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115. Effects of tillage and nitrogen fertilizers on CH4 and CO2 emissions and soil organic carbon in paddy fields of central China.

Author

Li Cheng-Fang, Zhou Dan-Na, Kou Zhi-Kui, Zhang Zhi-Sheng, Wang Jin-Ping, Cai Ming-Li, and Cao Cou-Gui

Subjects
Medicine, Science
Abstract

Quantifying carbon (C) sequestration in paddy soils is necessary to help better understand the effect of agricultural practices on the C cycle. The objective of the present study was to assess the effects of tillage practices [conventional tillage (CT) and no-tillage (NT)] and the application of nitrogen (N) fertilizer (0 and 210 kg N ha(-1)) on fluxes of CH(4) and CO(2), and soil organic C (SOC) sequestration during the 2009 and 2010 rice growing seasons in central China. Application of N fertilizer significantly increased CH(4) emissions by 13%-66% and SOC by 21%-94% irrespective of soil sampling depths, but had no effect on CO(2) emissions in either year. Tillage significantly affected CH(4) and CO(2) emissions, where NT significantly decreased CH(4) emissions by 10%-36% but increased CO(2) emissions by 22%-40% in both years. The effects of tillage on the SOC varied with the depth of soil sampling. NT significantly increased the SOC by 7%-48% in the 0-5 cm layer compared with CT. However, there was no significant difference in the SOC between NT and CT across the entire 0-20 cm layer. Hence, our results suggest that the potential of SOC sequestration in NT paddy fields may be overestimated in central China if only surface soil samples are considered.

Published
2012
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123. ARID1B, a molecular suppressor of erythropoiesis, is essential for the prevention of Monge's disease.

Author

Azad, Priti, Caldwell, Andrew B, Ramachandran, Srinivasan, Spann, Nathanael J, Akbari, Ali, Villafuerte, Francisco C, Bermudez, Daniela, Zhao, Helen, Poulsen, Orit, Zhou, Dan, Bafna, Vineet, Subramaniam, Shankar, and Haddad, Gabriel G

Subjects
Chromatin, Humans, Chronic Disease, Altitude Sickness, DNA-Binding Proteins, Transcription Factors, Erythropoiesis, Tumor Suppressor Protein p53, Hypoxia, Stem Cell Research, Clinical Research, Genetics, Stem Cell Research - Nonembryonic - Non-Human, Aetiology, Underpinning research, 1.1 Normal biological development and functioning, 2.1 Biological and endogenous factors, Medicinal and Biomolecular Chemistry, Biochemistry & Molecular Biology
Abstract

At high altitude Andean region, hypoxia-induced excessive erythrocytosis (EE) is the defining feature of Monge's disease or chronic mountain sickness (CMS). At the same altitude, resides a population that has developed adaptive mechanism(s) to constrain this hypoxic response (non-CMS). In this study, we utilized an in vitro induced pluripotent stem cell model system to study both populations using genomic and molecular approaches. Our whole genome analysis of the two groups identified differential SNPs between the CMS and non-CMS subjects in the ARID1B region. Under hypoxia, the expression levels of ARID1B significantly increased in the non-CMS cells but decreased in the CMS cells. At the molecular level, ARID1B knockdown (KD) in non-CMS cells increased the levels of the transcriptional regulator GATA1 by 3-fold and RBC levels by 100-fold under hypoxia. ARID1B KD in non-CMS cells led to increased proliferation and EPO sensitivity by lowering p53 levels and decreasing apoptosis through GATA1 mediation. Interestingly, under hypoxia ARID1B showed an epigenetic role, altering the chromatin states of erythroid genes. Indeed, combined Real-time PCR and ATAC-Seq results showed that ARID1B modulates the expression of GATA1 and p53 and chromatin accessibility at GATA1/p53 target genes. We conclude that ARID1B is a novel erythroid regulator under hypoxia that controls various aspects of erythropoiesis in high-altitude dwellers.

Published
2022

124. MLCTR: A Fast Scalable Coupled Tensor Completion Based on Multi-Layer Non-Linear Matrix Factorization

Author

Uddin, Ajim, Zhou, Dan, Tao, Xinyuan, Chou, Chia-Ching, and Yu, Dantong

Subjects
Computer Science - Machine Learning
Abstract

Firms earning prediction plays a vital role in investment decisions, dividends expectation, and share price. It often involves multiple tensor-compatible datasets with non-linear multi-way relationships, spatiotemporal structures, and different levels of sparsity. Current non-linear tensor completion algorithms tend to learn noisy embedding and incur overfitting. This paper focuses on the embedding learning aspect of the tensor completion problem and proposes a new multi-layer neural network architecture for tensor factorization and completion (MLCTR). The network architecture entails multiple advantages: a series of low-rank matrix factorizations (MF) building blocks to minimize overfitting, interleaved transfer functions in each layer for non-linearity, and by-pass connections to reduce the gradient diminishing problem and increase the depths of neural networks. Furthermore, the model employs Stochastic Gradient Descent(SGD) based optimization for fast convergence in training. Our algorithm is highly efficient for imputing missing values in the EPS data. Experiments confirm that our strategy of incorporating non-linearity in factor matrices demonstrates impressive performance in embedding learning and end-to-end tensor models, and outperforms approaches with non-linearity in the phase of reconstructing tensors from factor matrices.

Published
2021

126. Transcriptome-wide association study and Mendelian randomization in pancreatic cancer identifies susceptibility genes and causal relationships with type 2 diabetes and venous thromboembolism

Author

Lindstrom, Sara, Wang, Lu, Smith, Erin, Gordon, William, Van Hylckama Vlieg, Astrid, De Andrade, Mariza, Brody, Jennifer, Pattee, Jack, Haessler, Jeffrey, Brumpton, Ben, Chasman, Daniel, Suchon, Pierre, Chen, Ming-Huei, Turman, Constance, Germain, Marine, Wiggins, Kerri, MacDonald, James, Braekkan, Sigrid, Armasu, Sebastian, Pankratz, Nathan, Jackson, Rabecca, Nielsen, Jonas, Giulianini, Franco, Puurunen, Marja, Ibrahim, Manal, Heckbert, Susan, Bammler, Theo, Frazer, Kelly, McCauley, Bryan, Taylor, Kent, Pankow, James, Reiner, Alexander, Gabrielsen, Maiken, Deleuze, Jean-François, O'Donnell, Chris, Kim, Jihye, McKnight, Barbara, Kraft, Peter, Hansen, John-Bjarne, Rosendaal, Frits, Heit, John, Psaty, Bruce, Tang, Weihong, Kooperberg, Charles, Hveem, Kristian, Ridker, Paul, Morange, Pierre-Emmanuel, Johnson, Andrew, Kabrhel, Christopher, Trégouët, David-Alexandre, Smith, Nicholas, Tan, Marcus C.B., Isom, Chelsea A., Liu, Yangzi, Wu, Lang, Zhou, Dan, and Gamazon, Eric R.

Published
2024
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