Detecting stress caused by nitrogen deficit using deep learning techniques applied on plant electrophysiological data.

Plant electrophysiology carries a strong potential for assessing the health of a plant. Current literature for the classification of plant electrophysiology generally comprises classical methods based on signal features that portray a simplification of the raw data and introduce a high computational cost. The Deep Learning (DL) techniques automatically learn the classification targets from the input data, overcoming the need for precalculated features.

[From evidence to quality improvement to provide high value and patient centered care].

Evidence-based practice and quality improvement should be at the heart of healthcare and public health. However, their implementation remains insufficient which is reflected in Switzerland in the high frequency of low-value care, in the wide regional variation in care practices, and in the absence of quality monitoring for the majority of healthcare processes. It is necessary to strengthen the monitoring of quality, particularly that perceived by patients, to help strengthening high-value and patient centered care.

Electrophysiological assessment of plant status outside a Faraday cage using supervised machine learning.

Living organisms have evolved complex signaling networks to drive appropriate physiological processes in response to changing environmental conditions. Amongst them, electric signals are a universal method to rapidly transmit information. In animals, bioelectrical activity measurements in the heart or the brain provide information about health status.

In vivo and in vitro cytogenetic damage induced by sodium nitrite.

The mutagenicity of sodium nitrite was assayed by in vivo and in vitro experiments. The in vivo experiments were carried out in male rats and mice intragastrically treated twice, with an interval of 24 h, with nitrite in doses of 1.72, 5.