How does digital trade promote and reallocate the export technology complexity of the manufacturing industry? Evidence from 30 Chinese provinces, 2011-2020.

It is important for China to break the "low-end lock" of the manufacturing value chain worldwide by revealing how digital trade promotes and reallocates the export technology complexity of the manufacturing industry. Panel data for 30 provinces in China from 2011 to 2020 were employed to measure the digital trade development and export technology complexity of the manufacturing industry. Benchmark regression, intermediary effect regression, panel threshold and other models were used to test the promotion and reallocation of digital trade on the export technology complexity of the manufacturing industry. The findings are as follows: (1) Digital trade promotes the export technology complexity of the manufacturing industry, with significant regional heterogeneity (eastern, central and western regions), and the most obvious promotion in technology-intensive manufacturing. (2) Technological innovation and human capital play a reallocation role in the process of digital trade, affecting the technological complexity of manufacturing exports, with mediating effects of 14.19% and 8.61%, respectively. (3) Digital trade promotes and reallocates the export technology complexity of the manufacturing industry through industrial structure upgrading, and a nonlinear relationship was found. These results provide empirical support and a decision-making basis for digital trade in promoting the export technology complexity of the manufacturing industry. The development of digital trade should be encouraged; the differential development of digital trade in the eastern, central, and western regions should be boosted; importance should be attached to the intermediary incentive role of technological innovation and human capital; and the upgrading of the industrial structure should be promoted scientifically.

Genome-scale CRISPR screen identifies TRIM2 and SLC35A1 associated with porcine epidemic diarrhoea virus infection.

Porcine epidemic diarrhoea (PED) caused by the porcine epidemic diarrhoea virus (PEDV) is the most devastating disease in the global pig industry due to its high mortality rate in piglets. The host factors critical for PEDV replication are poorly understood. Here, we designed a pooled African green monkey genome-scale CRISPR/Cas9 knockout (VeroCKO) library containing 75,608 single guide RNAs targeting 18,993 protein-coding genes. Subsequently, we use the VeroCKO library to identify key host factors facilitating PEDV infection in Vero E6 cells. Several previously unreported genes associated with PEDV infection are highly enriched post-PEDV selection. We discovered that knocking out the tripartite motif 2 (TRIM2) and the solute carrier family 35 member A1 (SLC35A1) inhibited PEDV replication. Virtual screening and molecular docking approaches showed that chem-80,048,685 (M2) s ignificantly inhibited PEDV attachment and late replication by impeding SLC35A1. Furthermore, we found that knocking out SLC35A1 in Vero E6 cells upregulated a disintegrin and metalloprotease protein-17 (ADAM17) by splicing porcine aminopeptidase N (pAPN) and angiotensin-converting enzyme 2 (ACE2) ectodomains to reduce PEDV-infection in a CMP-Sialic Acid (CMP-SA) cell entry-independent manner. These findings provide a new perspective for a better understanding of host-pathogen interactions and new therapeutic targets for PEDV infection.

Molecular insight into CO2/N2 separation using a 2D-COF supported ionic liquid membrane.

The covalent organic framework (COF) shows great potential for use in gas separation because of its uniform and high-density sub-nanometer sized pores. However, most of the COF pore sizes are large, and there are mismatches with the gas pairs (3-6 Å), and the steric hindrance cannot work in gas selectivity. In this work, one type of COF (NUS-2) supported ionic liquid membrane (COF-SILM) was prepared for use in CO2/N2 separation. The separation performance was investigated using molecular dynamics simulation. There was an ultrahigh CO2 permeability up to 2.317 × 106 GPU, and a better CO2 selectivity was obtained when compared to that of N2. The physical mechanism of ultrahigh permeability and CO2 selectivity are discussed in detail. The ultrathin membrane, high-density pores and high transmembrane driving force are responsible for the ultrahigh permeability of CO2. The different adsorption capabilities of ionic liquid (IL) for CO2 and N2, as well as a gating effect, which allows CO2 passage and inhibits N2 passage, contribute to the better CO2 selectivity over N2. Moreover, the effects of the COF layer number and IL thickness on gas separation performance are also discussed. This work provides a molecular level understanding of the gas separation mechanism of COF-SILM, and the simulation results show one potential outstanding CO2 separation membrane for future applications.

Integrative analysis of transcriptomic and metabolomic profiles reveal the complex molecular regulatory network of meat quality in Enshi black pigs.

Improving meat quality is a crucial purpose of commercial production and breeding systems. In this study, multiomics techniques were used to investigate the molecular mechanisms that impact the excessive diversity of meat quality in Enshi black pigs. The results suggest that 120 differentially expressed genes (DEGs) and 171 significantly changed metabolites (SCMs) contribute to the content of intramuscular fat (IMF) through the processes of fat accumulation and regulation of lipolysis. A total of 141 DEGs and 47 SCMs may regulate meat color through the processes of nicotinate and nicotinamide metabolism. Herein, we found some candidate genes associated with IMF and meat color. We also presented a series of metabolites that are potentially available biological indicators to measure meat quality. This research provides further insight into the detection of intramuscular fat accumulation and meat color variation and provides a reference for molecular mechanisms in the regulation of IMF and meat color.

CRISPR screening of porcine sgRNA library identifies host factors associated with Japanese encephalitis virus replication.

Japanese encephalitis virus (JEV) is a mosquito-borne zoonotic flavivirus that causes encephalitis and reproductive disorders in mammalian species. However, the host factors critical for its entry, replication, and assembly are poorly understood. Here, we design a porcine genome-scale CRISPR/Cas9 knockout (PigGeCKO) library containing 85,674 single guide RNAs targeting 17,743 protein-coding genes, 11,053 long ncRNAs, and 551 microRNAs. Subsequently, we use the PigGeCKO library to identify key host factors facilitating JEV infection in porcine cells. Several previously unreported genes required for JEV infection are highly enriched post-JEV selection. We conduct follow-up studies to verify the dependency of JEV on these genes, and identify functional contributions for six of the many candidate JEV-related host genes, including EMC3 and CALR. Additionally, we identify that four genes associated with heparan sulfate proteoglycans (HSPGs) metabolism, specifically those responsible for HSPGs sulfurylation, facilitate JEV entry into porcine cells. Thus, beyond our development of the largest CRISPR-based functional genomic screening platform for pig research to date, this study identifies multiple potentially vulnerable targets for the development of medical and breeding technologies to treat and prevent diseases caused by JEV.

Identification of ACTB Gene as a Potential Safe Harbor Locus in Pig Genome.

Transgenic pigs play an important role in biomedicine and agriculture. The "safe harbor" locus maintains consistent foreign gene expression in cells and is important for transgenic pig generation. However, as only several safe harbor loci(Rosa26, pH11 and Pifs501) have been identified in pigs, meeting the needs of the insertion of various foreign genes is difficult. In this study, we develop a novel strategy for the efficient knock-in of gene-of-interest fragments into endogenous beta-actin(ACTB) gene via CRISPR/Cas9 mediated homologous recombination with normal expression of ACTB. Thus, we provide an alternative strategy to integrate exogenous genes into the pig genome that can be applied to agricultural breeding and biomedical models.

Multi-task Learning via Adaptation to Similar Tasks for Mortality Prediction of Diverse Rare Diseases.

The mortality prediction of diverse rare diseases using electronic health record (EHR) data is a crucial task for intelligent healthcare. However, data insufficiency and the clinical diversity of rare diseases make it hard for deep learning models to be trained. Mortality prediction for these patients with different diseases can be viewed as a multi-task learning problem with insufficient data but a large number of tasks. On the other hand, insufficient training data makes it difficult to train task-specific modules in multi-task learning models. To address the challenges of data insufficiency and task diversity, we propose an initialization-sharing multi-task learning method (Ada-SiT). Ada-Sit can learn the parameter initialization and dynamically measure the tasks' similarities, used for fast adaptation. We use Ada-SiT to train long short-term memory networks (LSTM) based prediction models on longitudinal EHR data. The experimental results demonstrate that the proposed model is effective for mortality prediction of diverse rare diseases.

Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction.

Clinical outcome prediction based on Electronic Health Record (EHR) helps enable early interventions for high-risk patients, and is thus a central task for smart healthcare. Conventional deep sequential models fail to capture the rich temporal patterns encoded in the long and irregular clinical event sequences in EHR. We make the observation that clinical events at a long time scale exhibit strong temporal patterns, while events within a short time period tend to be disordered co-occurrence. We thus propose differentiated mechanisms to model clinical events at different time scales. Our model learns hierarchical representations of event sequences, to adaptively distinguish between short-range and long-range events, and accurately capture their core temporal dependencies. Experimental results on real clinical data show that our model greatly improves over previous state-of-the-art models, achieving AUC scores of 0.94 and 0.90 for predicting death and ICU admission, respectively. Our model also successfully identifies important events for different clinical outcome prediction tasks.