bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sequencing by Binding rivals error-corrected Sequencing by Synthesis technology for accurate detection and quantification of minor (&lt;0.1%) subpopulation variants
Authors: Allender Christopher J. , Wike Candice , Ellis Dean , Lemmer Darrin , Porter Tanner , Pond Stephanie J.K. , Engelthaler David M. 
Published: May 2024

Abstract

Detecting very minor (< 1%) subpopulations using next-generation sequencing is a critical need for multiple applications including detection of drug resistant pathogens and somatic variant detection in oncology. To enable these applications, wet lab enhancements and bioinformatic error correction methods have been developed for ‘sequencing by synthesis’ technology to reduce its inherent sequencing error rate. A recently available sequencing approach termed ‘sequencing by binding’ claims to have higher base calling accuracy data “out of the box.” This paper evaluates the utility of using ‘sequencing by binding’ for the detection of ultra-rare subpopulations down to 0.001%.
Drug-likeness analysis, in silico admet profiling of selected compounds in Kedrostis fetidissima, and anti-bacterial activity of the plant extract.
Authors: Kommidi Saritha , Alivelu Munagala , Mohammad Mustafa 
Published: May 2024

Abstract

Plants are a treasure trove of bioactive compounds. One such plant is Kedrostis foetidissima, which belongs to the Cucurbitaceae family. It has very important phytoconstituents like cucurbitacins, rutin, quercetin, and other compounds. These compounds are responsible for various biological activities like anti-microbial, anti-diabetic, anti-cancer, anti-oxidant, anti-anemic, and wound healing properties. Quercetin-3-O- Rhamnoside (1), Rutin (2), 7, 10 - Hexa decadienoic acid methyl ester (3), Docosanoic acid (4), 3,7,11,15-Tetra methyl hexa decan-1-ol (5), and Cucurbitacin-B (6) compounds from this plant were tested for in silico ADMET profiling and drug likeness properties using the Swiss ADME online webserver and the pkCSM server. All the studied compounds are obeying Lipinski's rule of five except compounds 1 and 2, with two and three violations each. The entire selected of compounds has a good bioavailability score in the recommended range of 0 to 1. Compound 4 has high (0.85) and compound 1 and 2 have low (0.17) oral bioavailability scores. All the selected compounds from Kedrostis foetidissima have strong pharmacological activities. Supporting this, the selected plant methanol extracts of leaf, stem callus, and tuber have shown good in vitro antibacterial activity against Bacillus subtilis, Escherishia coli, and Proteus vulgaris.
Humidity and Oxygen Effects on Photochromic Properties of MTO/ITO Thin Films
Authors: Inoue Shuhei , Matsuo Ippei , Matsumura Yukihiko 
Published: May 2024

Abstract

The study investigates the photochromic properties of magnesium tin oxide (MTO) films deposited on indium tin oxide (ITO) substrates under various environmental conditions. The photochromic behavior involves the transition of the material to a black state upon ultraviolet (UV) irradiation and its return to transparency upon heating. MTO films deposited on ITO substrates exhibited significant coloration upon UV exposure, with a notable decrease in visible light transmittance. Structural analysis revealed the formation of metallic indium at the MTO/ITO interface during UV irradiation, suggesting a complex reaction mechanism involving oxygen and hydrogen radicals. Investigations into the effects of atmospheric gases and humidity on UV-induced coloration indicated that moisture plays a crucial role in promoting photochromism, while the presence of oxygen influences the degree of coloration. The study provides insights into the underlying processes driving the photochromic phenomenon in MTO/ITO systems and underscores the importance of environmental factors in modulating their optical properties. Further research is warranted to fully elucidate the role of oxygen and its derivatives in the photochromic mechanism of MTO films for potential applications in energy storage devices.
Analysis and circuit implementation of fractional-order memristive hyperchaotic system with enhanced memory
Authors: Ding Zhixia , Qiu Le , Li Sai , Yang Le , Wang Liheng 
Published: May 2024

Abstract

In this paper, based on the memory characteristics of fractional calculus, a new fractional-order memristor is proposed, which not only retain the basic characteristics of memristor, but also expand the influence of control parameters on system performance. Which has a stronger memorizability compared to other fractional-order memristor by analyzing the pinched hysteresis loop area. Based on the above fractional-order memristor, a fractional-order memristive hyperchaotic circuit is designed, such system is analyzed by using the Lyapunov Exponents and the bifurcation diagrams. It is found that the phase trajectory of the system expands and narrows under different parameters. In addition, there are two coexisting hyperchaotic attractors at different initial values. The SE complexity and C0 complexity of the system parameters are also given by the complexity algorithm. Finally, the fractional-order memristor and fractional-order memristive hyperchaotic circuits are realized through analog circuits.
Diagnostic value of magnification optical enhancement by experts and ResNet for detecting diminutive colorectal polyps: A retrospective study
Authors: Wan Xiaohan , Zhang Yan , Yang Huawei , Li Yanqing 
Published: May 2024

Abstract

Background
Deep learning models, such as narrow band imaging, are useful for identifying colorectal polyp pathological types using electronic chromoendoscopy. However, such findings remain unreported for i-scan optical enhancement (OE). Thus, this study proposes a ResNet-based model to distinguish between colorectal polyp types.
Methods
Endoscopic videos of 58 patients with colorectal polyps were collected, and their pathological types were determined by pathological examination. An experienced endoscopist was asked to predict the polyp pathological types shown in the videos to evaluate the diagnostic accuracy of the endoscopists. The same videos were extracted as images to train the ResNet model and evaluate its effectiveness. Finally, 100 endoscopic images were randomly selected for human–machine comparisons to examine the consistency between machine learning and endoscopist diagnoses.
Results
An accuracy of 89.77% was obtained by experienced endoscopists in distinguishing colorectal polyp types based on the entire video; the accuracy of the ResNet model based on video-extracted images was approximately 95%. Therefore, the ability of the ResNet model to classify colorectal polyps matched that of an experienced endoscopist.
Conclusions
I-scan OE provides a powerful tool for experienced endoscopists to differentiate between colorectal polyp pathological types; the ResNet model can help endoscopists achieve high diagnostic accuracy and reduce unnecessary invasive examinations.
Image Filtering to Increase the Efficiency in the Construction of Underwater Photomosaics
Authors: Castelló Pol Baños , Bonin-Font Francisco , Codina Gabriel Oliver 
Published: May 2024

Abstract

This paper describes an innovative methodology designed to filter out images with redundant information, initially selected to form photo-mosaics of the seabed. Collecting visual data of the sea bottom using underwater robots usually entails hundreds or even thousands of images if missions are performed in large areas and/or have long durations. All these images usually present multiple overlapping parts that need to be filtered out wisely in order to avoid useless repetitive information, questionable pixel aggregations, unreal colour blends, or the collapse of the computational resources dedicated to the mosaic building process. Experiments have been done with datasets grabbed from an Autonomous Underwater Vehicle while observing marine habitats of special ecological interest. Results show important reductions of visual data destined to the mosaics without significant loss of relevant information to be included in them, saving, drastically, time and data processing efforts.
Interplay of Aurora kinase A functional residues and Epstein-Barr Nuclear Antigen 1 in Epstein-Barr Virus associated Gastric cancer
Authors: Varshney Nidhi , Singh Siddharth , Kandpal Meenakshi , Saini Vaishali , Roberston Erle S. , Jha Hem Chandra 
Published: May 2024

Abstract

Background
Epstein-Barr virus (EBV), an oncogenic gamma-herpesvirus, belongs to group 1 carcinogen and is implicated in various cancers, including gastric cancer. Aurora Kinase A is a major mitotic protein kinase that regulates mitotic progression; overexpression and hyperactivation of AURKA commonly promote genomic instability in many tumours. However, the relationship of functional residues of AURKA and EBV in gastric cancer progression remains unknown.
Results
We reveal that AURKA overexpression and EBV infection induce aneuploidy in gastric epithelial cells. The AURKA (S89) N-terminal residue is critical for the centrosome maturation process in EBV-infected gastric epithelial cells. The kinase domain residues T287 and T288 of AURKA are essential for centrosome maturation and bipolar spindle formation in EBV-infected gastric cancer cells. We also show that AURKA 287/288 dm reduces the transcript expression of cell cycle markers involved in mitotic entry in EBV infection. This mutant also enhanced the protein expression of p53 and Rb, which was reduced in EBV infection and decreased the Survivin expression. Further, EBNA1, the latent gene of EBV, stabilises the AURKA in its wild-type form when it was mutated at the 89th position but unable to stabilise it when mutated at 287/288 position. These mutants also induce mitotic catastrophe by regulating the apoptosis and autophagy pathway in EBV infection. AURKA287/288 dm also promotes autophagosome formation even in EBV infection.
Conclusion
Thus, this study demonstrates that the AURKA kinase domain is essential for its functioning and progression of oncogenesis of EBV-infected gastric epithelial cells.
ML-Based Adaptive MAC Protocol for Real Time Data Transmission in Wireless Sensor Networks
Authors: Kakade Archana R Raut Archana 
Published: May 2024

Abstract

Wireless Sensor Networks (WSNs) are expressively utilized in various real-time control and monitoring applications. WSNs have been expanded considering the necessities in industrial time-bounded applications to support the dependable, time-bound delivery of data. Recently, Machine Learning (ML) algorithms have been used to address various WSN-related issues. The use of machine learning techniques supports dynamically modifying MAC settings based on traffic patterns and network conditions. In WSNs to control the communication between a large number of tiny, low-power sensor nodes while preserving energy and reducing latency, effective MAC protocols are essential. This paper addresses the ML-based Adaptive MAC (ML-MAC) protocol to provide a priority-based transmission system. In this research, depending upon the predictions of the machine learning model, the MAC parameters are dynamically adjusted to find priority-based channel access and the optimal routing path to meet the deadline of critical data packets. From the result analysis, the average throughput and delay of the proposed ML-MAC algorithm is improved as compared to the existing I-MAC protocol.
© 2017 Elsevier Inc. All rights reserved.
Pocket Convolution Mamba for Brain Tumor Segmentation
Authors: Zhang Hao , Zhao Yunhao , Wang Lianjie , Wang Jiashu , Zhang Wenyin , Chen Yeh-Cheng , Xiong Neal 
Published: May 2024

Abstract

In the field of brain tumor segmentation, models based on CNNs and Transformer have received a lot of attention. However, CNNs have limitations in long-range modeling, and although Transformers can model at long distances, they have quadratic computational complexity. Recently, State Space Models (SSM), exemplified by the Mamba model, can achieve linear computational complexity and are adept at long-distance interactions. In this paper, we propose Pocket Convolution Mamba (P-BTS), which utilizes the PocketNet paradigm, SSM, and patch contrastive learning to achieve an efficient segmentation model. Specifically, the encoder follows the PocketNet paradigm, the SSM is used at the highest level of the model encoder to capture rich semantic information, and patch contrastive learning is achieved through the results of dual-stream data. Meanwhile, we designed a Spatial Channel Attention (SCA) module to enhance control over spatial channels, and a Feature Complement Module (FCM) to facilitate the interaction between low-level features and high-level semantic information. We conducted comprehensive experiments on the BraTS2018 and BraTS2019 datasets, and the results show that P-BTS has excellent segmentation performance.
Predictive Analysis of Geomagnetic Disturbance Using Satellite Solar Wind Measurements
Authors: Salloum Said A. 
Published: May 2024

Abstract

Geomagnetic storms pose a significant risk to our increasingly technology-dependent society, with the potential to disrupt power grids, satellite operations, and global communication systems. These disturbances are primarily driven by solar wind, a stream of charged particles emitted by the Sun, which interacts with the Earth's magnetosphere. The dynamic nature of solar activity, notably characterized by the solar cycle's periodicity of approximately 11 years, further complicates the prediction and understanding of such events. A critical challenge in space weather forecasting is the development of reliable models that can accurately predict geomagnetic disturbances in a timely manner. Traditional modeling approaches often struggle to capture the complex temporal dependencies and the multivariate nature of solar wind data. To address this challenge, we developed a Long Short-Term Memory (LSTM) neural network model designed to harness the sequential nature of solar wind measurements. This study leverages LSTM's capability to retain long-term temporal relationships, utilizing a dataset composed of various solar wind parameters collected from NASA's ACE and NOAA's DSCOVR satellites. The model was trained to predict the disturbance storm time (Dst) index, a measure of geomagnetic activity, using a feature set that included interplanetary magnetic field components, solar wind proton density, speed, and ion temperature. The LSTM model demonstrated a substantial learning capacity, evidenced by a Root Mean Squared Error (RMSE) of 14.25, indicating strong predictive performance. In a binary classification setup, the model achieved an accuracy of approximately 80.35% with an Area Under the Curve (AUC) score of 0.818, signifying its effective discrimination between high and low geomagnetic disturbance events. These results underscore the potential of utilizing LSTM models for space weather forecasting, which could significantly enhance our ability to mitigate the risks associated with geomagnetic storms. The incorporation of sunspot data could allow for calibration to the solar cycle, further refining model predictions. This study lays the groundwork for future research aimed at integrating more diverse data sources and applying advanced machine learning techniques to improve the accuracy and reliability of geomagnetic disturbance forecasts.
CSFN-YOLOv5s: A Rapid Apple Detection Algorithm in the Natural Environment
Authors: Hui Yongyong , Zhao Chunyu , Song Zhaoyang , Zhao Xiaoqiang 
Published: May 2024

Abstract

In the natural environment, the rapid detection of apples is of great importance for picking robots. However, the complex growth conditions of apples, the occlusion of leaves and branches, and the distance can cause the problem of missed apple detection. To address this problem, an algorithm called CSFN-YOLOv5s for accurate and efficient apple detection in complex natural environments was developed. Firstly, the Context Augmentation Module - Spatial Pyramid Pooling with Feature Concatenation and Spatial Channel wise Pooling （CAM-SPPFCSPC ）framework is constructed to introduce additional background and context information, enhance the use of context information of the model, and help the model better understand the image information, so as to improve the robustness and generalization ability of the model. Secondly, with the application of four detection layers to obtain finer granular feature expression and a smaller receptive field, improve the accuracy of small target detection by finely capturing its detailed information. Thirdly, the Normalized Wasserstein Distance (NWD) was used to improve the sensitivity of IoU to the position deviation of small objects. Finally, a large number of experimental results show that CSFN-YOLOv5s has certain advantages for rapid identification of apples in natural and complex environments.
Enhancing Solar-to-Chemical Conversion through Tailoring Dimensions of Semiconductors in Biohybrid Systems
Authors: Guo* Mingming , Kong* Xinke , Cheng Wenbo , Yang Wenjun , Pi Shanshan , Wang Xin , Wang Xueyun , Xiang Yang , Duan Fangfang , Yang Chen , Wang Yuanyuan , Gao Xiang 
Published: May 2024

Abstract

Integrating light-harvesting semiconductor materials with biocatalysts offers a promising approach for solar-power production of fuels and fine chemicals. Despite significant advances, the influence of materials’ dimensions on energy utilization efficiency and the involved photoelectron transfer pathways remains largely to be explored. Here, we investigated the effect of dimensionality on the energy conversion efficiency in semiconductor nanomaterial-based biohybrid systems. We found that the intracellularly localized 2D nanoplatelets, particularly with core-crown heterostructures, were more efficient in supplying energy for microbial chemical production than the lower-dimensional nanomaterials. The biohybrids possessing the 2D nanoplatelets exhibited a 2.69-fold increase in 2,3-butanediol (BDO) production yield and achieved 2.35% solar-to-chemical conversion efficiency. Based on metabolomic and transcriptomic analyses, we identified a novel thiamine pyrophosphate (TPP)-mediated pathway of energy generation from photoexcited electrons. Furthermore, the addition of TPP enhanced the BDO production of the biohybrids under illumination. Our results demonstrate the potential to increase the solar-to-chemical conversion efficiency of semiconductor biohybrids by tailoring the dimension of semiconductor nanomaterials and engineering the intracellular electron transfer and energy generation pathways.

*Mingming Guo and Xinke Kong contributed equally to this work.
Nexus between Fair Pay and Say on Pay Votes
Authors: Arslan Muhammad 
Published: May 2024

Abstract

This study explores the magnitude of shareholders' say on pay votes and its impact on future CEO compensation. This study draws its sample from US Russell 3000 companies, consisting largest US companies, from 2011 to 2019. By creating a dummy variable, we further divided our sample into Russell 3000 and S&P 500 for peer comparison. The study employs descriptive statistics, correlation analysis, and pooled OLS regression and finds that future CEO compensation is significantly negatively associated with pay gap opposition. The coefficient and t-stat were higher for S&P 500 than for the Russell group. The study also finds that the CEO-to-Employee pay ratio positively correlates with shareholder dissent votes. The coefficient and t-stat were higher for the Russell group than for the S&P 500. Each additional point of CTE increases the shareholder dissent votes increases by 1.4% for the Russell 3,000 companies. The study has important implications for corporate directors, investors, and policymakers. The study contributes to corporate governance literature, particularly on executive compensation. Our findings supported the view of social comparison theory and contented that shareholders view CEO compensation as a biased evaluation of his contribution to the firm. We have developed a unique measure of the CEO-to-Employee pay ratio, which is based on SEC methodology. Our findings provide empirical evidence to investors and policymakers in the United States and other jurisdictions.
Healthcare Workers' Attitude Towards People Living With HIV
Authors: Saricicek Mehmet Sinan , Tuncer Ozge , Saricicek Ali Taylan 
Published: May 2024

Abstract

Background: HIV-related stigmatization among healthcare workers is still high. The aim of that research was to determine attitudes and behaviors towards people living with HIV (PLWH) to determine the ratio of stigmatization and prejudice towards PLWH among healthcare workers.
Methods: This was a single-center, national, cross-sectional, prospective, analytical survey study. Doctors and nurses at Izmir Bozyaka Training and Research Hospital were included in this study. The study was performed as a face-to-face and online survey. The sociodemographic data form and AIDS Attitude Scale (AAS) were administered to the participants. The results were analysed with the IBM SPSS 24.0 program. The significance level was accepted as p<0.05.
Results: A total of 404 people were included in our study.A total of 301 (74, 5%) of the participants were female, and 13 (25, 5%) were male. The average age of the participants was 33.25 ± 8.22 years. Most of the participants were aged 24-30 years (55, 9%), and the attitudes of healthcare workers towards PLWH increased with age. Compared with assistant doctors and nurses, specialist doctors have more positive and empathetic attitudes and less avoidance.
Conclusion: Participant attitudes towards PLWH are generally mild-high positive, but negative attitudes towards PLWH are common among healthcare workers. Regular education is needed for nurses and assistant doctors to increase confidence in HIV and reduce negative attitudes towards PLWH. However, additional quantitative studies are needed to identify interventionable points to increase empathy toward PLWH.
Fluoride removal by hydroxyapatite modified with anhydrous aluminum chloride
Authors: Ren Mengyue , Jia Xiangling , Huang Tianyin , Wu Bingdang , Yang Jingjing 
Published: May 2024

ABSTRACT

Efficient and low-cost removal of fluoride from water has attracted wide attention. Here, aluminum-modified hydroxyapatite (Al-HAP) was prepared by a homogeneous hydrothermal co-precipitation method. The physicochemical properties of the Al-HAP surface were characterized by SEM, XRD, FT-IR, BET, and zeta potential, and the adsorption performances were evaluated. It showed that Al-HAP has a larger specific surface area (121.97 m2/g, which is 2.3 times larger than that of HAP), more surface-active hydroxyls and positively charged at pH less than 7, which indicate that Al-HAP is beneficial to the adsorption of negatively charged fluoride. Al-HAP had a higher fluoride adsorption capacity (56.44 mg/g) than that of HAP (28.36 mg/g), and not sensitive to the interference of coexisting ions except CO32-. Based on the adsorption kinetics and adsorption isotherm experiments, the proposed two-stage kinetic model and Freundlich isotherm model can better describe the adsorption process. From the results of XPS and FT-IR, it indicated that the ion exchange between hydroxyl group on the surface and fluoride ions is the main driven force for the adsorption, and electrostatic adsorption is also helpful. The present study provides an improved HAP to effectively remove fluoride from water.
Study the effect of Darun Lepa in Darunaka
Authors: Sneha Batakurki  , Prashant Bhandi  
Published: May 2024

Darunaka is a Kapha Vataja (due to Kapha and Vata) condition featured with itching, dryness, and scaling of skin over the scalp with an itching sensation is considered a cardinal feature. It has become the most common cause of hair fall. Dandruff is a common scalp disorder, the cause may vary among individuals like fungal, bacterial, dry scalp, oil or irritated skin. In this study a cost effective and simple topical application of Lepa using Haritaki Churna, Amra Beeja Churna and Godugdha as a combination has been evaluated in Darunaka.