Assessing the vulnerability of women to sexually transmitted diseases STDS/ HIV: construction and validation of markers

Objective To construct and validate markers of vulnerability of women to STDs/HIV, taking into consideration the importance of STDs/HIV. Method Methodological study carried out in three stages: 1) systematic review and identification of elements of vulnerability in the scientific production; 2) selection of elements of vulnerability, and development of markers; 3) establishment of the expert group and validation of the markers (content validity). Results Five markers were validated: no openness in the relationship to discuss aspects related to prevention of STDs/HIV; no perception of vulnerability to STDs/HIV; disregard of vulnerability to STDs/ HIV; not recognizing herself as the subject of sexual and reproductive rights; actions of health professionals that limit women’s access to prevention of STDs/HIV. Each marker contains three to eleven components. Conclusion The construction of such markers constituted an instrument, presented in another publication, which can contribute to support the identification of vulnerabilities of women in relation to STDs/HIV in the context of primary health care services. The markers constitute an important tool for the operationalization of the concept of vulnerability in primary health care and to promote inter/multidisciplinary and inter/multi-sectoral work processes

Detection of mesoscale thermal fronts from 4 km data using smoothing techniques : gradient-based fronts classification and basin scale application

In order to optimize frontal detection in sea surface temperature fields at 4 km resolution, a combined statistical and expert-based approach is applied to test different spatial smoothing of the data prior to the detection process. Fronts are usually detected at 1 km resolution using the histogram-based, single image edge detection (SIED) algorithm developed by Cayula and Cornillon in 1992, with a standard preliminary smoothing using a median filter and a 3 x 3 pixel kernel. Here, detections are performed in three study regions (off Morocco, the Mozambique Channel and north-western Australia) and across the Indian Ocean basin using the combination of multiple windows (CMW) method developed by Nieto, Demarcq and McClatchie in 2012 which improves on the original Cayula and Cornillon algorithm. Detections at 4 km and 1 km resolution are compared. Fronts are divided into two intensity classes ("weak" and "strong") according to their thermal gradient A preliminary smoothing is applied prior to the detection using different convolutions: three type of filters (median, average and Gaussian) combined with four kernel sizes (3 x 3, 5 x 5, 7 x 7, and 9 x 9 pixels) and three detection window sizes (16 x 16,24 x 24 and 32 x 32 pixels) to test the effect of these smoothing combinations on reducing the background noise of the data and therefore on improving the frontal detection. The performance of the combinations on 4 km data are evaluated using two criteria: detection efficiency and front length. We find that the optimal combination of preliminary smoothing parameters in enhancing detection efficiency and preserving front length includes a median filter, a 16 x 16 pixel window size, and a 5 x 5 pixel kernel for strong fronts and a 7 x 7 pixel kernel for weak fronts. Results show an improvement in detection performance (from largest to smallest window size) of 71% for strong fronts and 120% for weak fronts. Despite the small window used (16 x 16 pixels), the length of the fronts has been preserved relative to that found with 1 km data. This optimal preliminary smoothing and the CMW detection algorithm on 4 km sea surface temperature data are then used to describe the spatial distribution of the monthly frequencies of occurrence for both strong and weak fronts across the Indian Ocean basin. In general, strong fronts are observed in coastal areas; whereas weak fronts, with some seasonal exceptions, are mainly located in the open ocean. This study shows that adequate noise reduction achieved by a preliminary smoothing of the data considerably improves the frontal detection efficiency as well as the global quality of the results. Consequently, the use of 4 km data enables frontal detections similar to 1 km data (using a standard median 3 x 3 convolution) in terms of detectability, length and location. This method is easily applicable to large regions or at the global scale, with far less constraints of data manipulation and processing time using 4 km data relative to 1 km data

Razvijanje strategije podjetja POS Elektronček

International audienceIn order to optimize frontal detection in sea surface temperature fields at 4 km resolution, a combined statistical and expert-based approach is applied to test different spatial smoothing of the data prior to the detection process. Fronts are usually detected at 1 km resolution using the histogram-based, single image edge detection (SIED) algorithm developed by Cayula and Cornillon in 1992, with a standard preliminary smoothing using a median filter and a 3 × 3 pixel kernel. Here, detections are performed in three study regions (off Morocco, the Mozambique Channel, and north-western Australia) and across the Indian Ocean basin using the combination of multiple windows (CMW) method developed by Nieto, Demarcq and McClatchie in 2012 which improves on the original Cayula and Cornillon algorithm. Detections at 4 km and 1 km of resolution are compared. Fronts are divided in two intensity classes (“weak” and “strong”) according to their thermal gradient. A preliminary smoothing is applied prior to the detection using different convolutions: three type of filters (median, average and Gaussian) combined with four kernel sizes (3 × 3, 5 × 5, 7 × 7, and 9 × 9 pixels) and three detection window sizes (16 × 16, 24 × 24 and 32 × 32 pixels) to test the effect of these smoothing combinations on reducing the background noise of the data and therefore on improving the frontal detection. The performance of the combinations on 4 km data are evaluated using two criteria: detection efficiency and front length. We find that the optimal combination of preliminary smoothing parameters in enhancing detection efficiency and preserving front length includes a median filter, a 16 × 16 pixel window size, and a 5 × 5 pixel kernel for strong fronts and a 7 × 7 pixel kernel for weak fronts. Results show an improvement in detection performance (from largest to smallest window size) of 71% for strong fronts and 120% for weak fronts. Despite the small window used (16 × 16 pixels), the length of the fronts has been preserved relative to that found with 1 km data. This optimal preliminary smoothing and the CMW detection algorithm on 4 km sea surface temperature data are then used to describe the spatial distribution of the monthly frequencies of occurrence for both strong and weak fronts across the Indian Ocean basin. In general strong fronts are observed in coastal areas whereas weak fronts, with some seasonal exceptions, are mainly located in the open ocean. This study shows that adequate noise reduction done by a preliminary smoothing of the data considerably improves the frontal detection efficiency as well as the global quality of the results. Consequently, the use of 4 km data enables frontal detections similar to 1 km data (using a standard median 3 × 3 convolution) in terms of detectability, length and location. This method, using 4 km data is easily applicable to large regions or at the global scale with far less constraints of data manipulation and processing time relative to 1 km data

Fish aggregating devices drift like oceanographic drifters in the near-surface currents of the Atlantic and Indian Oceans

Knowledge of ocean surface dynamics is crucial for oceanographic and climate research. The satellite-tracked movements of hundreds of drifters deployed by research and voluntary observing vessels provide high-frequency and high-resolution information on near-surface currents around the globe. Consequently, they constitute a major component of the Global Ocean Observing System (GOOS). However, maintaining this array is costly and in some oceanic regions such as the tropics, spatio-temporal coverage is limited. Here, we demonstrate that the GPS-buoy equipped fish aggregating devices (FADs) used in tropical tuna fisheries to increase fishing success are also capable of providing comparable near-surface current information. We analyzed millions of position data collected between 2008 and 2014 from more than 15,000 FADs and 2,000 drifters, and combined this information with remotely-sensed near-surface current data to demonstrate that the surface velocity components of FADs and drifters are highly correlated in the Atlantic and Indian Oceans. While it was noted that the subsurface structures of FADs did slow them down relative to the drifters, particularly in the Atlantic Ocean, this bias was measurable and could be accounted for in future studies. Our findings show that the physical meteorological and oceanographic data collected by fishermen could provide an invaluable source of information to the GOOS. Furthermore, by forging closer collaborations with the fishing industry and ensuring their contributions to global ocean databases are properly acknowledged, there is significant scope to capture this data more effectively

Fish aggregating devices drift like oceanographic drifters in the near-surface currents of the Atlantic and Indian Oceans

Knowledge of ocean surface dynamics is crucial for oceanographic and climate research. The satellite-tracked movements of hundreds of drifters deployed by research and voluntary observing vessels provide high-frequency and high-resolution information on near-surface currents around the globe. Consequently, they constitute a major component of the Global Ocean Observing System (GOOS). However, maintaining this array is costly and in some oceanic regions such as the tropics, spatio-temporal coverage is limited. Here, we demonstrate that the GPS-buoy equipped fish aggregating devices (FADs) used in tropical tuna fisheries to increase fishing success are also capable of providing comparable near-surface current information. We analyzed millions of position data collected between 2008 and 2014 from more than 15,000 FADs and 2,000 drifters, and combined this information with remotely-sensed near-surface current data to demonstrate that the surface velocity components of FADs and drifters are highly correlated in the Atlantic and Indian Oceans. While it was noted that the subsurface structures of FADs did slow them down relative to the drifters, particularly in the Atlantic Ocean, this bias was measurable and could be accounted for in future studies. Our findings show that the physical meteorological and oceanographic data collected by fishermen could provide an invaluable source of information to the GOOS. Furthermore, by forging closer collaborations with the fishing industry and ensuring their contributions to global ocean databases are properly acknowledged, there is significant scope to capture this data more effectively