Plum pudding random medium model of biological tissue toward remote microscopy from spectroscopic light scattering

Biological tissue has a complex structure and exhibits rich spectroscopic behavior. There is \emph{no} tissue model up to now able to account for the observed spectroscopy of tissue light scattering and its anisotropy. Here we present, \emph{for the first time}, a plum pudding random medium (PPRM) model for biological tissue which succinctly describes tissue as a superposition of distinctive scattering structures (plum) embedded inside a fractal continuous medium of background refractive index fluctuation (pudding). PPRM faithfully reproduces the wavelength dependence of tissue light scattering and attributes the "anomalous" trend in the anisotropy to the plum and the powerlaw dependence of the reduced scattering coefficient to the fractal scattering pudding. Most importantly, PPRM opens up a novel venue of quantifying the tissue architecture and microscopic structures on average from macroscopic probing of the bulk with scattered light alone without tissue excision. We demonstrate this potential by visualizing the fine microscopic structural alterations in breast tissue (adipose, glandular, fibrocystic, fibroadenoma, and ductal carcinoma) deduced from noncontact spectroscopic measurement

Impact of COVID-19 on lifestyle and financial behaviour: The implications to research in financial vulnerability

The outbreak of coronavirus pandemic in late 2019 posted unprecedented social-economic challenges and disruptions to societies and individuals. The “new-normal” styles of living and working could intertwined with other determinants complicating the investigation of individual’s financial vulnerability. The purpose of this paper is to conduct literature survey to review and consolidate the recent scattered literatures to identify some possible factors to be considered in the research related to financial vulnerability, including pandemic’s impact of COVID-19 to different aspects of personal finance issues, pandemic-driven digitisation of the economy activities, changes in financial behaviour and addiction to digital technology

Microtubule distribution in somatic cell nuclear transfer bovine embryos following control of nuclear remodeling type

This study was conducted to evaluate the microtubule distribution following control of nuclear remodeling by treatment of bovine somatic cell nuclear transfer (SCNT) embryos with caffeine or roscovitine. Bovine somatic cells were fused to enucleated oocytes treated with either 5 mM caffeine or 150 µM roscovitine to control the type of nuclear remodeling. The proportion of embryos that underwent premature chromosome condensation (PCC) was increased by caffeine treatment but was reduced by roscovitine treatment (p < 0.05). The microtubule organization was examined by immunostaining β- and γ-tubulins at 15 min, 3 h, and 20 h of fusion using laser scanning confocal microscopy. The γ-tubulin foci inherited from the donor centrosome were observed in most of the SCNT embryos at 15 min of fusion (91.3%) and most of them did not disappear until 3 h after fusion, regardless of treatment (82.9-87.2%). A significantly high proportion of embryos showing an abnormal chromosome or microtubule distribution was observed in the roscovitine-treated group (40.0%, p < 0.05) compared to the caffeine-treated group (22.1%). In conclusion, PCC is a favorable condition for the normal organization of microtubules, and inhibition of PCC can cause abnormal mitotic division of bovine SCNT embryos by causing microtubule dysfunction

Learning to Denoise Unreliable Interactions for Link Prediction on Biomedical Knowledge Graph

Link prediction in biomedical knowledge graphs (KGs) aims at predicting unknown interactions between entities, including drug-target interaction (DTI) and drug-drug interaction (DDI), which is critical for drug discovery and therapeutics. Previous methods prefer to utilize the rich semantic relations and topological structure of the KG to predict missing links, yielding promising outcomes. However, all these works only focus on improving the predictive performance without considering the inevitable noise and unreliable interactions existing in the KGs, which limits the development of KG-based computational methods. To address these limitations, we propose a Denoised Link Prediction framework, called DenoisedLP. DenoisedLP obtains reliable interactions based on the local subgraph by denoising noisy links in a learnable way, providing a universal module for mining underlying task-relevant relations. To collaborate with the smoothed semantic information, DenoisedLP introduces the semantic subgraph by blurring conflict relations around the predicted link. By maximizing the mutual information between the reliable structure and smoothed semantic relations, DenoisedLP emphasizes the informative interactions for predicting relation-specific links. Experimental results on real-world datasets demonstrate that DenoisedLP outperforms state-of-the-art methods on DTI and DDI prediction tasks, and verify the effectiveness and robustness of denoising unreliable interactions on the contaminated KGs

A Review on Computer Aided Diagnosis of Acute Brain Stroke.

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas