Marijuana is one of the most commonly used illicit recreational drugs and is widely used for medicinal purposes. The psychoactive ingredient in marijuana is ∆9-tetrahydrocannabinol (∆9-THC), whereas the major non-psychoactive ingredient is cannabidiol (CBD). Here, we exposed zebrafish embryos to ∆9-THC or CBD for 5 hours during the critical stage of development known as gastrulation. Embryos were allowed to develop normally and were examined at 2 and 5 days post fertilization. THC and CBD treated embryos exhibited reduced heart rates, axial malformations and shorter trunks. Cannabinoid treatment altered synaptic activity at neuromuscular junctions (NMJs), and fluorescent labelling of primary and secondary motor neurons indicated a change in branching patterns and a reduction in the number of axonal branches in the trunk musculature. Furthermore, there were alterations in the α-bungarotoxin labelling of nicotinic acetylcholine receptors at NMJs. Locomotion studies show that larvae exposed to THC or CBD during gastrulation exhibited drastic reductions in the number of C-start escape responses to sound stimuli, but not to touch stimuli. Together these findings indicate that zebrafish embryos exposed to ∆9-THC or CBD during the brief but critical period of gastrulation exhibited alterations in heart rate, motor neuronal morphology, synaptic activity at the NMJ and locomotor responses to sound.

In recent decades, human brain tumor detection has become one of the most challenging issues in medical science. In this paper, we propose a model that includes the template-based K means and improved fuzzy C means (TKFCM) algorithm for detecting human brain tumors in a magnetic resonance imaging (MRI) image. In this proposed algorithm, firstly, the template-based K-means algorithm is used to initialize segmentation significantly through the perfect selection of a template, based on gray-level intensity of image; secondly, the updated membership is determined by the distances from cluster centroid to cluster data points using the fuzzy C-means (FCM) algorithm while it contacts its best result, and finally, the improved FCM clustering algorithm is used for detecting tumor position by updating membership function that is obtained based on the different features of tumor image including Contrast, Energy, Dissimilarity, Homogeneity, Entropy, and Correlation. Simulation results show that the proposed algorithm achieves better detection of abnormal and normal tissues in the human brain under small detachment of gray-level intensity. In addition, this algorithm detects human brain tumors within a very short time—in seconds compared to minutes with other algorithms.

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This paper investigates a hybrid satellite-terrestrial cooperative relaying communications network (HSTCN) under independent and non-identical shadowed Rician and Nakagami-m fading channels. It evaluates the performance of such a network using amplify-and-forward (AF) cooperative relaying protocol. The maximal ratio combining (MRC) technique is used at the destination to combine the signals received from the source and cooperating relay nodes. An analytical approach is derived to evaluate the performance of the system in terms of outage probability and symbol error rate (SER). The closed form expressions for the probability density function (PDF), cumulative distribution function (CDF) and moment generating function (MGF) of the end-to-end signal to noise ratio (SNR) are also derived. The derived analytical expressions are applied to the general operating conditions with the help of satellite channel data available from the literature. The analytical results are compared with Monte Carlo simulations. The results show the improvement in the performance of the HSTCN under challenging fading conditions with the help of fixed multiple terrestrial relay nodes

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