The effect of environmental conditions on river macrobenthic communities was studied using a data... more The effect of environmental conditions on river macrobenthic communities was studied using a dataset consisting of 343 sediment samples from unnavigable watercourses in Flanders, Belgium. Artificial neural network models were used to analyse the relation among river characteristics and macrobenthic communities. The dataset included presence or absence of macroinvertebrate taxa and 12 physicochemical and hydromorphological variables for each sampling site. The abiotic variables served as input for the artificial neural networks to predict the macrobenthic community. The effects of the input variables on model performance were assessed in order to identify the most diagnostic river characteristics for macrobenthic community composition. This was done by consecutively eliminating the least important variables and, when beneficial for model performance, adding previously removed ones again. This stepwise input variable selection procedure was tested not only on a model predicting the entire macrobenthic community, but also on three models, each predicting an individual taxon. Additionally, during each step of the stepwise leave-one-out procedure, a sensitivity analysis was performed to determine the response of the predicted macroinvertebrate taxa to the input variables applied. This research illustrated that a combination of input variable selection with sensitivity analyses can contribute to the development of reliable and ecologically relevant ANN models. The river characteristics predicting presence or absence of the benthic macroinvertebrates best were the Julian day, conductivity, and dissolved oxygen content. These conditions reflect the importance of discharges of untreated wastewater that occurred during the period of investigation in nearly all Flemish rivers.
Although various models have been developed for prediction and forecasting of time series in vari... more Although various models have been developed for prediction and forecasting of time series in various engineering fields, there is no perfect model to forecast hydrologic time series. In recent decades, Artificial Neural Networks (ANNs) have been very common for prediction and forecasting of hydrologic time series because of their practicality in applications. This study proposed a post-process in an ANN model to improve the forecasting performance by rescaling the errors based on a correlation between observations trained data. The model proposed in this study was examined using precipitation data achieved from different four stations in the United States, and compared with the feedforward networks. It was observed that all error measures used in this study were improved through a rescaling post-process in the model for all stations. The strong point of the model lies in that the correlation approach is very easy to apply.
Although the concept of endoscopy has existed for centuries, a practical, working neuroendoscopic... more Although the concept of endoscopy has existed for centuries, a practical, working neuroendoscopic system did not emerge until last century, as a result of numerous contributions and refinements in optical technology, illumination sources, and instrumentation. Modern neuroendoscopy would not be a flourishing field, as it is today, without the dedication, innovation, and implementation of emerging technology by key contributors including Maximilian Nitze, Walter Dandy, and Harold Hopkins. Despite several inherent and unique limitations, neuroendoscopic surgery is now performed for a variety of intraventricular, skull base, and spinal operations. In this review, the history of neuroendoscopy, key players who envisioned how the inner workings of the human body could be visualized "through the looking glass," and current state and future potential for neuroendoscopic surgery are discussed. Future directions of neuroendoscopic surgery will likely be guided by further miniaturization in camera and optical technology, innovations in surgical instrumentation design, the introduction of robotics, multi-port minimally invasive surgery, and an enhanced ability to perform bimanual microdissection.
To facilitate decision support in freshwater ecosystem protection and restoration management, hab... more To facilitate decision support in freshwater ecosystem protection and restoration management, habitat suitability models can be very valuable. Data driven methods such as artificial neural networks (ANNs) are particularly useful in this context, seen their time-efficient development and relatively high reliability. However, specialized and technical literature on neural network modelling offers a variety of model development criteria to select model architecture, training procedure, etc. This may lead to confusion among ecosystem modellers and managers regarding the optimal training and validation methodology. This paper focuses on the analysis of ANN development and application for predicting macroinvertebrate communities, a species group commonly used in freshwater assessment worldwide. This review reflects on the different aspects regarding model development and application based on a selection of 26 papers reporting the use of ANN models for the prediction of macroinvertebrates. This analysis revealed that the applied model training and validation methodologies can often be improved and moreover crucial steps in the modelling process are often poorly documented. Therefore, suggestions to improve model development, assessment and application in ecological river management are presented. In particular, data pre-processing determines to a high extent the reliability of the induced models and their predictive relevance. This also counts for the validation criteria, that need to be better tuned to the practical simulation requirements. Moreover, the use of sensitivity methods can help to extract knowledge on the habitat preference of species and allow peer-review by ecological experts. The selection of relevant input variables remains a critical challenge as well. Model coupling is a missing crucial step to link human activities, hydrology, physical habitat conditions, water quality and ecosystem status. This last aspect is probably the most valuable aspect to enable decision support in water management based on ANN models.
ABSTRACT Iniencephaly is a rare congenital malformation consisting of a complex alteration of th... more ABSTRACT Iniencephaly is a rare congenital malformation consisting of a complex alteration of the embryonic development occurring around the third post-fertilization week and characterized by a hyper-retroflexion of the cephalic pole. We report a case of iniencephaly associated with acrania-encephalocele, spina bifida and abnormal ductus venosus in a fetus with trisomy 18 diagnosed at 12 week's gestation in a 41-year-old woman. A co-occurrence between aneuploidy and iniencephaly was documented and polymorphisms on folate metabolism-related genes were investigated in the parents to assess possible etiologic factors and recurrence risk for neural tube defects (NTD). An homozygous state for the MTRR polymorphism was diagnosed in the mother, identifying a clinical risk for NTD. Once iniencephaly or any other NTD are suspected, genetic analysis, second level ultrasound and fetal karyotype are recommended. Autopsy should also be performed in all cases of early ultrasound-based diagnosis of fetal malformations.
The effect of environmental conditions on river macrobenthic communities was studied using a data... more The effect of environmental conditions on river macrobenthic communities was studied using a dataset consisting of 343 sediment samples from unnavigable watercourses in Flanders, Belgium. Artificial neural network models were used to analyse the relation among river characteristics and macrobenthic communities. The dataset included presence or absence of macroinvertebrate taxa and 12 physicochemical and hydromorphological variables for each sampling site. The abiotic variables served as input for the artificial neural networks to predict the macrobenthic community. The effects of the input variables on model performance were assessed in order to identify the most diagnostic river characteristics for macrobenthic community composition. This was done by consecutively eliminating the least important variables and, when beneficial for model performance, adding previously removed ones again. This stepwise input variable selection procedure was tested not only on a model predicting the entire macrobenthic community, but also on three models, each predicting an individual taxon. Additionally, during each step of the stepwise leave-one-out procedure, a sensitivity analysis was performed to determine the response of the predicted macroinvertebrate taxa to the input variables applied. This research illustrated that a combination of input variable selection with sensitivity analyses can contribute to the development of reliable and ecologically relevant ANN models. The river characteristics predicting presence or absence of the benthic macroinvertebrates best were the Julian day, conductivity, and dissolved oxygen content. These conditions reflect the importance of discharges of untreated wastewater that occurred during the period of investigation in nearly all Flemish rivers.
Although various models have been developed for prediction and forecasting of time series in vari... more Although various models have been developed for prediction and forecasting of time series in various engineering fields, there is no perfect model to forecast hydrologic time series. In recent decades, Artificial Neural Networks (ANNs) have been very common for prediction and forecasting of hydrologic time series because of their practicality in applications. This study proposed a post-process in an ANN model to improve the forecasting performance by rescaling the errors based on a correlation between observations trained data. The model proposed in this study was examined using precipitation data achieved from different four stations in the United States, and compared with the feedforward networks. It was observed that all error measures used in this study were improved through a rescaling post-process in the model for all stations. The strong point of the model lies in that the correlation approach is very easy to apply.
Although the concept of endoscopy has existed for centuries, a practical, working neuroendoscopic... more Although the concept of endoscopy has existed for centuries, a practical, working neuroendoscopic system did not emerge until last century, as a result of numerous contributions and refinements in optical technology, illumination sources, and instrumentation. Modern neuroendoscopy would not be a flourishing field, as it is today, without the dedication, innovation, and implementation of emerging technology by key contributors including Maximilian Nitze, Walter Dandy, and Harold Hopkins. Despite several inherent and unique limitations, neuroendoscopic surgery is now performed for a variety of intraventricular, skull base, and spinal operations. In this review, the history of neuroendoscopy, key players who envisioned how the inner workings of the human body could be visualized "through the looking glass," and current state and future potential for neuroendoscopic surgery are discussed. Future directions of neuroendoscopic surgery will likely be guided by further miniaturization in camera and optical technology, innovations in surgical instrumentation design, the introduction of robotics, multi-port minimally invasive surgery, and an enhanced ability to perform bimanual microdissection.
To facilitate decision support in freshwater ecosystem protection and restoration management, hab... more To facilitate decision support in freshwater ecosystem protection and restoration management, habitat suitability models can be very valuable. Data driven methods such as artificial neural networks (ANNs) are particularly useful in this context, seen their time-efficient development and relatively high reliability. However, specialized and technical literature on neural network modelling offers a variety of model development criteria to select model architecture, training procedure, etc. This may lead to confusion among ecosystem modellers and managers regarding the optimal training and validation methodology. This paper focuses on the analysis of ANN development and application for predicting macroinvertebrate communities, a species group commonly used in freshwater assessment worldwide. This review reflects on the different aspects regarding model development and application based on a selection of 26 papers reporting the use of ANN models for the prediction of macroinvertebrates. This analysis revealed that the applied model training and validation methodologies can often be improved and moreover crucial steps in the modelling process are often poorly documented. Therefore, suggestions to improve model development, assessment and application in ecological river management are presented. In particular, data pre-processing determines to a high extent the reliability of the induced models and their predictive relevance. This also counts for the validation criteria, that need to be better tuned to the practical simulation requirements. Moreover, the use of sensitivity methods can help to extract knowledge on the habitat preference of species and allow peer-review by ecological experts. The selection of relevant input variables remains a critical challenge as well. Model coupling is a missing crucial step to link human activities, hydrology, physical habitat conditions, water quality and ecosystem status. This last aspect is probably the most valuable aspect to enable decision support in water management based on ANN models.
ABSTRACT Iniencephaly is a rare congenital malformation consisting of a complex alteration of th... more ABSTRACT Iniencephaly is a rare congenital malformation consisting of a complex alteration of the embryonic development occurring around the third post-fertilization week and characterized by a hyper-retroflexion of the cephalic pole. We report a case of iniencephaly associated with acrania-encephalocele, spina bifida and abnormal ductus venosus in a fetus with trisomy 18 diagnosed at 12 week's gestation in a 41-year-old woman. A co-occurrence between aneuploidy and iniencephaly was documented and polymorphisms on folate metabolism-related genes were investigated in the parents to assess possible etiologic factors and recurrence risk for neural tube defects (NTD). An homozygous state for the MTRR polymorphism was diagnosed in the mother, identifying a clinical risk for NTD. Once iniencephaly or any other NTD are suspected, genetic analysis, second level ultrasound and fetal karyotype are recommended. Autopsy should also be performed in all cases of early ultrasound-based diagnosis of fetal malformations.
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