bluuebunny/crossref_metadata_2025 huggingface dataset:
Host–virus interactions during infection with a wild-type ILTV strain or a glycoprotein G deletion mutant ILTV vaccine strain in an
ex vivo
system
Authors: Gopakumar Gayathri , Coppo Mauricio J.C. , Diaz-Méndez Andrés , Hartley Carol A. , Devlin Joanne M.
Published: January 2025

Previous studies have demonstrated the safety and efficacy of a live-attenuated glycoprotein G (gG) deletion mutant vaccine strain of ILTV (∆gG-ILTV). In the current study, transcriptional profiles of chicken tracheal organ cultures (TOCs), 24 h post inoculation with ∆gG-ILTV or the gG-expressing parent wild-type strain, CSW-1 ILTV were explored and compared with the mock-infected TOCs using RNA-seq analysis. Transcriptomes of the vaccine and wild-type ILTV were also compared with each other. Although no viral genes (except for gG) were differentially regulated between the two ILTV-infected TOCs, pair-wise comparison of the transcriptomes of the ∆gG-ILTV or the CSW-1 ILTV-infected TOCs (each compared with mock-infected TOCs) identified the similarities and differences in host gene transcription between them. Several immune checkpoint inhibitors with likely roles in ILTV-mediated immune augmentation, and gene ontologies indicating cytokine response, and cytokine signaling were upregulated in both TOCs. Additionally, several other biological processes, molecular functions, and cellular components were enriched uniquely in the ∆gG-ILTV-infected TOCs, including those that indicated modifications to tracheal extracellular matrix (ECM) structural components, which may have a role in immune modulation
in vivo
. This study has revealed that the modifications of transcription of host genes during the early stages of ILTV infection are not limited to changes in cytokine or chemokine gene transcription, but several other immune-related genes and ECM components. Moreover, their differential regulation in the
ex vivo
system appears to be influenced by gG expression, potentially affecting the outcome of ILTV infection
in vivo
.

IMPORTANCE

Infectious laryngotracheitis virus (ILTV) remains a serious threat to poultry industries worldwide, causing significant economic losses. The glycoprotein G (gG) of ILTV is a virulence factor and a chemokine-binding protein with immunoregulatory functions. The influence of gG on the transcription of select host chemokine and cytokine genes has been demonstrated previously. This study extends our understanding of the early and localized host–ILTV interactions using genome-wide transcriptome analysis of ILTV-infected chicken tracheal organ cultures, and the role of gG during the process. Differential regulation of genes encoding immune checkpoint inhibitors observed in this study may have a role in ILTV-induced inhibition of type I interferon response, or negative regulation of T cell responses, bringing clarity to these ILTV immune-evasion mechanisms. Furthermore, differential regulation of genes encoding certain structural components and receptors with roles in cell migration, in the absence of gG, is consistent with the immunomodulatory role of ILTV gG.
Virulence profile of carbapenem-resistant
Klebsiella pneumoniae
strains by an
in vivo
model of
Galleria mellonella
Authors: Guembe María , Hafian Rama , Díaz-Navarro Marta , Visedo Andrés , De Maio Flavio , Pimpinelli Fulvia , Cavallo Ilaria , Truglio Mauro , Sivori Francesca , Di Domenico Enea Gino
Published: January 2025

Klebsiella pneumoniae
is a significant healthcare-associated pathogen, notable for its diverse virulence and antibiotic resistance profiles. This study aimed to characterize the genotypic and phenotypic diversity of
K. pneumoniae
isolates and evaluate their virulence using the
Galleria mellonella
model. Biomass production, metabolic activity, capsule formation, and siderophore production were assessed in 27
K
.
pneumoniae
isolates from hospital-associated infections. Lethality curves were generated using the
G. mellonella
model, with survival monitored hourly from 16 to 48 hours. The most common sequence types (ST) identified were the high-risk clones ST307 (
N
= 10), ST512 (
N
= 8), ST101 (
N
= 7), and ST661 (
N
= 2). These STs were associated with distinct K-locus, including KL102, KL107, KL17, and KL39. Most isolates belonged to the O2afg locus (
N
= 18), with the
K. pneumoniae
carbapenemase genotype detected in 96.3% of strains. None of the isolates were classified as hypervirulent. Phenotypically, ST661 exhibited the highest biomass production despite showing similar metabolic activity to other STs. A positive correlation was observed between biomass and siderophore production, while capsule production was inversely correlated with biomass. In the
G. mellonella
model, ST661 demonstrated the highest virulence, resulting in 100% mortality by 48 hours, compared to survival rates of 21.4% for ST101, 38.0% for ST307, and 31.2% for ST512. These findings underscore the pathogenic potential of ST661 isolates with enhanced biofilm production. The
G. mellonella
model may serve as an effective
in vivo
system for evaluating the virulence of emerging
K. pneumoniae
lineages.

IMPORTANCE

We demonstrate that the
Galleria mellonella
model is a useful tool to analyze the virulence of carbapenem-resistant
Klebsiella pneumoniae
strains. Our findings highlight the pathogenicity of carbapenem-resistant
K pneumoniae
isolates, particularly the role of the ST661 that, despite being a rare lineage, harbors the blaVIM gene and is associated with high biofilm production and the highest mortality rates.
Gut colonization of
Bacteroides plebeius
suppresses colitis-associated colon cancer development
Authors: Chen Hung-Lin , Hu Po-Yuan , Chen Chang-Shan , Lin Wei-Han , Hsu Daniel K. , Liu Fu-Tong , Meng Tzu-Ching
Published: January 2025

Colon cancer development may be initiated by multiple factors, including chronic inflammation, genetic disposition, and gut dysbiosis. The loss of beneficial bacteria and increased abundance of detrimental microbes exacerbates disease progression.
Bacteroides plebeius
(
B. plebeius
) is a human gut microbe, and its colon colonization is enhanced by a seaweed-supplemented diet. We found that mice orally administered with
B. plebeius
and fed a diet containing 1% seaweed developed a unique gut microbial composition. By linear discriminant analysis effect size analysis, we found that
B. plebeius
colonization increased the abundance of
Blautia coccoides
and reduced the abundance of
Akkermansia
sp. and
Dubosiella
sp. We also showed that colonization of
B. plebeius
suppressed the colon tumor development induced by azoxymethane/dextran sulfate sodium in specific-pathogen-free mice, coinciding with a reduced abundance of
Muribaculaceae
sp.,
Closteridale
sp., and
Bilophila
sp. Moreover,
B. plebeius
colonization in gnotobiotic mice resulted in enhanced production of selected metabolites, including propionic, taurocholic, cholic, alpha-, and beta-muricholic, as well as ursodeoxycholic acids. Importantly, some of these metabolites show anti-inflammatory and tumor-suppressive effects. We conclude that
B. plebeius
is able to restructure the gut microbial community and produce beneficial metabolites, leading to inhibition of colitis-associated colon cancer development.

IMPORTANCE

This work delves into the pivotal role of gut microbiota in suppressing the progression of colitis-associated colon cancer. By investigating the impact of
Bacteroides plebeius
that can be colonized in mouse gut by feeding the animal with seaweed diet, we unveil a novel mechanism through which this beneficial bacterium reshapes the gut microbial community and produces metabolites with anti-inflammatory and tumor-suppressive properties. Such findings underscore the potential of harnessing specific microbes, like
B. plebeius
shown in this study, to modulate the gut ecosystem and mitigate the risk of colitis-associated colon cancer.
Robust detection of pathogenicHYDINvariants that cause primary ciliary dyskinesia using RNA-seq of nasal mucosa
Authors: Hijikata Minako , Morimoto Kozo , Ito Masashi , Wakabayashi Keiko , Miyabayashi Akiko , Keicho Naoto
Published: January 2025

Primary ciliary dyskinesia (PCD, OMIM 244400) is a rare genetic disorder that affects motile cilia and is characterised by impaired mucociliary clearance of the airway epithelium, which results in chronic upper and lower airway infections. While short-read next-generation sequencing technology has been used for the genetic testing of PCD, its effectiveness is limited in identifying variants in theHYDINgene because of the nearly identical pseudogeneHYDIN2. As we confirmed that theHYDIN2gene was not expressed in airway cells, we obtained nasal mucosa biopsy specimens for total RNA sequencing (RNA-seq) with library enrichment using exome oligos. Among the 34 nasal samples from patients suspected of having PCD, three aberrant splicing patterns inHYDINwere identified in two samples. Variant calls from RNA-seq combined with long-read amplicon sequencing of genomic DNA detected four pathogenic variants exclusively in theHYDINgene. Therefore, RNA-seq in combination with long-read sequencing significantly facilitates the accurate genetic diagnosis of PCD caused byHYDINvariants.
Applying the Aajiiqatigiingniq Research Methodology: approaches and lessons learned through collaborative survey development and analysis
Authors: Carter Natalie Ann , Tagalik Shirley , Ljubicic Gita
Published: January 2025

Aajiiqatigiingniq is an Inuit Qaujimajatuqangit principle used to discuss serious topics and come to consensus around decision-making. In working with Elders on community research issues, the Aqqiumavvik Society in Arviat, Nunavut developed the Aajiiqatigiingniq Research Methodology (ARM) as a participatory and fully inclusive Inuit research approach. We followed the ARM in establishing partnerships, and developing and facilitating a survey, to learn how Nunavut community members use and share available weather, water, ice, and climate information. The ARM guided our 5-year research process involving 19 Local Research Coordinators, along with 13 Nunavut-based, eight university, and three federal government collaborators. Through community research leadership and connecting long-term partnerships, we worked in eight Nunavut communities. In this paper, we present the opportunities and challenges of putting the ARM into practice through four research stages: (1) building relationships; (2) building shared understanding; (3) knowledge sharing; and (4) collaborative analysis. In this process, we have come to recognize our strength in connection, Local Research Coordinators as Highly Qualified Personnel, the four Rs of collaborative analysis, and the challenges of hurdling institutions. We share our approach and lessons learned to contribute to ongoing efforts and dialogue around decolonizing research and enhancing Inuit self-determination in research.
Simulation Research on Data Acquisition and Supervisory Control System of Distribution Network Based on Vector Regression Model
Authors: Wu Shujin , Yuan Ye , Wu Yuanjiang , Xu Wenli
Published: January 2025

With the gradual maturity of deep learning and the Internet of Things, a smart grid is urgently needed to be established. For the intellectualization of the power grid, non-intrusive load monitoring (NILM) technology is the key step of intellectualization. The traditional load monitoring method is an invasive scheme, in which monitoring sensors are usually installed at the output end of each grid load. This method requires a lot of manpower and material resources in terms of equipment installation and maintenance, which is difficult to sustain. Therefore, monitoring electrical appliances are installed at the entrance of the distribution network to decompose the classification and operation status of the individual electrical appliances in the grid. Aiming at the problem of long-term multi-state electrical apparatus monitoring, this paper mainly optimizes the two optimal deep learning models Seq2Seq and WindowGRU based on activation function optimization, regular function optimization and neural network structure optimization. PReLU, Leaky-ReLU and ELU are used and tested. For the optimization based on regular functions, the value of F1-score is 0.9700, which is 0.1534 more than the optimization algorithm Seq2SeqLR in this paper. Meanwhile, it can also reach 0.8100 for other devices, which is 0.2373 more than the nilmTCN designed in this paper. The Recall value of the identification is improved to 0.6673, which is significantly higher than that of other models. It is proved that the research contents of this paper can improve the effective analysis of load energy consumption data, and can minimize unnecessary energy consumption to achieve the purpose of saving electricity.
Application Analysis of Fuzzy Language Based on Evolutionary Neural Network in Art Teaching
Authors: Rongliang Tong
Published: January 2025

In order to improve the teaching effect of art teaching, the performance effect of fuzzy language in art teaching is studied. This paper analyzes the application of fuzzy language in art teaching, and evaluates its performance effect on this basis. After determining the application strategy of fuzzy language in art teaching, the evaluation index system is constructed. Evolutionary neural network is used to calculate the weight of evaluation index. Based on the evaluation index weight and fuzzy judgment matrix, the evaluation results of the performance effect of fuzzy language in art teaching are obtained. The experimental results show that the evaluation indicators selected by this method have high contribution and noise tolerance, and can accurately evaluate the performance effect of fuzzy language in art teaching, which proves that using fuzzy language in art teaching can effectively improve the quality of art teaching.
Design of an Algorithm for Rapid Estimation of the Unit Cost of Concrete in Construction Projects
Authors: Ningning Zhang
Published: January 2025

The estimation of the concrete unit cost in construction engineering plays a vital role in controlling construction costs and ensuring timely project completion. To achieve this, a fast estimation algorithm for concrete unit costs is proposed. To ensure accurate estimation and reduce the computational burden on the convolutional neural network (CNN) for rapid estimation, the factors influencing concrete costs are identified and arranged using the interpretive structural modeling (ISM) method. The factors affecting the concrete unit cost of high-rise construction are selected as the input data sequence for the CNN model. The feature map of the input data is extracted through the convolution layer. After applying the pooling operation to the feature map, the processed data is passed into the fully connected layer through the final pooling layer, where the final calculation is performed to obtain the estimated concrete unit cost. Experimental results indicate that to ensure fast convergence and minimize estimation errors in CNN estimation, optimal network parameters are determined through multiple experiments. The best configuration includes a [Formula: see text] convolution kernel and 11 convolutional cores. This algorithm, which uses the ISM method to select influential factors, achieves high accuracy in estimating the concrete unit cost. In actual engineering applications, the error between the estimated and actual results is minimal, with a maximum difference of only 1 yuan.
Fault Diagnosis Method of DC Microgrid Multi-Port Power Electronic Circuits Based on WOA-KELM Algorithm
Authors: Deye Jiang , Yiguang Wang
Published: January 2025

The faults in DC microgrid multi-port power electronic circuits are complex and diverse. In order to better adapt to different types of fault data and complex circuit environment, and improve the generalization ability and accuracy of fault diagnosis, a fault diagnosis method for DC microgrid multi-port power electronic circuits based on WOA-KELM algorithm is studied. Through the wavelet packet analysis algorithm, the wavelet coefficient energy of DC microgrid multi-port power electronic circuit signal is extracted as a fault feature, and it is encoded to speed up fault diagnosis efficiency; the WOA algorithm is used to optimize the penalty parameters of KELM algorithm to improve the accuracy of fault diagnosis; in the optimized KELM algorithm, the coded fault feature samples are input, and the fault diagnosis results of DC microgrid multi-port power electronic circuits are output. According to the powerful nonlinear mapping ability of the kernel function, the generalization ability of the KELM algorithm is enhanced, so that it can better adapt to different types of fault data and complex circuit environment. Experiments show that this method can effectively extract fault features of multi-port power electronic circuits and complete feature coding; this method can effectively diagnose the faults of DC microgrid multi-port power electronic circuits; in different complex circuit scenarios, the fault reliability of this method for diagnosing power circuit faults is higher than 0.6, that is, the fault diagnosis accuracy is high.
A Parametric Design Method for Landscape Garden Layout Based on Clustering Algorithm
Authors: Lin Zhu
Published: January 2025

Landscape design involves many elements, such as plants, water, terrain, roads, and buildings. Each element has its specific design requirements and aesthetic standards. The combination and layout of these elements need to consider multiple factors such as spatial relations, functional requirements, ecological balance, and cultural background, which makes the design process very complex. In order to quantify the landscape layout and scientifically layout the landscape architecture, a parametric design method of landscape architecture layout based on a clustering algorithm was proposed. First, the composition of the landscape is analyzed based on the traditional research results, and the required computer software foundation is discussed. Then, in the corresponding software environment, considering that the impact factors of landscape layout elements include numerical and nonnumerical types, [Formula: see text]-prototype clustering algorithm is used for processing. Specifically, the Euclidean distance of the [Formula: see text]-means clustering model is used to measure the numerical impact factors, while the Hamming distance of the [Formula: see text] model is used to measure the nonnumerical impact factors, so as to achieve effective clustering of mixed impact factors and determine the layout parameters accordingly. At the same time, the rule matrix method is used to establish the design rules. Finally, based on the above research results, the parametric layout of landscape architecture is realized through a series of processes such as design research, planning and layout, construction and form, construction and management. The experimental results demonstrate that this method effectively extracts layout parameters, of landscape architecture and obtains more clear and logical design rules, thus improving the final design effect.
Online Physical Education Teaching Resources Integration Method Based on Multi-Objective Optimization Algorithm
Authors: Mu Daokui
Published: January 2025

In order to improve the sharing level of online physical education teaching resources, the integration design of online physical education teaching resources is carried out by using MOOC resource information fusion technology and closed frequent item fusion calculation method, and the intelligent scheduling and management ability of online physical education teaching resources is improved. An online physical education teaching resource integration model based on big data information fusion and cluster scheduling is proposed. The factors of online physical education teaching demand scale are initialized, the structure of common factors of online physical education teaching resources is determined by factor analysis method, the orthogonal rotation of indicators by maximum variance method is adopted, the cloud computing model is adopted to analyze the integration structure of online physical education teaching resources, and the feature quantity of cross frequent items rules of online physical education teaching resources is extracted. The parallel K-means clustering model of online physical education resources integration is constructed, the fuzzy correlation degree features of online physical education resources are calculated, the extracted correlation features of online physical education resources are processed by C-means clustering method for big data fusion, the multi-objective optimization technology is adopted for online physical education resources integration and adaptive scheduling, and the adaptive scheduling ability of online physical education resources is improved. The SPSS statistical software is used for empirical analysis, and the results show that the integration degree of online physical education teaching resources by this method is high, which promotes the development of online physical education teaching.
Electronic and magnetic properties of inverse Heusler Fe2RhSi and Fe2RhGe: Ab initio and Monte Carlo study
Authors: Mouchou S. , Toual Y. , Azouaoui A. , Maouhoubi A. , Masrour R. , Rezzouk A. , Bouslykhane K. , Hourmatallah A. , Benzakour N.
Published: January 2025

We use Density Functional Theory and Monte Carlo simulation to investigate the structural, electronic, and magnetic properties of the ferromagnetic inverse Heusler alloys Fe2RhSi and Fe2RhGe. The metallic nature of both alloys is confirmed using GGA, GGA+U, and meta-GGA methods. The calculated magnetic moments are [Formula: see text] for Fe2RhSi and [Formula: see text] for Fe2RhGe, aligning well with experimental findings. Spin polarization calculations yield 70% for Fe2RhSi and 53.03% for Fe2RhGe using the GGA method, while other methods produce values below 30%. Additionally, GGA+U calculations indicate a martensite transition at approximately [Formula: see text] for both alloys without affecting their magnetic state. The Curie temperatures are around 900[Formula: see text]K for Fe2RhGe and Fe2RhSi, which closely match experimental data. The high spin polarization and Curie temperature make Fe2RhSi a promising candidate for spintronic applications.
Polarization force driven FHD flow over a permeable disc with geothermal viscosity
Authors: Ram Paras , Kumar Pulkit
Published: January 2025

In this paper, we investigate the Bödewadt boundary layer flow of ferrous oxide-based electrically non-conducting Nanofluid considering the influence of thermal radiation and viscous dissipation over a permeable disc with slip conditions. Here, viscosity is taken as a function of temperature and depth. The equation of energy incorporates the radiative heat flux as described by the Rosseland approximation. The Von Kármán Model alters the constitutive nonlinear coupled partial differential equations of motion, which include slip boundary conditions. The final system of equations in PDE is solved computationally using a shooting approach in MATLAB with ode45, and the results are elucidated, through graphs and tables. The effects of all the above-considered physical entities including the geothermal viscosity on the velocity profile and temperature distribution over the disc are examined. An exhaustive analysis of all the above-investigated results is presented for the record. It is observed that the geothermal viscosity with considered boundary conditions retards the motion of the fluid causing decay in the net molecular movement and the thermal diffusion.
Accuracy of a Superficial Landmark of the Recurrent Branch of the Median Nerve and Anatomical Features of Transverse Muscle Fibres Observed During Carpal Tunnel Release
Authors: KANAZUKA Aya , SUZUKI Takane , MATSUURA Yusuke , AKASAKA Tomoyo , KUNIYOSHI Kazuki , OHTORI Seiji
Published: January 2025

Background: Surgeons use anatomical landmarks like the scaphoid tubercle, pisiform, trapezial tubercle and hook of hamate, along with Kaplan cardinal line (KCL) to avoid injury to the recurrent motor branch (RMB) of the median nerve during carpal tunnel release. The presence of transverse muscle fibres (TMF) overlying the transverse carpal ligament (TCL) may suggest proximity of the RMB, but their anatomical relationship is unclear. In this study, we evaluated the accuracy of anatomical landmarks to the RMB, TMF origin and insertion, and examined the relationship between TMF presence and RMB running patterns.

Methods: We dissected 30 hands from 16 fresh-frozen cadavers. After marking the superficial landmarks, we made a skin incision to confirm the presence of TMF and examined their origins and insertions. We then opened the carpal tunnel, dissected the RMB and recorded each position on a coordinate system using a fluoroscopic imaging system.

Results: TMF were observed in 18 hands (60%): 13 were continuous with the abductor pollicis brevis (APB), 2 were continuous with the superficial head of the flexor pollicis brevis (FPB) and 3 were continuous with both. The bifurcation point of the RMB was significantly located 4.5 mm ulnar and 7.5 mm proximal to the superficial landmark at the median. The RMB was classified according to Poisel classification: 24 (80%) were of the extraligamentous type, 4 (13%) of the transligamentous type, 1 (3%) of the preligamentous type and 1 (3%) of the subligamentous type. Amongst these, the transligamentous/preligamentous/subligamentous types are at high risk for RMB injury during TCL incision. No significant association existed between TMF presence and these high-risk RMB types..

Conclusions: The actual RMB may be located ulnar and proximal to the superficial landmark, indicating that surgeons should be cautious about RMB damage even in the absence of TMF.
Twitter User Geolocation Based on Location Feature Enhancement
Authors: Zhang Meng , Luo Xiangyang , Huang Ningbo , Liu Yimin , Du Shaoyong
Published: January 2025

User location discovery from social media is crucial for location-based services like emergency awareness and event monitoring. Existing approaches generally integrate user-generated text features and social relationships, but insufficiently explore location-specific features and geographically proximate relationships, leading to suboptimal accuracy. In this paper, we propose a Twitter user geolocation method based on location feature enhancement, to better capture the location characteristics in users’ tweets and social relationships. Specifically, a user tweet representation algorithm based on location feature separation (TwLS) is designed. By leveraging words’ location-aware weight matrix and pre-trained embeddings, TwLS calculates a tweet representation for each user in every location, explicitly indicating the relevance between users and various locations. Additionally, we develop the local celebrity discovery method (LocCel) to construct social networks by identifying and preserving geographically concentrated high-degree nodes while filtering noise. Thereby LocCel enhances local relationships and strengthens location-proximate connections within the user social network. Experiments on two real-world datasets show that our method outperforms seven baselines, improving user geolocation accuracy by 3.1% ∼ 8.1% and 1.8% ∼ 8.8%, while reducing median error by 22.2% ∼ 52.8% and 19.4% ∼ 50.7%, respectively.
Natural Language Processing for Dialects of a Language: A Survey
Authors: Joshi Aditya , Dabre Raj , Kanojia Diptesh , Li Zhuang , Zhan Haolan , Haffari Gholamreza , Dippold Doris
Published: January 2025

State-of-the-art natural language processing (NLP) models are trained on massive training corpora, and report a superlative performance on evaluation datasets. This survey delves into an important attribute of these datasets: the dialect of a language. Motivated by the performance degradation of NLP models for dialectal datasets and its implications for the equity of language technologies, we survey past research in NLP for dialects in terms of datasets, and approaches. We describe a wide range of NLP tasks in terms of two categories: natural language understanding (NLU) (for tasks such as dialect classification, sentiment analysis, parsing, and NLU benchmarks) and natural language generation (NLG) (for summarisation, machine translation, and dialogue systems). The survey is also broad in its coverage of languages which include English, Arabic, German, among others. We observe that past work in NLP concerning dialects goes deeper than mere dialect classification, and extends to several NLU and NLG tasks. For these tasks, we describe classical machine learning using statistical models, along with the recent deep learning-based approaches based on pre-trained language models. We expect that this survey will be useful to NLP researchers interested in building equitable language technologies by rethinking LLM benchmarks and model architectures.
Proactive Resilient Adaptive Sensor Node Management with Neuro-Symbolic Systems for Optimized Coverage Efficiency in the Mission-Critical Applications
Authors: P Dineshkumar , K Geetha , C Rajan
Published: January 2025

Wireless sensor networks (WSNs) play a critical role in applications such as wildlife monitoring, disaster recovery, and precision agriculture, where continuous coverage and longevity are paramount amidst dynamic environmental challenges. To address these demands, the cellular adaptive energy forecasting and coverage optimization (CAEFCO) framework integrates localized neuro-symbolic energy forecasting (LNS-EF), a novel concept that combines symbolic reasoning with neural network learning directly on sensor nodes. LNS-EF enables nodes to not only predict energy depletion based on past consumption patterns and environmental factors but also incorporate rule-based contextual reasoning for enhanced decision-making. Alongside this, CAEFCO employs an anomaly detection module that identifies disruptions, such as sensor damage or environmental interference, allowing real-time task redistribution. This dual approach ensures seamless task reallocation while extending network lifetime. CAEFCO’s proactive methodology demonstrates a 97% reduction in data loss and an 85% improvement in network longevity, offering a breakthrough in the resilience and sustainability of WSNs in mission-critical and harsh environments.
Growth differentiation factor 15 is a glucose-downregulated gene acting as the cross talk between stroma and cancer cells of the human bladder
Authors: Chang Kang-Shuo , Chen Syue-Ting , Lin Wei-Yin , Hsu Shu-Yuan , Sung Hsin-Ching , Lin Yu-Hsiang , Feng Tsui-Hsia , Hou Chen-Pang , Juang Horng-Heng
Published: January 2025

This study investigates the counteraction of either CAPE or metformin with the AMPK activity increasing GDF15 expression in human bladder cells. The findings are the first study to indicate the secretion of GDF15 from cancer and stroma cells via autocrine or paracrine mechanisms. Our study suggests that GDF15, an antitumor gene in the human bladder induced by AMPK inducers, acts as a communication link between stroma and cancer cells in the human bladder.
Random Forest–Based Coal Mine Roof Displacement Prediction and Application
Authors: Li Hongxia , Wu Rong , Gao Jianan
Published: January 2025

Coal mine roof accident is one of the most important geological disasters faced by coal mine, and roof displacement is an important index to measure the effect of roadway control and construction safety. Therefore, this study puts forward the machine learning method to study the advance prediction of coal roadway roof displacement. By identifying six important influencing indexes of coal roadway roof displacement, the prediction dataset of coal roadway roof displacement is established and the correlation and importance of the indexes are analyzed. Based on Random Forest (RF), XGBoost, and Gradient Boosting Decision Tree (GBDT) algorithms, three kinds of roof displacement prediction models are established respectively. R2, mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), and root mean square error (RMSE) are selected to evaluate the performance of the models. The results show that the RF model has the best performance while the XGBoost model has the worst performance. When RF model is applied to coal mine roadway, the average R2 is 0.92, and the relative error of prediction is 1.76%–9.11%, which indicates that RF model has greater accuracy and applicability in advance prediction of coal roadway roof displacement. It can be used to predict the roof accidents of coal mines to ensure the safety and stability of the roadway enclosure rock during the construction period. The study will be helpful in planning of mining, improving the recovery rate of resources, and promoting the intelligent development of coal mine.
Study on the Deformation Characteristics of Railway Subgrade Structures Induced by Groundwater Level Changes
Authors: Li Baolong , Jiang Lulu , Wang Dongli , Yin Yi , Shen Yupeng , Han Yunxi
Published: January 2025

The groundwater extraction will lead to a drop in water level, causing a change in the distribution of additional stress in the strata. This phenomenon leads to uneven settlement of the railway subgrade. In severe cases, this could threaten the safe operation of trains. Therefore, the degree and main factors influencing groundwater extraction in the settlement of the railway subgrade need to be determined through research. Based on railway subgrade engineering, the first step involved monitoring the settlement resulting from groundwater extraction during the spring irrigation period. Following this, the settlement characteristics of the subgrade structure were analysed. Subsequently, based on the Biot consolidation theory of the soil, a numerical simulation model was established to study the threshold parameters of the groundwater extraction on site. Finally, the primary and secondary relationships of the influencing factors must be clarified. The research results show that: (1) According to the monitoring data, groundwater extraction resulted in a subgrade subsidence of 15.2 mm, exceeding the prescribed threshold, affecting the engineering quality. (2) By the numerical simulation results, the distance parameter of the wells is the most significant factor affecting the deformation of the subgrade, followed by the quantity of extraction. So, the distance between the wells and the tracks should be kept at more than 150 m; the pumping volume should not exceed 1500 m3/day. (3) Using horizontal jet grouting piles to reinforce the subgrade can reduce the maximum uneven deformation from 18.14 to 7.86 mm, an effective settlement control measure.
Intraoral Pleomorphic Adenoma of the Minor Salivary Glands: A Case Series of 10 Cases With Emphasis on Histopathological Features
Authors: Jain Neetu , Keshwar Shashi , Shrestha Ashish , Jaisani Mehul Rajesh , Maharjan Iccha Kumar , Agrawal Navin
Published: January 2025

Pleomorphic adenoma (PA), the most common salivary gland tumor, presents unique challenges due to its diverse clinicopathologic features. The objective of this case series is to highlight the implication of detailed histopathological examination to guide appropriate diagnosis. This study reviews 10 cases of PA diagnosed at B.P. Koirala Institute of Health Sciences, Nepal, between 2011 and 2023. Patients ranged from 16 to 71 years, with a male‐to‐female ratio of 1:2.3. Most lesions (eight cases) were located on the palate, with additional cases, one on the upper lip and one on the cheek mucosa. Lesion sizes ranged from 1 to 3 cm2 and durations from 2 months to 4 years. Clinically, all lesions were well encapsulated, nontender, and nonulcerated and had normal overlying mucosa. Histopathologically, cases included classical PA, myxoid, and cellular types. Common findings were ductal structures filled with eosinophilic material, a “swarm bee” appearance, plasmacytoid cells, and myxoid stroma. Squamous differentiation and psammoma bodies were observed in some cases. No osseous or cartilaginous components were detected. All cases were excised, with no recurrences reported during at least 2 years of follow‐up. Hence, proper diagnosis is crucial for effective management and long‐term outcomes of PA.
Novel p.Arg534del Mutation and MTHFR C667T Polymorphism in Fragile X Syndrome (FXS) With Autism Spectrum Phenotype: A Case Report
Authors: Hasan Hasan , Santos Ellery R. , Amrei Seyedeh Ala Mokhtabad , Tassone Flora , Randol Jamie Leah , Hagerman Paul , Hagerman Randi J.
Published: January 2025

Fragile X syndrome (FXS) presents with autism spectrum disorder (ASD), intellectual disability, developmental delay, seizures, hypotonia during infancy, joint laxity, behavioral issues, and characteristic facial features. The predominant mechanism is due to CGG trinucleotide repeat expansion of more than 200 repeats in the 5′UTR (untranslated region) of FMR1 (Fragile X Messenger Ribonucleoprotein 1) causing promoter methylation and transcriptional silencing. However, not all patients presenting with the characteristic phenotype and point/frameshift mutations with deletions in FMR1 have been described in the literature. It is believed that < 1% of cases are caused by point mutations. Genetic and functional testing of point mutations in FXS has yielded insights on KH domain RNA‐binding properties of FMRP (Fragile X Messenger Ribonucleoprotein Protein) and nuclear export of the protein. Here, we report a c.1599_1601del p.Arg534del novel mutation in FMR1 with homozygous C677T MTHFR polymorphism in a 12‐year‐old boy. He presents with unique phenotype of FXS with ASD, developmental delay, nonverbal learning disorder (NVLD), overall IQ in the 5th percentile with above average verbal IQ (66th percentile), difficulties with quantitative reasoning, dyspraxia, below average visual‐spatial skills (2nd percentile), difficulty with social pragmatics and social understanding, and executive dysfunction. He has a strong aptitude for music and exceptional aural skills. Identification of novel variants has helped in understanding functional aspects of FMRP. In addition, it aids families in genetic counseling and in administering therapies for children with FXS who present with atypical features.
Emerging Passive Cooling Technologies and Their Multidisciplinary Applications: An Integrative Review
Authors: Nqoro Xhamla , Taziwa Raymond , Hasheni Thabo , Giwa Solomon
Published: January 2025

The persistent rise in temperature driven by the emission of greenhouse gases presents a pressing contemporary challenge, fostering innovative cooling techniques. Currently, passive cooling technologies have gained attention in various research fields for their effectiveness in combating heat accumulation. Compared to traditional active cooling methods, which rely on electricity or other energy sources, passive cooling significantly reduces energy consumption and electricity demand. These technologies have demonstrated the potential for temperature reductions of ~1°C–24°C, translating to substantial electricity savings of about 2–300 kWh/year. This paper uses an integrative review approach to highlight the fundamental principles and design strategies underlying passive cooling technologies, such as phase change materials, radiative cooling, and evaporative cooling. Special emphasis is placed on their potential implementation, from preserving biological materials to cooling buildings, electronics, and personal clothing. Passive cooling methods offer cost savings over time due to lower maintenance and operational costs and potentially simpler designs.
Looking Back and Moving Forward: Exploring Community Connectors’ Experience With Implementing Social Prescribing
Authors: Esfandiari Elham , Chudyk Anna M. , Mulligan Kate , Miller William C. , Mortenson W. Ben , Newton Christie , Rush Kathy L. , Petrella Robert J. , Ashe Maureen C.
Published: January 2025

Social prescribing is a health and social model of care which is emerging globally. It is a multifaceted intervention shaped by various contextual factors that can affect its implementation. Our aim was to describe community connectors’ (link workers or navigators) perceptions and experiences delivering social prescribing programs, with a particular interest in identifying implementation factors or themes. We conducted 11 online semi‐structured interviews with community connectors who delivered social prescribing in British Columbia (BC), Canada. We used directed content analysis, and two authors explored interviews using an implementation perspective. We sorted findings using a deductive approach based on previously published guidance to consider program acceptability, adoption, reach, dose, fidelity, feasibility, and sustainability, and community connectors’ self‐efficacy in delivering the program. We identified factors or themes which could impact on social prescribing implementation, specifically: variability in people’s unmet social needs, identification of community resources, team relationships, and communication. Participants also shared their experiences and perspectives on community connectors’ training, support, and their roles and scope within the continuum of care. At the client level, participants noted some challenges for people to access services because of low income and/or digital literacy. They further provided suggestions for shaping the future of social prescribing. Overall, participants provided valuable insights into social prescribing implementation opportunities and challenges which contribute to understanding community connectors’ role within the wider scope of this quickly emerging health and social model of care.
Contribution of Daylighting to Saving Electrical Energy in Cameroon: Case Study of a Classroom in a Public School
Authors: Tagueu Merimé Souffo , Madjou Tanekou Lionelle Adelaїde , Ndzana Benoît
Published: January 2025

The need for electrical energy in Cameroon is growing and supply is not keeping up with demand. The solutions proposed so far relate to the construction of new hydroelectric dams and solar power plants, but this requires a lot of time and enormous financial resources. In this work, we develop a method for reducing the energy bill by adjusting lighting level while remaining within regulatory margins. This method highlights the use of a dimmer which allows the lighting level of the luminaires to be adjusted from 0% to 100% of their value. Thus, by combining the contribution of natural outdoor lighting with that of artificial indoor lighting, we are able to significantly reduce the quantity of electrical energy consumed by a classroom during the day. In our case study, by dividing the classroom into 99 measurement points, we collect data on illuminance levels by simulation using DIALux software and by direct measurement using a luxmeter. Applying the optimization algorithm designed for this purpose brings the average illuminance level down to 300lx following the EN12464 standard. Finally, taking into account the cost of kWh practiced in Cameroon, this saving goes up to 57.85% depending on the daily lighting level. This solution could make it possible to partially resolve the energy deficit in the time necessary for the construction of new power plants.
Effects of Tocotrienol on Cardiovascular Risk Markers in Patients With Chronic Kidney Disease: A Randomized Controlled Trial
Authors: Trugilho Liana , Alvarenga Lívia , Cardozo Ludmila , Paiva Bruna , Brito Jessyca , Barboza Isis , Almeida Jonatas , Anjos Juliana dos , Khosla Pramod , Ribeiro-Alves Marcelo , Mafra Denise
Published: January 2025

Tocotrienols, isomers of vitamin E, may provide an effective nutritional strategy to mitigate common cardiovascular risks such as dyslipidemia, inflammation, and oxidative stress in patients with chronic kidney disease (CKD). This double‐blind, placebo‐controlled, randomized clinical trial aimed to evaluate the effects of a tocotrienol‐rich fraction (TRF) supplementation (300 mg/day) on oxidative stress and inflammatory markers, including transcription factors in nondialysis (ND) and hemodialysis (HD) CKD patients for three months. Interleukin‐6, tumor necrosis factor‐α (IL‐6 and TNF‐α), C‐reactive protein (CRP), lipid peroxidation, biochemical parameters, and transcription factors such as NRF2 and NF‐κB mRNA expression were evaluated. Seventeen HD patients (9 in the placebo group, 8 in the TRF group) and 16 ND CKD patients (8 in the placebo group and 8 in the TRF group) completed the study. In HD patients, significant reductions were observed in LDL cholesterol (p = 0.04) and total plasma cholesterol levels (p = 0.01) after TRF intervention. CRP serum levels decreased significantly in ND CKD patients (p = 0.05) after TRF supplementation. Transcription factors NRF2 and NF‐κB mRNA expressions remained unaltered in both groups. This study suggests that TRF supplementation may mitigate dyslipidemia and inflammation, factors involved with increased cardiovascular risk, in CKD patients, with variations in efficacy between HD and ND patients.

Trial Registration: ClinicalTrials.gov identifier: NCT04900532
Computation of Differential, Integral Operators and Quantitative Structure–Property Analysis of Boron α‐Icosahedral Nanosheet
Authors: Hakami Khalil Hadi , Khan Abdul Rauf , Bhatti Saad Amin
Published: January 2025

In its crystalline state, the α‐icosahedral nanosheet of boron demonstrates superconductivity and thermal electronic properties. Mathematical research on a graph’s structure yields a graph descriptor, a numerical measure. Chemical graph theory employs connectivity descriptors to analyze molecular structures, providing crucial insights into many chemical compounds’ chemical and biological characteristics. These characteristics benefit physicists, chemists, and medical and pharmaceutical specialists. In this paper, the idea of reverse degree–based ‐polynomial is initiated, and differential and integral operators are computed. We formulate reverse degree–based topological descriptors based on this concept. In this paper, we examine the boron α‐icosahedral nanosheet for this technique. We looked at the physicochemical properties of boron α‐icosahedral nanosheets using reverse degree–based topological descriptors and best‐fit linear regression models to get an idea of what they are. Researchers are hoping that this strategy will lead them into new areas where they can investigate related studies.