Background The advent of natural orifice transluminal endoscopic surgery (NOTES) and single-incision laparoscopic surgery (SILS), surgery without skin scarring, is now challenging every surgeon to improve the esthetic results for patients. Minilaparoscopic cholecystectomy (MLC) represents a refinement in laparoscopic surgery, potentially as cosmetically effective as NOTES. Nevertheless, because of the increased cost and difficulty in managing the equipment, it has not been widely accepted among surgeons. Objective To report modifications of the minilaparoscopic technique that make it possible to conduct needlescopic procedures safely and effectively, thereby, considerably reducing costs and promoting the dissemination of this operation. Method One thousand consecutive patients who underwent MLC were analyzed, from January 2000 to May 2009 (78.7% women; average age 45.9 y). Surgical technique: after performing the pneumoperitoneum at the umbilical site, 4 trocars were inserted; 2 of 2 mm, 1 of 3 mm, and 1 of 10 mm in diameter, through which a laparoscope was inserted. Neither the 3-mm laparoscope, nor clips, nor manufactured endobags were used. The cystic artery was safely sealed by electrocautery near the gallbladder neck and the cystic duct was sealed with surgical knots. Removal of the gallbladder was carried out, in an adapted bag made with a glove wrist, through the 10-mm umbilical site. Results The operative time was 43 minutes. The average hospital stay was 16 hours. There was no conversion to open surgery; 2.8% of patients underwent conversion to standard (5 mm) laparoscopic cholecystectomy because of difficulties with the procedure; there were 1.9% minor umbilical site infections and 1.0% umbilical herniations. There was no mortality; no bowel injury, no bile duct injury, and no postoperative hemorrhage, only 1 patient with Luschka's duct bile leakage needed a reoperation. Conclusions The MLC technique shows no differences in risks as compared with other laparoscopic cholecystectomy procedures. It also entails a considerable reduction in cost, and, as it does not use the 3-mm laparoscope or disposable materials, it is possible to perform MLC on a larger number of patients. Owing to the near invisibility of scars, MLC may also be considered as cosmetically effective as NOTES and SILS.

Campeche Bay, located in the Gulf of Mexico, is a well-established fossil fuel producing region, with numerous oil rigs exploring oil and natural gas. In an effort to reduce negative impacts on marine ecosystems, Pemex continuously monitored Campeche Bay for oil slicks, i.e., naturally occurring oil seeps and manmade oil spills. A long-term dataset (2000–2012) of synthetic aperture radar measurements from both RADARSAT satellites (766) is leveraged to investigate the spatial-temporal distribution of oil slicks (14,210) in this region. The present study has a threefold goal: (1) describe the monitoring strategy completed by Pemex and the information produced during such monitoring; (2) investigate the spatial-temporal distribution of the oil slicks observed in Campeche Bay, centering on aspects related to their occurrence; and (3) demonstrate the usefulness of RADARSAT-derived information in the execution of effective long-term environmental applications to locate seeps and spills on the sea surface. The observations confirm the massive oil input contribution of the Cantarell Oil Seep to the Campeche Bay. Oil spills (96%) usually occur in water depths shallower than 100 m, whereas oil seeps (63%) commonly occur in waters deeper than 1,000 m. The successful long-term application of RADARSAT-derived information has been shown.

Sea-surface petroleum pollution is observed as “oil slicks” (i.e., “oil spills” or “oil seeps”) and can be confused with “look-alike slicks” (i.e., environmental phenomena, such as low-wind speed, upwelling conditions, chlorophyll, etc.) in synthetic aperture radar (SAR) measurements, the most proficient satellite sensor to detect mineral oil on the sea surface. Even though machine learning (ML) has become widely used to classify remotely-sensed petroleum signatures, few papers have been published comparing various ML methods to distinguish spills from look-alikes. Our research fills this gap by comparing and evaluating six traditional techniques: simple (naive Bayes (NB), K-nearest neighbor (KNN), decision trees (DT)) and advanced (random forest (RF), support vector machine (SVM), artificial neural network (ANN)) applied to different combinations of satellite-retrieved attributes. 36 ML algorithms were used to discriminate “ocean-slick signatures” (spills versus look-alikes) with ten-times repeated random subsampling cross validation (70-30 train-test partition). Our results found that the best algorithm (ANN: 90%) was >20% more effective than the least accurate one (DT: ~68%). Our empirical ML observations contribute to both scientific ocean remote-sensing research and to oil and gas industry activities, in that: (i) most techniques were superior when morphological information and Meteorological and Oceanographic (MetOc) parameters were included together, and less accurate when these variables were used separately; (ii) the algorithms with the better performance used more variables (without feature selection), while lower accuracy algorithms were those that used fewer variables (with feature selection); (iii) we created algorithms more effective than those of benchmark-past studies that used linear discriminant analysis (LDA: ~85%) on the same dataset; and (iv) accurate algorithms can assist in finding new offshore fossil fuel discoveries (i.e., misclassification reduction).

An Exploratory Data Analysis (EDA) aims to use Synthetic Aperture Radar (SAR) measurements for discriminating between two oil slick types observed on the sea surface: naturally-occurring oil seeps versus human-related oil spills—the use of satellite sensors for this task is poorly documented in scientific literature. A long-term RADARSAT dataset (2008–2012) is exploited to investigate oil slicks in Campeche Bay (Gulf of Mexico). Simple Classification Algorithms to distinguish the oil slick type are designed based on standard multivariate data analysis techniques. Various attributes of geometry, shape, and dimension that describe the oil slick Size Information are combined with SAR-derived backscatter coefficients—sigma-(σ_o), beta-(β_o), and gamma-(γ_o) naught. The combination of several of these characteristics is capable of distinguishing the oil slick type with ~70% of overall accuracy, however, the sole and simple use of two specific oil slick’s Size Information (i.e., area and perimeter) is equally capable of distinguishing seeps from spills. The data mining exercise of our EDA promotes a novel idea bridging petroleum pollution and remote sensing research, thus paving the way to further investigate the satellite synoptic view to express geophysical differences between seeped and spilled oil observed on the sea surface for systematic use.

Linear discriminant analysis (LDA) is a mathematically robust multivariate data analysis approach that is sometimes used for surface oil slick signature classification. Our goal is to rank the effectiveness of LDAs to differentiate oil spills from look-alike slicks. We explored multiple combinations of (i) variables (size information, Meteorological-Oceanographic (metoc), geo-location parameters) and (ii) data transformations (non-transformed, cube root, log₁₀). Active and passive satellite-based measurements of RADARSAT, QuikSCAT, AVHRR, SeaWiFS, and MODIS were used. Results from two experiments are reported and discussed: (i) an investigation of 60 combinations of several attributes subjected to the same data transformation and (ii) a survey of 54 other data combinations of three selected variables subjected to different data transformations. In Experiment 1, the best discrimination was reached using ten cube-transformed attributes: ~85% overall accuracy using six pieces of size information, three metoc variables, and one geo-location parameter. In Experiment 2, two combinations of three variables tied as the most effective: ~81% of overall accuracy using area (log transformed), length-to-width ratio (log- or cube-transformed), and number of feature parts (non-transformed). After verifying the classification accuracy of 114 algorithms by comparing with expert interpretations, we concluded that applying different data transformations and accounting for metoc and geo-location attributes optimizes the accuracies of binary classifiers (oil spill vs. look-alike slicks) using the simple LDA technique.

Therapeutic laparoscopy was incorporated into surgical practice more than 25 y ago. Several modifications have since been developed to further minimize surgical trauma and improve results. Minilaparoscopy, performed with 2- to 3-mm instruments was introduced ...