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We report on a diffusive analysis of the motion of flagellate protozoa species. These parasites are the etiological agents of neglected tropical diseases: leishmaniasis caused by Leishmania amazonensis and Leishmania braziliensis, African sleeping sickness caused by Trypanosoma brucei, and Chagas disease caused by Trypanosoma cruzi. By tracking the positions of these parasites and evaluating the variance related to the radial positions, we find that their motions are characterized by a short-time transient superdiffusive behavior. Also, the probability distributions of the radial positions are self-similar and can be approximated by a stretched Gaussian distribution. We further investigate the probability distributions of the radial velocities of individual trajectories. Among several candidates, we find that the generalized gamma distribution shows a good agreement with these distributions. The velocity time series have long-range correlations, displaying a strong persistent behavior (Hurst exponents close to one). The prevalence of \"universal\" patterns across all analyzed species indicates that similar mechanisms may be ruling the motion of these parasites, despite their differences in morphological traits. In addition, further analysis of these patterns could become a useful tool for investigating the activity of new candidate drugs against these and others neglected tropical diseases.

The performance of the previously optimized magnetic cross-linked enzyme aggregate of Eversa (Eversa-mCLEA) in the enzymatic synthesis of biolubricants by transesterification of waste cooking oil (WCO) with different alcohols has been evaluated. Eversa-mCLEA showed good activities using these alcohols, reaching a transesterification activity with isoamyl alcohol around 10-fold higher than with methanol. Yields of isoamyl fatty acid ester synthesis were similar using WCO or refined oil, confirming that this biocatalyst could be utilized to transform this residue into a valuable product. The effects of WCO/isoamyl alcohol molar ratio and enzyme load on the synthesis of biolubricant were also investigated. A maximum yield of around 90 wt.% was reached after 72 h of reaction using an enzyme load of 12 esterification units/g oil and a WCO/alcohol molar ratio of 1:6 in a solvent-free system. At the same conditions, the liquid Eversa yielded a maximum ester yield of only 34%. This study demonstrated the great changes in the enzyme properties that can be derived from a proper immobilization system. Moreover, it also shows the potential of WCO as a feedstock for the production of isoamyl fatty acid esters, which are potential candidates as biolubricants.

With 23.4 billion pairs made and 22 billion discarded in 2023, post-consumer footwear waste is a major environmental challenge, demanding a shift toward circular economy practices. In this work, post-consumer footwear waste is repurposed into thermal/acoustic insulation materials for building construction, producing four needle-punched nonwovens (two of them compressed) composed of a post-consumer leather (30%) and footwear waste mixture (40%) with recycled polyester fibers. Nonwovens exhibited higher strain values (95.9 and 77.1% for leather residue and footwear mixture residue, respectively) but lower tensile strength (1694 and 104.9 kPa) and Young’s modulus (1767.8 and 136.10 kPa). The compressed nonwovens demonstrated higher tensile strength (7360 and 3559 kPa) and Young’s modulus values (12992 and 4020.4 kPa) and reduced strain (56.6 and 96.9%). The thermal conductivity results revealed that the nonwovens exhibited lower values (0.040 and 0.046 W/(m·K)), indicating better insulation performance when compared with their compressed counterparts (0.060 and 0.058 W/(m·K)). The nonwovens demonstrated high sound absorption at higher frequencies, reaching peak absorption coefficients of 0.917 and 0.995, ideal for acoustic insulation. The compressed nonwovens exhibited improved absorption at lower and mid-frequencies, with maximum values of 0.510 and 0.519. Given the current lack of applications for recycled materials derived from post-consumer footwear, the findings offer a novel approach to address their recycling.

The preparation of highly porous magnetic crosslinked aggregates (pm-CLEA) of porcine pancreas lipase (PPL) is reported. Some strategies to improve the volumetric activity of the immobilized biocatalyst were evaluated, such as treatment of PPL with enzyme surface-modifying agents (polyethyleneimine or dodecyl aldehyde), co-aggregation with protein co-feeders (bovine serum albumin and/or soy protein), use of silica magnetic nanoparticles functionalized with amino groups (SMNPs) as separation aid, and starch as pore-making agent. The combination of enzyme surface modification with dodecyl aldehyde, co-aggregation with SMNPs and soy protein, in the presence of 0.8% starch (followed by hydrolysis of the starch with α-amylase), yielded CLEAs expressing high activity (immobilization yield around 100% and recovered activity around 80%), high effectiveness factor (approximately 65% of the equivalent free enzyme activity) and high stability at 40 °C and pH 8.0, i.e., PPL CLEAs co-aggregated with SMNPs/bovine serum albumin or SMNPs/soy protein retained 80% and 50% activity after 10 h incubation, respectively, while free PPL was fully inactivated after 2 h. Besides, highly porous magnetic CLEAs co-aggregated with soy protein and magnetic nanoparticles (pm-SP-CLEAs) showed good performance and reusability in the hydrolysis of tributyrin for five 4h-batches.

The need to monitor the Earth’s surface over a range of spatial and temporal scales is fundamental in ecosystems planning and management. Change-Vector Analysis (CVA) is a bi-temporal method of change detection that considers the magnitude and direction of change vector. However, many multispectral applications do not make use of the direction component. The procedure most used to calculate the direction component using multiband data is the direction cosine, but the number of output direction cosine images is equal to the number of original bands and has a complex interpretation. This paper proposes a new approach to calculate the spectral direction of change, using the Spectral Angle Mapper and Spectral Correlation Mapper spectral-similarity measures. The chief advantage of this approach is that it generates a single image of change information insensitive to illumination variation. In this paper the magnitude component of the spectral similarity was calculated in two ways: as the standard Euclidean distance and as the Mahalanobis distance. In this test the best magnitude measure was the Euclidean distance and the best similarity measure was Spectral Angle Mapper. The results show that the distance and similarity measures are complementary and need to be applied together.

The purpose of this research is to assess the yield and reaction rate potential of carbon dioxide (CO2) sequestration through mineralisation using readily available and inexpensive resources by exploiting waste materials. In this case, a blend of four different kinds of ashes and combustion by-products were used, namely, coal fly ash (CFA), flue gas desulphurization (FGD) residues, municipal solid waste incineration fly ashes (MSWI FA) and bottom ash (MSWI BA), produced at the same location. To highlight the impact of these materials on the carbonation process, various factors were analysed, including particle size distribution, immediately soluble contents, mineralogy, particles’ detailed structure, and chemical composition. After preparing the samples, two carbonation processes were tested: natural carbonation and accelerated carbonation. To evaluate the impact of the water content on the reaction rate and yield of the mineral carbonation, various liquid-to-solid (L/S) ratios were used. The results demonstrate that the water content and pressure play a significant role in the CO2 sequestration during the accelerated carbonation, the higher the L/S, the greater the yields, which can reach up to 152 g CO2/kg with MSWI FA, while no substantial difference seems to emerge in the case of the natural carbonation.

We have mapped the primary native and exotic vegetation that occurs in the Cerrado-Caatinga transition zone in Central Brazil using MODIS-NDVI time series (product MOD09Q1) data over a two-year period (2011–2013). Our methodology consists of the following steps: (a) the development of a three-dimensional cube composed of the NDVI-MODIS time series; (b) the removal of noise; (c) the selection of reference temporal curves and classification using similarity and distance measures; and (d) classification using support vector machines (SVMs). We evaluated different temporal classifications using similarity and distance measures of land use and land cover considering several combinations of attributes. Among the classification using distance and similarity measures, the best result employed the Euclidean distance with the NDVI-MODIS data by considering more than one reference temporal curve per class and adopting six mapping classes. In the majority of tests, the SVM classifications yielded better results than other methods. The best result among all the tested methods was obtained using the SVM classifier with a fourth-degree polynomial kernel; an overall accuracy of 80.75% and a Kappa coefficient of 0.76 were obtained. Our results demonstrate the potential of vegetation studies in semiarid ecosystems using time-series data.

Background One of the major challenges to leishmaniasis treatment is the emergence of parasites resistant to antimony. To study differentially expressed genes associated with drug resistance, we performed a comparative transcriptomic analysis between wild-type and potassium antimonyl tartrate (Sb III )-resistant Leishmania infantum lines using high-throughput RNA sequencing. Methods All the cDNA libraries were constructed from promastigote forms of each line, sequenced and analyzed using STAR for mapping the reads against the reference genome ( L. infantum JPCM5) and DESeq2 for differential expression statistical analyses. All the genes were functionally annotated using sequence similarity search. Results The analytical pipeline considering an adjusted p -value < 0.05 and fold change > 2.0 identified 933 transcripts differentially expressed (DE) between wild-type and Sb III -resistant L. infantum lines. Out of 933 DE transcripts, 504 presented functional annotation and 429 were assigned as hypothetical proteins. A total of 837 transcripts were upregulated and 96 were downregulated in the Sb III -resistant L. infantum line. Using this DE dataset, the proteins were further grouped in functional classes according to the gene ontology database. The functional enrichment analysis for biological processes showed that the upregulated transcripts in the Sb III -resistant line are associated with protein phosphorylation, microtubule-based movement, ubiquitination, host–parasite interaction, cellular process and other categories. The downregulated transcripts in the Sb III -resistant line are assigned in the GO categories: ribonucleoprotein complex, ribosome biogenesis, rRNA processing, nucleosome assembly and translation. Conclusions The transcriptomic profile of L. infantum showed a robust set of genes from different metabolic pathways associated with the antimony resistance phenotype in this parasite. Our results address the complex and multifactorial antimony resistance mechanisms in Leishmania , identifying several candidate genes that may be further evaluated as molecular targets for chemotherapy of leishmaniasis. Graphical Abstract

Mapping deforestation is an essential step in the process of managing tropical rainforests. It lets us understand and monitor both legal and illegal deforestation and its implications, which include the effect deforestation may have on climate change through greenhouse gas emissions. Given that there is ample room for improvements when it comes to mapping deforestation using satellite imagery, in this study, we aimed to test and evaluate the use of algorithms belonging to the growing field of deep learning (DL), particularly convolutional neural networks (CNNs), to this end. Although studies have been using DL algorithms for a variety of remote sensing tasks for the past few years, they are still relatively unexplored for deforestation mapping. We attempted to map the deforestation between images approximately one year apart, specifically between 2017 and 2018 and between 2018 and 2019. Three CNN architectures that are available in the literature—SharpMask, U-Net, and ResUnet—were used to classify the change between years and were then compared to two classic machine learning (ML) algorithms—random forest (RF) and multilayer perceptron (MLP)—as points of reference. After validation, we found that the DL models were better in most performance metrics including the Kappa index, F1 score, and mean intersection over union (mIoU) measure, while the ResUnet model achieved the best overall results with a value of 0.94 in all three measures in both time sequences. Visually, the DL models also provided classifications with better defined deforestation patches and did not need any sort of post-processing to remove noise, unlike the ML models, which needed some noise removal to improve results.

The Synthetic Aperture Radar (SAR) time series allows describing the rice phenological cycle by the backscattering time signature. Therefore, the advent of the Copernicus Sentinel-1 program expands studies of radar data (C-band) for rice monitoring at regional scales, due to the high temporal resolution and free data distribution. Recurrent Neural Network (RNN) model has reached state-of-the-art in the pattern recognition of time-sequenced data, obtaining a significant advantage at crop classification on the remote sensing images. One of the most used approaches in the RNN model is the Long Short-Term Memory (LSTM) model and its improvements, such as Bidirectional LSTM (Bi-LSTM). Bi-LSTM models are more effective as their output depends on the previous and the next segment, in contrast to the unidirectional LSTM models. The present research aims to map rice crops from Sentinel-1 time series (band C) using LSTM and Bi-LSTM models in West Rio Grande do Sul (Brazil). We compared the results with traditional Machine Learning techniques: Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (k-NN), and Normal Bayes (NB). The developed methodology can be subdivided into the following steps: (a) acquisition of the Sentinel time series over two years; (b) data pre-processing and minimizing noise from 3D spatial-temporal filters and smoothing with Savitzky-Golay filter; (c) time series classification procedures; (d) accuracy analysis and comparison among the methods. The results show high overall accuracy and Kappa (>97% for all methods and metrics). Bi-LSTM was the best model, presenting statistical differences in the McNemar test with a significance of 0.05. However, LSTM and Traditional Machine Learning models also achieved high accuracy values. The study establishes an adequate methodology for mapping the rice crops in West Rio Grande do Sul.