Search Results (3,329)

Campylobacter spp. constitute a group of microaerophilic bacteria that includes strains that are aerotolerant and capable of surviving in aerobic conditions. Recent studies have shown that aerotolerant strains are highly prevalent in meats, animals, and clinical settings. Changes in growth media and other environmental conditions can affect the aerotolerance of Campylobacter strains and must be considered when studying their aerotolerance in vitro. Polymicrobial interactions and biofilms also play a significant role in the ability of Campylobacter to survive oxygen exposure. Continuous subculturing may foster aerotolerance, and studies have demonstrated a positive correlation between aerotolerance and virulence and between aerotolerance and the ability to survive stressful environmental conditions. Various mechanisms and genetic origins for aerotolerance have been proposed; however, most of the potential genes involved in aerotolerance require further investigation, and many candidate genes remain unidentified. Research is also needed to investigate if there are any clinical implications for Campylobacter aerotolerance. Understanding the aerotolerance of Campylobacter remains an important target for further research, and it will be an important step towards identifying potential targets for intervention against this clinically important food-borne pathogen. Full article

Remote sensing image change detection is crucial for urban planning, environmental monitoring, and disaster assessment, as it identifies temporal variations of specific targets, such as surface buildings, by analyzing differences between images from different time periods. Current research faces challenges, including the accurate extraction of change features and the handling of complex and varied image contexts. To address these issues, this study proposes an innovative model named the Segment Anything Model-UNet Change Detection Model (SCDM), which incorporates the proposed center expansion and reduction method (CERM), Segment Anything Model (SAM), UNet, and fine-grained loss function. The global feature map of the environment is extracted, the difference measurement features are extracted, and then the global feature map and the difference measurement features are fused. Finally, a global decoder is constructed to predict the changes of the same region in different periods. Detailed ablation experiments and comparative experiments are conducted on the WHU-CD and LEVIR-CD public datasets to evaluate the performance of the proposed method. At the same time, validation on more complex DTX datasets for scenarios is supplemented. The experimental results demonstrate that compared to traditional fixed-size partitioning methods, the CERM proposed in this study significantly improves the accuracy of SOTA models, including ChangeFormer, ChangerEx, Tiny-CD, BIT, DTCDSCN, and STANet. Additionally, compared with other methods, the SCDM demonstrates superior performance and generalization, showcasing its effectiveness in overcoming the limitations of existing methods. Full article

In recent years, the High-Dynamic-Range (HDR) image has gained widespread popularity across various domains, such as the security, multimedia, and biomedical fields, owing to its ability to deliver an authentic visual experience. However, the extensive dynamic range and rich detail in HDR images present challenges in assessing their quality. Therefore, current efforts involve constructing subjective databases and proposing objective quality assessment metrics to achieve an efficient HDR Image Quality Assessment (IQA). Recognizing the absence of a systematic overview of these approaches, this paper provides a comprehensive survey of both subjective and objective HDR IQA methods. Specifically, we review 7 subjective HDR IQA databases and 12 objective HDR IQA metrics. In addition, we conduct a statistical analysis of 9 IQA algorithms, incorporating 3 perceptual mapping functions. Our findings highlight two main areas for improvement. Firstly, the size and diversity of HDR IQA subjective databases should be significantly increased, encompassing a broader range of distortion types. Secondly, objective quality assessment algorithms need to identify more generalizable perceptual mapping approaches and feature extraction methods to enhance their robustness and applicability. Furthermore, this paper aims to serve as a valuable resource for researchers by discussing the limitations of current methodologies and potential research directions in the future. Full article

Pine wilt disease (PWD) is a severe forest disease caused by the invasion of pine wood nematode (Bursaphelenchus xylophilus), which has caused significant damage to China’s forestry resources due to its short disease cycle and strong infectious ability. Benefiting from the development of unmanned aerial vehicle (UAV)-based remote sensing technology, the use of UAV images for the detection of PWD-infected trees has become one of the mainstream methods. However, current UAV-based detection studies mostly focus on multispectral and hyperspectral images, and few studies have focused on using red–green–blue (RGB) images for detection. This study used UAV-based RGB images to extract feature information using different color space models and then utilized semantic segmentation techniques in deep learning to detect individual PWD-infected trees. The results showed that: (1) The U-Net model realized the optimal image segmentation and achieved the highest classification accuracy with F1-score, recall, and Intersection over Union (IoU) of 0.9586, 0.9553, and 0.9221, followed by the DeepLabv3+ model and the feature pyramid networks (FPN) model. (2) The RGBHSV feature scheme outperformed both the RGB feature scheme and the hue saturation value (HSV) feature scheme, which were unrelated to the choice of the semantic segmentation techniques. (3) The semantic segmentation techniques in deep-learning models achieved superior model performance compared with traditional machine-learning methods, with the U-Net model obtaining 4.81% higher classification accuracy compared with the random forest model. (4) Compared to traditional semantic segmentation models, the newly proposed segment anything model (SAM) performed poorly in identifying pine wood nematode disease. Its success rate is 0.1533 lower than that of the U-Net model when using the RGB feature scheme and 0.2373 lower when using the HSV feature scheme. The results showed that the U-Net model using the RGBHSV feature scheme performed best in detecting individual PWD-infected trees, indicating that the proposed method using semantic segmentation technique and UAV-based RGB images to detect individual PWD-infected trees is feasible. The proposed method not only provides a cost-effective solution for timely monitoring forest health but also provides a precise means to conduct remote sensing image classification tasks. Full article

Scholars have connected Calvin’s idea of the imago Dei with the modern concept of inherent human dignity. Others have emphasized how Calvin tried to use the image of God to promote the significance of human dignity without connecting it with political theories. These arguments may be justifiable to some extent. However, my concern is that these scholars focus on the image of God in others rather than on how it transforms individuals who live out its renewal. For Calvin, the restoration of the image of God by the Holy Spirit aimed at enabling a holy and righteous life. In his thought, this life was marked by visible excellence, reflected in social virtues expressed toward others. Based on these insights, I show that, according to Calvin, treating others with dignity, honor, and love is not only made possible by recognizing them as bearers of God’s image but is primarily driven by the renewal of God’s image within those who display these actions and attitudes through the work of the Spirit. This fresh perspective on Calvin’s doctrine of the image of God and human dignity can be presented by analyzing Calvin’s pneumatological account of the imago Dei. Full article

Background: The extent of the SARS-CoV-2 short-term evolution under Remdesivir (RDV) exposure and whether it varies across different upper respiratory compartments are not fully understood. Methods: Patients hospitalized for COVID-19, with or without RDV therapy, were enrolled and completed up to three visits, in which they provided specimens from four respiratory compartments. Near full-length genome SARS-CoV-2 sequences were obtained from viral RNA, standard lineage and variant assignments were performed, and viral mutations in the RNA-dependent RNA polymerase (RdRp) region—the RDV target gene—were detected and compared between participants with and without RDV, across the four compartments, within participants across visits, and versus a larger sequence dataset. The statistical analysis used a generalized linear mixed-effects model. Results: A total of 139 sequences were obtained from 37 out of the 44 (84%) enrolled participants. The genotyping success varied across respiratory compartments, which ranged from 42% with oropharyngeal specimens to 67% with nasopharyngeal specimens and showed improvement with higher viral loads. No RdRp mutations known to be associated with RDV resistance were identified, and for 34 detected mutations at 32 amino acid positions that are not known as RDV-associated, there was no evidence of any associations with the RDV exposure, respiratory compartment, or time. At least 1 of these 34 mutations were detected in all participants, and some differed from the larger sequence dataset. Conclusions: This study highlighted the SARS-CoV-2 short-term genomic stability within hosts and across upper respiratory compartments, which suggests a lack of evolution of RDV resistance over time. This contributes to our understanding of SARS-CoV-2 genomic dynamics. Full article

Calcium is an important second messenger that is involved in almost all cellular processes. Disruptions in the regulation of intracellular Ca²⁺ levels ([Ca²⁺]_i) adversely impact normal physiological function and can contribute to various diseased conditions. STIM and Orai proteins play important roles in maintaining [Ca²⁺]_i through store-operated Ca²⁺ entry (SOCE), with STIM being the primary regulatory protein that governs the function of Orai channels. STIM1 and STIM2 are single-pass ER-transmembrane proteins with their N- and C-termini located in the ER lumen and cytoplasm, respectively. The N-terminal EF-SAM domain of STIMs senses [Ca²⁺]_ER changes, while the C-terminus mediates clustering in ER-PM junctions and gating of Orai1. ER-Ca²⁺ store depletion triggers activation of the STIM proteins, which involves their multimerization and clustering in ER-PM junctions, where they recruit and activate Orai1 channels. In this review, we will discuss the structure, organization, and function of EF-hand motifs and the SAM domain of STIM proteins in relation to those of other eukaryotic proteins. Full article

Dietary analysis of herbivorous insects relies on successfully eliminating surface contamination. If this cannot be performed reliably, then it will not be possible to differentiate between plants that the insect is feeding on and plants the insect has been in contact with, either directly or via pollen. Methods in the literature often use bleach and alcohol washes to remove contamination. We perform a controlled metabarcoding baseline study on a herbivorous, xylem-feeding insect, the Meadow Spittlebug (Philaenus spumarius), using Oxford Nanopore Technologies (ONT) sequencing, and identify possible contamination that persists after washes. Despite the reported success of methods in the literature, we find that contamination is still present, leading to possible false-positive results. We hypothesise that pollen is the main source of contamination, its robust nature making it difficult to remove, and conduct a further three experiments with the goal of removing pollen from the surface of Philaenus spumarius. This study investigates the effectiveness of robust bleach/Tween/alcohol washes, sterile gut excision (including combined with Distel application), and ultraviolet light as alternative sterilisation approaches. Overall, our findings indicate that we are unable to remove surface contamination and still detect signals that may originate in the gut. In no experiment did we unequivocally detect plant DNA that originated in the P. spumarius gut. Full article

The spatial distribution characteristics of iron ore components are important when measuring the difficulty of their beneficiation. Polarized light microscopy and scanning electron microscopy are traditional methods with some shortcomings, including complicated operation and low efficiency. Most of the laboratory hyperspectral imaging techniques that have emerged in recent years have been focused on the field of mineral resource exploration. In contrast, the mineral distribution and tectonic characteristics of iron ores have been relatively poorly studied in the field of beneficiation. To address the issue, 11 experimental samples of banded iron formation (BIF)-hosted iron ores were selected and tested using an imaging spectrometer. Then, based on the differences in spectral characteristic of the three main components (quartz, hematite, and magnetite) in the samples, the identification model of the spatial distribution of the iron ore components was established using the normalized spectral amplitude index (NSAI) and spectral angle mapper (SAM). The NSAI and SAM identify minerals based on spectral amplitude features and spectral morphological features of the sample, respectively. The spatial distribution of different minerals in the samples was tested using the model, and the test results demonstrated that the spatial distribution of the three components is consistent with the banded tectonic character of the sample. Upon comparison with the chemical test results, the mean absolute errors (MAE) of the model for quartz, hematite, and magnetite in the samples were 2.03%, 1.34%, and 1.55%, respectively, and the root mean square errors (RMSE) were 2.72%, 2.08%, and 1.85%, respectively, with the exception of one martite sample that reached an MAE of 10.17%. Therefore, the model demonstrates a high degree of accuracy. The research provides a new method to test the spatial distribution of iron ore components. Full article

Wearable technologies represent a significant advancement in facilitating communication between humans and machines. Powered by artificial intelligence (AI), human gestures detected by wearable sensors can provide people with seamless interaction with physical, digital, and mixed environments. In this paper, the foundations of a gesture-recognition framework for the teleoperation of infrared consumer electronics are established. This framework is based on force myography data of the upper forearm, acquired from a prototype novel soft pressure-based force myography (pFMG) armband. Here, the sub-processes of the framework are detailed, including the acquisition of infrared and force myography data; pre-processing; feature construction/selection; classifier selection; post-processing; and interfacing/actuation. The gesture recognition system is evaluated using 12 subjects’ force myography data obtained whilst performing five classes of gestures. Our results demonstrate an inter-session and inter-trial gesture average recognition accuracy of approximately 92.2% and 88.9%, respectively. The gesture recognition framework was successfully able to teleoperate several infrared consumer electronics as a wearable, safe and affordable human–machine interface system. The contribution of this study centres around proposing and demonstrating a user-centred design methodology to allow direct human–machine interaction and interface for applications where humans and devices are in the same loop or coexist, as typified between users and infrared-communicating devices in this study. Full article

Salivary gland cancers (SGC) are rare tumours with limited availability of systemic therapies. Some SGC subtypes overexpress HER2, and this represents a potential therapeutic target, but the evidence base is limited. This study sought to analyse real-world data on the efficacy of HER2-directed therapies in SGC. This is a retrospective observational study using anonymised data from commercial compassionate-use access registrations and a privately funded pharmacy prescribing register. Treatment duration was defined as the time from drug initiation to treatment discontinuation. Kaplan–Meier analysis of treatment duration was performed using R for Windows (v4.3.2). A case report is also provided of an exceptional responder. Eighteen patients were identified who received HER2-directed therapies for HER2-positive recurrent/metastatic SGC, and complete data on treatment duration was available for 15/18. Histology was salivary duct carcinoma in 13/18 patients, adenocarcinoma NOS in 4/18, and carcinoma ex pleomorphic adenoma in 1/18. The median treatment duration was 8.3 months (95% CI: 6.41-not reached), and the range was 1.0–47.0 months. Choice of HER2-directed therapy varied, with ado-trastuzumab emtasine being the most common (9/18). At the time of analysis, HER2-directed therapy was ongoing for 9/15, discontinued due to disease progression for 4/15, discontinued due to toxicity for 1/15, and 1/15 was discontinued for an unspecified reason. An exceptional responder experienced a complete response with a treatment duration of 47.0 months. These real-world data are comparable to the median PFS observed with HER2-directed therapies in phase II trials and support the use of HER2-directed therapies in this group. Full article

Learning to write the Arabic alphabet is crucial for Arab children’s cognitive development, enhancing their memory and retention skills. However, the lack of Arabic language educational applications may hamper the effectiveness of their learning experience. To bridge this gap, SamAbjd was developed, an interactive web application that leverages deep learning techniques, including air-writing recognition, to teach Arabic letters. SamAbjd was tailored to user needs through extensive surveys conducted with mothers and teachers, and a comprehensive literature review was performed to identify effective teaching methods and models. The development process involved gathering data from three publicly available datasets, culminating in a collection of 31,349 annotated images of handwritten Arabic letters. To enhance the dataset’s quality, data preprocessing techniques were applied, such as image denoising, grayscale conversion, and data augmentation. Two models were experimented with using a convolution neural network (CNN) and Visual Geometry Group (VGG16) to evaluate their effectiveness in recognizing air-written Arabic characters. Among the CNN models tested, the standout performer was a seven-layer model without dropout, which achieved a high testing accuracy of 96.40%. This model also demonstrated impressive precision and F1-score, both around 96.44% and 96.43%, respectively, indicating successful fitting without overfitting. The web application, built using Flask and PyCharm, offers a robust and user-friendly interface. By incorporating deep learning techniques and user feedback, the web application meets educational needs effectively. Full article

The purpose of the Social Accounting Matrix (SAM) is to improve the quality of the database for modelling, including, but not limited to, policy analysis, multiplier analysis, price analysis, and Computable General Equilibrium. This article contributes to constructing the 2017 national SAM for South Africa, incorporating regional accounts. Only in Limpopo Province of South Africa are agricultural industries, labour, and households captured at the district level, while agricultural industry, labour, and household accounts in other provinces remain unchanged. The main data sources for constructing a SAM are found from different sources, such as Supply and Use Tables, National Accounts, Census of Commercial Agriculture, Quarterly Labour Force Survey, South Africa Revenue Service, Global Insight (regional explorer), and South Africa Reserve Bank. The dataset recorded that land returns for irrigation agriculture were highest (18.2%) in the Northern Cape Province of South Africa compared to other provinces, whereas the Free State Province of South Africa rainfed agriculture had the largest shares (22%) for payment to land. Regarding intermediate inputs, rainfed agriculture in the Western Cape, Free State, and Kwazulu-Natal Provinces paid approximately 0.4% for using intermediate inputs. In terms of the districts, land returns for irrigation were highest in the Vhembe district of Limpopo Province of South Africa with 0.3%. Despite Mopani district of Limpopo Province of South Africa having the lowest land returns for irrigation agriculture, it has the highest share (1.6%) of payment to land from rainfed agriculture. The manufacturing and community service sectors had a trade deficit, whereas other sectors experienced a trade surplus. The main challenges found in developing a SAM are scarcity of data to attain the information needed for disaggregation for the sub-matrices and insufficient information from different data sources for estimating missing information to ensure the row and column totals of the SAM are consistent and complete. Full article

This study investigated the effectiveness of a five-session virtual professional development program designed to increase the knowledge of inclusive education practices among school-based professionals from 26 schools on a topic of their choice. Participants, including administrators, general and special education teachers, child study team members (i.e., school psychologists, school counselors, and social workers), and others, demonstrated statistically significant increases in content knowledge on Accommodations and Modifications, Universal Design for Learning (UDL), Co-teaching, and Differentiation, as measured by pre- and post-assessments. Utilizing multilevel modeling and dependent samples t-tests, the results confirm the usefulness of virtual professional development in building knowledge of inclusive education practices. The findings provide empirical support for virtual training and offer insights into best practices for delivering professional development in inclusive education, suggesting future research should investigate the long-term impacts on classroom practices and student outcomes. Full article

Accurate cultivated land parcel data are an essential analytical unit for further agricultural monitoring, yield estimation, and precision agriculture management. However, the high degree of landscape fragmentation and the irregular shapes of cultivated land parcels, influenced by topography and human activities, limit the effectiveness of parcel extraction. The visual semantic segmentation model based on the Segment Anything Model (SAM) provides opportunities for extracting multi-form cultivated land parcels from high-resolution images; however, the performance of the SAM in extracting cultivated land parcels requires further exploration. To address the difficulty in obtaining parcel extraction that closely matches the true boundaries of complex large-area cultivated land parcels, this study used segmentation patches with cultivated land boundary information obtained from SAM unsupervised segmentation as constraints, which were then incorporated into the subsequent multi-scale segmentation. A combined method of SAM unsupervised segmentation and multi-scale segmentation was proposed, and it was evaluated in different cultivated land scenarios. In plain areas, the precision, recall, and IoU for cultivated land parcel extraction improved by 6.57%, 10.28%, and 9.82%, respectively, compared to basic SAM extraction, confirming the effectiveness of the proposed method. In comparison to basic SAM unsupervised segmentation and point-prompt SAM conditional segmentation, the SAM unsupervised segmentation combined with multi-scale segmentation achieved considerable improvements in extracting complex cultivated land parcels. This study confirms that, under zero-shot and unsupervised conditions, the SAM unsupervised segmentation combined with the multi-scale segmentation method demonstrates strong cross-region and cross-data source transferability and effectiveness for extracting complex cultivated land parcels across large areas. Full article