bluuebunny/crossref_metadata_2025 huggingface dataset:
Noise Detection and Suppression From ECG Through Improved CEEEMD and Adaptive Wavelet Soft Thresholding
Authors: Gupta T. Raghavendra , Umanandhini D.
Published: November 2024

Electrocardiogram (ECG) is a noninvasive, effective and economical biomedical signal that is vital in diagnosing cardiovascular diseases. However, the acquiring process contaminates the ECG signal with several types of noises like Motion Artifacts, Power Line Interference and Baseline Wander. Hence, this paper proposes a new approach to detect and suppress the noises from ECG signals. The complete methodology comprises two stages: noise detection and noise suppression. The former stage applies Improved Complete Ensemble Empirical Mode Decomposition (CEEMD) to decompose the noisy ECG into Intrinsic Mode Functions (IMFs). Next, Maximum Absolute Amplitude (MAA) and Auto-Correlation Maximum Amplitude (AMA) are extracted and used to classify the type of noises from ECG. Then, the noisy ECG segments are processed through the second stage and decomposed into sub-bands through Discrete Wavelet Transform (DWT). Then, the sub-bands are categorized into noise-dominant and signal-dominant frequency bins, and only noise-dominant frequency bins are subjected to noise suppression through a newly proposed adaptive soft thresholding mechanism. The effectiveness of the proposed method is assessed by contaminating the ECG signals acquired from the MIT-BIH arrhythmia database with different noises at different Signal-Noise Ratios (SNRs). Three performance metrics, namely Output SNR, Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (PCC), are employed to explore the superiority of the proposed method over state-of-the-art methods, which considered EMD and CEEMD as decomposition filters. The proposed method improved by an average of 3.5 dB in output SNR and 0.0290 in RMSE.
Normal Gut Microbiomes in Diverse Populations: Clinical Implications
Authors: Shete Omprakash , Ghosh Tarini Shankar
Published: November 2024

The human microbiome is a sensor and modulator of physiology and homeostasis. Remarkable tractability underpins the promise of therapeutic manipulation of the microbiome. However, the definition of a normal or healthy microbiome has been elusive. This is in part due to the underrepresentation of minority groups and major global regions in microbiome studies to date. We review studies of the microbiome in different populations and highlight a commonality among health-associated microbiome signatures along with major drivers of variation. We also provide an overview of microbiome-associated therapeutic interventions for some widespread, widely studied diseases. We discuss sources of bias and the challenges associated with defining population-specific microbiome reference bases. We propose a roadmap for defining normal microbiome references that can be used for population-customized microbiome therapeutics and diagnostics.
Device Reliability of Negative Capacitance Source Pocket Double Gate TFETs: A Study on Temperature and Noise Effects
Authors: Babu K. Murali Chandra , Goel Ekta
Published: November 2024

This study investigates the reliability of a negative capacitance source pocket double gate tunnel field-effect transistor (NC-SP-DGTFET) by examining the effects of temperature and various noise components on its performance. The research focuses on key DC parameters, including the transfer characteristics, subthreshold swing, and the ION/IOFF ratio, evaluated across a temperature range from 250 to 450 K. Additionally, the study explores the radio-frequency performance of the device by assessing how temperature impacts transconductance (gm), cut-off frequency (fT), gate capacitance (Cgg), intrinsic delay, and the transconductance frequency product. Noise performance metrics are also analyzed, focusing on the drain current noise power spectral density (SID) and gate voltage noise power spectral density (SVG). The study considers the contributions of diffusion, generation-recombination (G-R), and flicker noise components and at 300 K, SID and SVG showed peak values of 5.08 × 10−26 A2/Hz and 2.67 × 10−16 V2/Hz, 5.73 × 10−18 A2/Hz and 3.22 × 10−10 V2/Hz, and 1.33 × 10−25 A2/Hz and 1.19 × 10−14 V2/Hz, respectively. The analysis reveals that flicker noise is predominant at lower frequencies, while diffusion noise becomes more significant at higher frequencies. However, G-R noise is the most dominant across all frequencies examined. These findings provide crucial insights for optimizing the design and performance of NC-SP-DGTFETs in low-power applications.
Cultured primary turtle hepatocytes: a cellular model for the study of temperature and anoxia
Authors: Myrka Alexander M. , Frost Ryan , Di Stefano Domenic , Plotnikov Sergey V. , Buck Leslie T.
Published: November 2024

We have optimized a methodology for two-dimensional (2-D) culturing of primary western painted turtle hepatocytes and used this model to study the effects of cyanide and temperature on structural rearrangement, and the effect of cyanide on mitochondrial distribution. Our results suggest that low temperature acclimation, either in vivo or in vitro, inhibits cell protrusions and structural rearrangement. Acute cyanide exposure did not inhibit structural rearrangement or alter mitochondrial distribution.
Hepatoprotective Effects of Medicinal Honey: Introducing a New Classification Based on Phenolic Content and Antioxidant Capacity
Authors: Zamani Ranjbar Garmroodi Bardia , Rajabi Moghadam Mahdieh , Zarban Asghar , Bideh Milad
Published: November 2024

Honey contains vital compounds capable of ameliorating oxidative stress damage. Two markers of total phenolic content (TPC) and antioxidant capacity (TAC) have been selected for the classification of honey samples for investigating the protective effects of different honey samples on hepatotoxicity induced by CCl4. Hence, 56 male Wistar rats in 8 groups were selected and were given a daily single dose (20%) of honey samples classified as weak, medium, and strong based on TPC and TAC. After four weeks of pretreatment, acute liver damage (ALD) was induced in treated groups using CCl4 mixed with corn oil in a 1:1 ratio. Following ALD induction, the animals were sacrificed. Various samples were then collected for analysis, including serum samples for biochemistry tests (ALT, AST, ALP, and FBS) and antioxidant status assessment (DPPH, FRAP, THIOL, and MDA); urine samples for antioxidant status evaluation (DPPH, FRAP, and MDA); liver tissue homogenate for oxidative stress marker analysis (SOD, CAT, GPx, and MDA); and tissue samples for histopathological examination. Biochemical results showed a significant decrease (p ≤ 0.05) in ALT, AST, ALP, and FBS values in ALD groups treated with honey; antioxidant and oxidative stress evaluations also exactly approved the same results by an insignificant decrease (p ≥ 0.05) in serum MDA and a significant increase (p ≤ 0.05) in THIOL and DPPH and also a reduction in urinary MDA and increase of urinary DPPH all compared to positive control (p ≤ 0.05). SOD, CAT, and GPx activities and MDA values in liver homogenate also ameliorated in honey‐treated group (p ≤ 0.05). Histopathological evaluations confirmed less damage in treated groups, especially in stronger honey. Potent honey samples based on TPC and TAC have more protective effects in hepatotoxicity.
A3669G Polymorphism of Glucocorticoid Receptor Is More Present in Patients With Pemphigus Vulgaris Than in Healthy Controls and Contributes to Steroid‐Resistance
Authors: Lepekhova Anfisa , Olisova Olga , Shimanovsky Nikolay , Teplyuk Natalia , Zhgelskaya Elizaveta , Punj Ridhima , Mardanova Alina , Dukhanin Alexander
Published: November 2024

There are two main isoforms of glucocorticoid receptor (GR) α‐ and β‐isoforms. GRβ isoform is the dominant inhibitor of the GRα receptor and plays a significant role in a poor response to systemic glucocorticoids (GCs) under numerous conditions. The A3669G (rs6198) polymorphism (SNP) in the untranslated region of human GR stabilizes the mRNA of the dominant‐negative GRβ isoform. However, the mechanisms which generate mRNA encoding the GRβ isoform have been poorly defined, especially in pemphigus patients who do not respond to GCs. The main aim was to study gene SNP of GR in patients with pemphigus vulgaris (PV) and healthy controls. We investigated whether the A3669G SNP of the human GRβ gene is a susceptibility allele for PV and contributes to GC resistance development. The presence of the A3669G SNP was determined by high‐resolution melting analysis and then confirmed by direct sequencing. GR A3669G SNP (AG genotype) occurred more frequently in PV patients (n = 72; 25%) compared with healthy controls (n = 92; 3.2%; p < 0.001). Allele G was significantly more presented in PV patients (p < 0.001). Out of 48 patients with AA genotype of A3669G SNP, only 6 had GC resistance, whereas 11 out of 18 with AG genotype developed GC resistance (p < 0.0001). The frequency of a poor response to GC in the group of patients with AG genotype was 4.89 times higher compared to AG negative one (p < 0.0001).

Our study showed that the A3669G SNP was more present in PV patients. Moreover, GC resistance appeared more frequently in patients with the AG genotype (p < 0.0001). Since those patients predominantly with severe pemphigus had GC resistance (p = 0.001), we feel that A3669G SNP (AG genotype) contributes to its development. However, more studies are needed to determine whether A3669G SNP of the human GR gene associated with the disease severity and poor response to GCs in these patients.
A Longitudinal Correlational Study of Psychological Resilience, Depression Disorder, and Brain Functional–Structural Hybrid Connectome in Breast Cancer
Authors: Liang Mu Zi , Zhou Jin , Chen Peng , Song Ya Lan , Li Shu Han , Liang Yu Yan , Hu Guang Yun , Hu Qu , Sun Zhe , Yu Yuan Liang , Molassiotis Alex , Knobf M. Tish , Ye Zeng Jie
Published: November 2024

Purposes: To evaluate the association between psychological resilience, depression disorder (DD), and brain functional–structural hybrid connectome in patients with breast cancer before treatment (T0) and at 1 year.

Methods: Between February 2017 and October 2019, 172 patients were longitudinally enrolled from a multicenter trial named as Be Resilient to Breast Cancer (BRBC) and completed resting‐state functional magnetic resonance imaging (rs‐fMRI) and diffusion tensor imaging (DTI) before the T0. Data‐driven multivoxel pattern analysis (MVPA) and correlational tractography (CT) were performed to identify distinct functional‐structural hybrid connectome. DD was diagnosed by psychiatry physicians according to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM‐5). Psychological resilience was collected by Resilience Scale Specific to Cancer (RS‐SC) and tested as the mediation variable between hybrid connectome and DD.

Results: Of the total sample of 172, 14.5% (N = 25) were diagnosed with DD. High psychological resilience was associated with a lower risk of DD (hazard ratio (HR) = 0.37, 95% confidence interval (CI), 0.17–0.82, p = 0.0368). Frontal pole right (88.0%) in rs‐fMRI and arcuate fasciculus_L (75.2%) in DTI were identified as main significant brain areas. Psychological resilience accounted for 10.01%–12.14% of direct effect between brain functional–structural hybrid connectome and 1‐year DD.

Conclusion: Psychological resilience predicts DD at 1 year and mediates the association between brain functional–structural hybrid connectome and DD at 1 year in patients diagnosed with breast cancer.

Trial Registration: ClinicalTrials.gov identifier: NCT03026374
Decomposition of the Coffee Value Chain Among Smallholder Farmers in Embu and Kirinyaga Counties in Central Kenya
Authors: Wambua Daniel Musau , Ndirangu Samuel N. , Mogaka Hezron , Gichimu Bernard M.
Published: November 2024

The coffee value chain is lengthy and complex with numerous actors and this exposes the farmers to inherent financial risks. This study sought to decompose the coffee value chain and its dynamics. The study was undertaken in Embu and Kirinyaga counties in Central Kenya and the target population was smallholder coffee farmers. The study used multistage stratified sampling techniques to draw a sample of 385 respondents. Majority (97%) of the sampled farmers were processing and marketing their coffee through cooperative societies. Socioeconomic analysis of these farmers showed that majority were middle‐aged, fairly educated and with adequate coffee farming experience but their cherry production was very low averaging 2.3 kgs per tree for 2022/2023 crop year. The cooperative societies were playing key roles in the coffee value chain including farmers’ training, input and credit provision, coffee processing and marketing. There were numerous coffee marketing challenges, including high middlemen involvement, which lowered the coffee prices and reduced the trade volumes. Value adding activities such as roasting, grinding, and packaging were rare and farmers’ involvement in the upstream value chain was minimal. The local demand for the produced coffee was very low with domestic consumption taking only 2% of the output. The multilevel mixed effect model results revealed that value adding and farmer involvement were found to have a significant positive influence on the traded volumes and coffee prices while middlemen involvement had a negative influence. There is need for enhanced value adding and farmers involvement in the upstream value chain as well as strengthening the cooperative societies’ role in coffee marketing for more accountability and increased incomes.
B∗ Algorithm: Multiobjective Path Planning for Flexible Buses Traffic System
Authors: Ke Zhida , Tsai Rong-Guei , Xu Chengtao , Yu Yicong , Huang Zhiming , Chen Xiaolan , Lin Lin , Luo Delin , He Shanna
Published: November 2024

Advances in vehicle intelligence have ushered in the rapid development of intelligent connected vehicles and the emergence of the Internet of Vehicles (IoV), greatly improving the passenger travel experience. However, as a new mode of transport, flexible public transportation presents challenges for operators in terms of reducing costs and improving passenger experiences through complex route planning. The present study introduces B∗ as a heuristic multiobjective route planning algorithm that addresses these challenges. Using the trajectory extraction procedure (TEP) and route assignment procedure (RAP), B∗ filters out inaccessible routes and plans efficient routes on the fly to save money and enhance the passenger experience. Experimental results show that B∗ outperforms traditional methods in terms of shorter driving distances and reduced passenger waiting times, highlighting its potential to optimize bus utilization and improve travel experiences.
From Nature to Application: Exploring Nature’s Elegance for Human Innovation
Authors: Zolovs Maksims , Piginka-Vjaceslavova Inga , Semjonov Aleksandr
Published: November 2024

Gastropod mucus, a complex and versatile substance produced by snails and slugs, plays a critical role in their biology, and holds promise for various applications. This review offers a comprehensive and insightful overview of the current state of knowledge on gastropod mucus, encompassing its composition, biological roles, and potential applications. We delve into the multiple intricate mechanisms by which snails and slugs employ mucus to achieve their various biological goals such as locomotion, defense against predators, protection from environmental threats, communication, mating, homing behavior, nutrient recycling, and regulating population density. The focus then shifts to an examination of the practical applications of gastropod mucus such as antipathogen properties, skincare and antiaging, surgical adhesive and drug delivery, dental and orthopedic application, engineering, and biomimicry, as revealed through various studies. We also critically assess the results and identify areas for further research. This critical review helps to refine our understanding of the true potential of gastropod mucus and guides future investigations. Finally, we discuss the challenges of studying gastropod mucus.
Solar Irradiation and Temperature Prediction Using LSTM Neural Network and Solar Energy Potential in Northern Cameroon for Solar Electric Vehicle Application
Authors: Douswekreo Saito , Ndoumbe Jean , Baba Aoua , Fedotova Marina , Offole Florence , Essola Dieudonne
Published: November 2024

The fight against global warming is becoming increasingly important because of the race to find new sources of energy. This work presents a method for estimating solar energy (SE) potential by applying LSTM neural networks to predict the parameters influencing photovoltaic production, namely, solar irradiation and temperature over 24 h. The database comes from MERRA‐2 and includes the meteorological parameters’ temperature, relative humidity, atmospheric pressure, wind speed, wind direction, solar irradiation and rainfall for 20 locations in northern Cameroon with hourly time steps over 23 years, from 2000 to 2022. The method consists of designing an LSTM neural model to obtain the lowest deviation between the real and predicted data. The performance criteria used to validate the model that predicts the two parameters are an accuracy of 92.45% and an RMSE of 20.9. The model was tested in two localities, Makary and Banyo, with the most important values out of the 20 localities. The database was used to estimate that the average SE potential is 2.193 MWh/m2/year in Makary, and the lowest potential is 1.949 MWh/m2/year in Banyo. This information can be used to select sites for solar power plant installations, solar photovoltaic energy management, solar electric vehicle (EV) fleet management and to select sites for the construction of EV recharging stations.
A Novel Pentapeptide From Tilapia Skin Gelatin Gastrointestinal Digests Which Inhibits Scar Formation Through Anti‐Inflammatory and Antiangiogenesis
Authors: Liu Yi , Zheng Haiyan , Lin Liyuan , Huang Huixue , Zhou Chunxia , Hong Pengzhi , Qian Zhong-Ji
Published: November 2024

Tilapia is a kind of fish with a huge amount of aquaculture, and a lot of byproducts are wasted in processing. Of these, fish skin is the most valued due to its high content of gelatin, which has good characteristics such as low hypoallergenic, biocompatibility, and permeability. In this study, a novel pentapeptide (Leu‐Ser‐Gly‐Tyr‐Gly, LYG) was isolated from gelatin gastrointestinal digests of tilapia skin and verified the anti‐inflammatory and antiangiogenic activity of LYG, which can inhibit scar formation. In addition, the active sites were analyzed using molecular docking. The key active amino acids Leu and Tyr of LYG were able to form hydrogen bonds and good hydrophobic structures with transforming growth factor β1 (TGF‐β1). Hyperplastic scar (HS) is a common fibrous proliferative disorder of the skin, and fibrosis is the process of scar formation. It is well known that the inhibition of collagen and angiogenesis is an effective way to combat HS. The results of this study show that LYG inhibited collagen synthesis and inflammatory stress in mouse fibroblasts (L929), suppressed the angiogenic signaling pathway in vascular endothelial cells (HUVECs), and reduced scar formation after wound healing in mice. This study provides new ideas for improving the diversified use of tilapia resources.
Supplementary Figure S6 from Galectin-9 Mediates the Functions of Microglia in the Hypoxic Brain Tumor Microenvironment
Authors: Lee Chanju , Yu Dahee , Kim Hyung-Seok , Kim Ki Sun , Chang Chi Young , Yoon Hee Jung , Won Su Bin , Kim Dae Yeon , Goh Eun Ah , Lee Yong Sun , Park Jong-Bae , Kim Sang Soo , Park Eun Jung
Published: November 2024

Supplementary Fig. S6
Supplementary Figure S4 from Galectin-9 Mediates the Functions of Microglia in the Hypoxic Brain Tumor Microenvironment
Authors: Lee Chanju , Yu Dahee , Kim Hyung-Seok , Kim Ki Sun , Chang Chi Young , Yoon Hee Jung , Won Su Bin , Kim Dae Yeon , Goh Eun Ah , Lee Yong Sun , Park Jong-Bae , Kim Sang Soo , Park Eun Jung
Published: November 2024

Supplementary Fig. S4
Supplementary Figure S15 from Galectin-9 Mediates the Functions of Microglia in the Hypoxic Brain Tumor Microenvironment
Authors: Lee Chanju , Yu Dahee , Kim Hyung-Seok , Kim Ki Sun , Chang Chi Young , Yoon Hee Jung , Won Su Bin , Kim Dae Yeon , Goh Eun Ah , Lee Yong Sun , Park Jong-Bae , Kim Sang Soo , Park Eun Jung
Published: November 2024

Supplementary Fig. S15
Table S1 Antibodies from PTEN Loss Shapes Macrophage Dynamics in High-Grade Serous Ovarian Carcinoma
Authors: Spear Sarah , Le Saux Olivia , Mirza Hasan B. , Iyer Nayana , Tyson Katie , Grundland Freile Fabio , Walton Josephine B. , Woodman Chloé , Jarvis Sheba , Ennis Darren P. , Aguirre Hernandez Carmen , Xu Yuewei , Spiliopoulou Pavlina , Brenton James D. , Costa-Pereira Ana P. , Cook David P. , Vanderhyden Barbara C. , Keun Hector C. , Triantafyllou Evangelos , Arnold James N. , McNeish Iain A.
Published: November 2024

Table of antibodies used in flow cytometry and immunohistochemistry.
Supplementary Methods from Low–Molecular Weight Cyclin E Confers a Vulnerability to PKMYT1 Inhibition in Triple-Negative Breast Cancer
Authors: Li Mi , Lulla Amriti R. , Wang Yan , Tsavaschidis Spyros , Wang Fuchenchu , Karakas Cansu , Nguyen Tuyen D.T. , Bui Tuyen N. , Pina Marc A. , Chen Mei-Kuang , Mastoraki Sofia , Multani Asha S. , Fowlkes Natalie W. , Sahin Aysegul , Marshall C. Gary , Hunt Kelly K. , Keyomarsi Khandan
Published: November 2024

This file contains all the information pertaining to supplementary methods that were referred to in the supplementary data.
Supplementary Video 1 from Live-Cell Invasive Phenotyping Uncovers ALK2 as a Therapeutic Target in LKB1-Mutant Lung Cancer
Authors: Koo Junghui , Seong Chang-Soo , Parker Rebecca E. , Herrera Amy , Dwivedi Bhakti , Arthur Robert A. , Dinasarapu Ashok Reddy , Johnston Henry Richard , Claussen Henry , Tucker-Burden Carol , Ramalingam Suresh S. , Fu Haian , Zhou Wei , Marcus Adam I. , Gilbert-Ross Melissa
Published: November 2024

Invasive phenotyping of K, KL, and KP HBECs
Figure S2 from Live-Cell Invasive Phenotyping Uncovers ALK2 as a Therapeutic Target in LKB1-Mutant Lung Cancer
Authors: Koo Junghui , Seong Chang-Soo , Parker Rebecca E. , Herrera Amy , Dwivedi Bhakti , Arthur Robert A. , Dinasarapu Ashok Reddy , Johnston Henry Richard , Claussen Henry , Tucker-Burden Carol , Ramalingam Suresh S. , Fu Haian , Zhou Wei , Marcus Adam I. , Gilbert-Ross Melissa
Published: November 2024

Figure S2
Figure S7 from The Neurodevelopmental Protein POGZ Suppresses Metastasis in Triple-Negative Breast Cancer by Attenuating TGFβ Signaling
Authors: Heath John , Mirabelli Caitlynn , Annis Matthew G. , Sabourin Valerie , Hebert Steven , Findlay Steven , Kim HaEun , Witcher Michael , Kleinman Claudia L. , Siegel Peter M. , Orthwein Alexandre , Ursini-Siegel Josie
Published: November 2024

Supplemental Figure 7 with legend
Figure S6 from The Neurodevelopmental Protein POGZ Suppresses Metastasis in Triple-Negative Breast Cancer by Attenuating TGFβ Signaling
Authors: Heath John , Mirabelli Caitlynn , Annis Matthew G. , Sabourin Valerie , Hebert Steven , Findlay Steven , Kim HaEun , Witcher Michael , Kleinman Claudia L. , Siegel Peter M. , Orthwein Alexandre , Ursini-Siegel Josie
Published: November 2024

Supplemental Figure 6 with legend
Figure S5 from The Neurodevelopmental Protein POGZ Suppresses Metastasis in Triple-Negative Breast Cancer by Attenuating TGFβ Signaling
Authors: Heath John , Mirabelli Caitlynn , Annis Matthew G. , Sabourin Valerie , Hebert Steven , Findlay Steven , Kim HaEun , Witcher Michael , Kleinman Claudia L. , Siegel Peter M. , Orthwein Alexandre , Ursini-Siegel Josie
Published: November 2024

Supplemental Figure 5 with legend
Supplementary Figure 4 from Hypoxia Stimulates PYGB Enzymatic Activity to Promote Glycogen Metabolism and Cholangiocarcinoma Progression
Authors: Pan Yani , Zhou Yue , Shen Yonghua , Xu Lei , Liu Hongwen , Zhang Nannan , Huang Tianlu , Meng Kui , Liu Yu , Wang Lishan , Bai Ge , Chen Qi , Zhu Yun , Zou Xiaoping , Wang Siliang , Wang Zhangding , Wang Lei
Published: November 2024

PYGB promotes CCA proliferation in vitro.
Figure S4 from The Neurodevelopmental Protein POGZ Suppresses Metastasis in Triple-Negative Breast Cancer by Attenuating TGFβ Signaling
Authors: Heath John , Mirabelli Caitlynn , Annis Matthew G. , Sabourin Valerie , Hebert Steven , Findlay Steven , Kim HaEun , Witcher Michael , Kleinman Claudia L. , Siegel Peter M. , Orthwein Alexandre , Ursini-Siegel Josie
Published: November 2024

Supplemental Figure 4 with Legend
Supplementary sure S1 from A Dual-Payload Antibody–Drug Conjugate Targeting CD276/B7-H3 Elicits Cytotoxicity and Immune Activation in Triple-Negative Breast Cancer
Authors: Zhou Zhuoxin “Zora” , Si Yingnan , Zhang Jiashuai , Chen Kai , George Ashley , Kim Seulhee , Zhou Lufang , Liu Xiaoguang “Margaret”
Published: November 2024

Supplementary Figure S1
Supplementary Figure 5 from DAZAP1 Phase Separation Regulates Mitochondrial Metabolism to Facilitate Invasion and Metastasis of Oral Squamous Cell Carcinoma
Authors: Zhang Jiayi , Ni Zihui , Zhang Yu , Guo Yan , Zhai Rundong , Wang Mengqi , Gong Zizhen , Wang Mengyao , Zeng Fanrui , Gu Ziyue , Chen Qianming , Liu Laikui , Wang Zhiyong , Zhu Weiwen
Published: November 2024

The COX16 overexpression rescues the DAZAP1-knockdown induced phenotypes of OSCC cell lines.
Supplementary Figure S2 from A Dual-Payload Antibody–Drug Conjugate Targeting CD276/B7-H3 Elicits Cytotoxicity and Immune Activation in Triple-Negative Breast Cancer
Authors: Zhou Zhuoxin “Zora” , Si Yingnan , Zhang Jiashuai , Chen Kai , George Ashley , Kim Seulhee , Zhou Lufang , Liu Xiaoguang “Margaret”
Published: November 2024

Supplementary Figure S2
Facial Filler Injections– Questions Patients Ask and Where They Find Answers
Authors: Monos Stylianos , Fritz Christian , Go Beatrice , Rajasekaran Karthik
Published: November 2024

Introduction: The most pressing questions patients ask about facial fillers and the sources to which patients are directed remain incompletely understood.
Methods: The search engine optimization tool Ahrefs was utilized to extract Google metadata on searches performed in the United States. The most frequently asked questions were categorized by topic, while websites were categorized by authoring organization. JAMA benchmark criteria were used for website information quality assessment.
Results: A total of 300 questions for the term “fillers” were extracted. The majority of search queries (24.0%) and monthly search volume (39.3%) pertained to procedural costs. The mean JAMA score for private practice sources (1.1 ± 0.57) was significantly lower than that of corporate sources (2.6 ± 0.55, p= 0.0003) but not significantly lower than academic pages (1.6 ± 1.34, p=0.483). With respect to monthly search volume, queries concerning lip fillers have been increasingly asked at a rate that exceeds other injection sites.
Conclusion: Online searches for facial fillers often involve the topic of cost, and frequently direct patients to websites that contain inadequate information on authorship, attribution, disclosure and currency. When compared to other anatomic sites, search queries involving lip fillers have increased over the last three years.
Epigenetic Alterations in Post-Traumatic Stress Disorder: Comprehensive Review of Molecular Markers
Authors: Golubeva Elizaveta , Zeltser Angelina , Zorkina Yana , Ochneva Aleksandra , Tsurina Anna , Andreyuk Denis , Kostyuk Georgiy , Morozova Anna
Published: November 2024

Background: Post-traumatic stress disorder (PTSD) can occur after a traumatic event. PTSD is characterized by nightmares, flashbacks and avoidance of stressors. It currently affects 2–8% of the population, with military personnel particularly susceptible. Studies show that environmental stressors can induce various epigenetic changes that shape the PTSD phenotype. Despite the significant impact of epigenetic factors on PTSD symptoms and susceptibility, they have not been widely discussed in the literature. This review focuses on describing epigenetic mechanisms in PTSD, especially DNA methylation, chromatin regulation, and noncoding RNA. Summary: The article includes relevant studies published from 2013 to 2023, excluding non-English-language studies or studies with insufficient data. This review investigated gene methylation changes in association with PTSD, including those related to the hypothalamic-pituitary-adrenal axis, brain-derived neurotrophic factor, neurotransmitters, and immune system functioning, as well as the role of histones and regulatory noncoding RNAs. Key Messages: Epigenetic alterations play a crucial role in shaping PTSD susceptibility, symptomatology, and long-term outcomes, highlighting their potential as important markers and therapeutic targets. Understanding these alterations can aid in developing clinical strategies to better predict, prevent, and treat PTSD. However, further large-scale longitudinal studies are needed to establish the temporal relationship between epigenetic changes and the onset of PTSD, as well as to classify other potential epigenetic mechanisms.
Energy analysis of active photovoltaic cooling system using water flow
Authors: Kristi Ant. Ardath , Susanto Erwin , Risdiyanto Agus , Junaedi Agus , Darussalam Rudi , Rachman Noviadi Arief , Fudholi Ahmad
Published: November 2024

An active water-cooling system is one of several technologies that has been proven to be able to reduce heat losses and increase electrical energy in photovoltaic (PV) module. This research discusses a comparative experimental study of three pump activation controls in cooling of PV module with the aim of evaluating specifically the PV output power, net energy gain, water flow rate, and module temperature reduction. The three pump activation controls being compared are continuously active during the test, active based on setpoint temperature, and active by controlling the pump voltage using pulse width modulation (PWM) control in adjusting water flow rate smoothly. The results show that controlling the pump voltage using PWM in the PV cooling process produces energy of 437.95 Wh, slightly lower than the others and the average module cooling temperature is 35.24 °C, higher of 1-3 °C than the others. Nevertheless, PWM control of cooling pump has resulted the percentage of net energy gain of 9.94%, greater than other controls, and with an average flow rate of 2.17 L/min, more efficient than the others. Thus, this control is quite effective as it can produce higher net PV energy yield and lower water consumption.
Integrated U-Net segmentation and gated recurrent unit classification for accurate brain tumor diagnosis from magnetic resonance imaging images
Authors: Sajjanar Ravikumar , Dixit Umesh D.
Published: November 2024

Early diagnosis and proper grouping of tumors in the brain are critical for successful therapy and positive outcomes for patients. This work proposes a complete technique for identifying brain tumors that employ sophisticated artificial intelligence methodologies and achieve an accuracy rate of 97.18%. The work makes use of the brain tumor magnetic resonance imaging (MRI) collection in Kaggle, which has 723 MRI scans classified as glioma, meningioma, pituitary tumor, and no tumor. These images are initially preprocessed, which includes scaling to a homogeneous size normalizing, and removal of noise to ensure uniformity and clarity. To improve the information set, generative adversarial networks (GANs) are used to perform data augmentation, producing artificial pictures that improve the database variety and resilience. To achieve exact cancer localization, the U-Net construction, recognized for its encoder-decoder design and skip links, is used to divide up tumor areas across images generated by MRI. The image segments are then input into gated recurrent units (GRUs), to analyze a collection of features to capture periods and differences between segments. The last classification is accomplished using an entirely linked layer and then a softmax stimulation, which provides the tumors classes. This method helps for medical experiments and clinical methods.
Fortifying industrial cybersecurity: a novel industrial internet of things architecture enhanced by honeypot integration
Authors: Kouari Oumaima El , Lazaar Saiida , Achoughi Tarik
Published: November 2024

The industrial internet of things (IIoT) has significantly transformed the industrial sectors by connecting devices, machines, and systems to enhance automation, efficiency, and decision-making. However, the increased interconnectivity also poses significant security challenges because IIoT devices control critical infrastructures and processes. Our work presents an implementation of a robust industrial cybersecurity strategy with a segmented network architecture, collaborative efforts between information technology (IT) and operational technology (OT) teams for enhanced resilience and effectiveness, and vertical honeypots across all Industry 4.0 levels integrated with Wazuh for log transmission and proactive threat response, alongside Snort intrusion detection system (IDS) monitoring network traffic. Additionally, we reinforce our architecture by Wazuh with Elasticsearch and Kibana as a security information and event management solution, facilitating data analysis and compliance enforcement through custom rulesets and cybersecurity threat intelligence (CTI) integration, with automatic updates for continuous adaptation against emerging threats.
Evaluating machine learning models for predictive analytics of liver disease detection using healthcare big data
Authors: Khaled Osama Mohareb , Elsherif Ahmed Zakareia , Salama Ahmed , Herajy Mostafa , Elsedimy Elsayed
Published: November 2024

Liver diseases rank among the most prevalent health issues globally, causing significant morbidity and mortality. Early detection of liver diseases allows for timely intervention, which can prevent the progression of such diseases to more severe stages such as cirrhosis or liver cancer. To this end, many machine learning models have been previously developed to early predict liver diseases among potential patients. However, each model has its accuracy and performance limitations. In this paper, we present a comprehensive comparison of three different machine learning models that can be employed to enhance the prediction and management of liver diseases. We utilize a big data set of 32,000 records to evaluate the performance of each model. First, we implement a preprocessing technique to rectify missing or corrupt data in liver disease datasets, ensuring data integrity. Afterwards, we compare the performance of three machine models: k-nearest neighbors (KNN), gaussian naive Bayes (Gaussian NB) and random forest (RF). We concluded that the RF algorithm demonstrates superior performance in our evaluation, excelling in both predictive accuracy and the ability to classify patients accurately regarding the presence of liver disease. Our results show that RF outperforms other models based on several performance metrics including accuracy: 97.3%, precision: 97%, recall: 96%, and f1-score: 95%.
Enhancing internet of things security against structured query language injection and brute force attacks through federated learning
Authors: Adamova Aigul , Zhukabayeva Tamara , Mukanova Zhanna , Oralbekova Zhanar
Published: November 2024

The internet of things (IoT) encompasses various devices for monitoring, data collection, tracking people and assets, and interacting with other gadgets without human intervention. Implementing a system for predicting the development and assessing the criticality of detected attacks is essential for ensuring security in IoT interactions. This work analyses existing methods for detecting attacks, including machine learning, deep learning, and ensemble methods, and explores the federated learning (FL) method. The aim is to study FL to enhance security, develop a methodology for predicting the development of attacks, and assess their criticality in real-time. FL enables devices and the aggregation server to jointly train a common global model while keeping the original data locally on each client. We demonstrate the performance of the proposed methodology against structured query language (SQL) injection and brute force attacks using the CICIOT2023 dataset. We used accuracy and F1 score metrics to evaluate the effectiveness of our proposed methodology. As a result, the accuracy in predicting SQL injection reached 100%, and for brute force attacks, it reached 98.25%. The high rates of experimental results clearly show that the proposed FL-based attack prediction methodology can be used to ensure security in IoT interactions.
Intrusion detection and prevention using Bayesian decision with fuzzy logic system
Authors: Sekar Satheeshkumar , Parvathy Palaniraj Rajidurai , Gupta Gopal Kumar , Rajagopalan Thiruvenkadachari , Basavaraddi Chethan Chandra Subhash Chandra Basappa , Padmanaban Kuppan , Murugan Subbiah
Published: November 2024

Nowadays, intrusion detection and prevention method has comprehended the notice to decrease the effect of intruders. denial of service (DoS) is an attack that formulates malicious traffic is distributed into an exacting network device. These attackers absorb with a valid network device, the valid device will be compromised to insert malicious traffic. To solve these problems, the Bayesian decision model with a fuzzy logic system based on intrusion detection and prevention (BDFL) is introduced. This mechanism separates the DoS packets based on the type of validation, such as packet and flow validation. The BDFL mechanism uses a fuzzy logic system (FLS) for validating the data packets. Also, the key features of the algorithm are excerpted from data packets and categorized into normal, doubtful, and malicious. Furthermore, the Bayesian decision (BD) decide two queues as malicious and normal. The BDFL mechanism is experimental in a network simulator environment, and the operations are measures regarding DoS attacker detection ratio, delay, traffic load, and throughput.
Low-cost integrated circuit packaging defect classification system using edge impulse and ESP32CAM
Authors: Kamaruddin Muhammad Adni , Mispan Mohd Syafiq , Jidin Aiman Zakwan , Mohd Nasir Haslinah , Mohd Nor Nurul Izza
Published: November 2024

Defects in integrated circuit (IC) packaging are inevitable. Several factors can cause defects in IC packaging such as material quality, errors in machine and human handling operations, and non-optimized processes. An automated optical inspection (AOI) is a typical method to find defects in the IC manufacturing field. Nevertheless, AOI requires human assistance in the event of uncertain defect classification. Human inspection often misses very tiny defects and is inconsistent throughout the inspection. Therefore, this study proposed a low-cost IC packaging defect classification system using edge impulse and ESP32-CAM. The method involves training a deep learning model (i.e., convolutional neural network (CNN)) using a dataset of non-defective and defective ICs on Edge Impulse. For defective ICs, the top surface of the ICs is deliberately scratched to imitate the cosmetic defects. ICs with scratch-free on their top surfaces are considered non-defective ICs. A successfully trained model using Edge Impulse is subsequently deployed on ESP32-CAM. The model is optimized to fit the limited resources of the ESP32-CAM. By using the built-in camera in ESP32-CAM, the trained model can perform a real-time image classification of non-defective/defective ICs. The proposed system achieves 86.1% prediction accuracy by using a 1,571 image dataset of defective and non-defective ICs.
Optimizing power consumption in novel electrical design for single ended comparator circuit
Authors: Zghoul Fadi Nessir , Migdadi Wafaa , Al-Mistarihi Mamoun
Published: November 2024

Contemporary society electronic technology has evolved into a pivotal component across various facets of our lives. Its indispensability is particularly evident in the advancement of medical, agricultural, industrial, and other sectors. As this technology continues to play a crucial role, optimizing its performance in terms of speed, accuracy, and energy consumption becomes paramount. This paper introduces a novel electrical design for the threshold inverter quantization comparator circuit aiming to meet the evolving demands of modern electronic applications. The proposed design enhances the classic threshold inverter quantization comparator’s performance by significantly reducing its power consumption. Through rigorous mathematical analysis and simulation results it is demonstrated that the proposed comparator design achieves a remarkable 50% reduction in power consumption compared to the conventional threshold inverter quantization comparator. Subsequently the newly devised design is applied to the construction of a 4-bit flash analog-to-digital converter using 0.35 μm complementary metal–oxide–semiconductor (CMOS) technology.
From concept to application: building and testing a low-cost light detection and ranging system for small mobile robots using time-of-flight sensors
Authors: García Andrés , Díaz Mauricio , Martínez Fredy
Published: November 2024

Advancements in light detection and ranging (LiDAR) technology have significantly improved robotics and automated navigation. However, the high cost of traditional LiDAR sensors restricts their use in small-scale robotic projects. This paper details the development of a low-cost LiDAR prototype for small mobile robots, using time-of-flight (ToF) sensors as a cost-effective alternative. Integrated with an ESP32 microcontroller for real-time data processing and Wi-Fi connectivity, the prototype facilitates accurate distance measurement and environmental mapping, crucial for autonomous navigation. Our approach included hardware design and assembly, followed by programming the ToF sensors and ESP32 for data collection and actuation. Experiments validated the accuracy of the ToF sensors under static, dynamic, and varied lighting conditions. Results show that our low-cost system achieves accuracy and reliability comparable to more expensive options, with an average mapping error within acceptable limits for practical use. This work offers a blueprint for affordable LiDAR systems, expanding access to technology for research and education, and demonstrating the viability of ToF sensors in economical robotic navigation and mapping solutions.
Refining thyroid function evaluation: a comparative study of preprocessing methods in diffuse reflectance spectroscopy
Authors: Shalini Wincent Anto Win , Rajalakshmi Thulasi , Suryakala Selvanayagam Vasanthadev
Published: November 2024

Thyroid dysfunction, comprising conditions such as hyperthyroidism and hypothyroidism, represents a substantial global health challenge, necessitating timely and precise diagnosis for effective therapeutic intervention and patient welfare. Conventional diagnostic modalities often involve invasive procedures, that could cause discomfort and inconvenience for individuals. The non-invasive techniques like diffuse reflectance spectroscopy (DRS) can offer a promising alternative. This study underscores the critical role of preprocessing methods in enhancing the accuracy of thyroid hormone functionality through a non-invasive approach. In the proposed study the spectral data acquired from the DRS setup are subjected to different preprocessing techniques to improve the efficacy of the prediction model. Thirty individuals with thyroid dysfunction were included in the study, and preprocessing methods such as baseline correction, multiplicative scatter correction (MSC), and standard normal variate (SNV), were systematically evaluated. The study highlights that SNV preprocessing outperformed other methods with a root mean square error (RMSE) of 0.005 and an R² of 0.99. In contrast, MSC resulted in an RMSE of 0.87 and an R² of 0.86, while baseline correction showed a RMSE of 0.84 and an unusual R² of 1.09, indicating potential issues. SNV proved to be the most effective technique.
Road feature extraction from LANDSAT-8 operational land imager images using simplified U-Net model
Authors: Reddy Sama Lenin Kumar , Rao Chandu Venkateswara , Kumar Pullakura Rajesh
Published: November 2024

Automatic road feature extraction from the remote sensing (RS) imagery has a significant role in various applications such as urban planning, transportation management, and environmental monitoring. In this paper, propose a method based on the U-Net model to extract the road features from the LANDSAT-8 operational land imager (OLI) images. This method aims to extract road features in OLI images that appear as curvilinear features and roads with widths greater than 25 meters, which are mostly covered within a single pixel of the OLI resolution of multi-spectral images. The U-Net architecture is well-known for its effectiveness in image segmentation tasks. However, to optimize the complexity in the U-Net model, simplified the architecture while retaining its key components and principles. The proposed model by decreasing the convolution layers and the parameters which are involved to optimize the model called as simplified U-Net model. To train this model, the label images were generated for LANDSAT-8 OLI images, by using the saturation based adaptive thresholding and morphology (SATM) method. This reduces the efforts to draw the labels in the vector format labels and convert to raster images. The model is able to effectively generate weights, which are able to extract the road features. This model weights applied on the OLI images which covers the urban and rural areas of India, producing the satisfactory results. The experimental results with the quantitative analysis presented in the paper.
Enhancing the reliance of emergency power supply systems for nuclear facilities using hybrid system
Authors: Saade Mohammed , El-Eissawi Hussein , Nada Adel S.
Published: November 2024

The performance of an atomic facility depends on the efficient supply of electricity, particularly emergency loads like monitoring and control equipment, radiation safety systems, and emergency lights. Most nuclear facilities rely on diesel generators to supply emergency loads during grid outages. Due to the diesel generator's imperfections, such as its starting time, it may fail to deliver power because it is unavailable due to maintenance, failure to start, or failure to run and supply the load. It cannot immediately supply the critical loads, resulting in a blackout and the release of radioactive substances into the environment. To address the previous issues, this paper proposes an improved method to enhance the reliability of nuclear facilities for providing electricity to safety and critical consumers during normal and emergency operating modes. The approach incorporates a photovoltaic (PV) system/battery, and its robustness and performance are tested using load flow and transient stability analysis. The simulation results demonstrated the effectiveness and speed of the proposed method when compared to the traditional method, as the emergency consumers were successfully powered within a very short time without fluctuations, and the voltage reduction and frequency were within the nominal values. The electrical transient analyzer program (ETAP) is used to validate these results.