Types Of Fingerprints Research Articles

(Invited) Electrochemically Synthesized High Density Chemical Sensor Arrays

Electronic detection of molecules is rapidly emerging as an alternative to the tranditional optical and electrochemical methods because of the small size, low-power consumption, improved sensing performance and most of all possibility of developing high density arrays for simulatenous analyses of multiple species in small sample volumes. Recently, one-dimensional nanostructures (e.g., carbon nanotubes (CNTs), inorganic, and organic nanowires) as conduction channels of field effect transistors (FETs) have been developed for detection of a variety of gaseous molecules with excellent low detection limit, sensitivity, and selectivity. These features are a consequence of dramatic decrease in characteristic length and increase in the ratio of surface to volume atoms, allowing for rapid diffusion into the bulk and for a more significant fraction of the atoms to participate in surface processes such as chemical and biochemical binding interactions. One-dimensional geometries also enhance response times by virtue of their two-dimensional mass transfer profile. Furthermore, nanowires are heralded for device miniaturization and sensor arrays, enabling duplicate elements to reduce false positives/negatives and pattern recognition systems termed electronic noses where each sensor in the array has a unique response to every analyte creating a fingerprint type response that increases sensitivity and selectivity. Finally, sensors are also attractive for their proven commercial viability, as this approach uses a single material behaving as both the sensitive layer and transducer to directly covert chemical information into an electronic signal without the need for labels, allowing for real-time, continuous monitoring. In this presentation, synthesis, functionalization, and assembly of various nanoengineered materials including CNTs and conducting polymer nanowires will be discussed to create “true” high density gaseous sensor arrays with superior sensing performance in cost-effective manner. Finally, Android based smartphone integratable sensor will be demonstrated.

Molecular ecology of Listeria spp., Salmonella, Escherichia coli O157:H7 and non-O157 Shiga toxin-producing E.coli in pristine natural environments in Northern Colorado.

Molecular subtyping is commonly used in foodborne disease surveillance and microbial source tracking. There is a knowledge gap regarding the molecular ecology of foodborne pathogens in non-food-associated environments. The objective of this study was to isolate and subtype foodborne pathogens from pristine natural environments with minimal anthropogenic inputs. Five locations (wilderness areas) in Northern Colorado were sampled during the spring, summer and fall over a 2-year period. Soil, water, sediment, surface soil and wildlife faecal samples were microbiologically analysed to detect Listeria, Salmonella and Shiga toxin-producing Escherichia coli (STEC), and resultant isolates were subtyped. Three samples tested positive for Listeria monocytogenes and 19 samples contained other Listeria spp. Salmonella was isolated from two samples, five samples contained non-O157 STEC, and E.coli O157:H7 was not detected. Two L. monocytogenes isolates from faecal samples collected from the same wilderness area over a year apart shared the same PFGE pattern, while all other isolates had a unique type. Our data indicate that (i) there was a rare presence of human foodborne pathogens in pristine natural environments in Northern Colorado, (ii) there was genetic diversity between organisms isolated within a given wilderness area, and (iii) the Northern Colorado climate and topography may contribute to the low occurrence of these organisms. Relatively little is known about the molecular ecology of foodborne pathogens in pristine natural environments. While foodborne pathogens were rarely detected in wildlife faecal and environmental samples from the wilderness areas in this study, some isolates shared DNA fingerprint types with human clinical isolates from same region during the same time frame, highlighting the need for environmental isolate subtype data. The availability of molecular subtyping data for non-food-associated foodborne pathogen isolates can facilitate epidemiological and microbial source tracking investigations.

Comparison study of finger prints pattern in some egyptians and libyan people

Our studyaimed at comparison study of classical types (loops, arches, whorls and mixed or composite) of finger prints of some Egyptian and Libyans peopole.

Factors affecting the presence, genetic diversity and antimicrobial sensitivity of Escherichia coli in poultry meat samples collected from Canberra, Australia.

To investigate the factors determining the clonal composition of Escherichia coli in poultry meat samples, 306 samples were collected from 16 shops, representing three supermarket chains and an independent butchery located in each of the four town centers of Canberra, Australia, during the summer, autumn and winter. A total of 3415 E. coli isolates were recovered and assigned to a phylogenetic group using the Clermont quadruplex PCR method, fingerprinted using repetitive element palindromic (REP) PCR and screened for their antimicrobial susceptibility profiles. The probability of detecting E. coli and the number of fingerprint types detected per sample, as well as the phylogroup membership of the isolates and their antimicrobial sensitivity profiles varied, with one or more of retailer, store, meat type, season and husbandry. The results of this study demonstrate that poultry meat products are likely to be contaminated with a genetically diverse community of E. coli and suggest that factors relating to the nature of the meat product and distribution chain are determinants of the observed diversity.

Characterization of Escherichia coli and Salmonella from Victoria, Australia, Dairy Farm Environments

Safe dairy food production starts at the farm level, with the presence of pathogens on farms potentially impacting the downstream food supply. Studies often commence with looking for pathogens in fecal material of farm animals, predominantly cows; however, pathogens may arise from other on-farm sources. In Australia, few studies have looked at the broader farm environment, particularly in relation to Escherichia coli and Salmonella. The present study characterized the genetic similarity of these pathogens from bovine, ovine, and caprine dairy farm environments and related this to the stx1, stx2, eae, or ehx virulence markers in E. coli and antibiotic resistance in Salmonella. E. coli isolates with indistinguishable genetic profiles and at least one of the virulence factors were found in multiple samples on the farms, although profiles were unique to each farm. E. coli O26 with stx1 from one bovine farm had a different fingerprint type than all of the other E. coli O26 isolates, which lacked the Shiga toxin genes. They were from a separate bovine farm and were themselves closely related. No antibiotic resistance was detected among Salmonella isolates to the 17 antibiotics tested. Three Salmonella serotypes were identified: Orion, Infantis, and Zanzibar. The published PCR serotyping method used misidentified Salmonella Zanzibar as Salmonella Javiana, which was revealed after conventional antisera-based serotyping; this illustrates the need for caution when using PCR techniques for Salmonella serotype identification. Of the three serotypes, Salmonella Orion was most prevalent and was potentially resident on the farm. This article describes the previously unreported genetic diversity of potentially pathogenic E. coli and Salmonella serotypes from the farm environments of three dairy animal species in Victoria, Australia.

Cross‐Classified Multilevel Modelling of the Effectiveness of Similarity‐Based Virtual Screening

The screening effectiveness of a chemical similarity search depends on a range of factors, including the bioactivity of interest, the types of similarity coefficient and fingerprint that comprise the similarity measure, and the nature of the reference structure that is being searched against a database. This study introduces the use of cross-classified multilevel modelling as a way to investigate the relative importance of these four factors when carrying out similarity searches on the ChEMBL database. Two principal conclusions can be drawn from the analyses: that the fingerprint plays a more important role than the similarity coefficient in determining the effectiveness of a similarity search, and that comparative studies of similarity measures should involve many more reference structures than has been the case in much previous work.

Optical Methods in Fingerprint Imaging for Medical and Personality Applications.

Over the years, analysis and induction of personality traits has been a topic for individual subjective conjecture or speculation, rather than a focus of inductive scientific analysis. This study proposes a novel framework for analysis and induction of personality traits. First, 14 personality constructs based on the “Big Five” personality factors were developed. Next, a new fingerprint image algorithm was used for classification, and the fingerprints were classified into eight types. The relationship between personality traits and fingerprint type was derived from the results of the questionnaire survey. After comparison of pre-test and post-test results, this study determined the induction ability of personality traits from fingerprint type. Experimental results showed that the left/right thumbprint type of a majority of subjects was left loop/right loop and that the personalities of individuals with this fingerprint type were moderate with no significant differences in the 14 personality constructs.

The Great Automatic Grammatizator: writing, labour, computers

What does it mean when we say that computers can ‘write’ and how are recent developments in neural networks and machine learning changing this capacity? This article examines the long-standing literary fear of authorship being replaced by machines while also interrogating the labour and credit implications that sit behind widely used structures of authorship in a technological age. The argument makes reference to one work of computer-generated writing – Johannes Helden & Hakan Jonson’s Evolution [2014] – and to one software paradigm (a character-based recurrent neural networks for language acquisition trained on the corpus of the journal Textual Practice). I here argue that unless we conceive more broadly of the criteria for ‘authorship’ as a labour function, and unless we take seriously the need to see textual production as social production, hybridized (but predominantly) machine identities will come to dominate a literary landscape.

Countering JPEG anti-forensics based on noise level estimation

Quantization artifact and blocking artifact are the two types of well-known fingerprints of JPEG compression. Most JPEG forensic techniques are focused on these fingerprints. However, recent research shows that these fingerprints can be intentionally concealed via anti-forensics, which in turn makes current JPEG forensic methods vulnerable. A typical JPEG anti-forensic method is adding anti-forensic dither to DCT transform coefficients and erasing blocking artifact to remove the trace of compression history. To deal with this challenge in JPEG forensics, in this paper, we propose a novel countering method based on the noise level estimation to identify the uncompressed images from those forged ones. The experimental results show that the proposed method achieves superior performance on several image databases with only one-dimensional feature. It is also worth emphasizing that the proposed threshold-based method has explicit physical meaning and is simple to be implemented in practice. Moreover, we analyze the strategies available to the investigator and the forger in the case of that they are aware of the existence of each other. Game theory is used to evaluate the ultimate performance when both sides adopt their Nash equilibrium strategies.

A Retrospective Study of Salmonella Enteritidis Isolated from Commercial Layer Flocks.

Consumption of shell eggs has been associated with Salmonella Enteritidis (SE) infections in humans in the United States. Because of this, the Pennsylvania Egg Quality Assurance Program (PEQAP) was developed and implemented in 1994. The PEQAP involves periodic flock testing and management practices to minimize SE contamination of shell eggs. Subsequently, the U.S. Food and Drug Administration (FDA) introduced a mandatory federal program in 2010 and 2012 for shell egg producers modeled closely after PEQAP to reduce the incidence and prevalence of SE during production, storage, and transport nationwide. In this study, a retrospective epidemiologic analysis was conducted by characterizing SE isolated from commercial layer environment samples and shell eggs submitted to the Animal Diagnostic Laboratory at The Pennsylvania State University using phage typing and pulsed-field gel electrophoresis (PFGE). The objective of this study was to determine the relatedness of SE isolates from hen house environments and shell eggs and to optimize the existing protocols of egg quality assurance programs by identifying the best layer-house environmental sampling time points in order to minimize SE contamination of shell eggs. A total of 94 SE isolates from 65 hen flocks on 35 premises in Pennsylvania recovered during 2007 to 2015 were used in this study. The SE phage type 8 and PFGE fingerprint type JEGX01.0004 most commonly associated with human SE infection was also the predominant type present in layer-house environments and shell eggs. This reconfirms hen house environmental monitoring is an effective method to identify SE-infected flocks. Further, the PEQAP program allowed SE detection of infected flocks earlier than the FDA program as it included an additional environmental test at 29-31 wk of age, enabling the earlier prevention of SE-contaminated shell eggs going to the market. Therefore, it is recommended to refine the sampling time points of the current FDA Egg Rule by adding hen house environmental testing at 29-31 wk of age.

CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods

Carcinogenicity refers to a highly toxic end point of certain chemicals, and has become an important issue in the drug development process. In this study, three novel ensemble classification models, namely Ensemble SVM, Ensemble RF, and Ensemble XGBoost, were developed to predict carcinogenicity of chemicals using seven types of molecular fingerprints and three machine learning methods based on a dataset containing 1003 diverse compounds with rat carcinogenicity. Among these three models, Ensemble XGBoost is found to be the best, giving an average accuracy of 70.1 ± 2.9%, sensitivity of 67.0 ± 5.0%, and specificity of 73.1 ± 4.4% in five-fold cross-validation and an accuracy of 70.0%, sensitivity of 65.2%, and specificity of 76.5% in external validation. In comparison with some recent methods, the ensemble models outperform some machine learning-based approaches and yield equal accuracy and higher specificity but lower sensitivity than rule-based expert systems. It is also found that the ensemble models could be further improved if more data were available. As an application, the ensemble models are employed to discover potential carcinogens in the DrugBank database. The results indicate that the proposed models are helpful in predicting the carcinogenicity of chemicals. A web server called CarcinoPred-EL has been built for these models (http://ccsipb.lnu.edu.cn/toxicity/CarcinoPred-EL/).

Computational models for the classification of mPGES-1 inhibitors with fingerprint descriptors.

Human microsomal prostaglandin [Formula: see text] synthase (mPGES)-1 is a promising drug target for inflammation and other diseases with inflammatory symptoms. In this work, we built classification models which were able to classify mPGES-1 inhibitors into two groups: highly active inhibitors and weakly active inhibitors. A dataset of 1910mPGES-1 inhibitors was separated into a training set and a test set by two methods, by a Kohonen's self-organizing map or by random selection. The molecules were represented by different types of fingerprint descriptors including MACCS keys (MACCS), CDK fingerprints, Estate fingerprints, PubChem fingerprints, substructure fingerprints and 2D atom pairs fingerprint. First, we used a support vector machine (SVM) to build twelve models with six types of fingerprints and found that MACCS had some advantage over the other fingerprints in modeling. Next, we used naïve Bayes (NB), random forest (RF) and multilayer perceptron (MLP) methods to build six models with MACCS only and found that models using RF and MLP methods were better than NB. Finally, all the models with MACCS keys were used to make predictions on an external test set of 41 compounds. In summary, the models built with MACCS keys and using SVM, RF and MLP methods show good prediction performance on the test sets and the external test set. Furthermore, we made a structure-activity relationship analysis between mPGES-1 and its inhibitors based on the information gain of fingerprints and could pinpoint some key functional groups for mPGES-1 activity. It was found that highly active inhibitors usually contained an amide group, an aromatic ring or a nitrogen heterocyclic ring, and several heteroatoms substituents such as fluorine and chlorine. The carboxyl group and sulfur atom groups mainly appeared in weakly active inhibitors.

ChemSAR: an online pipelining platform for molecular SAR modeling

BackgroundIn recent years, predictive models based on machine learning techniques have proven to be feasible and effective in drug discovery. However, to develop such a model, researchers usually have to combine multiple tools and undergo several different steps (e.g., RDKit or ChemoPy package for molecular descriptor calculation, ChemAxon Standardizer for structure preprocessing, scikit-learn package for model building, and ggplot2 package for statistical analysis and visualization, etc.). In addition, it may require strong programming skills to accomplish these jobs, which poses severe challenges for users without advanced training in computer programming. Therefore, an online pipelining platform that integrates a number of selected tools is a valuable and efficient solution that can meet the needs of related researchers.ResultsThis work presents a web-based pipelining platform, called ChemSAR, for generating SAR classification models of small molecules. The capabilities of ChemSAR include the validation and standardization of chemical structure representation, the computation of 783 1D/2D molecular descriptors and ten types of widely-used fingerprints for small molecules, the filtering methods for feature selection, the generation of predictive models via a step-by-step job submission process, model interpretation in terms of feature importance and tree visualization, as well as a helpful report generation system. The results can be visualized as high-quality plots and downloaded as local files.ConclusionChemSAR provides an integrated web-based platform for generating SAR classification models that will benefit cheminformatics and other biomedical users. It is freely available at: http://chemsar.scbdd.com.Graphical abstract.

The influence of the negative-positive ratio and screening database size on the performance of machine learning-based virtual screening.

The machine learning-based virtual screening of molecular databases is a commonly used approach to identify hits. However, many aspects associated with training predictive models can influence the final performance and, consequently, the number of hits found. Thus, we performed a systematic study of the simultaneous influence of the proportion of negatives to positives in the testing set, the size of screening databases and the type of molecular representations on the effectiveness of classification. The results obtained for eight protein targets, five machine learning algorithms (SMO, Naïve Bayes, Ibk, J48 and Random Forest), two types of molecular fingerprints (MACCS and CDK FP) and eight screening databases with different numbers of molecules confirmed our previous findings that increases in the ratio of negative to positive training instances greatly influenced most of the investigated parameters of the ML methods in simulated virtual screening experiments. However, the performance of screening was shown to also be highly dependent on the molecular library dimension. Generally, with the increasing size of the screened database, the optimal training ratio also increased, and this ratio can be rationalized using the proposed cost-effectiveness threshold approach. To increase the performance of machine learning-based virtual screening, the training set should be constructed in a way that considers the size of the screening database.

Clavibacter michiganensis subsp. michiganensis strains virulence and genetic diversity. a first study in Argentina

Tomato bacterial wilt and canker, caused by Clavibacter michiganensis subsp. michiganensis (Cmm) is considered one of the most important bacterial diseases of tomato worldwide. During the last two decades, severe outbreaks have occurred in greenhouses in the horticultural belt of Buenos Aires-La Plata, Argentina. Cmm strains collected in this area over a period of 14 years (2000–2013) were characterized for genetic diversity by rep-PCR genomic fingerprinting and level of virulence in order to have a better understanding of the source of inoculum and virulence variability. Analyses of BOX-, ERIC- and REP-PCR fingerprints revealed that the strains were genetically diverse; the same three fingerprint types were obtained in all three cases. No relationship could be established between rep-PCR clustering and the year, location or greenhouse origin of isolates, which suggests different sources of inoculum. However, in a few cases, bacteria with identical fingerprint types were isolated from the same greenhouse in different years. Despite strains differing in virulence, particularly within BOX-PCR groups, putative virulence genes located in plasmids (celA, pat-1) or in a pathogenicity island in the chromosome (tomA, chpC, chpG and ppaA) were detected in all strains. Our results suggest that new strains introduced every year via seed importation might be coexisting with others persisting locally. This study highlights the importance of preventive measures to manage tomato bacterial wilt and canker.

Diversity of the subspecies Bifidobacterium animalis subsp. lactis

Strains of Bifidobacterium animalis subsp. lactis are well-known health-promoting probiotics used commercially. B. animalis subsp. lactis has been isolated from different sources, and little is known about animal isolates of this taxon. The aim of this study was to examine the genotypic and phenotypic diversity between B. animalis subsp. lactis strains different animal hosts including Cameroon sheep, Barbary sheep, okapi, mouflon, German shepard and to compare to BB12, food isolates and the collection strain DSM 10140. Ten strains of B. animalis subsp. lactis from different sources were characterised by phenotyping, fingerprinting, and multilocus sequence typing (MLST). Regardless of origin, MLST and phylogenetic analyses revealed a close relationship between strains of B. animalis subsp. lactis with commercial and animal origin with the exception of isolates from ovine cheese, mouflon and German Shepard dog. Moreover, isolates from dog and mouflon showed significant differences in fermentation profiles and peptide mass fingerprints (MALDI-TOF). Results indicated phenotypic and genotypic diversity among strains of B. animalis subsp. lactis.

A noninvasive cancer detection strategy based on gold nanoparticle surface-enhanced raman spectroscopy of urinary modified nucleosides isolated by affinity chromatography

The search for tumor biomarkers in the urine for cancer diagnosis is currently a hot topic in clinical oncology, with potential for cancer screening and diagnosis. Modified nucleosides excreted through the urine are considered to be a general tumor marker for various cancer types. Herein, we explore a new method that utilizes surface-enhanced Raman scattering (SERS) spectroscopy to obtain a complete biochemical profile of urinary modified nucleosides. In our method, modified nucleosides are first isolated from urine sample utilizing the excellent separation ability of affinity chromatography; then supplemented with gold (Au) nanoparticles as substrate for SERS spectroscopy analysis. The obtained SERS spectra present rich diagnostic and fingerprinting type signatures of urinary modified nucleosides. The utility of this new method in cancer detection was evaluated by analyzing urine samples from three groups of subjects: nasopharyngeal cancer patients (n=62), esophageal cancer patients (n=55), and healthy volunteers (n=52). Partial least squares and linear discriminant analysis (PLS-DA) were used to analyze and classify the SERS spectra of urinary modified nucleosides from nasopharyngeal cancer, esophageal cancer, and the normal group, achieving diagnostic sensitivities of 95.2%, 90.9% and 98.1% and specificities of 97.2%, 98.2% and 95.7%, respectively. These results demonstrated great potential of this novel method for non-invasive and label-free cancer detection and screening.

Molecular Epidemiology of Carbapenem-Resistant Enterobacter cloacae in a University Hospital in Japan

Background Carbapenem-resistant Enterobacteriaceae (CRE) is becoming more significant concerns in the clinical settings. National surveillance for CRE in Japan has started since September 2014 under the criteria of either cefmetazole-MIC ≥ 64 µg/mL and imipenem-MIC ≥ 2 µg/mL or meropenem-MIC ≥ 2 µg/mL, which revealed that Enterobacter cloacae was the largest species detected. Understanding of the epidemiology and the drug-resistant mechanism are needed to prevent the spread of carbapenem-resistant E. cloacae (CREC).Methods A retrospective cohort analysis of patients who were positive for CREC from April 2012 to March 2016 at Nagoya University Hospital (a single tertiary hospital in Japan) was performed. Patient information was obtained from electric health records. Molecular epidemiology for CREC isolates was assessed by repetitive-sequence-PCR-based DNA fingerprinting (DiversiLab®, bioMerieux, France) and multilocus sequencing typing (MLST). Types of carbapenemases were determined by PCR-sequencing method. Clinical backgrounds and outcomes were compared between carbapenemase-producing E. cloacae (CPEC) cases and non-CPEC cases.ResultsWe identified 41 CREC isolates from 39 patients (19 CPEC isolates in 19 patients). Ten CPEC and 9 non-CPEC isolates were colonization (Table 1). CPEC cases were more likely to have longer hospital stay from admission to positive culture (median 31 days vs. 13.5 days, P = 0.0253) (Table 2) than non-CPEC cases, however, ages, underlying diseases, type of infection and outcomes did not differ significantly. IMP-60 gene was identified in 18 CPEC isolates. Molecular epidemiological study by DiversiLab® revealed 4 main CPEC clusters, including isolates detected months apart from different patients (Figure 1). MLST analysis showed 30 CREC isolates belonged to previously reported sequence type (ST). Eighteen CPEC isolates belonged to ST53, 78, 113, and 513, respectively, which were compatible with clusters classified by DiversiLab®.Conclusion CPEC could be propagated horizontally from patient to patient and possibly by plasmid dissemination. Strict infection control is necessary to prevent the spread of CREC.Disclosures All authors: No reported disclosures.

Comparative analysis of lip with thumbprints: An identification tool in personal authentication.

Context:Identification of person living or dead using diverse characteristics is the basis in forensic science. The uniqueness of lip and fingerprints and further, association between them can be useful in establishing facts in legal issues.Aims:The present study was carried out to determine the distribution of different lip print patterns among subjects having different thumbprint patterns and to determine the correlation between lip print patterns and thumbprint patterns.Materials and Methods:The study sample comprised 100 students randomly selected from Bapuji Dental College Hospital, Davangere, Karnataka, 50 males and 50 females aged between 18 and 20 years. Red colored lipstick was applied on the lips by a lipstick applicator brush. Lip and thumb impressions were made on No. 1 Whatman filter paper and visualized using magnifying lens. Three main types of fingerprints (loop, whorl and arch) were identified; Tsuchihashi Y classification of lip print patterns was followed in the study. Chi-square test was used to see the association between lip and thumbprints.Results:The correlation between lip and left thumb print patterns for gender identification was statistically significant. In both males and females, Type II lip pattern associated with loop finger pattern were most significant and in males, Type III lip pattern with whorl type of finger pattern showed statistical significance.Conclusion:We conclude that the correlation found between lip print and thumbprint can be utilized in the field of forensic science for gender identification.

A study on the genetic analysis of clinical isolates and vaccine strains of Bordetella pertussis by pulsed-field gel electrophoresis (PFGE)

Considering the circulation of Bordetella pertussis clinical strains among populations with high vaccination coverage, it is necessary to have a proper understanding of this bacterium causing whooping cough. Various techniques, which are available for studying B. pertussis, can facilitate a proper comparison between different populations. We genotypically analyzed a collection of two vaccine strains used for the production of killed pertussis vaccine during 2000-2014 at Razi Vaccine and Serum Research Institute, Karaj, Iran. Ten clinical and two reference (Tohama 1 and 18323) strains were used by means of pulsed-field gel electrophoresis (PFGE). The genetic profiles of the vaccine master and working seeds showed no significant changes in the frequency of fingerprint types in the vaccine strains; also, the homogeneity of the profiles was demonstrated. However, the clinical isolates showed heterogeneity in the genetic profiles. In addition, serotyping was performed with monoclonal antisera to agglutinogens 2 and 3. Analysis of the fimbriae showed that all ten clinical strains expressed Fim3.