UV‐selective optically transparent Zn(O,S)‐based solar cells

This work reports experimental evidence of a photovoltaic effect in transparent UV‐selective Zn(O,S)‐based heterojunctions. Zn(O,S) has a strong interest for the development of UV‐selective solar cells with high transparency in the visible region, required for the development of nonintrusive building‐integrated photovoltaic (BIPV) elements as transparent solar windows and glass‐based solar façades. By anion alloying, Zn(O,S) mixed crystal absorbers can be fabricated with different sulfur content across the whole compositional range. This allows adjustment of the bandgap of the absorbers in the 2.7-2.9 eV region, maximizing absorption in the UV, while keeping a high level of transparency. Zn(O,S) alloys with composition corresponding to S/(S + O) content ratios of 0.6 are successfully grown by sputtering deposition, and first glass/FTO/NiO/Zn(O,S)/ITO device prototypes are produced. The resulting devices present an average visible transmittance (AVT) of 75% and present photovoltaic effect. By introducing a thin C60 film as electron transport layer (ETL), charge extraction is enhanced, and devices show an efficiency of 0.5% and an AVT > 69%. The transparency of these devices can potentially allow for their ubiquitous installation in glazing systems as part of nonintrusive BIPV elements or to power Internet of Things (IoT) devices and sensors as an integrated transparent component

Knee Viscosupplementation: Cost-Effectiveness Analysis between Stabilized Hyaluronic Acid in a Single Injection versus Five Injections of Standard Hyaluronic Acid

Given the wide difference in price per vial between various presentations of hyaluronic acid, this study seeks to compare the effectiveness and treatment cost of stabilized hyaluronic acid (NASHA) in a single injection with standard preparations of hyaluronic acid (HA) in five injections in osteoarthritis (OA) of the knee. Fifty-four patients with knee osteoarthritis (Kellgren–Lawrence Grade II and III) and the Western Ontario and McMaster Universities Arthritis Index (WOMAC) pain score greater than 7, with a homogeneous distribution of age, sex, BMI, and duration of disease, were included in this study. Patients were randomized into two groups: Group I was treated with NASHA (Durolane®) and Group II with HA (Go-ON®). Patient’s evolution was followed up at the 1st, 2nd, 4th, 8th, 12th, and 26th week after treatment. A statistically significant improvement in WOMAC score was observed for patients treated with NASHA versus those who received HA at Week 26. In addition, the need for analgesia was significantly reduced at Week 26 in the NASHA-treated group. Finally, the economic analysis showed an increased cost of overall treatment with HA injections. Our data support the use of the NASHA class of products in the treatment of knee OA

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-

Hongos liquenizados y liquenícolas de la Sierra de Albarracín (Teruel, España)

Se presenta un catálogo de 462 hongos liquenizados y liquenícolas de la Sierra de Albarracín (Teruel, Aragón, España), como resultado de la IV Campaña de Recolección organizada por la Sociedad Española de Liquenología (SEL). Diplotomma hedinii es novedad para la Península Ibé- rica y Lepraria leuckertiana constituye una segunda cita peninsular, 69 taxones son novedad para Aragón y 86 lo son para la provincia de Teruel

Consensus Guidelines for Advancing Coral Holobiont Genome and Specimen Voucher Deposition

Coral research is being ushered into the genomic era. To fully capitalize on the potential discoveries from this genomic revolution, the rapidly increasing number of high-quality genomes requires effective pairing with rigorous taxonomic characterizations of specimens and the contextualization of their ecological relevance. However, to date there is no formal framework that genomicists, taxonomists, and coral scientists can collectively use to systematically acquire and link these data. Spurred by the recently announced “Coral symbiosis sensitivity to environmental change hub” under the “Aquatic Symbiosis Genomics Project” - a collaboration between the Wellcome Sanger Institute and the Gordon and Betty Moore Foundation to generate gold-standard genome sequences for coral animal hosts and their associated Symbiodiniaceae microalgae (among the sequencing of many other symbiotic aquatic species) - we outline consensus guidelines to reconcile different types of data. The metaorganism nature of the coral holobiont provides a particular challenge in this context and is a key factor to consider for developing a framework to consolidate genomic, taxonomic, and ecological (meta)data. Ideally, genomic data should be accompanied by taxonomic references, i.e., skeletal vouchers as formal morphological references for corals and strain specimens in the case of microalgal and bacterial symbionts (cultured isolates). However, exhaustive taxonomic characterization of all coral holobiont member species is currently not feasible simply because we do not have a comprehensive understanding of all the organisms that constitute the coral holobiont. Nevertheless, guidelines on minimal, recommended, and ideal-case descriptions for the major coral holobiont constituents (coral animal, Symbiodiniaceae microalgae, and prokaryotes) will undoubtedly help in future referencing and will facilitate comparative studies. We hope that the guidelines outlined here, which we will adhere to as part of the Aquatic Symbiosis Genomics Project sub-hub focused on coral symbioses, will be useful to a broader community and their implementation will facilitate cross- and meta-data comparisons and analyses.CV acknowledges funding from the German Research Foundation (DFG), grants 433042944 and 458901010. Open Access publication fees are covered by an institutional agreement of the University of Konstanz

14-3-3 Mediates Histone Cross-Talk during Transcription Elongation in Drosophila

Post-translational modifications of histone proteins modulate the binding of transcription regulators to chromatin. Studies in Drosophila have shown that the phosphorylation of histone H3 at Ser10 (H3S10ph) by JIL-1 is required specifically during early transcription elongation. 14-3-3 proteins bind H3 only when phosphorylated, providing mechanistic insights into the role of H3S10ph in transcription. Findings presented here show that 14-3-3 functions downstream of H3S10ph during transcription elongation. 14-3-3 proteins localize to active genes in a JIL-1–dependent manner. In the absence of 14-3-3, levels of actively elongating RNA polymerase II are severely diminished. 14-3-3 proteins interact with Elongator protein 3 (Elp3), an acetyltransferase that functions during transcription elongation. JIL-1 and 14-3-3 are required for Elp3 binding to chromatin, and in the absence of either protein, levels of H3K9 acetylation are significantly reduced. These results suggest that 14-3-3 proteins mediate cross-talk between histone phosphorylation and acetylation at a critical step in transcription elongation

Predicting the onset and persistence of episodes of depression in primary health care. The predictD-Spain study: Methodology

Background: The effects of putative risk factors on the onset and/or persistence of depression remain unclear. We aim to develop comprehensive models to predict the onset and persistence of episodes of depression in primary care. Here we explain the general methodology of the predictD-Spain study and evaluate the reliability of the questionnaires used. Methods: This is a prospective cohort study. A systematic random sample of general practice attendees aged 18 to 75 has been recruited in seven Spanish provinces. Depression is being measured with the CIDI at baseline, and at 6, 12, 24 and 36 months. A set of individual, environmental, genetic, professional and organizational risk factors are to be assessed at each follow-up point. In a separate reliability study, a proportional random sample of 401 participants completed the test-retest (251 researcher-administered and 150 self-administered) between October 2005 and February 2006. We have also checked 118,398 items for data entry from a random sample of 480 patients stratified by province. Results: All items and questionnaires had good test-retest reliability for both methods of administration, except for the use of recreational drugs over the previous six months. Cronbach's alphas were good and their factorial analyses coherent for the three scales evaluated (social support from family and friends, dissatisfaction with paid work, and dissatisfaction with unpaid work). There were 191 (0.16%) data entry errors. Conclusion: The items and questionnaires were reliable and data quality control was excellent. When we eventually obtain our risk index for the onset and persistence of depression, we will be able to determine the individual risk of each patient evaluated in primary health care.The research in Spain was funded by grants from the Spanish Ministry of Health (grant FIS references: PI04/1980, PI0/41771, PI04/2450, and PI06/1442), Andalusian Council of Health (grant references: 05/403, 06/278 and 08/0194), and the Spanish Ministry of Education and Science (grant reference SAF 2006/07192). The Malaga sample, as part of the predictD-International study, was also funded by a grant from The European Commission (reference QL4-CT2002-00683)