Journal of biomedical materials research. Part B, Applied biomaterials, Jan 15, 2014
The purpose of this study was to evaluate the efficacy of chitosan-alginate membrane to accelerat... more The purpose of this study was to evaluate the efficacy of chitosan-alginate membrane to accelerate wound healing in experimental cutaneous wounds. Two wounds were performed in Wistar rats by punching (1.5 cm diameter), treated with membranes moistened with saline solution (CAM group) or with saline only (SL group). After 2, 7, 14, and 21 days of surgery, five rats of each group were euthanized and reepithelialization was evaluated. The wounds/scars were harvested for histological, flow cytometry, neutrophil infiltrate, and hydroxyproline analysis. CAM group presented higher inflammatory cells recruitment as compared to SL group on 2(nd) day. On the 7(th) day, CAM group showed higher CD11b(+) level and lower of neutrophils than SL group. The CAM group presented higher CD4(+) cells influx than SL group on 2(nd) day, but it decreased during the follow up and became lower on 14(th) and 21(st) days. Higher fibroplasia was noticed on days 7 and 14 as well as higher collagenesis on 21(st) ...
Hyaluronidases are enzymes that degrade hyaluronan an important constituent of the extracellular ... more Hyaluronidases are enzymes that degrade hyaluronan an important constituent of the extracellular matrix. They have been used as a spreading agent, improving the absorption of drugs and facilitating the subcutaneous infusion of fluids. Here, we investigated the influence of bovine testes hyaluronidase (HYAL) during cutaneous wound healing in in vitro and in vivo assays. We demonstrated in the wound scratch assay that HYAL increased the migration and proliferation of fibroblasts in vitro at low concentration, e.g. 0.1 U HYAL enhanced the cell number by 20%. HYAL presented faster and higher reepithelialization in in vivo full-thickness excisional wounds generated on adult Wistar rats back skin already in the early phase at 2nd day post operatory compared to vehicle-control group. Wound closured area observed in the 16 U and 32 U HYAL treated rats reached 38% and 46% compared to 19% in the controls, respectively. Histological and biochemical analyses supported the clinical observations ...
BACKGROUND: Various studies have shown that phototherapy promotes the healing of cutaneous wounds... more BACKGROUND: Various studies have shown that phototherapy promotes the healing of cutaneous wounds. OBJECTIVE: To investigate the effect of phototherapy on healing of cutaneous wounds in nourished and undernourished rats. METHODS: Forty rats, 20 nourished plus 20 others rendered marasmus with undernourishment, were assigned to four equal groups: nourished sham, nourished Light Emitting Diode treated, undernourished sham and undernourished Light Emitting Diode treated. In the two treated groups, two 8-mm punch wounds made on the dorsum of each rat were irradiated three times per week with 3 J/cm 2 sq cm of combined 660 and 890nm light; wounds in the other groups were not irradiated. Wounds were evaluated with digital photography and image analysis, either on day 7 or day 14, with biopsies obtained on day 14 for histological studies. RESULTS: Undernourishment retarded the mean healing rate of the undernourished sham wounds (p < 0.01), but not the undernourished Light emission diode treated wounds, which healed significantly faster (p < 0.001) and as fast as the two nourished groups. Histological analysis showed a smaller percentage of collagen in the undernourished sham group compared with the three other groups, thus confirming our photographic image analysis data. CONCLUSION: Phototherapy reverses the adverse healing effects of undernourishment. Similar beneficial effects may be achieved in patients with poor nutritional status.
O ambiente organizacional tem sido objeto de estudo de diferentes areas do saber, tendo em vista ... more O ambiente organizacional tem sido objeto de estudo de diferentes areas do saber, tendo em vista o impacto que o mesmo gera sobre os individuos, grupos, organizacao e o meio ambiente. Neste cenario, algumas variaveis tem sido investigadas, entre elas satisfacao no trabalho e intencao de rotatividade. A primeira variavel pode ser compreendida como uma variavel atitudinal que indica o quanto o individuo gosta ou nao do seu trabalho, e a segunda variavel sinaliza se o individuo ja pensou ou tem interesse em deixar a organizacao em que trabalha. Para o desenvolvimento deste trabalho contou-se com amostra constituida por 171 pessoas de duas empresas do ramo alimenticio, com media de idade de 33 anos e 5 meses e tempo de casa de 6 anos e 2 meses. Os instrumentos foram um questionario socio demografico e duas escalas, uma de Satisfacao no Trabalho e outra de Intencao de Rotatividade. O objetivo foi verificar a relacao existente entre satisfacao no trabalho e intencao de rotatividade. Os ac...
Different activities are performed by people during the day and many aspects of life are associat... more Different activities are performed by people during the day and many aspects of life are associated with places of human mobility patterns. Among those activities, there are some that are recurrent and demand displacement of the individual between regular places like going to work, going to school, going back home from wherever the individual is located. To accomplish these recurrent daily activities, people tend to follow regular paths with similar temporal and spatial characteristics. In this paper, we propose a method for discovering common pathways across users’ habits. By using density-based clustering algorithms, we detect the users’ most preferable locations and apply a Gaussian Mixture Model (GMM) over these locations to automatically separate the trajectories that follow patterns of days and hours, in order to discover the representations of individual’s habits. Over the set of users’ habits, we search for the trajectories that are more common among them by using the Longest Common Sub-sequence (LCSS) algorithm considering the distance that pairs of users travel on the same path. To evaluate the proposed method we use a real-world GPS dataset. The results show that the method is able to find common routes between users that have similar habits paving the way for future recommendation, prediction and carpooling research techniques.
Machine Learning and Knowledge Discovery in Databases, 2020
Many aspects of life are associated with places of human mobility patterns and nowadays we are fa... more Many aspects of life are associated with places of human mobility patterns and nowadays we are facing an increase in the pervasiveness of mobile devices these individuals carry. Positioning technologies that serve these devices such as the cellular antenna (GSM networks), global navigation satellite systems (GPS), and more recently the WiFi positioning system (WPS) provide large amounts of spatio-temporal data in a continuous way. Therefore, detecting significant places and the frequency of movements between them is fundamental to understand human behavior. In this paper, we propose a method for discovering user habits without any a priori or external knowledge by introducing a densitybased clustering for spatio-temporal data to identify meaningful places and by applying a Gaussian Mixture Model (GMM) over the set of meaningful places to identify the representations of individual habits. To evaluate the proposed method we use two real-world datasets. One dataset contains high-density GPS data and the other one contains GSM mobile phone data in a coarse representation. The results show that the proposed method is suitable for this task as many unique habits were identified. This can be used for understanding users' behavior and to draw their characterizing profiles having a panorama of the mobility patterns from the data.
Human mobility patterns are associated with many aspects of our life. With the increase of the po... more Human mobility patterns are associated with many aspects of our life. With the increase of the popularity and pervasiveness of smartphones and portable devices, the Internet of Things (IoT) is turning into a permanent part of our daily routines. Positioning technologies that serve these devices such as the cellular antenna (GSM networks), global navigation satellite systems (GPS), and more recently the WiFi positioning system (WPS) provide large amounts of spatio-temporal data in a continuous way (data streams). In order to understand human behavior, the detection of important places and the movements between these places is a fundamental task. That said, the proposal of this work is a method for discovering user habits over mobility data without any a priori or external knowledge. Our approach extends a density-based clustering method for spatio-temporal data to identify meaningful places the individuals' visit. On top of that, a Gaussian mixture model (GMM) is employed over movements between the visits to automatically separate the trajectories accordingly to their key identifiers that may help describe a habit. By regrouping trajectories that look alike by day of the week, length, and starting hour, we discover the individual's habits. The evaluation of the proposed method is made over three real-world datasets. One dataset contains high-density GPS data and the others use GSM mobile phone data with 15-min sampling rate and Google Location History data with a variable sampling rate. The results show that the proposed pipeline is suitable for this task as other habits rather than just going from home to work and vice versa were found. This method can be used for understanding person behavior and creating their profiles revealing a panorama of human mobility patterns from raw mobility data.
Many aspects of our lives are associated with places and the activities we perform on a daily bas... more Many aspects of our lives are associated with places and the activities we perform on a daily basis. Most of them are recurrent and demand displacement of the individual between regular places like going to work, school or other important personal locations. To accomplish these recurrent daily activities, people tend to follow regular paths with similar temporal and spatial characteristics, especially because humans are frequently looking for uniformity to support their decisions and make their actions easier or even automatic. In this work, we propose a method for discovering common pathways across users' habits from human mobility data. By using a density-based clustering algorithm, we identify the most preferable locations the users visit, we apply a Gaussian mixture model over these places to automatically separate among all traces, the trajectories that follow patterns in order to discover the representations of individual's habits. By using the longest common sub-sequence algorithm, we search for the trajectories that are more similar over the set of users' habits trips by considering the distance that pairs of users or habits share on the same path. The proposed method is evaluated over two real-world GPS datasets and the results show that the approach is able to detect the most important places in a user's life, detect the routine activities and identify common routes between users that have similar habits paving the way for research techniques in carpooling, recommendation and prediction systems.
As pragas e doenças no cafeeiro, ocasionadas por bicho-mineiro, broca-do-café, ferrugem e cercosp... more As pragas e doenças no cafeeiro, ocasionadas por bicho-mineiro, broca-do-café, ferrugem e cercosporiose, chegam a atingir ate 50% de uma lavoura cafeeira, podendo causar grandes prejuízos aos cafeicultores. Sendo assim, os sistemas inteligentes são de suma importância para predizer esses danos ao cafeeiro. As redes neurais articiais do tipo perceptron multicamadas, foram os sistemas inteligentes utilizados neste trabalho para prever a porcentagem de ocorrência de pragas e incidência de doenças no cafeeiro. Foram utilizados dados meteorológicos, tais como: temperaturas mínima e máxima, precipitação pluviométrica, umidade relativa do ar, incidência de raios solares e pressão atmosféerica como variáveis de entrada do modelo. O valor dos dados referentes as pragas e doenças se deram quantitativamente e foram coletados no Campo Experimental da EPAMIG de São Sebastião do Paraíso, no sul de Minas Gerais. Foram empregadas as méetricas estatísticas RMSE e R2 para vericar o quão o modelo de r...
We propose a new family of distributions called the exponentiated Kumaraswamy-G class with three ... more We propose a new family of distributions called the exponentiated Kumaraswamy-G class with three extra positive parameters, which generalizes the Cordeiro and de Castro's family. Some special distributions in the new class are discussed. We derive some mathematical properties of the proposed class including explicit expressions for the quantile function, ordinary and incomplete moments, generating function, mean deviations, reliability, Rényi entropy and Shannon entropy. The method of maximum likelihood is used to fit the distributions in the proposed class. Simulations are performed in order to assess the asymptotic behavior of the maximum likelihood estimates. We illustrate its potentiality with applications to two real data sets which show that the extended Weibull model in the new class provides a better fit than other generalized Weibull distributions.
A class of univariate distributions called the exponentiated generalized class was recently propo... more A class of univariate distributions called the exponentiated generalized class was recently proposed in the literature. A four-parameter model within this class named the exponentiated generalized Gumbel distribution is defined. We discuss the shapes of its density function and obtain explicit expressions for the ordinary moments, generating and quantile functions, mean deviations, Bonferroni and Lorenz curves and Rényi entropy. The density function of the order statistic is derived. The method of maximum likelihood is used to estimate model parameters. We determine the observed information matrix. We provide a Monte Carlo simulation study to evaluate the maximum likelihood estimates of model parameters and two applications to real data to illustrate the importance of the new model.
Journal of biomedical materials research. Part B, Applied biomaterials, Jan 15, 2014
The purpose of this study was to evaluate the efficacy of chitosan-alginate membrane to accelerat... more The purpose of this study was to evaluate the efficacy of chitosan-alginate membrane to accelerate wound healing in experimental cutaneous wounds. Two wounds were performed in Wistar rats by punching (1.5 cm diameter), treated with membranes moistened with saline solution (CAM group) or with saline only (SL group). After 2, 7, 14, and 21 days of surgery, five rats of each group were euthanized and reepithelialization was evaluated. The wounds/scars were harvested for histological, flow cytometry, neutrophil infiltrate, and hydroxyproline analysis. CAM group presented higher inflammatory cells recruitment as compared to SL group on 2(nd) day. On the 7(th) day, CAM group showed higher CD11b(+) level and lower of neutrophils than SL group. The CAM group presented higher CD4(+) cells influx than SL group on 2(nd) day, but it decreased during the follow up and became lower on 14(th) and 21(st) days. Higher fibroplasia was noticed on days 7 and 14 as well as higher collagenesis on 21(st) ...
Hyaluronidases are enzymes that degrade hyaluronan an important constituent of the extracellular ... more Hyaluronidases are enzymes that degrade hyaluronan an important constituent of the extracellular matrix. They have been used as a spreading agent, improving the absorption of drugs and facilitating the subcutaneous infusion of fluids. Here, we investigated the influence of bovine testes hyaluronidase (HYAL) during cutaneous wound healing in in vitro and in vivo assays. We demonstrated in the wound scratch assay that HYAL increased the migration and proliferation of fibroblasts in vitro at low concentration, e.g. 0.1 U HYAL enhanced the cell number by 20%. HYAL presented faster and higher reepithelialization in in vivo full-thickness excisional wounds generated on adult Wistar rats back skin already in the early phase at 2nd day post operatory compared to vehicle-control group. Wound closured area observed in the 16 U and 32 U HYAL treated rats reached 38% and 46% compared to 19% in the controls, respectively. Histological and biochemical analyses supported the clinical observations ...
BACKGROUND: Various studies have shown that phototherapy promotes the healing of cutaneous wounds... more BACKGROUND: Various studies have shown that phototherapy promotes the healing of cutaneous wounds. OBJECTIVE: To investigate the effect of phototherapy on healing of cutaneous wounds in nourished and undernourished rats. METHODS: Forty rats, 20 nourished plus 20 others rendered marasmus with undernourishment, were assigned to four equal groups: nourished sham, nourished Light Emitting Diode treated, undernourished sham and undernourished Light Emitting Diode treated. In the two treated groups, two 8-mm punch wounds made on the dorsum of each rat were irradiated three times per week with 3 J/cm 2 sq cm of combined 660 and 890nm light; wounds in the other groups were not irradiated. Wounds were evaluated with digital photography and image analysis, either on day 7 or day 14, with biopsies obtained on day 14 for histological studies. RESULTS: Undernourishment retarded the mean healing rate of the undernourished sham wounds (p < 0.01), but not the undernourished Light emission diode treated wounds, which healed significantly faster (p < 0.001) and as fast as the two nourished groups. Histological analysis showed a smaller percentage of collagen in the undernourished sham group compared with the three other groups, thus confirming our photographic image analysis data. CONCLUSION: Phototherapy reverses the adverse healing effects of undernourishment. Similar beneficial effects may be achieved in patients with poor nutritional status.
O ambiente organizacional tem sido objeto de estudo de diferentes areas do saber, tendo em vista ... more O ambiente organizacional tem sido objeto de estudo de diferentes areas do saber, tendo em vista o impacto que o mesmo gera sobre os individuos, grupos, organizacao e o meio ambiente. Neste cenario, algumas variaveis tem sido investigadas, entre elas satisfacao no trabalho e intencao de rotatividade. A primeira variavel pode ser compreendida como uma variavel atitudinal que indica o quanto o individuo gosta ou nao do seu trabalho, e a segunda variavel sinaliza se o individuo ja pensou ou tem interesse em deixar a organizacao em que trabalha. Para o desenvolvimento deste trabalho contou-se com amostra constituida por 171 pessoas de duas empresas do ramo alimenticio, com media de idade de 33 anos e 5 meses e tempo de casa de 6 anos e 2 meses. Os instrumentos foram um questionario socio demografico e duas escalas, uma de Satisfacao no Trabalho e outra de Intencao de Rotatividade. O objetivo foi verificar a relacao existente entre satisfacao no trabalho e intencao de rotatividade. Os ac...
Different activities are performed by people during the day and many aspects of life are associat... more Different activities are performed by people during the day and many aspects of life are associated with places of human mobility patterns. Among those activities, there are some that are recurrent and demand displacement of the individual between regular places like going to work, going to school, going back home from wherever the individual is located. To accomplish these recurrent daily activities, people tend to follow regular paths with similar temporal and spatial characteristics. In this paper, we propose a method for discovering common pathways across users’ habits. By using density-based clustering algorithms, we detect the users’ most preferable locations and apply a Gaussian Mixture Model (GMM) over these locations to automatically separate the trajectories that follow patterns of days and hours, in order to discover the representations of individual’s habits. Over the set of users’ habits, we search for the trajectories that are more common among them by using the Longest Common Sub-sequence (LCSS) algorithm considering the distance that pairs of users travel on the same path. To evaluate the proposed method we use a real-world GPS dataset. The results show that the method is able to find common routes between users that have similar habits paving the way for future recommendation, prediction and carpooling research techniques.
Machine Learning and Knowledge Discovery in Databases, 2020
Many aspects of life are associated with places of human mobility patterns and nowadays we are fa... more Many aspects of life are associated with places of human mobility patterns and nowadays we are facing an increase in the pervasiveness of mobile devices these individuals carry. Positioning technologies that serve these devices such as the cellular antenna (GSM networks), global navigation satellite systems (GPS), and more recently the WiFi positioning system (WPS) provide large amounts of spatio-temporal data in a continuous way. Therefore, detecting significant places and the frequency of movements between them is fundamental to understand human behavior. In this paper, we propose a method for discovering user habits without any a priori or external knowledge by introducing a densitybased clustering for spatio-temporal data to identify meaningful places and by applying a Gaussian Mixture Model (GMM) over the set of meaningful places to identify the representations of individual habits. To evaluate the proposed method we use two real-world datasets. One dataset contains high-density GPS data and the other one contains GSM mobile phone data in a coarse representation. The results show that the proposed method is suitable for this task as many unique habits were identified. This can be used for understanding users' behavior and to draw their characterizing profiles having a panorama of the mobility patterns from the data.
Human mobility patterns are associated with many aspects of our life. With the increase of the po... more Human mobility patterns are associated with many aspects of our life. With the increase of the popularity and pervasiveness of smartphones and portable devices, the Internet of Things (IoT) is turning into a permanent part of our daily routines. Positioning technologies that serve these devices such as the cellular antenna (GSM networks), global navigation satellite systems (GPS), and more recently the WiFi positioning system (WPS) provide large amounts of spatio-temporal data in a continuous way (data streams). In order to understand human behavior, the detection of important places and the movements between these places is a fundamental task. That said, the proposal of this work is a method for discovering user habits over mobility data without any a priori or external knowledge. Our approach extends a density-based clustering method for spatio-temporal data to identify meaningful places the individuals' visit. On top of that, a Gaussian mixture model (GMM) is employed over movements between the visits to automatically separate the trajectories accordingly to their key identifiers that may help describe a habit. By regrouping trajectories that look alike by day of the week, length, and starting hour, we discover the individual's habits. The evaluation of the proposed method is made over three real-world datasets. One dataset contains high-density GPS data and the others use GSM mobile phone data with 15-min sampling rate and Google Location History data with a variable sampling rate. The results show that the proposed pipeline is suitable for this task as other habits rather than just going from home to work and vice versa were found. This method can be used for understanding person behavior and creating their profiles revealing a panorama of human mobility patterns from raw mobility data.
Many aspects of our lives are associated with places and the activities we perform on a daily bas... more Many aspects of our lives are associated with places and the activities we perform on a daily basis. Most of them are recurrent and demand displacement of the individual between regular places like going to work, school or other important personal locations. To accomplish these recurrent daily activities, people tend to follow regular paths with similar temporal and spatial characteristics, especially because humans are frequently looking for uniformity to support their decisions and make their actions easier or even automatic. In this work, we propose a method for discovering common pathways across users' habits from human mobility data. By using a density-based clustering algorithm, we identify the most preferable locations the users visit, we apply a Gaussian mixture model over these places to automatically separate among all traces, the trajectories that follow patterns in order to discover the representations of individual's habits. By using the longest common sub-sequence algorithm, we search for the trajectories that are more similar over the set of users' habits trips by considering the distance that pairs of users or habits share on the same path. The proposed method is evaluated over two real-world GPS datasets and the results show that the approach is able to detect the most important places in a user's life, detect the routine activities and identify common routes between users that have similar habits paving the way for research techniques in carpooling, recommendation and prediction systems.
As pragas e doenças no cafeeiro, ocasionadas por bicho-mineiro, broca-do-café, ferrugem e cercosp... more As pragas e doenças no cafeeiro, ocasionadas por bicho-mineiro, broca-do-café, ferrugem e cercosporiose, chegam a atingir ate 50% de uma lavoura cafeeira, podendo causar grandes prejuízos aos cafeicultores. Sendo assim, os sistemas inteligentes são de suma importância para predizer esses danos ao cafeeiro. As redes neurais articiais do tipo perceptron multicamadas, foram os sistemas inteligentes utilizados neste trabalho para prever a porcentagem de ocorrência de pragas e incidência de doenças no cafeeiro. Foram utilizados dados meteorológicos, tais como: temperaturas mínima e máxima, precipitação pluviométrica, umidade relativa do ar, incidência de raios solares e pressão atmosféerica como variáveis de entrada do modelo. O valor dos dados referentes as pragas e doenças se deram quantitativamente e foram coletados no Campo Experimental da EPAMIG de São Sebastião do Paraíso, no sul de Minas Gerais. Foram empregadas as méetricas estatísticas RMSE e R2 para vericar o quão o modelo de r...
We propose a new family of distributions called the exponentiated Kumaraswamy-G class with three ... more We propose a new family of distributions called the exponentiated Kumaraswamy-G class with three extra positive parameters, which generalizes the Cordeiro and de Castro's family. Some special distributions in the new class are discussed. We derive some mathematical properties of the proposed class including explicit expressions for the quantile function, ordinary and incomplete moments, generating function, mean deviations, reliability, Rényi entropy and Shannon entropy. The method of maximum likelihood is used to fit the distributions in the proposed class. Simulations are performed in order to assess the asymptotic behavior of the maximum likelihood estimates. We illustrate its potentiality with applications to two real data sets which show that the extended Weibull model in the new class provides a better fit than other generalized Weibull distributions.
A class of univariate distributions called the exponentiated generalized class was recently propo... more A class of univariate distributions called the exponentiated generalized class was recently proposed in the literature. A four-parameter model within this class named the exponentiated generalized Gumbel distribution is defined. We discuss the shapes of its density function and obtain explicit expressions for the ordinary moments, generating and quantile functions, mean deviations, Bonferroni and Lorenz curves and Rényi entropy. The density function of the order statistic is derived. The method of maximum likelihood is used to estimate model parameters. We determine the observed information matrix. We provide a Monte Carlo simulation study to evaluate the maximum likelihood estimates of model parameters and two applications to real data to illustrate the importance of the new model.
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