Copy number variation (CNV) is a form of structural variation in the human genome that provides medical insight into complex human diseases; while whole-genome sequencing is becoming more affordable, whole-exome sequencing (WES) remains an important tool in clinical diagnostics. Because of its discontinuous nature and unique characteristics of sparse target-enrichment-based WES data, the analysis and detection of CNV peaks remain difficult tasks. The Savitzky–Golay (SG) smoothing is well known as a fast and efficient smoothing method. However, no study has documented the use of this technique for CNV peak detection. It is well known that the effectiveness of the classical SG filter depends on the proper selection of the window length and polynomial degree, which should correspond with the scale of the peak because, in the case of peaks with a high rate of change, the effectiveness of the filter could be restricted. Based on the Savitzky–Golay algorithm, this paper introduces a novel adaptive method to smooth irregular peak distributions. The proposed method ensures high-precision noise reduction by dynamically modifying the results of the prior smoothing to automatically adjust parameters. Our method offers an additional feature extraction technique based on density and Euclidean distance. In comparison to classical Savitzky–Golay filtering and other peer filtering methods, the performance evaluation demonstrates that adaptive Savitzky–Golay filtering performs better. According to experimental results, our method effectively detects CNV peaks across all genomic segments for both short and long tags, with minimal peak height fidelity values (i.e., low estimation bias). As a result, we clearly demonstrate how well the adaptive Savitzky–Golay filtering method works and how its use in the detection of CNV peaks can complement the existing techniques used in CNV peak analysis.

Farmers’ satisfaction with new superior varieties (NSVs) is a critical strategy for boosting their adoption. Out of 48 national NSV hybrids produced, only three, including Nasa-29, JH-37, and Bima-10, have been widely distributed at the farmer level. However, no studies have been carried out to establish farmers’ satisfaction of any of the three hybrid maize varieties. As a result, the main aim of this study is to establish farmers’ satisfaction of three-hybrid maize. The survey was conducted in three South Sulawesi maize production districts: Bone, Gowa, and North Luwu, representing the east, west, and transitional zones. A total of 150 farmers from three districts were then deliberately chosen as respondents. Variables such as seed availability, cultivation technology, post-harvest, and product marketing were monitored during our assessment. The data were then analyzed using the importance performance analysis (IPA) method. The findings indicate that seed quality and quantity, disease resistance, low yield, and productivity are the variables that require intervention to improve farmer satisfaction with the superiority of the national hybrid maize NSV. The low price of seeds, the ability to grow at 15 days, the small size of the cob, ease of harvest, and the accessibility of marketing the product at the best price were then deemed adequate variables.

Objective: To perform molecular docking and pharmacokinetic prediction of momordicoside F2, beta-sitosterol, and cis-N-feruloyltyramine herbal derivatives as maltase-glucoamylase (MGAM) inhibitors for the treatment of diabetes.Methods: The herbal derivatives and standard drug miglitol were docked differently onto MGAM receptor using AutoDock Vina software. In addition, Lipinski’s rule, drug-likeness, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties were analyzed using Molinspiration, ADMET structure–activity relationship, and prediction of activity spectra for substances online tools.Results: Docking studies reveal that momordicoside F2, beta-sitosterol, and cis-N-feruloyltyramine derivatives have high binding affinity to the MGAM receptor (−7.8, −6.8, and −6.5 Kcal/Mol, respectively) as compared to standard drug miglitol (−5.3 Kcal/Mol). In addition, all the herbal derivatives indicate good bioavailability (topological polar surface area <140 Ȧ and Nrot <10) without toxicity or mutagenic effects.Conclusion: The molecular docking and pharmacokinetic information of herbal derivatives obtained in this study can be utilized to develop novel MGAM inhibitors having antidiabetic potential with better pharmacokinetic and pharmacodynamics profile.

Detection of complexes, or groups of functionally related proteins, is an importantchallenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient whenapplied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 nonoverlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL outperform AP, MCODE and SCPS algorithms withhigh clustering coefficient (0.751) network density and modularity index (0.630).This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks.

In this paper, we provide a simple forward-looking approach to compare rating methods with respect to their stability over time. Given a rating vector of entities involved in the comparison and a ranking indicated by the rating, the stability of the methods is measured by the change in rating vector and ranks of the entities over time from a forward-looking perspective. We investigate various linear algebraic rating methods and use the Euclidean distance and Kendall tau rank correlation to measure their stability in rating and ranking, respectively. The investigations are based on both rolling and expanding window approaches. We apply the methodology to sports as a widely known ranking and rating environment. The results suggest that PageRank and Massey rating methods provide better rating and ranking stability than simple methods, such as winning percentage, and more advanced ones, such as Colley’s least square and Keener’s eigenvector-based method. Finally, a simple way to examine the potential predictive power of the rating methods is also provided.