Pleosporales species are important plant pathogens, saprobes, and endophytes on a wide range of economically important plant hosts. The classification of Pleosporales has undergone various modifications in recent years due to the addition of many families described from multiple habitats with a high level of morphological deviation. Numerous asexual genera have been described in Pleosporales that can be either hyphomyceteous or coelomycetous. Phoma- or coniothyrium-like species are common and have been revealed as polyphyletic in the order Pleosporales and linked with several sexual genera. A total of 31 pleosporalean strains were isolated in different regions of Taiwan between 2017 and 2018 from the leaves of Camellia sinensis plants with symptoms of leaf spot disease. These strains were evaluated morphologically and genotypically using multi-locus sequence analyses of the ITS, LSU, SSU, rpb2, tef1 and tub2 genes. The results demonstrated the affiliation of these strains with the various families in Pleosporales and revealed the presence of one new genus (Neoshiraia) and eight new species (Alloconiothyrium camelliae, Amorocoelophoma camelliae, Leucaenicola camelliae, L. taiwanensis, Neoshiraia camelliae, N. taiwanensis, Paraconiothyrium camelliae and Paraphaeosphaeria camelliae). Furthermore, to the best of our understanding, Didymella segeticola, Ectophoma pomi and Roussoella mexican were reported for the first time from C. sinensis in Taiwan.As the number of genomics datasets grows rapidly, sample mislabeling has become a high stakes issue. We present CrosscheckFingerprints (Crosscheck), a tool for quantifying sample-relatedness and detecting incorrectly paired sequencing datasets from different donors. Crosscheck outperforms similar methods and is effective even when data are sparse or from different assays. Application of Crosscheck to 8851 ENCODE ChIP-, RNA-, and DNase-seq datasets enabled us to identify and correct dozens of mislabeled samples and ambiguous metadata annotations, representing ~1% of ENCODE datasets.Chlorella is a unicellular green microalga that has been used in fields such as bioenergy production and food supplementation. In this study, two promoters of N (nitrogen) deficiency-inducible Chlorella vulgaris N Deficiency Inducible (CvNDI) genes were isolated from Chlorella vulgaris UTEX 395. These promoters were used for the production of a recombinant protein, human granulocyte-colony stimulating factor (hG-CSF) in Chlorella vulgaris UTEX 395 and Chlorella sp. ArM0029B. To efficiently secrete the hG-CSF, the protein expression vectors incorporated novel signal peptides obtained from a secretomics analysis of Chlorella spp. see more After a stable transformation of those vectors with a codon-optimized hG-CSF sequence, hG-CSF polypeptides were successfully produced in the spent media of the transgenic Chlorella. To our knowledge, this is the first report of recombinant protein expression using endogenous gene components of Chlorella.Atypicalities in connectivity between brain regions have been implicated in a range of neurocognitive disorders. We require metrics to assess stable individual differences in connectivity in the developing brain, while facing the challenge of limited data quality and quantity. Here, we examine how varying core processing parameters can optimise the test-retest reliability of EEG connectivity measures in infants. EEG was recorded twice with a 1-week interval between sessions in 10-month-olds. EEG alpha connectivity was measured across different epoch lengths and numbers, with the phase lag index (PLI) and debiased weighted PLI (dbWPLI), for both whole-head connectivity and graph theory metrics. We calculated intra-class correlations between sessions for infants with sufficient data for both sessions (N's = 19-41, depending on the segmentation method). Reliability for the whole brain dbWPLI was higher across many short epochs, whereas reliability for the whole brain PLI was higher across fewer long epochs. However, the PLI is confounded by the number of available segments. Reliability was higher for whole brain connectivity than graph theory metrics. Thus, segmenting available data into a high number of short epochs and calculating the dbWPLI is most appropriate for characterising connectivity in populations with limited availability of EEG data.Identifying image features that are robust with respect to segmentation variability is a tough challenge in radiomics. So far, this problem has mainly been tackled in test-retest analyses. In this work we analyse radiomics feature reproducibility in two phases first with manual segmentations provided by four expert readers and second with probabilistic automated segmentations using a recently developed neural network (PHiseg). We test feature reproducibility on three publicly available datasets of lung, kidney and liver lesions. We find consistent results both over manual and automated segmentations in all three datasets and show that there are subsets of radiomic features which are robust against segmentation variability and other radiomic features which are prone to poor reproducibility under differing segmentations. By providing a detailed analysis of robustness of the most common radiomics features across several datasets, we envision that more reliable and reproducible radiomic models can be built in the future based on this work.Heterogeneity is an intrinsic characteristic of cancer. Even in isogenic tumors, cell populations exhibit differential cellular programs that overall supply malignancy and decrease treatment efficiency. In this study, we investigated the functional relationship among cell subtypes and how this interdependency can promote tumor development in a cancer cell line. To do so, we performed single-cell RNA-seq of MCF7 Multicellular Tumor Spheroids as a tumor model. Analysis of single-cell transcriptomes at two-time points of the spheroid growth, allowed us to dissect their functional relationship. As a result, three major robust cellular clusters, with a non-redundant complementary composition, were found. Meanwhile, one cluster promotes proliferation, others mainly activate mechanisms to invade other tissues and serve as a reservoir population conserved over time. Our results provide evidence to see cancer as a systemic unit that has cell populations with task stratification with the ultimate goal of preserving the hallmarks in tumors.