bluuebunny/crossref_metadata_2025_split huggingface dataset:
CT-based radiomics predicts CD38 expression: indirectly reflects clinical prognosis in epithelial ovarian cancer
Authors: Yao Yuan , Zhang Haijin , Liu Hui , Teng Chendi , Che Xuan , Bian Wei , Zhang Wenting , Wang Zhifeng 
Published: December 2023

Abstract

Background
Cluster of differentiation 38 (CD38) has been found to be highly expressed in various solid tumors, and its expression level may be associated with patient prognosis and survival. The study aimed to explore the prognostic value of CD38 expression for patients with epithelial ovarian cancer (EOC) and to construct two computed tomography (CT)-based radiomics models for CD38 expression prediction.
Methods
A total of 333 cases of EOC were enrolled from The Cancer Genome Atlas (TCGA) database for CD38 related bioinformatics and survival analysis. 56 intersection cases from TCGA and The Cancer Imaging Archive (TCIA) databases were selected for radiomics feature extraction and model construction. Logistic regression (LR) and support vector machine (SVM) models were constructed and internally validated using 5-fold cross-validation to assess the performance of the models for CD38 expression levels.
Results
High expression of CD38 was an independent protective factor (HR = 0.540) for overall survival (OS) in EOC patients. 5 radiomics features based on CT images were selected to build models for CD38 expression prediction. In the training set and internal validation set, for the receiver operating characteristic (ROC) Curve, the LR model reached area under the curve (AUC) of 0.739 and 0.732, while the SVM model achieved AUC values of 0.741 and 0.700, respectively. For the precision-recall (PR) Curve, the LR model and SVM model demonstrated AUC of 0.760 and 0.721. The calibration curves and decision curve analysis (DCA) provided evidence supporting the fitness and net benefit of the models.
Conclusions
High expression of CD38 can improve OS in EOC patients. CT-based radiomics models can be a new predictive tool for CD38 expression, offering possibilities for individualized survival assessment for patients with EOC.
Method for edge chipping monitoring based on vibration polar coordinate image feature analysis
Authors: Jiang Zhenxi , Wang Fuji , Mou WenPing , Zhu Shaowei , Fu Rao , Yu Zhiyong 
Published: December 2023

Abstract
Cutting edge chipping is a randomly-happened but critical tool failure, which could seriously affect machining quality without effectively and timely treatment especially in the automated machining process. Accurately and timely monitoring chipping is the first key step for properly handling edge chipping issues, while there are still gaps between academic research and industrial application as chipping initiation and propagation are always stochastic especially for milling complex structural parts with mass of ever-changing cutting conditions. This paper proposes a chipping monitoring method for automated milling process based on cutting vibration polar coordinate image features and residual neural network (ResNet) model, which lies on the change of cutting vibration signals that reveal just after an event of edge chipping has occurred. With the creatively proposed polar coordinate transform method, cutting vibration signals are first preprocessed for real-time chipping detection. The preprocessed signals with easily distinguished shape features of polar coordinate images, are then employed for developing a ResNet50 based classification model. And consequently, the cutting edge chipping could be monitored in almost real time. Experimental validation for real-time chipping detection has been conducted with the proposed method. The accuracy of No. 5 ~ 8 experiments are 92%, 88%, 83% and 84%, respectively. More importantly, the method is suited for edge chipping monitoring for milling complex structural parts, where effects of constantly changing cutting conditions have been eliminated.
Prediction model of the unbalanced expression of hormone receptor in HER2-negative Breast Cancer
Authors: Wang Tingrui , Wang Xingxu , Lin Zijing , Han Shanshan , Xiao Zhesi , Ming Jia 
Published: December 2023

Abstract
Purpose: Estrogen and Progesterone receptors(ER, PR) are essential indicators for Breast cancer(BC) therapy, but the unbalanced expression effect remains unclear. This study aimed to explore the features of HER2-negative BC with different expressions of ER and PR.
Methods: 106,742 women patients with BC in SEER from 2010 to 2015 were divided into four groups: ER-negativePR-negative (NN), ER-negativePR-positive (NP), ER-positvePR-negative (PN), ER-positivePR-positive (PP). The significant results of Cox Hazard regression were used to build the nomograms of NP and PN.
Results: Groups of ER-negative (median age<60) were younger than ER-positive. Proportions about Grade III-IV of NP and NN were significantly higher than PN and PP (III: 81.41%, 81.71%, 44.26%, 18.63%; IV: 0.73%, 0.61%, 0.29%, 0.10%). Metastasis rates in NN, NP, and PN were higher than in PP (3.45%,3.02%,3.26%, 2.07%). The survival line of NP was equal to NN (P=0.65) but lower than PP (P=0.00011); PN was lower than PP but significantly higher than NN(P<0.0001). C-index of NP and PN prediction models was 0.721 (95%CI: 0.679-0.761) and 0.750 (95%CI: 0.736-0.763).
Conclusion: HER2-negative BC with uneven expressions of ER and PR differs from those with consistent presentations. NP and NN have similar clinical features: worse grading, larger tumor diameter, and higher incidence of peripheral invasion and metastasis rates. The survival of NP and PN was worse than PP; NN was consistent with NP but lower than PN from the curve. The two nomograms were validated effectively and had a moderate ability.
Machine Learning Models for Mining Social Media Data for Effective Natural Disaster Assessment
Authors: Mittal Prahlada Varada , Karki Sejal , Parasher Siddharth , Narang Sukriti , Mittal Ankush 
Published: December 2023

Abstract
Satellite technology has emerged as a key tool for effective management and assessment of natural disasters. However, the challenge of accurately estimating impacted populations and assessing building damage, often obscured from aerial views, persists. To address this, the integration of imagery and textual data from social networks offers a promising solution. This study employs Twitter and Flickr datasets, using SVM, CNN, XGBoost, Logistic Regression, and Gradient Boost to extract insights. The sentiment analysis component categorizes disaster-affected individuals' emotions as panic, neutral, or non-panic. The Logistic Regression model excels in text classification, boasting an impressive 88.99% accuracy on the test dataset and 83.45% on training. The framework introduces an Aid analysis model, which gives us an impressive accuracy of 83.16% for the classification of tweets based on the aid sought by people through tweets. Image classification employs CNN, achieving 83.29% accuracy to comprehend disaster impact visually. Given real-time social media responses, the system assists government and organisations promptly, prioritising assistance. It serves as a dependable aid resource, enabling efficient responses tailored to affected communities. Thus, this approach holds potential to significantly enhance disaster relief efficacy.
A multiobjective metaheuristic-based container consolidation model for cloud application performance improvement
Authors: Bracke Vincent , Santos José , Wauters Tim , De Turck Filip , Volckaert Bruno 
Published: December 2023

Abstract

This work describes an approach to enhance container orchestration platforms with an autonomous and dynamic rescheduling system that aims at improving application service time by co-locating highly interdependent containers for network delay reduction. Unreasonable container consolidation may however lead to host CPU saturation, in turn impairing the service time. The multiobjective approach proposed in this work aims to improve application service time by minimizing both inter-server network traffic and CPU throttling on overloaded servers. To this extent, the Simulated Annealing combinatorial optimization heuristic is used and compared on its relative performance towards the optimal solution obtained by Mathematical Programming. Additionally, the impact of the proposed system is validated on a Kubernetes cluster hosting three concurrent applications, and this under varying load scenarios. The proposed rescheduling system systematically i) improves the application service time (up to 27.2% from our experiments) and ii) surpasses the improvement reached by the Kubernetes descheduler.
The mental health of college students: After the reduction of COVID-19 pandemic restrictions
Authors: He Lin , Zhu Chen , Xu Haoyuan , Xi Haofan , Zhang Haiqing , Bao Tianhao , Li Kuan 
Published: December 2023

Abstract

Among adolescents and young adults, self-reported baseline psychological distress was quite high during the COVID-19 pandemic. As a vulnerable group, college students suffer from serious psychological problems such as anxiety and depression during the COVID-19 pandemic. Despite the fact that COVID-19 no longer constitutes a public health emergency of international concern and the reduction of COVID-19 pandemic restrictions in March 2023, the change of college students' mental health remains unclear. In March and June 2023, college students in Kunming, China, were investigated by the Self-Rating Anxiety Scale, Self-Rating Depression Scale, and Pittsburgh Sleep Quality Index. After the COVID-19 pandemic restrictions were reduced, the results of the scale indicated anxiety (-0.6%, 21.6%-21%), mild anxiety (+ 0.5%, 16.50%-17.0%), moderate anxiety (-0.7%, 3.94%-3.2%), and severe anxiety (-0.4%, 1.2%-0.8%). Depression (-8.1%, 37.2%-29.1%), mild depression (-5.2%, 19.4%-14.2%), moderate depression (-2.2%, 16.7%-14.5%), or severe depression (-0.7%, 1.1%-0.3%) among college students. Good sleep quality (0%, 72.3%-72.3%) and poor sleep quality (0%, 27.7%-27.7%); anxiety or depression (-6.5%, 39.7%-33.2%) among college students. The incidence of anxiety and depression in college students was reduced, after the reduction of COVID-19 pandemic restrictions.
Single-photon superradiance in individual cesium lead halide quantum dots
Authors: Zhu Chenglian , Boehme Simon C. , Feld Leon G. , Moskalenko Anastasiia , Dirin Dmitry N. , Mahrt Rainer F. , Stöferle Thilo , Bodnarchuk Maryna I. , Efros Alexander L. , Sercel Peter C. , Kovalenko Maksym V. , Rainò Gabriele 
Published: December 2023

Abstract
The brightness of an emitter is ultimately described by Fermi’s golden rule, with a radiative rate proportional to its oscillator strength times the local density of photonic states (LDOS). Since the oscillator strength is an intrinsic material property, the quest for ever brighter emission has relied on LDOS engineering, via dielectric or plasmonic resonators1,2. In contrast, a much less explored avenue is to boost the oscillator strength, and hence the emission rate, via a collective behavior termed superradiance. Recently, it had been proposed3 that the latter can be realized via the giant oscillator-strength transitions of a weakly confined exciton in a quantum well when its coherent motion extends over many unit cells. Here, we demonstrate single-photon superradiance in perovskite quantum dots (QDs) with a sub-100 ps radiative decay time, almost as short as the reported exciton coherence time4. The characteristic dependence of radiative rates on QD size, composition, and temperature suggests the formation of giant transition dipoles, as confirmed by effective-mass calculations. The results aid the development of ultrabright, coherent quantum-light sources and attest that quantum effects, e.g., single-photon emission, persist in nanoparticles ten times larger than the exciton Bohr radius.
A comprehensive, open-source data model for wastewater-based epidemiology
Authors: Therrien Jean-David , Thomson Mathew , Sion Eugen-Sorin , Lee Ivan , Maere Thomas , Nicolaï Niels , Manuel Douglas G. , Vanrolleghem Peter A. 
Published: December 2023

Abstract

The recent SARS-COV-2 pandemic has sparked the adoption of wastewater-based epidemiology (WBE) as a low-cost way to monitor the health of populations. In parallel, the pandemic has encouraged researchers to openly share their data to serve the public better and accelerate science. However, environmental surveillance data are highly dependent on context and are difficult to interpret meaningfully across sites. This paper presents the second iteration of the Public Health Environmental Surveillance Open Data Model (PHES-ODM), an open-source dictionary and set of data tools to enhance the interoperability of environmental surveillance data and enable the storage of contextual (meta)data. The data model describes how to store environmental surveillance program data, metadata about measurements taken on various specimens (water, air, surfaces, sites, populations) and data about measurement protocols. The model provides software tools that support the collection and use of PHES-ODM formatted data, including performing PCR calculations and data validation, recording data into input templates, generating wide tables for analysis, and producing SQL database definitions. Fully open-source and already adopted by institutions in Canada, the European Union, and other countries, the PHES-ODM provides a path forward for creating robust, interoperable, open datasets for environmental public health surveillance for SARS-CoV-2 and beyond.
Interactions of Polyphenolic Gallotannins with Amyloidogenic
Polypeptides Associated with Alzheimer’s Disease: From Molecular
Insights to Physiological Significance
Authors: Khalifa Jihane , Bourgault Steve , Gaudreault Roger 
Published: December 2023

Abstract:
Polyphenols are natural compounds abundantly found in plants. They are known for
their numerous benefits to human health, including antioxidant properties and anti-inflammatory
activities. Interestingly, many studies have revealed that polyphenols can also modulate the formation
of amyloid fibrils associated with disease states and can prevent the formation of cytotoxic oligomer
species. In this review, we underline the numerous effects of four hydrolysable gallotannins
(HGTs) with high conformational flexibility, low toxicity, and multi-targeticity, e.g., tannic
acid, pentagalloyl glucose, corilagin, and 1,3,6-tri-O-galloyl-β-D-glucose, on the aggregation of
amyloidogenic proteins associated with the Alzheimer’s Disease (AD). These HGTs have demonstrated
interesting abilities to reduce, at different levels, the formation of amyloid fibrils involved
in AD, including those assembled from the amyloid β-peptide, the tubulin-associated unit, and the
islet amyloid polypeptide. HGTs were also shown to disassemble pre-formed fibrils and to diminish
cognitive decline in mice. Finally, this manuscript highlights the importance of further investigating
these naturally occurring HGTs as promising scaffolds to design molecules that can interfere
with the formation of proteotoxic oligomers and aggregates associated with AD pathogenesis.
Stability Indicating HPLC Method for In-vitro Determination of Pantoprazole Sodium and its Degradation Products in Simulated Gastric and Intestinal Fluids
Authors: Gupta Avani , Akhtar Juber , Rastogi Kailash Chandra , Badruddeen  , Khan Mohammad Irfan , Ahmad Mohammad 
Published: December 2023

Background:
A high-performance liquid chromatography (HPLC) method was developed
for the determination of Pantoprazole Sodium (PPZ) in the presence of its degradation products.
The degradation of PPZ was studied in simulated intestinal fluid (SIF) and simulated gastric
fluids (SGF) in various temperature conditions.

Aim:
This study aimed to establish a simple, sensitive, and rapid RP HPLC method for in-vitro determination
of Pantoprazole Sodium and its degradation products in simulated gastric and intestinal
fluids.

Objective:
Pantoprazole is acid labile drug. In order to determine pantoprazole in various oral dosage
forms, the stability-indicating assay of PPZ was performed in phosphate buffer (pH 6.8) representing
simulated intestinal fluid (SIF) and in 0.1 molars (M) Hydrochloric acid (HCl) as simulated
gastric fluid (SGF) at two different temperature conditions, i.e., 25°C and 0°C, respectively.

Method:
Pantoprazole sodium was obtained from the Akums laboratory in Haridwar. The analysis
was performed by high-performance liquid chromatography (HPLC), Shimadzu, equipped with
two LC-10 AD VP solvent-delivery modules, a SPD-10A UV–-visible detector, and a manual injector
valve with 20 μL sample loop. Phenomenex ODS analytical column (150 mm × 4.6 mm i.d.,
5 μm particles) was done under reversed-phase partition chromatographic conditions. The mobile
phase was phosphate buffer and acetonitrile (ACN) of pH 7.4, respectively, optimized in a 70:30
(v/v) ratio followed by filtration through a 0.45 μm membrane filter and degassed by ultrasonicator
before use. The mobile phase was delivered at the flow rate of 2 mL/min. The various parameters,
such as linearity, accuracy and precision of the analytical method, were studied.

Result:
The standard curve of PPZ was linear (R2>0.99) over the concentration range of 5-30
μg/mL, and the relative standard deviation (RSD) values for intra-day and inter-day variations were
in the range of 1.0-1.8%. The range of RSD was within ±2.

Conclusion:
The stability of PPZ in aqueous solution was pH dependent. The rate of degradation
increases with decreasing pH. The pH stability of pantoprazole was studied at the above-mentioned
temperature conditions. The PPZ peaks were analyzed by comparing them with fresh samples and
were stable in SIF solution after 24 hours elapsed time at pH 6.8. The obtained degraded peaks in
SGF (pH 1) were successfully separated from the PPZ.
Performance of Hybrid Asphalt Mixture through the Stability and Tensile Strength
Authors: Sani Wan Noor Hin Mior , Jaya Ramadhansyah Putra , Masri Khairil Azman 
Published: December 2023

Introduction:
The stability and tensile strength of asphalt mixtures play a crucial role in pavement durability, primarily due to the challenges associated with cracking. This study investigates the utilization of a hybrid asphalt mixture comprising hybrid materials: Palm Oil Fuel Ash (POFA), garnet waste and sawdust to enhance the modified asphalt performance.

Aims:
The objective is to examine the hybrid materials’ influence towards the mechanical properties of the hybrid asphalt mixture, specifically focusing on the Indirect Tensile Strength (ITS) as well as the Marshall stability test.

Methods:
To achieve this, hybrid materials were finely ground and sieved to 25µm before being incorporated into the mixture. The Marshall mix design procedure was conducted, utilizing hybrid binders at concentrations of 0% (control), 3% 6%, and 9%. The effects of the hybrid asphalt mixture on stability, flow, stiffness, and ITS were assessed at the optimal binder content.

Results:
The findings indicate that the hybrid asphalt mixture reveals enhanced Marshall stability, stiffness, flow, and ITS values compared to conventional asphalt mixtures. Notably, hybrid asphalt mixtures with 6% binder concentration demonstrate the most significant enhancement in terms of Marshall stability and ITS.

Conclusion:
This correlation suggests that the incorporation of POFA, garnet waste, and sawdust as hybrid materials in the hybrid asphalt mixture possesses a positive effect on the overall mechanical properties of the pavement.

Highlights:
• POFA, garnet waste, and sawdust increased the stability of the hybrid asphalt mixture.
• POFA, garnet waste, and sawdust increased the tensile strength of the hybrid asphalt mixture.
• Hybrid asphalt mixture performs positively towards flow and stiffness.
Identifying Symptoms Prior to Pancreatic Ductal Adenocarcinoma Diagnosis in Real-World Care Settings: Natural Language Processing Approach
Authors: Xie Fagen , Chang Jenny , Luong Tiffany , Wu Bechien , Lustigova Eva , Shrader Eva , Chen Wansu 
Published: December 2023

Background
Pancreatic cancer is the third leading cause of cancer deaths in the United States. Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, accounting for up to 90% of all cases. Patient-reported symptoms are often the triggers of cancer diagnosis and therefore, understanding the PDAC-associated symptoms and the timing of symptom onset could facilitate early detection of PDAC.

Objective
This paper aims to develop a natural language processing (NLP) algorithm to capture symptoms associated with PDAC from clinical notes within a large integrated health care system.

Methods
We used unstructured data within 2 years prior to PDAC diagnosis between 2010 and 2019 and among matched patients without PDAC to identify 17 PDAC-related symptoms. Related terms and phrases were first compiled from publicly available resources and then recursively reviewed and enriched with input from clinicians and chart review. A computerized NLP algorithm was iteratively developed and fine-trained via multiple rounds of chart review followed by adjudication. Finally, the developed algorithm was applied to the validation data set to assess performance and to the study implementation notes.

Results
A total of 408,147 and 709,789 notes were retrieved from 2611 patients with PDAC and 10,085 matched patients without PDAC, respectively. In descending order, the symptom distribution of the study implementation notes ranged from 4.98% for abdominal or epigastric pain to 0.05% for upper extremity deep vein thrombosis in the PDAC group, and from 1.75% for back pain to 0.01% for pale stool in the non-PDAC group. Validation of the NLP algorithm against adjudicated chart review results of 1000 notes showed that precision ranged from 98.9% (jaundice) to 84% (upper extremity deep vein thrombosis), recall ranged from 98.1% (weight loss) to 82.8% (epigastric bloating), and F1-scores ranged from 0.97 (jaundice) to 0.86 (depression).

Conclusions
The developed and validated NLP algorithm could be used for the early detection of PDAC.