bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the experiences of dancers who have achieved peak performance: on-stage, pre-stage, and post-stage
Authors: Nam Soo Mi , Park Hye Youn , Kim Min Joo
Published: May 2024

The aim of this study is to identify and classify the different attributes that contribute to peak performance among professional dancers, and to understand how these attributes change over time. We conducted an analysis using inductive content analysis on open-ended survey data collected from 42 formally trained professional dancers. Additionally, we analyzed interview data from seven professional dancers who demonstrated outstanding achievements in the field among the survey participants. The main themes that emerged were related to various temporal events of peak performance experience: pre-stage, on-stage, and post-stage. During the on-stage, peak performance was perceived by both internal and external factors. During the pre-stage, emphasis was placed on technical, cognitive, and artistic strategies during practice, whereas just before going on the stage, attention shifted to psychological and physical strategies. During the post-stage, dancers reported immediate changes in their psychological and physical states following the peak performance experience, and thereafter, the peak performance experience was noted to influence psychological, technical, and cognitive aspects. These findings provide valuable insights into the key characteristics that emerge throughout a series of peak performance experiences and are consistent with previous research.
Cognitive and emotional empathy in acute and remitted anorexia nervosa: a systematic review
Authors: Gray Indigo E. , Enticott Peter G. , Fuller-Tyszkiewicz Matthew , Kirkovski Melissa
Published: May 2024

Background

Impairments in empathy are well established in anorexia nervosa (AN). It is unclear, however, whether these deficits only occur in the acute phases of AN due to neurocognitive impacts of starvation (often referred to as context-dependent, or state-like), or if deficits remain once remission has been achieved (trait-like). This debate is commonly referred to as the ‘state vs trait’ debate.

Objective

This systematic review aims to summarise existing literature regarding empathy in AN, and to investigate whether empathy deficits in AN are state- or trait-based.

Method

A total of 1014 articles were identified, and seven articles remained after the screening process. These seven articles, comparing empathy across three groups (acute AN, remission of AN, and non-clinical controls), were evaluated and summarised in accordance with PRISMA guidelines. Articles were required to have included all three groups and report on either cognitive empathy and/or emotional empathy.

Results

The majority of studies were of satisfactory quality. The results identified were inconsistent, with few articles lending some support to the ‘state’ hypothesis and others producing nonsignificant results.

Conclusions

There is minimal literature comparing empathy in acute and remission phases of AN. While there were some inconsistencies in included articles, some data indicate that there may be slight improvements to emotional and cognitive empathy following recovery of AN. Further research is needed to better enrich knowledge regarding the role of state vs trait with regard to neurocognitive difficulties experienced by individuals with AN.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=335669, identifier CRD42022335669.
Knowledge and attitudes toward mild traumatic brain injury among patients and family members
Authors: He Jian , Chen Zhongliang , Zhang Jianjun , Liu Xiao
Published: May 2024

Introduction

Mild traumatic brain injury (mTBI) is a prevalent health issue with significant effects on patients' lives. Understanding and attitudes toward mTBI among patients and their families can influence management and outcomes. This study aimed to assess knowledge and attitudes toward mTBI in these groups.

Methods

A cross-sectional study was conducted at Zhejiang Hospital from July 1, 2023, to September 30, 2023. Patients with mTBI and their family members participated. Data were collected via an online questionnaire covering demographic information and mTBI knowledge and attitudes. Knowledge scores ranged from 0 to 20 and attitude scores from 8 to 40. Multivariate logistic regression identified factors influencing these scores.

Results

A total of 573 valid questionnaires were analyzed (289 males, 50.44%; 284 females, 49.56%). Among respondents, 258 (45.03%) had experienced a concussion. Mean knowledge and attitude scores were 11.00 ± 2.75 and 27.78 ± 4.07, respectively. Monthly per capita income of 5,000–10,000 RMB was negatively associated with knowledge and attitude scores (β = 0.160, 95% CI: [3.245 to 0.210], P = 0.026). Middle school education decreased the likelihood of positive attitudes toward mTBI (OR = 0.378, 95% CI: [0.1630.874], P = 0.023). mTBI due to falls was associated with increased likelihood of positive attitudes (OR = 3.588, 95% CI: [1.274–10.111], P = 0.016).

Discussion

Significant gaps in knowledge and attitudes toward mTBI exist among patients and their families, influenced by income and education levels. Personal experience with mTBI from falls correlates with more positive attitudes. These findings highlight the need for targeted educational interventions to improve understanding and attitudes, ultimately enhancing patient care and management. Comprehensive, accessible mTBI education is crucial for fostering positive attitudes and better knowledge among patients and their families.
Quantifying air quality co-benefits to industrial decarbonization: the local Air Emissions Tracking Atlas
Authors: Jordan Amy B. , Rodriguez Daniel S. , Bennett Jeffrey A. , Sale Kat , Gilhooley Christopher
Published: May 2024

Introduction

Many decarbonization technologies have the added co-benefit of reducing short-lived climate pollutants, such as particulate matter (PM), nitrogen oxides (NOx), and sulfur dioxide (SO2), creating a unique opportunity for identifying strategies that promote both climate change solutions and opportunities for air quality improvement. However, stakeholders and decision-makers may struggle to quantify how these co-benefits will impact public health for the communities most affected by industrial air pollution.

Methods

To address this problem, the LOCal Air Emissions Tracking Atlas (LOCAETA) fills a data availability and analysis gap by providing estimated air quality benefits from industrial decarbonization options, such as carbon capture and storage (CCS). These co-benefits are calculated using an algorithm that connects disparate datasets that separately report greenhouse gas emissions and other pollutants at U.S. industrial facilities.

Results

Version 1.0 of LOCAETA displays the estimated primary PM2.5 emission reduction co-benefits from additional pretreatment equipment for CCS on industrial and power facilities across the state of Louisiana, as well as the potential for VOC and NH3 generation. The emission reductions are presented in the tool alongside facility pollutant emissions information and relevant air quality, environmental, demographic, and public health datasets, such as air toxics cancer risk, satellite and in situ pollutant measurements, and population vulnerability metrics.

Discussion

LOCAETA enables regulators, policymakers, environmental justice communities, and industrial and commercial users to compare and contrast quantifiable public health benefits due to air quality impacts from various climate change mitigation strategies using a free and publicly-available tool. Additional pollutant reductions can be calculated using the same methodology and will be available in future versions of the tool.
Association between serum copper and blood glucose: a mediation analysis of inflammation indicators in the NHANES (2011–2016)
Authors: Cheng Zijing , Kong Yuzhe , Yang Wenqi , Xu Haitao , Tang Decheng , Zuo Yu
Published: May 2024

Background

The rising prevalence of diabetes underscores the need for identifying effective prevention strategies. Recent research suggests environmental factors, particularly heavy metals like copper, significantly influence health outcomes, including diabetes, through mechanisms involving inflammation and oxidative stress. This study aims to explore how serum copper levels affect blood glucose, employing NHANES data from 2011 to 2016, to provide insights into environmental health’s role in diabetes prevention and management.

Methods

The study analyzed data from 2,318 NHANES participants across three cycles (2011–2016), focusing on those with available data on serum copper, inflammatory markers, and blood glucose levels. We utilized principal component analysis for selecting inflammatory markers, mediation analysis to examine direct and indirect effects, multiple linear regression for assessing relationships between markers and glucose levels, and weighted quantile sum regression for evaluating individual and collective marker effects, adjusting for demographic variables and serum copper.

Results

Participants averaged 42.70 years of age, with a near-even split between genders. Average serum copper was 119.50 μg/dL, white blood cell count 6.82 × 109/L, and fasting blood glucose 107.10 mg/dL. Analyses identified significant mediation by inflammatory markers (especially white blood cells: 39.78%) in the copper-blood glucose relationship. Regression analyses highlighted a positive correlation between white blood cells (estimate: 1.077, 95% CI: 0.432 to 2.490, p = 0.013) and copper levels and a negative correlation for monocyte percentage (estimate: −1.573, 95% CI: 0.520 to −3.025, p = 0.003). Neutrophil percentage was notably influential in glucose levels. Sensitive analyses confirmed the study’s findings.

Conclusion

Serum copper levels significantly impact blood glucose through inflammatory marker mediation, highlighting the importance of considering environmental factors in diabetes management and prevention. These findings advocate for public health interventions and policies targeting environmental monitoring and heavy metal exposure reduction, emphasizing the potential of environmental health measures in combating diabetes incidence.
The association between children’s exposure to pesticides and asthma, wheezing, and lower respiratory tract infections. A systematic review and meta-analysis
Authors: Keleb Awoke , Daba Chala , Asmare Lakew , Bayou Fekade Demeke , Arefaynie Mastewal , Mohammed Anissa , Tareke Abiyu Abadi , Kebede Natnael , Tsega Yawkal , Endawkie Abel , Kebede Shimels Derso , Abera Kaleab Mesfin , Abeje Eyob Tilahun , Enyew Ermias Bekele
Published: May 2024

Background

Exposure to pesticides is a global public health problem, especially for children. Its association with chronic respiratory disease among children has attracted considerable attention, but the existing evidence remains inconclusive and cannot be certain. Therefore, this systematic review and meta-analysis aim to determine the global pooled effect size of association with pesticide exposure and asthma, wheezing, and respiratory tract infections among children.

Methods

A comprehensive search was conducted for relevant literature from electronic databases, including PubMed, Google Scholar, Hinari, Semantic Scholar, and Science Direct. Studies that provided effect size on the association between pesticide exposure and childhood asthma, wheezing, and respiratory tract infections in children were included. The articles were screened, data was extracted, and the quality of each study was assessed with four independent reviewers. Random effects models for significant heterogeneity and fixed effect models for homogeneous studies were conducted to estimate pooled effect sizes with 95% confidence intervals using Comprehensive Meta-Analysis version 3.3.070 and MetaXL version 2. Funnel plot and Higgins I2 statistics were used to determine the heterogeneity of the included studies. Subgroup analyses were computed based on the types of pesticide exposure, study design, sample size category, and outcome assessment technique.

Result

A total of 38 articles with 118,303 children less than 18 years of age were included in this meta-analysis. Pesticide exposure among children increased the risk of asthma by 24%; (OR = 1.24, 95% CI: 1.14–1.35) with extreme heterogeneity (I2 = 81%, p < 0.001). Exposure to pesticides increased the odds of developing wheezing among children by 34% (OR = 1.34, 95% CI: 1.14–1.57), with high heterogeneity (I2 = 79%, p < 0.001) and also increased the risk of developing lower respiratory tract infection by 79% (OR = 1.79, 95% CI: 1.45–2.21) with nonsignificant low heterogeneity (I2 = 30%, p-value = 0.18).

Conclusion

This meta-analysis provided valuable evidence supporting the association between childhood asthma, wheezing, and lower respiratory tract infection with pesticide exposure. The findings would contribute to a better understanding of the estimate of the effect of pesticide exposure on respiratory health in children and inform evidence-based preventive strategies and public health interventions.
Human-annotated rationales and explainable text classification: a survey
Authors: Herrewijnen Elize , Nguyen Dong , Bex Floris , van Deemter Kees
Published: May 2024

Asking annotators to explain “why” they labeled an instance yields annotator rationales: natural language explanations that provide reasons for classifications. In this work, we survey the collection and use of annotator rationales. Human-annotated rationales can improve data quality and form a valuable resource for improving machine learning models. Moreover, human-annotated rationales can inspire the construction and evaluation of model-annotated rationales, which can play an important role in explainable artificial intelligence.
A retrospective view on non-linear methods in chemometrics, and future directions
Authors: Westad Frank , Flåten Geir Rune
Published: May 2024

This perspective article reviews how the chemometrics community approached non-linear methods in the early years. In addition to the basic chemometric methods, some methods that fall under the term “machine learning” are also mentioned. Thereafter, types of non-linearity are briefly presented, followed by discussions on important aspects of modeling related to non-linear data. Lastly, a simulated data set with non-linear properties is analyzed for quantitative prediction and batch monitoring. The conclusion is that the latent variable methods to a large extent handle non-linearities by adding more linear combinations of the original variables. Nevertheless, with strong non-linearities between the X and Y space, non-linear methods such as Support Vector Machines might improve prediction performance at the cost of interpretability into both the sample and variable space. Applying multiple local models can improve performance compared to a single global model, of both linear and non-linear nature. When non-linear methods are applied, the need for conservative model validation is even more important. Another approach is pre-processing of the data which can make the data more linear before the actual modeling and prediction phase.
Comparison of traditional regression modeling vs. AI modeling for the prediction of dental caries: a secondary data analysis
Authors: Dey Priya , Ogwo Chukwuebuka , Tellez Marisol
Published: May 2024

Introduction

There are substantial gaps in our understanding of dental caries in primary and permanent dentition and various predictors using newer modeling methods such as Machine Learning (ML) algorithms and Artificial Intelligence (AI). The objective of this study is to compare the accuracy, precision, and differences between the caries predictive capability of AI vs. traditional multivariable regression techniques.

Methods

The study was conducted using secondary data stored in the Temple University Kornberg School of Dentistry electronic health records system (axiUm) of pediatric patients aged 6–16 years who were patients on record at the Pediatric Dentistry Clinic. The outcome variables considered in the study were the decayed–missing–filled teeth (DMFT) and the decayed–extracted–filled teeth (deft) scores. The predictors included age, sex, insurance, fluoride exposure, having a dental home, consumption of sugary meals, family caries experience, having special needs, visible plaque, medications reducing salivary flow, and overall assessment questions.

Results

The average DMFT score was 0.85 ± 2.15, while the average deft scores were 0.81 ± 2.15. For childhood dental caries, XGBoost was the best performing ML algorithm with accuracy, sensitivity. and Kappa as 81%, 84%, and 61%, respectively, followed by Support Vector Machine and Lasso Regression algorithms, both with 84% specificity. The most important variables for prediction found were age and visible plaque.

Conclusions

The machine learning model outperformed the traditional statistical model in the prediction of childhood dental caries. Data from a more diverse population will help improve the quality of caries prediction for permanent dentition where the traditional statistical method outperformed the machine learning model.
Machine learning versus deep learning in land system science: a decision-making framework for effective land classification
Authors: Southworth Jane , Smith Audrey C. , Safaei Mohammad , Rahaman Mashoukur , Alruzuq Ali , Tefera Bewuket B. , Muir Carly S. , Herrero Hannah V.
Published: May 2024

This review explores the comparative utility of machine learning (ML) and deep learning (DL) in land system science (LSS) classification tasks. Through a comprehensive assessment, the study reveals that while DL techniques have emerged with transformative potential, their application in LSS often faces challenges related to data availability, computational demands, model interpretability, and overfitting. In many instances, traditional ML models currently present more effective solutions, as illustrated in our decision-making framework. Integrative opportunities for enhancing classification accuracy include data integration from diverse sources, the development of advanced DL architectures, leveraging unsupervised learning, and infusing domain-specific knowledge. The research also emphasizes the need for regular model evaluation, the creation of diversified training datasets, and fostering interdisciplinary collaborations. Furthermore, while the promise of DL for future advancements in LSS is undeniable, present considerations often tip the balance in favor of ML models for many classification schemes. This review serves as a guide for researchers, emphasizing the importance of choosing the right computational tools in the evolving landscape of LSS, to achieve reliable and nuanced land-use change data.
N fertilization strategies for the use of P-rich organic amendments in the restoration of soil productivity—short-term responses in two soils
Authors: Sæbø Arne , Persson Tomas , Schröder Peter , Hanslin Hans Martin
Published: May 2024

To facilitate nutrient management and the use of manure as a feedstock for biogas production, manure is often separated into a solid and a liquid fraction. The former fraction is usually high in P and low in N, so when incorporated in the soil as fertilizer, it needs to be supplemented by N from, e.g., mineral fertilizers or nitrogen-fixing species. To explore strategies to manage N with solid-separated manure, we examined how the amount of digestate and the N:P ratio of pig digestate, i.e., manure that had partially undergone anaerobic digestion, affected the productivity of Westerwolds ryegrass and red clover in a pot experiment with one soil which was rich and another which was poor in plant nutrients. The soil and plant species treatments were combined with four doses of digestate, which gave plant available phosphorus (P) concentrations of 2, 4, 8, or 16 mg P100 g−1 soil. Ammonium nitrate was dosed to obtain factorial combinations of digestate amount and N:P ratios of 1.8, 4, 8, and 16. Clover was harvested once at the beginning of flowering (15 weeks after seeding), while Westerwolds ryegrass was allowed to regrow three times after being cut at the shooting stage (in total, 4 cuts, 6, 9, 12, and 15 weeks after seeding). Ryegrass yield increased by up to 2.9 times with digestate dosage. Interactions with the N:P ratio and soil type were weak. Hence, the effect of increasing the N:P ratio was additive across digestate dosages. Red clover biomass also increased by up to 39% with digestate dosage. Residual nutrients in the soil after red clover cultivation were affected by the initial differences in soil characteristics but not by digestate treatment or biomass of harvested red clover. A targeted N management is required to benefit from the P-rich digestate in grass cultivation, while the long-term effects of red clover culture on N input need further investigation.
Cardiopulmonary effects of apneustic anesthesia ventilation in anesthetized pigs: a new mode of ventilation for anesthetized veterinary species
Authors: Bukoski Alex , Downs John , Hodgson David S. , Le-Bert Carolina R. , Thomen Robert , Flors Lucia , Thombs Lori , Bailey James
Published: May 2024

Objective

To compare the cardiopulmonary effects of apneustic anesthesia ventilation (AAV) and conventional mechanical ventilation (CMV) in anesthetized pigs and to describe a new mode of ventilation for anesthetized veterinary species.

Study design

Randomized, crossover design without washout.

Animals

Twelve healthy, female white Landrace pigs.

Methods

Following ketamine-midazolam premedication and anesthetic induction with propofol, the trachea was intubated, and each pig was positioned in dorsal recumbency. Anesthesia was maintained with propofol and sufentanil infusions. Pigs were instrumented and their lungs were sequentially ventilated with each mode, in random order, for 1 h according to predefined criteria [fraction of inspired oxygen (FiO2) = 0.21, 10 mL kg−1 tidal volume (VT), and arterial carbon dioxide tension (PaCO2) within 40–45 mmHg]. Cardiopulmonary data were collected at baseline, 30 and 60 min. In 8 pigs, thoracic computed tomography (CT) was performed following the 60 min time point for each mode of ventilation and images were analyzed to quantify lung aeration. The effects of ventilation mode, time, and order were analyzed using repeated measures ANOVA. Paired t-tests were used to compare lung aeration between modes. Significance was defined as p < 0.05.

Results

Data from 12 pigs were analyzed. A significant effect of mode was found for heart rate, mean arterial pressure (MAP), pulmonary artery occlusion pressure, cardiac index (CI), stroke volume index, systemic vascular resistance, pulmonary vascular resistance, oxygen delivery index (DO2I), oxygen extraction ratio (O2ER), VT, arterial oxygen tension, arterial hemoglobin saturation, PaCO2, end-tidal carbon dioxide tension, alveolar dead space (VDalv/VTalv), venous admixture (Q.s/Q.t), mean airway pressure, and dynamic compliance index (CRSI). Order effects were also observed for some cardiovascular and respiratory variables. For the eight pigs that underwent thoracic CT, AAV resulted in significantly larger proportions of normally and hyperaerated lung while CMV resulted in larger proportions of hypoaerated and atelectatic lung.

Conclusions

In dorsally recumbent anesthetized pigs, ventilated with FiO2 = 0.21, both modes of ventilation supported adequate oxygenation while AAV resulted in higher CRSI, and lower VDalv/VTalv and Q.s/Q.t, compared with CMV. AAV was also associated with lower MAP, CI, and DO2I and higher O2ER compared with CMV. Further investigation of AAV in anesthetized animals is warranted.
Microbial assessment of commercial pet foods marketed in the United Arab Emirates
Authors: Hadid Michella , Alwan Nisreen , Dimassi Hani , Deghel Maria , El Khatib Sami , Serhan Mireille , Hassan Hussein F.
Published: May 2024

Examining the microbiological characteristics of pet food is imperative to safeguard the health and well-being of companion animals, pet owners, and the surrounding environment. Domestic animals, known for carrying harmful microorganisms, pose a significant health risk, especially in close proximity to people and children. Notably, no studies have previously investigated pet food quality in the Gulf Cooperation Council countries, in particular, the United Arab Emirates (UAE). This study examined the microbiological quality of all stock keeping units (SKUs) of pet foods marketed in UAE (n = 118). Parameters assessed include Total Aerobic Microbial Count (TAMC), Enterobacteriaceae, Total Yeast and Mold Count (TYMC), Salmonella, Listeria monocytogenes, and Clostridium species. Among the 118 samples, 33 (28%) exceeded the acceptable TAMC limit of 106 CFU/g, highlighting significant variations based on manufacturers and ingredients. Eight samples (7%) surpassed the maximum Enterobacteriaceae limit of 3 × 102 CFU/g. TYMC levels exhibited variation, with 33 (28%) exceeding the limit of 104 CFU/g. L. monocytogenes was identified in 44 (37%) of the samples, while Salmonella was not detected. Clostridium contamination was observed in 28 (24%) of the samples. Statistical analyses revealed associations between pet food characteristics and microbial quality, underscoring the imperative for international standards to ensure the safety of pet food. These findings carry significant implications for pet owners, regulatory bodies, and the pet food industry, emphasizing the need for ongoing efforts to enhance the overall quality and safety of pet food products.
Employing a Convolutional Neural Network to Classify Sleep Stages from EEG Signals Using Feature Reduction Techniques
Authors: Mohammed Maadh Rajaa , Sagheer Ali Makki
Published: May 2024

One of the most essential components of human life is sleep. One of the first steps in spotting abnormalities connected to sleep is classifying sleep stages. Based on the kind and frequency of signals obtained during a polysomnography test, sleep phases can be separated into groups. Accurate classification of sleep stages from electroencephalogram (EEG) signals plays a crucial role in sleep disorder diagnosis and treatment. This study proposes a novel approach that combines feature selection techniques with convolutional neural networks (CNNs) to enhance the classification performance of sleep stages using EEG signals. Firstly, a comprehensive feature selection process was employed to extract discriminative features from raw EEG data, aiming to reduce dimensionality and enhance the efficiency of subsequent classification using mutual information (MI) and analysis of variance (ANOVA) after splitting the dataset into two sets—the training set (70%) and testing set (30%)—then processing it using the standard scalar method. Subsequently, a 1D-CNN architecture was designed to automatically learn hierarchical representations of the selected features, capturing complex patterns indicative of different sleep stages. The proposed method was evaluated on a publicly available EDF-Sleep dataset, demonstrating superior performance compared to traditional approaches. The results highlight the effectiveness of integrating feature selection with CNNs in improving the accuracy and reliability of sleep stage classification from EEG signals, which reached 99.84% with MI-50. This approach not only contributes to advancing the field of sleep disorder diagnosis, but also holds promise for developing more efficient and robust clinical decision support systems.
Conceptualizing Agile Branding: Dimensions and Antecedents for Managing Brands in a Dynamic Environment
Authors: Pöhlmann Michaela , Seitz Jürgen , Jambrino-Maldonado Carmen , de las Heras-Pedrosa Carlos
Published: May 2024

Traditional branding approaches take too long to react to today’s dynamic environment. Agile branding offers a promising approach and might be the contemporary answer to the demands of our digitalized world. While scientists state that an agile approach to branding is essential nowadays, there is a lack of knowledge on how agile branding can be conceptualized and applied in practice. This article aims to provide a better understanding of agile branding by identifying its dimensions and antecedents. This study pursued a multi-method approach: a systematic literature review was conducted, followed by qualitative semi-structured interviews with eleven experts and a group discussion with five participants. Applying the dynamic capabilities perspective, the authors identify five dimensions, including 20 antecedents of agile branding: (a) leadership style and culture, (b) employee skills and competencies, (c) technology and methods, (d) stakeholder and feedback integration, and (e) ambidexterity. The critical challenge of agile branding is to balance brand continuity and adaptation. The study is the first to conceptualize agile branding, offering scientists and practitioners values and principles on how brands can be managed and further evolved in a constantly changing world to stay relevant in the market and ahead of the competition.
Tryptophan Promotes the Production of Xanthophyll Compounds in Yellow Abdominal Fat through HAAO
Authors: Liu Xiaojing , Men Lilin , Chen Yanji , Wang Yongli , Wang Yanke , Zhang Xu , Cui Huanxian , Guo Yuming , Wen Jie
Published: May 2024

Abdominal fat, which in the past was often regarded as waste and discarded, has in recent years been used as a fat source to produce meat by-products. Yellow abdominal fat has higher economic value. Therefore, improving the color of abdominal fat plays an important role in improving the appearance of meat products. This study aimed to identify the contributors and the regulatory network involved in the formation of yellow and white color in abdominal fat. We found that four xanthophyll compounds were significantly different in yellow and white abdominal fat chicken, including zeaxanthin, lutein, canthaxanthin, and β-cryptoxanthin. There were 551 different and 8 common metabolites significantly correlated with these 4 xanthophyll compounds. Similarly, a total of 54 common genes were identified in 4 common related pathways (Complement and coagulation cascades, Metabolic pathways, PPAR signaling pathway, Carbon metabolism) of the 8 common metabolites. The high expression of HAAO in the yellow abdominal fat group leads to the degradation of tryptophan and its intermediate 5-hydroxyindole, and subsequently to the formation of the four xanthophyll compounds. This process is also regulated by tyrosine, kynurenine 3-monooxygenase (KMO), homogentisate 1, 2-dioxygenase (HGD), etc. Together, these findings show the effect of tryptophan on abdominal fat color, as well as a negative regulatory effect of HAAO and 5-hydroxyindole on the production of xanthophyll compounds involved in abdominal fat coloration.
A Gestational Pectin Diet Could Improve the Health of Multiparous Sows by Modulating the Gut Microbiota and Cytokine Level during Late Pregnancy
Authors: Zheng Jie , Li Shuang , He Jiaqi , Liu Hao , Huang Yingyan , Jiang Xuemei , Zhao Xilun , Li Jian , Feng Bin , Che Lianqiang , Fang Zhengfeng , Xu Shengyu , Lin Yan , Hua Lun , Zhuo Yong , Wu De
Published: May 2024

This study aimed to investigate the effects of the dietary fiber pectin on the gut microbiota and health of parturient sows. A total of 30 parity 5–7, multiparous gestation sows (Large White × Landrace) were randomly assigned to two treatment groups after mating: Con (control, basic diet) and Pec (pectin, 3%). The sows received the two diets during gestation, and all sows were fed the same standard basic diet during lactation. The results of β-diversity showed that the composition of the gut microbiota was different in the Con and Pec groups. Compared with the sows in the Con group, the Pec sows showed a higher abundance of the gut bacteria Clostridium and Romboutsia and a lower abundance of harmful bacteria (Micrococcaceae, Coriobacteriaceae, Dorea, Actinomyces). On the other hand, the SCFA plasma concentration was increased in the Pec group, while pro-inflammatory cytokine (IL-6, IL-1β, and TNF-α) concentrations were decreased. In conclusion, the soluble dietary fiber pectin could improve the reproductive performance and health of sows by increasing the abundance of some commensal bacteria enhancing the metabolite SCFA levels and reducing the pro-inflammatory cytokine plasma levels.
Gum Arabic Increases Phagocytosis of Escherichia coli by Blood Leukocytes of Young and Old Healthy Volunteers
Authors: Freibrodt Christin , Baien Shima , von Köckritz-Blickwede Maren , de Buhr Nicole , Nau Roland , Seele Jana
Published: May 2024

Background: Gum arabic, a polysaccharide exudate from Acacia senegal (L.) Willdenow trees, has already been used by African native people in natural medicine. Methods: Using whole-blood samples from young (20–35 years) and older (>80 years) healthy volunteers (each group n = 10), the effect of an aqueous solution of GA on phagocytosis of Escherichia coli was examined with a gentamicin protection assay. Whole-blood samples of each volunteer were stimulated with GA and as a control with CpG oligodeoxynucleotides (Toll-like receptor -9 agonists) for 2 h, then co-incubated with E. coli for 30 min and thereafter treated with gentamicin for up to 240 min to kill extracellular bacteria. Then, whole-blood cells were lysed with distilled water, and colony-forming units were counted by quantitative plating. Cytokine enzyme-linked immunosorbent assay for the detection of TNF-α and IL-6 was performed using the blood supernatant. Results: The GA concentration tested (20 mg/mL) did not affect the viability of eukaryotic cells. Phagocytosis of E. coli by whole-blood leukocytes derived from young (p = 0.008) and older (p = 0.004) healthy volunteers was increased by 120.8% (young) and 39.2% (old) after stimulation with GA. In contrast, CpG only stimulated the bacterial phagocytosis by cells derived from young volunteers (p = 0.004). Stimulation of whole blood with GA increased the intracellular killing of E. coli in young (p = 0.045) and older volunteers (p = 0.008) and induced a TNF-α release in whole blood collected from older volunteers but not from younger ones (p = 0.008). Conclusions: These data encourage the isolation of active compounds of GA and the initiation of clinical trials addressing the preventive effect of GA on bacterial infections.
CRISPR Interference-Mediated Silencing of the mmpL3 Gene in Mycobacterium smegmatis and Its Impact on Antimicrobial Susceptibility
Authors: Yuliani Yonita , Ilmi Azizah Fitriana Nurul , Petsong Suthidee , Sawatpanich Ajcharaporn , Chirakul Sunisa , Chatsuwan Tanittha , Palaga Tanapat , Rotcheewaphan Suwatchareeporn
Published: May 2024

Background: The discovery of novel therapeutic agents, especially those targeting mycobacterial membrane protein large 3 (mmpL3), has shown promise. In this study, the CRISPR interference-Streptococcus thermophilus nuclease-deactivated Cas9 (CRISPRi-dCas9Sth1) system was utilized to suppress mmpL3 expression in Mycobacterium smegmatis, and its impacts on susceptibility to antimicrobial agents were evaluated. Methods: The repression of the mmpL3 gene was confirmed by RT-qPCR. The essentiality, growth curve, viability, and antimicrobial susceptibility of the mmpL3 knockdown strain were investigated. Results: mmpL3 silencing was achieved by utilizing 0.5 and 1 ng/mL anhydrotetracycline (ATc), resulting in reductions in the expression of 60.4% and 74.4%, respectively. mmpL3 silencing led to a significant decrease in bacterial viability when combined with one-half of the minimal inhibitory concentrations (MICs) of rifampicin, rifabutin, ceftriaxone, or isoniazid, along with 0.1 or 0.5 ng/mL ATc (p < 0.05). However, no significant difference was observed for clarithromycin or amikacin. Conclusions: The downregulation of the mmpL3 gene in mycobacteria was achieved through the use of CRISPRi-dCas9Sth1, resulting in growth deficiencies and resensitization to certain antimicrobial agents. The impact was dependent upon the level of gene expression.
Impact of Soil Fertilization with Pig Slurry on Antibiotic Residues and Resistance Genes: A Longitudinal Study
Authors: Massaccesi Luisa , Albini Elisa , Massacci Francesca Romana , Giusepponi Danilo , Paoletti Fabiola , Sdogati Stefano , Morena Francesco , Agnelli Alberto , Leccese Angelo , Magistrali Chiara Francesca , Galarini Roberta
Published: May 2024

The impact of soil fertilization with animal manure on the spread and persistence of antibiotic resistance in the environment is far from being fully understood. To add knowledge about persistence and correlations between antibiotic residues and antibiotic resistance genes (ARGs) in fertilized soil, a longitudinal soil mesocosm study was conducted. Soil samples were collected from the mesocosms immediately before spreading and then afterward at fifteen time points during a 320-day observation period. Eight ARGs (ermB, sul1, tetA, tetG, tetM, cfr, fexA, and optrA) and the class 1 integron-integrase gene, intI1, were determined in both pig slurry and soil, as well as residues of 36 antibiotics. Soil chemical and biochemical parameters were also measured. Twelve antibiotics were detected in the slurry in the range of 3 µg kg−1–3605 µg kg−1, with doxycycline, lincomycin, and tiamulin being the most abundant, whereas ermB, sul1, and tetM were the predominant ARGs. Before spreading, neither antibiotic residues nor ARGs were detectable in the soil; afterwards, their concentrations mirrored those in the slurry, with a gradual decline over the duration of the experiment. After about three months, the effect of the amendment was almost over, and no further evolution was observed.
Chemotherapy Regimen Optimization Using a Two-Archive Multi-Objective Squirrel Search Algorithm
Authors: Huo Lin , Liang Xi , Huo Donglin
Published: May 2024

Chemotherapy is one of the most effective treatments for cancer, but the efficacy of standard chemotherapy regimens is often limited by toxicities and the individual heterogeneity of cancers. Precise dosing is an important tool to improve efficacy and reduce significant differences in toxicity. However, most of the existing studies on chemotherapy optimization fail to fully consider the toxic side effects, drug resistance, and drug combinations, and thus the chemotherapy regimens obtained may face difficulty in achieving the expected efficacy and also affect the subsequent treatment. Therefore, this paper establishes a tumor growth model for the combination chemotherapy of cell cycle-specific and non-cycle-specific drugs and includes the factors of acquired drug resistance and toxic side effects, proposing an improved multi-objective Squirrel Search Algorithm, the TA-MOSSA, to solve the problem of accurate chemotherapy drug optimization. In this paper, experiments were conducted to analyze the efficacy of chemotherapy dosing regimens obtained by the TA-MOSSA based on the tumor growth model, and the results show that the TA-MOSSA can provide effective chemotherapy regimens for patients who take different treatment approaches.
Deep-Learning Correction Methods for Weather Research and Forecasting (WRF) Model Precipitation Forecasting: A Case Study over Zhengzhou, China
Authors: Zhang Jianbin , Gao Zhiqiu , Li Yubin
Published: May 2024

Systematic biases and coarse resolutions are major limitations of current precipitation datasets. Many studies have been conducted for precipitation bias correction and downscaling. However, it is still challenging for the current approaches to handle the complex features of hourly precipitation, resulting in the incapability of reproducing small-scale features, such as extreme events. In this study, we proposed a deep-learning model called PBT (Population-Based Training)-GRU (Gate Recurrent Unit) based on numerical model NWP gridded forecast data and observation data and employed machine-learning (ML) methods, such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Gradient-Boosted Decision Tree (GBDT), to correct the WRF hourly precipitation forecasts. To select the evaluation method, we conducted a sample balance experiment and found that when the proportion of positive and negative samples was 1:1, the Threat Score (TS) and accuracy scores were the highest, while the Probability of Detection (POD) score was slightly lower. The results showed that: (1) the overall errors of the PBT-GRU model were relatively smaller, and its root mean square error (RMSE) was only 1.12 mm, which was reduced by 63.04%, 51.72%, 58.36%, 37.43%, and 26.32% compared to the RMSE of WRF, SVM, KNN, GBDT, and RF, respectively; and (2) according to the Taylor diagram, the standard deviation (σn) and correlation coefficient (r) of PBT-GRU were 1.02 and 0.99, respectively, while the σn and r of RF were 1.12 and 0.98, respectively. Furthermore, the σn and r of the SVM, GBDT, and KNN models were between those of the above models, with values of 1.24 and 0.95, 1.15 and 0.97, and 1.26 and 0.93, respectively. Based on a comprehensive analysis of the TS, accuracy, RMSE, r and σn, the PBT-GRU model performed the best, with a significantly better correction effect than that of the ML methods, resulting in an overall performance ranking of PBT-GRU > RF > GBDT > SVM > KNN. This study provides a hint of the possibility that the proposed PBT-GRU model can outperform model precipitation correction based on a small sample of one-station data. Thus, due to its promising performance and excellent robustness, we recommend adopting the proposed PBT-GRU model for precipitation correction in business applications.
The Recovery and Re-Calibration of a 13-Month Aerosol Extinction Profiles Dataset from Searchlight Observations from New Mexico, after the 1963 Agung Eruption
Authors: Antuña-Marrero Juan-Carlos , Mann Graham W. , Barnes John , Calle Abel , Dhomse Sandip S. , Cachorro Victoria E. , Deshler Terry , Li Zhengyao , Sharma Nimmi , Elterman Louis
Published: May 2024

The recovery and re-calibration of a dataset of vertical aerosol extinction profiles of the 1963/64 stratospheric aerosol layer measured by a searchlight at 32° N in New Mexico, US, is reported. The recovered dataset consists of 105 aerosol extinction profiles at 550 nm that cover the period from December 1963 to December 1964. It is a unique record of the portion of the aerosol cloud from the March 1963 Agung volcanic eruption that was transported into the Northern Hemisphere subtropics. The data-recovery methodology involved re-digitizing the 105 original aerosol extinction profiles from individual Figures within a research report, followed by the re-calibration. It involves inverting the original equation used to compute the aerosol extinction profile to retrieve the corresponding normalized detector response profile. The re-calibration of the original aerosol extinction profiles used Rayleigh extinction profiles calculated from local soundings. Rayleigh and aerosol slant transmission corrections are applied using the MODTRAN code in transmission mode. Also, a best-estimate aerosol phase function was calculated from observations and applied to the entire column. The tropospheric aerosol phase function from an AERONET station in the vicinity of the searchlight location was applied between 2.76 to 11.7 km. The stratospheric phase function, applied for a 12.2 to 35.2 km altitude range, is calculated from particle-size distributions measured by a high-altitude aircraft in the vicinity of the searchlight in early 1964. The original error estimate was updated considering unaccounted errors. Both the re-calibrated aerosol extinction profiles and the re-calibrated stratospheric aerosol optical depth magnitudes showed higher magnitudes than the original aerosol extinction profiles and the original stratospheric aerosol optical depth, respectively. However, the magnitudes of the re-calibrated variables show a reasonable agreement with other contemporary observations. The re-calibrated stratospheric aerosol optical depth demonstrated its consistency with the tropics-to-pole decreasing trend, associated with the major volcanic eruption stratospheric aerosol pattern when compared to the time-coincident stratospheric aerosol optical depth lidar observations at Lexington at 42° N.
Useful Quantities and Diagram Types for Diagnosis and Monitoring of Electrochemical Energy Converters Using Impedance Spectroscopy: State of the Art, Review and Outlook
Authors: Kurzweil Peter , Scheuerpflug Wolfgang , Schell Christian , Schottenbauer Josef
Published: May 2024

The concept of pseudocapacitance is explored as a rapid and universal method for the state of health (SOH) determination of batteries and supercapacitors. In contrast to this, the state of the art considers the degradation of a series of full charge/discharge cycles. Lithium-ion batteries, sodium-ion batteries and supercapacitors of different cell chemistries are studied by impedance spectroscopy during lifetime testing. Faradaic and capacitive charge storage are distinguished by the relationship between the stored electric charge and capacitance. Batteries with a flat voltage–charge curve are best suited for impedance spectroscopy. There is a slight loss in the linear correlation between the pseudocapacitance and Ah capacity in regions of overcharge and deep discharge. The correct calculation of quantities related to complex impedance and differential capacitance is outlined, which may also be useful as an introductory text and tutorial for newcomers to the field. Novel diagram types are proposed for the purpose of the instant performance and failure diagnosis of batteries and supercapacitors.
Surface Modification Induces Oriented Zn(002) Deposition for Highly Stable Zinc Anode
Authors: Zhang Hongfei , Li Fujie , Li Zijin , Gao Liu , Xu Binghui , Wang Chao
Published: May 2024

Aqueous zinc metal batteries (AZMBs) are considered a promising candidate for grid-scale energy storage systems owing to their high capacity, high safety and low cost. However, Zn anodes suffer from notorious dendrite growth and undesirable surface corrosion, severely hindering the commercialization of AZMBs. Herein, a strategy for engineering a dense ZnO coating layer on Zn anodes using the atomic layer deposition (ALD) technique is developed, aiming to improve its long-term cycling stability with fewer Zn dendrites. The surface-modified Zn anode (ZnO@Zn) exhibits an excellent long-cycling life (680 h) and stable coulombic efficiency when being used in a symmetric cell. Moreover, the ZnO@Zn electrode shows a high stability with almost no capacity decay after 1100 cycles at 2C in a full cell using MnO2 as the cathode. The ZnO coating is conducive to reducing corrosion and the generation of by-products, thus increasing the reversibility of Zn2+/Zn stripping/plating. Particularly, density functional theory (DFT) calculation results reveal that the ZnO coating layer could effectively lower the adsorption energy of the Zn(002) plane in ZnO@Zn, inducing the preferential deposition of Zn2+ towards the (002) crystal plane with fewer Zn dendrites. The surface ZnO coating protocol provides a promising approach to achieve a dendrite-free Zn anode for stable AZMBs.