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Fil: Lopez, Beatriz Alicia. Universidad Nacional de Santiago del Estero. Instituto de Bionanotecnología del Noa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Bionanotecnología del Noa; Argentina

Fil: Rodriguez, Silvia del Carmen. Universidad Nacional de Santiago del Estero. Facultad de Agronomía y Agroindustrias. Instituto de Ciencias y Tecnologías Alimentarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet Noa Sur. Centro de Investigación en Biofísica Aplicada y Alimentos. - Universidad Nacional de Santiago del Estero. Centro de Investigación en Biofísica Aplicada y Alimentos; Argentina

The objective of this study was to evaluate the effect of antioxidant treatments on the conservation of cut eggplants (Solanum melongena L.), which was carried out in two stages. Initially, the effect of citric acid (CA), ascorbic acid (AA) and cysteine (Cys) solution at 0.5 and 1% on sensory attributes (general appearance and browning), and color parameters during storage were evaluated. Immersion in 1% AA was considered the best antioxidant since it maintained visual quality for 6 days. Subsequently, hot water dipping (HWD) treatments followed by the 1% AA solution were evaluated and optimized through the Response Surface Methodology (RSM). Sensory attributes, color parameters, respiration rate (RR), phenolic compounds (PhC), antioxidant capacity, as well as the activity of polyphenol oxidase (PPO) and peroxidase (POD) were assessed during cold storage. The results showed that HWD at 50 °C, 60 s and 1% AA was the optimal combination to control enzymatic browning and extend its fresh quality for up to 8 days. Furthermore, that combination of treatments reduced the PPO and POD activities and increased the PhC compared to the control (untreated), not finding significant differences between them in antioxidant capacity and RR. Therefore, the application of this combination would be the most appropriate to preserve the quality of the fresh-cut eggplants for 8 days of storage at 4 °C.

Keeping in mind the concept of green chemistry, this research aims to synthesize and characterize new ionic liquids (ILs) derived from N-cinnamyl imidazole with different sizes of alkyl chains (1, 6, 8, and 10 carbon atoms), and evaluate their antibacterial activity against Skin and soft tissue infections (SSTIs) causative bacteria. The antibacterial screening was carried out by agar well diffusion and the Minimum Inhibitory Concentration (MIC) and Half Maximum Inhibitory Concentration (IC50) of the different ILs were determined by microdilution in broth, also Molecular dynamics simulations were performed to study the interaction mechanism between ILs and membranes. The MIC value in Gram-positive bacteria showed that as the hydrocarbon chain increases, the MIC value decreases with a dose-dependent effect. Furthermore, Gram-negative bacteria showed high MIC values, which were also evidenced in the antibacterial screening. The molecular dynamics showed an incorporation of the ILs with the longer chain (10 C), corresponding to a passive diffusion towards the membrane surface, for its part, the ILs with the shorter chain due to its lack of hydrophobicity was not incorporated into the bilayer. Finally, the new ILs synthesized could be an alternative for the treatment of Gram-positive bacteria causative of SSTIs.

The isolation of pathogenic Leptospira is fundamental for a comprehensive characterization of circulating strains in endemic regions. Unfortunately, culture methods of Leptospira spp. are laborious and challenging. Here, we present a method for the isolation of these pathogenic bacteria from non-fresh serum samples, previously stored at 4–8 °C for several days. Briefly, 730 serum samples collected from leptospirosis-suspected patients (presenting acute signs) were screened for Leptospira DNA by real-time PCR. Thirty-one PCR-positive sera were then assessed for Leptospira isolation on specialized media for up to 6 months. Using this methodology, 11 Leptospira isolates were obtained, resulting in an isolation rate of 35.4% (11/31). Through whole-genome analysis, ten strains were identified as Leptospira santarosai and one strain as Leptospira borgpertersenii . The isolates were classified into six different serogroups, namely Hebdomadis, Shermani, Tarassovi, Pyrogenes, Ballum, and Grippotyphosa, demonstrating a wide diversity of Leptospira strains circulating in Costa Rica. This study reveals that serum is a suitable sample for Leptospira isolation in patients with positive PCR results, even after maintenance at cold conditions, promoting the use of serum for Leptospira isolation in reference laboratories around the world.

This paper presents an efficient cyberphysical platform for the smart management of smart territories. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart cities is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study where the bike renting service of Paris—Vélib’ Métropole has been managed. This platform could enable smart territories to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques.

Rotating machines are frequently subject to a wide range of rough conditions, resulting in mechanical failures and performance degradation. Thus, it is important to apply proper failure detection and recognition techniques, such as machine learning algorithms, to prevent these issues early. In industrial environments, little data exists regarding failure conditions, which hinders the training stage of the classification algorithms responsible for classifying the failures. Therefore, this work proposes a hybrid method of data augmentation to increase the number of minority class instances in order to improve classifier performance. The approach combines the synthetic minority over-sampling and the additive white Gaussian noise techniques to create a set of artificial signals. The results show that the proposal is able to achieve better results than applying those techniques separately and also when using an undersampling strategy. For comparison purposes, four machine learning classification methods were analyzed alongside our data augmentation proposal, namely, support vector machines, K-nearest neighbors, random forest and stacked sparse autoencoder. The proposed hybrid data augmentation method associated with stacked sparse autoencoder outperformed the other models obtaining an accuracy of 100% and a processing time of 0.13 s.

Failure detection from mechanical vibration analysis is crucial in industry machinery, with early discovery allowing for preventive action to be performed. This paper introduces a prototype of an IoT system capable of (i) identifying combined failures of a rotating machine and (ii) predicting failures, in a non-invasive manner. An embedded solution is devised, which is able to classify four types of operating conditions, namely (i) normal, (ii) imbalanced, (iii) imbalanced associated with horizontal misalignment, and (iv) imbalanced associated with vertical misalignment. The goal of the paper is to propose an automatic method of diagnosis and measurement of combined failures in rotating machines. The employed methodology combines a simulation bench and measuring the severity in a controlled environment. Three distinct machine learning techniques were compared for classification purposes: support vector machines, k -nearest neighbors, and random forests. The results obtained reveal the possibility of differentiating between the types of combined faults; an accuracy of 81.41% using a random forest classifier was achieved. A supervisory system was developed which is responsible for monitoring machines and sending wireless alert messages. The latter are sent to a control application, allowing for user interaction through mobile devices. Results reveal the possibility of differentiating between the types of combined faults, and also motor failure severity profile for different scenarios. Through the construction of severity profiles, when faults occurred, high vibration values were registered at elevated speeds. The proposed methodology can be used in any rotating machine that complies with the conditions imposed by ISO 10816.

Predictive maintenance has been employed to reduce maintenance costs and production losses and to prevent any failure before it occurs. The framework proposed in this work performs diesel engine prognosis by evaluating the absolute value of the failure severity using random forest (RF) and multilayer perceptron (MLP) neural networks. A database was implemented with 3500 failure scenarios to overcome the problem of inducing destructive failures in diesel engines. Diesel engine failure signals were developed with the zero-dimensional thermodynamic model inside a cylinder coupled with the crankshaft torsional vibration model. Artificial neural networks and random forest regression models were employed for classifying and quantifying failures. The methodology was applied alongside an engine simulator to assess effectiveness and accuracy. The best-fitting performance was obtained with the random forest regressor with an RMSE value of 0.10 ± 0.03%.

Inflammation is a critical defense against pathogens but can impair iron metabolism and erythropoiesis, potentially causing or exacerbating anemia during infection. However, the ecological and evolutionary relevance of this trade-off remains poorly understood. Naturally co-evolved host–parasite systems offer a unique opportunity to explore how inflammatory responses balance the benefits of pathogen control against potential physiological costs. We examined how systemic inflammation affects recovery from hookworm-associated anemia in South American fur seal ( Arctocephalus australis ) pups, aiming to determine whether inflammation facilitates recovery or imposes hematological constraints. We longitudinally monitored 83 pups over approximately 3 months on Guafo Island, Northern Chilean Patagonia, measuring hookworm burden, hematological parameters, iron concentration, and blood cytokines. Seventy-two percent of the pups developed clinical hookworm infection, and 47% of these became anemic. Among anemic pups, 54% recovered from anemia 2 months after infection. Changes in inflammatory markers, but not hookworm burden, iron concentration, or body condition, predicted recovery outcome. Sustained increases in IFN-γ and neutrophils reduced the likelihood of recovery, while increased IL-10 concentration favored recovery. These effects were independent of plasma iron concentration, although IL-6 was negatively correlated with lower plasma iron. Our findings show that prolonged systemic inflammation impairs recovery from anemia in a wild marine mammal, highlighting a physiological cost of inflammation in early life as a key ecological trade-off between immune defense and hematological resilience in natural host–parasite systems.