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"Rodrigues, A."
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Photodynamic Therapy Review: Principles, Photosensitizers, Applications, and Future Directions
Photodynamic therapy (PDT) is a minimally invasive therapeutic modality that has gained great attention in the past years as a new therapy for cancer treatment. PDT uses photosensitizers that, after being excited by light at a specific wavelength, react with the molecular oxygen to create reactive oxygen species in the target tissue, resulting in cell death. Compared to conventional therapeutic modalities, PDT presents greater selectivity against tumor cells, due to the use of photosensitizers that are preferably localized in tumor lesions, and the precise light irradiation of these lesions. This paper presents a review of the principles, mechanisms, photosensitizers, and current applications of PDT. Moreover, the future path on the research of new photosensitizers with enhanced tumor selectivity, featuring the improvement of PDT effectiveness, has also been addressed. Finally, new applications of PDT have been covered.
Journal Article
المعادلة الإلهية : رواية
\"المعادلة الإلهية\" هو استغراقه في النظريات العلمية الأكثر أهمية في تاريخ تطور العلوم، لا سيما تلك التي وضعها آينشتاين ونيوتن. ف \"المعادلة الإلهية\" هي كناية عن نص روائي يسرد فيه الكاتب أكثر النظريات دقة وتعقيدا. حاولت تلك النظريات تفكيك لغز الكون وبدايات تشكله ومن ثم تطوره وصولا إلى نهايته أو تكرار تشكله إلى ما لا نهاية. وهو أمر تطرقت إليه الأديان السماوية والوضعية ولكن من خلال دلالات رمزية ودروب استندت إلى الغيبية الميتافيزيقية والإيمان بأن ثمة خالقا لهذا الكون. تفسير لغز النشوء الكوني يتراوح في هذه الرواية الممتعة علميا وفلسفيا بين الأدلة العلمية والدلالات الدينية الرمزية لقصة الخلق.
Book
Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data
Background
The accurate prediction of genomic breeding values is central to genomic selection in both plant and animal breeding studies. Genomic prediction involves the use of thousands of molecular markers spanning the entire genome and therefore requires methods able to efficiently handle high dimensional data. Not surprisingly, machine learning methods are becoming widely advocated for and used in genomic prediction studies. These methods encompass different groups of supervised and unsupervised learning methods. Although several studies have compared the predictive performances of individual methods, studies comparing the predictive performance of different groups of methods are rare. However, such studies are crucial for identifying (i) groups of methods with superior genomic predictive performance and assessing (ii) the merits and demerits of such groups of methods relative to each other and to the established classical methods. Here, we comparatively evaluate the genomic predictive performance and informally assess the computational cost of several groups of supervised machine learning methods, specifically,
regularized regression
methods,
deep
,
ensemble
and
instance-based
learning algorithms, using one simulated animal breeding dataset and three empirical maize breeding datasets obtained from a commercial breeding program.
Results
Our results show that the relative predictive performance and computational expense of the groups of machine learning methods depend upon both the data and target traits and that for classical regularized methods, increasing model complexity can incur huge computational costs but does not necessarily always improve predictive accuracy. Thus, despite their greater complexity and computational burden, neither the adaptive nor the group regularized methods clearly improved upon the results of their simple regularized counterparts. This rules out selection of one procedure among machine learning methods for routine use in genomic prediction. The results also show that, because of their competitive predictive performance, computational efficiency, simplicity and therefore relatively few tuning parameters, the classical linear mixed model and regularized regression methods are likely to remain strong contenders for genomic prediction.
Conclusions
The dependence of predictive performance and computational burden on target datasets and traits call for increasing investments in enhancing the computational efficiency of machine learning algorithms and computing resources.
Journal Article
The art of music
\"The Art of music is a handsomely illustrated and rich interdisciplinary look at the mutual influence between music and the visual arts across cultures and eras. The book sheds new light on more familiar artists at the intersection of the visual and the musical, such as Wassily Kandinsky and Arnold Schoenberg, and presents new scholarship on less well-known examples in the arts of Asia, Africa, the Americas, and Europe, from antique pottery to contemporary video and sound art. Essays consider key works and themes such as synesthesia and other formal and theoretical crossovers, motifs of musicians, and performative and ritual functions of music, musical instruments, and art. With more than 250 color images illustrating works of art in diverse traditions, The Art of music offers enriching reading for scholars and general audiences alike\"-- Provided by publisher.
Book
Global Biodiversity Conservation Priorities
The location of and threats to biodiversity are distributed unevenly, so prioritization is essential to minimize biodiversity loss. To address this need, biodiversity conservation organizations have proposed nine templates of global priorities over the past decade. Here, we review the concepts, methods, results, impacts, and challenges of these prioritizations of conservation practice within the theoretical irreplaceability/vulnerability framework of systematic conservation planning. Most of the templates prioritize highly irreplaceable regions; some are reactive (prioritizing high vulnerability), and others are proactive (prioritizing low vulnerability). We hope this synthesis improves understanding of these prioritization approaches and that it results in more efficient allocation of geographically flexible conservation funding.
Journal Article
Simulation-Based Evaluation of the Impact of an Electrochromic Glazing on the Energy Use and Indoor Comfort of an Office Room
Electrochromic glazing alters its optical properties in the absence/presence of an electrical charge, varying from clear to dark to control daylighting and solar heat gains. This study aims to evaluate the impact of an electrochromic glazing, with indoor glare or temperature control, on the energy performance and thermal and visual comfort of an office room under three European climates, using a calibrated simulation model. The novelty of the paper lies in its combined performance assessment, using different standards and metrics. The results showed reduced climatization energy requirements with temperature control, but significantly increased artificial lighting energy use. Glare control achieved useful illuminance levels during 74–80% of working hours. Concerning temperature control, working hours within thermal comfort increased (21–43%) under a free-float regime. Moreover, the performance of this glazing was compared to that of a clear glazing with/without a reflective film and a thermochromic glazing for different solar orientations. The electrochromic glazing with glare control showed the highest energy savings (14–36%) for a western orientation, and the lowest negative impact on daylighting for a northern orientation. The best glare reduction was achieved with the reflective film. Considering the free-float regime, the electrochromic glazing, with temperature control, showed the highest increase in working hours within thermal comfort (6–9%) for a western orientation.
Journal Article
Current Status and Perspectives on Wire and Arc Additive Manufacturing (WAAM)
Additive manufacturing has revolutionized the manufacturing paradigm in recent years due to the possibility of creating complex shaped three-dimensional parts which can be difficult or impossible to obtain by conventional manufacturing processes. Among the different additive manufacturing techniques, wire and arc additive manufacturing (WAAM) is suitable to produce large metallic parts owing to the high deposition rates achieved, which are significantly larger than powder-bed techniques, for example. The interest in WAAM is steadily increasing, and consequently, significant research efforts are underway. This review paper aims to provide an overview of the most significant achievements in WAAM, highlighting process developments and variants to control the microstructure, mechanical properties, and defect generation in the as-built parts; the most relevant engineering materials used; the main deposition strategies adopted to minimize residual stresses and the effect of post-processing heat treatments to improve the mechanical properties of the parts. An important aspect that still hinders this technology is certification and nondestructive testing of the parts, and this is discussed. Finally, a general perspective of future advancements is presented.
Journal Article
The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia
Very high (spatial and temporal) resolution satellite (VHRS) and high-resolution unmanned aerial vehicle (UAV) imagery provides the opportunity to develop new crop disease detection methods at early growth stages with utility for early warning systems. The capability of multispectral UAV, SkySat and Pleiades imagery as a high throughput phenotyping (HTP) and rapid disease detection tool for wheat rusts is assessed. In a randomized trial with and without fungicide control, six bread wheat varieties with differing rust resistance were monitored using UAV and VHRS. In total, 18 spectral features served as predictors for stem and yellow rust disease progression and associated yield loss. Several spectral features demonstrated strong predictive power for the detection of combined wheat rust diseases and the estimation of varieties’ response to disease stress and grain yield. Visible spectral (VIS) bands (Green, Red) were more useful at booting, shifting to VIS–NIR (near-infrared) vegetation indices (e.g., NDVI, RVI) at heading. The top-performing spectral features for disease progression and grain yield were the Red band and UAV-derived RVI and NDVI. Our findings provide valuable insight into the upscaling capability of multispectral sensors for disease detection, demonstrating the possibility of upscaling disease detection from plot to regional scales at early growth stages.
Journal Article
Enhanced Photodynamic Therapy: A Review of Combined Energy Sources
Photodynamic therapy (PDT) has been used in recent years as a non-invasive treatment for cancer, due to the side effects of traditional treatments such as surgery, radiotherapy, and chemotherapy. This therapeutic technique requires a photosensitizer, light energy, and oxygen to produce reactive oxygen species (ROS) which mediate cellular toxicity. PDT is a useful non-invasive therapy for cancer treatment, but it has some limitations that need to be overcome, such as low-light-penetration depths, non-targeting photosensitizers, and tumor hypoxia. This review focuses on the latest innovative strategies based on the synergistic use of other energy sources, such as non-visible radiation of the electromagnetic spectrum (microwaves, infrared, and X-rays), ultrasound, and electric/magnetic fields, to overcome PDT limitations and enhance the therapeutic effect of PDT. The main principles, mechanisms, and crucial elements of PDT are also addressed.
Journal Article