Optical biopsy identification and grading of gliomas using label-free visible resonance Raman spectroscopy.

Glioma is one of the most refractory types of brain tumor. Accurate tumor boundary identification and complete resection of the tumor are essential for glioma removal during brain surgery. We present a method based on visible resonance Raman (VRR) spectroscopy to identify glioma margins and grades. A set of diagnostic spectral biomarkers features are presented based on tissue composition changes revealed by VRR. The Raman spectra include molecular vibrational fingerprints of carotenoids, tryptophan, amide I/II/III, proteins, and lipids. These basic in situ spectral biomarkers are used to identify the tissue from the interface between brain cancer and normal tissue and to evaluate glioma grades. The VRR spectra are also analyzed using principal component analysis for dimension reduction and feature detection and support vector machine for classification. The cross-validated sensitivity, specificity, and accuracy are found to be 100%, 96.3%, and 99.6% to distinguish glioma tissues from normal brain tissues, respectively. The area under the receiver operating characteristic curve for the classification is about 1.0. The accuracies to distinguish normal, low grade (grades I and II), and high grade (grades III and IV) gliomas are found to be 96.3%, 53.7%, and 84.1% for the three groups, respectively, along with a total accuracy of 75.1%. A set of criteria for differentiating normal human brain tissues from normal control tissues is proposed and used to identify brain cancer margins, yielding a diagnostic sensitivity of 100% and specificity of 71%. Our study demonstrates the potential of VRR as a label-free optical molecular histopathology method used for in situ boundary line judgment for brain surgery in the margins

Diversification of mitogenomes in three sympatric Altica flea beetles (Insecta, Chrysomelidae)

The Asian flea beetles Altica cirsicola, Altica fragariae and Altica viridicyanea are broadly sympatric and morphologically highly similar but feed on distantly related host plants. They have been suggested as a model for ecological speciation stud- ies. However, their phylogeny and species limits remain uncertain. In this study, we added mitochondrial genomes from multiple individuals of each species to the grow- ing database. Phylogenetic analyses based on 15 genes showed clear interspecific divergences of A. fragariae from the other species, but A. cirsicola and A. viridi- cyanea were not distinguishable by distance‐based or tree‐based methods of species delimitation due to non‐monophyly of mitogenomes relative to the morphologically defined entities, possibly affected by interspecific introgression. This was confirmed by wider sampling of mitochondrial COX1 (58 individuals) and the second internal transcribed spacer of nuclear ribosomal RNA cluster (ITS2; 68 individuals), which showed that ITS2, but not COX1, coincided with the morphological species limits. The full mitochondrial genomes are not able to shed further light on the species status, even with the most sensitive approach based on diagnostic characters, yet the whole mitogenome is useful to get improved estimates of intra‐ and interspecific variation, not affected by the stochastic error seen in individual genes

A Method Based on the Improved Matrix Pencil Algorithm Designed for Voltage Flicker Detection

Voltage fluctuation and flicker are becoming more and more serious, which need necessary detection and further management. An improved algorithm designed for detecting voltage flicker parameters has been proposed. The method transformed voltage flicker signal model into a sum of series exponential signal model. Two matrices were constructed and combined into a matrixpencil. Thus, the nonlinear problem was converted into a linear problem. Not only voltage amplitude and frequency but also phase information of voltage flicker can be extracted through the method. Moreover, due to the difficulty of modal order determination in the noise environment, a method of Hankel matrix rank estimation was put forward to determine modal order accurately. Voltage flicker experiments were taken in the noise background. The results show that the proposed method is superior in computational accuracy, order determination and anti-noise capability. As a result, the method could contribute a new idea for the voltage flicker signal parameter extraction

Th17/Treg Cells Imbalance and GITRL Profile in Patients with Hashimoto’s Thyroiditis

published_or_final_versio

The Role of Malic Enzyme on Promoting Total Lipid and Fatty Acid Production in Phaeodactylum tricornutum

To verify the function of malic enzyme (ME1), the ME1 gene was endogenously overexpressed in Phaeodactylum tricornutum. Overexpression of ME1 increased neutral and total lipid content and significantly increased saturated fatty acids (SFAs) and polyunsaturated fatty acids (PUFAs) in transformants, which varied between 23.19 and 25.32% in SFAs and between 49.02 and 54.04% in PUFAs, respectively. Additionally, increased ME1 activity was accompanied by elevated NADPH content in all three transformants, indicating that increased ME1 activity produced additional NADPH comparing with that of WT. These results indicated that ME1 activity is NADP-dependent and plays an important role in the NADPH levels required for lipid synthesis and fatty acid desaturation in P. tricornutum. Furthermore, our findings suggested that overexpression of endogenous ME1 represents a valid method for boosting neutral-lipid yield in diatom

Feature Selection for MAUC-Oriented Classification Systems

Feature selection is an important pre-processing step for many pattern classification tasks. Traditionally, feature selection methods are designed to obtain a feature subset that can lead to high classification accuracy. However, classification accuracy has recently been shown to be an inappropriate performance metric of classification systems in many cases. Instead, the Area Under the receiver operating characteristic Curve (AUC) and its multi-class extension, MAUC, have been proved to be better alternatives. Hence, the target of classification system design is gradually shifting from seeking a system with the maximum classification accuracy to obtaining a system with the maximum AUC/MAUC. Previous investigations have shown that traditional feature selection methods need to be modified to cope with this new objective. These methods most often are restricted to binary classification problems only. In this study, a filter feature selection method, namely MAUC Decomposition based Feature Selection (MDFS), is proposed for multi-class classification problems. To the best of our knowledge, MDFS is the first method specifically designed to select features for building classification systems with maximum MAUC. Extensive empirical results demonstrate the advantage of MDFS over several compared feature selection methods.Comment: A journal length pape

Carbon Monitor Cities, near-real-time daily estimates of CO2 emissions from 1500 cities worldwide

Building on near-real-time and spatially explicit estimates of daily carbon dioxide (CO2) emissions, here we present and analyze a new city-level dataset of fossil fuel and cement emissions. Carbon Monitor Cities provides daily, city-level estimates of emissions from January 2019 through December 2021 for 1500 cities in 46 countries, and disaggregates five sectors: power generation, residential (buildings), industry, ground transportation, and aviation. The goal of this dataset is to improve the timeliness and temporal resolution of city-level emission inventories and includes estimates for both functional urban areas and city administrative areas that are consistent with global and regional totals. Comparisons with other datasets (i.e. CEADs, MEIC, Vulcan, and CDP) were performed, and we estimate the overall uncertainty to be 21.7%. Carbon Monitor Cities is a near-real-time, city-level emission dataset that includes cities around the world, including the first estimates for many cities in low-income countries