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Multi-modal remote sensing (RS) involves the fusion of data from multiple sensors, such as RGB, Multispectral, Hyperspectral, Light Detection and Ranging, Synthetic Aperture Radar, etc., each capturing unique information across different regions of the electromagnetic spectrum. The fusion of different modalities can provide complementary information, allowing for a comprehensive understanding of the Earth's surface. Multi-modal RS image segmentation leverages various RS modalities to achieve pixel-level semantics classification. While deep learning has demonstrated promise in this domain, the limited availability of labeled multi-modal data poses a constraint on leveraging data-intensive techniques like deep learning to their full potential. To address this gap, we present Ticino, a novel multi-modal remote sensing dataset tailored for semantic segmentation. Ticino includes five modalities, including RGB, Digital Terrain Model, Panchromatic, and Hyperspectral images within the visual-near and short-wave infrared spectrum. Specifically annotated for Land Cover and Soil Agricultural Use, the dataset serves as a valuable resource for researchers in the field. Additionally, we conduct a comparative analysis, comparing single-modality with multi-modality deep learning techniques and evaluating the effectiveness of early fusion versus middle fusion approaches. This work aims to facilitate future research efforts in the domain by providing a robust benchmark dataset and insights into the effectiveness of various segmentation approaches.

Hyperspectral pansharpening is crucial for the improvement of the usability of images in various applications. However, it remains underexplored due to a scarcity of data. The primary goal of pansharpening is to enhance the spatial resolution of hyperspectral images by reconstructing missing spectral information without compromising consistency with the original data. This paper addresses the data gap by presenting a new hyperspectral dataset specifically designed for pansharpening and the evaluation of several deep learning strategies using this dataset. The new dataset has two crucial features that make it invaluable for deep learning hyperspectral pansharpening research. (1) It presents the highest cardinality of images in the state of the art, making it the first statistically relevant dataset for hyperspectral pansharpening evaluation, and (2) it includes a wide variety of scenes, ensuring robust generalization capabilities for various approaches. The data, collected by the ASI PRISMA satellite, cover about 262,200 km2 and their heterogeneity is ensured by a random sampling of the Earth’s surface. The analysis of the deep learning methods consists in the adaptation of these approaches to the PRISMA hyperspectral data and the quantitative and qualitative evaluation of their performance in this new scenario. The investigation included two settings: Reduced Resolution (RR) to evaluate the techniques in a controlled environment and Full Resolution (FR) for a real-world evaluation. In addition, for the sake of completeness, we have also included machine-learning-free approaches in both scenarios. Our comprehensive analysis reveals that data-driven neural network methods significantly outperform traditional approaches, demonstrating a superior adaptability and performance in hyperspectral pansharpening under both RR and FR protocols.

This paper presents a comparative analysis of transformerbased fusion methods applied to a novel multimodal dataset for remote sensing semantic segmentation. This investigation evaluates the impact of several fusion methods on the accuracy of the results. In particular, for early fusion, we investigate the Early Concatenation. For middle fusion, we investigate four methods, namely the Token Patch Embedding, Channel Patch Embedding, Token Fusion at Attention Level, and Cross-Attention. Finally, as a representative of late fusion, we investigate the use of Late Concatenation. The methods presented here are specifically designed to operate effectively with all modalities under investigation. Experiments conducted on the Ticino dataset show that Late Concatenation outperforms the best single modality RGB method of 4.04%, 2.24% and 3.47% respectively on accuracy, precision and mIoU. This study provides an opportunity to further explore fusion methods utilizing transformers, thereby enhancing our understanding of the potential of data fusion.

Lo studio del territorio è uno dei compiti più rilevanti per l'influenza che la Terra e la gestione delle sue risorse hanno sulla nostra vita come individui e come società. Dal luogo in cui viviamo alla distribuzione della popolazione, il cibo che consumiamo, la cultura e le relazioni sociali tra le diverse società del mondo sono in parte definite dalle caratteristiche delle terre che ci circondano. Queste sono le ragioni che creano la necessità di osservare e studiare la Terra. Questi studi hanno lo scopo di descrivere le caratteristiche dei terreni e possono essere collegati a molti compiti, che variano dalla classificazione, alla segmentazione, alla stima delle caratteristiche del suolo, ecc. con l'obiettivo finale di ottenere informazioni che sono fondamentali in molte applicazioni, dall'agricoltura di precisione allo studio della copertura e dell'uso del suolo. A tal fine, l'uso delle tecnologie di remote sensing è aumentato esponenzialmente, migliorando di conseguenza la disponibilità e la raccolta di dati. Questo incremento e l'uso di nuove tecnologie aprono il campo del remote sensing a due vantaggi cruciali: 1) la possibilità di utilizzare tecniche di intelligenza artificiale per l'osservazione della Terra e 2) dati che non solo aumentano in cardinalità ma anche nel tipo di informazioni che trasmettono. La prima di queste opportunità consente l'uso di tecniche incredibilmente efficienti, derivate in particolare dalla computer vision, che possono migliorare notevolmente la nostra capacità di studiare la Terra. La seconda ci permette di adottare strategie multimodali. Queste strategie mirano a combinare diversi tipi di dati (modalità), come immagini RGB, dati iperspettrali, LiDAR, ecc. per sfruttare le informazioni provenienti da ciascuno di essi. In molti compiti di computer vision, gli approcci multimodali si sono proposti come un nuovo passo per una migliore comprensione della realtà, migliorando così la nostra capacità di gestire le risorse, The study of the land is one of the most relevant tasks for the influence that Earth and the management of its resources have on our lives as individuals and as a society. From the location in which we live to the distribution of the population, the food we consume, the culture, and the socio-relationship between the different societies of the world are partially defined by the characteristics of the surrounding lands. These are the reasons that create our necessity of observing and studying the Earth. These studies have the scope to describe the features of the terrains and can be linked to many tasks, varying from classification, segmentation, estimation of soil characteristics, etc., with the final goal to obtain information that is fundamental in many applications from agriculture of precision to study of land cover and land use. To this end, the use of remote sensing technologies has exponentially increased, consequently enhancing the availability and collection of data. This increment and the use of new technologies open the remote sensing field to two crucial advantages: 1) the possibilities of using AI techniques for Earth Observation and 2) data that not only increase in cardinality but also in the kinds of information that they convey. The former of these opportunities allows for the use of incredibly efficient techniques derived in particular from computer vision that can greatly improve our ability to study Earth. The latter enables us to multimodal strategies. These strategies aim to combine different kinds of data (modalities), such as RGB images, hyperspectral data, LiDAR, etc., to exploit the information that comes from each of them. In many computer vision tasks, multimodal approaches have posed themselves as a new step for a better understanding of reality, thus improving our ability to handle data resources. However, in remote sensing applications, it is still difficult to consider these approaches together with AI techniques due to the lack of da

In this paper, we propose an unsupervised method for hyperspectral remote sensing image segmentation. The method exploits the mean-shift clustering algorithm that takes as input a preliminary hyperspectral superpixels segmentation together with the spectral pixel information. The proposed method does not require the number of segmentation classes as input parameter, and it does not exploit any a-priori knowledge about the type of land-cover or land-use to be segmented (e.g. water, vegetation, building etc.). Experiments on Salinas, SalinasA, Pavia Center and Pavia University datasets are carried out. Performance are measured in terms of normalized mutual information, adjusted Rand index and F1-score. Results demonstrate the validity of the proposed method in comparison with the state of the art.

In this paper, different machine learning methodologies have been evaluated for the estimation of the multiple soil characteristics of a continental-wide area corresponding to the European region, using multispectral Sentinel-3 satellite imagery and digital elevation model (DEM) derivatives. The results confirm the importance of multispectral imagery in the estimation of soil properties and specifically show that the use of DEM derivatives improves the quality of the estimates, in terms of (Formula presented.), by about 19% on average. In particular, the estimation of soil texture increases by about 43%, and that of cation exchange capacity (CEC) by about 65%. The importance of each input source (multispectral and DEM) in predicting the soil properties using machine learning has been traced back. It has been found that, overall, the use of multispectral features is more important than the use of DEM derivatives with a ration, on average, of 60% versus 40%.

Weaning is a critical period for piglets, in which unbalanced gut microbiota and/or pathogen colonisation can contribute to diseases that interfere with animal performance. Tannins are natural compounds that could be used as functional ingredients to improve gut health in pig farming thanks to their antibacterial, antioxidant, and antidiarrhoeal properties. In this study, a mixture of quebracho and chestnut tannins (1.25%) was evaluated for its efficacy in reducing the negative weaning effects on piglet growth. Microbiota composition was assessed by Illumina MiSeq 16S rRNA gene sequencing of DNA extracted from stools at the end of the trial. Sequence analysis revealed an increase in the genera Shuttleworthia, Pseudobutyrivibrio, Peptococcus, Anaerostipes, and Solobacterium in the tannin-supplemented group. Conversely, this dietary intervention reduced the abundance of the genera Syntrophococcus, Atopobium, Mitsuokella, Sharpea, and Prevotella. The populations of butyrate-producing bacteria were modulated by tannin, and higher butyrate concentrations in stools were detected in the tannin-fed pigs. Co-occurrence analysis revealed that the operational taxonomic units (OTUs) belonging to the families Veillonellaceae, Lachnospiraceae, and Coriobacteriaceae occupied the central part of the network in both the control and the tannin-fed animals. Instead, in the tannin group, the OTUs belonging to the families Acidaminococcaceae, Alcaligenaceae, and Spirochaetaceae characterised its network, whereas Family XIII Incertae Sedis occupied a more central position than in the control group. Conversely, the presence of Desulfovibrionaceae characterised the network of the control group, and this family was not present in the network of the tannin group. Moreover, the prediction of metabolic pathways revealed that the gut microbiome of the tannin group possessed an enhanced potential for carbohydrate transport and metabolism, as well as a lower abundance of pathways related to cell

Differences in attitudes on social issues such as abortion, immigration and sex are hugely divisive, and understanding their origins is among the most important tasks facing human behavioural sciences. Despite the clear psychological importance of parenthood and the motivation to provide care for children, researchers have only recently begun investigating their influence on social and political attitudes. Because socially conservative values ostensibly prioritize safety, stability and family values, we hypothesized that being more invested in parental care might make socially conservative policies more appealing. Studies 1 (preregistered; n = 376) and 2 (n = 1924) find novel evidence of conditional experimental effects of a parenthood prime, such that people who engaged strongly with a childcare manipulation showed an increase in social conservatism. Studies 3 (n = 2610, novel data from 10 countries) and 4 (n = 426 444, World Values Survey data) find evidence that both parenthood and parental care motivation are associated with increased social conservatism around the globe. Further, most of the positive association globally between age and social conservatism is accounted for by parenthood. These findings support the hypothesis that parenthood and parental care motivation increase social conservatism.

This paper investigates the effects of sugarcane bagasse fibers (SCBFs) on the mechanical strength and durability properties of compressed and stabilized earth blocks (CSEBs). CSEBs were fabricated using natural soil and SCBFs, collected from Lakeland, LA, with a manually operated compression machine. Nine different soil mix compositions containing different amounts of SCBFs (0%, 0.5%, and 1.0% by weight) and Type II Portland cement (0%, 6%, and 12% by weight) were considered. The flexural, dry compressive, and wet compressive strengths of the different CSEBs were evaluated experimentally. Durability was also examined by measuring mass loss, dry density, water absorption, and wet compressive strength after 12 wetting/drying cycles. The morphology and chemical composition of the CSEBs were investigated via scanning electron microscopy and energy-dispersive X-ray spectroscopy, whereas the mineralogical characteristics were evaluated using X-ray crystallography. The results show that including 0.5% to 1.0% by weight of SCBFs in CSEBs stabilized with 12% by weight of cement can significantly improve the CSEB mechanical properties without compromising their durability properties.

OBJECTIVES: End-of-life dreams and visions (ELDVs) have been reported throughout history. We aimed to synthesize the research literature on ELDVs to determine the proportions of patients, bereaved families, healthcare professionals, and volunteers reporting ELDVs; ELDV content, timing, and interpretation; and any evidence-based approaches to ELDV-related care. METHODS: A systematic review protocol was registered with PROSPERO (CRD4021282929). CINAHL, Medline, Embase, Emcare, and APA PsycInfo were searched for peer-reviewed English language articles reporting qualitative, quantitative, or mixed methods studies that explored reports of ELDVs by patients, bereaved families, healthcare professionals, or volunteers. Synthesis used both meta-analysis and a narrative approach. RESULTS: Of 2,045 papers identified by searches, 22 were included, describing 18 studies in a variety of settings. Meta-analyses indicated that 77% (95% confidence intervals [CIs] 69-84%) of patients (n = 119) reported an ELDV compared with 32% (95% CIs 21-44%) of bereaved relatives (n = 2,444) and that 80% (95% CIs 59-94%) of healthcare professionals (n = 171) reported either witnessing or being told of an ELDV in the preceding 5 years. Studies of volunteers reported 34% (95% CIs 20-48%) (n = 45) either witnessing or being told of an ELDV over their entire period of service, with 49% of volunteers (95% CIs 33-64%) (n = 39) reporting events occurring in the preceding year. ELDVs reported by patients, bereaved families, healthcare professionals, and volunteers were perceived as being a source of comfort. Healthcare professionals and volunteers expressed a need for further education on how to support patients experiencing ELDVs and their families. SIGNIFICANCE OF RESULTS: ELDVs are experienced by the majority of dying patients and need consideration in delivering holistic end-of-life care. Little if any research has been conducted in acute care facilities.

Sperm motility is directly related to the ability of sperm to move through the female reproductive tract to reach the ovum. Sperm motility is a complex trait that is influenced by environmental and genetic factors and is associated with male fertility, oocyte penetration rate, and reproductive success of cattle. In this study we carried out a GWAS in Italian Holstein bulls to identify candidate regions and genes associated with variations in progressive and total motility (PM and TM, respectively). After quality control, the final data set consisted of 5,960 records from 949 bulls having semen collected in 10 artificial insemination stations and genotyped at 412,737 SNPs (call rate >95%; minor allele frequency >5%). (Co)variance components were estimated using single trait mixed models, and associations between SNPs and phenotypes were assessed using a genomic BLUP approach. Ten windows that explained the greatest percentage of genetic variance were located on Bos taurus autosomes 1, 2, 4, 6, 7, 23, and 26 for TM and Bos taurus autosomes 1, 2, 4, 6, 8, 16, 23, and 26 for PM. A total of 150 genes for TM and 72 genes for PM were identified within these genomic regions. Gene Ontology enrichment analyses identified significant Gene Ontology terms involved with energy homeostasis, membrane functions, sperm-egg interactions, protection against oxidative stress, olfactory receptors, and immune system. There was significant enrichment of quantitative trait loci for fertility, calving ease, immune response, feed intake, and carcass weight within the candidate windows. These results contribute to understanding the architecture of the genetic control of sperm motility and may aid in the development of strategies to identify subfertile bulls and improve reproductive success.

Mycotoxins should be monitored in order to properly evaluate corn silage safety quality. In the present study, corn silage samples (n = 115) were collected in a survey, characterized for concentrations of mycotoxins, and scanned by a NIR spectrometer. Random Forest classification models for NIR calibration were developed by applying different cut-offs to classify samples for concentration (i.e., mu g/kg dry matter) or count (i.e., n) of (i) total detectable mycotoxins; (ii) regulated and emerging Fusarium toxins; (iii) emerging Fusarium toxins; (iv) Fumonisins and their metabolites; and (v) Penicillium toxins. An over- and under-sampling re-balancing technique was applied and performed 100 times. The best predictive model for total sum and count (i.e., accuracy mean +/- standard deviation) was obtained by applying cut-offs of 10,000 mu g/kg DM (i.e., 96.0 +/- 2.7%) or 34 (i.e., 97.1 +/- 1.8%), respectively. Regulated and emerging Fusarium mycotoxins achieved accuracies slightly less than 90%. For the Penicillium mycotoxin contamination category, an accuracy of 95.1 +/- 2.8% was obtained by using a cut-off limit of 350 mu g/kg DM as a total sum or 98.6 +/- 1.3% for a cut-off limit of five as mycotoxin count. In conclusion, this work was a preliminary study to discriminate corn silage for high or low mycotoxin contamination by using NIR spectroscopy.

Nutrigenomics is a multidisciplinary science focusing on the diet-genes interaction and exploiting different ‘omics’ technologies to comprehensively investigate the overall effects of different nutrients or diets on health. Recent scientific evidence highlighted the role of dietary polyphenols in modulating gene expression, transcription factors, and cellular processes, thus in potentially affecting health and diseases. Nutrigenomics contributes to the understanding of the molecular mechanisms underlying the direct beneficial effects of polyphenolic compounds in humans. This short review critically summarizes some recent findings on the nutrigenomics-related effects mediated by the major polyphenolic classes of nutritional interest.