Plaintext Research Articles

Exploring the role of NLRP3 infalmmasome in diabetes: a literature review and bibliometric analysis

BackgroundDiabetes has emerged as the foremost public health challenge of the 21st century, with a notable shift towards managing it through an inflammatory lens. This study seeks to investigate the role of NLRP3 infalmmasome in diabetes over the past ten years, leveraging bibliometric analysis to pinpoint prevailing trends, underscore critical focal points, and establish a roadmap for subsequent research endeavors.MethodA literature search was conducted based on the SCI-E database, and all recorded results were downloaded in plain text format for subsequent analysis. The analysis was carried out using Vosviewer1.6.18, citespace6.3R1, and Microsoft Excel 2021 software, focusing on the following terms: country, institution, author, journal, references, and keywords.ResultsFrom January 1, 2014, to December 31, 2023, a total of 1373 articles were retrieved, with China, the United States, and Italy contributing the majority of records. Harbin Medical University, Nanjing Medical University, and Central South University stand as the top three most productive institutions. “International Journal of Molecular Sciences” leads the way with the highest number of publications, closely followed by “Frontiers in Immunology” and “Frontiers in Pharmacology.” Authors Wang Wei boast the most publications, closely followed by Li Xiang and Wang Yan. Within the superimposed keyword network, four primary clusters emerge: (1) exploring the link between NLRP3 infalmmasome and inflammatory diseases like diabetes; (2) investigating the cellular-level pathogenesis of diabetes-related conditions; (3) examining diabetes characteristics and associated suppression techniques; (4) studying cell morphology alterations, including pyroptosis. Over the past five years, key topics in this field have revolved around the “heart”, “damage”, “caspase 1 activation”, “NLRP3”, and “diabetic kidney disease”.ConclusionThis paper has identified the hot spots and trends concerning the role of NLRP3 infalmmasome in diabetes, thereby providing a valuable reference for future research. Furthermore, it is anticipated that pyroptosis and diabetes-related diseases will become frontier research topics that may garner significant attention in the coming years.

Security Risks in the Encryption of Database Connection Strings

ABSTRACTBackgroundThere exists an important and open problem with the encrypting of database connection strings in that related connection strings start with well known identical initial sub‐strings. When these initial sub‐strings are longer than the encrypting algorithm's block size, the resulting encrypted text for related database connection strings will have identical initial sub‐strings which could increase the chance of breaking the encryption key used to encrypt the connection strings.AimsThe objective of our work was to find a simple, easy to implement, well known algorithm that would eliminate the identical initial sub‐strings in the encrypted text of related database connection strings.MethodsOur methodology was to apply an obfuscation function to the database connection strings before encrypting them with the 3DES and AES encryption algorithms. The obfuscation functions we chose for our tests were the Roman Keyword cipher or the Greek Scytale cipher.DiscussionThe results of applying the two obfuscation functions to the related database connection strings before encrypting them with the symmetric encryption algorithms. This process effectively eliminated the commonality in the encrypted text of the database connection strings.ConclusionWhile both the Keyword and Scytale obfuscation functions were able to eliminate the commonality in the encrypted text. We found that the Scytale function was superior to the Keyword function due to some minor limitations of the Keyword functions and the fact that the Scytale function geometrically increased the number of possible known strings within the plain text connection string before encryption.

How to present economic evaluations to non-technical audiences? Randomized trials with professionals and the general population

BackgroundCost-effectiveness analyses of health technologies have become a part of the decision-making process in healthcare policies. Nevertheless, economic results are not always presented in comprehensible formats for non-technical audiences, such as the general population, healthcare professionals or decision-makers. The purpose of this study was to observe which formats best convey the key message of an economic evaluation, and which are best received by two different audiences.MethodsThe summary of a hypothetical cost-effectiveness analysis was edited in different formats: infographic, plain language text and video-abstract for the general population; executive summary and policy brief for a specialized population, i.e. clinicians, clinical and non-clinical managers, or methodologists in health-technology assessment. Participants were randomly shown one of the formats, and data on objective and subjective comprehension, and perceived usefulness/acceptability were gathered by means of online questionnaires. Statistical differences between formats within each audience were analysed.ResultsIn the general population (N = 324), objective comprehension was statistically significantly better for infographic than for video-abstract (p = 0.005), and for plain text than for video-abstract (p = 0.024). There were no differences in subjective comprehension, but video-abstract was considered statistically significantly more useful to understand the information than plain text (p = 0.011). In the specialized population (N = 100), no statistically significant differences were observed for objective and subjective comprehension, although policy brief was perceived as statistically significantly more useful than executive summary (p = 0.005).ConclusionsA balance between effectivity of conveying the message and attractivity of the format needs to be sought, to facilitate non-technical audiences’ understanding of economic data and, consequently, perceive decision-making processes as more transparent and legitimate. The infographic and policy brief could be robust ways to present economic data to the general public and specialized audience, respectively.

Mapping the mitochondrial landscape in T2DM: key findings from 2003-2023.

T2DM, a chronic metabolic disorder, poses a significant threat to global public health. Mitochondria play a crucial role in the pathogenesis of T2DM. This study intends to investigate the correlation between mitochondria and T2DM over the past two decades (2003-2023) through bibliometric analysis. Its objectives are to pinpoint trends, emphasize research priorities, and establish a foundation for future investigations. A literature search was conducted using the SCI-E database. All recorded results were downloaded in plain text format for further analysis. The following terms were analyzed using Vosviewer 1.6.18, citespace 6.3r1, bibliometrix in RStudio (v.4.4.1), and Microsoft Excel 2021: country, institution, author, journal, references, and keywords. From January 1, 2003 to December 31, 2023, a total of 2,732 articles were retrieved. The United States, China, and Italy contributed most of the records. UNIVERSITY OF CALIFORNIA SYSTEM, INSTITUT NATIONAL DE LA SANTE ET DE LA RECHERCHE MEDICAL INSERM, and US DEPARTMENT OF VETERANS AFFAIRS were the top 3 most productive institutions. rocha milagros, victor victor m had the most publications, followed by roden michael, and petersen kf had the most citations together. DIABETES published the most articles on research on this topic, followed by AMERICAN JOURNAL OF PHYSIOLOGY-ENDOCRINOLOGY AND METABOLISM, DIABETOLOGIA. The key points of this topic are the relationship between mitochondria and T2DM, the skeletal muscle mitochondrial changes observed in T2DM, and the impact of mitochondrial dysfunction on T2DM. Over the past five years, particle dynamics, mitochondrial dysfunction, and mechanism research have emerged as significant focal points in this field. This paper successfully identified the key areas and emerging trends in the relationship between mitochondria and T2DM, thereby offering valuable insights for future research.

Identification of Exploited Unreliable Account Passwords in the Information Infrastructure Using Machine Learning Methods

Accounts are an integral part of most modern information systems and provide their owners with the ability to authenticate within the system. This paper presents an analysis of existing methods for detecting simple account passwords in automated systems. Their advantages and disadvantages are listed. A method was developed to detect simple exploitable passwords that administrators can use to supplement other existing methods to increase the overall security of automated systems against threats from accounts potentially compromised by attackers. The method was based on the analysis of commands executed in automated or manual modes with the indication of credentials in plain text. Minimum password strength requirements are provided based on the security level. A special case was considered in which all passwords analyzed in this way were found explicitly in the system logs. We developed a unified definition of the classification of passwords into simple and strong, and also developed machine learning technology for their classification. The method offers a flexible adaptation to a specific system, taking into account the level of significance of the information being processed and the password policy adopted, expressed in the possibility of retraining the machine learning model. The experimental method using machine learning algorithms, namely the ensemble of decision trees, for classifying passwords into strong and potentially compromised by attackers based on flexible password strength criteria, showed high results. The performance of the method is also compared against other machine learning algorithms, specifically XGBoost, Random Forest, and Naive Bayes. The presented approach also solves the problem of detecting events related to the use and storage of credentials in plain text. We used the dataset of approximately 770,000 passwords, allowing the machine learning model to accurately classify 98% of the passwords by their significance levels.

Surface ablation laser surgery: Bibliometric and visualized analysis from 2004 to 2023.

Surface ablation laser surgery has resurged in popularity recently because of its safety in correcting myopia and favorable postoperative corneal biomechanical properties. This study aimed to investigate the current focal points and future trends in surface ablation laser surgery over the last 2 decades. The Web of Science Core Collection was used as the primary data source to retrieve literature related to surface ablation laser surgery. All records, including full records and reference details, were exported in plain text format. VOSvivewer, CiteSpace, and Pajek were used to perform the bibliometric and visual analyses of the countries/regions, institutions, authors, journals, and keywords of relevant publications. A total of 3415 articles on surface ablation laser surgery were published in 253 journals. These articles were authored by 9681 individuals from 2751 institutions across 79 countries. The United States leads in terms of productivity and influence in this field. The Tehran University of Medical Sciences and Kymion GD were the most productive institutions and authors, whereas the University of Crete and Randleman JB were the most influential. The Journal of Cataract and Refractive Surgery was the most productive and influential in this area, and citation analysis revealed that the top 10 most-cited references focused primarily on postoperative wound healing and wavefront aberration. The keywords were grouped into the following 5 clusters: clinical effects and complications, special indications, iatrogenic corneal ectasia, haze, and pain management. High-frequency keywords in recent years included transepithelial photorefractive keratectomy, retreatment, transepithelial phototherapeutic keratectomy, and biomechanical properties. This bibliometric analysis examined the development trends, global cooperation, research hotspots, and future directions of surface ablation over the past 20 years.

Effectiveness of AR-Based Formative Peer Assessment on Chinese Writing Performance of Students with Different Cognitive Styles

The ability to communicate effectively in writing and produce clear and cohesive text is a necessary skill in both educational settings and the workplace, yet many young students struggle to organize their thoughts and engage in deep thinking. To address these challenges, an augmented reality (AR) application titled “Explore Wild Animals” has been used to help students organize information; however, it may not accommodate different cognitive styles. Integrating formative peer assessment (FPA) strategy into AR-based instruction can enhance knowledge construction and address diverse cognitive needs. This study, conducted from May to June 2023, empirically investigates the effects of FPA in an AR environment on the writing performance of learners with field-independent (FI) and field-dependent (FD) cognitive styles. A total of 89 fifth-grade pupils from China were randomly assigned to two groups: one group adopting FPA in an AR environment (AR-FPA), and the other group adopting FPA in a conventional PowerPoint (PPT) version 2410 environment (FPA). The results of a two-way analysis of covariance (ANCOVA) indicate that the AR-FPA group outperformed the FPA group in writing performance. Specifically, FI learners benefitted more from the AR-FPA approach, while FD learners performed better with the FPA approach. However, multiple linear regression analysis reveals that the peer feedback quality and features showed little to no significant correlation with feedback providers’ writing performance, regardless of cognitive style. These results highlight the effectiveness of integrating AR and FPA in enhancing educational outcomes, providing practical insights for promoting the sustainability of technology-enhanced learning and teaching practices.

Number theory based modern cryptography: RSA and Diffie-Hellman algorithms

Abstract. In the twenty-first century, the Internet has grown rapidly. Cryptography, with the protection of number theory as an important guarantee for the Internet, is critical to study, and algorithms must be updated to keep up with the evolution of the digital world. With the rapid spread of the Internet, security flaws in certain algorithms have been exposed, generating widespread alarm. This literature review focuses on the introductions of the Rivest-Shamir-Adleman (RSA) and Diffie-Hellman algorithms, integrating and summarizing previous research. The content of this literature review includes an introduction to algorithms and number theory, specific applications of these algorithms, and security issues associated with them. Finally, this literature review concludes that RSA and Diffie-Hellman are inefficient and insecure in some sectors of application, necessitating the use of alternative algorithms or the replacement of some unique encryption methods to achieve the desired level of safety.

Aristóteles, <i>Primeiros Analíticos</i> II, 23-27: tradução e notas

This is an annotated translation of the last five chapters of the second book of Aristotle’s Prior Analytics. My aim is to provide Portuguese-speaking readers with a clear text, supplemented by materials that elucidate and contextualize Aristotle’s work, addressing a notable gap in the Lusophone bibliographical universe. To achieve this, I have based my translation on Ross’s edition of the Aristotelian text and compared it with various existing translations and commentaries.

Is a Picture Worth 280 Characters?: Contextually Realistic Graphics vs. Plain Text in Survey Experiments

Abstract As survey experiments have become increasingly common in political science, some scholars have questioned whether inferences about the real world can be drawn from experiments involving hypothetical, text-based scenarios. In response to this criticism, some researchers recommended using realistic, context-heavy vignettes while others argue that abstract vignettes do not generate substantially different results. We contribute to this debate by evaluating whether incorporating contextually realistic graphics into survey experiment vignettes affects experimental outcomes. We field three original experiments that vary whether respondents are shown a realistic graphic or a plain text description during an international crisis. In our experiments, varying whether respondents are shown realistic graphics or plain text descriptions generally yields little difference in outcomes. Our findings have implications for survey methodology and experiments in political science – researchers may not need to invest the time to develop contextually realistic graphics when designing experiments.

Efficient Storage and Analysis of Genomic Data: A k-mer Frequency Mapping and Image Representation Method.

k-mer frequencies are crucial for understanding DNA sequence patterns and structure, with applications in motif discovery, genome classification, and short read assembly. However, the exponential increase in the dimension of frequency tables with increasing k-mer length poses storage challenges. In this study, we present a novel method for compressing k-mer data without information loss, aiming to optimize storage and analysis processes. We employed Chaos Game Representation (CGR) to map k-mers to coordinates and used these components to generate raster images of k-mers. The CGR maps were partitioned and labeled based on substrings, with each substring mapped to a subframe, creating a fractal-like structure. The entire k-mer frequency set of each genomic sequence was represented as a single image, with each pixel corresponding to a specific k-mer and its occurrence. This approach reduced file size by up to 16-fold compared to plain text and 3-fold compared to binary format. Furthermore, we demonstrated the feasibility of performing alignment-free similarity analyses on images derived from k-mer frequencies of whole genome sequences from 14 plant species. Our results highlight the potential of this method as a fast and efficient tool for accessing, processing, and analyzing large biological sequence datasets, enabling the retrieval of k-mer frequencies and image reconstruction.

Hidden in plain text: Uncovering hidden heterogeneity and social stratification in Ireland AD 1150-1800.

Between the Irish late medieval (AD 1150-c.1550) (LMP) and post-medieval (AD c.1550-1800) (PMP) periods, colonial practices changed land ownership. Contextualizing these periods within a biocultural political economy framework supports increasing social inequality in the PMP, which we expect to be reflected in different skeletal markers of longevity and stress. Therefore, we hypothesized that widespread exposure to stressors and resource deprivation contributed to lower longevity in the PMP compared to the LMP, and that there would be greater variation between sites in the frequency of linear enamel hypoplasias (LEH) in the PMP. We estimated age and counted matched pairs of LEH on 526 adult skeletons from the Irish counties of Dublin, Kildare, Louth, and Meath. Age-at-death was compared through Kaplan-Meier survival functions and non-parametric tests. Linear enamel hypoplasias prevalences were analyzed with Mann-Whitney U tests and Generalized Linear Models. Age-at-death distribution changed between the LMP and PMP for males, with males experiencing an increase in median-age-at-death. The same was untrue for females, who show no changes in median age-at-death. Analysis of LEH frequency per individual showed that variation between sites was significant in the post-medieval period but not in the late medieval period. These results suggest that social organization contributed to embodied health experiences that varied across time periods. Specifically, populations from the PMP appear to have embodied greater social inequality in their more varied susceptibility to stress. Our work demonstrates the strength of pairing historical texts with the analysis of skeletal remains to evaluate the impact of social structures on biological frailty.

Identify devices and events from non-IP heterogeneous IoT network traffic.

In recent years, notable advancements have been achieved in the realm of identifying IP-based Internet of Things (IoT) devices and events. Nevertheless, the majority of methods rely on extracting fingerprints or features from plain text IP-based packets, which limits their ability to accommodate heterogeneous IoT devices such as ZigBee and Z-Wave, and fails to address the challenge of limited traffic samples. To tackle these issues, we propose a novel approach based on IoT communication characteristics and featuring module extensibility. This method is presented to effectively identify IoT devices and events from non-IP heterogeneous IoT network traffic. To shield the differences caused by the heterogeneous IoT protocol, a heterogeneous sample extraction platform with an extensible structure is created to extract raw sequence samples from ZigBee and Z-Wave traffic, with potential for expansion to other protocols. To address the challenges arising from the scarcity of samples, a sample identification framework based on IoT communication characteristics is devised to create synthetic samples from the raw sequence samples, enabling concurrent processing of the raw and synthetic samples using an identification model featuring two separate sequence networks. Comparative assessments of our method against baseline sequence models and the latest techniques demonstrate the advantages of our approach in identifying non-IP heterogeneous IoT traffic. The experimental results indicate that our method achieves an average accuracy improvement of 29.7% compared to baseline models using only raw samples. Furthermore, our method shows improvements of 22.1%, 21.5%, and 21.8% in macro precision, macro recall, and macro F1-score, respectively, over the latest method.

Application of the KARP RABIN Algorithm for Plagiarism Detection System in Thesis Proposal Submission in the Department of Informatics Engineering STMIK Kaputama

Plagiarism is a serious threat, especially to academic honesty, so a detection system that can analyze various types of documents is needed. This research develops a plagiarism detection system using Optical Character Recognition (OCR) to convert image text into digital text. Rabin – Karp algorithm with rolling hash and Dice Coefficient Similarity is applied to measure similarities between documents. Testing is carried out on .doc, .txt, .jpg files. As a result, the system can detect plagiarism well in clear text and image documents, but accuracy can decrease in low-quality images. In conclusion, the similarity of content, sentence structure, and format affects the degree of similarity, while OCR techniques work effectively even though they are limited to low-quality images.

Efficient indexing and querying of annotations in a pangenome graph.

The current reference genome is the backbone of diverse and rich annotations. Simple text formats, like VCF or BED, have been widely adopted and helped the critical exchange of genomic information. There is a dire need for tools and formats enabling pangenomic annotation to facilitate such enrichment of pangenomic references. The Graph Alignment Format (GAF) is a text format, tab-delimited like BED/VCF files, which was proposed to represent alignments. GAF could also be used to store paths representing annotations in a pangenome graph, but there are no tools to index and query them efficiently. Here, we present extensions to vg and HTSlib that provide efficient sorting, indexing, and querying for GAF files. With this approach, annotations overlapping a subgraph can be extracted quickly. Paths are sorted based on the IDs of traversed nodes, compressed with BGZIP, and indexed with HTSlib/tabix via our extensions for the GAF format. Compared to the binary GAM format, GAF files are easier to edit or inspect because they are plain text, and we show that they are twice as fast to sort and half as large on disk. In addition, we updated vg annotate, which takes BED or GFF3 annotation files relative to linear sequences and projects them into the pangenome. It can now produce GAF files representing these annotations' paths through the pangenome. We showcase these new tools on several applications. We projected annotations for all Human Pangenome Reference Consortium Year 1 haplotypes, including genes, segmental duplications, tandem repeats and repeats annotations, into the Minigraph-Cactus pangenome (GRCh38-based v1.1). We also projected known variants from the GWAS Catalog and expression QTLs from the GTEx project into the pangenome. Finally, we reanalyzed ATAC-seq data from ENCODE to demonstrate what a coverage track could look like in a pangenome graph. These rich annotations can be quickly queried with vg and visualized using existing tools like the Sequence Tube Map or Bandage.

Cryptography in Network Security: a Mini Review

Network security and cryptography are crucial components in safeguarding data and communication in the digital age. Cryptography plays a vital role in network security by converting plain text into unreadable formats to protect confidentiality and integrity. As organizations generate vast amounts of data daily, ensuring secure data transfer over the internet becomes increasingly important, with network security challenges growing as society transitions to the digital era. Techniques such as encryption are used to protect enterprise information and communication from cyber threats, ensuring the integrity, confidentiality, and accessibility of computer networks and data. Encryption and network security are employed to protect data stored in systems and exchanged over wireless networks, with cryptography utilizing various algorithms and approaches to secure networks and data effectively.

Improved Execution Efficiency of FPE Scheme Algorithm Based on Structural Optimization

The model of preserving a format encryption scheme based on a Feistel structure has developed rapidly and has been widely used in recent years. In this paper, the software implementation of the FF1 algorithm for the model was presented, and its execution efficiency was evaluated. Then, the efficiency bottleneck problem and its causes were identified. Based on the above analysis results, optimization methods were given from the perspectives of prepossessing, algorithm structure, and format conversion function, and implementation plans were provided. Finally, the simulation results show that the optimized performance improvement is significant, and the degree of performance improvement increases with the increase in plain text length.

User-Centered Design Strategies for Age-Friendly Mobile News Apps

With the increasing elderly population, catering to the digital news needs of the elderly has become increasingly crucial. This study employs factor analysis to explore the requirements and challenges of elderly individuals when using mobile news applications, aiming to provide strategies and directions for age-friendly design of mobile news applications. The research first summarizes a series of key factors that influence elderly users when using mobile news applications, categorizing them into three primary factors: usability and navigation, visual presentation and readability, and interaction and touch controls. Further entropy weight analysis reveals that the usability and navigation factor holds the highest priority for improvement, followed by visual presentation and readability, and lastly, interaction and touch controls. Based on these findings, we propose a series of age-friendly design strategies, including rational interface layout, clear text and images, and user-friendly interaction controls. These strategies not only enhance the digital news experience for elderly users but also offer valuable guidance and innovation for the field of mobile application design.

Experimental determination of the sulfur K-shell fundamental parameters employing the holistic approach

Abstract Sulfur and its compounds are both very abundant in the environment and very important for technological applications such as batteries. X-ray spectroscopic characterizations of sulfur compounds for a quantitative determination of the present amount of sulfur often requires a good knowledge on the atomic fundamental parameters involved. These quantitatively describe the processes of photoionization and X-ray fluorescence emission, making them crucial for X-ray spectroscopy-based quantifications. 
By employing the recently demonstrated holistic approach, the atomic fundamental parameters of the sulfur K-shell were experimentally determined using the radiometrically calibrated instrumentation of the Physikalisch-Technische Bundesanstalt (PTB). The transition probabilities of the main K-shell fluorescence lines, the K-shell fluorescence yield, the K-shell Auger yield, the subshell photoionization cross section and fluorescence production cross section were determined by means of photon energy dependent X-ray fluorescence and transmission measurements on a thin InS coated silicon nitride membrane. The results are also downloadable from Zenodo as plain text.

Self-Invertible Key on Cipher Polygraphic Polyfunction with Eigen Matrix based IMIE

Hill Cipher’s System and its modifications are still practiced mainly in sending a secret message involving images. One drawback that the recipient of the message is trying to overcome is to get the decryption key from the cipher text to plain text during the decryption process. Previous studies have proven that using self-invertible keys can reduce the computation cost to obtain this key. This paper generates a self-invertible matrix based on Integer-Entry Matrix with all Integer Eigenvalues (IMIE). Subsequently, we executed it into Cipher Polygraphic Polyfunction Cryptosystem and observed the effect.