High-Pressure Phase Transition of the Oxonitridosilicate Chloride Ce4[Si4O3+xN7-x]Cl1-xOx with x = 0.12 and 0.18

The high-pressure behaviour of the oxonitridosilicate chlorides Ce4[Si4O3þxN7-x]Cl1-xOx, x = 0.12 and 0.18, is investigated by in situ powder synchrotron X-ray diffraction. Pressures up to 28 GPa are generated using the diamond-anvil cell technique. A reversible phase transition of first order occurs at pressures between 8 and 10 GPa. Within this pressure range the high- and the low-pressure phases are observed concomitantly. At the phase transition the unit cell volume is reduced by about 5%, and the cubic symmetry (space group P213) is reduced to orthorhombic (space group P212121) following a translationengleiche group-subgroup relationship of index 3. A fit of a third-order Birch-Murnaghan equation of state to the p-V data results in a bulk modulus B0 = 124(5) GPa with its pressure derivative B0 = 5(1) at V0 = 1134.3(4) Å3 for the low-pressure phase and in B0 = 153(10) GPa with B0 = 3.0(6) at V0 = 1071(3) Å3 for the high-pressure phase. The orthorhombic phase shows an anisotropic axial compression with the a axis (which is the shortest axis) being more compressible (k(a) = 0.0143(4) 1/GPa) than the b and c axes (k(b) = 0.0045(2), k(c) = 0.0058(2) 1/GPa). The experimental results confirm an earlier prediction of the pressureinduced instability of isotypic Ce4[Si4O4N6]O, and also show that the bulk modulus was predicted reasonably well

High-pressure polymeric nitrogen allotrope with the black phosphorus structure

Studies of polynitrogen phases are of great interest for fundamental science and for the design of novel high energy density materials. Laser heating of pure nitrogen at 140 GPa in a diamond anvil cell led to the synthesis of a polymeric nitrogen allotrope with the black phosphorus structure, bp-N. The structure was identified in situ using synchrotron single-crystal X-ray diffraction and further studied by Raman spectroscopy and density functional theory calculations. The discovery of bp-N brings nitrogen in line with heavier pnictogen elements, resolves incongruities regarding polymeric nitrogen phases and provides insights into polynitrogen arrangements at extreme densities

18-Crown-6 Coordinated Metal Halides with Bright Luminescence and Nonlinear Optical Effects

The crown-ether coordination compounds ZnX$_{2}$(18-crown-6), EuX$_{2}$(18-crown-6) (X: Cl, Br, I), MnI$_{2}$(18-crown-6), Mn$_{3}$Cl$_{6}$(18-crown-6)2, Mn$_{3}$I$_{6}$(18-crown-6)$_{2}$, and Mn$_{2}$I$_{4}$(18-crown-6) are obtained by ionic-liquid-based synthesis. Whereas MX$_{2}$(18-crown-6) (M: Zn, Eu) show conventional structural motives, Mn$_{3}$Cl$_{6}$(18-crown-6)$_{2}$, Mn$_{3}$I$_{6}$(18-crown-6)$_{2}$, and Mn$_{2}$I$_{4}$(18-crown-6) exhibit unusual single MnX$_{4}$ tetrahedra coordinated to the crown-ether complex. Surprisingly, some compounds show outstanding photoluminescence. Thus, rare Zn$^{2+}$-based luminescence is observed and unexpectedly efficient for ZnI$_{2}$(18-crown-6) with a quantum yield of 54%. Unprecedented quantum yields are also observed for Mn$_{3}$I$_{6}$(18-crown-6)$_{2}$, EuBr$_{2}$(18-crown-6), and EuI$_{2}$(18-crown-6) with values of 98, 72, and 82%, respectively, which can be rationalized based on the specific structural features. Most remarkable, however, is Mn$_{2}$I$_{4}$(18-crown-6). Its specific structural features with finite sensitizer–activator couples result in an extremely strong emission with an outstanding quantum yield of 100%. Consistent with its structural features, moreover, anisotropic angle-dependent emission under polarized light and nonlinear optical (NLO) effects occur, including second-harmonic generation (SHG). The title compounds and their optical properties are characterized by single-crystal structure analysis, X-ray powder diffraction, chemical analysis, density functional theory (DFT) calculations, and advanced spectroscopic methods

Clinical relevance of standardized mobile gait tests - reliability analysis between gait recordings at hospital and home in Parkinson’s disease: A pilot study

Background: Gait impairments in Parkinson’s disease (PD) are quantified using inertial sensors under standardized test settings in the hospital. Recent studies focused on the assessment of free-living gait in PD. However, the clinical relevance of standardized gait tests recorded at the patient’s home is unclear. Objective: To evaluate the reliability of supervised, standardized sensor-based gait outcomes at home compared to the hospital. Methods: Patients with PD (n=20) were rated by a trained investigator using the Unified Parkinson Disease Rating Scale (UPDRS-III). Gait tests included a standardized 4x10 m walk test and the Timed Up and Go Test (TUG). Tests were performed in the hospital (HOSPITAL) and at patients’ home (HOME), and controlled for investigator, time of the day, and medication. Statistics included reliability analysis using Intra-Class correlations and Bland-Altman plots. Results: UPDRS-III and TUG were comparable between HOSPITAL and HOME. PD patients’ gait at HOME was slower (gait velocity Δ=-0.07±0.11 m/s,-6.1 %), strides were shorter (stride length Δ=-9.2±9.4 cm;-7.3 %), and shuffling of gait was more present (maximum toe-clearance Δ=-0.7±2.5 cm;-8.8 %). Particularly, narrow walkways (< 85 cm) resulted in a significant reduction of gait velocity at home. Reliability analysis (HOSPITAL vs. HOME) revealed excellent ICC coefficients for UPDRS-III (0.950, p<0.000) and gait parameters (e.g. stride length: 0.898,p<0.000; gait velocity: 0.914,p<0.000; stance time: 0.922,p<0.000; stride time: 0.907,p<0.000). Conclusions: This pilot study underlined the clinical relevance of gait parameters by showing excellent reliability for supervised, standardized gait tests at HOSPITAL and HOME, even though gait parameters were different between test conditions

MaD GUI: An Open-Source Python Package for Annotation and Analysis of Time-Series Data

Developing machine learning algorithms for time-series data often requires manual annotation of the data. To do so, graphical user interfaces (GUIs) are an important component. Existing Python packages for annotation and analysis of time-series data have been developed without addressing adaptability, usability, and user experience. Therefore, we developed a generic open-source Python package focusing on adaptability, usability, and user experience. The developed package, Machine Learning and Data Analytics (MaD) GUI, enables developers to rapidly create a GUI for their specific use case. Furthermore, MaD GUI enables domain experts without programming knowledge to annotate time-series data and apply algorithms to it. We conducted a small-scale study with participants from three international universities to test the adaptability of MaD GUI by developers and to test the user interface by clinicians as representatives of domain experts. MaD GUI saves up to 75% of time in contrast to using a state-of-the-art package. In line with this, subjective ratings regarding usability and user experience show that MaD GUI is preferred over a state-of-the-art package by developers and clinicians. MaD GUI reduces the effort of developers in creating GUIs for time-series analysis and offers similar usability and user experience for clinicians as a state-of-the-art package

Automated assessment of foot elevation in adults with hereditary spastic paraplegia using inertial measurements and machine learning

Abstract Background Hereditary spastic paraplegias (HSPs) cause characteristic gait impairment leading to an increased risk of stumbling or even falling. Biomechanically, gait deficits are characterized by reduced ranges of motion in lower body joints, limiting foot clearance and ankle range of motion. To date, there is no standardized approach to continuously and objectively track the degree of dysfunction in foot elevation since established clinical rating scales require an experienced investigator and are considered to be rather subjective. Therefore, digital disease-specific biomarkers for foot elevation are needed. Methods This study investigated the performance of machine learning classifiers for the automated detection and classification of reduced foot dorsiflexion and clearance using wearable sensors. Wearable inertial sensors were used to record gait patterns of 50 patients during standardized 4 $$\times$$ ×  10 m walking tests at the hospital. Three movement disorder specialists independently annotated symptom severity. The majority vote of these annotations and the wearable sensor data were used to train and evaluate machine learning classifiers in a nested cross-validation scheme. Results The results showed that automated detection of reduced range of motion and foot clearance was possible with an accuracy of 87%. This accuracy is in the range of individual annotators, reaching an average accuracy of 88% compared to the ground truth majority vote. For classifying symptom severity, the algorithm reached an accuracy of 74%. Conclusion Here, we show that the present wearable gait analysis system is able to objectively assess foot elevation patterns in HSP. Future studies will aim to improve the granularity for continuous tracking of disease severity and monitoring therapy response of HSP patients in a real-world environment. </jats:sec

Methods for integrating postural control into biomechanical human simulations: a systematic review

AbstractUnderstanding of the human body’s internal processes to maintain balance is fundamental to simulate postural control behaviour. The body uses multiple sensory systems’ information to obtain a reliable estimate about the current body state. This information is used to control the reactive behaviour to maintain balance. To predict a certain motion behaviour with knowledge of the muscle forces, forward dynamic simulations of biomechanical human models can be utilized. We aim to use predictive postural control simulations to give therapy recommendations to patients suffering from postural disorders in the future. It is important to know which types of modelling approaches already exist to apply such predictive forward dynamic simulations. Current literature provides different models that aim to simulate human postural control. We conducted a systematic literature research to identify the different approaches of postural control models. The different approaches are discussed regarding their applied biomechanical models, sensory representation, sensory integration, and control methods in standing and gait simulations. We searched on Scopus, Web of Science and PubMed using a search string, scanned 1253 records, and found 102 studies to be eligible for inclusion. The included studies use different ways for sensory representation and integration, although underlying neural processes still remain unclear. We found that for postural control optimal control methods like linear quadratic regulators and model predictive control methods are used less, when models’ level of details is increasing, and nonlinearities become more important. Considering musculoskeletal models, reflex-based and PD controllers are mainly applied and show promising results, as they aim to create human-like motion behaviour considering physiological processes.</jats:p

Gait variability as digital biomarker of disease severity in Huntington’s disease

Abstract Background Impaired gait plays an important role for quality of life in patients with Huntington’s disease (HD). Measuring objective gait parameters in HD might provide an unbiased assessment of motor deficits in order to determine potential beneficial effects of future treatments. Objective To objectively identify characteristic features of gait in HD patients using sensor-based gait analysis. Particularly, gait parameters were correlated to the Unified Huntington’s Disease Rating Scale, total motor score (TMS), and total functional capacity (TFC). Methods Patients with manifest HD at two German sites (n = 43) were included and clinically assessed during their annual ENROLL-HD visit. In addition, patients with HD and a cohort of age- and gender-matched controls performed a defined gait test (4 × 10 m walk). Gait patterns were recorded by inertial sensors attached to both shoes. Machine learning algorithms were applied to calculate spatio-temporal gait parameters and gait variability expressed as coefficient of variance (CV). Results Stride length (− 15%) and gait velocity (− 19%) were reduced, while stride (+ 7%) and stance time (+ 2%) were increased in patients with HD. However, parameters reflecting gait variability were substantially altered in HD patients (+ 17% stride length CV up to + 41% stride time CV with largest effect size) and showed strong correlations to TMS and TFC (0.416 ≤ rSp ≤ 0.690). Objective gait variability parameters correlated with disease stage based upon TFC. Conclusions Sensor-based gait variability parameters were identified as clinically most relevant digital biomarker for gait impairment in HD. Altered gait variability represents characteristic irregularity of gait in HD and reflects disease severity