In 2015, global real estate was worth $217 trillion, which is approximately 2.7 times the global GDP; it also accounts for roughly 60% of all conventional global resources, making it one of the key factors behind any country’s economic growth and stability. The accessibility of spatial big data will help real estate investors make better judgement calls and earn additional profit. Since location is deemed necessary for real estate and consequent decision-making, digital maps have become a prime resource for real estate purchases, planning and development. Personalisation can assist in making judgments by identifying user desires and inclinations, which can then be recorded or captured as a user performs some interactions with a digital map. A personalised real estate portal can use this information to suggest properties, assist homeowners and provide valuable real estate analytics. This article presents a novel framework for recommending real estate to users. By monitoring user interactions through an online real estate portal, the framework can make personalised recommendations of real estate based on content, collaboration and location. The effectiveness of the recommendations was tested by the user feedback mechanism through a method of mean absolute precision, and the results show that 79% precise suggestions were generated, i.e., out of 5 recommendations produced, users were interested in at least 3. Along with that, a separate house price prediction model based on neural networks and classical regression techniques was also implemented to assist users in making an informed decision regarding prospects of real estate purchase.

Methicillin-resistant Staphylococcus aureus (MRSA) is a multi-drug resistant and opportunistic pathogen. The emergence of new clones of MRSA in both healthcare settings and the community warrants serious attention and epidemiological surveillance. However, epidemiological data of MRSA isolates from Pakistan are limited. We performed a whole-genome-based comparative analysis of two (P10 and R46) MRSA strains isolated from two provinces of Pakistan to understand the genetic diversity, sequence type (ST), and distribution of virulence and antibiotic-resistance genes. The strains belong to ST113 and harbor the SCCmec type IV encoding mecA gene. Both the strains contain two plasmids, and three and two complete prophage sequences are present in P10 and R46, respectively. The specific antibiotic resistance determinants in P10 include two aminoglycoside-resistance genes, aph(3’)-IIIa and aad(6), a streptothrin-resistance gene sat-4, a tetracycline-resistance gene tet(K), a mupirocin-resistance gene mupA, a point mutation in fusA conferring resistance to fusidic acid, and in strain R46 a specific plasmid associated gene ant(4’)-Ib. The strains harbor many virulence factors common to MRSA. However, no Panton-Valentine leucocidin (lukF-PV/lukS-PV) or toxic shock syndrome toxin (tsst) genes were detected in any of the genomes. The phylogenetic relationship of P10 and R46 with other prevailing MRSA strains suggests that ST113 strains are closely related to ST8 strains and ST113 strains are a single-locus variant of ST8. These findings provide important information concerning the emerging MRSA clone ST113 in Pakistan and the sequenced strains can be used as reference strains for the comparative genomic analysis of other MRSA strains in Pakistan and ST113 strains globally.

Inositol trisphosphate receptor (IP₃R) mediated Ca⁺² signaling is essential in determining the cell fate by regulating numerous cellular processes, including cell division and cell death. Despite extensive studies about the characterization of IP₃R in cancer, the underlying molecular mechanism initiating the cell proliferation and apoptosis remained enigmatic. Moreover, in cancer, the modulation of IP₃R in downstream signaling pathways, which control oncogenesis and cancer progression, is not well characterized. Here, we constructed a biological regulatory network (BRN), and describe the remodeling of IP₃R mediated Ca²⁺ signaling as a central key that controls the cellular processes in cancer. Moreover, we summarize how the inhibition of IP₃R affects the deregulated cell proliferation and cell death in cancer cells and results in the initiation of pro-survival responses in resistance of cell death in normal cells. Further, we also investigated the role of stereo-specificity of IP₃ molecule and its analogs in binding with the IP₃ receptor. Molecular docking simulations showed that the hydroxyl group at R₆ position along with the phosphate group at R₅ position in ‘R’ conformation is more favorable for IP₃ interactions. Additionally, Arg-266 and Arg-510 showed π–π and hydrogen bond interactions and Ser-278 forms hydrogen bond interactions with the IP₃ binding site. Thus, they are identified as crucial for the binding of antagonists.

Biological Regulatory Networks (BRNs) are responsible for developmental and maintenance related functions in organisms. These functions are implemented by the dynamics of BRNs and are sensitive to regulations enforced by specific activators and inhibitors. The logical modeling formalism by René Thomas incorporates this sensitivity with a set of logical parameters modulated by available regulators, varying with time. With the increase in complexity of BRNs in terms of number of entities and their interactions, the task of parameters estimation becomes computationally expensive with existing sequential SMBioNET tool. We extend the existing sequential implementation of SMBioNET by using a data decomposition approach using a Java messaging library called MPJ Express. The approach divides the parameters space into different regions and each region is then explored in parallel on High Performance Computing (HPC) hardware. The performance of the parallel approach is evaluated on BRNs of different sizes, and experimental results on multicore and cluster computers showed almost linear speed-up. This parallel code can be executed on a wide range of concurrent hardware including laptops equipped with multicore processors, and specialized distributed memory computer systems. To demonstrate the application of parallel implementation, we selected a case study of Hexosamine Biosynthetic Pathway (HBP) in cancer progression to identify potential therapeutic targets against cancer. A set of logical parameters were computed for HBP model that directs the biological system to a state of recovery. Furthermore, the parameters also suggest a potential therapeutic intervention that restores homeostasis. Additionally, the performance of parallel application was also evaluated on a network (comprising of 23 entities) of Fibroblast Growth Factor Signalling in Drosophila melanogaster. Qualitative modeling framework is widely used for investigating dynamics of biological regulatory networks. However, computation of model parameters in qualitative modeling is computationally intensive. In this work, we presented results of our Java based parallel implementation that provides almost linear speed-up on both multicore and cluster platforms. The parallel implementation is available at https://psmbionet.github.io .