Succinct Representations in Collaborative Filtering: A Case Study using Wavelet Tree on 1,000 Cores

User-Item (U-I) matrix has been used as the dominant data infrastructure of Collaborative Filtering (CF). To reduce space consumption in runtime and storage, caused by data sparsity and growing need to accommodate side information in CF design, one needs to go beyond the UI Matrix. In this paper, we took a case study of Succinct Representations in Collaborative Filtering, rather than using a U-I Matrix. Our key insight is to introduce Succinct Data Structures as a new infrastructure of CF. Towards this, we implemented a User-based K-Nearest-Neighbor CF prototype via Wavelet Tree, by first designing a Accessible Compressed Documents (ACD) to compress U-I data in Wavelet Tree, which is efficient in both storage and runtime. Then, we showed that ACD can be applied to develop an efficient intersection algorithm without decompression, by taking advantage of ACD’s characteristics. We evaluated our design on 1,000 cores of Tianhe-II supercomputer, with one of the largest public data set ml-20m. The results showed that our prototype could achieve 3.7 minutes on average to deliver the results

Sulforaphane Protects against Cardiovascular Disease via Nrf2 Activation

Cardiovascular disease (CVD) causes an unparalleled proportion of the global burden of disease and will remain the main cause of mortality for the near future. Oxidative stress plays a major role in the pathophysiology of cardiac disorders. Several studies have highlighted the cardinal role played by the overproduction of reactive oxygen or nitrogen species in the pathogenesis of ischemic myocardial damage and consequent cardiac dysfunction. Isothiocyanates (ITC) are sulfur-containing compounds that are broadly distributed among cruciferous vegetables. Sulforaphane (SFN) is an ITC shown to possess anticancer activities by both in vivo and epidemiological studies. Recent data have indicated that the beneficial effects of SFN in CVD are due to its antioxidant and anti-inflammatory properties. SFN activates NF-E2-related factor 2 (Nrf2), a basic leucine zipper transcription factor that serves as a defense mechanism against oxidative stress and electrophilic toxicants by inducing more than a hundred cytoprotective proteins, including antioxidants and phase II detoxifying enzymes. This review will summarize the evidence from clinical studies and animal experiments relating to the potential mechanisms by which SFN modulates Nrf2 activation and protects against CVD

Rapid Neutralization Testing System for Zika Virus Based on an Enzyme-Linked Immunospot Assay.

Zika virus (ZIKV) is a mosquito-borne flavivirus that has been associated with neuropathology in fetuses and adults, imposing a serious health concern. Therefore, the development of a vaccine is a global health priority. Notably, neutralization tests have a significant value for vaccine development and virus diagnosis. The cytopathic effect (CPE)-based neutralization test (Nt-CPE) is a common neutralization method for ZIKV. However, this method has some drawbacks, such as being time-consuming and labor-intensive and having low-throughput, which precludes its application in the detection of large numbers of specimens. To improve this problem, we developed a neutralization test based on an enzyme-linked immunospot assay (Nt-ELISPOT) for ZIKV and performed the assay in a 96-well format. A monoclonal antibody (mAb), 11C11, with high affinity and reactivity to ZIKV was used to detect ZIKV-infected cells. To optimize this method, the infectious dose of ZIKV was set at a multiplicity of infection (MOI) of 0.0625, and a detection experiment was performed after incubating for 24 h. As a result, under these conditions, the Nt-ELISPOT had good consistency with the traditional Nt-CPE to measure neutralizing titers of sera and neutralizing antibodies. Additionally, three neutralizing antibodies against ZIKV were screened by this method. Overall, we successfully developed an efficient neutralization test for ZIKV that is high-throughput and rapid. This Nt-ELISPOT can potentially be applied to detecting neutralizing titers of large numbers of specimens in vaccine evaluation and neutralizing antibody screening for ZIKV

Knowledge Extraction and Retrieval for Domain-Specific Documents

With the overwhelming amount of textual data created by more and more domain based information systems, it has been a significant challenge to identify the precise piece of relevant knowledge “nugget” from the tremendous amount of noisy, relevant and irrelevant data, including techno-geek and gobbledygook. When dealing with domain-specific text, many existing text mining methods fail to produce satisfying results, because they are unable to handle complex domain languages, understand semantic meaning, model latent business processes, or leverage domain resources and expertise. This motivates us to develop novel, effective extraction models and analyses to identify desired information from domain-specific documents, as well as associated retrieval models and analyses. In the dissertation, we study this research topic in three different domains, and approach the challenges in domain-specific text mining from multiple perspectives.In an enterprise service center, accurate and timely delivery of knowledge to service representatives becomes the cornerstone for delivering efficient customer service. There are two main steps in achieving this objective. The first step concerns efficient text mining to extract information of interest from the very long service request (SR) documents in the historical database. The second step concerns matching new service requests with previously solved service requests. Both lead to efficiencies by minimizing time spent by service personnel in accessing knowledge. In this scenario we present our text analytics system, the Service Request Analyzer and Recommender (SRAR), which is designed to improve the productivity in an enterprise service center for computer networking diagnostics and support. SRAR unifies a text preprocessor, a hierarchical classifier, and a service request recommender, to deliver critical, pertinent, and categorized knowledge for improved service efficiency. The novel feature we report here is identifying the components of the diagnostic process underlying the creation of the original text documents. This identification is crucial in the successful design and prototyping of SRAR and its hierarchical classifier elements. Equally, the use of domain knowledge and human expertise to generate features are indispensable components in improving the accuracy of knowledge extraction and retrieval. The empirical evaluation demonstrates the effectiveness of our framework and algorithms. We observe significant improvements of service time responsiveness during knowledge extraction and retrieval in the networking service center context at Cisco.In the healthcare domain, crucial information on a patient’s physical or mental conditions is provided by mentions of disorders in clinical notes. However, there are many surface forms of the same disorder concept documented in clinical text. Some are even recorded disjointedly, briefly, or intuitively. In this study, we propose a synergistic approach to extracting disorder concepts and variants. We exploit rich features to predict mention spans using supervised learning algorithms, including support vector machines (SVM). In addition to the explicit bag-of-words, orthographic, and morphologic features, we investigate semantic, syntactic, and sequential features for better capturing implicit relationships among words. More specifically, the two types of semantic features we propose based on medical ontology prove very effective. We supplement SVMs with a rule-based annotator and an unsupervised NLP system to improve the prediction accuracy and the generalization capability of the system. Ultimately, this synergistic system is able to produce state-of-the-art results on public challenge data sets.In the biomedical domain, we define the notion of concept, extract all types of concepts from biomedical documents, and design a concept-based information retrieval framework. Using this framework, we transform documents and queries from term space into concept space, perform semantic analysis among concepts, and estimate a concept-based relevance model for improved document retrieval. Our approach has three advantages. First, it only assumes independence between concepts, so is able to keep the strong dependencies between the words of a concept. Second, it unifies synonyms or different surface forms of a concept, leading to reduced dimensionality of the space, increased sample size of a concept, and consequently more accurate and reliable estimates of the relevance. Third, when domain resources are available, our approach enables the semantic analysis of query concepts, and thus identifies concepts related to the query, from which a more accurate distribution of relevance can be estimated. We compare our approach with three benchmark retrieval models on different types of data collections. The proposed approach demonstrates consistent and statistically significant improvements across collections, outperforming top benchmark conceptual language models by at least 9% and up to 20% on a number of metrics

Knowledge Extraction and Retrieval for Domain-Specific Documents

With the overwhelming amount of textual data created by more and more domain based information systems, it has been a significant challenge to identify the precise piece of relevant knowledge “nugget” from the tremendous amount of noisy, relevant and irrelevant data, including techno-geek and gobbledygook. When dealing with domain-specific text, many existing text mining methods fail to produce satisfying results, because they are unable to handle complex domain languages, understand semantic meaning, model latent business processes, or leverage domain resources and expertise. This motivates us to develop novel, effective extraction models and analyses to identify desired information from domain-specific documents, as well as associated retrieval models and analyses. In the dissertation, we study this research topic in three different domains, and approach the challenges in domain-specific text mining from multiple perspectives.In an enterprise service center, accurate and timely delivery of knowledge to service representatives becomes the cornerstone for delivering efficient customer service. There are two main steps in achieving this objective. The first step concerns efficient text mining to extract information of interest from the very long service request (SR) documents in the historical database. The second step concerns matching new service requests with previously solved service requests. Both lead to efficiencies by minimizing time spent by service personnel in accessing knowledge. In this scenario we present our text analytics system, the Service Request Analyzer and Recommender (SRAR), which is designed to improve the productivity in an enterprise service center for computer networking diagnostics and support. SRAR unifies a text preprocessor, a hierarchical classifier, and a service request recommender, to deliver critical, pertinent, and categorized knowledge for improved service efficiency. The novel feature we report here is identifying the components of the diagnostic process underlying the creation of the original text documents. This identification is crucial in the successful design and prototyping of SRAR and its hierarchical classifier elements. Equally, the use of domain knowledge and human expertise to generate features are indispensable components in improving the accuracy of knowledge extraction and retrieval. The empirical evaluation demonstrates the effectiveness of our framework and algorithms. We observe significant improvements of service time responsiveness during knowledge extraction and retrieval in the networking service center context at Cisco.In the healthcare domain, crucial information on a patient’s physical or mental conditions is provided by mentions of disorders in clinical notes. However, there are many surface forms of the same disorder concept documented in clinical text. Some are even recorded disjointedly, briefly, or intuitively. In this study, we propose a synergistic approach to extracting disorder concepts and variants. We exploit rich features to predict mention spans using supervised learning algorithms, including support vector machines (SVM). In addition to the explicit bag-of-words, orthographic, and morphologic features, we investigate semantic, syntactic, and sequential features for better capturing implicit relationships among words. More specifically, the two types of semantic features we propose based on medical ontology prove very effective. We supplement SVMs with a rule-based annotator and an unsupervised NLP system to improve the prediction accuracy and the generalization capability of the system. Ultimately, this synergistic system is able to produce state-of-the-art results on public challenge data sets.In the biomedical domain, we define the notion of concept, extract all types of concepts from biomedical documents, and design a concept-based information retrieval framework. Using this framework, we transform documents and queries from term space into concept space, perform semantic analysis among concepts, and estimate a concept-based relevance model for improved document retrieval. Our approach has three advantages. First, it only assumes independence between concepts, so is able to keep the strong dependencies between the words of a concept. Second, it unifies synonyms or different surface forms of a concept, leading to reduced dimensionality of the space, increased sample size of a concept, and consequently more accurate and reliable estimates of the relevance. Third, when domain resources are available, our approach enables the semantic analysis of query concepts, and thus identifies concepts related to the query, from which a more accurate distribution of relevance can be estimated. We compare our approach with three benchmark retrieval models on different types of data collections. The proposed approach demonstrates consistent and statistically significant improvements across collections, outperforming top benchmark conceptual language models by at least 9% and up to 20% on a number of metrics

Increasing Minority Recall Support Vector Machine Model for Imbalanced Data Classification

Imbalanced data classification is gaining importance in data mining and machine learning. The minority class recall rate requires special treatment in fields such as medical diagnosis, information security, industry, and computer vision. This paper proposes a new strategy and algorithm based on a cost-sensitive support vector machine to improve the minority class recall rate to 1 because the misclassification of even a few samples can cause serious losses in some physical problems. In the proposed method, the modification employs a margin compensation to make the margin lopsided, enabling decision boundary drift. When the boundary reaches a certain position, the minority class samples will be more generalized to achieve the requirement of a recall rate of 1. In the experiments, the effects of different parameters on the performance of the algorithm were analyzed, and the optimal parameters for a recall rate of 1 were determined. The experimental results reveal that, for the imbalanced data classification problem, the traditional definite cost classification scheme and the models classified using the area under the receiver operating characteristic curve criterion rarely produce results such as a recall rate of 1. The new strategy can yield a minority recall of 1 for imbalanced data as the loss of the majority class is acceptable; moreover, it improves the g-means index. The proposed algorithm provides superior performance in minority recall compared to the conventional methods. The proposed method has important practical significance in credit card fraud, medical diagnosis, and other areas

Effect of XingPiJieYu decoction on spatial learning and memory and cAMP-PKA-CREB-BDNF pathway in rat model of depression through chronic unpredictable stress

AbstractBackgroundDepression is a mental disorder characterized by a pervasive low mood and loss of pleasure or interest in usual activities, and often results in cognitive dysfunction. The disturbance of cognitive processes associated with depression, especially the impairment of learning and memory, exacerbates illness and increases recurrence of depression. XingPiJieYu (XPJY) is one of the most widely clinical formulas of traditional Chinese medicine (TCM) and can improve the symptoms of depression, including learning and memory. However, its regulatory effects haven’t been comprehensively studied so far. Recently, some animal tests have indicated that the cyclic adenosine monophosphate (cAMP)-protein kinase A (PKA)-cAMP response element-binding protein (CREB)-brain derived neurotrophic factor (BDNF) signaling pathway in hippocampus is closely related to depression and the pathogenesis of cognitive function impairments. The present study was performed to investigate the effect and mechanism of XPJY on depression and learning and memory in animal model.MaterialsThe rat model of depression was established by chronic unpredictable stress (CUS) for 21?days. The rats were randomly divided into six groups: control group, CUS group, CUS?+?XPJY (1.4?g/kg, 0.7?g/kg and 0.35?g/kg) groups, and CUS?+?sertraline (10?mg/kg) group. The sucrose preference, open field exploration and Morris water maze (MWM) were tested. The expression of cAMP, CREB, PKA and BDNF protein in hippocampus was examined with Elisa and Western Blot. The mRNA level of CREB and BDNF in hippocampus was measured with PCR.ResultsThe results demonstrated that rats subjected to CUS exhibited decreases in sucrose preference, total ambulation, percentage of central ambulation, rearing in the open field test and spatial performance in the MWM. CUS reduced the expression of cAMP, PKA, CREB and BDNF in hippocampus of model rats. These effects could be reversed by XPJY.ConclusionThe results indicated that XPJY can improve depression and related learning and memory and the effect of XPJY is partly exerted through the cAMP-PKA-CREB-BDNF signaling pathway

Comparison of ultrasonic shear wave elastography, AngioPLUS planewave ultrasensitive imaging, and optimized high-resolution magnetic resonance imaging in evaluating carotid plaque stability

Objective This study aimed to compare the efficiency of evaluating carotid plaque stability using ultrasonic shear wave elastography (SWE), AngioPLUS planewave ultrasensitive imaging (AP), and optimized high-resolution magnetic resonance imaging (MRI). Methods A total of 100 patients who underwent carotid endarterectomy at our hospital from October 2019 to August 2022 were enrolled. Based on the final clinical diagnosis, these patients were divided into vulnerable (n = 62) and stable (n = 38) plaque groups. All patients were examined using ultrasound SWE, AP, and optimized high-resolution MRI before surgery. The clinical data and ultrasound characteristics of patients of the two groups were compared. Considering the final clinical diagnosis as the gold standard, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of SWE, AP, high-resolution MRI, and the final clinical diagnosis of vulnerable plaque were calculated. Pearson’s correlation test was used to analyze the correlations of AP, SWE, and MRI results with the grading results of carotid artery stenosis. Results Statistically significant differences were noticed in terms of the history of smoking and coronary heart disease, plaque thickness, surface rules, calcified nodules, low echo area, and the degree of carotid artery stenosis between the two groups (P < 0.05). Considering the final clinical diagnosis as the gold standard, the sensitivity, specificity, PPV, and NPV of SWE-based detection of carotid artery vulnerability were 87.10% (54/62), 76.32% (29/38), 85.71% (54/63) and 78.38% (29/37), respectively, showing a general consistency with the final clinical results (Kappa = 0.637, P < 0.05). Considering the final clinical diagnosis as the gold standard, the sensitivity, specificity, PPV and NPV of AP-based detection of carotid artery vulnerability were 93.55% (58/62), 84.21% (32/38), 90.63% (58/64), and 88.89% (32/36), respectively, which agreed with the final clinical detection results (Kappa = 0.786, P < 0.05). Considering the final clinical diagnosis as the gold standard, the sensitivity, specificity, PPV and NPV of high-resolution MRI-based detection of carotid artery vulnerability were 88.71% (55/62), 78.95% (30/38), 87.30% (55/63), and 81.08% (30/37), respectively, showing consistency with the final clinical results (Kappa = 0.680, P < 0.05). AP, SWE, and MRI results were positively correlated with the results of carotid artery stenosis grading (P < 0.05). Conclusion AP technology is a non-invasive, inexpensive, and highly sensitive method to evaluate the stability of carotid artery plaques. This method can dynamically display the flow of blood in new vessels of plaque in real time and provide a reference for clinical diagnosis and treatment

Thermal Infrared Imagery Integrated with Multi-Field Information for Characterization of Pile-Reinforced Landslide Deformation

Physical model testing can replicate the deformation process of landslide stabilizing piles and analyze the pile-landslide interaction with multiple field information, thoroughly demonstrating its deformation and failure mechanism. In this paper, an integrated monitoring system was introduced. The instrumentation used included soil pressure cells, thermal infrared (TIR) imagery, 3D laser scanner, and digital photography. In order to precisely perform field information analysis, an index was proposed to analyze thermal infrared temperature captured by infrared thermography; the qualitative relationship among stress state and deformation as well as thermal infrared temperature is analyzed. The results indicate that the integrated monitoring system is expected to be useful for characterizing the deformation process of a pile-reinforced landslide. Difference value of TIR temperature ( T I R m ) is a useful indicator for landslide detection, and its anomalies can be selected as a precursor to landslide deformation