Cognitive Ability Score Differences on Mobile and Nonmobile Devices: The Role of Working Memory

In the last few decades there has been a dramatic shift in the way employment-related assessments are administered due to technological advancements. Mobile devices are increasingly used in employment-related assessments despite documented significant performance differences in scores on cognitive tests completed on mobile and nonmobile devices. These performance differences have been attributed to structural characteristic differences between mobile and nonmobile devices, which place differentiated information processing demands on test takers (Arthur, Keiser, & Doverspike, 2016). This relationship between the structural characteristic differences and information processing demands serves as the basis for Arthur et al.’s Structural Characteristics Information Processing (SCIP) model. The present study examines one component of this model, working memory, and the role it plays in the observed performance differences on mobile device cognitive assessments. Participants were recruited from the Texas A&M University Psychology Department Subject Pool (n = 196), and were randomly assigned to either a smartphone (n = 100) or desktop computer (n = 96) device condition to complete the specified cognitive and noncognitive assessments; they then completed a working memory test on a desktop computer. The relationship between participants’ working memory test scores and their cognitive and noncognitive test scores were examined to investigate whether the relationships differ as a function of the device type on which participants were tested. The results failed to show the expected device type differences for cognitive ability. However, as hypothesized, there was a stronger relationship between working memory and general mental ability (GMA) when the GMA test was completed on a smartphone compared to a desktop computer. Also as hypothesized, there was no significant difference between the smartphone and desktop device conditions on noncognitive test scores, nor in the working memory-noncognitive test score correlations for smartphones and desktop computers. The findings provide partial, initial support for Arthur et al.’s SCIP model, which can be utilized to explain the effects of internet-based testing devices on scores on employment-related assessments and tests

Theory and implementation of inelastic Constitutive Artificial Neural Networks

Nature has always been our inspiration in the research, design and development of materials and has driven us to gain a deep understanding of the mechanisms that characterize anisotropy and inelastic behavior. All this knowledge has been accumulated in the principles of thermodynamics. Deduced from these principles, the multiplicative decomposition combined with pseudo potentials are powerful and universal concepts. Simultaneously, the tremendous increase in computational performance enabled us to investigate and rethink our history-dependent material models to make the most of our predictions. Today, we have reached a point where materials and their models are becoming increasingly sophisticated. This raises the question: How do we find the best model that includes all inelastic effects to explain our complex data? Constitutive Artificial Neural Networks (CANN) may answer this question. Here, we extend the CANNs to inelastic materials (iCANN). Rigorous considerations of objectivity, rigid motion of the reference configuration, multiplicative decomposition and its inherent non-uniqueness, restrictions of energy and pseudo potential, and consistent evolution guide us towards the architecture of the iCANN satisfying thermodynamics per design. We combine feed-forward networks of the free energy and pseudo potential with a recurrent neural network approach to take time dependencies into account. We demonstrate that the iCANN is capable of autonomously discovering models for artificially generated data, the response of polymers for cyclic loading and the relaxation behavior of muscle data. As the design of the network is not limited to visco-elasticity, our vision is that the iCANN will reveal to us new ways to find the various inelastic phenomena hidden in the data and to understand their interaction. Our source code, data, and examples are available at doi.org/10.5281/zenodo.10066805Comment: 54 pages, 14 figures, 14 table

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SK15

Mitochondrial efficiency: lessons learned from transgenic mice

AbstractMetabolic research has, like most areas of research in the life sciences, been affected dramatically by the application of transgenic technologies. Within the specific area of bioenergetics it has been thought that transgenic approaches in mice would provide definitive proof for some longstanding metabolic theories and assumptions. Here we review a number of transgenic approaches that have been used in mice to address theories of mitochondrial efficiency. The focus is largely on genes that affect the coupling of energy substrate oxidation to ATP synthesis, and thus, mice in which the uncoupling protein (Ucp) genes are modified are discussed extensively. Transgenic approaches have indeed provided proof-of-concept in some instances, but in many other instances they have yielded results that are in contrast to initial hypotheses. Many studies have also shown that genetic background can affect phenotypic outcomes, and that the upregulated expression of genes that are related to the modified gene often complicates the interpretation of findings

Initial experience with positron emission tomography/computed tomography in addition to computed tomography and magnetic resonance imaging in preoperative risk assessment of endometrial cancer patients

Objective: Improved preoperative evaluation of lymph node status could potentially replace lymphadenectomy in women with endometrial cancer. PET/CT was routinely implemented in the preoperative workup of endometrial cancer at St Olav's University Hospital in 2016. Experience with PET/CT is limited, and there is no consensus about the use of PET/CT in the diagnostic workup of endometrial cancer. The aim of the study was to evaluate the diagnostic accuracy of PET/CT compared to standard CT/MRI in identifying lymph node metastases in endometrial cancer with histologically confirmed lymph node metastases as the standard of reference. We especially wanted to look at PET/CT as a supplement to the sentinel lymph node algorithm in the detection of paraaortic lymph nodes. Study design: A retrospective study included all women undergoing surgery for endometrial cancer from January 2016 through July 2019 at St Olav's University Hospital. Clinical data, results of CT, MRI, and PET/CT, and histopathological results were analyzed. Results: Among 185 patients included, 27 patients (15 %) had lymph node metastases. 17 (63 %) had pelvic lymph node metastases, one (4 %) had isolated paraaortic lymph node metastases, and 9 (33 %) had lymph node metastases in both the pelvis and the paraaortic region. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of PET/CT for the detection of lymph node metastases were 63 %, 98 %, 85 %, 94 %, and 93 %, respectively. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of CT/MRI were 41 %, 98 %, 73 %, 91 %, and 90 %, respectively (p = 0.07). For the 26 pelvic lymph node metastases, PET/CT had a sensitivity of 58 %, compared to 42 % for CT/MRI (p = 0.22). PET/CT detected all 10 paraaortic lymph node metastases, for a sensitivity of 100 %, compared to 50 % for CT/MRI (p = 0.06). Conclusions: PET is superior to CT/MRI for detection of lymph node metastases in endometrial cancer, particularly in detecting paraaortic lymph node metastases. The ability of preoperative PET to exclude paraaortic lymph node metastases may strengthen the credibility of the sentinel lymph node algorithm.publishedVersion© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)