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Accurately predicting the production rate and estimated ultimate recovery (EUR) of shale oil wells is vital for efficient shale oil development. Although numerical simulations provide accurate predictions, their high time, data, and labor demands call for a swifter, yet precise, method. This study introduces the DuongeCNNeLSTM (D-C-L) model, which integrates a convolutional neural network (CNN) with a long short-term memory (LSTM) network and is grounded on the empirical Duong model for physical constraints. Compared to traditional approaches, the D-C-L model demonstrates superior precision, efficiency, and cost-effectiveness in predicting shale oil production. [ABSTRACT FROM AUTHOR]

As the second most abundant natural polymer, accounting for approximately 30% of the organic carbon in the biosphere, lignin plays an essential role in plant development. However, a high lignin content affects the nutritional quality of alfalfa (Medicago sativa L.), the most widely cultivated perennial legume forage crop. Histological analysis indicated that G-lignin and S-lignin were present in the stem, leaf, and petiole of alfalfa, and the deposition of lignin increased gradually in descending internodes. Neutral detergent fiber (NDF), acid detergent fiber (ADF), and acid detergent lignin (ADL) contents continually increased from the top to the bottom of the stem, and ADL content showed a similar trend in leaves. Alfalfa leaves and stems from five different nodes (1, 2, 4, 6, and 8) were used as materials to investigate molecular regulatory mechanisms in lignin synthesis by RNA sequencing. Respectively 8074 and 7752 differentially expressed genes (DEGs) were identified in leaves and stems, and 1694 DEGs were common to the two tissues. "Phenylpropanoid biosynthesis" was the most enriched pathway in both leaves and stems, and 134 key regulatory genes in lignin synthesis were identified by a weighted gene co-expression network analysis. The NAC family transcription factor MsNST1 gene was highly expressed in old leaf and stem tissues. The deposition pattern of G- and S-lignin differed among M. truncatula wild-type, nst1 mutants, and overexpression lines, and the transcription levels of lignin synthesis genes such as HCT, F5H, and COMT in these three materials also differed. These results suggest that MsNST1 affects lignin synthesis in alfalfa. These findings provide a genetic basis and abundant gene resources for further study of the molecular mechanisms of lignin synthesis, laying a foundation for low-lignin alfalfa breeding research. [ABSTRACT FROM AUTHOR]

Grain size and weight are key components of wheat yield. Exploitation of major underlying quantitative trait loci (QTL) can improve yield potential in wheat breeding. A recombinant inbred line (RIL) population was constructed to detect QTL for thousand-grain weight (TGW), grain length (GL) and grain width (GW) across eight environments. Genomic regions associated with grain size and grain weight were identified on chromosomes 4A and 6A using bulked segregant exome sequencing (BSE-Seq) analysis. After constructing genetic maps, six major QTL detected in at least four individual environments and in best linear unbiased estimator (BLUE) datasets, explained 7.50%-23.45% of the phenotypic variation. Except for QGl. cib-4A, the other five QTL were co-located in two regions, namely QTgw/Gw.cib-4A and QTgw/Gw/Gl.cib-6A. Interactions of these QTL were analyzed. Unlike QTgw/Gw/Gl.cib-6A, QTgw/Gw.cib-4A and QGl.cib-4A had no effect on grain number per spike (GNS). The QTL were validated in a second cross using Kompetitive Allele Specific PCR (KASP) markers. Since QTgw/Gw.cib-4A was probably a novel locus, it and the KASP markers reported here can be used in wheat breeding. TraesCS4A03G0191200 was predicted to be potential candidate gene for QTgw/Gw.cib-4A based on the sequence differences, spatiotemporal expression patterns, gene annotation and haplotype analysis. Our findings will be useful for fine mapping and for marker-assisted selection in wheat grain yield improvement. [ABSTRACT FROM AUTHOR]

Alfalfa is the most widely cultivated perennial legume forage crop worldwide. Drought is one of the major environmental factors influencing alfalfa productivity. However, the molecular mechanisms underlying alfalfa responses to drought stress are still largely unknown. This study identified a drought-inducible gene of unknown function, designated as Medicago sativa DROUGHT-INDUCED UNKNOWN PROTEIN 1 (MsDIUP1). MsDIUP1 was localized to the nucleus, chloroplast, and plasma membranes. Overexpression of MsDIUP1 in Arabidopsis resulted in increased tolerance to drought, with higher seed germination, root length, fresh weight, and survival rate than in wild-type (WT) plants. Consistently, analysis of MsDIUP1 over-expression (OE) alfalfa plants revealed that MsDIUP1 also increased tolerance to drought stress, accompanied by physiological changes including reduced malondialdehyde (MDA) content and increased osmoprotectants accumulation (free proline and soluble sugar), relative to the WT. In contrast, disruption of MsDIUP1 expression by RNA interference (RNAi) in alfalfa resulted in a droughthypersensitive phenotype, with a lower chlorophyll content, higher MDA content, and less osmoprotectants accumulation than that of the WT. Transcript profiling of alfalfa WT, OE, and RNAi plants during drought stress showed differential responses for genes involved in stress signaling, antioxidant defense, and osmotic adjustment. Taken together, these results reveal a positive role for MsDIUP1 in regulating drought tolerance. [ABSTRACT FROM AUTHOR]