Real-time optical manipulation of cardiac conduction in intact hearts

Optogenetics has provided new insights in cardiovascular research, leading to new methods for cardiac pacing, resynchronization therapy and cardioversion. Although these interventions have clearly demonstrated the feasibility of cardiac manipulation, current optical stimulation strategies do not take into account cardiac wave dynamics in real time. Here, we developed an all‐optical platform complemented by integrated, newly developed software to monitor and control electrical activity in intact mouse hearts. The system combined a wide‐field mesoscope with a digital projector for optogenetic activation. Cardiac functionality could be manipulated either in free‐run mode with submillisecond temporal resolution or in a closed‐loop fashion: a tailored hardware and software platform allowed real‐time intervention capable of reacting within 2 ms. The methodology was applied to restore normal electrical activity after atrioventricular block, by triggering the ventricle in response to optically mapped atrial activity with appropriate timing. Real‐time intraventricular manipulation of the propagating electrical wavefront was also demonstrated, opening the prospect for real‐time resynchronization therapy and cardiac defibrillation. Furthermore, the closed‐loop approach was applied to simulate a re‐entrant circuit across the ventricle demonstrating the capability of our system to manipulate heart conduction with high versatility even in arrhythmogenic conditions. The development of this innovative optical methodology provides the first proof‐of‐concept that a real‐time optically based stimulation can control cardiac rhythm in normal and abnormal conditions, promising a new approach for the investigation of the (patho)physiology of the heart

Preliminary study on in vivo rooting of ornamental plants growing on peat-free growing media

Studies on the use of peat-free growing media to grow potted ornamental plants are nowadays increasing, due to environmental concerns around the exploitation of peat, but these kind of studies are lacking with respect to cutting production. In this work, we investigated rhizogenesis on cuttings of four ornamental species (Viburnum rhytidophyllum L., Pyracantha koidzumii × P. coccinea 'Mohave', Prunus laurocerasus L., Euonymus japonicus Thunb., Ligustrum sinense Lour.) planted on the following growing media: 1) peat:pumice 70:30 v v-1 (control); 2) coconut coir dust:pumice 70:30 v v-1; 3) coconut coir dust:green compost 55:45 v v-1; 4) coconut coir dust:green compost: Stabilized wood fiber 40:30:30 v v-1; 5) coconut coir dust 100 v v-1; 6) green compost 100 v v-1; 7) stabilized wood fiber 100 v v-1. Twelve cm-leafed-cuttings (with 4-6 leaves) were prepared and treated with 4000 ppm indole-3-butyric acid (IBA), 4000 ppm 1-naphthaleneacetic acid (NAA), 4000 ppm IBA+NAA or without hormones. After 120 days, rooting and shooting were evaluated considering root dry weight, length and the root area as main performance indicators. Cuttings grown on substrates with green compost and coconut coir dust generally tended to have same performances of cuttings grown on peat-based media while the cuttings grown on stabilized wood fiber media showed lower rooting grown. In general, data showed that green compost and coconut coir dust could represent excellent substitutes and alternatives to peat for the cultivation of cuttings in commercial nursery providing innovation elements regarding the total elimination of peat during the propagation phase of plants

Dynamic Bayesian network for crop growth prediction in greenhouses

The paper presents an Internet-of-Things based agricultural decision support system for crop growth. A dynamic Bayesian network (DBN) relates indicative parameters of crop development to environmental control parameters via unobserved (hidden) Markov states. The expectation-maximization algorithm is used to track the states and to learn the parameters of the DBN. The steady state information is then used to derive a predictor for the measurement data a few days ahead. The proposed DBN avoids time-consuming training cultivation cycles, as only data of the current cultivation cycle are available to the algorithm. Three cultivation cycles of lettuce have been used to test the performance of the proposed DBN. The environmental parameters were temperature, solar irradiance and vapor-pressure deficit. The measurement data include evapotranspiration at granularity equal one day, and leaf-area index and dry weight, at granularity equal one week. It turned out that accurate measurement data prediction a few days ahead is possible even if the number of data samples is low