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Alternaria species are cosmopolitan fungi darkly pigmented by melanin that infect numerous plant species causing economically important agricultural spoilage of various food crops. Alternaria spp. also infect animals, being described as entomopathogenic fungi but also infecting warm-blooded animals, including humans. Their clinical importance in human health, as infection agents, lay in the growing number of immunocompromised patients. Moreover, Alternaria spp. are considered some of the most abundant and potent sources of airborne sensitizer allergens causing allergic respiratory diseases, as severe asthma. Among the numerous strategies deployed by Alternaria spp. to attack their hosts, the production of toxins, carrying critical concerns to public health as food contaminant, and the production of hydrolytic enzymes such as proteases, can be highlighted. Alternaria proteases also trigger allergic symptoms in individuals with fungal sensitization, acting as allergens and facilitating antigen access to the host subepithelium. Here, we review the current knowledge about the mechanisms of Alternaria pathogenesis in plants and animals, the strategies used by Alternaria to cope with the host defenses, and the involvement Alternaria allergens and mechanisms of sensitization.

Aquaculture has been challenged to find alternative ingredients to develop innovative feed formulations that foster a sustainable future growth. Given the most recent trends in fish feed formulation on the use of alternative protein sources to decrease the dependency of fishmeal, it is fundamental to evaluate the implications of this new paradigm for fish health and welfare. This work intends to comprehensively review the impacts of alternative and novel dietary protein sources on fish gut microbiota and health, stress and immune responses, disease resistance, and antioxidant capacity. The research results indicate that alternative protein sources, such as terrestrial plant proteins, rendered animal by-products, insect meals, micro- and macroalgae, and single cell proteins (e.g., yeasts), may negatively impact gut microbiota and health, thus affecting immune and stress responses. Nevertheless, some of the novel protein sources, such as insects and algae meals, have functional properties and may exert an immunostimulatory activity. Further research on the effects of novel protein sources, beyond growth, is clearly needed. The information gathered here is of utmost importance, in order to develop innovative diets that guarantee the production of healthy fish with high quality standards and optimised welfare conditions, thus contributing to a sustainable growth of the aquaculture industry.

Seaweeds are an important source of nutrients and bioactive compounds and have a high potential as health boosters in aquaculture. This study evaluated the effect of dietary inclusion of Gracilaria gracilis biomass or its extract on the European seabass ( Dicentrarchus labrax ) gut microbial community. Juvenile fish were fed a commercial-like diet with 2.5% or 5% seaweed biomass or 0.35% seaweed extract for 47 days. The gut microbiome was assessed by 16S rRNA amplicon sequencing, and its diversity was not altered by the seaweed supplementation. However, a reduction in Proteobacteria abundance was observed. Random forest analysis highlighted the genera Photobacterium , Staphylococcus , Acinetobacter , Micrococcus and Sphingomonas, and their abundances were reduced when fish were fed diets with algae. SparCC correlation network analysis suggested several mutualistic and other antagonistic relationships that could be related to the predicted altered functions. These pathways were mainly related to the metabolism and biosynthesis of protective compounds such as ectoine and were upregulated in fish fed diets supplemented with algae. This study shows the beneficial potential of Gracilaria as a functional ingredient through the modulation of the complex microbial network towards fish health improvement.

The division of retrieval into multiple stages has evolved to balance efficiency and effectiveness among various ranking models. Faster but less accurate models are used to retrieve results from the entire corpus. Slower yet more precise models refine the ranking within the top candidate list. This study proposes a multi-stage learning-to-rank (MultiLTR) method. MultiLTR applies learning-to-rank techniques across multiple stages. It incorporates text from different fields such as titles, body content, and abstracts to produce a more comprehensive and accurate ranking. MultiLTR iteratively refines ranking accuracy through sequential processing phases. It dynamically selects top-performing rankers from a diverse candidate pool at each stage. Experiments were carried out on benchmark datasets, MQ2007 and MQ2008, using three categories of learning-to-rank algorithms. The results demonstrate that MultiLTR outperforms state-of-the-art ranking approaches, particularly in field-based ranking tasks. This study improves ranking accuracy and offers new insights into enhancing multi-stage ranking strategies.

In the area of consumer health search (CHS), there is an increasing concern about returning topically relevant and understandable health information to the user. Besides being used to rank topically relevant documents, Learning to Rank (LTR) has also been used to promote understandability ranking. Traditionally, features coming from different document fields are joined together, limiting the performance of standard LTR, since field information plays an important role in promoting understandability ranking. In this paper, a novel field-level Learning-to-Rank (f-LTR) approach is proposed, and its application in CHS is investigated by developing thorough experiments on CLEF’ 2016–2018 eHealth IR data collections. An in-depth analysis of the effects of using f-LTR is provided, with experimental results suggesting that in LTR, title features are more effective than other field features in promoting understandability ranking. Moreover, the fused f-LTR model is compared to existing work, confirming the effectiveness of the methodology.

Algae have been consumed for millennia in several parts of the world as food, food supplements, and additives, due to their unique organoleptic properties and nutritional and health benefits. Algae are sustainable sources of proteins, minerals, and fiber, with well-balanced essential amino acids, pigments, and fatty acids, among other relevant metabolites for human nutrition. This review covers the historical consumption of algae in Europe, developments in the current European market, challenges when introducing new species to the market, bottlenecks in production technology, consumer acceptance, and legislation. The current algae species that are consumed and commercialized in Europe were investigated, according to their status under the European Union (EU) Novel Food legislation, along with the market perspectives in terms of the current research and development initiatives, while evaluating the interest and potential in the European market. The regular consumption of more than 150 algae species was identified, of which only 20% are approved under the EU Novel Food legislation, which demonstrates that the current legislation is not broad enough and requires an urgent update. Finally, the potential of the European algae market growth was indicated by the analysis of the trends in research, technological advances, and market initiatives to promote algae commercialization and consumption.

Apparent diffusion coefficient (ADC) of magnetic resonance imaging (MRI) is an indispensable imaging technique in clinical neuroimaging that quantitatively assesses the diffusivity of water molecules within tissues using diffusion-weighted imaging (DWI). This study focuses on developing a robust machine learning (ML) model to predict the aggressiveness of gliomas according to World Health Organization (WHO) grading by analyzing patients’ demographics, higher-order moments, and grey level co-occurrence matrix (GLCM) texture features of ADC. A population of 722 labeled MRI-ADC brain image slices from 88 human subjects was selected, where gliomas are labeled as glioblastoma multiforme (WHO-IV), high-grade glioma (WHO-III), and low-grade glioma (WHO I-II). Images were acquired using 3T-MR systems and a region of interest (ROI) was delineated manually over tumor areas. Skewness, kurtosis, and statistical texture features of GLCM (mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence, and shade) were calculated using ADC values within ROI. The ANOVA f-test was utilized to select the best features to train an ML model. The data set was split into training (70%) and testing (30%) sets. The train set was fed into several ML algorithms and selected most promising ML algorithm using K-fold cross-validation. The hyper-parameters of the selected algorithm were optimized using random grid search technique. Finally, the performance of the developed model was assessed by calculating accuracy, precision, recall, and F1 values reported for the test set. According to the ANOVA f-test, three attributes; patient gender (1.48), GLCM energy (9.48), and correlation (13.86) that performed minimum scores were excluded from the dataset. Among the tested algorithms, the random forest classifier(0.8772 ± 0.0237) performed the highest mean-cross-validation score and selected to build the ML model which was able to predict tumor categories with an accuracy of 88.14% over the test set. The study concludes that the developed ML model using the above features except for patient gender, GLCM energy, and correlation, has high prediction accuracy in glioma grading. Therefore, the outcomes of this study enable to development of advanced tumor classification applications that assist in the decision-making process in a real-time clinical environment.

An outbreak of Chagas disease, possibly involving its vector Triatoma brasiliensis brasiliensis , was identified in the state of Rio Grande do Norte (RN). Given the historical significance of this vector in public health, the study aimed to evaluate its role in the transmission dynamics of the protozoan Trypanosoma cruzi in an area undergoing desertification in the Seridó region, RN, Brazil. We captured triatomines in sylvatic and anthropic ecotopes. Natural vector infection was determined using parasitological and molecular methods and we identified discrete typing units (DTUs) of T . cruzi by analyzing the COII gene of mtDNA, 24Sα rDNA, and mini-exon gene. Their blood meals sources were identified by amplification and sequencing of the mtDNA cytochrome b gene. A total of 952 T . b . brasiliensis were captured in peridomestic (69.9%) and sylvatic ecotopes (30.4%). A wide range of natural infection rates were observed in peridomestic (36.0% - 71.1%) and sylvatic populations (28.6% - 100.0%). We observed the circulation of TcI and TcII DTUs with a predominance of Tcl in sylvatic and peridomestic environments. Kerodon rupestris , rocky cavy (13/39), Homo sapiens , human (8/39), and Bos taurus , ox (6/39) were the most frequently detected blood meals sources. Thus, Triatoma b . brasiliensis is invading and colonizing the human dwellings. Furthermore, high levels of natural infection, coupled with the detection of TcI and TcII DTUs, and also the detection of K . rupestris and H . sapiens as blood meals sources of infected T . b . brasiliensis indicate a risk of T . cruzi transmission to human populations in areas undergoing desertification.

Given the continuous increase in the global population, the food manufacturers are advocated to either intensify the use of cropland or expand the farmland, making land cover and land usage dynamics mapping vital in the area of remote sensing. In this regard, identifying and classifying a high-resolution satellite imagery scene is a prime challenge. Several approaches have been proposed either by using static rule-based thresholds (with limitation of diversity) or neural network (with data-dependent limitations). This paper adopts the inductive approach to learning from surface reflectances. A manually labeled Sentinel-2 dataset was used to build a Machine Learning (ML) model for scene classification, distinguishing six classes (Water, Shadow, Cirrus, Cloud, Snow, and Other). This models was accessed and further compared to the European Space Agency (ESA) Sen2Cor package. The proposed ML model presents a Micro-F1 value of 0.84, a considerable improvement when compared to the Sen2Cor corresponding performance of 0.59. Focusing on the problem of optical satellite image scene classification, the main research contributions of this paper are: (a) an extended manually labeled Sentinel-2 database adding surface reflectance values to an existing dataset; (b) an ensemble-based and a Neural-Network-based ML models; (c) an evaluation of model sensitivity, biasness, and diverse ability in classifying multiple classes over different geographic Sentinel-2 imagery, and finally, (d) the benchmarking of the ML approach against the Sen2Cor package.

Query reformulation techniques optimize user queries to better align with documents, thus improving the performance of Information Retrieval (IR) systems. Previous methods have primarily focused on query expansion using techniques such as synonym replacement to improve recall. With the rapid advancement of Large Language Models (LLMs), the knowledge embedded within these models has grown. Research in prompt engineering has introduced various methods, with prompt chaining proving particularly effective for complex tasks. Directly prompting LLMs to reformulate queries has become a viable approach. However, existing LLM-based prompt methods for query reformulation often introduce irrelevant content into reformulated queries, resulting in decreased retrieval precision and misalignment with user intent. We propose a novel approach called Hierarchical Prompt Chaining for Query Reformulation (HiPC-QR). HiPC-QR employs a two-step prompt chaining technique to extract keywords from the original query and refine its structure by filtering out non-essential keywords based on the user’s query intent. This process reduces the query’s restrictiveness while simultaneously expanding essential keywords to enhance retrieval effectiveness. We evaluated the effectiveness of HiPC-QR on two benchmark retrieval datasets, namely MS MARCO and TREC Deep Learning.The experimental results show that HiPC-QR outperforms existing query reformulation methods on large-scale datasets in terms of both recall@10 and MRR@10.