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Roots are highly responsive to environmental signals encountered in the rhizosphere, such as nutrients, mechanical resistance and gravity. As a result, root growth and development is very plastic. If this complex and vital process is to be understood, methods and tools are required to capture the dynamics of root responses. Tools are needed which are high-throughput, supporting large-scale experimental work, and provide accurate, high-resolution, quantitative data. We describe and demonstrate the efficacy of the high-throughput and high-resolution root imaging systems recently developed within the Centre for Plant Integrative Biology (CPIB). This toolset includes (i) robotic imaging hardware to generate time-lapse datasets from standard cameras under infrared illumination and (ii) automated image analysis methods and software to extract quantitative information about root growth and development both from these images and via high-resolution light microscopy. These methods are demonstrated using data gathered during an experimental study of the gravitropic response of Arabidopsis thaliana.

Rolling circle amplification (RCA) for generation of distinct fluorescent signals in situ relies upon the self-collapsing properties of single-stranded DNA in commonly used RCA-based methods. By introducing a cross-hybridizing DNA oligonucleotide during rolling circle amplification, we demonstrate that the fluorophore-labeled RCA products (RCPs) become smaller. The reduced size of RCPs increases the local concentration of fluorophores and as a result, the signal intensity increases together with the signal-to-noise ratio. Furthermore, we have found that RCPs sometimes tend to disintegrate and may be recorded as several RCPs, a trait that is prevented with our cross-hybridizing DNA oligonucleotide. These effects generated by compaction of RCPs improve accuracy of visual as well as automated in situ analysis for RCA based methods, such as proximity ligation assays (PLA) and padlock probes.

Quantitative microscopy deals with the extraction of quantitative measurements from samples observed under a microscope. Recent developments in microscopy systems, sample preparation and handling techniques have enabled high throughput biological experiments resulting in large amounts of image data, at biological scales ranging from subcellular structures such as fluorescently tagged nucleic acid sequences to whole organisms such as zebrafish embryos. Consequently, methods and algorithms for automated quantitative analysis of these images have become increasingly important. These methods range from traditional image analysis techniques to use of deep learning architectures.Many biomedical microscopy assays result in fluorescent spots. Robust detection and precise localization of these spots are two important, albeit sometimes overlapping, areas for application of quantitative image analysis. We demonstrate the use of popular deep learning architectures for spot detection and compare them against more traditional parametric model-based approaches. Moreover, we quantify the effect of pre-training and change in the size of training sets on detection performance. Thereafter, we determine the potential of training deep networks on synthetic and semi-synthetic datasets and their comparison with networks trained on manually annotated real data. In addition, we present a two-alternative forced-choice based tool for assisting in manual annotation of real image data. On a spot localization track, we parallelize a popular compressed sensing based localization method and evaluate its performance in conjunction with different optimizers, noise conditions and spot densities. We investigate its sensitivity to different point spread function estimates.Zebrafish is an important model organism, attractive for whole-organism image-based assays for drug discovery campaigns. The effect of drug-induced neuronal damage may be expressed in the form of zebrafish shape deformation. First, we present an automated method for accurate quantification of tail deformations in multi-fish micro-plate wells using image analysis techniques such as illumination correction, segmentation, generation of branch-free skeletons of partial tail-segments and their fusion to generate complete tails. Later, we demonstrate the use of a deep learning-based pipeline for classifying micro-plate wells as either drug-affected or negative controls, resulting in competitive performance, and compare the performance from deep learning against that from traditional image analysis approaches.

Multiple sclerosis has been known to cause atrophy and deformation in the corpus callosum. In longitudinal studies, these changes have been typically quantified by using medical image analysis techniques for measuring and analyzing the size and shape of a corpus callosum cross-section embedded in a specially selected measurement plane. Our contributions are three fold: (i) We introduce and quantify the effects of the error in measurement plane selection and image interpolation on the cross-sectional area of the corpus callosum; (ii) We present a novel and clinically meaningful criterion for the measurement plane selection which addresses significant drawbacks with previous methods of plane selection. We present a nested optimization based framework for accurate identification and extraction of this plane with simultaneous segmentation of its corpus callosum cross-section; (iii) We present a medial shape representation based technique for longitudinal, regional and deformation-specific shape analysis of the corpus callosum. Keywords. multiple sclerosis; corpus callosum; atrophy; midsagittal plane selection; shape analysis; medical image analysis. Subject terms. medical image analysis; multiple sclerosis; shape analysis; longitudinal study.

Multi-scale models integrating biomolecular data from genetic, transcriptional, and translational levels, coupled with extracellular microenvironments can assist in decoding the complex mechanisms underlying system-level diseases such as cancer. To investigate the emergent properties and clinical translation of such cancer models, we present Theatre for in silico Systems Oncology (TISON, https://tison.lums.edu.pk), a next-generation web-based multi-scale modeling and simulation platform for in silico systems oncology. TISON provides a “zero-code” environment for multi-scale model development by seamlessly coupling scale-specific information from biomolecular networks, microenvironments, cell decision circuits, in silico cell lines, and organoid geometries. To compute the temporal evolution of multi-scale models, a simulation engine and data analysis features are also provided. Furthermore, TISON integrates patient-specific gene expression data to evaluate patient-centric models towards personalized therapeutics. Several literature-based case studies have been developed to exemplify and validate TISON’s modeling and analysis capabilities. TISON provides a cutting-edge multi-scale modeling pipeline for scale-specific as well as integrative systems oncology that can assist in drug target discovery, repositioning, and development of personalized therapeutics.

The social change and speedy civilized progress and the spread of globalization in addition to expansion of the volume of trade exchange, effected new modern and developed kinds of commodities and services that outmatched the quantity of incomes of some Yemeni families. Those families found themselves forced to squeeze some areas of spending in order to spare money enough to buy the new goods and face the new requirements of life and many families tended to abandon traditional habits of spending and consumption. This means that the Yemeni contemporary society is heading towards the consumer mode with all its aspects, influenced by the age of world popular consumer mode. Poverty survey conducted about the Yemeni family has revealed certain kinds and levels of poverty; acute poverty, food poverty, medium poverty and families below the line of poverty. His has resulted in that many Yemeni families began to face the problem of satisfying the necessary needs and the emergence of variant consumer modes. This has got reflected on the phenomenon of openness to the market and affected the process of marketing and some aspects of trade recession. To solve this problem some rich and donor countries and international organisations are trying to help the poor countries suffering from low economic growth to relatively overcome their crises and concentrating studies on the conditions of families. They allot some assistance and try to vitalize some investment and economic projects to provide suitable job opportunities and mitigate the severity of unemployment and consequently the intensity of poverty. This is especially important when taking into account the big army unemployment among the ranks of the youth and children begging on the streets.

Elderly and patients with comorbid conditions have higher risk of infection and complications. Vaccination hesitancy is defined as the refusal of vaccine or the delay in accepting it despite the availability of vaccines and vaccination services. This study was aimed at assessing knowledge, perception, and acceptability of healthcare staff towards different types of COVID-19 vaccination. A multicenter hospital-based descriptive cross-sectional study was implemented to study the knowledge, perception, and acceptability of healthcare staff towards COVID-19 vaccination. Multistage sampling technique was applied. Data were collected through a self-administered questionnaire filled by the participants. 400 participants were studied. 61% of the participants were females, and the most frequent age reported was between 18 and 35 years (67%). A statistically significant association (p=0.048) was found between knowledge about vaccination and professions. The most common vaccine type known and accepted was AstraZeneca vaccine. On assessing acceptability of COVID-19 vaccination, acceptance rate was high (63.8%) and 22.7% of the participants had already got vaccinated. The rejection rate among our staff was 27.4%. This study was conducted in April, 2021. Majority of our healthcare staff believed that vaccination is the key to combat the pandemic. One of the issues and concerns about vaccination was the safety and the risk of developing acute adverse events (p=0.001). Encouraging factor for vaccination was the fear of getting infection themselves and their families. The present study revealed the presence of good knowledge and acceptability among medical staff towards COVID-19 vaccinations in Sudan.

The emergence of nanotechnology offers a promising avenue for enhancing cancer treatment outcomes. In this context, biomimetic nanoparticles have emerged as an exciting frontier in the field of biomedicine. These nanoparticles can emulate essential biological functions, drawing from an abundant reservoir of cellular capabilities. This includes engaging in biological binding, precise homing to tumor sites, and interaction with immune cells. These inherent traits endow biomimetic nanoparticles with a suite of intelligent features, including biocompatibility, low immunogenicity, reduced toxicity, immune evasion, prolonged circulation, homotypic binding, enhanced tumor targeting, and the capability of precise delivery. By integrating biologically inspired coatings derived from cell membranes with nanoparticle cores, these carriers become highly versatile vessels for encapsulating a wide array of therapeutic agents. As a result, they are being extensively harnessed for the precise delivery of drugs and genes, underpinning numerous biomedical applications. This discussion delves into the challenges and opportunities presented by biomimetic nanoparticles and offers a comprehensive exploration of their fundamentals and recent breakthroughs, with an eye towards clinical translation. By bridging the gap between scientific innovation and clinical utility, biomimetic nanoparticles hold great promise for advancing the field of cancer treatment.