Conservation of symbiotic signaling since the most recent common ancestor of land plants.

Plants have colonized lands 450 million years ago. This terrestrialization was facilitated by developmental and functional innovations. Recent evo-devo approaches have demonstrated that one of these innovations was the mutualistic arbuscular mycorrhizal symbiosis (AMS).

DIENELACTONE HYDROLASE LIKE PROTEIN1 negatively regulates the KAI2-ligand pathway in Marchantia polymorpha.

Karrikins are smoke-derived butenolides that induce seed germination and photomorphogenesis in a wide range of plants. KARRIKIN INSENSITIVE2 (KAI2), a paralog of a strigolactone receptor, perceives karrikins or their metabolized products in Arabidopsis thaliana. Furthermore, KAI2 is thought to perceive an unidentified plant hormone, called KAI2 ligand (KL).

An ancestral function of strigolactones as symbiotic rhizosphere signals.

In flowering plants, strigolactones (SLs) have dual functions as hormones that regulate growth and development, and as rhizosphere signaling molecules that induce symbiosis with arbuscular mycorrhizal (AM) fungi. Here, we report the identification of bryosymbiol (BSB), an SL from the bryophyte Marchantia paleacea. BSB is also found in vascular plants, indicating its origin in the common ancestor of land plants.

Major components of the KARRIKIN INSENSITIVE2-dependent signaling pathway are conserved in the liverwort Marchantia polymorpha.

KARRIKIN INSENSITIVE2 (KAI2) was first identified as a receptor of karrikins, smoke-derived germination stimulants. KAI2 is also considered a receptor of an unidentified endogenous molecule called the KAI2 ligand. Upon KAI2 activation, signals are transmitted through the degradation of D53/SMXL proteins via MAX2-dependent ubiquitination.

Cuffless Blood Pressure Estimation from only the Waveform of Photoplethysmography using CNN.

Although the pulse transit time is generally used for blood pressure estimation without a cuff, a method of estimating blood pressure only from photoplethysmography (PPG) based on the relationship between pulse waveform and blood pressure has been studied. This can eliminate the need for an electrocardiogram and allow more continuous and simpler blood pressure measurement. Previous studies have proposed methods of machine learning by extracting features such as wave height and time difference, or generating features with an auto-encoder.

Features Extraction for Cuffless Blood Pressure Estimation by Autoencoder from Photoplethysmography.

Several studies have been proposed to estimate blood pressure (BP) with cuffless devices using only a Photoplethysmograph (PPG) sensor on the basis of the physiological knowledge that the PPG changes depend on the state of the cardiovascular system. In these studies, machine learning algorithms were used to extract various features from the wave height and the elapsed time from the rising point of the pulse wave to feature points have been used to estimate the BP. However, the accuracy is still not adequate to be used as medical equipment because their features cannot express fully information of the pulse waveform which changes according to the BP.