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Abstract This study presents a comprehensive dataset of hydrological information gathered from five key eastern basins in Jalisco, Mexico. The dataset encompasses approximately 50 limnological variables and phytoplankton counts specifically for one of these basins. Water‐quality data were collected by the State Water Commission of Jalisco, adhering to the methods outlined in the Official Mexican Norm ‘NOM‐127’. Monthly samplings were conducted to assess environmental variables such as pH, temperature, oxygen, nutrients and heavy metals. Monitoring has been ongoing for three basins since 2009, while the remaining two basins have been monitored since 2015 and 2020. Phytoplankton data were obtained from monthly samples taken by the University of Guadalajara between 2014 and 2019 in Lake Cajititlán. The original data were cleaned and organized using tidy data principles, with codes accessible on GitHub. To facilitate data exploration and visualization, we developed a user‐friendly web application with the Shiny package in R. This application enables users to explore the dataset through summary statistics tables, time series plots and phytoplankton community analysis. The dataset is accessible on Zenodo. The presented data hold significance for environmental and water‐quality assessment and applications in machine learning, neural network models, community ecology and broader environmental research. Notably, the raw data, publicly accessible from the State Water Commission of Jalisco, have been previously utilized for these purposes. This dataset offers value due to its diverse limnological and phytoplankton variables, an extended time frame of availability, a curated and streamlined accessibility process and the inclusion of a web application for intuitive exploration and visualization.

Summary: Background: HIV incidence estimation is critical for monitoring the HIV epidemic dynamics and the effectiveness of public health prevention interventions. We aimed to identify sexual and gender minorities (SGM) with recent HIV infections, factors associated with recent HIV infection, and to estimate annualised HIV incidence rates. Methods: Cross-sectional multicentre study in HIV testing services in Brazil and Peru (15 cities). Inclusion criteria: 18+ years, SGM assigned male at birth, not using pre-/post-exposure prophylaxis. We identified recent HIV infection using the Maxim HIV-1 LAg-Avidity EIA assay as part of a recent infection testing algorithm (RITA). Annualized HIV incidence was calculated using the UNAIDS/WHO incidence estimator tool. Multivariable logistic regression models were used to estimate factors associated with recent HIV infection. Trial registration: NCT05674682. Findings: From 31-Jan-2021 to 29-May-2022, 6899 individuals participated [Brazil: 4586 (66.5%); Peru: 2313 (33.5%)]; 5946 (86.2%) cisgender men, 751 (10.9%) transgender women and 202 (2.9%) non-binary/gender diverse. Median age was 27 (IQR: 23–34) years. HIV prevalence was 11.4% (N = 784/6899); 137 (2.0%) SGM were identified with recent HIV infection. The overall annualized HIV incidence rate was 3.88% (95% CI: 2.86–4.87); Brazil: 2.62% (95% CI: 1.78–3.43); Peru: 6.69% (95% CI: 4.62–8.69). Participants aged 18–24 years had higher odds of recent HIV infection compared to those aged 30+ years in both countries. Interpretation: Our results highlight the significant burden of HIV epidemic among SGM in large urban centres of Brazil and Peru. Public health policies and interventions to increase access to effective HIV prevention methods such as PrEP are urgently needed in Latin America. Funding: Unitaid, WHO (Switzerland), Ministry of Health from Brazil and Peru.

To ensure food security given the current scenario of climate change and the accompanying ecological repercussions, it is essential to search for new technologies and tools for agricultural production. Microorganism-based biostimulants are recognized as sustainable alternatives to traditional agrochemicals to enhance and protect agricultural production. Marine actinobacteria are a well-known source of novel compounds for biotechnological uses. In addition, former studies have suggested that coral symbiont actinobacteria may support co-symbiotic photosynthetic growth and tolerance and increase the probability of corals surviving abiotic stress. We have previously shown that this activity may also hold in terrestrial plants, at least for the actinobacteria Salinispora arenicola during induced heterologous symbiosis with a wild Solanaceae plant Nicotiana attenuata under in vitro conditions. Here, we further explore the heterologous symbiotic association, germination, growth promotion, and stress relieving activity of S. arenicola in tomato plants under agricultural conditions and dig into the possible associated mechanisms. Tomato plants were grown under normal and saline conditions, and germination, bacteria-root system interactions, plant growth, photosynthetic performance, and the expression of salt stress response genes were analyzed. We found an endophytic interaction between S. arenicola and tomato plants, which promotes germination and shoot and root growth under saline or non-saline conditions. Accordingly, photosynthetic and respective photoprotective performance was enhanced in line with the induced increase in photosynthetic pigments. This was further supported by the overexpression of thermal energy dissipation, which fine-tunes energy use efficiency and may prevent the formation of reactive oxygen species in the chloroplast. Furthermore, gene expression analyses suggested that a selective transport channel gene, SlHKT1,2, induced by S. arenicola may assist in relieving salt stress in tomato plants. The fine regulation of photosynthetic and photoprotective responses, as well as the inhibition of the formation of ROS molecules, seems to be related to the induced down-regulation of other salt stress response genes, such as SlDR1A-related genes or SlAOX1b. Our results demonstrate that the marine microbial symbiont S. arenicola establishes heterologous symbiosis in crop plants, promotes growth, and confers saline stress tolerance. Thus, these results open opportunities to further explore the vast array of marine microbes to enhance crop tolerance and food production under the current climate change scenario.

Ha sido frecuente asumir que el origen de la población en la sierra de Guadarrama tenía que ver con la explotación maderera del bosque. Sin embargo, el análisis de la documentación que se hace en este artículo demuestra que tal asentamiento permanente tuvo que ver con el pastoreo, derivando en una reiterada construcción de recursos aún existentes a la espera de ser estudiados a fondo.

La Guerra Civil española constituyó un espacio de competencia de las principales redes de inteligencia por el control de la información que puso a prueba y estableció estrategias decisivas en este conflicto y con consecuencias posteriores en la Segunda Guerra Mundial y la Guerra Fría. Así, el objetivo de este artículo es analizar estructuralmente este factor de clandestinidad poco conocido, para comprender las organizaciones españolas y extranjeras que participaron en este conflicto, sus objetivos de partida y las consecuencias de su presencia en el desarrollo de las grandes estructuras de inteligencia. Para ello, se desarrolla un análisis comparado de las estructuras involucradas tanto en el bando republicano como en el bando rebelde, para comprender sus estrategias y participación en la gestión de la información privilegiada.

En este artículo se ofrece una reflexión sobre las posibilidades que da la figura de signos distintivos a alimentos para potenciar el turismo rural en San Miguel y San José de Comondú, dos localidades serranas aisladas que conforman Los Comondú, uno de los oasis de mayor importancia en la parte sur de la península de Baja California, en el noroeste de México. En ambas comunidades, los productos agropecuarios representan un valor significativo para la economía familiar. Sin embargo, es preciso replantear sus procesos de organización y de producción para revalorar el patrimonio gastronómico, así como cuidar la inocuidad de los productos. Esto prepararía a los pobladores locales para incursionar en nuevos nichos de mercado e incorporar valor agregado a productos ya existentes, lo que a la vez apoyaría las iniciativas de turismo rural en la zona.

HyperSpectral (HS) images have been successfully used for brain tumor boundary detection during resection operations. Nowadays, these classification maps coexist with other technologies such as MRI or IOUS that improve a neurosurgeon’s action, with their incorporation being a neurosurgeon’s task. The project in which this work is framed generates an unified and more accurate 3D immersive model using HS, MRI, and IOUS information. To do so, the HS images need to include 3D information and it needs to be generated in real-time operating room conditions, around a few seconds. This work presents Graph cuts Reference depth estimation in GPU (GoRG), a GPU-accelerated multiview depth estimation tool for HS images also able to process YUV images in less than 5.5 s on average. Compared to a high-quality SoA algorithm, MPEG DERS, GoRG YUV obtain quality losses of −0.93 dB, −0.6 dB, and −1.96% for WS-PSNR, IV-PSNR, and VMAF, respectively, using a video synthesis processing chain. For HS test images, GoRG obtains an average RMSE of 7.5 cm, with most of its errors in the background, needing around 850 ms to process one frame and view. These results demonstrate the feasibility of using GoRG during a tumor resection operation.

Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-ionizing as well as non-invasive. As a consequence, they have been extensively applied in the medical field. HSI is being combined with machine learning (ML) processes to obtain models to assist in diagnosis. In particular, the combination of these techniques has proven to be a reliable aid in the differentiation of healthy and tumor tissue during brain tumor surgery. ML algorithms such as support vector machine (SVM), random forest (RF) and convolutional neural networks (CNN) are used to make predictions and provide in-vivo visualizations that may assist neurosurgeons in being more precise, hence reducing damages to healthy tissue. In this work, thirteen in-vivo hyperspectral images from twelve different patients with high-grade gliomas (grade III and IV) have been selected to train SVM, RF and CNN classifiers. Five different classes have been defined during the experiments: healthy tissue, tumor, venous blood vessel, arterial blood vessel and dura mater. Overall accuracy (OACC) results vary from 60% to 95% depending on the training conditions. Finally, as far as the contribution of each band to the OACC is concerned, the results obtained in this work are 3.81 times greater than those reported in the literature.

Durante la primera mitad del siglo XIII, los integrantes de la diócesis de Segovia, obispo y clerecía, se enfrentaron en una lucha sin solución que tenía como centro el reparto de las rentas asociadas a la diócesis. Sin embargo, la documentación existente en los archivos de la Catedral y de la diócesis de Segovia demuestra que el enfrentamiento iba más allá de una disputa económica local asociada al ámbito jurisdiccional, trascendiendo a las relaciones sociales del clero segoviano y a la mentalidad unida a las mismas.

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.

El Lago Cajititlán, enfrenta severos problemas de salud ambiental; por ello se emprendió un estudio de biodiversidad del fitoplancton, con la finalidad de detectar indicadores biológicos de contaminación y determinar la abundancia relativa de los géneros en dicho ecosistema. Las algas en el medio ambiente producen beneficios: como el oxígeno y la conversión de energía luminosa en química, reflejada en la acumulación de biomasa por medio de la fotosíntesis, o bien, daños colaterales como la producción de toxinas por algas cianófitas. Se establecieron 5 puntos de monitoreo y se recolectaron muestras con frecuencia mensual durante 2014. La identificación y clasificación morfológica se realizó por microscopía óptica. Este documento es el primero en describir las comunidades predominantes en el lago: clorófitas, seguido por cianófitas y la correlación evolutiva de los géneros, mediante el empleo de árboles filogenéticos. El estudio proporcionó expectativas en el área de investigación ya que se identificaron especies con aplicación en salud ambiental y salud humana, Scenedesmus spp. y Chlorella spp. Especies que son fuente de biomoléculas de alto valor nutricional como proteínas y vitaminas, como Monoraphidium spp. De aplicación en la síntesis de biocombustibles, Botryococcus spp. Con aplicación terapéutica como productoras de antioxidantes, inmunomoduladores o productoras de toxinas, como Pseudanabaena spp. Se ha observado una fuerte relación entre las especies de cianófitas encontradas Microcystis spp.; Anabaena spp. y la muerte de peces durante el verano.

Según contaba Diego de Colmenares en su Historia de la Insigne Ciudad de Segovia, hacia el año 1466, Enrique IV de Castilla otorgó privilegio de 38000 maravedís para instalar en la ciudad un Estudio donde se enseñara Filosofía, Gramática y Retórica. Sin embargo, ningún trabajo posterior refiere noticia alguna sobre la estructura, funcionamiento o financiación de la citada universidad. El presente artículo trata de indagar en los orígenes del Estudio segoviano, en su estructura docente y fuentes para la financiación, tratando de mostrar una institución educativa superior bajomedieval diferente a las existentes en la época.

The use of hyperspectral imaging (HSI) in the medical field is an emerging approach to assist physicians in diagnostic or surgical guidance tasks. However, HSI data processing involves very high computational requirements due to the huge amount of information captured by the sensors. One of the stages with higher computational load is the K-Nearest Neighbors (KNN) filtering algorithm. The main goal of this study is to optimize and parallelize the KNN algorithm by exploiting the GPU technology to obtain real-time processing during brain cancer surgical procedures. This parallel version of the KNN performs the neighbor filtering of a classification map (obtained from a supervised classifier), evaluating the different classes simultaneously. The undertaken optimizations and the computational capabilities of the GPU device throw a speedup up to 66.18× when compared to a sequential implementation.

This paper analyzes, in the context of the theoretical frame, the types of Information Systems and the Enterprise Applications, considered for the general performance of the organization in its different functional levels and their relationships with clients and suppliers. It also analyzes the evolution of the area of Information Systems and Information Technology in the Organizations and their interaction with their mission and strategy. It considers, in addition, the maturity of the companies in using transactional data to transform them into information and knowledge for the organization. Some cases are analyzed for finding recurrences that allow knowing which developments and implementations of Information Systems (IS) and Information Technologies (IT) were carried out in Argentina, during the reactivation period that followed the economical and institutional crises of years 2001 - 2002.

Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400–1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min), making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes.

This study presents a comprehensive dataset of hydrological information gathered from five key eastern basins in Jalisco, Mexico. The dataset encompasses approximately 50 limnological variables and phytoplankton counts specifically for one of these basins. Water‐quality data were collected by the State Water Commission of Jalisco, adhering to the methods outlined in the Official Mexican Norm 'NOM‐127'. Monthly samplings were conducted to assess environmental variables such as pH, temperature, oxygen, nutrients and heavy metals. Monitoring has been ongoing for three basins since 2009, while the remaining two basins have been monitored since 2015 and 2020. Phytoplankton data were obtained from monthly samples taken by the University of Guadalajara between 2014 and 2019 in Lake Cajititlán. The original data were cleaned and organized using tidy data principles, with codes accessible on GitHub. To facilitate data exploration and visualization, we developed a user‐friendly web application with the Shiny package in R. This application enables users to explore the dataset through summary statistics tables, time series plots and phytoplankton community analysis. The dataset is accessible on Zenodo. The presented data hold significance for environmental and water‐quality assessment and applications in machine learning, neural network models, community ecology and broader environmental research. Notably, the raw data, publicly accessible from the State Water Commission of Jalisco, have been previously utilized for these purposes. This dataset offers value due to its diverse limnological and phytoplankton variables, an extended time frame of availability, a curated and streamlined accessibility process and the inclusion of a web application for intuitive exploration and visualization. [ABSTRACT FROM AUTHOR]