Efficient extraction of lignin from moso bamboo by microwave-assisted ternary deep eutectic solvent pretreatment for enhanced enzymatic hydrolysis.

Applications of deep eutectic solvent (DES) systems to separate lignocellulosic components are of interest to develop environmentally friendly processes and achieve efficient utilization of biomass. To enhance the performance of a binary neutral DES (glycerol:guanidine hydrochloride), various Lewis acids (e.g., AlCl3·6H2O, FeCl3·6H2O, etc.) were introduced to synthesize a series of ternary DES systems; these were coupled with microwave heating and applied to moso bamboo. Among the ternary DES systems evaluated, the FeCl3-based DES effectively removed lignin (81.17%) and xylan (85.42%), significantly improving enzymatic digestibility of the residual glucan and xylan (90.15% and 99.51%, respectively). Furthermore, 50.74% of the lignin, with high purity and a well-preserved structure, was recovered. A recyclability experiment showed that the pretreatment performance of the FeCl3-based DES was still basically maintained after five cycles. Overall, the microwave-assisted ternary DES pretreatment approach proposed in this study appears to be a promising option for sustainable biorefinery operations.

Visualizing the Habenula Using 3T High-Resolution MP2RAGE and QSM: A Preliminary Study.

BACKGROUND AND PURPOSE: The habenula is a key node in the regulation of emotion-related behavior. Accurate visualization of the habenula and its reliable quantitative analysis is vital for the assessment of psychiatric disorders. To obtain high-contrast habenula images and allow them to be compatible with clinical applications, this preliminary study compared 3T MP2RAGE and quantitative susceptibility mapping with MPRAGE by evaluating the habenula segmentation performance. MATERIALS AND METHODS: Ten healthy volunteers were scanned twice with 3T MPRAGE and MP2RAGE and once with quantitative susceptibility mapping. Image quality and visibility of habenula anatomic features were analyzed by 3 radiologists using a 5-point scale. Contrast assessments of the habenula and thalamus were also performed. The reproducibility of the habenula volume from MPRAGE and MP2RAGE was evaluated by manual segmentation and the Multiple Automatically Generated Template brain segmentation algorithm (MAGeTbrain). T1 values and susceptibility were measured in the whole habenula and habenula geometric subregion using MP2RAGE T1-mapping and quantitative susceptibility mapping. RESULTS: The 3T MP2RAGE and quantitative susceptibility mapping demonstrated clear boundaries and anatomic features of the habenula compared with MPRAGE, with a higher SNR and contrast-to-noise ratio (all P < .05). Additionally, 3T MP2RAGE provided reliable habenula manual and MAGeTbrain segmentation volume estimates with greater reproducibility. T1-mapping derived from MP2RAGE was highly reliable, and susceptibility contrast was highly nonuniform within the habenula. CONCLUSIONS: We identified an optimized sequence combination (3T MP2RAGE combined with quantitative susceptibility mapping) that may be useful for enhancing habenula visualization and yielding more reliable quantitative data.

Tumor microenvironment(TME) and single-source dual-energy CT(ssDECT) on assessment of inconformity between RECIST1.1 and pathological remission in neoadjuvant immunotherapy of NSCLC.

BACKGROUND: The inconformity (IC) between pathological and imaging remissions after neoadjuvant immunotherapy in patients with NSCLC can affect the evaluation of curative effect of neoadjuvant therapy and the decision regarding the chance of surgery. MATERIALS AND METHODS: Patients who achieved disease control(CR/PR/SD) after neoadjuvant chemoimmunotherapy from a clinical trial (NCT04326153) and after neoadjuvant chemotherapy during the same period were enrolled in this study. All patients underwent radical resection and systematic mediastinal lymphadenectomy after neoadjuvant treatments. The pathological remission, immunohistochemistry (CD4, CD8, CD20, CD56, FoxP3, CD68, CD163, CD11b tumor-infiltrating lymphocytes, or macrophages), and single-source dual-energy computed tomography (ssDECT) scans were assessed. The IC between imaging remission by CT and pathological remission was investigated. The underlying cause of IC, the correlation between IC and DFS, and prognostic biomarkers were explored. RESULTS: After neoadjuvant immunotherapy, enhanced immune killing and reduced immunosuppressive performance were observed. 70 % of neoadjuvant chemoimmunotherapy patients were in high/medium IC level. Massive necrosis and repair around and inside the cancer nest were the main pathological changes observed 30-45 days post-treatment with PD1/PD-L1 antibody and were the main causes of IC between the pathology and imaging responses after neoadjuvant immunotherapy. High IC and preoperative CD8 expression (H score ≥ 3) indicate a high pathological response rate and prolonged DFS. Iodine material density ssDECT images showed that the iodine content in the lesion causes hyperattenuation in post-neoadjuvant lesion in PCR patient. CONCLUSIONS: Compared to chemotherapy and targeted therapy, the efficacy of neoadjuvant immunotherapy was underestimated based on the RECIST criteria due to the unique antitumor therapeutic mechanism. Preoperative CD8+ expression and ssDECT predict this IC and evaluate the residual tumor cells. This is of great significance for screening immune beneficiaries and making more accurate judgments about the timing of surgery.

BCAN: Bidirectional Correct Attention Network for Cross-Modal Retrieval.

As a fundamental topic in bridging the gap between vision and language, cross-modal retrieval purposes to obtain the correspondences' relationship between fragments, i.e., subregions in images and words in texts. Compared with earlier methods that focus on learning the visual semantic embedding from images and sentences to the shared embedding space, the existing methods tend to learn the correspondences between words and regions via cross-modal attention. However, such attention-based approaches invariably result in semantic misalignment between subfragments for two reasons: 1) without modeling the relationship between subfragments and the semantics of the entire images or sentences, it will be hard for such approaches to distinguish images or sentences with multiple same semantic fragments and 2) such approaches focus attention evenly on all subfragments, including nonvisual words and a lot of redundant regions, which also will face the problem of semantic misalignment. To solve these problems, this article proposes a bidirectional correct attention network (BCAN), which introduces a novel concept of the relevance between subfragments and the semantics of the entire images or sentences and designs a novel correct attention mechanism by modeling the local and global similarity between images and sentences to correct the attention weights focused on the wrong fragments. Specifically, we introduce a concept about the semantic relationship between subfragments and entire images or sentences and use this concept to solve the semantic misalignment from two aspects. In our correct attention mechanism, we design two independent units to correct the weight of attention focused on the wrong fragments. Global correct unit (GCU) with modeling the global similarity between images and sentences into the attention mechanism to solve the semantic misalignment problem caused by focusing attention on relevant subfragments in irrelevant pairs (RI) and the local correct unit (LCU) consider the difference in the attention weights between fragments among two steps to solve the semantic misalignment problem caused by focusing attention on irrelevant subfragments in relevant pairs (IR). Extensive experiments on large-scale MS-COCO and Flickr30K show that our proposed method outperforms all the attention-based methods and is competitive to the state-of-the-art. Our code and pretrained model are publicly available at: https://github.com/liuyyy111/BCAN.

An innovative and efficient hydrogen peroxide-citric acid pretreatment of bamboo residues to enhance enzymatic hydrolysis and ethanol production.

Organic peracids has attracted widespread attention from researchers in biomass pretreatment. As a weak acid with high production, low price and toxicity, citric acid (CA) was mixed with hydrogen peroxide at the room temperature to generate peroxy-citric acid with strong oxidative functions. An innovative and efficient pretreatment method using peroxy-citric acid (HPCA) was proposed to enhance enzymatic hydrolysis and bioethanol production of bamboo residues. After D. giganteus (DG) was pretreated with HPCA at 80 °C for 3 h, lignin of 95.36% and xylan of 55.41% was effectively removed, and the enzymatic saccharification yield of HPCA-treated DG enhanced by about 8-9 times compared with CA pretreated DG. The ethanol recovery of 17.18 g/L was achieved. This work provided a reference for mild biomass pretreatment, which will promote the large-scale application of organic peracids system in biorefinery processes.

AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study.

OBJECTIVES: Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence. METHODS: We included n = 1591 multicenter, multivendor chest CT scans and divided them into AI training and validation datasets to develop an AI algorithm (n = 991 CT scans; n = 462 COVID-19, and n = 529 CAP) from three centers in China. An independent Chinese and German test dataset of n = 600 CT scans from six centers (COVID-19 / CAP; n = 300 each) was used to test the performance of eight blinded radiologists and the AI algorithm. A subtest dataset (180 CT scans; n = 90 each) was used to evaluate the radiologists' performance without and with AI assistance to quantify changes in diagnostic accuracy, reporting time, and diagnostic confidence. RESULTS: The diagnostic accuracy of the AI algorithm in the Chinese-German test dataset was 76.5%. Without AI assistance, the eight radiologists' diagnostic accuracy was 79.1% and increased with AI assistance to 81.5%, going along with significantly shorter decision times and higher confidence scores. CONCLUSION: This large multicenter study demonstrates that AI assistance in CT-based differentiation of COVID-19 and CAP increases radiological performance with higher accuracy and specificity, faster diagnostic time, and improved diagnostic confidence. KEY POINTS: • AI can help radiologists to get higher diagnostic accuracy, make faster decisions, and improve diagnostic confidence. • The China-German multicenter study demonstrates the advantages of a human-machine interaction using AI in clinical radiology for diagnostic differentiation between COVID-19 and CAP in CT scans.

Coupled Attention Framework of Convolutional Neural Network Based on Computer Intelligence.

Using an attention mechanism based on the convolutional neural networks (CNNs) improves the performance of computer vision tasks by enhancing the representation of the features. The existing attention methods enhance the expression of the features by modeling the internal information of the features. However, due to the limited information flow of the previous features, these methods are difficult to calibrate features more completely. In this paper, we propose a Coupled Attention Framework (CAF) that is a simple attention framework for improving the performance of the existing attention methods. In the CAF, a coupling branch is added to an existing attention method to generate the input attention maps and enhance the input features of the convolution. The input attention is then spread to the output features through coupling between the input attention maps and convolution, the output features. The final result is the experimental results on various visual tasks. The results show that applying CAF to most of the existing attention methods can improve the performance with fewer parameters.

Alkaline deacetylation-aided hydrogen peroxide-acetic acid pretreatment of bamboo residue to improve enzymatic saccharification and bioethanol production.

Bamboo pretreatment with alkaline deacetylation-aided hydrogen peroxide-acetic acid (HPAC-NaOH) was investigated for producing high-value-added products. Comparing with HPAC pretreated D. sinicus, the post-treatment of alkaline deacetylation resulted in higher glucose yield of 91.3% and ethanol concentrations of 17.20 g/L, increased by about 20-27%. A strong negative correlation between the content of acetyl with cellulose accessibility and enzymatic hydrolysis yield was showed. The deacetylation of HPAC-DS contributed to the increase of cellulase adsorption capacities in substrates and the variations of hydrophilicity, cellulose crystallinity, and degree of polymerization, which can generate highly reactive cellulosic materials for enzymatic saccharification to produce bioethanol. The HPAC-NaOH pretreatment can provide a promising approach to improve the bioconversion of bamboo to biofuels, and has broad space for the biorefinery of bamboo in the south of China.

Investigation of hydrogen peroxide-acetic acid pretreatment to enhance the enzymatic digestibility of bamboo residues.

Bamboo biomass was widely considered as a promising substitute for lignocellulose to produce fermentable sugars and biofuels in the south of China. When P. amarus were treated using hydrogen peroxide and acetic Acid pretreatment in the presence of sulphuric acid at 60 ℃ for 2 h, 82.63% lignin was removed from the bamboo residue, and enzymatic saccharification yield of 79.3% and ethanol content of 13.31 g/L were obtained. Analysis indicated that HPAC pretreatment increased the hydrophilic and porous nature of substrate, which can improve the enzyme accessibility to cellulose. When HPAC-pretreated D. sinicus, B. lapidea, N. affinis, andD. giganteus were used as the substrates of enzymatic saccharification, glucose yields of 71-84% at 72 h were achieved. HPAC pretreatment was a highly efficient and environmentally friendly method for bamboo biorefinery in the south of China.

Enhancing enzymatic digestibility of bamboo residues using a three-constituent deep eutectic solvent pretreatment.

A three-constituent deep eutectic solvent (3c-DES) pretreatment with choline chloride-oxalic acid-ethylene glycol was applied to examine its effectiveness on bamboo residues. The 3c-DES pretreatment can remove 91.09% xylan and significantly improved the 72 h hydrolysis yield of D. sinicus by 6.3 and 1.7 times as compared with the liquid hot water and two-constituent deep eutectic solvent (2c-DES) pretreatment. The introduction of ethylene glycol (EG) into choline chloride (ChCl)/ oxalic acid (OA) decreased the content of surface lignin and the condensation of lignin, which contributed to the increase of hydrophilic nature and cellulose accessibility in substrates. Moreover, higher glucose (85.72%) and xylose (91.05%) yields of 3c-DES pretreated bamboo were achieved with the addition of Tween 80. The 3c-DES system provides an alternative approach for the development of efficient bamboo pretreatment, and had broad space for bamboo biorefinery in southern China.

Generalized Pose Decoupled Network for Unsupervised 3D Skeleton Sequence-Based Action Representation Learning.

Human action representation is derived from the description of human shape and motion. The traditional unsupervised 3-dimensional (3D) human action representation learning method uses a recurrent neural network (RNN)-based autoencoder to reconstruct the input pose sequence and then takes the midlevel feature of the autoencoder as representation. Although RNN can implicitly learn a certain amount of motion information, the extracted representation mainly describes the human shape and is insufficient to describe motion information. Therefore, we first present a handcrafted motion feature called pose flow to guide the reconstruction of the autoencoder, whose midlevel feature is expected to describe motion information. The performance is limited as we observe that actions can be distinctive in either motion direction or motion norm. For example, we can distinguish "sitting down" and "standing up" from motion direction yet distinguish "running" and "jogging" from motion norm. In these cases, it is difficult to learn distinctive features from pose flow where direction and norm are mixed. To this end, we present an explicit pose decoupled flow network (PDF-E) to learn from direction and norm in a multi-task learning framework, where 1 encoder is used to generate representation and 2 decoders are used to generating direction and norm, respectively. Further, we use reconstructing the input pose sequence as an additional constraint and present a generalized PDF network (PDF-G) to learn both motion and shape information, which achieves state-of-the-art performances on large-scale and challenging 3D action recognition datasets including the NTU RGB+D 60 dataset and NTU RGB+D 120 dataset.

A PET/CT nomogram incorporating SUVmax and CT radiomics for preoperative nodal staging in non-small cell lung cancer.

OBJECTIVES: To develop and validate a PET/CT nomogram for preoperative estimation of lymph node (LN) staging in patients with non-small cell lung cancer (NSCLC). METHODS: A total of 263 pathologically confirmed LNs from 124 patients with NCSLC were retrospectively analyzed. Positron-emission tomography/computed tomography (PET/CT) examination was performed before treatment according to the clinical schedule. In the training cohort (N = 185), malignancy-related features, such as SUVmax, short-axis diameter (SAD), and CT radiomics features, were extracted from the regions of LN based on the PET/CT scan. The Minimum-Redundancy Maximum-Relevance (mRMR) algorithm and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model were used for feature selection and radiomics score building. The radiomics score (Rad-Score) and SUVmax were incorporated in a PET/CT nomogram using the multivariable logistic regression analysis. The performance of the proposed model was evaluated with discrimination, calibration, and clinical application in an independent testing cohort (N = 78). RESULTS: The radiomics scores consisting of 14 selected features were significantly associated with LN status for both training cohort with AUC of 0.849 (95% confidence interval (CI), 0.796-0.903) and testing cohort with AUC of 0.828 (95% CI, 0.782-0.919). The PET/CT nomogram incorporating radiomics score and SUVmax showed moderate improvement of the efficiency with AUC of 0.881 (95% CI, 0.834-0.928) in the training cohort and AUC of 0.872 (95% CI, 0.797-0.946) in the testing cohort. The decision curve analysis indicated that the PET/CT nomogram was clinically useful. CONCLUSION: The PET/CT nomogram, which incorporates Rad-Score and SUVmax, can improve the diagnostic performance of LN metastasis. KEY POINTS: • The PET/CT nomogram (Int-Score) based on lymph node (LN) PET/CT images can reliably predict LN status in NSCLC. • Int-Score is a relatively objective diagnostic method, which can play an auxiliary role in the process of clinicians making treatment decisions.

Bi-Directional Exponential Angular Triplet Loss for RGB-Infrared Person Re-Identification.

RGB-Infrared person re-identification (RGB-IR Re-ID) is a cross-modality matching problem, where the modality discrepancy is a big challenge. Most existing works use Euclidean metric based constraints to resolve the discrepancy between features of images from different modalities. However, these methods are incapable of learning angularly discriminative feature embedding because Euclidean distance cannot measure the included angle between embedding vectors effectively. As an angularly discriminative feature space is important for classifying the human images based on their embedding vectors, in this paper, we propose a novel ranking loss function, named Bi-directional Exponential Angular Triplet Loss, to help learn an angularly separable common feature space by explicitly constraining the included angles between embedding vectors. Moreover, to help stabilize and learn the magnitudes of embedding vectors, we adopt a common space batch normalization layer. The quantitative and qualitative experiments on the SYSU-MM01 and RegDB dataset support our analysis. On SYSU-MM01 dataset, the performance is improved from 7.40% / 11.46% to 38.57% / 38.61% for rank-1 accuracy / mAP compared with the baseline. The proposed method can be generalized to the task of single-modality Re-ID and improves the rank-1 accuracy / mAP from 92.0% / 81.7% to 94.7% / 86.6% on the Market-1501 dataset, from 82.6% / 70.6% to 87.6% / 77.1% on the DukeMTMC-reID dataset.

Radiomics nomogram: A noninvasive tool for preoperative evaluation of the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules.

In this study, we aimed to establish a radiomics nomogram that noninvasively evaluates the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules (GGNs). Computed tomography (CT) images of 509 patients manifesting as GGNs were collected: 70% of cases were included in the training cohort and 30% in the validation cohort. The Max-Relevance and Min-Redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithm were used to select the radiomics features and construct a radiomics signature. Univariate and multivariate logistic regression were used to select the invasiveness-related clinical and CT morphological predictors. Age, smoking history, long diameter, and average CT value were retained as independent predictors of GGN invasiveness. A radiomics nomogram was established by integrating clinical and CT morphological features with the radiomics signature. The radiomics nomogram showed good predictive ability in the training set (area under the curve [AUC], 0.940; 95% confidence interval [CI], 0.916-0.964) and validation set (AUC, 0.946; 95% CI, 0.907-0.986). This radiomics nomogram may serve as a noninvasive and accurate predictive tool to determine the invasiveness of GGNs prior to surgery and assist clinicians in creating personalized treatment strategies.

Multiple hepatocellular adenomas associated with long-term administration of androgenic steroids for aplastic anemia: A case report and literature review.

INTRODUCTION: Anabolic steroids are widely administered to patients with aplastic anemia (AA) and are associated with numerous medical complications. To assist with future diagnoses, we report about a young boy with multiple hepatocellular adenomas (HAs) induced by long-term use of anabolic androgenic steroids (AAS) for AA and present a related literature review. PATIENT CONCERN: A 15-year-old boy who was diagnosed with AA in 2011 had been treated with stanozolol (6 mg per day) and ciclosporin A (120-150 mg per day) for almost 4 years. He presented with epigastric pain and fever, and abdominal computed tomography showed a lesion of heterogenous density measuring 13.5 × 13.0 × 8.0 cm in the left hepatic lobe, which was initially misdiagnosed as a liver abscess. DIAGNOSIS: The patient went into hemorrhagic shock twice after invasive manipulation that aimed at diagnosis and was finally diagnosed with HA using fine needle aspiration. INTERVENTIONS: The patient discontinued AAS and only reserved ciclosporin A for AA treatment. OUTCOMES: Follow-up abdominal computed tomography performed 4 years after AAS discontinuation showed obvious regression of the hepatic lesions. CONCLUSION: It is of great importance for hematologists to completely understand that the long-term use of AAS may cause HA, which carries a great risk of hemorrhage and malignant transformation.

A novel radiomic nomogram for predicting epidermal growth factor receptor mutation in peripheral lung adenocarcinoma.

To predict the epidermal growth factor receptor (EGFR) mutation status in patients with lung adenocarcinoma using quantitative radiomic biomarkers and semantic features. We analyzed the computed tomography (CT) images and medical record data of 104 patients with lung adenocarcinoma who underwent surgical excision and EGFR mutation detection from 2016 to 2018 at our center. CT radiomic and semantic features that reflect the tumors' heterogeneity and phenotype were extracted from preoperative non-enhanced CT scans. The least absolute shrinkage and selection operator method was applied to select the most distinguishable features. Three logistic regression models were built to predict the EGFR mutation status by combining the CT semantic with clinicopathological characteristics, using the radiomic features alone, and by combining the radiomic and clinicopathological features. Receiver operating characteristic (ROC) curve analysis was performed using five-fold cross-validation and the mean area under the curve (AUC) values were calculated and compared between the models to obtain the optimal model for predicting EGFR mutation. Furthermore, radiomic nomograms were constructed to demonstrate the performance of the model. In total, 1025 radiomic features were extracted and reduced to 13 features as the most important predictors to build the radiomic signature. The combined radiomic and clinicopathological features model was developed based on the radiomic signature, sex, smoking, vascular infiltration, and pathohistological type. The AUC was 0.90 ± 0.02 for the training, 0.88 ± 0.11 for the verification, and 0.894 for the test dataset. This model was superior to the other prediction models that used the combined CT semantic and clinicopathological features (AUC for the test dataset: 0.768) and radiomic features alone (AUC for the test dataset: 0.837). The prediction model built by radiomic biomarkers and clinicopathological features, including the radiomic signature, sex, smoking, vascular infiltration, and pathological type, outperformed the other two models and could effectively predict the EGFR mutation status in patients with peripheral lung adenocarcinoma. The radiomic nomogram of this model is expected to become an effective biomarker for patients with lung adenocarcinoma requiring adjuvant targeted treatment.

Sample Fusion Network: An End-to-End Data Augmentation Network for Skeleton-Based Human Action Recognition.

Data augmentation is a widely used technique for enhancing the generalization ability of deep neural networks for skeleton-based human action recognition (HAR) tasks. Most existing data augmentation methods generate new samples by means of handcrafted transforms. However, these methods often cannot be trained and then are discarded during testing because of the lack of learnable parameters. To solve those problems, a novel type of data augmentation network called a sample fusion network (SFN) is proposed. Instead of using handcrafted transforms, an SFN generates new samples via a long short-term memory (LSTM) autoencoder (AE) network. Therefore, an SFN and HAR network can be cascaded together to form a combined network that can be trained in an end-to-end manner. Moreover, an adaptive weighting strategy is employed to improve the complementarity between a sample and the new sample generated from it by an SFN, thus allowing the SFN to more efficiently improve the performance of the HAR network during testing. The experimental results on various datasets verify that the proposed method outperforms state-of-the-art data augmentation methods. More importantly, the proposed SFN architecture is a general framework that can be integrated with various types of networks for HAR. For example, when a baseline HAR model with three LSTM layers and one fully connected (FC) layer was used, the classification accuracy was increased from 79.53% to 90.75% on the NTU RGB+D dataset using a cross-view protocol, thus outperforming most other methods.

Partial titanium mesh explantation cured post-cranioplasty implant-associated scalp infection.

Titanium mesh cranioplasty is routinely used worldwide for skull defect patients given its advantages, such as stability and biocompatibility. However, there are very few reports concerning the treatment of implant-associated scalp infection, which is one of the most common complications. The aim of the study is to retrospectively evaluate a novel operation technique for the treatment of titanium mesh-associated scalp infection post-cranioplasty, namely partial titanium mesh explantation (PTME). A retrospective study was conducted in all patients who underwent surgical treatment for implant-associated scalp infection from January 2012 to September 2016 in our hospital. In total, 17 patients were selected for study analysis among 231 patients who underwent cranioplasty. The treatment success rate of PTME was 85.7%. There was no statistically significant difference in demographics and characteristics except for follow-up length of time between the PTME group and TTME (total titanium mesh explantation) group (Non-paired Student's t-test, P=0.037). While, The PTME group exhibited a significantly reduced skull defect area post-operation compared with the TTME group (Non-paired Student's t-test, P=0.002). Moreover, post-PTME skull area also exhibited a significantly reduced skull defect area compared with the pre-cranioplasty area in the same patient (Non-paired Student's t-test, P=0.006). Compared with traditional surgical treatment of implant-associated scalp infection, PTME combined with strict debridement and antibiotic therapy can cure implant-associated scalp infection. Moreover, PTME could preserve sufficient titanium mesh for brain protection and cosmesis.

A Feature Point Matching Based on Spatial Order Constraints Bilateral-Neighbor Vote.

Feature point matching is a fundamental and challenging problem in many computer vision applications. In this paper, a robust feature point matching algorithm named spatial order constraints bilateral-neighbor vote (SOCBV) is proposed to remove outliers for a set of matches (including outliers) between two images. A directed k nearest neighbor (knn) graph of match sets is generated, and the problem of feature point matching is formulated as a binary discrimination problem. In the discrimination process, the class labeled matrix is built via the spatial order constraints defined on the edges that connect a point to its knn. Then, the posterior inlier class probability of each match is estimated with the knn density estimation and spatial order constraints. The vote of each match is determined by averaging all posterior class probabilities that originate from its associative inliers set and is used for removing outliers. The algorithm iteratively removes outliers from the directed graph and recomputes the votes until the stopping condition is satisfied. Compared with other popular algorithms, such as RANSAC, RSOC, GTM, SOC and WGTM, experiments under various testing data sets demonstrate strong robustness for the proposed algorithm.