Commercializing targeted sprayer systems allows producers to reduce herbicide inputs but risks the possibility of not treating emerging weeds. Currently, targeted applications with the John Deere system allow for five spray sensitivity settings, and no published literature discusses the impact of these settings on detecting and spraying weeds of varying species, sizes, and positions in crops. Research was conducted in AR, IL, IN, MS, and NC in corn, cotton, and soybean to determine how various factors might influence the ability of targeted applications to treat weeds. These data included 21 weed species aggregated to six classes with height, width, and densities, ranging from 25 to 0.25 cm, 25 to 0.25 cm, and 14.3 to 0.04 plants m-2, respectively. Crop and weed density did not influence the likelihood of treating the weeds. As expected, the sensitivity setting alters the ability to treat weeds. Targeted applications (across sensitivity settings, median weed height and width, and density of 2.4 plants m-2) resulted in a treatment success of 99.6% to 84.4%, 99.1% to 68.8%, 98.9% to 62.9%, 99.1% to 70.3%, 98.0% to 48.3%, and 98.5% to 55.8% for Convolvulaceae, decumbent broadleaf weeds, Malvaceae, Poaceae, Amaranthaceae, and yellow nutsedge, respectively. Reducing the sensitivity setting reduced the ability to treat weeds. Size of weeds aided targeted application success, with larger weeds being more readily treated through easier detection. Based on these findings, various conditions could impact the outcome of targeted multi-nozzle applications. Additionally, the analyses highlight some of the parameters to consider when using these technologies.

Objectives/Goals: Cervical cancer is preventable through HPV vaccination and the detection/removal of precancerous lesions. Incidence and mortality rates have only decreased by 3–4% in the past decade. Despite having the tools to prevent all cervical cancers, they are not being fully utilized. Our goal is to identify barriers and design strategies to overcome them. Methods/Study Population: Women in urban (750) and rural (750) settings will be screened for the presence of high-oncogenic risk HPV (hrHPV) by self-vaginal swab, complete the Monitoring Blunting Style Scale, a validation scale to determine attentional style, and a structural barrier to care survey. A subset (Results/Anticipated Results: The study, launched in September 2024 at the Medicine Primary Care Clinic at UMC in New Orleans, has enrolled 16 women. Sample adequacy was high (82%), with 5 women having hrHPV present. Participants expressed high satisfaction and acceptance of the self-administered vaginal swab, with most samples demonstrating high quality. Surveys have been collected, and hrHPV-positive women have been referred for gynecological follow-up. Shreveport site will recruit women across over 20 rural clinical sites using a Mobile Health Unit to increase access in rural and underserved communities. Discussion/Significance of Impact: The baseline study will take 12–18 months. We will identify and address key barriers to follow-up gynecological care, including logistical issues (improving access and navigation), educational needs (developing culturally sensitive materials), and emotional support. We will create a care delivery model to eliminate cervical cancer in Louisiana.

Bargaining scholars predict rapid power shifts cause preventive war. But cases with rapidly shifting power often remain peaceful. To explain the dogs that don’t bark, we introduce instant, repeated, costly militarization into Powell’s (1999) conventional-weapons power transition model. First, we rationalize preventive war during long, slow, complete-information power shifts. Second, we find that where past research into conventional shifts predicts war, a grand bargain backed by the decliner’s threat of war emerges as a second equilibrium. Because war and a grand bargain both prevent power from shifting, declining powers deploy them under the same conditions. Our grand bargain survives war-causing hazards, and some latent shifts. It occurs after incremental militarization causes repeated appeasement-like concessions, and when power shifts are instant, slow or fast, and perfectly observed; suggesting conventional shifts induce grand bargains under surprising conditions. The Great Game’s end fits our grand bargain, but that British elites seriously considered war.

There is a growing focus on understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterise dietary patterns, such as latent class analysis and machine learning algorithms, may offer opportunities to characterise dietary patterns in greater depth than previously considered. However, there has not been a formal examination of how this wide range of approaches has been applied to characterise dietary patterns. This scoping review synthesised literature from 2005 to 2022 applying methods not traditionally used to characterise dietary patterns, referred to as novel methods. MEDLINE, CINAHL and Scopus were searched using keywords including latent class analysis, machine learning and least absolute shrinkage and selection operator. Of 5274 records identified, 24 met the inclusion criteria. Twelve of twenty-four articles were published since 2020. Studies were conducted across seventeen countries. Nine studies used approaches with applications in machine learning, such as classification models, neural networks and probabilistic graphical models, to identify dietary patterns. The remaining studies applied methods such as latent class analysis, mutual information and treelet transform. Fourteen studies assessed associations between dietary patterns characterised using novel methods and health outcomes, including cancer, cardiovascular disease and asthma. There was wide variation in the methods applied to characterise dietary patterns and in how these methods were described. The extension of reporting guidelines and quality appraisal tools relevant to nutrition research to consider specific features of novel methods may facilitate consistent reporting and enable synthesis to inform policies and programs.

Partial remission after major depressive disorder (MDD) is common and a robust predictor of relapse. However, it remains unclear to which extent preventive psychological interventions reduce depressive symptomatology and relapse risk after partial remission. We aimed to identify variables predicting relapse and to determine whether, and for whom, psychological interventions are effective in preventing relapse, reducing (residual) depressive symptoms, and increasing quality of life among individuals in partial remission. This preregistered (CRD42023463468) systematic review and individual participant data meta-analysis (IPD-MA) pooled data from 16 randomized controlled trials (n = 705 partial remitters) comparing psychological interventions to control conditions, using 1- and 2-stage IPD-MA. Among partial remitters, baseline clinician-rated depressive symptoms (p = .005) and prior episodes (p = .012) predicted relapse. Psychological interventions were associated with reduced relapse risk over 12 months (hazard ratio [HR] = 0.60, 95% confidence interval [CI] 0.43–0.84), and significantly lowered posttreatment depressive symptoms (Hedges’ g = 0.29, 95% CI 0.04–0.54), with sustained effects at 60 weeks (Hedges’ g = 0.33, 95% CI 0.06–0.59), compared to nonpsychological interventions. However, interventions did not significantly improve quality of life at 60 weeks (Hedges’ g = 0.26, 95% CI -0.06 to 0.58). No moderators of relapse prevention efficacy were found. Men, older individuals, and those with higher baseline symptom severity experienced greater reductions in symptomatology at 60 weeks. Psychological interventions for individuals with partially remitted depression reduce relapse risk and residual symptomatology, with efficacy generalizing across patient characteristics and treatment types. This suggests that psychological interventions are a recommended treatment option for this patient population.

Foliar-applied postemergence applications of glufosinate are often applied to glufosinate-resistant crops to provide nonselective weed control without significant crop injury. Rainfall, air temperature, solar radiation, and relative humidity near the time of application have been reported to affect glufosinate efficacy. However, previous research may have not captured the full range of weather variability to which glufosinate may be exposed before or following application. Additionally, climate models suggest more extreme weather will become the norm, further expanding the weather range to which glufosinate can be exposed. The objective of this research was to quantify the probability of successful weed control (efficacy ≥85%) with glufosinate applied to some key weed species across a broad range of weather conditions. A database of >10,000 North American herbicide evaluation trials was used in this study. The database was filtered to include treatments with a single postemergence application of glufosinate applied to waterhemp [Amaranthus tuberculatus (Moq.) Sauer], morningglory species (Ipomoea spp.), and/or giant foxtail (Setaria faberi Herrm.) <15 cm in height. These species were chosen because they are well represented in the database and listed as common and troublesome weed species in both corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] (Van Wychen 2020, 2022). Individual random forest models were created. Low rainfall (≤20 mm) over the 5 d before glufosinate application was detrimental to the probability of successful control of A. tuberculatus and S. faberi. Lower relative humidity (≤70%) and solar radiation (≤23 MJ m−1 d−1) on the day of application reduced the probability of successful weed control in most cases. Additionally, the probability of successful control decreased for all species when average air temperature over the first 5 d after application was ≤25 C. As climate continues to change and become more variable, the risk of unacceptable control of several common species with glufosinate is likely to increase.

A reflective analysis is presented on the potential added value that actuarial science can contribute to the field of health technology assessment. This topic is discussed based on the experience of several experts in health actuarial science and health economics. Different points are addressed, such as the role of actuarial science in health, actuarial judgment, data inputs and their quality, modeling methodologies and the use of decision-analytic models in the age of artificial intelligence, and the development of innovative pricing and payment models.

Single-stranded nucleic acid (ssNA) binding proteins must both stably protect ssNA transiently exposed during replication and other NA transactions, and also rapidly reorganize and dissociate to allow further NA processing. How these seemingly opposing functions can coexist has been recently elucidated by optical tweezers (OT) experiments that isolate and manipulate single long ssNA molecules to measure conformation in real time. The effective length of an ssNA substrate held at fixed tension is altered upon protein binding, enabling quantification of both the structure and kinetics of protein–NA interactions. When proteins exhibit multiple binding states, however, OT measurements may produce difficult to analyze signals including non-monotonic response to free protein concentration and convolution of multiple fundamental rates. In this review we compare single-molecule experiments with three proteins of vastly different structure and origin that exhibit similar ssNA interactions. These results are consistent with a general model in which protein oligomers containing multiple binding interfaces switch conformations to adjust protein:NA stoichiometry. These characteristics allow a finite number of proteins to protect long ssNA regions by maximizing protein–ssNA contacts while also providing a pathway with reduced energetic barriers to reorganization and eventual protein displacement when these ssNA regions are diminished.