Forecasting Exchange Rates: The Multi-State Markov-Switching Model with Smoothing

This paper presents an exchange rate forecasting model which combines the multi-state Markov-switching model with smoothing techniques. The model outperforms a random walk at short horizons and its superior forecastability appears to be robust over different sample spans. Our finding hinges on the fact that exchange rates tend to follow highly persistent trends and accordingly, the key to beating the random walk is to identify these trends. An attempt to link the trends in exchange rates to the underlying macroeconomic determinants further reveals that fundamentals-based linear models generally fail to capture the persistence in exchange rates and thus are incapable of outforecasting the random walk.Exchange Rate, Forecasting, Markov-Switching, Smoothing, HP-Filter

The Exchange Rate and Macroeconomic Determinants: Time-Varying Transitional Dynamics

In this paper, I consider modeling the effects of the macroeconomic determinants on the nominal exchange rate to be channeled through the transition probabilities in a Markovian process. The model posits that the deviation of the exchange rate from its fundamental value alters the market’s belief in the probability of the process staying in certain regime next period. This paper further takes into account the ARCH effects of the volatility of the exchange rate. Empirical results generally confirm that fundamentals can affect the evolution of the dynamics of the exchange rate in a nonlinear way through the transition probabilities. In addition, I find that the volatility of the exchange rate is associated with significant ARCH effects which are subject to regime change.Exchange Rate, Macroeconomic Determinants, Markov-Switching, ARCH, and Time-Varying

Tanshinone IIA mitigates peritoneal fibrosis by inhibiting EMT via regulation of TGF-β/smad pathway

Purpose: To explore the effects of tanshinone IIA (T-IIA) on Dianeal-N PD-4 (PDF)-induced expression of fibrogenic cytokines in human peritoneal mesothelial cells (HPMCs), and to elucidate the mechanisms of action involved. Methods: Seven groups of HPMCs were used in the study: control group, PDF group, T-IIA group, LY364947 group, and 2 transforming growth factor-β (TGF-β) groups (TGF-β+ 50 μM T-IIA and TGF-β+ 100 μM T- IIA). The expression levels of mRNA and protein of TGF-β, smad2, smad7, α-smooth muscle actin(α-SMA), fibronectin, collagen І, E-cadherin, N-cadherin, matrix metalloprotein-2(MMP-2), and MMP-9 in the various groups were determined by reverse transcription-polymerase chain reaction (RTPCR) and Western blotting as appropriate. Results: The expressions of α-SMA, fibronectin, collagen І, TGF-β and smad2 were significantly upregulated in HPMCs by PDF treatment, but smad7 was down-regulated, relative to the control group (p < 0.01).These PDF-induced effects were reversed by T-IIA (p < 0.05). Inhibition of TGF-β/smad pathway by LY364947 treatment led to significant decrease in the expressions of fibrosis-related proteins, when compared with PDF group (p < 0.05). TGF-β treatment also produced numerous spindleshaped HPMCs characteristic of epithelial-mesenchymal transition (EMT). However, this morphological transition was alleviated, and the expression levels of EMT-related proteins were significantly downregulated by exposure to the two doses of T-IIA (p < 0.05). Conclusion: Tanshinone IIA inhibits EMT in HPMCs by regulating TGF-β/smad pathway, thus mitigating peritoneal fibrosis. Therefore, T-IIA has promising potential as a new drug for the treatment of peritoneal dialysis (PD)-induced fibrosis. Keywords: Peritoneal dialysis, Peritoneal fibrosis, Tanshinone IIA, Epithelial-mesenchymal transitio

Effects of Post-Surgical Parenteral Nutrition on Patients with Gastric Cancer

Background/Aims: In this study, we investigated the effect of post-surgical parenteral nutrition on patients with gastric cancer (GC) and its possible mechanism. Methods: A total of 108 patients were invited to assess for eligibility and 28 patients were excluded. The eighty patients were randomized to either a study group (1 L peripheral intravenous nutrition, 700 kcal) or a control group (1 L isotonic electrolyte solution). Parenteral nutrition was started on day 1 post-surgery and maintained for 4-8 days. Levels of albumin (ALB), prealbumin (PAB), hemoglobin (Hb) were measured before and after treatment. Self-rating Scale of Life Quality (SSLQ) and Quality of life (QoL) was assessed to analyze the patients’ quality of life. Psychological status was evaluated using both the Hospital Anxiety and Depression Scale (HADS-A/D) and the Patient Health Questionnaire-9 (PHQ-9). Immune function was evaluated by flow cytometric analysis of the levels of CD3+, CD4+, and CD8+ cells. Results: Following post-surgical parenteral nutrition, the levels of ALB, PAB and Hb were significantly higher in the study group than those in the control group. QoL and SSLQ scores were also significantly increased, while HAD-A/D and PHQ-9 scores were significantly reduced. Furthermore, the percentages of CD3+ and CD4+ cells, but not CD8+ cells, as well as the CD4+/CD8+ ratio were significantly increased in the study group. There were no significant differences in these parameters between the control and study group prior to surgery. Conclusion: The results suggest that post-surgical parenteral nutrition can significantly improve the nutritional and psychological status, QoL, and immune function of patients treated surgically for GC

Research on the Electromagnetic-Heat-Flow Coupled Modeling and Analysis for In-Wheel Motor

In this paper, a 15 KW in-wheel motor (IWM) is taken as the research object, and the coupling factors among the electromagnetic field, temperature field and flow field are analyzed, and the strong and weak coupling factors between the three fields are clarified, and by identifying the strong and weak coupling factors between the three fields, a three-field coupling analysis model for IWM with appropriate complexity is established, and the validity of the model is verified. In a certain driving condition, the electromagnetic field, temperature field and flow field characteristics of IWM are analyzed with the multi-field coupling model. The result shows that, after the IWM runs 8440 s under driving conditions, in this paper, the IWM electromagnetic torque of the rated working condition is 134.2 Nm, and IWM the electromagnetic torque of the peak working condition is 451.36 Nm, and the power requirement of the motor can be guaranteed. The highest temperature of the IWM is 150 &deg C, which does not exceed the insulation grade requirements of the motor (155 &deg C), the highest temperature of the permanent magnet (PM) is 65.6 &deg C, and it does not exceed the highest operating temperature of the PM, and ensures the accurate calculation of components loss and the temperature of the motor. It can be found, through research, that the electromagnetic torque difference between unidirectional coupling and bidirectional coupling is 3.2%, the maximum temperature difference is 7.98% in the three-field coupling analysis of IWM under rated working conditions. Therefore, it is necessary to consider the influence of coupling factors on the properties of motor materials when analyzing the electromagnetic field, temperature field and flow field of IWM it also provides some reference value for the simulation analysis of IWM in the future. Document type: Articl

Aberrant hippocampal subregion networks associated with the classifications of aMCI subjects: a longitudinal resting-state study

Background: Altered hippocampal structure and function is a valuable indicator of possible conversion from amnestic type mild cognitive impairment (aMCI) to Alzheimer’s disease (AD). However, little is known about the disrupted functional connectivity of hippocampus subregional networks in aMCI subjects. Methodology/Principal Findings: aMCI group-1 (n = 26) and controls group-1 (n = 18) underwent baseline and after approximately 20 months follow up resting-state fMRI scans. Integrity of distributed functional connectivity networks incorporating six hippocampal subregions (i.e. cornu ammonis, dentate gyrus and subicular complex, bilaterally) was then explored over time and comparisons made between groups. The ability of these extent longitudinal changes to separate unrelated groups of 30 subjects (aMCI-converters, n = 6; aMCI group-2, n = 12; controls group-2, n = 12) were further assessed. Six longitudinal hippocampus subregional functional connectivity networks showed similar changes in aMCI subjects over time, which were mainly associated with medial frontal gyrus, lateral temporal cortex, insula, posterior cingulate cortex (PCC) and cerebellum. However, the disconnection of hippocampal subregions and PCC may be a key factor of impaired episodic memory in aMCI, and the functional index of these longitudinal changes allowed well classifying independent samples of aMCI converters from non-converters (sensitivity was 83.3%, specificity was 83.3%) and controls (sensitivity was 83.3%, specificity was 91.7%). Conclusions/Significance: It demonstrated that the functional changes in resting-state hippocampus subregional networks could be an important and early indicator for dysfunction that may be particularly relevant to early stage changes and progression of aMCI subjects

Identification and discovery of imaging genetic patterns using fusion self-expressive network in major depressive disorder

IntroductionMajor depressive disorder (MDD) is a prevalent mental illness, with severe symptoms that can significantly impair daily routines, social interactions, and professional pursuits. Recently, imaging genetics has received considerable attention for understanding the pathogenesis of human brain disorders. However, identifying and discovering the imaging genetic patterns between genetic variations, such as single nucleotide polymorphisms (SNPs), and brain imaging data still present an arduous challenge. Most of the existing MDD research focuses on single-modality brain imaging data and neglects the complex structure of brain imaging data.MethodsIn this study, we present a novel association analysis model based on a self-expressive network to identify and discover imaging genetics patterns between SNPs and multi-modality imaging data. Specifically, we first build the multi-modality phenotype network, which comprises voxel node features and connectivity edge features from structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI), respectively. Then, we apply intra-class similarity information to construct self-expressive networks of multi-modality phenotype features via sparse representation. Subsequently, we design a fusion method guided by diagnosis information, which iteratively fuses the self-expressive networks of multi-modality phenotype features into a single new network. Finally, we propose an association analysis between MDD risk SNPs and the multi-modality phenotype network based on a fusion self-expressive network.ResultsExperimental results show that our method not only enhances the association between MDD risk SNP rs1799913 and the multi-modality phenotype network but also identifies some consistent and stable regions of interest (ROIs) multi-modality biological markers to guide the interpretation of MDD pathogenesis. Moreover, 15 new potential risk SNPs highly associated with MDD are discovered, which can further help interpret the MDD genetic mechanism.DiscussionIn this study, we discussed the discriminant and convergence performance of the fusion self-expressive network, parameters, and atlas selection