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Abstract Fingerprint database clustering and localization using k‐medoids and k‐nearest neighbour (k‐NN) algorithms respectively typically use distance‐based fingerprint similarity metrics, with their performances dependent on the type of distance metric used. This paper proposes employing a pattern‐based metric, the context similarity coefficient (CSC), for both algorithms instead of traditional distance‐based metrics. The CSC accounts for fingerprint behaviour and the non‐linear relationships among fingerprints during the similarity measurement. The performance of both algorithms with the CSC as the similarity metric is evaluated on four publicly available fingerprint databases, using position root mean square error (RMSE) and silhouette score as performance metrics. These results are compared to those of the same algorithms using five distance‐based metrics: Euclidean, square Euclidean, Manhattan, cosine, and Chebyshev distances. The k‐medoids algorithm with CSC shows moderate clustering performance compared to the five distance‐based metrics considered. However, when combined with the k‐NN algorithm also using CSC, it achieves the highest localization accuracy, with at least a 29% improvement in position RMSE across all four databases. The results indicate that while k‐medoids with CSC may not create well‐separated clusters, combining it with the k‐NN algorithm with CSC as its similarity metric significantly enhances localization accuracy compared to distance‐based metrics.

The localization accuracy of a fingerprint-based localization system is dependent on several factors, one of which is the accuracy and efficiency at which the fingerprint database is clustered. Most highly efficient and accurate clustering algorithms have high time-dependent computational complexity (CC), which tends to limit their practical applicability. A technique that has yet to be explored is the sequential hybridization of multiple low-time CC clustering algorithms to produce a single moderate-time CC clustering algorithm with high localization accuracy. As a result, this paper proposes a clustering algorithm with a moderate time CC that is based on the sequential hybridization of the closest access point (CAP) and improved k-means clustering algorithms. The performance of the proposed sequential hybrid clustering algorithm is determined and compared to the modified affinity propagation clustering (m-APC), fuzzy c-mean (FCM), and 2-CAP algorithms presented in earlier research works using four experimentally generated and publicly available fingerprint databases. The performance metrics considered for the comparisons are the position root mean square error (RMSE) and clustering time based on big O notation. The simulation results show that the proposed sequential hybrid clustering algorithm has improved localization accuracy with position RMSEs of about 54%, 77%, and 52%, respectively, higher than those of the m-APC, FCM, and 2-CAP algorithms. In terms of clustering time, it is 99% and 79% faster than the m-APC and FCM algorithms, respectively, but 90% slower than the 2-CAP algorithm. The results have shown that it is possible to develop a clustering algorithm that has a moderate clustering time with very high localization accuracy through sequential hybridization of multiple clustering algorithms that have a low clustering time with poor localization accuracy. Doi: 10.28991/ESJ-2024-08-02-02 Full Text: PDF

This research aimed to evaluate the larvicidal activity of the lower doses of commercially synthetic Bacillus thuringiensis israelensis (Vectobac 12 Aqueous Solution (12AS)) against the fourth instar larva. One hundred and fifty blood-fed female anopheles mosquitoes were collected from different resting sites from Abuja Quarters in June 2022 using an aspirator and allowed to breed until the first instar larva appeared. The larvae were monitored and fed with 10% yeast until the third instar emerged. 240 healthy third instar larvae were selected and grouped into three treatments containing sixty (60) larvae each and replicated three times. The first, second and third treatments were respectively treated with 0.84, 0.42 and 0.21ml/l of Vectobac 12AS. Each treatment has a control containing twenty (20) larvae. Larval mortality was determined using a glass rod at an interval of 15 minutes for 24 hours. ANOVA was used to statistically analysed differences in the larval mortality between the treatment and probit analysis was used to determine the lethal concentration (LC) and the lethal time (LT). Mortality of 1(6.7%) and 3(5.0%) were observed in the first treatment (0.84ml/l) after 15 and 30minute of exposure respectively. The highest mortality of 60(100%) was observed in all the treatments after 24 hours of exposure. Statistically, there was no significant difference (F=0.081, P> 0.05). 2.35 ml/l, 5.54 ml/l and 8.15 ml/l was determined to be LC50, LC90 and LC99 respectively and LT50, LT90 and LT99 were found to be, 1809.29min and 2451.34min respectively. Conclusively vectobac 12AS has demonstrated a high level of efficacy as it revealed 100% larval mortality even at a lower recommended dose. Further research should be carried out to study the impact of other biological and environmental factors on the efficacy of vectobac 12AS.

AbstractThis paper examines the role of institutional investors foreign-owned firms and government-linked companies, in improving firm performance through the channel of IT investment decision. The data sample comprises information collected from the annual reports of 231 companies listed on Bursa Malaysia, spanning the years 2010 to 2019. This study concludes, on the basis of the Generalized Method of Moments (GMM) dynamic model, which mitigates the endogeneity issue linked to the equity multiple value model, that institutional ownership moderates the relationship between IT investment and firm performance in a positive direction. The results of the study shed light on the capacity of institutional investors to allay apprehensions regarding agency problems that arise from the rent-seeking conduct of managers. The implications of this finding are both theoretical and practical, given that IT expenditures continue to constitute a substantial proportion of the capital budgets of organisations. However, several studies indicate that there is no direct relationship between firm IT investment levels and firm performance. This study contributes to the body of knowledge by examining the influence of institutional investors on IT investment decisions. It does so by integrating agency theory, which explains performance shortfalls, and corporate governance, which oversees and controls managers’ inappropriate investment choices, thereby illuminating the antecedents of IT investment decisions. The present investigation would furnish significant insights for stakeholders, investors, and the general public, thereby augmenting their understanding of the pivotal significance of institutional ownership and guaranteeing that investments in IT are adequately regulated.

Emotional stress is believed to be associated with increased tumor progression. Stress-induced epigenetic modifications can contribute to the severity of disease and poor prognosis in cancer patients. The current study aimed to investigate the expression profiles along with the prognostic significance of psychological stress-related genes in metastatic breast cancer patients, to rationalize the molecular link between emotional stress and cancer progression. We profiled the expression of selected stress-associated genes (5-HTT, NR3C1, OXTR, and FKBP5) in breast cancer including the stress evaluation of all participants using the Questionnaire on Distress in Cancer Patients–short form (QSC-R10). A survival database, the Kaplan–Meier Plotter, was used to explore the prognostic significance of these genes in breast cancer. Our results showed relatively low expressions of 5-HTT (p = 0.02) and OXTR (p = 0.0387) in metastatic breast cancer patients as compared to the non-metastatic group of patients. The expression of NR3C1 was low in tumor grade III as compared to grade II (p = 0.04). Additionally, the expression of NR3C1 was significantly higher in patients with positive estrogen receptor status. However, no significant difference was found regarding FKBP5 expression in breast cancer. The results suggest a potential implication of these genes in breast cancer pathology and prognosis. [ABSTRACT FROM AUTHOR]

Natural products (NPs) continue to provide a structural template for the design of novel therapeutic agents and expedite the drug discovery process. The majority of FDA-approved pharmaceuticals used in medical practice can be traced back to natural sources, and NPs play a significant role in drug development. Curcumin, one of the most well-studied chemicals among the NPs, is currently the subject of intense investigation for its biological effects, including the prevention and treatment of cancer. Cancer has overtaken all other causes of death in the world today, with 19.3 million new cases and nearly 10 million deaths predicted in 2020. In the present investigation, we reported the synthesis of three semi-synthetic analogues of curcumin-bearing pyrimidinone moiety by the chemical modification of the diketone function of curcumin followed by their characterization by analytical techniques including infrared (IR), nuclear magnetic resonance (NMR), and mass spectral data. According to the National Cancer Institute (NCI US) methodology, the curcumin analogues (C1-C3) were tested for their anticancer efficacy against 59 cancer cell lines in a single dose assay. 1-(2,6-Dichlorophenyl)-4,6-bis((E)-4-hydroxy-3-methoxystyryl)pyrimidin-2(1H)-one (C2) demonstrated the most promising anticancer activity with mean percent growth inhibition (%GIs) of 68.22 in single dose assay at 10 µM. The compound exhibited >68 %GIs against 31 out of 59 cancer cell lines and was found to be highly active against all leukemia and breast cancer cell lines. The compound C2 showed a lethal effect on HT29 (colon cancer) with %GI of 130.44, while 99.44 %GI was observed against RPMI-8226 (Leukemia). The compound C2 displayed better anticancer activity against the panels of CNS, melanoma, ovarian, prostate, and breast cancer cell lines than curcumin and other anti-EGFR agents gefitinib and imatinib in single dose assay. The compound C2 also demonstrated potent anticancer activity in a 5-dose assay (0.001 to 100 µM) with GI50 values ranging from 1.31 to 4.68 µM; however, it was found to be non-selective with SR values ranging from 0.73 to 1.35. The GI50 values of compound C2 were found to be better than that of the curcumin against all nine panels of cancer cell lines. All of the curcumin analogues were subsequently investigated for molecular docking simulation against EGFR, one of the most attractive targets for antiproliferative action. In molecular docking studies, all the ligands were found to accommodate the active site of EGFR and the binding affinity of ligand C2 was found to be −5.086 kcal/mol. The ligand C2 exhibited three different types of interactions: H-bond (Thr790 and Thr854), π-cationic (Arg841), and aromatic H-bond (Asn842). The curcumin analogues reported in the current investigation may provide valuable therapeutic intervention for the prevention and treatment of cancer and accelerate anticancer drug discovery programs in the future. [ABSTRACT FROM AUTHOR]

The affinity propagation clustering (APC) algorithm is popular for fingerprint database clustering because it can cluster without pre-defining the number of clusters. However, the clustering performance of the APC algorithm heavily depends on the chosen fingerprint similarity metric, with distance-based metrics being the most commonly used. Despite its popularity, the APC algorithm lacks comprehensive research on how distance-based metrics affect clustering performance. This emphasizes the need for a better understanding of how these metrics influence its clustering performance, particularly in fingerprint databases. This paper investigates the impact of various distance-based fingerprint similarity metrics on the clustering performance of the APC algorithm. It identifies the best fingerprint similarity metric for optimal clustering performance for a given fingerprint database. The analysis is conducted across five experimentally generated online fingerprint databases, utilizing seven distance-based metrics: Euclidean, squared Euclidean, Manhattan, Spearman, cosine, Canberra, and Chebyshev distances. Using the silhouette score as the performance metric, the simulation results indicate that structural characteristics of the fingerprint database, such as the distribution of fingerprint vectors, play a key role in selecting the best fingerprint similarity metric. However, Euclidean and Manhattan distances are generally the preferable choices for use as fingerprint similarity metrics for the APC algorithm across most fingerprint databases, regardless of their structural characteristics. It is recommended that other factors, such as computational intensity and the presence or absence of outliers, be considered alongside the structural characteristics of the fingerprint database when choosing the appropriate fingerprint similarity metric for maximum clustering performance.

In fingerprint-based localization systems, clustering fingerprint databases is a proposed technique for improving localization accuracy while reducing localization time. Among various clustering algorithms, density-based spatial clustering of applications with noise (DBSCAN) stands out for its robustness to outliers and ability to accommodate fingerprint databases of various shapes. However, the clustering performance of the DBSCAN algorithm is heavily influenced by the type of similarity measure metric used, with most researchers using distance-based metrics. This paper aims to enhance DBSCAN clustering by using a pattern-based metric known as the context similarity coefficient (CSC) instead of distance-based metrics. The CSC metric examines received signal strength (RSS) measurement patterns that form fingerprint vectors and assesses both linear and non-linear relationships between these vectors to determine similarity. Four publicly available fingerprint databases were used to evaluate the clustering performance with silhouette scores as a performance metric. The performance of the DBSCAN algorithm with the CSC metric is determined and compared to Euclidean and Manhattan distances as similarity measure metrics. Simulation results indicate that achieving good clustering performance with the DBSCAN algorithm requires generating three or fewer clusters. The proposed CSC metric demonstrated the best clustering performance in two of four fingerprint databases and the second-best in another. However, computational complexity comparisons reveal that the CSC metric is highly computationally intensive and is suggested to be used on small to medium-sized fingerprint databases generated using an odd number of wireless APs deployed in a non-uniform or non-grid-like distribution.

This review delves into the pivotal role of the long non-coding RNA NEAT1 in cancer biology, particularly in lung cancer (LC). It emphasizes NEAT1's unique subcellular localization and active involvement in gene regulation and chromatin remodeling. The review highlights NEAT1's impact on LC development and progression, including cell processes such as proliferation, migration, invasion, and resistance to therapy, positioning it as a potential diagnostic marker and therapeutic target. The complex web of NEAT1's regulatory interactions with proteins and microRNAs is explored, alongside challenges in targeting it therapeutically. The review concludes optimistically, suggesting future avenues for research and personalized LC therapies, shedding light on NEAT1's crucial role in LC.

Distance-based metrics are the most common fingerprint similarity metrics used in fingerprint database clustering and localization processes in a fingerprint-based localization system. In this paper, however, a less common but promising pattern-based fingerprint similarity metric is proposed as an alternative to the distance-base metric. The proposed fingerprint similarity metric is based on an inverse weight (IW) normalization of the context similarity coefficient (CSC)-based similarity metric measure. The clustering and localization performance of the fingerprint-based localization system with the proposed IW-CSC-based fingerprint similarity metric is determined and compared to the square Euclidean, Manhattan, and cosine distance-based metrics. The k-means algorithm with a k-means++ cluster initialization process is considered for fingerprint database clustering, while the k-nearest neighbor (k-NN) algorithm is considered for localization. Based on the four fingerprint databases considered, the proposed IW-CSC-based metric has the slowest localization time with moderate clustering performance. However, it has the best localization performance, which is at least 52% higher than the localization performances of the three distance-base metrics considered. The proposed IW-CSC-based metric is recommended as an alternative to the distance-base metric only when improved localization performance is the primary objective of the fingerprint-based localization system. It is also recommended for use in small to medium-sized fingerprint databases for clustering and localization.

The modified Z-score (mZ-score) method has been used to detect outliers in time series received signal strength (RSS) observations. Its performance is dependent on the scale estimator used, and each has advantages and disadvantages over the others. One approach to developing a scale estimator that combines the advantages of two or more scale estimators is through scale estimator hybridization. In this paper, the outlier detection performance of a mZ-score method with different hybridization approaches for Sn and median absolute deviation (MAD) scale estimators is determined and analysed. Three different hybrid scale estimators are identified, namely weighted, maximum, and average hybrid scale estimators. The performance of the mZ-score method using the three different hybrid scale estimators is determined using three experimentally generated and publicly available time-series RSS datasets. Based on the simulation results, the weighted hybrid scale estimator results in the best outlier detection performance amongst the three hybrid scale estimators. When compared to the mean-shift-based outlier detection (MOD) technique, the k-means clustering-based technique, and the density-based spatial clustering (DBSCAN) technique, the mZ-score method with the weighted hybrid scale estimator performs better with little or no false alarm and false negative detections.

The emergence of drug resistance and the limited number of approved antitubercular drugs prompted identification and development of new antitubercular compounds to cure Tuberculosis (TB). In this work, an attempt was made to identify potential natural compounds that target mycobacterial proteins. Three plant extracts (A. aspera, C. gigantea and C. procera) were investigated. The ethyl acetate fraction of the aerial part of A. aspera and the flower ash of C. gigantea were found to be effective against M. tuberculosis H37Rv. Furthermore, the GC-MS analysis of the plant fractions confirmed the presence of active compounds in the extracts. The Mycobacterium target proteins, i.e., available PDB dataset proteins and proteins classified in virulence, detoxification, and adaptation, were investigated. A total of ten target proteins were shortlisted for further study, identified as follows: BpoC, RipA, MazF4, RipD, TB15.3, VapC15, VapC20, VapC21, TB31.7, and MazF9. Molecular docking studies showed that β-amyrin interacted with most of these proteins and its highest binding affinity was observed with Mycobacterium Rv1636 (TB15.3) protein. The stability of the protein-ligand complex was assessed by molecular dynamic simulation, which confirmed that β-amyrin most firmly interacted with Rv1636 protein. Rv1636 is a universal stress protein, which regulates Mycobacterium growth in different stress conditions and, thus, targeting Rv1636 makes M. tuberculosis vulnerable to host-derived stress conditions. [ABSTRACT FROM AUTHOR]