Search Results (4,816)

Predicting the relative solvent accessibility (RSA) of a protein is critical to understanding its 3D structure and biological function. RSA prediction, especially when homology transfer cannot provide information about a protein’s structure, is a significant step toward addressing the protein structure prediction challenge. Today, deep learning is arguably the most powerful method for predicting RSA and other structural features of proteins. In particular, recent breakthroughs in deep learning—driven by the integration of natural language processing (NLP) algorithms—have significantly advanced the field of protein research. Inspired by the remarkable success of NLP techniques, this study leverages pre-trained language models (PLMs) to enhance RSA prediction. We present a deep neural network architecture based on a combination of bidirectional recurrent neural networks and convolutional layers that can analyze long-range interactions within protein sequences and predict protein RSA using ESM-2 encoding. The final predictor, PaleAle 6.0, predicts RSA in real values as well as two-state (exposure threshold of 25%) and four-state (exposure thresholds of 4%, 25%, and 50%) discrete classifications. On the 2022 test set dataset, PaleAle 6.0 achieved over 82% accuracy for two-state RSA (RSA_2C) and 59.75% accuracy for four-state RSA (RSA_4C), with a Pearson correlation coefficient (PCC) of 77.88 for real-value RSA prediction. When evaluated on the more challenging 2024 test set, PaleAle 6.0 maintained a strong performance, achieving 79.74% accuracy in the two-state prediction and 55.30% accuracy in the four-state prediction, with a PCC of 73.08 for real-value predictions, outperforming all previously benchmarked predictors. Full article

Salmonella is diffused worldwide, and Salmonella enterica subsp. enterica is spread worldwide with many serovars associated with the infection of domestic bovines. The most spread are S. Dublin, S. Typhimurium and S. Infantis. S. Napoli is, however very rarely reported in domestic ruminants. Here, we report an outbreak of S. Napoli on a dairy cow farm in Northern Italy (Piacenza). A total of 18 S. Napoli isolates were recovered from aborted fetuses, feces, tissues and environmental samples. Whole genome sequencing suggested that all isolates belonged to the same cluster. After the application of stringent biocontainment and biosecurity measures, no further cases were reported. However, four months after the first case, the serovar was still isolated in environmental samples, underlying the importance of adopting the correct biosecurity and biocontainment measures in order to prevent the circulation and transmission of Salmonella within the farm. Full article

The endoplasmic reticulum and the internal nuclear compartments are intrinsically connected through the nuclear membrane, pores and lamina. High resolution imaging of each of these cellular features concurrently remains a significant challenge. To that end we have developed a new molecular nuclear membrane-endoplasmic reticulum (NM-ER) staining fluorophore with emission maxima at 650 nm. NM-ER is compatible with fixed and live cell imaging and stimulated emission depletion microscopy (STED) showing significant improvement in resolution when compared to comparable confocal laser scanning microscopy. The imaging versatility of NM-ER was illustrated through its compatible use with other fluorophores for co-imaging with DNA, nuclear pores and lamina allowing cellular abnormalities to be identified. NM-ER alone, or in use with other nuclear region labels could be an important tool for the investigation of nuclear transport and associated cellular processes. Full article

Weaning in piglets presents significant physiological and immunological challenges, including gut dysbiosis and increased susceptibility to post-weaning diarrhoea (PWD). Abrupt dietary, environmental, and social changes during this period disrupt the intestinal barrier and microbiota, often necessitating antimicrobial use. Sustainable dietary strategies are critical to addressing these issues while reducing reliance on antimicrobials. Reducing dietary crude protein mitigates the availability of undigested proteins for pathogenic bacteria, lowering harmful by-products like ammonia and branched-chain fatty acids, which exacerbate dysbiosis. Organic acid supplementation improves gastric acidification, nutrient absorption, and microbial balance, while also serving as an energy-efficient alternative to traditional grain preservation methods. Increasing intestinal butyrate, a key short-chain fatty acid with anti-inflammatory and gut-protective properties, is particularly promising. Butyrate strengthens intestinal barrier integrity by upregulating tight junction proteins, reduces inflammation by modulating cytokine responses, and promotes anaerobic microbial stability. Exogenous butyrate supplementation via salts provides immediate benefits, while endogenous stimulation through prebiotics (e.g., resistant starch) and probiotics promotes sustained butyrate production. These interventions selectively enhance butyrate-producing bacteria such as Roseburia and Faecalibacterium prausnitzii, further stabilising the gut microbiota. Integrating these strategies can enhance gut integrity, microbial resilience, and immune responses in weaned piglets. Their combination offers a sustainable, antimicrobial-free approach to improving health and productivity in modern pig production systems. Full article

Objective: The regulation of food intake is disturbed in obesity, possibly resulting from alterations in gut peptide secretion. We hypothesize that obesity is associated with attenuated systemic and tissue concentrations of the gut peptides PYY and GLP-1. Methods: A prospective single-center study in which we included 13 individuals with obesity (BMI 39.5 ± 2.8 kg/m²) and 11 lean individuals as controls (BMI 20.7 ± 1.2 kg/m²) matched for age and gender. We measured: (1) tissue concentrations and mRNA expression of GLP-1 and PYY in ileal and colonic biopsies taken during routine colonoscopy and (2) plasma concentrations of PYY and GLP-1 in response to a meal in the same group. Results: Plasma GLP-1 and PYY responses did not differ between individuals with obesity and lean controls. Neither were tissue concentrations and mRNA expression of both peptides different between both groups. Conclusions: Systemic and local PYY and GLP-1 concentrations in individuals with obesity do not differ from those in lean subjects. Full article

This study examines the impact of laser shock peening (LSP) on the mechanical properties, microstructural features, and elemental distribution of stainless steel 316L (SS316L) produced using wire arc additive manufacturing (WAAM). The investigation focuses on significant changes in mechanical behavior, surface topography, and porosity following LSP treatment, comparing these results to the untreated condition. LSP treatment significantly enhanced the ultimate tensile strength (UTS) and yield strength (YS) of WAAM-fabricated SS316L samples. The UTS of the as-manufactured WAAM specimen was 548 MPa, which progressively increased with higher LSP intensities to 595 MPa for LSP-1, 613 MPa for LSP-2, and 634.5 MPa for LSP-3, representing a maximum improvement of 15.8%. The YS showed a similar trend, increasing from 289 MPa in the as-manufactured specimen to 311 MPa (LSP-1) and 332 MPa (LSP-2), but decreasing to 259 MPa for LSP-3, indicating over-peening effects. Microstructural analysis revealed that LSP induced severe plastic deformation and reduced porosity from 14.02% to 4.18%, contributing to the improved mechanical properties. Energy dispersive spectroscopy (EDS) analysis confirmed the formation of an oxide layer post-LSP, with an increase in carbon (C) and oxygen (O) elements and a decrease in chromium (Cr) and nickel (Ni) elements on the surface, attributed to localized pressure and heat impacts. LSP-treated samples exhibited enhanced mechanical performance, with higher tensile strengths and improved ductility at higher laser intensities. This is due to LSP effectively enhancing the mechanical properties and structural integrity of WAAM-fabricated SS316L, reducing porosity, and refining the microstructure. These improvements make the material suitable for critical applications in the aerospace, automotive, and biomedical fields. Full article

For general insurance pricing, aligning losses with accurate premiums is crucial for insurance companies’ competitiveness. Traditional actuarial models often face challenges like data heterogeneity and mismeasured covariates, leading to misspecification bias. This paper addresses these issues from a Bayesian perspective, exploring connections between Bayesian hierarchical modeling, partial pooling techniques, and the Gustafson correction method for mismeasured covariates. We focus on Non-Differential Berkson (NDB) mismeasurement and propose an approach that corrects such errors without relying on gold standard data. We discover the unique prior knowledge regarding the variance of the NDB errors, and utilize it to adjust the biased parameter estimates built upon the NDB covariate. Using simulated datasets developed with varying error rate scenarios, we demonstrate the superiority of Bayesian methods in correcting parameter estimates. However, our modeling process highlights the challenge in accurately identifying the variance of NDB errors. This emphasizes the need for a thorough sensitivity analysis of the relationship between our prior knowledge of NDB error variance and varying error rate scenarios. Full article

Fumonisins, a class of mycotoxins predominantly produced by Fusarium species, represent a major threat to food safety and public health due to their widespread occurrence in staple crops including peanuts, wine, rice, sorghum, and mainly in maize and maize-based food and feed products. Although fumonisins occur in different groups, the fumonisin B series, particularly fumonisin B1 (FB1) and fumonisin B2 (FB2), are the most prevalent and toxic in this group of mycotoxins and are of public health significance due to the many debilitating human and animal diseases and mycotoxicosis they cause and their classification as by the International Agency for Research on Cancer (IARC) as a class 2B carcinogen (probable human carcinogen). This has made them one of the most regulated mycotoxins, with stringent regulatory limits on their levels in food and feeds destined for human and animal consumption, especially maize and maize-based products. Numerous countries have regulations on levels of fumonisins in foods and feeds that are intended to protect human and animal health. However, there are still gaps in knowledge, especially with regards to the molecular mechanisms underlying fumonisin-induced toxicity and their full impact on human health. Detection of fumonisins has been advanced through various methods, with immunological approaches such as Enzyme-Linked Immuno-Sorbent Assay (ELISA) and lateral flow immunoassays being widely used for their simplicity and adaptability. However, these methods face challenges such as cross-reactivity and matrix interference, necessitating the need for continued development of more sensitive and specific detection techniques. Chromatographic methods, including HPLC-FLD, are also employed in fumonisin analysis but require meticulous sample preparation and derivitization due to the low UV absorbance of fumonisins. This review provides a comprehensive overview of the fumonisin family, focusing on their biosynthesis, occurrence, toxicological effects, and levels of contamination found in foods and the factors affecting their presence. It also critically evaluates the current methods for fumonisin detection and quantification, including chromatographic techniques and immunological approaches such as ELISA and lateral flow immunoassays, highlighting the challenges associated with fumonisin detection in complex food matrices and emphasizing the need for more sensitive, rapid, and cost-effective detection methods. Full article

We investigate the application of deep learning in comparing gait cycle time series from two groups of healthy children, each assessed in different gait laboratories. Both laboratories used similar gait analysis protocols with minimal differences in data collection. Utilizing a ResNet-based deep learning model, we successfully identified the source laboratory of each dataset, achieving a high classification accuracy across multiple gait parameters. To address the inter-laboratory differences, we explored various pre-processing methods and time series properties that may have been detected by the algorithm. We found that the standardization of the time series values was a successful approach to decrease the ability of the model to distinguish between the two centers. Our findings also reveal that differences in the power spectra and autocorrelation structures of the datasets play a significant role in the model performance. Our study emphasizes the importance of standardized protocols and robust data pre-processing to enhance the transferability of machine learning models across clinical settings, particularly for deep learning approaches. Full article

Water distribution networks (WDNs) are critical infrastructures that directly impact urban development and citizens’ quality of life. Due to digitalization technologies, modern networks have evolved towards cyber-physical systems, allowing real-time management and monitoring of network components. However, the increasing volume of data from monitoring poses significant challenges to accurately estimate the hydraulic status of the system, mainly when anomalous events or unreliable readings occur. This paper presents a novel methodology for state estimation (SE) in WDNs by integrating convolutional graph networks (GCNs) with long short-term memory (LSTM) networks. The methodology is validated on two WDNs of different scales and complexities, evaluating the SE of the sensors. The capability of the GCN-LSTM model was assessed during the last two months of the time series by simulating failures to analyze its impact on sensor readings and estimation accuracy. The smaller network showed higher sensitivity of the sensors to detect failures, while the larger one evidenced more challenges in SE due to the sensor dispersion. Overall, the model achieved low prediction errors and high coefficient of determination values between the actual and simulated values, showing good performance. Likewise, the simulated failures showed that replacing the missing data with the hourly mean of the last week significantly improved the accuracy of the predictions, guaranteeing a robust SE in the event of sensor failures. This methodology provides a reliable tool for addressing various network configurations’ operational challenges. Full article

Prostate cancer (PCa) pathogenesis relies on intercellular communication, which can involve tunnelling nanotubes (TNTs) and extracellular vesicles (EVs). TNTs and EVs have been reported to transfer critical cargo involved in cellular functions and signalling, prompting us to investigate the extent of organelle and protein transfer in PCa cells and the potential involvement of the androgen receptor. Using live cell imaging microscopy, we observed extensive formation of TNTs and EVs operating between PCa, non-malignant, and immune cells. PCa cells were capable of transferring lysosomes, mitochondria, lipids, and endoplasmic reticulum, as well as syndecan-1, sortilin, Glut1, and Glut4. In mechanistic studies, androgen-sensitive PCa cells exhibited changes in cell morphology when stimulated by R1881 treatment. Overexpression assays of a newly designed androgen receptor (AR) plasmid revealed its novel localization in PCa cellular vesicles, which were also transferred to neighbouring cells. Selected molecular machinery, thought to be involved in intercellular communication, was investigated by knockdown studies and Western blotting/immunofluorescence/scanning electron microscopy (SEM). PCa TNTs and EVs transported proteins and organelles, which may contain specialist signalling, programming, and energy requirements that support cancer growth and progression. This makes these important intercellular communication systems ideal potential targets for therapeutic intervention. Full article

In this study, we describe the development of hydrogel formulations composed of micelles loading two natural antioxidants—resveratrol and rutin—and the evaluation of the effect of a by-product on the rheological and textural properties of the developed semi-solids. This approach aims to associate the advantages of hydrogels for topical administration of drugs and of lipid micelles that mimic skin composition for the delivery of poorly water-soluble compounds in combination therapy. Biomimetic micelles composed of L-α-phosphatidylcholine loaded with two distinct polyphenols (one non-flavonoid and one flavonoid) were produced using hot shear homogenisation followed by the ultrasonication method. All developed micelles were dispersed in a carbomer 940-based hydrogel to obtain three distinct semi-solid formulations, which were then characterised by analysing the thermal, rheological and textural properties. Olive pomace-based hydrogels were also produced to contain the same micelles as an alternative to respond to the needs of zero waste and circular economy. The thermograms showed no changes in the typical profiles of micelles when loaded into the hydrogels. The rheological analysis confirmed that the produced hydrogels achieved the ideal properties of a semi-solid product for topical administration. The viscosity values of the hydrogels loaded with olive pomace (hydrogels A) proved to be lower than the hydrogels without olive pomace (hydrogels B), with this ingredient having a considerable effect in reducing the viscosity of the final formulation, yet without compromising the firmness and cohesiveness of the gels. The texture analysis of both hydrogels A and B also exhibited the typical behaviour expected of a semi-solid system. Full article

Background: Curcumin and hesperetin are plant polyphenols known for their poor solubility. To address this limitation, we prepared amorphous PVP K30–phosphatidylcholine dispersions via hot-melt extrusion. Methods: This study aimed to evaluate the effects of the amounts of active ingredients and phosphatidylcholine, as well as the process temperature, on the performance of the dispersions. A Box–Behnken design was employed to assess these factors. Solid-state characterization and biopharmaceutical studies were then conducted. X-ray powder diffraction (XRPD) was used to confirm the amorphous nature of the dispersions, while differential scanning calorimetry (DSC) provided insight into the miscibility of the systems. Fourier-transform infrared spectroscopy (FTIR) was employed to assess the intermolecular interactions. The apparent solubility and dissolution profiles of the systems were studied in phosphate buffer at pH 6.8. In vitro permeability across the gastrointestinal tract and blood–brain barrier was evaluated using the parallel artificial membrane permeability assay. Results: The quantities of polyphenols and phospholipids were identified as significant factors influencing the biopharmaceutical performance of the systems. Solid-state analysis confirmed the formation of amorphous dispersions and the development of interactions among components. Notably, a significant improvement in solubility was observed, with formulations exhibiting distinct release patterns for the active compounds. Furthermore, the in vitro permeability through the gastrointestinal tract and blood–brain barrier was enhanced. Conclusions: The findings suggest that amorphous PVP K30–phosphatidylcholine dispersions have the potential to improve the biopharmaceutical properties of curcumin and hesperetin. Full article

Background: An estimated 10–15% of all genetic diseases are attributable to variants in noncanonical splice sites, auxiliary splice sites and deep-intronic variants. Most of these unstudied variants are classified as variants of uncertain significance (VUS), which are not clinically actionable. This study investigated two novel splice-altering variants, CHM NM_000390.4:c.941-11T>G and CACNA1F NM_005183.4:c.2576+4_2576+5del implicated in choroideremia and cone dystrophy (COD), respectively, resulting in significant visual loss. Methods: Next-generation sequencing was employed to identify the candidate variants in CHM and CACNA1F, which were confirmed using Sanger sequencing. Cascade analysis was undertaken when additional family members were available. Functional analysis was conducted by cloning genomic regions of interest into gateway expression vectors, creating variant and wildtype midigenes, which were subsequently transfected into HEK293 cells. RNA was harvested and amplified by RT-PCR to investigate the splicing profile for each variant compared to the wildtype. Novel variants were reclassified according to ACMG/AMP and ClinGen SVI guidelines. Results: Midigene functional analysis confirmed that both variants disrupted splicing. The CHM NM_000390.4:c.941-11T>G variant caused exon 8 skipping, leading to a frameshift and the CACNA1F NM_005183.4:c.2576+4_2576+5del variant caused a multimodal splice defect leading to an in-frame insertion of seven amino acids and a frameshift. With this evidence, the former was upgraded to likely pathogenic and the latter to a hot VUS. Conclusions: This study adds to the mutational spectrum of splicing defects implicated in retinal degenerations by identifying and characterising two novel variants in CHM and CACNA1F. Our results highlight the importance of conducting functional analysis to investigate the consequences of intronic splice-altering variants and the significance of reclassifying VUS to confirm a genetic diagnosis. Full article

Accurately predicting protein secondary structure (PSSP) is crucial for understanding protein function, which is foundational to advancements in drug development, disease treatment, and biotechnology. Researchers gain critical insights into protein folding and function within cells by predicting protein secondary structures. The advent of deep learning models, capable of processing complex sequence data and identifying meaningful patterns, offer substantial potential to enhance the accuracy and efficiency of protein structure predictions. In particular, recent breakthroughs in deep learning—driven by the integration of natural language processing (NLP) algorithms—have significantly advanced the field of protein research. Inspired by the remarkable success of NLP techniques, this study harnesses the power of pre-trained language models (PLMs) to advance PSSP prediction. We conduct a comprehensive evaluation of various deep learning models trained on distinct sequence embeddings, including one-hot encoding and PLM-based approaches such as ProtTrans and ESM-2, to develop a cutting-edge prediction system optimized for accuracy and computational efficiency. Our proposed model, Porter 6, is an ensemble of CBRNN-based predictors, leveraging the protein language model ESM-2 as input features. Porter 6 achieves outstanding performance on large-scale, independent test sets. On a 2022 test set, the model attains an impressive 86.60% accuracy in three-state (Q3) and 76.43% in eight-state (Q8) classifications. When tested on a more recent 2024 test set, Porter 6 maintains robust performance, achieving 84.56% in Q3 and 74.18% in Q8 classifications. This represents a significant 3% improvement over its predecessor, outperforming or matching state-of-the-art approaches in the field. Full article