bluuebunny/crossref_metadata_2025_split huggingface dataset:
Contrasting metabolic responses of two chickpea varieties to root infection by Phytophthora are influenced by arbuscular mycorrhizal inoculation
Authors: Weinberger Natascha V. , Powell Jeff R. , Mikhael Meena , Plett Krista L. , Plett Jonathan M. 
Published: March 2024

Abstract

Most terrestrial plants, including important crop plants like chickpea (Cicer arietinum), benefit from mutualistic symbiosis with arbuscular mycorrhizal (AM) fungi due to enhanced nutrition and improved immune responses. Although this latter phenomenon, so-called Mycorrhiza Induced Resistance (MIR), is well-reported and metabolic adjustments have been observed, the diversity of mechanisms by which AM fungi prime disease resistance are still poorly understood. Using two chickpea varieties (Sonali and Kyabra) we investigated how differential host genotypes may impact AM-mediated defence responses against a root rot pathogen, Phytophthora medicaginis. Inoculation with Rhizophagus irregularis significantly inhibited disease-related biomass losses in Kyabra, while no impact on plant biomass was observed in Sonali. Using root metabolomic profiling, we detected 143 metabolic features which exhibited significant variation between at least two experimental conditions. Most noteable, the two chickpea varieties tested largely showed opposing metabolomic responses to microbial exposure. Furthermore, co-innoculation of pathogenic and beneficial microbes led to a core set of differentially expressed metabolomic features in Kyabra and Sonali. These results demonstrate contrasting levels of metabolomic responses to microbial inoculation and reveal potential molecular features involved in MIR in a globally relevant crop.
Salidroside alleviates inflammatory reaction and sciatic nerve injury by inhibiting AKT/NF-κB pathway
Authors: Chen Linyan , Meng Yun , Shao Xiaoyu , Song Qimin , Che Fengyuan 
Published: March 2024

Abstract
Background
Peripheral nerve injury is a common traumatic nerve injury disease with poor prognosis. Salidroside is a natural compound extracted from the plant Rhodiola, which has been proved to have neuroprotective effect. This experiment studied the therapeutic effect of salidroside on peripheral nerve injury.
Methods
Establishment of sciatic nerve injury model in Sprague-Dawley adult rats by arterial compression. Bsso-Beattie-Bresnahan(BBB score, F-wave and Tce-MEP were used to compare the motor and nerve conduction functions of rats. Histological differences were observed by Histological assessments and transmission electron microscopy. The rat model of sciatic nerve injury was selected and salidroside injection was injected for 14 consecutive days. The effects of salidroside on motor and nerve conduction function of SNI rats were evaluated by behavioral and electrophysiological monitoring. Histological changes were observed by HE staining and transmission electron microscope. Establishment of Schwann cell inflammation model. The expression of ROS was detected. The expressions of inflammatory factors and nerve growth factors in sciatic nerve tissue and RSC96 cells of rats were detected by QRT-PCR and western blotting.
Results
The compression of arteries causes sciatic nerve injury in different degrees. The motor and nerve conduction function of rats decreased, myelin sheath and axon were damaged, and the level of inflammation increased. Salidroside improved the nerve function and morphology of rats, reduced neuroinflammation and promoted the expression of nerve growth factor. Salidroside down-regulated the expression of inflammation in Schwann cells treated with LPS, reduced the production of ROS and promoted the secretion of nerve growth factor.
Conclusion
Sciatic nerve injury caused by arterial entrapment produces persistent neuroinflammatory reaction. Salidroside reduces the expression of inflammatory factor TNF-α through AKT/NF-κB pathway, up-regulates the expression of NGF, promotes myelin sheath growth and axon regeneration, and improves the neurological function of SNI rats.
Bile acid metabolism Modulate intestinal immunity involved in ulcerative colitis progression
Authors: Huang Hua , Yan Shuai , Guo Tianwei , Hua Qiuwen , Wang Yongtong , Ji Lijiang 
Published: March 2024

Abstract

Background: Bile acid is an important participant in intestinal immunity and inflammation, which exert their immune regulatory effects by interacting with immune cells in the intestine，many immune cells are involved in the pathogenesis of ulcerative colitis (UC), but the underlying mechanisms between Bile acid and immunity are unknown.
Object: The aim of this study was to investigate the relationship between Bile acid metabolism (BCM) and inflammation in colonic tissues and to identify potential therapeutic targets for modulating immune responses.
Methods: A total of 2,318 differentially expressed genes (DEGs) and 35 BCM-associated DEGs were obtained, and the results of gene ontology analysis showed that DEGs were significantly enriched in the positive regulation of NK cell and T cell activation. Fifteen types of immune cells were identified in inflamed colon tissues.
Results: Five BCM-associated DEGs (APOA1, AMACR, PEX19, CH25H, and AQP9) were highly correlated with immune-associated DEGs and all were significantly up/down-regulated in immune cells. And we further found at the cellular level that AQP9 gene expression may be regulated by IFN-γ- JAK signaling pathway promotes CD8+T cell activation.
Conclusion: This study contributes to the understanding of the pathogenesis and the mechanism of BCM in UC immune cells.
Predictors of flares in recent-onset psoriatic arthritis. Results of a multivariable model based on machine learning
Authors: Queiro Rubén , Seoane-Mato Daniel , Agirregoikoa Eva Galindez , Cañete Juan D. , Gratacós Jordi , Juanola Xavier , Fiter Jordi , Marcos Ana González , Laiz Ana 
Published: March 2024

Abstract
Background
Predicting the occurrence of a flare using tools and information that are readily available in daily clinical practice would provide added value in disease management. Scarcely any studies address this issue. The aim was to identify patient- and disease-related characteristics predicting flares in recent-onset PsA.
Methods
We performed a multicenter observational prospective study (2-year follow-up, regular annual visits). The study population comprised patients aged ≥ 18 years, fullfilling the CASPAR criteria and less than 2 years since the onset of symptoms. Flares were defined as inflammatory episodes affecting the axial skeleton and/or peripheral joints (joints, digits or entheses), diagnosed by a rheumatologist. The dataset contained data for the independent variables from the baseline visit and from follow-up visit number 1. These were matched with the outcome measures from follow-up visits 1 and 2, respectively. We trained a logistic regression model and random forest–type and XGBoost machine learning algorithms to analyze the association between the outcome measure and the variables selected in the bivariate analysis. A k-fold cross-validation with k = 5 was performed.
Results
At the first follow-up visit, 37.6% of the patients who attended the clinic had experienced flares since the baseline visit. Of those who attended the second visit, 27.4% had experienced flares since the first visit. The number of observations for the multivariate analysis was 295.The variables predicting flares between visits were PsAID, number of digits with onychopathy, age-adjusted Charlson comorbidity index and level of physical activity. The mean values of the measures of validity of the machine learning algorithms were all high, especially sensitivity (95.71%. 95% CI: 79.84–100.00).
Conclusions
These findings provide guidance not only on general measures (regular physical activity), but also on therapy (drugs addressing nail disease).
Enhancing Class Diagram Dynamics: A Natural Language Approach with ChatGPT
Authors: Djaber ROUABHIA 
Published: March 2024

Abstract
The present research introduces an innovative methodology, assisted by artificial intelligence, aimed at enhancing the dynamics of class diagrams within the field of software engineering. This methodology employs advanced language models, such as ChatGPT, to address the limitations inherent in traditional manual methods, particularly when dealing with complex systems. The approach is iterative, analyzing natural language use cases in detail and extracting key insights through the language understanding capabilities of ChatGPT. These insights are then integrated into a UML class diagram, resulting in demonstrable improvements in both accuracy and completeness. The updated diagram, supplemented with explicit methods derived from use cases, offers a more precise delineation of functional responsibilities and improved class relationships. This leads to a more comprehensive understanding of system interactions. This methodology, which is both versatile and efficient, aligns with UML best practices and holds significant value for Agile development. While further evaluation is necessary, preliminary findings suggest that AI-driven approaches hold considerable potential for enhancing the dynamics of class diagrams and advancing software development practices.
Tracking The Molecular Dynamics of Lineage Switch Under CD19 CAR-T Treatment in Non-KMT2A Rearranged B-ALL Patients
Authors: Wang Jianxiang , Qiu Shaowei , Mei Yihan , Gu Runxia , Liu Yu , Chen Manling , Xing Haiyan , Tang Kejing , Tian Zheng , Rao Qing , Yang Donglin , Pang Aiming , Wei Shuning , Jia Yujiao , Wang Huijun , Feng Sizhou , Wei Hui , Zhu Ping , Wang Min , Wang Ying , Liu Wenbing 
Published: March 2024

Abstract

Lineage switch in B-acute lymphoblastic leukemia (B-ALL) patients is a rare event during CD19 chimeric antigen receptor (CAR)-T treatment. Some studies have reported KMT2A rearrangements (KMT2A-r) as a risk factor in lineage switch, but the underlying mechanism of non-KMT2A-r cases remains unclear. Here, we described two young adult B-ALL lineage switch cases without KMT2A-r. Our analysis revealed lineage-specific transcription factors and surface markers related alterations, while major gene mutations remain unchanged. By reconstructing developmental trajectories, B-progenitor-like blasts were found to be reprogrammed into myeloid blasts after CAR-T treatment. Interestingly, we observed the increase of BCOR/BCORL1 truncating mutation burden at myeloid relapse in both cases. By retrospective analysis, we found that BCOR/BCORL1 gene mutated patients possessed myeloid-related features, indicating it as a potential risk factor for lineage switch. In summary, we established a study paradigm about lineage switch by single-cell technologies, which may be applied to clinical practice.
Feeding ecology of two filter feeding sharks in the northwestern Pacific Ocean
Authors: YU CHIJU , JOUNG SHOOUJENG , HSU HUAHSUN , LIU KWANGMING , YAMAGUCHI ATSUKO 
Published: March 2024

Abstract

This study examines the feeding information of two filtering shark species, the megamouth shark (Megachasma pelagios) and the whale shark (Rhincodon typus). Specimens were obtained through the mandatory catch and reporting system in Taiwan, providing stomach composition of M. pelagios and tissue samples for two species. A total of 19 stomachs were examined, with five found to be high digested or empty. Analysis of the remaining 14 stomachs revealed that M. pelagios primarily consumes zooplanktonic prey, including krill (Euphausiacea), jellyfish (Medusozoa), shrimp larvae (Dendrobranchiata), squat lobsters (Anomura), and crab larvae (Brachyura), indicating a low trophic level diet. Stable isotope analysis (SIA) of 91 M. pelagios and 90 R. typus specimens inferred distinct feeding strategies and diet compositions between the two species. M. pelagios is characterized as an inactive feeder employing engulfment feeding, whereas R. typus is an active suction-filter feeder capable of preying on small fish, squid, and displaying ontogenetic changes in diet. Both species are believed born in less productive waters and transition to more nutrient-rich habitats as they mature. Ontogenetic dietary shifts were observed in R. typus, attributed to enhanced swimming abilities and specialized gill-rakers facilitating more active suction feeding. Conversely, no significant diet shift during growth was noted in M. pelagios; however, differing stable isotope signatures between male and female specimens suggest sexual segregation. Further research involving individuals of various size classes for both M. pelagios and R. typus is needed to corroborate these findings.
IABHE: Identity and Attribute-based Honey Encryption Algorithm and Deep Learning-based Key Generation for Securing Big Data in Hadoop Framework
Authors: Siyal Reshma , Long Jun 
Published: March 2024

Abstract
Nowadays, Hadoop is considered promising to store and reliably process big data in the cloud computing platform, and it also provides low-cost and flexible services for large amounts of data. However, the possibility of malicious attacks on stored or processed data has increased due to the absence of inherent security mechanisms in Hadoop. In this approach, an Identity and Attribute-Based Honey Encryption Algorithm (IABHE) is developed for the encryption of data to offer security to the big data in the Hadoop framework. Here, the system model of the cloud is considered initially. Then, acquired input big data is applied to the MapReduce framework, where data encryption is performed by IABHE, which incorporates identity and attribute-based encryption with honey encryption. Moreover, a deep learning model, PyramidNet is utilized for the generation of secret keys used in encryption. Moreover, the encrypted data obtained in the mapper phase is aggregated by performing polynomial interpolation in the reducer phase. Finally, encrypted data is securely saved in the cloud. Here, the efficiency of IABHE is evaluated utilizing various performance metrics and it attained better values of encryption time at 17.756sec, decryption time at 8.767sec, and key complexity at 0.368.
A Numerical Study on the effect of Rotating Cylinder as Active Flow Control Device for NACA 23015 Airfoil
Authors: Ishrak Md. Shah , Akhter Syeda Mohsina , Shuvo Md. Adnan Nur Bhuiyan 
Published: March 2024

Abstract
Enhancing aerodynamic efficiency through the use of active and passive flow control methods with airfoils is one of the currently researched areas in the aerospace sector. This paper investigates the lift and drag characteristics of a NACA 23015 airfoil at low-velocity ratios using a rotating cylinder as a flow control device. To conduct this study ANSYS Fluent was utilized for simulation. The analysis involved placing the rotating cylinder at locations along the chord length of both the lower surfaces of the airfoil. Additionally, different cylinder diameters were tested for each scenario to assess performance. This study was carried out at Reynolds number Re = 1.5 x 105 corresponding to free stream velocity 15 m/s at seven Angle of Attack (0°,5°,10°,15°,20°,25° and 30°) The findings revealed that incorporating rotating cylinders can significantly enhance the performance of the airfoil. Moreover, it was noted that placing the cylinder at the lower surface improves the aerodynamic performance more compared to that when placed at the leading edge or upper surface at lower angles of attack.
The value of different machine learning radiomics based on DCE-MRI in predicting axillary lymph node status of breast cancer
Authors: Wang Han , Gong Li 
Published: March 2024

Abstract

Purpose The objective of this research was to investigate the significance of different machine learning models based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) combined with clinicopathologic and radiological analysis in predicting axillary lymph node metastasis (ALNM) of primary breast cancer (BC).
Methods The clinical data of 605 patients with BC underwent preoperative DCE-MRI examination from The Cancer Imaging Archive (TCIA) publicly available dataset were retrospectively analyzed and casually seperated into training and test cohort at a ratio of 8:2. After dimensionality reduction and selection, a prediction model was established using machine learning algorithms. Clinicopathologic characteristics were analyzed using univariate and multivariate logistic regression to identify variables for constructing clinical models. Receiver operating characteristic (ROC) curves analysis was used to screen out the best radiomics and clinical models, and a combined model was established. Decision curve analysis (DCA) was used to assess the clinical significance of the combined model.
Results The combined model exhibited superior diagnostic predictive capability in determining the presence or absence of ALNM. The training and test cohorts yielded area under the curve (AUC) values of 0.890 and 0.854, respectively.Additionally, a distinct combined model was developed to distinguish between the N1 group (1-3 ALNM) and the N2-3 group (≥4 ALNM), demonstrating promising efficacy with AUC values of 0.973 and 0.835 in the training and test groups, respectively. Furthermore, the integrated model discriminated between N0, N1, and N2-3, yielding a micro AUC of 0.861 and a macro AUC of 0.812.
Conclusion The integration of radiomics and clinicopathologic characteristics demonstrated outstanding predictive capability for ALNM, potentially offering a non-invasive and effective approach for clinical decision-making.
Cascading fault prevention strategy based on economic dispatch of AC-DC systems
Authors: DENG Huiqiong , XIE Pan , HUANG Hongyu , SHEN Junfu , PAN Dengwei 
Published: March 2024

Abstract

To achieve effective prevention of cascading faults in the economic dispatch of AC and DC systems, this paper proposes a preventive control strategy considering both safety and economy, and constructs a bi-level nonlinear optimization model. Initially, a mathematical criterion for discriminating cascading tripping is formulated based on the action characteristics of relay protection. Subsequently, a static safety margin index, using unit active output as the control variable, is established to measure the safety level of the AC/DC system. Then, an economic index is derived by integrating network loss, unit power supply cost, and environmental management cost. The inner-layer model aims to solve the static safety margin, while the outer-layer model utilizes the safety margin solved by the inner layer as a constraint to minimize the comprehensive cost of the grid.Furthermore, a two-layer particle swarm algorithm is proposed to solve the bi-level preventive control model. Additionally, a Jacobi matrix preprocessing method combined with sparse storage technology is employed to enhance computational efficiency during the power flow calculation process. Comparative analysis with other algorithms is conducted to demonstrate the effectiveness of the proposed algorithm. Finally, the proposed algorithm and model are simulated and analyzed using the improved IEEE39 node system to verify the feasibility of the proposed strategy.
Integrated machine learning reveals the role of tryptophan metabolism in clear cell renal cell carcinoma and its association with patient prognosis
Authors: Li Fan , Hu Haiyi , Xu Zhehao , Ding Lifeng , Lu Zeyi , Mao Xudong , Wang Ruyue , Luo Wenqin , Lin Yudong , Li Yang , Chen Xianjiong , Zhu Ziwei , Lu Yi , Zhou Chenghao , Wang Mingchao , Xia Liqun , Li Gonghui , Gao Lei 
Published: March 2024

Abstract

Background
The application of precision oncology in clinical settings is currently constrained by limitations in tools for granular patient stratification and personalized treatment approaches. Dysregulated tryptophan metabolism has been identified as a key player in tumor development, including immune suppression, proliferation, metastasis, and metabolic reprogramming. Nonetheless, its exact function in clear cell renal cell carcinoma (ccRCC) is yet to be fully understood, and there is a notable absence of predictive models or signatures derived from it.
Methods
The role of tryptophan metabolism on tumor cells was investigated using single-cell RNA sequencing data. Genes associated with tryptophan metabolism were identified across both single-cell and bulk cell dimensions through the application of weighted gene co-expression network analysis (WGCNA) and its single cell data variant (hdWGCNA). A signature related to tryptophan metabolism was subsequently developed utilizing an integrated machine learning approach. This signature was examined in multi-omics data for its associations with patient clinical features, prognosis, cancer malignancy-related pathways, immune microenvironment, genomic characteristics, and responses to immunotherapy and targeted therapy. Finally, genes within the signature were validated through experiments including qRT-PCR, Western blot, CCK8 assay, and transwell assay.
Results
The dysregulated tryptophan metabolism was identified as a potential contributor to the malignant transformation of normal epithelial cells. The tryptophan metabolism-related signature (TMRS) exhibited strong predictive ability for overall survival (OS) and progression-free survival (PFS) in multiple datasets. Furthermore, elevated TMRS risk score was associated with increased tumor malignancy, significant metabolic reprogramming, an inflamed yet dysfunctional immune microenvironment, greater genomic instability, resistance to immunotherapy, and heightened sensitivity to specific targeted therapeutics. Experimental validation demonstrated differential expression of genes within the signature between RCC and adjacent normal tissues, with decreased expression of the gene DDAH1 linked to increased proliferation and metastasis of tumor cells.
Conclusion
This study explored the influence of abnormal tryptophan metabolism on clear cell renal cell carcinoma, and constructed a signature related to tryptophan metabolism that can accurately predict patient prognosis, evaluate tumor biological status, and guide patient personalized treatment, which is conducive to enabling more patients to benefit from precision oncology.