TitleCharacterization of transgenic cotton (Gossypium hirsutum L.) over-expressing Arabidopsis thaliana Related to ABA-insensitive3(ABI3)/Vivparous1 (AtRAV1) and AtABI5 transcription factors: improved water use efficiency through altered guard cell physio... Permalink

Beef volatile flavor compound (VFC)development at the center, mid, and surface layers of cooked steaks wasevaluated through eighteen cookery treatment combinations consisting of oven cookingtemperature (OT; 177°C, 246°C, and 343°C) and final internal steak temperature(IT; 57°C, 63°C, 68°C, 74°C, 79°C, 85°C). In total, seventy-two VFC were measuredrepresenting the Maillard reaction and lipid degradation. Five VFC wereimpacted by a three-way interaction of OT × IT × layer (P ≤ 0.030). TwoVFC were impacted by a two-way interaction OT × IT (P ≤ 0.010). SixteenVFC were impacted by a two-way interaction OT × layer (P ≤ 0.050). SixteenVFC were impacted by a two-way interaction IT × layer (P ≤ 0.050).Twenty VFC were impacted by main effect of layer (P ≤ 0.010). Eight VFCwere impacted by main effect of IT (P ≤ 0.050). Maillard compounds were formedprimarily at steak surfaces with a general increase in content with greater finalIT and OT to a lesser extent. Lipid derived compounds were diverse. Methyl estersand aldehydes were consistently in lower content at steak surfaces and primarilyfound within the inner portions of steaks. Conversely, certain alcohols and ketoneswere more prominent at steak surfaces. Development of compounds among layers wasalso consistently influenced by IT and OT. It may be concluded that flavor contributingcompounds vary among cooked beef steaks at different depths and cookery temperatures,such as OT and final IT, may be utilized to mediate the final volatile compoundcomposition. 

No abstract

Epilepsy is a chronic neurological disorder characterized by abnormal electrical activity in the brain, often leading to recurrent seizures. With approximately 50 million people worldwide affected by epilepsy, there is a pressing need for efficient and accurate methods to detect and diagnose seizures. Electroencephalogram (EEG) signals, which record the brain’s electrical activity, have emerged as a valuable tool in detecting epilepsy and other neurological disorders. Traditionally, the process of analyzing EEG signals for seizure detection has relied on manual inspection by experts, which is time-consuming, labor-intensive, and susceptible to human error. To address these limitations, researchers have turned to machine learning and deep learning techniques to automate the seizure detection process. In this work, we propose a novel method for epileptic seizure detection, leveraging the power of 1-D Convolutional layers in combination with Bidirectional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) and Average pooling Layer as a unit. The performance of the proposed model is verified on the Bonn dataset. Our proposed model offers significant advancements over existing approaches for the detection of epileptic seizures, particularly for 2, 3, 4, and 5-class classification tasks. To assess the robustness and generalizability of our proposed architecture, we employ 5-fold cross-validation. By dividing the dataset into five subsets and iteratively training and testing the model on different combinations of these subsets, we obtain robust performance measures, including accuracy, sensitivity, and specificity. We demonstrate the effectiveness of the proposed architecture in accurately detecting epileptic seizures from EEG signals by using EEG signals of varying lengths. The results indicate its potential as a reliable and efficient tool for automated seizure detection, paving the way for improved diagnosis and management of epilepsy.