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Fulltext Common institutional investors and the quality of management earnings forecasts-Empirical and machine learning evidences

Yang S, Li X, Jiang Z, Xiao M

PLoS One, 2023;18(10):e0290126.
PMID: 37844110 DOI: 10.1371/journal.pone.0290126

Based on the data of the Chinese A-share listed firms in China Shanghai and Shenzhen Stock Exchange from 2014 to 2021, this article explores the relationship between common institutional investors and the quality of management earnings forecasts. The study used the multiple linear regression model and empirically found that common institutional investors positively impact the precision of earnings forecasts. This article also uses graph neural networks to predict the precision of earnings forecasts. Our findings have shown that common institutional investors form external supervision over restricting management to release a wide width of earnings forecasts, which helps to improve the risk warning function of earnings forecasts and promote the sustainable development of information disclosure from management in the Chinese capital market. One of the marginal contributions of this paper is that it enriches the literature related to the economic consequences of common institutional shareholding. Then, the neural network method used to predict the quality of management forecasts enhances the research method of institutional investors and the behavior of management earnings forecasts. Thirdly, this paper calls for strengthening information sharing and circulation among institutional investors to reduce information asymmetry between investors and management.
Fulltext The recognition of multi-class protein folds by adding average chemical shifts of secondary structure elements

Feng Z, Hu X, Jiang Z, Song H, Ashraf MA

Saudi J Biol Sci, 2016 Mar;23(2):189-97.
PMID: 26980999 DOI: 10.1016/j.sjbs.2015.10.008

The recognition of protein folds is an important step in the prediction of protein structure and function. Recently, an increasing number of researchers have sought to improve the methods for protein fold recognition. Following the construction of a dataset consisting of 27 protein fold classes by Ding and Dubchak in 2001, prediction algorithms, parameters and the construction of new datasets have improved for the prediction of protein folds. In this study, we reorganized a dataset consisting of 76-fold classes constructed by Liu et al. and used the values of the increment of diversity, average chemical shifts of secondary structure elements and secondary structure motifs as feature parameters in the recognition of multi-class protein folds. With the combined feature vector as the input parameter for the Random Forests algorithm and ensemble classification strategy, we propose a novel method to identify the 76 protein fold classes. The overall accuracy of the test dataset using an independent test was 66.69%; when the training and test sets were combined, with 5-fold cross-validation, the overall accuracy was 73.43%. This method was further used to predict the test dataset and the corresponding structural classification of the first 27-protein fold class dataset, resulting in overall accuracies of 79.66% and 93.40%, respectively. Moreover, when the training set and test sets were combined, the accuracy using 5-fold cross-validation was 81.21%. Additionally, this approach resulted in improved prediction results using the 27-protein fold class dataset constructed by Ding and Dubchak.
Cost-effectiveness analysis of sorafenib, lenvatinib, atezolizumab plus bevacizumab and sintilimab plus bevacizumab for the treatment of advanced hepatocellular carcinoma in China

Gong H, Ong SC, Li F, Weng Z, Zhao K, Jiang Z

Cost Eff Resour Alloc, 2023 Mar 31;21(1):20.
PMID: 37004046 DOI: 10.1186/s12962-023-00435-x

BACKGROUND AND OBJECTIVE: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide, especially in China. According to the 2021 Chinese Society of Clinical Oncology guidelines, sorafenib, lenvatinib, atezolizumab combined with bevacizumab, and sintilimab combined with bevacizumab are recommended as first-line treatment options for advanced HCC. This study provides a cost-effectiveness analysis of these treatments from the patient perspective.
METHODS: A partitioned survival model was established using the TreeAge 2019 software to evaluate the cost-effectiveness. The model includes three states, namely progression-free survival, progressive disease, and death. Clinical data were derived from three randomized controlled studies involving patients with advanced HCC who received the following treatment: sorafenib and lenvatinib (NCT01761266); atezolizumab in combination with bevacizumab (NCT03434379); and sintilimab in combination with bevacizumab (NCT03794440). Cost and clinical preference data were obtained from the literature and interviews with clinicians.
RESULTS: All compared with sorafenib therapy, lenvatinib had an incremental cost-effectiveness ratio (ICER) of US$188,625.25 per quality-adjusted life year (QALY) gained; sintilimab plus bevacizumab had an ICER of US$75,150.32 per QALY gained; and atezolizumab plus bevacizumab had an ICER of US$144,513.71 per QALY gained. The probabilistic sensitivity analysis indicated that treatment with sorafenib achieved a 100% probability of cost-effectiveness at a threshold of US$36,600/QALY. One-way sensitivity analysis revealed that the results were most sensitive to the medical insurance reimbursement ratio and drug prices.
CONCLUSIONS: In this economic evaluation, therapy with lenvatinib, sintilimab plus bevacizumab, and atezolizumab plus bevacizumab generated incremental QALYs compared with sorafenib; however, these regimens were not cost-effective at a willingness-to-pay threshold of US$36,600 per QALY. Therefore, some patients may achieve preferred economic outcomes from these three therapies by tailoring the regimen based on individual patient factors.
Fulltext Development of Organo-Dispersible Graphene Oxide via Pseudo-Surface Modification for Thermally Conductive Green Polymer Composites

Sim S, Andou Y, Bashid HAA, Lim H, Altarawneh M, Jiang Z, et al.

ACS Omega, 2018 Dec 31;3(12):18124-18131.
PMID: 31458398 DOI: 10.1021/acsomega.8b02478

Graphene has attracted lots of researchers attention because of its remarkable conductivity in both electrically and thermally. However, it has poor dispersibility in organic solvents which limited its applications. Polymers with aromatic end group which act as an intercalator were prepared by ring-opening polymerization with ε-caprolactone by utilizing 1-naphthalene methanol (1-NM) as an initiator. These intercalators will exist between graphene oxide (GO) sheets to prevent aggregation via interactions. The attachment of 1-NM on polymer chains was supported by ultraviolet-visible spectra, size exclusion chromatography profiles, and 1H nuclear magnetic resonance spectra. Exfoliated structured functionalized GO (fGO)/polycaprolactone (PCL) (synthesized fGO) nanocomposites that dispersed well in acetone, chloroform, N,N-dimethylformamide, dimethyl sulfoxide, tetrahydrofuran, and toluene were successfully synthesized. This agreed well with the enlarged interlayer spacing in the optimized fGO as compared to that of GO from density functional theory simulations using the DMol3 module that implemented in the Materials Studio 6.0. Furthermore, its potential to be applied as green electronics in electronics, aerospace, and automotive industries was presented, by trailering the thermal conductivity enhancement from the incorporation of fGO/PCL with commercialized biodegradable polymers, PCL, and poly[(R)-3-hydroxybutyric acid].
Fulltext FUZ-SMO: A fuzzy slime mould optimizer for mitigating false alarm rates in the classification of underwater datasets using deep convolutional neural networks

Liang Zhang D, Jiang Z, Mohammadzadeh F, Hasani Azhdari SM, Abualigah L, Ghazal TM

Heliyon, 2024 Apr 15;10(7):e28681.
PMID: 38586386 DOI: 10.1016/j.heliyon.2024.e28681

Sonar sound datasets are of significant importance in the domains of underwater surveillance and marine research as they enable experts to discern intricate patterns within the depths of the water. Nevertheless, the task of classifying sonar sound datasets continues to pose significant challenges. In this study, we present a novel approach aimed at enhancing the precision and efficacy of sonar sound dataset classification. The integration of deep long-short-term memory (DLSTM) and convolutional neural networks (CNNs) models is employed in order to capitalize on their respective advantages while also utilizing distinctive feature engineering techniques to achieve the most favorable outcomes. Although DLSTM networks have demonstrated effectiveness in tasks involving sequence classification, attaining their optimal performance necessitates careful adjustment of hyperparameters. While traditional methods such as grid and random search are effective, they frequently encounter challenges related to computational inefficiencies. This study aims to investigate the unexplored capabilities of the fuzzy slime mould optimizer (FUZ-SMO) in the context of LSTM hyperparameter tuning, with the objective of addressing the existing research gap in this area. Drawing inspiration from the adaptive behavior exhibited by slime moulds, the FUZ-SMO proposes a novel approach to optimization. The amalgamated model, which combines CNN, LSTM, fuzzy, and SMO, exhibits a notable improvement in classification accuracy, outperforming conventional LSTM architectures by a margin of 2.142%. This model not only demonstrates accelerated convergence milestones but also displays significant resilience against overfitting tendencies.
Molecular mechanisms underlying lignocellulose degradation and antibiotic resistance genes removal revealed via metagenomics analysis during different agricultural wastes composting

Ma C, Lo PK, Xu J, Li M, Jiang Z, Li G, et al.

Bioresour Technol, 2020 Oct;314:123731.
PMID: 32615447 DOI: 10.1016/j.biortech.2020.123731

In this study, the differences on the physico-chemical parameters, lignocellulose degradation, dynamic succession of microbial community, gene expression of carbohydrate-active enzymes and antibiotics resistance genes were compared during composting systems of bagasse pith/pig manure (BP) and manioc waste/pig manure (MW). The results revealed that biodegradation rates of organic matter, cellulose, hemicellulose and lignin (29.14%, 17.53%,45.36% and 36.48%) in BP were higher than those (15.59%, 16.74%, 41.23% and 29.77%) in MW. In addition, the relative abundance of Bacillus, Luteimonas, Clostridium, Pseudomonas, Streptomyces and expression of genes encoding carbohydrate- active enzymes in BP were higher than those in MW based on metagenomics sequencing. During composting, antibiotics and antibiotic resistance genes were substantially reduced, but the removal efficiency was divergent in the both samples. Taken together, metagenomics analysis was a potential method for evaluating lignocellulose's biodegradation process and determining the elimination of antibiotic and antibiotic resistance genes from different composting sources of biomass.
How does biochar aging affect NH3 volatilization and GHGs emissions from agricultural soils?

Feng Y, Feng Y, Liu Q, Chen S, Hou P, Poinern G, et al.

Environ Pollut, 2022 Feb 01;294:118598.
PMID: 34861331 DOI: 10.1016/j.envpol.2021.118598

Biochar has been considered as a potential tool to mitigate soil ammonia (NH3) volatilization and greenhouse gases (GHGs) emissions in recent years. However, the aging effect of biochar on soils remains elusive, which introduces uncertainty on the effectiveness of biochar to mitigate global warming in a long term. Here, a meta-analysis of 22 published works of literature with 217 observations was conducted to systematically explore the aging effect of biochar on soil NH3 and GHGs emissions. The results show that, in comparison with the fresh biochar, the aging makes biochar more effective to decrease soil NH3 volatilization by 7% and less risk to contribute CH4 emissions by 11%. However, the mitigation effect of biochar on soil N2O emissions is decreased by 15% due to aging. Additionally, aging leads to a promotion effect on soil CO2 emissions by 25% than fresh biochar. Our findings suggest that along with aging, particularly the effect of artificial aging, biochar could further benefit the alleviation of soil NH3 volatilization, whereas its potential role to mitigate global warming may decrease. This study provides a systematic assessment of the aging effect of biochar to mitigate soil NH3 and GHGs, which can provide a scientific basis for the sustainable green development of biochar application.
Fulltext Clinical research linking Traditional Chinese Medicine constitution types with diseases: a literature review of 1639 observational studies

Liang X, Wang Q, Jiang Z, Li Z, Zhang M, Yang P, et al.

J Tradit Chin Med, 2020 08;40(4):690-702.
PMID: 32744037 DOI: 10.19852/j.cnki.jtcm.2020.04.019

OBJECTIVE: To analyze clinical studies on correlations between Traditional Chinese Medicine (TCM) body constitution types and diseases published in the past 10 years, and to provide an evidence base to support the use of such correlations for health maintenance and disease prevention.
METHODS: We searched five databases for the period April 2009 to December 2019: China National Knowledge Infrastructure Database, Wanfang Database, China Science and Technology Journal Database, PubMed and Embase. Three types of observational studies on correlation between constitution types and diseases were included: cross-sectional, case-control and cohort studies. Descriptive statistical methods were employed for data analysis.
RESULTS: A total of 1639 clinical studies were identified: 1452 (88.59%) cross-sectional studies, 115 (7.02%) case-control studies and 72 (4.39%) cohort studies covering 30 regions of China and five other countries (Malaysia, South Korea, Singapore, Thailand and France). The collection of studies comprised 19 disease categories and 333 different diseases. The 10 most commonly studied diseases were hypertension, diabetes, stroke, coronary atherosclerotic heart disease (CAHD), sleep disorders, neoplasm of the breast, dysmenorrhea, fatty liver disease, chronic viral hepatitis B and dyslipidemia. We found high distributions for each biased constitution type in different patient populations as follows: Qi-deficiency constitution in stroke, diabetes, chronic obstructive pulmonary disease, acquired immunodeficiency syndrome and hypertension; Yang-deficiency constitution in female infertility, osteoporosis, irritable bowel syndrome, gonarthrosis and dysmenorrhea; Yin-deficiency constitution in hypertension, diabetes, constipation, female climacteric states and osteoporosis; phlegm- dampness constitution in hypertension, stroke, fatty liver disease, diabetes and metabolic syndrome; damp-heat constitution in acne, chronic gastritis, chronic viral hepatitis B, human papillomavirus infection and hyperuricemia; blood-stasis constitution in CAHD, endometriosis and stroke; Qi-stagnation constitution in hyperplasia and neoplasms of the breast, insomnia, depression and thyroid nodules; and inherited-special constitution in asthma and allergic rhinitis.
CONCLUSION: Eight biased TCM constitutions were closely related to specific diseases, and could be used to guide individualized prevention and treatment. More rigorously designed studies are recommended to further verify the constitution-disease relationship.
Large language models for diabetes care: Potentials and prospects

Sheng B, Guan Z, Lim LL, Jiang Z, Mathioudakis N, Li J, et al.

Sci Bull (Beijing), 2024 Jan 04.
PMID: 38220476 DOI: 10.1016/j.scib.2024.01.004
A Horizon Scan of Emerging Issues for Global Conservation in 2019

Sutherland WJ, Broad S, Butchart SHM, Clarke SJ, Collins AM, Dicks LV, et al.

Trends Ecol Evol, 2019 01;34(1):83-94.
PMID: 30554808 DOI: 10.1016/j.tree.2018.11.001

We present the results of our tenth annual horizon scan. We identified 15 emerging priority topics that may have major positive or negative effects on the future conservation of global biodiversity, but currently have low awareness within the conservation community. We hope to increase research and policy attention on these areas, improving the capacity of the community to mitigate impacts of potentially negative issues, and maximise the benefits of issues that provide opportunities. Topics include advances in crop breeding, which may affect insects and land use; manipulations of natural water flows and weather systems on the Tibetan Plateau; release of carbon and mercury from melting polar ice and thawing permafrost; new funding schemes and regulations; and land-use changes across Indo-Malaysia.
Fulltext Announcing the Genome Atlas of Bamboo and Rattan (GABR) project: promoting research in evolution and in economically and ecologically beneficial plants

Zhao H, Zhao S, International Network for Bamboo and Rattan, Fei B, Liu H, Yang H, et al.

Gigascience, 2017 07 01;6(7):1-7.
PMID: 28637269 DOI: 10.1093/gigascience/gix046

Bamboo and rattan are widely grown for manufacturing, horticulture, and agroforestry. Bamboo and rattan production might help reduce poverty, boost economic growth, mitigate climate change, and protect the natural environment. Despite progress in research, sufficient molecular and genomic resources to study these species are lacking. We launched the Genome Atlas of Bamboo and Rattan (GABR) project, a comprehensive, coordinated international effort to accelerate understanding of bamboo and rattan genetics through genome analysis. GABR includes 2 core subprojects: Bamboo-T1K (Transcriptomes of 1000 Bamboos) and Rattan-G5 (Genomes of 5 Rattans), and several other subprojects. Here we describe the organization, directions, and status of GABR.
Fulltext Pan-Asian adapted ESMO Clinical Practice Guidelines for the diagnosis, staging and treatment of patients with metastatic breast cancer

Im SA, Gennari A, Park YH, Kim JH, Jiang ZF, Gupta S, et al.

ESMO Open, 2023 Jun;8(3):101541.
PMID: 37178669 DOI: 10.1016/j.esmoop.2023.101541

The most recent version of the European Society for Medical Oncology (ESMO) Clinical Practice Guidelines for the diagnosis, staging and treatment of patients with metastatic breast cancer (MBC) was published in 2021. A special, hybrid guidelines meeting was convened by ESMO and the Korean Society of Medical Oncology (KSMO) in collaboration with nine other Asian national oncology societies in May 2022 in order to adapt the ESMO 2021 guidelines to take into account the differences associated with the treatment of MBC in Asia. These guidelines represent the consensus opinions reached by a panel of Asian experts in the treatment of patients with MBC representing the oncological societies of China (CSCO), India (ISMPO), Indonesia (ISHMO), Japan (JSMO), Korea (KSMO), Malaysia (MOS), the Philippines (PSMO), Singapore (SSO), Taiwan (TOS) and Thailand (TSCO). The voting was based on the best available scientific evidence and was independent of drug access or practice restrictions in the different Asian countries. The latter were discussed when appropriate. The aim of these guidelines is to provide guidance for the harmonisation of the management of patients with MBC across the different regions of Asia, drawing from data provided by global and Asian trials whilst at the same time integrating the differences in genetics, demographics and scientific evidence, together with restricted access to certain therapeutic strategies.
To which world regions does the valence-dominance model of social perception apply?

Jones BC, DeBruine LM, Flake JK, Liuzza MT, Antfolk J, Arinze NC, et al.

Nat Hum Behav, 2021 01;5(1):159-169.
PMID: 33398150 DOI: 10.1038/s41562-020-01007-2

Over the past 10 years, Oosterhof and Todorov's valence-dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov's methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov's original analysis strategy, the valence-dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence-dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 5 November 2018. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.7611443.v1 .

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