Analytical characteristics of a qPCR-based molecular diagnostic assay-conceptual considerations for laboratory personnel

Dear Editor：Quantitative real-time PCR has revolutionized molecular diagnostics with its ease of use,increased sensitivity and specificity and low turnaround time.PCR/quantitative PCR（qPCR）-based assays offer a distinct advantage over other serological/conventional diagnostic approaches.The ability to diagnose infectious diseases has benefited from the availability of US FDA approved and Conformite Europeenne（CE）-marked qPCR-based in-vitro diagnostic kits from international companies.The high-quality kits are calibrated with the World Health Organization（WHO）reference standards and the National Institute for Biological Standards and Control（NIBSC）standards.

Analytical Method of Fault Characteristic and Non-unit Protection for HVDC Transmission Lines

An analytical method of fault characteristic for the HVDC system based on frequency response characteristics of boundary elements is presented here.The computational formulas of transfer function and input impedance are deduced using the distributed parameter model of HVDC transmission line,and the amplitude-to-frequency-characteristics of the transfer function and input impedance are analyzed.Based on the amplitude-to-frequency difference between internal and external faults,a non-unit protection method for VSC-HVDC transmission line is presented.Using the current ratio of high-to-low-frequency,this protection method can distinguish internal from external fault.The presented algorithm only uses local-end current,has high operation speed,and is easy to implement.Simulations on a±200 kV VSC-HVDC system are conducted to demonstrate the validity and feasibility of the developed protection method.

Landslide initiation and runout susceptibility modeling in the context of hill cutting and rapid urbanization: a combined approach of weights of evidence and spatial multicriteria

Rainfall induced landslides are a common threat to the communities living on dangerous hillslopes in Chittagong Metropolitan Area, Bangladesh. Extreme population pressure, indiscriminate hill cutting, increased precipitation events due to global warming and associated unplanned urbanization in the hills are exaggerating landslide events. The aim of this article is to prepare a scientifically accurate landslide susceptibility map by combining landslide initiation and runout maps. Land cover, slope, soil permeability, surface geology, precipitation, aspect, and distance to hill cut, road cut, drainage and stream network factor maps were selected by conditional independence test. The locations of 56 landslides were collected by field surveying. A weight of evidence(Wo E) method was applied to calculate the positive(presence of landslides) and negative(absence of landslides) factor weights. A combination of analytical hierarchical process(AHP) and fuzzymembership standardization(weighs from 0 to 1) was applied for performing a spatial multi-criteria evaluation. Expert opinion guided the decision rule for AHP. The Flow-R tool that allows modeling landslide runout from the initiation sources was applied. The flow direction was calculated using the modified Holmgren's algorithm. The AHP landslide initiation and runout susceptibility maps were used to prepare a combined landslide susceptibility map. The relative operating characteristic curve was used for model validation purpose. The accuracy of Wo E, AHP, and combined susceptibility map was calculated 96%, 97%, and 98%, respectively.

SNCDM: Spinal Tumor Detection from MRI Images Using Optimized Super-Pixel Segmentation

Conferring to the American Association of Neurological Surgeons(AANS)survey,85%to 99%of people are affected by spinal cord tumors.The symptoms are varied depending on the tumor’s location and size.Up-to-the-min-ute,back pain is one of the essential symptoms,but it does not have a specific symptom to recognize at the earlier stage.Numerous significant research studies have been conducted to improve spine tumor recognition accuracy.Nevertheless,the traditional systems are consuming high time to extract the specific region and features.Improper identification of the tumor region affects the predictive tumor rate and causes the maximum error-classification problem.Consequently,in this work,Super-pixel analytics Numerical Characteristics Disintegration Model(SNCDM)is used to segment the tumor affected region.Estimating the super-pix-els of the affected region by this method reduces the variance between the iden-tified pixels.Further,the super-pixels are selected according to the optimized convolution network that effectively extracts the vertebral super-pixels features.Derived super-pixels improve the network learning and training process,which minimizes the maximum error classification problem also the efficiency of the system was evaluated using experimental results and analysis.