The immense diversity of extracellular matrix (ECM) proteins confers distinct biochemical and biophysical properties that influence cell phenotype. The ECM is highly dynamic as it is constantly deposited, remodelled, and degraded during development until maturity to maintain tissue homeostasis. The ECM’s composition and organization are spatiotemporally regulated to control cell behaviour and differentiation, but dysregulation of ECM dynamics leads to the development of diseases such as cancer. The chemical cues presented by the ECM have been appreciated as key drivers for both development and cancer progression. However, the mechanical forces present due to the ECM have been largely ignored but recently recognized to play critical roles in disease progression and malignant cell behaviour. Here, we review the ways in which biophysical forces of the microenvironment influence biochemical regulation and cell phenotype during key stages of human development and cancer progression.

Role of Extracellular Matrix in Development and Cancer Progression.

Cell behaviour is strongly influenced by physical, mechanical contacts between cells and their extracellular matrix. We review how the transcriptional regulators YAP and TAZ integrate mechanical cues with the response to soluble signals and metabolic pathways to control multiple aspects of cell behaviour, including proliferation, cell plasticity and stemness essential for tissue regeneration. Corruption of cell-environment interplay leads to aberrant YAP and TAZ activation that is instrumental for multiple diseases, including cancer.

YAP/TAZ upstream signals and downstream responses

Advances in computing technology have fostered the development of new and powerful deep learning (DL) techniques, which have demonstrated promising results in a wide range of applications. Particularly, DL methods have been successfully used to classify remotely sensed data collected by Earth Observation (EO) instruments. Hyperspectral imaging (HSI) is a hot topic in remote sensing data analysis due to the vast amount of information comprised by this kind of images, which allows for a better characterization and exploitation of the Earth surface by combining rich spectral and spatial information. However, HSI poses major challenges for supervised classification methods due to the high dimensionality of the data and the limited availability of training samples. These issues, together with the high intraclass variability (and interclass similarity) –often present in HSI data– may hamper the effectiveness of classifiers. In order to solve these limitations, several DL-based architectures have been recently developed, exhibiting great potential in HSI data interpretation. This paper provides a comprehensive review of the current-state-of-the-art in DL for HSI classification, analyzing the strengths and weaknesses of the most widely used classifiers in the literature. For each discussed method, we provide quantitative results using several well-known and widely used HSI scenes, thus providing an exhaustive comparison of the discussed techniques. The paper concludes with some remarks and hints about future challenges in the application of DL techniques to HSI classification. The source codes of the methods discussed in this paper are available from: https://github.com/mhaut/hyperspectral_deeplearning_review .

Deep learning classifiers for hyperspectral imaging: A review

One of the most exciting developments in the field of bacterial pathogenesis in recent years is the discovery that many pathogens utilize complex nanomachines to deliver bacterially encoded effector proteins into target eukaryotic cells. These effector proteins modulate a variety of cellular functions for the pathogen's benefit. One of these protein-delivery machines is the type III secretion system (T3SS). T3SSs are widespread in nature and are encoded not only by bacteria pathogenic to vertebrates or plants but also by bacteria that are symbiotic to plants or insects. A central component of T3SSs is the needle complex, a supramolecular structure that mediates the passage of the secreted proteins across the bacterial envelope. Working in conjunction with several cytoplasmic components, the needle complex engages specific substrates in sequential order, moves them across the bacterial envelope, and ultimately delivers them into eukaryotic cells. The central role of T3SSs in pathogenesis makes them great targets for novel antimicrobial strategies.

/pdf/bacterial-type-iii-secretion-systems-specialized-3czy2cl5cf.pdf

Bacterial type III secretion systems: specialized nanomachines for protein delivery into target cells

The cell imposes multiple barriers to virus entry. However, viruses exploit fundamental cellular processes to gain entry to cells and deliver their genetic cargo. Virus entry pathways are largely defined by the interactions between virus particles and their receptors at the cell surface. These interactions determine the mechanisms of virus attachment, uptake, intracellular trafficking, and, ultimately, penetration to the cytosol. Elucidating the complex interplay between viruses and their receptors is necessary for a full understanding of how these remarkable agents invade their cellular hosts.

/pdf/the-cell-biology-of-receptor-mediated-virus-entry-ee81zxkrt4.pdf

The cell biology of receptor-mediated virus entry.

Agrin as a Mechanotransduction Signal Regulating YAP through the Hippo Pathway

Kaposi's sarcoma-associated herpesvirus (KSHV) infections of endothelial and B cells are etiologically linked with Kaposi's sarcoma (KS) and primary effusion B-cell lymphoma (PEL), respectively. KS endothelial and PEL B cells carry multiple copies of the nuclear episomal latent KSHV genome and secrete a variety of inflammatory cytokines, including interleukin-1β (IL-1β) and IL-18. The maturation of IL-1β and IL-18 depends upon active caspase-1, which is regulated by a multiprotein inflammasome complex induced by sensing of danger signals. During primary KSHV infection of endothelial cells, acting as a nuclear pattern recognition receptor, gamma interferon-inducible protein 16 (IFI16) colocalized with the KSHV genome in the nuclei and interacted with ASC and procaspase-1 to form a functional inflammasome (Kerur N et al., Cell Host Microbe 9:363-375, 2011). Here, we demonstrate that endothelial telomerase-immortalized human umbilical cells (TIVE) supporting KSHV stable latency (TIVE-LTC cells) and PEL (cavity-based B-cell lymphoma 1 [BCBL-1]) cells show evidence of inflammasome activation, such as the activation of caspase-1 and cleavage of pro-IL-1β and pro-IL-18. Interaction of ASC with IFI16 but not with AIM2 or NOD-like receptor P3 (NLRP3) was detected. The KSHV latency-associated viral FLIP (vFLIP) gene induced the expression of IL-1β, IL-18, and caspase-1 mRNAs in an NF-κB-dependent manner. IFI16 and cleaved IL-1β were detected in the exosomes released from BCBL-1 cells. Exosomal release could be a KSHV-mediated strategy to subvert IL-1β functions. In fluorescent in situ hybridization analyses, IFI16 colocalized with multiple copies of the KSHV genome in BCBL-1 cells. IFI16 colocalization with ASC was also detected in lung PEL sections from patients. Taken together, these findings demonstrated the constant sensing of the latent KSHV genome by IFI16-mediated innate defense and unraveled a potential mechanism of inflammation induction associated with KS and PEL lesions.

https://jvi.asm.org/content/jvi/87/8/4417.full.pdf

Kaposi's Sarcoma-Associated Herpesvirus Latency in Endothelial and B Cells Activates Gamma Interferon-Inducible Protein 16-Mediated Inflammasomes

In this paper, we review the current activity of image classification methodologies and techniques. Image classification is a complex process which depends upon various factors. Here, we discuss about the current techniques, problems as well as prospects of image classification. The main focus will be on advanced classification techniques which are used for improving classification accuracy. Additionally, some important issues relating to classification performance are also discussed.

A survey of image classification methods and techniques

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths globally. The identity and role of cell surface molecules driving complex biological events leading to HCC progression are poorly understood, hence representing major lacunae in HCC therapies. Here, combining SILAC quantitative proteomics and biochemical approaches, we uncover a critical oncogenic role of Agrin, which is overexpressed and secreted in HCC. Agrin enhances cellular proliferation, migration and oncogenic signalling. Mechanistically, Agrin's extracellular matrix sensor activity provides oncogenic cues to regulate Arp2/3-dependent ruffling, invadopodia formation and epithelial-mesenchymal transition through sustained focal adhesion integrity that drives liver tumorigenesis. Furthermore, Agrin signalling through Lrp4-muscle-specific tyrosine kinase (MuSK) forms a critical oncogenic axis. Importantly, antibodies targeting Agrin reduced oncogenic signalling and tumour growth in vivo. Together, we demonstrate that Agrin is frequently upregulated and important for oncogenic property of HCC, and is an attractive target for antibody therapy.

/pdf/an-oncogenic-role-of-agrin-in-regulating-focal-adhesion-cud1hp6zkt.pdf

Sayan Chakraborty

Papers

Agrin as a Mechanotransduction Signal Regulating YAP through the Hippo Pathway

Kaposi's Sarcoma-Associated Herpesvirus Latency in Endothelial and B Cells Activates Gamma Interferon-Inducible Protein 16-Mediated Inflammasomes

A survey of image classification methods and techniques

An oncogenic role of Agrin in regulating focal adhesion integrity in hepatocellular carcinoma

Firefly Algorithm for Optimization of Scaling Factors During Embedding of Manifold Medical Information: An Application in Ophthalmology Imaging