Improved 11α-hydroxycanrenone production by modification of cytochrome P450 monooxygenase gene in Aspergillus ochraceus

Eplerenone is a drug that protects the cardiovascular system. 11α-Hydroxycanrenone is a key intermediate in eplerenone synthesis. We found that although the cytochrome P450 (CYP) enzyme system in Aspergillus ochraceus strain MF018 could catalyse the conversion of canrenone to 11α-hydroxycanrenone, its biocatalytic efficiency is low. To improve the efficiency of 11α-hydroxycanrenone production, the CYP monooxygenase-coding gene of MF018 was predicted and cloned based on whole-genome sequencing results. A recombinant A. ochraceus strain MF010 with the high expression of CYP monooxygenase was then obtained through homologous recombination. The biocatalytic rate of this recombinant strain reached 93 % at 60 h without the addition of organic solvents or surfactants and was 17–18 % higher than that of the MF018 strain. Moreover, the biocatalytic time of the MF010 strain was reduced by more than 30 h compared with that of the MF018 strain. These results show that the recombinant A. ochraceus strain MF010 can overcome the limitation of substrate biocatalytic efficiency and thus holds a high potential for application in the industrial production of eplerenone

From Indeterminacy to Determinacy: Augmenting Logical Reasoning Capabilities with Large Language Models

Recent advances in LLMs have revolutionized the landscape of reasoning tasks. To enhance the capabilities of LLMs to emulate human reasoning, prior works focus on modeling reasoning steps using specific thought structures like chains, trees, or graphs. However, LLM-based reasoning continues to encounter three challenges: 1) Selecting appropriate reasoning structures for various tasks; 2) Exploiting known conditions sufficiently and efficiently to deduce new insights; 3) Considering the impact of historical reasoning experience. To address these challenges, we propose DetermLR, a novel reasoning framework that formulates the reasoning process as a transformational journey from indeterminate premises to determinate ones. This process is marked by the incremental accumulation of determinate premises, making the conclusion progressively closer to clarity. DetermLR includes three essential components: 1) Premise identification: We categorize premises into two distinct types: determinate and indeterminate. This empowers LLMs to customize reasoning structures to match the specific task complexities. 2) Premise prioritization and exploration: We leverage quantitative measurements to assess the relevance of each premise to the target, prioritizing more relevant premises for exploring new insights. 3) Iterative process with reasoning memory: We introduce a reasoning memory module to automate storage and extraction of available premises and reasoning paths, preserving historical reasoning details for more accurate premise prioritization. Comprehensive experimental results show that DetermLR outperforms all baselines on four challenging logical reasoning tasks: LogiQA, ProofWriter, FOLIO, and LogicalDeduction. DetermLR can achieve better reasoning performance while requiring fewer visited states, highlighting its superior efficiency and effectiveness in tackling logical reasoning tasks.Comment: Code repo: https://github.com/XiaoMi/DetermL

Finding regions of interest using location based social media

The discovery of regions of interest in city groups is increasingly important in recent years. In this light, we propose and investigate a novel problem called Region Discovery query (RD query) that finds regions of interest with respect to a user's current geographic location. Given a set of spatial objects O and a query location q, if a circular region ω is with high spatial-object density and is spatially close to q, it is returned by the query and is recommended to users. This type of query can bring significant benefit to users in many useful applications such as trip planning and region recommendation. The RD query faces a big challenge: how to prune the search space in the spatial and density domains. To overcome the challenge and process the RD query efficiently, we propose a novel collaboration search method and we define a pair of bounds to prune the search space effectively. The performance of the RD query is studied by extensive experiments on real and synthetic spatial data

Live birth after in vitro maturation versus standard in vitro fertilisation for women with polycystic ovary syndrome : protocol for a non-inferiority randomised clinical trial

Funding This study was supported by the National Key Research and Development Program of China (2016YFC1000201; 2018YFC1002104) and the National Science Foundation of China (81730038). The study funders had no rule in the study design, implementation, analysis, manuscript, preparation, or decision to submit this article for publication.Peer reviewedPublisher PD

Full Scale of Pore-Throat Size Distribution and Its Control on Petrophysical Properties of the Shanxi Formation Tight Sandstone Reservoir in the North Ordos Basin, China.

Pore-throat size distribution is a key factor controlling the storage capacity and percolation potential of the tight sandstone reservoirs. However, the complexity and strong heterogeneity make it difficult to investigate the pore structure of tight sandstone reservoirs by using conventional methods. In this study, integrated methods of casting thin section, scanning electron microscopy, high-pressure mercury intrusion (HPMI), and constant-pressure mercury intrusion (CPMI) were conducted to study the pore-throat size distribution and its effect on petrophysical properties of the Shanxi Formation tight sandstones in the northern Ordos Basin (China). Results show that pore types of the Shanxi tight sandstone reservoirs include intergranular pores, dissolution pores, intercrystalline micropores, and microfracture, while the throats are dominated by sheet-like and tube-shaped throats. The HPMI-derived pore-throat size ranges from 0.006 to 10 μm, and the pore-throats with a radius larger than 10 μm were less frequent. The pore body size obtained from CPMI shows similar characteristics with radii ranging from 100 to 525 μm, while the throat size varies greatly with radii ranging from 0.5 to 11.5 µm, resulting in a wide range of pore-throat radius ratio. The full range of pore size distribution curves obtained from the combination of HPMI and CPMI displays multimodal with radii ranging from 0.006 to 525 µm. Permeability of the tight sandstone reservoirs is primarily controlled by relatively larger pore throats with small proportions, and the permeability decreases as the proportions of smaller pore-throats increase. The pervading nanopores in the tight gas sandstone reservoirs contribute little to the permeability but play an important role in the reservoir storage capacity. A new empirical equation obtained by multiple regression indicates that r15 (pore-throat size corresponding to 15% mercury saturation) is the best permeability estimator for tight gas sandstone reservoirs, which yields the highest correlation coefficient of 0.9629 with permeability and porosity

Proposal for Measurement of the Two-body Neutron Decay using Microcalorimeter

The bound beta-decay (BoB) of neutron is also known as the two-body neutron decay, which is a rare decay mode into a hydrogen atom and an anti-neutrino. The state of neutrino can be exactly inferred by measuring the state of hydrogen atom, providing a possible pathway to explore new physics. However, this rare decay mode has not yet been observed so far since it was predicted in 1947. The challenge in observing this decay is not only that its cross section is extremely low, equivalent to about branching ratio of the order of $10^{-6}$ of the three-body decay, but also that the final-state hydrogen atom is neutral and has extremely low kinetic energy, which cannot be effectively detected. In this study, we propose a microcalorimeter-based scheme for measuring the kinetic energies of hydrogen atoms produced from BoB of ultracold neutrons, which has a great advantage in terms of accuracy of the energy measurement. In this study, first, several important issues that require rigorous considerations for the decay measurements and possible solutions are discussed. Then, the requirements of the neutron flux and the appropriate structure design of the microcalorimeter are present by theoretical calculations. In short, this paper outlines our proposed novel experimental scheme for observing the BoB mode, addressing the possible solutions to all the necessary problems

Rapid Identification of Asteraceae

Plants from Asteraceae family are widely used as herbal medicines and food ingredients, especially in Asian area. Therefore, authentication and quality control of these different Asteraceae plants are important for ensuring consumers’ safety and efficacy. In recent decades, electronic nose (E-nose) has been studied as an alternative approach. In this paper, we aim to develop a novel discriminative model by improving radial basis function artificial neural network (RBF-ANN) classification model. Feature selection algorithms, including principal component analysis (PCA) and BestFirst + CfsSubsetEval (BC), were applied in the improvement of RBF-ANN models. Results illustrate that in the improved RBF-ANN models with lower dimension data classification accuracies (100%) remained the same as in the original model with higher-dimension data. It is the first time to introduce feature selection methods to get valuable information on how to attribute more relevant MOS sensors; namely, in this case, S1, S3, S4, S6, and S7 show better capability to distinguish these Asteraceae plants. This paper also gives insights to further research in this area, for instance, sensor array optimization and performance improvement of classification model

Retrospective analysis of clinical characteristics and treatment of children and adolescents with depression

ObjectiveTo analyze the demographic and clinical characteristics and treatment among children and adolescents with depression in different age groups of onset.Methods635 children and adolescents with depression in a hospital from January 2014 to December 2021 were collected by e-case, and grouped according to age of onset, including 115 cases in childhood 8-12, 359 cases in early adolescence 13-1 and 161 cases in late adolescence 16-18, and the general conditions, clinical characteristics, and treatment were compared between the three groups.ResultsFemales had more onset and were more likely to have psychotic symptoms in childhood, short duration and hospitalization in early adolescence increased year by year, and males had more onset and less hospitalization in late adolescence. There were no statistical differences in medication regimen, suicide, length of hospitalization, or family history between the three groups.ConclusionChildren and adolescents with depression have their unique clinical characteristics at different age of onset and need to enhance prevention and individualized treatment

Design strategies of tumor-targeted delivery systems based on 2D nanomaterials

Conventional chemotherapy and radiotherapy are nonselective and nonspecific for cell killing, causing serious side effects and threatening the lives of patients. It is of great significance to develop more accurate tumor-targeting therapeutic strategies. Nanotechnology is in a leading position to provide new treatment options for cancer, and it has great potential for selective targeted therapy and controlled drug release. 2D nanomaterials (2D NMs) have broad application prospects in the field of tumor-targeted delivery systems due to their special structure-based functions and excellent optical, electrical, and thermal properties. This review emphasizes the design strategies of tumor-targeted delivery systems based on 2D NMs from three aspects: passive targeting, active targeting, and tumor-microenvironment targeting, in order to promote the rational application of 2D NMs in clinical practice.This work was supported by the Guangdong Basic and Applied Basic Research Foundation (Nos. 2021A1515110657 and 2022A1515010056), Shenzhen Science and Technology Program (Grant No. RCBS20210609104513023), National Natural Science Foundation of China (No. 81922037), and Shanghai Biomedical Science and Technology Support Project (No. 19441903600)