bluuebunny/crossref_metadata_2025_split huggingface dataset:
WITHDRAWN: Optimal configuration of photovoltaic/photothermal /AA-CAES capacity considering combined cooling, heating and power supply
Authors: Wu Chenxi , You wei , Zheng Jing , Huang Jianbo , He Zhongjie 
Published: March 2024

Abstract

In the dual carbon development environment, flexible combined renewable resources and energy storage system with IES(integrated energy system) becomes more and more important. Analyzing the coupling mechanism of multi-energy plays a significant role in low-carbon IES planning and operation. Therefore, in this paper, after fully considering the advantages of advanced adiabatic compressed air energy storage (AA-CAES) in the micro IES, the configuration and scheduling strategy of the micro IES with Solar photovoltaic/thermal (PV/T)/AA-CAES are optimized. Firstly, the CCHP(combined power/heating/cooling) characteristics of AA-CAES are modeled in detail. Its operation constraints are also established. Secondly, bi-level programming method is employed to solve the problem. Capacity is optimized in the upper level with the objective of minimizing the sum of benefit brought by AA-CAES and the fixed cost. Dispatch cost and the treating cost of carbon are optimized in the lower level. Finally, the obvious method proposed in this paper is simulated and analyzed with an industrial park .
Integrative Analysis of Toxicometabolomics and Toxicoproteomics Data: New Molecular Insights into Thiazolidinedione-Induced Cardiotoxicity
Authors: Sultan Abdullah Al , Rattray Zahra , Rattray Nicholas J. W. 
Published: March 2024

Abstract

Introduction
Despite the well-established efficacy of thiazolidinediones (TZDs), including pioglitazone and rosiglitazone, in type II diabetes management, their potential contribution to heart failure risk remains a significant area of uncertainty. This incomplete understanding, which persists despite decades of clinical use of TZDs, has generated ongoing controversy and unanswered questions regarding their safety profiles, ultimately limiting their broader clinical application.
Objective and Methods
This study presented a multi-omics approach, integrating toxicoproteomics and toxicometabolomics data with the goal of uncovering novel mechanistic insights into TZD cardiotoxicity and identifying molecular signatures predictive of side effect progression.
Results
Network analysis of proteo-metabolomic data revealed a distinct fingerprint of disrupted biochemical pathways, which were primarily related to energy metabolism. Downregulation of oxidative phosphorylation and fatty acid synthesis was coupled with increased activity in anaerobic glycolysis, the pentose phosphate pathway, and amino acid and purine metabolism. This suggests a potential metabolic shift in AC16 cells from fatty acid oxidation towards anaerobic glycolysis, potentially contributing to observed cardiotoxicity. Additionally, the study identified a marked disruption in the glutathione system, indicating an imbalanced redox state triggered by TZD exposure. Importantly, our analysis identified key molecular signatures across omics datasets, including prominent signatures of amino acids like L-ornithine, L-tyrosine and glutamine, which are established heart failure biomarkers, supporting their potential use for the early prediction of cardiotoxicity progression.
Conclusion
By uncovering a novel mechanistic explanation for TZD cardiotoxicity, this study simultaneously illuminates potential therapeutic interventions, opening avenues for future research to improve the safety profile of TZD agents.
Genetic Diversity Analysis and DNA Fingerprint Construction of Zanthoxylum Species Based on SSR and iPBS Markers
Authors: Zhang Xiaoxi , Chen Wei , Yang Zhiwu , Luo Chengrong , Zhang Weiwei , Xu Feng , Ye Jiabao , Liao Yongling 
Published: March 2024

Abstract

Zanthoxylum is a versatile economic tree species utilized for its spice, seasoning, oil, medicinal, and industrial raw material applications, and it has a lengthy history of cultivation and domestication in China. This has led to the development of numerous cultivars. However, the prevalence of mixed varieties and naming confusion significantly obstruct the effective use of Zanthoxylum resources and industry development. Consequently, conducting genetic diversity studies and variety identification on Zanthoxylum are crucial. This research analyzed the genetic traits of 80 Zanthoxylum cultivars using SSR and iPBS molecular markers, leading to the creation of a DNA fingerprint. This study identified 206 and 127 alleles with 32 SSR markers and 10 iPBS markers, respectively, yielding an average of 6.4 and 12.7 alleles (Na) per marker. The average polymorphism information content (PIC) for the SSR and iPBS markers was 0.710 and 0.281, respectively. The genetic similarity coefficients for the 80 Zanthoxylum accessions ranged from 0.0947 to 0.9868 and from 0.2206 to 1.0000, with mean values of 0.3864 and 0.5215, respectively, indicating substantial genetic diversity. Cluster analysis, corroborated by principal coordinate analysis (PCoA), categorized these accessions into three primary groups. Analysis of the genetic differentiation among the three Zanthoxylum (Z. bungeanum, Z. armatum, and Z. piperitum) populations using SSR markers revealed a mean genetic differentiation coefficient (Fst) of 0.335 and a gene flow (Nm) of 0.629, suggesting significant genetic divergence among the populations. Molecular variance analysis (AMOVA) indicated that 65% of the genetic variation occurred within individuals, while 35% occurred among populations. Bayesian model-based analysis of population genetic structure divided all materials into two groups. The combined PI and PIsibs value of the 32 SSR markers were 4.265 × 10− 27 and 1.282 × 10− 11, respectively, showing strong fingerprinting power. DNA fingerprints of the 80 cultivars were established using eight pairs of SSR primers, each assigned a unique numerical code. In summary, while both markers were effective at assessing the genetic diversity and relationships of Zanthoxylum species, SSR markers demonstrated superior polymorphism and variety discrimination compared to iPBS markers. These findings offer a scientific foundation for the conservation and sustainable use of Zanthoxylum species.
A Pandoraea sp. strain efficiently degrades chlorobenzene via monooxygenation pathways with high potential for groundwater bioremediation
Authors: Pan Lixu , Yuan Bo , Li Qingqing , Ouyang Ji , Yang Jie , Zhou Yan , Cui Changzheng 
Published: March 2024

Abstract
Chlorobenzene (CB) is often detected in contaminated soil and groundwater at in-service petrochemical facilities. Given the high safety requirements for these petrochemical facilities, a sustainable and low-consumption microbial remediation technology is preferred. The aim of this study was to isolate an efficient chlorobenzene-degrading bacterial strain and understand its degradation mechanism to be used for in-situ bioremediation of chlorobenzene-contaminated sites in in-service petrochemical enterprises. A degrading bacterium was isolated from chlorobenzene-contaminated soil at a pesticide plant, identified as Pandoraea sp. XJJ-1 (CCTCC M 2021057). This strain completely degraded 100 mg·L− 1 CB and showed extensive degradability across a range of pH (5.0–9.0), temperature (10°C-37°C), and CB concentrations (100–600 mg·L− 1). Notably, the degradation efficiency was 85.2% at 15°C, and the strain could also degrade six other aromatic hydrocarbons, including benzene, toluene, ethylbenzene, and xylene (o-, m-, p-). The metabolic pathway of chlorobenzene was inferred using Ultra Performance Liquid Chromatography (UPLC), Gas chromatography-mass spectrometry (GC-MS), and genomic analysis. In strain XJJ-1, CB was metabolized to o-chlorophenol and 3-chloroxychol by chlorobenzene monooxygenase, followed by ortho-cleavage by the action of 3-chlorocatechol 1,2-dioxygenase. This is the first report of chlorobenzene monooxygenase as the rate-limiting step in Pandoraea spp.. Strain XJJ-1, which exhibits excellent degradation ability for CB at low temperatures, was isolated in this study. Moreover, the presence of the chlorobenzene monooxygenation pathway metabolism in strain XJJ-1 is reported for the first time in Pandoraea. As a biomaterial with low temperature resistance and composite pollutant degradation capacity, strain XJJ-1 has potential applications prospects in the in-situ bioremediation of chlorobenzene-contaminated sites.
Patterns in Isotopic Compositions, Nucleon Numbers, and Magic Numbers
Authors: Lin Jingfeng 
Published: March 2024

Abstract
This paper identifies a distinct pattern in the nuclear stability of isotopes with a nucleon number A = S(S+1)-2. It posits that S represents an ordinal number corresponding to a shell, with the maximum observed value of S being 16. The relation A=S(S+1) lies in a close packed hexagonal arrangement pattern. In this configuration, as neutrons fill certain shells to form closed loops, and each neutron couples with one or more protons in adjacent shells, the total number of neutrons or protons corresponds to magic numbers. This deduction leads to the elucidation of the nucleon arrangement patterns for each nuclide. The paper presents the nucleon arrangement schemes for 20 key nuclides and traces the evolution of shell structures. Regularities in the nucleon numbers of isotopes with the highest relative abundance, magic number phenomena, and the inversion of elemental atomic masses serve as evidence supporting these nucleon arrangement patterns. The proposed regularities and patterns unexpectedly emerge through an intriguing toroidal helix model. This study requires reference to the concept of an “electron cloud,” assuming nucleons as a toroidal helical “nucleon cloud” to explore the interaction mechanisms among nucleons. The structural model is novel, and further research must comprehensively cover a wide range of nuclear characteristics.
Effect of embryos quality on the sex ratio of neonates
Authors: Zhu Jie , Ni Feng , Wang Cunli , Cao Zhenyi , Luan Kang , Wu Yan , Yin Hui-Qun 
Published: March 2024

Abstract
Purpose
To investigate the effect of embryos developmental stage and morphological grade on the sex ratio of neonates at birth.
Methods
This was a retrospective cohort study, and including 5339 singletons born from IVF/ICSI (fresh and frozen) embryos transfer cycles between 2011 and 2022 at the Center for Reproductive Medicine of the 901st Hospital of the Joint Logistics Support Force of PLA. Only single embryo transfer (SET) or equal grade embryos transfer and resulting in a singleton gestations and live birth were included. Donors (oocytes or sperm) and preimplantation genetic testing (PGT) treatment were excluded. Demographics, cycle characteristics and assisted reproductive technology (ART) procedures were analyzed. Logistic regression was used to evaluate the percentage of males at birth for association with embryos quality and morphological parameters.
Results
Transfer with good grade embryos (GGE) significantly increased percentage of male neonates in comparing with low grade embryos (LGE) (54.2% versus 46.0%, P = 0.001). After adjustment for potential confounding variables, logistic regression indicated that when compared to GGE, LGE decreased the percentage of male neonates (odd ratio (OR) 0.741, 95% CI 0.592–0.927, P = 0.009), as well as with low grade blastocysts (LGB) was significantly associated with reduced percentage of male neonates when compared to good grade blastocysts (GGB) (OR 0.740, 95% CI 0.591–0.926, P = 0.008), however, it has not shown a relationship between cleavage stage embryos (good and low grades) transfer and sex at birth. Moreover, in SET cycles, the speed of embryonic development (7–8 cells, ≥ 9 cells and ≤ 6 cells) in cleavage stage embryos was not shown to be significantly associated with the sex at birth (P = 0.467), in addition, the percentage of male neonates was found to be significantly different from the reference category (blastocyst grading AA, P < 0.0001), only for blastocyst grading BC (OR 0.309, 95% CI 0.141–0.676, P = 0.003) and CB (OR 0.501, 95% CI 0.253–0.992, P = 0.047), compared to the reference category in blastocyst stage embryo transfer cycles. Furthermore, the data were stratified by categorical variables (blastocyst quality and developmental culture days (day 5 and day 6/7)), after pairwise comparison, the result showed that day 5 LGB was significantly lower than day 5 GGB (46.7% versus 55.6%, P = 0.013), the variations were no statistically significant differences with day 6/7 LGB and day 6/7 GGB were found. Finally, there were no statistically significant differences in the percentage of males at birth between low grade cleavage stage embryos (LGC) and good grade cleavage stage embryos (GGC) (47.5% versus 52.9%, respectively).
Conclusion
The morphological scores of cleavage stage embryos were not associated with sex ratio bias. The transfer of GGB has a higher probability of being male than LGB, particularly for day 5 GGB.
Testing the reproducibility and effectiveness of deep learning models among clinics: sperm detection as a pilot study
Authors: Wang Jiaqi , Jin Yufei , Jiang Aojun , Chen Wenyuan , Shan Guanqiao , Gu Yifan , Ming Yue , Li Jichang , Yue Chunfeng , Huang Zongjie , Librach Clifford , Lin Ge , Wang Xibu , Zhao Huan , Sun Yu , Zhang Zhuoran 
Published: March 2024

Abstract
Background:
Deep learning has been increasingly investigated for assisting clinical in vitro fertilization (IVF). The first technical step in many tasks is to visually detect and locate sperm, oocytes, and embryos in images. For clinical deployment of such deep learning models, different clinics use different image acquisition hardware and different sample preprocessing protocols, raising the concern over whether the reported accuracy of a deep learning model by one clinic could be reproduced in another clinic. Here we aim to investigate the effect of each imaging factor on the reproducibility of object detection models, using sperm analysis as a pilot example.
Methods:
Ablation studies were performed using state-of-the-art models for detecting human sperm to quantitatively assess how model precision (false-positive detection) and recall (missed detection) were affected by imaging magnification, imaging mode, and sample preprocessing protocols. The results led to the hypothesis that the richness of image acquisition conditions in a training dataset deterministically affects model reproducibility. The hypothesis was tested by first enriching the training dataset with a wide range of imaging conditions, then validated through internal blind tests on new samples and external multi-center clinical validations.
Results:
Ablation experiments revealed that removing subsets of data from the training dataset significantly reduced model precision. Removing raw sample images from the training dataset caused the largest drop in model precision, whereas removing 20x images caused the largest drop in model recall. by incorporating different imaging and sample preprocessing conditions into a rich training dataset, the model achieved an intraclass correlation coefficient (ICC) of 0.97 (95% CI: 0.94-0.99) for precision, and an ICC of 0.97 (95% CI: 0.93-0.99) for recall. Multi-center clinical validation showed no significant differences in model precision or recall across different clinics and applications.
Conclusions:
The results validated the hypothesis that the richness of data in the training dataset is a key factor impacting model reproducibility. These findings highlight the importance of diversity in a training dataset for model evaluation and suggest that future deep learning models in andrology and reproductive medicine should incorporate comprehensive feature sets for enhanced reproducibility across clinics.
Development and validation of a realistic neonatal intestinal atresia simulator for the training of pediatric surgeons
Authors: Montero Javier Arredondo , Asfura Oscar Emilio Bueso , Riveros Blanca Paola Pérez , Calvo Nerea Martín , Pueyo Francisco Javier , Castaño Nicolás López de Aguileta 
Published: March 2024

Abstract
Background
Neonatal surgical pathology presents high technical complexity and few opportunities for training. Many of the neonatal surgical entities are not replicable in animal models. The use of realistic 3D models is a cost-effective and efficient alternative for the training of new generations of pediatric surgeons.
Methods
We conceptualized, designed, and produced an anatomically realistic model for the open correction of jejunoileal atresia. We validated it with two groups of participants (experts and non-experts) through face, construct, and content validity questionnaires.
Results
The model was validated by 11 experts and 9 non-experts. The mean procedure time for the experts and non-experts groups was 41 and 42 minutes respectively. Six non-experts and one expert did not complete the procedure at the designed time (45 minutes) (p = 0.02). The mean score of face validity was 3.1 out of 4. Regarding construct validity, we found statistically significant differences between groups for the correct calculation of the section length of the antimesenteric border (Nixon's technique) (p < 0.01). Concerning content validity, the mean score was 3.3 out of 4 in the experts group and 3.4 out of 4 in the non-experts group.
Conclusions
The present model is a realistic and low-cost valid option for the training of open correction of jejunoileal atresia. Future studies with larger sample sizes and blinded validators are needed before drawing definitive conclusions.
Development of intensity–duration–frequency relationships in Khon Kaen City, Thailand under changing climate using GCMs and a simple scaling method
Authors: Tedprasith Kanjana , Lohpaisankrit Worapong 
Published: March 2024

Abstract

This study analyses the annual maximum (AM) rainfall series (1991–2022) in Khon Kaen City, Thailand. The AM rainfall series ranging from 3 to 24 h was best fitted to the Log-Pearson Type-III distribution. Notably, our findings reveal linear relationships between the moments of rainfall intensities and durations establishing the practicality of the simple scaling method for disaggregating 24-h AM rainfall data. Additionally, the results of this method are influenced by factors such as sample size, rainfall durations and the chosen probability distribution. Comparisons between intensity–duration–frequency (IDF) curves obtained through the simple scaling method and those derived from traditional frequency analysis provide valuable insights. Furthermore, this method was applied to bias-corrected rainfall data of 15 global climate models facilitating the generation of future IDF curves under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios. Our results highlight that rainfall events in the SSP5-8.5 scenario are projected to exhibit higher intensities emphasizing the need to understand and prepare for increased rainfall extremes in the context of climate change. This research contributes valuable insights into rainfall analysis and prediction techniques, which are crucial for effective water resource management and climate adaptation strategies in the Khon Kaen region.
Treatment of greywater with nanofiltration for nutrient removal – 2-year experience from Helsingborg
Authors: Hall Ashley , Widén Amanda , Edefell Ellen , Davidsson Åsa , Kjerstadius Hamse 
Published: March 2024

Abstract

Source-separated sanitation and greywater treatment have become an increasingly attractive alternative to traditional wastewater management systems in recent years due to their potential to combat water scarcity, ease resource recovery, and meet tightening effluent demands. In Helsingborg, Sweden, source-separated wastewater from the new city district of Oceanhamnen is being collected and treated in a new treatment plant (RecoLab) to test, among other issues, how efficient greywater treatment can be in achieving low discharge limits for pollutants. The greywater treatment consists of activated sludge treatment, drum filter micro-sieving, and nanofiltration. In the first two years of operation, the robustness of the treatment system during periods with extreme conditions, e.g., very low and very high organic matter concentrations, was tested. The combination of biological treatment and nanofiltration has achieved stable effluent concentrations below 10 mg/L chemical oxygen demand, 2 mg/L total nitrogen, and 0.2 mg/L total phosphorus as average values for 22 months of operation with an average flow of 43 m3/day. The treatment system for greywater treatment thus shows the possibility to achieve low discharge limits and meet the new proposed effluent demands of the EU Urban Wastewater Treatment Directive.
Seasonal variations and water chemical control mechanism of water quality in a suburban river near a coal city: a case study in the Xinbian River of Suzhou City, Anhui Province, China
Authors: Chen Yuqi , Peng Weihua , Lin Manli , Qiu Xinyi , Liu Jiankui , Zhang Jinchen , Xu Dong 
Published: March 2024

Abstract
To study the seasonal change rules of water quality indicators and water chemical control mechanisms in the nearby city river Xinbian River, in Suzhou City, Anhui Province, China, nine points were selected and periodically sampled for 12 months. Totally 108 water samples were collected, and basic physical–chemical indicators and routine ions (e.g., Ca2+, Mg2+, Na+, K+, SO42−, Cl−, F−, NO3−, CO32−, and HCO3−) were measured. Piper diagram, Gibbs diagram, and ion ratio method were used. The result shows that the seasonal and upstream-to-downstream variations in pH were less varied, whereas the fluctuation in dissolved oxygen was large. Four ions, namely, SO42-, F−, Cl−, and Na+ generally first decreased overall and then changed from spring to winter. The maximum contents of SO42- (406.03 mg/L), Cl− (250.22 mg/L), and Na+ (269.99 mg/L) in single water samples appeared at the S9 sampling point (Suzhou control gate) in March 2020. The dominant hydrochemical types in summer were Na–Ca–HCO3 and Na–Mg–SO4, while Na–Mg–SO4 and Na–Mg–HCO3 were the main hydrochemical types in the other seasons. The control factors of water chemical composition vary according to the season. However, rock weathering (e.g., silicate dissolution) is the dominant control factor of water chemistry in the studied river section.
Estimating the water quality index based on interpretable machine learning models
Authors: Yang Shiwei , Liang Ruifeng , Chen Junguang , Wang Yuanming , Li Kefeng 
Published: March 2024

Abstract

The water quality index (WQI) is an important tool for evaluating the water quality status of lakes. In this study, we used the WQI to evaluate the spatial water quality characteristics of Dianchi Lake. However, the WQI calculation is time-consuming, and machine learning models exhibit significant advantages in terms of timeliness and nonlinear data fitting. We used a machine learning model with optimized parameters to predict the WQI, and the light gradient boosting machine achieved good predictive performance. The machine learning model trained based on the entire Dianchi Lake water quality data achieved coefficient of determination (R2), mean square error, and mean absolute error values of 0.989, 0.228, and 0.298, respectively. In addition, we used the Shapley additive explanations (SHAP) method to interpret and analyse the machine learning model and identified the main water quality parameter that affects the WQI of Dianchi Lake as NH4+-N. Within the entire range of Dianchi Lake, the SHAP values of NH4+-N varied from −9 to 3. Thus, in future water environmental governance, it is necessary to focus on NH4+-N changes. These results can provide a reference for the treatment of lake water environments.