Results 51 to 60 of about 48,878 (307)

Increased Risk of Sarcomas in Children With Congenital Anomalies: Findings From the Genetic Overlap Between Anomalies and Cancer in Kids (GOBACK) Registry Linkage Study

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Pediatric sarcomas are a heterogeneous group of tumors that contribute disproportionately to cancer mortality in children. Although congenital anomalies are among the strongest known risk factors for childhood cancer, the risk of specific sarcoma subtypes among affected individuals has not yet been thoroughly evaluated. Procedure We
Russ Wolters, Ji Yun Tark, Tiffany M. Chambers, Tania A. Desrosiers, Michael E. Scheurer, Charles Shumate, Wendy N. Nembhard, Mahsa M. Yazdy, Eirini Nestoridi, Amanda E. Janitz, Jean Paul Tanner, Russell S. Kirby, Jason L. Salemi, Yao Yu, Chad D. Huff, Sharon E. Plon, Philip J. Lupo, Jeremy M. Schraw   +17 more
wiley   +1 more source

Investigation on Diverse Sparse Signal Decomposition Techniques for Power Signal Representation

Energies
Power quality disturbance signals must be continuously monitored, stored, and transmitted for effective analysis, protection, and system planning in modern power systems.
Vivek Anjali, Preetha Parakkatu Kesava Panikkar   +1 more
doaj   +1 more source

Diversity and complexity in neural organoids

FEBS Letters, EarlyView.
Neural organoid research aims to expand genetic diversity on one side and increase tissue complexity on the other. Chimeroids integrate multiple donor genomes within single organoids. Self‐organising multi‐identity organoids, exogenous cell seeding, or enforced assembly of region‐specific organoids contribute to tissue complexity.
Ilaria Chiaradia, Madeline A. Lancaster
wiley   +1 more source

Design and analysis strategies for robust microbiome ageing research

FEBS Letters, EarlyView.
The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik, İsmail Güderer, Yi Wang, Tayyaba Alvi, Prasoon Pandey, Handan Melike Dönertaş   +5 more
wiley   +1 more source

A Sparse Auto Encoder Deep Process Neural Network Model and its Application

International Journal of Computational Intelligence Systems, 2017
Aiming at the problem of time-varying signal pattern classification, a sparse auto-encoder deep process neural network (SAE-DPNN) is proposed. The input of SAE-DPNN is time-varying process signal and the output is pattern category.
Xu Shaohua, Xue Jiwei, Li Xuegui
doaj   +1 more source

Application of Sparse Representation to Bartlett Spectra for Improved Direction of Arrival Estimation

Sensors, 2020
A new technique for high-resolution direction of arrival estimation is presented. The method utilizes the traditional Bartlett spectra and sparse representation to locate emitters in single and multiple emitter scenarios.
Jacob Compaleo, Inder J. Gupta
doaj   +1 more source

Exploiting Statistical Dependencies in Sparse Representations for Signal Recovery [PDF]

IEEE Transactions on Signal Processing, 2012
Signal modeling lies at the core of numerous signal and image processing applications. A recent approach that has drawn considerable attention is sparse representation modeling, in which the signal is assumed to be generated as a combination of a few atoms from a given dictionary.
Peleg, Tomer, Eldar, Yonina, Elad, Michael   +2 more
openaire   +3 more sources

Sparse model construction using coordinate descent optimization [PDF]

, 2013
We propose a new sparse model construction method aimed at maximizing a model’s generalisation capability for a large class of linear-in-the-parameters models.
Xia Hong, Hong, Xia, Sheng Chen, Chen, Sheng, Junbin Gao, Guo, Yi, Yi Guo, Guo, Yi (R18457), Gao, Junbin   +8 more
core   +1 more source

Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers

Molecular Oncology, EarlyView.
This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel, Peyman Zarrineh, Nathnael T. Tuffa, Jigar H. Sheth, Sumitra Mohan, Mudassar Iqbal, Sankari Nagarajan   +6 more
wiley   +1 more source

A Method of Rolling Bearing Fault Diagnose Based on Double Sparse Dictionary and Deep Belief Network

IEEE Access, 2020
Feature extraction is the key technology in the data-driven intelligent fault diagnosis methods of rolling bearing. However, the acquired features by the traditional methods, which mainly based on time-frequency domain, sometimes cannot well represent ...
Junfeng Guo, Pengfei Zheng
doaj   +1 more source