Estimation of biophysical parameters in wheat crops in Golestan province using ultra-high resolution images

Due to various factors which impress wheat growth variability, we need short–term monitoring of biophysical parameters using ultra–high resolution images. These provide an ability to monitor crops at the individual plant level. Two flight missions were carried out at altitude of 40 m with a commercial quad copter and a commercial camera. The images were taken before and after tillage over an 8.8 ha field. The 2 cm orthoimages and surface models were generated using photogrammetric software. Then, the image variables including ratio of the blue and green band, ratio of the red and blue band,ratio of the red and green band, plant height were extracted from orthoimages and surface models. Field measurement included the leaf area index, plant height and biomass for 15 plots each of area 1 m × 1 m. Due to a linear relationship between the biophysical parameters and image variables, it was used a multivariate regression model for modelling. The model using the image variables resulted in coefficient of determination (R²) of 0.95 and the lowest error measures (RMSE = 0.24). The results show that ultra–high resolution images can be used for monitoring of biophysical parameters in wheat crops but it is limited for large area.     

Effects of melatonin supplementation on disease activity,oxidative stress,inflammatory, and metabolic parameters in patients with rheumatoid arthritis: a randomized double-blind placebo-controlled trial

Clinical Rheumatology - Considering the pathologic significance of inflammation and oxidative stress in rheumatoid arthritis (RA) as well as the antioxidant, anti-inflammatory and hypolipidemic...     

Administration of Selenium Nanoparticles Reverses Streptozotocin-Induced Neurotoxicity in the male rats

Metabolic Brain Disease - Alzheimer’s disease is the most common neurodegenerative disease associated with deposition of amyloid-beta and the increased oxidative stress. High free radical...     

Efficacy of tight control strategy in the treatment of adult-onset Still disease

Clinical Rheumatology - Adult-onset Still’s disease (AOSD) characterized by a high spiking fever, skin rash, arthritis, and leukocytosis. The aim of the present study was considering the...     

Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach

Purpose

Functional magnetic resonance imaging (fMRI) in resting state can be used to evaluate the functional organization of the human brain in the absence of any task or stimulus. The functional connectivity (FC) has non-stationary nature and consented to be varying over time. By considering the dynamic characteristics of the FC and using graph theoretical analysis and a machine learning approach, we aim to identify the laterality in cases of temporal lobe epilepsy (TLE).

Methods

Six global graph measures are extracted from static and dynamic functional connectivity matrices using fMRI data of 35 unilateral TLE subjects. Alterations in the time trend of the graph measures are quantified. The random forest (RF) method is used for the determination of feature importance and selection of dynamic graph features including mean, variance, skewness, kurtosis, and Shannon entropy. The selected features are used in the support vector machine (SVM) classifier to identify the left and right epileptogenic sides in patients with TLE.

Results

Our results for the performance of SVM demonstrate that the utility of dynamic features improves the classification outcome in terms of accuracy (88.5% for dynamic features compared with 82% for static features). Selecting the best dynamic features also elevates the accuracy to 91.5%.

Conclusion

Accounting for the non-stationary characteristics of functional connectivity, dynamic connectivity analysis of graph measures along with machine learning approach can identify the temporal trend of some specific network features. These network features may be used as potential imaging markers in determining the epileptogenic hemisphere in patients with TLE.

     

A novel non-linear recursive filter design for extracting high rate pulse features in nuclear medicine imaging and spectroscopy

Applications in imaging and spectroscopy rely on pulse processing methods for appropriate data generation. Often, the particular method utilized does not highly impact data quality, whereas in some scenarios, such as in the presence of high count rates or high frequency pulses, this issue merits extra consideration. In the present study, a new approach for pulse processing in nuclear medicine imaging and spectroscopy is introduced and evaluated. The new non-linear recursive filter (NLRF) performs nonlinear processing of the input signal and extracts the main pulse characteristics, having the powerful ability to recover pulses that would ordinarily result in pulse pile-up. The filter design defines sampling frequencies lower than the Nyquist frequency.In the literature, for systems involving NaI(Tl) detectors and photomultiplier tubes (PMTs), with a signal bandwidth considered as 15 MHz, the sampling frequency should be at least 30 MHz (the Nyquist rate), whereas in the present work, a sampling rate of 3.3 MHz was shown to yield very promising results. This was obtained by exploiting the known shape feature instead of utilizing a general sampling algorithm. The simulation and experimental results show that the proposed filter enhances count rates in spectroscopy. With this filter, the system behaves almost identically as a general pulse detection system with a dead time considerably reduced to the new sampling time (300 ns). Furthermore, because of its unique feature for determining exact event times, the method could prove very useful in time-of-flight PET imaging.