofilm formation by tube method and modified CRA method, respectively. All E. coli isolates were showing invasiveness in congo red binding assay while none of the isolates was found haemolytic. Molecular detection revealed the presence of fimH (508bp) gene in 33.3% of tested samples while stx1 gene could not be detected in any isolates.

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