Public Platform Research Articles

Get Free Copy: a multi-repository search platform for biomedical publications

We introduce Get Free Copy (https://getfreecopy.com), a web-based platform designed to streamline the search for biomedical literature across major repositories like arXiv, bioRxiv, medRxiv, and PubMed Central (PMC). Addressing challenges posed by paywalls and fragmented databases, it offers a unified interface for efficient retrieval of free, legitimate copies of biomedical literature. The platform’s implementation involves a Node.js backend and dynamic front-end display, enhancing accessibility and research efficiency. As an open-source project, Get Free Copy represents a significant contribution to the open-access movement, inviting global researcher collaboration for further development.

Efficient Use of Biological Data in the Web 3.0 Era by Applying Nonfungible Token Technology.

CryptoKitties, a trendy game on Ethereum that is an open-source public blockchain platform with a smart contract function, brought nonfungible tokens (NFTs) into the public eye in 2017. NFTs are popular because of their nonfungible properties and their unique and irreplaceable nature in the real world. The embryonic form of NFTs can be traced back to a P2P network protocol improved based on Bitcoin in 2012 that can realize decentralized digital asset transactions. NFTs have recently gained much attention and have shown an unprecedented explosive growth trend. Herein, the concept of digital asset NFTs is introduced into the medical and health field to conduct a subversive discussion on biobank operations. By converting biomedical data into NFTs, the collection and circulation of samples can be accelerated, and the transformation of resources can be promoted. In conclusion, the biobank can achieve sustainable development through "decentralization."

Multi‐objective optimization‐based and fault localization‐oriented test case generation for novice programs

SummaryOnline judgment (OJ) systems are capable of evaluating program results by automatically executing test cases, significantly improving the efficiency of traditional guidance approaches. Moreover, existing studies attempt to assist novices through automated fault localization techniques to provide feedback to novices, which can help them quickly find the location of faulty statements. Among them, spectrum‐based fault localization (SBFL) techniques have been widely used for their lightweight and efficiency, which only requires coverage information and test results of test cases to conduct fault localization. However, manually constructing high‐quality test cases for a large number of OJ questions is tough work to complete. To solve this problem, we propose the novice program‐oriented Multi‐Objective Optimization‐Based Fault Localization‐Oriented Test Case Generation (MFTCG) for automatically generating test inputs. Specifically, we use multi‐objective optimization algorithms to evolve the test case in terms of both fault localization and faulty code detection capability. We conduct experiments with 8911 programs from the well‐known public OJ platform AtCoder. The results show that our proposed approach MFTCG can achieve the best fault localization performance compared with existing automated test case generation approaches in most cases and can achieve the similar faulty code detection capability compared to manually designed test cases.

Platform paradoxes and public service media legitimacy: a cross-national study

ABSTRACT The intricate relationship between public service media (PSM) and social media platforms has emerged as a critical factor significantly impacting the legitimacy of the PSM institution. This study adopts a discursive institutionalism lens to examine how six PSM organizations across Europe, Australia and Canada communicate their relationship and ideas about social media platforms via their annual reports over a 10-year period (2013–2022). Annual reports provide valuable insight into PSM organizations’ discursive processes that are aimed at generating (and justifying) public and political support. The analysis uncovers a complex and at times contradictory set of discourses revolving around audience attention and interaction, editorial integrity, and digital safety. While building platform presence is portrayed as crucial for sustained reach and relevance, PSM organizations also position themselves as counterweights to negative platform influences within the national media ecology. We observe a shift towards increasingly risk-oriented platform narratives over time, particularly concerning Facebook, resulting in a more deliberate social media strategy among some PSM organizations, and even disengagement with the platform.

The chromosome-level genome and functional database accelerate research about biosynthesis of secondary metabolites in Rosa roxburghii

Rosa roxburghii Tratt, a valuable plant in China with long history, is famous for its fruit. It possesses various secondary metabolites, such as L-ascorbic acid (vitamin C), alkaloids and poly saccharides, which make it a high nutritional and medicinal value. Here we characterized the chromosome-level genome sequence of R. roxburghii, comprising seven pseudo-chromosomes with a total size of 531 Mb and a heterozygosity of 0.25%. We also annotated 45,226 coding gene loci after masking repeat elements. Orthologs for 90.1% of the Complete Single-Copy BUSCOs were found in the R. roxburghii annotation. By aligning with protein sequences from public platform, we annotated 85.89% genes from R. roxburghii. Comparative genomic analysis revealed that R. roxburghii diverged from Rosa chinensis approximately 5.58 to 13.17 million years ago, and no whole-genome duplication event occurred after the divergence from eudicots. To fully utilize this genomic resource, we constructed a genomic database RroFGD with various analysis tools. Otherwise, 69 enzyme genes involved in L-ascorbate biosynthesis were identified and a key enzyme in the biosynthesis of vitamin C, GDH (L-Gal-1-dehydrogenase), is used as an example to introduce the functions of the database. This genome and database will facilitate the future investigations into gene function and molecular breeding in R. roxburghii.

How can open public data promote efficient and equitable green production? Evidence from eco-efficiency in China.

Nowadays, concurrent attention to economic development and ecological issues is becoming an important trend. In this paper, we measure the eco-efficiency of 285 Chinese cities from 2003 to 2019 using a non-radial directional distance function and the data envelopment analysis method, based on which we analyze the club convergence of cities' eco-efficiency using the logt test; we estimate the impact of open public data platforms on eco-efficiency and its convergence using a multi-period difference in difference model and panel-ordered logit model, respectively. We find that, first, open public data platforms improve cities' eco-efficiency by about 6.5%, and the impact mechanisms include scale efficiency, technical efficiency, and total factor productivity, or, at the micro level, increasing the economic agglomeration degree, boosting the amount of foreign investment used, and increasing green innovation level. Second, there are three convergence clubs of eco-efficiency in China's cities, whose average eco-efficiency trends are above, close to, and below average, respectively. Third, public data platforms significantly increase the probability of cities belonging to the convergence clubs of high and medium eco-efficiency (Clubs 1 and 2) and decrease the probability of belonging to the low one (Club 3). However, the mechanisms only include technical efficiency and total factor productivity, or the amount of foreign investment used and the green innovation level at the micro level.

Development and implementation of personal learning environment-based writing for publication scaffolding platform for Ph.D. Students

This research tackles the challenges Ph.D. students face in academic publishing, which are not fully addressed by existing support systems. It introduces the personal learning environment-based writing for publication (PLE-WfP) platform, created to offer extensive support in academic writing and publication. Developed and tested over ten months with thirteen Year 1 Ph.D. Chinese students at a Thai university, the platform uses a mixed-methods approach encompassing development, intervention, and feedback analysis through reflective diaries. The PLE-WfP platform, structured in individual, group, and submission stages, guides students from beginning writing to collaborative research and final publication, addressing common publishing challenges. Participants expressed a strong preference for the platform’s personalized assistance over conventional methods, highlighting its relevance, adaptability, promptness, and thoroughness. The importance of the academic community in enhancing collaborative writing, confidence, emotional regulation, and resource sharing was also emphasized. This study highlights the need for emotional and financial support in addition to cognitive and technical assistance in academic writing. By providing a novel scaffolding approach, this research contributes valuable insights and solutions to the academic community, demonstrating significant theoretical and practical benefits for global academic discourse.

PRINCIPLES OF COMPILING A GERMAN-UKRAINIAN DICTIONARY OF PARONYMS

This article addresses the principles of selecting paronyms and the methodology for compiling an electronic German-Ukrainian dictionary of paronyms using a corpus-based approach and systematic analysis to identify similarities and differences in the use of paronyms in speech. The author presents a concept of paronymy as a systemic phenomenon involving etymologically related words with similar morphemic structures but different meanings. By conducting a comparative analysis of collocations, the study identified 1,836 paronyms organized into paronymic series, each comprising two to nine components. The article examines existing dictionaries of paronyms and highlights their lexicographic shortcomings, such as excessive normative/prescriptive orientation, inadequate development of dictionary entries from both linguistic and didactic perspectives, and a lack of rigorous evaluations of modern data to inform dictionary compilation. The ideal paronym dictionary should rely on corpus data, providing reliable information with generalizations about individual word uses within conventionalized language usage. Corpus-based tools, such as those used for contrasting near-synonyms, systematically identify similarities and differences between paronyms in a contrastive manner. This process determines the degree of semantic closeness among expressions with similar contextual usage by comparing their immediate collocational patterns. The recorded semantic closeness or distance between paronyms is based on their contextual overlap in usage. The lexicographic processing of the selected paronyms was carried out with TshwaneLex, a computer-based dictionary creation software that allows lexicographers to develop dictionaries for any language without requiring advanced IT skills. TshwaneLex offers various features such as automatic cross-reference tracking, advanced dictionary comparison/merge functionality, and support for all world languages through full Unicode compatibility. The lemma editing process uses a tree-based interface to represent the hierarchical structure of the lemma, including different meanings, submeanings, word formations, multi-word units, examples of usage, cross-references, and more. Furthermore, the input/output architecture is designed to support the development of additional interfaces for various data sources as add-ons or plug-ins, facilitating other output formats. The article notes that a PHP-based software module is currently available for hosting the TshwaneLex dictionary on the internet, providing an accessible platform for electronic dictionary publication.

A novel self-supervised representation learning framework based on time-frequency alignment and interaction for mechanical fault diagnosis

Recently, supervised learning methods for intelligent mechanical fault diagnosis have achieved considerable advancements, while they heavily rely on labeled information and neglect massive unlabeled signals. Consequently, some self-supervised research has emerged and employs contrastive learning to mine general knowledge from unlabeled samples. However, these methods emphasize independent feature extraction and lack deep feature interaction, which limits better representation learning. Furthermore, it also restricts the performance improvement of fault diagnosis. To fill this gap, a novel self-supervised representation learning framework based on time-frequency alignment and interaction (TFAI) is proposed to improve the diagnosis reliability under limited labeled data. Comprising a dual encoder and a cross-modal encoder based on Transformers, the proposed TFAI model takes paired time-frequency data as input for self-supervised pretraining. Two self-supervised learning strategies, namely time-frequency alignment and time-frequency interaction, are proposed to enable the model to gain valuable knowledge from unlabeled data. The former leverages a time-frequency contrastive loss to drive the dual encoder to extract profound features from unlabeled data. The latter involves utilizing a time-frequency matching loss to guide the cross-modal encoder to perform deep feature fusion in an unsupervised manner. The well pretrained TFAI model can learn informative representations and can be fine-tuned for specific downstream fault diagnosis tasks to improve the diagnosis accuracy. By conducting three experimental cases on an axial flow pump system and a public gearbox platform system, the effectiveness, generalizability, and domain adaptability of the proposed method are validated.

Blockchain integration in healthcare: a comprehensive investigation of use cases, performance issues, and mitigation strategies.

Healthcare is a critical area where blockchain technology (BT) is being heralded as a potential game-changer for facilitating secure and efficient data sharing. The purpose of this review is to examine BT applications, performance challenges, and solutions in healthcare. To begin, This review paper explores popular blockchain networks for data exchange, encompassing both public and permissioned platforms, such as Ethereum and Hyperledger Fabric. This paper analyzes the potential applications of BT's decentralized, immutable, and smart contract capabilities in healthcare settings, including secure and interoperable health data exchange, patient consent management, drug supply chain oversight, and clinical trial management. The healthcare industry might greatly benefit from the increased privacy, transparency, and accessibility that these technologies provide. Despite BT's promising medical uses, the technology is not without its drawbacks. High energy consumption, throughput, and scalability are all concerns. We wrapped up by discussing the solutions that have been implemented, including consensus processes, scalability measures like sharding, and off-chain transactions that are designed to mitigate the drawbacks.

Feasibility and applicability of self-sampling based online cervical cancer screening: findings from the China online cervical cancer screening trial

ObjectiveThis study aims to evaluate the feasibility and applicability of an online cervical cancer screening program using a website as the public platform and self-collected HPV testing as the primary screening method.MethodsA website (mcareu.com) was developed to facilitate the online cervical cancer screening program by Peking University Shenzhen Hospital (PUSH). Women in Shenzhen could register for participation on the website by providing essential demographic data. Sampling kits and specimens were delivered through regular logistics. Eligible women collected vaginal samples by themselves using the provided kits and in referring of the graphic guidance. The specimens were tested for HPV at PUSH or a reference lab, and the results were accessible on the website through participants' personal accounts. Participants who tested positive for high-risk HPV were scheduled for colposcopy and biopsies. The demographic and social background data of the eligible participants were analyzed to evaluate the feasibility and applicability of the online screening approach.ResultsA total of 1712 applicants registered for participation, with 99.9% (1710/1712) completing registration with full data. The analysis included 1560 applicants aged 30–59, with an average age of 41.1 (± 7.6) years. Among them, 83.3% (1299/1560) provided self-collected samples for testing. Age-group analysis revealed an overall sample provision rate (SPR) exceeded 80% in all age groups. A significant difference in SPR was observed only between the 30–34 and 45–49 age groups (p < 0.05), while no significant differences were found among other age groups. 99.7% of the samples were tested qualified, and there was no significant difference in sampling failure rate among age groups. Analysis of demographic and social elements showed no significant impact on the rates of sample provision among groups in most of the social elements but the medical insurance and the monthly family-incomes.ConclusionThe findings demonstrate that online cervical cancer screening is reliable for self-registration, self-sampling, and self-ordering for specimen transportation. It is suitable for women of all ages needing to be screened, irrespective of social elements, and effectively facilitates screening for women with limited access to medical resources. Therefore, online screening holds promise as an effective approach to increase screening coverage.

A secure authentication and collaborative data-sharing model based on a blockchain network in the cloud

ABSTRACT In cloud computing, various organisations store their confidential information in private or public cloud platforms. The data sharing through the various cloud platforms increases the risk of security. To attain data sharing while sustaining data confidentiality, a secure sharing platform and collaboration model is needed. Hence, this paper introduces the novel secure authentication and collaborative data sharing (SecAuth_CDS) scheme for sharing data on blockchain. The entities involved in the SecAuth_CDS are third parties, data owners, smart contracts and blockchain networks. The security of the authentication process can be boosted by applying various security operations, such as Exclusive OR (EXOR), hashing, encryption and so on. The proposed method assures that the sharing is secure and trustworthy by blockchain. The experimental result reveals that the SecAuth_CDS performed effectively in terms of revenue, memory usage and computation cost with the values of 27, 62.20 MB and 0.003 sec.

DIMet: an open-source tool for differential analysis of targeted isotope-labeled metabolomics data.

Many diseases, such as cancer, are characterized by an alteration of cellular metabolism allowing cells to adapt to changes in the microenvironment. Stable isotope-resolved metabolomics (SIRM) and downstream data analyses are widely used techniques for unraveling cells' metabolic activity to understand the altered functioning of metabolic pathways in the diseased state. While a number of bioinformatic solutions exist for the differential analysis of SIRM data, there is currently no available resource providing a comprehensive toolbox. In this work, we present DIMet, a one-stop comprehensive tool for differential analysis of targeted tracer data. DIMet accepts metabolite total abundances, isotopologue contributions, and isotopic mean enrichment, and supports differential comparison (pairwise and multi-group), time-series analyses, and labeling profile comparison. Moreover, it integrates transcriptomics and targeted metabolomics data through network-based metabolograms. We illustrate the use of DIMet in real SIRM datasets obtained from Glioblastoma P3 cell-line samples. DIMet is open-source, and is readily available for routine downstream analysis of isotope-labeled targeted metabolomics data, as it can be used both in the command line interface or as a complete toolkit in the public Galaxy Europe and Workfow4Metabolomics web platforms. DIMet is freely available at https://github.com/cbib/DIMet, and through https://usegalaxy.eu and https://workflow4metabolomics.usegalaxy.fr. All the datasets are available at Zenodo https://zenodo.org/records/10925786.

Can public data availability affect stock price crash risk? Evidence from China

In the era of the digital economy, data has emerged as a critical factor of production. The exploration of methods to unlock the economic value inherent in the vast reservoirs of public data held by the government has now begun to draw the attention of scholars. However, little research has considered this issue from the perspective of financial market stability. Leveraging the staggered establishment of public data open platforms (PDOPs) across different cities of China and employing the staggered difference-in-differences (DID) model, we find that the establishment of PDOPs significantly decrease local firms' stock price crash risk (SPCR). Our results remain valid after various robustness tests, such as placebo tests, entropy balancing analysis, alternative measures of SPCR, as well as alternative difference-in-differences (DID) estimators. Furthermore, additional evidence suggests that the observed effects are driven by two mechanisms: improved operating performance and enhanced external monitoring. The former mechanism reduces the generation of bad news within firms, while the latter restricts managers' ability to hoard bad news. Further research findings show that the motivation of management to withhold bad news and the nature of firms' property rights are important factors affecting the relationship between public data availability and SPCR. Overall, our findings suggest that government can help improve capital market resource allocation efficiency by supplying public data to market participants.

Research on Promoting the Practice and Construction of Civilization in the New Era with High Quality in Minority Areas

To build the Center for the practice of civilization in the New Era into a public platform for disseminating scientific theories, a strong position for strengthening ideological and political work at the grassroots level, a spiritual home for carrying forward the new trends of The Times, and a broad stage for carrying out voluntary services with Chinese characteristics, a command center for integrating resources, making good use of existing resources and making good use of additional resources, and a service center for meeting the people’s ever-growing needs for a better life. The belief center, which guides people to be confident, inspires them to fight, and works with one heart and one mind, has created a “model” for civilized practice in the new era

DDE KGEditor: A data service system for knowledge graph construction in geoscience

AbstractDeep‐time Digital Earth (DDE) is an innovative international big science program, focusing on scientific propositions of earth evolution, changing Earth Science by coordinating global geoscience data, and sharing global geoscience knowledge. To facilitate the DDE program with recent advances in computer science, the geoscience knowledge graph plays a key role in organizing the data and knowledge of multiple geoscience subjects into Knowledge Graphs (KGs), which enables the calculation and inference over geoscience KGs for data mining and knowledge discovery. However, the construction of geoscience KGs is challenging. Though there have been some construction tools, they commonly lack collaborative editing and peer review for building high‐quality large‐scale geoscience professional KGs. To this end, a data service system or tool, DDE KG Editor, is developed to construct geoscience KGs. Specifically, it comes with several distinctive features such as collaborative editing, peer review, contribution records, intelligent assistance, and discussion forums. Currently, global geoscientists have contributed over 60,000 ontologies for 22 subjects. The stability, scalability, and intelligence of the system are regularly improving as a public online platform to better serve the DDE program.

A public mid-density genotyping platform for cultivated blueberry (Vaccinium spp.)

Small public breeding programmes have many barriers to adopting technology, particularly creating and using genetic marker panels for genomic-based decisions in selection. Here we report the creation of a DArTag panel of 3,000 loci distributed across the tetraploid genome of blueberry (Vaccinium corymbosum) for use in molecular breeding and genomic prediction. The creation of this marker panel brings cost-effective and rapid genotyping capabilities to public and private breeding programmes. The open access provided by this platform will allow genetic data sets generated on the marker panel to be compared and joined across projects, institutions and countries. This genotyping resource has the power to make routine genotyping a reality for any breeder of blueberry.

Active citizen or pest? Civic authorities, democratization and citizenship in inter-war England

Abstract In the 1920s, the art collector John Hilditch tirelessly undertook a public campaign to have his objects exhibited at the Manchester City Art Gallery. This article uses his struggle as a lens through which to examine how the relationship between the civic museum and its citizens was reshaped during the transition to mass democracy. Historians have explored how civic authorities responded to the challenge of mass democratization by encouraging their citizens to become ‘active’, but we know little about how the citizens responded to this call. Hilditch’s campaign allows us to see what public platforms citizens could negotiate to become ‘active’ citizens, and just how far they could influence civic policy.

Effectiveness of WeChat-assisted preoperative education to reduce perioperative anxiety in breast cancer patients: a prospective randomized controlled study protocol

BackgroundBreast cancer is the most prevalent cancer among women globally, and surgical procedures continue to be the primary treatment. However, over 50% of patients experience preoperative anxiety due to the unknown and fear associated with surgery. Although drug therapy is commonly used to address this anxiety, its side effects have led to a heated debate regarding its effectiveness. Consequently, non-pharmacological therapies, such as preoperative education, have emerged as an alternative approach to alleviate anxiety. WeChat, a widely popular social media platform, offers a public platform that can potentially be utilized for effective preoperative education. This study aims to evaluate the use of WeChat public platform as a tool for preoperative education in patients undergoing breast surgery.MethodsThis is a prospective, randomized, and controlled trial will involve 392 adult women scheduled for breast cancer resection. Participants will be randomly assigned to either the WeChat education group or the regular group. In addition to regular preoperative visits, the WeChat education group will also watch science videos through the WeChat public platform. The regular group will only receive education from ward nurses during preoperative visits. The primary outcome measure will be the incidence of preoperative anxiety, defined by scores of the State Anxiety Inventory (SAI) exceeding 40 points. Secondary outcome measures include the incidence of severe anxiety (SAI > 44) on the day before surgery, incidence of anxiety 72 h after surgery, incidence of severe anxiety 72 h after surgery, NRS scores for pain at rest and during activity 24, 48, and 72 h after surgery, incidence of nausea and vomiting within 24 h after surgery, subjective sleep score at 1 week postoperatively, quality of life QoR-15 scores at 1 and 3 months postoperatively, incidence of chronic pain at 3 months postoperatively, bowel function recovery, length of hospital stay, and hospitalization expenses.DiscussionThis is the first clinical trial to investigate the use of WeChat public platform for delivering preoperative education on perioperative anxiety in breast cancer patients. By utilizing the renowned WeChat public platform, our study aims to improve patient outcomes by providing video education that explains the disease, surgery, and anesthesia in a more accessible manner, thereby reducing the incidence of perioperative anxiety. If our hypothesis is confirmed, this non-pharmacological approach can be universally acknowledged as a cost-effective and practical method in clinical care. Its application can also be extended to other medical fields beyond breast cancer.Trial registrationClinicalTrials.gov, NCT05291494. Registered on 29 December 2021.

Causal relationship between gut microbiota and chronic renal failure: a two-sample Mendelian randomization study.

Observational studies and some experimental investigations have indicated that gut microbiota are closely associated with the incidence and progression of chronic renal failure. However, the causal relationship between gut microbiota and chronic renal failure remains unclear. The present study employs a two-sample Mendelian randomization approach to infer the causal relationship between gut microbiota and chronic renal failure at the genetic level. This research aims to determine whether there is a causal effect of gut microbiota on the risk of chronic renal failure, aiming to provide new evidence to support targeted gut therapy for the treatment of chronic renal failure. Employing genome-wide association study (GWAS) data from the public MiBioGen and IEU OpenGWAS platform, a two-sample Mendelian randomization analysis was conducted. The causal relationship between gut microbiota and chronic renal failure was inferred using five different methods: Inverse Variance Weighted, MR-Egger, Weighted Median, Simple Mode, and Weighted Mode. The study incorporated sensitivity analyses that encompassed evaluations for pleiotropy and heterogeneity. Subsequently, the results of the Mendelian randomization analysis underwent a stringent correction for multiple testing, employing the False Discovery Rate method to enhance the validity of our findings. According to the results from the Inverse Variance Weighted method, seven bacterial genera show a significant association with the outcome variable chronic renal failure. Of these, Ruminococcus (gauvreauii group) (OR = 0.82, 95% CI = 0.71-0.94, p = 0.004) may act as a protective factor against chronic renal failure, while the genera Escherichia-Shigella (OR = 1.22, 95% CI = 1.08-1.38, p = 0.001), Lactococcus (OR = 1.1, 95% CI = 1.02-1.19, p = 0.013), Odoribacter (OR = 1.23, 95% CI = 1.03-1.49, p = 0.026), Enterorhabdus (OR = 1.14, 95% CI = 1.00-1.29, p = 0.047), Eubacterium (eligens group) (OR = 1.18, 95% CI = 1.02-1.37, p = 0.024), and Howardella (OR = 1.18, 95% CI = 1.09-1.28, p < 0.001) may be risk factors for chronic renal failure. However, after correction for multiple comparisons using False Discovery Rate, only the associations with Escherichia-Shigella and Howardella remain significant, indicating that the other genera have suggestive associations. Sensitivity analyses did not reveal any pleiotropy or heterogeneity. Our two-sample Mendelian randomization study suggests that the genera Escherichia-Shigella and Howardella are risk factors for chronic renal failure, and they may serve as potential targets for future therapeutic interventions. However, the exact mechanisms of action are not yet clear, necessitating further research to elucidate their precise roles fully.