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Endocrine disrupting chemicals are long suspected to impair reproductive health. Bisphenol A (BPA) has estrogenic activity and therefore the capacity of interfering with endocrine pathways. No studies dissected its short-term effects on pregnancy and possible underlying mechanisms. Here, we studied how BPA exposure around implantation affects pregnancy, particularly concentrating on placentation and uterine remodeling. We exposed pregnant female mice to 50 µg/kg BPA/day or 0.1% ethanol by oral gavage from day 1 to 7 of gestation. High frequency ultrasound was employed to document the presence and size of implantations, placentas and fetuses throughout pregnancy. Blood velocity in the arteria uterina was analyzed by Doppler measurements. The progeny of mothers exposed to BPA was growth-restricted compared to the controls; this was evident in vivo as early as at day 12 as analyzed by ultrasound and confirmed by diminished fetal and placenta weights observed after sacrificing the animals at day 14 of gestation. The remodeling of uterine spiral arteries (SAs) was considerably impaired. We show that short-term exposure to a so-called “safe” BPA dose around implantation has severe consequences. The intrauterine growth restriction observed in more than half of the fetuses from BPA-treated mothers may owe to the direct negative effect of BPA on the remodeling of uterine SAs that limits the blood supply to the fetus. Our work reveals unsuspected short-term effects of BPA on pregnancy and urges to more studies dissecting the mechanisms behind the negative actions of BPA during early pregnancy.

Previously, we found that the ultraviolet filter benzophenone-3 (BP3) causes fetal growth restriction in mice when is applied when implantation occurs (first week of gestation). However, whether BP3 can affect gestation and fertility after implantation period is unknown. We aimed to study the effects on reproductive physiology of the offspring caused by perinatal exposure to BP3. C57BL/6 pregnant mice were dermally exposed to 50 mg BP3/kg bw.day or olive oil (vehicle) from gestation day 9 (gd9) to postnatal day 21 (pnd1). We observed no differences in mother’s weights, duration of gestation, number of pups per mother, onset of puberty or sex ratio. The weights of the pups exposed to benzophenone-3 were transiently lower than those of the control. Estrous cycle was not affected by perinatal exposure to BP3. Besides, we performed a fertility assessment by continuous breeding protocol: at 10 weeks of age, one F1 female and one F1 male mouse from each group was randomly chosen from each litter and housed together for a period of 6 months. We noticed a reduction in the number of deliveries per mother among dams exposed to BP3 during the perinatal period. To see if this decreased fertility could be associated to an early onset of oocytes depletion, we estimated the ovarian reserve of germ cells. We found reduced number of oocytes and primordial follicles in BP3. In conclusion, perinatal exposure to BP3 leads to a decline in the reproductive capacity of female mice in a continuous breeding protocol linked to oocyte depletion.

Open-source model management frameworks such as OCL and ATL tend to focus on manipulating models built atop the Eclipse Modelling Framework (EMF), a de facto standard for domain specific modelling. MATLAB Simulink is a widely used proprietary modelling framework for dynamic systems that is built atop an entirely different technical stack to EMF. To leverage the facilities of open-source model management frameworks with Simulink models, these can be transformed into an EMF-compatible representation. Downsides of this approach include the synchronisation of the native Simulink model and its EMF representation as they evolve; the completeness of the EMF representation, and the transformation cost which can be crippling for large Simulink models. We propose an alternative approach to bridge Simulink models with open-source model management frameworks that uses an “on-the-fly” translation of model management constructs into MATLAB statements. Our approach does not require an EMF representation and can mitigate the cost of the upfront transformation on large models. To evaluate both approaches we measure the performance of a model validation process with Epsilon (a model management framework) on a sample of large Simulink models available on GitHub. Our previous results suggest that, with our approach, the total validation time can be reduced by up to 80%. In this paper, we expand our approach to support the management of Simulink requirements and dictionaries, and we improve the approach to perform queries on collections of model elements more efficiently. We demonstrate the use of the Simulink requirements and dictionaries with a case study and we evaluate the optimisations on collection queries with an experiment that compares the performance of a set of queries on models with different sizes. Our results suggest an improvement by up to 99% on some queries.

UML profiles offer an intuitive way for developers to build domain-specific modelling languages by reusing and extending UML concepts. Eclipse Papyrus is a powerful open-source UML modelling tool which supports UML profiling. However, with power comes complexity, implementing non-trivial UML profiles and their supporting editors in Papyrus typically requires the developers to handcraft and maintain a number of interconnected models through a loosely guided, labour-intensive and error-prone process. We demonstrate how metamodel annotations and model transformation techniques can help manage the complexity of Papyrus in the creation of UML profiles and their supporting editors. We present Jorvik, an open-source tool that implements the proposed approach. We illustrate its functionality with examples, and we evaluate our approach by comparing it against manual UML profile specification and editor implementation using a non-trivial enterprise modelling language (Archimate) as a case study. We also perform a user study in which developers are asked to produce identical editors using both Papyrus and Jorvik demonstrating the substantial productivity and maintainability benefits that Jorvik delivers.

While the majority of research on Model-Based Software Engineering revolves around open-source modelling frameworks such as the Eclipse Modelling Framework, the use of commercial and closed-source modelling tools such as RSA, Rhapsody, MagicDraw and Enterprise Architect appears to be the norm in industry at present. This technical gap can prohibit industrial users from reaping the benefits of state-of-the-art research-based tools in their practice. In this paper, we discuss an attempt to bridge a proprietary UML modelling tool (PTC Integrity Modeller), which is used for model-based development of safety-critical systems at Rolls-Royce, with an open-source family of languages for automated model management (Epsilon). We present the architecture of our solution, the challenges we encountered in developing it, and a performance comparison against the tool’s built-in scripting interface. In addition, we use the bridge in a real-world industrial case study that involves the coordination with other bridges between proprietary tools and Epsilon.

Breast cancer is one of the leading causes of death for women worldwide, and early detection can help reduce the death rate. Infrared thermography has gained popularity as a non-invasive and rapid method for detecting this pathology and can be further enhanced by applying neural networks to extract spatial and even temporal data derived from breast thermographic images if they are acquired sequentially. In this study, we evaluated hybrid convolutional-recurrent neural network (CNN-RNN) models based on five state-of-the-art pre-trained CNN architectures coupled with three RNNs to discern tumor abnormalities in dynamic breast thermographic images. The hybrid architecture that achieved the best performance for detecting breast cancer was VGG16-LSTM, which showed accuracy (ACC), sensitivity (SENS), and specificity (SPEC) of 95.72%, 92.76%, and 98.68%, respectively, with a CPU runtime of 3.9 s. However, the hybrid architecture that showed the fastest CPU runtime was AlexNet-RNN with 0.61 s, although with lower performance (ACC: 80.59%, SENS: 68.52%, SPEC: 92.76%), but still superior to AlexNet (ACC: 69.41%, SENS: 52.63%, SPEC: 86.18%) with 0.44 s. Our findings show that hybrid CNN-RNN models outperform stand-alone CNN models, indicating that temporal data recovery from dynamic breast thermographs is possible without significantly compromising classifier runtime.

Background/Aims: No single classification system has so far effectively predicted the severity for Acute Pancreatitis (AP). This study compares the effectiveness of classification systems: Original Atlanta (OAC), Revised Atlanta (RAC), Determinant based classification (DBC), PANC 3, Harmless AP Score (HAPS), Japanese Severity Score (JSS), Symptoms Nutrition Necrosis Antibiotics and Pain (SNNAP), and Beside Index of Severity for AP (BISAP) in predicting outcomes in AP. Methods: Scores for BISAP, Panc 3, HAPS, SNNAP, OAC, RAC, and DBC were calculated for 221 adult patients hospitalized for AP. Receiver Operating Characteristic curve analysis and Akaike Information Criteria were used to compare the effectiveness of predicting need for surgery, intensive care unit (ICU) admission, readmission within 30 days, and length of hospital stay. Results: Both the RAC and the DBC strongly predict the length of hospital stay (p < 0.0001 for both) and ICU admission (p < 0.0001 for both). Additionally, both BISAP and PANC 3 showed weak predictive capacity at identifying length of stay and ICU admission. Conclusions: We suggest that BISAP and PANC3 be obtained within the initial 24 h of hospitalization to offer an early prediction of length of stay and ICU admission. Subsequently, RAC and DBC can offer further information later in the course of the disease.

Protein–DNA and protein–RNA interactions are central to gene regulation and genetic disease, yet experimental identification remains costly and complex. Machine learning (ML) offers an efficient alternative, though challenges persist in representing protein sequences due to residue variability, dimensionality issues, and the risk of losing biological context. Traditional approaches such as k-mer counting or neural network encodings provide standardized sequence representations but often demand high computational resources and may obscure functional information. To address these limitations, a novel encoding method based on interpolation of physicochemical properties (PCPs) is introduced. Discrete PCPs values are transformed into continuous functions using logarithmic enhancement, highlighting residues that contribute most to nucleic acid interactions while preserving biological relevance across variable sequence lengths. Statistical features extracted from the resulting spectra via Tsfresh are then used for binary classification of DNA- and RNA-binding proteins. Six classifiers were evaluated, and the proposed method achieved up to 99% accuracy, precision, recall, and F1 score when amino acid highlighting was applied, compared with 66% without highlighting. Benchmarking against k-mer and neural network approaches confirmed superior efficiency and reliability, underscoring the potential of this method for protein interaction prediction. Our framework may be extended to multiclass problems and applied to the study of protein variants, offering a scalable tool for broader protein interaction prediction.

Pulsed light (PL) treatment is considered an emerging method for processing liquid foods. Efficient liquid food treatment design requires determining fluence to estimate microorganisms’ inactivation kinetics and develop effective treatment protocols. Despite available tools to determine PL equipment fluence, these are often costly and overlook the inherent photothermal mechanism. This research aims to analyze doses irradiated by PL equipment at 5.74 cm and 10.82 cm from the luminesce source, estimating the dose distribution across a simplified petri-dish-based sample holder, and validating results through Escherichia coli ATCC 25922 inactivation in guava nectar and pineapple juice. Results showed that correcting emitted doses for distance and absorbance yielded suitable adjustments, but including the temperature and color change factors strongest aligned the theoretical estimations with actual doses. This underscores a more pronounced correlation between the calculated dose and the inactivation of E. coli in guava nectar and pineapple juice.

Plant-based materials are an important source of bioactive compounds (BC) with interesting industrial applications. Therefore, adequate experimental strategies for maximizing their recovery yield are required. Among all procedures for extracting BC (maceration, Soxhlet, hydro-distillation, pulsed-electric field, enzyme, microwave, high hydrostatic pressure, and supercritical fluids), the ultrasound-assisted extraction (UAE) highlighted as an advanced, cost-efficient, eco-friendly, and sustainable alternative for recovering BC (polyphenols, flavonoids, anthocyanins, and carotenoids) from plant sources with higher yields. However, the UAE efficiency is influenced by several factors, including operational variables and extraction process (frequency, amplitude, ultrasonic power, pulse cycle, type of solvent, extraction time, solvent-to-solid ratio, pH, particle size, and temperature) that exert an impact on the molecular structures of targeted molecules, leading to variations in their biological properties. In this context, a diverse design of experiments (DOEs), including full or fractional factorial, Plackett–Burman, Box-Behnken, Central composite, Taguchi, Mixture, D-optimal, and Doehlert have been investigated alone and in combination to optimize the UAE of BC from plant-based materials, using the response surface methodology and mathematical models in a simple or multi-factorial/multi-response approach. The present review summarizes the advantages and limitations of the most common DOEs investigated to optimize the UAE of bioactive compounds from plant-based materials.