A case study of energy,water and soil flow chains in an arid ecosystem

Little attention has been directed to the study of soil flow and the complex relationships among energy water and soil flow in terrestrial ecosystems. Soil plays an important role in arid ecosystems. After water soil is the second key factor in the development of an arid ecosystem since soil is the only part of the system capable of absorbing and storing water and nutrients during the hot and long summer period. The present work presents a case study of an ecological soil flow chain in an arid environment and analyses the relationship between this chain and the energy and water flow chains. The study was conducted at the Sde Boqer experiment site located in the northern Negev of Israel where average annual rainfall is 92 mn. Data collected during five consecutive years show that the soil movement process within the ecosystem studied cannot be considered as a purely physical phenomenon, but rather as a part of a complex system in which the burrowing and digging activity of Isopods and Porcupines plays an important role by providing disaggregated soil particles easy to remove by shallow flows. Although controlled by the spatial distribution of soil moisture the biological activity acts as a regulator of soil depth and thus of soil moisture. If this regulating role is deleted from the system a new ecosystem, more arid, can be expected to develop. It is therefore concluded that the study of state and flow variables of an arid ecosystem should consider altogether the water, soil, energy and mineral chains.     

Automated thermal imaging monitors the local response to cervical cancer brachytherapy

Malignant tumors have high metabolic and perfusion rates, which result in a unique temperature distribution as compared to healthy tissues. Here, we sought to characterize the thermal response of the cervix following brachytherapy in women with advanced cervical carcinoma. Six patients underwent imaging with a thermal camera before a brachytherapy treatment session and after a 7-day follow-up period. A designated algorithm was used to calculate and store the texture parameters of the examined tissues across all time points. We used supervised machine learning classification methods (K Nearest Neighbors and Support Vector Machine) and unsupervised machine learning classification (K-means). Our algorithms demonstrated a 100% detection rate for physiological changes in cervical tumors before and after brachytherapy. Thus, we showed that thermal imaging combined with advanced feature extraction could potentially be used to detect tissue-specific changes in the cervix in response to local brachytherapy for cervical cancer.