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Atomically thin two-dimensional transition metal dichalcogenides (TMDCs) have been regarded as ideal and promising nanomaterials that bring broad application prospects in extensive fields due to their ultrathin layered structure, unique electronic band structure, and multiple spatial phase configurations. TMDCs with different phase structures exhibit great diversities in physical and chemical properties. By regulating the phase structure, their properties would be modified to broaden the application fields. In this mini review, focusing on the most widely concerned molybdenum dichalcogenides (MoX2: X = S, Se, Te), we summarized their phase structures and corresponding electronic properties. Particularly, the mechanisms of phase transformation are explained, and the common methods of phase regulation or phase stabilization strategies are systematically reviewed and discussed. We hope the review could provide guidance for the phase regulation of molybdenum dichalcogenides nanomaterials, and further promote their real industrial applications. [ABSTRACT FROM AUTHOR]

The Japanese encephalitis virus (JEV) is a mosquito-borne flavivirus that causes viral encephalitis in humans, pigs and other mammals across Asia and the Western Pacific. Genetic screening tools such as CRISPR screening, DNA sequencing and RNA interference have greatly improved our understanding of JEV replication and its potential antiviral approaches. However, information on exon and intron mutations associated with JEV replication is still scanty. CRISPR-Cas9-mediated cytosine base editing can efficiently generate C: G-to-T: A conversion in the genome of living cells. One intriguing application of base editing is to screen pivotal variants for gene function that is yet to be achieved in pigs. Here, we illustrate that CRISPR-Cas9-mediated cytosine base editor, known as AncBE4max, can be used for the functional analysis of calreticulin (CALR) variants. We conducted a CRISPR-Cas9-mediated cytosine base editing screen using 457 single guide RNAs (sgRNAs) against all exons and introns of CALR to identify loss-of-function variants involved in JEV replication. We unexpectedly uncovered that two enriched sgRNAs targeted the same site in intron-2 of the CALR gene. We found that mutating four consecutive G bases in the intron-2 of the CALR gene to four A bases significantly inhibited JEV replication. Thus, we established a CRISPR-Cas9-mediated cytosine-base-editing point mutation screening technique in pigs. Our results suggest that CRISPR-mediated base editing is a powerful tool for identifying the antiviral functions of variants in the coding and noncoding regions of the CALR gene. [ABSTRACT FROM AUTHOR]

Managed pressure drilling (MPD) is the most effective means to ensure drilling safety, and MPD is able to avoid further deterioration of complex working conditions through precise control of the wellhead back pressure. The key to the success of MPD is the well control strategy, which currently relies heavily on manual experience, hindering the automation and intelligence process of well control. In response to this issue, an MPD knowledge graph is constructed in this paper that extracts knowledge from published papers and drilling reports to guide well control. In order to improve the performance of entity extraction in the knowledge graph, a few-shot Chinese entity recognition model CEntLM-KL is extended from the EntLM model, in which the KL entropy is built to improve the accuracy of entity recognition. Through experiments on benchmark datasets, it has been shown that the proposed model has a significant improvement compared to the state-of-the-art methods. On the few-shot drilling datasets, the F-1 score of entity recognition reaches 33%. Finally, the knowledge graph is stored in Neo4J and applied for knowledge inference. [ABSTRACT FROM AUTHOR]

Kicks can lead to well control risks during petroleum drilling, and even more serious kicks may lead to serious casualties, which is the biggest threat factor affecting the safety in the process of petroleum drilling. Therefore, how to detect kicks early and efficiently has become a focus practical problem. Traditional machine learning models require a large amount of labeled data, such kicked sample, and it is difficult to label data, which requires a lot of labor and time. To address the above issues, the deep forest is extended to a transfer learning model to improve the generalization ability. In this paper, a transfer learning model is built to detect kicks early. The source domain model adopts the deep forest model. Deep forest is an ensemble learning model with a hierarchical structure similar to deep learning. Each layer contains a variety of random forests. It is an integration of the model in depth and breadth. In the case of a small sample size (20–60 min), kick can be identified 10 min in advance. The deep forest model is established as the source domaining model, and a cascade forest is added at the last layer according to the transfer learning algorithm to form the classification model of this paper. The experimental results show that the kick prediction accuracy of the model is 80.13% by a confusion matrix. In the target domain, the proposed model performs better than other ensemble learning algorithms, and the accuracy is 5% lower than other SOTA transfer learning algorithms. [ABSTRACT FROM AUTHOR]

Oil drilling has always been considered a vital part of resource exploitation, and during which overflow is the most common and tricky threat that may cause blowout, a catastrophic accident. Therefore, to prevent further damage, it is necessary to detect overflow as early as possible. However, due to the unbalanced distribution and the lack of labeled data, it is difficult to design a suitable solution. To address this issue, an improved Transformer Framework based on self-supervised learning is proposed in this paper, which can accurately detect overflow 20 min in advance when the labeled data are limited and severely imbalanced. The framework includes a self-supervised pre-training scheme, which focuses on long-term time dependence that offers performance benefits over fully supervised learning on downstream tasks and makes unlabeled data useful in the training process. Next, to better extract temporal features and adapt to multi-task training process, a Transformer-based auto-encoder with temporal convolution layer is proposed. In the experiment, we used 20 min data to detect overflow in the next 20 min. The results show that the proposed framework can reach 98.23% accuracy and 0.84 F1 score, which is much better than other methods. We also compare several modifications of our framework and different pre-training tasks in the ablation experiment to prove the advantage of our methods. Finally, we also discuss the influence of important hyperparameters on efficiency and accuracy in the experiment. [ABSTRACT FROM AUTHOR]

Oil drilling is the core process of oil and natural gas resources exploitation. Well overflow is one of the biggest threats to safety drilling. Prediction of the overflow in advance can effectively avoid the occurrence of this kind of accident. However, the drilling history has unbalanced distribution, and labeling data is a time-consuming and laborious job. To address this issue, an overflow-prediction algorithm based on semi-supervised learning is designed in this paper, which can accurately predict overflow 10 min in advance when the labeled data are limited. Firstly, a three-step feature-selection algorithm is conducted to extract 22 features, and the time series samples are constructed through a 500-width sliding window with step size 1. Then, the Mean Teacher model with Jitter noise is employed to train the labeled and unlabeled data at the same time, in which a fused CNN-LSTM network is built for time-series prediction. Compared with supervised learning and other semi-supervised learning frameworks, the results show that the proposed model based on only 200 labeled samples is able to achieve the same effect as supervised learning method using 1000 labeled samples, and the prediction accuracy can reach 87.43% 10 min in advance. With the increase in the proportion of unlabeled samples, the performance of the model can sustain a rise within a certain range. [ABSTRACT FROM AUTHOR]

Under the idea of low carbon economy, natural gas has drawn widely attention all over the world and becomes one of the fastest growing energies because of its clean, high calorific value, and environmental protection properties. However, policy and political factors, supply-demand relationship and hurricanes can cause the jump in natural gas prices volatility. To address this issue, a deep learning model based on oil and gas news is proposed to predict natural gas price trends in this paper. In this model, news text embedding is conducted by BERT-Base, Uncased on natural gas-related news. Attention model is adopted to balance the weight of the news vector. Meanwhile, corresponding natural gas price embedding is conducted by a BiLSTM module. The Attention-weighted news vectors and price embedding are the inputs of the fused network with transformer is built. BiLSTM is used to extract used price information related with news features. Transformer is employed to capture time series trend of mixed features. Finally, the network achieves an accuracy as 79%, and the performance is better than most traditional machine learning algorithms. [ABSTRACT FROM AUTHOR]

Genetically modified pigs have shown considerable application potential in the fields of life science research and livestock breeding. Nevertheless, a barrier impedes the production of genetically modified pigs. There are too few safe harbor loci for the insertion of foreign genes into the pig genome. Only a few loci (pRosa26, pH11 and Pifs501) have been successfully identified to achieve the ectopic expression of foreign genes and produce gene-edited pigs. Here, we use CRISPR/Cas9-mediated homologous directed repair (HDR) to accurately knock the exogenous gene-of-interest fragments into an endogenous CKM gene in the porcine satellite cells. After porcine satellite cells are induced to differentiate, the CKM gene promoter simultaneously initiates the expression of the CKM gene and the exogenous gene. We infer preliminarily that the CKM gene can be identified as a potential muscle-specific safe harbor locus in pigs for the integration of exogenous gene-of-interest fragments. [ABSTRACT FROM AUTHOR]

The ectopic overexpression of foreign genes in animal genomes is an important strategy for gain-of-function study and establishment of transgenic animal models. Previous studies showed that two loci (Rosa26 and pH11) were identified as safe harbor locus in pig genomes, which means foreign genes can be integrated into this locus for stable expression. Moreover, integration of a transgene may interfere with the endogenous gene expression of the target locus after the foreign fragments are inserted. Here, we provide a new strategy for efficient transgene knock-in in the endogenous GAPDH gene via CRISPR/Cas9 mediated homologous recombination. This strategy has no influence on the expression of the endogenous GAPDH gene. Thus, the GAPDH locus is a new alternative safe harbor locus in the pig genome for foreign gene knock-ins. This strategy is promising for agricultural breeding and biomedical model applications. [ABSTRACT FROM AUTHOR]