Types Of Fingerprints Research Articles

Rapid Fingerprinting of High-Molecular-Weight Polymers by Laser Desorption-Ionization Using Through-Hole Alumina Membrane High-Resolution Mass Spectrometry

Residual acid found in the desorption ionization using through-holes alumina membranes (DIUTHAME) induces a reproducible protonation/in-source dissociation of polymers made of ester, amide, or siloxane moieties during their surface-assisted laser desorption ionization (SALDI) mass analysis. Deposited on the DIUTHAME chips in solution (solvent-based) or in pure form by melting the polymer powder in situ (solvent-free), high-molecular-weight nylons, silicone, or functionalized celluloses among other polymers are instantly fingerprinted by laser DIUTHAME high-resolution mass spectrometry (MS) with specific patterns resembling their direct analysis in real-time (DART) single-stage or tandem mass spectra. Depending on the polymer, two main types of fingerprints are observed with either the protonated monomer or product ions revealing the nature of the repeating unit or its functionalization. This technique allows a rapid molecular analysis of industrial homopolymers regardless of their molecular weight and complementary to DART with simple or no sample preparation and also promisingly applicable for copolymers.

CLASIFICATION OF TYPES OF FINGERPRINT WITH NOISE USING RED SOM

To identify the fingerprint type according to Henry's classification despite having noise present in the fingerprint image, the artificial neural network SOM of Teuvo Kohonen was used.

Discriminant models on mitochondrial toxicity improved by consensus modeling and resolving imbalance in training

Humans and animals may be exposed to tens of thousands of natural and synthetic chemicals during their lifespan. It is difficult to assess risk for all the chemicals with experimental toxicity tests. An alternative approach is to use computational toxicology methods such as quantitative structure–activity relationship (QSAR) modeling. Mitochondrial toxicity is involved in many diseases such as cancer, neurodegeneration, type 2 diabetes, cardiovascular diseases and autoimmune diseases. Thus, it is important to rapidly and efficiently identify chemicals with mitochondrial toxicity. In this study, five machine learning algorithms and twelve types of molecular fingerprints were employed to generate QSAR discriminant models for mitochondrial toxicity. A threshold moving method was adopted to resolve the imbalance issue in the training data. Consensus of the models by an averaging probability strategy improved prediction performance. The best model has correct classification rates of 81.8% and 88.3% in ten-fold cross validation and external validation, respectively. Substructures such as phenol, carboxylic acid, nitro and arylchloride were found informative through analysis of information gain and frequency of substructures. The results demonstrate that resolving imbalance in training and building consensus models can improve classification rates for mitochondrial toxicity prediction.

TILoc: Improving the Robustness and Accuracy for Fingerprint-Based Indoor Localization

In WLAN fingerprint-based indoor localization, signal noise in the measurement of received signal strength indicator (RSSI) often results in matching a set of disperse reference points (RPs), leading to unsatisfactory estimation and weak robustness. To mitigate the noise problem, we propose a novel indoor positioning strategy, torus intersection localization (TILoc), aiming to improve the robustness and accuracy of fingerprint-based indoor localization. In the online phase, we design a new type of online RSSI fingerprints by filtering out unstable access points (APs). We use part of robust APs to construct RP torus and take the RPs in the intersection of RP tori as the nearest RPs. For reducing sparse spikes noise, we apply robust principal component analysis (RPCA) to train offline and online fingerprints. In addition, we take the AP’s effect into consideration when we position a target. Our simulation and experiments show that the proposed algorithm outperforms other recent state-of-the-art algorithms in robustness and accuracy.

In Silico Prediction of Metabolic Epoxidation for Drug-like Molecules via Machine Learning Methods.

Epoxidation is one of the reactions in drug metabolism. Since epoxide metabolites would bind with proteins or DNA covalently, drugs should avoid epoxidation metabolism in the body. Due to the instability of epoxide, it is difficult to determine epoxidation experimentally. In silico models based on big data and machine learning methods are hence valuable approaches to predict whether a compound would undergo epoxidation. In this study, we collected 884 epoxidation data manually from various sources, and finally got 829 unique sites of epoxidation. Three types of molecular fingerprints with different lengths (1024, 2048 or 4096 bits) were used to describe the reaction sites. Six machine learning methods were used to build the classification models. The training set and test set were randomly divided into 8 : 2, and 54 models were constructed and evaluated. Four best models were selected for feature selection. The features were then chosen and verified by external validation set. The resulted optimal model had the accuracy and AUC (area under the curve) values at 0.873 and 0.944 for the test set, 0.838 and 0.987 for the external validation set, respectively. The models built in this study could accurately predict whether a compound will undergo epoxidation and which part is most susceptible to epoxidation, which is of great significance for drug design.

Gender classification from fused multi-fingerprint types

ABSTRACT There has been a growing need for demographic information in security applications. These range from token-based demographic information elicitation to automatic gender classification or age estimation from biometric traits. Gender has been classified from facial images, voice utterances, fingerprints, hand images and emerging biometrics in the literature using local statistical or structural feature descriptors extracted from these traits. We propose a deep learning based convolutional neural network architecture for classifying gender from fingerprints of each of the five finger types and evaluate performances across trained models. We demonstrate that performance can be improved by classifying gender from fingerprints of fused combinations amongst the five right-hand finger types. The default method has been to classify gender from the index finger. However, our results show that certain finger types classify a certain gender better than the other. Leveraging on these varying strengths of the finger types we employ a fusion scheme at the abstract level, of odd number of models, trained with these fingerprint types to improve performance. Male, female and overall classification accuracies of the best fusion model are 94.7%, 88.0% and 91.3%, thus, proffering 31.02%, 7.82% and 18.72% improvement, respectively.

Developing novel in silico prediction models for assessing chemical reproductive toxicity using the naïve Bayes classifier method.

Assessment of reproductive toxicity is one of the important safety considerations in drug development. Thus, in the present research, the naïve Bayes (NB)-classifier method was applied to develop binary classification models. Six important molecular descriptors for reproductive toxicity were selected by the genetic algorithm. Then, 110 classification models were developed using six molecular descriptors and10 types of fingerprints with 11 different maximum diameters. Among these established models, the model based on six molecular descriptors and the SciTegic extended-connectivity fingerprints with 20 maximum diameters (LCFC_20) displayed the best prediction performance for reproductive toxicity (NB-1), which gave a 0.884 receiver operating characteristic (ROC) score and 91.8% overall prediction accuracy for the Training Set, and produced a 0.888 ROC score and 83.0% overall accuracy for the external Test Set I. In addition, for the external rat multi-generation reproductive toxicity dataset (Test Set II), the NB-1 model generated a 0.806 ROC score and 85.1% concordance. The generated prediction results indicated that the NB-1 model could give robust and reliable predictions for a reproductive toxicity potential of chemicals. Thus, the established model could be applied to filter early-stage molecules for potential reproductive adverse effects. In addition, six important molecular descriptors and new structural alerts for reproductive toxicity were identified, which could help medicinal chemists rationally guide the optimization of lead compounds and select chemicals with the best prospects of being safe and effective.

A Novel Outdoor Positioning Technique Using LTE Network Fingerprints.

In recent years, wireless-based fingerprint positioning has attracted increasing research attention owing to its position-related features and applications in the Internet of Things (IoT). In this paper, by leveraging long-term evolution (LTE) signals, a novel deep-learning-based fingerprint positioning approach is proposed to solve the problem of outdoor positioning. Considering the outstanding performance of deep learning in image classification, LTE signal measurements are converted into location grayscale images to form a fingerprint database. In order to deal with the instability of LTE signals, prevent the gradient dispersion problem, and increase the robustness of the proposed deep neural network (DNN), the following methods are adopted: First, cross-entropy is used as the loss function of the DNN. Second, the learning rate of the proposed DNN is dynamically adjusted. Third, this paper adopted several data enhancement techniques. To find the best positioning fingerprint and method, three types of fingerprint and five positioning models are compared. Finally, by using a deep residual network (Resnet) and transfer learning, a hierarchical structure training method is proposed. The proposed Resnet is used to train with the united fingerprint image database to obtain a positioning model called a coarse localizer. By using the prior knowledge of the pretrained Resnet, feed-forward neural network (FFNN)-based transfer learning is used to train with the united fingerprint database to obtain a better positioning model, called a fine localizer. The experimental results convincingly show that the proposed DNN can automatically learn the location features of LTE signals and achieve satisfactory positioning accuracy in outdoor environments.

Evaluation for Correlation of Cheiloscopy and Dermatoglyphics in Gender Determination

Background: Identification of the suspect, living or dead with the help of physical charachteristics play a vital role in forensic science. Improved methods of gender determination and personal identification are important in solving criminal cases. Cheiloscopy and dactyloscopy have been proved to be of use in personal identification. The present study aims to evaluate correlation of Cheiloscopy and Dermatoglyphics in gender determinationMaterials and Method: The study was carried out from January 2017 to January 2019 on 2112 individuals (1056 males and 1056 females) aged from 15 to 60 years in KMCT Dental College, Calicut. For lip print analysis Suzuki and Tsuchihashi classification was followed. For finger print analysis impression of all the fingers were taken. For analysis, the images were cropped and abode photoshop software was used.Results: Type 1 and 1’ lip groove patterns were found to be predominant among males.Type 4 and Type 5 lip groove patterns were found to be predominant among females. Loop finger print patterns were found to be predominant among males.Whorl finger print patterns were found to be predominant among females. Positive correlation (+0.80) was found between loop finger print pattern and Type 1’ lip print pattern in males which is statistically significant (p=0.003).Positive correlation (+0.75) was found between whorl finger print pattern and Type IV lip groove pattern in females which is statistically significant. (p=0.005)Conclusion: There was definite correlation of Cheiloscopy and Dermatoglyphics in gender determination

Comparative study to analyse the correlation between dermatoglyphics and impacted teeth

Background and Aims: Dental impaction is a serious challenge to the dental practitioners. Treatment for impaction is a complex procedure. During embryogenesis, the ridged skin and teeth develop from the same layer,. hence, suggesting that the genetic information in the genome is dissipated during this period, and any disturbance affecting tooth development and structure will be simultaneously reflected through change in dermatoglyphic patterns. This study aims to analyse the correlation between dermatoglyphics pattern variations and impacted teeth. Settings and Design: 100 patients, 50 impacted and 50 healthy patients, were selected who are attending to the department of oral medicine and radiology between the age group of 18-40 years. Methods and Materials: The fingerprints of both the hands were obtained using the duplicating ink pad. The fingertip patterns were analysed according to the classical method, and configurational types were classified according to the topological method. Statistical Analysis: The data obtained were subjected to statistical analysis using SPSS version 20.0 and the test of proportion and Chi-square test was used for the analysis. Results: Whorl type of fingerprints were observed more in impacted teeth patients whereas loop type of fingerprints was observed more in healthy patients. Conclusions: There is a significant correlation between impacted teeth and Dermatoglyphics and it can be used as a predilection marker.

The Relation Between Left Thumb Fingerprint Types with Blood Groups and Gender Among Students of Al-Kindy College of Medicine, 2019

The Relation Between Left Thumb Fingerprint Types with Blood Groups and Gender Among Students of Al-Kindy College of Medicine, 2019

Machine Learning Models Based on Molecular Fingerprints and an Extreme Gradient Boosting Method Lead to the Discovery of JAK2 Inhibitors.

Developing Janus kinase 2 (JAK2) inhibitors has become a significant focus for small-molecule drug discovery programs in recent years because the inhibition of JAK2 may be an effective approach for the treatment of myeloproliferative neoplasm. Here, based on three different types of fingerprints and Extreme Gradient Boosting (XGBoost) methods, we developed three groups of models in that each group contained a classification model and a regression model to accurately acquire highly potent JAK2 kinase inhibitors from the ZINC database. The three classification models resulted in Matthews correlation coefficients of 0.97, 0.94, and 0.97. Docking methods including Glide and AutoDock Vina were employed to evaluate the virtual screening effectiveness of our classification models. The R2 of three regression models were 0.80, 0.78, and 0.80. Finally, 13 compounds were biologically evaluated, and the results showed that the IC50 values of six compounds were identified to be less than 100 nM. Among them, compound 9 showed high activity and selectivity in that its IC50 value was less than 1 nM against JAK2 while 694 nM against JAK3. The strategy developed may be generally applicable in ligand-based virtual screening campaigns.

Kullback-Leibler Divergence Based Probabilistic Approach for Device-Free Localization Using Channel State Information.

Recently, people have become more and more interested in wireless sensing applications, among which indoor localization is one of the most attractive. Generally, indoor localization can be classified as device-based and device-free localization (DFL). The former requires a target to carry certain devices or sensors to assist the localization process, whereas the latter has no such requirement, which merely requires the wireless network to be deployed around the environment to sense the target, rendering it much more challenging. Channel State Information (CSI)—a kind of information collected in the physical layer—is composed of multiple subcarriers, boasting highly fined granularity, which has gradually become a focus of indoor localization applications. In this paper, we propose an approach to performing DFL tasks by exploiting the uncertainty of CSI. We respectively utilize the CSI amplitudes and phases of multiple communication links to construct fingerprints, each of which is a set of multivariate Gaussian distributions that reflect the uncertainty information of CSI. Additionally, we propose a kind of combined fingerprints to simultaneously utilize the CSI amplitudes and phases, hoping to improve localization accuracy. Then, we adopt a Kullback–Leibler divergence (KL-divergence) based kernel function to calculate the probabilities that a testing fingerprint belongs to all the reference locations. Next, to localize the target, we utilize the computed probabilities as weights to average the reference locations. Experimental results show that the proposed approach, whatever type of fingerprints is used, outperforms the existing Pilot and Nuzzer systems in two typical indoor environments. We conduct extensive experiments to explore the effects of different parameters on localization performance, and the results demonstrate the efficiency of the proposed approach.

Development of Door Safety Fingerprint Verification using Neural Network

Fingerprints can be used as natural keys. The unique fingerprint of a human being is potential for the security system. This research develops the door safety fingerprint verification using Neural Network. The component used in this research is fingerprint C3 as input processed by Arduino Uno ATMega 2560. After a fingerprint scanning process is done, the microcontroller identifies the fingerprint image. From the microcontroller forwarded the relay module process with solenoid output. Arduino can be read by a computer using the Arduino IDE. Identification during registration via C3 sensor verified using the MATLAB application with the Neural Network method. The design created produces forward and backward solenoid motion, after being given an electric current between 5 to 12 Volts. The MATLAB application will verify the fingerprint image and display the image from the results of the verification done with the user. Research has been tested 10 times with various types of fingerprints with 100% verified so that this study was declared successful.

Prevalence and genetic diversity of Campylobacter spp. in the production chain of broiler chickens in Lebanon and its association with the intestinal protozoan Blastocystis sp

Campylobacter jejuni is recognized as the most common foodborne pathogen associated with human gastroenteritis worldwide. Broilers are frequently infected by the bacteria and are considered the main source of exposure to humans. However, despite its public health impact, no recent data are currently available in Lebanon about Campylobacter spp. in poultry and human population. Therefore, this study aimed to determine the prevalence and genetic diversity of Campylobacter spp. in 227 ceca and on 227 carcasses of broiler chickens collected in Lebanese slaughterhouses. Overall, the prevalence of Campylobacter was shown to reach 67.0% in ceca and 17.2% on carcasses of Lebanese poultry. The only 2 Campylobacter species identified were C. jejuni and C. coli, with a slightly higher prevalence of C. coli in ceca and of C. jejuni on carcasses. A high level of genetic diversity was reported among the 51 C. jejuni isolates selected, since 25 distinct profiles were identified according to the comparative genomic fingerprinting typing method based on a subset of 40 genes using the 90% similarity threshold. Predominant clusters observed in Lebanese poultry isolates were also frequently found among French human clinical cases, highlighting that broiler chickens represent a potential reservoir for human campylobacteriosis. In addition, a significantly higher prevalence of Campylobacter spp. was found in slaughterhouse workers than in a cohort of hospitalized patients with no contact with poultry, confirming that contaminated broiler chickens in slaughterhouse appeared to be a non-negligible source of Campylobacter spp. transmission. Interestingly, a significant association between Campylobacter spp. and Blastocystis sp. has been observed. This correlation suggested that the presence of Campylobacter spp. would be favored when Blastocystis sp. is present and, similarly, the absence of one would favor the absence of the other. This is the first large-scale investigation focusing on the impact of Campylobacter spp. in broiler chickens in Lebanon and confirmed the need to implement prevention and control measures in the poultry production to reduce the burden of campylobacteriosis in the human population.

Deep Convolutional Generative Adversarial Network (dcGAN) Models for Screening and Design of Small Molecules Targeting Cannabinoid Receptors.

A deep convolutional generative adversarial network (dcGAN) model was developed in this study to screen and design target-specific novel compounds for cannabinoid receptors. In the adversarial process of training, two models, the discriminator D and the generator G, are iteratively trained. D is trained to discover the hidden patterns among the input data to have the accurate discrimination of the authentic compounds and the "fake" compounds generated by G; G is trained to generate "fake" compounds to fool the well-trained D by optimizing the weights for matrix multiplication of data sampling. In order to determine the appropriate architecture and the input data structure for the involved convolutional neural networks (CNNs), the combinations of various network architectures and molecular fingerprints were explored. Well-developed CNN models including LeNet-5, AlexNet, ZFNet, and VGGNet were investigated. Four types of fingerprints, including MACCS, ECFP6, AtomPair, and AtomPair Count, were calculated to describe the small molecules with diverse structural characteristics. The limitation of generating fingerprints as output remains that the concrete molecular structures cannot be converted directly, while the generative models with convolutional networks provide promising opportunities to the screening of molecules and rational modifications afterward. This study demonstrated how computer-aided drug discovery could benefit from the recent advances in deep learning.

Chemical profiling of fingerprints using mass spectrometry

Fingerprints are widely used in forensic science for individualization purposes. However, not every fingermark found at a crime scene is suitable for comparison, for instance due to distortion of ridge detail, or when the reference fingerprint is not in the database. To still retrieve information from these fingermarks, several studies have been initiated into the chemical composition of fingermarks, which is believed to be influenced by several donor traits. Yet, it is still unclear what donor information can be retrieved from the composition of one’s fingerprint, mainly because of limited sample sizes and the focus on analytical method development. It this paper, we analyzed the chemical composition of 1852 fingerprints, donated by 463 donors during the Dutch music festival Lowlands in 2016. In a targeted approach we compared amino acid and lipid profiles obtained from different types of fingerprints. We found a large inter-variability in both amino acid and lipid content, and significant differences in l-(iso)leucine, l-phenylalanine and palmitoleic acid levels between male and female donors. In an untargeted approach we used full-scan MS data to generate classification models to predict gender (77.9% accuracy) and smoking habit (90.4% accuracy) of fingerprint donors. In the latter, putatively, nicotine and cotinine are used as predictors.

A SURVEY ON FINGERPRINT PATTERN RECOGNITION

In this advance technical time, we all need accuracy to any security system. Among all security system, biometric recognition process is very popular in that time. Not only security purpose, identification is the main cause of using biometric characteristic. A pin, password combination is not enough to secure all things because that’s tracking is possible, but a person biometric characteristic is unique, so it is near to impossible to by-pass. In this paper, we discuss about the fingerprint types such as arch, loop, and whorl. We also discuss how the fingerprint will be recognized; however, where this technique is used in very large scale and what is the future scope of this technique, we discuss what improvement is needed in future.

Quality Evaluation of Oviductus Ranae Based on PUFAs Using HPLC Fingerprint Techniques Combined with Chemometric Methods.

Oviductus Ranae is a nutritional product for both medicine and food. Its quality is uneven due to multiple factors. An efficient method was established to evaluate the quality of Oviductus Ranae using fingerprint techniques and chemometric methods based on the analysis of polyunsaturated fatty acids (PUFAs) in petroleum ether extract by high performance liquid chromatography (HPLC). During this process, 27 batches of Oviductus Ranae were analyzed by HPLC and two types of chromatographic fingerprints were established. The fingerprint that was obtained by matching six known peaks was used for the quantification of six PUFAs. Another fingerprint was obtained by matching sixteen peaks with a peak area ratio greater than 0.5% and it was used to classify the different qualities of Oviductus Ranae by further combining three different chemometric models. The 27 batches of Oviductus Ranae were divided into four categories, which was consistent with the analysis results of six PUFAs contents. The two high-quality samples with significantly higher contents were classified into one category, and samples with medium contents were divided into two categories, including eight and thirteen samples, respectively. The four inferior samples with lower contents were classified into one category. The results indicated that the newly developed method has potential application prospects for the quality evaluation of Oviductus Ranae.

Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method.

Human immunodeficiency virus type-1 and hepatitis C virus (HIV/HCV) coinfection occurs when a patient is simultaneously infected with both human immunodeficiency virus type-1 (HIV-1) and hepatitis C virus (HCV), which is common today in certain populations. However, the treatment of coinfection is a challenge because of the special considerations needed to ensure hepatic safety and avoid drug–drug interactions. Multitarget inhibitors with less toxicity may provide a promising therapeutic strategy for HIV/HCV coinfection. However, the identification of one molecule that acts on multiple targets simultaneously by experimental evaluation is costly and time-consuming. In silico target prediction tools provide more opportunities for the development of multitarget inhibitors. In this study, by combining Naïve Bayes (NB) and support vector machine (SVM) algorithms with two types of molecular fingerprints, MACCS and extended connectivity fingerprints 6 (ECFP6), 60 classification models were constructed to predict compounds that were active against 11 HIV-1 targets and four HCV targets based on a multiple quantitative structure–activity relationships (multiple QSAR) method. Five-fold cross-validation and test set validation were performed to measure the performance of the 60 classification models. Our results show that the 60 multiple QSAR models appeared to have high classification accuracy in terms of the area under the ROC curve (AUC) values, which ranged from 0.83 to 1 with a mean value of 0.97 for the HIV-1 models and from 0.84 to 1 with a mean value of 0.96 for the HCV models. Furthermore, the 60 models were used to comprehensively predict the potential targets of an additional 46 compounds, including 27 approved HIV-1 drugs, 10 approved HCV drugs and nine selected compounds known to be active against one or more targets of HIV-1 or HCV. Finally, 20 hits, including seven approved HIV-1 drugs, four approved HCV drugs, and nine other compounds, were predicted to be HIV/HCV coinfection multitarget inhibitors. The reported bioactivity data confirmed that seven out of nine compounds actually interacted with HIV-1 and HCV targets simultaneously with diverse binding affinities. The remaining predicted hits and chemical-protein interaction pairs with the potential ability to suppress HIV/HCV coinfection are worthy of further experimental investigation. This investigation shows that the multiple QSAR method is useful in predicting chemical-protein interactions for the discovery of multitarget inhibitors and provides a unique strategy for the treatment of HIV/HCV coinfection.