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Synthetic jet has shown promising applications in active flow control in recent years. Tomographic particle image velocity (Tomo-PIV) is an emerging flow field measurement, which can obtain three-dimensional flow field with a high spatial and temporal resolution to help us better understand the evolution of synthetic jet. Therefore, this paper uses a time-resolved Tomo-PIV system to measure and analyze the three-dimensional flow evolution of a submerged two-orifice synthetic jet. The measurement is conducted for stroke length L = 1.9, 2.6, and 3.0 while keeping the orifice diameter D and the distance between the two orifices s constant. Research results reveal that the three-dimensional flow evolution can be described as follows: first, two independent vortex rings form at the outlet of the orifice plate; next, these two vortex rings interact with each other and merge into a non-circular vortex ring, which then undergoes axis switching and vortex reconnection, with the tendency of splitting into two vortex rings. Furthermore, the position where the axis-switching finishes coincides with the location of the maximum mean streamwise velocity. When the stroke length of the synthetic jet is 2.6, the non-circular vortex ring undergoes a collision of vortices after the completion of the axis switching, resulting in the phenomenon of vortex ring bifurcation. However, the non-circular vortex ring fails to split into two vortex rings for stroke lengths of 1.9 and 3. Moreover, the entrainment of the synthetic jet increases with the increase in stroke length. [ABSTRACT FROM AUTHOR]

Anomalies in time series, also called "discord," are the abnormal subsequences. The occurrence of anomalies in time series may indicate that some faults or disease will occur soon. Therefore, development of novel computational approaches for anomaly detection (discord search) in time series is of great significance for state monitoring and early warning of real-time system. Previous studies show that many algorithms were successfully developed and were used for anomaly classification, e.g., health monitoring, traffic detection, and intrusion detection. However, the anomaly detection of time series was not well studied. In this paper, we proposed a long short-term memory- (LSTM-) based anomaly detection method (LSTMAD) for discord search from univariate time series data. LSTMAD learns the structural features from normal (nonanomalous) training data and then performs anomaly detection via a statistical strategy based on the prediction error for observed data. In our experimental evaluation using public ECG datasets and real-world datasets, LSTMAD detects anomalies more accurately than other existing approaches in comparison. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) is the most commonly diagnosed malignancy and the second leading cause of cancer-related death in American men. Androgen deprivation therapy (ADT) has become a standard treatment strategy for advanced PCa. Although a majority of patients initially respond to ADT well, most of them will eventually develop castration-resistant PCa (CRPC). Previous studies suggest that ADT-induced changes in the immune microenvironment (mE) in PCa might be responsible for the failures of various therapies. However, the role of the immune system in CRPC development remains unclear. To systematically understand the immunity leading to CRPC progression and predict the optimal treatment strategy in silico, we developed a 3D Hybrid Multi-scale Model (HMSM), consisting of an ODE system and an agent-based model (ABM), to manipulate the tumor growth in a defined immune system. Based on our analysis, we revealed that the key factors (e.g. WNT5A, TRAIL, CSF1, etc.) mediated the activation of PC-Treg and PC-TAM interaction pathways, which induced the immunosuppression during CRPC progression. Our HMSM model also provided an optimal therapeutic strategy for improving the outcomes of PCa treatment. [ABSTRACT FROM AUTHOR]

The extreme learning machine (ELM) is famous for its single hidden-layer feed-forward neural network which results in much faster learning speed comparing with traditional machine learning techniques. Moreover, extensions of ELM achieve stable classification performances for imbalanced data. In this paper, we introduce a hybrid method combining the extended Kalman filter (EKF) with cost-sensitive dissimilar ELM (CS-D-ELM). The raw data are preprocessed by EKF to produce inputs for the CS-D-ELM classifier. Experimental results show that the proposed method is more suitable for real-time fault diagnosis of air handling units than traditional approaches. [ABSTRACT FROM AUTHOR]

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology. [ABSTRACT FROM AUTHOR]

Automatic, accurate and online fault detection of heating ventilation air conditioning (HVAC) subsystems, such as chillers, is highly demanded in building management system (BMS) to prevent energy waste and high maintenance cost. However, most fault detection techniques require rich faulty training data which is usually unavailable. In this study, a novel hybrid method is proposed to detect faults for chiller subsystems without any faulty training data available, i.e. by training the normal data only. A hybrid feature selection algorithm is applied to the chiller dataset collected by ASHRAE project 1043-RP to select the most significant feature variables. An online classification framework is introduced by combining an Extended Kalman Filter (EKF) model and a recursive one-class support vector machine (ROSVM). Experiment results show that the proposing algorithm detects typical chiller faults with high accuracy rates and requires less feature variables compared to existing works. [ABSTRACT FROM AUTHOR]

• Effectively detect anomalies in multivariate time series (MTS) is challenging. • In practical applications, few algorithms are widely used. • A space-embedding strategy for anomaly detection (SES-AD) in MTS was developed. • SES-AD model works efficiently in benchmark datasets and real datasets. • SES-AD outperformed other classical approaches. Anomaly detection of time series has always been a hot topic in academia and industry. However, many existing multivariant time series methods suffer from common challenges due to increased dimensionality. In this study, we developed a space-embedding strategy for anomaly detection in multivariant time series (SES-AD). As a hybrid model, SES-AD did not directly search the discords from the original time series, but projected the raw sequence to a lower dimensional space so that the significant abrupt change points in the new space can be easily captured from the dissimilarity vector. Finally, the potential abnormities were determined by a statistical strategy. To verify the performance of our method, SES-AD was applied to an extensive number of multivariate time series. The experimental results suggest that SES-AD is more efficient than five existing approaches. Overall, the SES-AD model is suitable for solving anomaly detection for high-dimensional datasets and guarantees computational effectiveness and accuracy. [ABSTRACT FROM AUTHOR]

Background. Hepatocellular carcinoma (HCC) is a highly aggressive malignancy. Traditional Chinese Medicine (TCM), with the characteristics of syndrome differentiation, plays an important role in the comprehensive treatment of HCC. This study aims to develop a nonnegative matrix factorization- (NMF-) based feature selection approach (NMFBFS) to identify potential clinical symptoms for HCC patient stratification. Methods. The NMFBFS approach consisted of three major steps. Firstly, statistics-based preliminary feature screening was designed to detect and remove irrelevant symptoms. Secondly, NMF was employed to infer redundant symptoms. Based on NMF-derived basis matrix, we defined a novel similarity measurement of intersymptoms. Finally, we converted each group of redundant symptoms to a new single feature so that the dimension was further reduced. Results. Based on a clinical dataset consisting of 407 patient samples of HCC with 57 symptoms, NMFBFS approach detected 8 irrelevant symptoms and then identified 16 redundant symptoms within 6 groups. Finally, an optimal feature subset with 39 clinical features was generated after compressing the redundant symptoms by groups. The validation of classification performance shows that these 39 features obviously improve the prediction accuracy of HCC patients. Conclusions. Compared with other methods, NMFBFS has obvious advantages in identifying important clinical features of HCC. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) family, which is involved in cancer development, proliferation, apoptosis, and drug resistance, is a group of noncoding RNAs that modulate the expression of oncogenes and antioncogenes. Doxorubicin is an active cytotoxic agent for breast cancer treatment, but the acquisition of doxorubicin resistance is a common and critical limitation to cancer therapy. The aim of this study was to investigate whether miR-193b mediated the resistance of breast cancer cells to doxorubicin by targeting myeloid cell leukemia-1 (MCL-1). In this study, we found that miR-193b levels were significantly lower in doxorubicin-resistant MCF-7 (MCF-7/DOXR) cells than in the parental MCF-7 cells. We observed that exogenous miR-193b significantly suppressed the ability of MCF-7/DOXR cells to resist doxorubicin. It demonstrated that miR-193b directly targeted MCL-1 3'-UTR (3'-Untranslated Regions). Further studies indicated that miR-193b sensitized MCF-7/DOXR cells to doxorubicin through a mechanism involving the downregulation of MCL-1. Together, our findings provide evidence that the modulation of miR-193b may represent a novel therapeutic target for the treatment of breast cancer. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a LSSVR-based time series method (LTSM) to predict dynamic lateral deformation of retaining structure and ground surface settlement in deep foundation pit engineering. After reconstructing phase space, time-varying lateral displacement of each observation point on retaining structure can be predicted using its historic unary time series data. And then, the ground settlement nearby the deep foundation pit can be predicted using all the observed values of lateral deformation which were collected at the same time from different depths on the retaining structure. The experimental results show that LTSM achieved a high accuracy when predicting the lateral deformation and ground settlement. [Copyright &y& Elsevier]

The Library of Integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction that occur when cells are exposed to a variety of perturbations. It is helpful for understanding cell pathways and facilitating drug discovery. Here, we developed a novel approach to infer cell-specific pathways and identify a compound's effects using gene expression and phosphoproteomics data under treatments with different compounds. Gene expression data were employed to infer potential targets of compounds and create a generic pathway map. Binary linear programming (BLP) was then developed to optimize the generic pathway topology based on the mid-stage signaling response of phosphorylation. To demonstrate effectiveness of this approach, we built a generic pathway map for the MCF7 breast cancer cell line and inferred the cell-specific pathways by BLP. The first group of 11 compounds was utilized to optimize the generic pathways, and then 4 compounds were used to identify effects based on the inferred cell-specific pathways. Cross-validation indicated that the cell-specific pathways reliably predicted a compound's effects. Finally, we applied BLP to re-optimize the cell-specific pathways to predict the effects of 4 compounds (trichostatin A, MS-275, staurosporine, and digoxigenin) according to compound-induced topological alterations. Trichostatin A and MS-275 (both HDAC inhibitors) inhibited the downstream pathway of HDAC1 and caused cell growth arrest via activation of p53 and p21; the effects of digoxigenin were totally opposite. Staurosporine blocked the cell cycle via p53 and p21, but also promoted cell growth via activated HDAC1 and its downstream pathway. Our approach was also applied to the PC3 prostate cancer cell line, and the cross-validation analysis showed very good accuracy in predicting effects of 4 compounds. In summary, our computational model can be used to elucidate potential mechanisms of a compound's efficacy. [ABSTRACT FROM AUTHOR]

• Inhibitory effects of eight hydrocolloids on the formation of PhIP were investigated. • 1% (w/w) of κ-carrageenan caused a decreased formation of PhIP by around 90 %. • κ-Carrageenan inhibited PhIP formation via trapping both creatinine and phenylacetaldehyde. 2-amino-1-methyl-6-phenylimidazo[4,5- b ]pyridine (PhIP) is a mutagen and a rodent carcinogen mainly formed in thermally processed muscle foods. Hydrocolloids are widely used as thickeners, gelling agents and stabilizers to improve food quality in the food industry. In this study, the inhibitory effects of eight hydrocolloids on the formation of PhIP were investigated in both chemical models and beef patties. 1% (w/w) of carboxymethylcellulose V, κ-carrageenan, alginic acid, and pectin significantly reduced PhIP formation by 53 %, 54 %, 48 %, and 47 %, respectively in chemical models. In fried beef patties, κ-carrageenan appeared to be most capable of inhibiting PhIP formation among the eight tested hydrocolloids. 1% (w/w) of κ-carrageenan caused a decreased formation of PhIP by 90 %. 1% (w/w) of κ-carrageenan also significantly reduced the formation of other heterocyclic aromatic amines including MeIQx and 4,8-DiMeIQx by 64 % and 48 %, respectively in fried beef patties. Further mechanism study showed that κ-carrageenan addition decreased the PhIP precursor creatinine residue and reduced the content of Maillard reaction intermediates including phenylacetaldehyde and aldol condensation product in the chemical model. κ-Carrageenan may inhibit PhIP formation via trapping both creatinine and phenylacetaldehyde. The structures of adducts formed between κ-carrageenan and creatinine and κ-carrageenan and phenylacetaldehyde merits further study. [ABSTRACT FROM AUTHOR]

Efficient and accurate recognition of protein–DNA interactions is vital for understanding the molecular mechanisms of related biological processes and further guiding drug discovery. Although the current experimental protocols are the most precise way to determine protein–DNA binding sites, they tend to be labor-intensive and time-consuming. There is an immediate need to design efficient computational approaches for predicting DNA-binding sites. Here, we proposed ULDNA, a new deep-learning model, to deduce DNA-binding sites from protein sequences. This model leverages an LSTM-attention architecture, embedded with three unsupervised language models that are pre-trained on large-scale sequences from multiple database sources. To prove its effectiveness, ULDNA was tested on 229 protein chains with experimental annotation of DNA-binding sites. Results from computational experiments revealed that ULDNA significantly improves the accuracy of DNA-binding site prediction in comparison with 17 state-of-the-art methods. In-depth data analyses showed that the major strength of ULDNA stems from employing three transformer language models. Specifically, these language models capture complementary feature embeddings with evolution diversity, in which the complex DNA-binding patterns are buried. Meanwhile, the specially crafted LSTM-attention network effectively decodes evolution diversity-based embeddings as DNA-binding results at the residue level. Our findings demonstrated a new pipeline for predicting DNA-binding sites on a large scale with high accuracy from protein sequence alone. [ABSTRACT FROM AUTHOR]

Previous studies showed that individuals' traits could be used to explain the similarity of behavioral patterns across different occasions. Such studies have typically focused on personality traits, and have not been extended to psychological needs. Our study used a large dataset of 1,715,078 anonymous users' App usage records to examine whether the individual's needs-based profiles of App usage were consistent across different situations (as indexed by categories of App functions). Results showed a high level of consistency across situations in a user's choice of Apps based on the needs the Apps could satisfy. These results provide clear evidence in support of cross-category App recommendation systems. [ABSTRACT FROM AUTHOR]

Highlights • The work introduces a novel semi-supervised approach to detect and diagnose faults for AHUs. • 80% accuracy rate is reached using a training set with 8000 normal samples and only around 30 samples for each fault type. • This work addresses the tradeoff between the initial number of faulty samples and the final classification accuracy. • This work addresses the tradeoff between the initial number of faulty samples and the computational cost. • This work addresses the tradeoff between the threshold of confidently levels and the final classification accuracy. Abstract Modern data-driven fault detection and diagnosis (FDD) techniques show impressive high diagnostic accuracy in recognizing various air handling units (AHUs) faults. Most existing data-driven FDD approaches simply adopt supervised machine learning techniques that presume the availability of a sufficient number of faulty training data samples. However, in real-world AHU FDD scenarios, the number of faulty training samples is not enough to support supervised learning methods, since faults are usually fixed within short periods of time. In this study, a semi-supervised learning FDD framework is proposed to deal with the above problem. By using the proposed framework, the training pool can be enriched by iteratively inserting confidently labeled testing samples, which mimics the scenario of detecting faults the earliest possible. Furthermore, the proposed framework can be easily extended with various kinds of state-of-art classifiers. Three important tradeoffs are observed through a series of experiments. With a reasonably small number of faulty training data samples available, the performance of the proposed semi-supervised learning technique is comparable to the classic supervised FDD methods. [ABSTRACT FROM AUTHOR]

Most life activities in organisms are regulated through protein complexes, which are mainly controlled via Protein–Protein Interactions (PPIs). Discovering new interactions between proteins and revealing their biological functions are of great significance for understanding the molecular mechanisms of biological processes and identifying the potential targets in drug discovery. Current experimental methods only capture stable protein interactions, which lead to limited coverage. In addition, expensive cost and time consuming are also the obvious shortcomings. In recent years, various computational methods have been successfully developed for predicting PPIs based only on protein homology, primary sequences of protein or gene ontology information. Computational efficiency and data complexity are still the main bottlenecks for the algorithm generalization. In this study, we proposed a novel computational framework, HNSPPI, to predict PPIs. As a hybrid supervised learning model, HNSPPI comprehensively characterizes the intrinsic relationship between two proteins by integrating amino acid sequence information and connection properties of PPI network. The experimental results show that HNSPPI works very well on six benchmark datasets. Moreover, the comparison analysis proved that our model significantly outperforms other five existing algorithms. Finally, we used the HNSPPI model to explore the SARS-CoV-2-Human interaction system and found several potential regulations. In summary, HNSPPI is a promising model for predicting new protein interactions from known PPI data. [ABSTRACT FROM AUTHOR]

Interaction of myeloma cells with osteoclasts (OC) can enhance tumor cell expansion through activation of complex signaling transduction networks. Both cells reside in the bone marrow, a hypoxic niche. How OC-myeloma interaction in a hypoxic environment affects myeloma cell growth and their response to drug treatment is poorly understood. In this study, we i) cultured myeloma cells in the presence/absence of OCs under normoxia and hypoxia conditions and did protein profiling analysis using reverse phase protein array; ii) computationally developed an Integer Linear Programming approach to infer OC-mediated myeloma cell-specific signaling pathways under normoxic and hypoxic conditions. Our modeling analysis indicated that in the presence OCs, (1) cell growth-associated signaling pathways, PI3K/AKT and MEK/ERK, were activated and apoptotic regulatory proteins, BAX and BIM, down-regulated under normoxic condition; (2) β1 Integrin/FAK signaling pathway was activated in myeloma cells under hypoxic condition. Simulation of drug treatment effects by perturbing the inferred cell-specific pathways showed that targeting myeloma cells with the combination of PI3K and integrin inhibitors potentially (1) inhibited cell proliferation by reducing the expression/activation of NF-κB, S6, c-Myc, and c-Jun under normoxic condition; (2) blocked myeloma cell migration and invasion by reducing the expression of FAK and PKC under hypoxic condition. [ABSTRACT FROM AUTHOR]

This study proposes a novel approach, namely, skin flow complex algorithm (SFCA), to decompose the molecular skin surface into topological disks. The main contributions of SFCA include providing a simple decomposition and fast calculation of the molecular skin surface. Unlike most existing works which partition the molecular skin surface into sphere and hyperboloid patches, SFCA partitions the molecular skin surface into triangular quadratic patches and rectangular quadratic patches. Each quadratic patch is proven to be a topological disk and rendered by a rational Bézier patch. The skin surface is constructed by assembling all rational Bézier patches. Experimental results show that the SFCA is more efficient than most existing algorithms, and produces a triangulation of molecular skin surface which is decomposable, deformable, smooth, watertight and feature-preserved. [ABSTRACT FROM AUTHOR]

One avian H3N2 influenza virus, providing its PB1 and HA segments, reassorted with one human H2N2 virus and caused a pandemic outbreak in 1968, killing over 1 million people. After its introduction to humanity, the pandemic H3N2 virus continued adapting to humans and has resulted in epidemic outbreaks every influenza season. To understand the functional roles of the originally avian PB1 gene in the circulating strains of human H3N2 influenza viruses, we analyzed the evolution of the PB1 gene in all human H3N2 isolates from 1968 to 2019. We found several specific residues dramatically changed around 2002–2009 and remained stable through to 2019. Then, we verified the functions of these PB1 mutations in the genetic background of the early pandemic virus, A/Hong Kong/1/1968(HK/68), as well as a recent seasonal strain, A/Jiangsu/34/2016 (JS/16). The PB1 V709I or PB1 V113A/K586R/D619N/V709I induced higher polymerase activity of HK/68 in human cells. And the four mutations acted cooperatively that had an increased replication capacity in vitro and in vivo at an early stage of infection. In contrast, the backward mutant, A113V/R586K/N619D/I709V, reduced polymerase activity in human cells. The PB1 I709V decreased viral replication in vitro, but this mutant only showed less effect on mice infection experiment, which suggested influenza A virus evolved in human host was not always consisted with highly replication efficiency and pathogenicity in other mammalian host. Overall, our results demonstrated that the identified PB1 mutations contributed to the viral evolution of human influenza A (H3N2) viruses. [ABSTRACT FROM AUTHOR]

Substantial effort in recent years has been devoted to analyzing data based large-scale biological networks, which provide valuable insight into the topologies of complex biological networks but are rarely context specific and cannot be used to predict the responses of cell signaling proteins to specific ligands or compounds. In this work, we proposed a novel strategy to investigate kinase inhibitor induced pathway signatures by integrating multiplex data in Library of Integrated Network-based Cellular Signatures (LINCS), e.g. KINOMEscan data and cell proliferation/mitosis imaging data. Using this strategy, we first established a PC9 cell line specific pathway model to investigate the pathway signatures in PC9 cell line when perturbed by a small molecule kinase inhibitor GW843682. This specific pathway revealed the role of PI3K/AKT in modulating the cell proliferation process and the absence of two anti-proliferation links, which indicated a potential mechanism of abnormal expansion in PC9 cell number. Incorporating the pathway model for side effects on primary human hepatocytes, it was used to screen 27 kinase inhibitors in LINCS database and PF02341066, known as Crizotinib, was finally suggested with an optimal concentration 4.6 uM to suppress PC9 cancer cell expansion while avoiding severe damage to primary human hepatocytes. Drug combination analysis revealed that the synergistic effect region can be predicted straightforwardly based on a threshold which is an inherent property of each kinase inhibitor. Furthermore, this integration strategy can be easily extended to other specific cell lines to be a powerful tool for drug screen before clinical trials. [ABSTRACT FROM AUTHOR]

Data-driven Automatic fault detection and diagnosis (AFDD) for air handling units (AHUs) is crucial for ensuring the stable operation and energy consumption of the heating ventilation air-conditioning (HVAC) system. However, traditional machine learning methods often underperform when confronted with insufficient training sample data, especially when lacking samples from the fault types. Based on the issues of insufficient samples from the fault types and imbalanced training dataset, this study proposes a novel AFDD approach using transformer integrated conditional Wasserstein generative adversarial network and deep reinforcement learning (TCWGAN-DRL) to synthesize the fault data and select high quality synthetic data samples. Firstly, we utilize the proposed TransCWGAN to synthesize fault samples. Then, reinforcement learning is utilized to select high quality synthetic samples. Finally, the filtered samples and the real fault samples are merged to form the training dataset for conventional supervised learning classifiers. Experimental results demonstrate that the enriched training dataset can effectively improve the AFDD results and outperforms recently published existing methods, for instance, compared to the suboptimal model, our method exhibits an increase in fault recognition accuracy of 4.9%, 3.66%, and 4.02% when the number of real fault samples is 15, 20, and 30, respectively. [ABSTRACT FROM AUTHOR]

Tunnel settlement commonly occurs during the tunnel construction processes in large cities. Existing forecasting methods for tunnel settlements include model-based approaches and artificial intelligence (AI) enhanced approaches. Compared with traditional forecasting methods, artificial neural networks can be easily implemented, with high performance efficiency and forecasting accuracy. In this study, an extended machine learning framework is proposed combining particle swarm optimization (PSO) with support vector regression (SVR), back-propagation neural network (BPNN), and extreme learning machine (ELM) to forecast the surface settlement for tunnel construction in two large cities of China P.R. Based on real-world data verification, the PSO-SVR method shows the highest forecasting accuracy among the three proposed forecasting algorithms. [ABSTRACT FROM AUTHOR]

Background/Aims: MicroRNAs (miRNAs) are promising biomarkers for pancreatic cancer (PaCa). However, systemic and unified evaluations of the diagnostic value of miRNAs are lacking. Therefore, we performed a systematic evaluation based on miRNA expression profiling studies. Methods: We obtained miRNA expression profiling studies from Gene Expression Omnibus (GEO) and ArrayExpress (AE) databases and calculated the pooled sensitivity, specificity, and summary area under a receiver operating characteristic (ROC) curve for every miRNA. According to the area under the curve (AUC), we identified the miRNAs with diagnostic potentiality and validated their prognostic role in The Cancer Genome Atlas (TCGA) data. Gene Ontology (GO) annotations and pathway enrichments of the target genes of the miRNAs were evaluated using bioinformatics tools. Results: Ten miRNA expression profiling studies including 958 patients were used in this diagnostic meta-analysis. A total of 693 miRNAs were measured in more than 9 studies. The top 50 miRNAs with high predictive values for PaCa were identified. Among them, miR-130b had the best predictive value for PaCa (pooled sensitivity: 0.73 [95% confidence intervals (CI) 0.44-0.91], specificity: 0.81 [95% CI 0.59–0.93], and AUC: 0.84 [95% CI 0.73–0.95]). We identified nine miRNAs (miR-23a, miR-30a, miR-125a, miR-129-1, miR-181b-1, miR-203, miR-221, miR-222, and miR-1301) associated with overall survival in PaCa patients by combining our results with TCGA data. The results of a Cox model revealed that two miRNAs (miR-30a [hazard ratio (HR)=2.43, 95% CI 1.05-5.59; p=0.037] and miR-203 [HR=3.14, 95% CI 1.28-7.71; p=0.012]) were independent risk factors for prognosis in PaCa patients. In total, 405 target genes of the nine miRNAs were enriched with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and cancer-associated pathways such as Ras signaling pathways, phospholipase D signaling pathway, and AMP-activated protein kinase (AMPK) signaling pathway were revealed among the top 20 enriched pathways. There were significant negative correlations between miR-181b-1 and miR-125a expression levels and the methylation status of their promoter region. Conclusion: Our study performed a systematic evaluation of the diagnostic value of miRNAs based on miRNA expression profiling studies. We identified that miR-23a, miR-30a, miR-125a, miR-129-1, miR-181b-1, miR-203, miR-221, miR-222, and miR-1301 had moderate diagnostic value for PaCa and predicted overall survival in PaCa patients. [ABSTRACT FROM AUTHOR]

Fault detection and diagnosis (FDD) plays an essential role of maintaining large-scale heating ventilation air conditioning (HVAC) systems for industrial usage. The process of selecting important features is a crucial step for FDD methods of HVAC components, such as chillers. A suitable feature selection method selects the minimum number of features to save the number of installing sensors, and simultaneously maximizes the FDD accuracy. According to the surveyed related works, it is found that most existing works only focus on maximizing the classification accuracy, and miss two important points. First, the misclassification costs of false positive and false negative are different for chiller FDD. Second, the selected feature subsets must be sequential for real-world applications. In this study, a cost-sensitive and sequential feature selection algorithm for chiller FDD is proposed to select the most important features using a back-tracing sequential forward feature selection (BT-SFS) algorithm. The ASHRAE dataset collected by project number 1043-RP is utilized. Support vector machine (SVM), which is proved to be one of the most effective FDD classification method for chillers in existing works, is employed for accuracy measurement. This work fills in the gap between theoretical HVAC FDD methods and real-world HVAC FDD applications. [ABSTRACT FROM AUTHOR]

During mariculture period, maricultured macroalga Gracilaria lemaneiformis experienced seasonal temperature changes. In this study, we examine the effects of predicted ocean acidification on carbon and nitrogen accumulation and amino acids (AA) contents in G . lemaneiformis under different temperature levels. The results showed that G . lemaneiformis exhibited higher growth rates under high temperature conditions than under low temperature conditions, regardless of CO 2 levels. Moreover, compared with low temperature, the culture conditions of high temperature enhanced N accumulation in G . lemaneiformis , resulting in increased N and AA contents and lowered C/N ratios. While low temperature had no significant effects on the C and N acquisition, it would alter the N conversion and assimilation in this alga. It appeared that the effects resulted from ocean acidification on carbon and N accumulation were more pronounced at high temperature than at low temperature. Additionally, the enhancement percentages of growth, as well as the decrease extent of C/N ratios, resulted from acidified seawater conditions of culture, were more pronounced at high temperature than at low temperature. We suggested that ocean acidification owing to atmospheric CO 2 rising would be more effective on G . lemaneiformis growth and nutritional constituent accumulation during the middle and later periods of mariculture concurring with increased seasonal temperature. [ABSTRACT FROM AUTHOR]

Proteins and their interactions lie at the heart of most underlying biological processes. Consequently, correct detection of protein-protein interactions (PPIs) is of fundamental importance to understand the molecular mechanisms in biological systems. Although the convenience brought by high-throughput experiment in technological advances makes it possible to detect a large amount of PPIs, the data generated through these methods is unreliable and may not be completely inclusive of all possible PPIs. Targeting at this problem, this study develops a novel computational approach to effectively detect the protein interactions. This approach is proposed based on a novel matrix-based representation of protein sequence combined with the algorithm of support vector machine (SVM), which fully considers the sequence order and dipeptide information of the protein primary sequence. When performed on yeast PPIs datasets, the proposed method can reach 90.06% prediction accuracy with 94.37% specificity at the sensitivity of 85.74%, indicating that this predictor is a useful tool to predict PPIs. Achieved results also demonstrate that our approach can be a helpful supplement for the interactions that have been detected experimentally. [ABSTRACT FROM AUTHOR]

Optimal reduct is one of the challenging problems in rough set theory, and most of the existing algorithms cannot achieve the optimal reduct on high dimensional data sets. To explore an efficient algorithm for the optimal reduct problem, this paper proposes its generalization problem, which is defined as the K -size partial reduct problem. For this type of problem, an inefficient enumeration algorithm is first proposed. Then we enhance the enumeration algorithm through three improvements with the local search algorithm, i.e., fast initial solution construction, generation rules of solution, and dynamic object weighting strategy. The fast initial solution construction dramatically reduces the number of iterations, the generation rules of solution define a reasonable neighborhood structure and an effective candidate solution transfer model, and the dynamic object weighting strategy adjusts the iterative process to guide the algorithm to jump out of the local optimal solution. On the basis of these three improvements, an efficient local search-based K -size partial reduct algorithm is raised. Finally, a K -size partial reduct-based attribute reduction algorithm is designed by using the relationship between optimal reduct and K -size partial reduct. To validate the effectiveness of our proposed algorithms, we implemented a broad range of experimental simulations. The results of the experiments show the superiorities and innovations of the proposed algorithms compared with state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

• This work applies an unsupervised learning technique, generative adversarial network (GAN), to deal with the imbalanced training dataset problem in data-driven AHU FDD. • Detailed steps of applying GAN to generate artificial faulty training samples are demonstrated and deeply analyzed. • A complete AHU FDD framework integrating extensions of GAN is proposed. • The existing GAN-based AHU FDD framework has been optimized. • Comprehensive experimental results are shown before and after applying GAN extensions to the traditional supervised FDD framework to illustrate the importance of GAN. Supervised learning techniques have witnessed significant successes in fault detection and diagnosis (FDD) for heating ventilation and air-conditioning (HVAC) systems. Despite the good performance, these techniques heavily rely on balanced datasets that contain a large amount of both faulty and normal data points. In real-world scenarios, however, it is often very challenging to collect a sufficient amount of faulty training samples that are necessary for building a balanced training dataset. In this paper, we introduce a framework that utilizes the generative adversarial network (GAN) to address the imbalanced data problem in FDD for air handling units (AHUs). To this end, we first show the necessary procedures of applying GAN to increase the number of faulty training samples in the training pool and re-balance the training dataset. The proposed framework then uses supervised classifiers to train the re-balanced datasets. Finally, we present a comparative study that illustrates the advantages of the proposed method for FDD of AHU with various evaluation metrics. Our work demonstrates the promising prospects of performing robust FDD of AHU with a limited number of faulty training samples. [ABSTRACT FROM AUTHOR]