Effect of an Artificial Caudal Fin on the Performance of a Biomimetic Fish Robot Propelled by Piezoelectric Actuators

This paper addresses the design of a biomimetic fish robot actuated by piezoeeramic actuators and the effect of artificial caudal fins on the fish robot＇s performance. The limited bending displacement produced by a lightweight piezocomposite actuator was amplified and transformed into a large tail beat motion by means of a linkage system. Caudal fins that mimic the shape of a mackerel fin were fabricated for the purpose of examining the effect of caudal fm characteristics on thrust production at an operating frequency range. The thickness distribution of a real mackerel＇s fin was measured and used to design artificial caudal fins. The thrust performance of the biomimetic fish robot propelled by fins of various thicknesses was examined in terms of the Strouhal number, the Froude number, the Reynolds number, and the power consumption. For the same fm area and aspect ratio, an artificial caudal fin with a distributed thickness shows the best forward speed and the least power consumption.     

Stimulatory Effects of Extracellular Vesicles Derived from Leuconostoc holzapfelii That Exists in Human Scalp on Hair Growth in Human Follicle Dermal Papilla Cells

Human hair follicle dermal papilla cells (HFDPCs) located in hair follicles (HFs) play a pivotal role in hair follicle morphogenesis, hair cycling, and hair growth. Over the past few decades, probiotic bacteria (PB) have been reported to have beneficial effects such as improved skin health, anti-obesity, and immuno-modulation for conditions including atopic dermatitis and inflammatory bowel disease (IBD). PB can secrete 50~150 nm sized extracellular vesicles (EVs) containing microbial DNA, miRNA, proteins, lipids, and cell wall components. These EVs can regulate communication between bacteria or between bacteria and their host. Although numerous biological effects of PB-EVs have been reported, the physiological roles of Leuconostoc holzapfelii (hs-Lh), which is isolated from human scalp tissue, and the extracellular vesicles derived from them (hs-LhEVs) are largely unknown. Herein, we investigated the effects of hs-LhEVs on hair growth in HFDPCs. We show that hs-LhEVs increase cell proliferation, migration, and regulate the cell cycle. Furthermore, hs-LhEVs were found to modulate the mRNA expression of hair-growth-related genes in vitro. These data demonstrate that hs-LhEVs can reduce apoptosis by modulating the cell cycle and promote hair growth by regulation via the Wnt/β-catenin signal transduction pathway.     

In vitro machine learning-based CAR T immunological synapse quality measurements correlate with patient clinical outcomes

The human immune system consists of a highly intelligent network of billions of independent, self-organized cells that interact with each other. Machine learning (ML) is an artificial intelligence (AI) tool that automatically processes huge amounts of image data. Immunotherapies have revolutionized the treatment of blood cancer. Specifically, one such therapy involves engineering immune cells to express chimeric antigen receptors (CAR), which combine tumor antigen specificity with immune cell activation in a single receptor. To improve their efficacy and expand their applicability to solid tumors, scientists optimize different CARs with different modifications. However, predicting and ranking the efficacy of different "off-the-shelf" immune products (e.g., CAR or Bispecific T-cell Engager [BiTE]) and selection of clinical responders are challenging in clinical practice. Meanwhile, identifying the optimal CAR construct for a researcher to further develop a potential clinical application is limited by the current, time-consuming, costly, and labor-intensive conventional tools used to evaluate efficacy. Particularly, more than 30 years of immunological synapse (IS) research data demonstrate that T cell efficacy is not only controlled by the specificity and avidity of the tumor antigen and T cell interaction, but also it depends on a collective process, involving multiple adhesion and regulatory molecules, as well as tumor microenvironment, spatially and temporally organized at the IS formed by cytotoxic T lymphocytes (CTL) and natural killer (NK) cells. The optimal function of cytotoxic lymphocytes (including CTL and NK) depends on IS quality. Recognizing the inadequacy of conventional tools and the importance of IS in immune cell functions, we investigate a new strategy for assessing CAR-T efficacy by quantifying CAR IS quality using the glass-support planar lipid bilayer system combined with ML-based data analysis. Previous studies in our group show that CAR-T IS quality correlates with antitumor activities in vitro and in vivo. However, current manually quantified IS quality data analysis is time-consuming and labor-intensive with low accuracy, reproducibility, and repeatability. In this study, we develop a novel ML-based method to quantify thousands of CAR cell IS images with enhanced accuracy and speed. Specifically, we used artificial neural networks (ANN) to incorporate object detection into segmentation. The proposed ANN model extracts the most useful information to differentiate different IS datasets. The network output is flexible and produces bounding boxes, instance segmentation, contour outlines (borders), intensities of the borders, and segmentations without borders. Based on requirements, one or a combination of this information is used in statistical analysis. The ML-based automated algorithm quantified CAR-T IS data correlates with the clinical responder and non-responder treated with Kappa-CAR-T cells directly from patients. The results suggest that CAR cell IS quality can be used as a potential composite biomarker and correlates with antitumor activities in patients, which is sufficiently discriminative to further test the CAR IS quality as a clinical biomarker to predict response to CAR immunotherapy in cancer. For translational research, the method developed here can also provide guidelines for designing and optimizing numerous CAR constructs for potential clinical development.Trial Registration: ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00881920","term_id":"NCT00881920"}}NCT00881920.     

Gold and silver plasmonic nanoprobes trace the positions of histone codes

We visualized the distribution of heterochromatin in a single nucleus using plasmonic nanoparticle-conjugated H3K9me3 and H3K27me3 antibodies. Due to distance-dependent plasmonic coupling effects between nanoprobes, their scattering spectra shift to longer wavelengths as the distance between heterochromatin histone markers reduced during oncogene-induced senescence (OIS). These observations were supported by simulating scattering profiles based on considerations of particle numbers, interparticle distances, and the spatial arrangements of plasmonic nanoprobes. Using this plasmon-based colourimetric imaging, we estimated changes in distances between H3K9me3 and H3K27me3 during the formation of senescence-associated heterochromatin foci in OIS cells. We anticipate that the devised analytical technique combined with high-spatial imaging and spectral simulation will eventually lead to a new means of diagnosing and monitoring disease progression and cellular senescence.     

RNase H is an exo- and endoribonuclease with asymmetric directionality,depending on the binding mode to the structural variants of RNA:DNA hybrids

RNase H is involved in fundamental cellular processes and is responsible for removing the short stretch of RNA from Okazaki fragments and the long stretch of RNA from R-loops. Defects in RNase H lead to embryo lethality in mice and Aicardi-Goutieres syndrome in humans, suggesting the importance of RNase H. To date, RNase H is known to be a non-sequence-specific endonuclease, but it is not known whether it performs other functions on the structural variants of RNA:DNA hybrids. Here, we used Escherichia coli RNase H as a model, and examined its catalytic mechanism and its substrate recognition modes, using single-molecule FRET. We discovered that RNase H acts as a processive exoribonuclease on the 3′ DNA overhang side but as a distributive non-sequence-specific endonuclease on the 5′ DNA overhang side of RNA:DNA hybrids or on blunt-ended hybrids. The high affinity of previously unidentified double-stranded (ds) and single-stranded (ss) DNA junctions flanking RNA:DNA hybrids may help RNase H find the hybrid substrates in long genomic DNA. Our study provides new insights into the multifunctionality of RNase H, elucidating unprecedented roles of junctions and ssDNA overhang on RNA:DNA hybrids.     

The structure of AcrIE4-F7 reveals a common strategy for dual CRISPR inhibition by targeting PAM recognition sites

Bacteria and archaea use the CRISPR-Cas system to fend off invasions of bacteriophages and foreign plasmids. In response, bacteriophages encode anti-CRISPR (Acr) proteins that potently inhibit host Cas proteins to suppress CRISPR-mediated immunity. AcrIE4-F7, which was isolated from Pseudomonas citronellolis, is a fused form of AcrIE4 and AcrIF7 that inhibits both type I-E and type I-F CRISPR-Cas systems. Here, we determined the structure of AcrIE4-F7 and identified its Cas target proteins. The N-terminal AcrIE4 domain adopts a novel α-helical fold that targets the PAM interaction site of the type I-E Cas8e subunit. The C-terminal AcrIF7 domain exhibits an αβ fold like native AcrIF7, which disables target DNA recognition by the PAM interaction site in the type I-F Cas8f subunit. The two Acr domains are connected by a flexible linker that allows prompt docking onto their cognate Cas8 targets. Conserved negative charges in each Acr domain are required for interaction with their Cas8 targets. Our results illustrate a common mechanism by which AcrIE4-F7 inhibits divergent CRISPR-Cas types.