Browsing by Author "Zhang, Jing"
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- Community Characteristics and Trajectories of Adolescent Internalizing and Externalizing Behaviors: The Cumulative Advantage/Disadvantage and Subjective Appraisals of Social Support as MechanismsZhang, Jing (Virginia Tech, 2012-09-05)Studies examining neighborhood effects on adolescent outcomes have indicated that adolescents growing up in low-income neighborhoods are at higher risk of developing internalizing and externalizing behaviors. However, knowledge of the long-term effects of neighborhood disadvantages on internalizing and externalizing behaviors and the involved mechanisms across adolescence is limited. Using family life course theory and the cumulative advantage/disadvantage perspective, this study examined how community disadvantages in early adolescence accumulate over time to influence later internalizing and externalizing behaviors and the protective effects of subjective appraisals of social support by adolescents and their primary caregivers. I estimated a two-level growth curve model using three waves of data from the Project on Human Development in Chicago Neighborhoods (PHDCN). Results indicated subjective appraisals of social support by both adolescents and caregivers played a protective role to buffer the negative effects of community disadvantages on internalizing and externalizing behaviors across adolescence. These results provide insight for the development of intervention programs at both family and government levels to improve adolescent outcomes.
- Development of a Dihydroquinoline-Pyrazoline GluN2C/2D-Selective Negative Allosteric Modulator of the N-Methyl-d-aspartate ReceptorD'Erasmo, Michael P.; Akins, Nicholas S.; Ma, Peipei; Jing, Yao; Swanger, Sharon A.; Sharma, Savita K.; Bartsch, Perry W.; Menaldino, David S.; Arcoria, Paul J.; Bui, Thi-Thien; Pons-Bennaceur, Alexandre; Le, Phuong; Allen, James P.; Ullman, Elijah Z.; Nocilla, Kelsey A.; Zhang, Jing; Perszyk, Riley E.; Kim, Sukhan; Acker, Timothy M.; Taz, Azmain; Burton, Samantha L.; Coe, Kevin; Fritzemeier, Russell G.; Burnashev, Nail; Yuan, Hongjie; Liotta, Dennis C.; Traynelis, Stephen F. (American Chemical Society, 2023-08-11)Subunit-selective inhibition of N-methyl-d-aspartate receptors (NMDARs) is a promising therapeutic strategy for several neurological disorders, including epilepsy, Alzheimer’s and Parkinson’s disease, depression, and acute brain injury. We previously described the dihydroquinoline-pyrazoline (DQP) analogue 2a (DQP-26) as a potent NMDAR negative allosteric modulator with selectivity for GluN2C/D over GluN2A/B. However, moderate (<100-fold) subunit selectivity, inadequate cell-membrane permeability, and poor brain penetration complicated the use of 2a as an in vivo probe. In an effort to improve selectivity and the pharmacokinetic profile of the series, we performed additional structure-activity relationship studies of the succinate side chain and investigated the use of prodrugs to mask the pendant carboxylic acid. These efforts led to discovery of the analogue (S)-(−)-2i, also referred to as (S)-(−)-DQP-997-74, which exhibits >100- and >300-fold selectivity for GluN2C- and GluN2D-containing NMDARs (IC50 0.069 and 0.035 μM, respectively) compared to GluN2A- and GluN2B-containing receptors (IC50 5.2 and 16 μM, respectively) and has no effects on AMPA, kainate, or GluN1/GluN3 receptors. Compound (S)-(−)-2i is 5-fold more potent than (S)-2a. In addition, compound 2i shows a time-dependent enhancement of inhibitory actions at GluN2C- and GluN2D-containing NMDARs in the presence of the agonist glutamate, which could attenuate hypersynchronous activity driven by high-frequency excitatory synaptic transmission. Consistent with this finding, compound 2i significantly reduced the number of epileptic events in a murine model of tuberous sclerosis complex (TSC)-induced epilepsy that is associated with upregulation of the GluN2C subunit. Thus, 2i represents a robust tool for the GluN2C/D target validation. Esterification of the succinate carboxylate improved brain penetration, suggesting a strategy for therapeutic development of this series for NMDAR-associated neurological conditions.
- Fast Detection of Transformed Data LeaksShu, Xiaokui; Zhang, Jing; Yao, Danfeng (Daphne); Feng, Wu-chun (IEEE, 2016-03-01)
- muBLASTP: database-indexed protein sequence search on multicore CPUsZhang, Jing; Misra, Sanchit; Wang, Hao; Feng, Wu-chun (BioMed Central, 2016)Background: The Basic Local Alignment Search Tool (BLAST) is a fundamental program in the life sciences that searches databases for sequences that are most similar to a query sequence. Currently, the BLAST algorithm utilizes a query-indexed approach. Although many approaches suggest that sequence search with a database index can achieve much higher throughput (e.g., BLAT, SSAHA, and CAFE), they cannot deliver the same level of sensitivity as the query-indexed BLAST, i.e., NCBI BLAST, or they can only support nucleotide sequence search, e.g., MegaBLAST. Due to different challenges and characteristics between query indexing and database indexing, the existing techniques for query-indexed search cannot be used into database indexed search. Results: muBLASTP, a novel database-indexed BLAST for protein sequence search, delivers identical hits returned to NCBI BLAST. On Intel Haswell multicore CPUs, for a single query, the single-threaded muBLASTP achieves up to a 4.41-fold speedup for alignment stages, and up to a 1.75-fold end-to-end speedup over single-threaded NCBI BLAST. For a batch of queries, the multithreaded muBLASTP achieves up to a 5.7-fold speedups for alignment stages, and up to a 4.56-fold end-to-end speedup over multithreaded NCBI BLAST. Conclusions: With a newly designed index structure for protein database and associated optimizations in BLASTP algorithm, we re-factored BLASTP algorithm for modern multicore processors that achieves much higher throughput with acceptable memory footprint for the database index.
- Rapid Screening of Transformed Data Leaks with Efficient Algorithms and Parallel ComputingShu, Xiaokui; Zhang, Jing; Yao, Danfeng (Daphne); Feng, Wu-chun (ACM, 2015-03)The leak of sensitive data on computer systems poses a serious threat to organizational security. Organizations need to identify the exposure of sensitive data by screening the content in storage and transmission, i.e., to detect sensitive information being stored or transmitted in the clear. However, detecting the exposure of sensitive information is challenging due to data transformation in the content. Transformations (such as insertion, deletion) result in highly unpredictable leak patterns. Existing automata-based string matching algorithms are impractical for detecting transformed data leaks, because of its formidable complexity when modeling the required regular expressions. We design two new algorithms for detecting long and transformed data leaks. Our system achieves high detection accuracy in recognizing transformed leaks compared to the state-of-the-art inspection methods. We parallelize our prototype on graphics processing unit and demonstrate the strong scalability of our detection solution required by a sizable organization.
- Transforming and Optimizing Irregular Applications for Parallel ArchitecturesZhang, Jing (Virginia Tech, 2018-02-12)Parallel architectures, including multi-core processors, many-core processors, and multi-node systems, have become commonplace, as it is no longer feasible to improve single-core performance through increasing its operating clock frequency. Furthermore, to keep up with the exponentially growing desire for more and more computational power, the number of cores/nodes in parallel architectures has continued to dramatically increase. On the other hand, many applications in well-established and emerging fields, such as bioinformatics, social network analysis, and graph processing, exhibit increasing irregularities in memory access, control flow, and communication patterns. While multiple techniques have been introduced into modern parallel architectures to tolerate these irregularities, many irregular applications still execute poorly on current parallel architectures, as their irregularities exceed the capabilities of these techniques. Therefore, it is critical to resolve irregularities in applications for parallel architectures. However, this is a very challenging task, as the irregularities are dynamic, and hence, unknown until runtime. To optimize irregular applications, many approaches have been proposed to improve data locality and reduce irregularities through computational and data transformations. However, there are two major drawbacks in these existing approaches that prevent them from achieving optimal performance. First, these approaches use local optimizations that exploit data locality and regularity locally within a loop or kernel. However, in many applications, there is hidden locality across loops or kernels. Second, these approaches use "one-size-fits-all'' methods that treat all irregular patterns equally and resolve them with a single method. However, many irregular applications have complex irregularities, which are mixtures of different types of irregularities and need differentiated optimizations. To overcome these two drawbacks, we propose a general methodology that includes a taxonomy of irregularities to help us analyze the irregular patterns in an application, and a set of adaptive transformations to reorder data and computation based on the characteristics of the application and architecture. By extending our adaptive data-reordering transformation on a single node, we propose a data-partitioning framework to resolve the load imbalance problem of irregular applications on multi-node systems. Unlike existing frameworks, which use "one-size-fits-all" methods to partition the input data by a single property, our framework provides a set of operations to transform the input data by multiple properties and generates the desired data-partitioning codes by composing these operations into a workflow.