bluuebunny/crossref_metadata_2025 huggingface dataset:
Genotyping of cocoa trees (Theobroma cacao L.) from first and second cycle of reccurent and reciprocal selection program in Côte d'Ivoire
Authors: GUIRAUD Boguinard Sahin , TAHI Gnion Mathias , POKOU Désiré , TREBISSOU Inago Caudou , COULIBALY Klotioloma , N’GUESSAN Walet Pierre , MINAKOU Okayo Sandrine , ASSI Evelyne Maryse , GOGBE-DIBI Françoise , KOTAIX Jacques Alain , KOUAME Norbert N’Dri , N’GUESSAN Kouamé François
Published: July 2024

The recurrent and reciprocal cocoa breeding program in Côte d'Ivoire is in its 2nd cycle of crossing High Amazon Forastero (HA)2 and a mixture of Low Amazon Forastero and Trinitario (LA + T)2. It was created with the aim of improving the characteristics of these genetic groups. This study was carried out to evaluate the identity and genetic structure of cocoa trees selected on index for production and rotted pod rate, with a view to better assessing the genetic conformity of individuals within each genetic group. 152 cocoa trees were genotyped with 99 SNPs markers. These samples included 53 HA2 genotypes, 26 (BA + T)2 genotypes, 30 parents genotypes and 43 progenies from crosses between HA2 and (BA + T)2. Results indicate that observed heterozygosity (Ho = 0.35) is higher than expected heterozygosity (He = 0.31), showing an excess of heterozygotes. Strong genetic differentiation (Fst = 0.90) was revealed between HA2 and (BA + T)2 and weak genetic differentiation between these groups and their offspring (Fst = 0.03). Multivariate analysis revealed two main phylogenetic groups. Group 1 containing almost all HA2 individuals and group 2 comprising almost all (BA + T)2 genotypes. Indeed, genetic conformity of individuals was revealed within each group, with 94% of HA2 genotypes and 81% of (BA + T)2 genotypes. These results are a good indicator for avoiding errors when selecting the best cocoa trees within each genetic group, with a view to starting a new selection cycle.
Enhancing Urban Resilience to Flooding in Hydrogeological Risk Areas Through Big Data Analytics Using Deep Neuro-Fuzzy System
Authors: Malik Varun , Martin R. John , Mittal Ruchi , Ravali Ravula Sahithya , Almalki Khalid Jaber , Ramakrishnan Jayabrabu , Swapna SL , Mavaluru Dinesh , Goyal SB , Kumar Manoj
Published: July 2024

Urban areas worldwide are increasingly at risk from hydrogeological hazards, leading to severe consequences. Urban flooding and mismanagement of water resources, resulting in riverine flooding, are primary contributors to this risk. Utilizing big data, including mobile phone signals collected at high frequencies, alongside administrative data, is essential for developing risk exposure indicators in smaller urban regions. Accurately assessing human traffic flows and movements is crucial for mitigating the impacts of natural disasters and ensuring a high quality of life in smart cities. However, comprehensive solutions to these challenges are lacking in many countries. Therefore, this study focuses on analyzing the impact of traffic data flow analysis in hydrogeological risk areas. The study employs mobile phone signals as big data to analyze traffic flows and forecast exposure risks to aid decision-making. To ensure data reliability, a circle search integrated fully connected conditional neural network (CS-ConNN) is used for data cleaning, categorizing mobile phone signal data into normal, empty, and garbage. Additionally, the study uses a deep recurrent neuro fuzzy system (DRNFS) to analyze the compound seasonality of circulation flow data and forecast risks, providing alerts to individuals transiting through affected areas. The model is validated through a case study of "Mandolossa," and developed area prone to inundating near Brescia, using hourly data from September 2020 to August 2021. Experimental results and cross-validation demonstrate a forecasting accuracy of 98.975%.
Cost-benefit Analysis on the Implementation of Treated Wastewater Reuse: Case of Sekem Farm El-wahat, Egypt
Authors: WAHID Nadya , KHAIRY Wael , KRAUSE Regina
Published: July 2024

In a context of increasing pressure on water resources and severe aridity; expansion in the reuse of naturally treated wastewater can be a viable and low-cost solution particularly for irrigation forests, green non-fruit trees, fabric crops, industrial oils, and non-edible raw crops. Wastewater treatment is increasingly recognized as a potential means in El-Wahat El-Bahariya in Egypt. However, investment decisions concerning the reuse of treated wastewater in irrigation needs to be justified in terms of financial and economic feasibility and profitability. Therefore, this research study aims to conduct a cost-benefit analysis (CBA) of an investment project “reuse treated wastewater in irrigation compared to other modes of water irrigation projects. The CBA results revealed that the cost of the initial investment for the production of treated water used for irrigating green non-fruit trees, Bamboo trees, and Cactus in Sekem El-Wahat, is economically efficient with 88% compared to the exploitation of aquifer groundwater for irrigating the same crops. That project allows for an economic gain of about 4,428.5 €/ha compared to the cost of producing aquifer ground water in Sekem Farm El-Wahat is estimated at 10,800 €/year. Substituting the use of aquifer ground water for irrigating crops with reuse of treated wastewater helps reducing energy consumption and offers great financial benefits to the beneficial communities. A net benefit of the reuse of treated wastewater project is largely positive with NPV equivalent to 4599 €/year with a medium economic efficiency (BCR) of about 0.44.
Synergistic Influence of Surface Sanding Technique and Multi-Walled Carbon Nanotube Incorporation on the Mode I Fracture Behavior of Glass Composite Laminates
Authors: Nazari Roohollah , Hakimi Rashid , Daneshfar Mohammad , Talebi Behnam
Published: July 2024

This study sequentially investigates the enhancement of fracture behavior in adhesive joints. Initially, mechanical sanding techniques were explored, revealing significant improvements of 55% in fracture energy and 38% in load-bearing capacity with optimal 240-grit sanding. Subsequently, in a second stage, Multi-Walled Carbon Nanotubes (MWCNTs) were introduced at varying weight percentages (0.1%, 0.3%, and 0.5% by weight) into the adhesive joints. This combined approach aimed to synergistically examine the impact of surface pretreatments and nanoparticle integration on fracture behavior. Results highlight the potential for fracture resistance and enhanced load-carrying capabilities in reinforced specimens, with maximum load and fracture energy improvements of up to 92% and 50%, respectively, compared to unreinforced specimens and those subjected solely to sanding treatments. Furthermore, the fracture mode shifted from adhesive failure to cohesive substrate failure in reinforced specimens. This comprehensive investigation underscores the intricate interplay between mechanical preparation and nanomaterial incorporation in significantly improving adhesive joint performance
Association between triglyceride glucose body mass index and left ventricular systolic synchronicity in patients with type 2 diabetes
Authors: Yan Fei-fan , Lu Chun-feng , Wang Xiao-hua , Huang Hua-xing
Published: July 2024

Background: The triglyceride glucose-body mass index (TyG-BMI) is considered to be an alternative indicator of insulin resistance (IR) with greater clinical value in terms of cardiovascular risk. However, the relationship between TyG-BMI and left ventricular asynchrony, which determines heart function, is unclear. The purpose of this study was to explore the association between the TyG-BMI and left ventricular asynchrony in patients with type 2 diabetes.
Methods: This cross-sectional study included 614 patients with type 2 diabetes between September 2021 and June 2023. All patients initially screened with conventional echocardiography underwent subsequent evaluations, including speck-tracking echocardiography and real-time three-dimensional echocardiography. The systolic dyssynchrony index (SDI) was automatically derived from real-time three-dimensional echocardiography in order to assess the degree of left ventricular asynchrony among patients. The TyG-BMI was calculated, and the included patients were stratified according to TyG-BMI quartiles.
Results: The analysis of the 614 patients with type 2 diabetes who were ultimately included revealed that the SDI tended to increase as the TyG-BMI increased, with the SDI corresponding to the highest quartile being the highest. According to multiple linear regression analysis, the TyG-BMI is independently related to the SDI. Further exploratory subgroup analysis revealed that the TyG-BMI was more likely to be associated with SDI in patients ≤55 years of age with type 2 diabetes.
Conclusions: Our research revealed that the TyG-BMI of patients with type 2 diabetes was positively correlated with the SDI, and this correlation was more obvious in patients with type 2 diabetes who were ≤55 years old.
SentiLexIT: Advancing Italian Sentiment Analysis through Automated Lexicon Generation
Authors: de Melo Tiago , Merialdo Paolo
Published: July 2024

The proliferation of digital platforms has exponentially increased the volume of user-generated content, providing rich data sources for sentiment analysis. This study introduces SentiLexIT, a novel methodology for automatically constructing domain-specific sentiment lexicons for the Italian language using an unsupervised approach. We analyzed 321,817 user reviews from diverse categories such as Amazon.it and Google Reviews, applying Bayes' Theorem to derive sentiment scores that effectively categorize review sentiments into distinct polarities. Our methodology significantly outperforms existing Italian sentiment lexicons and two versions of ChatGPT, achieving an average F1-score of 0.92 across various domains. The paper highlights the method's effectiveness in handling linguistic nuances specific to Italian, providing a robust tool for sentiment analysis. This research contributes to the field by offering a scalable, precise methodology for sentiment lexicon construction without the need for manual annotation, addressing the gap in non-English sentiment analysis resources.
Parameter estimation and model selection for stochastic differential equations for biological growth
Authors: Baltazar-Larios Fernando , Delgado-Vences Francisco , Ornelas Arelly
Published: July 2024

In this paper, we consider stochastic versions of three classical growth models given by ordinary differential equations (ODEs). Indeed we use stochastic versions of Von Bertalanffy, Gompertz, and Logistic differential equations as models. We assume that each stochastic differential equation (SDE) has some crucial parameters to be estimated and we use the Maximum Likelihood Estimator (MLE) to estimate them. For estimating the diffusion parameter, we use the MLE for two cases and the quadratic variation of the data for one of the SDEs. We apply the Akaike information criterion (AIC) to choose the best model for the simulated data. We consider that the AIC is a function of the drift parameter. We conduct numerical experiments to validate our selection method. Subsequently, we also apply it to actual data.
The proposed methodology could be applied to datasets with discrete observations including highly sparse data. Indeed, we can use this method even in the extreme case where we have observed only one point for each path, under the condition that we observed a sufficient number of trajectories. For the last two cases, the data can be viewed as incomplete observations of a model with a tractable likelihood function; then, we propose a version of the Expectation Maximiza- tion (EM) algorithm to estimate these parameters. This type of dataset typically appears in fishery, for instance.
Bioactive Compounds Derived from Sargassum wightii Exhibit Antibacterial Activity against Multi-Drug Resistant Acinetobacter baumannii
Authors: Suvaithenamudhan Suvaiyarasan , Kumar Sundarraj Dinesh , Thirugnanasambandam Rajendran , Ponmalar Esaki Muthu , Ganesh Pitchaipillai Sankar , Mariappan Vanitha , Shankar Esaki M. , Rudrapathy Parthiban
Published: July 2024

Acinetobacter baumannii (A. baumannii) is a notorious nosocomial pathogen known for its ability to form biofilms, rendering it highly resistant to conventional antibiotics and immune clearance. In this study, the minimum inhibitory concentration (MIC) ranged from 70 µg/mL to 100 µg/mL remarkably, the tested ethanolic extracts inhibited A. baumannii biofilm development in a concentration-dependent manner. Epifluorescence microscopic analysis revealed a significant reduction in treated biofilm formation compared to the control. Additionally, gas chromatography-mass spectrometry (GC-MS) analysis of the ethanol extract of Sargassum wightii (S. wightii) identified 10 major compounds. Molecular docking studies were conducted to explore the interaction of small molecules from S. wightii with the BfmR protein of A. baumannii. The molecular docking of three ligand molecules (CAS No. 002302-12-7, 015120-94-2, and 146397-91-3) with the target BfmR-Ab revealed the lowest binding energies (∆Gbind) of -42.26 and − 50.49 (kcal/mol) for the ligands CAS No. 002302-12-7 and 146397-91-3, respectively, and the lowest Glide score of -4.067 (kcal/mol) for the ligand CAS No. 015120-94-2. These top three hit molecules exhibited the highest affinity as efficient ligands against BfmR of A. baumannii. Nevertheless, S. wightii demonstrated antibiofilm activities against the multidrug-resistant (MDR) pathogen A. baumannii, with bioactive compounds exhibiting promising drug-likeness and pharmacokinetic signatures.
Functional characterization of malate dehydrogenase gene, HcMDH1 in enhancing abiotic stress tolerance in kenaf (Hibiscus cannabinus L.)
Authors: Luo Dengjie , Li Zengqiang , Mubeen Samavia , Rehman Muzammal , Cao Shan , Wang Caijin , Yue Jiao , Pan Jiao , Jin Gang , Li Ru , Chen Tao , Chen Peng
Published: July 2024

Drought and salt stress are two important environmental factors that significantly restrict plant growth and production. Malate dehydrogenase is essential to life as it is engaged in numerous physiological processes in cells, particularly those related to abiotic stress reactions. However, a complete understanding on MDH family members in kenaf is not clear yet. In this study, subcellular localization analysis and a yeast transcriptional activation assay revealed that HcMDH1 was localized in chloroplasts but had no transcriptional activation activity. When exposed to salt or drought, yeast cells that express the HcMDH1 gene have a greater survival rate. Overexpression of HcMDH1 in Arabidopsisincreased seed germination rate and root growth when transgenic lines were exposed to varying concentrations of mannitol and NaCl. Subsequent physiological studies revealed that transgenic lines had higher concentrations of soluble carbohydrates, proline, and chlorophyll and lower concentrations of malondialdehyde (MDA) and reactive oxygen species (ROS). Furthermore, inhibiting HcMDH1 in kenaf using virus-induced gene silencing (VIGS) decreased salt and drought tolerance due to elevated ROS and MDA levels. In these silenced lines, the expression of six essential genes engaged in stress-resistance and photosynthesis, namely HcGAPDH, HcGLYK, HcFBA, HcFBPase, HcPGA, and HcLSD, is significantly altered under salt and drought stress. In summary, HcMDH1 is a positive regulator of salt and drought tolerance in kenaf, which may have implications for transgenic breeding.
Examining Preferences for Weight Loss Interventions Among Black Men and Women Experiencing Overweight or Obesity: A Qualitative Approach
Authors: Ray Snehaa , Idiong Christie I. , Anderson Rachel , Killion Kate , Antrum Curtis , Puglisi Michael , Kalinowski Jolaade , Stowers Kristen Cooksey , Carr Loneke T. Blackman
Published: July 2024

Background Behavioral interventions aiming to modify dietary habits and physical activity have been less effective in achieving clinically significant weight loss in Black adults. Weight loss needs vary by race and gender, so different strategies are needed to engage individuals in healthy behaviors. While there have been some research efforts focused on weight loss in Black women, participation rates of Black men in weight loss interventions are lower. This may perpetuate the development of obesity-related conditions such as diabetes, heart disease, and other cardiovascular problems. This qualitative study investigated the barriers and facilitators to healthy eating and physical activity and the weight loss desires among Black adults with overweight or obesity.Methods Black adults mainly residing in Hartford, Connecticut were recruited for the study. Semi-structured interviews were conducted with Black males and females who self-reported being overweight or obese. Preferences for weight loss specific to each gender, barriers, and facilitators for weight-related behaviors such as diet and physical activity, were examined through thematic analysis to inform the development of a culturally relevant behavioral weight loss intervention.Results The sample consisted of Black adults from diverse racial and ethnic backgrounds. Black females (n = 16) and Black males (n = 8) were mostly non-Hispanic Black Americans. The key themes that emerged were: 1. the creation of a weight loss program for Black adults, 2. healthy eating barriers, 3. physical activity barriers, 4. healthy living facilitators, and social support. Both Black females and Black males identified unique weight loss preferences. Community and personalization were preferred by Black females while Black males preferred personalized diet and exercise regimes for diverse health conditions, not limited to weight loss. While both genders referred to digital devices and apps for recording weight, diet and physical activity as a major facilitator to adopting healthy habits, social support in the form of culturally relevant information from healthcare providers was highly desired to be incorporated into the intervention.Conclusion The findings of this study hold relevance for designing and developing of weight loss programs that promote behavior change for Black adults and help reduce obesity-related health inequities within this population.
Adverse events of pegvaliase in the treatment of phenylketonuria: A pharmacovigilance study based on the FAERS database
Authors: Liu Junfa , Chen Zhe , Liu Xiongfei , Li Xiangyang , Dong Zhitao
Published: July 2024

Pegvaliase was approved by the FDA in 2018 for the treatment of phenylketonuria(PKU).However, the long-term safety of pegvaliase in a large sample population is unclear. The purpose of this study was to investigate the adverse events associated with pegvaliase in the real world by mining data from the FAERS database. We extract adverse event data on pegvaliase from the FAERS database and perform a disproportionality analysis utilizing ROR，PRR，BCPNN and MGPS. A total of 82 preferred terms (PTs) simultaneously met the criteria of all four algorithms. Common PTs include reactions at the injection site，joint pain, sensitivity reactions, headaches, skin reactions, injection site pruritus, and rash pruritic, corresponding to adverse events reported in clinical trials. Unexpected significant adverse events were identified, such as amino acid level decreased or increased, panic attacks, self-injurious ideation, disturbances in attention, tunnel vision, and so on. The median onset time for adverse events to pegvaliase is 15 days(IQR = 9-42 days), with the majority occurring within 30 days (n=509, 67.78%). The top three drugs most commonly prescribed in conjunction with pegvaliase are famotidine, cetirizine, and sapropterin. When pegvaliase is used in combination with these drugs, it is associated with an increase or decrease in the risk of certain adverse events. Our study provides crucial insights for the safer and more effective use of pegvaliase.
The Impact of Artificial Intelligent Tools on Decision Making Behavioral and Neural Dynamics
Authors: Molina-Perez Edmundo , Cortes Pedro , Molina Isaac , Sobrino Fernanda , Tellez Mario , Orozco Yessica , Castellón Mitzi , Popper Steven , Serra Luis
Published: July 2024

Decision-making is a multifaceted cognitive process influenced by task complexity, information availability, individual cognitive strategies, and environmental settings. Yet, the neural mechanisms guiding everyday choices remain incompletely understood. This gap intensifies when integrating real-time aids, such as artificial intelligence tools (AIT), as cognitive decisionsupport especially for complex and ambiguous problems. This study explores the neural mechanisms of decision-making and examines how AIT influences these processes. Combining behavioral assessments and neurophysiological measurements, we investigate the dynamic interplay between human cognition and AIT through behavioral execution and electroencephalogram (EEG) activity. Experimental data from 54 participants suggest that in low-complexity decision-making, AIT is largely ignored in favor of heuristics. In high-complexity contexts, AIT positively influences decision-making outcomes while also increasing capacity for engagement with a challenging task as registered by EEG cortical activity. This suggests a non-linear effect of AIT in decision-making strategies highlighting its role as a complement to —rather than a replacement of—human cognitive processes.
A novel optimization method via Box-Behnken Design integrated with back propagation neural network - genetic algorithm on hydrogen purification
Authors: Zhang Nannan , Hu Sumeng
Published: July 2024

High purity hydrogen is a necessary need for fuel cell. Pressure swing adsorption (PSA) technology is one of the effective methods for hydrogen purification. The layered bed PSA model is built and validated. To simplify the calculation of PSA purification performance, the quadratic regression equations are obtained by Box-Behnken design (BBD) method.With adsorption time, pressure equalization time and feed flow rate from PSA process as independent optimization parameters of the BBD method, the hydrogen purity and productivity as two responses. The genetic algorithm (GA) is introduced to the back propagation neural network (BPNN) to solve the optimization problem of the PSA process. In order to explore the performance of optimization algorithms, a novel optimization method is proposed in this work. With the BBD method is integrated with BPNN-GA model to optimize the structure (BBD-BPNN-GA). The results showed that the BBD-BPNN-GA model have a better performance with the MSE of 0.0005, while the mean square error (MSE) of BPNN-GA model is 0.0035. And the correlation coefficient of R-values are much closer to 1of the BBD-BPNN-GA model, which is illustrated that the BBD-BPNN-GA model can be effectively applied to the prediction and optimization of PSA process.
The combined predictive value of renal function markers and blood lipid levels for the occurrence of preeclampsia
Authors: Zhang Jindi
Published: July 2024

Background and Objective: Preeclampsia(PE), a prevalent complication of pregnancy, could severely affect the health of gravidas and fetuses. Consequently, developing a convenient, cost-effective, and reliable method for predicting the occurrence of PE is crucial. This study aimed to assess the predictive value of combining renal function markers with blood lipid levels for PE.
Methods:This study conducted a retrospective analysis of 527 cases of pregnant women dilivering in Hangzhou Women’s Hospital during January 2021 to December 2023, which were divided into PE group(n=263) and control group(n=264).The levels of serum renal function markers and blood lipid were compared in both groups. Logistic regression analysis was used to explore the factors associated with PE. Then the receiver operating characteristic (ROC) curve was used to evaluate the predictive value of combining renal function markers with blood lipid levels for PE.
Results: Univariate analysis showed that the PE group had higher serum triglyceride(TG), creatinine(Cr), uric acid(UA)and urea than the control group. Logistic regression analysis showed that the high levels of serum TG,Cr and UA were independent risk factors for PE (P< 0.01). The areas under the curve (AUC) were 0.703(95%CI=0.658-0.747), 0.734(95%CI=0.691-0.777) and 0.822(95%CI=0.786-0.857) respectively. The AUC, sensitivity and specificity of the joint detection of TG,Cr and UA were 0.864(95%CI=0.833-0.896), 76.4%,and 84.8%,which showing a good predictive performance.
Conclusions: The increased levels of serum TG,Cr and UA suggest an increased risk of PE. And their joint detection has a certain predictive valuefor PE, which perhaps could offer a convenient and efficient method for the early diagnosis of PE.
False spring events in the eastern part of the Baltic Sea region
Authors: Klimavičius Laurynas , Rimkus Egidijus
Published: July 2024

Spring frosts can cause substantial economic and ecological damage, especially when occurring after the growing season has begun and leading to compound climate events, known as false spring (FS) events. This study examines the recurrence, spatial distribution and intensity of FS events in the eastern part of the Baltic Sea region from 1950 to 2022. These events were identified by determining the dates of the last spring frost (LSF) and the start of the growing season (SGS) for each grid cell annually. FS events were distinguished when the LSF date occurred later than the date of the SGS. The sum of growing degree days (tbase = 5°C), accumulated until the event, and the minimum air temperature (tmin) during the day of the FS event were used to assess the intensity of FS events. Throughout the study period, LSF and SGS dates shifted earlier by an average of 6.3 and 10.6 days, respectively. In 85.9% of the grid cells, the SGS date changed more rapidly than the LSF date. Although the number of FS events increased over the 73 years in most of the study area, the changes were statistically insignificant according to the Mann–Kendal test. However, comparing 1950–1969 and 2003–2022, 85.9% of grid cells saw more FS events, with the largest increase in the eastern part of the study area and southwestern Lithuania. Significant changes were determined in 30% of cells according to the t-test.
Bimetallic Chalcogenides Enable Highly Efficient Polysulfide Capture and Conversion in Lean-Electrolyte Li-S Batteries
Authors: Wang Junhua , Sun Hao , Zhao Weiran , Pan Chongxiang , Li Rongrong , Yao Yuan , Chang Caiyun
Published: July 2024

The commercialization of Li-S batteries is obstructed by the sluggish redox kinetics and serious shuttling behaviors of polysulfides. Herein, we report a rationally structured sulfur host material to tackle these issues, i.e. (Co, Ni)9S8 nanoparticles uniformly decorated on electrospun carbon nanofibers. The (Co, Ni)9S8 nanoparticles are demonstrated to effectively capture polysulfides and catalytically promote their redox conversions. Moreover, the interlinked porous architecture of (Co, Ni)9S8@CNFs also contributes to alleviate volume expansion of sulfur cathode and provide the rapid electron transfer paths and Li-ion diffusion channels. Benefiting from these attributes, the (Co, Ni)9S8@CNFs cathode delivers an excellent rate capability and long cycling stability (capacity decay of 0.142% per cycle over 300 cycles). Additionally, the (Co, Ni)9S8@CNFs with high sulfur content (83.3%) and lean electrolyte (5 μL mg-1) shows high capacity of 556 mAh g-1 at 0.5 C and 590 mAh g-1 after 180 cycles at 0.2 C, demonstrating highly efficient utilization of sulfur and extraordinary potential for practical application of Li-S batteries.
Al8P8 double nanoring as a high-performance sensor for SF6 decomposed gases: A DFT-D4 study
Authors: Ullah Faizan , Arfan Nur Hazimah Binti Zainal , Ayub Khurshid , Mahmood Tariq , Sheikh Nadeem S.
Published: July 2024

The efficacy of an Al8P8 double nanoring as a sensor for sulfur hexafluoride (SF6) decomposition gases (H2S, HF, SO2, SO2F2, and SOF2) is investigated using density functional theory with the PBE0-D4 functional and def2-TZVP basis set. Additionally, highly accurate DLPNO-CCSD(T)/cc-pVTZ single-point energy calculations are employed to refine the interaction energies. Interaction energies ranging from − 43.31 to − 63.92 kJ mol− 1 are reported, with H2S exhibiting the strongest adsorption. SO2 adsorption induces the most significant change in the HOMO-LUMO gap, narrowing it to 1.34 eV from 3.18 eV, which suggests a substantial enhancement in electrical conductivity upon interaction. NCI analysis reveals a diverse range of interaction types, including hydrogen bonding and van der Waals interactions, contributing to the adsorption behavior. Rapid recovery times are observed, indicating the reusability of the sensor. The findings demonstrate that the Al8P8 double nanoring shows promise as a sensitive, selective, and reusable sensor, particularly for SO2, with potential applications in industrial gas leak detection and environmental safety monitoring.
High-throughput separation of microalgae on a runway-shaped channel with ordered semicircular micro-obstacles
Authors: Hu Sheng , Jin Shuai , Chen Xiaoming , Tong Ruijie
Published: July 2024

Microalgae serve as a valuable biological resource in many industrial applications. Thus, it is essential to obtain a high-efficiency separation technique for microalgae precisely. In this study, a runway-shaped microchannel with ordered semicircular micro-obstacles was introduced to conduct the separation of microalgae with different sizes. The runway-shaped microchannel combined the spiral characteristics with a series of semicircular micro-obstacles to realize the advantage of a sheathless, high-throughput, and low aspect ratio configuration. These micro-obstacles improved the performance of particle focusing, which can promote the microalgae separation effectively. These simulated results demonstrated that the runway-shaped channel with ordered semicircular micro-obstacles could form the evident distribution of local Dean vortices to separate particles with different size or volume. When the flow rate is considered 4mL/min, the experiment indicated that the microchannel could separate the Chlorella vulgaris and Haematococcus pluvialis in 94.6% and 81.5%, respectively. The microchannel with the high throughput and separation efficiency is competent to carry out the task of microalgal screening and artificial cultivation.
Microfluidic Platform for Stroke Risk Prediction: Evaluation of Blood Viscosity by Shear Rate Variations
Authors: Chang Yii-Nuoh , Yao Da-Jeng
Published: July 2024

The incidence of stroke is on the rise globally, affecting one in every four individuals each year. An early warning and prevention systems are urgently required. Blood viscosity is a correlation factor that is worthy to study in the stroke risk evaluation model. For the first time, a microfluidic platform was used as the in-vitro blood property evaluation for stroke risk prediction. It can be also used to evaluate the variation of non-Newtonian fluid viscosity under different specific shear rate conditions. The rigorous microarray design is providing the meticulous shear rate which simulating the variable of blood viscosity during pulsation within blood vessels. Furthermore, the systolic blood viscosity (SBV) and diastolic blood viscosity (DBV) can be calculated by using the developed pulsatility flow concept. The results demonstrate an impressive accuracy of 95% and excellent reproducibility while compared to traditional viscometers and rheometer within the human blood viscosity range of 1-10cP. This monitoring system is capable of being an indispensable component in the stroke risk evaluation platform.
L-shaped association between leisure-time physical activity and depressive symptoms in individuals with chronic inflammatory airway disease: data from the NHANES (2007-2018)
Authors: Li Wenqiang , Bai Jingshan , Ge Yanlei , He Qian
Published: July 2024

Background
Chronic inflammatory airway diseases (CIAD) are well-known risk factors for depression symptoms. There exists a complex interplay between leisure-time physical activity (LTPA) and depressive symptoms. However, the relationship between LTPA duration and depressive symptoms in CIAD patients remains unclear.
Methods
We collected data from National Health and Nutrition Examination Survey (NHANES) 2007–2018. Weighted logistic regression was employed to analyze the correlation between CIAD and depressive symptoms. Next, CIAD patients with LTPA information, the similar method was also employed to examine the association between LTPA and depressive symptoms. Finally, the dose-response relationship between LTPA duration and depressive symptoms was explored using restricted cubic spline (RCS) plot.
Results
Consistent with previous research findings, patients with CIAD are more prone to experiencing symptoms of depression (P < 0.0001). Compared to CIAD patients without LTPA, patients who engage in LTPA have a lower probability of experiencing depressive symptoms (P < 0.0001). Interestingly, we had discovered that there is an L-shaped relationship between the duration of LTPA and the prevalence of depressive symptoms (non-linear P = 0.001). There is a threshold effect between the duration of LTPA and depressive symptoms. Within12 h/week, the risk of depressive symptoms in CIAD patients decreases with increased duration of LTPA. However, beyond 12 h/week, this association no longer exists.
Conclusion
Our research indicates that CIAD patients can increase the prevalence of depressive symptoms. And LTPA over a period of time is negatively correlated with depressive symptoms, but only up to a total duration of 12 h/week.
Multilevel Learning for Enhanced Traffic Congestion Prediction Using Anomaly Detection and Ensemble Learning.
Authors: Khasawneh Mohammed A. , Daraghmeh Mustafa , Awasthi Anjali , Agarwal Anjali
Published: July 2024

Traffic congestion is a major challenge in modern transportation systems, leading to increased travel time, greater fuel consumption, and higher levels of harmful emissions. Accurately forecasting traffic congestion at a high level of precision is crucial for implementing effective traffic control strategies, such as managing traffic lights. To generate accurate and reliable predictions, it is crucial to precisely detect abnormal traffic patterns in the data, particularly in densely populated urban areas. This study presents a novel approach for forecasting traffic congestion by employing a multilevel learning technique that integrates both anomaly detection and ensemble learning methods. As an initial step in the learning process, we evaluate different anomaly detection techniques to identify unusual traffic patterns in different locations over time. After predicting the anomaly, the data pattern is cleaned accordingly, and a set of baseline learner models is trained as a secondary learning process. The top-performing models are chosen and undergo an ensemble process to combine their results, evaluating both stacking and voting ensemble methods as a third learning process. We evaluate the efficacy of the proposed strategy by employing a real-world traffic dataset and diverse evaluation metrics. The dataset undergoes several preprocessing techniques, including the windowing process with various settings, to convert the time series data into frequency patterns and produce a more generalized model. The comparative results of this study demonstrate that the proposed multilevel learning approach achieves superior prediction accuracy compared to the baseline models, underscoring the influence of the multilevel learning strategy on the accuracy of predictions. This study highlights the utilization of anomaly detection and ensemble learning to enhance the precision of traffic congestion prediction, thereby promoting further exploration of this approach in intelligent transportation systems.
ParaMask, a new method to identify multicopy genomic regions, corrects major biases in whole-genome sequencing data
Authors: Tjeng Bastiaan , Arimond Male , Grindeland Helene Bråten , Libera Andrea Dalla , Fulgione Andrea
Published: July 2024

Multicopy genomic regions are repeated sequences that can bias genomics analyses. Here, we present a method to identify and filter multicopy regions in population-level genomic data of any species. The broad applicability of this method stems from a flexible Expectation-Maximization framework to detect excess heterozygosity while simultaneously fitting inbreeding levels. By combining this signature with read ratio deviations, excess sequencing coverage, and a clustering technique, our method attains high power. We show that multicopy regions create biases that confound evolutionary genomics analyses, and that by identifying these regions with our method and filtering them, we can correct these biases.
Association between pan-immune-inflammation value and hyperlipidemia in the United States population
Authors: Yan Yu , Jia Shanshan , Huo Xingwei , Liu Lu , Ma Shuangliang , Chen Xiaoping
Published: July 2024

Object: To investigate the possible association between pan-immune-inflammation value (PIV) and hyperlipidemia.
Methods: The authors selected the relevant data from National Health and Nutrition Examination Survey (NHANES) for a detailed cross-sectional study. The independent variable used the logarithmic form of PIV-log10 (PIV). The definition of dependent variable-hyperlipidemiawas based on the National Cholesterol Education Program standards. Both variables were calculated from measured laboratory data. Weighted multivariate logistic regression analyses and restricted cubic splines (RCS) were conducted to analyze the association between PIV and hyperlipidemia. Stratified analyses were used to identify potential associations between PIV and hyperlipidemia with other covariates. The study also constructed the receiver operating characteristic (ROC) curve to assess the predictive value for hyperlipidemia of PIV compared to systemic immune-inflammation index (SII).
Results: In the study, 7,715 participants from NHANES were included. After adjusting for all confounders, PIV and hyperglycemia had an significantly positive association (OR (95%CI): 1.55 (1.17-2.06); P = 0.002). Compared to participants with lowest quartile (Q1) of PIV, participants with the highest quartile (Q4) had a significantly higher risk of hyperlipidemia (OR (95%CI): 1.47 (1.21-1.79); P < 0.001). The RCS curve showed a linear relationship between PIV and hyperlipidemia (P-nonlinear = 0.0633, P-overall < 0.001). The ROC curve found that compared with SII, PIV had a slightly higher predictive value (0.547 vs 0.542, P = 0.267).
Conclusion: This national cross-sectional study discovered that PIV had a significantly positive relationship with hyperlipidemia, particularly in young overweight individuals. More prospective studies are needed to verify whether the PIV is a more reliable and effective index for assessing the risk of hyperlipidemia.
Adiabatic control of the phase memory relaxation in the anticrossing region
Authors: Kandrashkin Yu. E.
Published: July 2024

In the vicinity of the anticrossing region of the spin sublevels, the effective magnetic moment of the electron spin is reduced, resulting in an increase in the lifetime of the spin coherence. Based on this phenomenon, the modified electron spin echo protocol is proposed to extend the coherence relaxation time. It includes the Hahn echo sequence with the additional detuning of the magnetic field applied during the intervals between two pulses and between the refocusing pulse and the echo. The simplest system with anticrossing sublevels is an ion whose electron spin is strongly coupled to its own nucleus. To realize the proposed protocol, a narrow anticrossing region is required. The promising candidate to realize the proposed protocol is the crystal [Ho(W5O18)2]9−, whose spin transitions have been studied in the recent paper (K. Kundu, et al., Commun Phys. 6 (2023) 38). Near the clock transition, the spin evolution of the electron-nuclear spin system is described by a fictitious spin ½, but with the magnetic properties dictated by the properties of the ion. The numerical study shows that the spin coherence is not destroyed by the magnetic field detuning, but it reduces the phase relaxation. In addition, the detuning result in a phase shift of the coherence, which can be used differentiate between the target signal and the noise.
Avian Migration-Mediated Cross-Species Transmission and Recombination driving the Diversity of Gammacoronaviruses and Deltacoronaviruses
Authors: Wu Zhiqiang , Xu Yuting , Han Yelin , Xu Panpan , Zhou Siyu , Zhao Peng , Wang Yuyang , Hu Jie , Ma Min , Li Zirong , Bo Shunqi , Zhao Chenyao , Ji Lei , Yuan Yue , Zhao Wenliang , Wang Jianwei , Jin Qi , He Guimei
Published: July 2024

In the wake of pandemics like COVID-19, which have zoonotic origins, the role of wildlife as reservoirs for emerging infectious diseases has garnered heightened attention. Migratory birds, traversing continents, represent a potent but under-researched vector for the spread of infectious diseases, including novel coronaviruses. This study delves into the genetic diversity and cross-species transmission dynamics of coronaviruses in migratory birds, presenting pivotal findings. From April 2019 to April 2023, we screened 5,263 migratory bird samples collected from Shanghai, China, identifying 317 coronavirus-positive samples belonging to five avian-related coronavirus subgenera and subsequently obtaining 120 complete genome sequences. To facilitate further research with a global perspective, the study curated all currently available 19,000 avian-associated CoVs and expanded the original 12 species to the current 16 species, including three novel coronavirus species identified in our study and one re-classified species from the public domain. The study illuminates the intricate genetic evolution and cross-species transmission dynamics of birds-related coronaviruses on a global scale. A notable aspect of our research is the identification of complex recombination patterns within the spike protein across different virus species and subgenera, highlighting migratory birds as a reservoir of coronavirus. Notably, the coronaviruses found in migratory birds, predominantly from the orders Anseriformes, Charadriiformes, and Pelecaniformes, with domestic ducks from Anseriformes playing a key role in bridging the transmission of coronaviruses between migratory and non-migratory birds. These findings reveal the genetic and recombination characteristics of coronaviruses in migratory birds, emphasizing the critical role of ecologically pivotal bird species in coronavirus cross-species transmission and genetic diversity shaping.
NuSTAR as an Axion Helioscope
Authors: Ruz Jaime , Todarello Elisa , Vogel Julia , Giannotti Maurizio , Grefenstette Brian , Hudson Hugh , Hannah Iain , Irastorza Igor , Kim Crystal , O'Shea Tomas , Regis Marco , Smith David , Taoso Marco , Bueno Javier Trujillo
Published: July 2024

The nature of dark matter in the Universe is still an open question in astrophysics and cosmology. Axions and axionlike particles (ALPs) offer a compelling solution, and traditionally ground-based experiments have eagerly, but to date unsuccessfully, searched for these hypothetical low-mass particles that are expected to be produced in large quantities in the strong electromagnetic fields in the interior of stars. This work offers a fresh look at axions and ALPs by leveraging their conversion into X-rays in the magnetic field of the Sun’s atmosphere rather than a laboratory magnetic field. Unique data acquired with the Nuclear Spectroscopic Telescope Array (NuSTAR) during the solar minimum in 2020 allows us to set stringent limits on the coupling of axions to photons using state-of-the-art magnetic field models of the solar atmosphere. We report pioneering limits on the axion-photon coupling strength of 6.9×10−12 GeV−1 at 95% confidence level for axion masses ma ≲ 2×10−7 eV, surpassing current ground-based searches and further probing unexplored regions of the axion-photon coupling parameter space up to axion masses of ma ≲ 5×10−4 eV.
Multidisciplinary Aeroelastic Optimization of High Aspect-Ratio Composite Aircraft Wings via Tow-Steering
Authors: Nikolaos Ziakos , Spyridon Kilimtzidis , Vassilis Prof. Dr. Kostopoulos
Published: July 2024

Innovative high aspect-ratio airframe designs are leading the way to a more sustainable future in aviation. These configurations enhance aerodynamic efficiency by reducing induced drag. Further performance improvements, especially regarding structural mass, are achieved with composite material airframes, that have been used progressively more in the aerospace industry due to their superior performance, lightweight design and inherent ‘tailorability’. On that end, towsteered composites have recently shown to enhance performance compared to conventional composites by taking advantage of the anisotropic behavior of fiber composites. However, these advancements can result in undesirable effects and specifically aeroelastic couplings due to increased flexibility, making the design and optimization of such airframes highly complex and computationally demanding. In our approach, a computational framework for the aeroelastic optimization of a composite high aspect-ratio commercial aircraft wing is proposed. Initially, we explore the effect of tow-steered composite skins on the mass of a fully-sized reference wing compared to conventional composites and subject to multidisciplinary constraints. We then aim to further investigate the effect of steered composites in various disciplines and thus introduce two single sub-optimization problems, namely the minimization of the stress aggregate function on the skins as well as the maximization of the flutter velocity. Finally, a multi-objective problem is formulated for the simultaneous optimization across three objectives. Multiple gains in performance are observed across all of the objectives in the aforementioned optimization problems, indicating the positive effect of tow-steered composites in the design of high aspect-ratio wings.
Sex-stratified analyses of comorbidities associated with an inpatient delirium diagnosis using real world data
Authors: Sirota Marina , Kodama Lay , Woldemariam Sarah , Tang Alice , Li Yaqiao , Kornak John , Allen Isabel (E) , Raphael Eva , Oskotsky Tomiko
Published: July 2024

Delirium is a detrimental mental condition often seen in older, hospitalized patients and is currently hard to predict. In this study, we leverage electronic health records (EHR) to identify 7,492 UCSF patients and 19,417 UC health system patients with an inpatient delirium diagnosis and the same number of control patients without delirium. We found significant associations between comorbidities or laboratory values and an inpatient delirium diagnosis, including metabolic abnormalities and psychiatric diagnoses. Some associations were sex-specific, including dementia subtypes and infections. We further explored the associations with anemia and bipolar disorder by conducting longitudinal analyses from the time of first diagnosis to development of delirium, demonstrating a significant relationship across time. Finally, we show that an inpatient delirium diagnosis leads to increased risk of mortality. These results demonstrate the powerful application of the EHR to shed insights into prior diagnoses and laboratory values that could help predict development of inpatient delirium and the importance of sex when making these assessments.