A middleware for a large array of cameras

Large arrays of cameras are increasingly being employed for producing high quality image sequences needed for motion analysis research. This leads to the logistical problem with coordination and control of a large number of cameras. In this paper, we used a lightweight multi-agent system for coordinating such camera arrays. The agent framework provides more than a remote sensor access API. It allows reconfigurable and transparent access to cameras, as well as software agents capable of intelligent processing. Furthermore, it eases maintenance by encouraging code reuse. Additionally, our agent system includes an automatic discovery mechanism at startup, and multiple language bindings. Performance tests showed the lightweight nature of the framework while validating its correctness and scalability. Two different camera agents were implemented to provide access to a large array of distributed cameras. Correct operation of these camera agents was confirmed via several image processing agents

Type and Timing of Rehabilitation Following Acute and Subacute Spinal Cord Injury: A Systematic Review

Objectives: The objective of this study was to conduct a systematic review of the literature to address the following clinical questions: In adult patients with acute and subacute complete or incomplete traumatic SCI, (1) does the time interval between injury and commencing rehabilitation affect outcome?; (2) what is the comparative effectiveness of different rehabilitation strategies, including different intensities and durations of treatment?; (3) are there patient or injury characteristics that affect the efficacy of rehabilitation?; and (4) what is the cost-effectiveness of various rehabilitation strategies? Methods: A systematic search was conducted for literature published through March 31, 2015 that evaluated rehabilitation strategies in adults with acute or subacute traumatic SCI at any level. Studies were critically appraised individually and the overall strength of evidence was evaluated using methods proposed by the GRADE (Grades of Recommendation Assessment, Development and Evaluation) working group. Results: The search strategy yielded 384 articles, 19 of which met our inclusion criteria. Based on our results, there was no difference between body weight–supported treadmill training and conventional rehabilitation with respect to improvements in Functional Independence Measure (FIM) Locomotor score, Lower Extremity Motor Scores, the distance walked in 6 minutes or gait velocity over 15.2 m. Functional electrical therapy resulted in slightly better FIM Motor, FIM Self-Care, and Spinal Cord Independence Measure Self-Care subscores compared with conventional occupational therapy. Comparisons using the Toronto Rehabilitation Institute Hand Function Test demonstrated no differences between groups in 7 of 9 domains. There were no clinically important differences in Maximal Lean Test, Maximal Sidewards Reach Test, T-shirt Test, or the Canadian Occupational Performance Measure between unsupported sitting training and standard in-patient rehabilitation. Conclusion: The current evidence base for rehabilitation following acute and subacute spinal cord injury is limited. Methodological challenges have contributed to this and further research is still needed. © 2017, © The Author(s) 2017

Artificial Intelligence and Authorship Editor Policy: ChatGPT, Bard Bing AI, and beyond

Artificial intelligence and large-language model chatbots have generated significant attention in higher education, and in research practice. Whether ChatGPT, Bard, Jasper Chat, Socratic, Bing AI, DialoGPT, or something else, these are all shaping how education and research occur. In this Editorial, we offer five editorial principles to guide decision-making for editors, which will also become policy for the Journal of University Teaching and Learning Practice. First, we articulate that non-human authorship does not constitute authorship. Second, artificial intelligence should be leveraged to support authors. Third, artificial intelligence can offer useful feedback and pre-review. Fourth, transparency of artificial intelligence usage is an expectation. And fifth, the use of AI in research design, conduct, and dissemination must comply with established ethical principles. In these five principles, we articulate a position of optimism for the new forms of knowledge and research we might garner. We see AI as a mechanism that may augment our current practices but will not likely replace all of them. However, we do issue caution to the limitations of large language models including possible proliferation of poor-quality research, Stochastic Parroting, and data hallucinations. As with all research, authors should be comfortably familiar with the underlying methods being used to generate data and should ensure a clear understanding of the AI tools being used prior to deployment for research

Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network.

PurposeTo assess feasibility of training a convolutional neural network (CNN) to automate liver segmentation across different imaging modalities and techniques used in clinical practice and apply this to enable automation of liver biometry.MethodsWe trained a 2D U-Net CNN for liver segmentation in two stages using 330 abdominal MRI and CT exams acquired at our institution. First, we trained the neural network with non-contrast multi-echo spoiled-gradient-echo (SGPR)images with 300 MRI exams to provide multiple signal-weightings. Then, we used transfer learning to generalize the CNN with additional images from 30 contrast-enhanced MRI and CT exams.We assessed the performance of the CNN using a distinct multi-institutional data set curated from multiple sources (n = 498 subjects). Segmentation accuracy was evaluated by computing Dice scores. Utilizing these segmentations, we computed liver volume from CT and T1-weighted (T1w) MRI exams, and estimated hepatic proton- density-fat-fraction (PDFF) from multi-echo T2*w MRI exams. We compared quantitative volumetry and PDFF estimates between automated and manual segmentation using Pearson correlation and Bland-Altman statistics.ResultsDice scores were 0.94 ± 0.06 for CT (n = 230), 0.95 ± 0.03 (n = 100) for T1w MR, and 0.92 ± 0.05 for T2*w MR (n = 169). Liver volume measured by manual and automated segmentation agreed closely for CT (95% limit-of-agreement (LoA) = [-298 mL, 180 mL]) and T1w MR (LoA = [-358 mL, 180 mL]). Hepatic PDFF measured by the two segmentations also agreed closely (LoA = [-0.62%, 0.80%]).ConclusionsUtilizing a transfer-learning strategy, we have demonstrated the feasibility of a CNN to be generalized to perform liver segmentations across different imaging techniques and modalities. With further refinement and validation, CNNs may have broad applicability for multimodal liver volumetry and hepatic tissue characterization

Iodine status in western Kenya: a community-based cross-sectional survey of urinary and drinking water iodine concentrations

Spot urinary iodine concentrations (UIC) are presented for 248 individuals from western Kenya with paired drinking water collected between 2016 and 2018. The median UIC was 271 µg L−1, ranging from 9 to 3146 µg L−1, unadjusted for hydration status/dilution. From these data, 12% were potentially iodine deficient ( 300 µg L−1). The application of hydration status/urinary dilution correction methods was evaluated for UICs, using creatinine, osmolality and specific gravity. The use of specific gravity correction for spot urine samples to account for hydration status/urinary dilution presents a practical approach for studies with limited budgets, rather than relying on unadjusted UICs, 24 h sampling, use of significantly large sample size in a cross-sectional study and other reported measures to smooth out the urinary dilution effect. Urinary corrections did influence boundary assessment for deficiency–sufficiency–excess for this group of participants, ranging from 31 to 44% having excess iodine intake, albeit for a study of this size. However, comparison of the correction methods did highlight that 22% of the variation in UICs was due to urinary dilution, highlighting the need for such correction, although creatinine performed poorly, yet specific gravity as a low-cost method was comparable to osmolality corrections as the often stated ‘gold standard’ metric for urinary concentration. Paired drinking water samples contained a median iodine concentration of 3.2 µg L−1 (0.2–304.1 µg L−1). A weak correlation was observed between UIC and water-I concentrations (R = 0.11)

Uniqueness of Ground States for Short-Range Spin Glasses in the Half-Plane

We consider the Edwards-Anderson Ising spin glass model on the half-plane $Z \times Z^+$ with zero external field and a wide range of choices, including mean zero Gaussian, for the common distribution of the collection J of i.i.d. nearest neighbor couplings. The infinite-volume joint distribution $K(J,\alpha)$ of couplings J and ground state pairs $\alpha$ with periodic (respectively, free) boundary conditions in the horizontal (respectively, vertical) coordinate is shown to exist without need for subsequence limits. Our main result is that for almost every J, the conditional distribution $K(\alpha|J)$ is supported on a single ground state pair.Comment: 20 pages, 3 figure

Trends in prenatal cares settings: association with medical liability

<p>Abstract</p> <p>Background</p> <p>Medical liability concerns centered around maternity care have widespread public health implications, as restrictions in physician scope of practice may threaten quality of and access to care in the current climate. The purpose of this study was to examine national trends in prenatal care settings based on medical liability climate.</p> <p>Methods</p> <p>Analysis of prenatal visits in the National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey, 1997 to 2004 (N = 21,454). To assess changes in rates of prenatal visits over time, we used the linear trend test. Multivariate logistic regression modeling was developed to determine characteristics associated with visits made to hospital outpatient departments.</p> <p>Results</p> <p>In regions of the country with high medical liability (N = 11,673), the relative number, or proportion, of all prenatal visits occurring in hospital outpatient departments increased from 11.8% in 1997–1998 to 19.4% in 2003–2004 (p < .001 for trend); the trend for complicated obstetrical visits (N = 3,275) was more pronounced, where the proportion of prenatal visits occurring in hospital outpatient departments almost doubled from 22.7% in 1997–1998 to 41.6% in 2003–2004 (p = .004 for trend). This increase did not occur in regions of the country with low medical liability (N = 9,781) where the proportion of visits occurring in hospital outpatient departments decreased from 13.3% in 1997–1998 to 9.0% in 2003–2004.</p> <p>Conclusion</p> <p>There has been a shift in prenatal care from obstetrician's offices to safety net settings in regions of the country with high medical liability. These findings provide strong indirect evidence that the medical liability crisis is affecting patterns of obstetric practice and ultimately patient access to care.</p