Sanitizing the water sources of local communities is important to control the spread of microbial resistance genes, especially those for water-borne illnesses. The activities of antibiotic resistance gene (ARG)-host pathogens pose a threat to public health, and it has been estimated that the infection will lead up to 10 million deaths globally by the year 2050. Hence, in this study, we aim to analyze the efficiency of our municipal wastewater treatment plant (WWTP) process in producing pathogen-free water by investigating the microbial composition between influent and effluent water sites. Shotgun metagenomics sequencing using the Illumina platform was performed on the influent and effluent samples of six different WWTP sites located in Johore, Malaysia. After raw data pre-processing, the non-redundant contigs library was then aligned against BLASTP for taxonomy profiling and the Comprehensive Antibiotic Resistance Database for ARG annotation. Interestingly, the alpha-diversity result reported that effluent site samples showed higher abundance and diverse heterogeneity compared to the influent site. The principal component analysis (PCA) and non-metric multidimensional scaling (NMDS) plots also suggested that effluent sites showed high variation in the genetic material due to loosely clustered sample plots, as compared to the tightly clustered influent samples. This study has successfully identified the top three abundant phyla in influent—Proteobacteria,Firmicutes, andBacteroidetes—and effluent—Proteobacteria,Actinobacteria, andBacteroidetes—water. Despite the overlap within the top three abundant phyla in influent and effluent sites (ProteobacteriaandBacteroidetes), the ARG composition heat map and drug class phenotype plot bar exhibits a general trend of a downward shift, showing the efficiency of WWTP in reducing opportunistic pathogens. Overall, it was demonstrated that our municipal WWTP efficiently eliminated pathogenic microbes from the influent water before its total discharge to the environment, though not with the total elimination of microorganisms. This metagenomics study allowed for an examination of our water source and showed the potential interaction of species and ARGs residing in the influent and effluent environment. Both microbial profile structure and co-occurrence network analysis provide integrated understanding regarding the diversity of microorganisms and interactions for future advanced water sanitation treatments.

‘Causal’ direction is of great importance when dealing with complex systems. Often big volumes of data in the form of time series are available and it is important to develop methods that can inform about possible causal connections between the different observables. Here we investigate the ability of the Transfer Entropy measure to identify causal relations embedded in emergent coherent correlations. We do this by firstly applying Transfer Entropy to an amended Ising model. In addition we use a simple Random Transition model to test the reliability of Transfer Entropy as a measure of ‘causal’ direction in the presence of stochastic fluctuations. In particular we systematically study the effect of the finite size of data sets.

Microcalcifications in mammogram images are primary indicators for detecting the early stages of breast cancer. However, dense tissues and noise in the images make it challenging to classify the microcalcifications. Currently, preprocessing procedures such as noise removal techniques are applied directly on the images, which may produce a blurry effect and loss of image details. Further, most of the features used in classification models focus on local information of the images and are often burdened with details, resulting in data complexity. This research proposed a filtering and feature extraction technique using persistent homology (PH), a powerful mathematical tool used to study the structure of complex datasets and patterns. The filtering process is not performed directly on the image matrix but through the diagrams arising from PH. These diagrams will enable us to distinguish prominent characteristics of the image from noise. The filtered diagrams are then vectorised using PH features. Supervised machine learning models are trained on the MIAS and DDSM datasets to evaluate the extracted features’ efficacy in discriminating between benign and malignant classes and to obtain the optimal filtering level. This study reveals that appropriate PH filtering levels and features can improve classification accuracy in early cancer detection.

Valued in hundreds of billions of Malaysian ringgit, the Bursa Malaysia Financial Services Index’s constituents comprise several of the strongest performing financial constituents in Bursa Malaysia’s Main Market. Although these constituents persistently reside mostly within the large market capitalization (cap), the existence of the individual constituent’s causal influence or intensity relative to each other’s performance during uncertain or even certain times is unknown. Thus, the key purpose of this paper is to identify and analyze the individual constituent’s causal intensity, from early 2018 (pre-COVID-19) to the end of the year 2021 (post-COVID-19) using Granger causality and Schreiber transfer entropy. Furthermore, network science is used to measure and visualize the fluctuating causal degree of the source and the effected constituents. The results show that both the Granger causality and Schreiber transfer entropy networks detected patterns of increasing causality from pre- to post-COVID-19 but with differing causal intensities. Unexpectedly, both networks showed that the small- and mid-caps had high causal intensity during and after COVID-19. Using Bursa Malaysia’s sub-sector for further analysis, the Insurance sub-sector rapidly increased in causality as the year progressed, making it one of the index’s largest sources of causality. Even after removing large amounts of weak causal intensities, Schreiber transfer entropy was still able to detect higher amounts of causal sources from the Insurance sub-sector, whilst Granger causal sources declined rapidly post-COVID-19. The method of using directed temporal networks for the visualization of temporal causal sources is demonstrated to be a powerful approach that can aid in investment decision making.