In the context of the 2HDM, and assuming that neutrinos acquire masses via the Weinberg operator, we perform a systematic analysis to determine the minimal quark and lepton flavor patterns, compatible with masses, mixing and CP violation data, realizable by Abelian symmetries. We determine four minimal models for quarks, where the number of independent parameters matches the number of observables. For the lepton sector, three minimal predictive models are identified. Namely, we find scenarios with a preference for the upper/lower octant of the θ23 atmospheric mixing angle, that exhibit lower bounds on the lightest neutrino masses currently probed by cosmology and testable at future neutrinoless double beta decay experiments, even for a normally ordered neutrino masses. We investigate the phenomenology of each model taking into account all relevant theoretical, electroweak precision observables, scalar sector constraints, as well as stringent quark flavor processes such as B¯→Xsγ, Bs→μ−μ+ and meson oscillations, and the charged lepton flavor-violating decays eα−→eβ−eγ+eδ− and eα→eβγ. We show that, in some cases, Abelian flavor symmetries provide a natural framework to suppress flavor-changing neutral couplings and lead to scenarios featuring heavy neutral/charged scalar masses below the TeV scale within the reach of current experiments. Published by the American Physical Society 2024

We study a simple extension of the Standard Model featuring a dark sector with an ultralight pseudo-Nambu-Goldstone boson as a dark matter candidate. We focus on the mass range O(10−20–10−10) eV, relevant for strong gravity applications, and explore its production and evolution in the early Universe. The model is formulated in such a way that dark matter does not couple directly to photons or other Standard Model particles avoiding some of the most stringent cosmological bounds related to axionlike particles. In this work, two different scenarios are considered depending on whether dark matter is produced in a preinflationary or postinflationary regime. We also discuss the effect from emergent topological defects such as cosmic strings and domain walls and estimate the spectrum of stochastic gravitational waves produced by their decay, enabling us to test the model at current and future gravitational-wave experiments. Published by the American Physical Society 2024

To robustly discover and explore phytocompounds, it is necessary to evaluate the interrelationships between the plant species, plant tissue, and the extraction process on the extract composition and to predict its cytotoxicity. The present work evaluated how Fourier Transform InfraRed spectroscopy can acquire the molecular profile of aqueous and ethanol-based extracts obtained from leaves, seeds, and flowers of Cynara Cardunculus, and ethanol-based extracts from Matricaria chamomilla flowers, as well the impact of these extracts on the viability of mammalian cells. The extract molecular profile enabled to predict the extraction yield, and how the plant species, plant tissue, and extraction process affected the extract's relative composition. The molecular profile obtained from the culture media of cells exposed to extracts enabled to capture its impact on cells metabolism, at a higher sensitivity than the conventional assay used to determine the cell viability. Furthermore, it was possible to detect specific impacts on the cell's metabolism according to plant species, plant tissue, and extraction process. Since spectra were acquired on small volumes of samples (25 µL), after a simple dehydration step, and based on a plate with 96 wells, the method can be applied in a rapid, simple, high-throughput, and economic mode, consequently promoting the discovery of phytocompounds.

ABSTRACT Introduction The unparalleled progress in science of the last decades has brought a better understanding of the molecular mechanisms of diseases. This promoted drug discovery processes based on a target approach. However, despite the high promises associated, a critical decrease in the number of first-in-class drugs has been observed. Areas Covered This review analyses the challenges, advances, and opportunities associated with the main strategies of the drug discovery process, i.e. based on a rational target approach and on an empirical phenotypic approach. This review also evaluates how the gap between these two crossroads can be bridged toward a more efficient drug discovery process. Expert opinion The critical lack of knowledge of the complex biological networks is leading to targets not relevant for the clinical context or to drugs that present undesired adverse effects. The phenotypic systems designed by considering available molecular mechanisms can mitigate these knowledge gaps. Associated with the expansion of the chemical space and other technologies, these designs can lead to more efficient drug discoveries. Technological and scientific knowledge should also be applied to identify, as early as possible, both drug targets and mechanisms of action, leading to a more efficient drug discovery pipeline.

This report addresses a challenge proposed at the 174th European Study Group in Industry to develop an algorithm to optimize processes on rubber soles manufacturing. The objective is to create an order distribution plan that maximizes efficiency, reduces setup times synchronizing molds changing, ensuring that delivery dates are met. This problem can be classified as a parallel machines scheduling problem with sequence dependent setup times. This work presents an heuristic that minimizes the makespan, accounting for the sequence dependent setup times, divided into two steps: (i) a client’s order will be partitioned into sets corresponding to the assigned slots; (ii) reorder the molds within each position of the press, so that setup times are simultaneous. We find solutions for real data, for cases with double and triple molds for a given size. Results show that synchronization of setup times due to heating and mold changes decrease significantly the makespan.

Bacterial infections and resistance to antibiotic drugs represent the highest challenges to public health. The search for new and promising compounds with anti-bacterial activity is a very urgent matter. To promote the development of platforms enabling the discovery of compounds with anti-bacterial activity, Fourier-Transform Mid-Infrared (FT-MIR) spectroscopy coupled with machine learning algorithms was used to predict the impact of compounds extracted from Cynara cardunculus against Escherichia coli. According to the plant tissues (seeds, dry and fresh leaves, and flowers) and the solvents used (ethanol, methanol, acetone, ethyl acetate, and water), compounds with different compositions concerning the phenol content and antioxidant and antimicrobial activities were obtained. A principal component analysis of the spectra allowed us to discriminate compounds that inhibited E. coli growth according to the conventional assay. The supervised classification models enabled the prediction of the compounds' impact on E. coli growth, showing the following values for accuracy: 94% for partial least squares-discriminant analysis; 89% for support vector machine; 72% for k-nearest neighbors; and 100% for a backpropagation network. According to the results, the integration of FT-MIR spectroscopy with machine learning presents a high potential to promote the discovery of new compounds with antibacterial activity, thereby streamlining the drug exploratory process.

There is growing evidence of the differential effects the COVID-19 pandemic had on small-scale fisheries across the world, with predominantly decreased landings during the time of restrictions and substantial negative socio-economic impact. We contribute to this evidence with an analysis of a comprehensive dataset of 5 million first sales of seafood transactions by 3715 small-scale fishers catching 351 species in Portugal from 2017 to the end of 2020. The analysis shows that the patterns observed in other countries, overall, apply to Portugal, with a decline in reported catch and number of fishers during the lockdown. However, there were notable regional differences. Most interestingly, two regions with historically very high tourism activity recorded a statistically significant increase in reported average daily catch volume per fishing operator during the lockdown, often for high-value species. We discuss these regional differences and potential explanations, as well as their policy implications.

The characterisation of the Martian radiation environment is essential to understand if the planet can sustain life and ultimately if its human exploration is feasible. The major components of the radiation environment in the Mars orbit, are Galactic Cosmic Rays (GCRs) and Solar Energetic Particle (SEP) events. Since Mars has a negligible magnetic field and a much thinner atmosphere compared to the Earth’s, its surface is exposed to GCR and eventual SEP events, as well as to secondary particles produced in the atmosphere and in the shallow layers of the planet. The Curiosity rover that has been exploring the surface of Mars since August 2012, carries in its Mars Science Laboratory (MSL), the Radiation Assessment Detector (RAD) which measures high-energy radiation, such as protons, energetic ions of various elements, neutrons, and gamma rays. That includes not only direct radiation from space, but also secondary radiation produced by the interaction of space radiation with the atmosphere and surface rocks and soil.The detailed Martian Energetic Radiation Environment Model (dMEREM) is a GEometry ANd Tracking (GEANT4) based model developed for ESA which enables to predict the radiation environment expected at different locations on the Martian orbit, atmosphere and surface, as a function of epoch, latitude and longitude, taking into account the specific atmospheric and soil composition. dMEREM can be interfaced to different Primary Particle Models, such as the ISO-15390 and the Badhwar - O'Neill (BON) 2014 or 2020 Galactic Cosmic Ray Flux Models, or the National Aeronautics and Space Administration (NASA) Emission of Solar Protons (ESP) model for solar energetic proton fluences. dMEREM is interfaced with the European Mars Climate Database from where it retrieves information on the atmosphere composition and density at specific locations and solar longitudes and Gamma Ray Spectrometer data aboard Mars Odyssey, for the description of Mars soil composition, although soil compositions for specific locations, including those locally sampled by Martian rovers can also be defined by the user. dMEREM provides the kinetic energy and directional spectra of all particle types produced in the interactions of energetic particles with the Martian Atmosphere and Soil.The dMEREM validation results using differential proton fluxes stopping in the RAD sensor head as measured by MSL/RAD in Gale crater from November 15, 2015 to January 15, 2016 and in the begin of September 2017 is presented. Although the RAD only measures a limited field-of-view in zenith angle of the Martian Particle Radiation Field, the good agreement between the RAD data and the dMEREM predictions for protons within the RAD field of-view, are used as the basis for the use of dMEREM in the assessment of the expected ionizing radiation field on the surface of Mars for particles coming from all directions, including albedo particles. This assessment is also used to make predictions of dosimetric quantities, such as Ambient Dose Equivalent and Effective Dose, relevant for Human Space Flight, for the considered data periods.  

Robust data normalization and analysis are pivotal in biomedical research to ensure that observed differences in populations are directly attributable to the target variable, rather than disparities between control and study groups. ArsHive addresses this challenge using advanced algorithms to normalize populations (e.g., control and study groups) and perform statistical evaluations between demographic, clinical, and other variables within biomedical datasets, resulting in more balanced and unbiased analyses. The tool's functionality extends to comprehensive data reporting, which elucidates the effects of data processing, while maintaining dataset integrity. Additionally, ArsHive is complemented by A.D.A. (Autonomous Digital Assistant), which employs OpenAI's GPT-4 model to assist researchers with inquiries, enhancing the decision-making process. In this proof-of-concept study, we tested ArsHive on three different datasets derived from proprietary data, demonstrating its effectiveness in managing complex clinical and therapeutic information and highlighting its versatility for diverse research fields.