Procjena izloženosti UV zračenju tijekom ljetnih mjeseci u Hrvatskoj s pomoću jednostavne približne formule

The Tropospheric Ultraviolet-Visible (TUV) model, version 4.2 developed by Madronich (2003) was usedto estimate the extent of ultraviolet (UV) exposure of general population in Croatia over the summer. Solarnoon values (13 h local time, CEST) of the ultraviolet index (UVI) for the period April to October 2004 were calculated for 61 cities in Croatia. The results showed that the risk of sunburn at 13 h local time inclear weather was high between April and September (UVI >7) and very high in July (UVI >10). In July, the UVI exceeded 8 between 11 h and 15 h local time. In this study, we developed a simple approximate formula to estimate UVI. The formula includes data on the time, date, altitude and clouds. The difference between our estimate and the TUV model for the summer months of June, July and August at 10 h to16 h local time was less than 10 %.Tropospheric Ultraviolet-Visible (TUV) model, verzija 4.2 autora S. Madronicha (2003.) upotrijebljen je zaprocjenu izloženosti ultraljubičastom (UV) zračenju stanovništva u Hrvatskoj. Podnevne vrijednosti (13 hprema lokalnom vremenu) ultraljubičastog indeksa (UVI) izračunane su za 61 mjesto u Hrvatskoj za razdobljetravanj - listopad. Rezultati pokazuju da je u 13 h prema lokalnom vremenu rizik od nastanka opeklina izazvanih sunčevim zračenjem u danima bez naoblake visok između travnja i rujna (UVI > 7) te da je rizikvrlo visok tijekom srpnja (UVI >10). U srpnju tijekom dana UV indeks prelazi vrijednost 8 između 11 h i15 h prema lokalnom vremenu. U ovom radu za procjenu UV indeksa razvijena je jednostavna približna formula. Formula omogućava procjenu UV indeksa na temelju podataka o datumu, satu, nadmorskoj visini i naoblaci. Prilikom usporedbe rezultata dobivenih formulom i točnih rezultata dobivenih TUV modelom za ljetne mjesece lipanj, srpanj i kolovoz te razdoblje od 10 h do 16 h među rezultatima dobivena je razlikamanja od 10 %

Produção de uma xilanase fúngica por Komagataella phaffii em biorreator.

As xilanases são enzimas que hidrolisam ligações Beta-14 presentes na hemicelulose que compõe a parede celular vegetal. Essas enzimas são produzidas por bactérias, fungos e plantas. Por apresentarem diversas aplicações, como na fabricação de pães, na indústria farmacêutica, na fabricação de celulose e na geração de biocombustíveis, o objetivo desse trabalho foi escalonar a produção de uma xilanase de origem fúngica (F43 GH 10-03) por Komagataella phaffii em biorreatores para futura aplicação biotecnológica. Os cultivos foram realizados com fluxo de ar entre 0,1 a 4,0 vvm, pO2 fixado em 25%, os inóculos com densidade ótica a 600 nm iniciais fixadas em 2, 4 e 6 e temperatura de 30°C. Após fase de crescimento celular, foi estabelecida uma alimentação com glicose 50% (m/v) (fluxo de 4 g/L/h). Incialmente, a xilanase foi produzida em frascos apresentando atividade de 1,5 UI/mL entre 24 a 72 h de cultivo. A produção da xilanase em biorreator realizada em três condições com DO600 inicial fixada em 2, 4 e 6 mostrou que todos os cultivos apresentaram atividade de xilanase a partir de 14,5 h, com secreção de xilanase ascendente, com atividade variando entre 12 e 23 UI/mL, e a DO600 variando entre 197 e 234, com produção de proteínas totais entre 0,26 e 0,48 g/L. A análise por eletroforese mostrou uma banda de 55 kDa correspondendo à xilanase F43 GH 10-03. Dos três cultivos realizados, a estratégia com DO600 inicial fixada em 4 apresentou a maior atividade específica (70,7 UI/mg de proteína). Essa condição pode ser utilizada para o início de estudos de otimização de produção dessa enzima em biorreator

Biodiesel: desafios e oportunidades.

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Mega-analysis methods in ENIGMA: the experience of the generalized anxiety disorder working group

The ENIGMA group on Generalized Anxiety Disorder (ENIGMA‐Anxiety/GAD) is part of a broader effort to investigate anxiety disorders using imaging and genetic data across multiple sites worldwide. The group is actively conducting a mega‐analysis of a large number of brain structural scans. In this process, the group was confronted with many methodological challenges related to study planning and implementation, between‐country transfer of subject‐level data, quality control of a considerable amount of imaging data, and choices related to statistical methods and efficient use of resources. This report summarizes the background information and rationale for the various methodological decisions, as well as the approach taken to implement them. The goal is to document the approach and help guide other research groups working with large brain imaging data sets as they develop their own analytic pipelines for mega‐analyses

Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis

Background: Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria screening, directly from dried human blood spots. Methods: Filter papers containing dried blood spots (DBS) were obtained from a cross-sectional malaria survey in 12 wards in southeastern Tanzania in 2018/19. The DBS were scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra in the range 4000 cm−1 to 500 cm−1. The spectra were cleaned to compensate for atmospheric water vapour and CO2 interference bands and used to train different classification algorithms to distinguish between malaria-positive and malaria-negative DBS papers based on PCR test results as reference. The analysis considered 296 individuals, including 123 PCR-confirmed malaria positives and 173 negatives. Model training was done using 80% of the dataset, after which the best-fitting model was optimized by bootstrapping of 80/20 train/test-stratified splits. The trained models were evaluated by predicting Plasmodium falciparum positivity in the 20% validation set of DBS. Results: Logistic regression was the best-performing model. Considering PCR as reference, the models attained overall accuracies of 92% for predicting P. falciparum infections (specificity = 91.7%; sensitivity = 92.8%) and 85% for predicting mixed infections of P. falciparum and Plasmodium ovale (specificity = 85%, sensitivity = 85%) in the field-collected specimen. Conclusion: These results demonstrate that mid-infrared spectroscopy coupled with supervised machine learning (MIR-ML) could be used to screen for malaria parasites in human DBS. The approach could have potential for rapid and high-throughput screening of Plasmodium in both non-clinical settings (e.g., field surveys) and clinical settings (diagnosis to aid case management). However, before the approach can be used, we need additional field validation in other study sites with different parasite populations, and in-depth evaluation of the biological basis of the MIR signals. Improving the classification algorithms, and model training on larger datasets could also improve specificity and sensitivity. The MIR-ML spectroscopy system is physically robust, low-cost, and requires minimum maintenance

Status of QUBIC, the Q&U Bolometer for Cosmology

The Q&U Bolometric Interferometer for Cosmology (QUBIC) is a novel kind of polarimeter optimized for the measurement of the B-mode polarization of the Cosmic Microwave Back-ground (CMB), which is one of the major challenges of observational cosmology. The signal is expected to be of the order of a few tens of nK, prone to instrumental systematic effects and polluted by various astrophysical foregrounds which can only be controlled through multichroic observations. QUBIC is designed to address these observational issues with a novel approach that combines the advantages of interferometry in terms of control of instrumental systematics with those of bolometric detectors in terms of wide-band, background-limited sensitivity.Comment: Contribution to the 2022 Cosmology session of the 33rd Rencontres de Blois. arXiv admin note: substantial text overlap with arXiv:2203.0894