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Fertilizers, pesticides and global warming are threatening freshwater aquatic ecosystems. Most of these are shallow ponds or slow-flowing streams or ditches dominated by submerged macrophytes, periphyton or phytoplankton. Regime shifts between the dominance of these primary producers can occur along a gradient of nutrient loading, possibly triggered by specific disturbances influencing their competitive interactions. However, phytoplankton dominance is less desirable due to lower biodiversity and poorer ecosystem function and services. In this study, we combined a microcosm experiment with a process-based model to test three hypotheses: 1) agricultural run-off (ARO), consisting of nitrate and a mixture of organic pesticides and copper, differentially affects primary producers and enhances the risk of regime shifts, 2) warming increases the risk of an ARO-induced regime shift to phytoplankton dominance and 3) custom-tailored process-based models support mechanistic understanding of experimental results through scenario comparison. Experimentally exposing primary producers to a gradient of nitrate and pesticides at 22°C and 26°C supported the first two hypotheses. ARO had direct negative effects on macrophytes, while phytoplankton gained from warming and indirect effects of ARO like a reduction in the competitive pressure exerted by other groups. We used the process-based model to test eight different scenarios. The best qualitative fit between modeled and observed responses was reached only when taking community adaptation and organism acclimation into account. Our results highlight the importance of considering such processes when attempting to predict the effects of multiple stressors on natural ecosystems.

Background Hypothyroidism is the most common endocrine disorder diagnosed in dogs, leading to deleterious effects on a dog’s life quality. This study aims to evaluate changes in the redox status in canine hypothyroidism. For this purpose, a comprehensive panel of antioxidants and oxidants biomarkers were measured in serum and saliva of 23 dogs with hypothyroidism, 21 dogs with non-thyroidal illness, and 16 healthy dogs. Among the antioxidants, cupric reducing antioxidant capacity (CUPRAC), ferric reducing ability of plasma (FRAP), Trolox equivalent antioxidant capacity (TEAC), thiol, paraoxonase type 1 (PON-1) and glutathione peroxidase (GPx) were determined in serum and CUPRAC, ferric reducing ability of saliva (FRAS) and TEAC in saliva. The oxidant biomarkers included were total oxidant status (TOS), peroxide-activity (POX-Act), reactive oxygen-derived compounds (d-ROMs), advanced oxidation protein products (AOPP), and thiobarbituric acid reactive substances (TBARS) in serum and AOPP and TBARS in saliva. Results Results showed a significantly higher TEAC, PON-1, GPx, TOS, POX-Act, and d-ROMs, and a significantly lower AOPP in serum of dogs with hypothyroidism. Meanwhile, significantly lower FRAS and AOPP were observed in saliva of dogs with hypothyroidism. Once salivary concentrations were corrected based on their total protein concentrations, the only analyte showing significant changes was TBARS which was significantly higher in dogs with hypothyroidism. Conclusions Our results show that dogs with hypothyroidism present alterations in the redox status in both serum and saliva. This study should be considered a preliminary study and further research addressing these changes should be made using larger populations.

University campuses are normally constituted of large buildings responsible for high energy demand, and are also important as demonstration sites for new technologies and systems. This paper presents the results of achieving energy sustainability in a testbed composed of a set of four buildings that constitute the Telecommunications Engineering School of the Universidad Politécnica de Madrid. In the paper, after characterizing the consumption of university buildings for a complete year, different options to achieve more sustainable use of energy are presented, considering the integration of renewable generation sources, namely photovoltaic generation, and monitoring and controlling electricity demand. To ensure the implementation of the desired monitoring and control, an internet of things (IoT) platform based on wireless sensor network (WSN) infrastructure was designed and installed. Such a platform supports a smart system to control the heating, ventilation, and air conditioning (HVAC) and lighting systems in buildings. Furthermore, the paper presents the developed IoT-based platform, as well as the implemented services. As a result, the paper illustrates how providing old existing buildings with the appropriate technology can contribute to the objective of transforming such buildings into nearly zero-energy buildings (nZEB) at a low cost.

This work has been funded by the Spanish Ministry of Science and Innovation and by the European Union through the FEDER funds and is part of the research projects Pulvexact (AGL2002-04260-C04-02), Optidosa (AGL2007-66093-C04-03) and Safespray (AGL2010-22304-C04-03).

Cyberbullying (CB) has emerged as a growing concern among adolescents, with nearly 10% of European children affected monthly and almost half experiencing it at least once. Unlike traditional bullying, CB thrives in digital environments where anonymity and impunity are prevalent. Despite its increasing prevalence, understanding the causal mechanisms behind CB remains challenging due to the limitations of conventional statistical methods, which often rely on correlations and are prone to spurious associations. In this paper, we introduce a novel human–machine consensus framework for causal discovery, aimed at supporting social scientists in unraveling the complex dynamics of CB. We leverage recent advances in data-driven causal inference, particularly the use of Directed Acyclic Graphs (DAGs), to identify and interpret causal relationships from observational data. Our approach integrates automatic causal discovery algorithms with expert knowledge, addressing the limitations of both purely algorithmic and purely expert-driven methods, and allows for the creation of a model ensemble estimation of the causal effects. To enhance interpretability and usability, we advocate for the use of Probabilistic Graphical Causal Models (PGCMs), or Bayesian Networks, which combine probabilistic reasoning with graphical representation. This hybrid methodology not only mitigates cognitive biases and inconsistencies in expert input but also fosters transparency and critical reflection in model construction. Cyberbullying serves as a compelling case study where ethical constraints preclude experimental designs, highlighting the value of interpretable, expert-informed causal models for guiding policy and intervention strategies.

Background Uterine leiomyosarcomas are very rare and highly aggressive tumors that have a high rate of recurrence and poor prognosis, even when early diagnosed. Due to their relative rarity, there is limited research on optimal management strategies. Case presentation A 60-year-old woman with a history of an asymptomatic uterine leiomyoma presented in October 2015 with postmenopausal bleeding and a friable vaginal cyst that bled when palpated. A partial cystectomy was performed, and malignant-like cystic and solid components were identified. Histopathology diagnosed an unclassifiable malignant epithelioid tumor. Subsequent imaging studies identified a malignant uterine tumor, a metabolically active vaginal lesion, and two benign leiomyomas. An anterior pelvic exenteration (colpectomy, hysterectomy, bilateral adnexectomy, total cystectomy, and cutaneous ureteroileostomy ad modum Bricker) were performed by laparotomy in March 2016. Examination of the surgical specimens identified a 75 × 75-mm leiomyoma, an 80 × 30-mm infiltrating mesenchymal uterine lesion with vascular invasion and tumor emboli, and a 60 × 30-mm perivascular vaginal tumor. Immunohistochemistry indicated a phenotypic transition from a uterine leiomyosarcoma to a vaginal epithelioid lesion; marker expression changed from the uterine tumor actin+/desmin+/caldesmon+/CD10− phenotype, through the tumor emboli, to an actin−/desmin−/caldesmon−/CD10+ phenotype in the vaginal lesion. A high-grade uterine mesenchymal tumor and vaginal metastasis were diagnosed. Adjuvant chemotherapy with docetaxel, gemcitabine, and doxorubicin commenced in May 2016 and treatment has been well tolerated. Conclusions Differentiating leiomyosarcoma from leiomyoma is challenging and few tools other than microscopic evaluation are available. Vaginal compromise in leiomyosarcoma usually results from tumor extension, not hematogenous metastasis. A vaginal metastasis is a very rare initial presentation. We have found only two cases like this described on published literature. The atypical clinical and histological presentation in our case complicated diagnosis and delayed treatment. An early diagnosis and complete surgical clearance gives the best chance of survival, and imaging tools should be applied early in instances of new suspicious malignant lesions.

The essential role of Information and Communication Technologies (ICT) in modern electricity grids makes it necessary to consider them when evaluating the scalability and replicability capabilities of smart grid systems. This paper proposes a novel step-by-step methodology to quantitatively perform an ICT scalability and replicability analysis (SRA) in a smart grid context. The methodology is validated and exemplified by applying it to two real case studies that are demonstrated in the EU-funded RESPONSE project and comprise solutions relying on different communication technologies. The results of the proposed methodology are summarised through ICT scalability and replicability maps, which are introduced in this paper as a quick way of obtaining an overview of the scalability and replicability capabilities of an ICT system and as an efficient way of estimating the feasibility of scenarios not covered in the SRA.

Internet of Things platforms for Smart Cities are technologically complex and deploying them at large scale involves high costs and risks. Therefore, pilot schemes that allow validating proof of concepts, experimenting with different technologies and services, and fine-tuning them before migrating them to actual scenarios, are especially important in this context. The IoT platform deployed across the engineering schools of the Universidad Politécnica de Madrid in the Moncloa Campus of International Excellence represents a good example of a test bench for experimentation with Smart City services. This paper presents the main features of this platform, putting special emphasis on the technological challenges faced and on the solutions adopted, as well as on the functionality, services and potential that the platform offers.

Information and Communication Technology (ICT) infrastructures are at the heart of emerging Smart Grid scenarios with high penetration of Distributed Energy Resources (DER). The scalability of such ICT infrastructures is a key factor for the large scale deployment of the aforementioned Smart Grid solutions, which could not be ensured by small-scale pilot demonstrations. This paper presents a novel methodology that has been developed in the scope of the H2020 project InteGrid, which enables the scalability analysis of ICT infrastructures for Smart Grids. It is based on the Smart Grid Architecture Model (SGAM) framework, which enables a standardized and replicable approach. This approach consists of two consecutive steps: a qualitative analysis that aims at identifying potential bottlenecks in an ICT infrastructure; and a quantitative analysis of the identified critical links under stress conditions by means of simulations with the aim of evaluating their operational limits. In this work the proposed methodology is applied to a cluster of solutions demonstrated in the InteGrid Slovenian pilot. This pilot consists of a Large Customer Commercial Virtual Power Plant (VPP) that provides flexibility in medium voltage for tertiary reserve and a Traffic Light System (TLS) to validate such flexibility offers. This approach creates an indirect Transmission System Operator (TSO)—Distribution System Operator (DSO) coordination scheme.

The traditional electricity grid has remained the same for most of last century, without major architectural improvements. However, its requirements, guidelines and goals do have changed, especially during the last few years, driven by the sustainability in energy generation and energy efficiency principles. Thus, taking greenhouse gases emissions and CO 2 footprint reduction as key objectives and information and communications technologies as key enabler technologies, a novel and revolutionary electric grid paradigm, the so-called Smart Grid, is emerging, in which energy efficiency and renewable generation play a central role. This paper presents an overview on the main requirements and features of Smart Grids to integrate energy efficiency, on the work done and to be done, on the enabler technologies, as well as on the expected impacts and the main benefits Smart Grids will bring.