bluuebunny/crossref_metadata_2025 huggingface dataset:
Supplementary Table 7 from Telomerase Upregulation Induces Progression of Mouse BrafV600E-Driven Thyroid Cancers and Triggers Nontelomeric Effects
Authors: Landa Iñigo , Thornton Caitlin E.M. , Xu Bin , Haase Jacob , Krishnamoorthy Gnana P. , Hao Jingzhu , Knauf Jeffrey A. , Herbert Zachary T. , Martínez Paula , Blasco María A. , Ghossein Ronald , Fagin James A.
Published: August 2023

Full list of significantly upregulated terms (q-value<0.05) in BrafV600E+K5-Tert tumors, compared to BrafV600E, from 20-week animals, employing the GSEA (gene set enrichment analysis) hallmarks of cancer database on RNAseq data.
Supplementary Table 6 from Telomerase Upregulation Induces Progression of Mouse BrafV600E-Driven Thyroid Cancers and Triggers Nontelomeric Effects
Authors: Landa Iñigo , Thornton Caitlin E.M. , Xu Bin , Haase Jacob , Krishnamoorthy Gnana P. , Hao Jingzhu , Knauf Jeffrey A. , Herbert Zachary T. , Martínez Paula , Blasco María A. , Ghossein Ronald , Fagin James A.
Published: August 2023

Full list of KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis “up terms” for the BrafV600E vs. BrafV600E+Tert-123C>T comparison.
Supplementary Table 5 from Telomerase Upregulation Induces Progression of Mouse BrafV600E-Driven Thyroid Cancers and Triggers Nontelomeric Effects
Authors: Landa Iñigo , Thornton Caitlin E.M. , Xu Bin , Haase Jacob , Krishnamoorthy Gnana P. , Hao Jingzhu , Knauf Jeffrey A. , Herbert Zachary T. , Martínez Paula , Blasco María A. , Ghossein Ronald , Fagin James A.
Published: August 2023

Full list of KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis “up terms” for the BrafV600E vs. BrafV600E+K5-Tert comparison.
Supplementary Figure 5 from Telomerase Upregulation Induces Progression of Mouse BrafV600E-Driven Thyroid Cancers and Triggers Nontelomeric Effects
Authors: Landa Iñigo , Thornton Caitlin E.M. , Xu Bin , Haase Jacob , Krishnamoorthy Gnana P. , Hao Jingzhu , Knauf Jeffrey A. , Herbert Zachary T. , Martínez Paula , Blasco María A. , Ghossein Ronald , Fagin James A.
Published: August 2023

In vivo dabrafenib plus trametinib treatment of mice.
Supplementary Figure 4 from Telomerase Upregulation Induces Progression of Mouse BrafV600E-Driven Thyroid Cancers and Triggers Nontelomeric Effects
Authors: Landa Iñigo , Thornton Caitlin E.M. , Xu Bin , Haase Jacob , Krishnamoorthy Gnana P. , Hao Jingzhu , Knauf Jeffrey A. , Herbert Zachary T. , Martínez Paula , Blasco María A. , Ghossein Ronald , Fagin James A.
Published: August 2023

Additional analyses of RNA sequencing data from mouse tumors with telomerase upregulation.
Supplementary Figure 2 from Telomerase Upregulation Induces Progression of Mouse BrafV600E-Driven Thyroid Cancers and Triggers Nontelomeric Effects
Authors: Landa Iñigo , Thornton Caitlin E.M. , Xu Bin , Haase Jacob , Krishnamoorthy Gnana P. , Hao Jingzhu , Knauf Jeffrey A. , Herbert Zachary T. , Martínez Paula , Blasco María A. , Ghossein Ronald , Fagin James A.
Published: August 2023

Additional features of Braf and Braf+Tert murine tumors.
Supplementary Figure Legends from Podoplanin Drives Amoeboid Invasion in Canine and Human Mucosal Melanoma
Authors: Shinada Masahiro , Kato Daiki , Motegi Tomoki , Tsuboi Masaya , Ikeda Namiko , Aoki Susumu , Iguchi Takaaki , Li Toshio , Kodera Yuka , Ota Ryosuke , Hashimoto Yuko , Takahashi Yosuke , Chambers James , Uchida Kazuyuki , Kato Yukinari , Nishimura Ryohei , Nakagawa Takayuki
Published: August 2023

Supplementary Figure Legends 1-12
Supplementary Figure 9 from Podoplanin Drives Amoeboid Invasion in Canine and Human Mucosal Melanoma
Authors: Shinada Masahiro , Kato Daiki , Motegi Tomoki , Tsuboi Masaya , Ikeda Namiko , Aoki Susumu , Iguchi Takaaki , Li Toshio , Kodera Yuka , Ota Ryosuke , Hashimoto Yuko , Takahashi Yosuke , Chambers James , Uchida Kazuyuki , Kato Yukinari , Nishimura Ryohei , Nakagawa Takayuki
Published: August 2023

PDPN promotes distant colonization.
Supplementary Figure 2 from Podoplanin Drives Amoeboid Invasion in Canine and Human Mucosal Melanoma
Authors: Shinada Masahiro , Kato Daiki , Motegi Tomoki , Tsuboi Masaya , Ikeda Namiko , Aoki Susumu , Iguchi Takaaki , Li Toshio , Kodera Yuka , Ota Ryosuke , Hashimoto Yuko , Takahashi Yosuke , Chambers James , Uchida Kazuyuki , Kato Yukinari , Nishimura Ryohei , Nakagawa Takayuki
Published: August 2023

PDPN regulates canine mucosal melanoma proliferation, survival, migration, and invasion.
Supplementary Figure S8 from Overlaid Transcriptional and Proteome Analyses Identify Mitotic Kinesins as Important Targets of Arylsulfonamide-Mediated RBM39 Degradation
Authors: Coomar Seemon , Mota Pedro , Penson Alexander , Schwaller Jürg , Abdel-Wahab Omar , Gillingham Dennis
Published: August 2023

Supplementary Figure S8
Supplementary Figure S6 from Overlaid Transcriptional and Proteome Analyses Identify Mitotic Kinesins as Important Targets of Arylsulfonamide-Mediated RBM39 Degradation
Authors: Coomar Seemon , Mota Pedro , Penson Alexander , Schwaller Jürg , Abdel-Wahab Omar , Gillingham Dennis
Published: August 2023

Supplementary Figure S6
Supplementary Figure S4 from Overlaid Transcriptional and Proteome Analyses Identify Mitotic Kinesins as Important Targets of Arylsulfonamide-Mediated RBM39 Degradation
Authors: Coomar Seemon , Mota Pedro , Penson Alexander , Schwaller Jürg , Abdel-Wahab Omar , Gillingham Dennis
Published: August 2023

Supplementary Figure S4
Supplementary Figure S3 from Overlaid Transcriptional and Proteome Analyses Identify Mitotic Kinesins as Important Targets of Arylsulfonamide-Mediated RBM39 Degradation
Authors: Coomar Seemon , Mota Pedro , Penson Alexander , Schwaller Jürg , Abdel-Wahab Omar , Gillingham Dennis
Published: August 2023

Supplementary Figure S3
Supplementary Figure S1 from Overlaid Transcriptional and Proteome Analyses Identify Mitotic Kinesins as Important Targets of Arylsulfonamide-Mediated RBM39 Degradation
Authors: Coomar Seemon , Mota Pedro , Penson Alexander , Schwaller Jürg , Abdel-Wahab Omar , Gillingham Dennis
Published: August 2023

Supplementary Figure S1
Supplementary Data from Overlaid Transcriptional and Proteome Analyses Identify Mitotic Kinesins as Important Targets of Arylsulfonamide-Mediated RBM39 Degradation
Authors: Coomar Seemon , Mota Pedro , Penson Alexander , Schwaller Jürg , Abdel-Wahab Omar , Gillingham Dennis
Published: August 2023

Supplementary Data
Supplementary Data from Overlaid Transcriptional and Proteome Analyses Identify Mitotic Kinesins as Important Targets of Arylsulfonamide-Mediated RBM39 Degradation
Authors: Coomar Seemon , Mota Pedro , Penson Alexander , Schwaller Jürg , Abdel-Wahab Omar , Gillingham Dennis
Published: August 2023

TMT Proteomics Datasets
Data from Telomerase upregulation induces progression of mouse BrafV600E-driven thyroid cancers and triggers non-telomeric effects
Authors: Landa Inigo , Thornton Caitlin EM , Xu Bin , Haase Jacob , Krishnamoorthy Gnana P. , Hao Jingzhu , Knauf Jeffrey A. , Herbert Zachary T. , Martínez Paula , Blasco Maria A. , Ghossein Ronald , Fagin James A.
Published: August 2023

Abstract
Mutations in the promoter of the telomerase reverse transcriptase (TERT) gene are the paradigm of a cross-cancer alteration in a non-coding region. TERT promoter mutations (TPMs) are biomarkers of poor prognosis in cancer, including thyroid tumors. TPMs enhance TERT transcription, which is otherwise silenced in adult tissues, thus reactivating a bona fide oncoprotein. To study TERT deregulation and its downstream consequences, we generated a Tert mutant promoter mouse model via CRISPR/Cas9 engineering of the murine equivalent locus (Tert-123C>T) and crossed it with thyroid-specific BrafV600E-mutant mice. We also employed an alternative model of Tert overexpression (K5-Tert). Whereas all BrafV600E animals developed well-differentiated papillary thyroid tumors, 29% and 36% of BrafV600E+Tert-123C>T and BrafV600E+K5-Tert mice progressed to poorly differentiated cancers at week 20, respectively. Tert-upregulated tumors showed increased mitosis and necrosis in areas of solid growth, and older animals displayed anaplastic-like features, i.e., spindle cells and macrophage infiltration. Murine Tert promoter mutation increased Tert transcription in vitro and in vivo, but temporal and intra-tumoral heterogeneity was observed. RNA-sequencing of thyroid tumor cells showed that processes other than the canonical Tert-mediated telomere maintenance role operate in these specimens. Pathway analysis showed that MAPK and PI3K/AKT signaling, as well as processes not previously associated with this tumor etiology, involving cytokine and chemokine signaling, were overactivated. These models constitute useful pre-clinical tools to understand the cell-autonomous and microenvironment-related consequences of Tert-mediated progression in advanced thyroid cancers and other aggressive tumors carrying TPMs.
Implications: Telomerase-driven cancer progression activates pathways that can be dissected and perhaps therapeutically exploited.
Supplementary Table 2 from Rates of Intervention and Cancer Detection on Initial versus Subsequent Whole-body MRI Screening in Li-Fraumeni Syndrome
Authors: Kagami Laura A.T. , Du Yun K. , Fernandes Conrad J. , Le Anh N. , Good Madeline , Duvall Melani M. , Baldino Sarah E. , Powers Jacquelyn , Zelley Kristin , States Lisa J. , Mathew Manoj C. , Katona Bryson W. , MacFarland Suzanne P. , Maxwell Kara N.
Published: August 2023

Supplementary Table 2 outlines prior studies of initial WBMRI screening in LFS cohorts both in pediatric and adult patient populations including length of the study, number of patients with findings, and number of cancers detected.
Supplementary Table 1 from Rates of Intervention and Cancer Detection on Initial versus Subsequent Whole-body MRI Screening in Li-Fraumeni Syndrome
Authors: Kagami Laura A.T. , Du Yun K. , Fernandes Conrad J. , Le Anh N. , Good Madeline , Duvall Melani M. , Baldino Sarah E. , Powers Jacquelyn , Zelley Kristin , States Lisa J. , Mathew Manoj C. , Katona Bryson W. , MacFarland Suzanne P. , Maxwell Kara N.
Published: August 2023

Supplementary Table 1 shows non-malignant findings identified on WBMRI screening in our patient cohort with corresponding patient IDs to cancer diagnoses identified on WBMRI screening.
TABLE 2 from Integrative Prognostic Machine Learning Models in Mantle Cell Lymphoma
Authors: Hill Holly A. , Jain Preetesh , Ok Chi Young , Sasaki Koji , Chen Han , Wang Michael L. , Chen Ken
Published: August 2023

Performance metrics of prognostic MCL models
Supplementary Table 5 from Integrative Prognostic Machine Learning Models in Mantle Cell Lymphoma
Authors: Hill Holly A. , Jain Preetesh , Ok Chi Young , Sasaki Koji , Chen Han , Wang Michael L. , Chen Ken
Published: August 2023

Coefficients from the Multivariate Generalized Linear Model
Supplementary Table 2 from Integrative Prognostic Machine Learning Models in Mantle Cell Lymphoma
Authors: Hill Holly A. , Jain Preetesh , Ok Chi Young , Sasaki Koji , Chen Han , Wang Michael L. , Chen Ken
Published: August 2023

Full feature list with abbreviations from the dataset
Supplementary Methods from Integrative Prognostic Machine Learning Models in Mantle Cell Lymphoma
Authors: Hill Holly A. , Jain Preetesh , Ok Chi Young , Sasaki Koji , Chen Han , Wang Michael L. , Chen Ken
Published: August 2023

Supplementary detailed methods
Supplementary Figure 2 from Integrative Prognostic Machine Learning Models in Mantle Cell Lymphoma
Authors: Hill Holly A. , Jain Preetesh , Ok Chi Young , Sasaki Koji , Chen Han , Wang Michael L. , Chen Ken
Published: August 2023

S2. Variable Importance from Full XGBoost Model (training cross-validation sets)
FIGURE 4 from Integrative Prognostic Machine Learning Models in Mantle Cell Lymphoma
Authors: Hill Holly A. , Jain Preetesh , Ok Chi Young , Sasaki Koji , Chen Han , Wang Michael L. , Chen Ken
Published: August 2023

Selected Kaplan–Meier plots from top XGBoost model features. The median OS time is represented by the dashed line. Differences between groups determined by the log-rank test were statistically significant. A, OS in ECOG performance category groups assigned at baseline. B, OS in morphology groups identified from baseline biopsies. The aggressive histology group comprises blastoid, pleomorphic, and combined blastoid and pleomorphic morphologies. The “other” category represents classic histology biopsies with blastoid or pleomorphic features or unclassifiable biopsies. C, OS in TP53 mutational status groups. The mutation was identified from baseline biopsy NGS. D, OS in B2M level category groups. The high B2M category was defined from B2M levels higher than 1.8 mcg/mL. E, OS in bone marrow involvement groups. “Involved” bone marrow was considered to be higher than 5% MCL bone marrow involvement in a baseline sample. F, OS in platelet count groups. Low platelets were defined as lower than 150,000 per μL.
FIGURE 2 from Integrative Prognostic Machine Learning Models in Mantle Cell Lymphoma
Authors: Hill Holly A. , Jain Preetesh , Ok Chi Young , Sasaki Koji , Chen Han , Wang Michael L. , Chen Ken
Published: August 2023

Hyperparameter fitting and model metrics. A, A scatterplot showing the corresponding mean AUC (from cross-fold validation sets) to hyperparameter values tuned by the 50-grid Latin hypercube on the “all features” XGBoost model. B, Comparison of ROC curves from XGBoost Models. Of the trained models, the “all features” model performed the best on the test set, followed by the two “parsimonious” models (the iMIPI and iMIPI-s) and the combined “clinical and NGS” and “clinical and cytogenetic models.” C, Comparison of ROC curves from the “all features” and “parsimonious” (20-feature) XGBoost models and the multivariate linear model (GLM).
FIGURE 1 from Integrative Prognostic Machine Learning Models in Mantle Cell Lymphoma
Authors: Hill Holly A. , Jain Preetesh , Ok Chi Young , Sasaki Koji , Chen Han , Wang Michael L. , Chen Ken
Published: August 2023

Selection process and workflow of prognostic models. A, Flowchart depicting MCL patient selection process for inclusion in models. B, Flowchart showing data availability for patient cohort (n = 794). All patients had clinicopathologic data. Most patients (n = 642) had cytogenetic and/or genomic data. C, Workflow of ML (XGBoost modeling). The dataset containing all 794 patients was split into a training/validation set and a test set. The test set was held from all initial preprocessing and hyperparameter tuning to avoid data leakage. Data preprocessing included removing zero and NZV features, dummy encoding categorical features, and collapsing low-frequency categorical variables into an “other” category. The training set was again split into 10 cross-fold validation sets where hyperparameters for the XGBoost model were tuned. The hyperparameter set with the highest mean ROC AUC was chosen for the final fit on the test set, and performance was evaluated to check the model's fit. Variable importance was visualized with a VIP and SHAP additive values. The fitted XGBoost model was launched using a REST API, demonstrating clinical utility.
Role of the Sympathetic Nervous System in Mild Chronic Inflammatory Diseases: Focus on Osteoarthritis
Authors: Sohn Rebecca , Jenei-Lanzl Zsuzsa
Published: August 2023

The sympathetic nervous system (SNS) is a major regulatory mediator connecting the brain and the immune system that influences accordingly inflammatory processes within the entire body. In the periphery, the SNS exerts its effects mainly via its neurotransmitters norepinephrine (NE) and epinephrine (E), which are released by peripheral nerve endings in lymphatic organs and other tissues. Depending on their concentration, NE and E bind to specific α- and β-adrenergic receptor subtypes and can cause both pro- and anti-inflammatory cellular responses. The co-transmitter neuropeptide Y, adenosine triphosphate, or its metabolite adenosine are also mediators of the SNS. Local pro-inflammatory processes due to injury or pathogens lead to an activation of the SNS, which in turn induces several immunoregulatory mechanisms with either pro- or anti-inflammatory effects depending on neurotransmitter concentration or pathological context. In chronic inflammatory diseases, the activity of the SNS is persistently elevated and can trigger detrimental pathological processes. Recently, the sympathetic contribution to mild chronic inflammatory diseases like osteoarthritis (OA) has attracted growing interest. OA is a whole-joint disease and is characterized by mild chronic inflammation in the joint. In this narrative article, we summarize the underlying mechanisms behind the sympathetic influence on inflammation during OA pathogenesis. In addition, OA comorbidities also accompanied by mild chronic inflammation, such as hypertension, obesity, diabetes, and depression, will be reviewed. Finally, the potential of SNS-based therapeutic options for the treatment of OA will be discussed.
Reducing the time needed to solve a traveling salesman problem by clustering with a Hierarchy-based algorithm
Authors: Nejad Anahita Sabagh , Fazekas Gabor
Published: August 2023

In this study, we compare a cluster-based whale optimization algorithm (WOA) with an uncombined method to find a more optimized solution for a traveling salesman problem (TSP). The main goal is to reduce the time of solving a TSP. First, we solve the TSP with the Whale optimization algorithm, later we solve it with the combined method of solving TSP which uses the clustering method, called BIRCH (balanced iterative reducing and clustering using hierarchies). Birch builds a clustering feature (CF) tree and then applies one of the clustering methods (for ex. K-means) to cluster data. Experiments performed on three datasets show that the convergence time improves by using the combined algorithm.
New approach to similarity detection by combining technique three-patch local binary patterns (TP-LBP) with support vector machine
Authors: Chater Ahmed , Benradi Hicham , Lasfar Abdelali
Published: August 2023

Recognition systems have received a lot of attention because of their various
uses in people's daily lives, for example in robotic intelligence, smart cameras,
security surveillance or even criminal identification. Determining the
similarity of faces by different face variations is based on robust algorithms.
The validation of our experiment is done on two sets of data. In this paper, we
compare two facial recognition system techniques according to the
recognition rate and the average authentication time: in order to increase the
accuracy rate and decrease the processing time. our approach is based on
feature extraction by two algorithms principal components analysis scaleinvariant feature transform (PCA-SIFT) and speeded up robust features (SURF), then uses the random sample consensus (RANSAC) technique to cancel outliers. Finally, face recognition is established on the basis of proximity determination. The second technique is based on the association of support vector machine (SVM) classifier with the key point recovery technique. the results obtained by the second technique is better for both databases: The recognition rate of the base olivetti research laboratory (ORL) should be 98.125800 and that of the Grimace base 97.2851500. The evaluation according to the time of the second technique does not exceed 300ms on average.
Glove based wearable devices for sign language-GloSign
Authors: Biju Soly Mathew , Al-Khatib Obada , Sheikh Hashir Zahid , Oroumchian Farhad
Published: August 2023

Loss of the capability to talk or hear has psychological and social effects on
the affected individuals due to the absence of appropriate interaction. Sign
Language is used by such individuals to assist them in communicating with
each other. This paper proposes a glove called GloSign that can convert
American sign language to characters. This glove consists of flex and inertial
measurement unit (IMU) sensors to identify gestures. The data from glove is
uploaded on IoT platform, which makes the glove portable and wireless. The
data from gloves is passed through a k-nearest neighbors (KNN) Algorithm
machine learning algorithm to improve the accuracy of the system. The
system was able to achieve an accuracy of 96.8%. The glove can also be used
to form sentences. The output is displayed on the screen or is converted to
speech. This glove can be used in communicating with people who don’t know
sign language.
Analysis of machine repair time prediction using machine learning at one of leading footwear manufacturers in Indonesia
Authors: Purmala Yulio Agefa , Sudarto Sumarsono
Published: August 2023

Machine breakdowns in the production line mostly finish in more than 18
minutes, since the machine that needs repair more time is done on the
production line, not in the machine warehouse. Historical machine
breakdown data is digitally recorded through the Andon system, but it is still
not being used adequately to aid decision-making. This research introduces
an analysis of historical machine breakdown data to provide predictions of
repair time intervals with a focus on finding the best algorithm accuracy.
The research method uses machine learning techniques with a classification
model. There are five algorithms used: logistic regression (LR), naive bayes
(NB), k-nearest neighbor (KNN), support vector machine (SVM), and
random forest (RF). The results of this study prove that historical machine
breakdown data can be optimized to predict machine repair time intervals in
the production line. The accuracy of LR algorithm is slightly better than the
other algorithms. Based on the receiver operating characteristic–area under
curve (ROC-AUC) performance evaluation metric, the quality value of the
accuracy of LR model is satisfied with a percentage of 69% with a
difference of 0.5% between the train and test data.
Comparative analysis of machine learning algorithms on myoelectric signal from intact and transradial amputated limbs
Authors: Kadhim Dhirgaam A. , Raheema Mitha N. , Hussein Jabbar S.
Published: August 2023

Control strategies of smart hand prosthesis-based myoelectric signals in
recent years don't provide the patients with the sensation of biological
control of prostheses hand fingers. Therefore, in current work
hyperparameters optimization in machine learning algorithm and hand
gesture recognition techniques were applied to the myoelectric signal-based
on residual muscles contraction of the amputees corresponding to intact
forearm limb movement to improve their biological control. In this paper,
myoelectric signals are extracted using the MYO armband to recognize ten
gestures from ten volunteers (healthy and transradial amputation) on the
forearm, thereafter the noise of myoelectric signals using a notch filter (NF)
is removed. The proposed classification system involved two machine
learning algorithms: (1) the decision tree (DT), tri-layered neural network
(TLNN), k-nearest-neighbor (KNN), support vector machine (SVM) and
ensemble boosted tree (EBT) classifiers. (2) the optimized machine learning
classifiers, i.e., OKNN, OSVM, OEBT with optical diffraction tomography
(ODT) and ommatidia detecting algorithm (ODA). The experimental results
of classifiers comparison pointed out an algorithm that outperformed with
high accuracy is OEBT closely followed by OKNN achieves an accuracy of
97.8% and 97.1% for intact forearm limb, while for transradial amputation
with an accuracy of 91.9% and 91.4%, respectively.
Machine learning classification analysis model community satisfaction with traditional market facilities as public service
Authors: Syahputra Hadi , Yanto Musli , Putra Muhammad Reza , Hadi Aulia Fitrul , Fenia Selvi Zola
Published: August 2023

Traditional markets are public service facilities that can be utilized by the
community. The market function is used place where sellers and buyers meet
in conducting transactions. This study aims to build a machine learning
classification analysis model in measuring community satisfaction with
traditional market facilities. The analytical methods used include Fuzzy.
multiple linear regression (MRL), artificial neural network (ANN), and
decision tree (DT). Fuzzy is used to generate a pattern of rules in determining
the level of satisfaction. MRL serves to measure and test the correlation of
rules that have been formed. The ANN method is used to carry out the
classification analysis process based on learning. In the final stage. DT is used
to describe the decision tree of the analysis process. This study presents the
results of machine learning analysis which is very good in determining
satisfaction with an accuracy rate of 99.99%. This result is influenced by fuzzy
logic which can develop a classification rule pattern of 32 patterns. MRL also
shows a significant correlation level of 81.1% based on the indicator variables.
Overall, the machine learning classification analysis model can provide
knowledge to be considered in the management of traditional markets as
public service facilities.
Classification and visualization: Twitter sentiment analysis of Malaysia’s private hospitals
Authors: Abu Samah Khyrina Airin Fariza , Nor Azharludin Nur Maisarah , Riza Lala Septem , Hasrol Jono Mohd Nor Hajar , Moketar Nor Aiza
Published: August 2023

Malaysia has many private’s hospitals. Thus, feedback is important to improve service quality, becoming reviews for other patients. Reviews use the channel service provided on social media, such as Twitter. Nevertheless, online reviews are unstructured and enormous in volume, which leads to difficulties in comparing private hospitals. In addition, no single websites compare private hospitals based on users’ interests, bilingual reviews, and less time-consuming. Due to that, this study aims to classify and visualize the Twitter sentiment analysis of private hospitals in Malaysia. The scope focuses on five factors: 1) administrative procedure, 2) cost, 3) communication, 4) expertise, and 5) service. Term frequency-inverse document frequency is used for text mining, information retrieval techniques, and the Naïve Bayes, a machine learning algorithm for the classification. The user can visualize the specified state’s private hospitals and compare them with any selected state. The system’s functionality and usability have been tested to ensure it meets the objectives. Functionality testing proved that the private hospital’s Twitter sentiment could be predicted based on the training and testing data as intended, with 77.13% and 77.96% accuracy for English and Bahasa Melayu, respectively, while the system usability scale based on the usability testing resulted in an average final score of 95.42%.
Improve malware classifiers performance using cost-sensitive learning for imbalanced dataset
Authors: Abdel Ouahab Ikram Ben , Elaachak Lotfi , Bouhorma Mohammed
Published: August 2023

In recent times, malware visualization has become very popular for malware
classification in cybersecurity. Existing malware features can easily identify
known malware that have been already detected, but they cannot identify new
and infrequent malwares accurately. Moreover, deep learning algorithms
show their power in term of malware classification topic. However, we found
the use of imbalanced data; the Malimg database which contains 25 malware
families don’t have same or near number of images per class. To address these
issues, this paper proposes an effective malware classifier, based on costsensitive deep learning. When performing classification on imbalanced data, some classes get less accuracy than others. Cost-sensitive is meant to solve this issue, however in our case of 25 classes, classical cost-sensitive weights wasn’t effective is giving equal attention to all classes. The proposed approach improves the performance of malware classification, and we demonstrate this improvement using two Convolutional Neural Network models using functional and subclassing programming techniques, based on loss, accuracy, recall and precision.
Content-based image retrieval based on corel dataset using deep learning
Authors: Hassan Rasha Qassim , Sultani Zainab N. , Dhannoon Ban N.
Published: August 2023

A popular technique for retrieving images from huge and unlabeled image databases are content-based-image-retrieval (CBIR). However, the traditional information retrieval techniques do not satisfy users in terms of time consumption and accuracy. Additionally, the number of images accessible to users are growing due to web development and transmission networks. As the result, huge digital image creation occurs in many places. Therefore, quick access to these huge image databases and retrieving images like a query image from these huge image collections provides significant challenges and the need for an effective technique. Feature extraction and similarity measurement are important for the performance of a CBIR technique. This work proposes a simple but efficient deep-learning framework based on convolutional-neural networks (CNN) for the feature extraction phase in CBIR. The proposed CNN aims to reduce the semantic gap between low-level and high-level features. The similarity measurements are used to compute the distance between the query and database image features. When retrieving the first 10 pictures, an experiment on the Corel-1K dataset showed that the average precision was 0.88 with Euclidean distance, which was a big step up from the state-of-the-art approaches.
Development of hybrid convolutional neural network and autoregressive integrated moving average on computed tomography image classification
Authors: Saleh Abdulrazak Yahya , Chin Chee Ka
Published: August 2023

One of the deadliest diseases in humans is lung cancer. Radiologists and experienced doctors spend much more time investigating the pulmonary nodules due to the high similarities between malignant and benign nodules. Recently, the computer-assisted diagnosis (CAD) tool for nodule detection can provide a second opinion for the doctor to diagnose lung cancer. Although machine learning technologies are extensively employed to identify lung cancer, the process of these methods is complex. The numerous researches have sought to automate the diagnosis of pulmonary nodules using convolutional neural networks (CNN) to aid radiologists in the lung screening process. However, CNN still confronts some challenges, including a significant number of false positives and limited performance in detecting lung cancer from computed tomography (CT) images. In this work, we proposed a hybrid of CNN and auto-regressive integrated moving average (ARIMA) for lung nodule classification using CT images to address the classification issue. The proposed hybrid CNN-ARIMA can classify CT images successfully with test accuracy, average sensitivity, average precision, average specificity, average F1-Score, and area under the curve (AUC) of 99.61%, 99.71%, 99.43%, 99.71%, 99.57%, and 1.000, respectively.
Analysis of the evolution of advanced transformer-based language models: experiments on opinion mining
Authors: Zekaouiu Nour Eddine , Yousfi Siham , Rhanoui Maryem , Mikram Mounia
Published: August 2023

Opinion mining, also known as sentiment analysis, is a subfield of natural language processing (NLP) that focuses on identifying and extracting subjective information in textual material. This can include determining the overall sentiment of a piece of text (e.g., positive or negative), as well as identifying specific emotions or opinions expressed in the text, that involves the use of advanced machine and deep learning techniques. Recently, transformer-based language models make this task of human emotion analysis intuitive, thanks to the attention mechanism and parallel computation. These advantages make such models very powerful on linguistic tasks, unlike recurrent neural networks that spend a lot of time on sequential processing, making them prone to fail when it comes to processing long text. The scope of our paper aims to study the behaviour of the cutting-edge Transformer-based language models on opinion mining and provide a high-level comparison between them to highlight their key particularities. Additionally, our comparative study shows leads and paves the way for production engineers regarding the approach to focus on and is useful for researchers as it provides guidelines for future research subjects.
Gravidez não planejada no Brasil: estudo nacional em oito hospitais universitários
Authors: Nilson Tainá Vieira , Amato Angélica Amorim , Resende Ceres Nunes , Primo Walquíria Quida Salles Pereira , Nomura Roseli Mieko Yamamoto , Costa Maria Laura , Opperman Maria Lúcia , Brock Marianna , Trapani Junior Alberto , Damasio Lia Cruz Vaz da Costa , Reis Nadia , Borges Vera , Araújo Ana Cristina , Ruano Rodrigo , Zaconeta Alberto Carlos Moreno
Published: August 2023

OBJETIVO: Estimar a prevalência de gestação não planejada (GNP) em oito hospitais públicos universitários, distribuídos nas cinco regiões que compõem o Brasil. MÉTODOS: Análise secundária de um estudo transversal multicêntrico nacional, realizado em oito hospitais universitários públicos, entre 1º de junho e 31 de agosto de 2020, no Brasil. Amostra por conveniência incluindo mulheres que deram à luz em período de sessenta dias consecutivos e atenderam aos seguintes critérios: maiores de 18 anos; idade gestacional acima de 36 semanas no parto; com recém-nascido único e vivo, sem malformações. RESULTADOS: Amostra composta por 1.120 puérperas, das quais 756 (67,5%) declararam que a gravidez não tinha sido programada. A mediana da prevalência de GNP foi de 59,7%. Observou-se diferença significativa na prevalência de GNP entre os hospitais: Campinas (54,8%), Porto Alegre (58,2%), Florianópolis (59%), Teresina (61,2%), Brasília (64,3%), São Paulo (64,6%), Campo Grande (73,9%) e Manaus (95,3%) (p < 0,001). Foram fatores significativamente associados a GNP a idade materna, cor negra, menor renda familiar, maior número de filhos, maior número de pessoas convivendo em casa e não ter parceiro. CONCLUSÃO: na amostra estudada, cerca de dois terços das gestações foram declaradas como não programadas. A prevalência de gestação não planejada teve relação com fatores sociais e demográficos e variou significativamente entre os hospitais universitários avaliados.
Prevalence of dynapenia and overlap with disability, depression, and executive dysfunction
Authors: Teixeira Ivan Abdalla , Coutinho Evandro Silva Freire , Marinho Valeska , Castro-Costa Erico , Deslandes Andrea Camaz
Published: August 2023

OBJECTIVE: This study aims to investigate handgrip strength and dynapenia prevalence among older adults stratified by Brazilian macroregions. Additionally, we aim to evaluate the overlap between dynapenia and Instrumental Activities of Daily Living (IADL) disability, depression, and executive dysfunction on a national basis and by each Brazilian macroregion. METHODS: This cross-sectional analysis was based on data from the Brazilian Longitudinal Study of Aging (ELSI-Brazil). A multistage cluster sample design was used, with a representative population-based study of non-institutionalized community-dwelling Brazilians aged ≥ 50 years from 70 municipalities across all five macroregions of the country. The outcome variable was dynapenia. Covariables were IADL disability, depression, and executive dysfunction. The Brazilian macroregions were used for stratification. In addition, the following additional variables were included: age group, gender, education level, macroregions (North, Northeast, Southeast, South, and Midwest), self-reported health, multimorbidity, and falls. RESULTS: A total of 8,849 (94%) of the sample provided complete information for the handgrip strength assessment and were included in this analysis. Dynapenia prevalence was higher in North and Northeast regions (28.5% and 35.1%, respectively). We identified statistically significant differences between different macroregions for dynapenia, IADL disability, and verbal fluency, with worse values in the North and Northeast regions. In the North and Northeast macroregions, nearly half of the subjects that presented executive dysfunction and IADL disability also had dynapenia. There was a more significant overlap in the prevalence of all four conditions in the North and Northeast regions (4.8% and 5.5%, respectively), whereas the overlap was smaller in the South (2.3%). There was also a smaller overlap in the prevalence of dynapenia and depression in the South (5.8%) compared with other macroregions. CONCLUSIONS: Macroregions in Brazil exhibit marked differences in the prevalence of dynapenia and in its overlap with IADL disability, depression, and executive dysfunction.
O aumento de entidades filantrópicas no SUS: o que esse cenário revela?
Authors: Silva João Felipe Marques da , Carvalho Brígida Gimenez , Pereira Adelyne Maria Mendes , Nunes Elisabete de Fátima Almeida , Mendonça Fernanda de Freitas , Santini Stela Maris Lopes , Andrade Silvia Karla Azevedo Vieira , Ferraz Edinalva de Moura
Published: August 2023

O artigo analisa aspectos da mudança da natureza jurídica de instituições privadas de assistência à saúde, de estabelecimentos “com” para “sem” fins lucrativos. Trata-se de uma pesquisa exploratória, apoiada no referencial de análise de políticas, com foco em dados secundários, provenientes do Sistema de Cadastro Nacional de Estabelecimentos de Saúde (SCNES), de 2012 a 2020, e estudo de caso. Os resultados apresentam aumento dessas entidades em todas as regiões do país e evidências de que se comportam como estabelecimentos com fins lucrativos. A mudança de natureza jurídica oculta um processo mais amplo de mercantilização implícita dos serviços de saúde, incentivado por políticas estatais e relacionado às isenções previstas em lei.
A completeness indicator of gestational and congenital syphilis information in Brazil
Authors: Oliveira Guilherme Lopes de , Ferreira Andrêa JF , Santana José Guilherme , Lana Raquel Martins , Cardoso Andrey Moreira , Teles Carlos , Fiaccone Rosemeire L. , Aquino Rosana , Soares Maria Auxiliadora Santos , Paixao Enny S. , Santos Idália Oliveira , Salvi Leonardo , Barreto Maurício L. , Ichihara Maria Yury
Published: August 2023

OBJECTIVE: To evaluate the quality of information on gestational syphilis (GS) and congenital syphilis (CS) on the Sistema de Informação de Agravos de Notificação (SINAN-Syphilis Brazil – Notifiable Diseases Information System) by compiling and validating completeness indicators between 2007 and 2018. METHODS: Overall, care, and socioeconomic completeness scores were compiled based on selected variables, by using ad hoc weights assigned by experts. The completeness scores were analysed, considering the region and area of residence, the pregnant woman’s race/colour, and the year of case notification. Pearson’s correlation coefficients were used to validate the scores obtained by the weighted average method, compared with the values obtained by principal component analysis (PCA). RESULTS: Most selected variables presented a good or excellent degree of completeness for GS and CS, except for clinical classification, pregnant woman’s level of education, partner’s treatment, and child’s race/colour, which were classified as poor or very poor. The overall (89.93% versus 89.69%) and socioeconomic (88.71% versus 88.24%) completeness scores for GS and CS, respectively, were classified as regular, whereas the care score (GS-90.88%, and CS-90.72%) was good, despite improvements over time. Differences in the overall, care and socioeconomic completeness scores according to region, area of residence, and ethnic-racial groups were reported for syphilis notifications. The completeness scores estimated by the weighted average method and PCA showed a strong linear correlation (> 0.90). CONCLUSION: The completeness of GS and CS notifications has been improving in recent years, highlighting the variables that form the care score, compared with the socioeconomic scores, despite differences between regions, area of residence, and ethnic-racial groups. The weighted average was a viable methodological alternative easily operationalised to estimate data completeness scores, allowing routine monitoring of the completeness of gestational and congenital syphilis records.
What Does Loss and Damage Mean at the Country Level? A Global Mapping Through Nationally Determined Contributions
Authors: Calliari Elisa , Ryder Ben
Published: August 2023

By analyzing the way climate change loss and damage (L&D) is framed in nationally determined contributions (NDCs), this article investigates how parties to the United Nations Framework Convention on Climate Change make sense of the concept. Building on an original database of 313 active and archived NDCs, we employ frame analysis to identify the countries that mention L&D in these documents; map how they frame it, both in terms of the types of impacts that are relevant for the national context and the responses that are planned or adopted; and explore how this has changed over time. We find that L&D is not perceived as a “small islands issue” anymore and that a growing number of middle- and high-income countries are referring to the concept in their NDCs. We also observe increasing levels of specificity about the types of economic and noneconomic L&D incurred or projected and about national responses, including those focused on knowledge generation, institutional arrangements, and sectoral adaptation measures. Theoretically, the article advances understandings of how national policy actors translate the ill-defined L&D global agenda for the national level. At the same time, it illustrates how they attempt to shape it by advancing nationally informed L&D framings, therefore hinting toward an emerging “two-level ideational game” in this area of global governance.
The Effects of Political Knowledge Use by Developing Country Negotiators in Loss and Damage Negotiations
Authors: Serdeczny Olivia
Published: August 2023

Does using knowledge politically to explain or justify predetermined policy positions make a difference? Most theory suggests no. This article traces how developing country negotiators used knowledge to further their interests in loss and damage (L&D) negotiations from 2003 to 2013. The analysis shows an institutional effect, whereby knowledge was used to establish L&D as a theme under the United Nations Framework Convention on Climate Change. At the same time, an indirect effect emerges at the individual level as knowledge provides actors with a sense of clarity and legitimacy that strengthens their resolve in defending political positions, leaving surprising traces during moments of bargaining. These insights invite critical reflections on the normative dimensions of political knowledge use.
Understanding the Politics and Governance of Climate Change Loss and Damage
Authors: Vanhala Lisa , Calliari Elisa , Thomas Adelle
Published: August 2023

This introduction to the 2023 special issue of Global Environment Politics brings questions related to politics and political processes to the forefront in the study of climate change loss and damage. The aim of avoiding the detrimental impacts of climate change has been at the heart of the international response to global climate change for more than thirty years. Yet the development of global governance responses to climate change loss and damage—those impacts that we cannot, do not or choose not to prevent or adapt to—has only over the last decade become a central theme within the discussions under the United Nations Framework Convention on Climate Change (UNFCCC). Loss and damage has also become a research topic of growing importance within an array of disciplines, from international law to the interdisciplinary environmental social sciences. However, the engagement of scholars working in the fields of political science and international relations has been more limited so far. This is surprising because questions about how to best respond to loss and damage are fundamentally political, as they derive from deliberative processes, invoke value judgments, imply contestation, demand the development of policies, and result in distributional outcomes. In this introduction we describe the context and contributions of the research articles in the special issue. By drawing on a wide range of perspectives from across the social sciences, the articles render visible the multifaceted politics of climate change loss and damage and help to account for the trajectory of governance processes.
Rethinking the Knowledge Transfer Process Through the Use of Metaverse: A Qualitative Study of Organizational Learning Approach for Remote Workplace
Authors: Lau Kung Wong
Published: August 2023

This paper aims to explore the possibilities of implementing the tacit knowledge transfer process and practices in the metaverse and remote workplaces. Tacit knowledge transfer is crucial for organizational knowledge management and maintaining an organization's sustainable development. The research team believes that the use of the metaverse in remote workplaces is expected to revolutionize established organizational learning, knowledge management, and tacit knowledge transfer models. This research attempts to understand employees’ experiences from three perspectives: (1) the acceptance by both senior and junior staff of using the metaverse for training; (2) the experiences of tacit knowledge transfer over the metaverse; and (3) the role of immersion and interactivity during the knowledge transfer process. The significance of this research is the theoretical investigation of the tacit knowledge transfer model in the metaverse. This research is explorative in nature; therefore, this research is not going to generalize or prove the effectiveness of using the metaverse in the tacit knowledge transfer process, but rather explore the factors and deepen our understanding of these fundamental attributes. The findings of this research suggest a modification of the classical model SECI Matrix by adding a new component of “integration” in the transfer process.
Enhancing Engagement and Learning Outcomes in Architectural Computing Design Education: A Study on the Implementation of Augmented Reality
Authors: Ozenen Gurkan
Published: August 2023

Augmented reality (AR) technology is being increasingly used in the fields of architecture and design to provide new and engaging ways to visualize and communicate design concepts. Since architectural models are the simplest approach to emphasize a design concept, the tools, materials, and technology used to construct physical models in architecture and design education may vary. This paper describes the implementation of AR technology in an architectural computing design course. The purpose of this study is to ascertain an efficient method of model-making for architectural education through the use of AR. The study involved the development of an AR application that allowed students to visualize their design concepts in a virtual environment. The paper presents the design of the application, the methods used to integrate AR technology into the course curriculum, and the results of a survey conducted among students to evaluate their experience with the technology. Architecture students’ experiences using AR technology in place of physically constructed physical models for design projects are discussed in this study as well. The study concluded that the use of AR technology in the course had a positive impact on student engagement, learning outcomes, and overall satisfaction.