Structural investigations of CeIrIn5{_5} and CeCoIn5{_5} on macroscopic and atomic length scales

For any thorough investigation of complex physical properties, as encountered in strongly correlated electron systems, not only single crystals of highest quality but also a detailed knowledge of the structural properties of the material are pivotal prerequisites. Here, we combine physical and chemical investigations on the prototypical heavy fermion superconductors CeIrIn${_5}$ and CeCoIn${_5}$ on atomic and macroscopic length scale to gain insight into their precise structural properties. Our approach spans from enhanced resolution X-ray diffraction experiments to atomic resolution by means of Scanning Tunneling Microscopy (STM) and reveal a certain type of local features (coexistence of minority and majority structural patterns) in the tetragonal HoCoGa$_5$-type structure of both compounds.Comment: 8 pages, 5 figures, submitted to JPSJ (SCES 2013

Association between Self-Reported Prior Nights’ Sleep and Single-Task Gait in Healthy Young Adults: An Exploratory Study Using Machine Learning

Failure to obtain 7-9 hours of sleep has been associated with decreased gait speed in young adults. While Machine Learning (ML) has been used to identify sleep quality in young adults, there are no current studies that have used ML to identify prior night’s sleep in a sample of young adults. PURPOSE: To use ML to identify prior night’s sleep in healthy young adults using single-task walking gait. METHODS: Participants (n=126, age 24.3±4.0yrs; 65% female) completed a survey on their prior night’s sleep and performed a 2-minute walk around a 6m track. Gait data were collected using inertial sensors. Participants were split into 2 groups (\u3c7hs or \u3e9hs: poor sleepers; 7-9hs: good sleepers) and gait characteristics were used to classify participants into each group using ML models via a 10-fold cross validation. A post-hoc ANCOVA was used to assess gait differences. RESULTS: Using Random Forest Classifiers (RFC), top 9 features were extracted. Classification results suggest a 0.79 correlation between gait parameters and prior night’s sleep. The RFC models had a 65.03% mean classification accuracy rate. Top 0.3% of the models had 100% classification accuracy rate. The top 9 features were primarily characteristics that measured variance between lower limb movements. Post-hoc analyses suggest significantly greater variances between lower limb characteristics. CONCLUSION: Good sleepers had more asymmetrical gait patterns (faster gait speed, less trunk motion). Poor sleepers had trouble maintaining gait speed (increased variance in cadence, larger stride lengths, and less time spent in single leg support time). Although the mechanisms of these gait changes are unknown, these findings provide evidence that gait is different for individuals who not receive 7-9 hours of sleep the night before. As evidenced by the high correlation co-efficient of our classification models, gait may be a good way of identifying prior night’s sleep

Crystal Chemistry and Physics of UCd11

In the phase diagram U-Cd, only one compound has been identified so far─UCd11 (space group Pm3̅m). Since the discovery of this material, the physical properties of UCd11 have attracted a considerable amount of attention. In particular, its complex magnetic phase diagram─as a result of tuning with magnetic field or pressure─is not well-understood. From a chemical perspective, a range of lattice parameter values have been reported, suggesting a possibility of a considerable homogeneity range, i.e., UCd11-x. In this work, we perform a simultaneous study of crystallographic features coupled with measurements of physical properties. This work sheds light on the delicate relationship between the intrinsic crystal chemistry and magnetic properties of UCd11

Crystal Chemistry and Physics of UCd₁₁

In the phase diagram U-Cd, only one compound has been identified so farUCd11 (space group Pm3̅m). Since the discovery of this material, the physical properties of UCd11 have attracted a considerable amount of attention. In particular, its complex magnetic phase diagramas a result of tuning with magnetic field or pressureis not well-understood. From a chemical perspective, a range of lattice parameter values have been reported, suggesting a possibility of a considerable homogeneity range, i.e., UCd11–x. In this work, we perform a simultaneous study of crystallographic features coupled with measurements of physical properties. This work sheds light on the delicate relationship between the intrinsic crystal chemistry and magnetic properties of UCd11

Association between Self-Reported Prior Night’s Sleep and Single-Task Gait in Healthy, Young Adults: A Study Using Machine Learning

Failure to obtain the recommended 7–9 h of sleep has been associated with injuries in youth and adults. However, most research on the influence of prior night’s sleep and gait has been conducted on older adults and clinical populations. Therefore, the objective of this study was to identify individuals who experience partial sleep deprivation and/or sleep extension the prior night using single task gait. Participants (n = 123, age 24.3 ± 4.0 years; 65% female) agreed to participate in this study. Self-reported sleep duration of the night prior to testing was collected. Gait data was collected with inertial sensors during a 2 min walk test. Group differences (<7 h and >9 h, poor sleepers; 7–9 h, good sleepers) in gait characteristics were assessed using machine learning and a post-hoc ANCOVA. Results indicated a correlation (r = 0.79) between gait parameters and prior night’s sleep. The most accurate machine learning model was a Random Forest Classifier using the top 9 features, which had a mean accuracy of 65.03%. Our findings suggest that good sleepers had more asymmetrical gait patterns and were better at maintaining gait speed than poor sleepers. Further research with larger subject sizes is needed to develop more accurate machine learning models to identify prior night’s sleep using single-task gait

Variations in management of A3 and A4 cervical spine fractures as designated by the AO Spine Subaxial Injury Classification System

© 2022 The authors.OBJECTIVE Optimal management of A3 and A4 cervical spine fractures, as defined by the AO Spine Subaxial Injury Classification System, remains controversial. The objectives of this study were to determine whether significant management variations exist with respect to 1) fracture location across the upper, middle, and lower subaxial cervical spine and 2) geographic region, experience, or specialty. METHODS A survey was internationally distributed to 272 AO Spine members across six geographic regions (North America, South America, Europe, Africa, Asia, and the Middle East). Participants’ management of A3 and A4 subaxial cervical fractures across cervical regions was assessed in four clinical scenarios. Key characteristics considered in the vignettes included degree of neurological deficit, pain severity, cervical spine stability, presence of comorbidities, and fitness for surgery. Respondents were also directly asked about their preferences for operative management and misalignment acceptance across the subaxial cervical spine. RESULTS In total, 155 (57.0%) participants completed the survey. Pooled analysis demonstrated that surgeons were more likely to offer operative intervention for both A3 (p < 0.001) and A4 (p < 0.001) fractures located at the cervicothoracic junction compared with fractures at the upper or middle subaxial cervical regions. There were no significant variations in management for junctional incomplete (p = 0.116) or complete (p = 0.342) burst fractures between geographic regions. Surgeons with more than 10 years of experience were more likely to operatively manage A3 (p < 0.001) and A4 (p < 0.001) fractures than their younger counterparts. Neurosurgeons were more likely to offer surgical stabilization of A3 (p < 0.001) and A4 (p < 0.001) fractures than their orthopedic colleagues. Clinicians from both specialties agreed regarding their preference for fixation of lower junctional A3 (p = 0.866) and A4 (p = 0.368) fractures. Overall, surgical fixation was recommended more often for A4 than A3 fractures in all four scenarios (p < 0.001). CONCLUSIONS The subaxial cervical spine should not be considered a single unified entity. Both A3 and A4 fracture subtypes were more likely to be surgically managed at the cervicothoracic junction than the upper or middle subaxial cervical regions. The authors also determined that treatment strategies for A3 and A4 subaxial cervical spine fractures varied significantly, with the latter demonstrating a greater likelihood of operative management. These findings should be reflected in future subaxial cervical spine trauma algorithms.N