Espensenpallesen9866
We note that extension of this methodology to estimate sub-country rates at provincial, state, or smaller levels of geography would be useful but pose additional statistical challenges.As the basal bricks, the dynamics and arrangement of nucleosomes orchestrate the higher architecture of chromatin in a fundamental way, thereby affecting almost all nuclear biology processes. Thanks to its rather simple protocol, assay for transposase-accessible chromatin using sequencing (ATAC)-seq has been rapidly adopted as a major tool for chromatin-accessible profiling at both bulk and single-cell levels; however, to picture the arrangement of nucleosomes per se remains a challenge with ATAC-seq. In the present work, we introduce a novel ATAC-seq analysis toolkit, named decoding nucleosome organization profile based on ATAC-seq data (deNOPA), to predict nucleosome positions. selleck inhibitor Assessments showed that deNOPA outperformed state-of-the-art tools with ultra-sparse ATAC-seq data, e.g. no more than 0.5 fragment per base pair. The remarkable performance of deNOPA was fueled by the short fragment reads, which compose nearly half of sequenced reads in the ATAC-seq libraries and are commonly discarded by state-of-the-art nucleosome positioning tools. However, we found that the short fragment reads enrich information on nucleosome positions and that the linker regions were predicted by reads from both short and long fragments using Gaussian smoothing. Last, using deNOPA, we showed that the dynamics of nucleosome organization may not directly couple with chromatin accessibility in the cis-regulatory regions when human cells respond to heat shock stimulation. Our deNOPA provides a powerful tool with which to analyze the dynamics of chromatin at nucleosome position level with ultra-sparse ATAC-seq data.
Metastatic neoplasms involving the stomach are rare and diagnostically challenging if clinical history of malignancy is absent or unavailable. This study was designed to identify the tumors that most frequently metastasize to the stomach and the morphologic features that can provide clues to investigate the possibility of metastasis and predict the primary sites.
All patients with metastatic neoplasms involving the stomach were included in the study. The H&E- and immunohistochemical-stained slides were reviewed, and all clinical, endoscopic, and radiologic information was recorded.
One hundred fifty patients, including 84 (56%) women and 66 (44%) men (mean age, 64 years), were identified. Gastric metastases were the initial presentation in 15% cases. Epithelial tumors (73.3%) comprised the largest group, followed by melanoma (20.6%), sarcomas (4%), germ cell tumors (1.3%), and hematolymphoid neoplasms (0.7%). Lobular breast carcinoma was the most common neoplasm overall in women, while in men, it was melanoma. Solid/diffuse growth pattern (75%) was more common compared with glandular morphology. The solid/diffuse category included lobular breast carcinoma (21.3%), melanoma (20.6%), and renal cell carcinoma (10.6%), while the glandular category was dominated by gynecologic serous carcinomas (7.3%) with papillary/micropapillary architecture.
Metastatic neoplasms should be considered in the differential diagnosis of gastric neoplasms, particularly those with a diffuse/solid growth pattern. Glandular neoplasms are difficult to differentiate from gastric primaries except for Müllerian neoplasms, which frequently show a papillary/micropapillary architecture.
Metastatic neoplasms should be considered in the differential diagnosis of gastric neoplasms, particularly those with a diffuse/solid growth pattern. Glandular neoplasms are difficult to differentiate from gastric primaries except for Müllerian neoplasms, which frequently show a papillary/micropapillary architecture.
Drug repurposing is a potential alternative to the traditional drug discovery process. Drug repurposing can be formulated as a recommender system that recommends novel indications for available drugs based on known drug-disease associations. This paper presents a method based on non-negative matrix factorization (NMF-DR) to predict the drug-related candidate disease indications. This work proposes a recommender system-based method for drug repurposing to predict novel drug indications by integrating drug and diseases related data sources. For this purpose, this framework first integrates two types of disease similarities, the associations between drugs and diseases, and the various similarities between drugs from different views to make a heterogeneous drug-disease interaction network. Then, an improved non-negative matrix factorization-based method is proposed to complete the drug-disease adjacency matrix with predicted scores for unknown drug-disease pairs.
The comprehensive experimental results show that NMF-DR achieves superior prediction performance when compared with several existing methods for drug-disease association prediction.
The program is available at https//github.com/sshaghayeghs/NMF-DR.
The program is available at https//github.com/sshaghayeghs/NMF-DR.
Diploid and polyploid Urochloa (including Brachiaria, Panicum and Megathyrsus species) C4 tropical forage grasses originating from Africa are important for food security and the environment, often being planted in marginal lands worldwide. We aimed to characterize the nature of their genomes, the repetitive DNA, and the genome composition of polyploids, leading to a model of the evolutionary pathways within the group including many apomictic species.
Some 362 forage grass accessions from international germplasm collections were studied, and ploidy determined using an optimized flow cytometry method. Whole-genome survey sequencing and molecular cytogenetic analysis were used to identify chromosomes and genomes in Urochloa accessions belonging to the 'brizantha' and 'humidicola' agamic complexes and U. maxima.
Genome structures are complex and variable, with multiple ploidies and genome compositions within the species, and no clear geographical patterns. Sequence analysis of nine diploid and polyploid acmodel of evolution at the whole-genome level in diploid and polyploid accessions showing processes of grass evolution. We support the retention of narrow species concepts for U. brizantha, U. decumbens, and U. ruziziensis, and do not consider diploids and polyploids of single species as cytotypes. The results and model will be valuable in making rational choices of parents for new hybrids, assist in use of the germplasm for breeding and selection of Urochloa with improved sustainability and agronomic potential, and will assist in measuring and conserving biodiversity in grasslands.
Algorithms for classifying chromosomes, like convolutional deep neural networks (CNNs), show promise to augment cytogeneticists' workflows, however, a critical limitation is their inability to accurately classify various structural chromosomal abnormalities. In hematopathology, recurrent structural cytogenetic abnormalities herald diagnostic, prognostic, and therapeutic implications, but are laborious for expert cytogeneticists to identify. Non-recurrent cytogenetic abnormalities also occur frequently cancerous cells. Here, we demonstrate the feasibility of using CNNs to accurately classify many recurrent cytogenetic abnormalities while being able to reliably detect non-recurrent, spurious abnormal chromosomes, as well as provide insights into dataset assembly, model selection, and training methodology that improve overall generalizability and performance for chromosome classification.
Our top-performing model achieved a mean weighted F1 score of 96.86% on the validation set and 94.03% on the test set. Gradient class activation maps indicated that our model learned biologically-meaningful feature maps, reinforcing the clinical utility of our proposed approach. Altogether, this work proposes a new dataset framework for training chromosome classifiers for use in a clinical environment, reveals that residual CNNs and cyclical learning rates confer superior performance, and demonstrates the feasibility of using this approach to automatically screen for many recurrent cytogenetic abnormalities while adeptly classifying non-recurrent abnormal chromosomes.
Software is freely available at https//github.com/DaehwanKimLab/Chromosome-ReAd. The data underlying this article cannot be shared publicly due to it being protected patient information.
Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.The growing expansion of data availability in medical fields could help improve the performance of machine learning methods. However, with healthcare data, using multi-institutional datasets is challenging due to privacy and security concerns. Therefore, privacy-preserving machine learning methods are required. link2 Thus, we use a federated learning model to train a shared global model, which is a central server that does not contain private data, and all clients maintain the sensitive data in their own institutions. The scattered training data are connected to improve model performance, while preserving data privacy. However, in the federated training procedure, data errors or noise can reduce learning performance. Therefore, we introduce the self-paced learning, which can effectively select high-confidence samples and drop high noisy samples to improve the performances of the training model and reduce the risk of data privacy leakage. We propose the federated self-paced learning (FedSPL), which combines the advantage of federated learning and self-paced learning. The proposed FedSPL model was evaluated on gene expression data distributed across different institutions where the privacy concerns must be considered. The results demonstrate that the proposed FedSPL model is secure, i.e. it does not expose the original record to other parties, and the computational overhead during training is acceptable. Compared with learning methods based on the local data of all parties, the proposed model can significantly improve the predicted F1-score by approximately 4.3%. We believe that the proposed method has the potential to benefit clinicians in gene selections and disease prognosis.
Recent advancements in single-cell RNA sequencing (scRNA-seq) have enabled time-efficient transcriptome profiling in individual cells. To optimize sequencing protocols and develop reliable analysis methods for various application scenarios, solid simulation methods for scRNA-seq data are required. link3 However, due to the noisy nature of scRNA-seq data, currently available simulation methods cannot sufficiently capture and simulate important properties of real data, especially the biological variation. In this study, we developed SCRIP, a novel simulator for scRNA-seq that is accurate and enables simulation of bursting kinetics.
Compared to existing simulators, SCRIP showed a significantly higher accuracy of stimulating key data features, including mean-variance dependency in all experiments. SCRIP also outperformed other methods in recovering cell-cell distances. The application of SCRIP in evaluating differential expression analysis methods showed that edgeR outperformed other examined methods in differential expression analyses, and ZINB-WaVE improved the AUC at high dropout rates.
