1,257 research outputs found
Accurate Modeling of the Effects of Fringing Area Interface Traps on Scanning Capacitance Microscopy Measurement
Scanning capacitance microscopy (SCM) is a dopant profile extraction tool with nanometre spatial resolution. While it is based on the high-frequency MOS capacitor theory, there are crucial second-order effects which make the extraction of dopant profile from SCM data a challenging task. Due to small size of the SCM probe, the trapped charges in the interface traps at the oxide-silicon dioxide interface surrounding the probe significantly affect the measured SCM data through the fringing electric field created by the trapped charges. In this paper, we present numerical simulation results to investigate the nature of SCM dC/dV data in the presence of interface traps. The simulation takes into consideration the traps response to the ac signal used to measure dC/dV as well as the fringing field of the trapped charge surrounding the probe tip. In the study, we present an error estimation of experimental SCM dopant concentration extraction when the interface traps and fringing field are ignored. The trap distribution in a typical SCM sample is also investigated
High-order dynamic Bayesian network learning with hidden common causes for causal gene regulatory network
Background: Inferring gene regulatory network (GRN) has been an important topic in Bioinformatics. Many computational methods infer the GRN from high-throughput expression data. Due to the presence of time delays in the regulatory relationships, High-Order Dynamic Bayesian Network (HO-DBN) is a good model of GRN. However, previous GRN inference methods assume causal sufficiency, i.e. no unobserved common cause. This assumption is convenient but unrealistic, because it is possible that relevant factors have not even been conceived of and therefore un-measured. Therefore an inference method that also handles hidden common cause(s) is highly desirable. Also, previous methods for discovering hidden common causes either do not handle multi-step time delays or restrict that the parents of hidden common causes are not observed genes.
Results: We have developed a discrete HO-DBN learning algorithm that can infer also hidden common cause(s) from discrete time series expression data, with some assumptions on the conditional distribution, but is less restrictive than previous methods. We assume that each hidden variable has only observed variables as children and parents, with at least two children and possibly no parents. We also make the simplifying assumption that children of hidden variable(s) are not linked to each other. Moreover, our proposed algorithm can also utilize multiple short time series (not necessarily of the same length), as long time series are difficult to obtain.
Conclusions: We have performed extensive experiments using synthetic data on GRNs of size up to 100, with up to 10 hidden nodes. Experiment results show that our proposed algorithm can recover the causal GRNs adequately given the incomplete data. Using the limited real expression data and small subnetworks of the YEASTRACT network, we have also demonstrated the potential of our algorithm on real data, though more time series expression data is needed
Near-infrared probe as a quality control tool for milk powder blending processes
This study aims to evaluate the suitability and reliability of Process Analytical Tools (PAT) in monitoring milk powder blending processes. The uniformity end point was predicted using a Near-Infrared (NIR) probe, and subsequently validated using offline Fourier Transform Infrared Spectroscopy (FTIR). A standard milk formulation (SMF) made up of 50% lactose, 40% skim milk powder, and 10% whey protein concentrate was used. Additionally, the detection limit of the NIR probe was investigated using vitamin C powder. The average predicted uniformity end point using the inline NIR fixed reference (63.89 ± 2.06 min), and dynamic reference conformity test (63.00 ± 5.25 min) were comparable with the offline FTIR measurement (56.6 ± 0.71 min). A three-component Partial Least Square Regression (PLSR) model was constructed and validated for vitamin C. The detection limit is 0.11%, which is higher than the vitamin C level commonly found in most infant milk formula (0.035%)
PET-Tool: a software suite for comprehensive processing and managing of Paired-End diTag (PET) sequence data
BACKGROUND: We recently developed the Paired End diTag (PET) strategy for efficient characterization of mammalian transcriptomes and genomes. The paired end nature of short PET sequences derived from long DNA fragments raised a new set of bioinformatics challenges, including how to extract PETs from raw sequence reads, and correctly yet efficiently map PETs to reference genome sequences. To accommodate and streamline data analysis of the large volume PET sequences generated from each PET experiment, an automated PET data process pipeline is desirable. RESULTS: We designed an integrated computation program package, PET-Tool, to automatically process PET sequences and map them to the genome sequences. The Tool was implemented as a web-based application composed of four modules: the Extractor module for PET extraction; the Examiner module for analytic evaluation of PET sequence quality; the Mapper module for locating PET sequences in the genome sequences; and the ProjectManager module for data organization. The performance of PET-Tool was evaluated through the analyses of 2.7 million PET sequences. It was demonstrated that PET-Tool is accurate and efficient in extracting PET sequences and removing artifacts from large volume dataset. Using optimized mapping criteria, over 70% of quality PET sequences were mapped specifically to the genome sequences. With a 2.4 GHz LINUX machine, it takes approximately six hours to process one million PETs from extraction to mapping. CONCLUSION: The speed, accuracy, and comprehensiveness have proved that PET-Tool is an important and useful component in PET experiments, and can be extended to accommodate other related analyses of paired-end sequences. The Tool also provides user-friendly functions for data quality check and system for multi-layer data management
Genetic Code Mutations: The Breaking of a Three Billion Year Invariance
The genetic code has been unchanging for some three billion years in its canonical ensemble of encoded amino acids, as indicated by the universal adoption of this ensemble by all known organisms. Code mutations beginning with the encoding of 4-fluoro-Trp by Bacillus subtilis, initially replacing and eventually displacing Trp from the ensemble, first revealed the intrinsic mutability of the code. This has since been confirmed by a spectrum of other experimental code alterations in both prokaryotes and eukaryotes. To shed light on the experimental conversion of a rigidly invariant code to a mutating code, the present study examined code mutations determining the propagation of Bacillus subtilis on Trp and 4-, 5- and 6-fluoro-tryptophans. The results obtained with the mutants with respect to cross-inhibitions between the different indole amino acids, and the growth effects of individual nutrient withdrawals rendering essential their biosynthetic pathways, suggested that oligogenic barriers comprising sensitive proteins which malfunction with amino acid analogues provide effective mechanisms for preserving the invariance of the code through immemorial time, and mutations of these barriers open up the code to continuous change
Ad hoc influenza vaccination during years of significant antigenic drift in a tropical city with 2 seasonal peaks
We evaluated the acceptability of an additional ad hoc influenza vaccination among the health care professionals following seasons with significant antigenic drift. Self-administered, anonymous surveys were performed by hard copy questionnaires in public hospitals, and by an on-line platform available to all healthcare professionals, from April 1st to May 31st, 2015. A total of 1290 healthcare professionals completed the questionnaires, including doctors, nurses, and allied health professionals working in both the public and private systems. Only 31.8% of participating respondents expressed an intention to receive the additional vaccine, despite that the majority of them agreed or strongly agreed that it would bring benefit to the community (88.9%), save lives (86.7%), reduce medical expenses (76.3%), satisfy public expectation (82.8%), and increase awareness of vaccination (86.1%). However, a significant proportion expressed concern that the vaccine could disturb the normal immunization schedule (45.5%); felt uncertain what to do in the next vaccination round (66.0%); perceived that the summer peak might not occur (48.2%); and believed that the summer peak might not be of the same virus (83.5%). Furthermore, 27.8% of all respondents expected that the additional vaccination could weaken the efficacy of previous vaccinations; 51.3% was concerned about side effects; and 61.3% estimated that there would be a low uptake rate. If the supply of vaccine was limited, higher priority groups were considered to include the elderly aged ≥65 years with chronic medical conditions (89.2%), the elderly living in residential care homes (87.4%), and long-stay residents of institutions for the disabled (80.7%). The strongest factors associated with accepting the additional vaccine included immunization with influenza vaccines in the past 3 years, higher perceived risk of contracting influenza, and higher perceived severity of the disease impact. The acceptability to an additional ad hoc influenza vaccination was low among healthcare professionals. This could have a negative impact on such additional vaccination campaigns since healthcare professionals are a key driver for vaccine acceptance. The discordance in perceived risk and acceptance of vaccination regarding self versus public deserves further evaluation
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