bluuebunny/crossref_metadata_2025 huggingface dataset:
Association of mutation profiles with metastasis in patients with non-small cell lung cancer
Authors: Li Yingxue , Zheng Zheng , Wang Li , Han Lin , Du Ying , Zhang Xuedong , Liu Xia , Xie Jiaping
Published: October 2024

Objective

This study focused on the analysis of the correlation between common gene mutation types and metastatic sites in NSCLC patients.

Methods

We retrospectively studied 1586 NSCLC patients and used fluorescence Polymerase chain reaction (PCR) to detect EGFR, ALK, ROS1, RET, MET, BRAF, HER2, KRAS, NRAS, and PIK3CA gene mutations, and also investigated sex, smoking status, age at diagnosis, histological type and TNM stage. In addition, we analyzed the site of metastasis in patients with stage IV NSCLC.

Results

The EGFR-mutation group more frequently metastasized to lung (18.9%, P = 0.004), brain (18.9%, P = 0.001) and bone (27.1%, P = 0.004) than wild-type patients. ALK-mutation group (71.0%, P < 0.001), BRAF-mutation group (82.4%, P = 0.005) and NRAS-mutation group (100%, P = 0.025) were more likely to metastasize than the wild-type group. In the ALK mutation, lung metastasis (24.2%, P = 0.013), brain (24.2%, P = 0.007), bone metastasis (32.3%, P = 0.024), liver metastasis (19.4%, P = 0.001), and pleural metastasis (29.0%, P = 0.021) were common. In the KRAS-mutation group, lung metastasis (21.7%, P = 0.012) and brain metastasis (23.3%, P = 0.001) were more common. Less metastasis occurred in the HER2-mutation group (28.3%, P = 0.014). There was no difference in the RET, MET and PIK3CA mutations.

Conclusion

Patients with ALK mutant, BRAF mutant or NRAS mutant were more prone to metastasis, while the HER 2 mutation group was less metastatic. Patients with EGFR mutant NSCLC are more likely to develop bone, lung, or brain metastasis.
Development and validation of a clinical prognostic model for BRAF V600E-mutated colorectal cancer patients based on pathological stage, microsatellite status, and primary tumor site
Authors: Ou Kai , Liu Xiu , Ma Xiaoting , Yang Lin
Published: October 2024

Objective

To develop and validate a prognostic model for patients with BRAF V600E-mutated colorectal cancer.

Methods

The clinical and pathological information of 206 patients with BRAF V600E-mutated colorectal cancer diagnosed in Cancer Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College from 2014 to 2021 was retrospectively collected. Least absolute shrinkage and selection operator (LASSO) regression, Cox regression, and nomograms were used to develop clinical prognostic models. The differentiation was measured using C-statistic, and the predicted variability was evaluated using the calibration curve. The prognostic model was externally validated using validation set data from 164 patients pooled from five studies.

Results

Our clinical prognostic model included three variables: pathological stage, microsatellite status, and primary tumor site. In internal validation, the model had a concordant index of 0.785 (95% CI [0.732–0.839]) and a concordant index of 0.754 (95% CI [0.698–0.810]) using pathological staging. External validation confirmed the robustness of the model with a consistency index of 0.670 (95% CI [0.617–0.724]) and a consistency index of 0.584 (95% CI [0.546–0.622]) using pathological staging. Likelihood ratio test results show that our model is better (internal validation, p = 5.141e−03; external validation, p = 2.728e−05). The calibration graph drawn based on the prediction and the actual situation is close to the 45° diagonal.

Conclusion

By adding microsatellite status and primary tumor site on the basis of pathological stage, we improved the discriminability and prediction accuracy of the model and successfully established a prognosis model for patients with BRAF V600E mutation of colorectal cancer.
Fine classification and phenological analysis of rice paddy based on multi-temporal general compact polarimetric SAR data
Authors: Guo Xianyu , Yin Junjun , Li Kun , Yang Jian
Published: October 2024

Fine classification and phenological information of rice paddy are of great significance for precision agricultural management. General compact polarimetric (CP) synthetic aperture radar (SAR) offers the advantage of providing rich polarimetric information, making it an important means of monitoring rice growth. Therefore, in response to the current challenges of difficulty in rice type classification and the small differences in phenological polarimetric characteristics, a novel strategy for fine classification and phenological analysis of rice paddy is proposed. This strategy thoroughly explores the polarimetric information of general CP SAR data and the target scattering characterization capabilities under different imaging modes. Firstly, the general CP SAR data is formalized using the standard CP descriptors, followed by the extraction of general CP features through the ΔαB/αB target decomposition method. optimal CP features are generated to achieve fine classification of rice paddy. Finally, 6 phenological stages of rice are analyzed based on the general CP features. The experiment results of rice classification show that the classification accuracy based on this strategy exceeds 90%, with a Kappa coefficient above 0.88. The highest classification accuracies were observed for transplanting hybrid rice paddy (T-H) and direct-sown japonica rice paddy (D-J), at 80.9% and 89.9%, respectively. The phenological evolution rule of the two rice types indicate that from early June (seedling stage) to late July (elongation stage), the CP feature variation trends of T-H and D-J are generally consistent. However, from October (mature stage) to November (harvest stage), the variation trends of the CP features for T-H and D-J are significantly different. The study found that from the booting-heading stage to the harvest stage, the linear π/4 mode outperforms circular and elliptical polarimetric modes in distinguishing different types of rice. Throughout the entire phenological period of rice growth, CP SAR of linear π/4 mode is surpasses that of other linear modes in discriminating different type of rice. The proposed strategy enables high-precision fine classification rice paddy, and the extracted general CP αB parameter effectively reflects the phenological change trends in rice growth.
Gene expression signatures of mutualism and pathogenesis in flax roots
Authors: Quintans Isadora Louise Alves da Costa Ribeiro , Vukicevich Eric , Kokkoris Vasilis , Packard Erica , Adhikary Dinesh , Hart Miranda M. , Deyholos Michael K.
Published: October 2024

Introduction

Fusarium wilt, a devastating soil-borne fungal disease in flax (Linum usitatissimum), is caused by Fusarium oxysporum f. sp. lini, a hemibiotrophic plant pathogen that penetrates plant roots. There are several reports of the molecular response of L. usitatissimum to F. oxysporum f. sp. lini; however, comparisons of the effects of mutualistic and pathogenic fungi on plants are more limited.

Methods

In this study, we have integrated phenotyping and RNA-Seq approaches to examine the response of flax to F. oxysporum f.sp. lini and to a mutualistic arbuscular mycorrhizal fungus (AMF) Rhizoglomus irregulare. R. irregulare is a common soil fungus and also widely used as a commercial inoculant to improve plant growth. We measured flax growth parameters after plant inoculation with each or both fungi, in comparison with non-inoculated control. We performed transcriptome analysis of root tissues collected at 9 and 14 days post-inoculation.

Results

We identified several differentially expressed genes (DEGs) in response to pathogenic and mutualistic fungi. These included genes related to ethylene and salicylic acid biosynthesis, carbohydrate binding, oxidoreductases, and sugar transmembrane transporters. Genes related to calcium signaling, nutrient transport, lipid metabolism, cell wall, and polysaccharide-modifying were up-regulated by R. irregulare; however, the same genes were down-regulated by F. oxysporum f. sp. lini when treated independently. In the combined treatment, genes related to cell wall modifications, hormone regulation and nutrient uptake were up-regulated. These results suggest that inoculation with R. irregulare reduced gene expression related to F. oxysporum f. sp. lini infection, leading to a reduced response to the pathogen. In response to AMF, flax prioritized mutualism-related gene expression over defense, reversing the growth inhibition caused by F. oxysporum f. sp.lini in the combined treatment.

Discussion

This research provides insights into the protective effects of AMF, revealing the pre-symbiotic gene expression profile of flax in response to mutualism in comparison with pathogenicity. Potential target genes for crop improvement were identified, especially defense related genes.
Numerical simulation of landscape ecological river flow structure based on vegetation patch distribution and fragmentation
Authors: Zhang Jingzhou , Zhang Shengtang , Li Shufang , Yu Zicheng , Wang Wenjun , Zhao Wenhao , Li Guohao , Zhou Zheng
Published: October 2024

The self-organizing biological characteristics of vegetation and human activities lead to the disruption of the continuous spatial attributes of natural watersheds, which are significant factors affecting river wetland ecosystems. To clarify the landscape ecological flow characteristics of vegetation patch distribution and fragmentation, this study used the three-dimensional Reynolds stress turbulence model in ANSYS Fluent software. The model considered three vegetation patch coverages under two different submersion states and four fragmentation types of vegetation patches under the same coverage conditions within specific vegetation areas. The flow characteristics of longitudinally discontinuous rigid vegetation patches, occupying half of the width of the river channel, were numerically simulated. The model’s applicability was verified by indoor open-channel flume experiments. The results indicated that: (1) The streamwise velocity in vegetated areas is significantly lower than in non-vegetated areas, and the difference in flow capacity between vegetated and non-vegetated areas increases with patch coverage and fragmentation degree. (2) In the non-submerged state, the maximum Reynolds stress in the vegetated area is located at the bottom of the vegetation and is negatively correlated with patch coverage but positively correlated with fragmentation degree. In the submerged state, the maximum Reynolds stress is located near the top of the canopy and is positively correlated with both patch coverage and fragmentation degree. (3) The longitudinal turbulent kinetic energy in the vegetated area is significantly higher than in the non-vegetated area. In the non-submerged state, the turbulent kinetic energy in the vegetated area is negatively correlated with patch coverage but positively correlated with fragmentation degree. In the submerged state, the turbulent kinetic energy of the longitudinal distribution in the free layer of the vegetated area is positively correlated with patch coverage, negatively correlated with fragmentation degree, and is only reflected in the upstream vegetation area.
Triplex real-time qPCR for the simultaneous detection of Botryosphaeriaceae species in woody crops and environmental samples
Authors: Romero-Cuadrado Laura , Aguado Ana , Ruano-Rosa David , Capote Nieves
Published: October 2024

Introduction

Species of Botryosphaeriaceae fungi are relevant pathogens of almond causing trunk cankers, extensive gumming, necrosis of internal tissues and plant dieback and dead, threatening almond productivity. A novel triplex quantitative real-time PCR (qPCR) assay was designed for the simultaneous detection and quantification of Neofusicoccum parvum, Botryosphaeria dothidea and the Botryosphaeriaceae family.

Material and methods

The method was validated in symptomatic and asymptomatic almond, avocado, blueberry and grapevine plants and in environmental samples, such as cropping soil and rainwater and in artificially inoculated trapped spores, demonstrating the same performance on several matrices.

Results and discussion

The limit of detection of the triplex qPCR was 10 fg of genomic DNA for the three fungal targets, with high correlation coefficients (R2) and amplification efficiencies between 90 and 120%. Although the triplex qPCR demonstrated to be more sensitive and accurate than the traditional plate culturing and further sequencing method, a substantial agreement (kappa index = 0.8052 ± 0.0512) was found between the two detection methods. The highly sensitive qPCR assay allows for accurate diagnosis of symptomatic plants and early detection of Botryosphaeriaceae fungi in asymptomatic plants (rootstocks and grafting scions from almond nurseries). Furthermore, the triplex qPCR successfully detected Botryosphaeriaceae fungi in environmental samples, such as cropping soils and rainwater. It was also capable of detecting as few as 10 conidia in artificially inoculated tapes. Therefore, the triplex qPCR is a valuable tool for accurate diagnosis, aiding in the implementation of suitable control measures. It enables preventive detection in asymptomatic samples, helping to avoid the introduction and spread of these pathogens in production fields. Moreover, it assists in identifying inoculum sources and quantifying inoculum levels in crop environments, contributing to a precise phytosanitary application schedule, thereby reducing production costs and preserving the environment.
Enhancing prediction accuracy of foliar essential oil content, growth, and stem quality in Eucalyptus globulus using multi-trait deep learning models
Authors: Mieres-Castro Daniel , Maldonado Carlos , Mora-Poblete Freddy
Published: October 2024

Eucalyptus globulus Labill., is a recognized multipurpose tree, which stands out not only for the valuable qualities of its wood but also for the medicinal applications of the essential oil extracted from its leaves. In this study, we implemented an integrated strategy comprising genomic and phenomic approaches to predict foliar essential oil content, stem quality, and growth-related traits within a 9-year-old breeding population of E. globulus. The strategy involved evaluating Uni/Multi-trait deep learning (DL) models by incorporating genomic data related to single nucleotide polymorphisms (SNPs) and haplotypes, as well as the phenomic data from leaf near-infrared (NIR) spectroscopy. Our results showed that essential oil content (oil yield) ranged from 0.01 to 1.69% v/fw and had no significant correlation with any growth-related traits. This suggests that selection solely based on growth-related traits did n The emphases (colored text) from revisions were removed throughout the article. Confirm that this change is fine. ot influence the essential oil content. Genomic heritability estimates ranged from 0.25 (diameter at breast height (DBH) and oil yield) to 0.71 (DBH and stem straightness (ST)), while pedigree-based heritability exhibited a broader range, from 0.05 to 0.88. Notably, oil yield was found to be moderate to highly heritable, with genomic values ranging from 0.25 to 0.60, alongside a pedigree-based estimate of 0.48. The DL prediction models consistently achieved higher prediction accuracy (PA) values with a Multi-trait approach for most traits analyzed, including oil yield (0.699), tree height (0.772), DBH (0.745), slenderness coefficient (0.616), stem volume (0.757), and ST (0.764). The Uni-trait approach achieved superior PA values solely for branching quality (0.861). NIR spectral absorbance was the best omics data for CNN or MLP models with a Multi-trait approach. These results highlight considerable genetic variation within the Eucalyptus progeny trial, particularly regarding oil production. Our results contribute significantly to understanding omics-assisted deep learning models as a breeding strategy to improve growth-related traits and optimize essential oil production in this species.
Integrated hormone and transcriptome profiles provide insight into the pericarp differential development mechanism between Mandarin ‘Shatangju’ and ‘Chunhongtangju’
Authors: Huang Yongjing , Zhu Congyi , Hu Yibo , Yan Sanjiao , Luo Zhimin , Zou Yanping , Wu Wen , Zeng Jiwu
Published: October 2024

Introduction

Citrus reticulata cv. ‘Chunhongtangju’ was mutated from Mandarin ‘Shatangju’, which has been identified as a new citrus variety. Mandarin ‘Chunhongtangju’ fruits were late-ripening for about two months than Mandarin ‘Shatangju’.

Methods

To understand the pericarp differential development mechanism in Mandarin ‘Shatangju’ (CK) and ‘Chunhongtangju’ (LM), hormones and transcriptome profiles of pericarps were performed in different development stages: Young fruit stage (CK1/LM1), Expansion and Turning color stage (CK2), Expansion stage (LM2), Turning color stage (LM3), and Maturity stage (CK3/LM4).

Results

In this study, the development of LM was significantly slower, and the maturity was significantly delayed. At the same stage, most hormones in Mandarin ‘Chunhongtangju’ pericarps were higher than that in ‘Shatangju’ such as gibberellin A24, cis(+)-12-oxophytodienoic acid, and L-phenylalanine. The deficiency of hormones in late-maturing pericarps was mainly manifested in ABA, 12-OHJA, MeSAG, and ABA-GE. Differences in transcriptome profiles between the two citrus varieties are primarily observed in energy metabolism, signal transduction such as MAPK signaling pathway and plant hormone signaling, and biosynthesis of secondary metabolites. After analyzing the hormones and transcriptome data, we found that the top genes and hormones, such as Cs_ont_5g020040 (transcription elongation factor, TFIIS), Cs_ont_7g021670 (BAG family molecular chaperone regulator 5, BAG5), Cs_ont_2g025760 (40S ribosomal protein S27, Rps27), 5-deoxystrigol, salicylic acid 2-O-β-glucosid, and gibberellin A24, contributed significantly to gene transcription and hormone synthesis.

Discussion

This study suggests that the variances of pericarp development between the two varieties are linked to variations in the transcription levels of genes associated with energy and secondary metabolism, signal transduction related genes. These findings expand our understanding of the complex transcriptional and hormonal regulatory hierarchy during pericarp development.
Genome-wide association study revealed some new candidate genes associated with flowering and maturity time of soybean in Central and West Siberian regions of Russia
Authors: Perfil`ev Roman , Shcherban Andrey , Potapov Dmitriy , Maksimenko Konstantin , Kiryukhin Sergey , Gurinovich Sergey , Panarina Veronika , Polyudina Revmira , Salina Elena
Published: October 2024

The duration of flowering and maturity is an important agricultural trait determining the suitability of a variety for cultivation in the target region. In the present study, we used genome-wide association analysis (GWAS) to search for loci associated with soybean flowering and maturity in the Central and West Siberian regions of Russia. A field experiment was conducted in 2021/2022 at two locations (Orel and Novosibirsk). A germplasm collection of 180 accessions was genotyped using SoySNP50K Illumina Infinium Bead-Chip. From the initial collection, we selected 129 unrelated accessions and conducted GWAS on this dataset using two multi-locus models: FarmCPU and BLINK. As a result, we identified 13 loci previously reported to be associated with duration of soybean development, and 17 new loci. 33 candidate genes were detected in these loci using analysis of co-expression, gene ontology, and literature data, with the best candidates being Glyma.03G177500, Glyma.13G177400, and Glyma.06G213100. These candidate genes code the Arabidopis orthologs TOE1 (TARGET OF EAT 1), SPL3 (SQUAMOSA PROMOTER BINDING PROTEIN LIKE 3), the DELLA protein, respectively. In these three genes, we found haplotypes which may be associated with the length of soybean flowering and maturity, providing soybean adaptation to a northern latitudes.
Genome-wide analysis of WRKY gene family and the dynamic responses of key WRKY genes involved in cadmium stress in Brassica juncea
Authors: Li Shaocui , Ji Qingqing , An Xia , Chen Changli , Luo Xiahong , Liu Tingting , Zou Lina
Published: October 2024

The WRKY transcription factors comprise one of the most extensive gene families and serve as pivotal regulators of plant responses to heavy metal stress. They contribute significantly to maintaining plant growth and development by enhancing plant tolerance. However, research on the role of WRKY genes in response to cadmium (Cd) stress in mustard is minimal. In this study, we conducted a genome-wide analysis of the mustard WRKY gene family using bioinformatics. The results revealed that 291 WRKY putative genes (BjuWRKYs) were identified in the mustard genome. These genes were categorized into seven subgroups (I, IIa-e and III) through phylogenetic analysis, with differences in motif composition between each subgroup. Homology analysis indicated that 31.62% of the genes originated from tandem duplication events. Promoter analysis revealed an abundance of abiotic stress-related elements and hormone-related elements within the BjuWRKY genes. Transcriptome analysis demonstrated that most BjuWRKY genes exhibited differential expression patterns at different Cd treatment stages in mustard. Furthermore, 10 BjuWRKY genes were confirmed to respond to Cd stress through the construction of a BjuWRKY protein interaction network, prediction of hub genes, and real-time fluorescence quantitative PCR analysis, indicating their potential involvement in Cd stress. Our findings provide a comprehensive insight into the WRKY gene family in mustard and establish a foundation for further studies of the functional roles of BjuWRKY genes in Cd stress response.
Transcriptomic and sugar metabolic analysis reveals molecular mechanisms of peach gummosis in response to Neofusicoccum parvum infection
Authors: Zhang Yang , Liu Yong , Gan Zhimeng , Du Wei , Ai Xiaoyan , Zhu Wei , Wang Huiliang , Wang Furong , Gong Linzhong , He Huaping
Published: October 2024

Peach gummosis, a devastating disease caused by Neofusicoccum parvum, significantly shortens peach tree lifespan and reduces the yield of peach trees. Despite its impact, the molecular mechanism underlying this disease remains largely unexplored. In this study, we used RNA-seq, sugar metabolism measurements, and an integrated transcriptional and metabolomic analysis to uncover the molecular events driving peach gummosis. Our results revealed that N. parvum infection drastically altered the transcripts of cell wall degradation-related genes, the log2Fold change in the transcript level of Prupe.1G088900 encoding xyloglucan endotransglycosylase decreased 2.6-fold, while Prupe.6G075100 encoding expansin increased by 2.58-fold at 12 hpi under N. parvum stress. Additionally, sugar content analysis revealed an increase in maltose, sucrose, L-rhamnose, and inositol levels in the early stages of infection, while D-galactose, D-glucose, D-fructose consistently declined as gummosis progressed. Key genes related to cell wall degradation and starch degradation, as well as UDP-sugar biosynthesis, were significantly upregulated in response to N. parvum. These findings suggest that N. parvum manipulates cell wall degradation and UDP-sugar-related genes to invade peach shoot cells, ultimately triggering gum secretion. Furthermore, weighted gene co-expression network analysis (WGCNA) identified two transcription factors, ERF027 and bZIP9, as central regulators in the downregulated and upregulated modules, respectively. Overall, this study enhances our understanding of the physiological and molecular responses of peach trees to N. parvum infection and provide valuable insights into the mechanisms of peach defense against biotic stresses.
Mapping vulnerability for increased COVID-19 susceptibility and worse outcomes: a scoping review
Authors: Couto Rodrigo de Macedo , dos Santos Daniel Gonçalves , Sanine Patrícia Rodrigues , Pires dos Santos Andrea , Kmetiuk Louise Bach , Biondo Alexander Welker , Boing Alexandra Crispim
Published: October 2024

Introduction

Infectious diseases can spread rapidly in the globalized world, and the complex intersection of individual, social, economic, and cultural factors make it difficult to identify vulnerabilities in the face of pandemics.

Methods

Therefore, this study aimed to identify vulnerability factors to infection and worse outcomes associated with COVID-19. This is a scoping review study of six databases that selected publications between 2019 and 2023, focusing on individual, social, and programmatic dimensions of vulnerability. The results were recorded in a spreadsheet and analyzed, considering the interrelationships among these dimensions.

Results

A total of 45 articles were included in the review. Content analysis was conducted using the theoretical framework of health vulnerability, which divides vulnerability into individual, social, and programmatic dimensions. Race/ethnicity, homelessness, incarceration, socioeconomic level, food insecurity, and remote areas were classified as social dimensions. On the other hand, cancer, cardiovascular disease, HIV/AIDS, alcoholism, advanced age, obesity, mental disorders, diabetes, kidney disease, and pregnancy were classified as individual dimensions. None of the publications found explicitly mentioned programmatic vulnerabilities.

Discussion

The research found that social vulnerabilities reinforce individual vulnerabilities, creating a vicious cycle. In addition, programmatic vulnerabilities reinforce this relationship. This study emphasizes that public policies should address these different dimensions of vulnerability. It suggests that this information should be incorporated into health surveillance and future decision-making to face new pandemics.

Systematic review registration

https://archive.org/details/osf-registrations-wgfmj-v1.
Recommendations for implementing digital alcohol interventions in primary care: lessons learned from a Norwegian feasibility study
Authors: Potthoff Sebastian , Brendryen Håvar , Bosnic Haris , Njå Anne Lill Mjølhus , Finch Tracy , Lid Torgeir Gilje
Published: October 2024

Introduction

Excessive alcohol consumption is a leading global risk factor for ill-health and premature death. Digital alcohol interventions can be effective at reducing alcohol consumption, but their widespread adoption is lagging behind. This study aimed to identify factors promoting or inhibiting the implementation of a digital alcohol intervention in Norwegian primary care, by using Normalization Process Theory (NPT).

Methods

A mixed methods feasibility study combining quantitative and qualitative methods. A digital alcohol intervention called “Endre” was implemented across four GP practices in Stavanger and Oslo. Usage of the intervention was logged on the digital platform. General practitioners (GPs) reported their perceived uptake of the intervention via a web-based survey. The Normalization MeAsure Development (NoMAD) survey was used to measure support staff's perceived normalization of the intervention. Qualitative data were analyzed using the NPT framework, with quantitative data analyzed descriptively and using χ2 and Wilcoxon signed-rank test for differences in current and future normalization.

Results

Thirty-seven GPs worked in the clinics and could recruit patients for the digital intervention. Thirty-six patients registered for the intervention. Nine patients dropped out early and 25 completed the intervention as intended. Low normalization scores at follow-up (n = 27) indicated that Endre did not become fully embedded in and across practices. Nonetheless, staff felt somewhat confident about their use of Endre and thought it may become a more integral part of their work in the future. Findings from six semi-structured group interviews suggested that limited implementation success may have been due to a lack of tailored implementation support, staff's lack of involvement, their diminished trust in Endre, and a lack of feedback on intervention usage. The outbreak of the Covid-19 pandemic further limited opportunities for GPs to use Endre.

Conclusion

This study investigated the real-world challenges of implementing a digital alcohol intervention in routine clinical practice. Future research should involve support staff in both the development and implementation of digital solutions to maximize compatibility with professional workflows and needs. Integration of digital solutions may further be improved by including features such as dashboards that enable clinicians to access and monitor patient progress and self-reported outcomes.
Reproductive toxicology: keeping up with our changing world
Authors: Miller Laura B. , Feuz Morgan B. , Meyer Ralph G. , Meyer-Ficca Mirella L.
Published: October 2024

Reproductive toxicology testing is essential to safeguard public health of current and future generations. Traditional toxicological testing of male reproduction has focused on evaluating substances for acute toxicity to the reproductive system, with fertility assessment as a main endpoint and infertility a main adverse outcome. Newer studies in the last few decades have significantly widened our understanding of what represents an adverse event in reproductive toxicology, and thus changed our perspective of what constitutes a reproductive toxicant, such as endocrine disrupting chemicals that affect fertility and offspring health in an intergenerational manner. Besides infertility or congenital abnormalities, adverse outcomes can present as increased likelihood for various health problems in offspring, including metabolic syndrome, neurodevelopmental problems like autism and increased cancer predisposition, among others. To enable toxicologic studies to accurately represent the population, toxicologic testing designs need to model changing population characteristics and exposure circumstances. Current trends of increasing importance in human reproduction include increased paternal age, with an associated decline of nicotinamide adenine dinucleotide (NAD), and a higher prevalence of obesity, both of which are factors that toxicological testing study design should account for. In this perspective article, we highlighted some limitations of standard testing protocols, the need for expanding the assessed reproductive endpoint by including genetic and epigenetic sperm parameters, and the potential of recent developments, including mixture testing, novel animal models, in vitro systems like organoids, multigenerational testing protocols, as well as in silico modelling, machine learning and artificial intelligence.
Do COVID-19 Infectious Disease Models Incorporate the Social Determinants of Health? A Systematic Review
Authors: John-Baptiste Ava A. , Moulin Marc , Li Zhe , Hamilton Darren , Crichlow Gabrielle , Klein Daniel Eisenkraft , Alemu Feben W. , Ghattas Lina , McDonald Kathryn , Asaria Miqdad , Sharpe Cameron , Pandya Ekta , Moqueet Nasheed , Champredon David , Moghadas Seyed M. , Cooper Lisa A. , Pinto Andrew , Stranges Saverio , Haworth-Brockman Margaret J. , Galvani Alison , Ali Shehzad
Published: October 2024

Objectives

To identify COVID-19 infectious disease models that accounted for social determinants of health (SDH).

Methods

We searched MEDLINE, EMBASE, Cochrane Library, medRxiv, and the Web of Science from December 2019 to August 2020. We included mathematical modelling studies focused on humans investigating COVID-19 impact and including at least one SDH. We abstracted study characteristics (e.g., country, model type, social determinants of health) and appraised study quality using best practices guidelines.

Results

83 studies were included. Most pertained to multiple countries (n = 15), the United States (n = 12), or China (n = 7). Most models were compartmental (n = 45) and agent-based (n = 7). Age was the most incorporated SDH (n = 74), followed by gender (n = 15), race/ethnicity (n = 7) and remote/rural location (n = 6). Most models reflected the dynamic nature of infectious disease spread (n = 51, 61%) but few reported on internal (n = 10, 12%) or external (n = 31, 37%) model validation.

Conclusion

Few models published early in the pandemic accounted for SDH other than age. Neglect of SDH in mathematical models of disease spread may result in foregone opportunities to understand differential impacts of the pandemic and to assess targeted interventions.

Systematic Review Registration:

[https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020207706], PROSPERO, CRD42020207706.
Numerical Analysis of Liquid Hydrogen Atomization in a Premixing Tube Using a Volume of Fluid-to-Discrete Particle Model Approach
Authors: Adam Abdalazeem , He Weifeng , Han Dong , Fan Yuxin , Musa Omer
Published: October 2024

This paper examines the atomization characteristics of liquid hydrogen fuel in a premixing tube under different operating conditions. Hydrogen fuel’s unique injection morphology and atomization behavior were analyzed using the Volume of Fluid-to-Discrete Particle Model (VOF to DPM) approach, coupled with the SST k−ω turbulence model and adaptive mesh refinement. The study revealed that the breakup and transformation of liquid surfaces into particles are significantly impacted by varying air velocities and injection pressure. Specifically, higher air velocities caused the liquid sheet to lengthen and narrow due to intensified vortices. However, the breakup was delayed at higher velocities, occurring at distances of 0.037 m and 0.043 m for air velocities of 10 m/s and 20 m/s, respectively. The research also highlights the significant role that injection pressure plays in fluid sheet breakup. Higher pressures promote better atomization and fuel–lair mixing, resulting in more particles with increased diameters. Notably, the fluid sheet exhibited a small angle of about 43.79° when using the velocity component corresponding to p1 = 0.5 MPa. Similarly, for p2 = 1 MPa and p3 = 2 MPa, the angles were measured to be approximately 47.5° and 49.5°, respectively. Additionally, the study observed that the injection expands in length and diameter as time progresses, indicating fuel dispersion. These insights have significant implications for the design principles of injectors in power generation technologies that utilize liquid hydrogen fuel.
Pulsar Signal Adaptive Surrogate Modeling
Authors: Kašpárek Tomáš , Chudý Peter
Published: October 2024

As the number of spacecraft heading beyond Earth’s orbit increased in recent years, autonomous navigation solutions have become increasingly important. One such solution is pulsar-based navigation. The availability of pulsar signals for simulations and HIL testing is essential for the development of pulsar-based navigation. This study proposes a method to develop a surrogate model of pulsar signals based on radio pulsar observations. The selection of suitable pulsars for the radio telescope is discussed, and a series of observations are conducted. The collected data are processed using the PRESTO software, and the pulsar parameters for the model are derived. Unlike current pulsar signal models, the proposed model anticipates pulsar signal parameters to change over time. It can provide dynamic input parameters for known synthetic pulsar signal generators, resulting in a more realistic signal.
Excellent Canopy Structure in Soybeans Can Improve Their Photosynthetic Performance and Increase Yield
Authors: He Shuyuan , Li Xiuni , Chen Menggen , Xu Xiangyao , Zhang Wenjing , Chi Huiling , Shao Panxia , Tang Fenda , Gong Tao , Guo Ming , Xu Mei , Yang Wenyu , Liu Weiguo
Published: October 2024

In the maize-soybean intercropping system, varying degrees of maize leaf shading are an important factor that reduces the uniformity of light penetration within the soybean canopy, altering the soybean canopy structure. Quantitative analysis of the relationship between the soybean canopy structure and canopy photosynthesis helps with breeding shade-tolerant soybean varieties for intercropping systems. This study examined the canopy structure and photosynthesis of intercropped soybeans during the shading stress period (28 days before the corn harvest), the high light adaptation period (15 days after the corn harvest), and the recovery period (35 and 55 days after the corn harvest), using a field high-throughput phenotyping platform and a plant gas exchange testing system (CAPTS). Additionally, indoor shading experiments were conducted for validation. The results indicate that shade-tolerant soybean varieties (STV varieties) have significantly higher yields than shade-sensitive soybean varieties (SSV varieties). This is attributable to the STV varieties having a larger top area, lateral width, and lateral external rectangular area. Compared to the SSV varieties, the four top areas of the STV varieties are, on average, 52.09%, 72.05%, and 61.37% higher during the shading stress, high light adaptation, and recovery periods, respectively. Furthermore, the average maximum growth rates (GRs) for the side mean width (SMW) and side rectangle area (SRA) of the STV varieties are 62.92% and 22.13% in the field, and 83.36% and 55.53% in the indoor environment, respectively. This results in a lower canopy overlap in STV varieties, leading to a more uniform light distribution within the canopy, which is reflected in higher photosynthetic rates (Pn), apparent quantum efficiency, and whole-leaf photosynthetic potential (WLPP) for the STV varieties, thereby enhancing their adaptability to shading stress. Above-ground dry matter accumulation was higher in STV varieties, with more assimilates stored in the source and sink, promoting assimilate accumulation in the grains. These results provide new insights into how the superior canopy structure and photosynthesis of shade-tolerant soybean varieties contribute to increased yield.
Effect of Sod Production on Physical, Chemical, and Biological Properties of Soils in North and South China
Authors: Qu Xinyue , Li Yue , Wang Chu , Qiao Jiayue , Zhu Kai , Sun Yan , Hu Qiannan
Published: October 2024

Lawns play a vital role in urban development, but the impact of sod production on soil properties has always been controversial. In this study, we examined the physical, chemical, and biological properties of sod production bases across different regions and years [including northern China (2.5, 3, 5, 6, 8, 10, 12 years), referred to as N-2.5, N-3, etc., and southern China (3, 10, 11, 14, 17 years), referred to as S-3, S-10, etc.], with tall fescue and Kentucky bluegrass planted in the north and bermudagrass or creeping bentgrass planted in the south. Sod production was found to increase soil bulk density while reducing porosity and field capacity, but these effects did not consistently intensify with longer production periods. Except for available phosphorus and available potassium, other soil nutrients (total carbon, total nitrogen, organic matter, alkali-hydrolyzable nitrogen, etc.) were either unaffected or increased at certain time points (S-11, S-14). Prolonged sod production (S-10, S-17) also boosted microbial content. In northern regions, organic matter and total nitrogen were the key factors influencing microbial community structure, whereas in southern regions, alkali-hydrolyzable nitrogen, electrical conductivity, available potassium, and organic matter were most influential. We also found that crop rotation, sand mulching, and deep plowing could enhance soil nutrient content and microbial activity in sod production.
A Machine Vision System for Monitoring Wild Birds on Poultry Farms to Prevent Avian Influenza
Authors: Yang Xiao , Bist Ramesh Bahadur , Subedi Sachin , Wu Zihao , Liu Tianming , Paneru Bidur , Chai Lilong
Published: October 2024

The epidemic of avian influenza outbreaks, especially high-pathogenicity avian influenza (HPAI), which causes respiratory disease and death, is a disaster in poultry. The outbreak of HPAI in 2014–2015 caused the loss of 60 million chickens and turkeys. The most recent HPAI outbreak, ongoing since 2021, has led to the loss of over 50 million chickens so far in the US and Canada. Farm biosecurity management practices have been used to prevent the spread of the virus. However, existing practices related to controlling the transmission of the virus through wild birds, especially waterfowl, are limited. For instance, ducks were considered hosts of avian influenza viruses in many past outbreaks. The objectives of this study were to develop a machine vision framework for tracking wild birds and test the performance of deep learning models in the detection of wild birds on poultry farms. A deep learning framework based on computer vision was designed and applied to the monitoring of wild birds. A night vision camera was used to collect data on wild bird near poultry farms. In the data, there were two main wild birds: the gadwall and brown thrasher. More than 6000 pictures were extracted through random video selection and applied in the training and testing processes. An overall precision of 0.95 (mAP@0.5) was reached by the model. The model is capable of automatic and real-time detection of wild birds. Missed detection mainly came from occlusion because the wild birds tended to hide in grass. Future research could be focused on applying the model to alert to the risk of wild birds and combining it with unmanned aerial vehicles to drive out detected wild birds.
Estimation of Maize Water Requirements Based on the Low-Cost Image Acquisition Methods and the Meteorological Parameters
Authors: Zhao Jiuxiao , Tao Jianping , Zhang Shirui , Li Jingjing , Li Teng , Shan Feifei , Zheng Wengang
Published: October 2024

This study aims to enhance maize water demand calculation. We calculate crop evapotranspiration (ETc) through mobile phone photography and meteorological parameters. In terms of crop coefficient (Kc) calculation, we utilize the mobile phone camera image driver to establish a real-time monitoring model of Kc based on plant canopy coverage (PGC) changes. The calculation of PGC is achieved by constructing a PGC classification network and a Convolutional Block Attention Module (CBAM)-U2Net is implemented by the segment network. For the reference crop evapotranspiration (ETo) calculation, we constructed a simplified ETo estimation model based on SVR, LSTM, Optuna LSTM, and GWO-SVM using a public meteorological data-driven program, and evaluated its performance. The results demonstrate that our method achieves high classification accuracy for the PGC 98.9% and segmentation accuracy for the CBAM-U2net-based segmentation network 95.68%. The Kc calculation model exhibits a root mean square error (RMSE) of 0.053. In terms of ETo estimation, the Optuna-LSTM model with four variables demonstrates the best estimation effect, with a correlation coefficient (R2) of 0.953. The final R2 between the estimated ETc value and the true value is 0.918, with an RMSE of 0.014. This method can effectively estimate the water demand of maize.
Enhancing Drought Tolerance in Barley through the Application of Watermelon Rind Hydrogels: A Novel Approach to Sustainable Agriculture
Authors: Teng Bingqin , Wu Jun , Zhong Yuan , Cai Liqun , Qi Peng , Luo Zhuzhu
Published: October 2024

Drought stress critically hinders agricultural productivity, especially in arid and semi-arid zones. The results of this study show that the application of watermelon rind-based hydrogels (WR hydrogels), synthesized from a watermelon rind, acrylic acid (AA), and acrylamide (AAm), significantly enhanced soil water retention by 77.46% at a 0.3% concentration, increasing the plant height by 28.98% and biomass by 35.28% under controlled greenhouse conditions (25 °C/20 °C day/night temperature cycle, with a 12 h photoperiod at 400–500 μmol·m−2·s−1 of illuminance and 30–40% relative humidity). The accumulation of proline and soluble sugars decreased, indicating reduced osmotic stress in barley subjected to mild and severe drought conditions (6–15% FC and 17–26% FC). The chlorophyll content rose by 16.36%, boosting photosynthetic activity. A correlation and principal component analysis further highlighted the positive effects of hydrogel addition on plant growth and drought resistance. These findings underscore the potential of WR hydrogels as an effective soil amendment for promoting sustainable agriculture in water-limited conditions.
Crotalaria juncea Genotype Biomass Accumulation in Northern Semi-Arid and Humid-Continental Climates
Authors: Eberle Carrie A. , Harris Donna K. , Jones Tyler Z. , Fowers Beth , Mealor Brian A.
Published: October 2024

Crotalaria juncea (sunn hemp) is a tropical forage legume used as a cover, forage, and fiber crop. Sunn hemp seed production occurs primarily in India because it requires short days to flower and set seed. Seeds available for production are typically non-specific genotypes instead of true breeding varieties. As sunn hemp is grown in more locations, understanding not only its performance in different growing conditions but also variations in genotype performance is critical for production management. We evaluated the growth and biomass accumulation of four genotypes (KMB1, KMB2, Thailand Original Sunn, and ‘Tropic Sunn’) of sunn hemp grown in northern semi-arid and humid-continental environments, Wyoming (Adams ‘22 and ‘23 (irrigated), Wyarno ‘23 (rainfed)) and Minnesota (Morris ‘22 and ‘23), USA. Thailand Original Sunn had the fastest growth rate (height over time) but the slowest canopy closure (NDVI over time), while KMB1 had the slowest growth rate but the fastest canopy closure. While growth rates varied among sunn hemp germplasm, there were no marked differences in biomass accumulation when harvested at 60 and 90 days after planting. Although the genotype did not have a significant effect on biomass accumulation, the environment affected not only growth but also biomass accumulation. At 60 DAP, the sunn hemp biomass averaged 1836, 489, 2459, 3334, and 731 kg ha−1 in the Adams ‘22, Adams ‘23, Morris ‘22, Morris ‘23, and Wyarno ‘23 environments, respectively. At 90 DAP, the sunn hemp biomass averaged 6459, 4573, 7979, 7403, and 2220 kg ha−1 in the Adams ‘22, Adams ‘23, Morris ‘22, Morris ‘23, and Wyarno ‘23 environments, respectively. The growth rate, canopy closure, and biomass accumulation differed when compared between the semi-arid environments and the humid-continental environment, with the humid-continental environment producing faster growth and higher biomass. These findings support the hypothesis that genotypes are likely to perform as predicted within growing regions, but there may be room to improve performance in different environments through selective breeding.