SCE-LSTM: Sparse Critical Event-Driven LSTM Model with Selective Memorization for Agricultural Time-Series Prediction

In the domain of agricultural product sales and consumption forecasting, the presence of infrequent yet impactful events such as livestock epidemics and mass media influences poses substantial challenges. These rare occurrences, termed Sparse Critical Events (SCEs), often lead to predictions converging towards average values due to their omission from input candidate vectors. To address this issue, we introduce a modified Long Short-Term Memory (LSTM) model designed to selectively attend to and memorize critical events, emulating the human memory’s ability to retain crucial information. In contrast to the conventional LSTM model, which struggles with learning sparse critical event sequences due to its handling of forget gates and input vectors within the cell state, our proposed approach identifies and learns from sparse critical event sequences during data training. This proposed method, referred to as sparse critical event-driven LSTM (SCE-LSTM), is applied to predict purchase quantities of agricultural and livestock products using sharp-changing agricultural time-series data. For these predictions, we collected structured and unstructured data spanning the years 2010 to 2017 and developed the SCE-LSTM prediction model. Our model forecasts monetary expenditures for pork purchases over a one-month horizon. Notably, our results demonstrate that SCE-LSTM provides the closest predictions to actual daily pork purchase expenditures and exhibits the lowest error rates when compared to other prediction models. SCE-LSTM emerges as a promising solution to enhance agricultural product sales and consumption forecasts, particularly in the presence of rare critical events. Its superior performance and accuracy, as evidenced by our findings, underscore its potential significance in this domain

t-Butyl pyridine and phenyl C-region analogues of 2-(3-fluoro-4-methylsulfonylaminophenyl)propanamides as potent TRPV1 antagonists

A series of 2-substituted 6-t-butylpyridine and 4-t-butylphenyl C-region analogues of 2-(3-fluoro-4-methylsulfonamidophenyl)propanamides were investigated for hTRPV1 antagonism. The analysis of structure activity relationships indicated that the pyridine derivatives generally exhibited a little better antagonism than did the corresponding phenyl surrogates for most of the series. Among the compounds, compound 7 showed excellent antagonism toward capsaicin activation with K-i = 0.1 nM and compound 60S demonstrated a strong antiallodynic effect with 83% MPE at 10 mg/kg in the neuropathic pain model. The docking study of 7S in our hTRPV1 homology model indicated that the interactions between the AFB-regions of 7S with Tyr511 and the interactions between the t-butyl and ethyl groups in the C-region of 7S with the two hydrophobic binding pockets of hTRPV1 contributed to the high potency. (C) 2017 Elsevier Ltd. All rights reserved.OAIID:RECH_ACHV_DSTSH_NO:T201724901RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A003772CITE_RATE:2.881DEPT_NM:약학과EMAIL:[email protected]_YN:YN