Background. Solving the class-imbalance problem of within-project software defect prediction (SDP) is an important research topic. Although some class-imbalance learning methods have been presented, ...there exists room for improvement. For cross-project SDP, we found that the class-imbalanced source usually leads to misclassification of defective instances. However, only one work has paid attention to this cross-project class-imbalance problem. Objective. We aim to provide effective solutions for both within-project and cross-project class-imbalance problems. Method. Subclass discriminant analysis (SDA), an effective feature learning method, is introduced to solve the problems. It can learn features with more powerful classification ability from original metrics. For within-project prediction, we improve SDA for achieving balanced subclasses and propose the improved SDA (ISDA) approach. For cross-project prediction, we employ the semi-supervised transfer component analysis (SSTCA) method to make the distributions of source and target data consistent, and propose the SSTCA+ISDA prediction approach. Results. Extensive experiments on four widely used datasets indicate that: 1) ISDA-based solution performs better than other state-of-the-art methods for within-project class-imbalance problem; 2) SSTCA+ISDA proposed for cross-project class-imbalance problem significantly outperforms related methods. Conclusion. Within-project and cross-project class-imbalance problems greatly affect prediction performance, and we provide a unified and effective prediction framework for both problems.
Image annotation has attracted a lot of research interest, and multi-label learning is an effective technique for image annotation. How to effectively exploit the underlying correlation among labels ...is a crucial task for multi-label learning. Most existing multi-label learning methods exploit the label correlation only in the output label space, leaving the connection between the label and the features of images untouched. Although, recently some methods attempt toward exploiting the label correlation in the input feature space by using the label information, they cannot effectively conduct the learning process in both the spaces simultaneously, and there still exists much room for improvement. In this paper, we propose a novel multi-label learning approach, named multi-label dictionary learning (MLDL) with label consistency regularization and partial-identical label embedding MLDL, which conducts MLDL and partial-identical label embedding simultaneously. In the input feature space, we incorporate the dictionary learning technique into multi-label learning and design the label consistency regularization term to learn the better representation of features. In the output label space, we design the partial-identical label embedding, in which the samples with exactly same label set can cluster together, and the samples with partial-identical label sets can collaboratively represent each other. Experimental results on the three widely used image datasets, including Corel 5K, IAPR TC12, and ESP Game, demonstrate the effectiveness of the proposed approach.
Major depressive disorder (MDD) is associated with alterations of GABAergic interneurons, notably somatostatin (Sst) as well as parvalbumin (Pvalb), in cortical brain areas. In addition, the ...antidepressant effects of rapid-acting drugs are thought to occur via inhibition of GABA interneurons. However, the impact of these interneuron subtypes in affective behaviors as well as in the effects of rapid-acting antidepressants remains to be determined. Here, we used a Cre-dependent DREADD-chemogenetic approach to determine if inhibition of GABA interneurons in the mPFC of male mice is sufficient to produce antidepressant actions, and conversely if activation of these interneurons blocks the rapid and sustained antidepressant effects of scopolamine, a nonselective acetylcholine muscarinic receptor antagonist. Chemogenetic inhibition of all GABA interneurons (Gad1+), as well as Sst+ and Pvalb+ subtypes in the mPFC produced dose and time-dependent antidepressant effects in the forced swim and novelty suppressed feeding tests, and increased synaptic plasticity. In contrast, stimulation of Gad1, Sst, or Pvalb interneurons in mPFC abolished the effects of scopolamine and prevented scopolamine induction of synaptic plasticity. The results demonstrate that transient inhibition of GABA interneurons promotes synaptic plasticity that underlies rapid antidepressant responses.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Exosomes have emerged as a novel mode of intercellular communication. Exosomes can shuttle bioactive molecules including proteins, DNA, mRNA, as well as non-coding RNAs from one cell to another, ...leading to the exchange of genetic information and reprogramming of the recipient cells. Increasing evidence suggests that tumor cells release excessive amount of exosomes, which may influence tumor initiation, growth, progression, metastasis, and drug resistance. In addition, exosomes transfer message from tumor cells to immune cells and stromal cells, contributing to the escape from immune surveillance and the formation of tumor niche. In this review, we highlight the recent advances in the biology of exosomes as cancer communicasomes. We review the multifaceted roles of exosomes, the small secreted particles, in communicating with other cells within tumor microenvironment. Given that exosomes are cell type specific, stable, and accessible from body fluids, exosomes may provide promising biomarkers for cancer diagnosis and represent new targets for cancer therapy.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Software defect prediction has always been a hot research topic in the field of software engineering owing to its capability of allocating limited resources reasonably. Compared with cross-project ...defect prediction (CPDP), heterogeneous defect prediction (HDP) further relaxes the limitation of defect data used for prediction, permitting different metric sets to be contained in the source and target projects. However, there is still a lack of a holistic understanding of existing HDP studies due to different evaluation strategies and experimental settings. In this paper, we provide an empirical study on HDP approaches. We review the research status systematically and compare the HDP approaches proposed from 2014 to June 2018. Furthermore, we also investigate the feasibility of HDP approaches in CPDP. Through extensive experiments on 30 projects from five datasets, we have the following findings: (1) metric transformation-based HDP approaches usually result in better prediction effects, while metric selection-based approaches have better interpretability. Overall, the HDP approach proposed by Li et al. (CTKCCA) currently has the best performance. (2) Handling class imbalance problems can boost the prediction effects, but the improvements are usually limited. In addition, utilizing mixed project data cannot improve the performance of HDP approaches consistently since the label information in the target project is not used effectively. (3) HDP approaches are feasible for cross-project defect prediction in which the source and target projects have the same metric set.
Defects on product surfaces affect quality of the product. Machine vision provides an efficient tool for the surface defect detection. Threshold is commonly used to separate objects from the image ...background in the vision-based inspection method. The Otsu method is one of the most used approaches to decide the threshold for a satisfied result when the image histogram is bimodal, but it fails when the histogram of an image is unimodal or close to unimodal. Defects in product surfaces can range from small to large sizes, which results in distributions of the image histogram change from unimodal to bimodal. An improved Otsu method, named the weighted object variance (WOV), is proposed in this research to detect defects on product surfaces. A parameter that equals the cumulative probability of defects occurrence is weighted on the object variance of between-class variance. The weight ensures that the threshold always be a value that locates at the valley of two peaks or at the left bottom rim of a single peak histogram. It is essential to have a high detection rate and low false alarm rate for the defect detection. Experimental results demonstrate the effectiveness of the improved Otsu method in the defect detection of various surfaces. Compared to other thresholding methods such as maximum entropy, Otsu, valley-emphasis, and modified valley-emphasis methods, the WOV method provides better segmentation results.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Person re-identification has been widely studied due to its importance in surveillance and forensics applications. In practice, gallery images are high resolution (HR), while probe images are usually ...low resolution (LR) in the identification scenarios with large variation of illumination, weather, or quality of cameras. Person re-identification in this kind of scenarios, which we call super-resolution (SR) person re-identification, has not been well studied. In this paper, we propose a semi-coupled low-rank discriminant dictionary learning (SLD 2 L) approach for SR person re-identification task. With the HR and LR dictionary pair and mapping matrices learned from the features of HR and LR training images, SLD 2 L can convert the features of the LR probe images into HR features. To ensure that the converted features have favorable discriminative capability and the learned dictionaries can well characterize intrinsic feature spaces of the HR and LR images, we design a discriminant term and a low-rank regularization term for SLD 2 L. Moreover, considering that low resolution results in different degrees of loss for different types of visual appearance features, we propose a multi-view SLD 2 L (MVSLD 2 L) approach, which can learn the type-specific dictionary pair and mappings for each type of feature. Experimental results on multiple publicly available data sets demonstrate the effectiveness of our proposed approaches for the SR person re-identification task.
X-linked inhibitor of apoptosis protein (XIAP) possesses a critical role in promotion of cell survival and maintenance of cellular homeostasis. In cancer, elevated XIAP expression has been associated ...with malignancy, poor prognosis, and treatment resistance. However, the underlying mechanisms of these effects remain unclear. XIAP has previously been proposed to promote tumor growth through suppression of autophagy. In this study, we examined the expression of XIAP and p62, two critical mediators of autophagy, in breast and colon cancer. We observed a negative correlation between XIAP and p62 expression in normal and cancer tissues of breast and colon, and that the ratio of XIAP and p62 expression determines the cancer phenotype. In vitro, we observed that XIAP interacted with p62 and also that XIAP depletion resulted in increased expression of p62. XIAP functioned as an ubiquitination E3 ligase towards p62 and suppressed p62 expression through ubiquitin-proteasomal degradation. Furthermore, XIAP enhanced cancer cell proliferation, viability, and colony formation in vitro via suppression of p62. In addition, we demonstrated that XIAP-enhanced tumor growth is dependent on depletion of p62 in vivo. Herein, we have therefore delineated a novel mechanism by which XIAP contributes to development and progression of breast and colon carcinoma.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
While limited choice of emissive organic linkers with systematic emission tunability presents a great challenge to investigate energy transfer (ET) over the whole visible light range with designable ...directions, luminescent metal‐organic frameworks (LMOFs) may serve as an ideal platform for such study due to their tunable structure and composition. Herein, five Zr6 cluster‐based LMOFs, HIAM‐400X (X=0, 1, 2, 3, 4) are prepared using 2,1,3‐benzothiadiazole and its derivative‐based tetratopic carboxylic acids as organic linkers. The accessible unsaturated metal sites confer HIAM‐400X as a pristine scaffold for linker installation. Six full‐color emissive 2,1,3‐benzothiadiazole and its derivative‐based dicarboxylic acids (L) were successfully installed into HIAM‐400X matrix to form HIAM‐400X‐L, in which the ET can be facilely tuned by controlling its direction, either from the inserted linkers to pristine MOFs or from the pristine MOFs to inserted linkers, and over the whole range of visible light. The combination of the pristine MOFs and the second linkers via linker installation creates a powerful two‐dimensional space in tuning the emission via ET in LMOFs.
Tunable energy transfer with designable direction, from second linkers to pristine MOFs or from pristine MOFs to second linkers, was achieved in the whole visible spectrum via installing color‐full emissive second linkers into the full‐color emissive pristine MOFs.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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