bluuebunny/crossref_metadata_2025 huggingface dataset:
Table. S4 from FZ-AD005, a Novel DLL3-Targeted Antibody–Drug Conjugate with Topoisomerase I Inhibitor, Shows Potent Antitumor Activity in Preclinical Models
Authors: Guo Qingsong , Gao Bei , Song Ruiwen , Li Weinan , Zhu Shulei , Xie Qian , Lou Sensen , Wang Lei , Shen Jiafei , Zhao Teng , Zhang Yifan , Wu Jinsong , Lu Wei , Yang Tong
Published: October 2024

Supplementary Table. S4
Table S2 from FZ-AD005, a Novel DLL3-Targeted Antibody–Drug Conjugate with Topoisomerase I Inhibitor, Shows Potent Antitumor Activity in Preclinical Models
Authors: Guo Qingsong , Gao Bei , Song Ruiwen , Li Weinan , Zhu Shulei , Xie Qian , Lou Sensen , Wang Lei , Shen Jiafei , Zhao Teng , Zhang Yifan , Wu Jinsong , Lu Wei , Yang Tong
Published: October 2024

Supplementary Table. S2
Figure S14 from PKMYT1 Is a Marker of Treatment Response and a Therapeutic Target for CDK4/6 Inhibitor-Resistance in ER+ Breast Cancer
Authors: Chen Anran , Kim Beom-Jun , Mitra Aparna , Vollert Craig T. , Lei Jonathan T. , Fandino Diana , Anurag Meenakshi , Holt Matthew V. , Gou Xuxu , Pilcher Jacob B. , Goetz Matthew P. , Northfelt Donald W. , Hilsenbeck Susan G. , Marshall C. Gary , Hyer Marc L. , Papp Robert , Yin Shou-Yun , De Angelis Carmine , Schiff Rachel , Fuqua Suzanne A.W. , Ma Cynthia X. , Foulds Charles E. , Ellis Matthew J.
Published: October 2024

Synergy and viability analysis of RP-6306 and gemcitabine combination in MCF7 parental, EDR, and EDR Palbo-R cells
Supplementary Table S2 from Blocking M2-Like Macrophage Polarization Using Decoy Oligodeoxynucleotide-Based Gene Therapy Prevents Immune Evasion for Pancreatic Cancer Treatment
Authors: Chen Chang-Jung , Wang Hao-Chen , Hou Ya-Chin , Wu Yi-Ying , Shieh Chi-Chang , Shan Yan-Shen
Published: October 2024

Supplementary Table S2. Sequences of qPCR primer in this study (H: human)
Supplementary Figure S2 from Blocking M2-Like Macrophage Polarization Using Decoy Oligodeoxynucleotide-Based Gene Therapy Prevents Immune Evasion for Pancreatic Cancer Treatment
Authors: Chen Chang-Jung , Wang Hao-Chen , Hou Ya-Chin , Wu Yi-Ying , Shieh Chi-Chang , Shan Yan-Shen
Published: October 2024

Supplementary Figure S2. IL-4 levels were increased in the co-culture condition medium of U937 cells and PANC-1 cells. The co-culture condition medium of U937 cells and PANC-1 cells were collected at 0, 12, and 72 hours. ELISA was performed to detect the concentrations of IL-4. The bar graphs depict relative protein expression. *, p < 0.05; ***, p < 0.001, significant differences between groups. Data represent the mean of 3 independent experiments.
Figure S6 from AKT Inhibition Sensitizes to Polo-Like Kinase 1 Inhibitor Onvansertib in Prostate Cancer
Authors: Nouri Mannan , Varkaris Andreas , Ridinger Maya , Dalrymple Susan L. , Dennehy Christopher M. , Isaacs John T. , Einstein David J. , Brennen W.N. , Balk Steven P.
Published: October 2024

Secondary screen synergy analysis results.
Supplemental Figure S2 from Discovery of Monovalent Direct Degraders of BRD4 that Act via the Recruitment of DCAF11
Authors: Parker Gregory S. , Toth Julia I. , Fish Sarah , Blanco Gabrielle , Kampert Taylor , Li Xiaoming , Yang Linette , Stumpf Craig R. , Steadman Kenneth , Jamborcic Aleksandar , Chien Stephen , Daniele Elizabeth , Dearie Alejandro , Leriche Geoffray , Bailey Simon , Thompson Peggy A.
Published: October 2024

Supplemental Figure S2 shows PLX-3618 binding data to both bromodomains of BRD4
Supplementary Table S5 from The SOS1 Inhibitor MRTX0902 Blocks KRAS Activation and Demonstrates Antitumor Activity in Cancers Dependent on KRAS Nucleotide Loading
Authors: Sudhakar Niranjan , Yan Larry , Qiryaqos Fadia , Engstrom Lars D. , Laguer Jade , Calinisan Andrew , Hebbert Allan , Waters Laura , Moya Krystal , Bowcut Vickie , Vegar Laura , Ketcham John M. , Ivetac Anthony , Smith Christopher R. , Lawson J. David , Rahbaek Lisa , Clarine Jeffrey , Nguyen Natalie , Saechao Barbara , Parker Cody , Elliott Adam J. , Vanderpool Darin , He Leo , Hover Laura D. , Fernandez-Banet Julio , Coma Silvia , Pachter Jonathan A. , Hallin Jill , Marx Matthew A. , Briere David M. , Christensen James G. , Olson Peter , Haling Jacob , Khare Shilpi
Published: October 2024

Supplementary Table S5 details the antitumor activity of MRTX0902 and avutometinib in human KRAS-MAPK pathway mutant tumor cell line-derived xenograft models.
Supplementary Table S4 from The SOS1 Inhibitor MRTX0902 Blocks KRAS Activation and Demonstrates Antitumor Activity in Cancers Dependent on KRAS Nucleotide Loading
Authors: Sudhakar Niranjan , Yan Larry , Qiryaqos Fadia , Engstrom Lars D. , Laguer Jade , Calinisan Andrew , Hebbert Allan , Waters Laura , Moya Krystal , Bowcut Vickie , Vegar Laura , Ketcham John M. , Ivetac Anthony , Smith Christopher R. , Lawson J. David , Rahbaek Lisa , Clarine Jeffrey , Nguyen Natalie , Saechao Barbara , Parker Cody , Elliott Adam J. , Vanderpool Darin , He Leo , Hover Laura D. , Fernandez-Banet Julio , Coma Silvia , Pachter Jonathan A. , Hallin Jill , Marx Matthew A. , Briere David M. , Christensen James G. , Olson Peter , Haling Jacob , Khare Shilpi
Published: October 2024

Supplementary Table S4 shows the antitumor activity of MRTX0902 and osimertinib in human EGFR-mutant tumor cell line-derived xenograft models.
Figure S4 from NK Receptor Signaling Lowers TCR Activation Threshold, Enhancing Selective Recognition of Cancer Cells by TAA-Specific CTLs
Authors: Dong Bowen , Obermajer Nataša , Tsuji Takemasa , Matsuzaki Junko , Bonura Cindy M. , Sander Cindy , Withers Henry , Long Mark D. , Chavel Colin , Olejniczak Scott H. , Minderman Hans , Kirkwood John M. , Edwards Robert P. , Storkus Walter J. , Romero Pedro , Kalinski Pawel
Published: October 2024

NKRs assist CTLs to recognize and conjugate cancer cells expressing low-level MHCI/peptide complexes.
Figure S2 from Atypical B Cells Promote Cancer Progression and Poor Response to Bacillus Calmette-Guérin in Non–Muscle Invasive Bladder Cancer
Authors: Yolmo Priyanka , Rahimi Sadaf , Chenard Stephen , Conseil Gwenaëlle , Jenkins Danielle , Sachdeva Kartik , Emon Isaac , Hamilton Jake , Xu Minqi , Rangachari Manu , Michaud Eva , Mansure Jose J. , Kassouf Wassim , Berman David M. , Siemens David R. , Koti Madhuri
Published: October 2024

Gating strategy for identifying different subsets of T cells.
Supplemental Table S7 from Machine Learning–Directed Conversion of Glioblastoma Cells to Dendritic Cell–Like Antigen-Presenting Cells as Cancer Immunotherapy
Authors: Liu Tianyi , Jin Dan , Le Son B. , Chen Dongjiang , Sebastian Mathew , Riva Alberto , Liu Ruixuan , Tran David D.
Published: October 2024

Top 20 Predicted master cell fate determinants. Yellow-highlighted CFDs are predicted by nSCORE. (Supporting the Materials and Methods Section)
Supplemental Table S4 from Machine Learning–Directed Conversion of Glioblastoma Cells to Dendritic Cell–Like Antigen-Presenting Cells as Cancer Immunotherapy
Authors: Liu Tianyi , Jin Dan , Le Son B. , Chen Dongjiang , Sebastian Mathew , Riva Alberto , Liu Ruixuan , Tran David D.
Published: October 2024

12 independent input parameters for nSCORE (Supporting the Materials and Methods Section)
Supplemental Figure S22 from Machine Learning–Directed Conversion of Glioblastoma Cells to Dendritic Cell–Like Antigen-Presenting Cells as Cancer Immunotherapy
Authors: Liu Tianyi , Jin Dan , Le Son B. , Chen Dongjiang , Sebastian Mathew , Riva Alberto , Liu Ruixuan , Tran David D.
Published: October 2024

The conversion of human GBM cells and glioma stem cells (GSC) to iDCs-APCs. (Supporting Main Figure 7)
Supplemental Figure S20 from Machine Learning–Directed Conversion of Glioblastoma Cells to Dendritic Cell–Like Antigen-Presenting Cells as Cancer Immunotherapy
Authors: Liu Tianyi , Jin Dan , Le Son B. , Chen Dongjiang , Sebastian Mathew , Riva Alberto , Liu Ruixuan , Tran David D.
Published: October 2024

Expression of PD-L1 in reprogrammed human GBM cells and GSCs. (Supporting Main Figure 7)
Supplemental Figure S2 from Machine Learning–Directed Conversion of Glioblastoma Cells to Dendritic Cell–Like Antigen-Presenting Cells as Cancer Immunotherapy
Authors: Liu Tianyi , Jin Dan , Le Son B. , Chen Dongjiang , Sebastian Mathew , Riva Alberto , Liu Ruixuan , Tran David D.
Published: October 2024

GeneRep-generated reference networks in GBM and breast cancer(Supporting the Materials and Methods Section)
Supplementary Figure 4 from Intermittent MEK Inhibition with GITR Costimulation Rescues T-cell Function for Increased Efficacy with CTLA-4 Blockade in Solid Tumor Models
Authors: Dong Lauren , Choi Hyejin , Budhu Sadna , Schulze Isabell , Verma Svena , Mangarin Levi M. , Estrada Nevarro Valeria , Mehanna Nezar , Khan Jonathan F. , Venkatesh Divya , Thach Daniel , Rosen Neal , Wolchok Jedd D. , Merghoub Taha
Published: October 2024

Supplementary Figure 4. Intermittent CKI27 treatment partially relieves suppressive effects of MEK inhibition on T cell proliferation, cytokine production, and effector function. (A-C) Human PBMCs were labelled with CTV, sub-optimally stimulated with 1:25 or 1:100 CD3/CD28 Dynabeads, and treated with DMSO, continuous CKI27 (96hr) or washout CKI27 (24hr on, 72hr off); n=2-3. (A) Proliferation fold change of CTVlow CD8+ and CD4+ T cells was calculated by normalizing to DMSO. (B) FACS analysis of co-inhibitory, co-stimulatory, and activation markers on CD8+ and CD4+ T cells. Heatmaps represent fold changes of positive percentages of each marker normalized to DMSO. (C) Cytokine analysis of supernatants collected from all PBMCs. Heatmap represents fold changes of concentrations (pg/mL) of proteins normalized to DMSO. Two-way ANOVA test with Bonferroni’s correction for multiple comparisons was used. Significance levels are indicated by asterisks (*p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001; ****p ≤ 0.0001)
Supplementary Figure 10 from Intermittent MEK Inhibition with GITR Costimulation Rescues T-cell Function for Increased Efficacy with CTLA-4 Blockade in Solid Tumor Models
Authors: Dong Lauren , Choi Hyejin , Budhu Sadna , Schulze Isabell , Verma Svena , Mangarin Levi M. , Estrada Nevarro Valeria , Mehanna Nezar , Khan Jonathan F. , Venkatesh Divya , Thach Daniel , Rosen Neal , Wolchok Jedd D. , Merghoub Taha
Published: October 2024

Supplementary Figure 10. The triple combination reduces tumor growth, is T cell dependent, and protects from re-challenge in LLC and CT26. (A-D) LLC tumor bearing mice were treated with vehicle, isotypes, GITR, CTLA-4, 5mg/kg 4on/3off CKI27, and/or CD8 for 4 weeks and tumor growth was monitored over time. (A) Average tumor growth (volume, mm3) of immunocompetent mice. (B) Average tumor growth (volume, mm3) of immunodeficient mice. (C) Average tumor growth (volume, mm3) of CD8 depleted mice. (D) Average tumor growth (volume, mm3) of mice from (A) that were re-challenged. (E-H) CT26 tumor bearing mice were treated with vehicle, isotypes, GITR, αCTLA-4, 2mg/kg 4on/3off CKI27, and/or αCD8 for 4 weeks and tumor growth was monitored over time. (E) Average tumor growth (volume, mm3) of immunocompetent mice. (F) Average tumor growth (volume, mm3) of immunodeficient mice. (G) Average tumor growth (volume, mm3) of CD8 depleted mice. (H) Average tumor growth (volume, mm3) of mice from (E) that were re-challenged. Two-way ANOVA test with Bonferroni’s correction for multiple comparisons was used. Significance levels are indicated by asterisks (*p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001; ****p ≤ 0.0001).
Figure 6 from Hypoxia-Induced Long Noncoding RNA HIF1A-AS2 Regulates Stability of MHC Class I Protein in Head and Neck Cancer
Authors: Liao Tsai-Tsen , Chen Yu-Hsien , Li Zih-Yu , Hsiao An-Ching , Huang Ya-Li , Hao Ruo-Xin , Tai Shyh-Kuan , Chu Pen-Yuan , Shih Jing-Wen , Kung Hsing-Jien , Yang Muh-Hwa
Published: October 2024

HIF1A-AS2 is associated with an aggressive clinical stage in HNSCC. A, The expression of HIF1A-AS2 in different clinical stages of patients with GDC-HNSCC (n = 515). The whiskers extend to the minimum and maximum values, with each individual value as a point superimposed on the graph. B, The expression of HIF1A in different clinical stages of patients with GDC-HNSCC (n = 531). The whiskers extend to the minimum and maximum values, with each individual value as a point superimposed on the graph. C, Top: Case No. 21958 represents the patient group with HIF1αhigh/HLA-ABClow /HIF1A-AS2+/CD8−, whereas Case No. 21454 represents the patient group with HIF1αlow/HLA-ABChigh/HIF1A-AS2−/CD8+. Positive staining of panCK indicates the tumor cells. The yellow arrows indicate the nuclear HIF1α, whereas the red arrows indicate the membranous HLA-ABC. Scale bar, 100 μm. D, Histogram for illustrating the in situ hybridization HIF1A-AS2 score in the tumor part and the CD8+ density of the HNSCC samples. The HIF1A-AS2 staining score was calculated by multiplying the score for staining intensity with the percentage of positive tumor cells. The intensity and the percentage of positive tumor cells were analyzed by QuPath. PanCK staining was used as a marker distinguishing tumors from stroma. Data represent the mean ± SD. *, P < 0.05; ***, P < 0.001.
Figure 5 from Hypoxia-Induced Long Noncoding RNA HIF1A-AS2 Regulates Stability of MHC Class I Protein in Head and Neck Cancer
Authors: Liao Tsai-Tsen , Chen Yu-Hsien , Li Zih-Yu , Hsiao An-Ching , Huang Ya-Li , Hao Ruo-Xin , Tai Shyh-Kuan , Chu Pen-Yuan , Shih Jing-Wen , Kung Hsing-Jien , Yang Muh-Hwa
Published: October 2024

HIF1A-AS2 interacts with NBR1 and MHC-I to mediate autophagic degradation of MHC-I. A, Co-immunoprecipitation (Co-IP) to detect ubiquitination of HLA-A with or without the existence of HIF1A-AS2. 293T cells were co-transfected with HA-ubiquitin, MYC-HLA-A, and HIF1A-AS2 (pCDH-GFP-HIF1A-AS2)/or control (pCDH-GFP) for 48 hours, and bafilomycin A1 treated for 12 hours before conducting the Co-IP experiment. B, Biotinylated HIF1A-AS2 RNA pull-down assay. Biotin-labeled HIF1A-AS2 and antisense HIF1A-AS2 RNAs were incubated with the cell lysate from IFNγ treated SAS cells and pulled down by streptavidin beads. The RNA interaction proteins, NBR1 and HLA-ABC, were analyzed by western blot. C, NBR1 and HLA-ABC interaction domain on HIF1A-AS2. RNAs corresponding to indicated biotin-labeled HIF1A-AS2 were incubated with the cell lysate from IFNγ treated SAS cells, followed by streptavidin pull-down. The RNA interaction proteins, NBR1, and HLA-ABC were analyzed via western blot. D, Co-IP assay to demonstrate the interaction between HA-NBR1 and MYC-HLA-A with the existence of HIF1A-AS2. 293T cells were co-transfected with HA-NBR1, MYC-HLA-A, and HIF1A-AS2 (pCDH-GFP-HIF1A-AS2)/or control (pCDH-GFP) for 48 hours, and bafilomycin A1 treated for 12 hours before conducting the Co-IP experiment.
FIgure S7 from Hypoxia-Induced Long Noncoding RNA HIF1A-AS2 Regulates Stability of MHC Class I Protein in Head and Neck Cancer
Authors: Liao Tsai-Tsen , Chen Yu-Hsien , Li Zih-Yu , Hsiao An-Ching , Huang Ya-Li , Hao Ruo-Xin , Tai Shyh-Kuan , Chu Pen-Yuan , Shih Jing-Wen , Kung Hsing-Jien , Yang Muh-Hwa
Published: October 2024

Multispectral immunofluorescent staining for analyzing the infiltrated immune cells in HNSCC samples.
Data from Augmenting CAR T-cell Functions with LIGHT
Authors: Cai Winson , Tanaka Kento , Mi Xiaoli , Rajasekhar Vinagolu K. , Khan Jonathan F. , Yoo Sarah , de Stanchina Elisa , Rahman Jahan , Mathew Serena , Abrahimi Parwiz , Souness Sydney , Purdon Terence J. , McDowell James R. , Meyerberg Jeremy , Fujino Takeshi , Healey John H. , Abdel-Wahab Omar , Scheinberg David A. , Brentjens Renier J. , Daniyan Anthony F.
Published: October 2024

Abstract
Chimeric antigen receptor (CAR) T-cell therapy has resulted in remarkable clinical success in the treatment of B-cell malignancies. However, its clinical efficacy in solid tumors is limited, primarily by target antigen heterogeneity. To overcome antigen heterogeneity, we developed CAR T cells that overexpress LIGHT, a ligand of both lymphotoxin-β receptor on cancer cells and herpes virus entry mediator on immune cells. LIGHT-expressing CAR T cells displayed both antigen-directed cytotoxicity mediated by the CAR and antigen-independent killing mediated through the interaction of LIGHT with lymphotoxin-β receptor on cancer cells. Moreover, CAR T cells expressing LIGHT had immunostimulatory properties that improved the cells’ proliferation and cytolytic profile. These data indicate that LIGHT-expressing CAR T cells may provide a way to eliminate antigen-negative tumor cells to prevent antigen-negative disease relapse.
Table S2 from Exosomal Thomsen–Friedenreich Glycoantigen: A New Liquid Biopsy Biomarker for Lung and Breast Cancer Diagnoses
Authors: Hsu Chang-Chieh , Su Yafei , Rittenhouse-Olson Kate , Attwood Kristopher M. , Mojica Wilfrido , Reid Mary E. , Dy Grace K. , Wu Yun
Published: October 2024

Supplementary Table S2. Characteristics of Breast Cancer Patients and Controls.
Supplementary Table 5 from SREBP-Dependent Regulation of Lipid Homeostasis Is Required for Progression and Growth of Pancreatic Ductal Adenocarcinoma
Authors: Ishida Chiaki T. , Myers Stephanie L. , Kubota Casie S. , Shao Wei , McGuire Meredith R. , Liu Chune , Ewachiw Theodore E. , Mukhopadhyay Debaditya , Ke Suqi , Wang Hao , Rasheed Zeshaan A. , Anders Robert A. , Espenshade Peter J.
Published: October 2024

Table S5. SREBP target genes analyzed using the Kaplan-Meier Plotter  
Supplementary Table 3 from SREBP-Dependent Regulation of Lipid Homeostasis Is Required for Progression and Growth of Pancreatic Ductal Adenocarcinoma
Authors: Ishida Chiaki T. , Myers Stephanie L. , Kubota Casie S. , Shao Wei , McGuire Meredith R. , Liu Chune , Ewachiw Theodore E. , Mukhopadhyay Debaditya , Ke Suqi , Wang Hao , Rasheed Zeshaan A. , Anders Robert A. , Espenshade Peter J.
Published: October 2024

Table S3. Detailed cause of death and observed lesions in KPC mouse cohorts
Supplementary Materials and Methods from SREBP-Dependent Regulation of Lipid Homeostasis Is Required for Progression and Growth of Pancreatic Ductal Adenocarcinoma
Authors: Ishida Chiaki T. , Myers Stephanie L. , Kubota Casie S. , Shao Wei , McGuire Meredith R. , Liu Chune , Ewachiw Theodore E. , Mukhopadhyay Debaditya , Ke Suqi , Wang Hao , Rasheed Zeshaan A. , Anders Robert A. , Espenshade Peter J.
Published: October 2024

SUPPLEMENTARY MATERIALS AND METHODS
Supplementary Figure 5 from SREBP-Dependent Regulation of Lipid Homeostasis Is Required for Progression and Growth of Pancreatic Ductal Adenocarcinoma
Authors: Ishida Chiaki T. , Myers Stephanie L. , Kubota Casie S. , Shao Wei , McGuire Meredith R. , Liu Chune , Ewachiw Theodore E. , Mukhopadhyay Debaditya , Ke Suqi , Wang Hao , Rasheed Zeshaan A. , Anders Robert A. , Espenshade Peter J.
Published: October 2024

FIGURE S5 – SCAP is required for PDAC cell growth and survival in low serum conditions.
Supplementary Figure 4 from SREBP-Dependent Regulation of Lipid Homeostasis Is Required for Progression and Growth of Pancreatic Ductal Adenocarcinoma
Authors: Ishida Chiaki T. , Myers Stephanie L. , Kubota Casie S. , Shao Wei , McGuire Meredith R. , Liu Chune , Ewachiw Theodore E. , Mukhopadhyay Debaditya , Ke Suqi , Wang Hao , Rasheed Zeshaan A. , Anders Robert A. , Espenshade Peter J.
Published: October 2024

FIGURE S4 – SCAP is required for human PDAC tumor growth in mouse orthotopic xenograft models
Comparative Assessment of Mitochondria Isolation Buffers for Optimizing Tissue-Specific Yields in Buffalo
Authors: Kumari Sweta , Sadeesh E.M.
Published: October 2024

Introduction: Mitochondrial studies are crucial for assessing livestock health and performance. While extensive research has been done on cattle and pigs, the influence of mitochondria in Indian buffalo remains unexplored. Therefore, in order to understand functions of mitochondria, their energy-related processes, or any additional mitochondrial traits in buffaloes, it is imperative to isolate high-yield mitochondria with purity and functionality. Mitochondria are extracted by few conventional buffers. These buffers were previously characterized for their effectiveness in isolating mitochondria from rodent and human tissues. Therefore, the present study is to assess the performance of mitochondria isolation buffers specifically in buffalo tissues. Methods: The study involved isolation of mitochondria from four different tissues, i.e., liver, brain, heart and muscles of slaughtered buffalo (n = 3), using: (i) Tris-Mannitol buffer (ii) Tris-Sucrose buffer, and (iii) MOPS-Sucrose buffer. Buffer efficiency in preserving high fidelity during mitochondria isolation was assessed by comparison with Cayman’s MitoCheck® Mitochondrial Isolation Kit (control). Further mitochondrial purity and functionality was assessed through comparative estimation of protein concentration and marker enzyme assays, respectively. Results: Our results revealed insights into the suitability of specific buffer for functional mitochondria isolation from specific type of buffalo tissue. Notably for obtaining high quality functional mitochondria from buffalo, MOPS-Sucrose buffer appeared optimal for soft tissues (liver and brain), while Tris-Mannitol buffer was efficient for hard tissues (muscles and heart). Conclusions: Thus, our research highlights the influence of buffer composition and tissue-specific variations in buffer effectiveness on mitochondrial activity in different tissues, leading to improved mitochondrial isolation in buffalo.
Influence of the use of ground enhancement materials on the reduction of electrical resistivity in grounding systems: a review
Authors: Hugo Martínez Ángeles , José Gabriel Ríos Moreno , Rodrigo Rafael Velázquez Castillo , Mario Trejo Perea
Published: October 2024

A grounding system (GS) is an indispensable component in an electrical system network, as it is responsible for conducting electrical discharges to the ground due to faults caused by lightning strikes or transient system failures. Globally, it is estimated that 40 lightning strikes occur per second on the planet, amounting to around 1.2 billion per year, resulting in daily losses of various electrical equipment and human fatalities ranging from 6,000 to 24,000. Additionally, soil resistivity, which impedes the flow of electricity from electrical discharges into the ground, leads to inadequate mitigation of electrical overload effects, resulting in poor GS performance. Consequently, the implementation of ground enhancement materials (GEMs) to reduce impedance to optimal levels becomes necessary. The objective of this review is to broadly examine the current status of GEMs reported in the literature for use in GS, focusing on their composition and their effectiveness in improving soil conductivity and dissipating electrical currents as well as to identify emerging trends and current challenges in the development and application of these materials, in order to provide information to guide future research in the design and implementation of efficient and safe GS.
Design of mean filter using field programmable gate arrays for digital images
Authors: Ai Duong Huu , Nguyen Van Loi , Luong Khanh Ty , Le Viet Truong
Published: October 2024

In this paper, we design and analysis of mean filter using field programmable gate arrays (FPGAs) for digital images, FPGAs are integrated circuits consisting of interconnections that connect programmable internal hardware blocks allows users to customize operations for a specific application. FPGA is an ideal choice for real-time image processing, these FPGA devices are controlled in Verilog or VHDL languages, allowing to design at different levels and adapt to design changes or even support new applications throughout the life of the component. Digital image filtering is the most important task in image processing and with the help of computers, image recognition involves identifying and classifying objects in an image. This paper design of mean filter for digital image processing, implementation and analysis of image processing algorithms on FPGAs. The results obtained on the FPGA are compared and analyzed with the results by MATLAB software.
Impact of high-k insulators on electrical properties of junctionless double gate strained transistor
Authors: Kaharudin Khairil Ezwan , Salehuddin Fauziyah , Mohd Zain Anis Suhaila , Jalaludin Nabilah Ahmad , Arith Faiz , Mat Junos Siti Aisah , Ahmad Ibrahim
Published: October 2024

High-k dielectric insulators are required to reduce leakage and increase transistor performance. They are able to impact the mobility of carriers in transistors positively, leading to better device performance in advanced transistor architecture. Nevertheless, an in-depth analysis of how high-k dielectric insulators influence transistor characteristics must be carried out to determine their suitability. The objective of this study is to investigate the impact of high-k insulators towards electrical properties of junctionless double gate strained transistor. The simulation works is done using process/device simulator Silvaco Athena/Atlas. Based on the retrieved results, the magnitude of ION, on-off ratio, gm, and Cint for TiO2-based device are approximately 63%, 99%, 62%, and 89% respectively higher than the lowest permittivity material-based device. The TiO2-based device also exhibits the lowest magnitude in IOFF and SS compared to others. However, a significant degradation in fT magnitude have been observed for TiO2-based device significantly due to its large capacitances
Aqua-stream: an IoT based smart water management system for sustainable living
Authors: Siraparapu Sri Ramya , Azad S. M. A. K.
Published: October 2024

Aqua-stream, an innovative internet of things (IoT) enabled water management system, utilizes the power of long short-term memory (LSTM) networks, a sophisticated time-series forecasting machine learning technique with Kafka. Aqua-stream seamlessly integrates LSTM within the Kafka streaming architecture for efficient real-time data processing, ensuring quick responses to emerging water management needs. LSTM is employed for real-time anomaly detection, dynamically analyzing streaming data to prevent leaks through automated shut-off valves. The system’s comprehensive dashboard utilizes LSTM insights for live water quality analysis; adaptive scheduling based on individual preferences and personalized recommendations, enhancing cost-effective water management. This streamlined approach extends to the smart gardening system, where LSTM guides automation for optimal plant care incorporating sensors to monitor soil moisture, temperature, and sunlight levels. This system automatically adjusts watering and lighting to ensure optimal conditions for plant growth. Users can control and monitor their garden remotely via a smartphone, facilitating plant care while saving water and energy. Aqua-stream redefines home water management, offering a holistic solution that combines intelligent water conservation with smart gardening for a sustainable and connected living experience. Aqua-stream represents a seamless integration of LSTM-based machine learning and IoT technologies, offering an intelligent, yet simplified, solution for sustainable and connected living.
Mitigating blackhole attacks in wireless body area network
Authors: Abdessamad Goumidi Mohammed , Ehlem Zigh , Adda Belkacem Ali-Pacha
Published: October 2024

In this paper, we aimed to develop a trusted secured routing Ad-hoc on-demand distance vector (AODV) protocol to fight against blackhole attacks within the wireless body area network (WBAN). The trusted secure routing protocol incorporates a routing strategy based on trust value to detect malicious nodes based on their trust value, a routing technique based on node residual energy to select the node with the highest residual energy during the communication process, and a hybrid cryptography algorithm that merges the Affine cipher with the modified RSA cipher algorithm to secure communication against malevolent biomedical sensor attacks. Simulation outcomes demonstrate that the suggested protocol outperforms the traditional AODV routing protocol in all evaluation metrics, including data rate, energy consumption, and packet delivery ratio. Its main strength is that it considers several factors, like illegitimate medical sensor detection, efficient network energy use, and secure data transmission, unlike similar secured routing protocols. Furthermore, the hybrid cipher algorithm improves the effectiveness and increases the security level of sensitive data compared to traditional cipher algorithms such as the Affine cipher and the RSA cipher.
MPCNN: a novel approach for detecting human Monkeypox from skin lesion images leveraging deep neural network
Authors: Kabir Sk. Shalauddin , Hosen Md. Apu , Moz Shahadat Hoshen , Galib Syed Md.
Published: October 2024

The global healthcare scenario encounters a substantial challenge caused by the widespread outbreak of Monkeypox affecting over 65 countries. Limited availability of polymerase chain reaction (PCR) tests and biochemical assays necessitates alternative strategies. This study explores the viability of computer-aided identification of Monkeypox through the analysis of skin lesion images, offering a potential solution, particularly in resource-constrained settings. Employing data augmentation techniques, we augment the dataset to enhance its robustness. Subsequently, we utilize various pre-trained deep learning models, including EfficientNetB3, VGG16, ResNet50, AlexNet, and EfficientNet for classification tasks related to Monkeypox and other diseases. The achieved accuracies for these models are 98.48%, 69.19%, 91.41%, 78.38%, and 94.44%, respectively. We introduce a novel modified convolutional neural network (CNN) architecture named MPCNN to further improve performance. Our proposed MPCNN model demonstrates exceptional accuracy, precisely identifying Monkeypox patients with a remarkable precision of 99.49%. This technological advancement in disease identification holds significant promise for enhancing healthcare strategies and response mechanisms in the context of global health concerns.
Recognizing Indonesian sign language (Bisindo) gesture in complex backgrounds
Authors: Saputra Muhammad Alfhi , Rakun Erdefi
Published: October 2024

Sign language, particularly Indonesian sign language (Bisindo), is vital for deaf individuals, but learning it is challenging. This study aims to develop an automated Bisindo recognition system suitable for diverse backgrounds. Previous research focused on greenscreen backgrounds and struggled with natural or complex backgrounds. To address this problem, the study proposes using Faster region-based convolutional neural networks (RCNN) and YOLOv5 for hand and face detection, MobileNetV2 for feature extraction, and long short-term memory (LSTM) for classification. The system is also designed to focus on computational efficiency. YOLOv5 model achieves the best result with a sentence accuracy (SAcc) of 49.29% and a word error rate (WER) of 16.42%, with a computational time of 0.0188 seconds, surpassing the baseline model. Additionally, the system achieved a SacreBLEU score of 67.77%, demonstrating its effectiveness in Bisindo recognition across various backgrounds. This research improves accessibility for deaf individuals by advancing automated sign language recognition technology.
A hybrid classification approach for automatically recognizing COVID-19 using deep transfer learning using chest radiographs
Authors: Pinjara Murthuja , Babu G. Anjan
Published: October 2024

Coronavirus 2019 causes COVID-19, a worldwide epidemic. It endangers millions globally. Early illness detection improves recovery and control. X-ray image processing is used to categorise and identify COVID-19 in the present study. Preprocessing, feature extraction using local binary pattern (LBP) and edge orient histogram (EOH), and classification utilising K-nearest neighbour (KNN), Navie Bayes, support vector machine (SVM), and transfer learning convolution neural networks (CNNs) are some of the stages that are implemented in the process. Other phases in the process include preprocessing, feature extraction, and preprocessing. LBP+KNN, EOH+KNN, LBP+SVM, EOH +SVM, CNN+LBP, and CNN+EOH are the outputs derived from the combinations of feature extraction operators and classifiers. Other possible outcomes are CNN+EOH and CNN+LBP. A total of 4,000 pictures were used as the basis for conducting an analysis of the performance of six different models. In order to train the models, 10-fold cross-validation was used, and their accuracy was measured accordingly. The evaluation results indicate a high level of accuracy in diagnosis, ranging from 90.2% to 97.56%. The CNN+LBP and CNN+EOH models have demonstrated superior performance compared to other models, achieving average accuracies ranging from 96.66% and 98.54%.
Breast cancer identification using machine learning and hyperparameter optimization
Authors: Arifin Toni , Prasetyo Agung Ignatius Wiseto , Junianto Erfian , Rachman Rizal , Wibowo Ilham Rachmat , Agustin Dari Dianata
Published: October 2024

Breast cancer identification can be analyzed through genomic analysis using gene expression data, one type of which is mRNA. This involves analyzing gene expression patterns of breast tissue samples to distinguish breast cancer from healthy tissue or to differentiate subtypes of different breast cancers. This research developed the right computational model for breast cancer classification using machine learning and hyperparameter optimization algorithms. The primary objective of this research is to utilize various machine learning algorithms to classify breast cancer based on gene expression and enhance the models developed in previous studies. This paper provides an extensive literature review of prior breast cancer classification research and offers new theoretical perspectives. This research used a problem-solving approach with conventional machine learning techniques, most notably the decision tree. It also evaluates other machine learning algorithms for comparison, including k-nearest neighbor, naïve bayes, random forest, extra tree classifier, and support vector machine. The evaluation process used classification reports that provide insight into the precision, recall, F1-score, and accuracy of each machine learning model. The evaluation results show that the performance of the decision tree algorithm model is superior and impressive, achieving 99.73% accuracy and a score of 1 for precision, recall, and F1-score.
Integrating computing techniques in preserving Makassar’s Pakarena dance heritage
Authors: Syahrir Nurlina , Alimuddin Alimuddin
Published: October 2024

This interdisciplinary study bridges the cultural heritage of the Pakarena dance, a key element of Makassar ethnicity, with contemporary computing and informatics to examine its social implications, particularly in terms of identity formation, community transmission, and narrative preservation. Emphasizing the dance’s crucial role within the Makassar community, the research employs digital technologies for comprehensive data collection, analysis, and dissemination. Utilizing a qualitative framework supplemented by digital ethnography, the methodology includes in-depth interviews and participant observation, enriched with advanced data analysis and virtual reality (VR) presentations. This innovative approach facilitates the digital capture of the narratives and experiences of dance practitioners, cultural experts, and community members, ensuring the preservation of the dance’s cultural narrative and expression. The study reveals that the Pakarena dance is not only a bearer of the Makassar community’s history and traditions but also a platform for individual creativity and cultural identity, adapting while preserving its core in the face of societal shifts. The findings highlight the potential of computing and informatics in cultural preservation, suggesting new methods for documenting, analyzing, and promoting intangible cultural heritage. The study advocates for the use of technology to enhance and perpetuate cultural heritage, especially for younger generations, in our increasingly digital era.
Automated blood cancer detection models based on EfficientNet-B3 architecture and transfer learning
Authors: Alshdaifat Nawaf , Owida Hamza Abu , Mustafa Zaid , Aburomman Ahmad , Abuowaida Suhaila , Ibrahim Abdullah , Alsharafat Wafa
Published: October 2024

In blood smear images, there are difficulties in diagnosing blood cancer diseases like leukemia and lymphoma because of their various forms that appear in the human body. In this paper, a method for automatic detection of blood cancer is suggested that uses the EfficientNet-B3 architecture along with transfer learning techniques to improve accuracy and efficiency. We first fine-tuned the EfficientNet-B3 model, which was pre-trained on a large dataset consisting of annotated blood smear images, to capture pertinent features linked with blood malignant cells. To expedite the training process and adapt the model to our task, we use transfer learning. The proposed approach’s results from our experiments show that it outperforms traditional deep learning models and state-of-the-art methods in blood cancer detection. Additionally, with high precision and recall rates, this model also detects different types of blood cancers with robustness in its performance since its accuracy is over 99%. This means that when used together with the EfficientNet-B3 architecture, transfer learning can help the developed methods generalize among different types of blood cancers and conditions.
Deep learning utilization in Sundanese script recognition for cultural preservation
Authors: Rosalina Rosalina , Afriliana Nunik , Utomo Wiranto Herry , Sahuri Genta
Published: October 2024

This study addresses the challenge of preserving the Sundanese script, a traditional writing system of the Sundanese community in Indonesia, which is at risk of being forgotten due to technological advancements. To tackle this problem, we propose a deep learning approach using the YOLOv8 model for the automatic recognition of Sundanese characters. Our methodology includes creating a comprehensive dataset, applying augmentation techniques, and annotating the characters. The trained model achieved a precision of 95% after 150 epochs, demonstrating its effectiveness in recognizing Sundanese characters. While some variability in accuracy was observed for certain characters and real-time applications, the results indicate the feasibility and promise of using deep learning for Sundanese script recognition. This research highlights the potential of technological solutions to digitize and preserve the Sundanese script, ensuring its continued legacy and accessibility for future generations. Thus, we contribute to cultural preservation by providing a method to safeguard the Sundanese script against obsolescence.
Automatic kidney disease prediction using deep learning techniques
Authors: Rubia Jency , Shibi Sherin , Lincy Babitha , Catherin Jenifer Pon , Vigneshwaran Vigneshwaran , Nithila Ezhil
Published: October 2024

The kidneys play an energetic role in eliminating excess products and fluids from the body, by a complex mechanism which is crucial for upholding a stable balance of body chemicals. Chronic kidney disease (CKD) is considered by an unhurried weakening in renal function that may eventually result in kidney injury or failure. The difficulty of diagnosing the illness rises as it worsens. However, using data from normal medical visits to evaluate the various phases of CKD could help with early detection and prompt care. Researchers suggest a classification strategy for CKD along with optimization strategies used in the learning process. The incorporation of artificial intelligence offers promise because it may often astonish with its skills and enable seemingly difficult undertakings. Modern machine learning techniques have been developed to detect renal illness in light of this. In the current study, a new deep learning model for CKD initial recognition and prediction is introduced. The main objective of the project is to build a strong deep neural network (DNN) and estimate its result outcomes in comparison to other leading-edge machine learning techniques. The outcomes demonstrate that the proposed strategy outperforms current approaches and has promise as a useful tool for CKD detection.
Colour sorting ROS-based robot evaluation under different lights and camera angles
Authors: Saaid Mohammad Farid , Thamrin Norashikin M. , Misnan Mohamad Farid , Mohamad Roslina , Romli Nurul A’qilah
Published: October 2024

Automated colour sorting, aided by mobile robots, is widely prevalent in the current manufacturing industry. Obstacles, such as fluctuating light conditions and camera angles, frequently hinder this procedure. Creating a colour sorting robot is a complex and time-consuming task, especially due to the vulnerability of the RGB colour space to detection errors in extreme brightness or darkness. In response to these concerns, we introduce a mobile robot that operates on the robot operating system (ROS) platform and incorporates OpenCV. This robot employs the hue, saturation, and value (HSV) colour space model for its image processing capabilities in recognising the colours and Welzl’s algorithm for the ball’s diameter estimation. The robot’s performance was assessed across various luminous fluxes and camera tilt angles. It demonstrated exceptional performance at 64 lm and a tilt angle of 40 degrees, achieving an average accuracy of 87.5% for detecting the colour of the ball, and 81.25% for determining its location based on colour. For the ball’s diameter estimation, it was found that the best estimation was received at 64 lm and 30 degrees, with both 96.32%.
Artificial intelligence-based weather prediction framework using neural networks
Authors: Kaushik Keshav , Chhabra Gunjan , Bharany Salil , Rehman Ateeq Ur , Hamam Habib
Published: October 2024

For humans, weather prediction is vital in making rational everyday choices and avoiding risk. Accurate weather forecasting is regarded as one of the world’s most difficult issues. New weather forecasting, unlike conventional techniques, relies on a mixture of computer simulations, observation (via balloons and satellites), and information of patterns and trends (via local weather analysts and weather stations). Predictions are rendered with fair precision using such techniques. Prediction algorithms based on complicated formulas run the majority of computational models used for prediction. This paper highlights the prediction of weather with the artificial neural networks (ANN) using the latest available smart computing devices. To assess the effectiveness of the model, comparison research is conducted with the other existing models in the same area. The result demonstrates that our approach is better in comparison to other similar research and products. The comparative analysis has been undergone which confirms the superiority of our proposed techniques with an accuracy of 90.4%.
DeepCervix: enhancing cervical cancer detection through transfer learning with VGG-16 architecture
Authors: Joshi Vaishali M. , Dandavate Prajkta P. , Rashmi R. , Shinde Gitanjali R. , Thune Neeta N. , Mirajkar Riddhi
Published: October 2024

Cervical cancer remains a significant global health concern, emphasizing the urgent need for improved detection methods to ensure timely treatment. This research introduces a sophisticated methodology leveraging recent advances in medical imaging and deep learning algorithms to enhance the accuracy and efficiency of cervical cancer detection. The proposed approach comprises meticulous data preprocessing to ensure the integrity of input images, followed by the training of deep learning models including ResNet-50, AlexNet, and VGG-16, renowned for their performance in computer vision tasks. Evaluation metrics such as accuracy, precision, recall, and F1-score demonstrate the efficacy of the methodology, with an outstanding accuracy rate of 98% achieved. The model’s proficiency in accurately distinguishing healthy cervical tissue from cancerous tissue is underscored by precision, recall, and F1-score values. The primary strength of this deep learning-based approach lies in its potential for early detection, promising significant impact on cervical cancer diagnosis and treatment outcomes. This methodology contributes to advancements in medical imaging techniques, facilitating improved outcomes in cervical cancer detection and treatment.
Artificial intelligence and machine learning implementation status on Latam: a systematic literature review
Authors: Carlos Palomino Vidal , Patricia Condori Obregon , Enrique Stolar Sirlupu
Published: October 2024

Artificial intelligence (AI) and machine learning (ML) are disruptive technologies nowadays. It is well known that many important organizations use them to improve their productivity and processes, and many new applications are being developed as well. In Latin America, the adoption of new technologies is slower than in other parts of the world, limited by budget and trained personnel. The present research is a systematic literature review (SLR) conducted to analyze the implementation status of AI and ML technologies in Latin America, analyzing the improvements that these technologies bring to organizations. The methodology used in this literature review was PRISMA, a popular method widely used in this type of research. The findings were that the most relevant areas using these types of technologies are education and health, identifying also that their implementation improves operative efficiency, technology innovation, and competitiveness. These findings also demonstrate the lack of efforts in implementation in other business sectors like administration, agriculture, and production, which provides a great opportunity to improve in these areas in the future.
Evolution in Linguistics – Conceptual Innovation, Metonymy, and Miscommunication
Authors: Verhagen Arie
Published: October 2024

Conceptual innovations in science (‘paradigm shifts’ in the sense of Kuhn) come with changes in the meaning of basic terminology in that field. Linguistics is no exception. But linguistics is in the peculiar position that it comprises the study of the meaning of linguistic items, and the way these meanings change. Some basic ideas of cognitive semantics, especially the concept of metonymy, shed light on the risk of miscommunication in a scientific field in a period of innovation. Paradoxically, these risks are instantiated in a controversy in the field of linguistics itself that was triggered by the then new, Darwinian understanding of evolution. The paper ends by exploring a recent theoretical innovation (the ‘usage-based’ approach, especially its most recent variants) that holds a promise for overcoming the controversies, provided theoretical linguists accept that terminology also in their own field is to be semantically more precise than in everyday language use.
Rethinking the Relationship between Subjectification, Intersubjectification, and Textualization from a Constructionalist Perspective
Authors: Traugott Elizabeth Closs
Published: October 2024

In the last forty years there have been several proposals regarding a generalizable historical ordering among subjectification, intersubjectification and textualization. To date, most hypotheses have been developed within the framework of grammaticalization, in which hypotheses about unidirectional paths of change have played a significant role. In this paper I ask two questions: 1: Is there evidence for a generalizable historical ordering among subjectification, intersubjectification and textualization? 2. What hypotheses about the relationship between these three change-types appear to be optimal from the perspective of Diachronic Construction Grammar? I argue that, although particular data may suggest one trajectory or another, it is not possible to generalize over the order of changes. I propose a constructionalist model in which constructions have communicative, among other, functions, and in which the properties subjective, intersubjective and textual are of equal status and may emerge simultaneously in certain domains of grammar, such as Discourse Structuring Markers.