Objectives/Goals: Mayo Clinic Florida’s Clinical Research Units develop over 200 clinical studies on average annually. Almost 30% of these projects are developed and then are unable to activate due to a variety of operational factors. To increase the success rate, a scoring tool was created to assess the risk associated with the development of these research projects. Methods/Study Population: A project team comprised of members of research administration and physician leadership developed a rapid project management (RPM) scoring tool to assess operational risk factors. The scoring algorithm was embedded into an existing REDCap database, using a combination of identified variables and calculated fields. All noncancer industry sponsor-initiated clinical studies were scored at intake. According to the following categories: enrollment timelines, study team capacity, and previous experience with the Sponsor. Studies with a score greater than the established threshold were referred to physician leadership for transparent discussions with the principal investigator regarding the identified study development-related risks. Results/Anticipated Results: The RPM tool has assessed close to 200 projects since implementation in June 2022. An interim analysis is being conducted of all projects assessed by the RPM tool dating from implementation to May 2024 to compare the outcomes of these studies with the given RPM score. We anticipate based on anecdotal evidence gathered during the course of this pilot project that the RPM tool will show a correlation between risks identified and study outcomes as defined as successful activation of trials, or rationale of project development failures. We anticipate a reduction in the amount of time elapsed and effort expended developing projects with scores reflecting identified project development-related risk factors. Discussion/Significance of Impact: The RPM tool provides an opportunity to allocate resources to studies with the greatest potential for successful activation. In the future, the RPM tool may be used to identify risk factors associated with enrollment and accrual of participants.

The United Nations 2030 Agenda for Sustainable Development sets a framework of universal Sustainable Development Goals (SDGs) to address challenges to society and the planet. Island invasive species eradications have well-documented benefits that clearly align with biodiversity conservation-related SDGs, yet the value of this conservation action for socioeconomic benefits is less clear. We examine the potential for island invasive vertebrate eradications to have ecological and socioeconomic benefits. Specifically, we examine: (1) how SDGs may have been achieved through past eradications; and (2) how planned future eradications align with SDGs and associated targets. We found invasive vertebrate eradication to align with 13 SDGs and 42 associated targets encompassing marine and terrestrial biodiversity conservation, promotion of local and global partnerships, economic development, climate change mitigation, human health and sanitation and sustainable production and consumption. Past eradications on 794 islands aligned with a median of 17 targets (range 13–38) by island. Potential future eradications on 292 highly biodiverse islands could align with a median of 25 SDG targets (range 15–39) by island. This analysis enables the global community to explicitly describe the contributions that invasive vertebrate management on islands can make towards implementing the global sustainable development agenda.

Partisan identification is a fundamental force in individual and mass political behavior around the world. Informed by scholarship on human sociality, coalitional psychology, and group behavior, this research argues that partisan identification, like many other group-based behaviors, is influenced by forces of evolution. If correct, then party identifiers should exhibit adaptive behaviors when making group-related political decisions. The authors test this assertion with citizen assessments of the relative physical formidability of competing leaders, an important adaptive factor in leader evaluations. Using original and novel data collected during the contextually different 2008 and 2012 U.S. presidential elections, as well as two distinct measures obtained during both elections, this article presents evidence that partisans overestimate the physical stature of the presidential candidate of their own party compared with the stature of the candidate of the opposition party. These findings suggest that the power of party identification on political behavior may be attributable to the fact that modern political parties address problems similar to the problems groups faced in human ancestral times.

Intensive field surveys were conducted in eastern Nebraska to determine the frequency distribution model and associated parameters of broadleaf and grass weed seedling populations. The negative binomial distribution consistently fit the data over time (1992 to 1993) and space (fields) for both the inter and intrarow broadleaf and grass weed seedling populations. The other distributions tested (Poisson with zeros, Neyman type A, logarithmic with zeros, and Poisson-binomial) did not fit the data as consistently as the negative binomial distribution. Associated with the negative binomial distribution is a k parameter. k is a nonspatial aggregation parameter related to the variance at a given mean value. The k parameter of the negative binomial distribution was consistent across weed density for individual weed species in a given field except for foxtail spp. populations. Stability of the k parameter across field sites was assessed using the likelihood ratio test There was no stable or common k value across field sites and years for all weed species populations. The lack of stability in k across field sites is of concern, because this parameter is used extensively in the development of parametric sequential sampling procedures. Because k is not stable across field sites, k must be estimated at the time of sampling. Understanding the variability in it is critical to the development of parametric sequential sampling strategies and understanding the dynamics of weed species in the field.

Intensive surveys were conducted in 2 fields in eastern Nebraska to determine the spatial stability of common sunflower, velvetleaf, green and yellow foxtail, and hemp dogbane over 4 yr (1992 to 1995). The 1st field was planted to soybean in 1992 and corn in 1993, 1994, and 1995. The 2nd field was planted to corn in 1992 and 1994 and soybean in 1993 and 1995. Weed density was sampled prior to postemergence herbicide application at approximately 800 locations per year in each field on a regular 7 m grid. The same locations were sampled every year. Weed density at locations between the sample sites was determined by linear triangulation interpolation. Weed seedling distribution was significantly aggregated, with large weed-free areas in both fields. Common sunflower, velvetleaf, and hemp dogbane patches were very persistent in diameter in the east-west and north-south directions and in location and area over 4 yr in the 1st field. Foxtail distribution and density continuously increased in each of the 4 yr in the first field and decreased in the 2nd field. A geographic information system was used to overlay maps from each year for a species. This showed that 36% of the sampled area was continuously free of common sunflower, 62.5% was free of hemp dogbane, and 11.5% was free of velvetleaf in the 1st field, but only 1% was free of velvetleaf in the 2nd field. The persistence of broadleaf weed patches suggests that weed seedling distributions mapped in one year are good predictors of future seedling distributions. Improved and more efficient sampling methods are needed.

An intensive survey of two farmer-managed corn and soybean fields in eastern Nebraska was conducted to investigate parametric sequential sampling of weed seedling populations using a multistage procedure to estimate k, of the negative binomial distribution. k is a nonspatial aggregation parameter related to the variance at a given mean value. Mean weed seedling density ranged from 0.18 to 3.11 plants 0.38 m−2 (linear meter of crop row) based on 806 sampling locations. The average value of k, derived from 200 multistage estimation procedures, ranged from 0.17 to 0.32. A sequential sampling plan was developed with the goal of estimating the mean with a coefficient of variation (CV) of 10, 20, 30, and 40% of the sample mean. A sampling plan was also constructed to estimate the mean within a specified distance H of the true mean (H(x̄)= 0.10, 0.50 and 1.0 plants 0.38 m−2) with 80, 85, and 90% confidence. Estimating mean weed seedling density within a specified CV of the true mean CV(x̄) using parametric sequential sampling techniques was superior to estimating the mean within a specified distance (H(x̄)) of the true mean when considering the frequency of sampling and probability of error, especially at intermediate k values. At a k: value of 0.32 and 0.25, the difference between the actual CV(x̄) obtained from sampling and the CV(x̄) specified by the sampler was minimal. However, the accuracy of weed seedling density estimates was reduced with decreasing k values below 0.25, especially as the specified CV(x̄) increased.

Site-specific weed management recommendations require knowledge of weed species, density, and location in the field. This study compared several sampling techniques to estimate weed density and distribution in two 65-ha no-till Zea mays–Glycine max rotation fields in eastern South Dakota. The most common weeds (Setaria viridis, Setaria glauca, Cirsium arvense, Ambrosia artemisiifolia, and Polygonum pensylvanicum) were counted by species in 0.1-m2 areas on a 15- by 30-m (1,352 points in each field) or 30- by 30-m (676 points in each field) grid pattern, and points were georeferenced and data spatially analyzed. Using different sampling approaches, weed populations were estimated by resampling the original data set. The average density for each technique was calculated and compared with the average field density calculated from the all-point data. All weeds had skewed population distributions with more than 60% of sampling points lacking the specific weed, but very high densities (i.e., > 100 plants m−2) were also observed. More than 300 random samples were required to estimate densities within 20% of the all-point means about 60% of the time. Sampling requirement increased as average density decreased. The W pattern produced average species densities that often were similar to the field averages, but information on patch location was absent. Weed counts taken on the 15- by 30-m grid were dependent spatially and weed contour maps were developed. Kriged maps presented both density and location of weed patches and could be used to establish management zones. However, grid-sampling production fields on a small enough scale to obtain spatially dependent data may have limited usefulness because of time, cost, and labor constraints.

An intensive field survey of an eastern Nebraska corn and soybean field was conducted to characterize the spatial structure and temporal stability of broadleaf weed seedling populations over two growing seasons. Anisotropy, the effect of direction on the relationship between observations, is present in the semivariogram for the velvetleaf and common sunflower populations in 1992 and 1993. The directional trends in aggregation are visible in kriged maps as elliptical shapes oriented east to west across the study area. In addition, there are two distinct zones of aggregation from north to south. These two distinct areas of aggregation are reflected as a ‘plateau’ in the north-south semivariogram. The distance over which this plateau extends indicates that the shape or size of the patch is contracting in the north-south direction (perpendicular to the crop row). The slope of the semivariogram in the east-west direction (aligned with the crop row) remains consistent from 1992 to 1993 suggesting that the shape of the patch is not changing. For sunflower populations, the slope of the north-south empirical semivariogram changes at 20 m, similar to the velvetleaf population semivariograms. This change, however, is reflected as a downward trend in the empirical semivariogram. The distance over which this trend occurs increases from 1992 to 1993 suggesting that seedling patch size was smaller in 1993 compared to 1992. Weed seedling establishment resulting from seed dispersal, differential seed and seedling mortality, or emergence may have resulted in the observed patch dynamics.

Three methods of predicting the impact of weed interference on crop yield and expected economic return were compared to evaluate the economic importance of weed spatial heterogeneity. Density of three weed species was obtained using a grid sampling scheme in 11 corn and 11 soybean fields. Crop yield loss was predicted assuming densities were homogeneous, aggregated following a negative binomial with known population mean and k, or aggregated with weed densities spatially mapped. Predicted crop loss was lowest and expected returns highest when spatial location of weed density was utilized to decide whether control was justified. Location-specific weed management resulted in economic gain as well as a reduction in the quantity of herbicide applied.

WeedSOFT is a state-of-the-art decision support system for weed management in the north central region of the United States, but its accuracy to predict corn yield loss associated with later-emerging weed communities has not been adequately assessed. We conducted experiments in 2004 and 2005 to compare observed and predicted corn yield related to four establishment times of mixed-species weed communities for validation of competitive index modifier (CIM) values in WeedSOFT. Weed communities were established at VE, V2, V4, and V6 corn (emergence, second-leaf, fourth-leaf, and sixth-leaf stages, respectively), and consisted largely of annual grass and moderately competitive annual broadleaf species. Compared to weed-free corn, yield loss occurred in each of seven site-years for weed communities established at VE corn, but in only one site-year for communities established at V2 corn. No corn yield loss was associated with weed communities established at V4 or V6 corn. For communities established at VE corn, predicted corn yield differed from observed yield in all but one site year, with predicted yield less than observed yield in three site-years, and greater than observed yield in two site-years; however, nonlinear regression analyses of yield data pooled over site-years showed that fitted values were similar between predicted and observed yield. For communities established at V2 and V4 corn, predicted yield was less than observed yield in six and five site-years, respectively. For communities established at V6 corn, predicted yield was less than observed yield in three of six site-years, but was similar to observed yield in three of six site-years. These results indicated that the CIM values in WeedSOFT tended to overestimate the competitiveness of late-emerging weed communities. Corn yield data from a pooled analysis of all site-years were used to generate a revised set of growth stage CIM values, which improved the accuracy of predicted corn yield. These results should improve weed management decisions and reduce the need for herbicide applications to late-emerging weeds.

Potential crop yield loss due to early-season weed competition is an important risk associated with postemergence weed management programs. WeedSOFT is a weed management decision support system that has the potential to greatly reduce such risk. Previous research has shown that weed emergence time can greatly affect the accuracy of corn yield loss predictions by WeedSOFT, but our understanding of its predictive accuracy for soybean yield loss as affected by weed emergence time is limited. We conducted experiments at several sites across the Midwestern United States to assess accuracy of WeedSOFT predictions of soybean yield loss associated with mixed-species weed communities established at emergence (VE), cotyledon (VC), first-node (V1), or third-node (V3) soybean. Weed communities across research sites consisted mostly of annual grass species and moderately competitive annual broadleaf species. Soybean yield loss occurred in seven of nine site-years for weed communities established at VE soybean, four site-years for weed communities established at VC soybean, and one site-year for weed communities established at V1 soybean. No soybean yield loss was associated with weed communities established at the V3 stage. Nonlinear regression analyses of predicted and observed soybean yield data pooled over site-years showed that predicted yields were less than observed yields at all soybean growth stages, indicating overestimation of soybean yield loss. Pearson correlation analyses indicated that yield loss functions overestimated the competitive ability of high densities of giant and yellow foxtail with soybean, indicating that adjustments to competitive index values or yield loss function parameters for these species may improve soybean yield loss prediction accuracy and increase the usefulness of WeedSOFT as a weed management decision support system.

Variation in crop–weed interference relationships has been shown for a number of crop–weed mixtures and may have an important influence on weed management decision-making. Field experiments were conducted at seven locations over 2 yr to evaluate variation in common lambsquarters interference in field corn and whether a single set of model parameters could be used to estimate corn grain yield loss throughout the northcentral United States. Two coefficients (I and A) of a rectangular hyperbola were estimated for each data set using nonlinear regression analysis. The I coefficient represents corn yield loss as weed density approaches zero, and A represents maximum percent yield loss. Estimates of both coefficients varied between years at Wisconsin, and I varied between years at Michigan. When locations with similar sample variances were combined, estimates of both I and A varied. Common lambsquarters interference caused the greatest corn yield reduction in Michigan (100%) and had the least effect in Minnesota, Nebraska, and Indiana (0% yield loss). Variation in I and A parameters resulted in variation in estimates of a single-year economic threshold (0.32 to 4.17 plants m−1 of row). Results of this study fail to support the use of a common yield loss–weed density function for all locations.

The clinical course of vascular dementia has been described as a stepwise deterioration over time. We studied the chronologic course of cognitive performance over 1-4 years in seven patients with known ischemic cerebrovascular disease whose dementia subtype was assigned according to the clinical history and the pattern of white matter lesions seen on magnetic resonance imaging (MRI). One patient had the lacunar state, three had subcortical arteriosclerotic encephalopathy and three had multiple cortical and subcortical strokes. All were being treated with an antiplatelet agent and six received antihypertensive therapy. Four of the seven vascular dementia patients improved cognitively over the first year. A fluctuating course was eventually seen in all patients. None showed stepwise deterioration of cognitive function over time. The MRI was useful in subclassifying vascular dementia patients, but the clinical course did not appear to vary as a function of the dementia subtype.

For 30 years, cyclical wind variability in OB stars has puzzled the astronomical community. Phenomenological models involving co-rotating bright spots provide a potential explanation for the observed variations, but the underlying physics remains unknown. We present recent results from hydrodynamical simulations constraining bright spot properties and compare them to what can be inferred from space-based photometry. We also explore the possibility that these spots are caused by magnetic fields and discuss the detectability of such fields.