bluuebunny/crossref_metadata_2025_split huggingface dataset:
Biological activities and biosorption potential of red algae (Corallina officinalis) to remove toxic malachite green dye
Authors: Youssef Elen Emad , Beshay Botros Y. , Tonbol Kareem , Makled Sarah O. 
Published: August 2023

AbstractThis research aims to use eco-friendly Corallina officinalis as an adsorbent for removing harmful malachite green dye streams from industrial effluent, promoting sustainable living and effective microbial growth inhibition. Corallina officinalis biomass was tested for textile dye biosorption, as well as its antibacterial, antioxidant, and cytotoxic properties. The effects of certain parameters, involving pH solution, initial dye concentration, algae dose, and contact time, were investigated on the sorption of dye. Fourier transform infrared spectroscopy and scanning electron microscopy were also used and, the results showed that the functional groups on the surface of algae played an important part in the biosorption process. It was noted that the kinetic data were significantly prominent by the Pseudo-second-order model with regression correlation coefficient $${r}_{2}^{2}$$

r

2

2

values with an average of 0.95232. The biosorption was compatible with both the Freundlich (R2 = 0.9843), and Langmuir (R2 = 0.9653) isotherms, and the maximum removal efficiency for dye reached up to 99.9% in 2 h, 27 °C, stirring speed 120 rpm, pH 6, initial dye concentration 20 mg L−1, and biomass dose 0.03 g L−1. Corallina officinalis had higher antimicrobial activity, with values of minimum inhibitory concentrations ranging from 0.156 to 5 mg mL−1. Corallina officinalis exerted significant radical scavenging activity against tested free radicals. The extract was examined for cytotoxic activity using nine cancer cell lines, which exhibited high cytotoxicity for colon adenocarcinoma with an IC50 value of 25.895 µg mL−1.
Veterinary education and experience shape beliefs about dog breeds Part 1: Pain sensitivity
Authors: Caddiell Rachel M. P. , White Philip , Lascelles B. Duncan X. , Royal Kenneth , Ange-van Heugten Kimberly , Gruen Margaret E. 
Published: August 2023

AbstractOver 95% of veterinarians report believing that dog breeds differ in pain sensitivity. Ratings made by veterinarians differ from those of the general public, suggesting these beliefs may be learned during veterinary training or clinical experiences. Therefore, the current study’s primary objective was to evaluate dog breed pain sensitivity ratings during veterinary training and compare these ratings to those of the general public and undergraduates in animal-health related fields. Using an online survey, members of the general public, undergraduates, veterinary students across all four years, and veterinary faculty and staff rated pain sensitivity of 10 different dog breeds, identified only by their pictures. Compared to the general public and undergraduates, veterinary students rated pain sensitivity across breeds of dog more similarly to veterinary faculty and staff. Further, when undergraduates had clinical experience, they also rated certain dog breeds in a similar way to the veterinary students and professionals. Our findings suggest that veterinary education and clinical experiences influence pain sensitivity ratings across dog breeds. Future research should identify how these pain sensitivity beliefs are communicated and whether these beliefs affect recognition and treatment of pain by veterinarians.
Transport pathways and kinetics of cerebrospinal fluid tracers in mouse brain observed by dynamic contrast-enhanced MRI
Authors: Zhu Yuran , Wang Guanhua , Kolluru Chaitanya , Gu Yuning , Gao Huiyun , Zhang Jing , Wang Yunmei , Wilson David L. , Zhu Xiaofeng , Flask Chris A. , Yu Xin 
Published: August 2023

AbstractRecent studies have suggested the glymphatic system as a key mechanism of waste removal in the brain. Dynamic contrast-enhanced MRI (DCE-MRI) using intracisternally administered contrast agents is a promising tool for assessing glymphatic function in the whole brain. In this study, we evaluated the transport kinetics and distribution of three MRI contrast agents with vastly different molecular sizes in mice. Our results demonstrate that oxygen-17 enriched water (H217O), which has direct access to parenchymal tissues via aquaporin-4 water channels, exhibited significantly faster and more extensive transport compared to the two gadolinium-based contrast agents (Gd-DTPA and GadoSpin). Time-lagged correlation and clustering analyses also revealed different transport pathways for Gd-DTPA and H217O. Furthermore, there were significant differences in transport kinetics of the three contrast agents to the lateral ventricles, reflecting the differences in forces that drive solute transport in the brain. These findings suggest the size-dependent transport pathways and kinetics of intracisternally administered contrast agents and the potential of DCE-MRI for assessing multiple aspects of solute transport in the glymphatic system.
Evaluation of transmission characteristics of CVD-grown graphene and effect of tuning electrical properties of graphene up to 50 GHz
Authors: Okuda Ryota , Niwano Kazuhiko , Hatada Kaname , Kokubu Kei , Suga Ryosuke , Watanabe Takeshi , Koh Shinji 
Published: August 2023

AbstractGraphene has been investigated as a transparent conductive film for use in a variety of devices, and in recent years it has shown promise for use in millimeter-wave devices as 5G technology. In this study, we applied single-layer (SL), triple-layer (3L), and P-type doped 3L graphene to coplanar waveguide (CPW) transmission lines and obtained transmission characteristics (S21) from 1 to 50 GHz, which covered the 5G band. Furthermore, an equivalent circuit model of the CPW used in the measurements was constructed and simulations were performed, which showed good agreement with the measured results. The results validated the transmission properties of the graphene and the contact impedance at the interface between electrodes and the graphene in CPW circuits, which are necessary parameters for designing antennas using graphene. In addition, by comparing the transmission loss of three types of graphene, the parameters for improving the transmission characteristics were clarified.
Radar sensor based machine learning approach for precise vehicle position estimation
Authors: Sohail Muhammad , Khan Abd Ullah , Sandhu Moid , Shoukat Ijaz Ali , Jafri Mohsin , Shin Hyundong 
Published: August 2023

AbstractEstimating vehicles’ position precisely is essential in Vehicular Adhoc Networks (VANETs) for their safe, autonomous, and reliable operation. The conventional approaches used for vehicles’ position estimation, like Global Positioning System (GPS) and Global Navigation Satellite System (GNSS), pose significant data delays and data transmission errors, which render them ineffective in achieving precision in vehicles’ position estimation, especially under dynamic environments. Moreover, the existing radar-based approaches proposed for position estimation utilize the static values of range and azimuth, which make them inefficient in highly dynamic environments. In this paper, we propose a radar-based relative vehicle positioning estimation method. In the proposed method, the dynamic range and azimuth of a Frequency Modulated Continuous Wave radar is utilized to precisely estimate a vehicle’s position. In the position estimation process, the speed of the vehicle equipped with the radar sensor, called the reference vehicle, is considered such that a change in the vehicle’s speed changes the range and azimuth of the radar sensor. For relative position estimation, the distance and relative speed between the reference vehicle and a nearby vehicle are used. To this end, only those vehicles are considered that have a higher possibility of coming in contact with the reference vehicle. The data recorded by the radar sensor is subsequently utilized to calculate the precision and intersection Over Union (IOU) values. You Only Look Once (YOLO) version 4 is utilized to calculate precision and IOU values from the data captured using the radar sensor. The performance is evaluated under various real-time traffic scenarios in a MATLAB-based simulator. Results show that our proposed method achieves 80.0% precision in position estimation and obtains an IOU value up to 87.14%, thereby outperforming the state-of-the-art.
Adsorptive removal of acid red 18 dye from aqueous solution using hexadecyl-trimethyl ammonium chloride modified nano-pumice
Authors: Kasraee Mahboobeh , Dehghani Mohammad Hadi , Hamidi Farshad , Mubarak Nabisab Mujawar , Karri Rama Rao , Rajamohan Natarajan , Solangi Nadeem Hussain 
Published: August 2023

AbstractDischarging untreated dye-containing wastewater gives rise to environmental pollution. The present study investigated the removal efficiency and adsorption mechanism of Acid Red 18 (AR18) utilizing hexadecyl-trimethyl ammonium chloride (HDTMA.Cl) modified Nano-pumice (HMNP), which is a novel adsorbent for AR18 removal. The HDTMA.Cl is characterized by XRD, XRF, FESEM, TEM, BET and FTIR analysis. pH, contact time, initial concentration of dye and adsorbent dose were the four different parameters for investigating their effects on the adsorption process. Response surface methodology-central composite design was used to model and improve the study to reduce expenses and the number of experiments. According to the findings, at the ideal conditions (pH = 4.5, sorbent dosage = 2.375 g/l, AR18 concentration = 25 mg/l, and contact time = 70 min), the maximum removal effectiveness was 99%. The Langmuir (R2 = 0.996) and pseudo-second-order (R2 = 0.999) models were obeyed by the adsorption isotherm and kinetic, respectively. The nature of HMNP was discovered to be spontaneous, and thermodynamic investigations revealed that the AR18 adsorption process is endothermic. By tracking the adsorption capacity of the adsorbent for five cycles under ideal conditions, the reusability of HMNP was examined, which showed a reduction in HMNP's adsorption effectiveness from 99 to 85% after five consecutive recycles.
Short-term streamflow modeling using data-intelligence evolutionary machine learning models
Authors: Martinho Alfeu D. , Hippert Henrique S. , Goliatt Leonardo 
Published: August 2023

AbstractAccurate streamflow prediction is essential for efficient water resources management. Machine learning (ML) models are the tools to meet this need. This paper presents a comparative research study focusing on hybridizing ML models with bioinspired optimization algorithms (BOA) for short-term multistep streamflow forecasting. Specifically, we focus on applying XGB, MARS, ELM, EN, and SVR models and various BOA, including PSO, GA, and DE, for selecting model parameters. The performances of the resulting hybrid models are compared using performance statistics, graphical analysis, and hypothesis testing. The results show that the hybridization of BOA with ML models demonstrates significant potential as a data-driven approach for short-term multistep streamflow forecasting. The PSO algorithm proved superior to the DE and GA algorithms in determining the optimal hyperparameters of ML models for each step of the considered time horizon. When applied with all BOA, the XGB model outperformed the others (SVR, MARS, ELM, and EN), best predicting the different steps ahead. XGB integrated with PSO emerged as the superior model, according to the considered performance measures and the results of the statistical tests. The proposed XGB hybrid model is a superior alternative to the current daily flow forecast, crucial for water resources planning and management.
Integrated analysis of inflammatory mRNAs, miRNAs, and lncRNAs elucidates the molecular interactome behind bovine mastitis
Authors: Hasankhani Aliakbar , Bakherad Maryam , Bahrami Abolfazl , Shahrbabak Hossein Moradi , Pecho Renzon Daniel Cosme , Shahrbabak Mohammad Moradi 
Published: August 2023

AbstractMastitis is known as intramammary inflammation, which has a multifactorial complex phenotype. However, the underlying molecular pathogenesis of mastitis remains poorly understood. In this study, we utilized a combination of RNA-seq and miRNA-seq techniques, along with computational systems biology approaches, to gain a deeper understanding of the molecular interactome involved in mastitis. We retrieved and processed one hundred transcriptomic libraries, consisting of 50 RNA-seq and 50 matched miRNA-seq data, obtained from milk-isolated monocytes of Holstein–Friesian cows, both infected with Streptococcus uberis and non-infected controls. Using the weighted gene co-expression network analysis (WGCNA) approach, we constructed co-expressed RNA-seq-based and miRNA-seq-based modules separately. Module-trait relationship analysis was then performed on the RNA-seq-based modules to identify highly-correlated modules associated with clinical traits of mastitis. Functional enrichment analysis was conducted to understand the functional behavior of these modules. Additionally, we assigned the RNA-seq-based modules to the miRNA-seq-based modules and constructed an integrated regulatory network based on the modules of interest. To enhance the reliability of our findings, we conducted further analyses, including hub RNA detection, protein–protein interaction (PPI) network construction, screening of hub-hub RNAs, and target prediction analysis on the detected modules. We identified a total of 17 RNA-seq-based modules and 3 miRNA-seq-based modules. Among the significant highly-correlated RNA-seq-based modules, six modules showed strong associations with clinical characteristics of mastitis. Functional enrichment analysis revealed that the turquoise module was directly related to inflammation persistence and mastitis development. Furthermore, module assignment analysis demonstrated that the blue miRNA-seq-based module post-transcriptionally regulates the turquoise RNA-seq-based module. We also identified a set of different RNAs, including hub-hub genes, hub-hub TFs (transcription factors), hub-hub lncRNAs (long non-coding RNAs), and hub miRNAs within the modules of interest, indicating their central role in the molecular interactome underlying the pathogenic mechanisms of S. uberis infection. This study provides a comprehensive insight into the molecular crosstalk between immunoregulatory mRNAs, miRNAs, and lncRNAs during S. uberis infection. These findings offer valuable directions for the development of molecular diagnosis and biological therapies for mastitis.
Reduction of GAS5 and FOXD3-AS1 long non-coding RNAs in patients with bipolar disorder
Authors: Zamani Bita , Mehrab Mohseni Mahdieh , Naghavi Gargari Bahar , Taheri Mohammad , Sayad Arezou , Shirvani-Farsani Zeinab 
Published: August 2023

AbstractBipolar disorder (BD) patients suffer from severe disability and premature death because of failure in prognosis, diagnosis, and treatment. Although neural mechanisms of bipolar have not been fully discovered, studies have shown long noncoding RNAs (lncRNAs) can play an important role in signaling pathways such as PI3K/AKT pathway. There has been little study on deregulated lncRNAs and the lncRNAs’ mode of action in the BD. Hence, we aimed to investigate the expression of PI3K/AKT pathway-related lncRNAs named TUG1, GAS5, and FOXD3-AS1 lncRNAs in the PMBC in 50 bipolar patients and 50 healthy controls. Our results showed that FOXD3-AS1 and GAS5 under-expressed significantly in bipolar patients compared to healthy controls (P = 0.0028 and P < 0.0001 respectively). Moreover, after adjustment, all P values remained significant (q value < 0.0001). According to the ROC curve, AUC (area under the curve), specificity, and sensitivity of these lncRNAs, GAS5 and FOXD3-AS1 might work as BD candidate diagnostic biomarkers. Taken together, the current results highlight that the dysregulation of FOXD3-AS1 and GAS5 may be associated with an increased risk of BD.
Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset
Authors: Song Ge , Li Siwei , Xing Jia 
Published: August 2023

AbstractAccurate and timely prediction of lightning occurrences plays a crucial role in safeguarding human well-being and the global environment. Machine-learning-based models have been previously employed for nowcasting lightning occurrence, offering advantages in computation efficiency. However, these models have been hindered by limited accuracy due to inadequate representation of the intricate mechanisms driving lightning and a restricted training dataset. To address these limitations, we present a machine learning approach that integrates aerosol features to more effectively capture lightning mechanisms, complemented by enriched satellite observations from the Geostationary Lightning Mapper (GLM). Through training a well-optimized LightGBM model, we successfully generate spatially continuous (0.25° by 0.25°) and hourly lightning nowcasts over the Contiguous United States (CONUS) during the summer season, surpassing the performance of competitive baselines. Model performance is evaluated using various metrics, including accuracy (94.3%), probability of detection (POD, 75.0%), false alarm ratio (FAR, 38.1%), area under curve of precision–recall curve (PRC-AUC, 0.727). In addition to the enriched dataset, the improved performance can be attributed to the inclusion of aerosol features, which has significantly enhanced the model. This crucial aspect has been overlooked in previous studies. Moreover, our model unravels the influence of aerosol composition and loading on lightning formation, indicating that high aerosol loading consisting of sulfates and organic compounds tends to enhance lightning activity, while black carbon inhibits it. These findings align with current scientific knowledge and demonstrate the immense potential for elucidating the complex mechanisms underlying aerosol-associated lightning phenomena.
Precision-dissipation trade-off for driven stochastic systems
Authors: Proesmans Karel 
Published: August 2023

AbstractOver the last few decades, stochastic thermodynamics has emerged as a framework to study the thermodynamics of small-scaled systems. The relation between entropy production and precision is one of the most prominent research topics in this field. In this paper, I answer the question how much dissipation is needed to follow a pre-determined trajectory. This will be done by deriving a trade-off relation between how precisely a mesoscopic system can follow a pre-defined trajectory and how much the system dissipates. In the high-precision limit, the minimal amount of dissipation is inversely proportional to the expected deviation from the pre-defined trajectory. Furthermore, I will derive the protocol that maximizes the precision for a given amount of dissipation. The optimal time-dependent force field is a conservative energy landscape which combines a shifted version of the initial energy landscape and a quadratic energy landscape. The associated time-dependent probability distribution conserves its shape throughout the optimal protocol. Potential applications are discussed in the context of bit erasure and electronic circuits.
Record low 2022 Antarctic sea ice led to catastrophic breeding failure of emperor penguins
Authors: Fretwell Peter T. , Boutet Aude , Ratcliffe Norman 
Published: August 2023

AbstractThe spring season of 2022 saw record low sea ice extent in Antarctica that persisted throughout the year. At the beginning of December, the Antarctic sea ice extent was tracking with the all-time low set in 2021. The greatest regional negative anomaly of this low extent was in the central and eastern Bellingshausen Sea region, west of the Antarctic Peninsula where, during November, some regions experienced a 100% loss in sea ice concentration. We provide evidence of a regional breeding failure of emperor penguin colonies due to sea ice loss using Sentinel2 satellite imagery. Of the five breeding sites in the region all but one experienced total breeding failure after sea ice break-up before the start of the fledging period of the 2022 breeding season. This is the first recorded incident of a widespread breeding failure of emperor penguins that is clearly linked with large-scale contractions in sea ice extent.
Noun and Verb Lexicons Differentially Predict Later Grammatical Development in Mandarin-Speaking Children With and Without Late Language Emergence
Authors: Liu Huei-Mei , Tsao Feng-Ming , Lin Chun-Yi , Rost Gwyneth , Guo Ling-Yu 
Published: August 2023

Purpose:
The current investigation evaluated the extent to which early noun, verb, and adjective lexicon sizes predicted later grammatical outcomes in Mandarin-speaking children with and without late language emergence (LLE) using a parent report.

Method:

In Study 1, the parents of 24 Mandarin-speaking children with typical language filled out the toddler version of
Mandarin–Chinese Communicative Development Inventory—Taiwan
(MCDI-T) when these children were 24 and 36 months old. In Study 2, the parents of 23 children with LLE completed the same form when these children were 24, 36, and 48 months old. Noun, verb, and adjective lexicon sizes and grammatical complexity scores were computed from the MCDI-T form for each child.

Results:
Study 1 showed that verb lexicon size, but not noun or adjective lexicon size, at 24 months predicted grammatical complexity scores at 36 months for children with typical language. Study 2 revealed that noun lexicon size, but not verb or adjective lexicon size, at 24 months predicted grammatical complexity scores at 36 months for children with LLE. Noun lexicon size at 36 months was also the only significant predictor for grammatical complexity scores at 48 months in children with LLE.

Conclusions:
Noun and verb lexicon size differentially predicted later grammatical outcomes in young Mandarin-speaking children with and without LLE. The finding suggested that children with LLE may have approached grammatical learning differently from their typical peers due to the small verb lexicon size in the early phase of language development.
Performance analysis of secure intelligent reflecting surface assisted ground to unmanned aerial vehicle transmission
Authors: Yi Shen , Zhiwen Pan , Bin Wang , Nan Liu , Xiaohu You 
Published: August 2023

AbstractThe authors propose a secure intelligent reflecting surface (IRS) assisted transmission system in the presence of a ground eavesdropper, where the source in the ground transmits confidential information to unmanned aerial vehicle (UAV), IRS is deployed to promote the transmission rate of the source to UAV. Closed‐form expression for secure transmission non‐outage probability is derived, and secure transmission performance is analyzed. Simulation validates the correctness of the derivation. Compared with benchmarks, results show that the IRS can improve the secure transmission of ground to UAV.