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Genome-wide association studies typically evaluate the autosomes and sometimes the X Chromosome, but seldom consider the Y or mitochondrial (MT) Chromosomes. We genotyped the Y and MT Chromosomes in heterogeneous stock (HS) rats (Rattus norvegicus), an outbred population created from 8 inbred strains. We identified 8 distinct Y and 4 distinct MT Chromosomes among the 8 founders. However, only 2 types of each nonrecombinant chromosome were observed in our modern HS rat population (generations 81-97). Despite the relatively large sample size, there were virtually no significant associations for behavioral, physiological, metabolome, or microbiome traits after correcting for multiple comparisons. However, both Y and MT Chromosomes were strongly associated with the expression of a few genes located on those chromosomes, which provided a positive control. Our results suggest that within modern HS rats there are no Y and MT Chromosomes differences that strongly influence behavioral or physiological traits. These results do not address other ancestral Y and MT Chromosomes that do not appear in modern HS rats, nor do they address effects that may exist in other rat populations, or in other species.

To make adaptive decisions, we build an internal model of the associative relationships in an environment and use it to make predictions and inferences about specific available outcomes. Detailed, identity-specific cue-reward memories are a core feature of such cognitive maps. Here we used fiber photometry, cell-type and pathway-specific optogenetic manipulation, Pavlovian cue-reward conditioning and decision-making tests in male and female rats, to reveal that ventral tegmental area dopamine (VTADA) projections to the basolateral amygdala (BLA) drive the encoding of identity-specific cue-reward memories. Dopamine is released in the BLA during cue-reward pairing; VTADA→BLA activity is necessary and sufficient to link the identifying features of a reward to a predictive cue but does not assign general incentive properties to the cue or mediate reinforcement. These data reveal a dopaminergic pathway for the learning that supports adaptive decision-making and help explain how VTADA neurons achieve their emerging multifaceted role in learning.

This article aimed to determine a workflow for more efficient large-scale crop mapping using a time series of images from the Sentinel-2 Satellite, statistical methods of attribute selection, and machine learning. The proposed methodology explores the best possible combination of spectral variables related to vegetation (16 vegetation indices in the RGB, NIR, SWIR, and Red Edge regions) to characterize different spectro-temporal profiles of Land Use and Land Cover (LULC) in spatially heterogeneous landscapes. First, we applied a data dimensionality reduction analysis using the PCA (Principal Component Analysis) method. Subsequently, the variables that showed the highest statistical correlation between each other were used in the spectro-temporal classification process, using the Random Forest, TempCNN, and LightTAE algorithms, following three different strategies: C1 (ALL), C2 (BE + IV (Red Edge)) and C3 (BE + IV (without Red Edge)), where ALL – All variables; BE – Spectral Bands; IV – Vegetation Indices. Given the results found, the C2 classification scenario (with bands B3, B4, B5, B6, B7, B8, and B8A and the NDRE1, RESI, and MSR indexes) demonstrated the best LULC classification accuracy at the crop pattern level, compared to the other scenarios, with average values of 0.91, 0.88, 0.91, 0.89, and 0.89 (Global Accuracy, Producer Accuracy, User Accuracy, Kappa index, and F1-Score, respectively, for the TempCNN model), the which emphasized the importance of both qualitative and quantitative variability of sampling data and variables based on the Red Edge region for improving LULC classification processes in large-scale heterogeneous landscapes.

The escalating presence of plastics in agriculture has raised concerns regarding Agricultural Plastic Waste (APW). Hitherto, a lack of comprehensive plasticulture data impedes effective waste management strategies, potentially resulting in plastic pollution and contributing to microplastic formation. APW locations and quantities are pivotal for territorial planning and the formulation of public policies on waste management and land use. Remote detection of agri-plastics has garnered increased consideration, particularly in mapping plastic-mulched farmlands (PMFs) dispersed in extensive regions. This study investigates whether the use of satellite image time series can map PMFs accurately. We assessed pixel-based classification using a 16-day composite time series of Sentinel-2 imagery (S2-16D) obtained from the Brazil Data Cube project. The Plastic-Mulched Landcover Index (PMLI) was joined into S2-16D bands because it is an index focused on PMF detection. Four classifiers (RF, MLP, L-TAE, and TCNN) were compared through agreed classification metrics. The most promising outcome showed an overall accuracy of 100% employing L-TAE but with visible noise in the map. The time series enhanced accuracy while minimizing background confusion, offering a viable solution for PMF detection. The PMF map presented herein represents an initial stride toward fostering circularity in plasticulture throughout South America.

A vexing observation in genome-wide association studies (GWASs) is that parallel analyses in different species may not identify orthologous genes. Here, we demonstrate that cross-species translation of GWASs can be greatly improved by an analysis of co-localization within molecular networks. Using body mass index (BMI) as an example, we show that the genes associated with BMI in humans lack significant agreement with those identified in rats. However, the networks interconnecting these genes show substantial overlap, highlighting common mechanisms including synaptic signaling, epigenetic modification, and hormonal regulation. Genetic perturbations within these networks cause abnormal BMI phenotypes in mice, too, supporting their broad conservation across mammals. Other mechanisms appear species specific, including carbohydrate biosynthesis (humans) and glycerolipid metabolism (rodents). Finally, network co-localization also identifies cross-species convergence for height/body length. This study advances a general paradigm for determining whether and how phenotypes measured in model species recapitulate human biology.

Abstract The advent of bioprinting has enabled the creation of precise three‐dimensional (3D) cell cultures suitable for biomimetic in vitro models. In this study, we developed a novel protocol for 3D printing methacrylated collagen (ColMa, or PhotoCol®) combined with tendon stem/progenitor cells (hTSPCs) derived from human tendon explants. Although pure ColMa has not previously been proposed as a printable hydrogel, this paper outlines a robust and highly reproducible pipeline for bioprinting this material. Indeed, we successfully fabricated a 3D bioengineered scaffold and cultured it for 21 days under perfusion conditions with medium supplemented with growth/differentiation factor‐5 (GDF‐5). This bioprinting pipeline and the culture conditions created an exceptionally favorable 3D environment, enabling the cells to proliferate, exhibit tenogenic behaviors, and produce a new collagen type I matrix, thereby remodeling the surrounding environment. Indeed, over the 21‐day culture period under perfusion condition, tenomodulin expression showed a significant upregulation on day 7, with a 2.3‐fold increase, compared to days 14 and 21. Collagen type I gene expression was upregulated nearly 10‐fold by day 14. This trend was further confirmed by western blot analysis, which revealed a statistically significant difference in tenomodulin expression between day 21 and both day 7 and day 14. For type I collagen, significant differences were observed between day 0 and day 21, as well as between day 0 and day 14, with a p‐value of 0.01. These results indicate a progressive over‐expression of type I collagen, reflecting cell differentiation towards a proper tenogenic phenotype. Cytokines, such as IL‐8 and IL‐6, levels peaked at 8566 and 7636 pg/mL, respectively, on day 7, before decreasing to 54 and 46 pg/mL by day 21. Overall, the data suggest that the novel ColMa bioprinting protocol effectively provided a conducive environment for the growth and proper differentiation of hTSPCs, showcasing its potential for studying cell behavior and tenogenic differentiation.

The growing threat of viral infections requires innovative therapeutic approaches to safeguard human health. Nanomaterials emerge as a promising solution to overcome the limitations associated with conventional therapies. The eco-friendly synthesis of silver nanoparticles (AgNPs) currently represents a method that guarantees antimicrobial efficacy, safety, and cost-effectiveness. This study explores the use of AgNPs derived from the peel (Lp-AgNPs) and juice (Lj-AgNPs) Citrus limon “Ovale di Sorrento”, cultivars of the Campania region. The antiviral potential was tested against viruses belonging to the Coronaviridae and Herpesviridae. AgNPs were synthesized by reduction method using silver nitrate solution mixed with aqueous extract of C. limon peel and juice. The formation of Lp-AgNPs and Lj-AgNPs was assessed using a UV–Vis spectrophotometer. The size, ζ-potential, concentration, and morphology of AgNPs were evaluated by dynamic light scattering (DLS), nanoparticle tracking analysis (NTA), and field emission-scanning electron microscopy (FE-SEM). Cytotoxicity was evaluated in a concentration range between 500 and 7.8 µg/mL on VERO-76 and HaCaT cells, with the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium test bromide (MTT). Antiviral activity consisted of virus pre-treatment, co-treatment, cellular pre-treatment, and post-infection tests versus HSV-1 and SARS-CoV-2 at a multiplicity of infections (MOI) of 0.01. Plaque reduction assays and real-time PCR provided data on the antiviral potential of tested compounds. Lp-AgNPs and Lj-AgNPs exhibited spherical morphology with respective diameters of 60 and 92 nm with concentrations of 4.22 and 4.84 × 1010 particles/mL, respectively. The MTT data demonstrated minimal cytotoxicity, with 50 % cytotoxic concentrations (CC50) of Lp-AgNPs and Lj-AgNPs against VERO cells of 754.6 and 486.7 µg/mL. Similarly, CC50 values against HaCaT were 457.3 µg/mL for Lp-AgNPs and 339.6 µg/mL for Lj-AgNPs, respectively. In the virus pre-treatment assay, 90 % inhibitory concentrations of HSV-1 and SARS-CoV-2 were 8.54–135.04 µg/mL for Lp-AgNPs and 6.13–186.77 µg/mL for Lj-AgNPs, respectively. The molecular investigation confirmed the antiviral data, recording a reduction in the UL54 and UL27 genes for HSV-1 and in the Spike (S) gene for SARS-CoV-2, following AgNP exposure. The results of this study suggest that Lp-AgNPs and Lj-AgNPs derived from C. Limon could offer a valid ecological, natural, local and safe strategy against viral infections.

BackgroundQuantitative Trait Locus (QTL) analysis and Genome-Wide Association Studies (GWAS) have the power to identify variants that capture significant levels of phenotypic variance in complex traits. However, effort and time are required to select the best methods and optimize parameters and pre-processing steps. Although machine learning approaches have been shown to greatly assist in optimization and data processing, applying them to QTL analysis and GWAS is challenging due to the complexity of large, heterogenous datasets. Here, we describe proof-of-concept for an automated machine learning approach, AutoQTL, with the ability to automate many complicated decisions related to analysis of complex traits and generate solutions to describe relationships that exist in genetic data.ResultsUsing a publicly available dataset of 18 putative QTL from a large-scale GWAS of body mass index in the laboratory rat, Rattus norvegicus, AutoQTL captures the phenotypic variance explained under a standard additive model. AutoQTL also detects evidence of non-additive effects including deviations from additivity and 2-way epistatic interactions in simulated data via multiple optimal solutions. Additionally, feature importance metrics provide different insights into the inheritance models and predictive power of multiple GWAS-derived putative QTL.ConclusionsThis proof-of-concept illustrates that automated machine learning techniques can complement standard approaches and have the potential to detect both additive and non-additive effects via various optimal solutions and feature importance metrics. In the future, we aim to expand AutoQTL to accommodate omics-level datasets with intelligent feature selection and feature engineering strategies.

ABSTRACT Hyperprolactinemia is a frequent cause of menstrual irregularity, galactorrhea, hypogonadism, and infertility. The most common etiologies of hyperprolactinemia can be classified as physiological, pharmacological, and pathological. Among pathological conditions, it is essential to distinguish prolactinomas from other tumors and pituitary lesions presenting with hyperprolactinemia due to pituitary stalk disconnection. Proper investigation considering clinical data, laboratory tests, and, if necessary, imaging evaluation, is important to identify the correct cause of hyperprolactinemia and manage the patient properly. This position statement by the Brazilian Federation of Gynecology and Obstetrics Associations (Febrasgo) and Brazilian Society of Endocrinology and Metabolism (SBEM) addresses the recommendations for measurement of serum prolactin levels and the investigations of symptomatic and asymptomatic hyperprolactinemia and medication-induced hyperprolactinemia in women.

ABSTRACT Dopamine agonists are the first line of treatment for patients with symptomatic hyperprolactinemia due to prolactinomas and in those with idiopathic hyperprolactinemia. Treatment with these agents is effective in 80%-90% of the cases. Infertility treatment of patients with hyperprolactinemia is also carried out with dopamine agonists, aiming for the normalization of prolactin levels. The risk of symptomatic growth of prolactinomas during pregnancy is dependent on the tumor's size, duration of previous treatments, and prolactin levels. Notably, the corresponding risk is relatively low in cases of microprolactinomas (

Adaptive reward-related decision making requires accurate prospective consideration of the specific outcome of each option and its current desirability. Often this information must be inferred based on the presence of predictive environmental events. The basolateral amygdala (BLA) and medial orbitofrontal cortex (mOFC) are two key nodes in the circuitry supporting such outcome expectations, but very little is known about the function of direct connections between these regions. Here, in male rats, we first anatomically confirmed the existence of bidirectional, direct projections between the mOFC and BLA and found that BLA projections to mOFC are largely distinct from those to lateral OFC (lOFC). Next, using pathway-specific chemogenetic inhibition and the outcome-selective Pavlovian-to-instrumental transfer and devaluation tests, we interrogated the function of the bidirectional mOFC-BLA connections in reward-directed behavior. We found evidence that the mOFC→BLA pathway mediates the use of environmental cues to understand which specific reward is predicted, information needed to infer which action to choose, and how desirable that reward is to ensure adaptive responses to the cue. By contrast, the BLA→mOFC pathway is not needed to use the identity of an expected reward to guide choice but does mediate adaptive responses to cues based on the current desirability of the reward they predict. These functions differ from those we previously identified for the lOFC-BLA circuit. Collectively, the data reveal the mOFC-BLA circuit as critical for the cue-dependent reward outcome expectations that influence adaptive behavior and decision making.SIGNIFICANCE STATEMENT To make good decisions we evaluate how advantageous a particular course of action would be. This requires understanding what rewarding outcomes can be expected and how desirable they currently are. Such prospective considerations are critical for adaptive decision making but disrupted in many psychiatric diseases. Here, we reveal that direct connections between the medial orbitofrontal cortex and basolateral amygdala mediate these functions. These findings are especially important in light of evidence of dysfunction in this circuit in substance use disorder and mental illnesses marked by poor decision making.

Abstract Within the chromatin, distal elements interact with promoters to regulate specific transcriptional programs. Histone acetylation, interfering with the net charges of the nucleosomes, is a key player in this regulation. Here, we report that the oncoprotein SET is a critical determinant for the levels of histone acetylation within enhancers. We disclose that a condition in which SET is accumulated, the severe Schinzel-Giedion Syndrome (SGS), is characterized by a failure in the usage of the distal regulatory regions typically employed during fate commitment. This is accompanied by the usage of alternative enhancers leading to a massive rewiring of the distal control of the gene transcription. This represents a (mal)adaptive mechanism that, on one side, allows to achieve a certain degree of differentiation, while on the other affects the fine and corrected maturation of the cells. Thus, we propose the differential in cis-regulation as a contributing factor to the pathological basis of SGS and possibly other the SET-related disorders in humans.

Sensitivity to cocaine and its associated stimuli ("cues") are important factors in the development and maintenance of addiction. Rodent studies suggest that this sensitivity is related, in part, to the propensity to attribute incentive salience to food cues, which, in turn, contributes to the maintenance of cocaine self-administration, and cue-induced relapse of drug-seeking. Whereas each of these traits has established links to drug use, the relatedness between the individual traits themselves has not been well characterized in preclinical models. To this end, the propensity to attribute incentive salience to a food cue was first assessed in two distinct cohorts of 2716 outbred heterogeneous stock rats (HS; formerly N:NIH). We then determined whether each cohort was associated with performance in one of two paradigms (cocaine conditioned cue preference and cocaine contextual conditioning). These measure the unconditioned locomotor effects of cocaine, as well as conditioned approach and the locomotor response to a cocaine-paired floor or context. There was large individual variability and sex differences among all traits, but they were largely independent of one another in both males and females. These findings suggest that these traits may contribute to drug-use via independent underlying neuropsychological processes.

ObjectiveObesity is influenced by genetic and environmental factors. Despite the success of human genome-wide association studies, the specific genes that confer obesity remain largely unknown. The objective of this study was to use outbred rats to identify the genetic loci underlying obesity and related morphometric and metabolic traits.MethodsThis study measured obesity-relevant traits, including body weight, body length, BMI, fasting glucose, and retroperitoneal, epididymal, and parametrial fat pad weight in 3,173 male and female adult N/NIH heterogeneous stock (HS) rats across three institutions, providing data for the largest rat genome-wide association study to date. Genetic loci were identified using a linear mixed model to account for the complex family relationships of the HS and using covariates to account for differences among the three phenotyping centers.ResultsThis study identified 32 independent loci, several of which contained only a single gene (e.g., Epha5, Nrg1, Klhl14) or obvious candidate genes (e.g., Adcy3, Prlhr). There were strong phenotypic and genetic correlations among obesity-related traits, and there was extensive pleiotropy at individual loci.ConclusionsThis study demonstrates the utility of HS rats for investigating the genetics of obesity-related traits across institutions and identify several candidate genes for future functional testing.

Land use and land cover (LULC) classification has long been an essential topic in Earth Observation research and plays a key role in the sustainable development of agriculture. This study evaluated the accuracy of LULC classification based on an initial clustering step in a heterogeneous agricultural landscape using PlanetScope imagery while checking for variability among their Normalized Difference Vegetation Index (NDVI) temporal signatures. We adopt an object-based image analysis to generate image-objects and then extract statistical information of PlanetScope spectral bands and vegetation indices as input information for classification. The exploratory analysis focused on the double crop class and calculated the distance between NDVI temporal signatures of paired land parcels. We applied an unsupervised clustering technique along with Random Forest algorithm based on multiple tests to classify and analyse gains and losses in accuracies produced by these approaches. Our results showed that the initial clustering method outperformed the non-clustered classification of LULC in overall accuracy measures. The exploratory analysis demonstrated that double crops might present high intra-class variability and diverse crop calendars for neighbour land parcels. The accuracies achieved represent promising opportunities for the sufficiently accurate classification of such areas, and the knowledge of the intra-class variability allows the analyst to infer the temporal dynamics of crop fields. We reinforce that further work could assess other types of classifiers, especially in areas with a large number of crop types and distinct management practices.

Postoperative (PO) complications after transsphenoidal surgery (TSS) are rare when performed in pituitary referral centers. Partial hypopituitarism is more frequent and somewhat expected. Meningitis, cerebrospinal fluid leaks, and visual deficits are unusual. Cerebrovascular complications, including cerebral vasospasm are rare, usually under-appreciated and not mentioned to the patient prior to the surgery. This is a report of a 51-year-old male with a non-functioning pituitary macroadenoma presenting with partial hypopituitarism and visual field loss. The patient was submitted to an uneventful TSS. On the first PO day, he developed a left palpebral ptosis with unequal pupils and impaired consciousness (12 points on Glasgow Coma Scale). CT scan revealed a perimesencephalic subarachnoid hemorrhage (SAH) grade 1 according to the modified Fisher scale. High-dose dexamethasone (16 mg/day) was initiated and the patient became more alert (Glasgow 14). On the fifth PO day, due to progression of the neurological deficits (left III, IV, and VI cranial nerves palsy, ataxia, dysdiadochokinesia, right dysmetria, and dysarthria), a magnetic resonance angiography was obtained and revealed a recent mesencephalic infarct without evident vasospasm. Nevertheless, nimodipine 60 mg 4/4 h was initiated. No improvement was seen after 3 days of treatment. The patient was discharged and put on rehabilitation, returning to normal gait and balance after 7 months. This, therefore, is a case of an unexpected mesencephalic infarct probably due to vasospasm induced by minor SAH. Although exceptionally rare, informing the patient about this event prior to TSS is important due to its significant neurological impact. More data are needed considering preventive treatment with nimodipine as soon as SAH is detected after TSS and whether it would improve neurological outcomes.

Background and Aims: Perianal fistula represents one of the most disabling manifestations of Crohn′s disease (CD) due to complete destruction of the affected mucosa, which is replaced by granulation tissue and associated with changes in tissue organization. To date, the molecular mechanisms underlying perianal fistula formation are not well defined. Here, we dissected the tissue changes in the fistula area and addressed whether a dysregulation of extracellular matrix (ECM) homeostasis can support fistula formation. Methods: Surgical specimens from perianal fistula tissue and the surrounding region of fistulizing CD were analyzed histologically and by RNA sequencing. Genes significantly modulated were validated by real-time polymerase chain reaction, Western blot, and immunofluorescence assays. The effect of the protein product of TNF-stimulated gene-6 (TSG-6) on cell morphology, phenotype, and ECM organization was investigated with endogenous lentivirus-induced overexpression of TSG-6 in Caco-2 cells and with exogenous addition of recombinant human TSG-6 protein to primary fibroblasts from region surrounding fistula. Proliferative and migratory assays were performed. Results: A markedly different organization of ECM was found across fistula and surrounding fistula regions with an increased expression of integrins and matrix metalloproteinases and hyaluronan (HA) staining in the fistula, associated with increased newly synthesized collagen fibers and mechanosensitive proteins. Among dysregulated genes associated with ECM, TNFAI6 (gene encoding for TSG-6) was as significantly upregulated in the fistula compared with area surrounding fistula, where it promoted the pathological formation of complexes between heavy chains from inter-alpha-inhibitor and HA responsible for the formation of a crosslinked ECM. There was a positive correlation between TNFAI6 expression and expression of mechanosensitive genes in fistula tissue. The overexpression of TSG-6 in Caco-2 cells promoted migration, epithelial–mesenchymal transition, transcription factor SNAI1, and HA synthase (HAs) levels, while in fibroblasts, isolated from the area surrounding the fistula, it promoted an activated phenotype. Moreover, the enrichment of an HA scaffold with recombinant human TSG-6 protein promoted collagen release and increase of SNAI1, ITGA4, ITGA42B, and PTK2B genes, the latter being involved in the transduction of responses to mechanical stimuli. Conclusions: By mediating changes in the ECM organization, TSG-6 triggers the epithelial–mesenchymal transition transcription factor SNAI1 through the activation of mechanosensitive proteins. These data point to regulators of ECM as new potential targets for the treatment of CD perianal fistula.

An in-vitro model of human bone marrow mesenchymal stem cells (hBM-MSCs) myogenic commitment by synergic effect of a differentiation media coupled with human primary skeletal myoblasts (hSkMs) co-culture was developed adopting both conventional static co-seeding and perfused culture systems. Static co-seeding provided a notable outcome in terms of gene expression with a significant increase of Desmin (141-fold) and Myosin heavy chain II (MYH2, 32-fold) at day 21, clearly detected also by semi-quantitative immunofluorescence. Under perfusion conditions, myogenic induction ability of hSkMs on hBM-MSCs was exerted by paracrine effect with an excellent gene overexpression and immunofluorescence detection of MYH2 protein; furthermore, due to the dynamic cell culture in separate wells, western blot data were acquired confirming a successful cell commitment at day 14. A significant increase of anti-inflammatory cytokine gene expression, including IL-10 and IL-4 (15-fold and 11-fold, respectively) at day 14, with respect to the pro-inflammatory cytokines IL-12A (7-fold at day 21) and IL-1β (1.4-fold at day 7) was also detected during dynamic culture, confirming the immunomodulatory activity of hBM-MSCs along with commitment events. The present study opens interesting perspectives on the use of dynamic culture based on perfusion as a versatile tool to study myogenic events and paracrine cross-talk compared to the simple co-seeding static culture.

The spread of antibiotic-resistant bacteria and the rise of emerging and re-emerging viruses in recent years constitute significant public health problems. Therefore, it is necessary to develop new antimicrobial strategies to overcome these challenges. Herein, we describe an innovative method to synthesize ligand-free silver nanoparticles by Pulsed Laser Ablation in Liquid (PLAL-AgNPs). Thus produced, nanoparticles were characterized by total X-ray fluorescence, zeta potential analysis, transmission electron microscopy (TEM), and nanoparticle tracking analysis (NTA). A 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay was performed to evaluate the nanoparticles’ cytotoxicity. Their potential was evaluated against the enveloped herpes simplex virus type 1 (HSV-1) and the naked poliovirus type 1 (PV-1) by plaque reduction assays and confirmed by real-time PCR and fluorescence microscopy, showing that nanoparticles interfered with the early stage of infection. Their action was also examined against different bacteria. We observed that the PLAL-AgNPs exerted a strong effect against both methicillin-resistant Staphylococcus aureus (S. aureus MRSA) and Escherichia coli (E. coli) producing extended-spectrum β-lactamase (ESBL). In detail, the PLAL-AgNPs exhibited a bacteriostatic action against S. aureus and a bactericidal activity against E. coli. Finally, we proved that the PLAL-AgNPs were able to inhibit/degrade the biofilm of S. aureus and E. coli.

Integrated crop–livestock systems (ICLS) are among the main viable strategies for sustainable agricultural production. Mapping these systems is crucial for monitoring land use changes in Brazil, playing a significant role in promoting sustainable agricultural production. Due to the highly dynamic nature of ICLS management, mapping them is a challenging task. The main objective of this research was to develop a method for mapping ICLS using deep learning algorithms applied on Satellite Image Time Series (SITS) data cubes, which consist of Sentinel-2 (S2) and PlanetScope (PS) satellite images, as well as data fused (DF) from both sensors. This study focused on two Brazilian states with varying landscapes and field sizes. Targeting ICLS, field data were combined with S2 and PS data to build land use and land cover classification models for three sequential agricultural years (2018/2019, 2019/2020, and 2020/2021). We tested three experimental settings to assess the classification performance using S2, PS, and DF data cubes. The test classification algorithms included Random Forest (RF), Temporal Convolutional Neural Network (TempCNN), Residual Network (ResNet), and a Lightweight Temporal Attention Encoder (L-TAE), with the latter incorporating an attention-based model, fusing S2 and PS within the temporal encoders. Experimental results did not show statistically significant differences between the three data sources for both study areas. Nevertheless, the TempCNN outperformed the other classifiers with an overall accuracy above 90% and an F1-Score of 86.6% for the ICLS class. By selecting the best models, we generated annual ICLS maps, including their surrounding landscapes. This study demonstrated the potential of deep learning algorithms and SITS to successfully map dynamic agricultural systems.

In recent years, nanocarriers have been widely used as an effective solution for oral administration of pharmaceuticals. However, there is still an urgent need to speed up their translation to clinical practice. Cost-effective and industrially scalable methodologies are still needed. Herein, the production of vitamin C-loaded liposomes for nutraceutical purposes has been investigated and optimized by adopting a High-Pressure Homogenizer. Initially, the impact of process parameters on particles size, distributions, and morphology was explored. The findings document that the pressure and cycle manipulation allow for control over liposome size and polydispersity, reaching a maximum encapsulation efficiency exceeding 80%. This significantly improves the storage stability of vitamin C, as demonstrated by monitoring its antioxidant activity. Furthermore, the in vitro simulation of gastrointestinal digestion shows that liposomes could protect the active substance from damage and control its release in the gastrointestinal fluid. Thus, the whole nanodelivery system can contribute to enhancing vitamin C bioavailability. In conclusion, the results indicate that this innovative approach to producing vitamin C liposomes holds promise for clinical translation and industrial scale-up. Indeed, by utilizing food-grade materials and straightforward equipment, it is possible to produce stable and functional liposomes suitable for health products.

IntroductionThe RNA-binding protein AU-rich-element factor-1 (AUF-1) participates to posttranscriptional regulation of genes involved in inflammation and cellular senescence, two pathogenic mechanisms of chronic obstructive pulmonary disease (COPD). Decreased AUF-1 expression was described in bronchiolar epithelium of COPD patients versus controls and in vitro cytokine- and cigarette smoke-challenged human airway epithelial cells, prompting the identification of epithelial AUF-1-targeted transcripts and function, and investigation on the mechanism of its loss.ResultsRNA immunoprecipitation-sequencing (RIP-Seq) identified, in the human airway epithelial cell line BEAS-2B, 494 AUF-1-bound mRNAs enriched in their 3’-untranslated regions for a Guanine-Cytosine (GC)-rich binding motif. AUF-1 association with selected transcripts and with a synthetic GC-rich motif were validated by biotin pulldown. AUF-1-targets’ steady-state levels were equally affected by partial or near-total AUF-1 loss induced by cytomix (TNFα/IL1β/IFNγ/10 nM each) and siRNA, respectively, with differential transcript decay rates. Cytomix-mediated decrease in AUF-1 levels in BEAS-2B and primary human small-airways epithelium (HSAEC) was replicated by treatment with the senescence- inducer compound etoposide and associated with readouts of cell-cycle arrest, increase in lysosomal damage and senescence-associated secretory phenotype (SASP) factors, and with AUF-1 transfer in extracellular vesicles, detected by transmission electron microscopy and immunoblotting. Extensive in-silico and genome ontology analysis found, consistent with AUF-1 functions, enriched RIP-Seq-derived AUF-1-targets in COPD-related pathways involved in inflammation, senescence, gene regulation and also in the public SASP proteome atlas; AUF-1 target signature was also significantly represented in multiple transcriptomic COPD databases generated from primary HSAEC, from lung tissue and from single-cell RNA-sequencing, displaying a predominant downregulation of expression.DiscussionLoss of intracellular AUF-1 may alter posttranscriptional regulation of targets particularly relevant for protection of genomic integrity and gene regulation, thus concurring to airway epithelial inflammatory responses related to oxidative stress and accelerated aging. Exosomal-associated AUF-1 may in turn preserve bound RNA targets and sustain their function, participating to spreading of inflammation and senescence to neighbouring cells.

Various approaches were developed considering the need to increase agricultural productivity in cultivated areas without more deforestation, such as the Integrated Crop livestock systems (ICLS). The ICLS could be composed of annual crops followed by pastureland with the presence of cattle. Due to the high temporal dynamic of rotation between crops over the season, monitoring these areas is a big challenge. Also, agricultural organizations worldwide highlight the need for early-season maps for this kind of work. In this context, this study evaluated the potential of open data (Sentinel-2) data to map ICLS areas. The performance of two classifiers was evaluated: one of Machine Learning (random forest) and the other of Deep Learning (LSTM). Three different time windows of data were tested (Entire season, 180 days, and 120 days). Using the RF classifier, it was possible to achieve satisfactory results (Overall accuracy higher than 80%) for the early season (180 days). However, further studies are needed to explain better the lower(when compared to Random Forest) accuracy achieved by LSTM net (0.79 % for 180 days) and compare the results achieved here with results for a study area with different rates of cloud cover.

With the recent evolution in the sensor's spatial resolution, such as the MultiSpectral Imager (MSI) of the Sentinel-2 mission, the need to use segmentation techniques in satellite images has increased. Although the advantages of image segmentation to delineate agricultural fields in images are already known, the literature shows that it is rarely used to consider temporal changes in highly managed regions with the intensification of agricultural activities. Therefore, this work aimed to evaluate a multitemporal segmentation method based on the coefficient of variation of spectral bands and vegetation indices obtained from Sentinel-2 images, considering two agricultural years (2018–2019 and 2019–2020) in an area with agricultural intensification. Images of the coefficient of variation represented the spectro-temporal dynamics within the study area. These images were also used to apply an edge detection filter (Sobel) to verify their performance. The region-based algorithm Watershed Segmentation (WS) was used in the segmentation process. Subsequently, to assess the quality of the segmentation results produced, the metrics Potential Segmentation Error (PSE), Number-of-Segments Ratio (NSR), and Euclidean Distance 2 (ED2) were calculated from manually delineated reference objects. The segmentation achieved its best performance when applied to the unfiltered coefficient of variation images of spectral bands with an ED2 equal to 7.289 and 2.529 for 2018–2019 and 2019–2020, respectively. There was a tendency for the WS algorithm to produce over-segmentation in the study area; however, its use proved to be effective in identifying objects in a dynamic area with the intensification of agricultural activities.