Does daily activity overlap of seven mesocarnivores vary based on human development?

Many species of wildlife alter their daily activity patterns in response to co-occurring species as well as the surrounding environment. Often smaller or subordinate species alter their activity patterns to avoid being active at the same time as larger, dominant species to avoid agonistic interactions. Human development can complicate interspecies interactions, as not all wildlife respond to human activity in the same manner. While some species may change the timing of their activity to avoid being active when humans are, others may be unaffected or may benefit from being active at the same time as humans to reduce predation risk or competition. To further explore these patterns, we used data from a coordinated national camera-trapping program (Snapshot USA) to explore how the activity patterns and temporal activity overlap of a suite of seven widely co-occurring mammalian mesocarnivores varied along a gradient of human development. Our focal species ranged in size from the large and often dominant coyote (Canis latrans) to the much smaller and subordinate Virginia opossum (Didelphis virginiana). Some species changed their activity based on surrounding human development. Coyotes were most active at night in areas of high and medium human development. Red fox (Vulpes vulpes) were more active at dusk in areas of high development relative to areas of low or medium development. However, because most species were primarily nocturnal regardless of human development, temporal activity overlap was high between all species. Only opossum and raccoon (Procyon lotor) showed changes in activity overlap with high overlap in areas of low development compared to areas of moderate development. Although we found that coyotes and red fox altered their activity patterns in response to human development, our results showed that competitive and predatory pressures between these seven widespread generalist species were insufficient to cause them to substantially alter their activity patterns.

Detection of acute promyelocytic leukemia in peripheral blood and bone marrow with annotation-free deep learning.

While optical microscopy inspection of blood films and bone marrow aspirates by a hematologist is a crucial step in establishing diagnosis of acute leukemia, especially in low-resource settings where other diagnostic modalities are not available, the task remains time-consuming and prone to human inconsistencies. This has an impact especially in cases of Acute Promyelocytic Leukemia (APL) that require urgent treatment. Integration of automated computational hematopathology into clinical workflows can improve the throughput of these services and reduce cognitive human error. However, a major bottleneck in deploying such systems is a lack of sufficient cell morphological object-labels annotations to train deep learning models. We overcome this by leveraging patient diagnostic labels to train weakly-supervised models that detect different types of acute leukemia. We introduce a deep learning approach, Multiple Instance Learning for Leukocyte Identification (MILLIE), able to perform automated reliable analysis of blood films with minimal supervision. Without being trained to classify individual cells, MILLIE differentiates between acute lymphoblastic and myeloblastic leukemia in blood films. More importantly, MILLIE detects APL in blood films (AUC 0.94 ± 0.04) and in bone marrow aspirates (AUC 0.99 ± 0.01). MILLIE is a viable solution to augment the throughput of clinical pathways that require assessment of blood film microscopy.

The need for measurement science in digital pathology.

Background: Pathology services experienced a surge in demand during the COVID-19 pandemic. Digitalisation of pathology workflows can help to increase throughput, yet many existing digitalisation solutions use non-standardised workflows captured in proprietary data formats and processed by black-box software, yielding data of varying quality. This study presents the views of a UK-led expert group on the barriers to adoption and the required input of measurement science to improve current practices in digital pathology. Methods: With an aim to support the UK's efforts in digitalisation of pathology services, this study comprised: (1) a review of existing evidence, (2) an online survey of domain experts, and (3) a workshop with 42 representatives from healthcare, regulatory bodies, pharmaceutical industry, academia, equipment, and software manufacturers. The discussion topics included sample processing, data interoperability, image analysis, equipment calibration, and use of novel imaging modalities. Findings: The lack of data interoperability within the digital pathology workflows hinders data lookup and navigation, according to 80% of attendees. All participants stressed the importance of integrating imaging and non-imaging data for diagnosis, while 80% saw data integration as a priority challenge. 90% identified the benefits of artificial intelligence and machine learning, but identified the need for training and sound performance metrics.Methods for calibration and providing traceability were seen as essential to establish harmonised, reproducible sample processing, and image acquisition pipelines. Vendor-neutral data standards were seen as a "must-have" for providing meaningful data for downstream analysis. Users and vendors need good practice guidance on evaluation of uncertainty, fitness-for-purpose, and reproducibility of artificial intelligence/machine learning tools. All of the above needs to be accompanied by an upskilling of the pathology workforce. Conclusions: Digital pathology requires interoperable data formats, reproducible and comparable laboratory workflows, and trustworthy computer analysis software. Despite high interest in the use of novel imaging techniques and artificial intelligence tools, their adoption is slowed down by the lack of guidance and evaluation tools to assess the suitability of these techniques for specific clinical question. Measurement science expertise in uncertainty estimation, standardisation, reference materials, and calibration can help establishing reproducibility and comparability between laboratory procedures, yielding high quality data and providing higher confidence in diagnosis.

Evaluation and Intercomparison of Five North American Dry Deposition Algorithms at a Mixed Forest Site.

To quantify differences between dry deposition algorithms commonly used in North America, five models were selected to calculate dry deposition velocity (V d) for O3 and SO2 over a temperate mixed forest in southern Ontario, Canada, where a 5-year flux database had previously been developed. The models performed better in summer than in winter with correlation coefficients for hourly V d between models and measurements being approximately 0.6 and 0.3, respectively. Differences in mean V d values between models were on the order of a factor of 2 in both summer and winter. All models produced lower V d values than the measurements of O3 in summer and SO2 in summer and winter, although the measured V d may be biased. There was not a consistent tendency in the models to overpredict or underpredict for O3 in winter. Several models produced magnitudes of the diel variation of V d (O3) comparable to the measurements, while all models produced slightly smaller diel variations than the measurements of V d (SO2) in summer. A few models produced larger diel variations than the measurements of V d for O3 and SO2 in winter. Model differences were mainly due to different surface resistance parameterizations for stomatal and nonstomatal uptake pathways, while differences in aerodynamic and quasi-laminar resistances played only a minor role. It is recommended to use ensemble modeling results for ecosystem impact assessment studies, which provides mean values of all the used models and thus can avoid too much overestimations or underestimations.

In-vivo high resolution AFM topographic imaging of Caenorhabditis elegans reveals previously unreported surface structures of cuticle mutants.

Atomic force microscopy (AFM) is a powerful method for topographic imaging of surfaces with nanometer resolution. AFM offers significant advantages over scanning electron microscopy (SEM) including the acquisition of quantitative 3D-images and biomechanical information. More importantly, for in-vivo biological imaging, AFM does not require sample dehydration/labeling. We show for the first time high-resolution topographical images of the cuticle of the model organism C. elegans under physiological conditions using AFM. C. elegans is used extensively for drug screening and to study pathogen adherence in innate immunity; both applications highly depend on the integrity of the nematode's cuticle. Mutations affecting both drug adsorption and pathogen clearance have been proposed to relate to changes in the cuticle structure, but never visually examined in high resolution. In this study we use AFM to visualize the topography of wild-type adult C. elegans as well as several cuticle collagen mutants and describe previously unseen anatomical differences.

Measurement of the repeatability of the prompt flashlamp-induced wavefront aberration on beamlines at the National Ignition Facility.

We have undertaken a measurement campaign to determine the repeatability of the prompt flashlamp-induced wavefront aberration on beamlines at the National Ignition Facility (NIF) and determine the extent to which shot-to-shot variations in this aberration may degrade the performance of a proposed adaptive optics system for the short-pulse Advanced Radiographic Capability beamline on NIF. In this paper we will describe the unique NIF configuration that was required to make this measurement, present the results of the experiment, and discuss the implications of these results for the adaptive optics system design.

Modelling disease spread and control in networks: implications for plant sciences.

Networks are ubiquitous in natural, technological and social systems. They are of increasing relevance for improved understanding and control of infectious diseases of plants, animals and humans, given the interconnectedness of today's world. Recent modelling work on disease development in complex networks shows: the relative rapidity of pathogen spread in scale-free compared with random networks, unless there is high local clustering; the theoretical absence of an epidemic threshold in scale-free networks of infinite size, which implies that diseases with low infection rates can spread in them, but the emergence of a threshold when realistic features are added to networks (e.g. finite size, household structure or deactivation of links); and the influence on epidemic dynamics of asymmetrical interactions. Models suggest that control of pathogens spreading in scale-free networks should focus on highly connected individuals rather than on mass random immunization. A growing number of empirical applications of network theory in human medicine and animal disease ecology confirm the potential of the approach, and suggest that network thinking could also benefit plant epidemiology and forest pathology, particularly in human-modified pathosystems linked by commercial transport of plant and disease propagules. Potential consequences for the study and management of plant and tree diseases are discussed.

Healthcare-associated infection in pediatric patients on extracorporeal life support: The role of multidisciplinary surveillance.

OBJECTIVE: To describe the use of a multidisciplinary approach to sepsis surveillance and evaluate impact on outcome. DESIGN: Prospective clinical study or clinical audit cycle. SETTING: Tertiary pediatric extracorporeal membrane oxygenation (ECMO) center. PATIENTS: Patients were 215 children supported with ECMO January 1999 to December 2004. INTERVENTIONS: A multidisciplinary team met monthly to evaluate cases of bloodstream infection and mediastinitis, review trends, and update unit policies. Changes in practice were made at the end of 2001 in order to address a perceived high rate of sepsis: a) reeducation; b) introduction of electively preprimed ECMO circuits; and c) preference for neck rather than chest cannulation in cardiac patients. Prophylactic antibiotics were used from preprocedure for 24 hrs only throughout the study. MEASUREMENTS AND MAIN RESULTS: Over the entire study period, 39 children had 47 septic episodes, with a rate of 24.9 per 1000 ECMO days. Multiple logistic regression analyses indicated that infection was associated with duration of ECMO support (odds ratio 1.24; 95% confidence interval 1.15, 1.35 per day) and case type: Closed vs. open chest was protective in cardiac patients (odds ratio 0.08; 95% confidence interval 0.01, 0.50). Infection increased the odds of death by 2.01 (95% confidence interval 1.00, 4.05), but this effect was less important than case type and ECMO days. After policy changes were implemented, there was a reduction in sepsis from 29.3 to 20.1 episodes per 1000 ECMO days. There was reduced sepsis in respiratory patients: neonates from 28.0 to 6.6 and pediatric patients from 42.4 to 16.9 episodes per 1000 ECMO days. Despite policy changes, sepsis remained a problem in cardiac patients with open sternum: 65.1 per 1000 ECMO days. CONCLUSIONS: ECMO support is a high-risk setup for nosocomial infection, in particular for cardiac patients with open sternum for whom antibiotic prophylaxis is justified. Multidisciplinary surveillance offers an excellent approach for quality improvement in this challenging field.

Refinements in acute dietary exposure assessments for chlorpyrifos.

Food pesticide residue data are used by the U.S. Environmental Protection Agency (EPA) to determine potential dietary risk from chronic and acute exposures. An acute dietary risk assessment determines the pesticide exposure resulting from a single-day consumption of food, and uses stepwise refinement of residue estimates to better judge actual exposures. All exposure refinements use estimates of the fraction of crops treated and food residues measured increasingly closer to the point of actual food consumption, without changes in the pesticide uses. Exposure distributions at all levels of data refinement were extremely right skewed. At the highest level evaluated, estimated exposures at the 99.9th percentile were 0.00087 mg/kgBW/day compared to 0.2648 mg/kgBW/day at the tolerance level for children 1-6 years, theoretically the highest-exposed population sub-group. The estimated exposure at the 99.9th percentile of the U.S. population was approximately twice the exposure at the 99th percentile and 33 times the exposure at the 90th percentile. This evaluation showed the calculated exposure at the highest tier of assessment was 300 times lower than the tolerance level assessment for children 1-6 years at the 99.9th percentile. Reduction in exposure estimates between these tiers was due to a combination of the following factors: food residue measurements in a specially designed market-basket study, government-sponsored monitoring data, probabilistic methodologies, market share information, and food processing data. This case study demonstrates that an improved understanding of the uncertainties of acute dietary exposure from pesticides is possible by using well-established statistical tools and applying them to comprehensive exposure information, including residue monitoring data, consumption data, and pesticide use information.

A teenager who firebombed his parents while in a dissociated state.

The paper describes a fifteen-year-old boy who firebombed his adoptive parents in their bed. The boy claimed to have no memory of the incident which occurred during the latter part of the night's sleep. Evidence is given for the diagnosis of Dissociative Disorder, and there is discussion of Sleep Walking Disorder and REM Behaviour Disorder in the differential diagnosis. The contributions made by intrapsychic and family processes are explored and the course of treatment described.