A fungal polysaccharide from Fomitopsis officinalis as a multi-target molecule to combat cancer.

Some macrofungi have a long history of being used as traditional or folk medicines, making significant contributions to human health. To discover bioactive molecules with potential anticancer properties, a homogeneous heteropolysaccharide (FOBP90-1) was purified from the medicinal macrofungus Fomitopsis officinalis. FOBP90-1 was found to have a molecular weight of 2.87 × 104 g/mol and mainly consist of →6)-α-d-Galp-(1→, →2,6)-α-d-Galp-(1→, →3)-α-l-Fucp-(1→, →6)-ß-d-Glcp-(1→, α-d-Manp-(1→, and 3-O-Me-α-l-Fucp-(1→ according to UV, FT-IR, methylation analysis, and NMR data. In addition to its structural properties, FOBP90-1 displayed anticancer activity in zebrafish models. The following mechanistic analysis discovered that the in vivo antitumor effect was linked to immune activation and angiogenesis inhibition. These effects were mediated by the interactions of FOBP90-1 with TLR-2, TLR-4, PD-L1, and VEGFR-2, as determined through a series of experiments involving cells, transgenic zebrafish, molecular docking simulations, and surface plasmon resonance (SPR). All the experimental findings have demonstrated that FOBP90-1, a purified fungal polysaccharide, is expected to be utilized as a cancer treatment agent.

Evaluating Perceptual Bias During Geometric Scaling of Scatterplots.

Scatterplots are frequently scaled to fit display areas in multi-view and multi-device data analysis environments. A common method used for scaling is to enlarge or shrink the entire scatterplot together with the inside points synchronously and proportionally. This process is called geometric scaling. However, geometric scaling of scatterplots may cause a perceptual bias, that is, the perceived and physical values of visual features may be dissociated with respect to geometric scaling. For example, if a scatterplot is projected from a laptop to a large projector screen, then observers may feel that the scatterplot shown on the projector has fewer points than that viewed on the laptop. This paper presents an evaluation study on the perceptual bias of visual features in scatterplots caused by geometric scaling. The study focuses on three fundamental visual features (i.e., numerosity, correlation, and cluster separation) and three hypotheses that are formulated on the basis of our experience. We carefully design three controlled experiments by using well-prepared synthetic data and recruit participants to complete the experiments on the basis of their subjective experience. With a detailed analysis of the experimental results, we obtain a set of instructive findings. First, geometric scaling causes a bias that has a linear relationship with the scale ratio. Second, no significant difference exists between the biases measured from normally and uniformly distributed scatterplots. Third, changing the point radius can correct the bias to a certain extent. These findings can be used to inspire the design decisions of scatterplots in various scenarios.