Prediction of fruit rot disease incidence in Arecanut based on weather parameters

Received: July 19th, 2022 ; Accepted: October 20th, 2022 ; Published: November 22nd, 2022 ; Correspondence: [email protected] occurrence of pests and diseases in arecanut crops has always been an important factor affecting the total production of arecanut. Arecanut is always dependent on environmental factors during its growth. Thus monitoring and early prediction of the occurrence of the disease would be very helpful for prevention and therefore more crop production. Here, we propose artificial intelligence-based deep learning models for fruit rot disease prediction. Historical data on fruit rot incidence in representative areas of arecanut production in Udupi along with historical weather data are the parameters used to develop region-specific models for the Udupi district. The fruit rot disease incidence score value is predicted using recurrent neural network variants (i.e., Vanilla LSTM, Vanilla GRU, stacked LSTM, and Bidirectional LSTM) for the first time. The predictive performance of the proposed models is evaluated by mean square error (MSE) along with the 5-fold cross-validation technique. Further, compared to other deep learning and machine learning models, the Vanilla LSTM model gives 1.5 MSE, while the Vanilla GRU model gives 1.3 MSE making it the best prediction model for arecanut fruit rot disease

Utility and lower limits of frequency detection in surface electrode stimulation for somatosensory brain-computer interface in humans

Objective: Stimulation of the primary somatosensory cortex (S1) has been successful in evoking artificial somatosensation in both humans and animals, but much is unknown about the optimal stimulation parameters needed to generate robust percepts of somatosensation. In this study, the authors investigated frequency as an adjustable stimulation parameter for artificial somatosensation in a closed-loop brain-computer interface (BCI) system. Methods: Three epilepsy patients with subdural mini-electrocorticography grids over the hand area of S1 were asked to compare the percepts elicited with different stimulation frequencies. Amplitude, pulse width, and duration were held constant across all trials. In each trial, subjects experienced 2 stimuli and reported which they thought was given at a higher stimulation frequency. Two paradigms were used: first, 50 versus 100 Hz to establish the utility of comparing frequencies, and then 2, 5, 10, 20, 50, or 100 Hz were pseudorandomly compared. Results: As the magnitude of the stimulation frequency was increased, subjects described percepts that were “more intense” or “faster.” Cumulatively, the participants achieved 98.0% accuracy when comparing stimulation at 50 and 100 Hz. In the second paradigm, the corresponding overall accuracy was 73.3%. If both tested frequencies were less than or equal to 10 Hz, accuracy was 41.7% and increased to 79.4% when one frequency was greater than 10 Hz (p = 0.01). When both stimulation frequencies were 20 Hz or less, accuracy was 40.7% compared with 91.7% when one frequency was greater than 20 Hz (p < 0.001). Accuracy was 85% in trials in which 50 Hz was the higher stimulation frequency. Therefore, the lower limit of detection occurred at 20 Hz, and accuracy decreased significantly when lower frequencies were tested. In trials testing 10 Hz versus 20 Hz, accuracy was 16.7% compared with 85.7% in trials testing 20 Hz versus 50 Hz (p < 0.05). Accuracy was greater than chance at frequency differences greater than or equal to 30 Hz. Conclusions: Frequencies greater than 20 Hz may be used as an adjustable parameter to elicit distinguishable percepts. These findings may be useful in informing the settings and the degrees of freedom achievable in future BCI systems

Hippocampal and Orbitofrontal Theta Band Coherence Diminishes During Conflict Resolution

Objective: Coherence between the hippocampus and other brain structures has been shown with the theta frequency (3–8 Hz). Cortical decreases in theta coherence are believed to reflect response accuracy efficiency. However, the role of theta coherence during conflict resolution is poorly understood in noncortical areas. In this study, coherence between the hippocampus and orbitofrontal cortex (OFC) was measured during a conflict resolution task. Although both brain areas have been previously implicated in the Stroop task, their interactions are not well understood. Methods: Nine patients were implanted with stereotactic electroencephalography contacts in the hippocampus and OFC. Local field potential data were sampled throughout discrete phases of a Stroop task. Coherence was calculated for hippocampal and OFC contact pairs, and coherence spectrograms were constructed for congruent and incongruent conditions. Coherence changes during cue processing were identified using a nonparametric cluster-permutation t test. Group analysis was conducted to compare overall theta coherence changes among conditions. Results: In 6 of 9 patients, decreased theta coherence was observed only during the incongruent condition (P < 0.05). Congruent theta coherence did not change from baseline. Group analysis showed lower theta coherence for the incongruent condition compared with the congruent condition (P < 0.05). Conclusions: Theta coherence between the hippocampus and OFC decreased during conflict. This finding supports existing theories that theta coherence desynchronization contributes to improved response accuracy and processing efficiency during conflict resolution. The underlying theta coherence observed between the hippocampus and OFC during conflict may be distinct from its previously observed role in memory

Utility and lower limits of frequency detection in surface electrode stimulation for somatosensory brain-computer interface in humans

Objective: Stimulation of the primary somatosensory cortex (S1) has been successful in evoking artificial somatosensation in both humans and animals, but much is unknown about the optimal stimulation parameters needed to generate robust percepts of somatosensation. In this study, the authors investigated frequency as an adjustable stimulation parameter for artificial somatosensation in a closed-loop brain-computer interface (BCI) system. Methods: Three epilepsy patients with subdural mini-electrocorticography grids over the hand area of S1 were asked to compare the percepts elicited with different stimulation frequencies. Amplitude, pulse width, and duration were held constant across all trials. In each trial, subjects experienced 2 stimuli and reported which they thought was given at a higher stimulation frequency. Two paradigms were used: first, 50 versus 100 Hz to establish the utility of comparing frequencies, and then 2, 5, 10, 20, 50, or 100 Hz were pseudorandomly compared. Results: As the magnitude of the stimulation frequency was increased, subjects described percepts that were “more intense” or “faster.” Cumulatively, the participants achieved 98.0% accuracy when comparing stimulation at 50 and 100 Hz. In the second paradigm, the corresponding overall accuracy was 73.3%. If both tested frequencies were less than or equal to 10 Hz, accuracy was 41.7% and increased to 79.4% when one frequency was greater than 10 Hz (p = 0.01). When both stimulation frequencies were 20 Hz or less, accuracy was 40.7% compared with 91.7% when one frequency was greater than 20 Hz (p < 0.001). Accuracy was 85% in trials in which 50 Hz was the higher stimulation frequency. Therefore, the lower limit of detection occurred at 20 Hz, and accuracy decreased significantly when lower frequencies were tested. In trials testing 10 Hz versus 20 Hz, accuracy was 16.7% compared with 85.7% in trials testing 20 Hz versus 50 Hz (p < 0.05). Accuracy was greater than chance at frequency differences greater than or equal to 30 Hz. Conclusions: Frequencies greater than 20 Hz may be used as an adjustable parameter to elicit distinguishable percepts. These findings may be useful in informing the settings and the degrees of freedom achievable in future BCI systems

Utilizing Light-field Imaging Technology in Neurosurgery

Traditional still cameras can only focus on a single plane for each image while rendering everything outside of that plane out of focus. However, new light-field imaging technology makes it possible to adjust the focus plane after an image has already been captured. This technology allows the viewer to interactively explore an image with objects and anatomy at varying depths and clearly focus on any feature of interest by selecting that location during post-capture viewing. These images with adjustable focus can serve as valuable educational tools for neurosurgical residents. We explore the utility of light-field cameras and review their strengths and limitations compared to other conventional types of imaging. The strength of light-field images is the adjustable focus, as opposed to the fixed-focus of traditional photography and video. A light-field image also is interactive by nature, as it requires the viewer to select the plane of focus and helps with visualizing the three-dimensional anatomy of an image. Limitations include the relatively low resolution of light-field images compared to traditional photography and video. Although light-field imaging is still in its infancy, there are several potential uses for the technology to complement traditional still photography and videography in neurosurgical education

Utilizing Light-field Imaging Technology in Neurosurgery

Traditional still cameras can only focus on a single plane for each image while rendering everything outside of that plane out of focus. However, new light-field imaging technology makes it possible to adjust the focus plane after an image has already been captured. This technology allows the viewer to interactively explore an image with objects and anatomy at varying depths and clearly focus on any feature of interest by selecting that location during post-capture viewing. These images with adjustable focus can serve as valuable educational tools for neurosurgical residents. We explore the utility of light-field cameras and review their strengths and limitations compared to other conventional types of imaging. The strength of light-field images is the adjustable focus, as opposed to the fixed-focus of traditional photography and video. A light-field image also is interactive by nature, as it requires the viewer to select the plane of focus and helps with visualizing the three-dimensional anatomy of an image. Limitations include the relatively low resolution of light-field images compared to traditional photography and video. Although light-field imaging is still in its infancy, there are several potential uses for the technology to complement traditional still photography and videography in neurosurgical education

Rare Variant Enrichment analysis Supports

Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome is characterized by aplasia of the female reproductive tract; the syndrome can include renal anomalies, absence or dysgenesis, and skeletal anomalies. While functional models have elucidated several candidate genes, onl