bluuebunny/crossref_metadata_2025 huggingface dataset:
Supplementary Table S1 from Fraction Dose Escalation of Hypofractionated Radiotherapy with Concurrent Chemotherapy and Subsequent Consolidation Immunotherapy in Locally Advanced Non–Small Cell Lung Cancer: A Phase I Study
Authors: Zhou Rui , Liu FangJie , Zhang HongMei , Wang DaQuan , Zhang PengXin , Zheng ShiYang , Liu YiMei , Chen Li , Guo JinYu , Zou YingYi , Rong Yu-Ming , Liu Hui , Qiu Bo
Published: July 2024

Supplementary Table S1. Dose constraints for organs at risk.
Supplementary Table S2 from A Modular Trial of Androgen Signaling Inhibitor Combinations Testing a Risk-Adapted Strategy in Patients with Metastatic Castration–Resistant Prostate Cancer
Authors: Aparicio Ana M. , Tidwell Rebecca S.S. , Yadav Shalini S. , Chen Jiun-Sheng , Zhang Miao , Liu Jingjing , Guo Shuai , Pilié Patrick G. , Yu Yao , Song Xingzhi , Vundavilli Haswanth , Jindal Sonali , Zhu Keyi , Viscuse Paul V. , Lebenthal Justin M. , Hahn Andrew W. , Soundararajan Rama , Corn Paul G. , Zurita-Saavedra Amado , Subudhi Sumit K. , Zhang Jianhua , Wang Wenyi , Huff Chad , Troncoso Patricia , Allison James P. , Sharma Padmanee , Logothetis Christopher J.
Published: July 2024

Supplementary Table S2. Pathogenic mutations in germline DNA.
Supplementary Figure S13 from A Machine Learning Algorithm Facilitates Prognosis Prediction and Treatment Selection for Barcelona Clinic Liver Cancer Stage C Hepatocellular Carcinoma
Authors: Han Ji W. , Lee Soon K. , Kwon Jung H. , Nam Soon W. , Yang Hyun , Bae Si H. , Kim Ji H. , Nam Heechul , Kim Chang W. , Lee Hae L. , Kim Hee Y. , Lee Sung W. , Lee Ahlim , Chang U I. , Song Do S. , Kim Seok-Hwan , Song Myeong J. , Sung Pil S. , Choi Jong Y. , Yoon Seung K. , Jang Jeong W.
Published: July 2024

Application to the patients with first-line and later-line systemic chemotherapy in the propensity-score matched cohort.
Supplementary Figure S3 from Cell-Free Human Papillomavirus DNA Is a Sensitive Biomarker for Prognosis and for Early Detection of Relapse in Locally Advanced Cervical Cancer
Authors: Sivars Lars , Jylhä Cecilia , Crona Guterstam Ylva , Zupancic Mark , Lindqvist Britta , Nordenskjöld Magnus , Tham Emma , Hellman Kristina
Published: July 2024

Supplementary Figure S3. Kaplan–Meier graphs of comparisons between patients with ctHPV DNA–positive (dotted, bottom lines) and ctHPV DNA–negative (solid, top lines) plasma at different timepoints.
Supplementary Figure S1 from Cell-Free Human Papillomavirus DNA Is a Sensitive Biomarker for Prognosis and for Early Detection of Relapse in Locally Advanced Cervical Cancer
Authors: Sivars Lars , Jylhä Cecilia , Crona Guterstam Ylva , Zupancic Mark , Lindqvist Britta , Nordenskjöld Magnus , Tham Emma , Hellman Kristina
Published: July 2024

Supplementary Figure S1. Dilution curves of the type-specific ctHPV DNA assays.
Data from A Cohort Study to Evaluate Genetic Predictors of Aromatase Inhibitor Musculoskeletal Symptoms: Results from ECOG-ACRIN E1Z11
Authors: Stearns Vered , Jegede Opeyemi A. , Chang Victor T.-S. , Skaar Todd C. , Berenberg Jeffrey L. , Nand Ranveer , Shafqat Atif , Jacobs Nisha L. , Luginbuhl William , Gilman Paul , Benson Al B. , Goodman Judie R. , Buchschacher Gary L. , Henry N. Lynn , Loprinzi Charles L. , Flynn Patrick J. , Mitchell Edith P. , Fisch Michael J. , Sparano Joseph A. , Wagner Lynne I.
Published: July 2024

AbstractPurpose:
Aromatase inhibitor (AI)–associated musculoskeletal symptoms (AIMSS) are common and frequently lead to AI discontinuation. SNPs in candidate genes have been associated with AIMSS and AI discontinuation. E1Z11 is a prospective cohort study designed to validate the association between 10 SNPs and AI discontinuation due to AIMSS.
Patients and Methods:
Postmenopausal women with stage I to III hormone receptor–positive breast cancer received anastrozole 1 mg daily and completed patient-reported outcome measures to assess AIMSS (Stanford Health Assessment Questionnaire) at baseline, 3, 6, 9, and 12 months. We estimated that 40% of participants would develop AIMSS and 25% would discontinue AI treatment within 12 months. Enrollment of 1,000 women with a fixed number per racial stratum provided 80% power to detect an effect size of 1.5 to 4. SNPs were found in ESR1 (rs2234693, rs2347868, and rs9340835), CYP19A1 (rs1062033 and rs4646), TCL1A (rs11849538, rs2369049, rs7158782, and rs7159713), and HTR2A (rs2296972).
Results:
Of the 970 evaluable women, 43% developed AIMSS and 12% discontinued AI therapy within 12 months. Although more Black and Asian women developed AIMSS than White women (49% vs. 39%, P = 0.017; 50% vs. 39%, P = 0.004, respectively), the AI discontinuation rates were similar across groups. None of the SNPs were significantly associated with AIMSS or AI discontinuation in the overall population or in distinct cohorts. The OR for rs2296972 (HTR2A) approached significance for developing AIMSS.
Conclusions:
We were unable to prospectively validate candidate SNPs previously associated with AI discontinuation due to AIMSS. Future analyses will explore additional genetic markers, patient-reported outcome predictors of AIMSS, and differences by race.
Microsatellite Instability, Tumor Mutational Burden, and Response to Immune Checkpoint Blockade in Patients with Prostate Cancer
Authors: Lenis Andrew T. , Ravichandran Vignesh , Brown Samantha , Alam Syed M. , Katims Andrew , Truong Hong , Reisz Peter A. , Vasselman Samantha , Nweji Barbara , Autio Karen A. , Morris Michael J. , Slovin Susan F. , Rathkopf Dana , Danila Daniel , Woo Sungmin , Vargas Hebert A. , Laudone Vincent P. , Ehdaie Behfar , Reuter Victor , Arcila Maria , Berger Michael F. , Viale Agnes , Scher Howard I. , Schultz Nikolaus , Gopalan Anuradha , Donoghue Mark T.A. , Ostrovnaya Irina , Stopsack Konrad H. , Solit David B. , Abida Wassim
Published: July 2024

Purpose:

Patients with microsatellite instability–high/mismatch repair-deficient (MSI-H/dMMR) and high tumor mutational burden (TMB-H) prostate cancers are candidates for pembrolizumab. We define the genomic features, clinical course, and response to immune checkpoint blockade (ICB) in patients with MSI-H/dMMR and TMB-H prostate cancers without MSI [TMB-H/microsatellite stable (MSS)].

Experimental Design:

We sequenced 3,244 tumors from 2,257 patients with prostate cancer. MSI-H/dMMR prostate cancer was defined as an MSIsensor score ≥10 or MSIsensor score ≥3 and <10 with a deleterious MMR alteration. TMB-H was defined as ≥10 mutations/megabase. PSA50 and RECIST responses were assigned. Overall survival and radiographic progression-free survival (rPFS) were compared using log-rank test.

Results:

Sixty-three (2.8%) men had MSI-H/dMMR, and 33 (1.5%) had TMB-H/MSS prostate cancers. Patients with MSI-H/dMMR and TMB-H/MSS tumors more commonly presented with grade group 5 and metastatic disease at diagnosis. MSI-H/dMMR tumors had higher TMB, indel, and neoantigen burden compared with TMB-H/MSS. Twenty-seven patients with MSI-H/dMMR and 8 patients with TMB-H/MSS tumors received ICB, none of whom harbored polymerase epsilon (polE) catalytic subunit mutations. About 45% of patients with MSI-H/dMMR had a RECIST response, and 65% had a PSA50 response. No patient with TMB-H/MSS had a RECIST response, and 50% had a PSA50 response. rPFS tended to be longer in patients with MSI-H/dMMR than in patients with TMB-H/MSS who received immunotherapy. Pronounced differences in genomics, TMB, or MSIsensor score were not detected between MSI-H/dMMR responders and nonresponders.

Conclusions:

MSI-H/dMMR prostate cancers have greater TMB, indel, and neoantigen burden than TMB-H/MSS prostate cancers, and these differences may contribute to profound and durable responses to ICB.
Multiplexed Spatial Profiling of Hodgkin Reed–Sternberg Cell Neighborhoods in Classic Hodgkin Lymphoma
Authors: Pourmaleki Maryam , Jones Caitlin J. , Mellinghoff Sabrina D. , Greenstein Brian D. , Kumar Priyadarshini , Foronda Miguel , Navarrete Daniel A. , Campos Carl , Roshal Mikhail , Schultz Nikolaus , Shah Sohrab P. , Schietinger Andrea , Socci Nicholas D. , Hollmann Travis J. , Dogan Ahmet , Mellinghoff Ingo K.
Published: July 2024

Purpose:

Classic Hodgkin lymphoma (cHL) is a B-cell lymphoma that occurs primarily in young adults and, less frequently, in elderly individuals. A hallmark of cHL is the exceptional scarcity (1%–5%) of the malignant Hodgkin Reed–Sternberg (HRS) cells within a network of nonmalignant immune cells. Molecular determinants governing the relationship between HRS cells and their proximal microenvironment remain largely unknown.

Experimental Design:

We performed spatially resolved multiplexed protein imaging and transcriptomic sequencing to characterize HRS cell states, cellular neighborhoods, and gene expression signatures of 23.6 million cells from 36 newly diagnosed Epstein–Barr virus (EBV)-positive and EBV-negative cHL tumors.

Results:

We show that MHC-I expression on HRS cells is associated with immune-inflamed neighborhoods containing CD8+ T cells, MHC-II+ macrophages, and immune checkpoint expression (i.e., PD1 and VISTA). We identified spatial clustering of HRS cells, consistent with the syncytial variant of cHL, and its association with T-cell–excluded neighborhoods in a subset of EBV-negative tumors. Finally, a subset of both EBV-positive and EBV-negative tumors contained regulatory T-cell–high neighborhoods harboring HRS cells with augmented proliferative capacity.

Conclusions:

Our study links HRS cell properties with distinct immunophenotypes and potential immune escape mechanisms in cHL.
Supplementary Table S1 from IL2 Targeted to CD8+ T Cells Promotes Robust Effector T-cell Responses and Potent Antitumor Immunity
Authors: Moynihan Kelly D. , Kumar Manu P. , Sultan Hussein , Pappas Danielle C. , Park Terrence , Chin S. Michael , Bessette Paul , Lan Ruth Y. , Nguyen Henry C. , Mathewson Nathan D. , Ni Irene , Chen Wei , Lee Yonghee , Liao-Chan Sindy , Chen Jessie , Schumacher Ton N.M. , Schreiber Robert D. , Yeung Yik A. , Djuretic Ivana M.
Published: July 2024

Supplementary Table S1, Molecules used in this study
Optimize the position of distributed generations in distribution grid by using improved loss sensitivity factor
Authors: Phan Dinh Chung , Luu Ngoc An
Published: July 2024

This research proposed a method to determine the optimal position of distributed generations in a distribution grid. The method is improved from the loss sensitivity factor method. An algorithm is developed to determine both the position and size of distributed generations. This algorithm is validated via IEEE 33 bus distribution grid in two cases of distributed generation size including unknown size and constant size. The results were analyzed and compared to other previous algorithms including loss sensitivity factor-based algorithm and other algorithms. Results indicated the optimal position of each distributed generation to minimize the power loss. Results also indicated that with the proposed algorithm, the loss reduction rate (LRR) is the highest in comparison to that with other previous algorithms.
A high efficiency boost converter topology with least component count
Authors: Swargiary Rikhit , Deva Sarma Kaushik Chandra
Published: July 2024

This paper presents a design and analysis of novel DC-DC boost converter with a least component count. The proposed converter produces high DC gain voltage in comparison to some recently presented high voltage DC-DC converter. Here one switch, one inductor, two capacitors and one diode are used to achieved a high voltage gain without compromising efficiency of converter. The converter’s performance is evaluated using theatrical, simulation and experimental methods, with results indicating a four times of input voltage and a fast-transient response at various duty cycles is achieved. Due to its low component count the proposed converter is compact and hence it offers an effective solution for various power applications.
Design of a tunable center frequency and small size cavity bandpass filter by separating capacitor-loaded resonators
Authors: Nguyen Van Son , Dang Hoang Anh , Tran Van Dung , Pham Cao Dai , Phan Thi Bich , Nguyen Van Trung , Dai Xuan Loi , Huy Long Tran
Published: July 2024

This paper presents a tunable center frequency and small size cavity bandpass filter design method. In this method, a capacitor-loaded open terminal coaxial resonator is employed to reduce the size of cavity filters. The resonator is designed and fabricated separately into two parts to achieve the flexible operating frequency purpose. The first part is called the base of the resonator which is simply a pillar and directly fabricated integrally with the cavity housing. The second part called the hat of the resonator is the main part causing the load capacitance in cavity filters. By using different heights of the base part or/and different shapes and sizes of the hat, the operating frequency of cavity filters can be changed flexibly. This method not only reduces the difficulty and cost of cavity filter processing but also makes cavity filters reconfigurable. To demonstrate the effectiveness of the method, a cavity filter sample with a center frequency of 3.45 GHz and a bandwidth of 80 MHz was designed, fabricated, and measured. The measured results show that the insertion loss was smaller than 1.33 dB in the whole bandwidth, one zero-point at 3.350 GHz reaching -68 dB, the rejection at 3.550 GHz was -41 dB, unloaded Q was 5,898, and the dimension of the filter was 128 mm×86 mm×23 mm.
An intelligent approach to detect and predict online fraud transaction using XGBoost algorithm
Authors: Bala Bala Santhosh , Yadav Pasupula Praveen , Reddy Mogathala Raghavendra
Published: July 2024

The most popular payment method in recent years is the credit card. Due to the E-commerce industry’s explosive growth, the usage of credit cards for online purchases have been greatly increased as a result frauds has increased. Banks have been facing challenges to detect the credit card system fraud in recent years. Credit card fraud happens when the card was stolen for any unauthorized purposes or if the fraudster utilizes the credit card information for his own use. In order to prevent credit card fraud, it is essential to build detection measures. While detecting credit card theft with machine learning (ML), the features of credit card frauds play an important and they must be carefully selected. A fraud detection algorithm must be created in order to correctly locate and stop fraudulent activity as technology advances along with the amount of fraud cases. ML methods are essential for identifying fraudulent transactions. The implementation of fraud detection models is particularly difficult because of the sensitive nature of the data, the unbalanced class distributions, and the lack of data. In this work, an intelligent approach to detect and predict online fraud transaction using extreme gradient boosting (XGBoost) algorithm is described. The XGBoost model predicts whether a transaction is fraud or not. This model will achieve better performance interarm of recall, precision, accuracy and F1-score for credit card fraud detection.
Intrusion detection system for cloud environment based on convolutional neural networks and PSO algorithm
Authors: Rosline Gnanam Jeba , Rani Pushpa
Published: July 2024

Authentication of clients and their applications to cloud services is a major concern. Network security and the identification of hostile activities are greatly aided by intrusion detection systems (IDS). In general, optimisation strategies can be applied to improve IDS model performance. Convolutional neural networks (CNN) and other deep learning (DL) algorithms is utilised to enhance IDS’s capability to identify and categories intrusions. IDSs can identify prior attacks, adapt to changing threats, and minimise false positives by utilising these strategies. In this work, a lightweight CNN is proposed for intrusion detection in cloud environment. The main contribution of this research is to use particle swarm optimization (PSO), ametaheuristic algorithm to find the CNNs optimal parameters that comprise the number of convolutional layers, the size of the filter utilized in the convolutional procedure, the number of convolutional filters, and the batch size. Heuristic based searches are useful for solving these kinds of problems. The experimental outcomes demonstrate that the proposed method reaches 91.70% of accuracy, 91.82% of precision, 91.99% of recall and 91.90% of F1-score. Cloud providers can gain from improved security measures by incorporating the proposed IDS paradigm into cloud settings, thereby minimizing unauthorized access and any data breaches.
Automatic human height measurement system based on camera sensor with deep-learning and linear regression analysis
Authors: Fadllullah Arif , Zulfia Rahmatuz , Pradana Awang , Yudhistira Akbar Muhammad Adhiya
Published: July 2024

This study proposes a new approach for automatically measuring human height using a camera sensor with deep learning and linear regression analysis. The camera sensor is used to capture real-time images of human objects. The image is then processed with a YOLO4-based convolutional neural network (CNN) to separate the region of interest (ROI) of the human object from the background. The pixel value of the ROI vertical line is then converted into height in centimeters by the linear regression equation. The system was tested on 40 primary samples, with 20 samples used as control data and 20 samples used as test data. From the results of testing 20 control data samples, the linear regression equation was obtained as y' = 0.4034x + 24.938, which was then applied to convert the system's predicted height in centimeters for 20 test samples. The test results for 20 test samples showed that an average F1_score was 1, the R_square obtained was 0.93, the root mean square errors (RMSE) was 0.02, and the percentage of accuracy was 99.00%. The test results showed that the system was able to automatically detect human height with a very high level of correlation/similarity and accuracy between actual and predicted height.
Exploring RoBERTa model for cross-domain suggestion detection in online reviews
Authors: Nandula Anuradha , Reddy Panuganti Vijayapal
Published: July 2024

Detecting suggestions in online review requires contextual understanding of review text, which is an important real-world application of natural language processing. Given the disparate text domains found in product reviews, a common strategy involves fine-tuning bidirectional encoder representations from transformers (BERT) models using reviews from various domains. However, there hasn't been an empirical examination of how BERT models behave across different domains in tasks related to detecting suggestion sentences from online reviews. In this study, we explore BERT models for suggestion classification that have been fine-tuned using single-domain and cross-domain Amazon review datasets. Our results indicate that while single-domain models achieved slightly better performance within their respective domains compared to cross-domain models, the latter outperformed single-domain models when evaluated on cross-domain data. This was also observed for single-domain data not used for fine-tuning the single-domain model and on average across all tests. Although fine-tuning single-domain models can lead to minor accuracy improvements, employing multi-domain models that perform well across domains can help in cold start problems and reduce annotation costs.
Biomedical image classification using seagull optimization with deep learning for colon and lung cancer diagnosis
Authors: Manoharan Thiyagarajan , Velvizhi Ramalingam , Juluru Tarun Kumar , Kamal Shoaib , Mallick Shrabani , Puliyanjalil Ezudheen
Published: July 2024

Traditional health care relies on biomedical image categorization to identify and treat various medical conditions. In machine learning and medical imaging, biomedical image classification for colon and lung cancer diagnosis is significant. The work focuses on building novel models and algorithms to accurately detect and categorize tumorous lesions using computer tomography (CT) scans and histopathology slides. These systems use image processing, deep learning (DL), and convolutional neural networks (CNN) to assist medical professionals diagnose cancer sooner and improve patient outcomes. Biomedical image classification using seagull optimization with deep learning (BIC-SGODL) addresses colon and lung cancer diagnosis. The BIC-SGODL method improves cancer diagnosis using hyperparameter optimized DL model. BIC-SGODL utilizes DenseNet to learn complicated features. The convolutional long short-term memory (CLSTM) standard captures spatiotemporal information in sequential picture data. Finally, the SGO method adjusts hyperparameters to improve model performance and generalization. BIC-SGODL performs well with biomedical image dataset simulations. Thus, medical picture cancer diagnosis may be automated using BIC-SGODL.
An efficient data compression and storage technique with key management authentication in cloud space
Authors: Pinnapati Surekha , Shivanna Prakasha
Published: July 2024

Cloud computing is one of the promising technologies that offers cost-effective choices for processing and storing the huge volumes of data. In today’s world, data is the most important asset that one can have but it needs to be handled and protected properly. Portability of data can be increased by reducing the size of the data to be stored because of the limited storage space. As a result, data compression has arisen significantly. Data compression is a useful technique for reducing data size and increasing the effectiveness of data transit and storage. Data compression reduces the size of a data file while using lossy or lossless compression. One of the newest techniques for data compression is data duplication, which can reduce the amount of data saved while removing unnecessary data and maintaining an exact copy of the data. This analysis presents an Efficient data compression and storage technique with key management authentication in cloud space. This approach uses Regressive probabilistic key encryption (RPKE) to encrypt the cloud data and Lempel-Ziv-77-Huffman coding (LZ77-HM) is used to compress the huge amounts of cloud data. The Performance of presented approach is evaluated in terms of compression ratio and compression rate.
A particle swarm optimization inspired global and local stability driven predictive load balancing strategy
Authors: Dey Niladri Sekhar , Raju Sangaraju Hrushi Kesava
Published: July 2024

In distributed systems and parallel computing, optimal load balancing is difficult. These abstract addresses load balancing in distributed situations, highlighting current solutions' flaws and emphasizing the need for new ones. Load balancing research includes centralized and distributed algorithms, heuristics, and predictive models. Despite various successful methods, workload adaptability, overhead reduction, and scaling to large systems remain unresolved. This study proposes a particle swarm optimization (PSO) load balancing method that considers global and local stability considerations. The proposed method uses PSO principles to balance exploration and exploitation and allocate resources among distributed nodes. Predictive components improve preventative load management by predicting workload changes. Global and local load balancing stability criteria distinguish this study. The recommended method considers global system-wide performance indicators, local node-level characteristics, and micro-level stability to maximize system efficiency. A dual-focus technique distinguishes the proposed load balancing strategy from others, solving dynamic distributed system challenges. The study examines load balancing system advances and suggests improvements and further research. More accurate prediction modeling, stability measures, and application-specific enhancements may be studied in the future. Experimental validation and real-world implementation of the recommended approach are necessary to determine its practicality and ability to handle modern distributed computing systems.
A lightweight distributed ELM-based security framework for the internet of vehicles
Authors: Karimy Aziz Ullah , Reddy Putta Chandrasekhar
Published: July 2024

The fast growth of internet of vehicles (IoV) has created a new area of connectedness, with promising safety and efficiency in transportation. However, this advancement in vehicle technology has come with significant cybersecurity risks, specifically through control area network (CAN) protocol and other communication techniques within vehicles. This experimental study suggests a machine learning (ML) based security approach based on the extreme learning machine (ELM) algorithm to address these challenges. Unlike customary neural networks, ELM is known for its fast processing, minimal training time, and high accuracy, making it preferably suitable for dynamic IoV environments. The methodology involves data preprocessing, feature selection, and employing ELM for attack classification; the algorithm’s performance is evaluated using CARHacking, NSL-KDD, and EdgeIIoT datasets. We also examine the significance of distributed processing to enhance the computational efficiency of the model, obtaining 89% accuracy in 3 ms run-time for external networks, and 83% accuracy with 9 ms run-time for intra-vehical networks. This newly proposed security mechanism using ELM shows very accurate results in detecting intrusions with a high recall rate and reduced computation time through distributed processing.
Clinical named entity extraction for extracting information from medical data
Authors: Kuttaiyapillai Dhanasekaran , Madasamy Anand , Ayyavu Shobanadevi , Sayeed Md Shohel
Published: July 2024

Clinical named entity extraction (NER) based on deep learning gained much attention among researchers and data analysts. This paper proposes a NER approach to extract valuable Parkinson’s disease-related information. To develop an effective NER method and to handle problems in disease data analytics, a unique NER technique applies a “recognize-map-extract (RME)” mechanism and aims to deal with complex relationships present in the data. Due to the fast-growing medical data, there is a challenge in the development of suitable deep-learning methods for NER. Furthermore, the traditional machine learning approaches rely on the time-consuming process of creating corpora and cannot extract information for specific needs and locations in certain situations. This paper presents a clinical NER approach based on a convolutional neural network (CNN) for better use of specific features around medical entities and analyzes the performance of the proposed approach through fine-tuning NER with effective pre-training on the BC5CDR dataset. The proposed method uses annotation of entities for various medical concepts. The second stage develops a clinically NER method. This proposed method shows interesting results on the performance measures achieving a precision of 92.57%, recall of 92.22%, and F1- measure of 91.6%.
Development of a machine learning algorithm for fake news detection
Authors: Sia Abdullah Nur Atiqah , Aniza Rusli Nur Ida , Yuslee Nurshaheeda Shazlin
Published: July 2024

With the extensive technological advancements and expansion, the persistent issues regarding the creation and rapid dissemination of fake news have become a prevalent and recurrent concern. The manipulation of news content has critical repercussions, such as causing public mistrust, fear, harm, and misinformation. Addressing that, this study developed a supervised machine learning algorithm that can accurately classify social media data as fake news. The methodology of the proposed fake news detection model involved five main components: data acquisition from Twitter, data preprocessing, data transformation, model development using Naïve Bayes, decision tree, and support vector machine (SVM) and model evaluation using accuracy, precision, recall and F1-score. The results revealed that decision tree recorded the highest accuracy for both textual data (100%) and metadata (94.54%) and consistently outperformed both Naïve Bayes and SVM in terms of precision, recall, and F1-score metrics, with a score of 100% for the classification of textual data-based datasets. Regarding the metadata-based classification, decision tree also demonstrated excellent performance, with the highest F1-score of 94% for fake news data. Meanwhile, SVM exhibited the highest precision and recall performance for the metadata-based classification. Overall, the application of the decision tree classifier was deemed the most effective in Twitter fake news detection.
Microarray classification using genetic algorithm and latin hypercube sampling
Authors: Awangditama Bangun Rizki , Suciati Nanik
Published: July 2024

Cancer, the second leading cause of global death, requires advanced diagnostic technology. Microarray gene expression technology plays an important role in comprehensively analyzing the genetic aspects of cancer. However, challenges such as high-dimensional attributes, limited samples, and varying gene presence rates hinder the accurate classification of microarray data. This study proposes a model that uses latin hypercube sampling (LHS) in genetic algorithms (GA) for Feature Selection in microarray data classification. LHS makes the chromosome samples in the initial population of GAs representative and diverse. The study used three microarray datasets with different numbers of features and classes. The results reveal that first, the use of GA alone tends to limit the exploration of the resulting feature space, while the use of LHS can expand the feature selection possibilities in the context of feature selection. Secondly, this study shows that microarray classification using GA with LHS (GALHS) consistently outperforms other feature selection methods such as based correlation features (BCF), principal component analysis (PCA), relief, and lasso. Thus, this research contributes to feature selection by applying LHS and GA to optimize the performance of microarray data classification models.
Cell nuclei image segmentation using U-Net and DeepLabV3+ with transfer learning and regularization
Authors: Koishiyeva Dina , Sydybayeva Madina , Belginova Saule , Yeskendirova Damelya , Azamatova Zhanerke , Kalpebayev Azamat , Beketova Gulzhanat
Published: July 2024

Semantic nuclei segmentation is a challenging area of computer vision. Accurate nuclei segmentation can help medics in diagnosing many diseases. Automatic nuclei segmentation can help medics in diagnosing many diseases such as cancer by providing automatic tissue analysis. Deep learning algorithms allow automatic feature extraction from medical images, however, hematoxylin and eosin (H&E) stained images are challenging due to variability in staining and textures. Using pre-trained models in deep learning speeds up development and improves their performance. This paper compares Deeplabv3+ and U-Net deep learning methods with the pre-trained models ResNet-50 and EfficientNetB4 embedded in their architecture. In addition, different regularization and dropout parameters are applied to prevent overtraining. The experiment was conducted on the PanNuke dataset consisting of nearly 8,000 histological images and annotated nuclei. As a result, the ResNet50-based DeepLabV3+ model with L2 regularization of 0.02 and dropout of 0.7 showed efficiency with dice coefficient (DCS) of 0.8356, intersection over union (IOU) of 0.7280, and loss of 0.3212 on the test set.
IPO Studies: The Effect of Corporate Governance Structure on Underpricing With Underwriter Reputation as a Moderating Variable
Authors: Dara Audina  , Sri Kurnia  , Raja Yulianita Sarazwati  , Chartady Rachmad , Marina Lidya
Published: July 2024

The purpose of this study is to determine the influence of corporate governance structure on underpricing in companies that conduct Initial Public Offering (IPO) on the Indonesia Stock Exchange, as well as to determine the reputation of underwriters moderating the influence of corporate governance structure on underpricing. The research method used is quantitative method. The population in this study was 218 companies and the sample obtained after using purposive sampling was 167 companies. Data analysis techniques in this study are descriptive statistical analysis, classical assumption test, multiple linear regression analysis, and hypothesis test using EViews 12 software. The results of this study show that the size of the board of commissioners has a negative and significant effect on underpricing. Meanwhile, the independence of the board of commissioners and institutional ownership do not have a significant effect on underpricing in companies that conduct an Initial Public Offering (IPO) on the Indonesia Stock Exchange. It can be concluded that the reputation of the underwriter is unable to moderate the influence of the size of the board of commissioners, the independence of the board of commissioners, and institutional ownership on underpricing. In addition, an Adjusted R-squared value of 0.078222 means that 7.82% of the underpricing rate is influenced by the size of the board of commissioners, the independence of the board of commissioners, institutional ownership, and the reputation of underwriters, while the rest is influenced by other variables that are not included in this study.
A 3-Dimensional Ablation At Patient With Tachycardia Right Ventricular Outflow Track (RVOT) Origin: First Experienced in Saiful Anwar General Hospital Malang
Authors: Cik Kahadi , Rizal Ardian
Published: July 2024

Background: Premature ventricular complexes (PVCs) occur commonly in general population. It can occur in patient without structural heart disease as well as those with any form of cardiac disease. Commonly PVCs are asymptomatic, but in patients with frequent PVCs causing palpitations, syncope or dizziness, easily to fatigue, or shortness of breath. PVCs has been connected with a more than two-fold higher risk of cardiovascular events including stroke, mortality and also associated with arrhythmias or cardiomyopathy. Medications can be used to suppress PVCs but when drug therapy is ineffective, PVC ablation is reasonable to eliminate symptoms and treat or prevent PVC-induced cardiomyopathy.
Case Presentation: A hypertension and menopause, 57-year-old woman, frequently complained about palpitation with shortness of breath, dizziness and sometimes near syncope since 2017, occurring both at rest and with mild activities. After serial examinations, found Frequent PVC (39%) with RVOT-Origin and reduced ejection fraction (EF 48%), then conclude with Suspect Tachycardia Induced cardiomyopathies. She performs cardiac electrophysiological study and continue with 3-Dimentional Ablation with Radiofrequency (RFA) at RVOT in Saiful Anwar Hospital, that is The First 3-Dimension Ablation in East Java. 1 year follow up shows no recurrency and successful to improve quality of life of the patient
Discussion: According to guideline, catheter ablation is class I recommendation for patients with symptomatic outflow tract ventricular arrhythmias if medications are ineffective, not tolerated, or patient’s choose
Conclusion: Three-Dimensional Ablation at patient with Right Ventricular Outflow Track Tachycardia, can successfully eliminate PVCs with no recurrency in 1 year follow-up.
Targeting Neuronal GPR65 With Delayed BTB09089 Treatment Improves Neurorehabilitation Following Ischemic Stroke
Authors: Chen Ru , Zhang Meng-Qi , Miao Yu-Lu , Zhang Shu-Han , Cheng Yao , Wang Shao-Shuai , Yin Jing , Yang Cai-Hong , Zhang Hui-Feng , Tang Li , Li Yan , Zhang Yu , Fan Yan-Ying
Published: July 2024

BACKGROUND:

GPR65 (G protein-coupled receptor 65) can sense extracellular acidic environment to regulate pathophysiological processes. Pretreatment with the GPR65 agonist BTB09089 has been proven to produce neuroprotection in acute ischemic stroke. However, whether delayed BTB09089 treatment and neuronal GPR65 activation promote neurorestoration remains unknown.

METHODS:

Ischemic stroke was induced in wild-type (WT) or GPR65 knockout (GPR65
−/−
) mice by photothrombotic ischemia. Male mice were injected intraperitoneally with BTB09089 every other day at days 3, 7, or 14 poststroke. AAV-Syn-GPR65 (adenoassociated virus-synapsin-GPR65) was utilized to overexpress GPR65 in the peri-infarct cortical neurons of GPR65
−/−
and WT mice. Motor function was monitored by grid-walk and cylinder tests. The neurorestorative effects of BTB09089 were observed by immunohistochemistry, Golgi-Cox staining, and Western blotting.

RESULTS:

BTB09089 significantly promoted motor outcomes in WT but not in GPR65
−/−
mice, even when BTB09089 was delayed for 3 to 7 days. BTB09089 inhibited the activation of microglia and glial scar progression in WT but not in GPR65
−/−
mice. Meanwhile, BTB09089 reduced the decrease in neuronal density in WT mice, but this benefit was abolished in GPR65
−/−
mice and reemerged by overexpressing GPR65 in peri-infarct cortical neurons. Furthermore, BTB09089 increased the GAP43 (growth-associated protein-43) and synaptophysin puncta density, dendritic spine density, dendritic branch length, and dendritic complexity by overexpressing GPR65 in the peri-infarct cortical neurons of GPR65
−/−
mice, which was accompanied by increased levels of p-CREB (phosphorylated cAMP-responsive element-binding protein). In addition, the therapeutic window of BTB09089 was extended to day 14 by overexpressing GPR65 in the peri-infarct cortical neurons of WT mice.

CONCLUSIONS:

Our findings indicated that delayed BTB09089 treatment improved neurological functional recovery and brain tissue repair poststroke through activating neuronal GRP65. GPR65 overexpression may be a potential strategy to expand the therapeutic time window of GPR65 agonists for neurorehabilitation after ischemic stroke.
New orchids in the flora of Vietnam VIII (Orchidaceae: Epidendroideae, tribes Malaxideae, Neottieae and Podochileae)
Authors: AVERYANOV LEONID V. , NGUYEN VAN CANH , VUONG TRUONG BA , NURALIEV MAXIM S. , NGUYEN KHANG SINH , MAISAK TATIANA V. , YUDOVA DARIA A. , SAIDOV NIKITA T. , NGUYEN CUONG HUU , NONG DUY VAN
Published: July 2024

The paper presents new data on the orchid diversity of Vietnam obtained mainly in the course of the fieldwork in 2020–2023, as well as from the investigation of the collections stored at the Herbaria of Russia and Vietnam. It includes descriptions of four species (Appendicula spicata, Dendrobium khanhii, D. vietii, Listera anthropophora) and one variety (Eria dacrydium var. cornuta) new to science, as well as reports of 18 species new to the flora of Vietnam (Bulbophyllum conchiferum, B. gibbosum, B. lasiochilum, B. malleolabrum, B. medusae, B. mucronatum, B. reclusum, B. siamense, B. sutepense, Dendrobium deltatum, D. densiflorum, D. lampongense, D. polyanthum, Oberonia maxima, Phreatia sulcata, Podochilus lucescens, Sunipia grandiflora and S. rimannii). In addition, a new name, Dendrobium nobile var. luteoalbum, and a new nomenclatural combination, Eria chienii, are proposed, and lectotypes for the names Bulbophyllum mucronatum, B. sutepense, Dendrobium deltatum and D. nobile var. luteoalbum are designated. For each of the studied species, relevant synonyms, studied specimens, ecology, phenology, distribution, conservation status assessment according to the IUCN Red List Categories and Criteria, brief taxonomic notes, and illustrations are provided. For the newly described taxa, morphological descriptions and notes on etymology are included.
On a small collection of Maeridae Krapp-Schickel, 2008 (Crustacea: Amphipoda) from Gujarat, India
Authors: THACKER DIMPLE , MYERS ALAN A. , TRIVEDI JIGNESHKUMAR N.
Published: July 2024

The present study was based on amphipod specimens collected from the coastal areas of Gujarat state, India. From the collection, four species belonging to the family Maeridae Krapp-Schickel, 2008 were identified. Two species, Ceradomaera plumosa Ledoyer, 1973 and Ceradocus (Denticeradocus) alama Myers & Nithyanandan, 2016 are reported for the first time from Gujarat state and India, respectively. Two more species, Maera gujaratensis sp. nov. and Quadrimaera okha sp. nov. are described as new species. Maera gujaratensis sp. nov. can be differentiated from its closely related congener M. hirondellei Chevreux, 1900 on the basis of shape of male gnathopod 1 coxa and number of setae present on the telson. Quadrimaera okha sp. nov. can be differentiated from its closely related congeners Q. brownorum Hughes, 2015 and Q. caliban (Ruffo, Krapp & Gable, 2000) by the non-lobate anterodistal corner of gnathopod 2 basis, by the shape of the propodus of the male gnathopod 2, and in having a comparatively smaller uropod 3 peduncle to rami ratio.
On some Himalayan species of the genus Mannerheimia Mäklin, 1880 (Coleoptera: Staphylinidae: Omaliinae: Anthophagini)
Authors: SHAVRIN ALEXEY V.
Published: July 2024

Taxonomic and faunistic data for four Himalayan species of the genus Mannerheimia Mäklin, 1880 are presented. Three species are redescribed and illustrated: M. cachemirica Coiffait, 1982 (India: Kashmir), M. emodensa Coiffait, 1977 (Nepal) and M. obscura Coiffait, 1982 (Pakistan, India, Nepal). A new synonymy is established: M. cachemirica = M. vicina Coiffait, 1982 syn. n. The habitus of M. scutellata Coiffait, 1982 (India: Kashmir) is illustrated. Mannerheimia obscura is recorded from Himachal Pradesh (India) and Nepal for the first time; a distributional map of this species is provided.
Genetic and phenotypic diversification in a widespread fish, the Sailfin Molly (Poecilia latipinna)
Authors: Tiedemann Ralph , Riesch Rüdiger , Tomowski Maxi , Havenstein Katja , Schlupp Jan , Berbel-Filho Waldir Miron , Schlupp Ingo
Published: July 2024

Widespread species often experience significant environmental clines over the area they naturally occupy. We investigated a widespread livebearing fish, the Sailfin molly (Poecilia latipinna) combining genetic, life-history, and environmental data, asking how structured populations are. Sailfin mollies can be found in coastal freshwater and brackish habitats from roughly Tampico, Veracruz in Mexico to Wilmington, North Carolina, in the USA. In addition, they are found inland on the Florida peninsula. Using microsatellite DNA, we genotyped 168 individuals from 18 populations covering most of the natural range of the Sailfin molly. We further determined standard life-history parameters for both males and females for these populations. Finally, we measured biotic and abiotic parameters in the field. We found six distinct genetic clusters based on microsatellite data, with very strong indication of isolation by distance. However, we also found significant numbers of migrants between adjacent populations. Despite genetic structuring we did not find evidence of cryptic speciation. The genetic clusters and the migration patterns do not match paleodrainages. Life histories vary between populations but not in a way that is easy to interpret. We suggest a role of humans in migration in the sailfin molly, for example in the form of a ship channel that connects southern Texas with Louisiana which might be a conduit for fish migration.
Postoperative hyperglycemia among adult non-diabetic surgical patients at University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia
Authors: Dejen Eshetu Tesfaye , Workie Misganaw Mengie , Zeleke Tadael Gudayu , Admass Biruk Adie , Melesse Debas Yaregal , Melkie Tadesse Belayneh
Published: July 2024

Background

Postoperative hyperglycemia is associated with morbidity and mortality in non-diabetic surgical patients. However, there is limited information on the extent and factors associated with postoperative hyperglycemia. This study assessed the magnitude and associated factors of postoperative hyperglycemia among non-diabetic adult patients who underwent elective surgery at University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia.

Methods

A facility-based cross-sectional study was conducted among 412 adult patients who underwent elective surgery at University of Gondar Comprehensive Specialized Hospital from April 14 to June 30, 2022 All consecutive postoperative non-diabetic elective surgical patients who were admitted to PACU during the data collection period and who fulfilled inclusion criteria were included in the study until the intended minimum sample size was achieved. And data were collected through interviews using a pretested semi-structured questionnaire. Postoperative hyperglycemia was defined as a blood glucose level of ≥ 140 mg/dl. Multivariable logistic regression was performed to identify the association between postoperative hyperglycemia and independent variables. Variables with a p-value less than 0.05 and a 95% confidence interval (CI) were considered statistically significant.

Results

A total of 405 patients’ data were evaluated with a response rate of 98.3%. The median (IQR) age was 40 (28-52) years. The prevalence of postoperative hyperglycemia was 34.1% (95% CI: 29.4–39.0). Factors significantly associated with postoperative hyperglycemia included being overweight (AOR = 5.45, 95% CI: 2.46-12.0), American Society of Anesthesiologists (ASA) classification II and III (AOR = 2.37, 95% CI: 1.17–4.79), postoperative low body temperature (AOR = 0.18, 95% CI: 0.069–0.48), blood loss ≥ 500 ml (AOR = 2.33, 95% CI: 1.27–4.27), long duration of surgery, mild pain (AOR = 5.17, 95% CI: 1.32–20.4), and moderate pain (AOR = 7.63, 95% CI: 1.811–32.20).

Conclusion and recommendation

One-third of the study participants had postoperative hyperglycemia. Weight, ASA classification, postoperative body temperature, duration of surgery, intraoperative blood loss, and postoperative pain were identified as a modifiable risk factors. Maintaining normal body temperature throughout the procedure, treating postoperative pain, and monitoring and controlling blood glucose level in patients at risk of hyperglycemia is crucial.
Exploring long-term cancer survivors’ care experiences and unmet needs: protocol for a qualitative study
Authors: Speckemeier Christian , Maus K , Bialobrzeski A , Jaspers B , Radbruch L , Hahn S , Wasem J , Grünwald V , Dirksen U , Neumann A
Published: July 2024

Background

The number of cancer survivors has increased in recent decades, and the majority of them suffer from sequelae of their disease and treatment. This study, which is part of the larger research project OPTILATER, aims to explore different aspects of care services for long-term survivors (≥ 5 years after initial cancer diagnosis) in Germany. The study places an emphasis on the situation of people from different age groups, with different socio-demographic and cultural backgrounds, and sexually and gender diverse individuals.

Methods

To investigate experiences related to follow-up care, focus groups (n = 2) will be conducted with members of patient advisory councils and advocacy groups, representatives of communities, healthcare workers and networks, as well as members of Associations of Statutory Health Insurance Physicians. Guided interviews will be carried out with patients and relatives (n = 40) to investigate needs, barriers and obstacles in terms of follow-up care. On this basis, additional focus groups (n = 2) will be carried out to derive possible scenarios for improving the consideration of needs. Focus groups and interviews will follow a semi-structured format and will be analysed content-analytically. Focus groups and interviews will be conducted online, recorded, transcribed, and analysed independently by two persons.

Discussion

The qualitative approach is considered suitable because of the exploratory research aims. The identification of experiences and barriers can reveal disparities and optimization potential in the care of long-term cancer survivors.