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Staphylococcus species are widespread in poultry environments and can cause various infections, often when the host’s defences are compromised. This manuscript reports on a co-infection of chickens with Staphylococcus lentus and Staphylococcus aureus associated with an outbreak of arthritis, synovitis, and osteomyelitis in an organic broiler breeder flock in Austria. Clinically, the affected flock showed weakness, lethargy, lameness, and increased mortality. Post-mortem examinations identified purulent arthritis and femoral head necrosis. Bacteriological analysis using MALDI-TOF MS identified both S. aureus and S. lentus in the affected joints. Antibiotic resistance testing revealed significant resistance, particularly in S. lentus. Histological analysis showed severe inflammation and bacterial colonies in the joints. While S. aureus is a common pathogen in poultry, S. lentus is less frequently reported. This study emphasises the need for detailed bacterial characterisation in outbreaks to better understand the role of less common pathogens like S. lentus. Further research is necessary to elucidate the impact of S. lentus on poultry health and its role in causing arthritis and synovitis, highlighting the importance of comprehensive investigation in such outbreaks.

Most permissionless blockchains inherently suffer from throughput limitations. Layer-2 systems, such as side-chains or Rollups, have been proposed as a possible strategy to overcome this limitation. Layer-2 systems interact with the main-chain in two ways. First, users can move funds from/to the main-chain to/from the layer-2. Second, layer-2 systems periodically synchronize with the main-chain to keep some form of log of their activity on the main-chain - this log is key for security. Due to this interaction with the main-chain, which is necessary and recurrent, layer-2 systems impose some load on the main-chain. The impact of such load on the main-chain has been, so far, poorly understood. In addition to that, layer-2 approaches typically sacrifice decentralization and security in favor of higher throughput. This paper presents an experimental study that analyzes the current state of Ethereum layer-2 projects. Our goal is to assess the load they impose on Ethereum and to understand their scalability potential in the long-run. Our analysis shows that the impact of any given layer-2 on the main-chain is the result of both technical aspects (how state is logged on the main-chain) and user behavior (how often users decide to transfer funds between the layer-2 and the main-chain). Based on our observations, we infer that without efficient mechanisms that allow users to transfer funds in a secure and fast manner directly from one layer-2 project to another, current layer-2 systems will not be able to scale Ethereum effectively, regardless of their technical solutions. Furthermore, from our results, we conclude that the layer-2 systems that offer similar security guarantees as Ethereum have limited scalability potential, while approaches that offer better performance, sacrifice security and lead to an increase in centralization which runs against the end-goals of permissionless blockchains.

Abstract In 2019, outbreaks of hepatitis-splenomegaly syndrome (HSS) were observed in six commercial layer chicken flocks, belonging to three different Polish farms, and characterized by increased mortality, hemorrhagic hepatitis with attached blood clots on the liver surface, and splenomegaly. Diseased flocks were initially investigated for the presence of avian hepatitis E virus (aHEV) – the etiological agent of HSS – by conventional reverse transcriptase polymerase chain reaction, which revealed aHEV sequences clustering separately from all known aHEV genotypes. Additionally, an aHEV genome was identified for the first time in common pheasants, from a flock in France, using Next Generation Sequencing. This genome clustered together with the Polish aHEVs here investigated. Complete genome aHEV sequences from the HSS outbreaks confirmed the divergent cluster, with a shared nucleotide sequence identity of 79.6–83.2% with other aHEVs, which we propose to comprise a novel aHEV genotype – genotype 7. Histology and immunohistochemistry investigations in the liver and spleen established an association between aHEV and the observed lesions in the affected birds, consolidating the knowledge on the pathogenesis of aHEV, which is still largely unknown. Thus, the present investigation extends the natural host range and genotypes of aHEV and strengthens knowledge on the pathogenesis of HSS.

The energy sector is currently undergoing a significant shift, driven by the growing integration of renewable energy sources and the decentralization of electricity markets, which are now extending into local communities. This transformation highlights the pivotal role of prosumers within these markets, and as a result, the concept of Renewable Energy Communities is gaining traction, empowering their members to curtail reliance on non-renewable energy sources by facilitating local energy generation, storage, and exchange. Also in a community, management efficiency depends on being able to predict future consumption to make decisions regarding the purchase, sale and storage of electricity, which is why forecasting the consumption of community members is extremely important. This study presents an innovative approach to manage community energy balance, relying on Machine Learning (ML) techniques, namely eXtreme Gradient Boosting (XGBoost), to forecast electricity consumption. Subsequently, a decision algorithm is employed for energy trading with the public grid, based on solar production and energy consumption forecasts, storage levels and market electricity prices. The outcomes of the simulated model demonstrate the efficacy of incorporating these techniques, since the system showcases the potential to reduce both the community electricity expenses and its dependence on energy from the centralized distribution grid. ML-based techniques allowed better results specially for bi-hourly tariffs and high storage capacity scenarios with community bill reductions of 9.8%, 2.8% and 5.4% for high, low, and average photovoltaic (PV) generation levels, respectively.

A conservação e restauro de arte contemporânea tem-se revelado complexa. Por vezes, a análise e o estudo material não são suficientes para solucionar as questões de conservação associadas aos objetos. A intenção do artista e os motivos que o levaram a selecionar determinados materiais e técnicas de execução podem ser dadas a conhecer ao conservador-restaurador pelo próprio autor da obra em investigação. Este conhecimento pode ser obtido através da entrevista, sendo esta considerada, em pleno século XXI, o primeiro passo do processo de documentação de uma obra (Beerkens, et al, 2012). Conscientes desta importância realizaram-se entrevistas a seis artistas com diferentes campos de atuação: escultura em gesso e cerâmica; azulejo; pintura de cavalete figurativa sobre madeira e tela; pintura de cavalete monocromática e hiper-realista; instalações efémeras compostas por elementos naturalistas, design gráfico sobre papel. Foram, no mesmo período, realizadas entrevistas a seis conservadores-restauradores, um de cada uma das áreas de especialidade. O objetivo deste estudo foi confrontar os resultados entre os artistas e os conservadores-restauradores entrevistados e comparar e confrontar os pontos de vista de cada profissional em relação aos materiais, envelhecimento e degradação das obras, às intervenções de conservação e restauro, entre outros. Apesar de algumas diferenças, evidentes e com sentido, constatou-se que, na sua maioria, quer para os conservadores-restauradores, quer para os artistas, apesar dos trabalhos serem realizados de modo distinto, os objetivos permanecem os mesmos: a conservação futura da obra.

This work presents an innovative ultra-sensitive biosensor having the Spike protein on carbon-based screen-printed electrodes (SPEs), for monitoring in point-of-care antibodies against SARS-CoV-2, a very important tool for epidemiological monitoring of COVID-19 infection and establishing vaccination schemes. In an innovative and simple approach, a highly conductive support is combined with the direct adsorption of Spike protein to enable an extensive antibody capture. The high conductivity was ensured by using carboxylated carbon nanotubes on the carbon electrode, by means of a simple and quick approach, which also increased the surface area. These were then modified with EDC/NHS chemistry to produce an amine layer and undergo Spike protein adsorption, to generate a stable layer capable of capturing the antibodies against SARS-CoV-2 in serum with great sensitivity. Electrochemical impedance spectroscopy was used to evaluate the analytical performance of this biosensor in serum. It displayed a linear response between 1.0 ​pg/mL and 10 ​ng/mL, with a detection limit of ∼0.7 ​pg/mL. The analysis of human positive sera containing antibody in a wide range of concentrations yielded accurate data, correlating well with the reference method. It also offered the unique ability of discriminating antibody concentrations in sera below 2.3 ​μg/mL, the lowest value detected by the commercial method. In addition, a proof-of-concept study was performed by labelling anti-IgG antibodies with quantum dots to explore a new electrochemical readout based on the signal generated upon binding to the anti-S protein antibodies recognised on the surface of the biosensor. Overall, the alternative serologic assay presented is a promising tool for assessing protective immunity to SARS-CoV-2 and a potential guide for revaccination.

Within the context of Industry 4.0, quality assessment procedures using data-driven techniques are becoming more critical due to the generation of massive amounts of production data. In this paper, we address the detection of abnormal screw tightening processes, which is a key industrial task. Since labeling is costly, requiring a manual effort, we focus on unsupervised detection approaches. In particular, we assume a computationally light low-dimensional problem formulation based on angle–torque pairs. Our work is focused on two unsupervised machine learning (ML) algorithms: isolation forest (IForest) and a deep learning autoencoder (AE). Several computational experiments were held by assuming distinct datasets and a realistic rolling window evaluation procedure. First, we compared the two ML algorithms with two other methods, a local outlier factor method and a supervised Random Forest, on older data related with two production days collected in November 2020. Since competitive results were obtained, during a second stage, we further compared the AE and IForest methods by adopting a more recent and larger dataset (from February to March 2021, totaling 26.9 million observations and related to three distinct assembled products). Both anomaly detection methods obtained an excellent quality class discrimination (higher than 90%) under a realistic rolling window with several training and testing updates. Turning to the computational effort, the AE is much lighter than the IForest for training (around 2.7 times faster) and inference (requiring 3.0 times less computation). This AE property is valuable within this industrial domain since it tends to generate big data. Finally, using the anomaly detection estimates, we developed an interactive visualization tool that provides explainable artificial intelligence (XAI) knowledge for the human operators, helping them to better identify the angle–torque regions associated with screw tightening failures.