The utility of intersimple sequence repeat (ISSR) markers for identification of English or Persian walnut (Juglans regia L.) cultivars was explored. Four cultivars were screened with 47 ISSR primers; eight of these primers, which generated reproducible and informative data, were selected for further study. Two individuals from each of 48 cultivars, including many currently important in the California walnut industry as well as accessions from Europe and Asia, were then examined with the eight ISSR primers. Polymerase chain reaction (PCR) products were separated on agarose gels and stained with ethidium bromide. Fifty-four bands were scored as present or absent in each cultivar; of these, 31 (57%) were polymorphic among the 48 cultivars. Combined data from the eight ISSR primers provided a unique fingerprint for each of the cultivars tested. Fifteen of the cultivars could be distinguished from all others with just one primer, 31 with a minimum of two primers, and two required three primers. Pairwise genetic distances between the cultivars were calculated and a dendrogram was generated using the neighbor-joining algorithm. Some of the groupings in the dendrogram corresponded to groups which, based on known pedigrees, are genealogically closely related. Others included accessions from diverse genetic and/or geographic origins. These results can be attributed to a combination of the limitations of the ISSR method for inferring genetic relationships, on the one hand, and the complex history of walnut cultivar development involving extensive exchange and breeding of germplasm from different geographic regions, on the other.

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Objective. Neuromodulation technology hold promise to treat several conditions where physiological mechanisms of neural activity have been affected. Personalization of neurostimulation protocols is a mandatory step to make the treatment efficient and the devices highly effective. The interfaces between the targeted nervous tissue and the neurotechnology (i.e., human-machine link or neural interface) usually requires a constant re-calibration of the neuromodulation parameters, due to many different biological and microtechnological phenomena happening over-time. This adaptation of the optimal stimulation parameters generally involves an expert-mediated re-calibration, with correspondent economic burden, compromised every-day usability and efficacy of the device, and consequent loss of time and increased discomfort of patients going back to clinics to get the device tuned. We aim to construct an adaptable AI-based system, able to compensate for these changes autonomously. Approach. We exploited Gaussian Process-based Bayesian Optimization (GPBO) methods to re-adjust the neurostimulation parameters in realistic neuroprosthetic data by integrating the temporal information into the process to tackle the issue of time variability. To this aim, we built a predictive model able to tune the neuromodulation parameters for two separate crucial scenarios where re-calibration is needed. In the first one, we built a model able to find the optimal active sites to use from a multichannel electrode, i.e. able to cover a certain function for a neuroprosthesis, which in this specific case was the evoked-sensation location variability. In the second one, we propose an algorithm able to adapt the injected charge required to obtain a functional neural activation (e.g., perceptual threshold variability). Main results. Our automatic algorithm can successfully adapt neurostimulation parameters to perceptual threshold changes and to evoked-sensation location changes over-time. These findings show an automatic and fast way of tackling the inevitable variability of neurostimulation parameters over time. It will potentially enable a widespread and a better usability of this technology upon validation in other frameworks, while decreasing the time and the costs of the treatment. This work suggests the exploitation of AI-based methods for developing the next generation of “smart” neuromodulation devices.