Bis{2-[(4-chloro­phen­yl)imino­meth­yl]pyrrol-1-ido-κ2 N,N′}bis­(dimethyl­amido-κN)titanium(IV) toluene monosolvate

The mononuclear title compound, [Ti(C11H8ClN2)2(C2H6N)2]·C7H8, was synthesized by the reaction of N-(4-chloro­phen­yl)-2-pyrrolylcarbaldimine with Ti(C2H6N)4. The TiIV ion is situated on a twofold rotation axis and displays a distorted octa­hedral geometry defined by four N atoms from two 2-[(4-chloro­phen­yl)imino­meth­yl]pyrrol-1-ide ligands and two N atoms from two dimethyl­amine ligands. The Ti—Npyrrole bond length [2.1041 (19) Å] is longer than the Ti—Ndimethyl­amine bond length [1.9013 (19) Å]; the imine N atom exhibits the longest Ti—N bond [2.3152 (17) Å]. The toluene solvent mol­ecule is located on a twofold rotation axis running through the C atom of the methyl group. Consequently, the H atoms of the latter are rotationally disordered. The compound contains no markable hydrogen-bonding inter­actions

Digital breast tomosynthesis-based peritumoral radiomics approaches in the differentiation of benign and malignant breast lesions

PURPOSEWe aimed to evaluate digital breast tomosynthesis (DBT)-based radiomics in the differentiation of benign and malignant breast lesions in women.METHODSA total of 185 patients who underwent DBT scans were enrolled between December 2017 and June 2019. The features of handcrafted and deep learning-based radiomics were extracted from the tumoral and peritumoral regions with different radial dilation distances outside the tumor. A 3-step method was used to select discriminative features and develop the radiomics signature. Discriminative clinical factors were identified by univariate logistic regression. The clinical fac- tors with P < .05 were used to build a clinical model with multivariate logistic regression. The radiomics nomogram was developed by integrating the radiomics signature and discriminative clinical factors. Discriminative performance of the radiomics signature, clinical model, nomo- gram, and breast imaging reporting and data system assessment were evaluated and compared with the receiver operating characteristic and decision curves analysis (DCA).RESULTSA total of 2 handcrafted and 2 deep features were identified as the most discriminative features from the peritumoral regions with 2 mm dilation distances and used to develop the radiomics signature. The nomogram incorporating the radiomics signature, age, and menstruation status showed the best discriminative performance with area under the curve (AUC) values of 0.980 (95% CI, 0.960 to 1.000; sensitivity =0.970, specificity =0.946) in the training cohort and 0.985 (95% CI, 0.960 to 1.000; sensitivity = 0.909, specificity = 0.966) in the validation cohort. DCA con- firmed the potential clinical usefulness of our nomogram.CONCLUSIONOur results illustrate that the radiomics nomogram integrating the DBT imaging features and clinical factors (age and menstruation status) can be considered as a useful tool in aiding the clinical diagnosis of breast cancer

Heritability enrichment of immunoglobulin G N-glycosylation in specific tissues

Genome-wide association studies (GWAS) have identified over 60 genetic loci associated with immunoglobulin G (IgG) N-glycosylation; however, the causal genes and their abundance in relevant tissues are uncertain. Leveraging data from GWAS summary statistics for 8,090 Europeans, and large-scale expression quantitative trait loci (eQTL) data from the genotype-tissue expression of 53 types of tissues (GTEx v7), we derived a linkage disequilibrium score for the specific expression of genes (LDSC-SEG) and conducted a transcriptome-wide association study (TWAS). We identified 55 gene associations whose predicted levels of expression were significantly associated with IgG N-glycosylation in 14 tissues. Three working scenarios, i.e., tissue-specific, pleiotropic, and coassociated, were observed for candidate genetic predisposition affecting IgG N-glycosylation traits. Furthermore, pathway enrichment showed several IgG N-glycosylation-related pathways, such as asparagine N-linked glycosylation, N-glycan biosynthesis and transport to the Golgi and subsequent modification. Through phenome-wide association studies (PheWAS), most genetic variants underlying TWAS hits were found to be correlated with health measures (height, waist-hip ratio, systolic blood pressure) and diseases, such as systemic lupus erythematosus, inflammatory bowel disease, and Parkinson’s disease, which are related to IgG N-glycosylation. Our study provides an atlas of genetic regulatory loci and their target genes within functionally relevant tissues, for further studies on the mechanisms of IgG N-glycosylation and its related diseases

Newly recorded moss genus and speciesin guangxi autonomous region of China based on collection from huaping national nature reserve

Thirty-one moss species are firstly reported in Guangxi Autonomous Region,China,based on the collection from Huaping National Nature Reserve.These species belong to Hypnaceae (four genera,nine species),Thuidiaceae (three genera,five species),Meteoriaceae (two genera,three species),Brachytheciaceae (two genera,three species),Leskeaceae (two genera,three species),Pottiaceae (two genera,three species),Mniaceae (two genera,two species),Sematophyllaceae (one species),Hypopterygiaceae (one species) and Diphysciaceae (one species).Among the 19 genera,Pelekium and Herzogiella are two newly recorded genera from Guangxi Autonomous Region

Robust tensor clustering with non-greedy maximization

Tensors are increasingly common in several areas such as data mining, computer graphics, and computer vision. Tensor clustering is a fundamental tool for data analysis and pattern discovery. However, there usually exist outlying data points in realworld datasets, which will reduce the performance of clustering. This motivates us to develop a tensor clustering algorithm that is robust to the outliers. In this paper, we propose an algorithm of Robust Tensor Clustering (RTC). The RTC firstly finds a lower rank approximation of the original tensor data using a L1 norm optimization function. Because the L1 norm doesn't exaggerate the effect of outliers compared with L2 norm, the minimization of the L1 norm approximation function makes RTC robust to outliers. Then we compute the HOSVD decomposition of this approximate tensor to obtain the final clustering results. Different from the traditional algorithm solving the approximation function with a greedy strategy, we utilize a non-greedy strategy to obtain a better solution. Experiments demonstrate that RTC has better performance than the state-ofthe- art algorithms and is more robust to outlier

HiPRIME: Hierarchical and passivity preserved interconnect macromodeling engine for RLKC power delivery

This paper proposes a general hierarchical analysis methodology, HiPRIME, to efficiently analyze RLKC power delivery systems. After partitioning the circuits into blocks, we develop and apply the IEKS (Improved Extended Krylov Subspace) method to build the multi-port Norton equivalent circuits which transform all the internal sources to Norton current sources at ports. Since there are no active elements inside the Norton circuits, passive or realizable model order reduction techniques such as PRIMA can be applied. The significant speed improvement, 700 times faster than Spice with less than 0.2 % error and 7 times faster than a state-of-the-art solver, InductWise, is observed. To further reduce the top-level hierarchy runtime, we develop a second-level model reduction algorithm and prove its passivity. I