Comparing Pulsed-dye Laser with Cryotherapy in the Treatment of Common Warts

INTRODUCTION: No modality has been identified as the treatment of chice for treating common warts. Cryothearpy and pulsed-dye laser (PDL) are among common modalities for treating these lesions. The aim of this study was to compare pulsed dye laser with cryotherapy in terms of efficacy and complications.METHODS: Of a total of 46 patients enrolled in this study, 7 patients withdrew the study, 20 patients underwent cryotherapy and 19 patients underwent PDL. Patients underwent a maximum of 4 therapeutic sessions at 3-week intervals in both groups. They were assessed for the remission rate (complete and partial),side effects and recurrence rate in each session and 1 month after termination of the treatments.RESULTS: At the end of the study complete remission was achieved in 37.8% of patients in cryotherapy group and in 52.3% of patients in PDL group. This difference wasn’t statistically significant (P=0.229), though after first and second sessions of treatment complete and excellent partial remission occurred more in PDL group with significant difference (P=0.007 and P=0.021). Pain and bulla formation occurred statistically higher in cryotherapy group (P=0.002 and P=0.001). Other complications were rare in both groups.CONCLUSION: In terms of efficacy, we couldn’t demonstrate the superiority of pulseddye laser therapy to cryotherapy in treating common warts. Both methods were safe for long-term complications but PDL was much safer for short-term complications

Effect of Laser-Assisted Hair Removal (LAHR) on the Quality of Life and Depression in Hirsute Females: A Single-Arm Clinical Trial

Introduction: Hirsutism, mainly due to poly cystic ovary syndrome (PCOS), causes stress, anxiety and depression in females. LAHR is currently accepted as a good treatment option for hirsutism. The goal of the current study was to ascertain how LAHR affected the degree of hirsutism, quality of life, and depression in hirsute females. Methods: A single arm before/after clinical trial was designed and performed in the Razi Hospital Laser Clinic over a 15-months period. All hirsute females visiting Razi hospital laser clinic, were enrolled and received three sessions of LAHR every 4-6 weeks if they were interested and signed an informed consent. Before the commencement of LAHR and six to eight weeks after the last session, the Ferriman-Gallwey score (hirsutism severity), Beck score (depression index) and DLQI score (quality of life index) were calculated and stored. Results: There were 80 subjects in all. The mean± (SD) of the Ferriman-Gallwey score was reduced from 7.05 ± 2.27 to 4.91 ± 2.41, p<0.001. Beck depression score’s mean± (SD) was reduced from 13.3 ± 8.7 to 10.2 ± 8.4, p<0.001 and mean± (SD) of DLQI score was decreased from 5.6 ± 5.2 to 3.5 ± 2.3, p<0.001. No significant complication were reported. Conclusion:   LAHR can improve hirsutism related depression and degradation of quality of life as well as hirsutism physical signs

Identification of debris flow initiation zones using topographic model and airborne laser scanning data

Empirical multivariate predictive models represent an important tool to estimate debris flow initiation areas. Most of the approaches used in modelling debris flows propagation and deposit phases required identifying release (starting point) area or source area. Initiation areas offer a good overview to point out where field investigation should be conducted to establish a detailed hazard map. These zones, usually, are arbitrarily chosen which affect the model outputs; hence, there is a need to have accurate and automated means of identifying the release area. In addition to this, the resolution of the terrain dataset also affects the results of the detection of source areas. In this study, airborne laser scanning (ALS) data was used because of its robustness in providing detailed terrain attributes at high resolution. Primary and secondary conditioning parameters were derived from digital elevation model (DEM) as input into the modelling process. Three models were executed at different spatial resolution scales: 5, 10 and 15 m, respectively. MARSpline multivariate data mining predictive approach was implemented using morphometric indices and topographical derived parameter as independent variables. A statistics validation was calculated to estimate the optimal pixel size, 1200 randomly sample data were generated from existing inventory data. Debris flows and no-debris flows were categorized, and the transform to continuous integer (1 and 0), respectively. To achieve this, the data set was divided into two, 70% (840) for the training dataset and 30% (360) for validation. The best model was selected based on the model performance using the generalized cross validation (GCV) and the receiver operating characteristic (ROC) curve/area under curve (AUC) values. Conditioning parameters were numerically optimized to identify the arbitrarily maximum model basis function for eleven variables, using MARSplines analysis (algorithm). The three most influencing topographic parameters identified are topographic roughness index (TRI), slope angle, and specific catchment area (SCA) with the percentage values of participation in the model of 100, 93, and 86%, respectively. The chosen function appeared to describe the analysed correlation sufficiently well. Consequently, three stages of optimization were made to determine the optimized source areas is possible with 10 m pixel size, 200 maximum basis functions and 3 maximum interactions, resulting into 82% ROC train and 80% test, GCV 0.189 and 85% correlation coefficient. The result will be of great contribution to the advancement of a broad understanding of the dynamics of debris flows hazard and mitigations at regional level which; that is resourceful for comprehensive slope management for safe urban planning decision-making process and debris flow disaster management

Identification of debris flow initiation zones using topographic model and airborne laser scanning data

Empirical multivariate predictive models represent an important tool to estimate debris flow initiation areas. Most of the approaches used in modelling debris flows propagation and deposit phases required identifying release (starting point) area or source area. Initiation areas offer a good overview to point out where field investigation should be conducted to establish a detailed hazard map. These zones, usually, are arbitrarily chosen which affect the model outputs; hence, there is a need to have accurate and automated means of identifying the release area. In addition to this, the resolution of the terrain dataset also affects the results of the detection of source areas. In this study, airborne laser scanning (ALS) data was used because of its robustness in providing detailed terrain attributes at high resolution. Primary and secondary conditioning parameters were derived from digital elevation model (DEM) as input into the modelling process. Three models were executed at different spatial resolution scales: 5, 10 and 15 m, respectively. MARSpline multivariate data mining predictive approach was implemented using morphometric indices and topographical derived parameter as independent variables. A statistics validation was calculated to estimate the optimal pixel size, 1200 randomly sample data were generated from existing inventory data. Debris flows and no-debris flows were categorized, and the transform to continuous integer (1 and 0), respectively. To achieve this, the data set was divided into two, 70% (840) for the training dataset and 30% (360) for validation. The best model was selected based on the model performance using the generalized cross validation (GCV) and the receiver operating characteristic (ROC) curve/area under curve (AUC) values. Conditioning parameters were numerically optimized to identify the arbitrarily maximum model basis function for eleven variables, using MARSplines analysis (algorithm). The three most influencing topographic parameters identified are topographic roughness index (TRI), slope angle, and specific catchment area (SCA) with the percentage values of participation in the model of 100, 93, and 86%, respectively. The chosen function appeared to describe the analysed correlation sufficiently well. Consequently, three stages of optimization were made to determine the optimized source areas is possible with 10 m pixel size, 200 maximum basis functions and 3 maximum interactions, resulting into 82% ROC train and 80% test, GCV 0.189 and 85% correlation coefficient. The result will be of great contribution to the advancement of a broad understanding of the dynamics of debris flows hazard and mitigations at regional level which; that is resourceful for comprehensive slope management for safe urban planning decision-making process and debris flow disaster management

Artificial neural networks : applications in morphometric and landscape features analysis

In this thesis a semi-automatic method is developed to analyze morphometric features and landscape elements based on Self Organizing Map (SOM) as a unsupervised Artificial Neural Network algorithm. Analysis and parameterization of topography into simple and homogenous land elements (landform) can play an important role as basic information in planning processes and environmental modeling. Landforms and land cover are the main components of landscapes. Landscapes are dynamic systems that involve interrelation between physical characteristics (such as landform, soil) and anthropogenic processes (such as land use). In morphometry (as general term of geomorphometry) - the qualitative and quantitative measurement of topography - morphometric parameters are calculated such as profile curvature and longitudinal curvature. They are then used in morphometric analysis to identify morphometric features like plane, channel, ridge, peak or pit. In February 2000 the Shuttle Radar Topography Mission (SRTM), collected data over 80% of the Earth's land surface, to derive a consistent digital elevation model (DEM) for allland areas between 60 degrees N and 56 degrees S latitude. This DEM with about 90 m grid spacing was used to generate morphometric parameters of first order (slope) and second order (minimum curvature, maximum curvatures and cross-sectional curvature) by fitting a bivariate quadratic surface. These surface curvatures are strongly related to landform features and geomorphological processes. The thesis starts with an overall introduction and literature review. Then two methods for morphometric analysis are compared: morphometric parameterization and feature extraction proposed by Wood (1996a), calculated with Geographic Information Systems (GIS) software and our method implemented with Self Organizing Map (SOM) as an nsupervised artificial neural networks paradigm. Finally in our method for landscape element analysis morphometric parameters and remotely sensed spectral data are combined. The emphasis is on morphologically homogeneous landscape elements characterized by similar slope and curvature conditions. SOM is used to reduce large multidimensional data sets to one output layer consisting of 20 map units. These map units are interpreted in terms of morphometric features, slope and land cover to identify and characterize landscape elements or geoecological units Both studies have demonstrated valuable methods for extraction of land information that can be used in geomorphologic applications and geoecosystem modeling. These methods allow important savings in field work and can be used as alternative to labor intensive manual methods. But results may depend on scale and quality of the DEM and the topographic situation; caution should be used in interpretation. Evaluation of these methods in other areas with different morphometric conditions and with multi-scale DEM remains to be done.QC 2010110

Artificial neural networks : applications in morphometric and landscape features analysis

In this thesis a semi-automatic method is developed to analyze morphometric features and landscape elements based on Self Organizing Map (SOM) as a unsupervised Artificial Neural Network algorithm. Analysis and parameterization of topography into simple and homogenous land elements (landform) can play an important role as basic information in planning processes and environmental modeling. Landforms and land cover are the main components of landscapes. Landscapes are dynamic systems that involve interrelation between physical characteristics (such as landform, soil) and anthropogenic processes (such as land use). In morphometry (as general term of geomorphometry) - the qualitative and quantitative measurement of topography - morphometric parameters are calculated such as profile curvature and longitudinal curvature. They are then used in morphometric analysis to identify morphometric features like plane, channel, ridge, peak or pit. In February 2000 the Shuttle Radar Topography Mission (SRTM), collected data over 80% of the Earth's land surface, to derive a consistent digital elevation model (DEM) for allland areas between 60 degrees N and 56 degrees S latitude. This DEM with about 90 m grid spacing was used to generate morphometric parameters of first order (slope) and second order (minimum curvature, maximum curvatures and cross-sectional curvature) by fitting a bivariate quadratic surface. These surface curvatures are strongly related to landform features and geomorphological processes. The thesis starts with an overall introduction and literature review. Then two methods for morphometric analysis are compared: morphometric parameterization and feature extraction proposed by Wood (1996a), calculated with Geographic Information Systems (GIS) software and our method implemented with Self Organizing Map (SOM) as an nsupervised artificial neural networks paradigm. Finally in our method for landscape element analysis morphometric parameters and remotely sensed spectral data are combined. The emphasis is on morphologically homogeneous landscape elements characterized by similar slope and curvature conditions. SOM is used to reduce large multidimensional data sets to one output layer consisting of 20 map units. These map units are interpreted in terms of morphometric features, slope and land cover to identify and characterize landscape elements or geoecological units Both studies have demonstrated valuable methods for extraction of land information that can be used in geomorphologic applications and geoecosystem modeling. These methods allow important savings in field work and can be used as alternative to labor intensive manual methods. But results may depend on scale and quality of the DEM and the topographic situation; caution should be used in interpretation. Evaluation of these methods in other areas with different morphometric conditions and with multi-scale DEM remains to be done.QC 2010110

Morphometric and Landscape Feature Analysis with Artificial Neural Networks and SRTM data : Applications in Humid and Arid Environments

This thesis presents a semi-automatic method to analyze morphometric features and landscape elements based on Self Organizing Map (SOM) as an unsupervised Artificial Neural Network algorithm in two completely different environments: 1) the Man and Biosphere Reserve “Eastern Carpathians” (Central Europe) as a complex mountainous humid area and 2) Lut Desert, Iran, a hyper arid region characterized by repetition of wind-eroded features. In 2003, the National Aeronautics and Space Administration (NASA) released the SRTM/ SIR-C band data with 3 arc seconds (approx. 90 m resolution) grid for approximately 80 % of Earth’s land surface. The X-band SRTM data were processed with a 1 arc second (approx. 30 m resolution) grid by the German space agency, DLR and the Italian space agency ASI, but due to the smaller X-SAR ground swath, large areas are not covered. The latest version 3.0 SRTM/C DEM and SRTM/X band DEM were re-projected to 90 and 30 m UTM grid and used to generate morphometric parameters of first order (slope) and second order (cross-sectional curvature, maximum curvatures and minimum curvature) by using a bivariate quadratic surface. The morphometric parameters are then used in a SOM to identify morphometric features (or landform elements) e.g. planar, channel, ridge in mountainous areas or yardangs (ridge) and corridors (valley) in hyper-arid areas. Geomorphic phenomena and features are scale-dependent and the characteristics of features vary when measured over different spatial extents or different spatial resolution. Morphometric parameters were derived for nine window sizes of the 90 m DEM ranging from 5 × 5 to 55 ×55. Analysis of the SOM output represents landform entities with ground areas from 450 m to 4950 m that is local to regional scale features. Effect of two SRTM resolutions, C and X bands is studied on morphometric feature identification. The difference change analysis revealed the quantity of resolution dependency of morphometric features. Increasing the DEM spatial resolution from 90 to 30 m (corresponding to X band) by interpolation resulted in a significant improvement of terrain derivatives and morphometric feature identification. Integration of morphometric parameters with climate data (e.g. Sum of active temperature above 10 ° C) in SOM resulted in delineation of morphologically homogenous discrete geo-ecological units. These units were reclassified to produce a Potential Natural Vegetation map. Finally, we combined morphometric parameters and remotely sensed spectral data from Landsat ETM+ to identify and characterize landscape elements. The single integrated data set of geo-ecosystems shows the spatial distribution of geomorphic, climatic and biotic/cultural properties in the Eastern Carpathians. The results demonstrate that a SOM is a very efficient tool to analyze geo-morphometric features under diverse environmental conditions and at different scales and resolution. Finer resolution and decreasing window size reveals information that is more detailed while increasing window size and coarser resolution emphasizes more regional patterns. It was also successfully applied to integrate climatic, morphometric parameters and Landsat ETM+ data for landscape analysis. Despite the stochastic nature of SOM, the results are not sensitive to randomization of initial weight vectors if many iterations are used. This procedure is reproducible with consistent results.Avhandlingen presenterar en halvautomatisk metod för att analysera morfometriska kännetecken och landskapselement som bygger på Self Organizing Map (SOM), en oövervakad Artificiell Neural Nätverk algoritm, i två helt skilda miljöer: 1) Man and Biosphere Reserve "Eastern Carpathians" (Centraleuropa) som är ett komplext, bergigt och humid område och 2) Lut öken, Iran, en extrem torr region som kännetecknas av återkommande vinderoderade objekt. Basen för undersökningen är det C-band SRTM digital höjd modell (DEM) med 3 bågsekunder rutnät som National Aeronautics and Space Administration släppte 2003 för ungefär 80 % av jordens yta. Dessutom används i ett mindre område X-band SRTM DEM med 1 bågsekund rutnät av den tyska rymdagenturen DLR. DEM transformerades till 90 och 30 m UTM nätet och därav genererades morfometriska parametrar av första (lutning) och andra ordning (tvärsnittböjning, största och minsta böjning). De morfometriska parametrar används sedan i en SOM för att identifiera morfometriska objekt (eller landform element) t.ex. plan yta, kanal, kam i bergsområden eller yardangs (kam) och korridorer (dalgångar) i extrem torra områden. Geomorfiska fenomen och objekt är skalberoende och kännetecken varierar med geografiska områden och upplösning. Morfometriska parametrar har härletts från 90 m DEM för nio fönsterstorlekar från 5 × 5 till 55 × 55. Resultaten representerar landform enheter för områden från 450 m till 4950 m på marken dvs. lokal till regional skala. Inflytande av två SRTM upplösningar i C och X-banden har studerats för identifikation av morfometriska objekt. Förändringsanalys visade storleken av upplösningsberoende av morfometriska objekt. Ökning av DEM upplösningen från 90 till 30 m (motsvarande X-bandet) genom interpolation resulterade i en betydande förbättring av terräng parametrar och identifiering av morfometriska objekt. Integration av morfometriska parametrar med klimatdata (t.ex. summan av aktiv temperatur över 10° C) i SOM resulterade i avgränsningen av homogena geoekologiska enheter. Dessa enheter ha används för att producera en karta av potentiell naturlig vegetation. Slutligen har vi kombinerat morfometriska parametrar och multispektrala fjärranalysdata från Landsat ETM för att identifiera och karaktärisera landskapselement. Dessa integrerade ekosystem data visar den geografiska fördelningen av morfometriska, klimatologiska och biotiska/kulturella egenskaper i östra Karpaterna. Resultaten visar att SOM är ett mycket effektivt verktyg för att analysera geomorfometriska egenskaper under skilda miljöförhållanden, i olika skalor och upplösningar. Finare upplösning och minskad fönsterstorlek visar information som är mer detaljerad. Ökad fönsterstorlek och grövre upplösning betonar mer regionala mönster. Det var också mycket framgångsrikt att integrera klimatiska och morfometriska parametrar med Landsat ETM data för landskapsanalys. Trots den stokastiska natur av SOM, är resultaten inte känsliga för slumpvisa värden i de ursprungliga viktvektorerna när många iterationer används. Detta förfarande är reproducerbart med bestående resultat.QC 2010092

Basal cell carcinoma associated with erythema ab igne

Erythema ab igne is a skin condition mainly caused by heat exposure. Erythema ab igne usually follows a favorable prognosis. However, it may increase the risk of developing cutaneous malignancy in the involved skin. Being familiar with the type of cutaneous malignancies that may arise in the site of erythema ab igne is considerably important. To our knowledge, this letter presents the first case that shows the association between erythema ab igne and basal cell carcinoma