bluuebunny/crossref_metadata_2025_split huggingface dataset:
Text Analysis to Enhance Flow of Ideas Fluency and Conjugation Accuracy of EFL Students’ Writing
Authors: Rustipa Katharina , Soepriatmadji Liliek , Rahayu Endang Yuliyani , Kasprabowo Teguh 
Published: September 2024

Empirical studies revealed the EFL students’ writing problems, such as incomplete paragraph development and conjugation accuracy. These problems affected the overall text coherence. These problems have inspired teacher researchers to investigate alternative instructional techniques to improve the flow of ideas and conjugation accuracy of EFL students' paragraphs. This current research aims to investigate mood and paragraph structure analysis effectiveness to develop the conjugation accuracy and flow of ideas fluency in EFL students' paragraphs. This study followed an experimental design utilizing control and experimental groups. The research subjects comprised 30 2nd-year undergraduate EFL learners who were at an intermediate level of English proficiency. Data for this research were collected using multiple methods, including tests, documentation, and interviews. The research results revealed that mood analysis and paragraph-structure analysis were effective techniques for improving the conjugation accuracy and idea flow fluency of EFL students' writing. Thus, it is recommended that mood and paragraph structure analyses be alternative techniques to teach paragraph writing.
Behavior of LSTM and Transformer deep learning models in flood simulation considering South Asian tropical climate
Authors: Madhushanka G.W.T.I. , Jayasinghe M. T. R. , Rajapakse R. A. 
Published: September 2024

ABSTRACT

The imperative for a reliable and accurate flood forecasting procedure stems from the hazardous nature of the disaster. In response, researchers are increasingly turning to innovative approaches, particularly machine learning models, which offer enhanced accuracy compared to traditional methods. However, a notable gap exists in the literature concerning studies focused on the South Asian tropical region, which possesses distinct climate characteristics. This study investigates the applicability and behavior of long short-term memory (LSTM) and transformer models in flood simulation considering the Mahaweli catchment in Sri Lanka, which is mostly affected by the Northeast Monsoon. The importance of different input variables in the prediction was also a key focus of this study. Input features for the models included observed rainfall data collected from three nearby rain gauges, as well as historical discharge data from the target river gauge. Results showed that the use of past water level data denotes a higher impact on the output compared to the other input features such as rainfall, for both architectures. All models denoted satisfactory performances in simulating daily water levels, with Nash–Sutcliffe Efficiency (NSE) values greater than 0.77 while the transformer encoder model showed a superior performance compared to encoder–decoder models.
Influence of weather research and forecasting model microphysics and cumulus schemes for forecasting monsoon rainfall over the Kelani River basin, Sri Lanka
Authors: Perera P. L. L. N. , Neluwala N. G. P. B. , Wijetunge J. J. 
Published: September 2024

ABSTRACT

Creating precise quantitative precipitation forecasts is essential for reducing losses and damages. This study aimed to identify the best microphysics and cumulus schemes for forecasting monsoon rainfall over the Kelani River basin, Sri Lanka, using the WRF-ARW model. Four extreme rainfall events from the 2020 and 2021 monsoon seasons were simulated with various microphysics and cumulus parameterizations to find the optimal combinations. These combinations were then tested for their ability to forecast two monsoon events with a 24-h lead time. Simulated and forecasted rainfalls were compared with observations from 15 gauging stations. Results indicate that WSM3 and WSM6 microphysics schemes with the Betts–Miller–Janjic (BMJ) cumulus scheme are optimal for simulating rainfall, with WSM3_BMJ being the most suitable for forecasting. The findings of this study provide valuable initial data for research in regions with similar environmental conditions, offering insights into the suitability of various physics schemes for simulating and forecasting monsoon rainfall, particularly under extreme conditions. Furthermore, given the prevalence of monsoons in many tropical and subtropical climates, these results will be instrumental in enhancing the use of numerical weather prediction models for forecasting monsoon rainfall on a global scale.
Molecular Subtypes Based on Mitochondrial Oxidative Stress-related Gene Signature and Tumor Microenvironment Infiltration Characterization of Colon Adenocarcinoma
Authors: Cong Peijia , Xu Ruixue , Tan Ziru , Wu Xiaolin , Lian Haifeng , Li Dan 
Published: September 2024

Background:
As the most common subtype of colorectal cancer, colorectal
adenocarcinoma (COAD) still needs better prognostic stratification methods and new intervention
targets. The mitochondrial stress response, linked to mitochondrial homeostasis
and cancer metabolism, warrants further investigation.

Methods:
We identified mitochondrial oxidative stress-related genes (MOS) associated
with COAD prognosis through the TCGA and GEO databases. Molecular subtype characteristics
were identified based on MOS gene signatures, and an MOS scoring system
was established to comprehensively evaluate its clinical value. Additionally, the effect
of one of the screened genes, NDRG1, was investigated through a series of in vitro experiments,
including Western blot, qRT-PCR, CCK8 assay, clone formation, and Transwell
assay, to explore its impact on COAD proliferation and migration ability.

Results:
Our analysis revealed that MOS gene signatures effectively distinguished
molecular subtypes of COAD, and the MOS scoring system was found to be independent
in predicting prognosis. Evaluation of microenvironment infiltration characteristics,
mutation characteristics, immunotherapy response, and drug sensitivity analysis further
suggested the potential clinical utility of this study. in vitro experimental results showed
that NDRG1 significantly affected the proliferation and migration of COAD cells, partially
verifying the reliability of our bioinformatics analysis.

Conclusion:
This study provides a novel perspective on the role of mitochondrial oxidative
stress in COAD, proposing innovative prognostic evaluation methods and potential
therapeutic targets, thus offering new directions for the clinical treatment of COAD.
Identification and Verification of a Prognostic Risk Signature in Oral Squamous Cell Carcinoma
Authors: Chen Rishou , Duan Junlin , Ye Yonglong , Xu Huan , Ding Yali , Liu Jun 
Published: September 2024

Introduction:
Oral squamous cell carcinoma (OSCC) is a prevalent malignant condition.
This study aimed to investigate the role of mTORC1 signaling and develop a prognostic
model for OSCC.

Materials and Methods:
The single-sample gene set enrichment analysis (ssGSEA) algorithm was
utilized to calculate the Z-Score of Hallmarks in OSCC, followed by univariate Cox regression
analysis to identify processes associated with prognosis. Weighted gene co-expression network
analysis (WGCNA) was performed using transcriptomic data from the cancer genome atlas
(TCGA) cohort to identify genes correlated with mTORC1 signaling. A six-gene prognostic model
was constructed using multifactorial Cox regression analysis and validated using an external dataset.

Results:
The study uncovered a strong linkage between mTORC1, glycolysis, hypoxia, and the
prognosis of OSCC. mTORC1 signaling emerged as the most significant risk factor, negatively
impacting patient survival. Additionally, a six-gene prognostic risk score model was developed
which provided a quantitative measure of patients' survival probabilities. Interestingly, within the
context of these findings, TP53 gene mutations were predominantly observed in the high-risk
group, potentially underlining the genetic complexity of this patient subgroup. Additionally, differential
immune cell infiltration and an integrated nomogram were also reported.

Conclusion:
This study highlights the importance of mTORC1 signaling in OSCC prognosis and
presents a robust prognostic model for predicting patient outcomes.
Exploring 5-Benzylthiazolidine-2, 4-Dione Derivatives as Promising PPAR-gamma Agonists through Computational Methods
Authors: Gyampoh Solomon , Nabi Minnatullah , Arora Pinky , Kumar Shubham 
Published: September 2024

Introduction:
Peroxisome Proliferator-Activated Receptors (PPARs) are nuclear receptors
involved in the regulation of glucose and lipid metabolism, making them potential targets
for treating type 2 diabetes mellitus. 5-Benzylthiazolidine-2, 4-dione derivatives, organic
compounds with a thiazolidine-2, 4-dione core and a benzyl moiety at the 5-position, have garnered
attention for their diverse pharmacological activities, including anti-inflammatory, anticancer,
anti-diabetic, anti-tubercular, and anti-viral properties.

Methods:
This research aimed to identify 5-benzylthiazolidine-2,4-dione derivatives as synthetic
PPAR-gamma agonists using computational methods, such as molecular docking and in silico
ADMET analysis. Virtual screening generated potential molecules, and molecular docking studies
using AutoDock Vina 1.5.6 were performed at the active site of the PPAR-gamma receptor
(PDBID: 4PRG).

Results:
Nine hits were identified based on their binding affinities and were further evaluated
using SwissADME for their ADME properties. These nine compounds demonstrated superior
ADME profiles and bioavailability compared to the standard drug Rosiglitazone.

Conclusion:
This study provides a comprehensive computational approach for discovering novel
PPAR-gamma receptor agonists, potentially advancing experimental diabetes research.
Lapatinib: A Potential Therapeutic Agent for Colon Cancer Targeting Ferroptosis
Authors: Sun Yue , Wang Dan , Yuan Chen , Lang Xiujuan , Fu Songbo 
Published: September 2024

Background:
Colon cancer poses a significant threat to the lives of several patients, impacting their
quality of life, thus necessitating its urgent treatment. Lapatinib, a new generation of targeted anti-tumor drugs for
clinical application, has yet to be studied for its molecular mechanisms in treating colon cancer.

Objectives:
This study aimed to uncover the underlying molecular mechanisms through which lapatinib exerts its
therapeutic effects in colon cancer treatment.

Methods:
We accessed pertinent data on patients with colon cancer from the Cancer Genome Atlas (TCGA)
database and performed bioinformatics analysis to derive valuable insights. The cell counting kit-8 (CCK8) assay
was employed to assess whether lapatinib has a potential inhibitory effect on the growth and proliferation of HT-
29 cells. Additionally, we employed western blot and real-time quantitative polymerase chain reaction methods to
investigate whether lapatinib regulates the expression of the ferroptosis-associated protein GPX4 in HT-29 cells.
Furthermore, we utilized specific assay kits to measure the levels of reactive oxygen species (ROS) and
malondialdehyde in HT-29 cells treated with lapatinib, aiming to elucidate the precise pattern of cell damage
induced by this compound.

Results:
GPX4 exhibited high expression levels in tissues from patients with colon cancer and was significantly
associated with patient prognosis and diagnosis. Lapatinib inhibited the growth and proliferation of the colon
cancer cell line HT-29. Additionally, lapatinib suppressed the expression of GPX4 in HT-29 cells, while the ferroptosis
inhibitor ferrostatin-1 (Fer-1) partially restored its expression. Lapatinib induced an increase in intracellular
ROS levels and malondialdehyde content in HT-29 cells, with Fer-1 partially restoring these levels.

Conclusion:
Our findings demonstrated that lapatinib could effectively suppress the mRNA and protein expression
of GPX4 in colon cancer cells, which elevates intracellular levels of ROS and malondialdehyde, ultimately
inducing ferroptosis in these cells. This mechanism underscores the potential of lapatinib as a therapeutic strategy
for targeting tumors.
Sleep-Related Disorders in Parkinson's Disease: Mechanisms, Diagnosis, and Therapeutic Approaches
Authors: Arias-Carrion Oscar , Ortega-Robles Emmanuel , Ortuno-Sahagun Daniel , Ramirez-Bermudez Jesus , Hamid Aya , Shalash Ali 
Published: September 2024

Background:
Parkinson's Disease (PD) is frequently associated with a spectrum of
sleep-related disorders, including insomnia, Excessive Daytime Sleepiness (EDS), REM sleep
Behaviour Disorder (RBD), Restless Legs Syndrome (RLS), and Sleep-related Breathing Disorders
(SBDs). These disorders significantly impact PD patients' Quality of Life (QoL) and present
unique diagnostic and therapeutic challenges.

Methods:
This review has explored the intricate relationship between PD and sleep-related disorders,
emphasizing their distinctive features and underlying neurobiological mechanisms. It aimed
to consolidate current knowledge to optimize clinical management and improve patient care. The
profound impact of these disorders on QoL has been evaluated, along with precise diagnostic
methodologies. Additionally, various therapeutic strategies, including pharmacological treatments,
nonpharmacological interventions, and device-aided therapies, have been examined.

Results:
Sleep-related disorders are prevalent among PD patients. Specifically, RBD exhibits a
prevalence of 40-50%, often preceding the onset of motor symptoms, indicating its potential as an
early marker of PD. Despite their significant impact on QoL, these non-motor symptoms are frequently
under-recognized and inadequately managed in clinical practice. Pharmacological treatments,
along with nonpharmacological interventions, like cognitive-behavioral therapy for insomnia
and lifestyle modifications, have shown varied efficacy. Device-aided therapies have also
demonstrated the potential to improve sleep-related disorders and overall non-motor symptom
burden.

Conclusion:
Effective management of sleep-related disorders in PD calls for personalized, comprehensive,
and multimodal therapeutic approaches. This requires the collaborative efforts of neurologists,
sleep specialists, psychiatrists, and other healthcare professionals. Future research
should focus on the intricate relationship between PD and sleep disorders, aiming to develop innovative
treatments and significantly improve patient outcomes.
Efektivitas Implementasi Strategi Non-Celebrity Endorsement Terhadap Market Share Dan Product Sales Pada Usaha Mikro Kecil Menengah
Authors: Pratama Maman Sigit , Waluyowati Nur Prima 
Published: September 2024

The increasing and developing technological advancement has created awareness among entrepreneurs to use its products. Micro, small, and medium-sized entrepreneurs in Malang City use such ease in information accesses and technological progress through social media for communication in their marketing activities. Further, Endorsement is one of the concepts in marketing strategy that is implemented through the utilization of social media. The objective of this research is to identify the effectiveness of non-celebrity Endorsement on the market share and product salesof micro, small, and medium-sized enterprises in Malang City. The data of this qualitative descriptive research was harvested from interviews, observations, and document studies. The research objects are micro, small, and medium-sized entrepreneurs in Malang City that use non-celebrity Endorsement as their marketing strategy. The analyses were performed using data reduction, data presentation, and conclusion drawing, while the data validation was conducted through source triangulation and data triangulation. This study finds that non-celebrity Endorsement is effective in increasing the market share and product salesof micro, small, and medium-sized enterprises in Malang City.

Abstrak
Kemanjuan teknologi yang semakin pesat dan terus mengalami perkembangan memberikan kesadaran bagi para pelaku usaha untuk ikut memanfaatkan kemajuan teknologi. Kemudahan akses informasi dari kemajuan teknologi dimanfaatkan oleh pelaku Usaha Mikro Kecil Menengah (UMKM) di Kota Malang dengan menggunakan media sosial sebagai alat komunikasi dalam kegiatan pemasaran. Endorsement merupakan konsep strategi pemasaran yang diimplementasikan melalui pemanfaatan media sosial. Penelitian ini bertujuan untuk menetahui efektivitas dari implementasi Non-Celebirty Endorsement terhadap market share dan product salesdari Usaha Mikro Kecil Menengah (UMKM) di Kota Malang. Penelitian ini menggunakan pendekatan kualitatif dengan jenis penelitian deskriptif dimana menggunakan data primer melalui wawancara, observasi, dan dokumentasi. Objek penelitian ini adalah pelaku usaha Mikro Kecil Menengah (UMKM) di Kota Malang yang menggunakan Non-Celebirty Endorsement sebagai strategi pemasaran. Tenik analisis pada penelitian ini menggunakan reduksi data, penyajian data, dan kesimpulan serta keabsahan data menggunakan triangulasi sumber dan triangulasi data. Hasil dari penelitian ini menunjukan bahwa implemntasi Endorsement non-Celebriity efektif sebagai strategi pemasaran terhadap market share dan product salesdari Usaha Mikro Kecil Menengah (UMKM) di Kota Malang.
Wastewater Surveillance Pilot at US Military Installations: Cost Model Analysis
Authors: Sanjak Jaleal S , McAuley Erin M , Raybern Justin , Pinkham Richard , Tarnowski Jacob , Miko Nicole , Rasmussen Bridgette , Manalo Christian J , Goodson Michael , Stamps Blake , Necciai Bryan , Sozhamannan Shanmuga , Maier Ezekiel J 
Published: September 2024

Abstract

Background
The COVID-19 pandemic highlighted the need for pathogen surveillance systems to augment both early warning and outbreak monitoring/control efforts. Community wastewater samples provide a rapid and accurate source of environmental surveillance data to complement direct patient sampling. Due to its global presence and critical missions, the US military is a leader in global pandemic preparedness efforts. Clinical testing for COVID-19 on US Air Force (USAF) bases (AFBs) was effective but costly with respect to direct monetary costs and indirect costs due to lost time. To remain operating at peak capacity, such bases sought a more passive surveillance option and piloted wastewater surveillance (WWS) at 17 AFBs to demonstrate feasibility, safety, utility, and cost-effectiveness from May 2021 to January 2022.

Objective
We model the costs of a wastewater program for pathogens of public health concern within the specific context of US military installations using assumptions based on the results of the USAF and Joint Program Executive Office for Chemical, Biological, Radiological and Nuclear Defense pilot program. The objective was to determine the cost of deploying WWS to all AFBs relative to clinical swab testing surveillance regimes.

Methods
A WWS cost projection model was built based on subject matter expert input and actual costs incurred during the WWS pilot program at USAF AFBs. Several SARS-CoV-2 circulation scenarios were considered, and the costs of both WWS and clinical swab testing were projected. Analysis was conducted to determine the break-even point and how a reduction in swab testing could unlock funds to enable WWS to occur in parallel.

Results
Our model confirmed that WWS is complementary and highly cost-effective when compared to existing alternative forms of biosurveillance. We found that the cost of WWS was between US $10.5-$18.5 million less expensive annually in direct costs as compared to clinical swab testing surveillance. When the indirect cost of lost work was incorporated, including lost work associated with required clinical swab testing, we estimated that over two-thirds of clinical swab testing could be maintained with no additional costs upon implementation of WWS.

Conclusions
Our results support the adoption of WWS across US military installations as part of a more comprehensive and early warning system that will enable adaptive monitoring during disease outbreaks in a more cost-effective manner than swab testing alone.
Establishing Medical Intelligence—Leveraging Fast Healthcare Interoperability Resources to Improve Clinical Management: Retrospective Cohort and Clinical Implementation Study
Authors: Brehmer Alexander , Sauer Christopher Martin , Salazar Rodríguez Jayson , Herrmann Kelsey , Kim Moon , Keyl Julius , Bahnsen Fin Hendrik , Frank Benedikt , Köhrmann Martin , Rassaf Tienush , Mahabadi Amir-Abbas , Hadaschik Boris , Darr Christopher , Herrmann Ken , Tan Susanne , Buer Jan , Brenner Thorsten , Reinhardt Hans Christian , Nensa Felix , Gertz Michael , Egger Jan , Kleesiek Jens 
Published: September 2024

Background
FHIR (Fast Healthcare Interoperability Resources) has been proposed to enable health data interoperability. So far, its applicability has been demonstrated for selected research projects with limited data.

Objective
This study aimed to design and implement a conceptual medical intelligence framework to leverage real-world care data for clinical decision-making.

Methods
A Python package for the use of multimodal FHIR data (FHIRPACK [FHIR Python Analysis Conversion Kit]) was developed and pioneered in 5 real-world clinical use cases, that is, myocardial infarction, stroke, diabetes, sepsis, and prostate cancer. Patients were identified based on the ICD-10 (International Classification of Diseases, Tenth Revision) codes, and outcomes were derived from laboratory tests, prescriptions, procedures, and diagnostic reports. Results were provided as browser-based dashboards.

Results
For 2022, a total of 1,302,988 patient encounters were analyzed. (1) Myocardial infarction: in 72.7% (261/359) of cases, medication regimens fulfilled guideline recommendations. (2) Stroke: out of 1277 patients, 165 received thrombolysis and 108 thrombectomy. (3) Diabetes: in 443,866 serum glucose and 16,180 glycated hemoglobin A1c measurements from 35,494 unique patients, the prevalence of dysglycemic findings was 39% (13,887/35,494). Among those with dysglycemia, diagnosis was coded in 44.2% (6138/13,887) of the patients. (4) Sepsis: In 1803 patients, Staphylococcus epidermidis was the primarily isolated pathogen (773/2672, 28.9%) and piperacillin and tazobactam was the primarily prescribed antibiotic (593/1593, 37.2%). (5) PC: out of 54, three patients who received radical prostatectomy were identified as cases with prostate-specific antigen persistence or biochemical recurrence.

Conclusions
Leveraging FHIR data through large-scale analytics can enhance health care quality and improve patient outcomes across 5 clinical specialties. We identified (1) patients with sepsis requiring less broad antibiotic therapy, (2) patients with myocardial infarction who could benefit from statin and antiplatelet therapy, (3) patients who had a stroke with longer than recommended times to intervention, (4) patients with hyperglycemia who could benefit from specialist referral, and (5) patients with PC with early increases in cancer markers.