The Effects Of Experience And Data Presentation Format On An Auditing Judgment

Prior research has examined the effects of information presentation format on decision outcomes in various settings, but has led to inconclusive results as to whether a tabular or graphical format is superior for decision making.  An important methodological difference in these studies is the use of inexperienced versus experienced participants.  This study examines the interaction of experience with presentation format in the application of auditing judgments (specifically, analytical review judgments) and finds that participant experience does matter.  In particular, where tabular information was most extensively used (i.e., in the task of correlation assessment), the performance advantage from using graphs was not as great for practitioners as for students, perhaps because of the experience practitioners possess with the use of tables.  Implications of this study for the interpretation of prior findings are discussed as well as directions for future research

Surficial and deep earth material prediction from geochemical compositions

Prediction of true classes of surficial and deep earth materials using multivariate spatial data is a common challenge for geoscience modelers. Most geological processes leave a footprint that can be explored by geochemical data analysis. These footprints are normally complex statistical and spatial patterns buried deep in the high-dimensional compositional space. This paper proposes a spatial predictive model for classification of surficial and deep earth materials derived from the geochemical composition of surface regolith. The model is based on a combination of geostatistical simulation and machine learning approaches. A random forest predictive model is trained, and features are ranked based on their contribution to the predictive model. To generate potential and uncertainty maps, compositional data are simulated at unsampled locations via a chain of transformations (isometric log-ratio transformation followed by the flow anamorphosis) and geostatistical simulation. The simulated results are subsequently back-transformed to the original compositional space. The trained predictive model is used to estimate the probability of classes for simulated compositions. The proposed approach is illustrated through two case studies. In the first case study, the major crustal blocks of the Australian continent are predicted from the surface regolith geochemistry of the National Geochemical Survey of Australia project. The aim of the second case study is to discover the superficial deposits (peat) from the regional-scale soil geochemical data of the Tellus Project. The accuracy of the results in these two case studies confirms the usefulness of the proposed method for geological class prediction and geological process discovery

Requirements for Selection of Conventional and Innate T Lymphocyte Lineages

SummaryMice deficient in the Tec kinase Itk develop a large population of CD8+ T cells with properties, including expression of memory markers, rapid production of cytokines, and dependence on Interleukin-15, resembling NKT and other innate T cell lineages. Like NKT cells, these CD8+ T cells can be selected on hematopoietic cells. We demonstrate that these CD8+ T cell phenotypes resulted from selection on hematopoietic cells—forcing selection on the thymic stroma reduced the number and innate phenotypes of mature Itk-deficient CD8+ T cells. We further show that, similar to NKT cells, selection of innate-type CD8+ T cells in Itk−/− mice required the adaptor SAP. Acquisition of their innate characteristics, however, required CD28. Our results suggest that SAP and Itk reciprocally regulate selection of innate and conventional CD8+ T cells on hematopoietic cells and thymic epithelium, respectively, whereas CD28 regulates development of innate phenotypes resulting from selection on hematopoietic cells

Association between community-based self-reported COVID-19 symptoms and social deprivation explored using symptom tracker apps: A repeated cross-sectional study in Northern Ireland

Objectives: The aim of the study was to investigate the spatial and temporal relationships between the prevalence of COVID-19 symptoms in the community-level and area-level social deprivation. Design: Spatial mapping, generalised linear models, using time as a factor and spatial-lag models were used to explore the relationship between self-reported COVID-19 symptom prevalence as recorded through two smartphone symptom tracker apps and a range of socioeconomic factors using a repeated cross-sectional study design. Setting: In the community in Northern Ireland, UK. The analysis period included the earliest stages of non-pharmaceutical interventions and societal restrictions or \u27lockdown\u27 in 2020. Participants: Users of two smartphone symptom tracker apps recording self-reported health information who recorded their location as Northern Ireland, UK. Primary outcome measures: Population standardised self-reported COVID-19 symptoms and correlation between population standardised self-reported COVID-19 symptoms and area-level characteristics from measures of multiple deprivation including employment levels and population housing density, derived as the mean number of residents per household for each census super output area. Results: Higher self-reported prevalence of COVID-19 symptoms was associated with the most deprived areas (p \u3c 0.001) and with those areas with the lowest employment levels (p \u3c 0.001). Higher rates of self-reported COVID-19 symptoms within the age groups, 18-24 and 25-34 years were found within the most deprived areas during the earliest stages of non-pharmaceutical interventions and societal restrictions (\u27lockdown\u27). Conclusions: Through spatial regression of self-reporting COVID-19 smartphone data in the community, this research shows how a lens of social deprivation can deepen our understanding of COVID-19 transmission and prevention. Our findings indicate that social inequality, as measured by area-level deprivation, is associated with disparities in potential COVID-19 infection, with higher prevalence of self-reported COVID-19 symptoms in urban areas associated with area-level social deprivation, housing density and age

A scoping review of mathematical models of Plasmodium vivax

Plasmodium vivax is one of the most geographically widespread malaria parasites in the world due to its ability to remain dormant in the human liver as hypnozoites and subsequently reactivate after the initial infection (i.e. relapse infections). More than 80% of P. vivax infections are due to hypnozoite reactivation. Mathematical modelling approaches have been widely applied to understand P. vivax dynamics and predict the impact of intervention outcomes. In this article, we provide a scoping review of mathematical models that capture P. vivax transmission dynamics published between January 1988 and May 2023 to provide a comprehensive summary of the mathematical models and techniques used to model P. vivax dynamics. We aim to assist researchers working on P. vivax transmission and other aspects of P. vivax malaria by highlighting best practices in currently published models and highlighting where future model development is required. We provide an overview of the different strategies used to incorporate the parasite's biology, use of multiple scales (within-host and population-level), superinfection, immunity, and treatment interventions. In most of the published literature, the rationale for different modelling approaches was driven by the research question at hand. Some models focus on the parasites' complicated biology, while others incorporate simplified assumptions to avoid model complexity. Overall, the existing literature on mathematical models for P. vivax encompasses various aspects of the parasite's dynamics. We recommend that future research should focus on refining how key aspects of P. vivax dynamics are modelled, including the accumulation of hypnozoite variation, the interaction between P. falciparum and P. vivax, acquisition of immunity, and recovery under superinfection

Molecular Characterization Reveals Diverse and Unknown Malaria Vectors in the Western Kenyan Highlands.

The success of mosquito-based malaria control is dependent upon susceptible bionomic traits in local malaria vectors. It is crucial to have accurate and reliable methods to determine mosquito species composition in areas subject to malaria. An unexpectedly diverse set of Anopheles species was collected in the western Kenyan highlands, including unidentified and potentially new species carrying the malaria parasite Plasmodium falciparum. This study identified 2,340 anopheline specimens using both ribosomal DNA internal transcribed spacer region 2 and mitochondrial DNA cytochrome oxidase subunit 1 loci. Seventeen distinct sequence groups were identified. Of these, only eight could be molecularly identified through comparison to published and voucher sequences. Of the unidentified species, four were found to carry P. falciparum by circumsporozoite enzyme-linked immunosorbent assay and polymerase chain reaction, the most abundant of which had infection rates comparable to a primary vector in the area, Anopheles funestus. High-quality adult specimens of these unidentified species could not be matched to museum voucher specimens or conclusively identified using multiple keys, suggesting that they may have not been previously described. These unidentified vectors were captured outdoors. Diverse and unknown species have been incriminated in malaria transmission in the western Kenya highlands using molecular identification of unusual morphological variants of field specimens. This study demonstrates the value of using molecular methods to compliment vector identifications and highlights the need for accurate characterization of mosquito species and their associated behaviors for effective malaria control

Surficial and Deep Earth Material Prediction from Geochemical Compositions

Prediction of true classes of surficial and deep earth materials using multivariate spatial data is a common challenge for geoscience modelers. Most geological processes leave a footprint that can be explored by geochemical data analysis. These footprints are normally complex statistical and spatial patterns buried deep in the high-dimensional compositional space. This paper proposes a spatial predictive model for classification of surficial and deep earth materials derived from the geochemical composition of surface regolith. The model is based on a combination of geostatistical simulation and machine learning approaches. A random forest predictive model is trained, and features are ranked based on their contribution to the predictive model. To generate potential and uncertainty maps, compositional data are simulated at unsampled locations via a chain of transformations (isometric log-ratio transformation followed by the flow anamorphosis) and geostatistical simulation. The simulated results are subsequently back-transformed to the original compositional space. The trained predictive model is used to estimate the probability of classes for simulated compositions. The proposed approach is illustrated through two case studies. In the first case study, the major crustal blocks of the Australian continent are predicted from the surface regolith geochemistry of the National Geochemical Survey of Australia project. The aim of the second case study is to discover the superficial deposits (peat) from the regional-scale soil geochemical data of the Tellus Project. The accuracy of the results in these two case studies confirms the usefulness of the proposed method for geological class prediction and geological process discovery.The first three authors acknowledge financial support through DAAD-UA grant CodaBlock CoEstimation. The National Geochemical Survey of Australia project was part of the Australian Governments Onshore Energy Security Program 2006–2011, from which funding support is gratefully acknowledged. The Tellus Project was carried out by GSNI and funded by The Department for Enterprise, Trade and Investment (DETINI) and The Rural Development Programme through the Northern Ireland Programme for Building Sustainable Prosperity

The ACE1 Electrical Impedance Tomography System for Thoracic Imaging

The design and performance of the active complex electrode (ACE1) electrical impedance tomography system for single-ended phasic voltage measurements are presented. The design of the hardware and calibration procedures allows for reconstruction of conductivity and permittivity images. Phase measurement is achieved with the ACE1 active electrode circuit which measures the amplitude and phase of the voltage and the applied current at the location at which current is injected into the body. An evaluation of the system performance under typical operating conditions includes details of demodulation and calibration and an in-depth look at insightful metrics, such as signal-to-noise ratio variations during a single current pattern. Static and dynamic images of conductivity and permittivity are presented from ACE1 data collected on tank phantoms and human subjects to illustrate the system\u27s utility