Small-Molecule Therapeutic Perspectives for the Treatment of Progeria

Hutchinson–Gilford progeria syndrome (HGPS), or progeria, is an extremely rare disorder that belongs to the class of laminopathies, diseases characterized by alterations in the genes that encode for the lamin proteins or for their associated interacting proteins. In particular, progeria is caused by a point mutation in the gene that codifies for the lamin A gene. This mutation ultimately leads to the biosynthesis of a mutated version of lamin A called progerin, which accumulates abnormally in the nuclear lamina. This accumulation elicits several alterations at the nuclear, cellular, and tissue levels that are phenotypically reflected in a systemic disorder with important alterations, mainly in the cardiovascular system, bones, skin, and overall growth, which results in premature death at an average age of 14.5 years. In 2020, lonafarnib became the first (and only) FDA approved drug for treating progeria. In this context, the present review focuses on the different therapeutic strategies currently under development, with special attention to the new small molecules described in recent years, which may represent the upcoming first-in-class drugs with new mechanisms of action endowed with effectiveness not only to treat but also to cure progeria.Depto. de Química OrgánicaFac. de Ciencias QuímicasTRUEThe Progeria Research FoundationMinisterio de Ciencia e Innovaciónpu

Análisis del Sistema Europeo Común de Asilo como instrumento para la gestión de la crisis migratoria en la Unión Europea.

En este trabajo se lleva a cabo un análisis descriptivo de la evolución de la figura del asilo desde mediados del siglo XX hasta la constitución del actual Sistema Europeo Común de Asilo. Para posteriormente identificar las afecciones que hicieron colapsar el Sistema ante un contexto de excepcionales flujos migratorios. Y a su vez, determinar los elementos que han obstaculizado una intervención europea con la sensibilidad humanitaria requerida ante tal coyuntura. Para seguidamente, evaluar las propuestas de reforma presentadas en el seno de la Unión ante tales déficits. (CASTELLANO

Análisis del Sistema Europeo Común de Asilo como instrumento para la gestión de la crisis migratoria en la Unión Europea.

En este trabajo se lleva a cabo un análisis descriptivo de la evolución de la figura del asilo desde mediados del siglo XX hasta la constitución del actual Sistema Europeo Común de Asilo. Para posteriormente identificar las afecciones que hicieron colapsar el Sistema ante un contexto de excepcionales flujos migratorios. Y a su vez, determinar los elementos que han obstaculizado una intervención europea con la sensibilidad humanitaria requerida ante tal coyuntura. Para seguidamente, evaluar las propuestas de reforma presentadas en el seno de la Unión ante tales déficits. (CASTELLANO

Robust multi-year climate impacts of volcanic eruptions in decadal prediction systems

Major tropical volcanic eruptions have a large impact on climate, but there have only been three major eruptions during the recent relatively well-observed period. Models are therefore an important tool to understand and predict the impacts of an eruption. This study uses five state-of-the-art decadal prediction systems that have been initialized with the observed state before volcanic aerosols are introduced. The impact of the volcanic aerosols is found by subtracting the results of a reference experiment where the volcanic aerosols are omitted. We look for the robust impact across models and volcanoes by combining all the experiments, which helps reveal a signal even if it is weak in the models. The models used in this study simulate realistic levels of warming in the stratosphere, but zonal winds are weaker than the observations. As a consequence, models can produce a pattern similar to the North Atlantic Oscillation in the first winter following the eruption, but the response and impact on surface temperatures is weaker than in observations. Reproducing the pattern, but not the amplitude, may be related to a known model error. There are also impacts in the Pacific and Atlantic Oceans. This work contributes towards improving the interpretation of decadal predictions in the case of a future large tropical volcanic eruption

Estimating wheat grain yield using Sentinel-2 imagery and exploring topographic features and rainfall effects on wheat performance in Navarre, Spain

Reliable methods for estimating wheat grain yield before harvest could help improve farm management and, if applied on a regional level, also help identify spatial factors that influence yield. Regional grain yield can be estimated using conventional methods, but the typical process is complex and labor-intensive. Here we describe the development of a streamlined approach using publicly accessible agricultural data, field-level yield, and remote sensing data from Sentinel-2 satellite to estimate regional wheat grain yield. We validated our method on wheat croplands in Navarre in northern Spain, which features heterogeneous topography and rainfall. First, this study developed stepwise multilinear equations to estimate grain yield based on various vegetation indices, which were measured at various phenological stages in order to determine the optimal timings. Second, the most suitable model was used to estimate grain yield in wheat parcels mapped from Sentinel-2 satellite images. We used a supervised pixel-based random forest classification and the estimates were compared to government-published post-harvest yield statistics. When tested, the model achieved an R2 of 0.83 in predicting grain yield at field level. The wheat parcels were mapped with an accuracy close to 86% for both overall accuracy and compared to offcial statistics. Third, the validated model was used to explore potential relationships of the mapped per-parcel grain yield estimation with topographic features and rainfall by using geographically weighted regressions. Topographic features and rainfall together accounted for an average for 11 to 20% of the observed spatial variation in grain yield in Navarre. These results highlight the ability of our method for estimating wheat grain yield before harvest and determining spatial factors that influence yield at the regional scale

Immunofluorescence and High-Resolution Microscopy Reveal New Insights in Human Globozoospermia

Globozoospermia is a rare and severe type of teratozoospermia characterized by the presence of round-headed, acrosomeless spermatozoa with cytoskeleton defects. Current data support a negative relationship between globozoospermia and intracytoplasmic sperm injection (ICSI) outcomes, revealing the need to perform exhaustive studies on this type of sperm disorder. The aim of this study was to evaluate different structural, functional and molecular sperm biomarkers in total globozoospermia with proper embryo development after ICSI. The combination of field-emission scanning electron microscopy (FE-SEM) and transmission electron microscopy (TEM) allowed us to identify and correlate eight morphological patterns with both types of microscopy. Additionally, results reported a high percentage of coiled forms, with cytoplasmic retentions around the head and midpiece. By fluorescent microscopy, we detected that most of the sperm showed tubulin in the terminal piece of the flagellum and less than 1% displayed tyrosine phosphorylation in the flagellum. Moreover, we did not detect chaperone Heat shock-related 70 kDa protein 2 (HSPA2) in 85% of the cells. Overall, these findings provide new insights into globozoospermia, which could have potential implications in improving sperm selection methods for assisted reproductive techniques.This research was funded by the Human Fertility Professorship and Departamento de Biotecnología of the Universidad de Alicante (VIGROB-186)

Wh-Islands in L2 Spanish and L2 English: Between Poverty of the Stimulus and Data Assessment

This paper sheds light on the acquisition of wh-islands in L2 English spoken by native speakers of Spanish and L2 Spanish spoken by native speakers of English as well as on the distribution of wh-islands in L1 Spanish. A grammaticality judgment task with a 7-point Likert scale provides evidence that wh-island effects are present in L1 and L2 Spanish as well as L1 and L2 English. The L1 Spanish facts challenge the received view of wh-islands in this language, in keeping with recent developments which show that islands are more widely attested across languages than previously thought. These facts also highlight the dialogue between L2 research and replication studies thanks to the use of native control groups

A RE Methodology to achieve Accurate Polygon Models and NURBS Surfaces by Applying Different Data Processing Techniques

The scope of this work is to present a reverse engineering (RE) methodology to achieve accurate polygon models for 3D printing or additive manufacturing (AM) applications, as well as NURBS (Non-Uniform Rational B-Splines) surfaces for advanced machining processes. The accuracy of the 3D models generated by this RE process depends on the data acquisition system, the scanning conditions and the data processing techniques. To carry out this study, workpieces of different material and geometry were selected, using X-ray computed tomography (XRCT) and a Laser Scanner (LS) as data acquisition systems for scanning purposes. Once this is done, this work focuses on the data processing step in order to assess the accuracy of applying different processing techniques. Special attention is given to the XRCT data processing step. For that reason, the models generated from the LS point clouds processing step were utilized as a reference to perform the deviation analysis. Nonetheless, the proposed methodology could be applied for both data inputs: 2D cross-sectional images and point clouds. Finally, the target outputs of this data processing chain were evaluated due to their own reverse engineering applications, highlighting the promising future of the proposed methodology.This research was funded by the he Department of Economic Development, Sustainability and Environment of the Basque Government for funding the KK-2020/00094 (INSPECTA) research project and the Spanish Ministry of Science and Innovation for funding the ALASURF project (PID2019-109220RB-I00)

Subcutaneous coding via laser directed energy deposition (DED-LB) for unalterable component identification

Part identification has become a priority in manufacturing, especially when it comes to the additive manufacturing (AM) industry, where the counterfeit printing of almost any digitalized component is possible. As a means of avoiding forgery, the development of non-detectable labels, or digital passports is necessary. In the present work, a novel methodology for printing subcutaneous coding on aeronautical parts is proposed thanks to the multimaterial capabilities of the laser based directed energy deposition (DED-LB). The coding is based on embedding a dot pattern of a high-density alloy on a small area of a lower density component, which once covered and finished generates a pattern only visible by X-ray imagining. The viability of the proposed methodology is proven by embedding WC particles on a Ti6Al4V substrate. Firstly, the most relevant process parameters are optimized to ensure a sharp and a readable code, and their geometry is analysed by means of industrial Computed Tomography (CT). Also, the metallurgical quality and chemical composition of the generated dots is evaluated by Scanning Electron Microscope (SEM). Finally, a demonstrator part is fabricated with a hidden code. The code readability and the mechanical properties are tested to ensure the feasibility of the developed methodology.This work was supported by the ELKARTEK program of the Dept. of Economic Development, Sustainability and Environment of the Basque Government [grants KK-2022/00080 and KK-2023/00096], and by the MCIN/AEI/10.13039/501100011033/ and ERDF A way of making Europe [grant PID2022-141946OB-C21]

Automatic UAV-based Airport Pavement Inspection Using Mixed Real and Virtual Scenarios

Runway and taxiway pavements are exposed to high stress during their projected lifetime, which inevitably leads to a decrease in their condition over time. To make sure airport pavement condition ensure uninterrupted and resilient operations, it is of utmost importance to monitor their condition and conduct regular inspections. UAV-based inspection is recently gaining importance due to its wide range monitoring capabilities and reduced cost. In this work, we propose a vision-based approach to automatically identify pavement distress using images captured by UAVs. The proposed method is based on Deep Learning (DL) to segment defects in the image. The DL architecture leverages the low computational capacities of embedded systems in UAVs by using an optimised implementation of EfficientNet feature extraction and Feature Pyramid Network segmentation. To deal with the lack of annotated data for training we have developed a synthetic dataset generation methodology to extend available distress datasets. We demonstrate that the use of a mixed dataset composed of synthetic and real training images yields better results when testing the training models in real application scenarios.Comment: 12 pages, 6 figures, published in proceedings of 15th International Conference on Machine Vision (ICMV