The First Latin American Consensus on Superficial and Perforating Venous Mapping.

With the aim of obtaining a map which is useful as a diagnostic tool and therapeutical orientation, complementing the written report of duplex ultrasound venous study, Latin-American Scientific Societies of Phlebology, Vascular Surgery and Vascular Imaging were invited to participate, through their regional representatives, to the First Consensus of Superficial and Perforating Venous Mapping. A consensus process using a modified Delphi method was carried out. An International Working Group was formed, which developed a Prototype of the Venous Mapping that worked as a starting point for consensus, and was presented in a first virtual meeting of 54 experts (societies' representatives) when the methodology was explained. For the consensus process, two rounds of self-administrated questionnaires with feedback were used. In the first questionnaire a 100% consensus was obtained in the 15 statements (an agreement range of 85.2% to 100%) In the analysis of qualitative data, three categories according to the actions to implement were identified - actions which involved no action, minor changes and major changes. This analysis was used to build the second questionnaire, which reached a consensus in its six statements (agreement range of 87.1% to 98.1%). A final consensus on every field proposed was established with the approval of all the experts consulted and it was presented at a third online meeting. The document of the superficial and perforating venous mapping reached by consensus is presented hereafter.

Improving Sentiment Classification Performance through Coaching Architectures.

Intelligent systems have been developed for years to solve specific tasks automatically. An important issue emerges when the information used by these systems exhibits a dynamic nature and evolves. This fact adds a level of complexity that makes these systems prone to a noticeable worsening of their performance. Thus, their capabilities have to be upgraded to address these new requirements. Furthermore, this problem is even more challenging when the information comes from human individuals and their interactions through language. This issue happens more easily and forcefully in the specific domain of Sentiment Analysis, where feelings and opinions of humans are in constant evolution. In this context, systems are trained with an enormous corpus of textual content, or they include an extensive set of words and their related sentiment values. These solutions are usually static and generic, making their manual upgrading almost unworkable. In this paper, an automatic and interactive coaching architecture is proposed. It includes a ML framework and a dictionary-based system both trained for a specific domain. These systems converse about the outcomes obtained during their respective learning stages by simulating human interactive coaching sessions. This leads to an Active Learning process where the dictionary-based system acquires new information and improves its performance. More than 800, 000 tweets have been gathered and processed for experiments. Outstanding results were obtained when the proposed architecture was used. Also, the lexicon was updated with the prior and new words related to the corpus used which is important to reach a better sentiment analysis classification.

Analysis of Accelerometer and GPS Data for Cattle Behaviour Identification and Anomalous Events Detection.

In this paper, a method to classify behavioural patterns of cattle on farms is presented. Animals were equipped with low-cost 3-D accelerometers and GPS sensors, embedded in a commercial device attached to the neck. Accelerometer signals were sampled at 10 Hz, and data from each axis was independently processed to extract 108 features in the time and frequency domains. A total of 238 activity patterns, corresponding to four different classes (grazing, ruminating, laying and steady standing), with duration ranging from few seconds to several minutes, were recorded on video and matched to accelerometer raw data to train a random forest machine learning classifier. GPS location was sampled every 5 min, to reduce battery consumption, and analysed via the k-medoids unsupervised machine learning algorithm to track location and spatial scatter of herds. Results indicate good accuracy for classification from accelerometer records, with best accuracy (0.93) for grazing. The complementary application of both methods to monitor activities of interest, such as sustainable pasture consumption in small and mid-size farms, and to detect anomalous events is also explored. Results encourage replicating the experiment in other farms, to consolidate the proposed strategy.

Detecting Emotional Evolution on Twitter during the COVID-19 Pandemic Using Text Analysis.

Early in 2020, an unexpected and hazardous situation occurred threatening and challenging all of humankind. A new coronavirus called SARS-CoV-2 was first identified in Wuhan, China, and its related disease, called COVID-19, has induced one of the most dangerous crises at a global level since World War II. The ultra-fast transmission rate of the virus and the high mortality rate led the World Health Organization (WHO) to officially declare the situation a pandemic. Governments, for their part, were forced to implement unprecedented mobility restrictions and cease a large part of their economic activities. These facts triggered multiple reactions from people who expressed their feelings mainly through social networks (like Twitter), using them as vectors of information and opinion. In this paper, a study carried out in different Spanish speaking countries (Chile, Mexico, Peru, and Spain) is presented, which addresses the manner in which the evolution of the pandemic outbreak has affected the emotions expressed by individuals on Twitter over the last 13 months (from March 2020 to March 2021). We used a total of 3 million tweets to achieve this task. We made use of a well-known framework called EmoWeb to capture the dynamic variation in the sentimental value of pandemic-related words. The results reflect to what degree the pandemic and its derived problems have influenced and affected the population of the selected countries in different ways. The outcomes also illustrate the evolution over time of opinions published on Twitter regarding several topics related to COVID-19.

Runtime and Architecture Support for Efficient Data Exchange in Multi-Accelerator Applications.

Heterogeneous parallel computing applications often process large data sets that require multiple GPUs to jointly meet their needs for physical memory capacity and compute throughput. However, the lack of high-level abstractions in previous heterogeneous parallel programming models force programmers to resort to multiple code versions, complex data copy steps and synchronization schemes when exchanging data between multiple GPU devices, which results in high software development cost, poor maintainability, and even poor performance. This paper describes the HPE runtime system, and the associated architecture support, which enables a simple, efficient programming interface for exchanging data between multiple GPUs through either interconnects or cross-node network interfaces. The runtime and architecture support presented in this paper can also be used to support other types of accelerators. We show that the simplified programming interface reduces programming complexity. The research presented in this paper started in 2009. It has been implemented and tested extensively in several generations of HPE runtime systems as well as adopted into the NVIDIA GPU hardware and drivers for CUDA 4.0 and beyond since 2011. The availability of real hardware that support key HPE features gives rise to a rare opportunity for studying the effectiveness of the hardware support by running important benchmarks on real runtime and hardware. Experimental results show that in a exemplar heterogeneous system, peer DMA and double-buffering, pinned buffers, and software techniques can improve the inter-accelerator data communication bandwidth by 2×. They can also improve the execution speed by 1.6× for a 3D finite difference, 2.5× for 1D FFT, and 1.6× for merge sort, all measured on real hardware. The proposed architecture support enables the HPE runtime to transparently deploy these optimizations under simple portable user code, allowing system designers to freely employ devices of different capabilities. We further argue that simple interfaces such as HPE are needed for most applications to benefit from advanced hardware features in practice.

Maxillary sinus-floor elevation: an animal model.

OBJECTIVES: To find an animal model for modified Caldwell-Luc procedure training. The animal model should have (1) a proper cortical thickness in the lateral wall; (2) a similar morphology and resistance of the Schneiderian membrane in humans; and, finally, (3) an oral approach. MATERIALS AND METHODS: Twelve fresh heads (four Merino sheep, four Murciano-Granadina goats and four Large-White pigs). Two skulls from each of these species were also used. Several three-dimensional imagings from the skulls of each species were acquired using a C-arm. Two fresh heads of each species were used to perform the modified Caldwell-Luc procedure. Two fresh heads of each species were firstly frozen at -30 degrees C for 48 h and then sawed in blocks containing only the target region of the maxillary sinus. RESULTS: The average thickness was 2.03 mm in goat and sheep and 2.80 in pig. Releasing and elevation of the Schneiderian membrane from the sinus floor were easy in the three species. The approach of the maxillary sinus in sheep, goat and pig from the buccal vestibule required a previous surgical enlargement of the buccal vestibule. CONCLUSIONS: In conclusion, the cortical bone thickness and Schneider membrane characteristics in Merina sheep and Murciano-Granadina goat allow a perfect training for the modified Caldwell-Luc procedure. However, the approach from the oral cavity needs, in these species, a previous enlargement of the buccal vestibule. The excessive thickness of the cortical bone restricts the use of pigs for this technique.

Incidencia del cáncer de vesícula biliar en hospital Luis Vernaza y factores de riesgos más frecuentes 1998-2002 / To incidence of gall bladder cancer, at the Luis Vernaza hospital and risk factor more frequent 1998-2002

Tipo de estudio: Transversal, observacional y descriptivo; se realizó en el hospital “Luis Vernaza”, Guayaquil; 1998-2002.Objetivos:•Determinar la incidencia del cáncer de vesícula biliar.•Identificar los factores de riesgo presentes en nuestro medio.•Determinar el motivo de consulta más frecuente.•Precisar si el paciente es diagnosticado prequirúrgicamente.Resultados: Se encontró 36 pacientes con cáncer de vesícula biliar, sexo femenino 75%, edad promedio 68; años, acudieron con dolor abdominal agudo en el 86%, fue diagnosticado prequirúrgicamente en el 36% de los casos.Conclusiones: La incidencia del cáncer de vesícula biliar no se ha incrementado; puede estar relacionado con el aumento de colecistectomías. Los factores de riesgo más frecuentes son: edad avanzada, sexo femenino, antecedentes de litiasis biliar; el motivo de consulta principal fue el cuadro abdominal agudo, por lo que el diagnóstico preoperatorio, es errado en la mayoría de los casos.

Type of study: A transversal, observational and descriptive study was done at the Luis Vernaza Hospital in Guayaquil since 1998-2002.Objectives: •To determine the incidence of the gall-bladder cancer•Identify the risk factor more prevalent in our environment•Determine the motive of the consult more frequent•To know if the patient has presurgycal diagnosesResults: We found 36 patients with this pathology, female sex 75%, average age is around 68, main symptom was acute abdominal pain in 86 % had diagnoses presurgycal in the 36%.Conclusions: The incidence of the gallbladder cancer isn’t increased; this should be related to increase of colecistectomy. The risk factor more frequent are; elderly age, female sex, antecedent of gallstones disease and the main motive of the consult was acute abdominal pain, for this reason were wrong in the most frequent of the cases.