Browsing by Author "Huang, Lei"
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Item HEAT REDISTRIBUTION AND SEA LEVEL RISE IN THE SOUTH INDIAN OCEAN DURING THE ARGO ERAHuang, LeiThe South Indian Ocean (SIO) has been characterized as one of the major heat accumulators among the oceanic basins due to its remarkable warming during the Argo period, which are primarily attributed to the enhanced Indonesian Throughflow (ITF) heat transport. Nevertheless, the temperature increase is not evenly distributed in the whole basin but occurred along with several quasi-decadal cooling events in some specific regions of the SIO. First, we found that compared with the remarkable warming in the upper layer of the Southeast Indian Ocean (SEIO) during the recent global surface warming slowdown, the upper layer of the tropical SWIO undergoes a cooling trend during 2005–2011. This cooling trend is jointly forced by latent heat flux and horizontal heat advection associated with the changes in the surface atmosphere circulation. Second, the observational datasets suggest that the intermediate layer of the subtropical SIO displays significant cooling during the period 2010–2016 mainly because of the upward motion of relatively colder water from the deeper ocean. Compared to the decade-long warming in the upper layer of the SIO, which has been studied extensively, our understanding of temperature change in the intermediate layer is relatively limited. This study further reveals a quasi-decadal temperature cycle in the intermediate layer of the subtropical SIO during the Argo period, which is characterized by a shorter warming period during 2004–2009 and subsequent cooling during 2010–2016. Decomposition of temperature changes suggests that this quasi-decadal temperature variability is primarily driven by the heaving component, which is tightly associated with the local wind variability driven by local and remote climate forcings, whereas the spice change largely contributed by the SAM-related water mass transmission from higher latitudes, is of secondary importance. The impact of water mass spread may be more prominent on longer-term thermal variability in the tropical and subtropical regions. In addition to the quasi-decadal signal, the tropical SWIO displays strong multi-decadal variations. It has been reported that the SWIO experienced a sea level fall from the 1960s to the early 2000s. However, based on up-to-date satellite observations, this study reveals a sea level rise of 4.1±0.6 cm/decade in the tropical SWIO during 2002-2020. The mass component increases at a rate of 1.7±0.1 cm/decade, accounting for 41% of the total sea level rise and indicating an essential impact of mass change on sea level variability in the tropical SWIO. 29% of the total sea level rise is attributed to the steric sea level rise of 1.2±0.5 cm/decade in the upper 2000m. The steric sea level rise in the upper 300m can be attributed to wind-driven Ekman pumping and surface heat flux, whereas it is only of secondary importance to the steric sea level change in the upper 2000m. Conversely, thermal expansion below the thermocline (300-2000m), primarily caused by water mass spread from the Southern Ocean, induces a major contribution to the total change in the upper 2000m. Compared to existing studies that have primarily focused on the wind-driven thermal variations above the thermocline to the sea level variability, this study emphasizes the importance of ocean mass and deeper ocean changes in a warming climate.Item Network-level study of protein-protein interactions: analysis and prediction(University of Delaware, 2017) Huang, LeiWith continuous efforts in identifying protein-protein interactions (PPIs) through both high-throughput wet-lab experiments and computational methods, an increasing number of new PPIs have been discovered and validated, enabling sizeable (even genome wide) PPI networks to be formed. Therefore, it has become feasible and also imperative to study PPIs, as a whole, at the network level; to gain knowledge about the network topology and evolution; and to leverage the newly gained knowledge to advance the reconstruction of PPI networks, which are still quite sparse in most cases, by inferring de novo PPIs that are difficult to predict without a network context. ☐ In this dissertation, we systematically studied the PPI networks in terms of network evolution analysis and network completion with predicting de novo PPIs, and have proposed and developed a suite of novel methods from selecting evolutionary models to utilizing network evolution and topology, and leveraging multiple heterogeneous data sources for predicting PPIs. ☐ PPI evolution analysis aims at identifying the underlying evolution/growth mechanism of PPI networks, which plays a crucial role for understanding PPIs as a network system and for predicting new interactions. By exploring the state-of-the-art PPI network evolution models, we developed a novel sampling method based on Approximate Bayesian Computation and modified Differential Evolution algorithm to select the most fitting evolution model for different PPI networks. The results from our analysis based on Human and Yeast PPI networks show that different PPI networks may have different evolution/growth models: for Human PPI networks, Duplication-Attachment is the predominant mechanism while Scale-Free is the predominant mechanism for Yeast PPI networks. Equipped with the evolution models for different PPI networks, we designed a novel PPI prediction method to include the evolution information into the geometric embedding, which consequently improves the PPI prediction performance by about 15%. ☐ Despite of the rapid growth, PPI networks by and large remain incomplete and sparsely disconnected for most organisms, and therefore network completion poses a grand challenge in systems biology. Many traditional network-level PPI prediction methods use only connectivity information of existing edges to predict PPIs. However, from a PPI prediction perspective, what is particularly useful is to incorporate pairwise features for node pairs that are not currently linked by a direct edge but may become linked. In this dissertation, we developed novel PPI network inference methods that can utilize pairwise features for all node pairs, regardless whether they are currently directly connected or not. In particular, our methods can help integrate various heterogeneous feature kernels, e.g. gene co-expression kernel, protein sequence similarity kernel, etc., to build the PPI inference matrix, whose element is interpreted as probability of how likely the two corresponding proteins will interact. Specifically, we adopt two strategies to optimize weights for various feature kernels to build the kernel fusion and eventually the PPI inference matrix. Tested on Yeast PPI data and compared with two control methods, our proposed methods shows a significant improvement in performance as measured by receiver operating characteristic. ☐ Another challenge of PPI prediction is how to train prediction model over extremely sparse and disconnected PPI networks. Many of existing network level methods assume the training network should be connected. However, that assumption greatly affects their predictive power and limits the application area because current golden standard PPI networks are actually very sparse and disconnected. We developed a novel PPI prediction method based on deep learning neural network and regularized Laplacian kernel. We use the neural network to implicitly simulate and guide the evolution process of a PPI network by using rows of an ancient network as inputs and rows of the disconnected training network as labels. After the training step, an evolved PPI network whose rows are outputs of the neural network can be obtained. Then we predict PPIs by applying the regularized Laplacian kernel to the transition matrix that is built upon the evolved PPI network. The results from cross-validation experiments show that the PPI prediction accuracy for yeast data and human data can be further improved. Meanwhile, the transition matrix based on the evolved PPI network can also be used to leverage complementary information from the disconnected training network and multiple heterogeneous data sources. ☐ In sum, the work in this dissertation contributes to the understanding of PPI networks, especially, those that are large and sparse, by novel methods in selecting network evolutionary models and leveraging network topology and heterogeneous features to improve the prediction performance. We believe methods proposed in this dissertation will be useful tools to help researchers further analyze PPI data and predict PPIs.Item Protein-protein interaction prediction based on multiple kernels and partial network with linear programming(BioMed Central, 2016-08-01) Huang, Lei; Liao, Li; Wu, Cathy H.; Lei Huang, Li Liao and Cathy H. Wu; Huang, Lei; Liao, Li; Wu, Cathy H.BACKGROUND: Prediction of de novo protein-protein interaction is a critical step toward reconstructing PPI networks, which is a central task in systems biology. Recent computational approaches have shifted from making PPI prediction based on individual pairs and single data source to leveraging complementary information from multiple heterogeneous data sources and partial network structure. However, how to quickly learn weights for heterogeneous data sources remains a challenge. In this work, we developed a method to infer de novo PPIs by combining multiple data sources represented in kernel format and obtaining optimal weights based on random walk over the existing partial networks. RESULTS: Our proposed method utilizes Barker algorithm and the training data to construct a transition matrix which constrains how a random walk would traverse the partial network. Multiple heterogeneous features for the proteins in the network are then combined into the form of weighted kernel fusion, which provides a new "adjacency matrix" for the whole network that may consist of disconnected components but is required to comply with the transition matrix on the training subnetwork. This requirement is met by adjusting the weights to minimize the element-wise difference between the transition matrix and the weighted kernels. The minimization problem is solved by linear programming. The weighted kernel fusion is then transformed to regularized Laplacian (RL) kernel to infer missing or new edges in the PPI network, which can potentially connect the previously disconnected components. CONCLUSIONS: The results on synthetic data demonstrated the soundness and robustness of the proposed algorithms under various conditions. And the results on real data show that the accuracies of PPI prediction for yeast data and human data measured as AUC are increased by up to 19 % and 11 % respectively, as compared to a control method without using optimal weights. Moreover, the weights learned by our method Weight Optimization by Linear Programming (WOLP) are very consistent with that learned by sampling, and can provide insights into the relations between PPIs and various feature kernel, thereby improving PPI prediction even for disconnected PPI networks.Item Quasi-Decadal Temperature Variability in the Intermediate Layer of Subtropical South Indian Ocean During the Argo Period(Journal of Geophysical Research: Oceans, 2023-07-28) Huang, Lei; Zhuang, Wei; Wu, Zelun; Zhang,Yang; Meng, Lingsheng; Edwing, Deanna; Yan, Xiao-HaiIt has been reported that the subtropical South Indian Ocean (SIO) has been rapidly warming over the past two decades and can therefore be characterized as one of the major heat accumulators among the oceanic basins. However, this strong warming is not uniformly distributed in the vertical direction. In comparison to the decade-long warming in the upper layer (0–300 m) in 2004–2013, the intermediate layer (300–1,000 m) displays a shorter warming during 2004–2009 and an intense cooling during 2010–2016. By decomposing temperature variations into heaving and spice components, and performing a heat budget analysis, we show that temperature variations in the intermediate layer during these two periods are primarily contributed by isopycnal migrations driven by local wind forcing. Local wind change in the subtropical SIO can be explained by the Indian Ocean Dipole and El Niño–Southern Oscillation during 2004–2016, while Southern Annular Mode (SAM) favors anomalous wind change in mid-latitudes and the formation of basin-wide wind change in the SIO. Additionally, wind forcing in the Subantarctic Mode Water (SAMW) formation region, which is closely linked to the SAM, modulates the anomalous spreading of SAMW into the interior of the subtropical SIO. This, therefore, leads to the SAMW intrusion being of secondary importance to the quasi-decadal temperature variability. Our findings demonstrate the independence of wind-driven temperature changes on the quasi-decadal scale in the intermediate layer of the subtropical SIO under the overall warming background of SIO waters. Key Points - Quasi-decadal temperature variations occur in the intermediate layer (300–1,000 m) of subtropical South Indian Ocean (SIO) - Local wind-driven heaving process is the major driver, spice component arising from the Subantarctic Mode Water intrusion is of secondary importance - The local wind change in the subtropical SIO can be well explained by the combined effects of El Niño–Southern Oscillation, Indian Ocean Dipole and Southern Annular Mode Plain Language Summary Compared to the decade-long warming in the upper layer of the South Indian Ocean (SIO), which has been studied extensively, our understanding of temperature change in the intermediate layer is relatively limited. This study reveals a quasi-decadal temperature cycle in the intermediate layer of the subtropical SIO during the Argo period, which is characterized by a shorter warming period during 2004–2009 and subsequent cooling during 2010–2016. Decomposition of temperature changes suggests that this quasi-decadal temperature variability is primarily driven by the heaving component, which is tightly associated with local wind variability driven by local and remote forcings, whereas the spice change largely contributed by the SAM-related water mass transmission from higher latitudes, is of secondary importance. Thus, this study expands our knowledge of temperature variability in the SIO and demonstrates that the quasi-decadal variability of intermediate layer temperatures in the subtropical SIO serves as a crucial archive for both global and local climate change.Item Rapid Sea Level Rise in the Tropical Southwest Indian Ocean in the Recent Two Decades(Geophysical Research Letters, 2023-12-27) Huang, Lei; Zhuang, Wei; Lu, Wenfang; Zhang, Yang; Edwing, Deanna; Yan, Xiao-HaiIt has been reported that the sea level falls in the tropical Southwest Indian Ocean (SWIO) from the 1960s to the early 2000s. However, a rising trend of 4.05 ± 0.56 cm/decade has occurred during the recent two decades with our analysis showing that manometric sea level contributes 41% to this sea level rise. 30% of this rise is due to steric sea level (SSL) change in the upper 2,000 m with SSL rise in the upper 300 m of secondary importance. Conversely, thermal expansion below the thermocline (300–2,000 m), likely caused by water mass spread from the Southern Ocean, induces major contribution to SSL changes. Compared to existing studies demonstrating the contribution of thermal variations above the thermocline to sea level variability in the tropical SWIO, this study emphasizes the importance of ocean mass and deeper ocean changes in a warming climate. Key Points - Rapid sea level rise occurs in the tropical Southwest Indian Ocean (SWIO) since the early 2000s - The ocean mass addition and the upper 2,000 m ocean warming contribute significantly to the total sea level rise - The upper 2,000 m ocean warming is primarily attributed to thermal expansion below the thermocline associated with the spread of water masses Plain Language Summary Global ocean sea level change is spatially and temporally nonuniform due to oceanic and atmospheric dynamics. The tropical Southwest Indian Ocean (SWIO) experienced a sea level fall from the 1960s to the early 2000s. However, a rapid sea level rise has occurred over the last two decades in the tropical SWIO that is faster than the global average. The ocean mass increase due to extra water input leads to an essential impact on sea level rise in the tropical SWIO. Compared to previous studies demonstrating the effect of thermal expansion in the upper 300 m, this study shows larger contributions from deeper ocean (300–2,000 m) warming over the past two decades. Overall, this study highlights the importance of ocean mass and deeper water thermal structure in regulating tropical SWIO sea level rise in a changing climate, as well as the need for observations and direct assessment of the abyssal ocean beneath 2,000 m.