Mining-energy public policy of lithium in Mexico: tension between nationalism and globalism

This article addresses Mexico's present situation in the lithium industry and its near future, ceteris paribus. Mexico's short- and long-term lithium supply will not improve by the exploration and exploitation planned by the nationalistic objectives of the current government. This analysis demonstrates that significant changes must be made to Mexico's energy policy to promote the development of lithium due to five risks: manufacturing capacity, misaligned incentives, industrial policies, geographic concentration, and limited international coordination. Therefore, although the world's largest lithium mine was found in Sonora in 2019, Mexico's policy approaches to nationalize lithium exploration and exploitation will not allow the country to capitalize on the boom of this industry, as happened in Bolivia. In the short term, Mexico's policies will create an exploration deficit due to the country's lack of know-how and investment. Thus, Mexico will not extract lithium in the long term nor benefit from the demand increase and development of a value chain, especially in North America. Given these risks, this article postulates that Mexico's lithium policy should be revised to open its market to foreign investment and use this nascent market to a good advantage

Anti-Inflammatory and Antioxidant Activities of Methanol Extracts and Alkaloid Fractions of four Mexican Medicinal Plants of Solanaceae

Background: Methanol extracts and alkaloid fractions of different parts of four plant species belonging to Solanaceae family and used in Mexican traditional medicine were investigated for their total phenolic contents, anti-inflammatory and antioxidant properties.Materials and Methods: The total phenolic compounds of each extract was determined according to the Folin-Ciocalteu method, while the in vitro radical scavenging activities of the extracts were assessed using the DPPH and ABTS radicals. The in vivo anti-inflammatory activity was determined using the TPA-induced mouse ear edema model.Results: The methanol extracts contained the highest concentrations of phenolic compounds and also exhibited the best reducing power on the DPPH and ABTS radicals, in a concentration-dependent fashion. However, the anti-inflammatory activity did not follow the same trend, as some alkaloid fractions that showed low radical reducing power exhibited the strongest anti-inflammatory activity.Conclusion: The methanol extract obtained from the flowers of Nicotiana glauca presented the best overall performance with the largest amount of phenolic compounds (111 μg garlic acid equivalents/g of extract), the best antioxidant activity (94.80% inhibition of DPPH and 97.57% of ABTS) and the highest anti-inflammatory activity (81.93% inhibition of the inflammation).Keywords: Solanaceae family, antioxidant activity, anti-inflammatory activity

Morphological Estimation of Cellularity on Neo-Adjuvant Treated Breast Cancer Histological Images

This paper describes a methodology that extracts key morphological features from histological breast cancer images in order to automatically assess Tumour Cellularity (TC) in Neo-Adjuvant treatment (NAT) patients. The response to NAT gives information on therapy efficacy and it is measured by the residual cancer burden index, which is composed of two metrics: TC and the assessment of lymph nodes. The data consist of whole slide images (WSIs) of breast tissue stained with Hematoxylin and Eosin (H&E) released in the 2019 SPIE Breast Challenge. The methodology proposed is based on traditional computer vision methods (K-means, watershed segmentation, Otsu’s binarisation, and morphological operations), implementing colour separation, segmentation, and feature extraction. Correlation between morphological features and the residual TC after a NAT treatment was examined. Linear regression and statistical methods were used and twenty-two key morphological parameters from the nuclei, epithelial region, and the full image were extracted. Subsequently, an automated TC assessment that was based on Machine Learning (ML) algorithms was implemented and trained with only selected key parameters. The methodology was validated with the score assigned by two pathologists through the intra-class correlation coefficient (ICC). The selection of key morphological parameters improved the results reported over other ML methodologies and it was very close to deep learning methodologies. These results are encouraging, as a traditionally-trained ML algorithm can be useful when limited training data are available preventing the use of deep learning approaches

The relationship between truncation and phosphorylation at the C-terminus of tau protein in the paired helical filaments of Alzheimer's disease

Acknowledgements: Authors want to express their gratitude to Dr. P. Davies (Albert Einstein College of Medicine, Bronx, NY, USA) and Lester I. Binder (NorthWestern, Chicago, IL, USA) for the generous gift of mAbs (TG-3, Alz-50, and MC1), and (TauC-3), respectively, and to M. en C. Ivan J. Galván-Mendoza for his support in confocal microscopy, and Ms. Maricarmen De Lorenz for her secretarial assistance. We also want to express our gratitude to the Mexican Families who donate the brain of their loved ones affected with Alzheimer's disease, and made possible our research. This work was financially supported by CONACyT grant, No. 142293 (For R.M).Peer reviewedPublisher PD

Morphological estimation of Cellularity on Neo-adjuvant treated breast cancer histological images

This paper describes a methodology that extracts morphological features from histological breast cancer images stained for Hematoxilyn and Eosin (H&E). Cellularity was estimated and the correlation between features and the residual tumour size cellularity after a Neo-Adjuvant treatment (NAT) was examined. Images from whole slide imaging (WSI) were processed automatically with traditional computer vision methods to extract twenty two morphological parameters from the nuclei, epithelial region and the global image. The methodology was applied to a set of images from breast cancer under NAT. The data came from the BreastPathQ Cancer Cellularity Challenge 2019, and consisted of 2579 patches of 255×255 pixels of H&E histopatological samples from NAT treatment patients. The methodology automatically implements colour separation, segmentation and morphological analysis using traditional algorithms (K-means grouping, watershed segmentation, Otsu’s binarisation). Linear regression methods were applied to determine strongest correlation between the parameters and the cancer cellularity. The morphological parameters showed correlation with the residual tumour cancer cellularity. The strongest correlations corresponded to the stroma concentration value (r = −0.9786) and value from HSV image colour space (r = −0.9728), both from a global image parameters

Estimation of cellularity in tumours treated with Neoadjuvant therapy: A comparison of Machine Learning algorithms

This paper describes a method for residual tumour cellularity (TC) estimation in Neoadjuvant treatment (NAT) of advanced breast cancer. This is determined manually by visual inspection by a radiologist, then an automated computation will contribute to reduce time workload and increase precision and accuracy. TC is estimated as the ratio of tumour area by total image area estimated after the NAT. The method proposed computes TC by using machine learning techniques trained with information on morphological parameters of segmented nuclei in order to classify regions of the image as tumour or normal. The data is provided by the 2019 SPIE Breast challenge, which was proposed to develop automated TC computation algorithms. Three algorithms were implemented: Support Vector Machines, Nearest K-means and Adaptive Boosting (AdaBoost) decision trees. Performance based on accuracy is compared and evaluated and the best result was obtained with Support Vector Machines. Results obtained by the methods implemented were submitted during ongoing challenge with a maximum of 0.76 of prediction probability of success

Relationship between weight status and aerobic capacity in school children in Tijuana, Mexico

INTRODUCTION: Overweight and obesity in children can deteriorate physical and psychological health in the short, mid, and long term; alterations like dyslipidemia, hyperinsulinemia, glucose intolerance and other cardiovascular risk factors like prehypertension and hypertension occur more frequently in children and teens with obesity. PURPOSE: The aim of this study was to determine the relationship between the weight status and the aerobic capacity of schoolers in Tijuana, Mexico. METHODS: This study’s samples were constituted by 275 children, 135 girls and 140 boys from 5th and 6th grade, between the ages of 10-12, currently enrolled in the morning and evening shifts. Weight, height, body-mass index and the maximum oxygen consumption (20 meter Shuttle Run Test) were evaluated. To identify relationship between the weight status with the aerobic capacity, the Pearson correlation coefficient was used. RESULTS: The overweight and obesity prevalence were 29% and 13% in boys and 33% and 12% in girls respectably. It was observed a moderate negative correlation but statistically significant between the weight status with the aerobic capacity (r= -0.437, p=0.001). CONCLUSION: In this population, the greater the weight was associated with low aerobic capacity. In conclusion, the aerobic capacity could be affected due to overweight, obesity, and a superior corporal weight than the recommended one for a certain height