ChatGPT in a Contract Drafting Class

Our presentation will discuss the impact of ChatGPT on contract drafting pedagogy. Specifically, we will examine ChatGPT’s basis of knowledge and whether it has sufficient theoretical foundation to be used as a pedagogical tool; whether ChatGPT’s practical application supports proven methods of instructional delivery; and ChatGPT’s functionality as an assessment tool. 1. ChatGPT’s basis of knowledge and whether it has sufficient theoretical foundation to be used as a pedagogical tool Our presentation will compare the pretraining of ChatGPT and to the typical Contract Drafting pedagogy. We will start by showing the program on a screen and asking it how it was trained and what principles it follows to draft contracts. We will then compare this to the principles students are taught to apply in a typical contract drafting class. Our working hypotheses is that ChatGPT draws from internet resources and follows basic principles of clear writing. In contrast, transactional attorneys typically either draft from scratch following whatever conventions they were trained on, or they start with a form or sample document and then revise it for a specific transaction. 2. Whether ChatGPT’s practical application supports proven methods of instructional delivery We will then show an example of how ChatGPT works by giving it a prompt that would be typical in any contract drafting class and asking it to draft the applicable contract from scratch. It takes less than a minute for the program to draft the type of contract students typically draft in class. We will examine the sufficiency of ChatGPT’s output. The next step will be to highlight errors and other issues with the document ChatGPT drafts. We will ask the program if it can draft the contract following certain conventions (since drafting courses are typically taught according to conventions) and see whether it can actually do what it says it can do (it can’t). Ultimately, we will look at techniques that can be used to improve the quality and completeness of the documents ChatGPT produces so that it aligns more with the way contract drafting classes are typically taught. The program works better, for example, if you ask more specific questions and provide feedback on the output

United States Midwest Soil and Weather Conditions Influence Anaerobic Potentially Mineralizable Nitrogen

Nitrogen provided to crops through mineralization is an important factor in N management guidelines. Understanding of the interactive effects of soil and weather conditions on N mineralization needs to be improved. Relationships between anaerobic potentially mineralizable N (PMNan) and soil and weather conditions were evaluated under the contrasting climates of eight US Midwestern states. Soil was sampled (0–30 cm) for PMNan analysis before pre-plant N application (PP0N) and at the V5 development stage from the pre-plant 0 (V50N) and 180 kg N ha−1 (V5180N) rates and incubated for 7, 14, and 28 d. Even distribution of precipitation and warmer temperatures before soil sampling and greater soil organic matter (SOM) increased PMNan. Soil properties, including total C, SOM, and total N, had the strongest relationships with PMNan (R2 ≤ 0.40), followed by temperature (R2 ≤ 0.20) and precipitation (R2 ≤ 0.18) variables. The strength of the relationships between soil properties and PMNan from PP0N, V50N, and V5180N varied by ≤10%. Including soil and weather in the model greatly increased PMNan predictability (R2 ≤ 0.69), demonstrating the interactive effect of soil and weather on N mineralization at different times during the growing season regardless of N fertilization. Delayed soil sampling (V50N) and sampling after fertilization (V5180N) reduced PMNan predictability. However, longer PMNan incubations improved PMNan predictability from both V5 soil samplings closer to the PMNan predictability from PP0N, indicating the potential of PMNan from longer incubations to provide improved estimates of N mineralization when N fertilizer is applied

Arcuate AgRP, but not POMC neurons, modulate paraventricular CRF synthesis and release in response to fasting

Background: The activation of the hypothalamic–pituitary–adrenal (HPA) axis is essential for metabolic adaptation in response to fasting. However, the neurocircuitry connecting changes in the peripheral energy stores to the activity of hypothalamic paraventricular corticotrophin-releasing factor (CRFPVN) neurons, the master controller of the HPA axis activity, is not completely understood. Our main goal was to determine if hypothalamic arcuate nucleus (ARC) POMC and AgRP neurons can communicate fasting-induced changes in peripheral energy stores, associated to a fall in plasma leptin levels, to CRFPVN neurons to modulate the HPA axis activity in mice. Results: We observed increased plasma corticosterone levels associate with increased CRFPVN mRNA expression and increased CRFPVN neuronal activity in 36 h fasted mice. These responses were associated with a fall in plasma leptin levels and changes in the mRNA expression of Agrp and Pomc in the ARC. Fasting-induced decrease in plasma leptin partially modulated these responses through a change in the activity of ARC neurons. The chemogenetic activation of POMCARC by DREADDs did not affect fasting-induced activation of the HPA axis. DREADDs inhibition of AgRPARC neurons reduced the content of CRFPVN and increased its accumulation in the median eminence but had no effect on corticosterone secretion induced by fasting. Conclusion: Our data indicate that AgRPARC neurons are part of the neurocircuitry involved in the coupling of PVNCRF activity to changes in peripheral energy stores induced by prolonged fasting.Fil: Alves Fernandes, Alan Carlos. University Of Ribeirao Preto; BrasilFil: Pereira de Oliveira, Franciane. Universidade Federal de Sao Paulo.; BrasilFil: Fernandez, Gimena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Multidisciplinario de Biología Celular. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Multidisciplinario de Biología Celular. Universidad Nacional de La Plata. Instituto Multidisciplinario de Biología Celular; ArgentinaFil: da Guia Vieira, Luane. University Of Ribeirao Preto; BrasilFil: Gugelmin Rosa, Cristiane. University Of Ribeirao Preto; BrasilFil: do Nascimento, Taís. University Of Ribeirao Preto; BrasilFil: de Castro França, Suzelei. University Of Ribeirao Preto; BrasilFil: Donato Jr, Jose. Universidade de Sao Paulo. Departamento de Fisiología; BrasilFil: Vella, Kristen R.. Weill Cornell Medical College; Estados UnidosFil: Antunes Rodrigues, Jose. University Of Ribeirao Preto; BrasilFil: Mecawi , André. Universidade Federal de Sao Paulo.; BrasilFil: Perello, Mario. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Multidisciplinario de Biología Celular. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Multidisciplinario de Biología Celular. Universidad Nacional de La Plata. Instituto Multidisciplinario de Biología Celular; ArgentinaFil: Leico Kagohara Elias, Lucila. University Of Ribeirao Preto; BrasilFil: Rorato, Rodrigo. Universidade Federal de Sao Paulo.; Brasi

Predicting Economic Optimal Nitrogen Rate with the Anaerobic Potentially Mineralizable Nitrogen Test

Estimates of mineralizable N with the anaerobic potentially mineralizable N (PMNan) test could improve predictions of corn (Zea mays L.) economic optimal N rate (EONR). A study across eight US midwestern states was conducted to quantify the predictability of EONR for single and split N applications by PMNan. Treatment factors included different soil sample timings (pre-plant and V5 development stage), planting N rates (0 and 180 kg N ha−1), and incubation lengths (7, 14, and 28 d) with and without initial soil NH4–N included with PMNan. Soil was sampled (0–30 cm depth) before planting and N application and at V5 where 0 or 180 kg N ha−1 were applied at planting. Evaluating across all soils, PMNan was a weak predictor of EONR (R2 ≤ 0.08; RMSE, ≥67 kg N ha−1), but the predictability improved (15%) when soils were grouped by texture. Using PMNan and initial soil NH4–N as separate explanatory variables improved EONR predictability (11–20%) in fine-textured soils only. Delaying PMNan sampling from pre-plant to V5 regardless of N fertilization improved EONR predictability by 25% in only coarse-textured soils. Increasing PMNan incubations beyond 7 d modestly improved EONR predictability (R2 increased ≤0.18, and RMSE was reduced ≤7 kg N ha−1). Alone, PMNan predicts EONR poorly, and the improvements from partitioning soils by texture and including initial soil NH4–N were relatively low (R2 ≤ 0.33; RMSE ≥ 68 kg N ha−1) compared with other tools for N fertilizer recommendations

United States Midwest Soil and Weather Conditions Influence Anaerobic Potentially Mineralizable Nitrogen

Nitrogen provided to crops through mineralization is an important factor in N management guidelines. Understanding of the interactive effects of soil and weather conditions on N mineralization needs to be improved. Relationships between anaerobic potentially mineralizable N (PMNan) and soil and weather conditions were evaluated under the contrasting climates of eight US Midwestern states. Soil was sampled (0–30 cm) for PMNan analysis before pre-plant N application (PP0N) and at the V5 development stage from the pre-plant 0 (V50N) and 180 kg N ha−1 (V5180N) rates and incubated for 7, 14, and 28 d. Even distribution of precipitation and warmer temperatures before soil sampling and greater soil organic matter (SOM) increased PMNan. Soil properties, including total C, SOM, and total N, had the strongest relationships with PMNan (R2 ≤ 0.40), followed by temperature (R2 ≤ 0.20) and precipitation (R2 ≤ 0.18) variables. The strength of the relationships between soil properties and PMNan from PP0N, V50N, and V5180N varied by ≤10%. Including soil and weather in the model greatly increased PMNan predictability (R2 ≤ 0.69), demonstrating the interactive effect of soil and weather on N mineralization at different times during the growing season regardless of N fertilization. Delayed soil sampling (V50N) and sampling after fertilization (V5180N) reduced PMNan predictability. However, longer PMNan incubations improved PMNan predictability from both V5 soil samplings closer to the PMNan predictability from PP0N, indicating the potential of PMNan from longer incubations to provide improved estimates of N mineralization when N fertilizer is applied

A functional definition to distinguish ponds from lakes and wetlands

Ponds are often identified by their small size and shallow depths, but the lack of a universal evidence-based definition hampers science and weakens legal protection. Here, we compile existing pond definitions, compare ecosystem metrics (e.g., metabolism, nutrient concentrations, and gas fluxes) among ponds, wetlands, and lakes, and propose an evidence-based pond definition. Compiled definitions often mentioned surface area and depth, but were largely qualitative and variable. Government legislation rarely defined ponds, despite commonly using the term. Ponds, as defined in published studies, varied in origin and hydroperiod and were often distinct from lakes and wetlands in water chemistry. We also compared how ecosystem metrics related to three variables often seen in waterbody definitions: waterbody size, maximum depth, and emergent vegetation cover. Most ecosystem metrics (e.g., water chemistry, gas fluxes, and metabolism) exhibited nonlinear relationships with these variables, with average threshold changes at 3.7 ± 1.8 ha (median: 1.5 ha) in surface area, 5.8 ± 2.5 m (median: 5.2 m) in depth, and 13.4 ± 6.3% (median: 8.2%) emergent vegetation cover. We use this evidence and prior definitions to define ponds as waterbodies that are small (< 5 ha), shallow (< 5 m), with < 30% emergent vegetation and we highlight areas for further study near these boundaries. This definition will inform the science, policy, and management of globally abundant and ecologically significant pond ecosystems.Fil: Richardson, David C.. State University of New York at New Paltz; Estados UnidosFil: Holgerson, Meredith A.. Cornell University; Estados UnidosFil: Farragher, Matthew J.. University of Maine; Estados UnidosFil: Hoffman, Kathryn K.. No especifíca;Fil: King, Katelyn B. S.. Michigan State University; Estados UnidosFil: Alfonso, María Belén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; ArgentinaFil: Andersen, Mikkel R.. No especifíca;Fil: Cheruveil, Kendra Spence. Michigan State University; Estados UnidosFil: Coleman, Kristen A.. University of York; Reino UnidoFil: Farruggia, Mary Jade. University of California at Davis; Estados UnidosFil: Fernandez, Rocio Luz. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Hondula, Kelly L.. No especifíca;Fil: López Moreira Mazacotte, Gregorio A.. Leibniz - Institute of Freshwater Ecology and Inland Fisheries; AlemaniaFil: Paul, Katherine. No especifíca;Fil: Peierls, Benjamin L.. No especifíca;Fil: Rabaey, Joseph S.. University of Minnesota; Estados UnidosFil: Sadro, Steven. University of California at Davis; Estados UnidosFil: Sánchez, María Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Ecología, Genética y Evolución de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Ecología, Genética y Evolución de Buenos Aires; ArgentinaFil: Smyth, Robyn L.. No especifíca;Fil: Sweetman, Jon N.. State University of Pennsylvania; Estados Unido

High-throughput functional analysis of autism genes in zebrafish identifies convergence in dopaminergic and neuroimmune pathways

Advancing from gene discovery in autism spectrum disorders (ASDs) to the identification of biologically relevant mechanisms remains a central challenge. Here, we perform parallel in vivo functional analysis of 10 ASD genes at the behavioral, structural, and circuit levels in zebrafish mutants, revealing both unique and overlapping effects of gene loss of function. Whole-brain mapping identifies the forebrain and cerebellum as the most significant contributors to brain size differences, while regions involved in sensory-motor control, particularly dopaminergic regions, are associated with altered baseline brain activity. Finally, we show a global increase in microglia resulting from ASD gene loss of function in select mutants, implicating neuroimmune dysfunction as a key pathway relevant to ASD biology

Relating four‐day soil respiration to corn nitrogen fertilizer needs across 49 U.S. Midwest fields

Soil microbes drive biological functions that mediate chemical and physical processes necessary for plants to sustain growth. Laboratory soil respiration has been proposed as one universal soil health indicator representing these functions, potentially informing crop and soil management decisions. Research is needed to test the premise that soil respiration is helpful for profitable in‐season nitrogen (N) rate management decisions in corn (Zea mays L.). The objective of this research was two‐fold: (i) determine if the amount of N applied at the time of planting effected soil respiration, and (ii) evaluate the relationship of soil respiration to corn yield response to fertilizer N application. A total of 49 N response trials were conducted across eight states over three growing seasons (2014–2016). The 4‐day Comprehensive Assessment of Soil Health (CASH) soil respiration method was used to quantify soil respiration. Averaged over all sites, N fertilization did not impact soil respiration, but at four sites soil respiration decreased as N fertilizer rate applied at‐planting increased. Across all site‐years, soil respiration was moderately related to the economical optimum N rate (EONR) (r2 = 0.21). However, when analyzed by year, soil respiration was more strongly related to EONR in 2016 (r2 = 0.50) and poorly related for the first two years (r2 \u3c 0.20). These results illustrate the factors influencing the ability of laboratory soil respiration to estimate corn N response, including growing‐season weather, and the potential of fusing soil respiration with other soil and weather measurements for improved N fertilizer recommendations

Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale

Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a 'Big Data' approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence-only or presence-absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi-source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter- or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi-source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA-based techniques and satellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, execution of workflows and the production process/cycle as well as approaching technical interoperability among research infrastructures, (iv) allowing semantic interoperability by developing and adopting standards and tools for capturing consistent data and metadata, and (v) ensuring legal interoperability by endorsing open data or data that are free from restrictions on use, modification and sharing. Addressing these challenges is critical for biodiversity research and for assessing progress towards conservation policy targets and sustainable development goals

HPV vaccine decision making in pediatric primary care: a semi-structured interview study

<p>Abstract</p> <p>Background</p> <p>Despite national recommendations, as of 2009 human papillomavirus (HPV) vaccination rates were low with < 30% of adolescent girls fully vaccinated. Research on barriers to vaccination has focused separately on parents, adolescents, or clinicians and not on the decision making process among all participants at the point of care. By incorporating three distinct perspectives, we sought to generate hypotheses to inform interventions to increase vaccine receipt.</p> <p>Methods</p> <p>Between March and June, 2010, we conducted qualitative interviews with 20 adolescent-mother-clinician triads (60 individual interviews) directly after a preventive visit with the initial HPV vaccine due. Interviews followed a guide based on published HPV literature, involved 9 practices, and continued until saturation of the primary themes was achieved. Purposive sampling balanced adolescent ages and practice type (urban resident teaching versus non-teaching). Using a modified grounded theory approach, we analyzed data with NVivo8 software both within and across triads to generate primary themes.</p> <p>Results</p> <p>The study population was comprised of 20 mothers (12 Black, 9 < high school diploma), 20 adolescents (ten 11-12 years old), and 20 clinicians (16 female). Nine adolescents received the HPV vaccine at the visit, eight of whom were African American. Among the 11 not vaccinated, all either concurrently received or were already up-to-date on Tdap and MCV4. We did not observe systematic patterns of vaccine acceptance or refusal based on adolescent age or years of clinician experience. We identified 3 themes: (1) Parents delayed, rather than refused vaccination, and when they expressed reluctance, clinicians were hesitant to engage them in discussion. (2) Clinicians used one of two strategies to present the HPV vaccine, either presenting it as a routine vaccine with no additional information or presenting it as optional and highlighting risks and benefits. (3) Teens considered themselves passive participants in decision making, even when parents and clinicians reported including them in the process.</p> <p>Conclusions</p> <p>Programs to improve HPV vaccine delivery in primary care should focus on promoting effective parent-clinician communication. Research is needed to evaluate strategies to help clinicians engage reluctant parents and passive teens in discussion and measure the impact of distinct clinician decision making approaches on HPV vaccine delivery.</p