bluuebunny/crossref_metadata_2025_split huggingface dataset:
Convergence of the Laplace and the alternative multipole expansion approximation series for the Coulomb potential
Authors: Jobunga E. O. , Wandera C. O. , Okeyo O. S. 
Published: September 2023

AbstractMultipole expansion is a powerful technique used in many-body physics to solve dynamical problems involving correlated interactions between constituent particles. The Laplace multipole expansion series of the Coulomb potential is well established in literature. We compare its convergence with our recently developed perturbative and analytical alternative multipole expansion series of the Coulomb potential. In our working, we confirm that the Laplace and the analytical alternative multipole expansion series are equivalent as expected. In terms of performance, the perturbative alternative multipole expansion series underapproximate the expected results to some extent while the Laplace and the analytical alternative multipole expansion series yield results which are relatively accurate but oscillatory in nature even with a higher number of angular momentum terms employed. As a practical example, we have evaluated the Slater double integrals for two-electron systems using the multipole expansion techniques and a mean field approximation. The estimated results show that only spherical interactions are dominant while the higher-order interactions are negligible. To highlight the discrepancy in the application of each of the formulations of the multipole expansion series for the electron-electron interaction potential, an estimation of the non-relativistic groundstate energies of some helium-like systems, evaluated using the spherical approximation of the multipole potential, is provided. Our findings are likely to be useful in the treatment of the Coulomb potential in electronic structure calculations as well as in celestial mechanics.
Effect of forest-based biochar on maturity indices and bio-availability of heavy metals during the composting process of organic fraction of municipal solid waste (OFMSW)
Authors: Hassanzadeh Moghimi Omid , Nabi Bidhendi Gholamreza , Daryabeigi Zand Ali , Rabiee Abyaneh Maryam , Nabi Bidhendi Amir 
Published: September 2023

AbstractThe main objective of this study was to investigate the effect of biochar on the composting process of the organic fraction of municipal solid waste (OFMSW) under real conditions. Different doses of biochar (1%, 3%, and 5%) were mixed with compost piles to evaluate the variation of temperature, moisture content (MC), organic matter (OM), carbon (C), nitrogen (N), C/N ratio, and heavy metal (HM) contents in comparison with the control treatment (with 0% biochar addition). The results of this study showed that the compost piles combined with different doses of biochar had higher MC. The use of biochar as an additive, even at low doses (1%), was able to increase the compost quality through the reduction of N losses during the composting process. The highest reduction of OM during the composting process was observed in the control pile (without biochar addition) by 48.06%, whereas biochar affected the biodegradability of OM and prevented the reduction of nutrients during the composting process under real conditions. The contents of HMs (Pb, Zn, Ni, Cd, and Cu) showed a significant reduction in all of the compost piles combined with biochar in comparison with the control treatment. Considering that in terms of all compost quality indicators, the piles combined with biochar can regarded as high standard product, the composts obtained from combining the OFMSW with different biochar doses have desirable features to be used as an amendment agent to improve agricultural soil quality.
Associations of appetitive traits with growth velocities from infancy to childhood
Authors: Olwi Duaa Ibrahim , Day Felix R. , Cheng Tuck Seng , Olga Laurentya , Petry Clive J. , Hughes Ieuan A. , Smith Andrea D. , Ong Ken K. 
Published: September 2023

AbstractSeveral studies have reported associations between appetitive traits and weight gain during infancy or childhood, but none have directly compared these associations across both age periods. Here, we tested the associations between appetitive traits and growth velocities from birth to childhood. Appetitive trait data were collected using the Children’s Eating Behaviour Questionnaire (CEBQ) in 149 children from the Cambridge Baby Growth Study at age 9–17 years. These participants also provided anthropometric measurements during infancy (birth, 3, 12, 18, and 24 months) and childhood (5 to 11 years). Standardized growth velocities (in weight, length/height, BMI, and body fat percentage) for 0–3 months, 3–24 months, and 24 months to childhood were estimated using individual linear-spline models. Associations between each of the eight CEBQ traits and each growth velocity were tested in separate multilevel linear regression models, adjusted for sex, age at CEBQ completion, and the corresponding birth measurement (weight, length, BMI, or body fat percentage). The three food-approach traits (food responsiveness, enjoyment of food and emotional overeating) were positively associated with infancy and childhood growth velocities in weight, BMI, and body fat percentage. By contrast, only one of the food-avoidant traits, satiety responsiveness, was negatively associated with all growth velocities. Significant associations were mostly of similar magnitude across all age periods. These findings reveal a broadly consistent relationship between appetitive traits with gains in weight and adiposity throughout infancy and childhood. Future interventions and strategies to prevent obesity may benefit from measuring appetitive traits in infants and children and targeting these as part of their programs.
Sleep impairment and altered pattern of circadian biomarkers during a long-term Antarctic summer camp
Authors: Moraes Michele Macedo , Marques Alice Lamounier , Borges Leandro , Hatanaka Elaine , Heller Debora , Núñez-Espinosa Cristian , Gonçalves Dawit Albieiro Pinheiro , Soares Danusa Dias , Wanner Samuel Penna , Mendes Thiago Teixeira , Arantes Rosa Maria Esteves 
Published: September 2023

AbstractAntarctic expeditions include isolation and exposure to cold and extreme photoperiods (with continuous natural light during summer) that may influence psychophysiological responses modulated by luminosity and sleep. We assessed changes in night sleep patterns by actigraphy, salivary biomarkers, and perceptual variables in seven participants in the following time points along a 50-day camping expedition in Antarctica (Nelson Island): Pre-Field (i.e., on the ship before camp), Field-1, Field-2, Field-3, Field-4 (from 1st to 10th, 11th to 20th, 21st to 35th and 36th to 50th days in camp, respectively), and Post-Field (on the ship after camp). We also characterized mood states, daytime sleepiness, and sleep quality by questionnaires. Staying in an Antarctic camp reduced sleep efficiency (5.2%) and increased the number of awakenings and wakefulness after sleep onset (51.8% and 67.1%, respectively). Furthermore, transient increases in time in bed (16.5%) and sleep onset latency (4.8 ± 4.0 min, from Pre- to Field-3) was observed. These changes were accompanied by an altered pattern of the emerging circadian marker β-Arrestin-1 and a trend to reduce nocturnal melatonin [57.1%; P = 0.066, with large effect size (ES) from Pre-Field to Field-2 (ES = 1.2) and Field-3 (ES = 1.2)]. All changes returned to Pre-Field values during the Post-Field. The volunteers reported sleep-related physical complaints (feeling of cold and pain, discomfort to breathe, and cough or loud snoring), excessive daytime sleepiness, and reduced vigor during the camp. Thus, a 50-day camp alters neuroendocrine regulation and induces physical discomfort, which may explain the impaired sleep pattern and the consequent daytime sleepiness and mood changes.
Discovery of Nedd4 auto-ubiquitination inhibitors
Authors: Yong Darren , Green Stuart R. , Ghiabi Pegah , Santhakumar Vijayaratnam , Vedadi Masoud 
Published: September 2023

AbstractE3 ubiquitin ligases are critical to the protein degradation pathway by catalyzing the final step in protein ubiquitination by mediating ubiquitin transfer from E2 enzymes to target proteins. Nedd4 is a HECT domain-containing E3 ubiquitin ligase with a wide range of protein targets, the dysregulation of which has been implicated in myriad pathologies, including cancer and Parkinson's disease. Towards the discovery of compounds disrupting the auto-ubiquitination activity of Nedd4, we developed and optimized a TR-FRET assay for high-throughput screening. Through selective screening of a library of potentially covalent compounds, compounds25and81demonstrated apparent IC50values of 52 µM and 31 µM, respectively. Tandem mass spectrometry (MS/MS) analysis confirmed that25and81were covalently bound to Nedd4 cysteine residues (Cys182 and Cys867). In addition,81also adducted to Cys627. Auto-ubiquitination assays of Nedd4 mutants featuring alanine substitutions for each of these cysteines suggested that the mode of inhibition of these compounds occurs through blocking the catalytic Cys867. The discovery of these inhibitors could enable the development of therapeutics for various diseases caused by Nedd4 E3 ligase dysregulation.
A novel combined intelligent algorithm prediction model for the risk of the coal and gas outburst
Authors: Wang Zhie , Xu Jingde , Ma Jun , Cai Zhuowen 
Published: September 2023

AbstractThe mechanism of coal and gas outburst disasters is perplexing, and the evaluation methods of outburst disasters based on various sensitive indicators often have some imprecision and fuzziness. With the concept of accurate and intelligent mining in coal mines proposed in China, selecting quantifiable parameters for machine learning risk prediction can avoid the deviation caused by human subjectivity, and improve the accuracy of coal and gas outburst prediction. Aiming at the shortcomings of the support vector machine (SVM) such as low noise resistance and being prone to be influenced by parameters easily, this research proposed a prediction method based on a grey wolf optimizer to optimize the support vector machine (GWO-SVM). To coordinate the global and local optimization ability of the GWO, Tent Chaotic Mapping and DLH strategies were introduced to improve the optimization ability of the GWO and reduce the local optimal probability. The improved prediction model IGWO-SVM was used to predict the coal and gas outburst. The results showed that this model has faster training speed and higher classification prediction accuracy than the SVM and GWO-SVM models, which the accuracy rate reaching 100%. Finally, to obtain the correlation between the parameters of the coal and gas outburst prediction parameters, the random forest algorithm was used for training, and the three parameters with the highest feature importance were selected to rebuild the data set for machine learning. The accuracy of the IGWO-SVM outburst prediction model based on Random Forest was still 100%. Therefore, even if some prediction parameters are missing, the outburst can still be effectively predicted by using the RF-IGWO-SVM model, which is beneficial for the model application and underground safety management.
A metagenomic catalog for exploring the plastizymes landscape covering taxa, genes, and proteins
Authors: Jahanshahi Donya Afshar , Ariaeenejad Shohreh , Kavousi Kaveh 
Published: September 2023

AbstractThere are significant environmental and health concerns associated with the current inefficient plastic recycling process. This study presents the first integrated reference catalog of plastic-contaminated environments obtained using an insilico workflow that could play a significant role in discovering new plastizymes. Here, we combined 66 whole metagenomic data from plastic-contaminated environment samples from four previously collected metagenome data with our new sample. In this study, an integrated plastic-contaminated environment gene, protein, taxa, and plastic degrading enzyme catalog (PDEC) was constructed. These catalogs contain 53,300,583 non-redundant genes and proteins, 691 metagenome-assembled genomes, and 136,654 plastizymes. Based on KEGG and eggNOG annotations, 42% of recognized genes lack annotations, indicating their functions remain elusive and warrant further investigation. Additionally, the PDEC catalog highlights hydrolases, peroxidases, and cutinases as the prevailing plastizymes. Ultimately, following multiple validation procedures, our effort focused on pinpointing enzymes that exhibited the highest similarity to the introduced plastizymes in terms of both sequence and three-dimensional structural aspects. This encompassed evaluating the linear composition of constituent units as well as the complex spatial conformation of the molecule. The resulting catalog is expected to improve the resolution of future multi-omics studies, providing new insights into plastic-pollution related research.
Quantum correlations in the frustrated XY model on the honeycomb lattice
Authors: Satoori Sahar , Mahdavifar Saeed , Vahedi Javad 
Published: September 2023

AbstractWe have investigated the spin-1/2 XY frustrated antiferromagnetic Heisenberg honeycomb model, which features an intermediate region in its ground state phase diagram that is not well understood. The two dominant phases in the diagram are the quantum spin-liquid (QSL) and the antiferromagnetic Ising order. Quantum correlations suggest that the QSL phase is likely to exhibit entanglement. To explore this possibility, we utilized numerical Lanczos and density matrix renormalization group (DMRG) methods to calculate concurrence, quantum discord (QD), and entanglement entropy. The results of our study indicate the existence of quantum entanglement within the intermediate region, implying a greater probability for the dominance of the quantum spin-liquid (QSL) phase over the antiferromagnetic Ising order. This discovery underscores the importance of considering quantum correlations in comprehending the model’s behavior and provides insight into the complex nature of quantum systems.
Carcinogenic and non-carcinogenic health risk assessment of organic compounds and heavy metals in electronic cigarettes
Authors: Zhao Siyuan , Zhang Xi , Wang Junji , Lin Jianzai , Cao Deyan , Zhu Meilin 
Published: September 2023

AbstractE-cigarettes are now very popular in the world. Compared to traditional cigarettes, e-cigarettes are often considered safer and healthier. However, their safety remains controversial and requires further research and regulation. In this study, we aimed to understand the possible hazards to humans of four compounds (formaldehyde, acetaldehyde, acrolein, and acetone) and seven heavy metals (arsenic, cadmium, manganese, lead, copper, nickel, and chromium) contained in e-cigarette liquids and aerosols and perform a health risk assessment. We searched PubMed, CNKI, and other databases for relevant literature to obtain data on organic compounds and heavy metals in e-cigarette liquids and aerosols, and conducted acute, chronic, and carcinogenic risk assessments of various chemicals by different exposure routes. This study showed that exposure to four organic compounds and seven heavy metals in e-cigarette aerosols and e-liquids can cause varying levels of health risks in humans through different routes, with the inhalation route posing a higher overall risk than dermal exposure and oral intake. Various chemicals at high exposure doses can produce health risks beyond the acceptable range. E-cigarette designers must improve their products by changing the composition of the e-liquid and controlling the power of the device to reduce the health effects on humans.
Temperature and prey density drive growth and otolith formation of the world's most valuable fish stock
Authors: Ofelio Claudia , Moyano Marta , Sswat Michael , Rioual Fanny , Moullec Fabien , Aguirre-Velarde Arturo , Peck Myron A. 
Published: September 2023

AbstractPeruvian anchovy (Engraulis ringens) represents the largest single-species fishery worldwide. Knowledge on how temperature and prey availability influences growth and age estimation during marine fish early life stages is critical for predicting bottom-up processes impacting stock productivity under changing environmental conditions. We reared Peruvian anchovy larvae at two temperatures (14.5 and 18.5 °C) and prey concentrations [high (HF), and low (LF)] from 6 to 30 days post-hatch (dph) to measure growth rate and examine daily deposition of otolith increments. Peruvian anchovy larvae grew faster at 18.5 °C compared to 14.5 °C. Larvae reared at low prey concentration (18.5-LF) and low temperature (14.5-HF) grew 61 and 35% slower, respectively, than those at high prey and warm temperature (18.5-HF). Age and growth rates of larvae were well depicted in the otolith microstructure of well-fed larvae at 18.5 °C. However, larvae reared at 18.5-LF or 14.5-HF, had only 55 and 49% of the expected number of daily otolith increments. Our results suggest caution when attempting to explore how ocean processes regulate small pelagic stocks, the productivity of which are largely driven by changes in the survival and growth of young larvae.
Sharpen data-driven prediction rules of individual large earthquakes with aid of Fourier and Gauss
Authors: Cho In Ho 
Published: September 2023

AbstractPredicting individual large earthquakes (EQs)’ locations, magnitudes, and timing remains unreachable. The author’s prior study shows that individual large EQs have unique signatures obtained from multi-layered data transformations. Via spatio-temporal convolutions, decades-long EQ catalog data are transformed into pseudo-physics quantities (e.g., energy, power, vorticity, and Laplacian), which turn into surface-like information via Gauss curvatures. Using these new features, a rule-learning machine learning approach unravels promising prediction rules. This paper suggests further data transformation via Fourier transformation (FT). Results show that FT-based new feature can help sharpen the prediction rules. Feasibility tests of large EQs ($$M\ge$$M≥6.5) over the past 40 years in the western U.S. show promise, shedding light on data-driven prediction of individual large EQs. The handshake among ML methods, Fourier, and Gauss may help answer the long-standing enigma of seismogenesis.
Motivation moderates gender differences in navigation performance
Authors: Schinazi Victor R. , Meloni Dario , Grübel Jascha , Angus Douglas J. , Baumann Oliver , Weibel Raphael P. , Jeszenszky Péter , Hölscher Christoph , Thrash Tyler 
Published: September 2023

AbstractGender differences in navigation performance are a recurrent and controversial topic. Previous research suggests that men outperform women in navigation tasks and that men and women exhibit different navigation strategies. Here, we investigate whether motivation to complete the task moderates the relationship between navigation performance and gender. Participants learned the locations of landmarks in a novel virtual city. During learning, participants could trigger a top-down map that depicted their current position and the locations of the landmarks. During testing, participants were divided into control and treatment groups and were not allowed to consult the map. All participants were given 16 minutes to navigate to the landmarks, but those in the treatment group were monetarily penalized for every second they spent completing the task. Results revealed a negative relationship between physiological arousal and the time required to locate the landmarks. In addition, gender differences in strategy were found during learning, with women spending more time with the map and taking 40% longer than men to locate the landmarks. Interestingly, an interaction between gender and treatment group revealed that women in the control group required more time than men and women in the treatment group to retrieve the landmarks. During testing, women in the control group also took more circuitous routes compared to men in the control group and women in the treatment group. These results suggest that a concurrent and relevant stressor can motivate women to perform similarly to men, helping to diminish pervasive gender differences found in the navigation literature.
Physiologically-based pharmacokinetic modeling for optimal dosage prediction of olaparib when co-administered with CYP3A4 modulators and in patients with hepatic/renal impairment
Authors: Gao Dongmei , Wang Guopeng , Wu Honghai , Ren Jiawei 
Published: September 2023

AbstractThis study aimed to develop a physiologically-based pharmacokinetic (PBPK) model to predict the maximum plasma concentration (Cmax) and trough concentration (Ctrough) at steady-state of olaparib (OLA) in Caucasian, Japanese and Chinese. Furthermore, the PBPK model was combined with mean and 95% confidence interval to predict optimal dosing regimens of OLA when co-administered with CYP3A4 modulators and administered to patients with hepatic/renal impairment. The dosing regimens were determined based on safety and efficacy PK threshold Cmax (< 12,500 ng/mL) and Ctrough (772–2500 ng/mL). The population PBPK model for OLA was successfully developed and validated, demonstrating good consistency with clinically observed data. The ratios of predicted to observed values for Cmax and Ctrough fell within the range of 0.5 to 2.0. When OLA was co-administered with a strong or moderate CYP3A4 inhibitor, the recommended dosing regimens should be reduced to 100 mg BID and 150 mg BID, respectively. Additionally, the PBPK model also suggested that OLA could be not recommended with a strong or moderate CYP3A4 inducer. For patients with moderate hepatic and renal impairment, the dosing regimens of OLA were recommended to be reduced to 200 mg BID and 150 mg BID, respectively. In cases of severe hepatic and renal impairment, the PBPK model suggested a dosing regimen of 100 mg BID for OLA. Overall, this present PBPK model can determine the optimal dosing regimens for various clinical scenarios involving OLA.
An artificial intelligence model for the radiographic diagnosis of osteoarthritis of the temporomandibular joint
Authors: Talaat Wael M. , Shetty Shishir , Al Bayatti Saad , Talaat Sameh , Mourad Louloua , Shetty Sunaina , Kaboudan Ahmed 
Published: September 2023

AbstractThe interpretation of the signs of Temporomandibular joint (TMJ) osteoarthritis on cone-beam computed tomography (CBCT) is highly subjective that hinders the diagnostic process. The objectives of this study were to develop and test the performance of an artificial intelligence (AI) model for the diagnosis of TMJ osteoarthritis from CBCT. A total of 2737 CBCT images from 943 patients were used for the training and validation of the AI model. The model was based on a single convolutional network while object detection was achieved using a single regression model. Two experienced evaluators performed a Diagnostic Criteria for Temporomandibular Disorders (DC/TMD)-based assessment to generate a separate model-testing set of 350 images in which the concluded diagnosis was considered the golden reference. The diagnostic performance of the model was then compared to an experienced oral radiologist. The AI diagnosis showed statistically higher agreement with the golden reference compared to the radiologist. Cohen’s kappa showed statistically significant differences in the agreement between the AI and the radiologist with the golden reference for the diagnosis of all signs collectively (P = 0.0079) and for subcortical cysts (P = 0.0214). AI is expected to eliminate the subjectivity associated with the human interpretation and expedite the diagnostic process of TMJ osteoarthritis.