SHINE: Deep Learning-Based Accessible Parking Management System

The ongoing expansion of urban areas facilitated by advancements in science and technology has resulted in a considerable increase in the number of privately owned vehicles worldwide, including in South Korea. However, this gradual increment in the number of vehicles has inevitably led to parking-related issues, including the abuse of disabled parking spaces (hereafter referred to as accessible parking spaces) designated for individuals with disabilities. Traditional license plate recognition (LPR) systems have proven inefficient in addressing such a problem in real-time due to the high frame rate of surveillance cameras, the presence of natural and artificial noise, and variations in lighting and weather conditions that impede detection and recognition by these systems. With the growing concept of parking 4.0, many sensors, IoT and deep learning-based approaches have been applied to automatic LPR and parking management systems. Nonetheless, the studies show a need for a robust and efficient model for managing accessible parking spaces in South Korea. To address this, we have proposed a novel system called, Shine, which uses the deep learning-based object detection algorithm for detecting the vehicle, license plate, and disability badges (referred to as cards, badges, or access badges hereafter) and verifies the rights of the driver to use accessible parking spaces by coordinating with the central server. Our model, which achieves a mean average precision of 92.16%, is expected to address the issue of accessible parking space abuse and contributes significantly towards efficient and effective parking management in urban environments

Scalable, Non-denaturing Purification of Phosphoproteins Using Ga³⁺-IMAC: N2A and M1M2 Titin Components as Study case

The purification of phosphorylated proteins in a folded state and in large enough quantity for biochemical or biophysical analysis remains a challenging task. Here, we develop a new implementation of the method of gallium immobilized metal chromatography (Ga3+-IMAC) as to permit the selective enrichment of phosphoproteins in the milligram scale and under native conditions using automated FPLC instrumentation. We apply this method to the purification of the UN2A and M1M2 components of the muscle protein titin upon being monophosphorylated in vitro by cAMP-dependent protein kinase (PKA). We found that UN2A is phosphorylated by PKA at its C-terminus in residue S9578 and M1M2 is phosphorylated in its interdomain linker sequence at position T32607. We demonstrate that the Ga3+-IMAC method is efficient, economical and suitable for implementation in automated purification pipelines for recombinant proteins. The procedure can be applied both to the selective enrichment and to the removal of phosphoproteins from biochemical samples

PTRF/Cavin-1 and MIF Proteins Are Identified as Non-Small Cell Lung Cancer Biomarkers by Label-Free Proteomics

With the completion of the human genome sequence, biomedical sciences have entered in the “omics” era, mainly due to high-throughput genomics techniques and the recent application of mass spectrometry to proteomics analyses. However, there is still a time lag between these technological advances and their application in the clinical setting. Our work is designed to build bridges between high-performance proteomics and clinical routine. Protein extracts were obtained from fresh frozen normal lung and non-small cell lung cancer samples. We applied a phosphopeptide enrichment followed by LC-MS/MS. Subsequent label-free quantification and bioinformatics analyses were performed. We assessed protein patterns on these samples, showing dozens of differential markers between normal and tumor tissue. Gene ontology and interactome analyses identified signaling pathways altered on tumor tissue. We have identified two proteins, PTRF/cavin-1 and MIF, which are differentially expressed between normal lung and non-small cell lung cancer. These potential biomarkers were validated using western blot and immunohistochemistry. The application of discovery-based proteomics analyses in clinical samples allowed us to identify new potential biomarkers and therapeutic targets in non-small cell lung cancer

Genome-Scale Modeling of Light-Driven Reductant Partitioning and Carbon Fluxes in Diazotrophic Unicellular Cyanobacterium Cyanothece sp. ATCC 51142

Genome-scale metabolic models have proven useful for answering fundamental questions about metabolic capabilities of a variety of microorganisms, as well as informing their metabolic engineering. However, only a few models are available for oxygenic photosynthetic microorganisms, particularly in cyanobacteria in which photosynthetic and respiratory electron transport chains (ETC) share components. We addressed the complexity of cyanobacterial ETC by developing a genome-scale model for the diazotrophic cyanobacterium, Cyanothece sp. ATCC 51142. The resulting metabolic reconstruction, iCce806, consists of 806 genes associated with 667 metabolic reactions and includes a detailed representation of the ETC and a biomass equation based on experimental measurements. Both computational and experimental approaches were used to investigate light-driven metabolism in Cyanothece sp. ATCC 51142, with a particular focus on reductant production and partitioning within the ETC. The simulation results suggest that growth and metabolic flux distributions are substantially impacted by the relative amounts of light going into the individual photosystems. When growth is limited by the flux through photosystem I, terminal respiratory oxidases are predicted to be an important mechanism for removing excess reductant. Similarly, under photosystem II flux limitation, excess electron carriers must be removed via cyclic electron transport. Furthermore, in silico calculations were in good quantitative agreement with the measured growth rates whereas predictions of reaction usage were qualitatively consistent with protein and mRNA expression data, which we used to further improve the resolution of intracellular flux values

Glycome and Proteome Components of Golgi Membranes Are Common between Two Angiosperms with Distinct Cell-Wall Structures

The plant endoplasmic reticulum-Golgi apparatus is the site of synthesis, assembly, and trafficking of all noncellulosic polysaccharides, proteoglycans, and proteins destined for the cell wall. As grass species make cell walls distinct from those of dicots and noncommelinid monocots, it has been assumed that the differences in cell-wall composition stem from differences in biosynthetic capacities of their respective Golgi. However, immunosorbence-based screens and carbohydrate linkage analysis of polysaccharides in Golgi membranes, enriched by flotation centrifugation from etiolated coleoptiles of maize (Zea mays) and leaves of Arabidopsis (Arabidopsis thaliana), showed that arabinogalactan-proteins and arabinans represent substantial portions of the Golgi-resident polysaccharides not typically found in high abundance in cell walls of either species. Further, hemicelluloses accumulated in Golgi at levels that contrasted with those found in their respective cell walls, with xyloglucans enriched in maize Golgi, and xylans enriched in Arabidopsis. Consistent with this finding, maize Golgi membranes isolated by flotation centrifugation and enriched further by free-flow electrophoresis, yielded >200 proteins known to function in the biosynthesis and metabolism of cell-wall polysaccharides common to all angiosperms, and not just those specific to cell-wall type. We propose that the distinctive compositions of grass primary cell walls compared with other angiosperms result from differential gating or metabolism of secreted polysaccharides post-Golgi by an as-yet unknown mechanism, and not necessarily by differential expression of genes encoding specific synthase complexes

Pathological relationships involving iron and myelin may constitute a shared mechanism linking various rare and common brain diseases.

We previously demonstrated elevated brain iron levels in myelinated structures and associated cells in a hemochromatosis Hfe (-/-) xTfr2 (mut) mouse model. This was accompanied by altered expression of a group of myelin-related genes, including a suite of genes causatively linked to the rare disease family 'neurodegeneration with brain iron accumulation' (NBIA). Expanded data mining and ontological analyses have now identified additional myelin-related transcriptome changes in response to brain iron loading. Concordance between the mouse transcriptome changes and human myelin-related gene expression networks in normal and NBIA basal ganglia testifies to potential clinical relevance. These analyses implicate, among others, genes linked to various rare central hypomyelinating leukodystrophies and peripheral neuropathies including Pelizaeus-Merzbacher-like disease and Charcot-Marie-Tooth disease as well as genes linked to other rare neurological diseases such as Niemann-Pick disease. The findings may help understand interrelationships of iron and myelin in more common conditions such as hemochromatosis, multiple sclerosis and various psychiatric disorders