Oscillation-specific nodal alterations in early to middle stages Parkinsons disease.

Background: Different oscillations of brain networks could carry different dimensions of brain integration. We aimed to investigate oscillation-specific nodal alterations in patients with Parkinsons disease (PD) across early stage to middle stage by using graph theory-based analysis. Methods: Eighty-eight PD patients including 39 PD patients in the early stage (EPD) and 49 patients in the middle stage (MPD) and 36 controls were recruited in the present study. Graph theory-based network analyses from three oscillation frequencies (slow-5: 0.01-0.027 Hz; slow-4: 0.027-0.073 Hz; slow-3: 0.073-0.198 Hz) were analyzed. Nodal metrics (e.g. nodal degree centrality, betweenness centrality and nodal efficiency) were calculated. Results: Our results showed that (1) a divergent effect of oscillation frequencies on nodal metrics, especially on nodal degree centrality and nodal efficiency, that the anteroventral neocortex and subcortex had high nodal metrics within low oscillation frequencies while the posterolateral neocortex had high values within the relative high oscillation frequency was observed, which visually showed that network was perturbed in PD; (2) PD patients in early stage relatively preserved nodal properties while MPD patients showed widespread abnormalities, which was consistently detected within all three oscillation frequencies; (3) the involvement of basal ganglia could be specifically observed within slow-5 oscillation frequency in MPD patients; (4) logistic regression and receiver operating characteristic curve analyses demonstrated that some of those oscillation-specific nodal alterations had the ability to well discriminate PD patients from controls or MPD from EPD patients at the individual level; (5) occipital disruption within high frequency (slow-3) made a significant influence on motor impairment which was dominated by akinesia and rigidity. Conclusions: Coupling various oscillations could provide potentially useful information for large-scale network and progressive oscillation-specific nodal alterations were observed in PD patients across early to middle stages

Prediction of early neurologic deterioration in patients with perforating artery territory infarction using machine learning: a retrospective study

BackgroundEarly neurological deterioration (END) is a frequent complication in patients with perforating artery territory infarction (PAI), leading to poorer outcomes. Therefore, we aimed to apply machine learning (ML) algorithms to predict the occurrence of END in PAI and investigate related risk factors.MethodsThis retrospective study analyzed a cohort of PAI patients, excluding those with severe stenosis of the parent artery. We included demographic characteristics, clinical features, laboratory data, and imaging variables. Recursive feature elimination with cross-validation (RFECV) was performed to identify critical features. Seven ML algorithms, namely logistic regression, random forest, adaptive boosting, gradient boosting decision tree, histogram-based gradient boosting, extreme gradient boosting, and category boosting, were developed to predict END in PAI patients using these critical features. We compared the accuracy of these models in predicting outcomes. Additionally, SHapley Additive exPlanations (SHAP) values were introduced to interpret the optimal model and assess the significance of input features.ResultsThe study enrolled 1,020 PAI patients with a mean age of 60.46 (range 49.11–71.81) years. Of these, 30.39% were women, and 129 (12.65%) experienced END. RFECV selected 13 critical features, including blood urea nitrogen (BUN), total cholesterol (TC), low-density-lipoprotein cholesterol (LDL-C), apolipoprotein B (apoB), atrial fibrillation, loading dual antiplatelet therapy (DAPT), single antiplatelet therapy (SAPT), argatroban, the basal ganglia, the thalamus, the posterior choroidal arteries, maximal axial infarct diameter (measured at < 15 mm), and stroke subtype. The gradient-boosting decision tree had the highest area under the curve (0.914) among the seven ML algorithms. The SHAP analysis identified apoB as the most significant variable for END.ConclusionOur results suggest that ML algorithms, especially the gradient-boosting decision tree, are effective in predicting the occurrence of END in PAI patients

Transcriptome analysis of Bupleurum chinense focusing on genes involved in the biosynthesis of saikosaponins

<p>Abstract</p> <p>Background</p> <p><it>Bupleurum chinense </it>DC. is a widely used traditional Chinese medicinal plant. Saikosaponins are the major bioactive constituents of <it>B. chinense</it>, but relatively little is known about saikosaponin biosynthesis. The 454 pyrosequencing technology provides a promising opportunity for finding novel genes that participate in plant metabolism. Consequently, this technology may help to identify the candidate genes involved in the saikosaponin biosynthetic pathway.</p> <p>Results</p> <p>One-quarter of the 454 pyrosequencing runs produced a total of 195, 088 high-quality reads, with an average read length of 356 bases (NCBI SRA accession SRA039388). A <it>de novo </it>assembly generated 24, 037 unique sequences (22, 748 contigs and 1, 289 singletons), 12, 649 (52.6%) of which were annotated against three public protein databases using a basic local alignment search tool (E-value ≤1e-10). All unique sequences were compared with NCBI expressed sequence tags (ESTs) (237) and encoding sequences (44) from the <it>Bupleurum </it>genus, and with a Sanger-sequenced EST dataset (3, 111). The 23, 173 (96.4%) unique sequences obtained in the present study represent novel <it>Bupleurum </it>genes. The ESTs of genes related to saikosaponin biosynthesis were found to encode known enzymes that catalyze the formation of the saikosaponin backbone; 246 cytochrome P450 (<it>P450</it>s) and 102 glycosyltransferases (<it>GT</it>s) unique sequences were also found in the 454 dataset. Full length cDNAs of 7 <it>P450</it>s and 7 uridine diphosphate <it>GT</it>s (<it>UGT</it>s) were verified by reverse transcriptase polymerase chain reaction or by cloning using 5' and/or 3' rapid amplification of cDNA ends. Two <it>P450</it>s and three <it>UGT</it>s were identified as the most likely candidates involved in saikosaponin biosynthesis. This finding was based on the coordinate up-regulation of their expression with <it>β-AS </it>in methyl jasmonate-treated adventitious roots and on their similar expression patterns with <it>β-AS </it>in various <it>B. chinense </it>tissues.</p> <p>Conclusions</p> <p>A collection of high-quality ESTs for <it>B. chinense </it>obtained by 454 pyrosequencing is provided here for the first time. These data should aid further research on the functional genomics of <it>B. chinense </it>and other <it>Bupleurum </it>species. The candidate genes for enzymes involved in saikosaponin biosynthesis, especially the <it>P450</it>s and <it>UGT</it>s, that were revealed provide a substantial foundation for follow-up research on the metabolism and regulation of the saikosaponins.</p

Mechanical And Electrical Properties Of Carbon Nanotube Buckypaper Reinforced Silicon Carbide Nanocomposites

The nanocomposite was produced via phenolic resin infiltrating into a carbon nanotube (CNT) buckypaper preform containing B4C fillers and amorphous Si particles followed by an in-situ reaction between resin-derived carbon and Si to form SiC matrix. The buckypaper preform combined with the in-situ reaction avoided the phase segregation and increased significantly the volume fraction of CNTs. The nanocomposites prepared by this new process were dense with the open porosities less than 6%. A suitable CNT-SiC bonding was achieved by creating a B4C modified interphase layer between CNTs and SiC. The hardness increased from 2.83 to 8.58 GPa, and the indentation fracture toughness was estimated to increase from 2.80 to 9.96 MPa m1/2, respectively, by the reinforcing effect of B4C. These nanocomposites became much more electrically conductive with high loading level of CNTs. The in-plane electrical resistivity decreased from 124 to 74.4 μω m by introducing B4C fillers

Effects of Travel Speed on the Microstructure and Abrasion Resistance of Hardfacing Alloys Deposited with Composite Powder Particles and Solid Wire

Composite powder particles (CPP) preset on base metals were fused with a solid wire to form a melt by the intense heat provided by the arc. An X-ray diffractometer, scanning electron microscopy, and energy dispersive spectrometer were employed to investigate the effects of travel speed on the microstructure and abrasion resistance. It was found that the microstructure of hardfacing alloys with CPP consists of &gamma;-Fe, M7C3, and (Ti, V) C. With an increase in the travel speed from 3.5 to 6 mm/s, the microstructure with CPP changed from a hypoeutectic to hypereutectic structure. For hardfacing alloys with CPP, the increase in the travel speed not only contributed to a reduction of the dilution ratio of base metals, but also deliberately increased the volume fraction of primary M7C3-type carbides, which indicated that the bonding function executed on powder components led to a significant improvement in abrasion resistance and increased the utilization ratio of the alloying elements. The wear mechanism of hardfacing alloys included micro-cutting of abrasive particles and micro-spalling

Face Alignment Using View-Based Direct Appearance Models

Accurate face alignment is the prerequisite for many computer vision problems, such as face recognition, synthesis and 3-D face modeling. In this paper, a novel appearance model, called Direct Appearance Model (DAM), is proposed and its extended view-based models are applied for multiview face alignment. Similar to the active appearance model (AAM), DAM also makes ingenious use of both shape and texture constraints; however, it doesn&apos;t combine them as in AAM, texture information is used directly to predict the shape and estimate the position and appearance (hence the name DAM). The way that DAM models shapes and tex- tures has the following advantages as compared with AAM: (1) DAM subspaces include admissible appearances previously unseen in AAM, (2) It can converge more quickly and has higher accuracy, and (3) the memory requirement is cut down to a large extent. Extensive experiments are presented to evaluate the DAM alignment in comparison with AAM. I

Circulating MiR-125b as a Marker Predicting Chemoresistance in Breast Cancer

Background: Chemotherapy is an important component in the treatment paradigm for breast cancers. However, the resistance of cancer cells to chemotherapeutic agents frequently results in the subsequent recurrence and metastasis. Identification of molecular markers to predict treatment outcome is therefore warranted. The aim of the present study was to evaluate whether expression of circulating microRNAs (miRNAs) can predict clinical outcome in breast cancer patients treated with adjuvant chemotherapy. Methodology/Principal Findings: Circulating miRNAs in blood serum prior to treatment were determined by quantitative Real-Time PCR in 56 breast cancer patients with invasive ductal carcinoma and pre-operative neoadjuvant chemotherapy. Proliferating cell nuclear antigen (PCNA) immunostaining and TUNEL were performed in surgical samples to determine the effects of chemotherapy on cancer cell proliferation and apoptosis, respectively. Among the miRNAs tested, only miR-125b was significantly associated with therapeutic response, exhibiting higher expression level in non-responsive patients (n = 26, 46%; p = 0.008). In addition, breast cancers with high miR-125b expression had higher percentage of proliferating cells and lower percentage of apoptotic cells in the corresponding surgical specimens obtained after neoadjuvant chemotherapy. Increased resistance to anticancer drug was observed in vitro in breast cancer cells with ectopic miR-125b expression; conversely, reducing miR-125b level sensitized breast cancer cells to chemotherapy. Moreover, we demonstrated that th