For instance, defoliation may suppress ABA signaling induced by water deficits , which would reduce berry ripening stimulation . In fact, ABA biosynthesis in leaves relies mostly on leaf induction rather than root signaling . There is a feedback mechanism between the production of auxin and cytokinin by leaves and roots, respectively; where auxins are biosynthesized by developing leaves and shoot tips and therefore stimulating root growth, and roots produce cytokinins that stimulate the growth of the aerial portions. Therefore, defoliation may not only reduce the amount of reduced carbon allocated to berries, but also to the ripening stimuli through endogenous plant growth regulators. Fruits are the fate of many growth regulators depending on the berry developmental stage , and thus, changes in crop load could potentially alter the balance between the amount stimuli and fates. Therefore, leaf area to fruit mass is typically correlated to berry must soluble solids as in the 2nd year of study . Although, it is possible to find situations in which crop level does not compensate for defoliation as in the 2nd year of study . In fact, it is rather unusual to find lack of effects on ripening when two thirds of the clusters are removed from such an early stage as the present study. Finding only a significant effect of fruit removal in total soluble solids in the 2nd year could also suggest cumulative effects of crop level. It could be hypothesized that in the 1st year bearing 100% of the clusters, growing raspberries in container while not showing a reduction in soluble solids, may have taken a toll on plant reserves.

However, neither root mass nor starch content were impacted by the crop level in our work. Palliotti and Cartechini performed cluster thinning on three varieties over three seasons and found that cluster thinning did not affect must soluble solids. In years where rainfall was more abundant , results of cluster thinning were compensated with larger berries , with compensation of berry size similar to our results. Precisely, these kinds of results are those that disturbed the correlation between leaf area to fruit mass and berry total soluble solids . This suggested that larger berries may offer higher resistance to increases in berry total soluble solids regardless of leaf area to fruit mass . This hypothesis was supported by the response of grapevines submitted to water deficits that had smaller berries with higher soluble solids despite having much lower carbon assimilation rates .Development, which encompassed the timing of all physiological events recorded , was delayed clearly by defoliation when treatments were in place, which excluded bud break and flowering. The initiation of each of these pheno-phases is quite complex as it may require more than one preexisting condition. For instance, the release from dormancy is often associated to the fulfillment of a chilling/thermal time accumulation requirement , which supported the observation that all grapevines in the same site as this experiment would have a similar date of bud break. However, entering and exiting dormancy is also concomitant with major events of mobilization of soluble carbohydrates that condition the response of the latent bud . Similarly, veraison may be modeled with thermal time but ultimately requires a sucrose stimulus . Leaf senescence of deciduous plants is largely induced by shorter days and cooler temperatures, but as evidenced in our work, defoliation treatments delayed it.

Other studies have suggested that leaves are able to sense source strength and delay leaf senescence accordingly . However, in our work, source strength was achieved through more leaves rather than better leaf net carbon assimilation performance. In both experimental years we witnessed the 33%L treatments assimilate more carbon compared to 100%L to compensate. However, it remains to be seen in future works if this is in fact a carbon starvation effect or an artifact of plant water status. In fact, high sugar levels are one of the signals inducing natural leaf senescence , and this can be modulated. Interestingly, this response was not conditioned by sink strength or differences in leaf area in the final year, only by the practice of defoliation in our study.In the third year of study, no treatments were applied and therefore, all effects observed are attributable to cumulative effect of previous years’ conditions. The so-called carryover effects have been discussed in relation to indirect observations, where the treatments were applied for several years or when historical series were analyzed . In the case of defoliation, much direct evidence of carryover effects exists. For instance, Jermini et al. showed how defoliation caused by downy mildew induced severe reductions in the successive year’s yield. Bennett et al. also reported severe reductions in yield, and these were attributed to reductions in clusters per vine, to reductions in berries per cluster, but not to changes in berry mass. In that study, there were changes in inflorescence per vine and flowers per inflorescence. Therefore, promoting root and canopy growth over the years has a strong cumulative effect on yields. Alternate bearing is an issue in some tree fruit crops such as mango, avocado, olive, pistachio, citrus, etc. [reviewed by Monselise and Goldschmidt ] and fruit removal in those crops is not only performed aiming for in-season effects, but also to maintain consistent yield over the seasons. The carryover effects of crop level in grapevine have been reported less frequently than defoliation. Our results suggested that in fact grapevine is a perennial crop not very sensitive to alternate bearing.

Yield was associated with dormant season precipitation or root and shoot starch content at bud burst . In our results, starch content of roots was only affected by defoliation in July and September samplings, which are coetaneous with sucrose stimulus to berry ripening. As root starch content fully recovered in all treatments, root mass was the only factor that would explain changes in yield in the successive season . In fact, in 2019, grapevines that were defoliated during the two previous seasons had lower root mass and fruit load as a carryover effect, which led to a faster recovery of starch reserves. Likewise, the carry over effects of defoliation were evident in leaf area, berries per cluster, and yield in the final year.Extracellular voltage recordings from in vitro cell cultures support the investigation of neural activity and dynamics. These recordings allow us to assess information processing in complex neuronal networks and enable discovery on a scale from single neuron firing patterns to local and long-range functional connectivity, network synchrony, and oscillatory activity. Longitudinal recordings are essential to capture features of neurodevelopment and dynamics: basic physiological properties of neuron development, how 2D and 3D cultures grow and change activity patterns, and what rhythms the activity may follow. Recordings across time are essential to study response to electrical or drug stimulus over weeks and months. Thus, longitudinal electrophysiology recordings can inform in vitro drug discovery and genetic screens. The further combination of longitudinal recordings and large numbers of parallel experimental replicates allow investigations to progress significantly faster and makes new experiments feasible. Also, scaling up experiments generates the large volume of data necessary for taking advantage of Machine Learning algorithms and creates a faster turnaround between hypothesis, experiment, and re-testing. In vitro culture models serve as a flexible system that is much easier to scale up than animal models, large plastic pots for plants especially when paired with developments in robotic automation, microfluidics, and probes. Longitudinal recordings from multi-channel experiments demand vast amounts of data and memory. The data is challenging to manage, especially since out-of-the-box hardware and software are often offline. Storage on physical disks usually requires manual monitoring to prevent running out of disk space and laborious transfer of data for backup or processing. Furthermore, many recording systems require a designated workspace for experiments with a physical computer nearby with cables or wireless transmission to stream data. Several open-source efforts have been created to provide more affordable and modifiable recording equipment. However, no software solutions exist to easily manage and control a large amount of electrophysiology equipment and data at once. Recent advances in commodity hardware allow for more affordable computing devices. The Internet of Things allows many devices to come online when needed and be relinquished when not needed, and protocols have been developed to effectively and securely manage and communicate with these devices. Affordable, internet-connected devices have already been developed for ECG, EEG, EMG, and heart rate variability monitoring. Furthermore, commodity cloud compute from major companies as well as academic coalitions has become widely available and many tools for downstream analysis to process voltage recordings are already offered online. However, data acquisition for in vitro cultures remains relatively isolated, as no platform exists to stream data online to link with these analysis infrastructures. One solution is to write software add-ons for existing data acquisition systems.

However, not all existing data acquisition systems are flexible or open in terms of data formats, programmability, and remote control. Additionally, channel count and price range are not always suitable for the desired application. To address these issues we created Piphys, an all-in-one electrophysiology and processing system, that can simultaneously record data from multiple channels in the μV scale and stream it to the cloud. The user interacts with the device through a dashboard website to view data and control experiment parameters. Both hardware and software are made available as open source. Piphys is based on a Raspberry Pi computer and eliminates the need for a desktop or laptop computer to manage an electrophysiology experiment or for an operator to be present in the lab to start a recording. The Raspberry Pi comes with a Unix-based operating system that can be easily programmed with many existing software libraries and tools. Overall, the low price and extreme flexibility of the Raspberry Pi significantly lowers the cost of the entire electrophysiology system, providing an opportunity for broader education and research opportunities. Piphys can be used with a wide range of electrode probes including, but not limited to, rigid 2D and flexible 3D microelectrode arrays, silicon probes, and tetrodes. The system is built for long-term experiments with the goal of full automation using programs that can optimize experimental variables. Here we detail the Piphys system’s functionality and validate its accuracy and reliability for measuring neural activity.The Piphys hardware records from neural tissue remotely using our versatile circuit board connecting to Intan RHD series recording chips to perform highly sensitive analog to digital conversion. Data from the Intan can be optionally preprocessed on-site using a Raspberry Pi computer and streamed to a cloud service where deeper sorting and analysis of detected spikes can be performed. Spike sorting analysis measures neural activity changes over time in individual neurons and networks of neurons, using features like spike waveform, frequency of activity, and correlation to the activity of nearby neurons.Design elements —The Raspberry Pi Model 3 B+ is a low-cost, small-scale, single-board computer. It can be programmed to interface with customized hardware with a standard data communication protocol. It also has an expandable memory space configured by a removable SD card. The Intan RHD2132 bio-amplifier chip is the key driver of the shield biopotential-sensing functionality. The chip amplifies voltage signals sensed by the electrodes and converts the analog signals to digital values for storage inside the Raspberry Pi computer. Circuit design—The key physical innovation in Piphys is a hardware expansion board that enables a Raspberry Pi computer to interface with an Intan RHD2132 bio-amplifier chip to perform electrophysiology. Firstly, the expansion shield provides different levels of power derivative from the +5V source input. The system requires a +5V 2.5A DC power source which is provided via a micro-USB or barrel jack. In this set of experiments we used a power supply plugged into the wall outlet. The +5V input powers both the Pi and shield, and can be supplied either through the power barrel on the shield or through the micro-USB on the Pi for flexibility. On the shield, the power source is filtered through ferrite beads to remove high-frequency power line noise. The +5V source is converted to a +3.5V source for the Intan RHD2132 bio-amplifier chip and a +3.3V for the SN65LVDT41 chip. Conversion is performed by low-noise linear voltage regulators to smooth and isolate any fluctuations from the power supply. Secondly, the expansion shield provides translation between signal types.