Matthew Patterson

The aim of this study was to determine if anterior cruciate ligament reconstructed (ACL-R) female athletes exhibit altered lower limb kinematic profiles during jump landing when compared to a non-injured age, sex, and activity matched control group. Fourteen ACL-R and 14 non-injured control subjects performed 3 vertical drop jump (DVJ) trials. Lower limb kinematics were recorded at 200 Hz. Peak and time-averaged angular displacements were quantified and utilized for between-group analysis. The ACL-R group displayed altered hip joint frontal and transverse plane kinematic alterations, and knee joint frontal and sagittal plane kinematic alterations. Specifically the ACL-R group displayed an increased adducted (p < 0.05) and internally rotated (p < 0.05) hip joint position, both peak and time-averaged, following landing. The ACL-R group also displayed a decreased adducted (p < 0.05) and flexed (p < 0.05) position of the knee joint following landing. The observed aberrant lower limb kinematics could pre-dispose ACL-R athletes to potential future knee joint injuries. Further studies are required to determine in a prospective manner whether such deficits increase the incidence of recurrent ACL injury, and whether specific sensorimotor protocols following ACL reconstruction can minimize these kinematic deficits.

Context: Deficits in lower limb kinematics and postural stability are predisposing factors to the development of knee ligamentous injury. The extent to which these deficits are present after anterior cruciate ligament (ACL) reconstruction is still largely unknown. The primary hypothesis of the present study was that female athletes who have undergone ACL reconstruction and who have returned to sport participation would exhibit deficits in dynamic postural stability as well as deficiencies in hip- and knee-joint kinematics when compared with an age-, activity-, and sex-matched uninjured control group. Objective: To investigate dynamic postural stability as quantified by the Star Excursion Balance Test (SEBT) and simultaneous hip- and knee-joint kinematic profiles in female athletes who have undergone ACL reconstruction. Design: Descriptive laboratory study. Setting: University motion-analysis laboratory. Patients or Other Participants: Fourteen female athletes who had previously undergone ACL reconstruction (ACL-R) and 17 age- and sex-matched uninjured controls. Intervention(s): Each participant performed 3 trials of the anterior, posterior-medial, and posterior-lateral directional components of the SEBT. Main Outcome Measure(s): Reach distances for each directional component were quantified and expressed as a percentage of leg length. Simultaneous hip- and knee-joint kinematic profiles were recorded using a motion-analysis system. Results: The ACL-R group had decreased reach distances on the posterior-medial (P &amp;lt; .01) and posterior-lateral (P &amp;lt; .01) directional components of the SEBT. During performance of the directional components of the SEBT, ACL-R participants demonstrated altered hip-joint frontal-, sagittal-, and transverse-plane kinematic profiles (P &amp;lt; .05), as well as altered knee-joint sagittal-plane kinematic profiles (P &amp;lt; .05). Conclusions: Deficits in dynamic postural stability and concomitant altered hip- and knee-joint kinematics are present after ACL reconstruction and return to competitive activity. The extent to which these deficits influence potential future injury is worthy of investigation.

For the last 40 years, actigraphy or wearable accelerometry has provided an objective, low-burden and ecologically valid approach to assess real-world sleep and circadian patterns, contributing valuable data to epidemiological and clinical insights on sleep and sleep disorders. The proper use of wearable technology in sleep research requires validated algorithms that can derive sleep outcomes from the sensor data. Since the publication of the first automated scoring algorithm by Webster in 1982, a variety of sleep algorithms have been developed and contributed to sleep research, including many recent ones that leverage machine learning and / or deep learning approaches. However, it remains unclear how these algorithms compare to each other on the same data set and if these modern data science approaches improve the analytical validity of sleep outcomes based on wrist-worn acceleration data. This work provides a systematic evaluation across 8 state-of-the-art sleep algorithms on a common sleep data set with polysomnography (PSG) as ground truth. Despite the inclusion of recently published complex algorithms, simple regression-based and heuristic algorithms demonstrated slightly superior performance in sleep-wake classification and sleep outcome estimation. The performance of complex machine learning and deep learning models seem to suffer from poor generalization. This independent and systematic analytical validation of sleep algorithms provides key evidence on the use of wearable digital health technologies for sleep research and care.

When one of the electrons in a diatomic molecule is highly excited, the molecular system is, in many respects, similar to a hydrogen atom; a single electron moves in the field of a small, singly charged core. These states, called Rydberg states of the diatomic molecule, can essentially be characterized by hydrogenic quantum numbers $n$ and $l$, provided that $n$ is large enough so that the orbital radius of the Rydberg electron is large compared to the dimensions of the ion core. However, unlike the hydrogen atom, the ionic core generates not only a Coulomb field, but higher multipole potentials as well. In addition, the ionic core can be vibrationally and rotationally excited with excitation energies greater than the binding energy of the Rydberg electron. When this happens, auto-ionization can occur. This process is very similar to the nuclear internal-conversion process in which an excited nucleus, instead of emitting a $\ensuremath{\gamma}$ ray, de-excites by giving up its energy directly to an atomic electron. By taking advantage of the similarity between the autoionization and internal-conversion processes, auto-ionization lifetimes for Rydberg states of ${\mathrm{H}}_{2}$ and HD are calculated. These results are compared with experimental results and with those obtained from an alternative approach.

The use of inertial sensors to characterize pathological gait has traditionally been based on the calculation of temporal and spatial gait variables from inertial sensor data. This approach has proved successful in the identification of gait deviations in populations where substantial differences from normal gait patterns exist; such as in Parkinsonian gait. However, it is not currently clear if this approach could identify more subtle gait deviations, such as those associated with musculoskeletal injury. This study investigates whether additional analysis of inertial sensor data, based on quantification of gyroscope features of interest, would provide further discriminant capability in this regard. The tested cohort consisted of a group of anterior cruciate ligament reconstructed (ACL-R) females and a group of non-injured female controls, each performed ten walking trials. Gait performance was measured simultaneously using inertial sensors and an optoelectronic marker based system. The ACL-R group displayed kinematic and kinetic deviations from the control group, but no temporal or spatial deviations. This study demonstrates that quantification of gyroscope features can successfully identify changes associated with ACL-R gait, which was not possible using spatial or temporal variables. This finding may also have a role in other clinical applications where small gait deviations exist.