Muhammad Asad

The frequent occurrence of localized and seasonal droughts has caused severe economic losses in maize production in South China. To promote sustainable maize production, selecting and breeding drought-tolerant varieties is vital for addressing water scarcity. Drought stress affects all aspects of crop morphological performance. In this study, the morphological performance of 285 maize inbred lines under drought stress was investigated using D-value analysis, correlation analysis, principal component analysis, cluster analysis and stepwise regression analysis. All indicators were significantly different in the regular treatment compared to the drought treatment. Specifically, survival rate, root fresh weight, root dry weight, plant dry weight, root/crown ratio, and plant fresh weight were used as indicators for drought-tolerance evaluation. Furthermore, the drought-tolerant inbred line CML323 and the drought-sensitive inbred line CB2-49-1 were screened by comprehensively evaluating D values. The drought-tolerant inbred line CML323 exhibits higher leaf relative water content, chlorophyll content, proline content, and ascorbate peroxidase and peroxidase activity while having lower malondialdehyde content, consequently demonstrating excellent drought tolerance. This study provides valuable insights into drought-tolerance indicators and reference materials for breeding maize varieties.

Artificial Intelligence and Deepfake Technologies have brought a new dimension to the generation of fake data, making it easier and faster than ever before—this fake data could include text, images, sounds, videos, etc. This has brought new challenges that require the faster development of tools and techniques to avoid fraudulent activities at pace and scale. Our focus in this research study is to empirically evaluate the use and effectiveness of deep learning models such as Convolutional Neural Networks (CNNs) and Patch-based Neural Networks in the context of successful identification of real and fake images. We chose the healthcare domain as a potential case study where the fake medical data generation approach could be used to make false insurance claims. For this purpose, we obtained publicly available skin cancer data and used recently introduced stable diffusion approaches—a more effective technique than prior approaches such as Generative Adversarial Network (GAN)—to generate fake skin cancer images. To the best of our knowledge, and based on the literature review, this is one of the few research studies that uses images generated using stable diffusion along with real image data. As part of the exploratory analysis, we analyzed histograms of fake and real images using individual color channels and averaged across training and testing datasets. The histogram analysis demonstrated a clear change by shifting the mean and overall distribution of both real and fake images (more prominent in blue and green) in the training data whereas, in the test data, both means were different from the training data, so it appears to be non-trivial to set a threshold which could give better predictive capability. We also conducted a user study to observe where the naked eye could identify any patterns for classifying real and fake images, and the accuracy of the test data was observed to be 68%. The adoption of deep learning predictive approaches (i.e., patch-based and CNN-based) has demonstrated similar accuracy (~100%) in training and validation subsets of the data, and the same was observed for the test subset with and without StratifiedKFold (k = 3). Our analysis has demonstrated that state-of-the-art exploratory and deep-learning approaches are effective enough to detect images generated from stable diffusion vs. real images.

Abstract Mountain landscapes can be fragmented due to various human activities such as tourism, road construction, urbanization, and agriculture.

BACKGROUND Dicer1 plays a crucial role in regulating the development and reproduction of insects.

School of Agriculture and Food Sustainability, The University of Queensland, Gatton Campus, Brisbane, QLD 4343, Australia

Correction to: Scientific Reports https://doi.org/10.1038/s41598-024-71689-5 , published online 05 September 2024

Clustering samples based on similarity remains a significant challenge, especially when the goal is to accurately capture the underlying data clusters of complex arbitrary shapes. Existing density-based clustering techniques are known to be best suited for capturing arbitrarily shaped clusters. However, a key limitation of these methods is the difficulty in automatically finding the optimal set of parameters adapted to dataset characteristics, which becomes even more challenging when the data contain inherent noise. In our recent work, we proposed a Differential Evolution-based DENsity CLUstEring (DE-DENCLUE) to optimise DENCLUE parameters. This study evaluates DE-DENCLUE for its robustness in finding accurate clusters in the presence of noise in the data. DE-DENCLUE performance is compared against three other density-based clustering algorithms—DPC based on weighted local density sequence and nearest neighbour assignment (DPCSA), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Variable Kernel Density Estimation–based DENCLUE (VDENCLUE)—across several datasets (i.e., synthetic and real). The study has consistently shown superior results for DE-DENCLUE compared to other models for most datasets with different noise levels. Clustering quality metrics such as the Silhouette Index (SI), Davies–Bouldin Index (DBI), Adjusted Rand Index (ARI), and Adjusted Mutual Information (AMI) consistently show superior SI, ARI, and AMI values across most datasets at different noise levels. However, in some cases regarding DBI, the DPCSA performed better. In conclusion, the proposed method offers a reliable and noise-resilient clustering solution for complex datasets.

Abstract With the expansion of the manufacturing sector, it has become crucial to incorporate sustainable production methods in order to remain competitive in the market.

School of Agriculture and Food Sustainability, The University of Queensland, Gatton, QLD 4343, Australia

IntroductionAlthough metabolic dysfunction-associated fatty liver disease (MAFLD) is becoming more common in individuals with hepatocellular carcinoma (HCC), it is still unknown how this condition relates to postoperative complications of HCC. While hepatitis B/C virus (HBV/HCV) infection and alcohol use are primary risk factors, MAFLD has emerged as a significant contributor to HCC incidence. Understanding the prognostic impact of MAFLD on HCC outcomes, particularly post-radical resection, is essential.ObjectiveThis study aims to evaluate the prognostic significance of MAFLD on postoperative outcomes in HCC patients, following radical hepatectomy, with a focus on gender-specific mortality differences.MethodologyA retrospective cohort study was conducted at Pakistan Navy Station Shifa Hospital, Bahria University Medical and Dental College, Karachi, Pakistan. Consecutive HCC patients who underwent radical resection between May 2023 and April 2024 were included. MAFLD was diagnosed based on hepatic steatosis and metabolic dysfunction criteria. Data on demographics, clinical features, and outcomes were collected from electronic medical records. The primary outcome was overall survival (OS), and the secondary outcomes included recurrence-free survival (RFS). Statistical analyses involved multivariate Cox regression and Kaplan-Meier survival curves using IBM SPSS Statistics for Windows, Version 27 (Released 2020; IBM Corp., Armonk, NY, USA).ResultsMAFLD patients exhibited higher median body mass index (BMI) (25.3 kg/m² vs. 23.5 kg/m², p 0.05). Notably, MAFLD significantly increased mortality in female HCC patients, but not in males. Significant predictors of progression included Child-Pugh grade B, tumour size, and microvascular invasion.ConclusionMAFLD does not significantly impact OS or RFS following radical resection of HCC. However, MAFLD is associated with increased mortality in female patients, highlighting the need for gender-specific monitoring and management strategies in MAFLD-related HCC cases. Further large-scale studies are required to confirm these findings and elucidate the underlying mechanisms.