Predictability effects and parafoveal processing of compound words in natural Chinese reading

We report a boundary paradigm eye movement experiment to investigate whether the predictability of the second character of a two-character compound word affects how it is processed prior to direct fixation during reading. The boundary was positioned immediately prior to the second character of the target word, which itself was either predictable or unpredictable. The preview was either a pseudocharacter (nonsense preview), or an identity preview. We obtained clear preview effects in all conditions, but more importantly, skipping probability for the second character of the target word and the whole target word from pretarget was greater when it was predictable than when it was not predictable from the preceding context. Interactive effects for later measures on the whole target word (gaze duration and go-past time) were also obtained. These results demonstrate that predictability information from preceding sentential context and information regarding the likely identity of upcoming characters are used concurrently to constrain the nature of lexical processing during natural Chinese reading

LC–MS-based serum metabolomics analysis for the screening and monitoring of colorectal cancer

BackgroundColorectal Cancer (CRC) is a prevalent digestive system tumour with significant mortality and recurrence rates. Serum metabolomics, with its high sensitivity and high throughput, has shown potential as a tool to discover biomarkers for clinical screening and monitoring of the CRC patients.MethodsSerum metabolites of 61 sex and age-matched healthy controls and 62 CRC patients (before and after surgical intervention) were analyzed using a ultra-performance liquid chromatography-high resolution mass spectrometer (UPLC-MS). Statistical methods and pathway enrichment analysis were used to identify potential biomarkers and altered metabolic pathways.ResultsOur analysis revealed a clear distinction in the serum metabolic profile between CRC patients and healthy controls (HCs). Pathway analysis indicated a significant association with arginine biosynthesis, pyrimidine metabolism, pantothenate, and CoA biosynthesis. Univariate and multivariate statistical analysis showed that 9 metabolites had significant diagnostic value for CRC, among them, Guanosine with Area Under the Curve (AUC) values of 0.951 for the training group and0.998 for the validation group. Furthermore, analysis of four specific metabolites (N-Phenylacetylasparticacid, Tyrosyl-Gamma-glutamate, Tyr-Ser and Sphingosine) in serum samples of CRC patients before and after surgery indicated a return to healthy levels after an intervention.ConclusionOur results suggest that serum metabolomics may be a valuable tool for the screening and monitoring of CRC patients

Fully endoscopic transforaminal discectomy for thoracolumbar junction disc herniation with or without calcification under general anesthesia: Technical notes and preliminary outcomes

ObjectiveTo evaluate the feasibility, safety, and outcomes of percutaneous endoscopic transforaminal discectomy (PETD) for thoracolumbar junction disc herniation (TLDH) with or without calcification.MethodsThis study included 12 patients diagnosed with TLDH with or without calcification who met the inclusion criteria and underwent surgery for PETD from January 2019 to December 2021. The mean patient age, operation time, hospitalization time, time in bed, and complications were recorded. Patients were followed up for at least 9 months. Visual analog scale (VAS) scores for low-back and leg or thoracic radicular pain and modified Japanese Orthopedic Association score (m-JOA) scores were preoperatively evaluated, at 1 day and 3, 6, and 12 months postoperatively or at last follow-up. The modified MacNab criteria were used to evaluate clinical efficacy at 12 months postoperatively or at last follow-up.ResultsThe mean patient age, operation time, hospitalization time, and time in bed were 53 ± 13.9 years, 101.3 ± 9.2 min, 4.5 ± 1.3 days, and 18.0 ± 7.0 h, respectively. The mean VAS scores of low-back and leg or thoracic radicular pain improved from 5.8 ± 1.5 and 6.5 ± 1.4 to 2.0 ± 0.9 and 1.3 ± 0.5, respectively (P < 0.05). The m-JOA score improved from 7.5 ± 1.2 to 10.0 ± 0.7 (P < 0.05). The overall excellent–good rate of the modified MacNab criteria was 83.3%. No severe complications occurred.ConclusionFully endoscopic transforaminal discectomy and ventral decompression under general anesthesia is a safe, feasible, effective, and minimally invasive method for treating herniated discs with or without calcification at thoracolumbar junction zone

Radiomics Signature on Computed Tomography Imaging: Association With Lymph Node Metastasis in Patients With Gastric Cancer

Background: To evaluate whether radiomic feature-based computed tomography (CT) imaging signatures allow prediction of lymph node (LN) metastasis in gastric cancer (GC) and to develop a preoperative nomogram for predicting LN status.Methods: We retrospectively analyzed radiomics features of CT images in 1,689 consecutive patients from three cancer centers. The prediction model was developed in the training cohort and validated in internal and external validation cohorts. Lasso regression model was utilized to select features and build radiomics signature. Multivariable logistic regression analysis was utilized to develop the model. We integrated the radiomics signature, clinical T and N stage, and other independent clinicopathologic variables, and this was presented as a radiomics nomogram. The performance of the nomogram was assessed with calibration, discrimination, and clinical usefulness.Results: The radiomics signature was significantly associated with pathological LN stage in training and validation cohorts. Multivariable logistic analysis found the radiomics signature was an independent predictor of LN metastasis. The nomogram showed good discrimination and calibration.Conclusions: The newly developed radiomic signature was a powerful predictor of LN metastasis and the radiomics nomogram could facilitate the preoperative individualized prediction of LN status

Electro-mechanical braking system spindle unbalance axis trajectory purification and feedback method

The brake disc spindle of the electromechanical braking system will be accompanied by vibration unbalanced fault during the rotation operation, which will affect the braking performance of the brake system. In view of this phenomenon, based on the ensemble empirical mode decomposition algorithm and related energy operation theory, an offline purification program for the unbalanced axial trajectory of the spindle of electromechanical braking system are designed. Meanwhile, an online braking control feedback procedure for the axial trajectory are designed based on Cspace controller. Based on the designed program and braking theory, an experimental bench of the electro-mechanical braking system was set up, and experiments were conducted on the purification of the axial trajectory and the braking of the fault feedback respectively. The results show that the EEMD algorithm and the related energy operation theory can purify the unbalanced axial trajectory of the brake discs of the braking system and draw an ideal axial trajectory fault map; meanwhile, the experimental data of the fault feedback braking shows that the time required from the unbalanced fault monitoring to the completion of the braking process of the braking system is 1.278 s, which can effectively achieve the purpose of emergency braking in case of sudden failure. Through research, a new idea is provided for the development of electro-mechanical braking system fault detection and feedback

Performance Evaluation of Missing-Value Imputation Clustering Based on a Multivariate Gaussian Mixture Model.

BACKGROUND:It is challenging to deal with mixture models when missing values occur in clustering datasets. METHODS AND RESULTS:We propose a dynamic clustering algorithm based on a multivariate Gaussian mixture model that efficiently imputes missing values to generate a "pseudo-complete" dataset. Parameters from different clusters and missing values are estimated according to the maximum likelihood implemented with an expectation-maximization algorithm, and multivariate individuals are clustered with Bayesian posterior probability. A simulation showed that our proposed method has a fast convergence speed and it accurately estimates missing values. Our proposed algorithm was further validated with Fisher's Iris dataset, the Yeast Cell-cycle Gene-expression dataset, and the CIFAR-10 images dataset. The results indicate that our algorithm offers highly accurate clustering, comparable to that using a complete dataset without missing values. Furthermore, our algorithm resulted in a lower misjudgment rate than both clustering algorithms with missing data deleted and with missing-value imputation by mean replacement. CONCLUSION:We demonstrate that our missing-value imputation clustering algorithm is feasible and superior to both of these other clustering algorithms in certain situations