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Micro-archaeological data from sites located in central and eastern Europe show that, in comparison with other Upper Paleolithic hunter-gatherers, Gravettian foragers used fire more intensively and for a wider range of purposes. At these sites, this shift in pyrotechnology overlaps with the onset of periglacial conditions. Gravettian occupations of non-periglacial regions have been poorly investigated with micro-archaeological methods, and it remains to be further demonstrated whether these foragers also made a similar intensive and multipurpose use of fire. To further investigate this topic, we studied the sequence preserved at the cave of Fuente del Salín, in Cantabria, where previous excavations unearthed potential fire residues of Gravettian age. Using micromorphology, µ-X-ray fluorescence, and Scanning Electron Microscopy we reconstructed multiple phases of human visits to the site. Our results show that, during the main Gravettian occupation, foragers made intensive use of fire, as indicated by abundant heated bones and seashells, charcoals, amorphous char, fat-derived char, and in situ remains of potential stacked open hearths as well as burnt grass beddings. The intensive burning, systematic reuse of combustion features, and multiple purposes of the fires at Fuente del Salín are comparable with Gravettian sites from central and eastern Europe, indicating that these fire-use behaviors probably do not reflect a regional adaptation to periglacial environments but a cultural trait of the Gravettian tradition across Europe.

Autonomous navigation within airport environments presents significant challenges, mostly due to the scarcity of accessible and labeled data for training autonomous systems. This study introduces an innovative approach to assess the performance of vision-based models trained on synthetic datasets, with the goal of determining whether simulated data can train and validate navigation operations in complex airport environments. The methodology includes a comparative analysis employing image processing techniques and object detection algorithms. A comparative analysis of two different datasets was conducted: a synthetic dataset that mirrors real airport scenarios, generated using the Microsoft Flight Simulator 2020®video game, and a real-world dataset. The results indicate that models trained on a combination of both real and synthetic images perform much better in terms of adaptability and accuracy compared to those trained only on one type of dataset. This analysis makes a significant contribution to the field of autonomous airport navigation and offers a cost-effective and practical solution to overcome the challenges of dataset acquisition and algorithm validation. It is thus believed that this study lays the groundwork for future advancements in the field.

Chronic lymphocytic leukemia (CLL) is a heterogeneous disease without a well-defined genetic alteration responsible for the onset of the disease. Several lines of evidence coincide in identifying stimulatory and growth signals delivered by B-cell receptor (BCR), and co-receptors together with NFkB pathway, as being the driving force in B-cell survival in CLL. However, the molecular mechanism responsible for this activation has not been identified. Based on the hypothesis that BCR activation may depend on somatic mutations of the BCR and related pathways we have performed a complete mutational screening of 301 selected genes associated with BCR signaling and related pathways using massive parallel sequencing technology in 10 CLL cases. Four mutated genes in coding regions (KRAS, SMARCA2, NFKBIE and PRKD3) have been confirmed by capillary sequencing. In conclusion, this study identifies new genes mutated in CLL, all of them in cases with progressive disease, and demonstrates that next-generation sequencing technologies applied to selected genes or pathways of interest are powerful tools for identifying novel mutational changes.

We describe the design and validation of a vision-based system that allows the dynamic identification of ramp signals performed by airport ground staff. This ramp signals’ recognizer increases the autonomy of unmanned vehicles and prevents errors caused by visual misinterpretations or lack of attention from the pilot of manned vehicles. This system is based on supervised machine learning techniques, developed with our own training dataset and two models. The first model is based on a pre-trained Convolutional Pose Machine followed by a classifier, for which we have evaluated two possibilities: A Random Forest and a Multi-Layer Perceptron based classifier. The second model is based on a single Convolutional Neural Network that classifies the gestures directly imported from real images. When experimentally tested, the first model proved to be more accurate and scalable than the second one. Its strength relies on a better capacity to extract information from the images and transform the domain of pixels into spatial vectors, which increases the robustness of the classification layer. The second model instead is more adequate for gestures’ identification in low visibility environments, such as during night operations, conditions in which the first model appeared to be more limited, segmenting the shape of the operator. Our results support the use of supervised learning and computer vision techniques for the correct identification and classification of ramp hand signals performed by airport marshallers.

The objectives of this study were (1) to determine the percentage of emergency department (ED) visits due to musculoskeletal pain (MSP) by children 3–14 years of age during a period of 1 year; (2) to determine the most frequent presenting complaints; and (3) to characterize their etiology. A cross-sectional study was performed on children aged 3–14 11/12 years attended at the ED of a tertiary hospital due to MSP. The demographic and clinical characteristics of the patients were reviewed 5 days each month for 12 consecutive months. Study days were selected by computer-generated simple random sampling. Out of 4,531 visits to the ED, 826 were due to MSP (18.2 %; 95 % CI 17.1–19.4 %). When compared with children with no skeletal complaints, children with MSP had a similar sex distribution but were older (mean ± SD 7 ± 3.5 years vs 9.9 ± 3.1 years; p  < 0.0001). The most common complaints were pain at the wrist (19 %), ankle (19 %) and finger (15 %). The most common etiology was trauma (88.4 %), including contusions (38 %), fractures (21 %) and sprains (18 %). Children with hip (6.7 ± 3 years; p  < 0.0001) and elbow (7.8 ± 3.5 years; p  < 0.0001) complaints were younger than children with pain in other locations, whereas children with wrist pain (10.5 ± 2.6 years; p  < 0.002) and joint sprains (10.7 ± 2.7 years; p  < 0.0001) were older. Fractures were more frequent in boys (64 vs 36 %, p  = 0.008; OR 1.6; CI 1.1–2.2). Visits to the ED due to MSP increased with age. Pain at three locations represented 50 % of the presenting complaints. Trauma was the principal etiology, but fractures only represented one-fifth of the total.

Microscopy in the context of biomedical research is demanding new tools to automatically detect and capture objects of interest. The few extant packages addressing this need, however, have enjoyed limited uptake due to complexity of use and installation. To overcome these drawbacks, we developed iMSRC , which combines ease of use and installation with high flexibility and enables applications such as rare event detection and high-resolution tissue sample screening, saving time and resources.

This work describes the design, implementation, and performance evaluation of an orthogonal frequency division multiple access (OFDMA) time-division duplexing (TDD) physical layer (PHY) compliant with the worldwide interoperability for microwave access (WiMAX) standard using a costeffective software-defined radio (SDR) platform containing field programmable gate array (FPGA) and digital signal processor (DSP) modules. We show that the proposed SDR architecture is capable of supporting the wide variety of configuration options described in the WiMAX standard while fulfilling the stringent requirements of WiMAX OFDMA TDD PHYs. The architecture allows for the implementation of all TDD functionalities in the downlink and the uplink at both the base station and the mobile station. The proposed design is shown to efficiently use the available FPGA and DSP resources. We also carried out specific experiments that take into account the frame and the downlink map messages detection over ITU-R wireless channel models to illustrate the performance of the proposed design. Finally, we discuss the utilization of the proposed hardware architecture to implement the wirelessMAN-advanced air interface.

The Cave of Altamira (Spain), a UNESCO World Heritage site, contains one of the most fragile and inaccessible Paleolithic rock-art environments in Europe, where geomatics documentation is constrained not only by severe spatial, lighting and safety limitations but also by conservation-driven restrictions on time, access and operational procedures. This study applies a confined-space UAV equipped with LiDAR-based SLAM navigation to document and assess the stability of the vertical rock wall leading to “La Hoya” Hall, a structurally sensitive sector of the cave. Twelve autonomous and assisted flights were conducted, generating dense LiDAR point clouds and video sequences processed through videogrammetry to produce high-resolution 3D meshes. A Mask R-CNN deep learning model was trained on manually segmented images to explore automated crack detection under variable illumination and viewing conditions. The results reveal active fractures, overhanging blocks and sediment accumulations located on inaccessible ledges, demonstrating the capacity of UAV-SLAM workflows to overcome the limitations of traditional surveys in confined subterranean environments. All datasets were integrated into the DiGHER digital twin platform, enabling traceable storage, multitemporal comparison, and collaborative annotation. Overall, the study demonstrates the feasibility of combining UAV-based SLAM mapping, videogrammetry and deep learning segmentation as a reproducible baseline workflow to inform preventive conservation and future multitemporal monitoring in Paleolithic caves and similarly constrained cultural heritage contexts.

This work describes the design, implementation, and performance evaluation of an orthogonal frequency division multiple access (OFDMA) time-division duplexing (TDD) physical layer (PHY) compliant with the worldwide interoperability for microwave access (WiMAX) standard using a costeffective software-defined radio (SDR) platform containing field programmable gate array (FPGA) and digital signal processor (DSP) modules. We show that the proposed SDR architecture is capable of supporting the wide variety of configuration options described in the WiMAX standard while fulfilling the stringent requirements of WiMAX OFDMA TDD PHYs. The architecture allows for the implementation of all TDD functionalities in the downlink and the uplink at both the base station and the mobile station. The proposed design is shown to efficiently use the available FPGA and DSP resources. We also carried out specific experiments that take into account the frame and the downlink map messages detection over ITU-R wireless channel models to illustrate the performance of the proposed design. Finally, we discuss the utilization of the proposed hardware architecture to implement the wirelessMAN-advanced air interface.[PUBLICATION ABSTRACT]

Se aisló y caracterizó un ADNc que codifica una sintasa de ácido 1-aminociclopropano-1-carboxílico (Ca-ACS1) a partir de embriones cigóticos de garbanzo (Cicer arietinum). Este gen, que pertenece a una familia multigénica, mostró homología con otras sintasas de ácido 1-aminociclopropano-1-carboxílico de varias especies de plantas superiores (por ejemplo, Pisum sativum Ps-ACS2 y Glycine max Gm-ACS1), y su expresión en semillas en desarrollo no parece ser constitutiva, ya que la abundancia de ARNm de Ca-ACS1 fue más alta al inicio de la embriogénesis (etapa 1), media (etapas 3–6) y baja (etapas de desecación y semilla seca). Sin embargo, los niveles de transcripción de Ca-ACS1 no se correlacionaron con el contenido de ACC ni con la actividad de ACS.