Centro de gestión de riesgos para monitoreo de redes, en la Facultad de Ingeniería, Ciencias Físicas y Matemática.

La Facultad de Ingeniería, Ciencias Físicas y Matemática desde el laboratorio de cómputo se encarga de realizar el monitoreo de red en la facultad, que incluye: el Instituto de Investigación y Postgrado, Laboratorio de Sanitaria, Laboratorio de Hidráulica, Biblioteca y secretarías, además se incorpora el monitoreo para la Facultad de Ingeniería Química. La facultad no disponía de un inventario de activos ni de la valoración de cada uno de ellos, razón por la cual fue necesario conocer los activos informáticos y su riesgo, para proteger la información, este estudio se lo realizó mediante la Metodología de Análisis y Gestión de Riesgos de los Sistemas de Información Magerit v2.0. Partiendo de los resultados obtenidos en la valoración de activos, se desarrolló módulos para monitorear servidores aplicaciones, de bases de datos y dispositivos de interconexión denominada Centro de Gestión de Riesgos para Monitoreo de Redes CGRMR, que ayudará a visualizar y conocer eventos generados en la red, detectar los posibles problemas como caídas de servicio y ataques de intrusos, para que el administrador de la red pueda tomar medidas correctivas.The Facultad de Ingenieria, Ciencias Fisicas y Matematica from the computer lab is responsible to make monitoring in the faculty network, including: Postgraduate and Research Institute, Laboratorio de Sanitaria, Laboratorio de Hidraulica, Library and secretariats, and is incorporates monitoring for the Facultad de Ingenieria Quimica. The faculty did not have an asset inventory and valuation of each of them, which is why it was necessary to know the IT assets and risk, to protect information; this study was made by the Analysis and Management Methodology Risk Information System Magerit v2.0. Based on the results of the valuation of assets, we development modules to monitor application servers, data base servers and interconnection devices called Center of Management of Risks for Monitoring network CGRMR, that help you visualize and learn about events generated in the network, detect potential problems such as falls service and intrusion attacks, for the network administrator can take corrective action

The Forest Observation System, building a global reference dataset for remote sensing of forest biomass

International audienceForest biomass is an essential indicator for monitoring the Earth's ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (aGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. aGB and canopy height estimates with their associated uncertainties are derived at a 0.25 ha scale from field measurements made in permanent research plots across the world's forests. all plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Isolation and screening for antimicrobial activity of bacteria from sediments in Sitio Kay Reyna, Barangay Lumanyag, Lian, Batangas

A total of eighty bacterial colonies were isolated from soil collected from Sitio Kay Reyna, Barangay Lumanyag, Lian, Batangas. The bacterial isolates were screened for antimicrobial activity on three gram negative pathogens, Escherichia coli, Pseudomonas aeruginosa and Salmonella typhi a gram positive, Staphylococcus aureus and a fungus, Candida albicans.Bacterial isolates, DM3, DM5, DM6, DM7, DM9, DM11, DM13, DM14, DM16, DM20, DM21, DM24, DM25, DM27, DM30, DM31, DM40, DM43, DM55 and DM72 showed inhibitory activity against some of the test organisms. Seven of the isolates (DM5, DM7, DM13, DM14, DM16, DM55 and DM72) were positive against Candida albicans four (DM5, DM7, DM13 and DM14) were positive against Salmonella typhi, two (DM5 and DM7) against Pseudomonas aeruginosa, and eighteen (all isolates except DM55 and DM72) against Staphylococcus aureus. None of the bacterial isolates inhibited Escherichia coli. Relative to this experiment, isolates DM5 and DM7 were the most active, inhibiting four of the five test organims.The general morphological characteristics of the soil isolates were irregular in form, flat elevation, smooth colony formation with undulate margins and translucent density. Most of the isolated bacteria were gram negative rods

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