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An investigation was undertaken during rabi-summer season of 2016-17 in the Department of Seed Science and Technology, OUAT, Bhubaneswar to study the efficiency of some osmoprotectants in mitigating high temperature stress by improving growth, seed yield and quality of rice cv. Naveen. The field experiment was conducted in Split Plot Design, with three replications. The main plot factor was three dates of sowing (D1 - 30th November, D2 - 15th December and D3 - 30th December). The sub-plot factor was foliar spray of chemicals at vegetative and seed filling stages: Control (T0), 600 ppm Glycine betaine (T1), 400 ppm Salicylic acid (T2), 800 ppm Salicylic acid (T3), 10 ppm Ascorbic acid + 1.3% Citric acid (T4), 150 ppm α-Tocopherol (T5), 1.0% Potassium chloride (T6), 5 ppm Brassinolides (T7) and 10 ppm Brassinolides (T8). Observations on various phenological, seed yield and quality parameters were recorded. With advancement in sowing time, there was significant decrease in the chlorophyll content of leaves. The chlorophyll content of leaves after two days of spraying as slightly higher than that prior to spraying in all the treatments. Maximum chlorophyll content was recorded with spray treatment of 800 ppm salicylic acid at both the stages of observation. Significantly higher pollen viability and seed set were recorded with first date of sowing (D1), compared to second and third dates of sowing (D2 and D3, respectively). Among the treatments, pollen viability was highest with 10 ppm brassinolides and 400 ppm salicylic acid. Seed set was highest with 10 ppm brassinolides and 800 ppm salicylic acid. With advancement in sowing time, there was decrease in membrane stability index of leaves. The MSI of leaves after two days of spraying was slightly higher than that prior to spraying in all treatments. Maximum MSI was recorded with spray treatment of 400 ppm salicylic acid (T2) during vegetative and seed filling stages. The treatments T2, T4 and T8 produced highest increase in MSI compared to that before spraying during both stages. Higher seed yield was obtained from D1, compared to delayed sowing. Mean seed yield from D1 was 53.76 q/ha, as against 48.99 q/ha and 44.51 q/ha from D2 and D3, respectively. Spraying of 800 ppm salicylic acid and 10 ppm brassinolides gave highest seed yields from each date of sowing. Seed yields from D3T0 and D2T0 (35.33 q/ha and 39.72 q/ha, respectively) were significantly lower than all treatment combinations, including D1T0 (51.83 q/ha). Increase in seed yields due to various treatments as against the control was more pronounced in D2 and D3. The first date of sowing (D1) produced seeds with higher 1000-seed weight (22.62 g), compared to D2 and D3 (20.27 g and 19.36 g, respectively). Among the treatments, higher 1000-seed weights were recorded with the treatments T3 and T8 (21.51 g and 21.43 g, respectively), while lowest 1000-seed weight (18.76 g) was recorded with the Control. Seeds produced from D1 recorded higher germination and seed vigour. All the treatments recorded improvement in germination and vigour of seeds with three dates of sowing. Treatments T3, T7 and T8 gave highest improvement in seed germination over control. Under accelerated ageing treatment, T3 recorded highest germination followed by T8. Dates of sowing and treatment sprays had no influence on electrical conductivity of seed leachate. Hence, the present study demonstrated that foliar sprays at vegetative and seed filling stages with 800 ppm salicylic acid or 10 ppm ascorbic acid + 1.3% citric acid or 10 ppm brassinolides were quite effective in alleviating adverse effects of high temperature during flowering and seed set, through increased chlorophyll content of leaves, improved membrane stability, higher pollen viability and seed set, thus improving seed yield and quality of rice. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to decode intended and overt speech from neuromagnetic signals while the participants performed spontaneous overt speech tasks without cues or prompts (stimuli)., Method: Magnetoencephalography (MEG), a noninvasive neuroimaging technique, was used to collect neural signals from seven healthy adult English speakers performing spontaneous, overt speech tasks. The participants randomly spoke the words yes or no at a self-paced rate without cues. Two machine learning models, namely, linear discriminant analysis (LDA) and one-dimensional convolutional neural network (1D CNN), were employed to classify the two words from the recorded MEG signals., Results: LDA and 1D CNN achieved average decoding accuracies of 79.02% and 90.40%, respectively, in decoding overt speech, significantly surpassing the chance level (50%). The accuracy for decoding intended speech was 67.19% using 1D CNN., Conclusions: This study showcases the possibility of decoding spontaneous overt and intended speech directly from neural signals in the absence of perceptual interference. We believe that these findings make a steady step toward the future spontaneous speech-based brain-computer interface.

Neural speech decoding aims at providing natural rate communication assistance to patients with locked-in state (e.g. due to amyotrophic lateral sclerosis, ALS) in contrast to the traditional brain-computer interface (BCI) spellers which are slow. Recent studies have shown that Magnetoencephalography (MEG) is a suitable neuroimaging modality to study neural speech decoding considering its excellent temporal resolution that can characterize the fast dynamics of speech. Gradiometers have been the preferred choice for sensor space analysis with MEG, due to their efficacy in noise suppression over magnetometers. However, recent development of optically pumped magnetometers (OPM) based wearable-MEG devices have shown great potential in future BCI applications, yet, no prior study has evaluated the performance of magnetometers in neural speech decoding. In this study, we decoded imagined and spoken speech from the MEG signals of seven healthy participants and compared the performance of magnetometers and gradiometers. Experimental results indicated that magnetometers also have the potential for neural speech decoding, although the performance was significantly lower than that obtained with gradiometers. Further, we implemented a wavelet based denoising strategy that improved the performance of both magnetometers and gradiometers significantly. These findings reconfirm that gradiometers are preferable in MEG based decoding analysis but also provide the possibility towards the use of magnetometers (or OPMs) for the development of the next-generation speech-BCIs.

Advancing the knowledge about neural speech mechanisms is critical for developing next-generation, faster brain computer interface to assist in speech communication for the patients with severe neurological conditions (e.g., locked-in syndrome). Among current neuroimaging techniques, Magnetoencephalography (MEG) provides direct representation for the large-scale neural dynamics of underlying cognitive processes based on its optimal spatiotemporal resolution. However, the MEG measured neural signals are smaller in magnitude compared to the background noise and hence, MEG usually suffers from a low signal-to-noise ratio (SNR) at the single-trial level. To overcome this limitation, it is common to record many trials of the same event-task and use the time-locked average signal for analysis, which can be very time consuming. In this study, we investigated the effect of the number of MEG recording trials required for speech decoding using a machine learning algorithm. We used a wavelet filter for generating the denoised neural features to train an Artificial Neural Network (ANN) for speech decoding. We found that wavelet based denoising increased the SNR of the neural signal prior to analysis and facilitated accurate speech decoding performance using as few as 40 single-trials. This study may open up the possibility of limiting MEG trials for other task evoked studies as well.

Decoding speech directly from the brain has the potential for the development of the next generation, more efficient brain computer interfaces (BCIs) to assist in the communication of patients with locked-in syndrome (fully paralyzed but aware). In this study, we have explored the spectral and temporal features of the magnetoencephalography (MEG) signals and trained those features with convolutional neural networks (CNN) for the classification of neural signals corresponding to phrases. Experimental results demonstrated the effectiveness of CNNs in decoding speech during perception, imagination, and production tasks. Furthermore, to overcome the long training time issue of CNNs, we leveraged principal component analysis (PCA) for spatial dimension reduction of MEG data and transfer learning for model initialization. Both PCA and transfer learning were found to be highly beneficial for faster model training. The best configuration (50 principal coefficients + transfer learning) led to more than 10 times faster training than the original setting while the speech decoding accuracy remained at a similarly high level.