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The large-scale conversion of forests to agriculture has caused biodiversity loss, climate change, and disrupted dietary fatty acid balances, with adverse public health effects. Wild edibles like pine nuts, especially Pinus cembroides, provide sustainable solutions by supporting ecosystems and offering economic value. However, variability in seed quality limits market potential, and lipidomic studies on P. cembroides remain sparse. This paper underscores the ecological, social, and nutritional value of P. cembroides while advocating for advanced research to enhance its use as a non-timber forest resource in Mexico’s communal areas. It explores various analytical techniques, such as nuclear magnetic resonances (NMR), chromatography coupled with mass spectrometry (HPLC-MS, GC-MS) and GC coupled with flame ionization detector (GC-FID), highlighting extraction methods like derivatization, purification, and thin-layer chromatography. Likewise, some considerations are addressed for the treatment of data obtained in the detection of fatty acids from bioformatics and the evaluation of the data through statistical methods and artificial intelligence and deep learning. These approaches aim to improve fatty acid profiling and seed quality assessments, fostering the species economic viability and supporting sustainable livelihoods in rural communities, encouraging researchers across the country to explore the fatty acid composition of different P. cembroides populations can drive valuable insights into its nutritional and ecological significance. Such efforts can enhance understanding of regional variations, promote sustainable use, and elevate the specie’s economic and scientific value.

In this paper, a mask region-based convolutional neural network (Mask R-CNN) is used to improve the performance of machine vision in the challenging task of detecting peduncles and fruits of green sweet peppers (Capsicum annuum L.) in greenhouses. One of the most complicated stages of the sweet pepper harvesting process is to achieve a precise cut of the peduncle or stem because this type of specialty crop cannot be grabbed and pulled by the fruit since the integrity and value of the product are compromised. Therefore, accurate peduncle detection becomes vital for the autonomous harvesting of sweet peppers. ResNet-101 combined with the feature pyramid network (FPN) architecture (ResNet-101 + FPN) is adopted as the backbone network for feature extraction and object representation enhancement at multiple scales. Mask images of fruits and peduncles are generated, focused on green sweet pepper, which is the most complex color variety due to its resemblance to the background. In addition to bounding boxes, Mask R-CNN provides binary masks as a result of instance segmentation, which would help improve the localization process in 3D space, the next phase of the autonomous harvesting process of sweet peppers, since it isolates the pixels belonging to the object and demarcates its boundaries. The prediction results of 1148 fruits on 100 test images showed a precision rate of 84.53%. The prediction results of 265 peduncles showed a precision rate of 71.78%. The mean average precision rate with an intersection over union at 50 percent (mAP@IoU=50) for model-wide instance segmentation was 72.64%. The average detection time for sweet pepper fruit and peduncle using high-resolution images was 1.18 s. The experimental results show that the proposed implementation manages to segment the peduncle and fruit of the green sweet pepper in real-time in an unmodified production environment under occlusion, overlap, and light variation conditions with effectiveness not previously reported for simultaneous 2D detection models of peduncles and fruits of green sweet pepper.

The case report of a 34 years patient with health history is described. He was assisted in the intermunicipal visit of II Frente, province of Santiago de Cuba, due to a lesion in the common peroneal nerve during 8 months, in the lateral face of the superior third of the right leg, because of a pig bite, which was not diagnosed initially, but instead it was treated as a wound without neurological lesion. A surgical procedure was carried out, with a tendon transposition of the posterior tibial muscle. The foot was immobilized with a plaster splint for 15 days, the suture was removed 6 weeks later and rehabilitation was indicated. Twelve weeks later he began to walk without difficulty and he was able to return to his daily activities.