Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer’s disease (AD). Identifying MCI subjects who are at high risk of converting to AD is crucial for effective treatments. In this study, a deep learning approach based on convolutional neural networks (CNN), is designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data. First, MRI images are prepared with age-correction and other processing. Second, local patches, which are assembled into 2.5 dimensions, are extracted from these images. Then, the patches from AD and normal controls (NC) are used to train a CNN to identify deep learning features of MCI subjects. After that, structural brain image features are mined with FreeSurfer to assist CNN. Finally, both types of features are fed into an extreme learning machine classifier to predict the AD conversion. The proposed approach is validated on the standardized MRI datasets from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project. This approach achieves an accuracy of 79.9% and an area under the receiver operating characteristic curve (AUC) of 86.1% in leave-one-out cross validations. Compared with other state-of-the-art methods, the proposed one outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity. Results demonstrate great potentials of the proposed CNN-based approach for the prediction of MCI-to-AD conversion with solely MRI data. Age correction and assisted structural brain image features can boost the prediction performance of CNN.

2019 Novel Coronavirus (2019-nCoV) is a virus identified as the cause of the outbreak of pneumonia first detected in Wuhan, China. Investigations on the transmissibility, severity, and other features associated with this virus are ongoing. Currently, there is no vaccine or therapeutic antibody to prevent the infection, and more time is required to develop an effective immune strategy against the pathogen. In contrast, specific inhibitors targeting the key protease involved in replication and proliferation of the virus are the most effective means to alleviate the epidemic. The main protease of SARS-CoV is essential for the life cycle of the virus, which showed 96.1% of similarity with the main proteaseof 2019-nCoV, is considered to be an attractive target for drug development. In this study, we have identified 4 small molecular drugs with high binding capacity with SARS-CoV main protease by high-throughput screening based on the 8,000 clinical drug libraries, all these drugs have been widely used in clinical applications with guaranteed safety, which may serve as promising candidates to treat the infection of 2019-nCoV.

This paper presents an effective approach for resume information extraction to support automatic resume management and routing. A cascaded information extraction (IE) framework is designed. In the first pass, a resume is segmented into a consecutive blocks attached with labels indicating the information types. Then in the second pass, the detailed information, such as Name and Address, are identified in certain blocks (e.g. blocks labelled with Personal Information), instead of searching globally in the entire resume. The most appropriate model is selected through experiments for each IE task in different passes. The experimental results show that this cascaded hybrid model achieves better F-score than flat models that do not apply the hierarchical structure of resumes. It also shows that applying different IE models in different passes according to the contextual structure is effective.

Objective:We evaluated 3-D computed tomography angiography (3-D CTA) in the diagnosis of the nutcracker phenomenon, and its significance in postoperative follow up. Patients and Methods: Three-dimensional CTA was used to compare the anatomical relations of the left renal vein with the aorta and the superior mesenteric artery in patients with the nutcracker phenomenon and in a control group. Four patients with the nutcracker phenomenon received a surgical procedure of the transposition of the left renal vein. The 3-D CTA was used for all patients during postoperation follow-up testing. Results: The 3-D CTA showed a compression of the left renal vein between the aorta and the superior mesenteric artery (SMA) and the abnormal acute angle between them. The angles and distances between the SMA and the aorta were 39.3 ± 4.3° and 3.1 ± 0.2 mm in the patient groups and 90 ± 10° and 12 ± 1.8 mm in the control groups, respectively. Differences in angles and distances were statistically significant between the two groups (P < 0.05). Surgical transposition of the left renal vein was performed successfully. Postoperative 3-D CTA revealed the distance between the SMA and the aorta was nearly normal. Conclusion:The reconstruction imaging of the renal vein by means of 3-D CTA revealed that unusual hematuria was due to compression of the left renal vein; therefore it may be a useful alternative imaging technique instead of conventional examinations. The non-invasive 3-D CTA may be a useful tool in the diagnosis of the nutcracker phenomenon and follow-up testing.

This paper presents the results of the International Benchmarking of Longitudinal Train Dynamics Simulators which involved participation of nine simulators (TABLDSS, UM, CRE-LTS, TDEAS, PoliTo, TsDyn, CARS, BODYSIM and VOCO) from six countries. Longitudinal train dynamics results and computing time of four simulation cases are presented and compared. The results show that all simulators had basic agreement in simulations of locomotive forces, resistance forces and track gradients. The major differences among different simulators lie in the draft gear models. TABLDSS, UM, CRE-LTS, TDEAS, TsDyn and CARS had general agreement in terms of the in-train forces; minor differences exist as reflections of draft gear model variations. In-train force oscillations were observed in VOCO due to the introduction of wheelâ\u80\u93rail contact. In-train force instabilities were sometimes observed in PoliTo and BODYSIM due to the velocity controlled transitional characteristics which could have generated unreasonable transitional stiffness. Regarding computing time per train operational second, the following list is in order of increasing computing speed: VOCO, TsDyn, PoliTO, CARS, BODYSIM, UM, TDEAS, CRE-LTS and TABLDSS (fastest); all simulators except VOCO, TsDyn and PoliTo achieved faster speeds than real-time simulations. Similarly, regarding computing time per integration step, the computing speeds in order are: CRE-LTS, VOCO, CARS, TsDyn, UM, TABLDSS and TDEAS (fastest)

Schizophrenic subject is thought as a self-disorder patient related with abnormal brain functional network. It has been hypothesized that self-disorder is associated with the deficient functional integration of multisensory body signals in schizophrenic subjects. To further verify this assumption, 53 chronic schizophrenic subjects and 67 healthy subjects were included in this study and underwent resting-state functional magnetic resonance imaging. The data-driven methods, whole-brain temporal variability of fractional amplitude of low-frequency fluctuations and regional homogeneity (ReHo), were used to investigate dynamic local functional connectivity and dynamic local functional activity changes in schizophrenic subjects. Patients with schizophrenia exhibited increased temporal variability ReHo and fractional amplitude of low-frequency fluctuations across time windows within sensory and perception network (such as occipital gyrus, precentral and postcentral gyri, superior temporal gyrus, and thalamus). Critically, the increased dynamic ReHo of thalamus is significantly correlated with positive and total symptom of schizophrenic subjects. Our findings revealed that deficit in sensory and perception functional networks might contribute to neural physiopathology of self-disorder in schizophrenic subjects.

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Siloxane benzoxazine monomer (Ba-s) was synthesized using phenol, 3-aminopropyltriethoxysilane (KH-550), and paraformaldehyde, and then introduced onto halloysite nanotubes (HNTs) surface as a coupling agent to obtain functionalized HNTs (mHNTs). 1, 6-hexanediamine/phenol benzoxazine (Ba-h) was selected as a matrix to blend with mHNTs at the mass ratios of 1:0, 9:1, 7:1, 6:1, 4:1,7:3, and 3:2, respectively. The ring-opening copolymerization behaviors of the nanocomposites were investigated by differential scanning calorimetry. The results implied that the polymerization temperature of Ba-h was decreased by the grafted HNTs. The results of thermogravimetric analysis and dynamic mechanical analysis displayed that the obtained copolymers exhibit different degrees of improvement in the thermal stability and mechanical properties of Ba-h. These enhancements are attributed to the homogeneous dispersion of HNTs and the strong matrix-nanoparticle interactions based on SEM and TEM results.