Relationship between serum uric acid levels and the outcome of STN-DBS in Parkinson's disease.

BACKGROUND: Uric acid is a natural antioxidant and it has been shown that low levels of uric acid may be a risk factor for the development of Parkinson's disease. We aimed to investigate the relationship between uric acid and improvement of motor symptoms in patients with Parkinson's disease after subthalamic nucleus deep brain stimulation. METHODS: We analyzed the correlation between serum uric acid levels in 64 patients with Parkinson's disease and the rate of improvement of motor symptoms 2 years after subthalamic nucleus deep brain stimulation. RESULTS: A non-linear correlation was observed between uric acid levels and the rate of motor symptom improvement after subthalamic nucleus deep brain stimulation, during both the drug-off and drug-on periods. CONCLUSIONS: Uric acid is positively associated with the rate of motor symptom improvement in subthalamic nucleus deep brain stimulation within a certain range.

CGHS Black Hole Analog Moving Mirror and Its Relativistic Quantum Information as Radiation Reaction.

The Callan-Giddings-Harvey-Strominger black hole has a spectrum and temperature that correspond to an accelerated reflecting boundary condition in flat spacetime. The beta coefficients are identical to a moving mirror model, where the acceleration is exponential in laboratory time. The center of the black hole is modeled by the perfectly reflecting regularity condition that red-shifts the field modes, which is the source of the particle creation. In addition to computing the energy flux, we find the corresponding moving mirror parameter associated with the black hole mass and the cosmological constant in the gravitational analog system. Generalized to any mirror trajectory, we derive the self-force (Lorentz-Abraham-Dirac), consistently, expressing it and the Larmor power in connection with entanglement entropy, inviting an interpretation of acceleration radiation in terms of information flow. The mirror self-force and radiative power are applied to the particular CGHS black hole analog moving mirror, which reveals the physics of information at the horizon during asymptotic approach to thermal equilibrium.

Dynamic computational optical fringe mitigation in tunable laser absorption spectroscopy.

In optical spectroscopic systems where unwanted optical scattering cannot be eliminated, Fabry-Pérot etalons cause unpredictable changes in the spectral background. Frequent system calibration is then required to maintain the desired measurement accuracy, which presents a major limitation to the spectrometer. We introduce a computational approach to mitigate the adverse effects of optical fringing without hardware modifications. Motivated by experimental observations of complicated fringe behaviors, we simplify the problem by decomposing the fringe background into component etalons that can be addressed according to their individual characteristics. The effectiveness of the proposed method is demonstrated on a silicon photonic methane sensor, where accurate measurements of methane concentration are obtained from spectral data strongly affected by optical fringes.

Adaptive thermal stabilization of an integrated photonic spectrometer using parasitic interference fringes.

Parasitic fringe drift from unwanted scatterings limits the long-term stability of waveguide-based optical spectrometers. Yet their spectral features provide relevant information that can be used to improve performance of the spectrometer. We show that fringe drift can be extracted and utilized to perform accurate thermal stabilization, especially in the case of integrated waveguide sensors. In this Letter, effective stabilization of a methane silicon photonic sensor is demonstrated, and significant reduction in fringe noise is clearly observed.

Active silicon integrated nanophotonics: ferroelectric BaTiO3 devices.

The integration of complex oxides on silicon presents opportunities to extend and enhance silicon technology with novel electronic, magnetic, and photonic properties. Among these materials, barium titanate (BaTiO3) is a particularly strong ferroelectric perovskite oxide with attractive dielectric and electro-optic properties. Here we demonstrate nanophotonic circuits incorporating ferroelectric BaTiO3 thin films on the ubiquitous silicon-on-insulator (SOI) platform. We grow epitaxial, single-crystalline BaTiO3 directly on SOI and engineer integrated waveguide structures that simultaneously confine light and an RF electric field in the BaTiO3 layer. Using on-chip photonic interferometers, we extract a large effective Pockels coefficient of 213 ± 49 pm/V, a value more than six times larger than found in commercial optical modulators based on lithium niobate. The monolithically integrated BaTiO3 optical modulators show modulation bandwidth in the gigahertz regime, which is promising for broadband applications.

Electrical tuning and switching of an optical frequency comb generated in aluminum nitride microring resonators.

Aluminum nitride (AlN) has been shown to possess both strong Kerr nonlinearity and electro-optic Pockels effect. By combining these two effects, here we demonstrate on-chip reversible on/off switching of the optical frequency comb generated by an AlN microring resonator. We optimize the design of gating electrodes and the underneath resonator structure to effectively apply an electric field without increasing the optical loss. The switching of the comb is monitored by measuring one of the frequency comb peaks while varying the electric field. The controlled comb electro-optic response is investigated for direct comparison with the transient thermal effect.

Optical frequency comb generation from aluminum nitride microring resonator.

Aluminum nitride (AlN) is an appealing nonlinear optical material for on-chip wavelength conversion. Here we report optical frequency comb generation from high-quality-factor AlN microring resonators integrated on silicon substrates. By engineering the waveguide structure to achieve near-zero dispersion at telecommunication wavelengths and optimizing the phase matching for four-wave mixing, frequency combs are generated with a single-wavelength continuous-wave pump laser. Further, the Kerr coefficient (n2) of AlN is extracted from our experimental results.

Low-loss, silicon integrated, aluminum nitride photonic circuits and their use for electro-optic signal processing.

Photonic miniaturization requires seamless integration of linear and nonlinear optical components to achieve passive and active functions simultaneously. Among the available material systems, silicon photonics holds immense promise for optical signal processing and on-chip optical networks. However, silicon is limited to wavelengths above 1.1 µm and does not provide the desired lowest order optical nonlinearity for active signal processing. Here we report the integration of aluminum nitride (AlN) films on silicon substrates to bring active functionalities to chip-scale photonics. Using CMOS-compatible sputtered thin films we fabricate AlN-on-insulator waveguides that exhibit low propagation loss (0.6 dB/cm). Exploiting AlN's inherent Pockels effect we demonstrate electro-optic modulation up to 4.5 Gb/s with very low energy consumption (down to 10 fJ/bit). The ultrawide transparency window of AlN devices also enables high speed modulation at visible wavelengths. Our low cost, wideband, carrier-free photonic circuits hold promise for ultralow power and high-speed signal processing at the microprocessor chip level.

Development and Validation of a Prediction Model for Anxiety Improvement after Deep Brain Stimulation for Parkinson Disease.

BACKGROUND: Parkinson's disease (PD) represents one of the most frequently seen neurodegenerative disorders, while anxiety accounts for its non-motor symptom (NMS), and it has greatly affected the life quality of PD cases. Bilateral subthalamic nucleus deep brain stimulation (STN-DBS) can effectively treat PD. This study aimed to develop a clinical prediction model for the anxiety improvement rate achieved in PD patients receiving STN-DBS. METHODS: The present work retrospectively enrolled 103 PD cases undergoing STN-DBS. Patients were followed up for 1 year after surgery to analyze the improvement in HAMA scores. Univariate and multivariate logistic regression were conducted to select factors affecting the Hamilton Anxiety Scale (HAMA) improvement. A nomogram was established to predict the likelihood of achieving anxiety improvement. Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA), and calibration curve analysis were conducted to verify nomogram performance. RESULTS: The mean improvement in HAMA score was 23.9% in 103 patients; among them, 68.9% had improved anxiety, 25.2% had worsened (Preop) anxiety, and 5.8% had no significant change in anxiety. Education years, UPDRS-III preoperative score, and HAMA preoperative score were independent risk factors for anxiety improvement. The nomogram-predicted values were consistent with real probabilities. CONCLUSIONS: Collectively, a nomogram is built in the present work for predicting anxiety improvement probability in PD patients 1 year after STN-DBS. The model is valuable for determining expected anxiety improvement in PD patients undergoing STN-DBS.

Prediction of STN-DBS for Parkinson's disease by uric acid-related brain function connectivity: A machine learning study based on resting state function MRI.

Introduction: Parkinson's disease (PD) is a neurodegenerative disorder characterized by dyskinesia and is closely related to oxidative stress. Uric acid (UA) is a natural antioxidant found in the body. Previous studies have shown that UA has played an important role in the development and development of PD and is an important biomarker. Subthalamic nucleus deep brain stimulation (STN-DBS) is a common treatment for PD. Methods: Based on resting state function MRI (rs-fMRI), the relationship between UA-related brain function connectivity (FC) and STN-DBS outcomes in PD patients was studied. We use UA and DC values from different brain regions to build the FC characteristics and then use the SVR model to predict the outcome of the operation. Results: The results show that PD patients with UA-related FCs are closely related to STN-DBS efficacy and can be used to predict prognosis. A machine learning model based on UA-related FC was successfully developed for PD patients. Discussion: The two biomarkers, UA and rs-fMRI, were combined to predict the prognosis of STN-DBS in treating PD. Neurosurgeons are provided with effective tools to screen the best candidate and predict the prognosis of the patient.

High Q micro-ring resonators fabricated from polycrystalline aluminum nitride films for near infrared and visible photonics.

We demonstrate wideband integrated photonic circuits in sputter-deposited aluminum nitride (AlN) thin films. At both near-infrared and visible wavelengths, we achieve low propagation loss in integrated waveguides and realize high-quality optical resonators. In the telecoms C-band (1520-1580 nm), we obtain the highest optical Q factor of 440,000. Critical coupled devices show extinction ratio above 30 dB. For visible wavelengths (around 770 nm), intrinsic quality factors in excess of 30,000 is demonstrated. Our work illustrates the potential of AlN as a low loss material for wideband optical applications.

Primulinanana (Gesneriaceae), a new species from eastern Guangxi, China.

Primulinanana C.Xiong, W.C.Chou & F.Wen, a new species of Gesneriaceae from limestone areas of Guangxi, China, is described and illustrated here. It morphologically resembles P.yangshuoensis Y.G.Wei & F.Wen in papillose leaf surface, but can be easily distinguished from the latter by noting a combination of characteristics, especially in its leaf blades, leaf blade indumentum characteristic, calyx lobes, corolla and the disc. We found only one population at the type locality, about 200 mature individuals. According to the IUCN Red List Categories and Criteria (Version 3.1), the new species is provisionally assessed as Critically Endangered (CR).

A New Application of Functional Zonal Image Reconstruction in Programming for Parkinson's Disease Treated Using Subthalamic Nucleus-Deep Brain Stimulation.

Objective: Programming plays an important role in the outcome of deep brain stimulation (DBS) for Parkinson's disease (PD). This study introduced a new application for functional zonal image reconstruction in programming. Methods: Follow-up outcomes were retrospectively compared, including first programming time, number of discomfort episodes during programming, and total number of programming sessions between patients who underwent image-reconstruction-guided programming and those who underwent conventional programming. Data from 142 PD patients who underwent subthalamic nucleus (STN)-DBS between January 2017 and June 2019 were retrospectively analyzed. There were 75 conventional programs and 67 image reconstruction-guided programs. Results: At 1-year follow-up, there was no significant difference in the rate of stimulus improvement or superposition improvement between the two groups. However, patients who underwent image reconstruction-guided programming were significantly better at the first programming time, number of discomfort episodes during programming, and total number of programming sessions than those who underwent conventional programming. Conclusion: Imaging-guided programming of directional DBS leads was possible and led to reduced programming time and reduced patient side effects compared with conventional programming.

Nomogram to Predict Cognitive State Improvement after Deep Brain Stimulation for Parkinson's Disease.

PURPOSE: Parkinson's disease (PD) is a common neurodegenerative disease, for which cognitive impairment is a non-motor symptom (NMS). Bilateral subthalamic nucleus deep brain stimulation (STN-DBS) is an effective treatment for PD. This study established a nomogram to predict cognitive improvement rate after STN-DBS in PD patients. METHODS: We retrospectively analyzed 103 PD patients who underwent STN-DBS. Patients were followed up to measure improvement in MoCA scores one year after surgery. Univariate and multivariate logistic regression analyses were used to identify factors affecting improvement in cognitive status. A nomogram was developed to predict this factor. The discrimination and fitting performance were evaluated by receiver operating characteristics (ROC) analysis, calibration diagram, and decision curve analysis (DCA). RESULTS: Among 103 patients, the mean improvement rate of the MoCA score was 37.3% and the median improvement rate was 27.3%, of which 64% improved cognition, 27% worsened cognition, and 8.7% remained unchanged. Logistic multivariate regression analysis showed that years of education, UPDRSIII drug use, MoCA Preop, and MMSE Preop scores were independent factors affecting the cognitive improvement rate. A nomogram model was subsequently developed. The C-index of the nomogram was 0.98 (95%CI, 0.97-1.00), and the area under the ROC was 0.98 (95%CI 0.97-1.00). The calibration plot and DCA demonstrated the goodness-of-fit between nomogram predictions and actual observations. CONCLUSION: Our nomogram could effectively predict the possibility of achieving good cognitive improvement one year after STN-DBS in patients with PD. This model has value in judging the expected cognitive improvement of patients with PD undergoing STN-DBS.

Nomogram for Predicting Depression Improvement after Deep Brain Stimulation for Parkinson's Disease.

BACKGROUND: Parkinson's disease is a common neurodegenerative disease, with depression being a common non-motor symptom. Bilateral subthalamic nucleus deep brain stimulation is an effective method for the treatment of Parkinson's disease. Thus, this study aimed to establish a nomogram of the possibility of achieving a better depression improvement rate after subthalamic nucleus deep brain stimulation in patients with Parkinson's disease. METHODS: We retrospectively analyzed 103 patients with Parkinson's disease who underwent subthalamic nucleus deep brain stimulation and were followed up for the improvement of their Hamilton Depression scale scores 1 year postoperatively. Univariate and multivariate logistic regression analyses were used to select factors affecting the improvement rate of depression. A nomogram was then developed to predict the possibility of achieving better depression improvement. Furthermore, the discrimination and fitting performance was evaluated using a calibration diagram, receiver operating characteristics, and decision curve analysis. RESULTS: The mean and median improvement rates of Hamilton Depression scores were 13.1 and 33.3%, respectively. Among the 103 patients, 70.8% had an improved depression, 23.3% had a worsened depression, and 5.8% remained unchanged. Logistic multivariate regression analysis showed that age, preoperative Parkinson's Disease Questionnaire, Hamilton Anxiety, and Hamilton Depression scores were independent factors for the possibility of achieving a better depression improvement rate. Based on these results, a nomogram model was developed. The nomogram had a C-index of 0.78 (95% confidence interval: 0.69-0.87) and an area under the receiver operating characteristics of 0.78 (95% confidence interval: 0.69-0.87). The calibration plot and decision curve analysis further demonstrated goodness-of-fit between the nomogram predictions and actual observations. CONCLUSION: We developed a nomogram to predict the possibility of achieving good depression improvement 1 year after subthalamic nucleus deep brain stimulation in patients with Parkinson's disease, which showed a certain value in judging the expected depression improvement of these patients.

Stereotactic co-axial projection imaging for augmented reality neuronavigation: a proof-of-concept study.

Background: Lack of intuitiveness and poor hand-eye coordination present a major technical challenge in neurosurgical navigation. Methods: We developed an integrated dexterous stereotactic co-axial projection imaging (sCPI) system featuring orthotopic image projection for augmented reality (AR) neurosurgical navigation. The performance characteristics of the sCPI system, including projection resolution and navigation accuracy, were quantitatively verified. The resolution of the sCPI was tested with a USAF1951 resolution test chart. The stereotactic navigation accuracy of the sCPI was measured using a calibration panel with a 7×7 circle array pattern. In benchtop validation, the navigation accuracy of the sCPI and the BrainLab Kick Navigation Station was compared using a skull phantom with 8 intracranial targets. Finally, we demonstrated the potential clinical application of sCPI through a clinical trial. Results: The resolution test showed that the resolution of the sCPI was 1.3 mm. In a stereotactic navigation accuracy test, the maximum and minimum error of the sCPI was 2.9 and 0.3 mm, and the mean error was 1.5 mm. The stereotactic navigation accuracy test also showed that the navigation error of the sCPI would increase with the pitch and yaw angle, but there was no obvious difference in navigation errors caused by different yaw directions, which meant that the navigation error is unbiased across all directions. The benchtop validation showed that the average navigation errors for the sCPI system and the Kick Navigation Station were 1.4±0.8 and 1.8±0.7 mm, the medians were 1.3 and 1.9 mm, and the average preparation times were 3 min 24 sec and 6 min 8 sec, respectively. The clinical feasibility of sCPI-assisted neurosurgical navigation was demonstrated in a clinical study. In comparison with the BrainLab device, the sCPI system required less time for preoperative preparation and enhanced the clinician experience in intraoperative visualization and navigation. Conclusions: The sCPI technique can be potentially used in many surgical applications for intuitive visualization of medical information and intraoperative guidance of surgical trajectories.

Orthographic Pooling: Learned Maximum Intensity Projection for Vertebrae Labelling.

Maximum intensity projection (MIP) is a standard volume-rendering technique for 3D volumetric data processing. For example, given a 3D CT data, it simply projects the voxel values with its maximum intensity on a specific view to output a 2D image. Recently, MIP is further combined with Btrfly Net for vertebrae labelling task. However, this simple reformations of 3D data leads to loss of rich context information in volumetric data. In this paper, we propose a learned orthographic pooling approach instead of image processing based MIP. Typically, a simple conv-simple and bottleneck pooling modules are introduced to learn the orthographic projection of 3D data and output 2D intermediate feature maps. To this end, the learned orthographic pooling helps preserve detail information of 3D context during projection. Furthermore, an unified Btrfly Net is provided for vertebrae labelling by integrating the orthographic pooling sub-network. The novel Btrfly Net with orthographic pooling sub-network is evaluated on the 2014 MICCAI vertebra localization challenge dataset. Compared to original Butfly Net with MIP, orthographic pooling, the learned MIP largely boosts the performance of vertebrae labelling.

Pileadanxiaensis (Urticaceae), a new species in the Danxia landform from Guangdong, China including a description of the entire chloroplast genome.

Pileadanxiaensis L.F.Fu, A.K.Monro & Y.G.Wei, a new species of Urticaceae from Danxia landform, Guangdong, China, is described and photographed. Phylogenetic analyses based on three DNA regions (ITS, trnL-F and rbcL) suggest that the new species belongs to P.sect.Pilea. Within the section, the new species is morphologically most similar to P.sinocrassifolia and P.peploides. Plastid genome and ribosomal DNA (rDNA) sequences of the new species are assembled and annotated. The plastid genome is 151,857 bp in length and comprises two inverted repeats (IRs) of 25,307 bp separated by a large single-copy of 82,836 bp and a small single-copy of 18,407 bp. A total of 113 functional genes are recovered, comprising 79 protein-coding genes, 30 tRNA genes, and four rRNA genes. A global conservation assessment suggests that P.danxiaensis should be classified as of Least Concern (LC).

GHz optomechanical resonators with high mechanical Q factor in air.

We demonstrate wheel-shaped silicon optomechanical resonators for resonant operation in ambient air. The high finesse of optical whispering gallery modes (loaded optical Q factor above 500,000) allows for efficient transduction of the wheel resonator's mechanical radial contour modes of frequency up to 1.35 GHz with high mechanical Q factor around 4,000 in air.