Effect of High-Tunnel and Open-Field Production on the Yield, Cannabinoids, and Volatile Profiles in Industrial Hemp (Cannabis sativa L.) Inflorescence.

This study discovered the impact of high-tunnel (i.e., unheated greenhouse) and open-field production on two industrial hemp cultivars (SB1 and CJ2) over their yield parameters, cannabinoid development, and volatile profiles. Development of neutral cannabinoids (CBD, THC, and CBC), acidic cannabinoids (CBDA, THCA, and CBCA), and total cannabinoids during floral maturation were investigated. The volatile profiles of hemp flowers were holistically compared via HS-SPME-GC/MS. Findings indicated a high tunnel as an efficient practice for achieving greater total weight, stem number, and caliper, especially in the SB1 cultivar. Harvesting high-tunnel-grown SB1 cultivars during early flower maturation could obtain a high CBD yield while complying with THC regulations. Considering the volatile profiles, hemp flowers mainly consisted of mono- and sesquiterpenoids, as well as oxygenated mono- and sesquiterpenoids. Volatile analysis revealed the substantial impact of cultivars on the volatile profile compared to the production systems.

Timing is Everything: Surgical Outcomes for SARS-CoV-2 Positive Patients.

BACKGROUND: A debate remains on how long to postpone surgery after testing positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We aimed to determine surgical outcomes at different time points after a positive SARS-CoV-2 test. METHODS: This cohort study included non-preoperative critically ill adult surgical patients from 5/2020-5/2021 and a subset of SARS-CoV-2 positive patients 15-30 days before surgery from 5/2020-12/2021. Demographics, comorbidities, surgical variables, and outcomes were compared between SARS-CoV-2 positive patients within 50 days before surgery to SARS-CoV-2 negative surgical patients. Cases were stratified based on the timing of SARS-CoV-2 positivity before surgery in days (< 15, 15-30, > 30). Outcomes were compared between strata and against SARS-CoV-2 negative controls. A multivariable model was built to determine the association that the timing of SARS-CoV-2 positivity has on the odds of a major complication. RESULTS: The SARS-CoV-2 positive cohort had 262 patients compared to 1,840 SARS-CoV-2 negative patients. Timing strata contained 145 (< 15 days), 53 (15-30 days), and 64 (> 30 days). The SARS-CoV-2 positive group had a higher incidence of comorbidities (87.4% vs. 57.2%) and underwent more emergent surgery (45.7% vs. 9.3%). The odds of major complications in patients positive for SARS-CoV-2 before surgery were 1.88 (1.13-3.15) (< 15 days), 0.43 (0.14-1.30) (15-30 days), and 0.98 (0.44-2.21) (31-50 days) times the odds in SARS-CoV-2 negative surgery patients when controlling for other variables. CONCLUSION: Timing of SARS-CoV-2 positivity before surgery has an impact on major complications. In certain cases, it may be appropriate to postpone surgery 14 days after SARS-CoV-2 positivity.

Neurophysiological Evidence for Cognitive Map Formation during Sequence Learning.

Humans deftly parse statistics from sequences. Some theories posit that humans learn these statistics by forming cognitive maps, or underlying representations of the latent space which links items in the sequence. Here, an item in the sequence is a node, and the probability of transitioning between two items is an edge. Sequences can then be generated from walks through the latent space, with different spaces giving rise to different sequence statistics. Individual or group differences in sequence learning can be modeled by changing the time scale over which estimates of transition probabilities are built, or in other words, by changing the amount of temporal discounting. Latent space models with temporal discounting bear a resemblance to models of navigation through Euclidean spaces. However, few explicit links have been made between predictions from Euclidean spatial navigation and neural activity during human sequence learning. Here, we use a combination of behavioral modeling and intracranial encephalography (iEEG) recordings to investigate how neural activity might support the formation of space-like cognitive maps through temporal discounting during sequence learning. Specifically, we acquire human reaction times from a sequential reaction time task, to which we fit a model that formulates the amount of temporal discounting as a single free parameter. From the parameter, we calculate each individual's estimate of the latent space. We find that neural activity reflects these estimates mostly in the temporal lobe, including areas involved in spatial navigation. Similar to spatial navigation, we find that low-dimensional representations of neural activity allow for easy separation of important features, such as modules, in the latent space. Lastly, we take advantage of the high temporal resolution of iEEG data to determine the time scale on which latent spaces are learned. We find that learning typically happens within the first 500 trials, and is modulated by the underlying latent space and the amount of temporal discounting characteristic of each participant. Ultimately, this work provides important links between behavioral models of sequence learning and neural activity during the same behavior, and contextualizes these results within a broader framework of domain general cognitive maps.

Virtual resection predicts surgical outcome for drug-resistant epilepsy.

Patients with drug-resistant epilepsy often require surgery to become seizure-free. While laser ablation and implantable stimulation devices have lowered the morbidity of these procedures, seizure-free rates have not dramatically improved, particularly for patients without focal lesions. This is in part because it is often unclear where to intervene in these cases. To address this clinical need, several research groups have published methods to map epileptic networks but applying them to improve patient care remains a challenge. In this study we advance clinical translation of these methods by: (i) presenting and sharing a robust pipeline to rigorously quantify the boundaries of the resection zone and determining which intracranial EEG electrodes lie within it; (ii) validating a brain network model on a retrospective cohort of 28 patients with drug-resistant epilepsy implanted with intracranial electrodes prior to surgical resection; and (iii) sharing all neuroimaging, annotated electrophysiology, and clinical metadata to facilitate future collaboration. Our network methods accurately forecast whether patients are likely to benefit from surgical intervention based on synchronizability of intracranial EEG (area under the receiver operating characteristic curve of 0.89) and provide novel information that traditional electrographic features do not. We further report that removing synchronizing brain regions is associated with improved clinical outcome, and postulate that sparing desynchronizing regions may further be beneficial. Our findings suggest that data-driven network-based methods can identify patients likely to benefit from resective or ablative therapy, and perhaps prevent invasive interventions in those unlikely to do so.

High interictal connectivity within the resection zone is associated with favorable post-surgical outcomes in focal epilepsy patients.

Patients with drug-resistant focal epilepsy are often candidates for invasive surgical therapies. In these patients, it is necessary to accurately localize seizure generators to ensure seizure freedom following intervention. While intracranial electroencephalography (iEEG) is the gold standard for mapping networks for surgery, this approach requires inducing and recording seizures, which may cause patient morbidity. The goal of this study is to evaluate the utility of mapping interictal (non-seizure) iEEG networks to identify targets for surgical treatment. We analyze interictal iEEG recordings and neuroimaging from 27 focal epilepsy patients treated via surgical resection. We generate interictal functional networks by calculating pairwise correlation of iEEG signals across different frequency bands. Using image coregistration and segmentation, we identify electrodes falling within surgically resected tissue (i.e. the resection zone), and compute node-level and edge-level synchrony in relation to the resection zone. We further associate these metrics with post-surgical outcomes. Greater overlap between resected electrodes and highly synchronous electrodes is associated with favorable post-surgical outcomes. Additionally, good-outcome patients have significantly higher connectivity localized within the resection zone compared to those with poorer postoperative seizure control. This finding persists following normalization by a spatially-constrained null model. This study suggests that spatially-informed interictal network synchrony measures can distinguish between good and poor post-surgical outcomes. By capturing clinically-relevant information during interictal periods, our method may ultimately reduce the need for prolonged invasive implants and provide insights into the pathophysiology of an epileptic brain. We discuss next steps for translating these findings into a prospectively useful clinical tool.