Blue-light background impairs visual exogenous attention shift.

Previous research into the effects of blue light on visual-spatial attention has yielded mixed results due to a lack of properly controlling critical factors like S-cone stimulation, ipRGCs stimulation, and color. We adopted the clock paradigm and systematically manipulated these factors to see how blue light impacts the speed of exogenous and endogenous attention shifts. Experiments 1 and 2 revealed that, relative to the control light, exposure to the blue-light background decreased the speed of exogenous (but not endogenous) attention shift to external stimuli. To further clarify the contribution(s) of blue-light sensitive photoreceptors (i.e., S-cone and ipRGCs), we used a multi-primary system that could manipulate the stimulation of a single type of photoreceptor without changing the stimulation of other photoreceptors (i.e., the silent substitution method). Experiments 3 and 4 revealed that stimulation of S-cones and ipRGCs did not contribute to the impairment of exogenous attention shift. Our findings suggest that associations with blue colors, such as the concept of blue light hazard, cause exogenous attention shift impairment. Some of the previously documented blue-light effects on cognitive performances need to be reevaluated and reconsidered in light of our findings.

Identifying Features that Enhance Older Adults' Acceptance of Robots: A Mixed Methods Study.

BACKGROUND: With global aging, robots are considered a promising solution for handling the shortage of aged care and companionships. However, these technologies would serve little purpose if their intended users do not accept them. While the socioemotional selectivity theory predicts that older adults would accept robots that offer emotionally meaningful relationships, selective optimization with compensation model predicts that older adults would accept robots that compensate for their functional losses. OBJECTIVE: The present study aims to understand older adults' expectations for robots and to compare older adults' acceptance ratings for 2 existing robots: one of them is a more human-like and more service-oriented robot and the other one is a more animal-like and more companion-oriented robot. METHODS: A mixed methods study was conducted with 33 healthy, community-dwelling Taiwanese older adults (age range: 59-82 years). Participants first completed a semi-structured interview regarding their ideal robot. After receiving information about the 2 existing robots, they then completed the Unified Theory of Acceptance and Use of Technology questionnaires to report their pre-implementation acceptance of the 2 robots. RESULTS: Interviews were transcribed for conventional content analysis with satisfactory inter-rater reliability. From the interview data, a collection of older adults' ideal robot characteristics emerged with highlights of humanlike qualities. From the questionnaire data, respondents showed a higher level of acceptance toward the more service-oriented robot than the more companion-oriented robot in terms of attitude, perceived adaptiveness, and perceived usefulness. From the mixed methods analyses, the finding that older adults had a higher level of positive attitude towards the more service-oriented robot than the more companion-oriented robot was predicted by higher expectation or preference for robots with more service-related functions. CONCLUSION: This study identified older adults' preference toward more functional and humanlike robots. Our findings provide practical suggestions for future robot designs that target the older population.

Improving bone strength prediction in human proximal femur specimens through geometrical characterization of trabecular bone microarchitecture and support vector regression.

We investigate the use of different trabecular bone descriptors and advanced machine learning tech niques to complement standard bone mineral density (BMD) measures derived from dual-energy x-ray absorptiometry (DXA) for improving clinical assessment of osteoporotic fracture risk. For this purpose, volumes of interest were extracted from the head, neck, and trochanter of 146 ex vivo proximal femur specimens on multidetector computer tomography. The trabecular bone captured was characterized with (1) statistical moments of the BMD distribution, (2) geometrical features derived from the scaling index method (SIM), and (3) morphometric parameters, such as bone fraction, trabecular thickness, etc. Feature sets comprising DXA BMD and such supplemental features were used to predict the failure load (FL) of the specimens, previously determined through biomechanical testing, with multiregression and support vector regression. Prediction performance was measured by the root mean square error (RMSE); correlation with measured FL was evaluated using the coefficient of determination R2. The best prediction performance was achieved by a combination of DXA BMD and SIM-derived geometric features derived from the femoral head (RMSE: 0.869 ± 0.121, R2: 0.68 ± 0.079), which was significantly better than DXA BMD alone (RMSE: 0.948 ± 0.119, R2: 0.61 ± 0.101) (p < 10-4). For multivariate feature sets, SVR outperformed multiregression (p < 0.05). These results suggest that supplementing standard DXA BMD measurements with sophisticated femoral trabecular bone characterization and supervised learning techniques can significantly improve biomechanical strength prediction in proximal femur specimens.

Predicting the Biomechanical Strength of Proximal Femur Specimens with Minkowski Functionals and Support Vector Regression.

Regional trabecular bone quality estimation for purposes of femoral bone strength prediction is important for improving the clinical assessment of osteoporotic fracture risk. In this study, we explore the ability of 3D Minkowski Functionals derived from multi-detector computed tomography (MDCT) images of proximal femur specimens in predicting their corresponding biomechanical strength. MDCT scans were acquired for 50 proximal femur specimens harvested from human cadavers. An automated volume of interest (VOI)-fitting algorithm was used to define a consistent volume in the femoral head of each specimen. In these VOIs, the trabecular bone micro-architecture was characterized by statistical moments of its BMD distribution and by topological features derived from Minkowski Functionals. A linear multi-regression analysis and a support vector regression (SVR) algorithm with a linear kernel were used to predict the failure load (FL) from the feature sets; the predicted FL was compared to the true FL determined through biomechanical testing. The prediction performance was measured by the root mean square error (RMSE) for each feature set. The best prediction result was obtained from the Minkowski Functional surface used in combination with SVR, which had the lowest prediction error (RMSE = 0.939 ± 0.345) and which was significantly lower than mean BMD (RMSE = 1.075 ± 0.279, p<0.005). Our results indicate that the biomechanical strength prediction can be significantly improved in proximal femur specimens with Minkowski Functionals extracted from on MDCT images used in conjunction with support vector regression.

Topoisomerase I inhibitor evodiamine acts as an antibacterial agent against drug-resistant Klebsiella pneumoniae.

Topoisomerase inhibitors have been developed in a variety of clinical applications. We investigated the inhibitory effect of evodiamine on E. coli topoisomerase I, which may lead to an anti-bacterial effect. Evodiamine inhibits the supercoiled plasmid DNA relaxation that is catalyzed by E. coli topoisomerase I, and computer-aided docking has shown that the Arg161 and Asp551 residues of topoisomerase I interact with evodiamine. We investigated the bactericidal effect of evodiamine against multidrug-resistant Klebsiella pneumoniae. Evodiamine showed a significantly lower minimal inhibitory concentration value (MIC 128 µg/mL) compared with antibiotics (>512 µg/mL) against the clinical isolate of K. pneumoniae. The results suggested that evodiamine is a potential agent against drug-resistant bacteria.

Predicting the Biomechanical Strength of Proximal Femur Specimens through High Dimensional Geometric Features and Support Vector Regression.

Estimating local trabecular bone quality for purposes of femoral bone strength prediction is important for improving the clinical assessment of osteoporotic hip fracture risk. In this study, we explore the ability of geometric features derived from the Scaling Index Method (SIM) in predicting the biomechanical strength of proximal femur specimens as visualized on multi-detector computed tomography (MDCT) images. MDCT scans were acquired for 50 proximal femur specimens harvested from human cadavers. An automated volume of interest (VOI)-fitting algorithm was used to define a consistent volume in the femoral head of each specimen. In these VOIs, the non-linear micro-structure of the trabecular bone was characterized by statistical moments of its BMD distribution and by local scaling properties derived from SIM. Linear multi-regression analysis and support vector regression with a linear kernel (SVRlin) were used to predict the failure load (FL) from the feature sets; the predicted FL was compared to the FL values determined through biomechanical testing. The prediction performance was measured by the root mean square error (RMSE) for each image feature on independent test set. The best prediction result was obtained from the SIM feature set with SVRlin, which had the lowest prediction error (RMSE = 0.842 ± 0.209) and which was significantly lower than the conventionally used mean BMD (RMSE = 1.103 ± 0.262,, p<0.005). Our results indicate that the biomechanical strength prediction can be significantly improved in proximal femur specimens on MDCT images by using high-dimensional geometric features derived from SIM with support vector regression.