bluuebunny/crossref_metadata_2025 huggingface dataset:
Data from The Oncogenic PI3K-Induced Transcriptomic Landscape Reveals Key Functions in Splicing and Gene Expression Regulation
Authors: Ladewig Erik , Michelini Flavia , Jhaveri Komal , Castel Pau , Carmona Javier , Fairchild Lauren , Zuniga Adler G. , Arruabarrena-Aristorena Amaia , Cocco Emiliano , Blawski Ryan , Kittane Srushti , Zhang Yuhan , Sallaku Mirna , Baldino Laura , Hristidis Vasilis , Chandarlapaty Sarat , Abdel-Wahab Omar , Leslie Christina , Scaltriti Maurizio , Toska Eneda
Published: August 2023

Abstract
The phosphoinositide 3–kinase (PI3K) pathway regulates proliferation, survival, and metabolism and is frequently activated across human cancers. A comprehensive elucidation of how this signaling pathway controls transcriptional and cotranscriptional processes could provide new insights into the key functions of PI3K signaling in cancer. Here, we undertook a transcriptomic approach to investigate genome-wide gene expression and transcription factor activity changes, as well as splicing and isoform usage dynamics, downstream of PI3K. These analyses uncovered widespread alternatively spliced isoforms linked to proliferation, metabolism, and splicing in PIK3CA-mutant cells, which were reversed by inhibition of PI3Kα. Analysis of paired tumor biopsies from patients with PIK3CA-mutated breast cancer undergoing treatment with PI3Kα inhibitors identified widespread splicing alterations that affect specific isoforms in common with the preclinical models, and these alterations, namely PTK2/FRNK and AFMID isoforms, were validated as functional drivers of cancer cell growth or migration. Mechanistically, isoform-specific splicing factors mediated PI3K-dependent RNA splicing. Treatment with splicing inhibitors rendered breast cancer cells more sensitive to the PI3Kα inhibitor alpelisib, resulting in greater growth inhibition than alpelisib alone. This study provides the first comprehensive analysis of widespread splicing alterations driven by oncogenic PI3K in breast cancer. The atlas of PI3K-mediated splicing programs establishes a key role for the PI3K pathway in regulating splicing, opening new avenues for exploiting PI3K signaling as a therapeutic vulnerability in breast cancer.
Significance:
Transcriptomic analysis reveals a key role for the PI3K pathway in regulating RNA splicing, uncovering new mechanisms by which PI3K regulates proliferation and metabolism in breast cancer.

See related commentary by Claridge and Hopkins, p. 2216

Watch the interview with Eneda Toska, PhD, recipient of the 2023 Cancer Research Early Career Award: https://vimeo.com/847434965
Supplementary Table S1 from Socioeconomic Status at Birth and Breast Tissue Composition in Adolescence and Adulthood
Authors: Kehm Rebecca D. , Lilge Lothar , Walter E. Jane , White Melissa , Herbstman Julie B. , Perera Frederica P. , Miller Rachel L. , Terry Mary Beth , Tehranifar Parisa
Published: August 2023

Supplementary Table S1 shows the characteristics of adolescent girls by household income at birth in the Columbia Breast Cancer and the Environment Research Program (Columbia-BCERP) Study, n=165.
Supplementary Table S1 from Characteristics of Cancer Epidemiology Studies That Employ Metabolomics: A Scoping Review
Authors: Yu Catherine T. , Farhat Zeinab , Livinski Alicia A. , Loftfield Erikka , Zanetti Krista A.
Published: August 2023

Supplementary Table S1 shows metabolite super-pathways examined in semi-targeted population-based cancer metabolomics studies.
Supplementary Figure S2 from Characteristics of Cancer Epidemiology Studies That Employ Metabolomics: A Scoping Review
Authors: Yu Catherine T. , Farhat Zeinab , Livinski Alicia A. , Loftfield Erikka , Zanetti Krista A.
Published: August 2023

Supplementary Figure S2 shows geographic distribution of participant recruitment for metabolomic epidemiology studies of cancer.
Supplementary Table 3 from Body Composition, Relative Dose Intensity, and Adverse Events among Patients with Colon Cancer
Authors: Cheng En , Caan Bette J. , Cawthon Peggy M. , Evans William J. , Hellerstein Marc K. , Shankaran Mahalakshmi , Nyangau Edna , Campbell Kristin L. , Lee Catherine , Binder Alexandra M. , Meyerhardt Jeffrey A. , Schmitz Kathryn H. , Cespedes Feliciano Elizabeth M.
Published: August 2023

Supplementary Table 3. The Adjusted Associations of Scaled Body Composition Measurements (Per SD Increase) with Reduced RDI and Relative Changes (%) in the Number of Moderate and Severe Adverse Events
Supplementary Table 2 from Body Composition, Relative Dose Intensity, and Adverse Events among Patients with Colon Cancer
Authors: Cheng En , Caan Bette J. , Cawthon Peggy M. , Evans William J. , Hellerstein Marc K. , Shankaran Mahalakshmi , Nyangau Edna , Campbell Kristin L. , Lee Catherine , Binder Alexandra M. , Meyerhardt Jeffrey A. , Schmitz Kathryn H. , Cespedes Feliciano Elizabeth M.
Published: August 2023

Supplementary Table 2. Sex-Specific Standard Deviations of Body Composition Measurements (CT SMA, DXA ALM, D3Cr Muscle, CT TAT, and DXA TBF)
Table S2 from Associations between Ambient Air Pollutants and Clonal Hematopoiesis of Indeterminate Potential
Authors: Leiser Claire L. , Whitsel Eric A. , Reiner Alexander , Rich Stephen S. , Rotter Jerome I. , Taylor Kent D. , Tracy Russel P. , Kooperberg Charles , Smith Albert Vernon , Manson JoAnn E. , Mychaleckyj Josyf C. , Bick Alexander G. , Szpiro Adam A. , Kaufman Joel D.
Published: August 2023

Supplementary Table 2 shows additional results for non smokers and never smokers in the MESA and WHI cohorts.
Table S1 from Associations between Ambient Air Pollutants and Clonal Hematopoiesis of Indeterminate Potential
Authors: Leiser Claire L. , Whitsel Eric A. , Reiner Alexander , Rich Stephen S. , Rotter Jerome I. , Taylor Kent D. , Tracy Russel P. , Kooperberg Charles , Smith Albert Vernon , Manson JoAnn E. , Mychaleckyj Josyf C. , Bick Alexander G. , Szpiro Adam A. , Kaufman Joel D.
Published: August 2023

Supplementary table 1 shows additional results for correction for sampling in the WHI cohort
Table S2 from Risk of Colorectal Cancer and Colorectal Cancer Mortality Beginning One Year after a Negative Fecal Occult Blood Test, among Screen-Eligible 76- to 85-Year-Olds
Authors: Dalmat Ronit R. , Ziebell Rebecca A. , Kamineni Aruna , Phipps Amanda I. , Weiss Noel S. , Breslau Erica S. , Burnett-Hartman Andrea N. , Corley Douglas A. , Doria-Rose V. Paul , Green Beverly B. , Halm Ethan A. , Levin Theodore R. , Schottinger Joanne E. , Chubak Jessica
Published: August 2023

Table S-2 displays cumulative risk of death from other causes (competing risk to CRC mortality estimates#), stratified by patient characteristics at index date.
Supplementary Table 2 from Patterns of Evidence-Based Care for the Diagnosis, Staging, and First-line Treatment of Breast Cancer by Race–Ethnicity: A SEER-Medicare Study
Authors: Herbach Emma L. , Nash Sarah H. , Lizarraga Ingrid M. , Carnahan Ryan M. , Wang Kai , Ogilvie Amy C. , Curran Michaela , Charlton Mary E.
Published: August 2023

Supplementary Table 2 shows crude and adjusted rate ratios for all applicable outcomes stratified by stage at diagnosis.
Data from Patterns of evidence-based care for the diagnosis, staging, and first-line treatment of breast cancer by race-ethnicity: a SEER-Medicare study
Authors: Herbach Emma L. , Nash Sarah H. , Lizarraga Ingrid M. , Carnahan Ryan M. , Wang Kai , Ogilvie Amy C. , Curran Michaela , Charlton Mary E.
Published: August 2023

Abstract
Background: Racial and ethnic disparities in guideline-recommended breast cancer treatment are well documented, however studies including diagnostic and staging procedures necessary to determine treatment indications are lacking. The purpose of this study was to characterize patterns in delivery of evidence-based services for the diagnosis, clinical workup, and first-line treatment of breast cancer by race-ethnicity.

Methods: SEER-Medicare data were used to identify women diagnosed with invasive breast cancer between 2000 and 2017 at age 66 or older (n = 215,605). Evidence-based services included diagnostic procedures (diagnostic mammography and breast biopsy), clinical workup (stage and grade determination, lymph node biopsy, and HR and HER2 status determination), and treatment initiation (surgery, radiation, chemotherapy, hormone therapy, and HER2-targeted therapy). Poisson regression was used to estimate rate ratios (RRs) and 95% Confidence Intervals (CIs) for each service.

Results: Black and American Indian/Alaska Native (AIAN) women had significantly lower rates of evidence-based care across the continuum from diagnostics through first-line treatment compared to non-Hispanic White (NHW) women. AIAN women had the lowest rates of HER2-targeted therapy and hormone therapy initiation. While Black women also had lower initiation of HER2-targeted therapy than NHW, differences in hormone therapy were not observed.

Conclusions: Our findings suggest patterns along the continuum of care from diagnostic procedures to treatment initiation may differ across race-ethnicity groups.

Impact: Efforts to improve delivery of guideline-concordant treatment and mitigate racial-ethnic disparities in healthcare and survival should include procedures performed as part of the diagnosis, clinical workup, and staging processes.
Heritable Traits and Lung Cancer Risk: A Two-Sample Mendelian Randomization Study
Authors: Pettit Rowland W. , Byun Jinyoung , Han Younghun , Ostrom Quinn T. , Coarfa Cristian , Bondy Melissa L. , Amos Christopher I.
Published: August 2023

Introduction:

Lung cancer is a complex polygenic disorder. Analysis with Mendelian randomization (MR) allows for genetically predicted risks to be estimated between exposures and outcomes.

Methods:

We analyzed 345 heritable traits from the United Kingdom Biobank and estimated their associated effects on lung cancer outcomes using two sample MR. In addition to estimating effects with overall lung cancer, adenocarcinoma, small cell lung cancer, and squamous cell lung cancers, we performed conditional effect modeling with multivariate MR (MVMR) and the traits of alcohol use, smoking initiation, average pre-tax income, and educational attainment.

Results:

Univariate MR provided evidence for increased age at first sexual intercourse (OR, 0.55; P = 6.15 × 10−13), educational attainment (OR, 0.24; P = 1.07 × 10−19), average household income (OR, 0.58; P = 7.85 × 10−05), and alcohol usually taken with meals (OR, 0.19; P = 1.06 × 10−06) associating with decreased odds of overall lung cancer development. In contrast, a lack of additional educational attainment (OR, 8.00; P = 3.48 × 10−12), body mass index (OR, 1.28; P = 9.00 × 10−08), pack years smoking as a proportion of life span (OR, 9.93; P = 7.96 × 10−12), and weekly beer intake (OR, 3.48; P = 4.08 × 10−07) were associated with an increased risk of overall lung cancer development.

Conclusions:

Many heritable traits associated with an increased or inverse risk of lung cancer development. Effects vary based on histologic subtype and conditional third trait exposures.

Impact:

We identified several heritable traits and presented their genetically predictable impact on lung cancer development, providing valuable insights for consideration.
Study Representativeness Table 1 from Randomized Phase II Trial of Dendritic Cell/Myeloma Fusion Vaccine with Lenalidomide Maintenance after Upfront Autologous Hematopoietic Cell Transplantation for Multiple Myeloma: BMT CTN 1401
Authors: Chung David J. , Shah Nina , Wu Juan , Logan Brent , Bisharat Lina , Callander Natalie , Cheloni Giulia , Anderson Kenneth , Chodon Thinle , Dhakal Binod , Devine Steve , Somaiya Dutt Poorvi , Efebera Yvonne , Geller Nancy , Ghiasuddin Haider , Hematti Peiman , Holmberg Leona , Howard Alan , Johnson Bryon , Karagkouni Dimitra , Lazarus Hillard M. , Malek Ehsan , McCarthy Philip , McKenna David , Mendizabal Adam , Nooka Ajay , Munshi Nikhil , O'Donnell Lynn , Rapoport Aaron P. , Reese Jane , Rosenblatt Jacalyn , Soiffer Robert , Stroopinsky Dina , Uhl Lynne , Vlachos Ioannis S. , Waller Edmund K. , Young James W. , Pasquini Marcelo C. , Avigan David
Published: August 2023

Study representation of underserved communities
Supplementary Table S2 from A Phase Ib Trial of AVID200, a TGFβ 1/3 Trap, in Patients with Myelofibrosis
Authors: Mascarenhas John , Migliaccio Anna Rita , Kosiorek Heidi , Bhave Rupali , Palmer Jeanne , Kuykendall Andrew , Mesa Ruben , Rampal Raajit K. , Gerds Aaron T. , Yacoub Abdulraheem , Pettit Kristen , Talpaz Moshe , Komrokji Rami , Kremyanskaya Marina , Gonzalez Agapito , Fabris Frank , Johnson Kathryn , Dougherty Mikaela , McGovern Erin , Arango Ossa Juan , Domenico Dylan , Farnoud Noushin , Weinberg Rona Singer , Kong Amy , Najfeld Vesna , Vannucchi Alessandro Maria , Arciprete Francesca , Zingariello Maria , Falchi Mario , Salama Mohamed E. , Mead-Harvey Carolyn , Dueck Amylou , Varricchio Lilian , Hoffman Ronald
Published: August 2023

Patient listing of prior myelofibrosis treatment and time since discontinuation if known
Data from Rates of intervention and cancer detection on initial versus subsequent whole body MRI screening in Li-Fraumeni Syndrome
Authors: Kagami Laura AT. , 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

Abstract
Li-Fraumeni Syndrome (LFS) is a hereditary cancer predisposition syndrome with up to 90% lifetime cancer risk. Cancer screening, including annual whole-body MRI (WBMRI), is recommended due to known survival advantage, with cancer detection rate of 7% on initial screening. Intervention and cancer detection rates on subsequent screenings are unknown. Clinical data for pediatric and adult LFS patients (n=182) were reviewed, including instances of WBMRI screening and interventions based on screening results. For each WBMRI screening, interventions including biopsy and secondary imaging, as well as rate of cancer diagnosis, were analyzed comparing initial versus subsequent WBMRI. Of the total cohort (n=182), we identified 68 adult patients and 50 pediatric patients who had undergone at least two WBMRI screenings, with a mean of 3.8+1.9 (adults) and 4.0+2.1 (pediatric) screenings. Findings on initial screening led to an imaging or invasive intervention in 38% of adults and 20% of children. On follow up, overall intervention rates were lower for adults (19%, p=0.0026) and stable for children (19%, p=NS). Thirteen cancers were detected overall (7% of adult and 14% of pediatric scans), on both initial (pediatric: 4%, adult: 3%) and subsequent (pediatric: 10%, adult: 6%) screenings. Rates of intervention after WBMRI screening decreased significantly in adults between first and subsequent exams and remained stable in pediatric patients. Cancer detection rates were similar on screening (3-4% initial, 6-10% subsequent) for both children and adults. These findings provide important data for counseling LFS patients about screening outcomes.
A comparative study of machine learning algorithms for virtual learning environment performance prediction
Authors: Ismanto Edi , Ghani Hadhrami Ab. , Md Saleh Nurul Izrin Binti
Published: August 2023

Virtual learning environment is becoming an increasingly popular study
option for students from diverse cultural and socioeconomic backgrounds
around the world. Although this learning environment is quite adaptable,
improving student performance is difficult due to the online-only learning
method. Therefore, it is essential to investigate students' participation and
performance in virtual learning in order to improve their performance. Using
a publicly available Open University learning analytics dataset, this study
examines a variety of machine learning-based prediction algorithms to
determine the best method for predicting students' academic success, hence
providing additional alternatives for enhancing their academic achievement.
Support vector machine, random forest, Nave Bayes, logical regression, and
decision trees are employed for the purpose of prediction using machine
learning methods. It is noticed that the random forest and logistic regression
approach predict student performance with the highest average accuracy
values compared to the alternatives. In a number of instances, the support
vector machine has been seen to outperform the other methods.
Predictive machine learning applying cross industry standard process for data mining for the diagnosis of diabetes mellitus type 2
Authors: Garcia-Rios Victor , Marres-Salhuana Marieta , Sierra-Liñan Fernando , Cabanillas-Carbonell Michael
Published: August 2023

Currently, type 2 diabetes mellitus is one of the world's most prevalent
diseases and has claimed millions of people's lives. The present research aims
to know the impact of the use of machine learning in the diagnostic process
of type 2 diabetes mellitus and to offer a tool that facilitates the diagnosis of
the dis-ease quickly and easily. Different machine learning models were
designed and compared, being random forest was the algorithm that generated
the model with the best performance (90.43% accuracy), which was integrated
into a web platform, working with the PIMA dataset, which was validated by
specialists from the Peruvian League for the Fight against Diabetes
organization. The result was a decrease of (A) 88.28% in the information
collection time, (B) 99.99% in the diagnosis time, (C) 44.42% in the diagnosis
cost, and (D) 100% in the level of difficulty, concluding that the application
of machine learning can significantly optimize the diagnostic process of type
2 diabetes mellitus.
Hybrid travel time estimation model for public transit buses using limited datasets
Authors: Prakash Ashwini Bukanakere , Sumathi Ranganathaiah , Sudhira Honnudike Satyanarayana
Published: August 2023

A reliable transit service can motivate commuters to switch their traveling
mode from private to public. Providing necessary information to passengers
will reduce the uncertainties encountered during their travel and improve
service reliability. This article addresses the challenge of predicting dynamic
travel times in urban areas where real-time traffic flow information is
unavailable. In this perspective, a hybrid travel time estimation model
(HTTEM) is proposed to predict the dynamic travel time using the predicted
travel times of the machine learning model and the preceding trip details. The
proposed model is validated using the location data of public transit buses of,
Tumakuru, India. From the numerical results through error metrics, it is found
that HTTEM improves the prediction accuracy, finally, it is concluded that the
proposed model is suitable for estimating travel time in urban areas with
heterogeneous traffic and limited traffic infrastructure.
Analysis of clustering and association using data mining technique for elderly health condition dataset
Authors: Panthong Rattanawadee , Wongkanthiya Thawin
Published: August 2023

Data survey on the elderly health condition in each year aimed to investigate
the performance result on the elderly health care and to evaluate the elderly’s
health and health promotion. Thus, in analyzing the data, it mainly relied on
the mining data technique for the evaluating health condition. This study
presented the data analysis by clustering method. Then, the data was taken
from each group to find the association rule. The analysis results showed that
the elderly’s health condition data could be classified into four different
groups; cluster 1 (25%) were male elderly with high blood pressure and
smoking cigarette, cluster 2 (25%) were female elderly with no the congenital
disease but the result from the eye sight examination, it was found that they
were long-sighted, cluster 3 (24%) were female elderly with no the congenital
disease but having the insomnia and osteoarthritis and cluster 4 (26%) were
female elderly with high blood pressure and diabetes. It also indicated that
each group had the rule showing the correlation between the data in each
group having the minimum value of confidence at 0.8 and the minimum value
of support not less than 0.5.
Clustering algorithms for analysing electronic medical record: A mapping study
Authors: Zaman Shah Siti Nur Shahidah , Rosli Marshima Mohd
Published: August 2023

Electronic Medical Records (EMRs) contain patients’ history related to their medication, vaccine, test results and insurance information. EMRs need to be stored to facilitate the application of clinical treatment and prevention protocols. Clustering algorithms automate the process of information extraction and support health data management. Hence, in this mapping study, we systematically examine the literature on clustering algorithms used for analysing EMRs. We focus on studies published in 2016-2021 to present an overview of clustering techniques used in these studies to analyse medical data. We found 27 studies on clustering techniques, clustering technique problems and the evaluation parameters for analysing EMRs. However, although several studies have focused on this topic, only a few have taken the significant step of examining the clustering techniques used for analysing medical data particularly electronic medical record. Our results highlight that three clustering techniques have been used to analyse medical data, namely, the partitioning, the hierarchical and the density-based algorithms. We identified several clustering technique problems and 10 different evaluation parameters. The results suggest that researchers should focus on analysing medical data that will drive data-driven decision-making by management and promote a data-driven culture to ensure health care quality.
Machine learning based recommender system for e-commerce
Authors: Loukili Manal , Messaoudi Fayçal , Ghazi Mohammed El
Published: August 2023

Nowadays, e-commerce is becoming an essential part of business for many reasons, including the simplicity, availability, richness and diversity of products and services, flexibility of payment methods and the convenience of shopping remotely without losing time. These benefits have greatly optimized the lives of users, especially with the technological development of mobile devices and the availability of the Internet anytime and anywhere. Because of their direct impact on the revenue of e-commerce companies, recommender systems are considered a must in this field. Recommender systems detect items that match the customer's needs based on the customer's previous actions and make them appear in an interesting way. Such a customized experience helps to increase customer engagement and purchase rates as the suggested items are tailored to the customer's interests. Therefore, perfecting recommendation systems that allow for more personalized and accurate item recommendations is a major challenge in the e-marketing world. In our study, we succeeded in developing an algorithm to suggest personal recommendations to customers using association rules via the Frequent-Pattern-Growth algorithm. Our technique generated good results with a high average probability of purchasing the next product suggested by the recommendation system.
COVID-19 digital x-rays forgery classification model using deep learning
Authors: Abd El-Latif Eman I. , Khalifa Nour Eldeen
Published: August 2023

Nowadays, the internet has become a typical medium for sharing digital
images through web applications or social media and there was a rise in
concerns about digital image privacy. Image editing software’s have prepared
it incredibly simple to make changes to an image's content without leaving
any visible evidence for images in general and medical images in particular.
In this paper, the COVID-19 digital x-rays forgery classification model
utilizing deep learning will be introduced. The proposed system will be able
to identify and classify image forgery (copy-move and splicing) manipulation.
Alexnet, Resnet50, and Googlenet are used in this model for feature extraction
and classification, respectively. Images have been tampered with in three
classes (COVID-19, viral pneumonia, and normal). For the classification of
(Forgery or no forgery), the model achieves 0.9472 in testing accuracy. For
the classification of (Copy-move forgery, splicing forgery, and no forgery),
the model achieves 0.8066 in testing accuracy. Moreover, the model achieves
0.796 and 0.8382 for 6 classes and 9 classes problems respectively.
Performance indicators like Recall, Precision, and F1 Score supported the
achieved results and proved that the proposed system is efficient for detecting
the manipulation in images.

 
Classification of meat using the convolutional neural network
Authors: Purnamasari Detty , Bachrudin Koko , Suryana Dede Herman , Robert Robert
Published: August 2023

Every animal meat has different color and texture, for example, beef has a dark red color with a chewy texture, while pork has a pale red color and smooth fiber. A previous study has classified types of meat using gray level co-ocurrence matrix (GLCM), hue saturation value (HSV), and color intensity. In this research, we created meat classification between beef, pork, and horse meat using a convolutional neural network (CNN) develop in jupyter notebook, using the MobileNetV2 model, and 315 meat images as a dataset divided into 3 groups, 70% image for the training dataset, 20% image for the testing dataset, and 10% image for validation dataset. Before dividing the image into 3 groups, the image is resized to 224×224, and convert the color to grayscale. The model is trained with a training dataset, the epoch of 50, and Adam optimizer, the results show an accuracy of 93.15%.
Global-local attention with triplet loss and label smoothed crossentropy for person re-identification
Authors: Tran Nha , Nguyen Toan , Nguyen Minh , Luong Khiet , Lam Tai
Published: August 2023

Person re-identification (Person Re-ID) is a research direction on tracking and identifying people in surveillance camera systems with non-overlapping camera perspectives. Despite much research on this topic, there are still some practical problems that Person Re-ID has not yet solved, in reality, human objects can easily be obscured by obstructions such as other people, trees, luggage, umbrellas, signs, cars, motorbikes. In this paper, we propose a multibranch deep learning network architecture. In which one branch is for the representation of global features and two branches are for the representation of local features. Dividing the input image into small parts and changing the number of parts between the two branches helps the model to represent the features better. In addition, we add an attention module to the ResNet50 backbone that enhances important human characteristics and eliminates irrelevant information. To improve robustness, the model is trained by combining triplet loss and label smoothing cross-entropy loss (LSCE). Experiments are carried out on datasets Market1501, and duke multi-target multi-camera (DukeMTMC) datasets, our method achieved 96.04% rank-1, 88,11% mean average precision (mAP) on the Market1501 dataset, and 88.78% rank-1, 78,6% mAP on the DukeMTMC dataset. This method achieves performance better than some state-of-the-art methods.
Dyslexia deep clustering using webcam-based eye tracking
Authors: Ikermane Mohamed , Mouatasim Abdelkrim El
Published: August 2023

Dyslexia is a neurodevelopmental impairment that causes difficulties in reading and can have significant academic, social, and economic impacts. In Morocco, Dyslexia accounts for 37% of children's school failures. Early detection of dyslexia is crucial to help children reach their academic potential and prevent low self-esteem. To address this issue, a dyslexia screening tool using webcam-based eye tracking was developed for the Arabic language. The tool was tested on a dataset of 61 students from three primary schools in southern Morocco, and the results showed that using Arabic dyslexic-friendly typefaces improved reading performance, particularly for those with low reading performance. Deep clustering was used to reduce the dimensionality of the dataset, and the subjects were gathered using unsupervised k-means based on AutoEncoder output. The three clusters produced showed a significant difference in many dyslexia traits, such as the number and duration of fixations, as well as the saccade period. These findings suggest that webcam-based eye-tracking techniques have the potential to be used as an initial dyslexia diagnosis tool to assess if a child exhibits some of the typical symptoms of dyslexia and whether they should seek a professional full dyslexia diagnosis.
Exploring the impacts of using the artificial intelligence voice-enabled chatbots on customers interactions in the United Arab Emirates
Authors: Abdo Asad , Yusof Shafiz Mohd
Published: August 2023

Over the past decade, chatbots have experienced a significant increase in
popularity, especially since the outbreak of COVID-19. In the United Arab
Emirates, most businesses have accelerated their digital transformation and
are relying on chatbots as a primary way to interact with customers. However,
many of these chatbots lack a voice input option for customers. This study
investigates the benefits and challenges of incorporating artificial intelligence
(AI) voice-enabled chatbots into United Arab Emirates (UAE) businesses and
how this impacts customer experience. Qualitative research techniques were
used to gather information, and the results demonstrate that implementing AI
chatbots with voice input and sentiment analysis features can enhance the
customer experience by making it more efficient and convenient.
Additionally, the study found that AI chatbots can ultimately save businesses
time and money, and while they may reduce the need for human agents, they
will not replace them entirely. Finally, an implementation framework and
suggestions are provided for businesses that are interested in adopting AI
voice-enabled chatbots for customer interactions.
A novel meta-embedding technique for drug reviews sentiment analysis
Authors: Khine Aye Hninn , Wettayaprasit Wiphada , Duangsuwan Jarunee
Published: August 2023

Traditional word embedding models have been used in the feature extraction process of deep learning models for sentiment analysis. However, these models ignore the sentiment properties of words while maintaining the contextual relationships and have inadequate representation for domainspecific words. This paper proposes a method to develop a meta embedding model by exploiting domain sentiment polarity and adverse drug reaction (ADR) features to render word embedding models more suitable for medical sentiment analysis. The proposed lexicon is developed from the medical blogs corpus. The polarity scores of the existing lexicons are adjusted to assign new polarity score to each word. The neural network model utilizes sentiment lexicons and ADR in learning refined word embedding. The refined embedding obtained from the proposed approach is concatenated with original word vectors, lexicon vectors, and ADR feature to form a meta-embedding model which maintains both contextual and sentimental properties. The final meta-embedding acts as a feature extractor to assess the effectiveness of the model in drug reviews sentiment analysis. The experiments are conducted on global vectors (GloVE) and skip-gram word2vector (Word2Vec) models. The empirical results demonstrate the proposed meta-embedding model outperforms traditional word embedding in different performance measures.
Using machine learning to improve a telco self-service mobile application in Indonesia
Authors: Garini Jwalita Galuh , Hidayanto Achmad Nizar , Fina Agri
Published: August 2023

The use of mobile applications extends to the telecommunication sector, mainly due to COVID-19. Failure to provide it can cause dissatisfaction and result in the removal of the mobile application. Moreover, this leads to lost service opportunities, so paying attention to the mobile application's quality is essential. There has yet to be a study on measuring the service quality of a self-service mobile application in the telecommunication sector using online customer reviews. This study uses sentiment analysis and topic modeling to determine the service quality of a self-service mobile application in the telecommunication sector from reviews on Google Play Store and Apple App Store. This study uses myIndiHome as a case study. The total data obtained from both platforms are 20,452 reviews. Sentiment analysis was performed using Naïve Bayes, support vector machine, and logistic regression, while topic modeling was performed using latent dirichlet allocation. The results show that logistic regression performs better than support vector machine and Naïve Bayes. Meanwhile, topic modeling shows that the positive review data has three topics, including application features, products/services, and application interfaces. Moreover, the negative review data has five topics, including application availability, application feature reliability, application processing speed, bugs, and application reliability.
Prototyping of e-fisherman web server to support Indonesian fishermen’s activities
Authors: Fuada Syifaul , Adiono Trio
Published: August 2023

This paper presents a webserver for Indonesian fishermen, to support fishing activities. This is one of the sub-systems of e-Nelayan (in English: eFisherman) architecture, which was connected to e-Nelayan Apps; it helps to provide interaction between two users, including the administrators and fishermen. Using hypertext preprocessor (PHP) language, the website was developed to function on an Apache web server, with the adaptation of my structured query language (MySQL) framework for the database. This system was subsequently divided into two parts: (1) the front-end, which is responsible for the accessibility of data collection and (2) the back-end, where administrators update or modify crucial information: price, fishing result, illegal activity report, save our ship! (SOS) potential fish zone, and ship tracking. The administrators are unable to update the real-time weather information for the front-end part. The application was found to record the information obtained from the fishermen through the e-Nelayan apps and meteorology, climatology, and geophysical agency (BMKG in Indonesian). This web system is expected to carry out the following functions: to ensure easier interactions between fishermen and administrators, to enable easy update of information, to promote monitoring and recording of results, and to ensure fishermen’s safety.
Low-rate distributed denial of service attacks detection in software defined network-enabled internet of things using machine learning combined with feature importance
Authors: Abizar Muhammad , Ihzanor Syahputra Muhammad Ferry Septian , Habibullah Ahmad Rizky , Kusuma Aditya Christian Sri , Setiawan Sumadi Fauzi Dwi
Published: August 2023

One of the main challenges in developing the internet of things (IoT) is the existence of availability problems originated from the low-rate distributed denial of service attacks (LRDDoS). The complexity of IoT makes the LRDDoS hard to detect because the attack flow is performed similarly to the regular traffic. Integration of software defined IoT (SDN-Enabled IoT) is considered an alternative solution for overcoming the specified problem through a single detection point using machine learning approaches. The controller has a resource limitation for implementing the classification process. Therefore, this paper extends the usage of Feature Importance to reduce the data complexity during the model generation process and choose an appropriate feature for generating an efficient classification model. The research results show that the Gaussian Naïve Bayes (GNB) produced the most effective outcome. GNB performed better than the other algorithms because the feature reduction only selected the independent feature, which had no relation to the other features.
Automated classification of age-related macular degeneration from optical coherence tomography images using deep learning approach
Authors: Nagamani Gilakara Muni , Sudhakar Theerthagiri
Published: August 2023

Early detection of macular diseases can prevent vision loss. Manual screening can be unreliable due to the similarity in the pathological presentations of common retinal illnesses like age-related macular degeneration (AMD). Researchers are becoming more interested in the accurate automated computer-based detection of macular diseases. Using healthy optical coherence tomography (OCT) images, the drusens (early stage) and choroidal neovascularization (CNV) (late stage) of AMD are thus classified using a completely different approach in this paper. The new deep learning (DL) model is proposed for multiple OCT image segmentation of ophthalmological diseases using attention-based nested U-Net (ANU-Net). The flower pollination optimization algorithm (FPOA) is used to optimize the hyperparameters of the network. The SqueezeNet-based classification can be made in the pre-processed images. A dataset from the University of California San Diego (UCSD) is used to evaluate the proposed method. 98.7% accuracy, 99.8% specificity, and 99.7% sensitivity are achieved by the proposed method. The proposed method produces better identification results for automated preliminary diagnosis of macular diseases in hospitals and eye clinics due to the positive classification results.
A novel modified dandelion optimizer with application in power system stabilizer
Authors: Aribowo Widi , Suprianto Bambang , Prapanca Aditya
Published: August 2023

This article presents a newly developed modification of the dandelion optimizer (DO). The proposed method is a chaotic algorithmic integrity and modification of the original dandelion optimizer. Dandelion is one of the plants that rely on wind for seed propagation. This article presents the tuning of the power system stabilizer with the method proposed in a case study of a single machine system. The validation of the proposed method uses the benchmark function and performance on a single engine system against transient response. The method used as a comparison in this article is the whale optimization algorithm (WOA), grasshopper optimization algorithm (GOA) and the original dandelion optimizer (DO). The simulation results show that the proposed method, which is a modified dandelion optimizer, provides promising performance.
Modelling mechanisms for measurable and detection based on artificial intelligence
Authors: Abdul Qader Raghad Abdul Hadi , Mohammad Marwa Jassim
Published: August 2023

One of the trendiest areas in the field of materials science is Artificial Intelligence (AI) based physical applications. Typically, more time and resources are needed for traditional experiments and statistical methods. Thus, there is a growing need for applications of AI in the simulation and investigation of novel materials. Usually, there are significant restrictions because there are not any benchmark datasets, sophisticated pre-processing mechanisms, prediction modelling mechanisms, or simulation tools in the literature on materials. This work aims to attempt for examining computational and experimental data-based AI processes. In addition, the state of research into developing new materials and utilizing AI in material modelling tools is implemented. As long as, AI can be used in materials to improve efficiency and prediction accuracy. Also, it is very difficult to determine great learning models, involving data preparation, model architecture, data management, and simulation techniques. Finally, it has been discussed the challenges in realizing AI-based applications in the field of materials science.
Aerial image semantic segmentation based on 3D fits a small dataset of 1D
Authors: Ahmed Shouket Abdulrahman , Desa Hazry , T. Hussain Abadal-Salam
Published: August 2023

Time restrictions and lack of precision demand that the initial technique be abandoned. Even though the remaining datasets had fewer identified classes than initially planned for the study, the labels were more accurate. Because of the need for additional data, a single network cannot categorize all the essential elements in a picture, including bodies of water, roads, trees, buildings, and crops. However, the final network gains some invariance in detecting these classes with environmental changes due to the different geographic positions of roads and buildings discovered in the final datasets, which could be valuable in future navigation research. At the moment, binary classifications of a single class are the only datasets that can be used for the semantic segmentation of aerial images. Even though some pictures have more than one classification, images of roads and buildings were only found in a significant number of samples. Then, the building datasets were pooled to produce a larger dataset and for the constructed models to gain some invariance on image location. Because of the massive disparity in sample size, road datasets needed to be integrated.
First record of the heart urchin Plagiobrissus grandis (Gmelin, 1791) (Echinodermata: Echinoidea) in the coastal region of Paraná, southern Brazil
Authors: Guilherme Pablo Damian Borges , Bueno Maristela de Lima , Metri Rafael , Baptista-Metri Cassiana , Loose Robin Hilbert , Tavares Yara Aparecida Garcia
Published: August 2023

This study presents the first record of the cordiform echinoid Plagiobrissus grandis (Gmelin, 1791) (Echinoidea: Spatangoida: Brissidae) in Paraná’s shallow inner shelf, in addition to a brief description of its location, body measurements, sex determination, and reproductive status. Two specimens were recorded between 2015 (bycatch by artisanal fishing) and 2016 (observed by scuba diving). The only specimen – with severe signs of weakness – captured was donated to Associação MarBrasil and kept in an aquarium for rehabilitation (8 days), where it was possible to observe digging movements, burying skills (partially), and camouflage or covering behavior. The organism was sent to the UNESPAR campus of Paranaguá and was photographed, measured (length: 155.65 mm, width: 118.93 mm, and maximum test height: 41.31 mm), fixed, and properly preserved. Digitized images of the histological preparation showed that it was a female in the recovery stage (or nutritional reserve) of the reproductive cycle. Regardless of their limitations, the current records for the Brissidae family in South Atlantic are still concentrated on the Brazilian coast, contributing to the understanding of zoogeographic, ecological, morphophysiological, and behavioral aspects of the spatangoid echinoids.
Trabalhadores, tecnologia e saúde: a construção da cardiologia como especialidade médica no Brasil (décadas de 1930 e 1940)
Authors: Kropf Simone Petraglia
Published: August 2023

The article examines the constitution of cardiology as a medical specialty in Brazil in the 1930s and 1940s. In the context of the implementation of the social protection system by the Getúlio Vargas administration, cardiovascular diseases gained visibility in the debate on the health and productivity of urban workers. Some physicians argued that the diagnosis, treatment, and prevention of these diseases required specialized knowledge and practices, such as electrocardiography. The dissemination of this technology in Brazil benefited from exchanges with US doctors, intensified at the time by the Good Neighbor Policy. We argue that the establishment of cardiology as a specialty resulted from the association between demands for the reproduction of the workforce, socio-professional interests in the medical field, and transnational networks of inter-American scientific cooperation.
Sentido e historicidade nos trópicos: Sérgio Buarque de Holanda e as aporias do historicismo
Authors: Cunha Marcelo Durão Rodrigues da , Nicodemo Thiago Lima
Published: August 2023

Tendo como base estudos transnacionais que apontam para a relevância do historicismo enquanto conceito-chave do pensamento histórico, neste artigo objetivamos revisitar a reapropriação do termo, realizada na obra tardia de Sérgio Buarque de Holanda. De forma geral, buscamos sustentar que a aproximação do autor com o historicismo representou o capítulo brasileiro de uma tendência à relativização e adaptação do conceito moderno de história à realidade multiétnica e multicultural da América Latina. Assim, desenvolvemos a hipótese de que, em especial no texto O atual e o inatual em L. von Ranke (1974), Sérgio Buarque se preocupava com o processo de crise da velha matriz europeia de entendimento da história na modernidade, atentando para percepções alternativas e descontínuas sobre a questão do sentido na história.
Expenditure of biological drugs for rheumatoid arthritis treatment in the Brazilian public health system
Authors: Mega Tacila Pires , Silva Rondineli Mendes da
Published: August 2023

OBJECTIVE: This work aims to analyze the quantity and expenses related to biological drugs used for the treatment of rheumatoid arthritis (RA) in outpatient public care within the Brazilian Unified Health System (SUS). METHODS: It is a cross-sectional descriptive study based on secondary data from a historical series, referring to the purchase, volume, and the number of patients treated with different biological drugs (infliximabe, etanercept, adalimumab, rituximab, abatacept, tocilizumab, golimumab, and certolizumab pegol) for RA treatment in outpatient care from 2012 to 2017. The data were extracted from the SUS Outpatient Information System database-SIA/SUS and included ten drugs used for RA treatment. The study assessed the quantity and expenditure of these drugs, the number of RA patients treated, and the expenditure by RA subtypes. The National Broad Consumer Price Index was used to adjust the expenditures for December 2017. RESULTS: Th e M inistry o f H ealth a llocated approximately $ 500 m illion t o provide a bout 2 million units of biological drugs for RA patients from 2012 to 2017. The supply of adalimumab 40 mg and etanercept 50 mg accounted for 68.3% of the total expenditure. The subtypes “other rheumatoid arthritis with rheumatoid factor” (ICD-10 M05.8), “rheumatoid arthritis without rheumatoid factor” (ICD-10 M06.0), and “Felty’s syndrome” (M05. 0) represented 84.5% of the total expenditures. The proportion of patients treated with biological drugs increased by 33.0%. There was a significant 83.0% increase in the number of patients using biological drugs compared to the overall number of RA patients treated during the study period. CONCLUSIONS: The results obtained allow us to draw a more recent profile of expenditure on RA treatment and indicate trends in the use of biological drugs for this condition, generating data that can support management decisions in public health policies.