Enhanced Coexistence of Quantum Key Distribution and Classical Communication over Hollow-Core and Multi-Core Fibers.

In this paper, we investigate the impact of classical optical communications in quantum key distribution (QKD) over hollow-core fiber (HCF), multi-core fiber (MCF) and single-core fiber (SCF) and propose wavelength allocation schemes to enhance QKD performance. Firstly, we theoretically analyze noise interference in QKD over HCF, MCF and SCF, such as spontaneous Raman scattering (SpRS) and four-wave mixing (FWM). To mitigate these noise types and optimize QKD performance, we propose a joint noise suppression wavelength allocation (JSWA) scheme. FWM noise suppression wavelength allocation and Raman noise suppression wavelength allocation are also proposed for comparison. The JSWA scheme indicates a significant enhancement in extending the simultaneous transmission distance of classical signals and QKD, reaching approximately 100 km in HCF and 165 km in MCF under a classical power per channel of 10 dBm. Therefore, MCF offers a longer secure transmission distance compared with HCF when classical signals and QKD coexist in the C-band. However, when classical signals are in the C-band and QKD operates in the O-band, the performance of QKD in HCF surpasses that in MCF. This research establishes technical foundations for the design and deployment of QKD optical networks.

Corpus callosum atrophy and cognitive decline in early Alzheimer's disease: longitudinal MRI study.

BACKGROUND: We investigated the rate of corpus callosum (CC) atrophy and its association with cognitive decline in early Alzheimer's disease (AD). METHODS: We used publicly available longitudinal MRI data corresponding to 2 or more visits from 137 subjects characterized using the Clinical Dementia Rating (CDR) score. We classified these subjects into 3 groups according to the progression of their cognitive status: a healthy control group (CDR 0→0, n = 72), a decliner group (CDR 0→0.5, n = 14) and an AD group (CDR 0.5→0.5/1, n = 51). We measured the CC area on the midsagittal plane and calculated the atrophy rate between 2 or more visits. The correlation between the CC atrophy rate and annualized Mini Mental State Examination (MMSE) change was also analyzed. RESULTS: The results indicated that the baseline CC area was larger in the healthy control group compared to the AD group, whereas the CC atrophy rate was higher in the AD group relative to the control and decliner groups. The CC atrophy rate was also correlated with the annualized MMSE change in AD patients (p < 0.05). CONCLUSION: Callosal atrophy is present even in early AD and subsequently accelerates, such that the rate of CC atrophy is associated with cognitive decline in AD patients.

STN localization using local field potentials based on wavelet packet features and stacking ensemble learning.

BACKGROUND: DBS entails the insertion of an electrode into the patient brain, enabling Subthalamic nucleus (STN) stimulation. Accurate delineation of STN borders is a critical but time-consuming task, traditionally reliant on the neurosurgeon experience in deciphering the intricacies of microelectrode recording (MER). While clinical outcomes of MER have been satisfactory, they involve certain risks to patient safety. Recently, there has been a growing interest in exploring the potential of local field potentials (LFP) due to their correlation with the STN motor territory. METHOD: A novel STN detection system, integrating LFP and wavelet packet transform (WPT) with stacking ensemble learning, is developed. Initial steps involve the inclusion of soft thresholding to increase robustness to LFP variability. Subsequently, non-linear WPT features are extracted. Finally, a unique ensemble model, comprising a dual-layer structure, is developed for STN localization. We harnessed the capabilities of support vector machine, Decision tree and k-Nearest Neighbor in conjunction with long short-term memory (LSTM) network. LSTM is pivotal for assigning adequate weights to every base model. RESULTS: Results reveal that the proposed model achieved a remarkable accuracy and F1-score of 89.49% and 91.63%. COMPARISON WITH EXISTING METHODS: Ensemble model demonstrated superior performance when compared to standalone base models and existing meta techniques. CONCLUSION: This framework is envisioned to enhance the efficiency of DBS surgery and reduce the reliance on clinician experience for precise STN detection. This achievement is strategically significant to serve as an invaluable tool for refining the electrode trajectory, potentially replacing the current methodology based on MER.

[Effect of treatments of hydrogen peroxide and sodium dithionite-citrate-bicarbonate on clay minerals of red earth sediments].

As classical procedures for pretreatment of soil sediments, hydrogen peroxide (H2O2) and sodium dithionite-citrate-bicarbonate (DCB) treatment methods are very important in removing the organic matter and iron oxides acting as cementing agents in the soils. However, both of these methods have less been focused on the effect on the clay minerals when separating. Here, we report the comparable methods between H2O2 and DCB to reveal their effect on clay minerals in red earth sediments using X-ray diffraction (XRD). The XRD results suggested that mineral particles can be totally decentralized by either H2O2 or DCB method in the soils and high purity clay minerals can be obtained by separating quartz and other impurities from clay minerals effectively. However, the XRD data were distorted by the DCB treatment owning to the cation exchange between Na+ and interlayer cation. On the contrary, the authentic data can be obtained by H2O2 treatment. Therefore, the H2O2 treatment seems to be a more appropriate method to obtain authentic information of clay mineralogy when separating of clay minerals from red earth sediments.

Surface deformation tracking in monocular laparoscopic video.

Image-guided surgery has been proven to enhance the accuracy and safety of minimally invasive surgery (MIS). Nonrigid deformation tracking of soft tissue is one of the main challenges in image-guided MIS owing to the existence of tissue deformation, homogeneous texture, smoke and instrument occlusion, etc. In this paper, we proposed a piecewise affine deformation model-based nonrigid deformation tracking method. A Markov random field based mask generation method is developed to eliminate tracking anomalies. The deformation information vanishes when the regular constraint is invalid, which further deteriorates the tracking accuracy. Atime-series deformation solidification mechanism is introduced to reduce the degradation of the deformation field of the model. For the quantitative evaluation of the proposed method, we synthesized nine laparoscopic videos mimicking instrument occlusion and tissue deformation. Quantitative tracking robustness was evaluated on the synthetic videos. Three real videos of MIS containing challenges of large-scale deformation, large-range smoke, instrument occlusion, and permanent changes in soft tissue texture were also used to evaluate the performance of the proposed method. Experimental results indicate the proposed method outperforms state-of-the-art methods in terms of accuracy and robustness, which shows good performance in image-guided MIS.

Atomic insights into thickness-dependent deformation mechanism and mechanical properties of Ag/PMMA ultra-thin nanofilms.

In this work, the nanoindentations on bilayer composite nanofilms composed of metal Ag and polymer PMMA were simulated using molecular dynamics. The effects of the thickness of Ag and PMMA on the elastic moduli of the composite films were analyzed from Hertz contact theory, dislocation evolution and atomic migration. The results show that the maximum penetration depth that the Hertz model could well describe is about 6 Å, and this limiting value is almost independent on the film thickness. The deformation mode of the Ag films gradually changes from bending mode to indentation mode with an increase in Ag thickness, which improves the elastic modulus of the composite films. The rule of mixtures could give a theoretical prediction about the elastic modulus of the composite film close to the nanoindentation, and Hertz theory could also be used as long as the thickness of Ag films exceeded a certain value. The introduction of a PMMA layer impedes the development of dislocation in the Ag layer and improves the elastic limit of the composite films. This work provides an important basis for experimentally measuring the overall elastic modulus of metal/polymer composite film based on nanoindentation or extracting the elastic modulus of metal film from the overall indentation response of the composite film.

[Mineralogy and genesis of mixed-layer clay minerals in the Jiujiang net-like red soil].

Mineralogy and genesis were investigated using X-ray diffraction (XRD), Fourier infrared absorption spectroscopy (FTIR) and high resolution transmission electron microscopy (HRTEM) to understand the mineralogy and its genesis significance of mixed-layer clay minerals in Jiujiang red soil section. XRD and FTIR results show that the net-like red soil sediments are composed of illite, kaolinite, minor smectite and mixed-layer illite-smectite and minor mixed-layer kaolinite-smectite. HRTEM observation indicates that some smectite layers have transformed into kaolinite layers in net-like red soil. Mixed-layer illite-smectite is a transition phase of illite transforming into smectite, and mixed-layer kaolinite-smectite is a transitional product relative to kaolinite and smectite. The occurrence of two mixed-layer clay species suggests that the weathering sequence of clay minerals in net-like red soil traversed from illite to mixed-layer illite-smectite to smectite to mixed-layer kaolinite-smectite to kaolinite, which indicates that net-like red soil formed under a warm and humid climate with strengthening of weathering.

sEMG-Based Gesture Recognition Using Deep Learning From Noisy Labels.

Gesture recognition for myoelectric prosthesis control utilizing sparse multichannel surface Electromyography (sEMG) is a challenging task, and from a Muscle-Computer Interface (MCI) standpoint, the performance is still far from optimal. However, the design of a well-performed sEMG recognition system depends on the flexibility of the input-output function and the dataset's quality. To improve the performance of MCI, we proposed a novel gesture recognition framework that (i) Enrich the spectral information of the sparse sEMG signals by constructing a fused map image (denoted as sEMG-Map) that integrates a multiresolution decomposition (by means of orthogonal wavelets) through the raw signals then rely upon the Convolutional Neural Network (CNN) capacity to exploit the composite hierarchies in the constructed sEMG-Map input. (ii) Deals with the label noise by proposing a data-centric method (denoted as ALR-CNN) that synchronously refines the falsely labeled samples and optimizes the CNN model based on two basic assumptions. First, the deep model accuracy improves as the training progress. Second, a set of successive learnable max-activated outputs of a well-performed deep model is a reliable estimator for motion detection in the muscle activation pattern. Our proposed framework is evaluated on three large-scale public databases. The average classification accuracy is 95.50%, 95.85%, and 85.58% for NinaPro DB2, NinaPro DB7, and NinaPro DB3, respectively. The experimental results verify the effectuality of the proposed method and show high accuracy.

Facial landmark-guided surface matching for image-to-patient registration with an RGB-D camera.

BACKGROUND: Fiducial marker-based image-to-patient registration is the most common way in image-guided neurosurgery, which is labour-intensive, time consuming, invasive and error prone. METHODS: We proposed a method of facial landmark-guided surface matching for image-to-patient registration using an RGB-D camera. Five facial landmarks are localised from preoperative magnetic resonance (MR) images using deep learning and RGB image using Adaboost with multi-scale block local binary patterns, respectively. The registration of two facial surface point clouds derived from MR images and RGB-D data is initialised by aligning these five landmarks and further refined by weighted iterative closest point algorithm. RESULTS: Phantom experiment results show the target registration error is less than 3 mm when the distance from the camera to the phantom is less than 1000 mm. The registration takes less than 10 s. CONCLUSIONS: The proposed method is comparable to the state-of-the-arts in terms of the accuracy yet more time-saving and non-invasive.

Real-time biomechanical modelling of the liver using LightGBM model.

BACKGROUND: Accurate and real-time biomechanical modelling of the liver is a major challenge in computer-assisted surgery. Finite element method is often used to predict the deformation of organs for its high modelling accuracy. However, its high computation cost hinders its application in real time, such as virtual surgery simulations. METHOD: A liver model with biomechanical properties similar to real one is created using finite element method and a data set of the liver deformation with different forces (whose magnitude ranges from 0.1 to 0.5 N in omni-direction) acting on different surface points is generated. The mechanical behaviour of liver is simulated in real time by a tree-based LightGBM regression model trained with the generated data set. RESULTS: In comparison with the Random Forest and XGBoost, the LightGBM model achieves the best accuracy with 0.0774 mm, 0.0786 mm, 0.0801 mm in the mean absolute error (MAE) and 0.0591 mm, 0.0609 and 0.0622 mm in the root mean square error (RMSE) along x, y and z axis, respectively. In addition, it only takes 33 ms for the LightGBM model to estimate the deformation of the liver, which is much faster than finite element model (29.91 s). CONCLUSION: These results lay a foundation for the future development of real-time virtual surgery systems of simulating liver deformation during minimally invasive surgeries using our method.

Deep convolutional neural network for the automated detection of Subthalamic nucleus using MER signals.

BACKGROUND: Deep brain stimulation (DBS) surgery has been extensively conducted for treating advanced Parkinson's disease (PD) patient's symptoms. DBS hinges on the localization of the subthalamic nucleus (STN) in which a permanent electrode should be accurately placed to produce electrical current. Microelectrode recording (MER) signals are routinely recorded in the procedure of DBS surgery to validate the planned trajectories. However, manual MER signals interpretation with the goal of detecting STN borders requires expertise and prone to inter-observer variability. Therefore, a computerized aided system would be beneficial to automatic detection of the dorsal and ventral borders of the STN in MER. NEW METHOD: In this study, a new deep learning model based on convolutional neural system for automatic delineation of the neurophysiological borders of the STN along the electrode trajectory was developed. COMPARISON WITH EXISTING METHODS: The proposed model does not involve any conventional standardization, feature extraction or selection steps. RESULTS: Promising results of 98.67% accuracy, 99.03% sensitivity, 98.11% specificity, 98.79% precision and 98.91% F1-score for subject based testing were achieved using the proposed convolutional neural network (CNN) model. CONCLUSIONS: This is the first study on the analysis of MER signals to detect STN using deep CNN. Traditional machine learning (ML) algorithms are often cumbersome and suffer from subjective evaluation. Though, the developed 10-layered CNN model has the capability of extracting substantial features at the convolution stage. Hence, the proposed model has the potential to deliver high performance on STN region detection which shows perspective in aiding the neurosurgeon intraoperatively.

Prediction of acute kidney injury in ICU with gradient boosting decision tree algorithms.

PURPOSE: To predict acute kidney injury (AKI) in a large intensive care unit (ICU) database. MATERIALS AND METHODS: A total of 30,020 ICU admissions with 17,222 AKI episodes were extracted from the Medical Information Mart from Intensive Care (MIMIC)-III database. These were randomly divided into a training set and an independent testing set in a ratio of 4:1. Data pertaining to demographics, admission information, vital signs, laboratory tests, critical illness scores, medications, comorbidities, and intervention measures were collected. Logistic regression, random forest, LightGBM, XGBoost, and an ensemble model was used for early prediction of AKI occurrence and important feature extraction. The SHAP analysis was adopted to reveal the impact of prediction for each feature. RESULTS: The ensemble model had the best overall performance for predicting AKI before 24 h, 48 h and 72 h. The F1 values were 0.915, 0.893, and 0.878, respectively. AUCs were 0.923, 0.903, and 0.895, respectively. CONCLUSIONS: Based on readily available electronic medical record (EMR) data, gradient boosting decision tree models are highly accurate at early AKI prediction in critically ill patients.

Brainstem atrophy in the early stage of Alzheimer's disease: a voxel-based morphometry study.

Postmortem studies on patients with Alzheimer's disease (AD) have confirmed that the dorsal raphe nucleus (DRN) in the brainstem is the first brain structure affected in the earliest stage of AD. The present study examined the brainstem in the early stage of AD using magnetic resonance (MR) imaging. T1-weighted MR images of the brains of 81 subjects were obtained from the publicly available Open Access Series of Imaging Studies (OASIS) database, including 27 normal control (NC) subjects, 27 patients with very mild AD (AD-VM) and 27 patients with mild AD (AD-M). The brainstem was interactively segmented from the MR images using ITK-SNAP. The present voxel-based morphometry (VBM) study was designed to investigate the brainstem differences between the AD-VM/AD-M groups and the NC group. The results showed bilateral loss in the pons and the left part of the midbrain in the AD-M group compared to the NC group. The AD-M group showed greater loss in the left midbrain than the AD-VM group (PFWEcorrected < 0.05). The results revealed that brainstem atrophy occurs in the early stages of AD (Clinical Dementia Rating = 0.5 and 1.0). Most of these findings were also investigated in a multicenter dataset. This is the first VBM study that provides evidence of brainstem alterations in the early stage of AD.

A non-contact interactive stereo display system for exploring human anatomy.

Stereoscopic display based on Virtual Reality (VR) can facilitate clinicians observing 3 D anatomical models with the depth cue which lets them understand the spatial relationship between different anatomical structures intuitively. However, there are few input devices available in the sterile field of the operating room for controlling 3 D anatomical models. This paper presents a cost-effective VR application for stereo display of 3 D anatomical models with non-contact interaction. The system is integrated with hand gesture interaction and voice interaction to achieve non-contact interaction. Hand gesture interaction is based on Leap Motion. Voice interaction is implemented based on Bing Speech for English language and Aitalk for Chinese language. A local database is designed to record the anatomical terminologies organized in a tree structure, and provided to the speech recognition engine for querying these uncommon words. Ten participants were asked to practice the proposed system and compare it with the common interactive manners. The results show that our system is more efficient than the common interactive manner and prove the feasibility and practicability of the proposed system used in the sterile field.

Automatic Extraction of the Centerline of Corpus Callosum from Segmented Mid-Sagittal MR Images.

The centerline, as a simple and compact representation of object shape, has been used to analyze variations of the human callosal shape. However, automatic extraction of the callosal centerline remains a sophisticated problem. In this paper, we propose a method of automatic extraction of the callosal centerline from segmented mid-sagittal magnetic resonance (MR) images. A model-based point matching method is introduced to localize the anterior and posterior endpoints of the centerline. The model of the endpoint is constructed with a statistical descriptor of the shape context. Active contour modeling is adopted to drive the curve with the fixed endpoints to approximate the centerline using the gradient of the distance map of the segmented corpus callosum. Experiments with 80 segmented mid-sagittal MR images were performed. The proposed method is compared with a skeletonization method and an interactive method in terms of recovery error and reproducibility. Results indicate that the proposed method outperforms skeletonization and is comparable with and sometimes better than the interactive method.

Real-time probe tracking using EM-optical sensor for MRI-guided cryoablation.

BACKGROUND: A method of real-time, accurate probe tracking at the entrance of the MRI bore is developed, which, fused with pre-procedural MR images, will enable clinicians to perform cryoablation efficiently in a large workspace with image guidance. METHODS: Electromagnetic (EM) tracking coupled with optical tracking is used to track the probe. EM tracking is achieved with an MRI-safe EM sensor working under the scanner's magnetic field to compensate the line-of-sight issue of optical tracking. Unscented Kalman filter-based probe tracking is developed to smooth the EM sensor measurements when occlusion occurs and to improve the tracking accuracy by fusing the measurements of two sensors. RESULTS: Experiments with a spine phantom show that the mean targeting errors using the EM sensor alone and using the proposed method are 2.21 mm and 1.80 mm, respectively. CONCLUSION: The proposed method achieves more accurate probe tracking than using an EM sensor alone at the MRI scanner entrance.

Kalman filter-based EM-optical sensor fusion for needle deflection estimation.

PURPOSE: In many clinical procedures such as cryoablation that involves needle insertion, accurate placement of the needle's tip at the desired target is the major issue for optimizing the treatment and minimizing damage to the neighboring anatomy. However, due to the interaction force between the needle and tissue, considerable error in intraoperative tracking of the needle tip can be observed as needle deflects. METHODS: In this paper, measurements data from an optical sensor at the needle base and a magnetic resonance (MR) gradient field-driven electromagnetic (EM) sensor placed 10 cm from the needle tip are used within a model-integrated Kalman filter-based sensor fusion scheme. Bending model-based estimations and EM-based direct estimation are used as the measurement vectors in the Kalman filter, thus establishing an online estimation approach. RESULTS: Static tip bending experiments show that the fusion method can reduce the mean error of the tip position estimation from 29.23 mm of the optical sensor-based approach to 3.15 mm of the fusion-based approach and from 39.96 to 6.90 mm, at the MRI isocenter and the MRI entrance, respectively. CONCLUSION: This work established a novel sensor fusion scheme that incorporates model information, which enables real-time tracking of needle deflection with MRI compatibility, in a free-hand operating setup.

A novel 4-DOF surgical instrument with modular joints and 6-Axis Force sensing capability.

BACKGROUND: It is difficult for surgeons to exert appropriate forces during delicate operations due to lack of force feedback in robot-assisted minimally invasive surgery (RMIS). A 4-DOF surgical grasper with a modular wrist and 6-axis force sensing capability is developed. METHODS: A grasper integrated with a miniature force and torque sensor based on the Stewart platform is designed, and a cable tension decomposition mechanism is designed to alleviate influence of the cable tension to the sensor. A modularized wrist consisting of four joint units is designed to facilitate integration of the sensor and eliminate coupled motion of the wrist. RESULTS: Sensing ranges of this instrument are ±10 N and ±160 N mm, and resolutions are 1.2% in radial directions, 5% in axial direction, and 4.2% in rotational directions. An ex vivo experiment shows that this instrument prototype successfully measures the interaction forces. CONCLUSIONS: A 4-DOF surgical instrument with modular joints and 6-axis force sensing capability is developed. This instrument can be used for force feedback in RMIS. Copyright © 2016 John Wiley & Sons, Ltd.

Corpus callosum atrophy associated with the degree of cognitive decline in patients with Alzheimer's dementia or mild cognitive impairment: a meta-analysis of the region of interest structural imaging studies.

Individual structural neuroimaging studies of the corpus callosum (CC) in Alzheimer's disease (AD) and mild cognitive impairment (MCI) with the region of interest (ROI) analysis have yielded inconsistent findings. The aim of this study was to conduct a meta-analysis of structural imaging studies using ROI technique to measure the CC midsagittal area changes in patients with AD or MCI. Databases of PubMed, the Cochrane Library, the ISI Web of Science, and Science Direct from inception to June 2014 were searched with key words "corpus callosum" or "callosal", plus "Alzheimer's disease" or "mild cognitive impairment". Twenty-three studies with 603 patients with AD, 146 with MCI, and 638 healthy controls were included in this meta-analysis. Effect size was used to measure the difference between patients with AD or MCI and healthy controls. Significant callosal atrophy was found in MCI patients with an effect size of -0.36 (95% CI, -0.57 to -0.14; P = 0.001). The degree of the CC atrophy in mild AD was less severe than that in moderate AD with a mean effect size -0.69 (95% CI, -0.89 to -0.49) versus -0.92 (95% CI, -1.16 to -0.69), respectively. Comparing with healthy controls, patients with MCI had atrophy in the anterior portion of the CC (i.e., rostrum and genu). In contrast, patients with AD had atrophy in both anterior and posterior portions (i.e., splenium). These results suggest that callosal atrophy may be related to the degree of cognitive decline in patients with MCI and AD, and it may be used as a biomarker for patients with cognitive deficit even before meeting the criteria for AD.

Prognostic value of CA4/DG volumetry with 3T magnetic resonance imaging on postoperative outcome of epilepsy patients with dentate gyrus pathology.

PURPOSE: Hippocampal sclerosis (HS), the most common feature of mesial temporal lobe epilepsy (MTLE), is widely accepted as surgical indication for refractory epilepsy. Pathological hallmarks in hippocampal dentate gyrus (DG), including granule cell loss (GCL) and granule cell dispersion (GCD), are known to be closely related to the status epilepticus and spontaneous seizure. Our aim was to assess the association between volumetric changes in the hippocampal CA4/DG determined with 3-Tesla (3T) magnetic resonance imaging (MRI) and the postoperative seizure outcomes in MTLE patients with or without dentate gyrus pathology (DGP). METHODS: High-resolution T2- and T1-weighted three-dimensional (3D) MRI scans were performed on 39 MTLE patients before surgery with a 3T Philips scanner. ITK-SNAP software was used for segmentation and volumetry of the CA4/DG segment, and NASP software was used for 3D reconstructions of the CA4/DG region. Immunostaining for Neuronal Nuclei (NeuN) was performed on resected hippocampal specimens after surgery to verify the accuracy of CA4/DG segmentation and histopathological changes in DG. RESULTS: The CA4/DG subfield could be precisely segmented with high-resolution 3T MRI and confirmed by comparison of NeuN-immunoreactive slices with MRI results. MTLE patients with DGP showed smaller CA4/DG volume and favorable postoperative seizure outcomes. CONCLUSION: The volumetry of CA4/DG was associated with the pathological changes in DG in MTLE patients. The volumetry of CA4/DG with preoperative 3T MRI could predict the postoperative seizure outcomes in those patients.