bluuebunny/crossref_metadata_2025_split huggingface dataset:
Melittin - A Main Component of Bee Venom: A Promising Therapeutic Agent for Neuroprotection through Nrf2/HO-1 Pathway Activation
Authors: Yoo Jaehee , Nguyen Cong Duc , Ha Hai-Anh , Jeong Sang Jun , Yang Ji Hye , Lee Gihyun , Shin Jeong Cheol , Kim Jae-Hong 
Published: July 2024

Abstract

The Nrf2/HO-1 pathway, known for its significant role in regulating innate antioxidant defense mechanisms, is increasingly being recognized for its potential in neuroprotection studies. Derived from bee venom, melittin's neuroprotective effects are raising interest. This study confirms that melittin specificity upregulated the weaken Nrf2/HO-1 signaling in mice brain. Interestingly, we also revealed melittin’s efficient tactic, as the restored redox balance alone gradually stabilized other regulations of the mouse hippocampus. Using a scopolamine-induced, a common and effective neurodegeneration model in mice, chemical analysis revealed that melittin crosses the compromised blood-brain barrier, accumulates in the hippocampus, and significantly enhances neurogenesis and cognitive function in scopolamine-induced mice. Careful observation in mice showed: first signs of changes within 5 hours after melittin administration were the restoration of the Nrf2/HO-1 system and suppresses oxidative stress. After this event, from 7 to 12.5 hours after administration were the rebalancing of inflammation, apoptosis, neurotrophic factors, cholinergic function, and mitochondrial performance. This chain reaction underscores the redox balance's role in reviving multiple neuronal functions. Evidence of enhancement in mouse hippocampus led to further exploration with hippocampal cell line HT22. Immunofluorescence analysis showed melittin-induced Nrf2 translocation to the nucleus, which would initiating the translation of antioxidant genes like HO-1. Pathway inhibitors pinpointed melittin's direct influence on the Nrf2/HO-1 pathway. 3D docking models and pull-down assays suggested melittin's direct interaction with Keap1, Nrf2/HO-1’s activator. Overall, this study not only highlighted melittin specifically effect on Nrf2/HO-1, thus, rebalancing cellular redox, but also showed that this is a effective multi-effect therapeutic strategy against neurodegeneration.
Analysis of phase-change heat transfer in gold particles irradiated by an ultrashort-pulse laser under scattering effect
Authors: Shi Shunze , Zhang Mengya , Li Ling 
Published: July 2024

Abstract

The phase-change heat transfer in gold (Au) particles, vertically irradiated by a single-pulse laser, was investigated using a two-temperature model combined with the results of uneven particle surface light intensity distribution due to scattering. By coupling the interface energy balance equation and the phase tracking method of nucleation kinetics, the position of the solid-liquid interface was determined and the impact of laser parameters on the sintering process was investigated. As the laser irradiated the Au particles vertically, the melting process mainly occurred at both poles of the particles. The melting began at the bottom and resulted in a relatively small melting volume. As the laser energy density rose, the melting volume of the particles also increased. During the laser irradiation phase, the particle size increase led to an increase in the light intensity, and hence, temperature, at the bottom of the particle due to the scattering effect. At the end of laser irradiation, larger particle sizes resulted in a higher heat transfer volume per unit of light surface area. This led to lower temperatures, followed by particle melting, and a faster decline of the temperature at the bottom of the particle.
Sculpting Stochastic Dynamics: Manipulating Conductivity and Noise for Transitioning between Stochastic and Inverse Stochastic Resonances in Liquid- Crystal Electroconvection
Authors: Huh Jong-Hoon , Higashi Takumi , Sato Yuki 
Published: July 2024

Abstract

Noise can play a constructive role in nature and various engineering systems. Over the past four decades, noise-induced stochastic resonances (SRs) have been extensively documented, showing enhancement in system performance. Additionally, inverse SR has been observed in various systems. Typically, these resonances were studied independently. A transition between these resonances was recently observed in an alternating current-driven liquid-crystal electroconvection (EC) system using combined amplitude and phase noises. This study uses internal (material) and external (noise) parameters to demonstrate the control of this transition. Specifically, the nonmonotonic threshold voltage behavior of the EC system, indicative of the resonances, was numerically examined using additional parameters. Experimental tests were conducted to confirm the effects of these parameters. The findings reveal that the transition between these resonances can be systematically controlled to meet specific needs, whether desirable or undesirable system performances. Notably, this study illustrates how to modify the behavior of both resonances in colored noise by adjusting its cutoff frequency and steepness and phase noise, which is often overlooked. Moreover, this study provides valuable insights for various noise-related applications.
Deviatoric Stress-Induced Transition of Self-Trapped Exciton Emissions
Authors: Liu Kai-Kai , Lv Chao-Fan , Yang Xigui , Wu Yanran , Liang Yachuan , Qin Jinxu , Lai Shoulong , Liu Hang , Zang Jinhao , Cheng Shaobo , Shan Chong-Xin 
Published: July 2024

Abstract

Self-trapped exciton (STE) emissions, featured by their broad spectral band and little self-absorption, have garnered considerable attention for advanced lighting and imaging applications. However, the exploration of an effective strategy to facilitate multiple STE states, modulate the emission energy and the cover range remains a great challenge. Here, we introduce deviatoric stress, facilitating an extrinsic STE (ex-STE) state and the transition between ex-STE and another intrinsic STE (in-STE) emissions in pyramidal ZnO nanocrystals, achieving a remarkable transition of emission energy from yellow-green (2.34 eV) to deep-blue (2.88 eV). Combined in-situ stress monitoring and optical experiments demonstrate that the ex-STE state originates from a potential well generated by plastic deviatoric deformation induced by deviatoric stress. Spectroscopic and dynamical characterization of these two STE emissions reveal a transition process in the carrier’s relaxation pathway from ex-STE to in-STE, and conversely at much higher pressure. These findings demonstrate that deviatoric stress is a robust tool to drive STE emissions and provide deep insights into the carrier dynamics evolution of STE emissions.
Structural basis for transcription activation through cooperative recruitment of MntR
Authors: ahuja Shivani , Shi Haoyuan , Fu Yu , Kodyte Vilmante , Andreas Amelie , Sachla Ankita , Miller Keiki , Shrestha Ritu , Helmann John , Glasfeld Arthur 
Published: July 2024

Abstract

The manganese transport regulator (MntR) from B. subtilis is a dual regulatory protein that responds to heightened Mn2+ availability in the cell by both repressing the expression of uptake transporters and activating the expression of efflux proteins. Recent work indicates that, in its role as an activator, MntR binds several sites upstream of the genes encoding Mn2+ exporters, leading to a cooperative response to manganese. Here, we use cryo-EM to explore the molecular basis of gene activation by MntR and report a structure of four MntR dimers bound to four 18-base pair sites across an 84-base pair regulatory region of the mneP promoter. Our structures, along with solution studies including mass photometry and in vivo transcription assays, reveal that MntR dimers employ polar and non-polar contacts to bind cooperatively to an array of low-affinity DNA-binding sites. These results reveal the molecular basis for cooperativity in the activation of manganese efflux.
Tumor-specific high-valence bismuth(V) nanoplatform potentiates cancer immunotherapy with exogenous excitation-free endogenous H2O2- and O2-independent ROS generation
Authors: Li Wanwan , Tang Yizhang , Yu Xujiang , He Liangrui , Tang Meng , Yue Wenji , Chen Ruitong , Zhao Jie , Pan Qi 
Published: July 2024

Abstract

Reactive oxygen species (ROS) with evoked immunotherapy holds tremendous promise for cancer treatment but has limitations due to its dependence on exogenous excitation and/or endogenous H2O2 and O2. Here we report a versatile oxidizing pentavalent bismuth(V) nanoplatform (NaBiVO3-PEG) can generate ROS in an excitation-free and H2O2- and O2-independent manner. Upon exposure to tumor microenvironment, NaBiVO3-PEG undergoes continuous H+-accelerated hydrolysis with •OH and 1O2 generation through a two-electron transfer reaction with H2O and intrinsic lattice oxygen transformation. Simultaneous release of sodium counterions after endocytosis triggers caspase-1-mediated pyroptosis. NaBiVO3-PEG intratumorally administered initiates robust therapeutic efficacies against both primary and distant tumors and activates systemic immune responses to combat tumor metastasis. Alternatively, NaBiVO3-PEG intravenously administered can efficiently accumulate at the tumor site for further real-time computed tomography monitoring and synergistic immunotherapy and radiotherapy. Overall, this work offers a new nanomedicine based on a high-valence bismuth(V) nanoplatform and underscores its great potential for cancer immunotherapy.
Fructose metabolism is associated with anesthesia/surgery induced lactate production
Authors: Zhang Lei , Liu Jianhui , Miao Zhengjie , Zhou Ren , Wang Hao , Li Xiang , Liu Jiehui , Zhang Jingya , Yan Jia , Jiang Hong 
Published: July 2024

Abstract

Background: Elderly individuals display excessive lactate levels that may contribute to development of cognitive impairment following surgery, including delayed neurocognitive recovery (dNCR). Since the origin of this increased lactate is unknown, here we assessed associations between metabolic pathways and postoperative dNCR.
Methods: This study included 43 patients (≥65 years old) who had surgery under general anaesthesia. We also used a mouse model in which 20-month-old mice were exposed under sevoflurane to induce postoperative dNCR. Metabolomics were used to measure metabolites in the serum of patients and brains of mice following anaesthesia/surgery. Isotope labelling and metabolic flux were used to analyse flow and distribution of specific metabolites in metabolic pathways.
Results: Among 43 patients, 17 developed dNCR. Metabolomics showed significantly decreased postoperative serum fructose 1-phosphate levels in dNCR compared to non-dNCR patients. Similar results were found in the mouse model. Isotope labelling and metabolic flux experiments in mice showed fructose but not glucose entered glycolysis, increasing lactate levels after anaesthesia/surgery. Administration of intraperitoneal fructose inhibitors to mice effectively inhibited the increased lactate levels and cognitive dysfunction following anaesthesia/surgery. We also found anaesthesia/surgery increased IL-6 levels in mice, and that IL-6 may function upstream in fructose activation.
Conclusions: These results suggest that anaesthesia/surgery activates fructose metabolism, producing excessive lactate and ultimately contributing to postoperative cognitive impairment. Fructose metabolism is thus a potential therapeutic target for dNCR.
Evolving Neuro-Fuzzy Systems for Real-Time Data Processing in Internet of Medical Things
Authors: Nyirenda Clement , Chilipa Richard 
Published: July 2024

Abstract

Evolving neuro-fuzzy systems (ENFS) have shown great promise in analysis of streaming data, integrating artificial
neural networks and fuzzy logic to create self-learning, data-driven models suitable for online (real-time) applications. In healthcare, ENFS have the potential to transform the Internet of Medical Things (IoMT) by enabling real-time data processing, decision support, and timely emergency response. In this study six
evolving fuzzy min-max neural network models have been implemented using Python and Kafka, and evaluated on the MIT-BIH Arrhythmia dataset comprising 109,446 samples corresponding to shapes of heartbeats for the normal case and the cases affected by different arrhythmias and myocardial infarction. To achieve online continual learning, the prequential test-then-train method has been used to sequentially classify the data instances from
scratch and update the system structure along with the arrival of new data samples. The Enhanced Fuzzy Min-Max Neural Network (EFMMNN) achieved the highest accuracy of 98.11%. Future work will explore deploying these evolving fuzzy systems in real-world healthcare settings using edge-fog computing.
Particle Swarm Optimization of Deep Learning Models for the Determination of Vehicle Hijacking Tweets
Authors: Nyirenda Clement , Patel Taahir A. 
Published: July 2024

Abstract

The problem of vehicle hijacking is increasingly prevalent in many parts of the world, including South Africa, causing travelers to live in constant fear of becoming victims. To address this, this work employs the usage of social media data to identify hijacking incidents in urban environments by using the city of Cape Town in South Africa as the case study. More specifically, the incorporated data is obtained from the Twitter social media platform. However, this form of data tends to be informal and unstructured. Hence, this led to the issue of data relevance, which was addressed by using Machine Learning algorithms to classify tweets. Two deep learning algorithms were employed: a Convolutional Neural Network (CNN), and a Multi-layer Feed-forward Neural Network (MLFNN). However, due to model complexity, Particle Swarm Optimization (PSO) was also implemented to produce the model variations, PSOCNN and PSOMLFNN. It was found that the PSO-optimized models outperformed the standard non-PSO models. The PSOMLFNN approach was also identified to be the best-performing model amongst the employed selection by obtaining an accuracy of 96.15%. Hence, it was successfully employed along with the Tweet data to produce vehicle hijacking maps. In future work, further machine learning and optimization algorithms will be tested to identify the best-performing algorithms for this task as well as extend the hijacking maps to a mobile application for drivers on the South African roads.
Water quality ensemble prediction model for the urban water reservoir based on the hybrid long short-term memory (LSTM) network analysis
Authors: He Kai , Liu Yu , Yuan Jinlong , He Zhidong , Yin Qidong , Xu Dongjian , Zhao Xinfeng , Hu Maochuan , Lu Haoxian 
Published: July 2024

ABSTRACT

The water quality of drinking water reservoirs directly impacts the water supply safety for urban residents. This study focuses on the Da Jing Shan Reservoir, a crucial drinking water source for Zhuhai City and the Macau Special Administrative Region. The aim is to establish a prediction model for the water quality of drinking water reservoirs, which can serve as a vital reference for water plants when formulating their water supply plans. In this research, after smoothing the data using the Hodrick-Prescott filter, we utilized the long short-term memory (LSTM) network model to create a water quality prediction model for the Da Jing Shan Reservoir. Simulation calculations reveal that the model's fitting degree is consistently above 60%. Specifically, the prediction accuracy for pH, dissolved oxygen (DO), and biochemical oxygen demand (BOD) in the water quality prediction model aligns with actual results by more than 70%, effectively simulating the reservoir's water quality changes. Moreover, for parameters such as pH, DO, BOD, and total phosphorus, the relative forecasting error of the LSTM model is less than 10%, confirming the model's validity. The results of this study offer an essential model reference for predicting water quality for the Da Jing Shan Reservoir.
Impact of El Niño, Indian Ocean dipole, and Madden–Julian oscillation on land surface temperature in Kuching City Sarawak, during the periods of 1997/1998 and 2015/2016: a pilot study
Authors: Anak Kemarau Ricky , Boo Wee Hin , Sakawi Zaini , Dambul Ramzah , Suab Stanley Anak , Wan Mohd Jaafar Wan Shafrina , Eboy Oliver Valentine , Fakhry Norzin Muhammad Ammar 
Published: July 2024

ABSTRACT
The severe El Niño events of 1997/1998 and 2015/2016 caused significant disruptions in Southeast Asia, particularly in Borneo, resulting in hazardous haze and acute water shortages. This study examines the influence of El Niño, the Indian Ocean Dipole (IOD), and the Madden–Julian oscillation (MJO) on regional climate, using time-series data from February 1993 to December 2020. Data from El Niño, IOD, and MJO indices were integrated with Landsat 5 and 8 land surface temperature records, allowing for a detailed analysis of their combined effects on regional temperature and precipitation patterns. Time-series trend decomposition and the generalized linear mixed model approach identified the Oceanic Niño Index (ONI) as a significant driver of temperature increases and dry spell occurrences during the peak El Niño years. On the other hand, ONI correlated strongly with mean monthly temperatures, underscoring its dominant influence. In addition, the IOD was found to significantly affect regional temperatures with a regression coefficient of 0.38867 (p = 0.0455), indicating its significant but less pronounced impact compared with ONI. These findings clarify the dynamics between key climate indices and their impact on regional climate extremes, offering critical insights for improving climate resilience and adaptation in tropical regions.
Advancements of Glucose Monitoring Biosensor: Current State, Generations of Technological Progress, and Innovation Dynamics
Authors: Dua Arpita , Debnath Abhijit , Kumar Kunal , Mazumder Rupa , Mazumder Avijit , Singh Rajesh , Mangal Saloni , Sanchitra Jahanvi , Khan Fahad , Tripathi Soumya , Vishwas Sukriti , Chaudhary Hema , Sharma Parul , Srivastava Shikha 
Published: July 2024

Abstract:
Glucose monitoring is essential for managing diabetes, and continuous glucose monitoring
biosensors can offer real-time monitoring with little invasiveness. However, challenges
remain in improving sensor accuracy, selectivity, and overall performance. This article aims to
review current trends and recent advancements in glucose-monitoring biosensors while evaluating
their benefits and limitations for diabetes monitoring. An analysis of current literature on
transdermal glucose sensors was conducted, focusing on detection techniques, novel nanomaterials,
and integrated sensor systems. Recent research has led to advancements in electrochemical,
optical, electromagnetic, and sonochemical sensors for transdermal glucose detection. The use of
novel nanomaterials and integrated sensor designs has improved sensitivity, selectivity, and accuracy.
However, issues like calibration requirements, motion artifacts, and skin irritation persist.
Transdermal glucose sensors show promise for non-invasive, convenient diabetes monitoring
but require further enhancements to address limitations in accuracy, reliability, and biocompatibility.
Continued research and innovation focusing on sensor materials, designs, and surface
chemistry is needed to optimize biosensor performance and utility. The study offers a comprehensive
analysis of the present status of technological advancement and highlights areas that
need more research.
LBSA-DRIVER: A Novel Approach to Identifying Cancer Driver Genes
Using List-Based Simulated Annealing
Authors: Atay Yilmaz , Ngobesing Lionel Alangeh , Cingiz Mustafa Ozgur 
Published: July 2024

Introduction:
Cancer driver genes are genes responsible for cancer genesis; thus, identifying
cancer-related genes is crucial in fostering cancer treatment. The accuracy in identifying cancer
driver genes within the vast pool of normal passenger genes directly influences the efficacy of
treatment approaches.

Objective:
This research aimed to effectively identify cancer driver genes using the List-based Simulated
Annealing (LBSA) optimization technique.

Method:
The proposed model (LBSA-DRIVER) harnesses a list-based simulated annealing algorithm
within a bipartite network to pinpoint cancer driver genes. The process begins with creating a
bipartite graph that integrates gene mutations and expression data from carefully chosen datasets.
The LBSA algorithm is then applied to the generated graph to identify and rank the genes, drawing
insights from a biological interaction network.

Result:
Following the algorithm's development, rigorous experimental analyses have been conducted
using four benchmark datasets from The Cancer Genome Atlas (TCGA) database. The datasets
used were the Breast Cancer dataset (BRCA), Prostate Adenocarcinoma dataset (PRAD), Ovarian
cancer dataset (OV), and Glioblastoma Multiforme dataset (GBM).

Conclusion:
Our findings, including precision, recall, F-score, and accuracy metrics, provide strong
evidence of the effectiveness of the proposed model in identifying driver genes.
An Overview of Optical Sensing of Copper and its Removal by Different Techniques
Authors: Dutta Srishti , Banjara Dishen Kumar , Pandey Abhilash , Sharma Vansikha , Sahu Devanand , Kumari Niraj , Patra Goutam Kumar 
Published: July 2024

:
The identification of heavy metals by sensing technologies is a crucial area of study since these metals are present in the environment and are
hazardous. The in-depth analyses of the probes' structures and sensing capabilities improve our understanding of how to design and develop probes
for the same metal in the future. The third most common metal ion and trace element, copper (Cu2+), is essential to all living things and is involved
in several activities. However, different diseases are caused by excess or deficiency of Cu2+ ions, depending on what the cell requires. For all of
these reasons, optical sensors have concentrated on quick, highly sensitive, and selective real-time detection of Cu2+ ions. Fluorescence in the
refractive index-adsorption from the interactions between light and matter can be measured using optical sensors. Furthermore, due to their strong
advantages which include real-time detection, simplicity and naked eye recognition, low cost, high specificity against analytes, quick reaction, and
the requirement for less complex equipment during analysis they have attracted a lot of attention in recent years. In this review, we covered many
fluoro and chemosensors for the detection of copper, along with their sensing parameters in various mediums and thorough structural analyses.
This review also covers the extraction of copper from the aqueous medium. The use of membrane processes, adsorption, and electrocoagulation is
examined, and the difficulties associated with their application have been presented.