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The microwave response of superconducting devices can be affected by nonlinearity effects of both intrinsic and extrinsic origin. In this study, we report on the nonlinear behavior of NbTi microwave resonators, in the presence of dc magnetic fields up to 4 T. The aim of this work is to characterize the vortex-induced nonlinearity, which in these conditions of frequency (11 GHz) and fields is expected to give the major contribution to dissipation, when the circulating rf current exceeds a given threshold. Nonlinearity is investigated by analyzing Q-degradation and resonance curve distortion as a function of the input rf power, while the emergence of sharp discontinuities is associated to the existence of an rf limiting current density. The current densities corresponding to the onset of these features are compared to the critical current density from dc measurements, helping us to outline a comprehensive picture. Moreover, the pinning constant was extracted as a function of temperature by means of a Gittleman–Rosenblum analysis, revealing the prominent role of δTc−type pinning. We also analyzed the effects of introducing controlled artificial disorder and pinning sites through 1.5-MeV proton irradiation. After irradiation, we observed an increase of both the pinning constant and the in-field nonlinearity threshold and limiting current.

Proteins involved in antibiotic resistance (resistome) and with antimicrobial activity are present in biological specimens. This study aims to explore the presence and abundance of antimicrobial peptides (AMPs) and resistome proteins in bovine milk from diverse breeds and from intensive (Pezzata rossa, Bruna alpina, and Frisona) and non-intensive farming (Podolica breeds). Liquid atmospheric pressure matrix-assisted laser desorption/ionization (LAP-MALDI) mass spectrometry (MS) profiling, bottom-up proteomics, and metaproteomics were used to comprehensively analyze milk samples from various bovine breeds in order to identify and characterize AMPs and to investigate resistome proteins. LAP-MALDI MS coupled with linear discriminant analysis (LDA) machine learning was employed as a rapid classification method for Podolica milk recognition against the milk of other bovine species. The results of the LAP-MALDI MS analysis of milk coupled with the linear discriminant analysis (LDA) demonstrate the potential of distinguishing between Podolica and control milk samples based on MS profiles. The classification accuracy achieved in the training set is 86% while it reaches 98.4% in the test set. Bottom-up proteomics revealed approximately 220 quantified bovine proteins (identified using the Bos taurus database), with cathelicidins and annexins exhibiting higher abundance levels in control cows (intensive farming breeds). On the other hand, the metaproteomics analysis highlighted the diversity within the milk’s microbial ecosystem with interesting results that may reflect the diverse environmental variables. The bottom-up proteomics data analysis using the Comprehensive Antibiotic Resistance Database (CARD) revealed beta-lactamases and tetracycline resistance proteins in both control and Podolica milk samples, with no relevant breed-specific differences observed.

Antimicrobial resistance (AMR) poses a critical global health threat, necessitating innovative strategies to combat infectious diseases. Plants and their extracts offer alternatives/support to traditional antibiotics, and their diverse phytocomplexes with antimicrobial properties can be exploited. The Mediterranean hosts a high number of biodiverse plant species known for their bioactive compounds. This study focuses on identifying plant species and phytochemical constituents with antimicrobial activity against Escherichia coli (E. coli). Utilizing a systematic literature review and meta-analysis approach, we screened 3037 entries, yielding 70 studies suitable for MIC (minimum inhibitory concentration) annotation. The results highlighted Lavandula spp., Plectranthus spp. and Lupinus jaimehintoniana as the most effective plants with average MICs of, respectively, 0.144 mg/mL, 0.260 mg/mL, and 0.140 mg/mL. These results might help to fight AMR via the discovery of complementary natural antimicrobial agents to support the development of green veterinary pharmacology. Further exploration of these resources promises valuable insights for future support to antimicrobial strategies.

Deltamethrin is used against plant pests (e.g., mites and ants) and, in farm animals, against biting insects because of its acaricidal and repellent effects against ticks, thus protecting the sheep and cattle from the transmission of pathogens. However, its impact on the environment still needs to be fully evaluated. This study evaluates the impact of this pyrethroid on the nitrogen-fixing nodules in Medicago truncatula, a model legume. This research compares nodular biomass and root weight between a deltamethrin-treated section and an untreated control section of this legume. Our results indicate a significant reduction in the biomass of nitrogen-fixing nodules in the treated grove, suggesting that deltamethrin negatively affects the symbiotic relationship between M. truncatula and nitrogen-fixing bacteria. This reduction in nodule formation can impair soil fertility and plant growth, highlighting an ecological risk associated with pyrethroid’s use in livestock farming. These findings underscore the need for a shift towards Green Veterinary Pharmacology (GVP), which promotes environmentally sustainable practices in managing livestock health. By minimizing our reliance on harmful chemical treatments, GVP offers viable solutions to protect and enhance ecosystem services such as biological nitrogen fixation that are essential for maintaining soil health and agricultural productivity.

The Podolica cattle breed is widespread in southern Italy, and its productivity is characterized by low yields and an extraordinary quality of milk and meats. Most of the milk produced is transformed into “Caciocavallo Podolico” cheese, which is made with 100% Podolica milk. Fourier Transform Infrared Spectroscopy (FTIR) is the technique that, in this research work, was applied together with machine learning to discriminate 100% Podolica milk from contamination of other Calabrian cattle breeds. The analysis on the test set produced a misclassification percentage of 6.7%. Among the 15 non-Podolica samples in the test set, 2 were misclassified and recognized as Podolica milk even though the milk was from other species. The correct classification rate improved to 100% when the same method was applied to the recognition of Podolica and Pezzata Rossa milk produced by the same farm. Furthermore, this technique was tested for the recognition of Podolica milk mixed with milk from other bovine species. The multivariate model and the respective confusion matrices obtained showed that all the 14 Podolica samples (test set) mixed with 40% non-Podolica milk were correctly classified. In addition, Pezzata Rossa milk produced by the same farm was detected as a contaminant in Podolica milk from the same farm down to concentrations as little as 5% with a 100% correct classification rate in the test set. The method described yielded higher accuracy values when applied to the discrimination of milks from different breeds belonging to the same farm. One of the reasons for this phenomenon could be linked to the elimination of the environmental variable. However, the results obtained in this work demonstrate the possibility of using FTIR to discriminate between milks from different breeds.