Real-Time Monitoring of Gas–Solid Fluidized-Bed Granulation and Coating Process: Evolution of Particle Size,Fluidization Regime Transitions,and Psychometric Parameters

Moisture content and particle size are critical parameters in fluidized-bed granulation and coating. In this study, microcrystalline cellulose was granulated and coated. The particle size was monitored in real time by a Parsum IPP70 probe. The fluidization regime was assessed by Gaussian spectral analysis. The bed temperature and gas humidity profile were monitored to evaluate the drying efficiency. The defluidization phase was detected in the early stages with frequencies below 6.0 Hz. The Parsum probe showed the growth of the particles due to agglomeration. The psychometric parameters indicated the lower potential energy for drying. Therefore, monitoring in real time is important to detect failures.     

Kinetics of Soybean Oil Hydrolysis on Niobium Catalysts

The catalytic hydrolysis of soybean oil was used as an alternative for the production of monoglycerides (MG) and diglycerides (DG). The reactions were conducted in a stainless-steel tubular reactor in the temperature range of 240–290 °C, on niobium phosphate (NBP) and niobium oxide (NBO) as catalysts. In the hydrolysis reactions at 270 °C, the maximum selectivities of the products of interest were obtained at 22 % MG and 48 % DG for the reaction with NBP, and 7 % MG and 33 % DG with NBO, for 59 % and 36 % of triglyceride conversion in 10 min, respectively. The proposed kinetic model presented a good fit of the theoretical model with the experimental data, showing that the previous hypotheses considered for the mechanism development are suitable for describing the kinetics of soybean oil hydrolysis.     

Phytochemical and bioactive compounds identification of Ocimum tenuiflorum leaves of methanol extract and its fraction with an anti-diabetic potential

Phytochemical screening assay on crude extracts of Ocimum tenuiflorum L. leaves and methanol fractions was performed in this study. Phytochemical qualitative assay on aqueous crude extract and aqueous fraction did not show any steroid and terpenoid. However, ethanol and butanol fractions also did not show steroid in the screening test. The result of HPLC identification of active crude extracts and active fractions showed the possible bioactive compounds in this plant extracts, which may control diabetes. These findings showed that the active crude extract (methanol) and its active fractions (ethyl acetate, and butanol) showed the presence of polyphenolic active constituents such as 3,4-dimethoxycinnamic acid, caffeic acid, diosmetin, luteolin, kaempferol, rosmarinic acid, apigenin, and genistein that control the blood glucose in diabetic rats. Furthermore, isolation of the active components may pave the way for the development of new agents for the treatment of diabetes and its complications.     

Genomic studies of milk-related traits in water buffalo (Bubalus bubalis) based on single-step genomic best linear unbiased prediction and random regression models

Genomic selection has been widely implemented in many livestock breeding programs, but it remains incipient in buffalo. Therefore, this study aimed to (1) estimate variance components incorporating genomic information in Murrah buffalo; (2) evaluate the performance of genomic prediction for milk-related traits using single- and multitrait random regression models (RRM) and the single-step genomic best linear unbiased prediction approach; and (3) estimate longitudinal SNP effects and candidate genes potentially associated with time-dependent variation in milk, fat, and protein yields, as well as somatic cell score (SCS) in multiple parities. The data used to estimate the genetic parameters consisted of a total of 323,140 test-day records. The average daily heritability estimates were moderate (0.35 ± 0.02 for milk yield, 0.22 ± 0.03 for fat yield, 0.42 ± 0.03 for protein yield, and 0.16 ± 0.03 for SCS). The highest heritability estimates, considering all traits studied, were observed between 20 and 280 d in milk (DIM). The genetic correlation estimates at different DIM among the evaluated traits ranged from ?0.10 (156 to 185 DIM for SCS) to 0.61 (36 to 65 DIM for fat yield). In general, direct selection for any of the traits evaluated is expected to result in indirect genetic gains for milk yield, fat yield, and protein yield but also increase SCS at certain lactation stages, which is undesirable. The predicted RRM coefficients were used to derive the genomic estimated breeding values (GEBV) for each time point (from 5 to 305 DIM). In general, the tuning parameters evaluated when constructing the hybrid genomic relationship matrices had a small effect on the GEBV accuracy and a greater effect on the bias estimates. The SNP solutions were back-solved from the GEBV predicted from the Legendre random regression coefficients, which were then used to estimate the longitudinal SNP effects (from 5 to 305 DIM). The daily SNP effect for 3 different lactation stages were performed considering 3 different lactation stages for each trait and parity: from 5 to 70, from 71 to 150, and from 151 to 305 DIM. Important genomic regions related to the analyzed traits and parities that explain more than 0.50% of the total additive genetic variance were selected for further analyses of candidate genes. In general, similar potential candidate genes were found between traits, but our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the traits across parities. These results contribute to a better understanding of the genetic architecture of milk production traits in dairy buffalo and reinforce the relevance of incorporating genomic information to genetically evaluate longitudinal traits in dairy buffalo. Furthermore, the candidate genes identified can be used as target genes in future functional genomics studies.     

Pleurotusostreatus and Agaricus subrufescens: investigation of chemical composition and antioxidant properties of these mushrooms cultivated with different handmade and commercial supplements

In this study, the chemical composition, total phenolic content and antioxidant activity (DPPH, ORAC and FRAP assays) of A. subrufescens and P. ostreatus, cultivated with handmade and commercials supplements, were compared. Additionally, the compounds ergosterol, saccharopine, and hexitol were identified in A. subrufescens by HPLC-MS/MS. The antioxidant compound p-coumaric acid and dihexoses was found in both mushroom species. A. subrufescens presented higher total phenolic content (73.8 ± 0.6 mg GAE 100 g⁻¹) and antioxidant activity than P. ostreatus (16.6 ± 0.5 mg GAE 100 g⁻¹). The handmade supplement based on the waste of noble grains presented statistically similar phenolic content to the mushrooms cultivated with commercial ones Spawn Mate II SE (86.1 ± 1.4 and 92.9 ± 0.3 mg GAE 100 g⁻¹, respectively). Therefore, the results support the use of handmade supplements based on agro-wastes as a viable alternative to the use of high-cost commercial ones.     

Methodology for Estimating Streamflow by Water Balance and Rating Curve Methods Based on Logistic Regression

Both water balance (WB) and rating curve (RC) are methods for estimating streamflow. The first is mostly used to estimate reservoir outflows, while the second is usually adopted in hydrometeorological network streamflow gauges. While WB uses hourly collected data, the RC estimates streamflow using current water level and extrapolation techniques. The objective of this study was to analyze variations in the reservoir’s hourly outflow at Queimado Hydroelectric Power Plant (HPP Queimado) and to propose a method to evaluate whether the estimate of the daily outflows, obtained by the WB method, is similar to the flow values obtained at a conventional station. The logistic regression (LR) model was used because it is a method that adopts binary, categorically dependent variables to identify the event of interest. The results showed that the values of streamflow, obtained from an average of two daily readings, were a good representation of the flows in the region. The LR was able to identify atypical data, especially in the rainy season. This means that data consistency analysis can be faster and safer, when adequately employed and considering the proposed conditions, contributing to both management policies and the management of water resources.

     

Monthly Streamflow Modeling Based on Self-Organizing Maps and Satellite-Estimated Rainfall Data

Hydrological data provide valuable information for the decision-making process in water resources management, where long and complete time series are always desired. However, it is common to deal with missing data when working on streamflow time series. Rainfall-streamflow modeling is an alternative to overcome such a difficulty. In this paper, self-organizing maps (SOM) were developed to simulate monthly inflows to a reservoir based on satellite-estimated gridded precipitation time series. Three different calibration datasets from Três Marias Reservoir, composed of inflows (targets) and 91 TRMM-estimated rainfall data (inputs), from 1998 to 2019, were used. The results showed that the inflow data homogeneity pattern influenced the rainfall-streamflow modeling. The models generally showed superior performance during the calibration phase, whereas the outcomes varied depending on the data homogeneity pattern and the chosen SOM structure in the testing phase. Regardless of the input data homogeneity, the SOM networks showed excellent results for the rainfall-runoff modeling, presenting Nash–Sutcliffe coefficients greater than 0.90.

Graphical Abstract