Antimicrobial activity, mechanical and thermal properties of cassava starch films incorporated with beeswax and propolis.

Edible films can be formed from different polymeric compounds. The use of starch has gained extra value; because it can be used in combination with plasticizers and lipids, helping to improve mechanical properties. Besides, with the addition of an antimicrobial, the function of these films can be extended. The objective of this research was to evaluate the effect of native cassava starch, beeswax and ethanolic propolis extract (EPE) on the mechanical, thermal and inhibitory properties against the Aspergillus niger fungus. An experimental Box-Behnken design with three factors: cassava starch concentration (2-4%w/v), beeswax (0.5-0.9%w/w) and EPE (1-4%v/w) was used. The films obtained were opaque and with low mechanical properties. EPE concentration affected tensile strength, elongation at break (EB) and Young's modulus (YM), and cassava starch content only affected EB and YM. In thermal properties, the weight loss was affected by the cassava starch-beeswax interaction, where the most loss occurred at high levels of these factors in the temperature range of 200-360 °C. The films reduced the growth of the Aspergillus niger by 51%, where the beeswax-EPE interaction had a significant positive effect. The characteristics of the developed films suggest that they would be more acceptable as fruit and vegetable coatings.

Deep learning, data ramping, and uncertainty estimation for detecting artifacts in large, imbalanced databases of MRI images.

Magnetic resonance imaging (MRI) is increasingly being used to delineate morphological changes underlying neurological disorders. Successfully detecting these changes depends on the MRI data quality. Unfortunately, image artifacts frequently compromise the MRI utility, making it critical to screen the data. Currently, quality assessment requires visual inspection, a time-consuming process that suffers from inter-rater variability. Automated methods to detect MRI artifacts could improve the efficiency of the process. Such automated methods have achieved high accuracy using small datasets, with balanced proportions of MRI data with and without artifacts. With the current trend towards big data in neuroimaging, there is a need for automated methods that achieve accurate detection in large and imbalanced datasets. Deep learning (DL) is the ideal MRI artifact detection algorithm for large neuroimaging databases. However, the inference generated by DL does not commonly include a measure of uncertainty. Here, we present the first stochastic DL algorithm to generate automated, high-performing MRI artifact detection implemented on a large and imbalanced neuroimaging database. We implemented Monte Carlo dropout in a 3D AlexNet to generate probabilities and epistemic uncertainties. We then developed a method to handle class imbalance, namely data-ramping to transfer the learning by extending the dataset size and the proportion of the artifact-free data instances. We used a 34,800 scans (98% clean) dataset. At baseline, we obtained 89.3% testing accuracy (F1 = 0.230). Following the transfer learning (with data-ramping), we obtained 94.9% testing accuracy (F1 = 0.357) outperforming focal cross-entropy (92.9% testing accuracy, F1 = 0.304) incorporated for comparison at handling class imbalance. By implementing epistemic uncertainties, we improved the testing accuracy to 99.5% (F1 = 0.834), outperforming the results obtained in previous comparable studies. In addition, we estimated aleatoric uncertainties by incorporating random flips to the MRI volumes, and demonstrated that aleatoric uncertainty can be implemented as part of the pipeline. The methods we introduce enhance the efficiency of managing large databases and the exclusion of artifact images from big data analyses.

Rheological Behavior of Passion Fruit (Passiflora edulis) Peel Extract.

In this work, the rheological behavior of passion fruit peel extract was determined at different temperatures (5-40 °C) and peel content in the extract (40-55% w/w). The extract was obtained after blanching the passion fruit peels at 95 °C for 5 min, then they were crushed to reduce their size, water was added, and finally, they were subjected to liquefaction and subsequent filtration. Rheological measurements were made using a rheometer with a plate and plate geometry. Extract samples were adequately described by the power-law model exhibiting pseudoplastic behavior, without the presence of thixotropy. The temperature did not influence the flow behavior index, but the consistency coefficient did. The dynamic study (the temperature sweep test) showed that passion fruit peel extract exhibits a more elastic than viscous behavior, typical of a gel.

Effects of Yam (Dioscorea rotundata) Mucilage on the Physical, Rheological and Stability Characteristics of Ice Cream.

In the present investigation, yam mucilage was evaluated as a stabilizer and emulsifier in the formulation of vanilla flavored ice cream; physicochemical, rheological, and stability characteristics were determined. A completely randomized bifactorial design was used (yam mucilage: Carboxymethylcellulose ratio with the following levels: 100:0, 80:20, 50:50, and 20:80, and stabilizers concentration with levels of 0.4 and 0.8%). Results showed an increase in the protein content present in ice cream mixture as the amount of mucilage increases. Rheologically, it was found that ice cream has the characteristic behavior of a pseudoplastic fluid, presenting a viscoelastic structure where elastic behavior predominates. In addition, ratios with a higher content of mucilage incorporated a greater volume of air and presented the longest melting times, delaying drops falling time; in the same way mucilage gives ice cream a freezing temperature between -6.1 to -2.8 °C, indicating that the application of mucilage in food industry is possible due to its nutritional value, and it gives ice cream stability properties.

Post-industrial context of cassava bagasse and trend of studies towards a sustainable industry: A scoping review - Part I.

Background: The cassava starch industry is recognized as a source of negative externalities caused by the agroindustrial waste 'cassava bagasse'. Even though options for bioconversion of cassava bagasse have been introduced, it is also true that hundreds of tons of this waste are produced annually with the consequent negative environmental impact. This agroindustrial context highlights the need for further research in technological proposals aimed at lowering the water contained in cassava bagasse. Methods: We report a scoping review of studies from 2010-2021 that mention the uses of cassava bagasse, as well as the technological options that have become effective for drying fruits and vegetables. The method used for selecting articles was based on the Preferred Reporting Items for Systematic Review and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) method. Articles selected were taken from the databases of ScienceDirect, Google Scholar, Scopus and Springer. Results : This review highlights fruit and vegetable osmotic dehydration and drying studies assisted by the combination of emerging technologies of osmotic pressure, ultrasound, and electrical pulses. Studies that take advantage of cassava bagasse have focused on biotechnological products, animal and human food industry, and development of biofilms and biomaterials. Conclusions: In this review, we found 60 studies out of 124 that show the advantages of the residual components of cassava bagasse for the development of new products. These studies do not mention any potential use of bagasse fiber for post-industrial purposes, leaving this end products' final use/disposal unaddressed. A viable solution is osmotic dehydration and drying assisted with electrical pulse and ultrasound that have been shown to improve the drying efficiency of fruits, vegetables and tubers. This greatly improves the drying efficiency of agro-industrial residues such as husks and bagasse, which in turn, directly impacts its post-industrial use.

PaísDI: Feasibility and effectiveness of an advocacy program for adults with intellectual disability and their stakeholders' groups in Chile.

BACKGROUND: People with intellectual disability in Chile face individual and collective barriers to social participation. Lack of knowledge about their rights and tools for effective self-advocacy seem to be key elements that need to be improved to facilitate participation. METHOD: We present PaísDI, a 16 h long manualised program created by self-advocates in collaboration with an interdisciplinary team, with four modules: rights and intellectual disability, leadership in intellectual disability, effective communication and financial considerations of social projects. This quasi-experimental study had 349 participants, divided in three groups: people with intellectual disability, relatives and professionals. Feasibility and effectiveness where measured. RESULTS: The program is shown to be viable and effective, especially in its impact on self-perception for self-advocacy activities. CONCLUSION: The discussion highlights Chile's historic debt in creating policies that promote self-determination, knowledge and the empowerment of people with intellectual disability, to bolster their participation as citizens.

Comparison of Language and Memory Lateralization by Functional MRI and Wada Test in Epilepsy.

The intracarotid sodium amobarbital procedure (ISAP or Wada test) lateralizes cerebral functions to the cerebral hemispheres preoperatively. Functional magnetic resonance imaging (fMRI) is increasingly used to characterize preoperative language and memory lateralization. In this study, concordance of fMRI with Wada was examined in patients with medically intractable seizures. The relationship of the distance between the epileptogenic focus to functional activation area with patients' post-operative deficits in language was also analyzed. 27 epilepsy patients with preoperative fMRI and Wada data were analyzed using established fMRI paradigms for language and memory. Activation of Broca's and Wernicke's areas were measured in three dimensions. Language and memory lateralization were determined, and standard neuropsychiatry Wada test procedures were used for comparison. The shortest distance between a language area to the border of surgical focus (LAD) was also measured and compared with postoperative language deficits. Our study found that concordance between fMRI and Wada testing was 0.41 (Kappa's 'fair to good' concordance) for language dominance and 0.1 (Kappa's 'poor' concordance) for memory. No significant correlation was found between LAD and post-op language deficit (p=0.439). A correlation was found between LAD and post-op memory deficit (p=0.049; the further distance from surgical lesion to language area is associated with less post-operative memory loss). Females demonstrated significantly increased postoperative seizure improvement (Fisher's p-value=0.0296; female=8; male=6). A significant association between handedness (right-handed subjects) and postoperative seizure improvement was found (p=0.02) as well as a significant trend for interaction of gender and handedness on postoperative seizure improvement (p=0.09). Overall, our results demonstrate fMRI as a useful preoperative adjunct to Wada testing for language lateralization in patients with medically intractable seizures.

Development and characterization of dual-modified yam (Dioscorea rotundata) starch-based films.

The current consumer demand for fresh food and the interest in caring for the environment have driven the development of biodegradable film packaging to replace synthetic films to preserve the integrity of food. The objective of this work was to evaluate the effects of starch modifications (oxidized, cross-linked, and dual: oxidized/cross-linked), starch concentration (1 and 2%), and glycerol concentration (5 and 15%) on water vapor permeability (WVP), mechanical properties (tensile strength and elongation), optical, and structural properties of films based on "hawthorn" yam starch. The WVP of the films was 4.4 × 10-10 to 1.5 × 10-9 g/m∗s∗Pa, where the films with oxidized yam starch showed a 58.04% reduction concerning the native starch. The tensile strength of oxidized yam starch films showed a decrease of 17.51% with an increase in glycerol concentration. For the 1% starch concentration, elongation increased by 17.03% when the glycerol concentration was increased from 5 to 15%. Modification of starch, starch concentration, and glycerol have a significant effect on the barrier, mechanical, physical, and structural properties of films made with yam starch, where films made with oxidized yam starches at a concentration of 1% starch and 5% glycerol showed the best responses of the properties evaluated.

Using Deep Learning Algorithms to Automatically Identify the Brain MRI Contrast: Implications for Managing Large Databases.

Neuroimaging science has seen a recent explosion in dataset size driving the need to develop database management with efficient processing pipelines. Multi-center neuroimaging databases consistently receive magnetic resonance imaging (MRI) data with unlabeled or incorrectly labeled contrast. There is a need to automatically identify the contrast of MRI scans to save database-managing facilities valuable resources spent by trained technicians required for visual inspection. We developed a deep learning (DL) algorithm with convolution neural network architecture to automatically infer the contrast of MRI scans based on the image intensity of multiple slices. For comparison, we developed a random forest (RF) algorithm to automatically infer the contrast of MRI scans based on acquisition parameters. The DL algorithm was able to automatically identify the MRI contrast of an unseen dataset with <0.2% error rate. The RF algorithm was able to identify the MRI contrast of the same dataset with 1.74% error rate. Our analysis showed that reduced dataset sizes caused the DL algorithm to lose generalizability. Finally, we developed a confidence measure, which made it possible to detect, with 100% specificity, all MRI volumes that were misclassified by the DL algorithm. This confidence measure can be used to alert the user on the need to inspect the small fraction of MRI volumes that are prone to misclassification. Our study introduces a practical solution for automatically identifying the MRI contrast. Furthermore, it demonstrates the powerful combination of convolution neural networks and DL for analyzing large MRI datasets.

Estimating cortical column sensory networks in rodents from micro-electrocorticograph (µECoG) recordings.

Micro-electrocorticograph (µECoG) arrays offer the flexibility to record local field potentials (LFPs) from the surface of the cortex, using high density electrodes that are sub-mm in diameter. Research to date has not provided conclusive evidence for the underlying signal generation of µECoG recorded LFPs, or if µECoG arrays can capture network activity from the cortex. We studied the pervading view of the LFP signal by exploring the spatial scale at which the LFP can be considered elemental. We investigated the underlying signal generation and ability to capture functional networks by implanting, µECoG arrays to record sensory-evoked potentials in four rats. The organization of the sensory cortex was studied by analyzing the sensory-evoked potentials with two distinct modeling techniques: (1) The volume conduction model, that models the electrode LFPs with an electrostatic representation, generated by a single cortical generator, and (2) the dynamic causal model (DCM), that models the electrode LFPs with a network model, whose activity is generated by multiple interacting cortical sources. The volume conduction approach modeled activity from electrodes separated < 1000 µm, with reasonable accuracy but a network model like DCM was required to accurately capture activity > 1500 µm. The extrinsic network component in DCM was determined to be essential for accurate modeling of observed potentials. These results all point to the presence of a sensory network, and that µECoG arrays are able to capture network activity in the neocortex. The estimated DCM network models the functional organization of the cortex, as signal generators for the µECoG recorded LFPs, and provides hypothesis-testing tools to explore the brain.

Delineating potential epileptogenic areas utilizing resting functional magnetic resonance imaging (fMRI) in epilepsy patients.

Seizure localization includes neuroimaging like electroencephalogram, and magnetic resonance imaging (MRI) with limited ability to characterize the epileptogenic network. Temporal clustering analysis (TCA) characterizes epileptogenic network congruent with interictal epileptiform discharges by clustering together voxels with transient signals. We generated epileptogenic areas for 12 of 13 epilepsy patients with TCA, congruent with different areas of seizure onset. Resting functional MRI (fMRI) scans are noninvasive, and can be acquired quickly, in patients with different levels of severity and function. Analyzing resting fMRI data using TCA is quick and can complement clinical methods to characterize the epileptogenic network.

Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm.

High-resolution three-dimensional magnetic resonance imaging (3D-MRI) is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM) algorithm in the quality assessment of structural brain images, using global and region of interest (ROI) automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy) of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

Development and application of edible coatings in minimally processed fruit / Desarrollo y aplicación de recubrimientos comestibles en frutas mínimamente procesadas

The consumer's interest to purchase safe, nutritious, minimally processed, and healthy food has increased consumption of various fruits and vegetables. Generally, the quality of fruits depends on nutritional, microbiological and organoleptic properties, all of which are exposed to dynamic changes during harvesting, storage, and marketing. These changes are mainly due to the interactions between the fruits and its surroundings or migration among different inner components, which can result in loss of moisture and some volatile compounds.

Alterations in regional homogeneity of resting-state brain activity in mesial temporal lobe epilepsy.

PURPOSE: The purpose of the present study was to identify abnormal areas of regional synchronization in patients with mesial temporal lobe epilepsy and hippocampus sclerosis (mTLE-HS) compared to healthy controls, by applying a relatively novel method, the Regional Homogeneity (ReHo) method to resting state fMRI (RS-fMRI) data. METHODS: Eyes closed RS-fMRI data were acquired from 10 mTLE-HS patients (four right-side, six left-side) and 15 age- and gender-matched healthy subjects, and were analyzed by using ReHo. For group analysis, four right-side MTLE-HS patients' functional images were flipped, in order to make a homogeneous left MTLE-HS group (10 cases) and increase the sample size. KEY FINDINGS: Compared to the healthy control group, patients showed significantly increased ReHo in ipsilateral parahippocampal gyrus, midbrain, insula, corpus callosum, bilateral sensorimotor cortex, and frontoparietal subcortical structures, whereas decreased ReHo was observed mainly in default mode network (DMN) (including precuneus and posterior cingulate gyrus, bilateral inferior lateral parietal, and mesial prefrontal cortex) and cerebellum in patients relative to the control group. SIGNIFICANCE: This study identified that ReHo pattern in mTLE-HS patients was altered compared to healthy controls. We consider decreased ReHo in DMN to be responsible for wide functional impairments in cognitive processes. We propose that the increased ReHo in specific regions may form a network that might be responsible for seizure genesis and propagation.

Bio-swarm-pipeline: a light-weight, extensible batch processing system for efficient biomedical data processing.

A streamlined scientific workflow system that can track the details of the data processing history is critical for the efficient handling of fundamental routines used in scientific research. In the scientific workflow research community, the information that describes the details of data processing history is referred to as "provenance" which plays an important role in most of the existing workflow management systems. Despite its importance, however, provenance modeling and management is still a relatively new area in the scientific workflow research community. The proper scope, representation, granularity and implementation of a provenance model can vary from domain to domain and pose a number of challenges for an efficient pipeline design. This paper provides a case study on structured provenance modeling and management problems in the neuroimaging domain by introducing the Bio-Swarm-Pipeline. This new model, which is evaluated in the paper through real world scenarios, systematically addresses the provenance scope, representation, granularity, and implementation issues related to the neuroimaging domain. Although this model stems from applications in neuroimaging, the system can potentially be adapted to a wide range of bio-medical application scenarios.

Mortalidad diferencial en el Distrito Federal, 1978 / Differential mortality in the Federal District, 1978

El estudio de la mortalidad es indispensable para compreender el proceso salud-enfermedad en los países subdesarrollados. Este trabajo se inscribe entre los primeros que analizan las diferencias en la muerte de 43.634 residentes en el Distrito Federal durante 1978, a partir de tres ejes: la ocupación, el lugar de residencia y una clasificación de las causas de muerte. Para obtener resultados se utilizó una metodología estadística compleja que los autores proponen como adecuada a las características del sistema estadístico mexicano. En los resultados se presentan tasas específicas de mortalidad por estratos ocupacionales, por lugar de residencia y por calidad abatible de las causas de muerte; asimismo una combinación de estos tres ejes. Las diferenciales de mortalidad indican fuertes contrastes entre los grupos de población, particularmente marcados en las muertes infantiles, que son congruentes con observaciones efectuadas en otros países. Los autores señalan que este tipo de estudios es indispensable para evaluar el sistema de salud mexicano y dan apoyo para orientar la formación de recursos humanos

Resecciones pulmonares por carcinoma / Pulmonary resections due to carcinoma

Se analizaron 171 pacientes a quienes se realizó resección pulmonar por carcinoma (70 porciento de los explorados). La edad constituyó factor fundamental en la mortalidad operatoria. La lobectomía provocó la mitad del riesgo quirúrgico de la neumonectomía. La mortalidad quirúrgica no se relacionó con el sexo ni con la localización tumoral ni con la etapa clínica. La sobrevida global a 5 años fue del 25,76 porciento para los operados solamente, y de 31 porciento para los pacientes que recibieron tratamiento radiante previo. La mejor posibilidad de sobrevida correspondió a los pacientes asintomáticos. Los carcinomas indiferenciados tuvieron un pronóstico peor que los epidermoides y adenocarcinomas. La presencia de metástasis regionales ganglionares y extensión a otras estructuras establece un criterio pronóstico malo en cuanto a sobrevida(AU)