Differentiable Forward and Backward Fixed-Point Iteration Layers

Recently, several studies proposed methods to utilize some classes of optimization problems in designing deep neural networks to encode constraints that conventional layers cannot capture. However, these methods are still in their infancy and require special treatments, such as analyzing the KKT condition, for deriving the backpropagation formula. In this paper, we propose a new layer formulation called the fixed-point iteration (FPI) layer that facilitates the use of more complicated operations in deep networks. The backward FPI layer is also proposed for backpropagation, which is motivated by the recurrent back-propagation (RBP) algorithm. But in contrast to RBP, the backward FPI layer yields the gradient by a small network module without an explicit calculation of the Jacobian. In actual applications, both the forward and backward FPI layers can be treated as nodes in the computational graphs. All components in the proposed method are implemented at a high level of abstraction, which allows efficient higher-order differentiations on the nodes. In addition, we present two practical methods of the FPI layer, FPI_NN and FPI_GD, where the update operations of FPI are a small neural network module and a single gradient descent step based on a learnable cost function, respectively. FPI\_NN is intuitive, simple, and fast to train, while FPI_GD can be used for efficient training of energy networks that have been recently studied. While RBP and its related studies have not been applied to practical examples, our experiments show the FPI layer can be successfully applied to real-world problems such as image denoising, optical flow, and multi-label classification

Sociodemographic factors associated with the use of mental health services in depressed adults: results from the Korea National Health and Nutrition Examination Survey (KNHANES)

Background: The aims of this study were to determine the utilization of mental health services (MHSs) by adults with a depressive mood and to identify the influencing sociodemographic factors, using a nationwide representative Korean sample. Methods: The study included 2735 subjects, aged 19 years or older, who had experienced a depressive mood continuously for over 2 weeks within the previous year, using the data from the KNHANES IV (Fourth Korea National Health and Nutrition Examination Survey), which was performed between 2007 and 2009, and involved a nationally representative sample of the Korean community population who were visited at home. A multivariate logistic regression analysis was used to estimate the adjusted odd ratios (ORs) and 95% confidence intervals (CIs) for the use of MHSs, which was defined as using healthcare institutions, consulting services, and inpatient or outpatient treatments due to mental health problems. Results: MHSs had been used by 9.6% of the subjects with a depressive mood. The use of the MHSs was significantly associated with age, education level, and employment status, after adjusting for sociodemographic and health-related factors. Specifically, the OR for the nonuse of MHSs by the elderly (≥65 years) relative to subjects aged 19–34 years was 2.55 (95% CI = 1.13–5.76), subjects with a lower education level were less likely to use MHSs compared to those with a higher education level (7–9 years, OR = 2.35, 95% CI = 1.19–4.64; 10–12 years, OR = 1.66, 95% CI = 1.07–2.56; ≥13 years, reference), and the OR of unemployed relative to employed was 0.47 (95% CI = 0.32–0.67). Conclusions: Among Korean adults with a depressive mood, the elderly, those with a lower education level, and the employed are less likely to use MHSs. These findings suggest that mental health policies should be made based on the characteristics of the population in order to reduce untreated patients with depression. Greater resources and attention to identifying and treating depression in older, less educated, and employed adults are warranted

Paclitaxel-Induced Immune Suppression Is Associated with NF-B Activation Via Conventional PKC Isotypes in Lipopolysaccharide-Stimulated 70Z/3 Pre-B Lymphocyte Tumor Cells

ABSTRACT Paclitaxel, a potent antitumor agent, has been shown to be lipopolysaccharide (LPS) mimetic in mice, stimulating signaling pathways and gene expression indistinguishably from LPS. In the present study, we showed the intracellular signaling pathway of paclitaxel-induced nuclear factor-B (NF-B) activation and its suppressive effect on LPS-induced signaling in murine 70Z/3 pre-B cells. Stimulation of 70Z/3 cells with LPS for 30 min caused activation of NF-B in the nuclei by detection of DNA-protein binding specific to NF-B. Similarly, paclitaxel also produced a marked and dose-related NF-B activation. However, pretreatment of cells with 10 M paclitaxel for 18 h resulted in complete inhibition of LPS-mediated NF-B activation. Interestingly, the activity of IB kinase (IKK-␤), which plays an essential role in NF-B activation through IB phosphorylation, was largely enhanced in paclitaxel-treated cells, detected as IB␣ phosphorylation. Because protein kinase C (PKC) is implicated in the activation of NF-B via IKK-␤, the effect of paclitaxel on PKC activation was also measured. It was shown that NF-B nuclear translocation and DNA binding in response to paclitaxel was completely blocked by the conventional PKC inhibitor, Gö 6976. Moreover, immunoblotting analysis with paclitaxel-treated cell extract demonstrated that the conventional PKC isotype PKC-␣ was found to be involved in the regulation of paclitaxel-induced NF-B activation, as determined by electrophoretic mobility shift of PKC. Therefore, these data suggest that paclitaxel may activate IKK-␤ via conventional PKC isotypes, resulting in NF-B activation and, finally, desensitization of LPS-inducible signaling pathway in 70Z/3 pre-B cells

Review on the current trends in tongue diagnosis systems

AbstractTongue diagnosis is an essential process to noninvasively assess the condition of a patient's internal organs in traditional medicine. To obtain quantitative and objective diagnostic results, image acquisition and analysis devices called tongue diagnosis systems (TDSs) are required. These systems consist of hardware including cameras, light sources, and a ColorChecker, and software for color correction, segmentation of tongue region, and tongue classification. To improve the performance of TDSs, various types TDSs have been developed. Hyperspectral imaging TDSs have been suggested to acquire more information than a two-dimensional (2D) image with visible light waves, as it allows collection of data from multiple bands. Three-dimensional (3D) imaging TDSs have been suggested to provide 3D geometry. In the near future, mobile devices like the smart phone will offer applications for assessment of health condition using tongue images. Various technologies for the TDS have respective unique advantages and specificities according to the application and diagnostic environment, but this variation may cause inconsistent diagnoses in practical clinical applications. In this manuscript, we reviewed the current trends in TDSs for the standardization of systems. In conclusion, the standardization of TDSs can supply the general public and oriental medical doctors with convenient, prompt, and accurate information with diagnostic results for assessing the health condition

Extraction and separation of hexavalent molybdenum from acidic sulfate solutions using Alamine 336 as an extractant

Extraction and separation of hexavalent molybdenum from acidic sulfate solutions using Alamine 336 as an extractant and kerosene as diluent is described. Variation of acid concentration influences the percentage of extraction of hexavalent molybdenum indicating the ion exchange type mechanism. Effect of concentration of Alamine 336 on the extraction of hexavalent molybdenum in the presence of divalent copper and trivalent iron is also presented. The upper limit of concentration of extractant for extraction of hexavalent molybdenum free from divalent copper and trivalent iron is observed with 0.1 mol/L of Alamine 336. However, when the concentration of Alamine 336 is increased to 1.0 mol/L, about 48% of copper is extracted along with molybdenum but without any iron. The method is suitable for the separation of molybdenum from copper and iron contained leach liquor. The optimized experimental parameters such as phase contact time, effect of extractant, metal, loading capacity of extractant and followed by strip ping studies with ammonia, hydrogen peroxide, sodium hydroxide, sodium thiosulfate and thiourea is presented. From the above experimental data we proposed the aqueous mechanism for hexavalent molybdenum extraction processes

Differentiable Forward and Backward Fixed-Point Iteration Layers

Recently, several studies have proposed methods to utilize some classes of optimization problems in designing deep neural networks to encode constraints that conventional layers cannot capture. However, these methods are still in their infancy and require special treatments, such as the analysis of the Karush-Kuhn-Tucker (KKT) condition, to derive the backpropagation formula. In this paper, we propose a new formulation called the fixed-point iteration (FPI) layer, which facilitates the use of more complicated operations in deep networks. The backward FPI layer, which is motivated by the recurrent backpropagation (RBP) algorithm, is also proposed. However, in contrast to RBP, the backward FPI layer yields the gradient using a small network module without explicitly calculating the Jacobian. In actual applications, both forward and backward FPI layers can be treated as nodes in the computational graphs. All the components of our method are implemented at a high level of abstraction, which allows efficient higher-order differentiations on the nodes. In addition, we present two practical methods, the neural net FPI (FPI_NN) layer and the gradient descent FPI (FPI_GD) layer, whereby the FPI update operations are a small neural network module and a single gradient descent step based on a learnable cost function, respectively. FPI_NN is intuitive and simple, while FPI_GD can be used to efficient train energy function networks that have been studied recently. While RBP and related studies have not been applied to practical examples, our experiments show that the FPI layer can be successfully applied to real-world problems such as image denoising, optical flow, and multi-label classification.Y