Information Content of USDA Rice Reports and Price Reactions of Rice Futures

Rice is a predominant food staple in many regions of the world, and it is important to determine how efficiently the U.S. rice market helps to ensure world food security. This question can be answered by gauging the price discovery performance of the U.S. rice futures market and the economic usefulness of the U.S. government’s supply and demand forecasts. So, to this end, we employ two event study approaches: (1) to examine variability in returns on report-release days as compared to returns on pre- and post-report days, and (2) to regress price reactions on changes in usage and production information. It is found that the USDA provides the rice futures markets with valuable information and rice futures respond to the information in an economically consistent manner

Markets and Supply Chains: An Investigation of the Institutions Influencing the Farm-Supply Chain Interface

Farm-level operations have lasting and amplified impacts that promulgate the entire supply chain, and the farm is increasingly in the forefront of today’s headlines on topics such as social responsibility, environmental sustainability, traceability, and food safety. Despite its significance, however, the farm remains a ‘black box’ and has traditionally operated independently with little information-sharing, trust, or collaboration with buyers downstream. This dissertation begins to unpack this ‘black box’ by employing different methodologies to identify the factors influencing exchange in the farm-supply chain interface. In Essay 1, I examine why the farm continues to be a challenge for ‘traditional’ collaborative approaches to buyer-supplier exchange. I use an interpretive approach to identify the individual and institutional factors influencing farmers’ operations decision-making. Field interviews reveal that farmers approach buyer-supplier exchange differently and tend to rely more heavily on market mechanisms to coordinate activities with buyers and inform their decision-making. In Essay 2, I build on this finding to examine the institutional factors influencing exchange in the spot market, which accounts for a majority of the total value of agricultural commodity production. I use a proprietary data set and time series econometrics to investigate how spot market exchanges between farmers and buyers are influenced by the futures market—an institution serving critical informational and risk management functions in the industry. In line with the predictions of Austrian economics, the findings indicate that farmers and buyers use the information conveyed by the futures market as they negotiate prices in the spot market. In Essay 3, I build on this finding and further explore how the futures market influences spot market exchanges by examining how information asymmetry affects the price adjustment process. I draw on economic theory to develop hypotheses that are tested using a proprietary data set and nonlinear time series econometrics. The findings suggest that buyers exploit their informational advantage by adjusting spot market prices asymmetrically. Taken together, the three essays demonstrate how institutions influence decision-making and exchange in the agricultural supply chain and offer important insights for theory, practice, and public policy

An experimental test of green management information system effects on carrier selection: weigh station and tollbooth bypass technology adoption

In a highly competitive price-driven industry, carriers are continuously searching for opportunities to differentiate their offerings, minimize operational costs, and appeal to shippers. At the same time, environmental sustainability has evolved from being trendy jargon into a requirement for competitive supply chain management. It is at the intersection of these two modern topics that the current study identifies a new carrier selection attribute based on a specialized type of green management information system. We apply social exchange theory to hypothesize carrier price and green technology adoption effects on shipper purchase intent. The hypothesized direct and interaction effects are tested by way of a vignette-based experiment, with a sample of full-time working professionals. The supported hypotheses collectively suggest that the adoption of weigh station and tollbooth bypass technology, as a type of environmentally sustainable information system, positively affects transportation carrier selection and attenuates the negative effect of a carrier’s price on shippers’ purchase intentions. These research findings offer unique theoretical, practical, and policy implications surrounding the trucking carrier selection decision

Best practices in research mentoring in clinical science.

The growth of clinical science as a field depends on the work of engaged mentors nurturing future generations of scientists. Effective research mentoring has been shown to predict positive outcomes, including greater scholarly productivity, reduced attrition, and increased satisfaction with training and/or employment, which ultimately may enhance the quality of the clinical-science research enterprise. Barriers to effective research mentoring, however, pose significant challenges for both mentees and mentors, as well as for labs, training programs, and/or departments. We discuss some key issues as they apply to clinical-science mentoring and note how they are affected across different developmental levels (undergraduate, postbaccalaureate, doctoral, internship, postdoctoral associates, and early career faculty). Although we do not proclaim expertise on these issues-and have struggled with them in our own careers-we believe an open discussion around best mentoring practices will enhance our collective effectiveness and help mentees and our field to flourish. (PsycINFO Database Record (c) 2019 APA, all rights reserved)

Examining Associations Between Smartphone Use and Clinical Severity in Frontotemporal Dementia: Proof-of-Concept Study

BackgroundFrontotemporal lobar degeneration (FTLD) is a leading cause of dementia in individuals aged <65 years. Several challenges to conducting in-person evaluations in FTLD illustrate an urgent need to develop remote, accessible, and low-burden assessment techniques. Studies of unobtrusive monitoring of at-home computer use in older adults with mild cognitive impairment show that declining function is reflected in reduced computer use; however, associations with smartphone use are unknown.ObjectiveThis study aims to characterize daily trajectories in smartphone battery use, a proxy for smartphone use, and examine relationships with clinical indicators of severity in FTLD.MethodsParticipants were 231 adults (mean age 52.5, SD 14.9 years; n=94, 40.7% men; n=223, 96.5% non-Hispanic White) enrolled in the Advancing Research and Treatment of Frontotemporal Lobar Degeneration (ARTFL study) and Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects (LEFFTDS study) Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) Mobile App study, including 49 (21.2%) with mild neurobehavioral changes and no functional impairment (ie, prodromal FTLD), 43 (18.6%) with neurobehavioral changes and functional impairment (ie, symptomatic FTLD), and 139 (60.2%) clinically normal adults, of whom 55 (39.6%) harbored heterozygous pathogenic or likely pathogenic variants in an autosomal dominant FTLD gene. Participants completed the Clinical Dementia Rating plus National Alzheimer's Coordinating Center Frontotemporal Lobar Degeneration Behavior and Language Domains (CDR+NACC FTLD) scale, a neuropsychological battery; the Neuropsychiatric Inventory; and brain magnetic resonance imaging. The ALLFTD Mobile App was installed on participants' smartphones for remote, passive, and continuous monitoring of smartphone use. Battery percentage was collected every 15 minutes over an average of 28 (SD 4.2; range 14-30) days. To determine whether temporal patterns of battery percentage varied as a function of disease severity, linear mixed effects models examined linear, quadratic, and cubic effects of the time of day and their interactions with each measure of disease severity on battery percentage. Models covaried for age, sex, smartphone type, and estimated smartphone age.ResultsThe CDR+NACC FTLD global score interacted with time on battery percentage such that participants with prodromal or symptomatic FTLD demonstrated less change in battery percentage throughout the day (a proxy for less smartphone use) than clinically normal participants (P<.001 in both cases). Additional models showed that worse performance in all cognitive domains assessed (ie, executive functioning, memory, language, and visuospatial skills), more neuropsychiatric symptoms, and smaller brain volumes also associated with less battery use throughout the day (P<.001 in all cases).ConclusionsThese findings support a proof of concept that passively collected data about smartphone use behaviors associate with clinical impairment in FTLD. This work underscores the need for future studies to develop and validate passive digital markers sensitive to longitudinal clinical decline across neurodegenerative diseases, with potential to enhance real-world monitoring of neurobehavioral change

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships

Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis

Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15-90. The effects of dementia, mild cognitive impairment, Parkinson\u27s disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia

Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis.

Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15–90. The effects of dementia, mild cognitive impairment, Parkinson’s disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia (p \u3c 0.001), while neither depression nor ADHD showed consistent associations with VLM scores (p \u3e 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders

Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis.

Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15-90. The effects of dementia, mild cognitive impairment, Parkinson's disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia (p 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders

Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity.

Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant