Changes in the microflora of Serra cheese: evolution throughout ripening time, lactation period and axial location

Changes in the microflora and physicochemical characteristics in Serra da Estrela cheese were examined using a three-way factorial design over a 3.5 day ripening period, throughout the lactation period, and for several slices taken normal to the cheese axis. Lactic acid bacteria were the major components of the microflora during ripening throughout the lactation period, but no statistically signtficant differences were detected between the surface and the interior of the cheese. Both enterobacteriaceae and staphylococci in the curd were at maximum numbers during the first week and were still at significant numbers towards the end of the maturation period. The numbers of staphylococci were statistically different between slices and at different times within the lactation period, whereas the numbers of enterobacteriaceae showed statisticalty signrficant variations only throughout the lactation period. Ripening time, lactation period and axial location signtficantly affected the number of yeasts. AN factors studied had statistically significant effects on pH, salt and moisture content; the hypotheses that ripening time, lactation period, and axial location had effects on salt and moisture contents were accepted, in this order, at increasingly lower levels of signtficance; the hypotheses that ripening time, axial location, and lactation period had effects on pH were accepted, in this order, at increasingly lower levels of significance

Improving light harvesting in polymer photodetector devices through nanoindented metal mask films

To enhance light harvesting in organic photovoltaic devices, we propose the incorporation of a metal (aluminum) mask film in the system’s usual layout. We fabricate devices in a sandwich geometry, where the mask (nanoindented with a periodic array of holes of sizes d and spacing s) is added between the transparent electrode and the active layer formed by a blend of the semiconducting polymer P3HT and substituted fullerene. Its function is to promote trapping of the incident light into the device’s cavity (the region corresponding to the active layer). For d, we set a value that allows light diffraction through the holes in the relevant absorption range of the polymer. To optimize the mask structure, we consider a very simple model to determine the s leading to trapped fields that are relatively intense and homogeneous within the device. From measurements of the action spectra, we show that, indeed, such architecture can considerably improve the resulting photocurrent efficiencies—one order of magnitude in the best situation studied.

First in-beam studies of a Resistive-Plate WELL gaseous multiplier

We present the results of the first in-beam studies of a medium size (10$\times$10 cm$^2$) Resistive-Plate WELL (RPWELL): a single-sided THGEM coupled to a pad anode through a resistive layer of high bulk resistivity ($\sim$10$^9 \Omega$cm). The 6.2~mm thick (excluding readout electronics) single-stage detector was studied with 150~GeV muons and pions. Signals were recorded from 1$\times$1 cm$^2$ square copper pads with APV25-SRS readout electronics. The single-element detector was operated in Ne\(5% $\mathrm{CH_{4}}$) at a gas gain of a few times 10$^4$, reaching 99$\%$ detection efficiency at average pad multiplicity of $\sim$1.2. Operation at particle fluxes up to $\sim$10$^4$ Hz/cm$^2$ resulted in $\sim$23$\%$ gain drop leading to $\sim$5$\%$ efficiency loss. The striking feature was the discharge-free operation, also in intense pion beams. These results pave the way towards robust, efficient large-scale detectors for applications requiring economic solutions at moderate spatial and energy resolutions.Comment: Accepted by JINS

Morphometric patterns and preferential uses of Capsicum peppers in the State of Roraima, Brazilian Amazonia.

O objetivo deste estudo foi verificar distinções no padrão morfológico de frutos de pimentas do gênero Capsicum spp. (Solanaceae) utilizados por grupos tradicionais indígenas (vivendo em aldeias) e não-indígenas (derivado da migração/colonização, contendo ou não miscigenação, situados fora de áreas indígenas), em Roraima, norte da Amazônia brasileira. Para tanto foi utilizado um banco de dados com 182 subamostras de Capsicum spp. coletadas em 39 localidades daquele estado (14 indígenas e 25 não-indígenas). As localidades foram caracterizadas também por tipos fitofisionômicos predominantes (savana ou floresta) e por zona domiciliar do usuário (rural ou urbana). Os resultados indicaram haver diferenças morfológicas nos frutos de pimenta relacionadas tanto a fitofisionomia, quanto a zona domiciliar da área de coleta, mas não a origem étnica do grupo. Isto indica que as diferenças estão relacionadas mais a fatores inerentes aos tratos culturais, fortemente influenciados pelo ambiente, que à preferência do usuário. Os morfotipos com frutos de menor peso e alta pungêngia das espécies C. chinense e C. frutescens, independente da cor, foram os preferidos dos usuários indígenas e não-indígenas para produção de molhos e jiquitaia (pó de pimenta). A 'aderência cultural' é sugerida para explicar o uso preferencial comum de pimentas entre os agrupamentos étnicos analisados em Roraima

Machine learning in infection management using routine electronic health records:tools, techniques, and reporting of future technologies

Background: Machine learning (ML) is increasingly being used in many areas of health care. Its use in infection management is catching up as identified in a recent review in this journal. We present here a complementary review to this work. Objectives: To support clinicians and researchers in navigating through the methodological aspects of ML approaches in the field of infection management. Sources: A Medline search was performed with the keywords artificial intelligence, machine learning, infection∗, and infectious disease∗ for the years 2014–2019. Studies using routinely available electronic hospital record data from an inpatient setting with a focus on bacterial and fungal infections were included. Content: Fifty-two studies were included and divided into six groups based on their focus. These studies covered detection/prediction of sepsis (n = 19), hospital-acquired infections (n = 11), surgical site infections and other postoperative infections (n = 11), microbiological test results (n = 4), infections in general (n = 2), musculoskeletal infections (n = 2), and other topics (urinary tract infections, deep fungal infections, antimicrobial prescriptions; n = 1 each). In total, 35 different ML techniques were used. Logistic regression was applied in 18 studies followed by random forest, support vector machines, and artificial neural networks in 18, 12, and seven studies, respectively. Overall, the studies were very heterogeneous in their approach and their reporting. Detailed information on data handling and software code was often missing. Validation on new datasets and/or in other institutions was rarely done. Clinical studies on the impact of ML in infection management were lacking. Implications: Promising approaches for ML use in infectious diseases were identified. But building trust in these new technologies will require improved reporting. Explainability and interpretability of the models used were rarely addressed and should be further explored. Independent model validation and clinical studies evaluating the added value of ML approaches are needed

Eficiência de métodos de emasculação em cultivares de alface americanas.

O objetivo deste trabalho foi o de avaliar a eficiência de três métodos de emasculação em duas cultivares de alface americana. Para tanto, realizou-se um experimento nas casas de vegetação da Embrapa Hortaliças cujo delineamento experimental foi inteiramente casualizados com vinte repetições.Resumo 11

A podridão do tronco do sisal

bitstream/CNPA/18273/1/COMTEC281.pd

Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors

Funding Information: This research was funded by Fundação para a Ciência e Tecnologia (FCT) Portugal, grant number UIDB/50006/2020 (LAQV-REQUIMTE), UIDP/04378/2020 and UIDB/04378/2020 (UCIBIO) and LA/P/0140/2020 (i4HB), the European Commission GLYCOTwinning (GA 101079417), the EJPRD ProDGNE (EJPRD/0001/2020 EU 825575) and SI I&DT, DCMatters (AVISO Nº 17/SI/2019) REF 47212. F.P. gratefully acknowledges FCT for an Assistant Research Position (CEECIND/01649/2021). Publisher Copyright: © 2023 by the authors.Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein–protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.publishersversionpublishe