Veterinarians and Their Perception of the Treatment of Animal Abuse Cases in the Criminal Justice System

Americans love their companion animals. In 2015, nearly two-thirds (65%) of all households in the U.S. housed at least one pet (APPA, n.d.). This love does not translate to policy, however, as many animals are left defenseless. Interestingly, the Animal Legal Defense Fund (2016) has ranked Illinois’ animal protection laws as the strongest in the nation for the last eight years. Extant animal abuse research is almost exclusively concerned with the ramifications that the abuse has for humans, and there is a dearth of social science research that examines veterinarians and the criminal justice system. Extremely limited research on this subject suggests that veterinarians are not satisfied with how the criminal justice system handles reports of animal abuse. The current study was designed to explore veterinarians’ familiarity with animal abuse and experience with the criminal justice system. The researcher was particularly interested in whether veterinarians were satisfied with the criminal justice system’s response to animal abuse. Additionally, the study explored what these individuals believed an appropriate response to animal abuse would consist of. There were not any hypotheses as the research was meant to be purely exploratory. To achieve these goals, the researcher conducted semi-structured qualitative interviews with veterinarians. Participants were recruited by way of a convenience sampling procedure with McLean County, Illinois serving as the research site. Findings indicate that veterinarians seldom encounter animal abuse, and they interact with the criminal justice system even less frequently. The interactions they have had with the criminal justice system have not been pleasant, with every participant being left unsatisfied after participating in criminal cases. If these veterinarians had their way, the criminal justice system would take animal abuse more seriously and punish animal abusers more harshly

Ocean carbon from space: Current status and priorities for the next decade

The ocean plays a central role in modulating the Earth\u27s carbon cycle. Monitoring how the ocean carbon cycle is changing is fundamental to managing climate change. Satellite remote sensing is currently our best tool for viewing the ocean surface globally and systematically, at high spatial and temporal resolutions, and the past few decades have seen an exponential growth in studies utilising satellite data for ocean carbon research. Satellite-based observations must be combined with in-situ observations and models, to obtain a comprehensive view of ocean carbon pools and fluxes. To help prioritise future research in this area, a workshop was organised that assembled leading experts working on the topic, from around the world, including remote-sensing scientists, field scientists and modellers, with the goal to articulate a collective view of the current status of ocean carbon research, identify gaps in knowledge, and formulate a scientific roadmap for the next decade, with an emphasis on evaluating where satellite remote sensing may contribute. A total of 449 scientists and stakeholders participated (with balanced gender representation), from North and South America, Europe, Asia, Africa, and Oceania. Sessions targeted both inorganic and organic pools of carbon in the ocean, in both dissolved and particulate form, as well as major fluxes of carbon between reservoirs (e.g., primary production) and at interfaces (e.g., air-sea and land–ocean). Extreme events, blue carbon and carbon budgeting were also key topics discussed. Emerging priorities identified include: expanding the networks and quality of in-situ observations; improved satellite retrievals; improved uncertainty quantification; improved understanding of vertical distributions; integration with models; improved techniques to bridge spatial and temporal scales of the different data sources; and improved fundamental understanding of the ocean carbon cycle, and of the interactions among pools of carbon and light. We also report on priorities for the specific pools and fluxes studied, and highlight issues and concerns that arose during discussions, such as the need to consider the environmental impact of satellites or space activities; the role satellites can play in monitoring ocean carbon dioxide removal approaches; economic valuation of the satellite based information; to consider how satellites can contribute to monitoring cycles of other important climatically-relevant compounds and elements; to promote diversity and inclusivity in ocean carbon research; to bring together communities working on different aspects of planetary carbon; maximising use of international bodies; to follow an open science approach; to explore new and innovative ways to remotely monitor ocean carbon; and to harness quantum computing. Overall, this paper provides a comprehensive scientific roadmap for the next decade on how satellite remote sensing could help monitor the ocean carbon cycle, and its links to the other domains, such as terrestrial and atmosphere

Estimating uncertainty in ecosystem budget calculations

© The Authors, 2010. This article is distributed under the terms of the Creative Commons Attribution-Noncommercial License. The definitive version was published in Ecosystems 13 (2010): 239-248, doi:10.1007/s10021-010-9315-8.Ecosystem nutrient budgets often report values for pools and fluxes without any indication of uncertainty, which makes it difficult to evaluate the significance of findings or make comparisons across systems. We present an example, implemented in Excel, of a Monte Carlo approach to estimating error in calculating the N content of vegetation at the Hubbard Brook Experimental Forest in New Hampshire. The total N content of trees was estimated at 847 kg ha−1 with an uncertainty of 8%, expressed as the standard deviation divided by the mean (the coefficient of variation). The individual sources of uncertainty were as follows: uncertainty in allometric equations (5%), uncertainty in tissue N concentrations (3%), uncertainty due to plot variability (6%, based on a sample of 15 plots of 0.05 ha), and uncertainty due to tree diameter measurement error (0.02%). In addition to allowing estimation of uncertainty in budget estimates, this approach can be used to assess which measurements should be improved to reduce uncertainty in the calculated values. This exercise was possible because the uncertainty in the parameters and equations that we used was made available by previous researchers. It is important to provide the error statistics with regression results if they are to be used in later calculations; archiving the data makes resampling analyses possible for future researchers. When conducted using a Monte Carlo framework, the analysis of uncertainty in complex calculations does not have to be difficult and should be standard practice when constructing ecosystem budgets

Characterizing Ham and Loin Quality as Hot Carcass Weight Increases to an Average of 119 Kilograms

The objective was to characterize ham and loin quality of carcasses ranging from 78 to 145 kg (average ∼119 kg). Hot carcass weight (HCW), back fat depth, and loin depth was measured on 666 carcasses. Loin pH, instrumental and visual color and iodine value of clear plate fat (all 3 layers) was measured on approximately 90% of the population. Quality measurements of the ham, 14 d aged loin and chop, and loin chop shear force (SSF) were evaluated on approximately 30% of the population. Myosin heavy chain fiber type determination was completed on 49 carcasses. Slopes of regression lines and coefficients of determination between HCW and quality traits were calculated using the REG procedure in SAS and considered significantly different from 0 at P ≤ 0.05. As HCW increased, loin depth (b1 = 0.2496, P 0.15) and did not explain more than 1% (R2 ≤ 0.01) of the variation in 1 d loin color or pH. Loins from heavier carcasses were more tender (decreased SSF; b1 = –0.0674, P 0.22) muscle fiber type percentage or area. These results suggest that increasing HCW to an average of 119 kg did not compromise pork quality

Effects of Increased Pork Hot Carcass Weights. II: Loin Quality Characteristics and Palatability Ratings

The objective of this study was to evaluate the effects of increased pork hot carcass weight on loin quality and palatability of top loin chops. Pork loins (N = 200) were collected from 4 different hot carcass weight groups: A light weight (LT; less than 111.8 kg), medium-light weight (MLT; 111.8 to 119.1 kg), medium-heavy weight (MHVY; 119.1 to 124.4), and a heavyweight group (HVY; 124.4 and greater). Following fabrication, chops were assigned to fat and moisture analysis, Warner-Bratzler shear force (WBSF), consumer sensory panels, or trained sensory panels. Chops from the HVY group were rated as more (P 0.05) consumer flavor liking ratings. Hot carcass weight treatment did not contribute (P > 0.05) to the percentage of chops rated acceptable for flavor and overall liking. The greatest (P 0.05), with greater (P < 0.05) overall tenderness ratings compared to chops from LT carcasses. These results indicate chops from heavier weight carcasses may have improved tenderness and juiciness compared to chops from lighter carcasses

Effects of Increased Pork Hot Carcass Weights. I: Chop Thickness Impact on Consumer Visual Ratings

The objective of this study was to evaluate the effect of increased pork hot carcass weights on consumer visual acceptability and purchase intent ratings of top loin chops cut to various thicknesses in a price labeled versus unlabeled retail display scenario. Pork loins (N = 200) were collected from 4 different hot carcass weight groups: light weight (LT; less than 111.8 kg), medium-light weight (MLT; 111.8 to 119. kg), medium-heavy weight (MHVY; 119.1 to 124.4 kg), and a heavy weight group (HVY; 124.4 kg and greater). Loins were fabricated into 4 pairs of chops of specified thicknesses (1.27, 1.91, 2.54, and 3.18 cm). One chop from each pair was assigned to be packaged with or without a label. Consumers assessed chops for appearance, desirability, and purchase intent. For both appearance and purchase intent ratings, chops from HVY carcasses were given more desirable (P < 0.05) ratings compared to LT chops. Consumers gave greater (P < 0.05) appearance ratings to thicker cut chops. There was a hot carcass weight × chop thickness interaction (P < 0.05) for the percentage of consumers that indicated the chop was desirable overall. Regardless of hot carcass weight group, chops with a thickness of 1.27 cm had the lowest (P < 0.05) percentage of consumers indicate they were desirable overall. A greater (P < 0.05) percentage of consumers indicated “yes” they would purchase chops cut to a thickness of 2.54 cm compared to all other thicknesses. Additionally, there was a greater (P < 0.05) percentage of consumers who indicated they would purchase unlabeled chops compared to labeled chops. These results, within the population sampled, indicate that carcass weight and chop thickness can affect consumer preference and thus should be considered by retailers when marketing fresh pork loin chops

Alliance of Genome Resources Portal: unified model organism research platform

The Alliance of Genome Resources (Alliance) is a consortium of the major model organism databases and the Gene Ontology that is guided by the vision of facilitating exploration of related genes in human and well-studied model organisms by providing a highly integrated and comprehensive platform that enables researchers to leverage the extensive body of genetic and genomic studies in these organisms. Initiated in 2016, the Alliance is building a central portal (www.alliancegenome.org) for access to data for the primary model organisms along with gene ontology data and human data. All data types represented in the Alliance portal (e.g. genomic data and phenotype descriptions) have common data models and workflows for curation. All data are open and freely available via a variety of mechanisms. Long-term plans for the Alliance project include a focus on coverage of additional model organisms including those without dedicated curation communities, and the inclusion of new data types with a particular focus on providing data and tools for the non-model-organism researcher that support enhanced discovery about human health and disease. Here we review current progress and present immediate plans for this new bioinformatics resource

Alliance of Genome Resources Portal: unified model organism research platform

The Alliance of Genome Resources (Alliance) is a consortium of the major model organism databases and the Gene Ontology that is guided by the vision of facilitating exploration of related genes in human and well-studied model organisms by providing a highly integrated and comprehensive platform that enables researchers to leverage the extensive body of genetic and genomic studies in these organisms. Initiated in 2016, the Alliance is building a central portal (www.alliancegenome.org) for access to data for the primary model organisms along with gene ontology data and human data. All data types represented in the Alliance portal (e.g. genomic data and phenotype descriptions) have common data models and workflows for curation. All data are open and freely available via a variety of mechanisms. Long-term plans for the Alliance project include a focus on coverage of additional model organisms including those without dedicated curation communities, and the inclusion of new data types with a particular focus on providing data and tools for the non-model-organism researcher that support enhanced discovery about human health and disease. Here we review current progress and present immediate plans for this new bioinformatics resource

Priorities for synthesis research in ecology and environmental science

ACKNOWLEDGMENTS We thank the National Science Foundation grant #1940692 for financial support for this workshop, and the National Center for Ecological Analysis and Synthesis (NCEAS) and its staff for logistical support.Peer reviewedPublisher PD

Priorities for synthesis research in ecology and environmental science

ACKNOWLEDGMENTS We thank the National Science Foundation grant #1940692 for financial support for this workshop, and the National Center for Ecological Analysis and Synthesis (NCEAS) and its staff for logistical support.Peer reviewedPublisher PD