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This paper aims to evaluate the statistical association between exposure to air pollution and forced expiratory volume in the first second (FEV1) in both asthmatic and non-asthmatic children and teenagers, in which the response variable FEV1 was repeatedly measured on a monthly basis, characterizing a longitudinal experiment. Due to the nature of the data, an robust linear mixed model (RLMM), combined with a robust principal component analysis (RPCA), is proposed to handle the multicollinearity among the covariates and the impact of extreme observations (high levels of air contaminants) on the estimates. The Huber and Tukey loss functions are considered to obtain robust estimators of the parameters in the linear mixed model (LMM). A finite sample size investigation is conducted under the scenario where the covariates follow linear time series models with and without additive outliers (AO). The impact of the time-correlation and the outliers on the estimates of the fixed effect parameters in the LMM is investigated. In the real data analysis, the robust model strategy evidenced that RPCA exhibits three principal component (PC), mainly related to relative humidity (Hmd), particulate matter with a diameter smaller than 10 µm (PM10) and particulate matter with a diameter smaller than 2.5 µm (PM2.5).

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Moisture activated dry granulation (MADG) is an attractive granulation process. However, only a few works have explored modified drug release achieved by MADG, and to the best of the authors knowledge, none of them have explored gastroretention. The aim of this study was to explore the applicability of MADG process for developing gastroretentive placebo tablets, aided by SeDeM diagram. Floating and swelling capacities have been identified as critical quality attributes (CQAs). After a formulation screening step, the type and concentration of floating matrix formers and of binders were identified as the most relevant critical material attributes (CMAs) to investigate in ten formulations. A multiple linear regression analysis (MLRA) was applied against the factors that were varied to find the design space. An optimized product based on principal component analysis (PCA) results and MLRA was prepared and characterized. The granulate was also assessed by SeDeM. In conclusion, granulates lead to floating tablets with short floating lag time (<2 min), long floating duration (>4 h), and showing good swelling characteristics. The results obtained so far are promising enough to consider MADG as an advantageous granulation method to obtain gastroretentive tablets or even other controlled delivery systems requiring a relatively high content of absorbent materials in their composition.

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This study aimed to evaluate the chemical composition and antioxidant activity of phenolic extracts from monofloral and polyfloral honey samples obtained from different Brazilian regions. The chemical composition (total content of phenolic compounds and flavonoids) of extracts were measured by using colorimetric assays and analyzed by high performance liquid chromatographic (HPLC-DAD). The antioxidant activity was evaluated by chemical and biochemical assays (reducing power assay, 1,1-diphenyl-2-picrylhydrazyl (DPPH⋅) and 2,2-azino-bis(3-ethylbenzothiazoline-6-sulphonic) acid (ABTS⋅+) scavenger assays. It was also investigated the ability of extracts in attenuate lipid peroxidation induced by Fe2+ in phospholipids. The obtained results clearly demonstrated that the botanical origin and geographical region of honey collection influenced the chemical composition and antioxidant activity of extracts. Furthermore, the samples were constituted by phenolic acids and flavonoids, which p-coumaric acid was predominant among the compounds identified. All samples were able to scavenge free radicals and inhibit lipid peroxidation, and good correlations were obtained between the flavonoid content and honey color. In conclusion, the obtained extracts were constituted by antioxidant compounds, which reinforce the usage of honey in human diets, and demonstrated that the region of honey collection strong influenced in the chemical composition and, consequently, its biological effect.

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Candida auris and Candida haemulonii are two emerging opportunistic pathogens that have caused an increase in clinical cases in the recent years worldwide. The differentiation of some Candida species is highly laborious, difficult, costly, and time-consuming depending on the similarity between the species. Thus, this study aimed to develop a new, faster, and less expensive methodology for differentiating between C. auris and C. haemulonii based on near-infrared (NIR) spectroscopy and multivariate analysis. C. auris CBS10913 and C. haemulonii CH02 were separated in 15 plates per species, and three isolated colonies of each plate were selected for Fourier transform near-infrared (FT-NIR) analysis, totaling 90 spectra. Subsequently, principal component analysis (PCA) and variable selection algorithms, including the successive projections algorithm (SPA) and genetic algorithm (GA) coupled with linear discriminant analysis (LDA), were employed to discern distinctive patterns among the samples. The use of PCA, SPA, and GA algorithms associated with LDA achieved 100% sensitivity and specificity for the discriminations. The SPA-LDA and GA-LDA algorithms were essential in selecting the variables (infrared wavelengths) of most importance for the models, which could be attributed to binding of cell wall structures such as polysaccharides, peptides, proteins, or molecules resulting from yeasts' metabolism. These results show the high potential of combined FT-NIR and multivariate analysis techniques for the classification of Candida-like fungi, which can contribute to faster and more effective diagnosis and treatment of patients affected by these microorganisms.

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The differential effects of cellular and ultrastructural characteristics on the optical properties of adaxial and abaxial leaf surfaces in the genus Tradescantia highlight the intricate relationships between cellular arrangement and pigment distribution in the plant cells. We examined hyperspectral and chlorophyll a fluorescence (ChlF) kinetics using spectroradiometers and optical and electron microscopy techniques. The leaves were analysed for their spectral properties and cellular makeup. The biochemical compounds were measured and correlated with the biophysical and ultrastructural features. The main findings showed that the top and bottom leaf surfaces had different amounts and patterns of pigments, especially anthocyanins, flavonoids, total phenolics, chlorophyll-carotenoids, and cell and organelle structures, as revealed by the hyperspectral vegetation index (HVI). These differences were further elucidated by the correlation coefficients, which influence the optical signatures of the leaves. Additionally, ChlF kinetics varied between leaf surfaces, correlating with VIS-NIR-SWIR bands through distinct cellular structures and pigment concentrations in the hypodermis cells. We confirmed that the unique optical properties of each leaf surface arise not only from pigmentation but also from complex cellular arrangements and structural adaptations. Some of the factors that affect how leaves reflect light are the arrangement of chloroplasts, thylakoid membranes, vacuoles, and the relative size of the cells themselves. These findings improve our knowledge of the biophysical and biochemical reasons for leaf optical diversity, and indicate possible implications for photosynthetic efficiency and stress adaptation under different environmental conditions in the mesophyll cells of Tradescantia plants.

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The rubber tree, Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell. Arg., is the sole plant worldwide utilized for the commercial production of natural rubber. Following years of breeding, there exists a wide array of germplasm differentiation in rubber trees. The exploration of diversity and population structure within rubber tree germplasm resources, alongside the establishment of core germplasm resources, is instrumental in elucidating the genetic background and facilitating the effective utilization and management of these resources. By employing SNP molecular marker technology, 195 rubber tree resources were amplified, their genetic diversity analyzed, and a fingerprint map was subsequently constructed. Through this process, the cold-resistant core germplasm of rubber trees was identified. The results revealed that the PIC, He, and pi values ranged from 0.0905 to 0.3750, 0.095 to 0.5000, and 0.0953 to 0.5013, respectively. Both group structure analysis and cluster analysis delineated the accessions into two groups, signifying a simple group structure. A core germplasm bank was established with a sampling ratio of 10%, comprising 21 accessions divided into two populations. Population G1 consists of 20 accessions, while population G2 comprises 1 accession. The research findings have led to the creation of a molecular database that is anticipated to contribute to the management and subsequent breeding applications of rubber tree accessions.

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PURPOSE: This paper aimed to contextualize the process of public hospital providing services, based on the measurement of the performance of Federal University Hospitals (HUFs) of Brazil, using the technique of multivariate statistics of principal component analysis. DESIGN/METHODOLOGY/APPROACH: This research presented a descriptive and quantitative character, as well as exploratory purpose and followed the inductive logic, being empirically structured in two stages, that is, the application of principal component analysis (PCA) in four healthcare performance dimensions; subsequently, the full reapplication of principal component analysis in the most highly correlated variables, in module, with the first three main components (PC1, PC2 and PC3). FINDINGS: From the principal component analysis, considering mainly component I, with twice the explanatory power of the second (PC2) and third components (PC3), it was possible to evidence the efficient or inefficient behavior of the HUFs evaluated through the production of medical residency, by specialty area. Finally, it was observed that the formation of two groups composed of seven and eight hospitals, that is, Groups II and IV shows that these groups reflect similarities with respect to the scores and importance of the variables for both hospitals' groups. RESEARCH LIMITATIONS/IMPLICATIONS: Among the main limitations it was observed that there was incomplete data for some HUFs, which made it impossible to search for information to explain and better contextualize certain aspects. More specifically, a limited number of hospitals with complete information were dealt with for 60% of SIMEC/REHUF performance indicators. PRACTICAL IMPLICATIONS: The use of PCA multivariate technique was of great contribution to the contextualization of the performance and productivity of homogeneous and autonomous units represented by the hospitals. It was possible to generate a large quantity of information in order to contribute with assumptions to complement the decision-making processes in these organizations. SOCIAL IMPLICATIONS: Development of public policies with emphasis on hospitals linked to teaching centers represented by university hospitals. This also involved the projection of improvements in the reach of the efficiency of the services of assistance to the public health, from the qualified formation of professionals, both to academy, as to clinical practice. ORIGINALITY/VALUE: The originality of this paper for the scenarios of the Brazilian public health sector and academic area involved the application of a consolidated performance analysis technique, that is, PCA, obtaining a rich work in relation to the extensive exploitation of techniques to support decision-making processes. In addition, the sequence and the way in which the content, formed by object of study and techniques, has been organized, generates a particular scenario for the measurement of performance in hospital organizations.

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The food industry has grown with the demands for new products and their authentication, which has not been accompanied by the area of analysis and quality control, thus requiring novel process analytical technologies for food processes. An electronic tongue (e-tongue) is a multisensor system that can characterize complex liquids in a fast and simple way. Here, we tested the efficacy of an impedimetric microfluidic e-tongue setup - comprised by four interdigitated electrodes (IDE) on a printed circuit board (PCB), with four pairs of digits each, being one bare sensor and three coated with different ultrathin nanostructured films with different electrical properties - in the analysis of fresh and industrialized coconut water. Principal Component Analysis (PCA) was applied to observe sample differences, and Partial Least Squares Regression (PLSR) was used to predict sample physicochemical parameters. Linear Discriminant Analysis (LDA) and Partial Least Square - Discriminant Analysis (PLS-DA) were compared to classify samples based on data from the e-tongue device. Results indicate the potential application of the microfluidic e-tongue in the identification of coconut water composition and determination of physicochemical attributes, allowing for classification of samples according to soluble solid content (SSC) and total titratable acidity (TTA) with over 90% accuracy. It was also demonstrated that the microfluidic setup has potential application in the food industry for quality assessment of complex liquid samples.

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Agriculture is an essential economic activity in Brazil. However, it is also the main source of water quality degradation. Monitoring catchments with agricultural land use is a way to generate information on a scale to identify causes and sources of water quality degradation. This work used monitoring data derived from hydrology and the quality of surface and underground water in an intensive agricultural catchment in the Atlantic Forest biome. The Fortaleza River catchment is located in the western part of Santa Catarina state in southern Brazil and has 62 km2 of drainage area. Hydrological and water quality monitoring was conducted for 7 years at two fluviometric stations, three lysimeters, one meteorological station, and one piezometer. Data on precipitation, temperature, water flow, surface runoff, drainage, and water quality were used. Statistical analyses were also developed. Precipitation between 2013 and 2019 presented a homogeneous distribution in monthly and annual data, with January and July the months with the highest and lowest values, respectively. Statistical difference in the average and Q95 flows was found in upstream and downstream fluviometric sections. In terms of quality, statistical differences were identified for ammonium, nitrate, and potassium concentrations, which had higher concentrations in lysimeter runoff, indicating direct influence of agricultural activity on water quality. Principal component analysis (PCA) indicated that (i) surface water presented a positive relationship in Component 1 for the magnesium-calcium, sulphate-chloride, and acetate-bromide groups and a negative relationship for phosphate-nitrate; (ii) in lysimeters, the positive relationship occurred for Component 2 for the phosphate-chloride and sulphate-nitrate groups and was negative for ammonium-lithium and calcium-potassium-magnesium; and (iii) in piezometer, positive relationships were found for chloride-sodium and phosphate-nitrite pairs, while negative relationships were found for calcium-magnesium.

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Coat color is a factor affecting heat tolerance in tropical ruminant and a particular coat color can determine which is more resilient to environmental changes. The aim of this study was to measure the level of adaptation of Morada Nova sheep with different coat color by using an Adaptability Index (AI). Adult ewes were used, including two different coat colors of Morada Nova sheep (red and white) with mean of body weight of 28.02 ± 5.70 kg and 31.47 ± 3.41 kg, respectively. Physiology parameters, hematology, electrolytes, acid-base status, mineral, renal functions, metabolites, enzymes, and proteins were measured. AI was designed using a multivariate approach (principal component analysis) to "weigh" the influence of each variable in the animal responses. The variables more important for adaptive aspects of Red Morada Nova were: haematology, electrolytes and acid-base status. The hemoglobin (HG), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), sodium (Na+), oxygen pressure (PO2), glucose (GLU) and albumin (ALB) were significantly higher in Red Morada Nova sheep and hydrogen carbonate (HCO3), base excess (BE), total carbon dioxide concentration (TCO2) and URE were significantly higher in the white phenotype. The variables more important for adaptive aspects of White Morada Nova sheep were: (K+), total protein (TP), PO2, HG, cholesterol (CHO), rectal temperature (RT) and glucose (GLU). Both phenotypes showed a high adaptation level, however, a higher value was generated for the Red Morada Nova sheep (81.97). This study concludes that both phenotypes of the Morada Nova sheep breed are well adapted to the climatic condition of the Brazilian tropical region using different adaptive mechanisms.

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