Precision Spine (United States)

Resonant metasurfaces, characterized by their compact integration and strong field localization, combined with the superior nonlinear properties of low-dimensional materials, provide a promising platform for developing more efficient and integrated nonlinear devices. Enhancing nonlinear conversion efficiency is crucial; at the same time, integrating multiple functionalities into a single compact design is also urgently demanded for versatile nonlinear optical devices. In this work, we propose a polarization-independent quasi-bound state in continuum (q-BIC) metasurface integrated with a thin layer 3R-MoS2, which enables giant enhancement of second harmonic generation (SHG), as well as full-Poincaré polarization tunability of SHG emission by precisely controlling the polarization of the fundamental wave. Our work paves the way for tailoring exceptional optical nonlinearity and tunable polarization control in nonlinear light sources.

Manual therapy (MT) is frequently used to manage temporomandibular disorders (TMDs), yet patient responses vary significantly. Predictive models may help clinicians tailor treatments to individual patients. The primary aim of this study was to externally validate a previously developed prediction model for identifying patients with TMDs who are more likely to benefit from MT. Additionally, new prognostic models to predict outcomes at a one-month follow-up were developed. A cohort of 124 adults with a diagnosis of a TMD received a four-week MT program (one session per week) applied to craniomandibular structures. Predictors collected at baseline included clinical and psychosocial variables: pain during mouth opening, pain localisation, treatment expectations, and the central sensitisation inventory (CSI). The primary outcome was a ≥ 30% pain reduction post-treatment. Model performance was assessed using discrimination, calibration, and decision curve analysis. New models predicting one-month outcomes were developed and internally validated via bootstrapping. The original model showed strong discrimination and overall fit in the validation cohort (AUC = 0.95, R2 = 0.75). Except for pain location, the predictors in the original model also showed excellent discrimination in the developed model based on the outcome at the one-month follow-up (AUC = 0.96). A significant interaction between treatment expectations and CSI was found, with high CSI negatively affecting the outcome in people with positive treatment expectations. This study externally validated a clinical prediction model for pain reduction in people with TMDs following MT, confirming previously identified predictors of good outcome such as pain during mouth opening, positive treatment expectations, localisation of pain in the craniocervical region, and lower CSI scores. A link to a web-based calculator of the prediction model is provided. The interaction between treatment expectations and CSI suggests that CSI plays a key role in shaping treatment response, modulating the influence of treatment expectation. Future studies with a control group are needed to confirm these results and distinguish true treatment effect modifiers from general prognostic factors. ClinicalTrials.gov ID: NCT03990662.

Utilizing robots for autonomous target search in complex and unknown environments can greatly improve the efficiency of search and rescue missions. However, existing methods have shown inadequate performance due to hardware platform limitations, inefficient viewpoint selection strategies, and conservative motion planning. In this work, we propose HEATS, which enhances the search capability of mobile manipulators in complex and unknown environments. We design a target viewpoint planner tailored to the strengths of mobile manipulators, ensuring efficient and comprehensive viewpoint planning. Supported by this, a whole-body motion planner integrates global path search with local IPC optimization, enabling the mobile manipulator to safely and agilely visit target viewpoints, significantly improving search performance. We present extensive simulated and real-world tests, in which our method demonstrates reduced search time, higher target search completeness, and lower movement cost compared to classic and state-of-the-art approaches. Our method will be open-sourced for community benefit<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>.

Disasters caused by natural hazards are increasingly threatening lives and climate change is further increasing the impacts affecting the lives and livelihoods of the communities. Mitigating the risks from disasters caused by natural hazards is very essential to reduce the damages. The solutions against natural hazards are designed with the aim to enhance safety and can be further enhanced by integrating smart alert and monitoring systems. This paper covers an innovative system named HelloMac Geo which is Internet of Things (IoT) based alert and monitoring system developed to verify if a rockfall or debris flow protection system is impacted or to monitor geostability. The paper describes HelloMac Geo system in detail and its different applications, and it presents interesting installation along RD 900, a pivotal roadway through the scenic Ubaye Valley in the Alpes-de-Haute-Provence, linking France to Italy and another in Sonico, northern Italy, amidst the Alpine landscape of Val Rabbia.

We consider the problem of solving the optimal transport problem between two empirical distributions with missing values. Our main assumption is that the data is missing completely at random (MCAR), but we allow for heterogeneous missingness probabilities across features and across the two distributions. As a first contribution, we show that the Wasserstein distance between empirical Gaussian distributions and linear Monge maps between arbitrary distributions can be debiased without significantly affecting the sample complexity. Secondly, we show that entropic regularized optimal transport can be estimated efficiently and consistently using iterative singular value thresholding (ISVT). We propose a validation set-free hyperparameter selection strategy for ISVT that leverages our estimator of the Bures-Wasserstein distance, which could be of independent interest in general matrix completion problems. Finally, we validate our findings on a wide range of numerical applications.

Introduction Thoracolumbar burst fractures are the most typical variant of injury to the spine. However, debate over the optimum treatment approach continues. Although spinal fusion has long been considered the gold standard in spinal fixation, the non-fusion procedure has recently gained prominence. Objectives To evaluate fusion versusnon-fusion procedures in surgically treated thoracolumbar burst fractures. Methods This quasi-experimental study included 64 patients with single-level thoracolumbar fractures treated between July 2014 and June 2019. Patients were randomly allocated into two cohorts of 32 (50%) each based on registration numbers: those with odd numbers underwent fusion surgery (Group I), and those with even numbers underwent non-fusion surgery (Group II). All patients received short-segment posterior instrumentation, including the fractured vertebra. Radiological, functional, and neurological outcomes were assessed and contrasted. SPSS version 25.0 (IBM Corp., Armonk, NY) was used to conduct the statistical analysis. Results No statistically significant difference was observed between the two groups in terms of radiological outcomes, functional outcomes, neurological improvement, or implant failure rates. The non-fusion group, however, exhibited reduced operative duration and hemorrhage, absence of donor site morbidity, and preserved a greater number of motion segments. No notable intraoperative or postoperative complications were noted, nor were any substantial differences observed in either group. Conclusion Routine spinal fusion may be unnecessary for surgically repaired thoracolumbar burst fractures, as the non-fusion method provides similar outcomes with additional advantages.