Accurate Identification System Research Articles

CRISPR-Cas12a-activated proximity catalytic hairpin assembly strategy for sensitive gene analysis in Streptococcus mutans

The cnm gene in Streptococcus mutans (S. mutans) mediates bacterial binding to extracellular matrix and invasion within host cells, and has been found to be correlated with the occurrence of brain microbleeds. Hence, it is crucial to develop a reliable method to evaluate cnm-positive S. mutans. Herein, we propose a novel diagnostic platform utilizing Clustered regularly interspaced short palindromic repeats (CRISPR)-Cas12a system for accurate identification of the cnm gene, along with proximity catalytic hairpin assembly (CHA) for enhanced signal amplification, for the detection and analysis of the cnm gene in S. mutans. When the cnm gene is present, it activates trans-cleavage activity of Cas12a to trigger the proximity CHA. In contrast to conventional CHA, the hairpin probes in proximity CHA were linked in a sequential manner using a liner ssDNA sequence. Additionally, the CHA process was facilitated by the Duplex-Specific Nuclease (DSN), which enhanced the efficiency and speed of the reaction. The approach exhibits several notable benefits, including i) the target recognition capability of crRNA in CRISPR-Cas system enhances the detection specificity; ii) Cas12a-crRNA is independent on the PAM during recognizing target dsDNA; iii) the use of the DSN and the proximity design of the H1-H2 complex has significantly enhanced the reactive efficiency of proximity CHA. As a result, the method exhibits a low limit of detection of 47 aM and a high selectivity to cnm gene. Moreover, the approach has been effectively employed in clinical settings to accurately differentiate S. mutans from a combination of microorganisms.

Automatic Microfluidic Harmonized RAA-CRISPR Diagnostic System for Rapid and Accurate Identification of Bacterial Respiratory Tract Infections.

Respiratory tract infections (RTIs) pose a grave threat to human health, with bacterial pathogens being the primary culprits behind severe illness and mortality. In response to the pressing issue, we developed a centrifugal microfluidic chip integrated with a recombinase-aided amplification (RAA)-clustered regularly interspaced short palindromic repeats (CRISPR) system to achieve rapid detection of respiratory pathogens. The limitations of conventional two-step CRISPR-mediated systems were effectively addressed by employing the all-in-one RAA-CRISPR detection method, thereby enhancing the accuracy and sensitivity of bacterial detection. Moreover, the integration of a centrifugal microfluidic chip led to reduced sample consumption and significantly improved the detection throughput, enabling the simultaneous detection of multiple respiratory pathogens. Furthermore, the incorporation of Chelex-100 in the sample pretreatment enabled a sample-to-answer capability. This pivotal addition facilitated the deployment of the system in real clinical sample testing, enabling the accurate detection of 12 common respiratory bacteria within a set of 60 clinical samples. The system offers rapid and reliable results that are crucial for clinical diagnosis, enabling healthcare professionals to administer timely and accurate treatment interventions to patients.

A facile fabrication strategy constructed multilayer piezoresistive pressure sensor for intelligent recognition system towards privacy protection

Privacy and security concerns have become a focal point of attention for humans, so that it is crucial to develop a secure and efficient identity recognition smart system. This work proposes a piezoresistive pressure sensor (PPS) based intelligent recognition system for privacy protection, wherein the PPS consists of multiple layers of electrospun fiber membranes with a bionic pine needle shaped structure. The PPS exhibits high sensitivity of 9040.46 kPa−1 at 0–1 kPa, 679.7 kPa−1 at 1–7 kPa, and 50.04 kPa−1 at 7–20 kPa, with a fast response/recovery time of 30 ms and excellent stability. Given these prominent features, the specific application in privacy protection has been construced. PPS was firstly exploited as an intelligent handwriting pad to obtain handwriting signals from different users and combined with convolutional neural network (LeNet-5) and long short-term memory (LSTM) network models to establish a handwriting recognition intelligent system for accurate identification of user identities. Further, a password unlocking control system was succesfully created based on the PPS array and support vector machine (SVM) network model for dual verification of passwords and behavior patterns. This work can easily and effectively detect people’s daily behavioral characteristics for identity authentication, and has enormous application potential in fields such as handwriting recognition, security defense, and privacy protection.

Pharmacognostical study of Croton Crassifolius Geisel

ObjectivesCroton Crassifolius Geisel of the genus Croton in the Euphorbia family is a widely distributed herb in South China. It has medicinal value for the treatment of a sore throat, soothing tendons, activating collateral, and treating rheumatoid arthritis. Although it has been traditionally used in Chinese medicine, there has been no systematic study on its identification and classification of the authenticity of this plant. The purpose of this study is to provide a scientific basis for the identification and classification of the authenticity of this plant. MethodsAn accurate and effective identification system was established through morphological characteristics, microscopic characteristics, physical and chemical parameter determination, phytochemical screening, and DNA barcoding analysis. ResultsThe physicochemical results revealed that this plant might contain flavonoids, cardiac glycosides, and alkaloids. The ITS locus in the nuclear genome in the chloroplast genome was screened and evaluated and Neighbor-Joining phylogenetic trees can effectively identify Croton Crassifolius Geisel to a certain extent. ConclusionThis research contributed to the development of species identification.

Efficient pollen grain classification using pre-trained Convolutional Neural Networks: a comprehensive study

Pollen identification is necessary for several subfields of geology, ecology, and evolutionary biology. However, the existing methods for pollen identification are laborious, time-consuming, and require highly skilled scientists. Therefore, there is a pressing need for an automated and accurate system for pollen identification, which can be beneficial for both basic research and applied issues such as identifying airborne allergens. In this study, we propose a deep learning (DL) approach to classify pollen grains in the Great Basin Desert, Nevada, USA. Our dataset consisted of 10,000 images of 40 pollen species. To mitigate the limitations imposed by the small volume of our training dataset, we conducted an in-depth comparative analysis of numerous pre-trained Convolutional Neural Network (CNN) architectures utilizing transfer learning methodologies. Simultaneously, we developed and incorporated an innovative CNN model, serving to augment our exploration and optimization of data modeling strategies. We applied different architectures of well-known pre-trained deep CNN models, including AlexNet, VGG-16, MobileNet-V2, ResNet (18, 34, and 50, 101), ResNeSt (50, 101), SE-ResNeXt, and Vision Transformer (ViT), to uncover the most promising modeling approach for the classification of pollen grains in the Great Basin. To evaluate the performance of the pre-trained deep CNN models, we measured accuracy, precision, F1-Score, and recall. Our results showed that the ResNeSt-110 model achieved the best performance, with an accuracy of 97.24%, precision of 97.89%, F1-Score of 96.86%, and recall of 97.13%. Our results also revealed that transfer learning models can deliver better and faster image classification results compared to traditional CNN models built from scratch. The proposed method can potentially benefit various fields that rely on efficient pollen identification. This study demonstrates that DL approaches can improve the accuracy and efficiency of pollen identification, and it provides a foundation for further research in the field.

Biomimetic Flexible High‐Sensitivity Near‐Infrared II Organic Photodetector for Photon Detection and Imaging

AbstractThe flexible near‐infrared (NIR) organic photodetector is an essential prerequisite for the next‐generation visual system for accurate information identification. However, prevailing limitations in NIR organic photodetectors, including high noise, weak responsivity, and low detectivity, directly impede the translation to practical applications. Herein, ultranarrow bandgap acceptors, featuring a 2D highly electron‐donating central core (DTPC), named as YZ and YZ1, are designed and synthesized for NIR organic photodetectors. It is found that the introduction of highly rigid DTPC effectively reduces energetic disorder in active films. Moreover, extending the end conjugation not only broadens the absorption spectrum but also diminishes trap states and improves charge transport. Consequently, the YZ1‐based organic photodetector exhibits an impressive detection range from 300 to 1050 nm with a high responsivity of 0.27 A W−1 at 1000 nm and specific detectivity of 9.24 × 1013 Jones, comparable to commercial silicon photodiodes. Inspired by biological eyes, large‐area parallelly independent photodetector arrays (2000 pixels) for an artificial vision system are fabricated, which enables a clear matter identification in the NIR region. These findings and results simultaneously provide critical insights into molecular design and bridge the gap between material science and device engineering, accelerating the application of NIR organic photodetectors in next‐generation wearable optoelectronics.

Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals

User authentication has become necessary in different life domains. Traditional authentication methods like personal information numbers (PINs), password ID cards, and tokens are vulnerable to attacks. For secure authentication, methods like biometrics have been developed in the past. Biometric information is hard to lose, forget, duplicate, or share because it is a part of the human body. Many authentication methods focused on electrocardiogram (ECG) signals have achieved great success. In this paper, we have developed cardiac biometrics for human identification using a deep learning (DL) approach. Cardiac biometric systems rely on cardiac signals that are captured using the electrocardiogram (ECG), photoplethysmogram (PPG), and phonocardiogram (PCG). This study utilizes the ECG as a biometric modality because ECG signals are a superior choice for accurate, secure, and reliable biometric-based human identification systems, setting them apart from PPG and PCG approaches. To get better performance in terms of accuracy and computational time, we have developed an ensemble approach based on VGG16 pre-trained transfer learning (TL) and Long Short-Term Memory (LSTM) architectures to optimize features. To develop this authentication system, we have fine-tuned this ensemble network. In the first phase, we preprocessed the ECG biosignal to remove noise. In the second phase, we converted the 1-D ECG signals into a 2-D spectrogram image using a transformation phase. Next, the feature extraction step is performed on spectrogram images using the proposed ensemble DL technique, and finally, those features are identified by the boosting machine learning classifier to recognize humans. Several experiments were performed on the selected dataset, and on average, the proposed system achieved 98.7% accuracy, 98.01% precision, 97.1% recall, and 0.98 AUC. In this paper, we have compared the developed approach with state-of-the-art biometric authentication systems. The experimental results demonstrate that our proposed system outperformed the human recognition competition.

Molecular diagnosis and differentiation of Meloidogyne arenaria, Meloidogyne javanica and Meloidogyne incognita using SNP-based KASP assays

Root-knot nematodes (RKNs) are one of the most economically important plant-parasitic nematodes in many crops. Meloidogyne arenaria, M. incognita and M. javanica are the most common species attacking tomato, pepper, eggplant, cucumber, melon, watermelon and zucchini, which have economic importance in vegetable growing areas. Thus, a rapid and accurate identification system is required for RKNs. In this study, Kompetitive Allele-Specific PCR (KASP) genotyping assays were developed to identify three common RKNs. The novel technique developed in this study utilized only two primers: one allele-specific forward primer and a reverse common primer. Meloidogyne arenaria, M. incognita and M. javanica were separated easily from each other and other RKN species. This is the first report on three species identified by the KASP assay. The results showed that the KASP assays are suitable for the high-throughput identification of these species. The developed system can be used to identify three RKN species in research, routine and quarantine laboratories.

An automated deep learning system for accurate identification of pneumonia and covid-19 from chest X-Ray images

An automated deep learning system for accurate identification of pneumonia and covid-19 from chest X-Ray images

Tea leaf disease detection and identification based on YOLOv7 (YOLO-T)

A reliable and accurate diagnosis and identification system is required to prevent and manage tea leaf diseases. Tea leaf diseases are detected manually, increasing time and affecting yield quality and productivity. This study aims to present an artificial intelligence-based solution to the problem of tea leaf disease detection by training the fastest single-stage object detection model, YOLOv7, on the diseased tea leaf dataset collected from four prominent tea gardens in Bangladesh. 4000 digital images of five types of leaf diseases are collected from these tea gardens, generating a manually annotated, data-augmented leaf disease image dataset. This study incorporates data augmentation approaches to solve the issue of insufficient sample sizes. The detection and identification results for the YOLOv7 approach are validated by prominent statistical metrics like detection accuracy, precision, recall, mAP value, and F1-score, which resulted in 97.3%, 96.7%, 96.4%, 98.2%, and 0.965, respectively. Experimental results demonstrate that YOLOv7 for tea leaf diseases in natural scene images is superior to existing target detection and identification networks, including CNN, Deep CNN, DNN, AX-Retina Net, improved DCNN, YOLOv5, and Multi-objective image segmentation. Hence, this study is expected to minimize the workload of entomologists and aid in the rapid identification and detection of tea leaf diseases, thus minimizing economic losses.

Utilizing Convolutional Neural Networks for Fingerprint-Based Attendance Monitoring

The traditional method of taking attendance using paper sheets is prone to errors like impersonation, loss, or theft. To solve this issue, automatic attendance systems utilizing identification technology such as barcode badges, electronic tags, touch screens, magnetic stripe cards, and biometrics have been implemented. Biometric technology uses physiological or behavioral characteristics for identification purposes, but traditional biometric systems have limitations such as vulnerability to damage or alteration over time, and variations in occlusions, poses, facial expressions, and illumination can affect face recognition accuracy. Fingerprint identification relies on the distinctiveness of fingerprints and involves comparing two impressions of the friction ridges on human fingers or toes to determine if they belong to the same individual. There are five primary categories of fingerprints: arch, tented arch, left loop, right loop, and whorl. Various algorithms have been developed to recognize fingerprints using minutiaebased matching, which involves identifying key features like ridge ending and bifurcation. Deep learning algorithms, particularly convolutional neural networks, have been successful in improving identification accuracy by extracting features automatically from fingerprint images. In recent times, securing personal data has become increasingly important, and the Convolutional Neural Network (CNN) identification system is recommended for improving accuracy and performance. This paper proposes a fingerprint identification system that combines three models: CNN, Softmax, and Random Forest (RF) classifiers. The conventional system uses K-means and DBSCAN algorithms to separate the foreground and background regions and extracts features using CNNs and dropout approach. The Softmax acts as a recognizer. The proposed algorithm is evaluated on a public database and shows promising results, providing an accurate and efficient biometric identification system.

Plant Club: An all-in-one Plant based Application

Plant diseases can have significant economic and environmental impacts, including reduced crop yields and the spread of disease to other plants. Plant identification can also be a challenging task, even for experts, due to the wide range of plant species and the various factors that can influence their appearance. In this project, we propose to develop a comprehensive solution for plant disease identification and management using deep learning and social media. Our solution will include a mobile app for identifying common plant diseases, a deep learning system for accurate and efficient plant identification, and a social networking site for plant lovers to share photos, tips, and advice. We will also integrate e- commerce functionality, allowing plant sellers to easily list and sell their plants online through popular platforms such as Etsy and eBay. Our solution aims to improve plant health, reduce the economic and environmental impacts of plant diseases, and enhance the online plant shopping experience for customers. Key Words: Plant E-Commerce, Identification, Plant social media, Machine Learning, Disease Identification.

Ventral and Dorsal Stream EEG Channels: Key Features for EEG-Based Object Recognition and Identification.

Object recognition and object identification are multifaceted cognitive operations that require various brain regions to synthesize and process information. Prior research has evidenced the activity of both visual and temporal cortices during these tasks. Notwithstanding their similarities, object recognition and identification are recognized as separate brain functions. Drawing from the two-stream hypothesis, our investigation aims to understand whether the channels within the ventral and dorsal streams contain pertinent information for effective model learning regarding object recognition and identification tasks. By utilizing the data we collected during the object recognition and identification experiment, we scrutinized EEGNet models, trained using channels that replicate the two-stream hypothesis pathways, against a model trained using all available channels. The outcomes reveal that the model trained solely using the temporal region delivered a high accuracy level in classifying four distinct object categories. Specifically, the object recognition and object identification models achieved an accuracy of 89% and 85%, respectively. By incorporating the channels that mimic the ventral stream, the model's accuracy was further improved, with the object recognition model and object identification model achieving an accuracy of 95% and 94%, respectively. Furthermore, the Grad-CAM result of the trained models revealed a significant contribution from the ventral and dorsal stream channels toward the training of the EEGNet model. The aim of our study is to pinpoint the optimal channel configuration that provides a swift and accurate brain-computer interface system for object recognition and identification.

Identification and Characterization of a Retrospective Cohort of Secondary Hemophagocytic Lymphohistiocytosis (sHLH) Patients in the US

Introduction: Hemophagocytic lymphohistiocytosis (HLH) is a rare and life-threatening hyperinflammatory syndrome induced by aberrantly activated myeloid and T cells. HLH may be primary (i.e., due to genetic mutations) which typically presents in children, or secondary (i.e., triggered by an infection, malignancy, or rheumatologic condition) which is most common in adults. Without treatment, HLH is universally fatal; even with existing treatment, overall mortality ranges from 50-75%. Most of our understanding of HLH comes from studies in the pediatric population, and limited information is available on the diagnosis and treatment of patients with secondary HLH (sHLH). The aim of this study is to compile a historical cohort of sHLH patients from large US medical systems to better understand and refine the sHLH clinical definition, describe the epidemiology (including incidence, prevalence, and natural history of the disease), and characterize demographic, clinical, treatment characteristics, survival, and healthcare resource utilization (HRU) of the cohort compared with a matched comparison group. Methods: A historical cohort study was designed to identify sHLH patients aged ≥12 years from de-identified Electronic Medical Record (EMR) systems at health care centers in the US. The EMR systems track patients longitudinally over multiple years and are linked at the patient level by a unique identifier that is consistent across service locations and time. Three different methods (Figure 1) and chart reviews were used to enable identification of all possible sHLH patients and to better understand initial recorded diagnosis, differences in treatment and outcomes, and to determine a simpler system for accurate identification of sHLH cases in the healthcare system. As sHLH is a rare disorder that can be complex to diagnose and treat, these algorithms were developed to identify patients that may or may not have been assigned an ICD code for HLH. Approximately 5.4 million unique patient records were reviewed. Comparison of patients without sHLH were also identified from the EMR systems, matched on birth year, sex, and county of residence. Results: Patient electronic data from 2009-2021 were reviewed using all three methods. Of >800,000 unique inpatient records, 903 unique de-duplicated patients were identified for chart review. 57 patients were identified using HLH-related ICD codes (Method 1). An additional 846 patients were identified using HLH-related treatments (dexamethasone and etoposide) and elevated ferritin (≥1,000 to <10,000 ng/mL) in patient charts (Method 2) and/or by elevated ferritin (≥10,000 ng/mL) or sCD25 lab test ordered (with cohorts stratified by sCD25 lab values ≤3,900 U/mL and >3,900 U/mL) (Method 3). Potential HLH patient charts were then adjudicated via clinician review using HLH-2004 criteria and clinical judgment. Conclusion: Since sHLH is a rare, heterogenous, and complex disease to diagnosis and treat, HLH-related ICD codes (Method 1) may not be sufficient to capture the spectrum of sHLH patients. Both HLH-related treatments in charts (Method 2) and elevated ferritin and sCD25 values (Method 3) could prove useful in identifying additional sHLH patients. Moreover, Method 3 could show promise as a simplified strategy for identifying and diagnosing patients with sHLH. This process could inform the clinical definition and true burden of sHLH, particularly those patients who never received an sHLH diagnosis, as well as the clinical, demographic, and treatment characteristics and outcomes of this unique patient population. Figure 1. Flow Diagram of Patient Eligibility and MethodsFigure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Automatic identification of benign pigmented skin lesions from clinical images using deep convolutional neural network

ObjectiveWe aimed to develop a computer-aided detection (CAD) system for accurate identification of benign pigmented skin lesions (PSLs) from images captured using a digital camera or a smart phone.MethodsWe collected a total of 12,836 clinical images which had been classified and location-labeled for training and validating. Four models were developed and validated; you only look once, v4 (YOLOv4), you only look once, v5 (YOLOv5), single shot multibox detector (SSD) and faster region-based convolutional neural networks (Faster R-CNN). The performance of the models was compared with three trained dermatologists, respectively. The accuracy of the best model was further tested and validated using smartphone-captured images.ResultsThe accuracies of YOLOv4, YOLOv5, SSD and Faster R-CNN were 0.891, 0.929, 0.852 and 0.874, respectively. The precision, sensitivity and specificity of YOLOv5 (the best model) were 0.956, 0.962 and 0.952, respectively. The accuracy of YOLOv5 model for images captured using a smart-phone was 0.905. The CAD based YOLOv5 system can potentially be used in clinical identification of PSLs.ConclusionWe developed and validated a CAD system for automatic identification of benign PSLs using digital images. This approach may be used by non-dermatologists to easily diagnose by taking a photo of skin lesion and guide on management of PSLs.

Abstract 2939: Automation meets reliability: Use of Oncomine࣪precision assay on the Genexus࣪system for accurate identification of cancer biomarkers in FFPE and liquid biopsy samples

Abstract Accurate and early detection of oncogenic markers may one day be the key to fighting cancer. However, with complex workflows, long turnaround times, and numerous user touch points of most sequencing platforms does not make the process attainable. In contrast, the fully automated Genexus࣪ system provides a specimen-to-report workflow for cancer research with minimal user touchpoints and single day turnaround time. FFPE (formalin-fixed paraffin embedded) tissues and liquid biopsy are two of the main sample types used in oncology research. Here we report the use of the Oncomine࣪ Precision Assay (OPA) with the Genexus࣪ System which provides a comprehensive genetic profile across 50 key genes using DNA and RNA from FFPE tissues or cfTNA (cell free total nucleic acid) from liquid biopsy samples. To evaluate the nucleic acid (NA) extraction and sequencing performance of the Genexus࣪ system, contrived control samples with known variants and clinical research samples were used in the OPA FFPE and liquid biopsy workflows (n = 30). NA was quantified using the Genexus࣪ purification instrument’s onboard Qubit࣪ quantitation feature after purification. Output plates with extracted NA were transferred to the Genexus࣪ Integrated Sequencer for library preparation and sequencing using the OPA assay. Data were analyzed using the Ion torrent Genexus࣪ software to evaluate assay performance and variant calling.The OPA assay only requires 10ng of DNA and RNA from FFPE samples and 20ng of cfTNA from liquid biopsy samples. Genexus࣪ purification instrument onboard quantitation data showed successful extraction of NA exceeding the required yields for library preparation. Excess NA was automatically aliquoted into an archive plate and stored for future use. Sequencing results for four samples of FFPE or liquid biopsy were reported within 24 hours. Both Control and clinical research samples showed expected assay metrices including read coverage, molecular coverage, and uniformity. The results reported all expected variants at correct allele frequencies, including BRAF V600E, KRAS G12C, PIK3CA N345K, AKT1 etc. Overall, this study demonstrates that the Genexus࣪ system provides a user-friendly workflow with automated NA purification, quantitation, sample dilution, library preparation, sequencing, and data analysis with minimal hands-on time that can be performed with limited expertise to obtain results within 24 hours. Reliable identification of variants from control and clinical research samples of FFPE and liquid biopsy origin with optimal assay metrics demonstrates the successful use of the OPA assay and Genexus࣪ system that can be confidently used in clinical oncology research.(For Research Use Only. Not for use in diagnostic procedures.) Citation Format: Thilanka Jayaweera, Nicholas Siepert, Brian Hradecky, Emilia Ostrowska, Iris Casuga, kristy Lea, Priyanka Kshatriya, Jian Gu, Jeff Schageman, Ru Cao, Angie Cheng, Kelli Bramlett. Automation meets reliability: Use of Oncomine࣪precision assay on the Genexus࣪system for accurate identification of cancer biomarkers in FFPE and liquid biopsy samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2939.

DNA Barcoding of Potential Mosquito Disease Vectors (Diptera, Culicidae) in Jazan Region, Saudi Arabia.

The conventional morphological characterization of mosquito species remains heavily used for species identification in Jazan, Saudi Arabia. It requires substantial expertise and time, as well as having difficulty in confirming identity of morphologically similar species. Therefore, to establish a reliable and accurate identification system that can be applied to understanding spatial distribution of local mosquito species from the Jazan region, DNA barcoding was explored as an integrated tool for mosquito species identification. In this study, 44 adult mosquito specimens were analyzed, which contain 16 species belong to three genera of potential mosquito disease vectors (Aedes, Anopheles, and Culex). The specimens were collected from the Jazan region located in southwest Saudi Arabia. These included old and preserved mosquito voucher specimens. In addition, we assessed the genetic distance based on the generated mitochondrial partial COI DNA barcodes to detect cryptic diversity across these taxa. Nine mosquito species belonging to three genera were successfully barcoded and submitted to GenBank, namely: Aedes aegypti, Aedes caspius, Aedes vexans, Aedes vittatus, Anopheles arabiensis, Culex pipiens, Culex quinquefasciatus, Culex sitiens, and Culex tritaeniorhynchus. Of these nine species, Aedes vexans, Aedes vittatus, Culex sitiens, and Culex tritaeniorhynchus were registered in GenBank for the first time from Saudi Arabia. The DNA barcodes generated a 100% match to known barcodes of these mosquito species, that also matched with the morphological identification. Ae. vexans was found to be either a case of cryptic species (subspecies) or a new species from the region. However, more research has to be conducted to prove the latter. This study directly contributes to the development of a molecular reference library of mosquito species from the Jazan region and Saudi Arabia. The library is essential for confirmation of species in support of existing mosquito surveillance and control programmes.

A wavelet-based capsule neural network for ECG biometric identification

Electrocardiogram (ECG) signals have received a high level of attention from the biometric research community due to their unique nature for each person, which makes them suitable for developing accurate and reliable human identification systems. Although most existing ECG-based biometric recognition methods have received prominent results, several consecutive heartbeat segments are used in their approaches to achieve high accuracy, which is challenging to apply in biometric systems deployed in real-world applications. This paper proposes a new approach for human identification via ECG, based on a combination of Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) along with a novel kind of deep learning technique known as Capsule network. The CWT is used to transform a single heartbeat signal into the time–frequency domain, and the DWT is adopted to extract spectral information of 2D frequency-time scalogram images to further improve accuracy. The discrete wavelet coefficients are then used as input to the capsule network promoting the recognition performance due to its high learning capacities. To support real-life practicality of ECG biometric identification system, the effectiveness and efficiency of our approach were evaluated over four databases that include normal and abnormal ECG records: PTB Diagnosis ECG (PTB), MIT-BIH Arrhythmia, MIT-BIH Normal Sinus Rhythm (NSRDB), and the MIT-BIH ST Change (STDB) databases. Experimental results demonstrate that our proposed method was able to achieve high identification accuracies and outperforming other state-of-the-art methods, by achieving an accuracy of 99.5%, 98.1%, 98.2%, and 100% on the PTB, MIT-BIH arrhythmia, STDB, and NSRDB respectively. Furthermore, the approach showed very good generalization ability since the training and test sets were completely different, which demonstrates the feasibility to promote the application of our approach in practice.

Using Deep Learning to Identify Costa Rican Native Tree Species From Wood Cut Images.

Tree species identification is critical to support their conservation, sustainable management and, particularly, the fight against illegal logging. Therefore, it is very important to develop fast and accurate identification systems even for non-experts. In this research we have achieved three main results. First, we developed—from scratch and using new sample collecting and processing protocols—an dataset called CRTreeCuts that comprises macroscopic cross-section images of 147 native tree species from Costa Rica. Secondly, we implemented a CNN for automated tree species identification based on macroscopic images of cross-sections of wood. For this CNN we apply the fine-tuning technique with VGG16 as a base model, pre-trained with the ImageNet data set. This model is trained and tested with a subset of 75 species from CRTreeCuts. The top-1 and top-3 accuracies achieved in the testing phase are 70.5% and 80.3%, respectively. The Same-Specimen-Picture Bias (SSPB), which is known to erroneously increase accuracy, is absent in all experiments. Finally, the third result is Cocobolo, an Android mobile application that uses the developed CNN as back-end to identify Costa Rican tree species from images of cross-sections of wood.

Classification and Diagnosis of Lymphoma’s Histopathological Images Using Transfer Learning

Current cancer diagnosis procedure requires expert knowledge and is time-consuming, which raises the need to build an accurate diagnosis support system for lymphoma identification and classification. Many studies have shown promising results using Machine Learning and, recently, Deep Learning to detect malignancy in cancer cells. However, the diversity and complexity of the morphological structure of lymphoma make it a challenging classification problem. In literature, many attempts were made to classify up to four simple types of lymphoma. This paper presents an approach using a reliable model capable of diagnosing seven different categories of rare and aggressive lymphoma. These Lymphoma types are Classical Hodgkin Lymphoma, Nodular Lymphoma Predominant, Burkitt Lymphoma, Follicular Lymphoma, Mantle Lymphoma, Large B-Cell Lymphoma, and T-Cell Lymphoma. Our proposed approach uses Residual Neural Networks, ResNet50, with a Transfer Learning for lymphoma’s detection and classification. The model used results are validated according to the performance evaluation metrics: Accuracy, precision, recall, F-score, and kappa score for the seven multi-classes. Our algorithms are tested, and the results are validated on 323 images of 224 × 224 pixels resolution. The results are promising and show that our used model can classify and predict the correct lymphoma subtype with an accuracy of 91.6%.