Search results for "Recurrent"
showing 10 items of 256 documents
Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran
2021
The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses. In this study, we applied two novel deep learning algorithms, the recurrent neural network (RNN) and convolutional neural network (CNN), for national-scale landslide susceptibility mapping of Iran. We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors (altitude, slope degree, profile curvature, distance to river, aspect, plan curvature, distance to road, distance to fault, rainfall, geology and land-sue) to construct a geospatial database and divided the data into the training and the testing dataset. We then d…
Molecular phylogeny and forms of photosynthesis in tribe Salsoleae (Chenopodiaceae).
2016
Evolution of C3–C4 intermediate and C4 lineages are resolved in Salsoleae (Chenopodiaceae), and a model for structural and biochemical changes for the evolution of the Salsoloid form of C4 is considered.
Endometrial Receptivity Analysis (ERA): data versus opinions
2021
Abstract This article summarises and contextualises the accumulated basic and clinical data on the ERA test and addresses specific comments and opinions presented by the opponent as part of an invited debate. Progress in medicine depends on new technologies and concepts that translate to practice to solve long-standing problems. In a key example, combining RNA sequencing data (transcriptomics) with artificial intelligence (AI) led to a clinical revolution in personalising disease diagnosis and fostered the concept of precision medicine. The reproductive field is no exception. Translation of endometrial transcriptomics to the clinic yielded an objective definition of the limited time period …
Type of chromosome abnormality affects embryo morphology dynamics.
2016
Objective To study the differences in the cleavage time between types of embryo chromosomal abnormalities and elaborate algorithm to exclude aneuploid embryos according to the likelihood to be euploid. Design Retrospective cohort study. Setting University affiliated private center. Patient(s) Preimplantational genetic screening patients (n = 112) including cases of advanced maternal age, repeated implantation failure, and recurrent miscarriage. A total of 485 embryos were analyzed. Intervention(s) None. Main Outcome Measure(s) All biopsied embryos were cultured in an incubator with time-lapse technology, cleavage timing from insemination to day 3 and all kinetic parameters that have been de…
Intratumoral Heterogeneity and Longitudinal Changes in Gene Expression Predict Differential Drug Sensitivity in Newly Diagnosed and Recurrent Gliobla…
2020
Background: Inevitable recurrence after radiochemotherapy is the major problem in the treatment of glioblastoma, the most prevalent type of adult brain malignancy. Glioblastomas are notorious for a high degree of intratumor heterogeneity manifest through a diversity of cell types and molecular patterns. The current paradigm of understanding glioblastoma recurrence is that cytotoxic therapy fails to target effectively glioma stem cells. Recent advances indicate that therapy-driven molecular evolution is a fundamental trait associated with glioblastoma recurrence. There is a growing body of evidence indicating that intratumor heterogeneity, longitudinal changes in molecular biomarkers and spe…
The role of surgery in platinum-resistant ovarian cancer: A call to the scientific community.
2021
Abstract In the last decade, a growing attention has been focused on identifying effective therapeutic strategies also in the orphan clinical setting of women with platinum-resistant disease. In this context, secondary cytoreductive surgery (SCS) remains a potential approach only in women with platinum sensitive relapse, but experimental data have been published supporting the role of SCS also in patients with platinum-resistant recurrence. In particular, surgery is emerging as a potential option in specific subgroups of women, such as those patients with low-grade serous histology, or low-volume relapse with disease located in the so-called pharmacological sanctuaries. Furthermore, contras…
Deep learning architectures for prediction of nucleosome positioning from sequences data
2018
Abstract Background Nucleosomes are DNA-histone complex, each wrapping about 150 pairs of double-stranded DNA. Their function is fundamental for one of the primary functions of Chromatin i.e. packing the DNA into the nucleus of the Eukaryote cells. Several biological studies have shown that the nucleosome positioning influences the regulation of cell type-specific gene activities. Moreover, computational studies have shown evidence of sequence specificity concerning the DNA fragment wrapped into nucleosomes, clearly underlined by the organization of particular DNA substrings. As the main consequence, the identification of nucleosomes on a genomic scale has been successfully performed by com…
Deep learning network for exploiting positional information in nucleosome related sequences
2017
A nucleosome is a DNA-histone complex, wrapping about 150 pairs of double-stranded DNA. The role of nucleosomes is to pack the DNA into the nucleus of the Eukaryote cells to form the Chromatin. Nucleosome positioning genome wide play an important role in the regulation of cell type-specific gene activities. Several biological studies have shown sequence specificity of nucleosome presence, clearly underlined by the organization of precise nucleotides substrings. Taking into consideration such advances, the identification of nucleosomes on a genomic scale has been successfully performed by DNA sequence features representation and classical supervised classification methods such as Support Vec…
Deep Learning Architectures for DNA Sequence Classification
2017
DNA sequence classification is a key task in a generic computational framework for biomedical data analysis, and in recent years several machine learning technique have been adopted to successful accomplish with this task. Anyway, the main difficulty behind the problem remains the feature selection process. Sequences do not have explicit features, and the commonly used representations introduce the main drawback of the high dimensionality. For sure, machine learning method devoted to supervised classification tasks are strongly dependent on the feature extraction step, and in order to build a good representation it is necessary to recognize and measure meaningful details of the items to cla…
A Physiology-Based Model of Human Bile Acid Metabolism for Predicting Bile Acid Tissue Levels After Drug Administration in Healthy Subjects and BRIC …
2019
Drug-induced liver injury (DILI) is a matter of concern in the course of drug development and patient safety, often leading to discontinuation of drug-development programs or early withdrawal of drugs from market. Hepatocellular toxicity or impairment of bile acid (BA) metabolism, known as cholestasis, are the two clinical forms of DILI. Whole-body physiology-based modelling allows a mechanistic investigation of the physiological processes leading to cholestasis in man. Objectives of the present study were: (1) the development of a physiology-based model of the human BA metabolism, (2) population-based model validation and characterisation, and (3) the prediction and quantification of alter…