bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of long-read sequencing and MLPA combined with long-PCR sequencing of CYP21A2 mutations in patients with 21-OHD
Authors: Lan Tian , Wang Jin , Chen Kaibi , Zhang Jianru , Chen Xiaohong , Yao Hui
Published: November 2024

Background

21-Hydroxylase deficiency (21-OHD) is caused by mutations in the CYP21A2 gene. Due to the complex structure and the high genetic heterogeneity of the CYP21A2 gene, genetic testing for 21-OHD is currently facing challenges. Moreover, there are no comparative studies on detecting CYP21A2 mutations by both second-generation sequencing and long-read sequencing (LRS, also known as third-generation sequencing).

Objective

To detect CYP21A2 variations in 21-OHD patients using targeted capture with LRS method based on the PacBio (Pacific Biosciences) Sequel II platform.

Methods

A total of 67 patients with 21-OHD were admitted in Wuhan Children’s Hospital. The full sequence of CYP21A2 gene was analyzed by targeted capture combined with LRS based on the PacBio Sequel II platform. The results were compared with those of long-polymerase chain reaction (Long-PCR) combined with multiplex ligation probe amplification (MLPA) detection. Based on the in vitro study of 21-hydroxylase activity of common mutations, the patient genotypes were divided into groups of Null, A, B, and C, from severe to mild. The correlation between different genotype groups and clinical typing was observed.

Results

The study analyzed a total of 67 patients. Among them, 44 (65.67%) were males and 23 (34.33%) were females, with a male-to-female ratio of approximately 1.9:1. A total of 27 pathogenic variants were identified in the 67 patients, of which micro-conversion accounted for 61.9%, new variants of CYP21A2 accounted for 8.2%; deletion accounted for 22.4% (CYP21A2 single deletion and chimeric TNXA/TNXB accounted for 12.7%, chimeric CYP21A1P/CYP21A2 accounted for 9.7%); and duplication accounted for 3.0% (CYP21A2 Gene Duplication). I2G was the most common variant (26.9%). Targeted capture LRS and MLPA combined with Long-PCR detection of CYP21A2 mutations showed 30 detection results with differences. The overall genotype-phenotype correlation was 82.1%. The positive predictive rate of the Null group for salt wasting (SW) type was 84.6%, the A group for SW type was 88.9%, the group B for simple virilization (SV) type was 82.4%, and the group C for SV type was 62.5%. The correlation coefficient rs between the severity of the phenotype and the genotype group was 0.682 (P < 0.05).

Conclusion

Targeted capture combined with LRS is an integrated approach for detecting CYP21A2 mutations, allowing precise determination of connected sites for multiple deletions/insertions and cis/trans configurations without analyzing parental genomic samples. The overall genotype-phenotype correlation for 21-OHD is generally strong, with higher associations observed between genotype and phenotype for group Null, A, and B mutations, and larger genotype-phenotype variation in group C mutations. Targeted capture with LRS sequencing offers a new method for genetic diagnosis in 21-OHD patients.
Oxidative balance score and the potential for suffering rheumatoid arthritis: a cross-sectional study
Authors: Zhang Yimin , Yu Hao , Fu Jianfei , Zhuo Renjie , Xu Jin , Liu Liya , Dai Manyun , Li Zhen
Published: November 2024

Objective

Our study was conducted to explore the link between oxidative balance score (OBS) and rheumatoid arthritis (RA).

Methods

A total of 21,415 participants were included in our research from five cycles (2011–2012, 2013–2014, 2015–2016, 2017–2018, and 2017–2020) of the National Health and Nutrition Examination Survey (NHANES). Moreover, 20 elements related to diet as well as lifestyle were combined to calculate OBS. The relationship between OBS and RA was assessed by employing multivariable regression analysis, and further exploration was carried out through subgroup analysis, restricted cubic spline analysis, and sensitivity analysis. Multiple covariates were selected to adjust the model for more robust results.

Results

In our cross-sectional study, a higher OBS has a protective effect on the development of RA (OR = 0.98, 95% CI: 0.97 to 0.99). In contrast to individuals aged ≥60, the result is more prominent in the population aged 20–60 (OR = 0.97, 95% CI: 0.96 to 0.98). Marital status appears to introduce interference in the relationship between OBS and RA, and unmarried individuals exhibited different outcomes (OR = 1.02, 95% CI: 0.99 to 1.04) compared to others. The positive influence of OBS was more evident in patients with chronic kidney disease and cardiovascular disease, while it was stronger in individuals without diabetes and liver disease.

Conclusion

A higher OBS correlates with a reduced odd of RA. Further studies are needed to shoot more sights on improving dietary habits and lifestyles to gain proper OBS and explore whether OBS can be one of the measurements utilized to measure the risk of RA.
Integrating immunotherapy with conventional treatment regime for breast cancer patients- an amalgamation of armamentarium
Authors: Nandi Deeptashree , Sharma Dipali
Published: November 2024

Immunotherapy stands as the frontrunner in treatment strategies imparting efficient remission in various types of cancer. In fact, emerging breakthroughs with immune checkpoint inhibitors (ICI) in a spectrum of cancers have evoked interest in research related to the potential effects of immunotherapy in breast cancer patients. A major challenge with breast cancer is the molecular heterogeneity that limits the efficacy of many therapeutic regimes. Clinical trials have shown favorable clinical outcomes with immunotherapeutic options in some subtypes of breast cancer. However, ICI monotherapy may not be sufficient for all breast cancer patients, emphasizing the need for combinatorial approaches. Ongoing research is focused on untangling the interplay of ICI with established as well as novel anticancer therapeutic regimens in preclinical models of breast cancer. Our review will analyze the existing research regarding the mechanisms and clinical impact of immunotherapy for the treatment of breast cancer. We shall evaluate the role of immune cell modulation for improved therapeutic response in breast cancer patients. This review will provide collated evidences about the current clinical trials that are testing out the implications of immunotherapy in conjunction with traditional treatment modalities in breast cancer and summarize the potential future research directions in the field. In addition, we shall underline the recent findings related to microbiota modulation as a key regulator of immune therapy response in cancer patients and its plausible applications in breast cancer.
Identification and validation of mRNA profiles linked to ATP- induced cell death represent a novel prognostic model for breast cancer
Authors: Zhang Zhongwen , Zhang Haolong , Zhang Zheng , Sandai Doblin , Lu Ping , Zhang Haoling , Wu Jianjun
Published: November 2024

Background

Cell death mechanisms are integral to the pathogenesis of breast cancer (BC), with ATP-induced cell death (AICD) attracting increasing attention due to its distinctive specificity and potential therapeutic applications.

Methods

This study employed genomic methodologies to investigate the correlation between drug sensitivity and types of AICD in BC. Initially, data from TCGA were utilized to construct a prognostic model and classification system for AICD. Subsequently, a series of bioinformatics analyses assessed the prognostic and clinical significance of this model within the context of BC.

Results

Analysis revealed a cohort of 18 genes associated with AICD, exhibiting prognostic relevance. Survival analyses indicated that overall survival rates were significantly lower in high-risk populations compared to their low-risk counterparts. Furthermore, prognostic indicators linked to AICD demonstrated high accuracy in predicting survival outcomes in BC. Immunological assessments indicated heightened expression of anti-tumor infiltrating immune cells and immune checkpoint molecules in low-risk populations, correlating with various anti-tumor immune functions. Ultimately, a comprehensive prognostic model related to AICD was developed through univariate analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis. As Adenosine triphosphate (ATP) concentration increased, the viability of BC cells exhibited a general decline at each time point. Notably, ATP diminished the mitochondrial membrane potential in BC cells while enhancing it in normal breast epithelial cells. Additionally, ATP inhibited the migration of BC cells and promoted their apoptosis. ATP also stimulated reactive oxygen species (ROS) production in MCF-10A cells, with implications for the immune response in BC cells. Compared to the control group, expression levels of CLIC6, SLC1A1, and CEMIP were significantly reduced in the ATP intervention group, whereas ANO6 expression was elevated. ANO6, CEMIP, and CLIC6 share genetic variants with BC, while SLC1A1 does not exhibit genetic causal variation with the disease.

Conclusion

A valuable prognostic model associated with AICD has been established, capable of accurately predicting BC prognosis. The induction of cell death by ATP appears to play a protective role in BC progression. These findings carry significant implications for the implementation of personalized and tailored treatment strategies for BC patients.
Phytoplankton carbon to chlorophyll a model development: a review
Authors: Gui Jiang , Sun Jun
Published: November 2024

The cellular carbon content and chlorophyll a (Chl a) concentration are two of the most significant indices for assessing phytoplankton biomass. Recording and monitoring these biomasses are essential tasks in phytoplankton research, and the carbon-to-chlorophyll a (C:Chl a) ratio serves as a crucial conversion tool between them. Although the C:Chl a ratio varies widely, it is influenced by external environmental factors, making modeling studies of C:Chl a particularly important. This paper provides an overview of the historical development of the C:Chl a model, beginning with early empirical models and progressing to the development of mechanistic models. This discussion is followed by an examination of existing gaps and future challenges in current C:Chl a modeling, particularly the potential underestimation of carbon biomass in existing C:Chl a models for dinoflagellates exhibiting multiple growth strategies. Finally, it is suggested that future C:Chl a models should strive to achieve a balance between reliability and applicability.
Allogeneic fibroblasts ameliorate intervertebral disc degeneration by reducing osteophytes in rabbits
Authors: Chen Chen , Huang Yizhuo , Shi Lei , Zhou Li , Zhou Shenao , Wan Hongjin , Yang Xiao , Zhao Jie
Published: November 2024

Introduction

Low back pain (LBP) was commonly induced by intervertebral disc degeneration (IVDD), which is accompanied by the loss of disc height and osteophyte generation. Cell-based therapy is a promising treatment for preventing the degeneration of interverbral disc. In our study, allogeneic fibroblasts are shown to ameliorate intervertebral disc degeneration by reducing osteophytes in rabbits.

Methods

We established a rabbits-derived fibroblast (Rab-Fib) which could be expanded in vitro and constructed puncture-induced intervertebral disc degeneration rabbit model. Histologic and imaging examinations and analyses were performed after 2 weeks, 3 months, and 12 months.

Results

Our data indicate that stable and reliably-extracted allogeneic fibroblasts can effectively ameliorate intervertebral disc degeneration by reducing osteophytes.

Conclusion

Our study provides a basis for advancing the further translation of fibroblasts in intervertebral disc therapy.
The latest advancements in Sosuga virus (SOSV) research
Authors: Markarian Nathan M. , Abrahamyan Levon
Published: November 2024

The last 60 years have seen the emergence of several zoonotic viruses, some of which originate from bats. Among these are Nipah virus, Marburg virus and Ebola viruses, which have high case fatality rates, and pose significant public health risks. In 2012, another zoonotic paramyxovirus from bats, known as Sosuga Virus (SOSV), was discovered in a hospitalized biologist who had returned from a trip to Africa. Given the potential public health threats of the SOSV, investigating its pathogenesis, epidemiology and developing antiviral strategies are crucial to control possible future outbreaks. Thus, in this review, we explore the latest advancements in understanding SOSV since its discovery, focusing on its pathogenesis, animal models and the development of antiviral strategies. By examining the current literature, this review aims to provide a comprehensive overview to guide future studies and help public health efforts in better mitigating potential SOSV outbreaks.
Network-based statistics reveals an enhanced subnetwork in prefrontal cortex in mild cognitive impairment: a functional near-infrared spectroscopy study
Authors: Wu Peirong , Lv Zeping , Bi Yinuo , Li Yijiang , Chen Hong , Jiang Jianfan , Pang Suyan , Zhao Xin , Jiang Wenyu
Published: November 2024

Background

Mild cognitive impairment (MCI) is generally considered to have a high risk of progression to Alzheimer’s disease. Our study aimed to investigate the abnormal functional connectivity (FC) in prefrontal cortex (PFC) in patients with MCI and explore the relationship between the observed changes and cognitive function.

Methods

Sixty-seven patients with MCI and 71 healthy individuals were recruited for this study. All participants underwent the Montreal Cognitive Assessment (MoCA) and functional near-infrared spectroscopy (fNIRS) examinations.

Results

Compared with healthy controls (HC), the patients with MCI exhibited significantly lower MoCA scores (p < 0.001). Through FC analysis, an enhanced subnetwork was observed in the right prefrontal cortex of the MCI group, covering four pairs of channel connections: CH12-CH15, CH12-CH16, CH13-CH15, and CH13-CH16. Moreover, the FC values of these four channel pairs and the education duration were significantly correlated with MoCA scores. Subsequently, a multiple linear regression model was performed to observe the independent factors of cognition decline, serving the education duration and the average FC values of subnetwork as independent variables and the MoCA scores as the dependent variable. The regression model showed a total of 25.7% explanation power (adjusted R2 = 0.257, F = 24.723, p < 0.001).

Conclusion

Our study suggested that the enhanced subnetwork within the right PFC may be involved in the pathophysiology of MCI and serve as a potential target for the treatment of MCI.
Transcriptome and metabolome profiling unveil the accumulation of chlorogenic acid in autooctoploid Gongju
Authors: Zhao Li , Cao Yu , Shan Gaomeng , Zhou Jiayi , Li Xintong , Liu Peng , Wang Yansong , An Songhao , Gao Ri
Published: November 2024

Background

Gongju is recognized as one of the four traditional Chinese medicinal herbs, and its main constituents are chlorogenic acid (CGA) and its derivative material. CGA content in autooctoploid Gongju are considerably elevated compared with those in parental tetraploid Gongju at different flowering stages. However, the underlying molecular mechanisms governing the regulation CGA content remain poorly understood.

Methods

Therefore, we conducted integrated transcriptome and metabolome analyses of different flowering stages in autooctoploid and tetraploid Gongju to elucidate the underlying molecular mechanisms governing CGA biosynthesis.

Results

Transcriptome analysis showed that the number of differentially expressed genes in the budding stage (BS), early flowering stage (EF), and full flowering stage (FF) of tetraploid and octoploid Gongju were 3859, 11,211, and 6837, respectively. A total of 563, 466, and 394 differential accumulated metabolites were respectively identified between the bud stages of tetraploid and octoploid Gongju (4BS vs. 8BS), between the early flowering stages of tetraploid and octoploid Gongju (4EF vs. 8EF), and the full flowering stages of tetraploid and octoploid Gongju (4FF vs. 8FF) groups. The integrated analysis of transcriptomics and metabolomics revealed that the expression of pma6460 and mws0178, which are key enzymes involved in the CGA synthesis pathway, increased during the flowering stages of octoploid Gongju relative to that of tetraploid Gongju. The expression levels of CmHQT and CmC3H genes associated with CGA synthesis were higher in octoploid plants than in tetraploid plants at various flowering stages. To investigate the potential regulation of transcription factors involved in CGA synthesis, we analyzed the coexpression of CmC3H and CmHQT with CmMYBs and CmbHLHs. Results indicated that transcription factors, such as CmMYB12 (Cluster-30519.0), CmMYB26 (Cluster-75874.0), CmMYB5 (Cluster-94106.0), CmMYB1 (Cluster-71968.7), CmbHLH62 (Cluster-32024.1), CmbHLH75 (Cluster-62341.0), CmbHLH62 (Cluster-32024.8), CmbHLH75 (Cluster-60210.0), and CmbHLH16 (Cluster-90665.1) play a pivotal role in CGA synthesis regulation. The present study provides novel insights into the molecular mechanisms underlying CGA accumulation in autopolyploid Gongju.
Stochastic simulation to optimize rice breeding at IRRI
Authors: Seck Fallou , Prakash Parthiban Thathapalli , Covarrubias-Pazaran Giovanny , Gueye Tala , Diédhiou Ibrahima , Bhosale Sankalp , Kadaru Suresh , Bartholomé Jérôme
Published: November 2024

Introduction

Genetic improvement in rice increased yield potential and improved varieties for farmers over the last decades. However, the demand for rice is growing while its cultivation faces challenges posed by climate change. To address these challenges, rice breeding programs need to adopt efficient breeding strategies to provide a steady increase in the rate of genetic gain for major traits. The International Rice Research Institute (IRRI) breeding program has evolved over time to implement faster and more efficient breeding techniques such as rapid generation advance (RGA) and genomic selection (GS). Simulation experiments support data-driven optimization of the breeding program toward the desired rate of genetic gain for key traits.

Methods

This study used stochastic simulations to compare breeding schemes with different cycle times. The objective was to assess the impact of different genomic selection strategies on medium- and long-term genetic gain. Four genomic selection schemes were simulated, representing the past approaches (5 years recycling), current schemes (3 years recycling), and two options for the future schemes (both with 2 years recycling).

Results

The 2-Year within-cohort prediction scheme showed a significant increase in genetic gain in the medium-term horizon. Specifically, it resulted in a 22%, 24%, and 27% increase over the current scheme in the zero, intermediate, and high genotype-by-environment interaction (GEI) contexts, respectively. On the other hand, the 2-Year scheme based on between-cohort prediction was more efficient in the long term, but only in the absence of GEI. Consistent with our expectations, the shortest breeding schemes showed an increase in genetic gain and faster depletion of genetic variance compared to the current scheme.

Discussion

These results suggest that higher rates of genetic gain are achievable in the breeding program by further reducing the cycle time and adjusting the target population of environments. However, more attention is needed regarding the crossing strategy to use genetic variance optimally.
Technology-facilitated sexual violence among Italian youths: validation of the technology-facilitated sexual violence victimization scale
Authors: Orsolini Laura , Yılmaz-Karaman İmran Gökçen , Selvi Kerim , Reina Salvatore , Longo Giulio , Volpe Umberto
Published: November 2024

Introduction

Technology-facilitated sexual violence (TFSV), i.e. the use of digital communication technologies for facilitating sexual violence and harassment, represents a concern due to its exponential growth, particularly among youths. Few studies investigated TFSV, mainly due to the lack of a validated assessment tool, such as the TFSV-Victimization scale (TFSV-VS). Our study aimed to investigate the TFSV phenomenon in a sample of Italian young adults (aged 18-24), through the validation of the Italian translated version of TFSV-VS.

Methods

The study consisted of two phases: 1) validation of the Italian version of TFSV-VS; b) evaluation of TFSV lifetime and during the last 12 months among Italian youths.

Results

In our sample, 78.6% and 56.1% of subjects reported a lifetime and 12-months TFSV victimization, respectively. Digital sexual (70.4% and 46.6%), gender and/or sexuality-based harassment (43% and 29.6%) are those most represented. A gendered and sexuality-based pattern in lifetime TFSV was observed, mainly observed among females (p=0.005) and sexual/gender minority (SGM)(p=0.001). Being female (p<0.001) and perceiving low social support (p = 0.030) were associated with higher subjective distress related to traumatic TFSV experiences. Being female (p<0.001), younger (p=0.006) and perceiving low social support (p=0.030) were associated with the highest psychological distress due to TFSV.

Conclusions

Italian TFSV-VS shows good psychometric properties. Our findings observed a gendered trend of TFSV, by suggesting TFSV as a phenomenon influenced by SGM belonging.
Helping Plants Fight Back Against Pests and Diseases
Authors: Mauch-Mani Brigitte , Flors Victor
Published: November 2024

Feeding the world’s growing population is a huge task, made even more challenging by pests and diseases that threaten crops. For years, pesticides and other chemical treatments have been the common solution, but these chemicals can be dangerous for the environment, and they have been used so much that many of them no longer work well. The world needs a better solution. Fortunately, plants have natural defenses against pests and diseases. Through a process called induced resistance (IR), plants can “ramp up” these defenses, making them stronger in the face of multiple threats. Researchers are exploring ways to activate IR in healthy plants to protect them against future dangers. This fascinating strategy could help to reduce pesticide use—a win-win situation for both farmers and the environment! In this article, we will explain the natural defenses of plants, describe why IR might be a great way to protect crops, and discuss the scientific challenges still to be faced on the path to sustainable farming.
Global Empirical Model of Sporadic-E Occurrence Rates
Authors: Parsch Eli V. , Franz Anthony L. , Dao Eugene V. , Wu Dong L. , Swarnalingam Nimalan , Salinas Cornelius C. J. H. , Emmons Daniel J.
Published: November 2024

Intense ionization enhancements in the Earth’s ionosphere, known as sporadic-E (Es), can degrade and severely disrupt the propagation of radio signals. Although many previous studies have analyzed the characteristics and morphologies of sporadic-E, few efforts have attempted to model global Es occurrence rates (ORs) at high time resolutions. This study develops a global empirical model of blanketing sporadic-E occurrence rates using a Karhunen–Loéve Expansion (KLE) of a global OR climatology built with Global Navigation Satellite System radio occultation (GNSS-RO) and ionosonde observations. Using an fbE ≥ threshold of 3 MHz, the model outputs a blanketing sporadic-E OR for a given geomagnetic latitude, longitude, day of year, and local solar time. The model outputs are compared to digisonde observations at four sites with varying geomagnetic latitudes, resulting in correlation coefficients ranging from 0.5 to 0.9 for monthly averaged observations and an uncertainty of 11%. Furthermore, the average uncertainty is estimated to be 12%. This Global Empirical Model of Sporadic-E Occurrence Rates (GEMSOR) is capable of providing blanketing sporadic-E OR estimates for global radio frequency (RF) operations.
Research on the Characteristics of High-Performance Textured Ceramic Materials and Their Application in Composite Rod Transducers
Authors: Tian Fenghua , Liu Yiming , Tian Wenqiang , Wang Lei , Hao Baoan , Yang Shuai
Published: November 2024

Recently, textured piezoelectric ceramics have become a hot topic in the field of piezoelectric materials. Due to their high cost-effectiveness, textured ceramics are expected to be the material of choice for the next generation of acoustic transducers. In this study, we investigated the coercive field (Ec), piezoelectric constant (d33), and dielectric constant (ε33) of textured PIN-PSN-PT ceramics under different torques, in response to the demand for the development of composite rod transducer technology for transmitting and receiving. Based on the obtained data, a wideband composite rod transducer was designed and fabricated using textured PIN-PSN-PT ceramics with high performance. Compared with conventional PZT piezoelectric ceramic transducers of the same size, the wideband composite rod transducer made with textured ceramics extends the frequency band to a lower frequency of 6.5 kHz, improves the emission performance by 2 dB, and enhances the reception performance by 2 dB. Compared with conventional PZT piezoelectric ceramics in the same frequency band, the acoustic performance is comparable, but there is a volume reduction of 59.23% and a weight reduction of 49.7%, solving the technical bottleneck of developing composite rod transducers that are miniaturized and lightweight. The research results of this study have important reference value for the engineering application of textured ceramic materials in acoustic transducers.
Work Team Effectiveness: Importance of Organizational Culture, Work Climate, Leadership, Creative Synergy, and Emotional Intelligence in University Employees
Authors: Paredes-Saavedra Maribel , Vallejos María , Huancahuire-Vega Salomón , Morales-García Wilter , Geraldo-Campos Luis
Published: November 2024

(1) Background: In university contexts, the effectiveness of work teams is vital for institutional success and the personal development of an institution’s members. Objective: Our aim is to understand the relationships between emotional intelligence, team leadership, organizational culture, work climate, and creative synergy with team effectiveness. (2) Methods: We used a cross-sectional predictive design study using structural equation modeling (SEM), in which 512 surveys of employees of a private Peruvian university were analyzed. (3) Results: Leadership and emotional intelligence were found to significantly improve team effectiveness. In addition, organizational culture, work climate, and creative synergy act as mediators in these relationships, enhancing team effectiveness. The adjusted model presented adequate incremental (x2=9452.498, gl=3391, p<0.001) and comparative (TLI=0.998, CFI=0.998, RMSEA=0.017 y SRMR=0.033) goodness-of-fit indices. (4) Conclusions: The results showed that the development of emotional competencies and leadership skills is essential to optimize the effectiveness of work teams in universities. This integrated model not only provides a solid theoretical framework for future research, but also offers practical recommendations for improving the management and performance of work teams.
Effect of Selected Determinants on the Sustainability of SMEs: Does Gender Matter?
Authors: Dvorsky Jan , Frajtova-Michalikova Katarina , Zvarikova Katarina
Published: November 2024

The paper aims to find disparities in the perception of sustainability-related criteria’s impact on the sustainability of the SMEs in the countries of the V4 based on gender. The case study was conducted on 1398 responses from owners or top managers in Visegrad Group countries (V4). The questionnaire contained 52 questions. Data collection was conducted by an external agency using the CAWI methodology. The statistical hypotheses were verified by applying structural equation modelling (SEM method). The empirical findings are important for many authors and researchers. The environmental aspect of entrepreneurship is the most important factor, having a strong positive impact on the sustainability of SMEs. This includes active involvement in environmental protection, environmental responsibility as a part of SME governance, and the perception of owners regarding the environment in firm management. Gender disparities are important in evaluating financial management, the level of digitalisation in a firm, human resource management, and business ethics, and their effect on the sustainability of SMEs.
Interactive Multiple-Model Learning Filter for Spacecraft Pursuit–Evasion Game Strategy Switch Based on Long Short-Term Memory Network
Authors: Wang Chuangge , Chen Danhe , Liao Wenhe
Published: November 2024

Aiming to address the problem of pursuit and interception for spacecraft using multiple evasion strategies, a pursuit strategy involving the use of an interactive multiple-model filter (IMM) in a pursuit–evasion game is considered, where the Evader adopts a switchable evasion strategy based on a linear quadratic method and zero-effort miss method. In this case, an improved interactive multiple-model feedback-learning filter method based on a long short-term memory network (LSTM-IMML) is proposed to estimate the Evader’s strategy mode, with the resulting estimation allowing the Pursuer to then switch its own strategy to the appropriate pursuit strategy to intercept the Evader. Also, the improved interactive multiple-model feedback learning filter can feed back the fusion estimation of the last-time state to the next-time state to improve estimation accuracy. An LSTM-based probability estimation network is designed to accurately estimate the probability of different modes. The proposed LSTM-IMML method can be used in the pursuit–evasion game when the Evader is able to switch its evasion strategy. The simulation results show that the LSTM-IMML method has better state estimation accuracy, and the mode probability estimation of the Evader is more exact and stable.
Situation Awareness Discrimination Based on Physiological Features for High-Stress Flight Tasks
Authors: Qian Chunying , Liu Shuang , Wanyan Xiaoru , Feng Chuanyan , Li Zhen , Sun Wenye , Wang Yihang
Published: November 2024

Situation awareness (SA) discrimination is significant, allowing for the pilot to maintain task performance and ensure flight safety, especially during high-stress flight tasks. Although previous research has attempted to identify and classify SA, existing SA discrimination models are predominantly binary and rely on traditional machine learning methods with limited physiological modalities. The current study aimed to construct a triple-class SA discrimination model for pilots facing high-stress tasks. To achieve this, a flight simulation experiment under typical high-stress tasks was carried out and deep learning algorithms (multilayer perceptron (MLP) and the attention mechanism) were utilized. Specifically, eye-tracking (ET), heart rate variability (HRV), and electroencephalograph (EEG) modalities were chosen as the model’s input features. Comparing the unimodal models, the results indicate that EEG modality surpasses ET and HRV modalities, and the attention mechanism structure has advantageous implications for processing the EEG modalities. The most superior model fused the three modalities at the decision level, with two MLP backbones and an attention mechanism backbone, achieving an accuracy of 83.41% and proving that the model performance would benefit from multimodal fusion. Thus, the current research established a triple-class SA discrimination model for pilots, laying the foundation for the real-time evaluation of SA under high-stress aerial operating conditions and providing a reference for intelligent cockpit design and dynamic human–machine function allocation.
The Influence of Ice Accretion on the Thermodynamic Performance of a Scientific Balloon: A Simulation Study
Authors: Liu Qiang , He Lan , Yang Yanchu , Zhao Kaibin , Li Tao , Zhu Rongchen , Wang Yanqing
Published: November 2024

A scientific balloon is the ideal platform for carrying out long-duration missions for scientific research in the stratosphere. However, when a scientific balloon ascends through icy clouds and reaches supercooled droplets, there is a risk of ice accretion on the balloon’s surface. Ice accretion on the balloon can threaten flight safety and the accomplishment of missions and can even result in disastrous accidents. A comprehensive simulation platform was developed to simulate the influence of ice accretion on the thermodynamic performance of a scientific balloon to provide quantitative data support for balloon design and flight operations. The simulation platform consisted of two parts: one based on ANSYS software to solve the accretion model and the other a program developed with MATLAB software to solve the thermodynamic model. The results suggest that, in certain cloud environments, there is a risk of ice accretion on a balloon’s surface; the extra ice mass added to the balloon may prevent it from ascending through icy clouds and instead keep it floating at the base of these clouds.
Winter Wheat Yield Estimation by Fusing CNN–MALSTM Deep Learning with Remote Sensing Indices
Authors: Li Changchun , Zhang Lei , Wu Xifang , Chai Huabin , Xiang Hengmao , Jiao Yinghua
Published: November 2024

A rapid and accurate determination of large-scale winter wheat yield is significant for food security and policy formulation. In this study, meteorological data and enhanced vegetation index (EVI) were used to estimate the winter wheat yield in Henan Province, China, by constructing a deep learning model. The deep learning model combines CNN feature extraction and makes full use of the sequence data processing capability of the LSTM and a multi-head attention mechanism to develop a novel CNN–MALSTM estimation model, which can capture the information of input sequences in different feature subspaces to enhance the expressiveness of the model. A CNN–LSTM baseline model was also constructed for comparison. Compared with the baseline model (R2 = 0.75, RMSE = 646.53 kg/ha, and MAPE = 8.82%), the proposed CNN–MALSTM model (R2 = 0.79, RMSE = 576.01 kg/ha, MAPE = 7.29%) could more accurately estimate the yield. Based on the cross-validation with one year of left-out data and the input of the fertility period by fertility period to explore the sensitivity of the model to data from different fertility periods to the final yield, an annual yield distribution map of Henan Province was constructed. Through cross-validation, the stability of the model in different years was assessed. The results showed that the model could obtain the best prediction of the yield approximately 20 days in advance. In terms of the spatial distribution of the yield in Henan Province on a yearly basis, the estimated yield showed an overall uptrend from west to east, consistent with the trend in the statistical yearbook of the yield for Henan Province. Thus, it can be concluded that the proposed CNN–MALSTM model can provide stable yield estimation results.
Assessing Consumer Interest in Sustainable and Ethically Certified Tropical Fruits in the Central and Eastern European Region
Authors: Hejkrlik Jiri , Rondevaldova Johana , Chaloupkova Petra
Published: November 2024

Tropical fruit consumption has increased globally, with 95% of production in low- and middle-income countries, often with a questionable social and environmental impact. This study explores the potential of sustainable tropical fruit consumption in Central and Eastern Europe. The researchers surveyed 2266 Czech respondents on their attitudes toward tropical fruits and the role of ethical certification in their purchasing decisions. Using a structural equation model (SEM), the study identified the factors influencing consumers’ decisions to buy Fairtrade fruit, focusing on awareness of related global issues, including their environmental and economic impacts. The findings indicate that despite the increasing supply and consumer awareness of tropical fruits in the Czech market, most respondents preferred traditional tropical fruits like bananas (99%), pineapples (94%), mangoes (78%), and avocados (65%). The study found that 42% of respondents were familiar with Fairtrade and that 55% intended to buy it, but that consumers often purchased it unintentionally due to retailers’ marketing strategies. The results of the SEM showed that consumers’ ethical shopping preferences and environmental awareness significantly contributed to Fairtrade purchasing behaviour, whereas economic and global challenges did not have a substantial impact. Therefore, for the long-term sustainability of the ethical tropical fruit sector, greater consumer education on the social and economic aspects of ethical products is needed.
Identification and Correlation Analysis of Key Clostridia and LAB Species in Alfalfa Silages Prepared with Different Cultivars and Additives
Authors: Zheng Mingli , Li Qin , Mao Peichun , Tian Xiaoxia , Guo Yuxia , Meng Lin
Published: November 2024

Clostridial fermentation is the determining process causing the spoilage of direct-cut alfalfa silage, and the application of lactic acid bacteria (LAB) inoculant is considered as the most promising technology for inhibiting clostridial fermentation. In order to screen target-based LAB strains, identification and correlation analysis of key Clostridia and LAB species in alfalfa silage were conducted in this study. Three alfalfa cultivars (Sanditi, SD; Celsius, CE; SW5909, SW) were harvested at the early bloom stage and ensiled without (CK) or with LAB inoculant (LB) and sucrose (SC) for 60 d. Single-molecule real-time sequencing was used to identify dominant Clostridia and LAB species, and LAB with significant inhibitory effects on dominant Clostridia was screened via correlation network analysis. The results showed that silages CK and LB encountered severe clostridial fermentation as indicated by large amounts of butyric acid (BA) and ammoniacal nitrogen (NH3-N) production. Compared to silages CK and LB, SC treatment decreased (p < 0.05) BA and NH3-N concentrations, as well as decreasing (p < 0.05) the bacterial community indexes of Shannon and Chao1. Lactiplantibacillus pentosus was the first dominant LAB in silage CK of alfalfa SD and CE. The first dominant LAB in silage LB was also identified as L. pentosus, rather than Lentilactobacillus buchneri and Lactiplantibacillus plantarum in the used inoculant. L. buchneri became more abundant in silage SC of alfalfa SD and CE, accounting for the high fermentation quality of these silages. Clostridium tyrobutyricum, Clostridium luticellarii, Garciella sp._GK3, Clostridium sporogenes, Clostridium perfringens, and Clostridium sp._BTY5 were the most dominant Clostridia species in alfalfa silage. Furthermore, Enterococcus faecalis, L. buchneri, and L. pentosus exhibited significant (p < 0.05) inhibitory effects on C. tyrobutyricum, C. luticellarii, and Garciella sp._GK3, respectively, which were the top three Clostridia species associated with clostridial fermentation. In conclusion, E. faecalis, L. buchneri, and L. pentosus were screened and can be used as potential LAB inoculants for the targeted inhibition of clostridial fermentation.
YOLOv8-GABNet: An Enhanced Lightweight Network for the High-Precision Recognition of Citrus Diseases and Nutrient Deficiencies
Authors: Dai Qiufang , Xiao Yungao , Lv Shilei , Song Shuran , Xue Xiuyun , Liang Shiyao , Huang Ying , Li Zhen
Published: November 2024

Existing deep learning models for detecting citrus diseases and nutritional deficiencies grapple with issues related to recognition accuracy, complex backgrounds, occlusions, and the need for lightweight architecture. In response, we developed an improved YOLOv8-GABNet model designed specifically for citrus disease and nutritional deficiency detection, which effectively addresses these challenges. This model incorporates several key enhancements: A lightweight ADown subsampled convolutional block is utilized to reduce both the model’s parameter count and its computational demands, replacing the traditional convolutional module. Additionally, a weighted Bidirectional Feature Pyramid Network (BiFPN) supersedes the original feature fusion network, enhancing the model’s ability to manage complex backgrounds and achieve multiscale feature extraction and integration. Furthermore, we introduced important features through the Global to Local Spatial Aggregation module (GLSA), focusing on crucial image details to enhance both the accuracy and robustness of the model. This study processed the collected images, resulting in a dataset of 1102 images. Using LabelImg, bounding boxes were applied to annotate leaves affected by diseases. The dataset was constructed to include three types of citrus diseases—anthracnose, canker, and yellow vein disease—as well as two types of nutritional deficiencies, namely magnesium deficiency and manganese deficiency. This dataset was expanded to 9918 images through data augmentation and was used for experimental validation. The results show that, compared to the original YOLOv8, our YOLOv8-GABNet model reduces the parameter count by 43.6% and increases the mean Average Precision (mAP50) by 4.3%. Moreover, the model size was reduced from 50.1 MB to 30.2 MB, facilitating deployment on mobile devices. When compared with mainstream models like YOLOv5s, Faster R-CNN, SSD, YOLOv9t, and YOLOv10n, the YOLOv8-GABNet model demonstrates superior performance in terms of size and accuracy, offering an optimal balance between performance, size, and speed. This study confirms that the model effectively identifies the common diseases and nutritional deficiencies of citrus from Conghua’s “Citrus Planet”. Future deployment to mobile devices will provide farmers with instant and precise support.
Harvesting Light: The Interrelation of Spectrum, Plant Density, Secondary Metabolites, and Cannabis sativa L. Yield
Authors: Reichel Philipp , Munz Sebastian , Hartung Jens , Graeff-Hönninger Simone
Published: November 2024

The approaching legalisation and associated increasing demand for medicinal and recreational Cannabis sativa L. will lead to a growing relevance for lighting systems designed for Cannabis sativa L. The interplay between plant density, light spectrum, light distribution, yield, and secondary metabolite distribution within the plant has not yet been studied. To fill this knowledge gap, a CBD-dominant Cannabis sativa L. strain was grown in a greenhouse experiment with two plant densities (2.66 and 12 plants −1 m−2) under two different light spectra. The chosen light spectra were two LED fixtures, Solray385 (SOL) and AP67, with an R: FR ratio of 12.9 and 3.7, respectively. The results indicated that light-induced effects on individual plants can be transferred to the plant stock. A low R: FR ratio induced a 16% increase in dry flower yield in the last ten days of flowering, while a change in the light spectrum could increase the potential maximum plant density per square metre. The two spectra did not affect (CBD + CBDA) yield, as a lower flower yield compensated for a higher concentration. CBDA concentration was not significantly affected by plant density. In contrast, the higher density led to an increased total cannabidiol concentration (CBD + CBDA) and altered the distribution of terpenes. Here, the light distribution over the plant stock is particularly decisive, as a more homogenous illumination led to an increased terpene concentration of up to 41%. A Photon Conversion Efficacy (PCE) of 0.05 g mol−1 under SOL and 0.06 g mol−1 under AP67 was achieved. Plants in the centre under the highest light intensity of 1200 PAR showed up to 48% reduced efficacy. These results strongly suggest that light intensity needs to be fine-tuned to the cultivation system to prevent a reduction in efficacy, resulting in yield and quality losses.
Recognition of Heat-Damaged Corn Seeds Based on Fusion of Laser Ultrasonic Signal and Infrared Image Features
Authors: Lu Tao , Wang Zihua , Zhao Zhongyi , Zhao Zhike
Published: November 2024

Corn is widely cultivated on a global scale. However, high temperatures during storage and transportation can lead to thermal damage to the kernels, negatively impacting their quality. Traditional methods for identifying heat-damaged grains primarily rely on manual inspection, which is characterized by low efficiency and accuracy. This study proposes a novel identification method that integrates laser ultrasonic signals with infrared image texture features. A pulsed laser stimulates the seeds to generate laser ultrasonic signals, while an infrared camera captures infrared images of the seeds. We extract time-domain, frequency-domain, and Hilbert-domain features from the laser ultrasonic signals, in addition to texture features from the infrared images. These features are combined using Canonical Correlation Analysis (CCA). Subsequently, the fused features are classified using a Backpropagation (BP) neural network, Support Vector Machine (SVM), and Particle Swarm Optimization–Support Vector Machine (PSO–SVM). The results indicate that the recognition rate achieved with the fused ‘signal-image’ features reaches 99.17%, providing a novel approach for detecting heat-damaged corn seeds.
Estimation of Canopy Water Content by Integrating Hyperspectral and Thermal Imagery in Winter Wheat Fields
Authors: Gao Chenkai , Liu Shuimiao , Wu Pengnian , Wang Yanli , Wu Ke , Li Lingyun , Wang Jinghui , Liu Shilong , Gao Peimeng , Zhao Zhiheng , Shao Jing , Yu Haolin , Guan Xiaokang , Wang Tongchao , Wen Pengfei
Published: November 2024

Rapid and accurate estimation of canopy water content (CWC) is important for agricultural water management and food security. Due to the complexity of dynamic changes in water transport during plant growth, estimation of CWC using a single sensor often leads to high uncertainty in the results. Multi-sensor data fusion is one of the solutions to this problem, but suitable spectral preprocessing methods and data fusion methods still need further research. The objectives of this study were to characterize the performance of two varieties at different growth stages under five water stress conditions and screen hyperspectral sensitive spectral bands by using continuous wavelet transform (CWT) and a successive projection algorithm (SPA). Ultimately, the CWC prediction model of winter wheat hyperspectral characteristic bands and thermal imaging information fusion was created using the GRA algorithm. The results showed that canopy temperature parameters and spectral parameters responded significantly to water deficits in winter wheat. Using the CWT-SPA method, a total of 285 hyperspectral feature bands with wavelet decomposition scales ranging from one to eight were selected. The sensitive bands were mainly distributed in the following ranges: 545–561, 746–1348, 1561–1810, and 2122–2430 nm. The GRA algorithm has good multi-source data model fusion capability, and its constructed prediction model based on hyperspectral and thermal image fusion has high accuracy on the canopy water content in winter wheat (R2 = 0.930, RMSE = 5.44%, nRMSE = 7.94%). Compared to the single-feature spectral model (R2 = 0.864, RMSE = 5.92%, nRMSE = 8.63%) and thermal image CWC prediction model (R2 = 0.813, RMSE = 7.22%, nRMSE = 10.49%), the model prediction accuracy based on the GRA algorithm is increased by 7.64% and 13.69%, respectively.