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Corn ( Zea mays ) biomass accumulation and nutrient uptake by the six-leaf collar (V6) growth stage are low, and therefore, synchronizing nutrient supply with crop demand could potentially minimize nutrient loss and improve nutrient use efficiency. Knowledge of corn’s response to nutrient stress in the early growth stages could inform such nutrient management. Field studies were conducted to assess corn recovery from when no fertilizer application is made until the V6 growth stage, and thereafter, applying fertilizer rates as those in non-stressed conditions. The early season nutrient stress and non-stress conditions received the same amount of nutrients. As the availability of nutrients for plant uptake is largely dependent on soil moisture, corn recovery from the early season nutrient stress was assessed under different soil moisture regimes induced via irrigation scheduling at 50% and 80% field capacity under overhead and subsurface drip irrigation (SSDI) systems. Peanut ( Arachis hypogaea ) was the previous crop under all conditions, and the fields were under cereal rye ( Secale cereale ) cover crop prior to planting corn. At the V6 growth stage, the nutrient concentrations of the early season-stressed crops, except for copper, were above the minimum threshold of sufficiency ranges reported for corn. However, the crops showed poor growth, with biomass accumulation being reduced by over 50% compared to non-stressed crops. Also, the uptake of all nutrients was significantly lower under the early season nutrient stress conditions. The recovery of corn from the early season nutrient stress was low. Compared to non-stress conditions, the early season nutrient stress caused 1.58 Mg ha -1 to 3.4 Mg ha -1 yield reduction. The percent yield reduction under the SSDI system was 37.6-38.2% and that under the overhead irrigation system was 11.7-13%. The high yield reduction from the early season nutrient stress under the SSDI system was because of water stress conditions in the topsoil soil layer. The findings of the study suggest ample nutrient supply in the early season growth stage is critical for corn production, and thus, further studies are recommended to determine the optimum nutrient supply for corn at the initial growth stages.

The phytocannabinoid cannabinol (CBN) has a potential mechanism of action as an alternative sleep aid but there is minimal evidence to support its effectiveness. The aim of this randomized, double-blind, placebo-controlled study was to assess the safety and effects of three formulations of a hemp-derived CBN sleep aid, TruCBN™ [25 mg (n = 206), 50 mg (n = 205), 100 mg (n = 203)], on sleep quality (PROMIS Sleep Disturbance 8A), relative to placebo (n = 204). The effectiveness and safety of these formulations relative to 4 mg of melatonin (n = 202) was assessed. Exploratory measures were stress (PROMIS Stress 4A), anxiety (Anxiety 4A), pain (PROMIS™ PEG), and well-being (WHO 5). All groups and the 4 mg melatonin group experienced significant improvement in sleep quality relative to the placebo group with no significant differences between any group and the melatonin group. Participants taking 100 mg showed a larger decrease in stress compared to the placebo group. There were no significant differences in anxiety, pain, well-being, or the frequency of side effects between any group and the placebo group. There was no significant difference in improvements in sleep quality between any of the treatment groups and the 4 mg melatonin group. Orally ingested CBN, at 25 mg, 50 mg, and 100 mg, is a safe and effective alternative for the improvement of sleep.

Background/Objectives: Anxiety and stress are interrelated and connected to reduced health-related quality of life. Botanicals such as Apocynum venetum L. (A. venetum) have been shown to improve health outcomes. No human studies have been conducted in a diverse large group of healthy adults in the US. The purpose of this randomized, double-blind, placebo-controlled study was to evaluate the effects of Venetron® Rafuma (A. venetum leaf extract) on self-reported anxiety levels and overall health outcomes compared to placebo. Methods: Healthy adults (N = 476) seeking improvement in self-reported anxiety and stress were randomly assigned to receive 50 mg of A. venetum (n = 234) or placebo (n = 242) for 6 weeks. Feelings of anxiety were assessed at baseline and weekly using Patient-Reported Outcomes Measurement Information System (PROMIS™) Anxiety 8A. Perceived stress, sleep quality, and cognitive function were evaluated at baseline and weekly using validated assessments. A linear mixed-effects regression model was used to compare the change in health outcome scores between active and placebo groups. Results: A total of 370 participants completed at least one additional assessment and were included in the analysis: 179 in the active arm and 191 in the placebo arm. There was a significant difference between the groups in the rate of improvement in perceived stress and sleep disturbance. The active group was significantly more likely to experience a Minimal Clinically Important Difference (MCID) in their perceived stress and marginally significantly more likely to experience an MCID in their feelings of anxiety. Participants who reported experiencing side effects did not significantly differ between arms. Conclusions: Venetron® may be safe and effective therapy for stress and sleep disturbance among those suffering from feelings of anxiety.

Contemporary row‐crop practices in the mid‐southern United States contribute to declining water tables and impairment of downstream waters, threatening the sustainability of irrigated agriculture and vital ecosystem services. This research was conducted to determine whether agro‐hydrology can be altered by cover crop or traffic pattern. The effects of cover crop Austrian winter pea [Pisum sativum var. arvense (L.) Poiret], crimson clover [Trifolium incarnatum L.], cereal rye [Secale cereal L.], and tillage radish [Raphanus sativus L.] and traffic pattern (traffic, nontraffic) on runoff, erosion, and nutrient transport from a furrow irrigation event were investigated at Stoneville, MS on a Commerce very fine sandy loam (a fine‐silty, mixed, superactive, nonacid, thermic Fluvaquentic Endoaquept). Relative to winter fallow, the inclusion of a cover crop reduced erosion by at least 16% but had no effect on runoff or the transport of total P, total Kjeldahl nitrogen (TKN), soluble reactive phosphate (SRP), NH4–N, or NO3–N. Eliminating traffic in furrows reduced runoff, erosion, and SRP transport up to 13%. Traffic pattern had no effect on the transport of total P, TKN, NH4–N, or NO3–N. Regardless of treatment, total N loss never exceeded 0.2% of N applied as inorganic fertilizer. This research demonstrates that cover crop and equipment traffic pattern can be manipulated in reduced tillage systems to improve the sustainability of irrigated agriculture and ecosystem services in the mid‐southern United States. Core Ideas Cover crop had no effect on runoff but reduced erosion at least 16%. Eliminating traffic in furrows decreased runoff and erosion by 11%. N transport from simulated irrigation events was <0.2% of annual N inputs. Traffic compaction is an overlooked hindrance to irrigation efficiency.

Eye tracking technology has become a powerful tool for understanding how people view and understand visual stimuli. These technologies have been used for years to study how developers read source code when accomplishing various software engineering tasks, however the methods used make little distinction between viewing source code as opposed to generic visual stimuli. Current eye tracking approaches rely predominately on Areas of Interest (AOIs) to match a user’s gazes to specific areas of a fixed image. This approach supports the viewing of smaller code snippets where each line or token can be assigned an AOI. However, files must be predetermined and the AOIs must be plotted manually, meaning that using a larger source code project more reminiscent of real-world systems becomes unfeasible. Today’s software developers work with software containing a multitude of source code files that may be scrolled through and switched between at any given time and iTrace is designed to give developers that freedom during eye tracking studies. The thesis presents the development, architecture, and usage of the iTrace project as an eye tracking infrastructure for recording data within commonly used development environments. iTrace ties directly into development environments such as Microsoft Visual Studio and Eclipse with the use of editor plugins to gather information dynamically and directly from the source. This approach permits the user to scroll through and switch between files as needed during the recording session. A robust post-processing toolkit allows iTrace to automatically generate additional syntactic information by matching the line and column a gaze falls on to a token within the source code file that is viewed. This type of contextual information is often lost when using AOIs where a code snippet is typically isolated from the larger body of source code. Additionally, several fixation filters are currently implemented which can be used to group gazes into fixations directly within the iTrace infrastructure. All this information is gathered into an easy-to-use SQLite database. The more realistic setting iTrace supports allows researchers to verify how well previous AOI based results extend to a more realistic development setting and develop new studies that can fully leverage the features of an integrated development environment.

BackgroundPhysician responsiveness to patient preferences for depression treatment may improve treatment adherence and clinical outcomes.ObjectiveTo examine associations of patient treatment preferences with types of depression treatment received and treatment adherence among Veterans initiating depression treatment.DesignPatient self-report surveys at treatment initiation linked to medical records.SettingVeterans Health Administration (VA) clinics nationally, 2018–2020.ParticipantsA total of 2582 patients (76.7% male, mean age 48.7 years, 62.3% Non-Hispanic White)Main MeasuresPatient self-reported preferences for medication and psychotherapy on 0–10 self-anchoring visual analog scales (0=“completely unwilling”; 10=“completely willing”). Treatment receipt and adherence (refilling medications; attending 3+ psychotherapy sessions) over 3 months. Logistic regression models controlled for socio-demographics and geographic variables.Key ResultsMore patients reported strong preferences (10/10) for psychotherapy than medication (51.2% versus 36.7%, McNemar χ21=175.3, p<0.001). A total of 32.1% of patients who preferred (7–10/10) medication and 21.8% who preferred psychotherapy did not receive these treatments. Patients who strongly preferred medication were substantially more likely to receive medication than those who had strong negative preferences (odds ratios [OR]=17.5; 95% confidence interval [CI]=12.5–24.5). Compared with patients who had strong negative psychotherapy preferences, those with strong psychotherapy preferences were about twice as likely to receive psychotherapy (OR=1.9; 95% CI=1.0–3.5). Patients who strongly preferred psychotherapy were more likely to adhere to psychotherapy than those with strong negative preferences (OR=3.3; 95% CI=1.4–7.4). Treatment preferences were not associated with medication or combined treatment adherence. Patients in primary care settings had lower odds of receiving (but not adhering to) psychotherapy than patients in specialty mental health settings. Depression severity was not associated with treatment receipt or adherence.ConclusionsMismatches between treatment preferences and treatment type received were common and associated with worse treatment adherence for psychotherapy. Future research could examine ways to decrease mismatch between patient preferences and treatments received and potential effects on patient outcomes.

Water level declines in the Mississippi River Valley Alluvial Aquifer (MRVAA) are attributed largely to withdrawals for rice (Oryza sativa L.) irrigation. This study was performed to determine if alternative irrigation strategies for rice could reduce withdrawal from the MRVAA without having an adverse effect on yield and profitability. Research was conducted at 19 on‐farm locations across the Delta region of Mississippi from 2014 through 2016 to determine the effects of irrigation water management practice, i.e., conventional flood via cascade (CONV), multiple side inlet (MSI), and MSI coupled with alternate wetting and drying (AWD), on aquifer withdrawal, rough rice grain yield, irrigation water use efficiency (IWUE), and net returns above irrigation costs. Compared to CONV and MSI, up to 39% less water was applied to AWD (P ˂ .0001). Rice grain yield for AWD was not different from either CONV or MSI (P = .1343), despite substantial reductions in water use. Relative to standard irrigation strategies, AWD maintained or increased net returns up to$238 ha−1 for pumping depths from 5.5 m to 122 m and diesel prices from $ 0.42 L−1 to $0.98 L−1 (P ≤ .0003). Irrigation water use efficiency was up to 59% greater for AWD relative to conventional systems due to the positive effects of the former on water use while maintaining yield (P = .0034). These data demonstrate that AWD can reduce withdrawal from the MRVAA while maintaining or improving yield and net returns relative to irrigation strategies currently employed across the midsouthern USA rice belt.

Inadequate sleep is a global health concern. Sleep is multidimensional and complex; new multi-ingredient agents are needed. This study assessed the comparative effects of two multi-ingredient supplements on sleep relative to placebo. Adults (N = 620) seeking better sleep were randomly assigned to receive one of three study products. Sleep A (contained lower (0.35 mg THC and higher levels of botanicals (75 mg each hops oil and valerian oil), Sleep B (contained higher THC (0.85 mg) and lower botanicals (20 mg each hops oil and valerian oil) or placebo) for 4 weeks. Sleep disturbance was assessed at baseline and weekly using NIH’s Patient-Reported Outcomes Measurement Information System (PROMIS™) Sleep Disturbance SF 8A survey. Anxiety, stress, pain, and well-being were assessed using validated measures at baseline and weekly. A linear mixed-effects regression model was used to assess the change in health outcome score between active product groups and the placebo. There was a significant difference in sleep disturbance, anxiety, stress, and well-being between Sleep A and placebo. There was no significant difference in any health parameter between Sleep B and placebo. Side effects were mild or moderate. There were no significant differences in the frequency of side effects between the study groups. A botanical blend containing a low concentration of THC improved sleep disturbance, anxiety, stress, and well-being in healthy individuals that reported better sleep as a primary health concern.

Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA). A 2018-2020 national sample of = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample. In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors. Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan. This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018-2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample. 32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables. Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.