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BACKGROUND: Dr. Ney Bienvenido Arias Lora emerged not only as a pioneer in neurosurgery but a community leader in the Dominican Republic (DR), contributing significantly to a region where both fundamental and neurosurgical resources were scarce. This account aims to shed light on the remarkable life and career of Dr. Arias Lora, emphasizing the critical role he played in establishing and advancing neurosurgery in the DR. METHODS: This paper relies on original bibliographic materials, providing an in-depth analysis of Dr. Arias Lora's life. Through a thorough examination of his career, we aim to highlight his pioneering efforts in the Caribbeans where neurosurgical expertise was nearly nonexistent during his time. RESULTS: Dr. Arias Lora, born in 1926, and became the first neurosurgeon in the DR in 1959. He played a pivotal role in establishing the Neurosurgery Residency Program at the Hospital Salvador B. Gautier and was instrumental in the development of neurosurgery training in his home country and the Caribbeans. Beyond his medical contributions, Dr. Arias Lora served as an educator, authoring significant works, and holding prestigious academic positions. His legacy is reflected in the "Dr. Ney Arias Lora Traumatology Hospital" in Santo Domingo, a testament to his dedication to neurosurgery and public service. CONCLUSIONS: Dr. Ney Bienvenido Arias Lora's life and achievements stand as a testament to the transformative impact dedicated individuals can have on the advancement of neurosurgery. Despite the intricacies inherent in the field of neurosurgery and broader societal challenges, his story serves as an inspiration.
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Neurocirurgiões , Neurocirurgia , História do Século XX , República Dominicana , Neurocirurgia/história , Neurocirurgiões/história , História do Século XXI , HumanosRESUMO
The presence of green areas in urbanized cities is crucial to reduce the negative impacts of urbanization. However, these areas can influence the signal quality of IoT devices that use wireless communication, such as LoRa technology. Vegetation attenuates electromagnetic waves, interfering with the data transmission between IoT devices, resulting in the need for signal propagation modeling, which considers the effect of vegetation on its propagation. In this context, this research was conducted at the Federal University of Pará, using measurements in a wooded environment composed of the Pau-Mulato species, typical of the Amazon. Two machine learning-based propagation models, GRNN and MLPNN, were developed to consider the effect of Amazonian trees on propagation, analyzing different factors, such as the transmitter's height relative to the trunk, the beginning of foliage, and the middle of the tree canopy, as well as the LoRa spreading factor (SF) 12, and the co-polarization of the transmitter and receiver antennas. The proposed models demonstrated higher accuracy, achieving values of root mean square error (RMSE) of 3.86 dB and standard deviation (SD) of 3.8614 dB, respectively, compared to existing empirical models like CI, FI, Early ITU-R, COST235, Weissberger, and FITU-R. The significance of this study lies in its potential to boost wireless communications in wooded environments. Furthermore, this research contributes to enhancing more efficient and robust LoRa networks for applications in agriculture, environmental monitoring, and smart urban infrastructure.
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Designing and deploying telecommunications and broadcasting networks in the challenging terrain of the Amazon region pose significant obstacles due to its unique morphological characteristics. Within low-power wide-area networks (LPWANs), this research study introduces a comprehensive approach to modeling large-scale propagation loss channels specific to the LoRaWAN protocol operating at 915 MHz. The objective of this study is to facilitate the planning of Internet of Things (IoT) networks in riverside communities while accounting for the mobility of end nodes. We conducted extensive measurement campaigns along the banks of Universidade Federal do Pará, capturing received signal strength indication (RSSI), signal-to-noise ratio (SNR), and geolocated point data across various spreading factors. We fitted the empirical close-in (CI) and floating intercept (FI) propagation models for uplink path loss prediction and compared them with the Okumura-Hata model. We also present a new model for path loss with dense vegetation. Furthermore, we calculated received packet rate statistics between communication links to assess channel quality for the LoRa physical layer (PHY). Remarkably, both CI and FI models exhibited similar behaviors, with the newly proposed model demonstrating enhanced accuracy in estimating radio loss within densely vegetated scenarios, boasting lower root mean square error (RMSE) values than the Okumura-Hata model, particularly for spreading factor 9 (SF9). The radius coverage threshold, accounting for node mobility, was 945 m. This comprehensive analysis contributes valuable insights for the effective deployment and optimization of LoRa-based IoT networks in the intricate environmental conditions of the Amazon region.
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Introducción: El Campamento Tortuguero de Cedeño ha sido el sitio menos investigado del Golfo de Fonseca, donde se protege a la tortuga golfina en Honduras desde 1975. Objetivo: Evaluar la anidación de la tortuga Golfina (Lepidochelys olivacea) durante la temporada de veda entre el 2011 y 2021 en Campamento Tortuguero Cedeño, Choluteca, Honduras. Métodos: Entre 2011 a 2021, se llevó a cabo el monitoreo diario de las actividades de anidación durante la veda del 1 al 25 de septiembre. Los patrullajes se realizaron entre las 6:00-18:00 h, y las 18:00-5:00 h. Se registró el número total de tortugas que anidaban y se recogieron sus huevos, que se transportaron al criadero, donde se tabularon los resultados de las puestas y las crías. Resultados: Se registró un total 1 065 tortugas de L. olivacea, 95 051 huevos recolectados, 1 065 nidos marcados en tres playas que fueron reubicados en viveros artificiales y una eclosión exitosa de 62 747 neonatos. La playa Las Doradas fue el sitio con el mayor número de tortugas anidadoras, seguido de Los Delgaditos y por último Cedeño. El promedio de la frecuencia de anidación fue de 96 nidos. Del 2011 al 2021 el esfuerzo de recolección de los nidos aumentó en un 91.6 %, pasando de 84 a 161 nidos. El número de personas patrullando se asoció con la cantidad de nidos detectados en las playas. Conclusión: Los esfuerzos de monitoreo y conservación para la especie han indicado que ha habido un incremento en la anidación de L. olivacea en las tres playas, con un mayor incremento en Playa Las Doradas. Este escenario comprueba la funcionalidad de la veda en esta zona.
Introduction: The Cedeño Turtle Camp has been the least researched site in the Fonseca Gulf, where Olive Ridley Turtles in Honduras have been protected since 1975. Objective: To evaluate the nesting of Olive Ridley turtles (Lepidochelys olivacea) during the closed season from 2011 to 2021 in Campamento Tortuguero Cedeño, Choluteca, Honduras. Methods: From 2011 to 2021, daily monitoring of nesting activities was conducted during the closed season from the 1st to 25th of September. Patrols were conducted between 6:00-18:00 h, and 18:00-5:00 h. The total number of nesting turtles was recorded, and their eggs were collected and transported to the hatchery, where clutch and hatchling performance were tabulated. Results: A total of 1 065 L. olivacea turtles were recorded, 95 051 eggs collected, 1 065 nests marked on three beaches that were relocated in artificial hatcheries and a successful hatching of 62 747 hatchlings. Las Doradas beach was the site with the highest number of nesting turtles, followed by Los Delgaditos and lastly Cedeño. The average nesting frequency was 96 nests. From 2011 to 2021 the nest collection effort increased by 91.6 %, from 84 to 161 nests. The number of people patrolling was associated with the number of nests detected on the beaches. Conclusion: Monitoring and conservation efforts for L. olivacea in the Campamento Tortuguero Cedeño show a positive trend in nesting with a greater increase in Playa Las Doradas. This scenario proves the functionality of the closed season in this area.
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Animais , Implantação do Embrião , Tartarugas/embriologia , HondurasRESUMO
This paper aims to provide a metaheuristic approach to drone array optimization applied to coverage area maximization of wireless communication systems, with unmanned aerial vehicle (UAV) base stations, in the context of suburban, lightly to densely wooded environments present in cities of the Amazon region. For this purpose, a low-power wireless area network (LPWAN) was analyzed and applied. LPWAN are systems designed to work with low data rates but keep, or even enhance, the extensive area coverage provided by high-powered networks. The type of LPWAN chosen is LoRa, which operates at an unlicensed spectrum of 915 MHz and requires users to connect to gateways in order to relay information to a central server; in this case, each drone in the array has a LoRa module installed to serve as a non-fixated gateway. In order to classify and optimize the best positioning for the UAVs in the array, three concomitant bioinspired computing (BIC) methods were chosen: cuckoo search (CS), flower pollination algorithm (FPA), and genetic algorithm (GA). Positioning optimization results are then simulated and presented via MATLAB for a high-range IoT-LoRa network. An empirically adjusted propagation model with measurements carried out on a university campus was developed to obtain a propagation model in forested environments for LoRa spreading factors (SF) of 8, 9, 10, and 11. Finally, a comparison was drawn between drone positioning simulation results for a theoretical propagation model for UAVs and the model found by the measurements.
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Algoritmos , Dispositivos Aéreos não Tripulados , Humanos , Cidades , Simulação por Computador , FloresRESUMO
Sub-GHz communication provides long-range coverage with low power consumption and reduced deployment cost. LoRa (Long-Range) has emerged, among existing LPWAN (Low Power Wide Area Networks) technologies, as a promising physical layer alternative to provide ubiquitous connectivity to outdoor IoT devices. LoRa modulation technology supports adapting transmissions based on parameters such as carrier frequency, channel bandwidth, spreading factor, and code rate. In this paper, we propose SlidingChange, a novel cognitive mechanism to support the dynamic analysis and adjustment of LoRa network performance parameters. The proposed mechanism uses a sliding window to smooth out short-term variations and reduce unnecessary network re-configurations. To validate our proposal, we conducted an experimental study to evaluate the performance concerning the Signal-to-Noise Ratio (SNR) parameter of our SlidingChange against InstantChange, an intuitive mechanism that considers immediate performance measurements (parameters) for re-configuring the network. The SlidingChange is compared with LR-ADR too, a state-of-the-art-related technique based on simple linear regression. The experimental results obtained from a testbed scenario demonstrated that the InstanChange mechanism improved the SNR by 4.6%. When using the SlidingChange mechanism, the SNR was around 37%, while the network reconfiguration rate was reduced by approximately 16%.
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This article focuses on the development of a system based on the long-range network (LoRa), which is used for monitoring the agricultural sector and is implemented in areas of the Andean region of Ecuador. The LoRa network is applied for the analysis of climatic parameters by monitoring temperature, relative humidity, soil moisture and ultraviolet radiation. It consists of two transmitter nodes and one receiver node, a LoRa Gateway with two communication channels for data reception and one for data transmission, and an IoT server. In addition, a graphical user interface has been developed in Thinger.io to monitor the crops and remotely control the actuators. The research conducted contains useful information for the deployment of a LoRa network in agricultural crops located in mountainous areas above 2910 m.a.s.l., where there are terrains with irregular orography, reaching a coverage of 50 hectares and a range distance of 875 m to the farthest point in the community of Chirinche Bajo, Ecuador. An average RSSI of the radio link of -122 dBm was obtained in areas with a 15% slope and 130 m difference in height according to the Gateway, where the presence of vegetation, eucalyptus trees and no line-of-sight generated interference to the radio signal. The success rate of PDR packet delivery with an SF of nine, had a better performance, with values of no less than 76% and 92% in uplink and downlink respectively. Finally, the technological gap is reduced, since the network reaches places where traditional technologies do not exist, allowing farmers to make timely decisions in the production process in the face of adverse weather events.
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Raios Ultravioleta , Tecnologia sem Fio , Agricultura , Produtos Agrícolas , Equador , SoloRESUMO
The Internet of Things (IoT) device scenario has several emerging technologies. Among them, Low-Power Wide-Area Networks (LPWANs) have proven to be efficient connections for smart devices. These devices communicate through gateways that exchange points with the central server. This study proposes an empirical and statistical methodology based on measurements carried out in a typical scenario of Amazonian cities composed of forests and buildings on the Campus of the Federal University of Pará (UFPA) to apply an adjustment to the coefficients in the UFPA propagation model. Furthermore, an Evolutionary Particle Swarm Optimization (EPSO) metaheuristic with multi-objective optimization was applied to maximize the coverage area and minimize the number of gateways to assist in the planning of a LoRa network. The results of simulations using the Monte Carlo method show that the EPSO-based gateway placement optimization methodology can be used to plan future LPWAN networks. As reception sensitivity is a decisive factor in the coverage area, with -108 dBm, the optimal solution determined the use of three gateways to cover the smart campus area.
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Strawberries are sensitive fruits that are afflicted by various pests and diseases. Therefore, there is an intense use of agrochemicals and pesticides during production. Due to their sensitivity, temperatures or humidity at extreme levels can cause various damages to the plantation and to the quality of the fruit. To mitigate the problem, this study developed an edge technology capable of handling the collection, analysis, prediction, and detection of heterogeneous data in strawberry farming. The proposed IoT platform integrates various monitoring services into one common platform for digital farming. The system connects and manages Internet of Things (IoT) devices to analyze environmental and crop information. In addition, a computer vision model using Yolo v5 architecture searches for seven of the most common strawberry diseases in real time. This model supports efficient disease detection with 92% accuracy. Moreover, the system supports LoRa communication for transmitting data between the nodes at long distances. In addition, the IoT platform integrates machine learning capabilities for capturing outliers in collected data, ensuring reliable information for the user. All these technologies are unified to mitigate the disease problem and the environmental damage on the plantation. The proposed system is verified through implementation and tested on a strawberry farm, where the capabilities were analyzed and assessed.
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Fragaria , Internet das Coisas , Agricultura , Fazendas , UmidadeRESUMO
Energy efficiency is an issue that is currently gaining relevance, high electricity demands worldwide generate a negative impact on the planet caused by the natural depletion of resources associated with production processes. In this regard, the technologies associated with the Internet of Things (IoT) are considered as a tool to optimize processes and resources through the monitoring of variables. In this context, this work proposes a low-cost electronic system with IoT architecture used in the monitoring of electrical variables, this becomes a support tool in the estimation of energy consumption in internal distribution electrical circuits of homes or small industries. This device generates information to recognize consumption patterns and load balances per electrical phase, contains two hardware modules and a software user interface. The first is an electronic node that includes a high-performance polyphase meter based on the Atmel M90E32AS chip, which is controlled by an ESP32 chip, for wireless communication is used a Radio Frequency (RF) module in the 915 MHz band and LoRa protocol based on the Semtech SX1278 transceiver, this node is able to measure and transmit variables such as current, voltage, active energy, reactive energy, power factor and other electrical variables in circuits of up to three phases. For the study, a calibration process was carried out in an accredited laboratory (Metrex S.A. in Colombia), then tests were performed by monitoring a three-phase 110V electrical circuit in a small factory, with the information generated it was possible to identify consumption patterns over a period of seven consecutive days, important data such as times when energy is wasted due to improper use of loads connected to the network, electric stoves, computer equipment turned on during non-working hours are examples of the results obtained.
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The 5G deployment brings forth the usage of Unmanned Aerial Vehicles (UAV) to assist wireless networks by providing improved signal coverage, acting as relays or base-stations. Another technology that could help achieve 5G massive machine-type communications (mMtc) goals is the Long Range Wide-Area Network (LoRaWAN) communication protocol. This paper studied these complementary technologies, LoRa and UAV, through measurement campaigns in suburban, densely forested environments. Downlink and uplink communication at different heights and spreading factors (SF) demonstrate distinct behavior through our analysis. Moreover, a neural network was trained to predict the measured signal-to-noise ratio (SNR) behavior and results compared with SNR regression models. For the downlink scenario, the neural network results show a root mean square error (RMSE) variation between 1.2322-1.6623 dB, with an error standard deviation (SD) less than 1.6730 dB. For the uplink, the RMSE variation was between 0.8714-1.3891 dB, with an error SD less than 1.1706 dB.
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Redes de Comunicação de Computadores , Redes Neurais de Computação , Razão Sinal-RuídoRESUMO
Nowadays, conventional agriculture farms lack high-level automated management due to the limited number of installed sensor nodes and measuring devices. Recent progress of the Internet of Things (IoT) technologies will play an essential role in future smart farming by enabling automated operations with minimum human intervention. The main objective of this work is to design and implement a flexible IoT-based platform for remote monitoring of agriculture farms of different scales, enabling continuous data collection from various IoT devices (sensors, actuators, meteorological masts, and drones). Such data will be available for end-users to improve decision-making and for training and validating advanced prediction algorithms. Unlike related works that concentrate on specific applications or evaluate technical aspects of specific layers of the IoT stack, this work considers a versatile approach and technical aspects at four layers: farm perception layer, sensors and actuators layer, communication layer, and application layer. The proposed solutions have been designed, implemented, and assessed for remote monitoring of plants, soil, and environmental conditions based on LoRaWAN technology. Results collected through both simulation and experimental validation show that the platform can be used to obtain valuable analytics of real-time monitoring that enable decisions and actions such as, for example, controlling the irrigation system or generating alarms. The contribution of this article relies on proposing a flexible hardware and software platform oriented on monitoring agriculture farms of different scales, based on LoRaWAN technology. Even though previous work can be found using similar technologies, they focus on specific applications or evaluate technical aspects of specific layers of the IoT stack.
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Agricultura , Comunicação , Agricultura/métodos , Chile , Fazendas , Humanos , SoftwareRESUMO
Deaths caused by respiratory and cardiovascular diseases have increased by 10%. Every year, exposure to high levels of air pollution is the cause of 7 million premature deaths and the loss of healthy years of life. Air pollution is generally caused by the presence of CO, NO2, NH3, SO2, particulate matter PM10 and PM2.5, mainly emitted by economic activities in large metropolitan areas. The problem increases considerably in the absence of national regulations and the design, installation, and maintenance of an expensive air quality monitoring network. A smart multi-sensor system to monitor air quality is proposed in this work. The system uses an unmanned aerial vehicle and LoRa communication as an alternative for remote and in-situ atmospheric measurements. The instrumentation was integrated modularly as a node sensor to measure the concentration of carbon monoxide (CO), nitrogen dioxide (NO2), ammonia (NH3), sulfur dioxide (SO2), and suspended particulate mass PM10 and PM2.5. The optimal design of the multi-sensor system has been developed under the following constraints: A low weight, compact design, and low power consumption. The integration of the multi-sensor device, UAV, and LoRa communications as a single system adds aeeded flexibility to currently fixed monitoring stations.
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Poluentes Atmosféricos , Poluição do Ar , Poluentes Atmosféricos/análise , Poluição do Ar/análise , Dióxido de Nitrogênio , Material Particulado , Dispositivos Aéreos não TripuladosRESUMO
It has been demonstrated that LoRa-based wide area networks (WANs) can cover extended areas under harsh propagation conditions. Traditional LoRaWAN solutions based on single-hop access face important drawbacks related to the presence of blind spots. This paper aims to tackle blind spots and performance issues by using a relaying approach. Many researchers investigating multi-hop solutions consider a fixed spreading factor (SF). This simplifies synchronization and association processes, but does not take advantage of the orthogonality between the virtual channels (i.e., frequency, SF) that help to mitigate blind spots. This paper proposes a time-slotted spreading factor hopping (TSSFH) mechanism that combines virtual channels and time slots into a frame structure. Pseudo-random scheduling is used inside blind spots, which simplifies the end-devices' communication process and network organization. The results show how collisions decrease inside blind spots when more communication opportunities become available as more relaying nodes can be listening in different cells (i.e., frequency, SF-offset, time-offset). This has a direct impact on the collision-free packet delivery ratio (PDR) metric, which improves when more listening windows are opened, at the expense of faster battery depletion.
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Comunicação , Polissacarídeo-LiasesRESUMO
This paper analyzes the feasibility of the coexistence of telemetry and alarm messages employing Long-Range Wide-Area Network (LoRaWAN) technology in industrial environments. The regular telemetry messages come from periodic measurements from the majority of sensors while the alarm messages come from sensors whose transmissions are triggered by rarer (random) events that require highly reliable communication. To reach such a strict requirement, we propose here strategies of allocation of spreading factor, by treating alarm and regular (telemetry) messages differently. The potential of such allocation strategies has also been investigated under retransmission and diversity of gateways. Both indoor industrial plant and open-field scenarios are investigated. We compare the proposed solution with a benchmark scenario-where no alarm is considered-by using system level simulation. Our results show that it is possible to achieve high reliability with reasonably low delay for the alarm messages without significantly affecting the performance of the regular links.
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Tuberculosis (TB) is an important infectious disease caused by Mycobacterium tuberculosis (Mtb) and responsible for thousands of deaths every year. Although there are antimycobacterial drugs available in therapeutics, just few new chemical entities have reached clinical trials, and in fact, since introduction of rifampin only two important drugs had reached the market. Pyrazinoic acid (POA), the active agent of pyrazinamide, has been explored through prodrug approach to achieve novel molecules with anti-Mtb activity, however, there is no activity evaluation of these molecules against non-replicating Mtb until the present. Additionally, pharmacokinetic must be preliminary evaluated to avoid future problems during clinical trials. In this paper, we have presented six POA esters as prodrugs in order to evaluate their anti-Mtb activity in replicating and non-replicating Mtb, and these showed activity highly influenced by medium composition (especially by albumin). Lipophilicity seems to play the main role in the activity, possibly due to controlling membrane passage. Novel duplicated prodrugs of POA were also described, presenting interesting activity. Cytotoxicity of these prodrugs set was also evaluated, and these showed no important cytotoxic profile.
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Antituberculosos/farmacologia , Ésteres/farmacologia , Mycobacterium tuberculosis/efeitos dos fármacos , Pró-Fármacos/farmacologia , Pirazinamida/análogos & derivados , Animais , Antituberculosos/síntese química , Antituberculosos/toxicidade , Proliferação de Células/efeitos dos fármacos , Chlorocebus aethiops , Relação Dose-Resposta a Droga , Ésteres/síntese química , Ésteres/toxicidade , Concentração Inibidora 50 , Testes de Sensibilidade Microbiana , Viabilidade Microbiana/efeitos dos fármacos , Estrutura Molecular , Mycobacterium tuberculosis/crescimento & desenvolvimento , Pró-Fármacos/síntese química , Pró-Fármacos/toxicidade , Pirazinamida/síntese química , Pirazinamida/farmacologia , Pirazinamida/toxicidade , Relação Estrutura-Atividade , Células VeroRESUMO
Resumen Desde 2006 a 2012, la anidación de la tortuga lora (Lepidochelys olivacea) fue monitoreada en bahía Drake, un enclave reconocido internacionalmente por su excelente oferta eco-turística que se localiza en la región noroeste de la península de Osa. Sorprendentemente, esta área dispone de playas de anidación solitaria de tortuga lora que han permanecido casi desadvertidas hasta fechas recientes. Durante este periodo de monitoreo, 958 nidos fueron registrados en playa Drake (promedio anual: 136.9; densidad: 3.80 nidos/100m de playa), de los cuales 363 (37.9%) fueron reubicados a un vivero. Antes de 2006, la pérdida anual de nidos fue superior al 85% debido al saqueo en playa; desde 2006, el porcentaje del saqueo de nidos se mantuvo en un promedio del 10.1%. Además, un total de 335 hembras fueron identificadas con placas metálicas; el promedio de la longitud curva del caparazón fue de 66.1cm; el promedio del ancho curvo del caparazón fue de 70.2cm, y el tamaño promedio por nidada fue de 96.3 huevos. El promedio del éxito de eclosión para los nidos reubicados en vivero fue de 79.2%, y más de 61 000 neonatos fueron liberados al mar durante este periodo. Este proyecto es un ejemplo de una iniciativa exitosa de conservación, eco-turismo y desarrollo comunitario.
Abstract The nesting of the Olive Ridley (Lepidochelys olivacea) sea turtle was studied from 2006 to 2012 in Drake Bay, Costa Rica, an important solitary nesting site and center of eco-tourism in the Osa Peninsula. During this period, 958 nests were recorded (mean: 136.9 nests per season; density: 3.8 nests/100m of beach per season), of which 38% were relocated to a hatchery. The incidence of poaching was reduced from 85% in 2005 to a mean of 10.1% from 2006-2012. A total of 335 nesting females were tagged; the mean curved length of carapace was 66.1cm, the mean curved width was 70.2cm, and the mean number of eggs per nest was 96.3. A mean rate of reproductive success of 79.2% was obtained and over 61 000 hatchlings were liberated from the hatchery. This project is an example of a successful community-based conservation and eco-tourism initiative. Rev. Biol. Trop. 63 (Suppl. 1): 117-129. Epub 2015 April 01.
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Tartarugas/classificação , Estudos de Casos e Controles , Conservação dos Recursos Naturais/economia , Costa RicaRESUMO
The aerobic cloacal and nasal bacterial flora of 45 apparently healthy female olive ridley sea turtles (Lepidochelys olivacea )was studied at Nancite nesting beach,in Santa Rosa National Park (Costa Rican North Pacific)during July and August 2002.Bacterial samples were obtained by inserting sterile swabs directly into the cloaca and the nasal cavities of the turtles.Ninety-nine aerobic bacterial isolates, including 10 Gram-negative and 5 Gram-positive bacteria, were recovered.The most common bacteria cultured were Aeromonas spp. (13/45) and Citrobacter freundi (6/45)from cloacal samples and Bacillus spp.(32/45), Staphylococcus aureus (6/45)and Corynebacterium spp.(5/45)from nasal ducts.The results of the present study showed that the aerobic bacterial flora of nesting female olive ridleys was composed of several potential human and animal microbe pathogens.
Con el objetivo de determinar la flora normal aerobia, cloacal y nasal de la tortuga lora (Lepidochelys olivacea ), entre los meses de julio y agosto del 2002,se colectaron muestras bacteriológicas de 45 quelonios aparentemente sanos,durante el desove en Playa Nancite,Parque Nacional Santa Rosa, Costa Rica, a través del uso de hisopos estériles que se introdujeron en la cloaca y en uno de los conductos nasales. De las muestras recolectadas se obtuvieron e identificaron un total de 99 aislamientos, incluyendo 10 grupos de Gram-negativos y 5 de Gram-positivos. De cada tortuga se obtuvo un promedio de 0.7 bacterias de la cloaca y 1.4 de las cavidades nasales. Las bacterias más frecuente halladas fueron Aeromonas spp.(13/45) y Citrobacter freundi (6/45) en la cloaca, y Bacillus spp. (32/45),Staphylococcus aureus (6/45)y Corynebacterium spp.(5/45)en las cavidades nasales. En este investigación, la flora microbiana de las tortugas lora resultó constituida por microorganismos potencialmente patógenos para el ser humano y las tortugas.