Evaluation of the Thermal, Chemical, Mechanical, and Microbial Stability of New Nanohybrids Based on Carboxymethyl-Scleroglucan and Silica Nanoparticles for EOR Applications

Scleroglucan (SG) is resistant to harsh reservoir conditions such as high temperature, high shear stresses, and the presence of chemical substances. However, it is susceptible to biological degradation because bacteria use SG as a source of energy and carbon. All degradation effects lead to viscosity loss of the SG solutions, affecting their performance as an enhanced oil recovery (EOR) polymer. Recent studies have shown that nanoparticles (NPs) can mitigate these degradative effects. For this reason, the EOR performance of two new nanohybrids (NH-A and NH-B) based on carboxymethyl-scleroglucan and amino-functionalized silica nanoparticles was studied. The susceptibility of these products to chemical, mechanical, and thermal degradation was evaluated following standard procedures (API RP 63), and the microbial degradation was assessed under reservoir-relevant conditions (1311 ppm and 100 °C) using a bottle test system. The results showed that the chemical reactions for the nanohybrids obtained modified the SG triple helix configuration, impacting its viscosifying power. However, the nanohybrid solutions retained their viscosity during thermal, mechanical, and chemical degradation experiments due to the formation of a tridimensional network between the nanoparticles (NPs) and the SG. Also, NH-A and NH-B solutions exhibited bacterial control because of steric hindrances caused by nanoparticle modifications to SG. This prevents extracellular glucanases from recognizing the site of catalysis, limiting free glucose availability and generating cell death due to substrate depletion. This study provides insights into the performance of these nanohybrids and promotes their application in reservoirs with harsh conditions.

Mycobacterium leprae's Infective Capacity Is Associated with Activation of Genes Involved in PGL-I Biosynthesis in a Schwann Cells Infection Model

Peripheral nerves and Schwann cells (SCs) are privileged and protected sites for initial colonization, survival, and spread of leprosy bacillus. Mycobacterium leprae strains that survive multidrug therapy show a metabolic inactivation that subsequently induces the recurrence of typical clinical manifestations of leprosy. Furthermore, the role of the cell wall phenolic glycolipid I (PGL-I) in the M. leprae internalization in SCs and the pathogenicity of M. leprae have been extensively known. This study assessed the infectivity in SCs of recurrent and non-recurrent M. leprae and their possible correlation with the genes involved in the PGL-I biosynthesis. The initial infectivity of non-recurrent strains in SCs was greater (27%) than a recurrent strain (6.5%). In addition, as the trials progressed, the infectivity of the recurrent and non-recurrent strains increased 2.5- and 2.0-fold, respectively; however, the maximum infectivity was displayed by non-recurrent strains at 12 days post-infection. On the other hand, qRT-PCR experiments showed that the transcription of key genes involved in PGL-I biosynthesis in non-recurrent strains was higher and faster (Day 3) than observed in the recurrent strain (Day 7). Thus, the results indicate that the capacity of PGL-I production is diminished in the recurrent strain, possibly affecting the infective capacity of these strains previously subjected to multidrug therapy. The present work opens the need to address more extensive and in-depth studies of the analysis of markers in the clinical isolates that indicate a possible future recurrence.

Analysis of the cytotoxic and genotoxic effects in a population chronically exposed to coal mining residues

During coal mining activities, many compounds are released into the environment that can negatively impact human health. Particulate matter, polycyclic aromatic hydrocarbons (PAHs), metals, and oxides are part of the complex mixture that can affect nearby populations. Therefore, we designed this study to evaluate the potential cytotoxic and genotoxic effects in individuals chronically exposed to coal residues from peripheral blood lymphocytes and buccal cells. We recruited 150 individuals who lived more than 20 years in La Loma-Colombia and 120 control individuals from the city of Barranquilla without a history of exposure to coal mining. In the cytokinesis-block micronucleus cytome (CBMN-Cyt) assay, significant differences in the frequency of micronucleus (MN), nucleoplasmic bridge (NPB), nuclear bud (NBUD), and apoptotic cells (APOP) were observed between the two groups. In the buccal micronucleus cytome (BM-Cyt) assay, a significant formation of NBUD, karyorrhexis (KRX), karyolysis (KRL), condensed chromatin (CC), and binucleated (BN) cells was observed in the exposed group. Considering the characteristics of the study group, a significant correlation for CBMN-Cyt was found between NBUD and vitamin consumption, between MN or APOP and meat consumption, and between MN and age. Moreover, a significant correlation for BM-Cyt was found between KRL and vitamin consumption or age, and BN versus alcohol consumption. Using Raman spectroscopy, a significant increase in the concentration of DNA/RNA bases, creatinine, polysaccharides, and fatty acids was detected in the urine of individuals exposed to coal mining compared to the control group. These results contribute to the discussion on the effects of coal mining on nearby populations and the development of diseases due to chronic exposure to these residues.

Surface, satellite ozone variations in Northern South America during low anthropogenic emission conditions: a machine learning approach

2020 presented the ideal conditions for studying the air quality response to several emission reductions due to the COVID-19 lockdowns. Numerous studies found that the tropospheric ozone increased even in lockdown conditions, but its reasons are not entirely understood. This research aims to better understand the ozone variations in Northern South America. Satellite and reanalysis data were used to analyze regional ozone variations. An analysis of two of the most polluted Colombian cities was performed by quantifying the changes of ozone and its precursors and by doing a machine learning decomposition to disentangle the contributions that precursors and meteorology made to form O₃. The results indicated that regional ozone increased in most areas, especially where wildfires are present. Meteorology is associated with favorable conditions to promote wildfires in Colombia and Venezuela. Regarding the local analysis, the machine learning ensemble shows that the decreased titration process associated with the NO plummeting owing to mobility reduction is the main contributor to the O₃ increase (≈50%). These tools lead to conclude that (i) the increase in O₃ produced by the reduction of the titration process that would be associated with an improvement in mobile sources technology has to be considered in the new air quality policies, (ii) a boost in international cooperation is essential to control wildfires since an event that occurs in one country can affect others and (iii) a machine learning decomposition approach coupled with sensitivity experiments can help us explain and understand the physicochemical mechanism that drives ozone formation.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11869-023-01303-6.

Cooper-pair distribution function [Formula: see text] for superconducting [Formula: see text] and [Formula: see text]

Cooper-pair distribution function, [Formula: see text], is a recent theoretical proposal that reveals information about the superconductor state through the determination of the spectral regions where Cooper pairs are formed. This is built from the well-established Eliashberg spectral function and phonon density of states, calculated by first-principles. From this function is possible to obtain the [Formula: see text] parameter, which is proportional to the total number of Cooper pairs formed at a critical temperature [Formula: see text]. Herein, we reported [Formula: see text] function of the compressed [Formula: see text] and [Formula: see text] high-[Formula: see text] conventional superconductors, including the effect of stable sulfur isotopes in [Formula: see text]. [Formula: see text] suggests that the vibration energy range of 10-70 meV is where the Cooper pairs are possible for these superconductors, pointing out the possible importance of the low-energy region on the electron-phonon superconductivity. This has been confirmed by the fact that a simple variation in the low-frequency region induced for the substitution of S atoms in [Formula: see text] by its stable isotopes can lead to important changes in [Formula: see text]. The results also show proportionality between [Formula: see text] parameter and experimental or theoretical [Formula: see text] values.

Genomic regions associated with physiological, biochemical and yield-related responses under water deficit in diploid potato at the tuber initiation stage revealed by GWAS

Water deficit, which is increasing with climate change, is a serious threat to agricultural sustainability worldwide. Dissection of the genetic architecture of water deficit responses is highly desirable for developing water-deficit tolerant potato cultivars and enhancing the resilience of existing cultivars. This study examined genetic variation in response to water deficit in a panel of diploid potato and identified the QTL governing this trait via a genome-wide association study (GWAS). A panel of 104 diploid potato accessions were evaluated under both well-watered and water deficit treatments at tuber initiation stage. Drought stress index (DTI) was calculated to assess tolerance of the diploid potato genotypes to water deficit. The GWAS was conducted using a matrix of 47K single nucleotide polymorphisms (SNP), recently available for this population. We are reporting 38 QTL, seven for well-watered conditions, twenty-two for water deficit conditions and nine for DTI which explain between 12.6% and 44.1% of the phenotypic variance. A set of 6 QTL were found to be associated with more than one variable. Marker WDP-9.21 was found associated with tuber fresh weigh under WD and gene annotation analysis revealed co-localization with the Glucan/water dikinase (GWD) gene. Of the nine QTL detected from DTI on chromosomes 2,3,5,8,10 and 12, three candidate genes with a feasible role in water deficit response were identified. The findings of this study can be used in marker-assisted selection (MAS) for water- deficit tolerance breeding in potato.

Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise

Physical exercise (PE) has become an essential tool for different rehabilitation programs. High-intensity exercises (HIEs) have been demonstrated to provide better results in general health conditions, compared with low and moderate-intensity exercises. In this context, monitoring of a patients' condition is essential to avoid extreme fatigue conditions, which may cause physical and physiological complications. Different methods have been proposed for fatigue estimation, such as: monitoring the subject's physiological parameters and subjective scales. However, there is still a need for practical procedures that provide an objective estimation, especially for HIEs. In this work, considering that the sit-to-stand (STS) exercise is one of the most implemented in physical rehabilitation, a computational model for estimating fatigue during this exercise is proposed. A study with 60 healthy volunteers was carried out to obtain a data set to develop and evaluate the proposed model. According to the literature, this model estimates three fatigue conditions (low, moderate, and high) by monitoring 32 STS kinematic features and the heart rate from a set of ambulatory sensors (Kinect and Zephyr sensors). Results show that a random forest model composed of 60 sub-classifiers presented an accuracy of 82.5% in the classification task. Moreover, results suggest that the movement of the upper body part is the most relevant feature for fatigue estimation. Movements of the lower body and the heart rate also contribute to essential information for identifying the fatigue condition. This work presents a promising tool for physical rehabilitation.

Attentional bias during emotional processing: Behavioral and electrophysiological evidence from an Emotional Flanker Task

Threatening stimuli seem to capture attention more swiftly than neutral stimuli. This attention bias has been observed under different experimental conditions and with different types of stimuli. It remains unclear whether this adaptive behaviour reflects the function of automatic or controlled attention mechanisms. Additionally, the spatiotemporal dynamics of its neural correlates are largely unknown. The present study investigates these issues using an Emotional Flanker Task synchronized with EEG recordings. A group of 32 healthy participants saw response-relevant images (emotional scenes from IAPS or line drawings of objects) flanked by response-irrelevant distracters (i.e., emotional scenes flanked by line drawings or vice versa). We assessed behavioural and ERP responses drawn from four task conditions (Threat-Central, Neutral-Central, Threat-Peripheral, and Neutral-Peripheral) and subjected these responses to repeated-measures ANOVA models. When presented as response-relevant targets, threatening images attracted faster and more accurate responses. They did not affect response accuracy to targets when presented as response-irrelevant flankers. However, response times were significantly slower when threatening images flanked objects than when neutral images were shown as flankers. This result replicated the well-known Emotional Flanker Effect. Behavioural responses to response-relevant threatening targets were accompanied by significant modulations of ERP activity across all time-windows and regions of interest and displayed some meaningful correlations. The Emotional Flanker Effect was accompanied by a modulation over parietal and central-parietal regions within a time-window between 550-690ms. Such a modulation suggests that the attentional disruption to targets caused by response-irrelevant threatening flankers appears to reflect less neural resources available, which are seemingly drawn away by distracting threatening flankers. The observed spatiotemporal dynamics seem to concur with understanding of the important adaptive role attributed to threat-related attention bias.

A Novel Method for Objective Selection of Information Sources Using Multi-Kernel SVM and Local Scaling

Advancement on computer and sensing technologies has generated exponential growth in the data available for the development of systems that support decision-making in fields such as health, entertainment, manufacturing, among others. This fact has made that the fusion of data from multiple and heterogeneous sources became one of the most promising research fields in machine learning. However, in real-world applications, to reduce the number of sources while maintaining optimal system performance is an important task due to the availability of data and implementation costs related to processing, implementation, and development times. In this work, a novel method for the objective selection of relevant information sources in a multimodality system is proposed. This approach takes advantage of the ability of multiple kernel learning (MKL) and the support vector machines (SVM) classifier to perform an optimal fusion of data by assigning weights according to their discriminative value in the classification task; when a kernel is designed for representing each data source, these weights can be used as a measure of their relevance. Moreover, three algorithms for tuning the Gaussian kernel bandwidth in the classifier prediction stage are introduced to reduce the computational cost of searching for an optimal solution; these algorithms are an adaptation of a common technique in unsupervised learning named local scaling. Two real application tasks were used to evaluate the proposed method: the selection of electrodes for a classification task in Brain-Computer Interface (BCI) systems and the selection of relevant Magnetic Resonance Imaging (MRI) sequences for detection of breast cancer. The obtained results show that the proposed method allows the selection of a small number of information sources.