bluuebunny/crossref_metadata_2025 huggingface dataset:
Supplementary Table S15 from Oncogenic Drivers and Therapeutic Vulnerabilities in KRAS Wild-Type Pancreatic Cancer
Authors: Singh Harshabad , Keller Rachel B. , Kapner Kevin S. , Dilly Julien , Raghavan Srivatsan , Yuan Chen , Cohen Elizabeth F. , Tolstorukov Michael , Andrews Elizabeth , Brais Lauren K. , da Silva Annacarolina , Perez Kimberly , Rubinson Douglas A. , Surana Rishi , Giannakis Marios , Ng Kimmie , Clancy Thomas E. , Yurgelun Matthew B. , Schlechter Benjamin L. , Clark Jeffrey W. , Shapiro Geoffrey I. , Rosenthal Michael H. , Hornick Jason L. , Nardi Valentina , Li Yvonne Y. , Gupta Hersh , Cherniack Andrew D. , Meyerson Matthew , Cleary James M. , Nowak Jonathan A. , Wolpin Brian M. , Aguirre Andrew J.
Published: November 2023

List Of Germline Variants In Pancreatic Cancer Cohort
Supplementary Figure S7 from Effective Reprogramming of Patient-Derived M2-Polarized Glioblastoma-Associated Microglia/Macrophages by Treatment with GW2580
Authors: Fermi Valentina , Warta Rolf , Wöllner Amélie , Lotsch Catharina , Jassowicz Lena , Rapp Carmen , Knoll Maximilian , Jungwirth Gerhard , Jungk Christine , Dao Trong Philip , von Deimling Andreas , Abdollahi Amir , Unterberg Andreas , Herold-Mende Christel
Published: November 2023

Gene expression profile of BLZ945- and PLX3397-treated pGBM patient-derived organoids.
Supplementary Figure S8 from Oncogenic Drivers and Therapeutic Vulnerabilities in KRAS Wild-Type Pancreatic Cancer
Authors: Singh Harshabad , Keller Rachel B. , Kapner Kevin S. , Dilly Julien , Raghavan Srivatsan , Yuan Chen , Cohen Elizabeth F. , Tolstorukov Michael , Andrews Elizabeth , Brais Lauren K. , da Silva Annacarolina , Perez Kimberly , Rubinson Douglas A. , Surana Rishi , Giannakis Marios , Ng Kimmie , Clancy Thomas E. , Yurgelun Matthew B. , Schlechter Benjamin L. , Clark Jeffrey W. , Shapiro Geoffrey I. , Rosenthal Michael H. , Hornick Jason L. , Nardi Valentina , Li Yvonne Y. , Gupta Hersh , Cherniack Andrew D. , Meyerson Matthew , Cleary James M. , Nowak Jonathan A. , Wolpin Brian M. , Aguirre Andrew J.
Published: November 2023

Germline genetic analysis of KRAS WT versus KRAS mutant pancreatic cancer
Supplementary Figure S7 from Oncogenic Drivers and Therapeutic Vulnerabilities in KRAS Wild-Type Pancreatic Cancer
Authors: Singh Harshabad , Keller Rachel B. , Kapner Kevin S. , Dilly Julien , Raghavan Srivatsan , Yuan Chen , Cohen Elizabeth F. , Tolstorukov Michael , Andrews Elizabeth , Brais Lauren K. , da Silva Annacarolina , Perez Kimberly , Rubinson Douglas A. , Surana Rishi , Giannakis Marios , Ng Kimmie , Clancy Thomas E. , Yurgelun Matthew B. , Schlechter Benjamin L. , Clark Jeffrey W. , Shapiro Geoffrey I. , Rosenthal Michael H. , Hornick Jason L. , Nardi Valentina , Li Yvonne Y. , Gupta Hersh , Cherniack Andrew D. , Meyerson Matthew , Cleary James M. , Nowak Jonathan A. , Wolpin Brian M. , Aguirre Andrew J.
Published: November 2023

Copy number gains in MAPK genes are infrequent in KRAS WT, MAPK Negative pancreatic cancer
Supplementary Figure S11 from Oncogenic Drivers and Therapeutic Vulnerabilities in KRAS Wild-Type Pancreatic Cancer
Authors: Singh Harshabad , Keller Rachel B. , Kapner Kevin S. , Dilly Julien , Raghavan Srivatsan , Yuan Chen , Cohen Elizabeth F. , Tolstorukov Michael , Andrews Elizabeth , Brais Lauren K. , da Silva Annacarolina , Perez Kimberly , Rubinson Douglas A. , Surana Rishi , Giannakis Marios , Ng Kimmie , Clancy Thomas E. , Yurgelun Matthew B. , Schlechter Benjamin L. , Clark Jeffrey W. , Shapiro Geoffrey I. , Rosenthal Michael H. , Hornick Jason L. , Nardi Valentina , Li Yvonne Y. , Gupta Hersh , Cherniack Andrew D. , Meyerson Matthew , Cleary James M. , Nowak Jonathan A. , Wolpin Brian M. , Aguirre Andrew J.
Published: November 2023

Cox proportional hazard regression shows improved OS for KRAS WT pancreatic cancer after adjusting for patient age and disease stage
Supplementary Table 6 from Integration of Genomic Sequencing Drives Therapeutic Targeting of PDGFRA in T-Cell Acute Lymphoblastic Leukemia/Lymphoblastic Lymphoma
Authors: Paolino Jonathan , Dimitrov Boris , Apsel Winger Beth , Sandoval-Perez Angelica , Rangarajan Amith Vikram , Ocasio-Martinez Nicole , Tsai Harrison K. , Li Yuting , Robichaud Amanda L. , Khalid Delan , Hatton Charlie , Gillani Riaz , Polonen Petri , Dilig Anthony , Gotti Giacomo , Kavanagh Julia , Adhav Asmani A. , Gow Sean , Tsai Jonathan , Li Yen Der , Ebert Benjamin L. , Van Allen Eliezer M. , Bledsoe Jacob , Kim Annette S. , Tasian Sarah K. , Cooper Stacy L. , Cooper Todd M. , Hijiya Nobuko , Sulis Maria Luisa , Shukla Neerav N. , Magee Jeffrey A. , Mullighan Charles G. , Burke Michael J. , Luskin Marlise R. , Mar Brenton G. , Jacobson Matthew P. , Harris Marian H. , Stegmaier Kimberly , Place Andrew E. , Pikman Yana
Published: November 2023

Results of bone marrow and PDX panel sequencing from patient 146.
Supplementary Table 1 from RANK Expression as an Independent Predictor for Response to Neoadjuvant Chemotherapy in Luminal-Like Breast Cancer: A Translational Insight from the GeparX Trial
Authors: Link Theresa , Blohmer Jens-Uwe , Schmitt Wolfgang D. , Kuhlmann Jan Dominik , Just Marianne , Untch Michael , Stotzer Oliver , Fasching Peter A. , Thill Marc , Reinisch Mattea , Schneeweiss Andreas , Wimberger Pauline , Seiler Sabine , Huober Jens , Jackisch Christian , Rhiem Kerstin , Hanusch Claus , Sinn Bruno V. , Nekljudova Valentina , Loibl Sibylle , Denkert Carsten
Published: November 2023

Distribution of baseline parameters according to dichotomized RANK expression status (RANKlow vs. RANKhigh)
Supplementary Table 3 from Integration of Genomic Sequencing Drives Therapeutic Targeting of PDGFRA in T-Cell Acute Lymphoblastic Leukemia/Lymphoblastic Lymphoma
Authors: Paolino Jonathan , Dimitrov Boris , Apsel Winger Beth , Sandoval-Perez Angelica , Rangarajan Amith Vikram , Ocasio-Martinez Nicole , Tsai Harrison K. , Li Yuting , Robichaud Amanda L. , Khalid Delan , Hatton Charlie , Gillani Riaz , Polonen Petri , Dilig Anthony , Gotti Giacomo , Kavanagh Julia , Adhav Asmani A. , Gow Sean , Tsai Jonathan , Li Yen Der , Ebert Benjamin L. , Van Allen Eliezer M. , Bledsoe Jacob , Kim Annette S. , Tasian Sarah K. , Cooper Stacy L. , Cooper Todd M. , Hijiya Nobuko , Sulis Maria Luisa , Shukla Neerav N. , Magee Jeffrey A. , Mullighan Charles G. , Burke Michael J. , Luskin Marlise R. , Mar Brenton G. , Jacobson Matthew P. , Harris Marian H. , Stegmaier Kimberly , Place Andrew E. , Pikman Yana
Published: November 2023

Clinical characteristics for patients reported in this study.
Supplementary Figure 2 from Integration of Genomic Sequencing Drives Therapeutic Targeting of PDGFRA in T-Cell Acute Lymphoblastic Leukemia/Lymphoblastic Lymphoma
Authors: Paolino Jonathan , Dimitrov Boris , Apsel Winger Beth , Sandoval-Perez Angelica , Rangarajan Amith Vikram , Ocasio-Martinez Nicole , Tsai Harrison K. , Li Yuting , Robichaud Amanda L. , Khalid Delan , Hatton Charlie , Gillani Riaz , Polonen Petri , Dilig Anthony , Gotti Giacomo , Kavanagh Julia , Adhav Asmani A. , Gow Sean , Tsai Jonathan , Li Yen Der , Ebert Benjamin L. , Van Allen Eliezer M. , Bledsoe Jacob , Kim Annette S. , Tasian Sarah K. , Cooper Stacy L. , Cooper Todd M. , Hijiya Nobuko , Sulis Maria Luisa , Shukla Neerav N. , Magee Jeffrey A. , Mullighan Charles G. , Burke Michael J. , Luskin Marlise R. , Mar Brenton G. , Jacobson Matthew P. , Harris Marian H. , Stegmaier Kimberly , Place Andrew E. , Pikman Yana
Published: November 2023

PDGFRA mutant response to IL-3 withdrawal and ponatinib. Retreatment of mice with PDGFRA mutant PDX leads to response.
Supplementary Figure 1 from Integration of Genomic Sequencing Drives Therapeutic Targeting of PDGFRA in T-Cell Acute Lymphoblastic Leukemia/Lymphoblastic Lymphoma
Authors: Paolino Jonathan , Dimitrov Boris , Apsel Winger Beth , Sandoval-Perez Angelica , Rangarajan Amith Vikram , Ocasio-Martinez Nicole , Tsai Harrison K. , Li Yuting , Robichaud Amanda L. , Khalid Delan , Hatton Charlie , Gillani Riaz , Polonen Petri , Dilig Anthony , Gotti Giacomo , Kavanagh Julia , Adhav Asmani A. , Gow Sean , Tsai Jonathan , Li Yen Der , Ebert Benjamin L. , Van Allen Eliezer M. , Bledsoe Jacob , Kim Annette S. , Tasian Sarah K. , Cooper Stacy L. , Cooper Todd M. , Hijiya Nobuko , Sulis Maria Luisa , Shukla Neerav N. , Magee Jeffrey A. , Mullighan Charles G. , Burke Michael J. , Luskin Marlise R. , Mar Brenton G. , Jacobson Matthew P. , Harris Marian H. , Stegmaier Kimberly , Place Andrew E. , Pikman Yana
Published: November 2023

Thermodynamic cycle of PDGFRA mutations.
Supplementary Figure S6 from Survivin Dendritic Cell Vaccine Safely Induces Immune Responses and Is Associated with Durable Disease Control after Autologous Transplant in Patients with Myeloma
Authors: Freeman Ciara L. , Atkins Reginald , Varadarajan Indumathy , Menges Meghan , Edelman Jeffrey , Baz Rachid , Brayer Jason , Castaneda Puglianini Omar , Ochoa-Bayona Jose Leonel , Nishihori Taiga , Shain Kenneth H. , Shah Bijal , Chen Dung Tsa , Kelley Linda , Coppola Domenico , Alsina Melissa , Antonia Scott , Anasetti Claudio , Locke Frederick L.
Published: November 2023

Supplemental figure S6: Venn diagram illustrating the number of patients with immune responses (total n=13)
Supplementary Figure S2 from Survivin Dendritic Cell Vaccine Safely Induces Immune Responses and Is Associated with Durable Disease Control after Autologous Transplant in Patients with Myeloma
Authors: Freeman Ciara L. , Atkins Reginald , Varadarajan Indumathy , Menges Meghan , Edelman Jeffrey , Baz Rachid , Brayer Jason , Castaneda Puglianini Omar , Ochoa-Bayona Jose Leonel , Nishihori Taiga , Shain Kenneth H. , Shah Bijal , Chen Dung Tsa , Kelley Linda , Coppola Domenico , Alsina Melissa , Antonia Scott , Anasetti Claudio , Locke Frederick L.
Published: November 2023

Supplemental Figure S2. Survivin expression in neoplastic cells by immunohistochemistical staining from pre-treatment bone marrow of enrolled subjects
Data from Oncogenic Drivers and Therapeutic Vulnerabilities in KRAS Wild-Type Pancreatic Cancer
Authors: Singh Harshabad , Keller Rachel B. , Kapner Kevin S. , Dilly Julien , Raghavan Srivatsan , Yuan Chen , Cohen Elizabeth F. , Tolstorukov Michael , Andrews Elizabeth , Brais Lauren K. , da Silva Annacarolina , Perez Kimberly , Rubinson Douglas A. , Surana Rishi , Giannakis Marios , Ng Kimmie , Clancy Thomas E. , Yurgelun Matthew B. , Schlechter Benjamin L. , Clark Jeffrey W. , Shapiro Geoffrey I. , Rosenthal Michael H. , Hornick Jason L. , Nardi Valentina , Li Yvonne Y. , Gupta Hersh , Cherniack Andrew D. , Meyerson Matthew , Cleary James M. , Nowak Jonathan A. , Wolpin Brian M. , Aguirre Andrew J.
Published: November 2023

AbstractPurpose:
Approximately 8% to 10% of pancreatic ductal adenocarcinomas (PDAC) do not harbor mutations in KRAS. Understanding the unique molecular and clinical features of this subset of pancreatic cancer is important to guide patient stratification for clinical trials of molecularly targeted agents.
Experimental Design:
We analyzed a single-institution cohort of 795 exocrine pancreatic cancer cases (including 785 PDAC cases) with a targeted multigene sequencing panel and identified 73 patients (9.2%) with KRAS wild-type (WT) pancreatic cancer.
Results:
Overall, 43.8% (32/73) of KRAS WT cases had evidence of an alternative driver of the MAPK pathway, including BRAF mutations and in-frame deletions and receptor tyrosine kinase fusions. Conversely, 56.2% of cases did not harbor a clear MAPK driver alteration, but 29.3% of these MAPK-negative KRAS WT cases (12/41) demonstrated activating alterations in other oncogenic drivers, such as GNAS, MYC, PIK3CA, and CTNNB1. We demonstrate potent efficacy of pan-RAF and MEK inhibition in patient-derived organoid models carrying BRAF in-frame deletions. Moreover, we demonstrate durable clinical benefit of targeted therapy in a patient harboring a KRAS WT tumor with a ROS1 fusion. Clinically, patients with KRAS WT tumors were significantly younger in age of onset (median age: 62.6 vs. 65.7 years; P = 0.037). SMAD4 mutations were associated with a particularly poor prognosis in KRAS WT cases.
Conclusions:
This study defines the genomic underpinnings of KRAS WT pancreatic cancer and highlights potential therapeutic avenues for future investigation in molecularly directed clinical trials.

See related commentary by Kato et al., p. 4527
Table S4 from Pathogenic Germline Variants in BRCA1/2 and p53 Identified by Real-world Comprehensive Cancer Genome Profiling Tests in Asian Patients
Authors: Satake Tomoyuki , Kondo Shunsuke , Tanabe Noriko , Mizuno Takaaki , Katsuya Yuki , Sato Jun , Koyama Takafumi , Yoshida Tatsuya , Hirata Makoto , Yamamoto Noboru
Published: November 2023

Characteristics of patients with BRCA1/2 variants
Table S3 from Pathogenic Germline Variants in BRCA1/2 and p53 Identified by Real-world Comprehensive Cancer Genome Profiling Tests in Asian Patients
Authors: Satake Tomoyuki , Kondo Shunsuke , Tanabe Noriko , Mizuno Takaaki , Katsuya Yuki , Sato Jun , Koyama Takafumi , Yoshida Tatsuya , Hirata Makoto , Yamamoto Noboru
Published: November 2023

Comparison of pathogenic germline variants
TABLE 1 from Pathogenic Germline Variants in BRCA1/2 and p53 Identified by Real-world Comprehensive Cancer Genome Profiling Tests in Asian Patients
Authors: Satake Tomoyuki , Kondo Shunsuke , Tanabe Noriko , Mizuno Takaaki , Katsuya Yuki , Sato Jun , Koyama Takafumi , Yoshida Tatsuya , Hirata Makoto , Yamamoto Noboru
Published: November 2023

Demographic and clinical characteristics of patients
Blockade of HMGB1 Reduces Inflammation and Pruritus in Atopic Dermatitis by Inhibiting Skin Fibroblasts Activation
Authors: Zhou Lingxuan , Yuan Xiaohui , Hu Yongyan , Zhu Siyu , Li Junxiang , Wang Chenyu , Jing Miao , Liu Lingling , Xu Zhe , Zhao Zuotao , Zhao Jiahui
Published: November 2023

Introduction: Atopic dermatitis (AD) is a chronic inflammatory skin disease characterized by relapsed eczema and serious pruritus. High-mobility group box 1 protein (HMGB1) is a nuclear-binding protein and serves as an alarmin to promote inflammatory responses. Methods: In this study, we established an AD mouse model by topical use of MC903 on ears and then used a specific HMGB1-binding peptide cIY8 and a HMGB1 inhibitor of glycyrrhizin to investigate HMGB1 on fibroblast activation in the pathogenesis of AD-like symptoms. Results: Topical use of cIY8 and oral use of glycyrrhizin significantly improved the MC903-induced AD-like symptoms and pathological changes of the ears and scratching behavior in an AD mouse model; cIY8 treatment inhibited the higher mRNAs of IL-1α, IL-4, IL-5, IL-13, and IL-31 in the ears. In human fibroblasts, HMGB1 caused nuclear translocation of NF-kB, and the nuclear translocation could be inhibited by pre-treatment of HMGB1 with cIY8, suggesting that NF-κB signaling pathway participates in the HMGB1-induced inflammation of AD in fibroblasts and that cIY8 effectively impedes the function of HMGB1. Glycyrrhizin inhibited the Ca2+ signaling induced by ionomycin in mouse primary fibroblasts. The fibroblast-related proteins of α-SMA, Hsp47, and vimentin and the pruritus-related proteins of IL-33 and periostin were increased in the ears of the AD mouse model, the ratio of EdU incorporation became higher in mouse fibroblasts treated with MC903, and the higher proliferation and inflammatory responses of the fibroblasts could be reversed by glycyrrhizin treatment. Conclusions: Fibroblast activation by HMGB1 is one of the critical processes in the development of inflammation and pruritus in the AD mouse model. The specific HMGB1-binding peptide cIY8 and the HMGB1 inhibitor glycyrrhizin inactivate skin fibroblasts to alleviate the inflammation and pruritus in the AD mouse model. Peptide cIY8 may be topically used to treat AD patients in the future.
Clinical Features, Treatment Modalities, and Outcomes of Elderly Thymoma Patients: A Propensity-Matched Study Based on the SEER Database
Authors: Wang Zhidong , Gong Cheng , Zhang Youpu , Wang Min , Chao Ce , Qian Yongxiang , Di Dongmei , Wang Bin
Published: November 2023

Introduction: Thymoma is a common mediastinal tumor, but few studies have been performed in thymoma patients 80 years or older. This study aimed to analyze the clinical features, treatment modalities, and survival outcomes of thymoma patients at least 80 years old and compare these features to those of patients younger than 80 years old. Method: Data from thymoma patients in the Surveillance, Epidemiology and End Results database between 2000 and 2019 were selected. Clinical features, treatment modalities of the two age groups were compared. Survival rates were calculated by the Kaplan-Meier method and the log-rank test was used to compare survival rates between two groups. Propensity score matching was used based on whether surgery was performed. Univariate and multivariate Cox proportional-hazards regression analyses were performed to identify independent prognostic factors. Results: Compared with the younger patients, the patients aged 80 years or older had a similar distribution of Masaoka-Koga tumor stage, a higher proportion of type A thymoma, and a lower recurrence rate in the early stage. In elderly patients after propensity score matching, the overall survival and cancer-specific survival were better in the surgery group with complete resection and compared with patients of different ages, elderly patients showed similar benefit from surgery as younger patients were observed. Conclusion: In thymoma patients aged 80 years or older, surgery still plays an important role in survival outcome. Compared with younger patients, older patients have unique clinical features.
Hierarchal attribute based cryptographic model to handle security services in cloud environment: a new model
Authors: Rajarao Banavathu , Sreenivasulu Meruva
Published: November 2023

The sharing of information in the cloud is a unique element of the environment, but there is a risk that the information may land with the wrong people. To counterattack this problem, security-associated methodologies were used to secure the information that was readily available to clients. Despite the lack of benefits, this provides productive/adaptability and dependability in access control strategies between clients in the sharing of information. The novel hierarchal attribute-based cryptographic security model (NHACSM) is being proposed to provide adaptability, versatility, and access control in sharing information in the appropriate climate. This model allows clients to share information in a hierarchal way, allowing for a productive assessment of access control strategy and improved security. The NHACSM method is used to reduce the total time values for different user instances compared to conventional approaches, for example, attribute-set-based encryption (ASBE), key-policy attribute-based encryption (KP-ABE), and ciphertext-policy attribute-based encryption (CP-ABE). With respect to 10 instances existing methods achieve 2.7, 2.5, and 2.3 respectively, and also compared to 20, 30, 40, and 50 instances, our proposed method is low. The encryption and decryption time evaluation values and performance evaluation of different approaches, ASBE, CP-ABE, were taken into account when increasing the user instance.
Gated recurrent unit decision model for device argumentation in ambient assisted living
Authors: Kumar G. S. Madhan , Prakash S. P. Shiva , Krinkin Kirill
Published: November 2023

The increasing elderly population worldwide is facing a variety of social, physical, and cognitive issues, such as walking problems, falls, and difficulties in performing daily activities. To support elderly people, continuous monitoring and supervision are needed. Due to the busy modern lifestyle of caretakers, taking care of elderly people is difficult. As a result, many elderly people prefer to live independently at home without any assistance. To help such people, an ambient assisted living (AAL) environment is provided that monitors and evaluates the daily activities of elderly individuals. An AAL environment has heterogeneous devices that interact, and exchange information of the activities performed by the users. The devices can be involve in an argumentation about the occurrence of an activity thus leading to generate conflicts. To address this issue, the paper proposes a gated recurrent unit (GRU) learning techniques to facilitate decision-making for device argumentation during activity occurrences. The proposed model is used to initially classify user activities and each sensor value status. Then a novel method is used to identify argumentation among devices for activity occurrences in the classified user activities. Later, the GRU decision making model is used to resolve the argumentation and to identify the target activity that occurred. The result of the proposed model is compared with other existing techniques. The proposed model outperformed the other existing methods with an accuracy of 85.45%, precision of 72.32%, recall of 65.83%, and F1-Score of 60.22%.
Memory built-in self-repair and correction for improving yield: a review
Authors: Sontakke Vijay , Atchina Delsikreo
Published: November 2023

Nanometer memories are highly prone to defects due to dense structure, necessitating memory built-in self-repair as a must-have feature to improve yield. Today’s system-on-chips contain memories occupying an area as high as 90% of the chip area. Shrinking technology uses stricter design rules for memories, making them more prone to manufacturing defects. Further, using 3D-stacked memories makes the system vulnerable to newer defects such as those coming from through-silicon-vias (TSV) and micro bumps. The increased memory size is also resulting in an increase in soft errors during system operation. Multiple memory repair techniques based on redundancy and correction codes have been presented to recover from such defects and prevent system failures. This paper reviews recently published memory repair methodologies, including various built-in self-repair (BISR) architectures, repair analysis algorithms, in-system repair, and soft repair handling using error correcting codes (ECC). It provides a classification of these techniques based on method and usage. Finally, it reviews evaluation methods used to determine the effectiveness of the repair algorithms. The paper aims to present a survey of these methodologies and prepare a platform for developing repair methods for upcoming-generation memories.
Partitioning intensity inhomogeneity colour images via Saliency-based active contour
Authors: Syukri Mazlin Muhammad , Jumaat Abdul Kadir , Embong Rohana
Published: November 2023

Partitioning or segmenting intensity inhomogeneity colour images is a challenging problem in computer vision and image shape analysis. Given an input image, the active contour model (ACM) which is formulated in variational framework is regularly used to partition objects in the image. A selective type of variational ACM approach is better than a global approach for segmenting specific target objects, which is useful for applications such as tumor segmentation or tissue classification in medical imaging. However, the existing selective ACMs yield unsatisfactory outcomes when performing the segmentation for colour (vector-valued) with intensity variations. Therefore, our new approach incorporates both local image fitting and saliency maps into a new variational selective ACM to tackle the problem. The euler-lagrange (EL) equations were presented to solve the proposed model. Thirty combinations of synthetic and medical images were tested. The visual observation and quantitative results show that the proposed model outshines the other existing models by average, with the accuracy of 2.23% more than the compared model and the Dice and Jaccard coefficients which were around 12.78% and 19.53% higher, respectively, than the compared model.
Wireless channel-based ciphering key generation: effect of aging and treatment
Authors: Almamori Aqiel , Adil Abbas Mohammed
Published: November 2023

Key generation for data cryptography is vital in wireless communications security. This key must be generated in a random way so that can not be regenerated by a third party other than the intended receiver. The random nature of the wireless channel is utilized to generate the encryption key. However, the randomness of wireless channels deteriorated over time due to channel aging which casing security threats, particularly for spatially correlated channels. In this paper, the effect of channel aging on the ciphering key generations is addressed. A proposed method to randomize the encryption key each coherence time is developed which decreases the correlation between keys generated at consecutive coherence times. When compared to the conventional method, the randomness improvement is significant at each time interval. The simulation results show that the proposed method improves the randomness of the encrypting keys.
Fire detection using deep learning methods
Authors: Bayegizova Aigulim , Abdikerimova Gulzira , Kaliyeva Samal , Shaikhanova Aigul , Shangytbayeva Gulmira , Sugurova Laura , Sugur Zharkynay , Saimanova Zagira
Published: November 2023

Fire detection is an important task in the field of safety and emergency prevention. In recent years, deep learning methods have shown high efficiency in solving various computer vision problems, including detecting objects in images. In this paper, monitoring wildfires was considered, which allows you to quickly respond to them and prevent their spread using deep learning methods. For the experiment, images from the satellite and images from the FireWatch sensor were taken as initial data. In this work, the deep learning algorithms you only look once (YOLO), convolutional neural network (CNN), and fast recurrent neural network (FastRNN) were considered, which makes it possible to determine the accuracy of a natural fire. As a result of the experiments, an automated fire recognition algorithm using YOLOv4 deep learning methods was created. It is expected that the results of the study will show that deep learning methods can be successfully applied to detect fire in images. This may lead to the development of automated monitoring systems capable of quickly and reliably detecting fire situations, which will help improve safety and reduce the risk of fires.
Affective e-learning approaches, technology and implementation model: a systematic review
Authors: Adebiyi Marion Olubunmi , Adebiyi Abayomi Aduragba , Olaniyan Deborah , Orenyi Bajeh Amos
Published: November 2023

A systematic literature study including articles from 2016 to 2022 was done to evaluate the various approaches, technologies, and implementation models involved in measuring student engagement during learning. The review’s objective was to compile and analyze all studies that investigated how instructors can gauge students’ mental states while teaching and assess the most effective teaching methods. Additionally, it aims to extract and assess expanded methodologies from chosen research publications to offer suggestions and answers to researchers and practitioners. Planning, carrying out the analysis, and publishing the results have all received significant attention in the research approach. The study’s findings indicate that more needs to be done to evaluate student participation objectively and follow their development for improved academic performance. Physiological approaches should be given more support among the alternatives. While deep learning implementation models and contactless technology should interest more researchers. And, the recommender system should be integrated into e-learning system. Other approaches, technologies, and methodology articles, on the other hand, lacked authenticity in conveying student feeling.
The preliminary study of carbon x-change rakyat using blockchain application
Authors: Putro Wahyu Sasongko , Rahmi Nitia , Asditama Raditya Yoga , Akbar Nur Arifin
Published: November 2023

Today’s air pollution is detrimental to the environment, particularly in Indonesia. Carbon dioxide (CO2) and nitrogen oxide (NOx) are present in the atmosphere due to air pollution. Many individuals employ reforestation to lessen the influence of CO2 and NOx gases on the atmosphere. However, in the digitalized era, lowering carbon emissions may also be accomplished through a carbon credit exchange. Thus, in this study we investigate the performance of the carbon x-change rakyat (CXR) based on blockchain platform utilizing the stress test approach. We provided four scenarios with 10,000 to 100,000 transactions evaluated on the CXR blockchain system i.e., transfer, insert, remove, and update. The outcome demonstrates CXR’s effectiveness with 100% success and 0% failure rate based on testing and statistical computations calculation. The mean absolute error (MAE), variance accounted for (VAF), and percent error (PE) are obtained with values ranging from 0.38% to 4.67%. In this study, the transaction per-second (TPS) is used to calculate include error request (IER) and exclude error request (EER) values around 312 to 746 milliseconds (ms). In addition, the TPS of CXR based on blockchain platform is a capability to create and trace database carbon certificate ownership (nonfinancial activity). It means CXR based on the blockchain platform has a fast response to process carbon certificate ownership for transactions across local and international countries in the world.
Human-machine interactions based on hand gesture recognition using deep learning methods
Authors: Zholshiyeva Lazzat , Manbetova Zhanat , Kaibassova Dinara , Kassymova Akmaral , Tashenova Zhuldyz , Baizhumanov Saduakas , Yerzhanova Akbota , Aikhynbay Kulaisha
Published: November 2023

Human interaction with computers and other machines is becoming an increasingly important and relevant topic in the modern world. Hand gesture recognition technology is an innovative approach to managing computers and electronic devices that allows users to interact with technology through gestures and hand movements. This article presents deep learning methods that allow you to efficiently process and classify hand gestures and hand gesture recognition technologies for interacting with computers. This paper discusses modern deep learning methods such as convolutional neural networks (CNN) and recurrent neural networks (RNN), which show excellent results in gesture recognition tasks. Next, the development and implementation of a human-machine interaction system based on hand gesture recognition is discussed. System architectures are described, as well as technical and practical aspects of their application. In conclusion, the article summarizes the research results and outlines the prospects for the development of hand gesture recognition technology to improve human-machine interaction. The advantages and limitations of the technology are analyzed, as well as possible areas of its application in the future.
Deep learning based Arabic short answer grading in serious games
Authors: Soulimani Younes Alaoui , El Achaak Lotfi , Bouhorma Mohammed
Published: November 2023

Automatic short answer grading (ASAG) has become part of natural language processing problems. Modern ASAG systems start with natural language preprocessing and end with grading. Researchers started experimenting with machine learning in the preprocessing stage and deep learning techniques in automatic grading for English. However, little research is available on automatic grading for Arabic. Datasets are important to ASAG, and limited datasets are available in Arabic. In this research, we have collected a set of questions, answers, and associated grades in Arabic. We have made this dataset publicly available. We have extended to Arabic the solutions used for English ASAG. We have tested how automatic grading works on answers in Arabic provided by schoolchildren in 6th grade in the context of serious games. We found out those schoolchildren providing answers that are 5.6 words long on average. On such answers, deep learning-based grading has achieved high accuracy even with limited training data. We have tested three different recurrent neural networks for grading. With a transformer, we have achieved an accuracy of 95.67%. ASAG for school children will help detect children with learning problems early. When detected early, teachers can solve learning problems easily. This is the main purpose of this research.
Stock price forecasting in Indonesia stock exchange using deep learning: a comparative study
Authors: Haryono Agus  Tri , Sarno Riyanarto , Sungkono Kelly Rossa
Published: November 2023

In 2022, the Indonesia stock exchange (IDX) listed 825 companies, making it challenging to identify low-risk companies. Stock price forecasting and price movement prediction are vital issues in financial works. Deep learning has previously been implemented for stock market analysis, with promising results. Because of the differences in architecture and stock issuers in each study report, a consensus on the best stock price forecasting model has yet to be reached. We present a methodology for comparing the performance of convolutional neural networks (CNN), gated recurrent units (GRU), long short-term memory (LSTM), and graph convolutional networks (GCN) layers. The four layers types combination yields 11 architectures with two layers stacked maximum, and the architectures are performance compared in stock price predicting. The dataset consists of open, highest, lowest, closed price, and volume transactions and has 2,588,451 rows from 727 companies in IDX. The best performance architecture was chosen by a vote based on the coefficient of determination (R2), mean squared error (MSE), root mean square error (RMSE), mean absolute percent error (MAPE), and f1-score. TFGRU is the best architecture, producing the finest results on 315 companies with an average score of RMSE is 553.327, MAPE is 0.858, and f1-score is 0.456.
Optimizing olive disease classification through transfer learning with unmanned aerial vehicle imagery
Authors: Raouhi El Mehdi , Lachgar Mohamed , Hrimech Hamid , Kartit Ali
Published: November 2023

Early detection of diseases in growing olive trees is essential for reducing costs and increasing productivity in this crucial economic activity. The quality and quantity of olive oil depend on the health of the fruit, making accurate and timely information on olive tree diseases critical to monitor growth and anticipate fruit output. The use of unmanned aerial vehicles (UAVs) and deep learning (DL) has made it possible to quickly monitor olive diseases over a large area indeed of limited sampling methods. Moreover, the limited number of research studies on olive disease detection has motivated us to enrich the literature with this work by introducing new disease classes and classification methods for this tree. In this study, we present a UAV system using convolutional neuronal network (CNN) and transfer learning (TL). We constructed an olive disease dataset of 14K images, processed and trained it with various CNN in addition to the proposed MobileNet-TL for improved classification and generalization. The simulation results confirm that this model allows for efficient diseases classification, with a precision accuracy achieving 99% in validation. In summary, TL has a positive impact on MobileNet architecture by improving its performance and reducing the training time for new tasks.
Enhancing feature selection with a novel hybrid approach incorporating genetic algorithms and swarm intelligence techniques
Authors: Benghazouani Salsabila , Nouh Said , Zakrani Abdelali , Haloum Ihsane , Jebbar Mostafa
Published: November 2023

Computing advances in data storage are leading to rapid growth in large-scale datasets. Using all features increases temporal/spatial complexity and negatively influences performance. Feature selection is a fundamental stage in data preprocessing, removing redundant and irrelevant features to minimize the number of features and enhance the performance of classification accuracy. Numerous optimization algorithms were employed to handle feature selection (FS) problems, and they outperform conventional FS techniques. However, there is no metaheuristic FS method that outperforms other optimization algorithms in many datasets. This motivated our study to incorporate the advantages of various optimization techniques to obtain a powerful technique that outperforms other methods in many datasets from different domains. In this article, a novel combined method GASI is developed using swarm intelligence (SI) based feature selection techniques and genetic algorithms (GA) that uses a multi-objective fitness function to seek the optimal subset of features. To assess the performance of the proposed approach, seven datasets have been collected from the UCI repository and exploited to test the newly established feature selection technique. The experimental results demonstrate that the suggested method GASI outperforms many powerful SI-based feature selection techniques studied. GASI obtains a better average fitness value and improves classification performance.
Multi-task learning using non-linear autoregressive models and recurrent neural networks for tide level forecasting
Authors: Nikentari Nerfita , Wei Hua-Liang
Published: November 2023

Tide level forecasting plays an important role in environmental management and development. Current tide level forecasting methods are usually implemented for solving single task problems, that is, a model built based on the tide level data at an individual location is only used to forecast tide level of the same location but is not used for tide forecasting at another location. This study proposes a new method for tide level prediction at multiple locations simultaneously. The method combines nonlinear autoregressive moving average with exogenous inputs (NARMAX) model and recurrent neural networks (RNNs), and incorporates them into a multi-task learning (MTL) framework. Experiments are designed and performed to compare single task learning (STL) and MTL with and without using non-linear autoregressive models. Three different RNN variants, namely, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are employed together with non-linear autoregressive models. A case study on tide level forecasting at many different geographical locations (5 to 11 locations) is conducted. Experimental results demonstrate that the proposed architectures outperform the classical single-task prediction methods.
Falácias pseudocéticas e pseudocientíficas do “Contradossiê das evidências sobre a homeopatia”
Authors:   Universidade de São Paulo. Faculdade de Medicina, Teixeira Marcus Zulian
Published: November 2023

Em vista da homeopatia estar fundamentada em pressupostos científicos distintos dos empregados pela prática médica convencional, frequentemente, é alvo de críticas infundadas disseminadas por indivíduos que, de forma sistemática, negam os princípios homeopáticos e qualquer evidência científica que os comprovem, por estarem envoltos em um negacionismo dogmático que impede uma análise correta e isenta de preconceitos. São pseudocéticos disfarçados em pseudocientistas. Para esclarecer médicos, pesquisadores, profissionais de saúde e a população em geral, desmistificando posturas dogmáticas culturalmente arraigadas e a falácia pseudocética de que "não existem evidências científicas para homeopatia", em 2017, a Câmara Técnica de Homeopatia do Conselho Regional de Medicina do Estado de São Paulo (CT-Homeopatia, Cremesp) elaborou e publicou o "Dossiê Especial Evidências Científicas em Homeopatia", disponibilizado livremente na Revista de Homeopatia (São Paulo), periódico científico da Associação Paulista de Homeopatia (APH). Englobando nove revisões narrativas em diversas linhas de pesquisa homeopática (histórico-social, educação médica, farmacológica, básicas, clínica, segurança do paciente e patogenética) e dois ensaios clínicos randomizados e placebos-controlados desenvolvidos por membros da CT-Homeopatia, contendo centenas de artigos científicos publicados em inúmeras revistas científicas indexadas e revisadas por pares, este dossiê destaca para a classe médica e científica, bem como para o público em geral, o 'estado da arte' da pesquisa em homeopatia.
Trigonometria: dos conceitos básicos até problemas olímpicos
Authors:   Universidade de São Paulo. Faculdade de Zootecnia e Engenharia de Alimentos, Linares Juan López , Bruno-Alfonso Alexys ,   Universidade de São Paulo. Faculdade de Zootecnia e Engenharia de Alimentos
Published: November 2023

A trigonometria é importante porque fornece uma base para entender e analisar ângulos, formas e relações espaciais. Suas aplicações são amplas e se estendem a vários campos, tornando-se um ramo essencial da matemática em contextos teóricos e práticos. Este material didático foi utilizado durante algumas das aulas do curso "Geometria Olímpica com GeoGebra" para professores de Matemática do Ensino Fundamental e Médio de todo o Brasil. O texto conta com 40 figuras que facilitam acompanhar a resolução. Todas têm como complemento links para os gráficos interativos no site do GeoGebra e, vários, a resolução em vídeo do YouTube. A discussão é organizada em quatro capítulos: Fundamentos de trigonometria; Construções, exercícios e desafios; Conjugados Isogonais; Problemas de olimpíadas internacionais. O diferencial na utilização do GeoGebra está baseado na disponibilidade gratuita do software, tanto online como aplicativos para computadores e celulares. As construções geométricas podem ser feitas de forma dinâmica, onde exploram-se diversas configurações de um mesmo problema. O GeoGebra serve tanto como calculadora gráfica e numérica, utilizada para a verificação, como ferramenta para a apresentação, passo a passo, de uma demonstração rigorosa. O GeoGebra também convida o leitor a interagir, a pôr as mão na massa.
Cooperative Response to Endocardial Notch Reveals Interaction With Hippo Pathway
Authors: Luna-Zurita Luis , Flores-Garza Brenda Giselle , Grivas Dimitrios , Siguero-Álvarez Marcos , de la Pompa José Luis
Published: November 2023

BACKGROUND:

The endocardium is a crucial signaling center for cardiac valve development and maturation. Genetic analysis has identified several human endocardial genes whose inactivation leads to bicuspid aortic valve formation and calcific aortic valve disease, but knowledge is very limited about the role played in valve development and disease by noncoding endocardial regulatory regions and upstream factors.

METHODS:

We manipulated Notch signaling in mouse embryonic endocardial cells by short-term and long-term coculture with OP9 stromal cells expressing Notch ligands and inhibition of Notch activity. We examined the transcriptional profile and chromatin accessibility landscape for each condition, integrated transcriptomic, transcription factor occupancy, chromatin accessibility, and proteomic datasets. We generated in vitro and in vivo models with CRISPR-Cas9-edited deletions of various noncoding regulatory elements and validated their regulatory potential.

RESULTS:

We identified primary and secondary transcriptional responses to Notch ligands in the mouse embryonic endocardium, and a NOTCH-dependent transcriptional signature in valve development and disease. By defining the changes in the chromatin accessibility landscape and integrating with the landscape in developing mouse endocardium and adult human valves, we identify potential noncoding regulatory elements, validated selected candidates, propose interacting cofactors, and define the timeframe of their regulatory activity. Additionally, we found cooperative transcriptional repression with Hippo pathway by inhibiting nuclear Yap (Yes-associated protein) activity in the endocardium during cardiac valve development.

CONCLUSIONS:

Sequential Notch-dependent transcriptional regulation in the embryonic endocardium involves multiple factors. Notch activates certain noncoding elements through these factors and simultaneously suppresses elements that could hinder cardiac valve development and homeostasis. Biorxviv:
https://www.biorxiv.org/content/10.1101/2023.03.23.533882v1.full
.
Nitric Oxide Modulates Ca
2+
Leak and Arrhythmias via S-Nitrosylation of CaMKII
Authors: Power Amelia S. , Asamudo Esther U. , Worthington Luke P.I. , Alim Chidera C. , Parackal Raquel E. , Wallace Rachel S. , Ebenebe Obialunanma V. , Heller Brown Joan , Kohr Mark J. , Bers Donald M. , Erickson Jeffrey R.
Published: November 2023

BACKGROUND:

Nitric oxide (NO) has been identified as a signaling molecule generated during β-adrenergic receptor stimulation in the heart. Furthermore, a role for NO in triggering spontaneous Ca
2+
release via
S
-nitrosylation of CaMKIIδ (Ca
2+
/calmodulin kinase II delta) is emerging. NO donors are routinely used clinically for their cardioprotective effects on the heart, but it is unknown how NO donors modulate the proarrhythmic CaMKII to alter cardiac arrhythmia incidence. We test the role of
S
-nitrosylation of CaMKIIδ at the Cysteine-273 inhibitory site and cysteine-290 activating site in cardiac Ca
2+
handling and arrhythmogenesis before and during β-adrenergic receptor stimulation.

METHODS:

We measured Ca
2+
-handling in isolated cardiomyocytes from C57BL/6J wild-type (WT) mice and mice lacking CaMKIIδ expression (CaMKIIδ-KO) or with deletion of the
S
-nitrosylation site on CaMKIIδ at cysteine-273 or cysteine-290 (CaMKIIδ-C273S and -C290A knock-in mice). Cardiomyocytes were exposed to NO donors,
S
-nitrosoglutathione (GSNO; 150 μM), sodium nitroprusside (200 μM), and β-adrenergic agonist isoproterenol (100 nmol/L).

RESULTS:

Both WT and CaMKIIδ-KO cardiomyocytes responded to isoproterenol with a full inotropic and lusitropic Ca
2+
transient response as well as increased Ca
2+
spark frequency. However, the increase in Ca
2+
spark frequency was significantly attenuated in CaMKIIδ-KO cardiomyocytes. The protection from isoproterenol-induced Ca
2+
sparks and waves was mimicked by GSNO pretreatment in WT cardiomyocytes but lost in CaMKIIδ-C273S cardiomyocytes. When GSNO was applied after isoproterenol, this protection was not observed in WT or CaMKIIδ-C273S but was apparent in CaMKIIδ-C290A. In Langendorff-perfused isolated hearts, GSNO pretreatment limited isoproterenol-induced arrhythmias in WT but not CaMKIIδ-C273S hearts, while GSNO exposure after isoproterenol sustained or exacerbated arrhythmic events.

CONCLUSIONS:

We conclude that prior
S
-nitrosylation of CaMKIIδ at cysteine-273 can limit subsequent β-adrenergic receptor–induced arrhythmias, but that
S
-nitrosylation at cysteine-290 might worsen or sustain β-adrenergic receptor–induced arrhythmias. This has important implications for the administration of NO donors in the clinical setting.
Prey size preference in the tokay gecko (Gekko gecko)
Authors: Probst Anja , Ringler Eva , Szabo Birgit
Published: November 2023

The optimal foraging theory posits that animals aim to maximize energy intake while minimizing predation and handling costs during foraging. Most observed animal behaviour supports this theory, but occasional deviations provide insights into the ecological factors that shape foraging decisions. We tested prey-size preference using a two-choice test between different prey sizes in tokay geckos. We expected geckos to prefer larger prey and decision latencies to be longer when discrimination was more difficult and when small prey was offered. Geckos preferred larger prey when the size difference was large, although decision latency remained consistent. This aligns with prior research on sit-and-wait predators. Together with previous findings showing freezing behaviour after prey capture in tokay geckos, our findings suggest a strong influence of predator avoidance on foraging decisions opening up a new avenue for future research investigating the link between decision making and predator avoidance in tokay geckos.