Global prevalence of Plasmodium infection in wild birds: A systematic review and meta-analysis.

Avian malaria is a vector-borne parasitic disease caused by Plasmodium infection transmitted to birds by mosquitoes. The aim of this systematic review was to analyze the global prevalence of malaria and risk factors associated with infection in wild birds. A systematic search of the databases CNKI, WanFang, VIP, PubMed, and ScienceDirect was performed from database inception to 24 February 2023. The search identified 3181 retrieved articles, of which 52 articles met predetermined inclusion criteria. Meta-analysis was performed using the random-effects model. The estimated pooled global prevalence of Plasmodium infection in wild birds was 16%. Sub-group analysis showed that the highest prevalence was associated with adult birds, migrant birds, North America, tropical rainforest climate, birds captured by mist nets, detection of infection by microscopy, medium quality studies, and studies published after 2016. Our study highlights the need for more understanding of Plasmodium prevalence in wild birds and identifying risk factors associated with infection to inform future infection control measures.

Identification of CD161 expression as a novel prognostic biomarker in breast cancer correlated with immune infiltration.

Background: CD161 has been identified as a prognostic biomarker in many neoplasms, but its role in breast cancer (BC) has not been fully explained. We aimed to investigate the molecular mechanism and prognostic value of CD161 in BC. Methods: CD161 expression profile was extracted from TIMER, Oncomine, UALCAN databases, and verified by the Gene Expression Omnibus (GEO) database and quantitative real-time polymerase chain reaction (qRT-PCR). The prognostic value of CD161 was assessed via GEPIA, Kaplan-Meier plotter and PrognoScan databases. The Cox regression and nomogram analyses were conducted to further validate the association between CD161 expression and survival. Gene set enrichment analysis (GSEA), Gene Ontology (GO) analysis, and KEGG pathway enrichment analysis were performed to probe the tumor-associated annotations of CD161. CIBERSORT and ssGSEA were employed to investigate the correlation between CD161 expression and immune cell infiltration in BC, and the result was verified by TIMER and TISIDB. Results: Multiple BC cohorts showed that CD161 expression was decreased in BC, and a high CD161 expression was associated with a preferable prognosis. Therefore, we identified the combined model including CD161, age and PR status to predict the survival (C index = 0.78) of BC patients. Functional enrichment analysis indicated that CD161 and its co-expressed genes were closely related to several cancerous and immune signaling pathways, suggesting its involvement in immune response during cancer development. Moreover, immune infiltration analysis revealed that CD161 expression was correlated with immune infiltration. Conclusion: Collectively, our findings revealed that CD161 may serve as a potential biomarker for favorable prognosis and a promising immune therapeutic target in BC.

ADTIDO: Detecting the Tired Deck Officer with Fusion Feature Methods.

The incidence of maritime accidents can be significantly reduced by identifying the deck officer's fatigue levels. The development of car driver fatigue detectors has employing electroencephalogram (EEG)-based technologies in recent years and made it possible to swiftly and accurately determine the level of a driver's fatigue. However, individual variability and the sensitivity of EEG signals reduce the detection precision. Recently, another type of video-based technology for detecting driver fatigue by recording changes in the drivers' eye characteristics has also been explored. In order to improve the classification performance of EEG-based approaches, this paper introduces the ADTIDO (Automatic Detect the TIred Deck Officers) algorithm, an EEG-based classification method of deck officers' fatigue level, which combines a video-based approach to record the officer's eye closure time for each time window. This paper uses a Discrete Wavelet Transformer (DWT) and decomposes the EEG signals into six sub-signals, from which we extract various EEG-based features, e.g., MAV, SD, and RMS. Unlike the traditional video-based method of calculating the Eyelid Closure Degree (ECD), this paper then obtains the ECD values from the EEG signals. The ECD-EEG fusion features are then created and used as the inputs for a classifier by combining the ECD and EEG feature sets. In addition, the present work develops the definition of "fatigue" at the individual level based on the real-time operational reaction time of the deck officer. To verify the efficacy of this research, the authors conducted their trials by using the EEG signals gathered from 21 subjects. It was found that Bidirectional Gated Recurrent Unit (Bi-GRU) networks outperform other classifiers, reaching a classification accuracy of 90.19 percent, 1.89 percent greater than that of only using EEG features as inputs. By combining the ADTIDO channel findings, the classification accuracy of deck officers' fatigue levels finally reaches 95.74 percent.

An intelligent identification system combining image and DNA sequence methods for fruit flies with economic importance (Diptera: Tephritidae).

BACKGROUND: Images and DNA sequences are two important methods for identifying fruit fly species. In addition, the identification of insect species complexes is highly problematic when attempting to utilize automatic identification methods in an actual environment. We integrated the image and DNA sequence identification methods into a single system for the first time and explored an open interactive multi-image comparison function for solving the problem of species complexes. The Automated Fruit Fly Identification System 1.0 (AFIS1.0) was updated to AFIS2.0 by employing different models and developing the system under a novel framework. RESULTS: AFIS2.0 was developed using 83 species belonging to eight genera in the Tephritidae, which includes most pests of this family. The system applies the Mask Region Convolutional Neural Network (Mask R-CNN) and discriminative deep metric learning (AlexNet based) methods for image identification, integrates Blast+ for DNA sequence comparison and specific weighting for the fusion result. At the species level, the best classification success rate for wing images (as the Top 1 species in the species list of outcomes) reached 90%, and the average classification success rate for wing, thorax, and abdomen images (as the Top 5 species in the species list of outcomes) was 94%. CONCLUSION: AFIS2.0 is more accurate and convenient than AFIS1.0 and can be beneficial for users with or without specific expertise regarding Tephritidae. It also provides a more compact and fluent computer system for fruit fly identification, and can be easily applied in practice. © 2021 Society of Chemical Industry.

Construction, implementation and testing of an image identification system using computer vision methods for fruit flies with economic importance (Diptera: Tephritidae).

BACKGROUND: Many species of Tephritidae are damaging to fruit, which might negatively impact international fruit trade. Automatic or semi-automatic identification of fruit flies are greatly needed for diagnosing causes of damage and quarantine protocols for economically relevant insects. RESULTS: A fruit fly image identification system named AFIS1.0 has been developed using 74 species belonging to six genera, which include the majority of pests in the Tephritidae. The system combines automated image identification and manual verification, balancing operability and accuracy. AFIS1.0 integrates image analysis and expert system into a content-based image retrieval framework. In the the automatic identification module, AFIS1.0 gives candidate identification results. Afterwards users can do manual selection based on comparing unidentified images with a subset of images corresponding to the automatic identification result. The system uses Gabor surface features in automated identification and yielded an overall classification success rate of 87% to the species level by Independent Multi-part Image Automatic Identification Test. CONCLUSION: The system is useful for users with or without specific expertise on Tephritidae in the task of rapid and effective identification of fruit flies. It makes the application of computer vision technology to fruit fly recognition much closer to production level. © 2016 Society of Chemical Industry.

Learning locality preserving graph from data.

Machine learning based on graph representation, or manifold learning, has attracted great interest in recent years. As the discrete approximation of data manifold, the graph plays a crucial role in these kinds of learning approaches. In this paper, we propose a novel learning method for graph construction, which is distinct from previous methods in that it solves an optimization problem with the aim of directly preserving the local information of the original data set. We show that the proposed objective has close connections with the popular Laplacian Eigenmap problem, and is hence well justified. The optimization turns out to be a quadratic programming problem with n(n-1)/2 variables (n is the number of data points). Exploiting the sparsity of the graph, we further propose a more efficient cutting plane algorithm to solve the problem, making the method better scalable in practice. In the context of clustering and semi-supervised learning, we demonstrated the advantages of our proposed method by experiments.

A hybrid approach to detect and localize texts in natural scene images.

Text detection and localization in natural scene images is important for content-based image analysis. This problem is challenging due to the complex background, the non-uniform illumination, the variations of text font, size and line orientation. In this paper, we present a hybrid approach to robustly detect and localize texts in natural scene images. A text region detector is designed to estimate the text existing confidence and scale information in image pyramid, which help segment candidate text components by local binarization. To efficiently filter out the non-text components, a conditional random field (CRF) model considering unary component properties and binary contextual component relationships with supervised parameter learning is proposed. Finally, text components are grouped into text lines/words with a learning-based energy minimization method. Since all the three stages are learning-based, there are very few parameters requiring manual tuning. Experimental results evaluated on the ICDAR 2005 competition dataset show that our approach yields higher precision and recall performance compared with state-of-the-art methods. We also evaluated our approach on a multilingual image dataset with promising results.

Learning multiview face subspaces and facial pose estimation using independent component analysis.

An independent component analysis (ICA) based approach is presented for learning view-specific subspace representations of the face object from multiview face examples. ICA, its variants, namely independent subspace analysis (ISA) and topographic independent component analysis (TICA), take into account higher order statistics needed for object view characterization. In contrast, principal component analysis (PCA), which de-correlates the second order moments, can hardly reveal good features for characterizing different views, when the training data comprises a mixture of multiview examples and the learning is done in an unsupervised way with view-unlabeled data. We demonstrate that ICA, TICA, and ISA are able to learn view-specific basis components unsupervisedly from the mixture data. We investigate results learned by ISA in an unsupervised way closely and reveal some surprising findings and thereby explain underlying reasons for the emergent formation of view subspaces. Extensive experimental results are presented.