Are Innovation Output and Economic Output Strongly Related in Emerging Industrial Clusters? Evidence from China

For many countries, innovation-driven development has become a prevalent consensus because innovation can effectively stimulate economic growth. Emerging industries are innovation-intensive with high potential economic benefit. However, is it assured that high innovation output means high economic benefit? In October of 2010, China State Council initiated the Decision of Speeding up Cultivation and Development of Strategic Emerging Industries, signifying top-down policy mobilization to advance emerging industries. According to seven types of emerging industries defined in the Decision, we collected data from official industrial databases to figure out spatial divergence of emerging industries in terms of innovation output and economic benefit over the years from 2000 to 2011. We construct twodimension scatter diagrams based on number of granted patents as the indicator of innovation output and industrial locational quotient as the indicator of industrial economic benefit. The result shows that China has seen preliminary spatial clustering of key emerging industries across regions and industries in the light of innovation output and economic benefit. However, not all regions with high innovation output have high economic benefit. The spatial divergence is closely related to region-specific and industry-specific characteristics. We offer policy implications to facilitate targeted emerging industries with more detailed policy and regional endowment

Translational Bioinformatics for Human Reproductive Biology Research: Examples, Opportunities and Challenges for a Future Reproductive Medicine

Since 1978, with the first IVF (in vitro fertilization) baby birth in Manchester (England), more than eight million IVF babies have been born throughout the world, and many new techniques and discoveries have emerged in reproductive medicine. To summarize the modern technology and progress in reproductive medicine, all scientific papers related to reproductive medicine, especially papers related to reproductive translational medicine, were fully searched, manually curated and reviewed. Results indicated whether male reproductive medicine or female reproductive medicine all have made significant progress, and their markers have experienced the progress from karyotype analysis to single-cell omics. However, due to the lack of comprehensive databases, especially databases collecting risk exposures, disease markers and models, prevention drugs and effective treatment methods, the application of the latest precision medicine technologies and methods in reproductive medicine is limited.This research was funded by Project of Natural Science Foundation of Gansu Province (20JR5RA363); Project of Gansu Provincial Education Department (2020B-003)

Upcycling of PET oligomers from chemical recycling processes to PHA by microbial co-cultivation

Polyethylene terephthalate (PET) is the most widely consumed polyester plastic and can be recycled by many chemical processes, of which glycolysis is most cost-effective and commercially viable. However, PET glycolysis produces oligomers due to incomplete depolymerization, which are undesirable by-products and require proper disposal. In this study, the PET oligomers from chemical recycling processes were completely bio-depolymerized into monomers and then used for the biosynthesis of biodegradable plastics polyhydroxyalkanoates (PHA) by cocultivation of two engineered microorganisms Escherichia coli BL21 (DE3)-LCCICCG and Pseudomonas putida KT2440-ΔRDt-ΔZP46C-M. E. coli BL21 (DE3)-LCCICCG was used to secrete the PET hydrolase LCCICCG into the medium to directly depolymerize PET oligomers. P. putida KT2440-ΔRDt-ΔZP46C-M that mastered the metabolism of aromatic compounds was engineered to accelerate the hydrolysis of intermediate products mono-2- (hydroxyethyl) terephthalate (MHET) by expressing IsMHETase, and biosynthesize PHA using ultimate products terephthalate and ethylene glycol depolymerized from the PET oligomers. The population ratios of the two microorganisms during the co-cultivation were characterized by fluorescent reporter system, and revealed the collaboration of the two microorganisms to bio-depolymerize and bioconversion of PET oligomers in a single process. This study provides a biological strategy for the upcycling of PET oligomers and promotes the plastic circular economy

Molecular insights into functional differences between mcr-3- and mcr-1-mediated colistin resistance

The global emergence of plasmid-mediated colistin resistance genes mcr-1 and mcr-3 has threatened the role of the “last resort” drug colistin in the defense against infections caused by multidrug-resistant Gram-negative bacteria. However, functional differences between these two genes in mediating colistin resistance remains poorly understood. Protein sequence alignment of MCR-3 and MCR-1 was therefore conducted in Clustal Omega to identify sequence divergence. The molecular recognition of lipid A head group phosphatidylethanolamine and MCR-3 enzyme was studied by homology modeling and molecular docking, with the catalytic mechanism of MCR-3 also being explored. Thr277 in MCR-3 was validated as the key amino acid residue responsible for the catalytic reaction using site-directed mutagenesis and was shown to act as a nucleophile. Lipid A modification induced by the MCR-3 and MCR-1 enzymes was confirmed by electrospray ionization time-of-flight mass spectrometry. Far-UV circular dichroism spectra of the MCR-3 and MCR-1 enzymes suggested that MCR-3 was more thermostable than MCR-1, with a melting temperature of 66.19°C compared with 61.14°C for MCR-1. These data provided molecular insight into the functional differences between mcr-3 and mcr-1 in conferring colistin resistance

Causality of anti-Helicobacter pylori IgG levels on myocardial infarction and potential pathogenesis: a Mendelian randomization study

BackgroundPrevious observational studies have shown that a potential relationship between anti-Helicobacter pylori (H. pylori) IgG levels and Myocardial Infarction (MI). Nevertheless, the evidence for the causal inferences remains disputable. To further clarify the relationship between anti-H. pylori IgG levels and MI and explore its pathogenesis, we conducted a Mendelian randomization (MR) analysis.MethodsIn this study, we used two-sample Mendelian Randomization (MR) to assess the causality of anti-H. pylori IgG levels on MI and potential pathogenesis, 12 single nucleotide polymorphisms (SNPs) related to anti-H. pylori IgG levels were obtained from the European Bioinformatics Institute (EBI). Summary data from a large-scale GWAS meta-analysis of MI was utilized as the outcome dataset. Summary data of mediators was obtained from the FinnGen database, the UK Biobank, the EBI database, MRC-IEU database, the International Consortium of Blood Pressure, the Consortium of Within family GWAS. Inverse variance weighted (IVW) analysis under the fixed effect model was identified as our main method. To ensure the reliability of the findings, many sensitivity analyses were performed.ResultsOur study revealed that increases of anti-H. pylori IgG levels were significantly related to an increased risk of MI (OR, 1.104; 95% CI,1.042–1.169; p = 7.084 × 10−4) and decreases in HDL cholesterol levels (β, −0.016; 95% CI, −0.026 to −0.006; p = 2.02 × 10−3). In addition, there was no heterogeneity or pleiotropy in our findings.ConclusionThis two-sample MR analysis revealed the causality of anti-H. pylori IgG levels on MI, which might be explained by lower HDL cholesterol levels. Further research is needed to clarify the results

Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning

Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks, but focus on conversational tasks has been rather limited. This is partly due to the high cost of obtaining non-English conversational data, which results in limited coverage. In this work, we introduce XSGD for cross-lingual alignment pretraining, a parallel and large-scale multilingual conversation dataset that we created by translating the English-only Schema-Guided Dialogue (SGD) dataset (Rastogi et al., 2020) into 105 other languages. XSGD contains approximately 330k utterances per language. To facilitate aligned cross-lingual representations, we develop an efficient prompt-tuning-based method for learning alignment prompts. We also investigate two different classifiers: NLI-based and vanilla classifiers, and test cross-lingual capability enabled by the aligned prompts. We evaluate our model's cross-lingual generalization capabilities on two conversation tasks: slot-filling and intent classification. Our results demonstrate the strong and efficient modeling ability of NLI-based classifiers and the large cross-lingual transfer improvements achieved by our aligned prompts, particularly in few-shot settings. In addition, we highlight the nice results of our approach compared to LLMs such as text-davinci-003 and ChatGPT in both zero-shot and few-shot settings. While LLMs exhibit impressive performance in English, their cross-lingual capabilities in other languages, particularly low-resource languages, are limited