Gestión de la prevención de riesgos laborales en la industria conservera Conserfrut

Este Trabajo Fin de Máster, representa la Gestión de Prevención Riesgos Laborales de una empresa del sector de secundario, como es la industria conservera, en particular la empresa CONSERFRUT S.L. El actual documento tiene como finalidad implantar un marco de actuación y una directiva concreta para que la empresa CONSERFRUT S.L, conforme un sistema preventivo eficaz, donde se integre todos sus procesos productivos y la actividad empresarial a la que pertenece, asegurando la integridad física y moral de los trabajadores como lo establece la Ley 31/1995 de 8 de noviembre de prevención de riesgos laborales, la Ley 54/2003 de 12 de diciembre de Reforma del marco normativo de la prevención de riesgos laborales, el Real Decreto 39/1997 de 30 de enero por el que se aprueba el Reglamento de los Servicios de Prevención

Clinical and molecular study of the extracellular matrix protein 1 gene in a spanish family with lipoid proteinosis

[Background] Lipoid proteinosis (LP) is a rare autosomal recessive disorder characterized by a hoarse voice, variable scarring, and infiltration of the skin and mucosa. This disease is associated with mutations of the gene encoding extracellular matrix protein 1 (ECM1). [Case Report]This was a clinical and molecular study of a new case of LP with a severe phenotype. A 35-year-old female born to nonconsanguineous parents developed dermatological and extracutaneous symptoms in her 9th month of life. The neurological abnormalities of the disease began to appear at the age of 19 years. Computed tomography revealed cranial calcifications. [Conclusions]The diagnosis of LP was confirmed by histopathological findings and direct sequencing of ECM1. A new homozygous nonsense mutation was identified in exon 7 of ECM1, c.1076G>A (p.Trp359*). This mutation was not detected in 106 chromosomes of healthy individuals with a similar demographic origin. Microsatellite markers around ECM1 were used to construct the haplotype in both the parents and the patient. Reports on genotype-phenotype correlations in LP point to a milder phenotype in carriers of missense mutations in the Ecm1a isoform, whereas mutations in the Ecm1b isoform are thought to be associated with more severe phenotypes. The present findings in a Spanish patient carrying a truncating mutation in exon 7 revealed complete dermatological and neurological manifestations. © 2014 Korean Neurological Association.The authors thank the patient and her family for their participation, and the financial support of grants from MICINN (no. SAF2007-60508) and Consejería de Ciencia Junta de Andalucía (no. CVI02790).Peer Reviewe

Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks

[EN] Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was trained with five challenging and heterogeneous MR prostate datasets (and two US datasets), with segmentations from many different experts with varying segmentation criteria. The model achieves a consistently strong performance in all datasets independently (mean Dice Similarity Coefficient -DSC- above 0.91 for all datasets except for one), outperforming the inter-expert variability significantly in MR (mean DSC of 0.9099 vs. 0.8794). When evaluated on the publicly available Promise12 challenge dataset, it attains a similar performance to the best entries. In summary, the model has the potential of having a significant impact on current prostate procedures, undercutting, and even eliminating, the need of manual segmentations through improvements in terms of robustness, generalizability and output resolutionThis work has been partially supported by a doctoral grant of the Spanish Ministry of Innovation and Science, with reference FPU17/01993Pellicer-Valero, OJ.; González-Pérez, V.; Casanova Ramón-Borja, JL.; Martín García, I.; Barrios Benito, M.; Pelechano Gómez, P.; Rubio-Briones, J.... (2021). Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks. Applied Sciences. 11(2):1-17. https://doi.org/10.3390/app11020844S117112Marra, G., Ploussard, G., Futterer, J., & Valerio, M. (2019). Controversies in MR targeted biopsy: alone or combined, cognitive versus software-based fusion, transrectal versus transperineal approach? World Journal of Urology, 37(2), 277-287. doi:10.1007/s00345-018-02622-5Ahdoot, M., Lebastchi, A. H., Turkbey, B., Wood, B., & Pinto, P. A. (2019). Contemporary treatments in prostate cancer focal therapy. Current Opinion in Oncology, 31(3), 200-206. doi:10.1097/cco.0000000000000515Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. doi:10.1145/3065386Allen, P. D., Graham, J., Williamson, D. C., & Hutchinson, C. E. (s. f.). Differential Segmentation of the Prostate in MR Images Using Combined 3D Shape Modelling and Voxel Classification. 3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006. doi:10.1109/isbi.2006.1624940Freedman, D., Radke, R. J., Tao Zhang, Yongwon Jeong, Lovelock, D. M., & Chen, G. T. Y. (2005). Model-based segmentation of medical imagery by matching distributions. IEEE Transactions on Medical Imaging, 24(3), 281-292. doi:10.1109/tmi.2004.841228Klein, S., van der Heide, U. A., Lips, I. M., van Vulpen, M., Staring, M., & Pluim, J. P. W. (2008). Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Medical Physics, 35(4), 1407-1417. doi:10.1118/1.2842076Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 234-241. doi:10.1007/978-3-319-24574-4_28He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV). doi:10.1109/iccv.2017.322Shelhamer, E., Long, J., & Darrell, T. (2017). Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640-651. doi:10.1109/tpami.2016.2572683He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2016.90Milletari, F., Navab, N., & Ahmadi, S.-A. (2016). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. 2016 Fourth International Conference on 3D Vision (3DV). doi:10.1109/3dv.2016.79Zhu, Q., Du, B., Turkbey, B., Choyke, P. L., & Yan, P. (2017). Deeply-supervised CNN for prostate segmentation. 2017 International Joint Conference on Neural Networks (IJCNN). doi:10.1109/ijcnn.2017.7965852To, M. N. N., Vu, D. Q., Turkbey, B., Choyke, P. L., & Kwak, J. T. (2018). Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging. International Journal of Computer Assisted Radiology and Surgery, 13(11), 1687-1696. doi:10.1007/s11548-018-1841-4Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2017.243Zhu, Y., Wei, R., Gao, G., Ding, L., Zhang, X., Wang, X., & Zhang, J. (2018). Fully automatic segmentation on prostate MR images based on cascaded fully convolution network. Journal of Magnetic Resonance Imaging, 49(4), 1149-1156. doi:10.1002/jmri.26337Wang, Y., Ni, D., Dou, H., Hu, X., Zhu, L., Yang, X., … Wang, T. (2019). Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound. IEEE Transactions on Medical Imaging, 38(12), 2768-2778. doi:10.1109/tmi.2019.2913184Lemaître, G., Martí, R., Freixenet, J., Vilanova, J. C., Walker, P. M., & Meriaudeau, F. (2015). Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review. Computers in Biology and Medicine, 60, 8-31. doi:10.1016/j.compbiomed.2015.02.009Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., … Madabhushi, A. (2014). Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge. Medical Image Analysis, 18(2), 359-373. doi:10.1016/j.media.2013.12.002Zhu, Q., Du, B., & Yan, P. (2020). Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation. IEEE Transactions on Medical Imaging, 39(3), 753-763. doi:10.1109/tmi.2019.2935018He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. 2015 IEEE International Conference on Computer Vision (ICCV). doi:10.1109/iccv.2015.123Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359. doi:10.1109/tkde.2009.191Smith, L. N. (2017). Cyclical Learning Rates for Training Neural Networks. 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). doi:10.1109/wacv.2017.58Abraham, N., & Khan, N. M. (2019). A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). doi:10.1109/isbi.2019.8759329Lei, Y., Tian, S., He, X., Wang, T., Wang, B., Patel, P., … Yang, X. (2019). Ultrasound prostate segmentation based on multidirectional deeply supervised V‐Net. Medical Physics, 46(7), 3194-3206. doi:10.1002/mp.13577Orlando, N., Gillies, D. J., Gyacskov, I., Romagnoli, C., D’Souza, D., & Fenster, A. (2020). Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images. Medical Physics, 47(6), 2413-2426. doi:10.1002/mp.14134Karimi, D., Zeng, Q., Mathur, P., Avinash, A., Mahdavi, S., Spadinger, I., … Salcudean, S. E. (2019). Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images. Medical Image Analysis, 57, 186-196. doi:10.1016/j.media.2019.07.005PROMISE12 Resultshttps://promise12.grand-challenge.org/Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2020). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 203-211. doi:10.1038/s41592-020-01008-

Changes in the Microbial Composition of the Rhizosphere of Hop Plants Affected by Verticillium Wilt Caused by Verticillium nonalfalfae

This article belongs to the Special Issue Plant Root Interaction with Associated Microbiomes[EN] Verticillium wilt is a devastating disease affecting many crops, including hops. This study aims to describe fungal and bacterial populations associated with bulk and rhizosphere soils in a hop field cultivated in Slovenia with the Celeia variety, which is highly susceptible to Verticillium nonalfalfae. As both healthy and diseased plants coexist in the same field, we focused this study on the detection of putative differences in the microbial communities associated with the two types of plants. Bacterial communities were characterized by sequencing the V4 region of the 16S rRNA gene, whereas sequencing of the ITS2 region was performed for fungal communities. The bacterial community was dominated by phyla Proteobacteria, Acidobacteriota, Bacteroidota, Actinobacteriota, Planctomycetota, Chloroflexi, Gemmatimonadota, and Verrucomicrobiota, which are typically found in crop soils throughout the world. At a fungal level, Fusarium sp. was the dominant taxon in both bulk and rhizosphere soils. Verticillium sp. levels were very low in all samples analyzed and could only be detected by qPCR in the rhizosphere of diseased plants. The rhizosphere of diseased plants underwent important changes with respect to the rhizosphere of healthy plants where significant increases in potentially beneficial fungi such as the basidiomycetes Ceratobasidium sp. and Mycena sp., the zygomycete Mortierella sp., and a member of Glomeralles were observed. However, the rhizosphere of diseased plants experienced a decrease in pathogenic basidiomycetes that can affect the root system, such as Thanatephorus cucumeris (the teleomorph of Rhizoctonia solani) and Calyptella spSIThis work was financed through a PRIMA grant (Section 2-2021) and is part of the project PCI2022-132966, funded by the Ministry of Science and Innovation (MCIN), the State Investigation Agency (MCIN/AEI/10.13039/501100011033), and by the European Union “NextGenerationEU”/Recovery Plant, Transformation and Resilience (PRTR). Carla Calvo-Peña was supported by a predoctoral contract from the Junta de Castilla y León (Consejería de Educación) and the European Social Fund. Ana Ibañez was supported by a “Margarita Salas” modality postdoctoral grant (Reference no.: UP2021-025) through the University of León awarded by the Spanish Ministry of Universities within the Recovery, Transformation, and Resilience Plan (modernization and digitalization of the educational system), for which funding comes from the European Recovery Instrument European Union—NextGenerationE

Oral Calcidiol Is More Effective Than Cholecalciferol Supplementation to Reach Adequate 25(OH)D Levels in Patients with Autoimmune Diseases Chronically Treated with Low Doses of Glucocorticoids: A "Real-Life" Study

Glucocorticoids (GCs) are the cornerstone of the therapy in many autoimmune and inflammatory diseases. However, it is well known that their use is a double edged sword, as their beneficial effects are associated almost universally with unwanted effects, as, for example glucocorticoid-induced osteoporosis (GIO). Over the last years, several clinical practice guidelines emphasize the need of preventing bone mass loss and reduce the incidence of fractures associated with GC use. Calcium and vitamin D supplementation, as adjunctive therapy, are included in all the practice guidelines. However, no standard vitamin D dose has been established. Several studies with postmenopausal women show that maintaining the levels above 30-33 ng/mL help improve the response to bisphosphonates. It is unknown if the response is the same in GIO, but in the clinical practice the levels are maintained at around the same values. In this study we demonstrate that patients with autoimmune diseases, undergoing glucocorticoid therapy, often present suboptimal 25(OH)D levels. Patients with higher body mass index and those receiving higher doses of glucocorticoids are at increased risk of having lower levels of 25(OH)D. In these patients, calcidiol supplementations are more effective than cholecalciferol to reach adequate 25(OH)D levels

Induction of Engineered Residual Stresses Fields and Associate Surface Properties Modification by Short Pulse Laser Shock Processing

Laser shock processing (LSP) is consolidating as an effective technology for the improvement of metallic materials surface properties involving their fatigue life. The main acknowledged advantage of the LSP technique consists on its capability of inducing a relatively deep compression residual stresses field into metallic alloy pieces allowing an improved mechanical behaviour, explicitly the life improvement of the treated specimens against wear, crack growth and stress corrosion cracking. Progress accomplished by the authors in the line of practical development of the LSP technique at an experimental level, aiming its integral assessment from an interrelated theoretical and experimental point of view, is presented in this paper. Concretely, experimental results on the residual stress profiles and associated surface properties modification successfully reached in typical materials (especially Al and Ti alloys) under different LSP irradiation conditions are presented, a correlated analysis of the residual stress profiles obtained under different irradiation strategies and the evaluation of the corresponding induced surface properties as roughness and wear resistance being also presented. Through a coupled theoretical- experimental analysis the real possibilities of the LSP technique as a possible substitutive of related traditional surface modification techniques as, for example, shot peening