Recording behaviour of indoor-housed farm animals automatically using machine vision technology: a systematic review

Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building

Mechanisms, Mediators, and Moderators of the Effects of Exercise on Chemotherapy-Induced Peripheral Neuropathy

Chemotherapy-induced peripheral neuropathy (CIPN) is an adverse effect of neurotoxic antineoplastic agents commonly used to treat cancer. Patients with CIPN experience debilitating signs and symptoms, such as combinations of tingling, numbness, pain, and cramping in the hands and feet that inhibit their daily function. Among the limited prevention and treatment options for CIPN, exercise has emerged as a promising new intervention that has been investigated in approximately two dozen clinical trials to date. As additional studies test and suggest the efficacy of exercise in treating CIPN, it is becoming more critical to develop mechanistic understanding of the effects of exercise in order to tailor it to best treat CIPN symptoms and identify who will benefit most. To address the current lack of clarity around the effect of exercise on CIPN, we reviewed the key potential mechanisms (e.g., neurophysiological and psychosocial factors), mediators (e.g., anti-inflammatory cytokines, self-efficacy, and social support), and moderators (e.g., age, sex, body mass index, physical fitness, exercise dose, exercise adherence, and timing of exercise) that may illuminate the relationship between exercise and CIPN improvement. Our review is based on the studies that tested the use of exercise for patients with CIPN, patients with other types of neuropathies, and healthy adults. The discussion presented herein may be used to (1) guide oncologists in predicting which symptoms are best targeted by specific exercise programs, (2) enable clinicians to tailor exercise prescriptions to patients based on specific characteristics, and (3) inform future research and biomarkers on the relationship between exercise and CIPN

A Multicenter Study of the Causes and Consequences of Optimistic Expectations About Prognosis by Surrogate Decision-Makers in ICUs.

ObjectivesOptimistic expectations about prognosis by surrogate decision-makers in ICUs are common, but there are few data about the causes and clinical consequences. Our objective was to determine the causes of optimistic expectations about prognosis among surrogates and whether it is associated with more use of life support at the end of life.DesignProspective, multicenter cohort study from 2009 to 2012.SettingTwelve ICUs from multiple regions of the United States.SubjectsThe surrogates and physicians of 275 incapacitated ICU patients at high risk of death.InterventionsNone.Measurements and main resultsSurrogates and physicians completed a validated instrument assessing their prognostic expectations for hospital survival. We determined the proportion of surrogates with optimistic expectations, defined as a prognostic estimate that was at least 20% more optimistic than the physician's, then determined how frequently this arose from surrogates miscomprehending the physicians' prognosis versus holding more hopeful beliefs compared with the physician. We used multivariable regression to examine whether optimistic expectations were associated with length of stay, stratified by survival status, and time to withdrawal of life support among nonsurvivors. Overall, 45% of surrogates (95% CI, 38-51%) held optimistic expectations about prognosis, which arose from a combination of misunderstanding the physician's prognostic expectations and from holding more hopeful beliefs compared with the physician. Optimistic expectations by surrogates were associated with significantly longer duration of ICU treatment among nonsurvivors before death (β coefficient = 0.44; 95% CI, 0.05-0.83; p = 0.027), corresponding to a 56% longer ICU stay. This difference was associated with a significantly longer time to withdrawal of life support among dying patients whose surrogates had optimistic prognostic expectations compared with those who did not (β coefficient = 0.61; 95% CI, 0.16-1.07; p = 0.009).ConclusionsThe prevalent optimism about prognosis among surrogates in ICUs arises both from surrogates' miscomprehension of physicians' prognostications and from surrogates holding more hopeful beliefs. This optimism is associated with longer duration of life support at the end of life

Unveiling the autoreactome: Proteome-wide immunological fingerprints reveal the promise of plasma cell depleting therapy

The prevalence and burden of autoimmune and autoantibody mediated disease is increasing worldwide, yet most disease etiologies remain unclear. Despite numerous new targeted immunomodulatory therapies, comprehensive approaches to apply and evaluate the effects of these treatments longitudinally are lacking. Here, we leverage advances in programmable-phage immunoprecipitation (PhIP-Seq) methodology to explore the modulation, or lack thereof, of proteome-wide autoantibody profiles in both health and disease. We demonstrate that each individual, regardless of disease state, possesses a distinct set of autoreactivities constituting a unique immunological fingerprint, or "autoreactome", that is remarkably stable over years. In addition to uncovering important new biology, the autoreactome can be used to better evaluate the relative effectiveness of various therapies in altering autoantibody repertoires. We find that therapies targeting B-Cell Maturation Antigen (BCMA) profoundly alter an individual's autoreactome, while anti-CD19 and CD-20 therapies have minimal effects, strongly suggesting a rationale for BCMA or other plasma cell targeted therapies in autoantibody mediated diseases

A Multilab Replication of the Induced-Compliance Paradigm of Cognitive Dissonance

According to cognitive-dissonance theory, performing counterattitudinal behavior produces a state of dissonance that people are motivated to resolve, usually by changing their attitude to be in line with their behavior. One of the most popular experimental paradigms used to produce such attitude change is the induced-compliance paradigm. Despite its popularity, the replication crisis in social psychology and other fields, as well as methodological limitations associated with the paradigm, raise concerns about the robustness of classic studies in this literature. We therefore conducted a multilab constructive replication of the induced-compliance paradigm based on Croyle and Cooper (Experiment 1). In a total of 39 labs from 19 countries and 14 languages, participants (N = 4,898) were assigned to one of three conditions: writing a counterattitudinal essay under high choice, writing a counterattitudinal essay under low choice, or writing a neutral essay under high choice. The primary analyses failed to support the core hypothesis: No significant difference in attitude was observed after writing a counterattitudinal essay under high choice compared with low choice. However, we did observe a significant difference in attitude after writing a counterattitudinal essay compared with writing a neutral essay. Secondary analyses revealed the pattern of results to be robust to data exclusions, lab variability, and attitude assessment. Additional exploratory analyses were conducted to test predictions from cognitive-dissonancetheory. Overall, the results call into question whether the induced-compliance paradigm provides robust evidence for cognitive dissonance

Expanding the diversity of mycobacteriophages: insights into genome architecture and evolution.

Mycobacteriophages are viruses that infect mycobacterial hosts such as Mycobacterium smegmatis and Mycobacterium tuberculosis. All mycobacteriophages characterized to date are dsDNA tailed phages, and have either siphoviral or myoviral morphotypes. However, their genetic diversity is considerable, and although sixty-two genomes have been sequenced and comparatively analyzed, these likely represent only a small portion of the diversity of the mycobacteriophage population at large. Here we report the isolation, sequencing and comparative genomic analysis of 18 new mycobacteriophages isolated from geographically distinct locations within the United States. Although no clear correlation between location and genome type can be discerned, these genomes expand our knowledge of mycobacteriophage diversity and enhance our understanding of the roles of mobile elements in viral evolution. Expansion of the number of mycobacteriophages grouped within Cluster A provides insights into the basis of immune specificity in these temperate phages, and we also describe a novel example of apparent immunity theft. The isolation and genomic analysis of bacteriophages by freshman college students provides an example of an authentic research experience for novice scientists