bluuebunny/crossref_metadata_2025 huggingface dataset:
IL20RB is a Prognostically Significant Gene and a Potential Biomarker in Papillary Renal Cell Carcinoma
Authors: Jiang Tiancheng , Zhang Lei , Mao Weipu , Zhu Yongkun , Wang Ganlin , Wang Guoqing , Chen Shuqiu , Zhang Zhenglin
Published: October 2024

Background
Papillary renal cell carcinoma (pRCC) is the second most common type of renal cancer after clear cell renal cell carcinoma. While diagnostic and therapeutic approaches for pRCC often draw on experiences from clear cell renal cell carcinoma, specific targets for diagnosis and treatment remain elusive. IL20RB, the interleukin 20 receptor subunit β, plays a crucial role in IL20R heterodimerization. Initially associated with a variety of skin diseases, recent studies have implicated IL20RB in various malignancies, yet its role in pRCC remains unclear.
Methods
Prognostic value was determined using the TCGA database and GEPIA, with validation through the analysis of our center’s samples. Subsequent, survival analysis of pRCC patients with high and low IL20RB expression was conducted using R software (version 4.2.1). To further understand the function of IL20RB in pRCC, immune infiltration analysis as well as KEGG/GO enrichment analysis were performed using TRNA sequencing expression profiles from the TCGA database. Protein-protein interactions (PPI) between IL20RB and other proteins in human species were explored using the STRING (String-db.org) database, and drug sensitivity analysis was conducted using the GDSC database.
Results
Our results revealed specific expression of IL20RB in pRCC tumor tissues, with high expression associated with a poorer prognosis and significantly positively correlated with clinical features. Additionlly, IL20RB may function as a cytokine receptor involved in the immune-inflammatory response in the pRCC microenvironment.
Conclusions
IL20RB plays an important role in pRCC and may serve as a potential prognostic oncogene, offering a potential target for future therapeutic interventions.
Relationship of oxidative balance score to gallstone prevalence and gallbladder surgery in American adults: evidence from NHANES 2017-2020
Authors: Wei Shouxin , Yu Sijia , Xu Zhengwen , Qian Chuan , Jia Yindong
Published: October 2024

Gallstones represent a common gastrointestinal disorder, affecting approximately 10-20% of the global population. Oxidative stress plays a pivotal role in the pathogenesis of gallstones; however, there remains a lack of effective clinical indicators to predict their formation. This study addresses this gap by investigating the relationship between oxidative balance score (OBS) and the incidence of gallstones and gallbladder surgery, utilizing data from the National Health and Nutrition Examination Survey (NHANES) from 2017 to 2020. Methods: This study investigated the association between OBS and the occurrence of gallstones and gallbladder surgery by analyzing data from NHANES for the period 2017–2020, adjusting for potential confounders such as age, sex, race, and comorbidities. Various statistical analyses were applied, including multivariate logistic regression, subgroup analyses, and smoothed curve-fitting models.Results: The results revealed a significant negative correlation between OBS and the risks of both gallstones and gallbladder surgery, with each one-unit increase in OBS associated with a 2.6% reduction in gallstone risk (OR=0.974, 95% CI: 0.958-0.990) and a 3.3% reduction in the risk of gallbladder surgery (OR=0.967, 95% CI: 0.950-0.983). Subgroup analyses confirmed the consistency of these associations across various demographic and health-related factors. Furthermore, a dose-response relationship was observed between higher OBS and a reduced risk of gallbladder surgery.Conclusions: This study demonstrates that a higher OBS is associated with a lower incidence of gallstones and reduced rates of gallbladder surgery, offering new insights into the link between oxidative stress levels and gallstone formation as well as surgical intervention. The findings underscore the potential of OBS as a valuable tool for identifying at-risk populations and informing personalized prevention strategies for gallstone disease.
CXCL12 regulates breast cancer metastasis and T lymphocyte infiltration
Authors: Liu Xian-Fu , Chen Yan-Song , Tang Jing-Wei , Zhang Hao , Zhang Xiao-Jing
Published: October 2024

Purpose: Breast cancer (BC) is a malignant tumor with the highest incidence rate in women. This work explored the function of CXCL12 in tumor metastasis.
Methods: CXCL12 protein expression levels were assessedby IHC in breast cancer tissues. String revealed thatCXCL12 interacts with CXCL10 protein molecules. The GEPIA2 database revealed that CXCL12 was negatively correlated with CXCL10.CXCL12. The effects of CXCL12 on invasion and migration were detected by scratch and transwell experiments in breast cancer cells. CD4+ T and CD8+ T cells in the inflammatory microenvironment of breast cancer patients were evaluated with the NGDC database and verified by IHC.
Results: CXCL12 knockdown inhibited migration and invasion and enhanced the expression and secretion of CXCL10 in BC. CXCL10 isresponsible for the recruitment of CD4+ and CD8+ T lymphocytes into tumors and enhances antitumoreffects. The single-cell data showed that the patients in the CXCL10+CD4+/CD8+ T-cell group and the CXCL12-CD4+/CD8+ T-cell group had better prognoses.
Conclusions:CXCL12 promoted BC migration and invasion. On the otherhand, CXCL12 inhibited the expression and secretion of CXCL10, further inhibiting T lymphocyteinfiltration and promoting breast cancer metastasis in the TME.
Influence of the filling technique on endodontic retreatment in curved mesial canals of mandibular molars – An in vitro study
Authors: Copelli Felipe Andretta , Oda Lisa Yurie , Leal Renata Maira Souza , Rodrigues Clarissa Teles , Duarte Marco Antonio Hungaro , Cavenago Bruno Cavalini
Published: October 2024

Objectives: To evaluate the influence of the filling technique associated with a bioceramic sealer on the remaining filling material and the impact on the remaining dentin after the filling removal in the retreatment of curved mesial canals of mandibular molars.
Materials and Methods:Eighteen curved mesial roots of mandibular molars with Vertucci type IV canals were prepared up to instrument 35/.05. Two groups were established according to the filling technique: single-cone (n=18) and continuous wave of condensation (n=18). Bio-C Sealer was used for both techniques. The filling was removed using the Reciproc Blue R25 instrument, and the repreparation was carried out using the Reciproc Blue R40 instrument. The samples were scanned by micro-CT before and after the filling removal. For statistical analysis, the data normality was checked, and parametric tests were used for normal distribution and non-parametric tests for non-normal distribution.
Results: The remaining filling material was observed in both groups during retreatment, with no differences. Analyses of transportation and centering ability also indicated similarities between the two groups.
Conclusions: Endodontic retreatment in curved mesial canals of mandibular molars filled with single-cone or continuous wave of condensation techniques and filled with bioceramic sealer was similar. The use of reciprocating instruments for filling removal and repreparation was efficient in curved mesial canals of mandibular molars, with no complications.
Clinical relevance: The filling technique did not influence the non-surgical endodontic retreatment in curved mesial canals. Regardless of the technique, it was not possible to remove all the filling material from the root canals.
Numerical simulation of soliton solutions of nonlinear Fitzhugh-Nagumo equation by using LOOCV with exponential B-spline with Significant Applications in Neurosciences
Authors: RANI RICHA , ARORA GEETA
Published: October 2024

This study focuses on solving the one-dimensional nonlinear Fitzhugh-Nagumo (FHN) equation using a novel technique called the “Exponential modified cubic B-spline differential quadrature method” combined with “leave-one-out cross-validation”. The inclusion of leave-one-out cross-validation (LOOCV) is essential for finding the optimal value of the parameter \(\:\lambda\:\), which is a key component in the exponential modified cubic B-spline basis functions, thereby enhancing the accuracy and robustness of the results. By incorporating this unique combination of LOOCV and the exponential modified cubic B-spline differential quadrature method, the research introduces a new computational approach that could be of considerable interest to scholars in the field. This method has been applied to four different examples of the Fitzhugh-Nagumo equation, with outcomes detailed in tables and figures. This paper presents the methodology and results of a study on the equation, emphasizing its significance and applications in neuroscience. The Fitzhugh-Nagumo model is highlighted as a versatile tool across various scientific, engineering, and mathematical fields, with a particular focus on its role in understanding the complex dynamics of neural systems and its potential impact on future research and real-world problems.
Application and research prospects of straw in clean production
Authors: Ye Haodong , Wen Jun , Ouyang Shasha
Published: October 2024

The abundance of straw resources provides a fertile ground for research on the current state of their clean production and circular application. Leveraging the Web of Science (WOS) database as the primary data source and utilizing the CiteSpace literature analysis tool, this study conducts a visual analysis of research literature related to rice straw. It explores parameters such as publication volume, authors, research institutions, countries, keywords, and more, providing a knowledge map for better understanding. The research findings offer an objective and scientific overview of the quantity and annual trends in literature pertaining to the application of straw materials in clean production. Moreover, the study identifies high-frequency keywords, prolific authors, publishing institutions, and research frontiers and directions, thereby establishing a research framework based on straw for clean production application. This study also serves as valuable reference material for scholars in related fields, providing insights for future diversified research endeavors.
Comparison of Hetrombopag and Eltrombopag added to first-line immunosuppressive therapy in severe aplastic anemia
Authors: Zhang Baohang , Yang Wenrui , Kang Rui , Shi Yimeng , Hu Xiangrong , Zhang Li , Jing Liping , Yuan Weiping , Shi Jun , Zhang Fengkui , Zhao Xin
Published: October 2024

The addition of thrombopoietin receptor agonists (TPO-RAs) to immunosuppressive therapy (IST) improves the hematologic response rate and quality in patients with severe aplastic anemia (SAA). However, no studies have yet reported on whether there are differences in the efficacy of TPO-RAs. Here we analyzed the clinical data of SAA patients who received hetrombopag (HPAG) or eltrombopag (EPAG) as part of first-line standard IST between March 2020 and June 2022 to compare the efficacy of HPAG and EPAG in SAA patients. Sixty-seven patients were enrolled in the HPAG group and 42 patients in the EPAG group, with a similar proportion of very severe aplastic anemia (VSAA) patients between the two groups (26.9% vs 33.3%, P = 0.613). The overall hematologic response (OR) rates of the HPAG group at 3 and 6 months after IST were 50.7% and 65.6%, respectively, close to that of the EPAG group (50%, P = 0.973; 73.8%, P = 0.494). The rates of complete response (CR) at 3 and 6 months were 13.4% and 31.3% in the HPAG group, respectively, which were like those in the EPAG group (11.9% and 28.6%), with no statistical difference (P = 1.00 and 0.59). The median time to first response (3.0 months vs 3.2 months, P = 0.79) was similar in HPAG and EPAG. We further analyzed data of VSAA patients. The OR rate, CR rate and the time to obtain first response in VSAAs were comparable between the two groups. The median follow-up time of HPAG group was 22.38 (3-33.27) months, and that of EPAG group was 33.9 (9.4–49.2) months. The overall survival (OS) rates were 91.0% and 92.8% in HPAG group and EPAG group (P = 0.53), respectively. Monosomy 7 was detected in 1 patient in EPAG group and her disease transformed to acute myelocytic leukemia (AML) at 25 months after ATG treatment. One patient in HPAG had trisomy 8 at 9 months of ATG treatment, and bone marrow examination showed no disease progression. Conclusion: The addition of HPAG to standard IST in SAA patients showed similar response rates and response quality to that of EPAG. HPAG could be an alternative of EPAG for the first-line treatment of SAA patients.
Using Dynamic Token Embedding Compression to Optimize Inference Process in Large Language Models
Authors: Gavrilo Diana , Johansson Christopher , Petrovich Thomas , Martins Gabriel , Edwards Samuel
Published: October 2024

Large-scale deep learning architectures, while transformative for language understanding and generation, impose substantial computational and memory demands during inference, often limiting their practical deployment in constrained environments. Introducing a novel methodology, Dynamic Token Embedding Compression (DTEC), this work addresses these challenges through a selective token embedding mechanism that dynamically adjusts embedding dimensionality based on contextual relevance during inference. DTEC optimizes memory usage and inference time by applying high compression to low-relevance tokens while preserving dimensionality for those deemed critical to context, resulting in significant improvements in resource efficiency. Experimental results demonstrate that DTEC reduces inference time by 25.6% and memory consumption by 30.2% on average across various text lengths without compromising model accuracy or output quality. Moreover, DTEC effectively lowers hallucination rates, enhancing model fidelity and strengthening its application across tasks requiring precision and reliability. With its adaptive token prioritization, DTEC emerges as an efficient framework for resource-limited environments and a promising approach for real-time, scalable LLM deployment.
Inequality in human development amplifies climate-related disaster risk
Authors: Teber Khalil , Sippel Sebastian , Krause Melanie , Zscheischler Jakob , Mahecha Miguel
Published: October 2024

The severity of climate disaster impacts is shaped not only by the intensity of the
events themselves but also by the exposure, vulnerability, and adaptive capac-
ity of affected communities. The lack of globally comparable data integrating
local societal and climatic conditions poses significant challenges to understand-
ing which factors transform hazardous events into disasters. Existing research
often neglects both hazard intensity and within-country inequality when analyz-
ing the relationship between climate hazard events and their outcomes.
Here, analyzing over 7,000 climate-related disasters from 1990 to 2020, we show
that human development drives major shifts in global exposure and impact pat-
terns, with societal vulnerability outweighing hazard intensity in driving impacts.
Regions with low subnational Human Development Index (sHDI) scores expe-
rience disproportionately higher human losses and economic damage, despite
reduced hazard exposure over the past three decades. For instance, individuals
in these regions are 8.2 times (95% CI: 2.16–23.06) more likely to be killed by
storms compared to those in very high sHDI regions. Our findings also indicate
that within-country inequality exacerbates disaster risk in low- and medium-sHDI
regions. These results underscore the critical role of human development in miti-
gating disaster risks and highlight the link between socioeconomic conditions and
disaster vulnerability.
Treatment with Ascorbyl Glucoside Arginine Complex ameliorates solar lentigos
Authors: Takada Mariko , Numano Kayoko , Nakano Masahiko , Yamamoto Akio , Imokawa Genji
Published: October 2024

Little is known about anti-pigmenting effects of whitening agents on solar lentigo (SLs). To characterize the anti-pigmenting effects of newly designed ascorbyl glucoside arginine complex (AGAC) on SLs, lotions with or without 28% AGAC were applied twice daily for 24 weeks in a double-blind half-face study of 27 Japanese females with SLs. Pigmentation scores were evaluated using a photo-scale and skin colors were assessed using a color difference meter and a Mexameter. Treatment with the test lotion elicited a significant decrease in pigmentation scores at 24 weeks com-pared to week 0, with a significant decrease in pigmentation scores at 24 weeks compared to the placebo lotion-treated SLs. In the test lotion-treated SLs, the lightness (L) values and melanin index (MI) reflecting pigmentation level significantly (p<0.0001) increased and decreased, respectively, at 12 and 24 weeks of treatment compared to week 0. Comparisons of increased L values or decreased MI values between the test and placebo lotion-treated SLs demonstrated that the test lotion-treated SLs had significantly higher increased L or decreased MI values than the placebo lotion-treated SLs both at 12 and 24 weeks of treatment. The sum of our results strongly indicates that AGAC is distinctly effective in ameliorating the hyperpigmentation levels of SLs at a visibly recognizable level by the subjects without any hypo-pigmenting effects or skin irritation.
Role and mechanism of Pim-2 kinase inhibitors-induced immunogenic cell death in multiple myeloma
Authors: Liu Zhaoyun , Shen Hongli , Che Mengting , Zhao Xianghong , Wang Hao , Yang Chun , Fu Rong
Published: October 2024

Background Immune dysfunction is an important part of pathogenesis in multiple myeloma, and restoring anti-myeloma immunity has become a key research direction.
Methods This article demonstrates through in vivo and in vitro experiments whether and how Pim-2 kinase inhibitors induce immunogenic cell death in multiple myeloma.
Results In this study, Pim-2 kinase inhibitors up-regulate IRE1 phosphorylation, promote XBP1 and CHOP transcription, thereby mediating endoplasmic reticulum stress in MM cells. Endoplasmic reticulum stress(ER-stress) and increased reactive oxygen species(ROS) levels can promote the expression of damage related molecular patterns and promote immunogenic cell death in MM cells. In addition, Pim-2 kinase inhibitors-treated MM cell lines can up-regulate the expression of activation molecules on the surface of Dendritic cells(DCs) from MM patients, promote T lymphocyte differentiation from Naïve T cells to effector memory T cells, and promote the expression of T lymphocyte functional molecules. In vivo, it is shown that Pim-2 kinase inhibitors can simulate human DCs maturation and activate functional T lymphocytes.
Conclusions As a consequence, these data improve our knowledge about how Pim-2 kinase inhibitors regulates anti-myeloma immunity and provide justification for applying Pim-2 kinase inhibitors in multiple myeloma(MM) treatment.
Krakauer Bürger Heinrich Smedt und die Rechnungen seiner Flandernreise 1401-1402
Authors: Bartoszewicz Agnieszka , Starzynski Marcin
Published: October 2024

Dieser Artikel präsentiert eine kritische Edition der Rechnungen, die Heinrich Smedt, ein Krakauer Kaufmann, 1401-1402 schrieb.
vorbisteringhe deß heringheß uthe deme Sunde
Authors: Lehmann Oliver , Schmidt Jörn O. , Voss Rüdiger
Published: October 2024

Gesalzener Hering wurde eine der wichtigsten Handelswaren zur Zeit der Hanse.
Über (Miss-)Kommunikation bei mittelalterlichen Sprachkontakten
Authors: Ivanov Sergey
Published: October 2024

Der vorliegende Beitrag beschäftigt sich mit den Herausforderungen in der Kommunikation im Handel des östlichen Ostseeraums. Die hier in den Blick genommenen Beziehungen zwischen Riga und Polozk waren intensiv und umfangreich; dennoch bleibt weitestgehend offen, wie genau die Kommunikation verlief, welche Sprachen benutzt wurden und in wessen Verantwortung die Übersetzung lag. Dieser Beitrag untersucht den einzigen lateinischen Brief, der von Polozk an Riga im 15. Jh. geschickt wurde. Weiterhin wird behandelt, welche Rolle das Latein in der Kommunikation zwischen den Städten spielte und ob die Situationen in Polozk und Novgorod in Bezug auf Übersetzungen sich unterschieden. Besondere Aufmerksamkeit gilt dabei der möglichen Beteiligung von ‚Schlesiern‘ als Dolmetscher am Polozker Hofe, wie sie in der letzten überarbeiteten Ausgabe der Polozker Urkunden vorgeschlagen wird. Die Auseinandersetzung mit dem Text und seinem zeitlichen Kontext spricht dabei für schlesische Übersetzertätigkeit und erlaubt die Hypothese, dass ihre Erwähnung zudem auf handelspolitische Verschiebungen anspielt.
ЛЕС БРОСЕЛИАНД В СОВРЕМЕННОЙ ФРАНЦУЗСКОЙ БРЕТАНИ КАК ТЕРРИТОРИЯ ВООБРАЖАЕМОГО ПРОСТРАНСТВА
Authors: М.К. ЛЮБАРТ
Published: October 2024

Лес Броселианд — мифический лес средневековых легенд артурианского цикла, был в течение XIX–XX вв. идентифицирован с частью реального леса Пемпон (Бретань, Франция). Он стал территорией «материализации» чудесных персонажей, героев, легендарных событий, что выражается в нахождении в нем «тех самых мест», где происходило то или иное легендарное событие, деятельности туристических и культурных организаций, иллюстрирующих (иногда с помощью аниматоров, артистов, художников) мифические события. Лес, пользующийся большой популярностью и из-за природной ценности, стал также неким пространством, где воплощаются мифологические исторические представления, фантазии как профессиональных творческих людей, обычных посетителей, а также приверженцев неоязыческих культов, прежде всего неодруидизма.

The Broceliand forest is a mythical forestof medieval Arthurian legends,which was identified duringthe XIX–XX centuries witha part of the real forestof Paempon (Brittany, France). It has become the territoryof the «materialization» of wonderful characters, heroes, legendary events, which is expressed in finding in it«the very places» where this or that legendary event took place, the activitiesof tourist and cultural organizations illustrating (sometimes withthe help of animators, artists, artists) mythical events.The forest, which is very popular because ofits natural value, has also become a kind of space where mythological historical ideas, fantasies of professional creative people, ordinary visitors, as well as adherents of neo-pagan cults, primarily neo-Druidism, are embodied.
Decorative properties of cultivars of Liquidambar styraciflua L.
Authors: Svitylko I. M.
Published: October 2024

Purpose. To determine the structure, and color of the leaf, the bark of the branches, the habit of the crown, the linear characteristics of the plants, the features of flowering and fruiting of Liquidambar styraciflua L. and its cultivars. Provide recommendations for their use in landscaping. Methods.
The subjects of the research were cultivars of L. styraciflua, which were found in arboretums, parks, squares, garden centers, nurseries and in green areas throughout Ukraine. The plants were evaluated according to a complex scale of ornamental value of woody plants, which includes four levels of ornamental value of trees and shrubs: very high, high, medium and low. The methods of observation, analysis, comparison, data summarization, photo­fixation and descriptive­taxation method were used. Results. A comprehensive assessment of the decorative effect of L. styraciflua cultivars (‘Worplesdon’, ‘Gum Ball’, ‘Rotundiloba’, ‘Albomarginata Manon’, ‘Oakville Highlight’, ‘Slender Silhouette’, ‘Fastigiatа’, ‘Pasquali’, ‘Stared’, ‘Thea’, ‘Aurea’, ‘Golden Treasure’, ‘Variegata’, ‘Golden Sun’, ‘Stella’, ‘Stella Rossa’) against species plants was carried out. Based on the results of the research, a table was compiled to help landscape architects, nursery and garden center workers, botanists and ecologists, students and staff of forestry and botanical institutions in the selection of plants. Recommendations are given for the use of L. styraciflua cultivars in landscaping. Conclusions. According to the degree of ornamentality, the vast majo­rity of L. styraciflua cultivars (13) belong to group I (very high, 65–90 points). They are recommended for use in urban and domestic landscaping and for the creation of various landscape objects. Group II (high ornamental value, 51–64 points) contains two cultivars, and group III (moderate ornamental value, 41–50 points) contains only one cultivar.
Cancer-associated Fibroblasts (CAFs) Regulate Lung Cancer Malignant Progression by Transferring SERPINE2 (PN1) Via Exosomes
Authors: Chen Yu , Zhu Sihong , Yang Ling , Lu YuFen , Ye Xiaoqun
Published: October 2024

Background and Aim:

Cancer-associated fibroblasts (CAFs), one of the
most abundant stromal cell types in the tumor microenvironment (TME), are potential
targets for cancer treatments such as lung cancer. However, the underlying
mechanism by which CAFs promote lung cancer progression remains elusive.

Methods:

We obtained primary CAFs, normal fibroblasts (NFs), and their exosomes
and constructed protease nexin-1 (PN1) stably silenced or over-expressed CAFs cells
using lentivirus. Bioinformatics was used to obtain the expression of PN1 in lung
cancer and normal tissues, the relationship with overall survival, and the enriched
pathways. The MTT and Transwell assays were performed to detect the proliferation,
migration, and invasion abilities of lung cancer cells after treatment. Western blotting,
qRT-PCR, immunohistochemistry, and xenograft models were used to illustrate the
role of CAFs in lung cancer progression via exosomes.

Results:

CAFs-derived exosomes, in which PN1 was more highly expressed than that
in NFs-derived ones, effectively promoted the proliferation, migration, and invasion
potentials of lung cancer cells A549 and H1975. Meanwhile, the PN1 expression was
higher in lung cancer tissues than that in normal tissues and was negatively
associated with the overall survival rate of lung cancer patients. More importantly,
over-expressing or silencing of PN1 in A549 and H1975 cells promoted or inhibited cell
proliferation, migration, and invasion, correspondingly. Furthermore, treated with PN1
overexpressing CAFs-derived exosomes, the lung cancer cells proliferation, migration,
and invasion varied positively and were accompanied by activation of Toll-like and NF-
κB signaling pathways. However, this phenomenon can be reversed by AN-3485, an
antagonist of the Toll-like pathway. Finally, overexpression of PN1 leads to
accelerated tumor growth by increasing the expression of the proliferation biomarker
Ki67 and activation of the NF-κB signaling pathway in vivo.

Conclusion:

CAFs promoted lung cancer progression by transferring PN1 and
activating the Toll-like/NF-κB signaling pathway via exosomes.
In Vitro Antibacterial Activity of Adiantum capillus Veneris Extraction with Methanol, Chloroform, and Ether Solvents against Methicillin-resistant Staphylococcus aureus
Authors: Ebrahimzadeh Mahdiyeh , Rahbari Solmaz , Doust Reza Hosseini , Mojab Faraz
Published: October 2024

Background:

The increasing problem of multi-drug resistant (MDR) pathogens is a worldwide
concern, especially in the pharmaceutical industry. At the same time, medicinal plants have
renewed interest because of their wide variety of bioactive phytochemicals, which could be used to
develop new antimicrobial drugs. This renewed interest is partly due to the growing resistance to
traditional drugs and their associated side effects.

Methods:

The objective of this study is to assess the antimicrobial properties of the total extract and
various fractions of Adiantum capillus veneris against Methicillin-resistant Staphylococcus aureus
(MRSA). The aerial parts of Adiantum capillus veneris were subjected to extraction using methanol,
chloroform, and ether, and the resulting extracts were tested for their antimicrobial activity against
MRSA. Additionally, essential oil was obtained from the aerial parts using a Clevenger apparatus
and boiling water. Furthermore, Gas Chromatography-Mass Spectrometry (GC/MS) was utilized to
analyze the phytochemicals isolated from the extracts of Adiantum capillus veneris.

Results:

The essential oil was obtained through distillation and then analyzed using GC/MS. The
antimicrobial activity was evaluated using the agar diffusion method.

Conclusion:

GC/MS analysis revealed that the composition was primarily phytol (59.9%), constituting
99.3% of phyto-constituents. However, both the total extract and the individual fractions exhibited
no inhibitory effects against MRSA strains
Two-Dimensional MXene (Ti3C2Tx)-based Nano-Photosensitizers for Enhanced Photothermal Ablation of Tumor Cells
Authors: Sundramoorthy Ashok K.
Published: October 2024

Cancer remains one of the leading causes of death globally, accounting for approximately
one in every six deaths. Traditional cancer therapies, including surgery, chemotherapy, chemoimmunotherapy,
and radiation, face numerous challenges and limitations. In this context, we explore the
advantages of photothermal therapy (PTT) using two-dimensional (2D) MXene-based nanocomposites
for cancer treatment. MXenes, composed of abundant and non-toxic elements, such as titanium
(Ti), carbon (C), fluorine (F), and oxygen (O), demonstrate low toxicity and are promising candidates
in photothermal cancer therapies. Their ultrathin planar nanostructure, high photothermal conversion
efficiency, strong near-infrared (NIR) responsiveness, and chemically modifiable surfaces enhance
their therapeutic potential. Recent innovations include the development of folic acid-functionalized
Au@c-Ti3C2 nanostructures, a skin-mountable electrostimulation patch (eT-patch), ionic gels containing
MXene (Ti3C2Tx), and composite scaffolds made of MXene, collagen, silk fibroin, and quercetin.
These MXene-based photosensitive compounds offer efficient targeting and selective treatment
of cancer cells, highlighting their significant role in advancing cancer therapies.
A BEHAVIORAL APPROACH TO DETECT SOMNOLENCE OF CAB DRIVERS USING CONVOLUTIONAL NEURAL NETWORK
Authors:   Sona College of Technology, India, V Vinodhini , M Abishek ,   Sona College of Technology, India, K Divya ,   Sona College of Technology, India
Published: October 2024

The Road Traffic Accident Statistics concluded that fatal road accident 60% is caused by vehicle collision of taxi drivers. World Health Organization (WHO) is constantly initiating global road safety measures to minimize road accidents but, the cause for fatal injuries is primarily due to driving fatigue. Most people rely on cabs as the main transport. To provide obligate care of passengers, a computer vision-based technique is needed to detect the somnolence of drivers. Our proposed model CabSafety is based non-intrusive computer vision technique using Convolutional Neural Network (CNN). A tiny camera is fixed focusing the driver’s face to monitor the behavioral changes like an eye blink, yawing, watery eye, mouth movement, and head position. The measures of the driver’s eye are concentrated to identify sleepiness under stimulator or test conditions. The efficiency of the proposed model provides better results compared to the existing technique. The image from the camera is processed by OpenCV and Keras/Tensor flow. CNN classifier is used to detect eye status. The prediction from the CNN classifier produces an alarm to alert the driver.
ARTIFICIAL NEURAL NETWORKS TO DETECT FACIAL ABNORMALITIES THROUGH CEPHALOMETRIC RADIOGRAPHY USING BJORK ANALYSIS
Authors:   Annamalai University, India, S Anbazhagan
Published: October 2024

The dental and skeletal relationships in the head are studied in Cephalometric analysis. This research work addresses Bjork’s analysis for the classification of patients. In this research work, the backpropagation neural network (BPNN), and generalized regression neural network (GRNN) classifiers are used and studied for the diagnosis of Cephalometric analysis. In this study, a total of 304 (male 109, female 195) patient’s case records were collected for this study. All the collected clinical data are used for classification. For training and testing the proposed models, patients'''' data were separated by four-fold cross-validation. Based on Bjork analysis, experimental results show that GRNN provided achieving the performance of 97.39% of good classification results when compared to the BPNN model. The GRNN approach is feasible and was found to be achieving a performance of 97.39% of the correct detection of patients.
CNN-RNN BASED HANDWRITTEN TEXT RECOGNITION
Authors:   PSG Institute of Technology and Applied Research, India, G R Hemanth , M Jayasree ,   PSG Institute of Technology and Applied Research, India, Venii S Keerthi ,   PSG Institute of Technology and Applied Research, India, P Akshaya ,   PSG Institute of Technology and Applied Research, India, R Saranya ,   PSG Institute of Technology and Applied Research, India
Published: October 2024

At present most of the scripts are handwritten due to the ease of using a pen tip in place of a keyboard, hence errors are common due to illegibility of the human handwriting. To avoid this problem handwriting recognition is essential. Offline handwritten Text recognition (OHTR) has become one of the major areas of research in recent times because of the need to eliminate errors due to misinterpretation of handwritten text and the need for automation to improve efficiency. The application of this system can be seen in fields like handwritten application interpretations, postal address recognition, signature verification, and various others. In this project, offline handwritten Text recognition is performed using Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) which uses the architecture of Recurrent Neural network (RNN) and connectionist temporal classification (CTC). The neural network is trained and tested using the IAM database containing handwritten English text. The implementation of this work is done using image segmentation-based handwritten text recognition where OpenCV is used for performing image processing and TensorFlow is used for training and text recognition. This whole system is developed using python and the output is displayed in a word file.
COVID-19 DISEASE IDENTIFICATION USING HYBRID ENSEMBLE MACHINE LEARNING APPROACH
Authors:   Guru Gobind Singh Indraprastha University, India, Yadav Jyotsna , Bhardwaj Richa ,   Guru Gobind Singh Indraprastha University, India
Published: October 2024

Corona viral infected disease 2019 (Covid-19) has created a pandemic in year 2020 taking many lives and affecting millions of people. Due to lack of sufficient testing resources and healthcare systems, many countries and hospitals are not able to test this disease as the workload on the existing laboratories is increasing. In the proposed work, we have used hybrid ensemble machine learning models to predict this disease based on clinical variables and standard clinical laboratory tests. The main motive of the ensemble model is that combination of classifiers will classify the unseen data samples more accurately and chances for misclassification is very less as compared to the classification made by a single classifier. The performance comparison from various classification techniques is also done to show that hybrid ensemble classifier has outperformed decision tree and Support Vector Machine based classification algorithms.
GENE BICLUSTERING ON LARGE DATASETS USING FUZZY C-MEANS CLUSTERING
Authors:   HKBK College of Engineering, India, M Ramkumar , J Gowrishankar ,   Jain University, India, V Amirtha Preeya ,   Presidency University, India, T Pushpa ,   HKBK College of Engineering, India, T Karthikeyan ,   University of Technology and Applied Sciences, Oman
Published: October 2024

The current study employs biclustering to alleviate some of the drawbacks associated with gene expression data grouping. Different biclustering algorithms are used in this study to detect unique gene activity in various contexts and reduce the duplication of broad gene information. Furthermore, machine learning or heuristic algorithms have become widely utilised for biclustering due to their suitability in problems where populations of potential solutions allow examination of a larger percentage of the research area. To begin with, gene expression data biclusters frequently contain data that is the same under a variety of different situations of gene expression. Therefore, the biclustering technique is particularly effective if the matrix lines and columns are merged immediately. Submatrices can be identified using the Large Average Sub matrix. A Fuzzy C-Means algorithm is also used to ensure that the sub-matrix can be expanded to include more rows and columns for further analysis. The sub-matrices and component precision and strength are factored into the system design. It uses biclustering techniques to differentiate gene expression information. On the Garber dataset, the simulation is run in Java. Using the average match score for non-overlapping modules, the influence of noise on overlapping modules using constant bicluster and additive bicluster, and the overall run duration, the study is assessed.
RICE PLANT DISEASE IDENTIFICATION DECISION SUPPORT MODEL USING MACHINE LEARNING
Authors:   Sage University, India, Shrivastava Gaurav , Patidar Harish ,   Sage University, India
Published: October 2024

In this paper, we propose a decision support system for Indian rice farmers for identifying diseases. In a country like India, food security is an essential concern. Additionally, diseases in plants can cause a significant loss. Early-stage detection of diseases can help in improving the production of rice. In this context, first we investigate the recent contributed efforts in the field of plant disease detection by analysing plant leaves using machine learning and image processing techniques. Next, the datasets and relevant algorithms are concluded. Then, a machine learning model has been presented. The model includes the edge feature extraction using canny edge detection technique, colour features are extracted using grid colour movement, and the texture analysis is performed using Local Binary Pattern (LBP). In the next step, using the extracted features, we have prepared a combined feature vector to train the Machine Learning (ML) algorithms namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). These machine learning algorithms are organized in such a manner that the proposed decision support model can identify and differentiate the leaf plants. Additionally, it also recognizes the rice plants when we query. Secondly, the model is also able to recognize rice plant diseases. The first scenario of the experiment has been carried out using Plant Village dataset. The second scenario of experiment uses the rice plant disease dataset obtained from Kaggle with three classes. The second dataset used which is known as the Mendeley dataset which contains five different diseases as class labels. The experimental study with the implemented system confirms the superiority of ANN to be used with the proposed decision support system as compared to the SVM algorithm in terms of accuracy and time consumption. Finally, future work has also been highlighted.
PREDICTION OF SEED PURITY AND VARIETY IDENTIFICATION USING IMAGE MINING TECHNIQUES
Authors:   Bishop Heber College, India, M Suganthi , J.G.R. Sathiaseelan ,   Bishop Heber College, India
Published: October 2024

Seed is a little embryonic plant that can be used to introduce plant infections to new areas while also allowing them to survive from one cropping season to the next. Seed health is a well-known component in modern agricultural science for achieving the required plant population and yield. Seed-borne fungus are a major biotic constraint in seed production around the world. The detection of seed-borne pathogens by seed health testing is a crucial step in the treatment of crop diseases. Speed and accuracy are critical requirements for longterm economic growth, competitiveness, and sustainability in agricultural output. Because human judgements in identifying objects and situations are variable, subjective, and delayed, seed prediction activities are costly and unreliable. Machine vision technology provides a nondestructive, cost-effective, quick, and accurate option for automated procedures. Seed variety, seed type (country seed or hybrid seed), seed health, and purity prediction were the four basic processes. We began the first procedure by aligning the seed bodies in the same direction using a seed orientation approach. Then, to detect atypical physical seed samples, a quality screening procedure was used. Their physical characteristics, such as shape, colour, and texture, were retrieved to serve as data representations for the prediction. This research introduces a new fuzzy cognitive map (FCM) model based on deep learning neural networks that predicts seed purity tests using data from biological investigations. The relevant data features from the seed test are extracted by FCM, which then effectively initialises the deep neural networks. The Levenberg–Marquardt (LM) technique for deep neural networks was discovered to improve seed purity test prediction. Four statistical machine learning algorithms (BP-ANN, Multivariate regression, and FCMLM deep learning). Furthermore, we demonstrated an improvement in the system''''s overall performance in terms of data quality, including seed orientation and quality screening. In independent numerical testing, the correlation coefficient between predicted values and true values acquired from experiments reached 0.9.
DESIGN OF CATEGORICAL DATA CLUSTERING USING MACHINE LEARNING ENSEMBLE
Authors:   Srinivas University, India, N. Yuvaraj , A Jayanthiladevi ,   Srinivas University, India
Published: October 2024

Cluster analysis of data is a crucial tool for discovering and making sense of a dataset underlying structure. It has been put to use in many contexts and many different fields with great success. In addition, new innovations in the last decade have piqued the interest of clinical researchers, scientists, and biologists. As the number of dimensions in a data set grows, the consensus function of traditional ensemble clustering often fails to generate final clusters. The main problem with conventional ensemble clustering is exactly this. The proposed work employs a similarity measure between links to identify which clusters contain the unknown datasets. To this end, this study proposes employing an improved ensemble framework for clustering categorical datasets. More specifically, it employs ensemble machine learning methods to categorize data. Multiple machine learning algorithms are incorporated into this model. Objective performance indicators are used to compare a model to more traditional approaches to determine how effective each the proposed method is.
A DEEP LEARNING MODEL FOR IMPROVING THE RICE PLANT DISEASE DETECTION PERFORMANCE
Authors:   Sage University, India, Shrivastava Gaurav , Barua Kuntal ,   Sage University, India
Published: October 2024

Rice is one of the most utilized grains in India. It is a seasonal crop which mostly grows between June to October. This crop mostly grows in natural conditions and its production has a significant influence on different diseases in the plant. Early stage detection of diseases can help in improving the production. In this paper, an analysis and study on deep learning models for getting accurate rice plant disease detection is presented. In this context, first the recent contributions on detecting the diseases by analysing the plant leaf images are reviewed. Then, a comparison among sequential model and 2D-CNN model has been performed. The experimental analysis demonstrates that 2D-CNN outperforms as compared to the simple sequential model. The experiments are extended by including the different image feature selection models. In order to extract features, sobel based edge detection, Local Binary Pattern (LBP) based texture analysis and their combinations i.e. sobel and LBP, Sobel, LBP and color, and a combination of color and sobel are used. The experiments are performed on Kaggle based rice plant disease detection dataset and the performance in terms of precision, recall, f1-score and accuracy has been measured. The experimental evaluation highlights two major points (1) the CNN does not require additional features for better classification consequences (2) the highly trained models are able to respond faster as compared to less trained models. Based on the obtained performance, a more accurate model for plant disease detection is designed.
COMPARATIVE STUDY OF XAI USING FORMAL CONCEPT LATTICE AND LIME
Authors:   Sri Sathya Sai Institute of Higher Learning, India, Venkatsubramaniam Bhaskaran , Baruah Pallav Kumar ,   Sri Sathya Sai Institute of Higher Learning, India
Published: October 2024

Local Interpretable Model Agnostic Explanation (LIME) is a technique to explain a black box machine learning model using a surrogate model approach. While this technique is very popular, inherent to its approach, explanations are generated from the surrogate model and not directly from the black box model. In sensitive domains like healthcare, this need not be acceptable as trustworthy. These techniques also assume that features are independent and provide feature weights of the surrogate linear model as feature importance. In real life datasets, features may be dependent and a combination of a set of features with their specific values can be the deciding factor rather than individual feature importance. They also generate random instances around the point of interest to fit the surrogate model. These random instances need not be part of the original source or may even turn out to be meaningless. In this work, we compare LIME to explanations from the formal concept lattice. This does not use a surrogate model but a deterministic approach by generating synthetic data that respects implications in the original dataset and not randomly generating it. It obtains crucial feature combinations with their values as decision factors without presuming dependence or independence of features. Its explanations not only cover the point of interest but also global explanation of the model, similar and contrastive examples around the point of interest. The explanations are textual and hence easier to comprehend than comprehending weights of a surrogate linear model to understand the black box model.
APPLICATION OF TIME SERIES ANALYSIS IN FORECASTING COAL PRODUCTION AND CONSUMPTION IN THE PHILIPPINES
Authors:   University of Mindanao Digos College, Philippines, E. Parreño Samuel John
Published: October 2024

Energy is the backbone of a country’s economic and technological development. For a country to be competitive in the global market and secure sustainable development, energy must be efficiently used. The Philippines’s main source of energy is coal. Historical data shows that the country’s energy and coal consumption has been continuously increasing. With this, careful energy planning is required to develop policies that ensure sufficient energy supply in the future. This paper focuses on forecasting coal production and consumption in the Philippines. For the purpose of forecasting, the autoregressive integrated moving average (ARIMA) model is applied to reveal that ARIMA (1, 2, 0) is the best model to forecast coal production. For coal consumption, the best model identified was ARIMA (0, 2, 1). The models have undergone residual analyses and forecast evaluations to ensure that the ‘best’ models found are statistically appropriate models. The forecasts show that in the following years, Philippine coal production will decrease, while the coal consumption rate will increase. In addition, it is predicted that the Philippines will need to import a total of 133.1983 MMT of coal to meet the coal consumption from 2021 to 2025.