Chromosome 2p14 Is Linked to Susceptibility to Leprosy

BACKGROUND: A genetic component to the etiology of leprosy is well recognized but the mechanism of inheritance and the genes involved are yet to be fully established. METHODOLOGY: A genome-wide single nucleotide polymorphism (SNP) based linkage analysis was carried out using 23 pedigrees, each with 3 to 7 family members affected by leprosy. Multipoint parametric and non-parametric linkage analyses were performed using MERLIN 1.1.1. PRINCIPAL FINDINGS: Genome-wide significant evidence for linkage was identified on chromosome 2p14 with a heterogeneity logarithm of odds (HLOD) score of 3.51 (rs1106577) under a recessive model of inheritance, while suggestive evidence was identified on chr.4q22 (HLOD 2.92, rs1349350, dominant model), chr. 8q24 (HLOD 2.74, rs1618523, recessive model) and chr.16q24 (HLOD 1.93, rs276990 dominant model). Our study also provided moderate evidence for a linkage locus on chromosome 6q24-26 by non-parametric linkage analysis (rs6570858, LOD 1.54, p = 0.004), overlapping a previously reported linkage region on chromosome 6q25-26. CONCLUSION: A genome-wide linkage analysis has identified a new linkage locus on chromosome 2p14 for leprosy in Pedigrees from China

Expression and Bioinformatic Analysis of Ornithine Aminotransferase in Non-small Cell Lung Cancer

Background and objective It has been proven that ornithine aminotransferase (OAT) might play an important role in the oncogenesis and progression of numerous malignant tumors. The aim of this study is to detect the mRNA and protein expression of OAT in non-small cell lung cancer (NSCLC), as well as to analyze the bioinformatic features and binary interactions. Methods OAT mRNA expression was detected in A549 and 16HBE cell lines by reverse transcription-polymerase chain reaction. OAT protein expression was determined in 55 cases of NSCLC and 17 cases of adjacent non-tumor lung tissues by immunohistochemical staining. The bioinformatic features and binary interactions of OAT were analyzed. Gene ontology annotation and signal pathway analysis were performed. Results OAT mRNA expression in A549 cells was 2.85-fold lower than that in 16HBE cells. OAT protein expression was significantly higher in NSCLC tissues than that in adjacent non-tumor lung tissues. A significant difference of OAT protein expression was existed between squamous cell lung cancer and adenocarcinoma (P<0.05), but was not correlated with the gender, age, lymph node metastasis, tumor size, and TNM stages. Bioinformatic analysis suggested that OAT was a highly homologous and stable protein located in the mitochondria. An aminotran-3 domain and several sites of phosphorylation, which may function in signal transduction, gene transcription, and molecular transit, were found. In the 54 selected binary interactions of OAT, TNF and TRAF6 play roles in the NF-κB pathway. Conclusion OAT may play an important role in the oncogenesis and progression of NSCLC. Thus, OAT may be a novel biomarker for the diagnosis of NSCLC or a new target for its treatment

Adaptive Weight Multi-channel Matching Pursuit Algorithm-Based Strong Shielding Removal Method

The strata with large impedance differences are presented as strong amplitude seismic events on the seismic profile, which obscure useful information of nearby reservoirs and need interpretive target processing to remove the strong shielding. Therefore, this study proposes a multi-channel matching pursuit algorithm based on adaptive weight to remove strong shielding. Moreover, to address the problem of poor matching accuracy and spatial continuity of normal multi-channel matching pursuit at area where strong tectonic changes occur, a multi-channel matching pursuit de-strong shielding method based on adaptive weights is proposed. First, we used the layer structure information to flatten the strong reflection layer locally to weaken the influence of stratigraphic structure changes on the extraction of the strong reflection layer. Subsequently, we introduced a correlation coefficient between adjacent seismic traces and the central trace as the weight of multi trace averaging, which improved the stability and lateral continuity of the matching results. The result analysis of model processing tests and practical seismic data application shows that the proposed method can effectively strip the strong reflections and highlight the effective reservoir information; the well seismic match degree is significantly improved with higher matching accuracy, better spatial continuity, and better striping effect than those of the conventional matching pursuit algorithm

Determining an Optimal Cutoff of Serum β-Human Chorionic Gonadotropin for Assisting the Diagnosis of Intracranial Germinomas.

BACKGROUND:Beta (β)-human chorionic gonadotropin (β-HCG) is used to confirm the diagnosis and plan treatment of intracranial germinomas. However, the cutoff values of serum β-HCG in diagnosis of intracranial germinomas reported in the literature are inconsistent. To establish an appropriate cutoff value of serum β-HCG for diagnosis of intracranial germinomas, we retrospectively reviewed the records of intracranial tumor patients who received serum β-HCG and α-fetoprotein (AFP) tests for diagnostic purposes at our hospital from 2005 to 2014. METHODS:A total of 93 intracranial germinomas and 289 intracranial non-germ cell tumors were included in this study. Receiver operating characteristic (ROC) analysis was used to evaluate the sensitivity and specificity of 3 cutoffs (0.1, 0.4, and 0.5 mIU/mL) for diagnosing intracranial germinomas. The serum β-HCG level of intracranial germinoma patients was further analyzed to investigate the effect of metastasis status and tumor location on serum β-HCG level. RESULTS:The area under the ROC curve was 0.81 (P < .001), suggesting β-HCG is an effective marker. Of the 3 cutoff values, 0.1 mIU/mL possessed a highest sensitivity (66.67%) and good specificity (91%). Although there was no β-HCG level difference between metastatic and non-metastatic intracranial germinoma patients, the diagnostic rate of metastatic neurohypophyseal germinomas was significantly higher than that of its non-metastatic counterpart (P < .05), implying that the location of the germinoma might need to be considered when β-HCG is used as a marker to predict metastasis. CONCLUSIONS:Determining an optimal cutoff of serum β-HCG is helpful for assisting the diagnosis of intracranial germinoma

Overvoltage and Oscillation Suppression Circuit with Switching Losses Optimization and Clamping Energy Feedback for SiC MOSFET

The snubber circuit is a cost-effective solution to reduce the severe turn-off overvoltage and oscillation caused by the fast switching characteristics of SiC MOSFET. However, the turn-on switching losses will significantly increase as the snubber decouples the power loop parasitic inductance during the switching process. In this article, the effect of the power loop parasitic inductance on the switch performance of SiC MOSFETs and the quasi-zero voltage switching (QZVS) turn-on condition are studied. On this basis, an SiC MOSFET overvoltage and oscillation suppression circuit (OVSC) with not only the switching losses optimization feature but also the clamping energy feedback characteristic is proposed. The overvoltage and oscillation can be effectively suppressed by clamping capacitors, and those capacitors do not participate in the switching process until the overvoltage occurs, which is of benefit to the switching losses reduction compared with the other normal snubber circuits. Besides, the turn-off overvoltage and oscillation energy stored in the clamping capacitors can be feedback to dc and load side through a passive branch composed of an energy feedback inductor and a diode. The experimental studies and comprehensive comparison studies are carried out to verify the validity of the proposed OVSC. The experimental results show that OVSC has excellent overvoltage and oscillation suppression performance and can significantly reduce the switching losses

Physical informed neural networks with soft and hard boundary constraints for solving advection-diffusion equations using Fourier expansions

Deep learning methods have gained considerable interest in the numerical solution of various partial differential equations (PDEs). One particular focus is on physics-informed neural networks (PINNs), which integrate physical principles into neural networks. This transforms the process of solving PDEs into optimization problems for neural networks. In order to address a collection of advection-diffusion equations (ADE) in a range of difficult circumstances, this paper proposes a novel network structure. This architecture integrates the solver, which is a multi-scale deep neural network (MscaleDNN) utilized in the PINN method, with a hard constraint technique known as HCPINN. This method introduces a revised formulation of the desired solution for advection-diffusion equations (ADE) by utilizing a loss function that incorporates the residuals of the governing equation and penalizes any deviations from the specified boundary and initial constraints. By surpassing the boundary constraints automatically, this method improves the accuracy and efficiency of the PINN technique. To address the ``spectral bias'' phenomenon in neural networks, a subnetwork structure of MscaleDNN and a Fourier-induced activation function are incorporated into the HCPINN, resulting in a hybrid approach called SFHCPINN. The effectiveness of SFHCPINN is demonstrated through various numerical experiments involving advection-diffusion equations (ADE) in different dimensions. The numerical results indicate that SFHCPINN outperforms both standard PINN and its subnetwork version with Fourier feature embedding. It achieves remarkable accuracy and efficiency while effectively handling complex boundary conditions and high-frequency scenarios in ADE.Comment: 2