Investigating the role of body force or strength (Quwa) in causing diseases in Iranian traditional medicine and the role of cellular energy in causing diseases in conventional medicine.

Although much of conventional medicine has its roots in traditional medicine, from the point of view of Iranian medicine, health means when organs function naturally, and disease occurs when organs cannot performproperly. Doing actions naturally requires force, some actions require one force and some require two or more. The forces of the human body include the natural forces, the spiritual forces, and the vital forces, with the help of which human actions are performed. Therefore, in order to perform actions normally, a person must have all the forces of his body in a proper situation. According to the principles of modern medicine, the function of the body and cells depends on the energy content of cells, and most of the chemical reactions in the cells are related to the availability of energy in foods for various cell physiological systems. Any event that leads to a drop in energy production or energy loss or a reduction in cells' access to energy can lead to a range of related diseases. On the other hand, if the body cells have enough access to energy and perform all functions well, the disease will not occur and the ability to fight possible diseases will be higher. The aim of this study was to investigate the role of potency in causing diseases in Iranian medicine and the role of cellular energy in causing diseases in conventional medicine. It was concluded that all principles of health refer to the optimization of energy production and consumption in the cells. Accordingly, more energy available to the cell leads to the normal function of cells and the higher ability of the body cells to fight disease.

Estimating and interpreting nonlinear receptive field of sensory neural responses with deep neural network models.

Our understanding of nonlinear stimulus transformations by neural circuits is hindered by the lack of comprehensive yet interpretable computational modeling frameworks. Here, we propose a data-driven approach based on deep neural networks to directly model arbitrarily nonlinear stimulus-response mappings. Reformulating the exact function of a trained neural network as a collection of stimulus-dependent linear functions enables a locally linear receptive field interpretation of the neural network. Predicting the neural responses recorded invasively from the auditory cortex of neurosurgical patients as they listened to speech, this approach significantly improves the prediction accuracy of auditory cortical responses, particularly in nonprimary areas. Moreover, interpreting the functions learned by neural networks uncovered three distinct types of nonlinear transformations of speech that varied considerably from primary to nonprimary auditory regions. The ability of this framework to capture arbitrary stimulus-response mappings while maintaining model interpretability leads to a better understanding of cortical processing of sensory signals.

Towards reconstructing intelligible speech from the human auditory cortex.

Auditory stimulus reconstruction is a technique that finds the best approximation of the acoustic stimulus from the population of evoked neural activity. Reconstructing speech from the human auditory cortex creates the possibility of a speech neuroprosthetic to establish a direct communication with the brain and has been shown to be possible in both overt and covert conditions. However, the low quality of the reconstructed speech has severely limited the utility of this method for brain-computer interface (BCI) applications. To advance the state-of-the-art in speech neuroprosthesis, we combined the recent advances in deep learning with the latest innovations in speech synthesis technologies to reconstruct closed-set intelligible speech from the human auditory cortex. We investigated the dependence of reconstruction accuracy on linear and nonlinear (deep neural network) regression methods and the acoustic representation that is used as the target of reconstruction, including auditory spectrogram and speech synthesis parameters. In addition, we compared the reconstruction accuracy from low and high neural frequency ranges. Our results show that a deep neural network model that directly estimates the parameters of a speech synthesizer from all neural frequencies achieves the highest subjective and objective scores on a digit recognition task, improving the intelligibility by 65% over the baseline method which used linear regression to reconstruct the auditory spectrogram. These results demonstrate the efficacy of deep learning and speech synthesis algorithms for designing the next generation of speech BCI systems, which not only can restore communications for paralyzed patients but also have the potential to transform human-computer interaction technologies.

Evaluation of the cytotoxicity of Satureja spicigera and its main compounds.

Satureja spicigera (Lamiaceae) grows wildly in Northwest of Iran. In this study, bioassay-guided isolation and identification of the main compounds has been reported using various chromatographic methods and comparison of their spectral data with those reported in the literature. Brine shrimp lethality and four cancerous cell lines HT29/219, Caco(2), NIH-3T3, and T47D were used for cytotoxicity evaluations. From the aerial parts of S. spicigera, nine known compounds including two flavanones, 5,7,3',5'-tetrahydroxy flavanone (8) and 5,4'-dihydroxy-3'-methoxyflavanone-7-(6''-O-α-L-rhamnopyranosyl)-ß-D-glucopyranoside (9), one dihydrochalcone, nubigenol (7), together with thymoquinone (1), thymol (2), carvacrol (3), ß-sitosterol (4), ursolic acid (5) and oleanolic acid (6) were identified. Among the isolated chalcone and flavanones, compound 8 was effective against Artemia salina larva (LC(50)= 2 µg/mL) and only the compound 9 demonstrated IC(50) value of 98.7 µg/mL on the T47D (human, breast, ductal carcinoma). Other compounds did not show significant inhibition of the cell growth.