Search Results (1,167)

Background: The reported data on African universal neonatal hearing screening (UNHS) practices tend to be quite scarce, despite the developments in hearing screening the last two decades. The objective of this systematic review was (a) to identify the most recent (in a 20-year span) literature information about NHS/UNHS programs in Africa and (b) to provide data on the procedures used to assess the population, the intervention policies, and on the estimated prevalence of congenital hearing loss with an emphasis on bilateral hearing loss cases. Methods: Queries were conducted via the PubMed, Scopus, and Google Scholar databases for the time window of 2004–2024. The mesh terms used were “OAE”, “universal neonatal hearing screening”, “congenital hearing loss”, “well babies”, and “Africa”. Only research articles and review papers were considered as good candidates. The standard English language filter was not used, to identify information from non-English-speaking scientific communities and groups. Results: Data from 15 papers were considered, reflecting the neonatal hearing practices of nine African states. No country-wide NHS programs were reported. The various screening realities are implemented within big urban centers, leaving the residents of rural areas unassisted. For the latter, proposals based on tele-medicine protocols have been suggested. The data on HL prevalence are also incomplete, but the available data refer to rates from 3 to 360 subjects per 1000. These data cannot be taken at face value but within the small sample size context in which they were acquired. Regarding the causes of HL, very few data have been reported; consanguinity is the most attributed factor, at least in the Sub-Saharan African states. For the majority of the programs, no data were reported on hearing loss prevalence/incidence or on any strategies to restore hearing. Conclusions: The information on the African neonatal hearing screening are quite scarce, and it is an urgent need to convince audiologists from the African localized programs to publish their hearing screening data. Full article

Oil palms (Elaeis guineensis Jacq.) are increasingly cultivated throughout the humid tropics and are reported to have high transpiration rates. A potential contribution of stem water storage to transpiration has been discussed in previous studies. We assessed the water-use characteristics of oil palms at different horizontal and vertical positions in the plant by using three sap flux techniques, i.e., thermal dissipation probes, the heat ratio method and heat field deformation sensors. In a radial profile of the stem, sap flux densities were low at the outer margin, increased to 2.5 cm under the bark and remained relatively high to the innermost measured depth at 7.5 cm. In a vertical profile of the stem and with further sensors in leaf petioles, we found only small time lags in sap flux densities. Time lags along the flow path are often used for analyzing the contribution of water storage to transpiration. Thus, the small observed time differences in our study would leave only little room for the contribution of water storage to transpiration. However, water storage might still contribute to transpiration in ways that are not detected by time lag analysis. Such mechanisms may be explored in future studies. Full article

This study proposes a method for estimating the spectral images of fluorescence spectral distributions emitted from plant grains and leaves without using a spectrometer. We construct two types of multiband imaging systems with six channels, using ordinary off-the-shelf cameras and a UV light. A mobile phone camera is used to detect the fluorescence emission in the blue wavelength region of rice grains. For plant leaves, a small monochrome camera is used with additional optical filters to detect chlorophyll fluorescence in the red-to-far-red wavelength region. A ridge regression approach is used to obtain a reliable estimate of the spectral distribution of the fluorescence emission at each pixel point from the acquired image data. The spectral distributions can be estimated by optimally selecting the ridge parameter without statistically analyzing the fluorescence spectra. An algorithm for optimal parameter selection is developed using a cross-validation technique. In experiments using real rice grains and green leaves, the estimated fluorescence emission spectral distributions by the proposed method are compared to the direct measurements obtained with a spectroradiometer and the estimates obtained using the minimum norm estimation method. The estimated images of fluorescence emissions are presented for rice grains and green leaves. The reliability of the proposed estimation method is demonstrated. Full article

The bacterium Xanthomonas campestris poses a significant threat to global agriculture due to its ability to infect leaves, fruits, and stems under various climatic conditions. Its rapid spread across large crop areas results in economic losses, compromises agricultural productivity, increases management costs, and threatens food security, especially in small-scale agricultural systems. To address this issue, this study developed a model that combines fuzzy logic and neural networks, optimized with intelligent algorithms, to detect symptoms of this foliar disease in 15 essential crop species under different environmental conditions using images. For this purpose, Sugeno-type fuzzy inference systems and adaptive neuro-fuzzy inference systems (ANFIS) were employed, configured with rules and clustering methods designed to address cases where diagnostic uncertainty arises due to the imprecision of different agricultural scenarios. The model achieved an accuracy of 93.81%, demonstrating robustness against variations in lighting, shadows, and capture angles, and proving effective in identifying patterns associated with the disease at early stages, enabling rapid and reliable diagnoses. This advancement represents a significant contribution to the automated detection of plant diseases, providing an accessible tool that enhances agricultural productivity and promotes sustainable practices in crop care. Full article

Infrared (IR) small target detection is a crucial component of infrared imaging systems and is vital for applications in surveillance, security, and early warning systems. However, most existing algorithms for detecting small targets in infrared imagery encounter difficulties in achieving both high accuracy and speed, particularly in complex scenes. Additionally, infrared image sequences frequently exhibit gradual background changes as well as sudden alterations, which further complicates the task of detecting small targets. To address these issues, a dual-region search method (DRSM) is proposed and combined with multi-directional filtering, min-sum fusion, and clustering techniques, forming an infrared small moving target detection method in complex scenes. First, a multi-directional filter bank is proposed and it causes the original infrared image sequence to retain only point-like features after the filtering. Then, several consecutive filtered feature maps are superimposed into one, where the moving target will leave a trajectory due to its motion characteristics. Finally, based on the trajectory, a dual-region search strategy is employed to pinpoint the exact location of the target. The experimental outcomes show that, compared to alternative algorithms, the proposed approach outperforms others in terms of detection accuracy and speed, particularly in diverse real-world complex scenarios. Full article

Thioredoxin z (TRX z) plays a significant role in chloroplast development by regulating the transcription of chloroplast genes. In this study, we identified a pentatricopeptide repeat (PPR) protein, rice albino seedling-lethal (RAS), that interacts with OsTRX z. This interaction was initially discovered by using a yeast two-hybrid (Y2H) screening technique and was further validated through Y2H and bimolecular fluorescence complementation (BiFC) experiments. RAS contains 16 PPR motifs and features a small MutS-related (SMR) domain at its C-terminus. CRISPR/Cas9-generated ras mutants exhibited an albino seedling-lethal phenotype characterized by abnormal chloroplast structures and a significantly reduced chlorophyll content. RAS localizes to the chloroplast and is predominantly expressed in young leaves. Mutations in RAS affect RNA editing at the rpl2, rps14, and ndhA sites, as well as RNA splicing at the rpl2, atpF, and ndhA transcripts within the chloroplast. Furthermore, the expression levels of genes associated with chloroplast formation are altered in the ras mutant. Both OsTRX z and RAS were found to interact with chloroplast signal recognition particle (cpSRP) proteins, indicating that their proper localization within the chloroplast may be dependent on the SRP pathway. Collectively, our findings highlight the critical role of RAS in chloroplast development, as it is involved in RNA processing and the regulation of chloroplast gene expression. Full article

Loquat leaves, flowers, and other organs contain abundant antioxidant substances, which have wide applications in medicine, health, and food industries. This study aims to provide theoretical guidance for loquat hybrid parent and combination selection and a basis for high-quality loquat strain screening and development. For comprehensive antioxidant profiling, we used “Ninghaibai” and “Oobusa” loquat and their F₁ generation as experimental materials to determine the total phenol, flavonoid, DPPH, ABTS, and FRAP content in the leaves and flowers of 56 strains. Five traits, including total phenols, flavonoids, DPPH, ABTS, and FRAP, were widely separated and normally distributed in the flowers of 56 F₁ loquat strains, exhibiting the genetic basis of these quantitative traits. However, these traits displayed widely separated and slightly skewed distribution in the leaves of the F₁ generation. The total phenols, flavonoids, DPPH, and FRAP showed a trend of small inheritance in the leaves. However, the ABTS showed a trend of medium and high inheritance in leaves and flowers, respectively. Through cluster and principal component analyses, a comprehensive antioxidant activity evaluation was conducted. Ten strains with comprehensive scores greater than 1 for antioxidant activity in leaves and flowers were selected. Among them, the top three strains with high antioxidant capacity were ND107, “Oobusa”, and ND128. These results suggest that hybrid breeding guided by the genetic characteristics of each trait can improve the possibility of cultivating new varieties with high antioxidant activity. Full article

Repeated expeditions across various regions of Georgia in the early 2000s led to the identification of 434 wild grapevine individuals (Vitis vinifera L. subsp. sylvestris (C.C. Gmel.) Hegi) across 127 different sites, with 45% of these sites containing only a single vine and only 7% more than 9 vines. A total of 70 accessions were propagated in a germplasm collection, 41 of them were descripted from the ampelographic point of view and 32 from the phenological one. The geographical and ecological analysis confirmed that wild grapevines primarily grow in humid environments with warm and fully humid climates, often near rivers. They favor deep, fertile, and evolved soils, mainly alluvial and cinnamonic types (80%), with a marginal presence on strongly eroded soils. Their main natural vegetations are forests and open woodlands, with some individuals in the Southeast found in steppes. The altitudinal range spans from 0 to 1200 m, with 80% of vines distributed between 400 and 900 m. The phenological analysis revealed significant differences among the accessions but no difference among populations, with only a slight variation in bud-break timing, indicating a high level of synchronicity overall. Flowering timing proved to be the most uniform stage, suggesting minimal environmental pressure on genetic adaptation. The mature leaf morphology exhibited significant polymorphism, though leaves were generally three- or five-lobed, weak-wrinkling, and -blistering, with a low density of hairs. Bunch and berry morphology were more uniform. Bunches were consistently very small, cylindrical, and never dense or winged. Berries were also very small, mostly globular, always blue-black in color, and non-aromatic. A striking feature was the frequency of red flesh coloration, which ranged from weak to strong, with uncolored flesh being rare. The Georgian population of wild grapevines was found to be fragmented, often consisting of scattered single individuals or small groups. Therefore, we believe it is urgent for Georgia to implement specific protection measures to preserve this vital genetic resource. Full article

Purpose: Naegleria fowleri is the main etiologic agent implicated in primary amoebic meningoencephalitis (PAM). It is also known as the brain-eating amoeba because of the severe brain inflammation following infection, with a survival rate of about 5%. This review aims to identify Naegleria fowleri infections and evaluate patients’ progression. This literature review emphasizes the importance of rapid diagnosis and treatment of infected patients because only prompt initiation of appropriate therapy can lead to medical success. Compared to other articles of this kind, this one analyzes a large number of reported cases and all the factors that affected patients’ evolution. Materials and methods: Two independent reviewers used “Naegleria fowleri” and “case report” as keywords in the Clarivate Analytics—Web of Science literature review, obtaining 163 results. The first evaluation step was article title analysis. The two reviewers determined if the title was relevant to the topic. The first stage removed 34 articles, leaving 129 for the second stage. Full-text articles were evaluated after reading the abstract, and 77 were eliminated. This literature review concluded with 52 articles. Key findings: This review included 52 case report articles, 17 from the USA, eight from India, seven from China, four from Pakistan, two from the UK, and one each from Thailand, Korea, Japan, Italy, Iran, Norway, Turkey, Costa Rica, Zambia, Australia, Taiwan, and Venezuela, and Mexico. This study included 98 patients, with 17 women (17.4%) and 81 men (82.6%). The cases presented in this study show that waiting to start treatment until a diagnosis is confirmed can lead to rapid worsening and bad outcomes, especially since there is currently no drug that works very well as a treatment and the death rate is around 98%. Limitations: The lack of case presentation standardization may lead to incomplete case information in the review since the cases did not follow a writing protocol. The small number of global cases may also lead to misleading generalizations, especially about these patients’ treatment. Due to the small number of cases, there is no uniform sample of patients, making it difficult to determine the exact cause of infection. Full article

The domestication and breeding of wheat genotypes through the years has led to the loss in their genetic variation, making them more prone to different abiotic stresses. Boron (B) toxicity is one of the stresses decreasing the wheat cultivars’ yield in arid and semi-arid regions around the world. Wild wheat progenitors, such as Triticum urartu Thumanian ex Gandilyan, possess a broader gene pool that harbors several genes conferring tolerance to various biotic and abiotic stresses. Unfortunately, T. urartu is not well-explored at the molecular level for its tolerance towards B toxicity in soil. In this study, for the first time, we compared the transcriptomic changes in the leaves of a high B-tolerant T. urartu genotype, PI662222, grown in highly toxic B (10 mM B in the form of boric acid) with the ones grown in the control (3.1 μM B) treatment in hydroponic conditions. The obtained results suggest that several mechanisms are involved in regulating the response of the studied T. urartu genotype toward B toxicity. All the growth parameters of T. urartu genotype, including root–shoot length, root fresh weight, and root–shoot dry weight, were less affected by high boron (10 mM) as compared to the boron-tolerant bread wheat cultivar. With a significant differential expression of 654 genes, 441 and 213 genes of T. urartu genotype were down- and upregulated, respectively, in the PI662222 leaves in high B in comparison to the control treatment. While key upregulated genes included those encoding RNA polymerase beta subunit (chloroplast), ATP synthase subunit gamma, chloroplastic, 60S ribosomal protein, and RNA-binding protein 12-like, the main downregulated genes included those encoding photosystem II protein D, ribulose bisphosphate carboxylase small subunit, and peroxidase 2-like. Interestingly, both Gene Ontology enrichment and KEGG pathways emphasized the possible involvement of the genes related to the photosynthetic process and apparatus in the high B tolerance of the T. urartu genotype. The further functional characterization of the identified potential T. urartu genes will facilitate their utilization in crop improvement programs for B toxicity stress. Full article

Pepper production is a critical component of the global agricultural economy, with exports reaching a remarkable $6.9B in 2023. This underscores the crop’s importance as a major economic driver of export revenue for producing nations. Botrytis cinerea, the causative agent of gray mold, significantly impacts crops like fruits and vegetables, including peppers. Early detection of this pathogen is crucial for a reduction in fungicide reliance and economic loss prevention. Traditionally, visual inspection has been a primary method for detection. However, symptoms often appear after the pathogen has begun to spread. This study employs the Deep Learning algorithm YOLO for single-class segmentation on plant images to extract spatial details of pepper leaves. The dataset included hyperspectral images at discrete wavelengths (460 nm, 540 nm, 640 nm, 775 nm, and 875 nm) from derived vegetation indices (CVI, GNDVI, NDVI, NPCI, and PSRI) and from RGB. At an Intersection over Union with a 0.5 threshold, the Mean Average Precision (mAP50) achieved by the leaf-segmentation solution YOLOv11-Small was 86.4%. The extracted leaf segments were processed by multiple Transformer models, each yielding a descriptor. These descriptors were combined in ensemble and classified into three distinct classes using a K-nearest neighbor, a Long Short-Term Memory (LSTM), and a ResNet solution. The Transformer models that comprised the best ensemble classifier were as follows: the Swin-L (P:4 × 4–W:12 × 12), the ViT-L (P:16 × 16), the VOLO (D:5), and the XCIT-L (L:24–P:16 × 16), with the LSTM-based classification solution on the RGB, CVI, GNDVI, NDVI, and PSRI image sets. The classifier achieved an overall accuracy of 87.42% with an F1-Score of 81.13%. The per-class F1-Scores for the three classes were 85.25%, 66.67%, and 78.26%, respectively. Moreover, for B. cinerea detection during the initial as well as quiescent stages of infection prior to symptom development, qPCR-based methods (RT-qPCR) were used for quantification of in planta fungal biomass and integrated with the findings from the AI approach to offer a comprehensive strategy. The study demonstrates early and accurate detection of B. cinerea on pepper plants by combining segmentation techniques with Transformer model descriptors, ensembled for classification. This approach marks a significant step forward in the detection and management of crop diseases, highlighting the potential to integrate such methods into in situ systems like mobile apps or robots. Full article

In agricultural production, the nitrogen content of sugarcane is assessed with precision and the economy, which is crucial for balancing fertilizer application, reducing resource waste, and minimizing environmental pollution. As an important economic crop, the productivity of sugarcane is significantly influenced by various environmental factors, especially nitrogen supply. Traditional methods based on manually extracted image features are not only costly but are also limited in accuracy and generalization ability. To address these issues, a novel regression prediction model for estimating the nitrogen content of sugarcane, named SC-ResNeXt (Enhanced with Self-Attention, Spatial Attention, and Channel Attention for ResNeXt), has been proposed in this study. The Self-Attention (SA) mechanism and Convolutional Block Attention Module (CBAM) have been incorporated into the ResNeXt101 model to enhance the model’s focus on key image features and its information extraction capability. It was demonstrated that the SC-ResNeXt model achieved a test R² value of 93.49% in predicting the nitrogen content of sugarcane leaves. After introducing the SA and CBAM attention mechanisms, the prediction accuracy of the model improved by 4.02%. Compared with four classical deep learning algorithms, SC-ResNeXt exhibited superior regression prediction performance. This study utilized images captured by smartphones combined with automatic feature extraction and deep learning technologies, achieving precise and economical predictions of the nitrogen content in sugarcane compared to traditional laboratory chemical analysis methods. This approach offers an affordable technical solution for small farmers to optimize nitrogen management for sugarcane plants, potentially leading to yield improvements. Additionally, it supports the development of more intelligent farming practices by providing precise nitrogen content predictions. Full article

Coffee yield exhibits plant-level variability; however, due to operational issues, especially in smaller operations, the scouting and management of coffee yields are often hindered. Thus, a cell-size approach at the field level is proposed as a simple and efficient solution to overcome these constraints. This study aimed to present the feasibility of a cell-size approach to characterize spatio-temporal coffee production based on soil and plant attributes and yield (biennial effects) and to assess strategies for enhanced soil fertilization recommendations and economic results. The spatio-temporal study was conducted using a database composed of yield and soil and plant attributes from four harvest seasons of coffee plantation in the southeast region of Brazil. We used small plots as cells, where soil, leaf, and yield samples were taken, and the average value of each variable was assigned to each cell. The results indicated that macro- and micronutrient contents in the soil and leaves exhibited spatio-temporal heterogeneity between cells, suggesting that customized coffee tree management practices could be employed. The cell-size sampling strategy identified regions of varying yield over time and associated them with their biennial effect, enabling the identification of profitable areas to direct resource and input management in subsequent seasons. This approach optimized the recommendation of potassium and phosphate fertilizers on farms, demonstrating that localized management is feasible even with low spatial resolution. The cell-size approach proved to be adequate on two coffee farms and can be applied in scenarios with limited resources for high-density sampling, especially for small- and medium-sized farms. Full article

Background: Olive leaves are a rich source of polyphenols, predominantly secoiridoids, flavonoids, and simple phenols, which exhibit various biological properties. Extracts prepared from olive leaves are associated with hypoglycemic, hypotensive, diuretic, and antiseptic properties. Upon ingestion, a substantial fraction of these polyphenols reaches the colon where they undergo extensive metabolism by the gut microbiota. Host characteristics, like age, can influence the composition of the gut microbiome, potentially affecting the biotransformation of these compounds. Therefore, it can be hypothesised that differences in the gut microbiome between young and elderly individuals may impact the biotransformation rate and the type and amount of metabolites formed. Methods: An in vitro biotransformation model was used to mimic the conditions in the stomach, small intestine and colon of two age groups of healthy participants (20–30 years old, ≥65 years old), using oleuropein as a single compound and an olive leaf extract as test compounds. The bacterial composition and metabolite content were investigated. Results: The study revealed that, while the same metabolites were formed in both age groups, in the young age group, less metabolite formation was observed, likely due to a reduced viable cell count. Most biotransformation reactions took place within the first 24 h of colon incubation, and mainly, deglycosylation, hydrolysis, flavonoid ring cleavage, and demethylation reactions were observed. A bacterial composition analysis showed a steep drop in α-diversity after 24 h of colon incubation, likely due to favourable experimental conditions for certain bacterial species. Conclusions: Both age groups produced the same metabolites, suggesting that the potential for polyphenols to exert their health-promoting benefits persists in healthy older individuals. Full article

SMALL AUXIN UP-REGULATED RNA (SAURs) genes are acknowledged as auxin-responsive genes that play crucial roles in modulating adaptive growth under abiotic stress conditions. Low temperatures constitute a primary limiting factor that significantly impairs the development, growth, and fruit quality of watermelon plants during the winter and spring seasons. Despite their potential importance, SAURs have not yet been thoroughly investigated or characterized in watermelon. In this study, we identified a positive regulator of the chilling stress response among watermelon SAURs, designated as ClSAUR1. Subcellular localization analysis demonstrated that the protein is directed to both the nucleus and cytoplasm. Quantitative real-time PCR (qRT-PCR) analysis indicated that ClSAUR1 is ubiquitously expressed across various watermelon tissues, with pronounced expression in the roots and leaves. Moreover, qRT-PCR and promoter::β-glucuronidase (GUS) staining assays revealed that the expression of ClSAUR1 is significantly upregulated in response to exogenous abscisic acid (ABA) and chilling stress. The overexpression of ClSAUR1 in tobacco lines was contrasted and analyzed, revealing an increased tolerance to chilling stress. This was evidenced by a reduced degree of wilting and chlorosis compared to wild-type (WT) plants. Furthermore, the overexpressed lines showed reduced reactive oxygen species (ROS) accumulation and increased antioxidant enzyme activity. The qRT-PCR results further indicated that the expression levels of genes associated with abscisic acid (ABA), antioxidant enzymes, and CBF–COR cold-responsive pathways were upregulated in the transgenic tobacco lines. This study provides new insights into the role of ClSAURs in enhancing the cold resistance of watermelon. Full article