Surface Structures Involved in Plant Stomata and Leaf Colonization by Shiga-Toxigenic Escherichia Coli O157:H7

Shiga-toxigenic Escherichia coli (STEC) O157:H7 uses a myriad of surface adhesive appendages including pili, flagella, and the type 3 secretion system (T3SS) to adhere to and inflict damage to the human gut mucosa. Consumption of contaminated ground beef, milk, juices, water, or leafy greens has been associated with outbreaks of diarrheal disease in humans due to STEC. The aim of this study was to investigate which of the known STEC O157:H7 adherence factors mediate colonization of baby spinach leaves and where the bacteria reside within tainted leaves. We found that STEC O157:H7 colonizes baby spinach leaves through the coordinated production of curli, the E. coli common pilus, hemorrhagic coli type 4 pilus, flagella, and T3SS. Electron microscopy analysis of tainted leaves revealed STEC bacteria in the internal cavity of the stomata, in intercellular spaces, and within vascular tissue (xylem and phloem), where the bacteria were protected from the bactericidal effect of gentamicin, sodium hypochlorite or ozonated water treatments. We confirmed that the T3S escN mutant showed a reduced number of bacteria within the stomata suggesting that T3S is required for the successful colonization of leaves. In agreement, non-pathogenic E. coli K-12 strain DH5α transformed with a plasmid carrying the locus of enterocyte effacement (LEE) pathogenicity island, harboring the T3SS and effector genes, internalized into stomata more efficiently than without the LEE. This study highlights a role for pili, flagella, and T3SS in the interaction of STEC with spinach leaves. Colonization of plant stomata and internal tissues may constitute a strategy by which STEC survives in a nutrient-rich microenvironment protected from external foes and may be a potential source for human infection

Detection and depth estimation for domestic waste in outdoor environments by sensors fusion

In this work, we estimate the depth in which domestic waste are located in space from a mobile robot in outdoor scenarios. As we are doing this calculus on a broad range of space (0.3 - 6.0 m), we use RGB-D camera and LiDAR fusion. With this aim and range, we compare several methods such as average, nearest, median and center point, applied to those which are inside a reduced or non-reduced Bounding Box (BB). These BB are obtained from segmentation and detection methods which are representative of these techniques like Yolact, SOLO, You Only Look Once (YOLO)v5, YOLOv6 and YOLOv7. Results shown that, applying a detection method with the average technique and a reduction of BB of 40%, returns the same output as segmenting the object and applying the average method. Indeed, the detection method is faster and lighter in comparison with the segmentation one. The committed median error in the conducted experiments was 0.0298 ${\pm}$ 0.0544 m.Comment: This work has been submitted to IFAC WC 2023 for possible publicatio

LiLO: Lightweight and low-bias LiDAR Odometry method based on spherical range image filtering

In unstructured outdoor environments, robotics requires accurate and efficient odometry with low computational time. Existing low-bias LiDAR odometry methods are often computationally expensive. To address this problem, we present a lightweight LiDAR odometry method that converts unorganized point cloud data into a spherical range image (SRI) and filters out surface, edge, and ground features in the image plane. This substantially reduces computation time and the required features for odometry estimation in LOAM-based algorithms. Our odometry estimation method does not rely on global maps or loop closure algorithms, which further reduces computational costs. Experimental results generate a translation and rotation error of 0.86\% and 0.0036{\deg}/m on the KITTI dataset with an average runtime of 78ms. In addition, we tested the method with our data, obtaining an average closed-loop error of 0.8m and a runtime of 27ms over eight loops covering 3.5Km.Comment: This paper is under review at the journal "Autonomous Robots" (Springer

Characterization of Gelidium corneum's (Florideophyceae, Rhodophyta) vegetative propagation process under increasing levels of temperature and irradiance

Climate change is affecting Gelidium corneum (Hudson) J.V. Lamouroux fields in the Bay of Biscay by reducing its cover and biomass. Understanding those changes requires a good characterization of the responses of this species to different stressors, particularly the effects on key processes such as the vegetative propagation. Here, we aimed to characterize the interactive effect of temperature (15, 20 and 25◦C) and irradiance (5-10, 55-60 and 95-100 μmol*m2*s- 1) on two phases of the vegetative propagation process: the re-attachment capacity and the survival of re-attached fragments. The study findings revealed significant effects of both temperature and irradiance in the re-attachment capacity of the species, with higher rates of attachment registered at 20 ◦C and 5-10 μmol*m- 2*s-1 after 10, 20 and 30 days of culture. However, the interaction effects were not significant at any time interval. At higher or lower temperatures and increasing irradiances, the attachment capacity was reduced. On the other hand, irradiance was demonstrated to be the main factor controlling the survival of rhizoids. In fact, higher levels of irradiance generated severe damage on rhizoids, and thus, conditioned the development of new plants. According to this, it seems clear that the vegetative propagation process of this species is expected to become more vulnerable as both variables are expected to rise due to climate change. An increased vulnerability of this species may have several implications from an ecological and economic perspective, so we encourage to continue exploring the factors and processes controlling its distribution in order to adopt better management actions in the future.This work was funded by the National Plan for Research in Science and Technological Innovation from the Spanish Government 2017–2020 [grant number C3N-pro project PID2019-105503RB-I00] and co-funded by the European Regional Development’s funds. Samuel Sainz-Villegas and Begoña Sánchez-Astráin acknowledge the financial support received under predoctoral grants from the Spanish Ministry of Science, Innovation and Universities [grant numbers: FPU18/03573 and PRE2020-096255, respectively]. This work is part of the PhD project of Samuel Sainz-Villegas

A genetic algorithm for robust berth allocation and quay crane assignment

Scheduling problems usually obtain the optimal solutions assuming that the environment is deterministic. However, actually the environment is dynamic and uncertain. Thus, the initial data could change and the initial schedule obtained might be unfeasible. To overcome this issue, a proactive approach is presented for scheduling problems without any previous knowledge about the incidences that can occur. In this paper, we consider the berth allocation problem and the quay crane assignment problem as a representative example of scheduling problems where a typical objective is to minimize the service time. The robustness is introduced within this problem by means of buffer times that should be maximized to absorb possible incidences or breakdowns. Therefore, this problem becomes a multi-objective optimization problem with two opposite objectives: minimizing the total service time and maximizing the robustness or buffer time

Expression and functional analysis of the hydrogen peroxide biosensors HyPer and HyPer2 in C2C12 myoblasts/myotubes and single skeletal muscle fibres

[EN] Hydrogen peroxide (H2O2) is generated in cells and plays an important role as a signalling molecule. It has been reported that H2O2 is involved in physiological and pathological processes in skeletal muscle. However, H2O2 detection in cells with traditional techniques produces frequent artefacts. Currently, the HyPer biosensor detects intracellular H2O2 specifcally in real time using fuorescence microscopy. The aim of this study was to develop and optimize approaches used to express the HyPer biosensor in diferent models of skeletal muscle cells, such as the C2C12 myoblast/myotube cell line and mature skeletal muscle fbres isolated from C57BL/6J mice, and to measure intracellular H2O2 in real time in these cells. The results show that the expression of the HyPer biosensor in skeletal muscle cells is possible. In addition, we demonstrate that HyPer is functional and that this biosensor detects changes and fuctuations in intracellular H2O2 in a reversible manner. The HyPer2 biosensor, which is a more advanced version of HyPer, presents improved properties in terms of sensitivity in detecting lower concentrations of H2O2 in skeletal muscle fbres. In conclusion, the expression of the HyPer biosensor in the diferent experimental models combined with fuorescence microscopy techniques is a powerful methodology to monitor and register intracellular H2O2 specifcally in skeletal muscle. The innovation of the methodological approaches presented in this study may present new avenues for studying the role of H2O2 in skeletal muscle pathophysiology. Furthermore, the methodology may potentially be adapted to yield other specifc biosensors for diferent reactive oxygen and nitrogen species or metabolites involved in cellular functions

¿Es capaz “ChatGPT” de aprobar el examen MIR de 2022? Implicaciones de la inteligencia artificial en la educación médica en España

Artificial intelligence and natural language processing models have made an entrance into the field of medical education. Among them, the ChatGPT model has been used to try to solve different international medical exams. However, there is no literature which addresses this phenomenon in Europe or other Spanish-speaking countries. The present paper aims at evaluating the ability to answer questions of the ChatGPT model in the 2022 MIR, which grants access to the Spanish postgraduate training system. To this end, a cross-sectional descriptive analysis has been carried out in which all the questions of the 2022 MIR exam have been solved by this technology. ChatGPT was able to answer 51.4% of the questions correctly, which is approximately 69 net answers on said exam. According to estimates for this year, it would have obtained a 7688 position, which would be slightly below the population’s median, but would allow it to pass the cut-off score and choose a large number of specialties. These results are similar to those obtained in the existing literature, slightly worse to those obtained  by this tool in the American USMLE exams. The development of AI is  an opportunity for medical students and residents to learn, but it is also a risk in many ways. It is essential to train future specialists in the new reality of artificial intelligence so that they are able to use them and obtain benefits in a reasoned and safe manner.La inteligencia artificial y los modelos de procesamiento de lenguaje natural han irrumpido con fuerza en el ámbito de la educación médica. Entre ellos, el modelo ChatGPT ha sido utilizado para intentar resolver distintos exámenes de medicina a nivel internacional. Sin embargo, prácticamente no existe literatura en Europa ni países de habla hispana. El presente trabajo pretende evaluar la capacidad de responder preguntas del modelo ChatGPT en el examen MIR 2022. Para ello, se ha llevado a cabo un análisis transversal y descriptivo en el que se han introducido la totalidad de las preguntas del examen MIR 2022 en dicho modelo. ChatGPT ha sido capaz de responder de manera acertada un 51,4% de las preguntas, lo que supone aproximadamente 69 netas en el examen MIR. Según estimaciones para este año, obtendría un 7688, lo que estaría ligeramente por debajo de la mediana de la población presentada, pero que le permitiría pasar la nota de corte y escoger un gran número de especialidades. El resultado es similar a los obtenidos en la bibliografía previa, ligeramente por debajo de los resultados obtenidos por dicha herramienta en los exámenes americanos USMLE. Este tipo de modelos suponen una oportunidad para el aprendizaje de los estudiantes de medicina y los residentes, pero también supone un riesgo en muchos sentidos. Es fundamental formar a los futuros especialistas en la nueva realidad de la inteligencia artificial para que sean capaces de utilizarlas y obtener beneficios de manera razonada y segura

Minimal invasive surgery in craniostenosis

En el presente trabajo se describe la experiencia en craneoestenosis con cirugía mínimamente invasiva, evaluando el diseño y eficacia de un nuevo craneotomo en cadáveres así como su aplicación clínica en un caso de sinostósis sagital con instrumentación endoscópica. Este procedimiento es sin duda un gran recurso en el tratamiento de las craneoestenosis brindando los beneficios de la cirugía mínimamente invasiva, eliminando la necesidad de grandes incisiones, disminuyendo el sangrado quirúrgico, reduciendo estancia hospitalaria y disminuyendo la morbilidad operatoria In this paper, we describe the experience with the use of endoscopic craniofacial procedures, evaluating the design and the efficacy of a new craniotome in cadavers and his clinical application in a case of sagittal synostosis for an endoscopic assisted cranioplasty. This procedure is a great option in the treatment of craniosynostosis, giving the benefits of minimal invasive surgery and eliminating the needing of big incisions, long hospital stay and reducing the postoperative morbidit

Optimizing the Curie temperature of pseudo-binary RxR'2-xFe17 (R,R' = rare earth) for magnetic refrigeration

Several pseudo-binary RxR'2-xFe17 alloys (with R = Y, Ce, Pr, Gd and Dy) were synthesized with rhombohedral Th2Zn17-type crystal structure determined from x-ray and neutron powder diffraction. The choice of compositions was done with the aim of tuning the Curie temperature (TC) in the 270 ± 20 K temperature range, in order to obtain the maximum magneto-caloric effect around room temperature. The investigated compounds exhibit broad isothermal magnetic entropy changes, ΔSM(T), with moderate values of the refrigerant capacity, even though the values of ΔSMPeak are relatively low compared with those of the R2Fe17 compounds with R = Pr or Nd. The reduction on the ΔSMPeak is explained in terms of the diminution in the saturation magnetization value. Furthermore, the ΔSM(T) curves exhibit a similar caret-like behavior, suggesting that the magneto-caloric effect is mainly governed by the Fe-sublattice. A single master curve for ΔSM/ΔSMPeak(T) under different values of the magnetic field change are obtained for each compound by rescaling of the temperature axis.España MICINN MAT2011-27573-C04Basque Government IT-347-07CONACYT CB-2010-01-156932Slovak R&D Agency VVCE-0058-0

Visual Servoing NMPC Applied to UAVs for Photovoltaic Array Inspection

The photovoltaic (PV) industry is seeing a significant shift toward large-scale solar plants, where traditional inspection methods have proven to be time-consuming and costly. Currently, the predominant approach to PV inspection using unmanned aerial vehicles (UAVs) is based on photogrammetry. However, the photogrammetry approach presents limitations, such as an increased amount of useless data during flights, potential issues related to image resolution, and the detection process during high-altitude flights. In this work, we develop a visual servoing control system applied to a UAV with dynamic compensation using a nonlinear model predictive control (NMPC) capable of accurately tracking the middle of the underlying PV array at different frontal velocities and height constraints, ensuring the acquisition of detailed images during low-altitude flights. The visual servoing controller is based on the extraction of features using RGB-D images and the Kalman filter to estimate the edges of the PV arrays. Furthermore, this work demonstrates the proposal in both simulated and real-world environments using the commercial aerial vehicle (DJI Matrice 100), with the purpose of showcasing the results of the architecture. Our approach is available for the scientific community in: https://github.com/EPVelasco/VisualServoing_NMPCComment: This paper is under review at the journal "IEEE Robotics and Automation Letters