Liquid ultraviolet matrix-assisted laser desorption/ionization mass spectrometry for automated proteomic analysis

We have combined several key sample preparation steps for the use of a liquid matrix system to provide high analytical sensitivity in automated ultraviolet - matrix-assisted laser desorption/ ionisation - mass spectrometry (UV-MALDI-MS). This new sample preparation protocol employs a matrix-mixture which is based on the glycerol matrix-mixture described by Sze et al. U. Am. Soc. Mass Spectrom. 1998, 9, 166-174). The low-ferntomole sensitivity that is achievable with this new preparation protocol enables proteomic analysis of protein digests comparable to solid-state matrix systems. For automated data acquisition and analysis, the MALDI performance of this liquid matrix surpasses the conventional solid-state MALDI matrices. Besides the inherent general advantages of liquid samples for automated sample preparation and data acquisition the use of the presented liquid matrix significantly reduces the extent of unspecific ion signals in peptide mass fingerprints compared to typically used solid matrices, such as 2,5-dihydrox-ybenzoic acid (DHB) or alpha-cyano-hydroxycinnamic acid (CHCA). In particular, matrix and lowmass ion signals and ion signals resulting from cation adduct formation are dramatically reduced. Consequently, the confidence level of protein identification by peptide mass mapping of in-solution and in-gel digests is generally higher.

Automated Nuclear Analysis of Leishmania major Telomeric Clusters Reveals Changes in Their Organization during the Parasite's Life Cycle

Parasite virulence genes are usually associated with telomeres. The clustering of the telomeres, together with their particular spatial distribution in the nucleus of human parasites such as Plasmodium falciparum and Trypanosoma brucei, has been suggested to play a role in facilitating ectopic recombination and in the emergence of new antigenic variants. Leishmania parasites, as well as other trypanosomes, have unusual gene expression characteristics, such as polycistronic and constitutive transcription of protein-coding genes. Leishmania subtelomeric regions are even more unique because unlike these regions in other trypanosomes they are devoid of virulence genes. Given these peculiarities of Leishmania, we sought to investigate how telomeres are organized in the nucleus of Leishmania major parasites at both the human and insect stages of their life cycle. We developed a new automated and precise method for identifying telomere position in the three-dimensional space of the nucleus, and we found that the telomeres are organized in clusters present in similar numbers in both the human and insect stages. While the number of clusters remained the same, their distribution differed between the two stages. The telomeric clusters were found more concentrated near the center of the nucleus in the human stage than in the insect stage suggesting reorganization during the parasite's differentiation process between the two hosts. These data provide the first 3D analysis of Leishmania telomere organization. The possible biological implications of these findings are discussed.

Development of automated image analysis tools for verification of radiotherapy field accuracy with an electronic portal imaging device

The successful management of cancer with radiation relies on the accurate deposition of a prescribed dose to a prescribed anatomical volume within the patient. Treatment set-up errors are inevitable because the alignment of field shaping devices with the patient must be repeated daily up to eighty times during the course of a fractionated radiotherapy treatment. With the invention of electronic portal imaging devices (EPIDs), patient's portal images can be visualized daily in real-time after only a small fraction of the radiation dose has been delivered to each treatment field. However, the accuracy of human visual evaluation of low-contrast portal images has been found to be inadequate. The goal of this research is to develop automated image analysis tools to detect both treatment field shape errors and patient anatomy placement errors with an EPID. A moments method has been developed to align treatment field images to compensate for lack of repositioning precision of the image detector. A figure of merit has also been established to verify the shape and rotation of the treatment fields. Following proper alignment of treatment field boundaries, a cross-correlation method has been developed to detect shifts of the patient's anatomy relative to the treatment field boundary. Phantom studies showed that the moments method aligned the radiation fields to within 0.5mm of translation and 0.5$\sp\circ$ of rotation and that the cross-correlation method aligned anatomical structures inside the radiation field to within 1 mm of translation and 1$\sp\circ$ of rotation. A new procedure of generating and using digitally reconstructed radiographs (DRRs) at megavoltage energies as reference images was also investigated. The procedure allowed a direct comparison between a designed treatment portal and the actual patient setup positions detected by an EPID. Phantom studies confirmed the feasibility of the methodology. Both the moments method and the cross-correlation technique were implemented within an experimental radiotherapy picture archival and communication system (RT-PACS) and were used clinically to evaluate the setup variability of two groups of cancer patients treated with and without an alpha-cradle immobilization aid. The tools developed in this project have proven to be very effective and have played an important role in detecting patient alignment errors and field-shape errors in treatment fields formed by a multileaf collimator (MLC). ^

Automated data analysis to rapidly derive and communicate ecological insights from satellite-tag data : A case study of reintroduced red kites

Acknowledgements We thank Andrew Spink (Noldus Information Technology) and the Blogging Birds team members Peter Kindness and Abdul Adeniyi for their valuable contributions to this paper. John Fryxell, Chris Thaxter and Arjun Amar provided valuable comments on an earlier version. The study was part of the Digital Conservation project of dot.rural, the University of Aberdeen’s Digital Economy Research Hub, funded by RCUK (grant reference EP/G066051/1).

From "ecological tourism" to "ecotourism": lexical analysis of an emerging tourism

From ecological tourism to ecotourism: lexical analysis of an emerging tourism. This article deals with the lexicon created in connection with a recent form of tourism: the ecological tourism or ecotourism. The rise of this type of tourism encourages the creation of new concepts and products that are named with new words and expressions with different procedures of formation. From the name itself ecotourism, then expressed as the acronym ecotourism, we analyze the formation of other related words, as well as their formal variation and use. For this, we have worked with a specific corpus of electronic tourist texts and different digital sources and databases.

Dynalog: an automated dynamic analysis framework for characterizing android applications

Android is becoming ubiquitous and currently has the largest share of the mobile OS market with billions of application downloads from the official app market. It has also become the platform most targeted by mobile malware that are becoming more sophisticated to evade state-of-the-art detection approaches. Many Android malware families employ obfuscation techniques in order to avoid detection and this may defeat static analysis based approaches. Dynamic analysis on the other hand may be used to overcome this limitation. Hence in this paper we propose DynaLog, a dynamic analysis based framework for characterizing Android applications. The framework provides the capability to analyse the behaviour of applications based on an extensive number of dynamic features. It provides an automated platform for mass analysis and characterization of apps that is useful for quickly identifying and isolating malicious applications. The DynaLog framework leverages existing open source tools to extract and log high level behaviours, API calls, and critical events that can be used to explore the characteristics of an application, thus providing an extensible dynamic analysis platform for detecting Android malware. DynaLog is evaluated using real malware samples and clean applications demonstrating its capabilities for effective analysis and detection of malicious applications.

Automated tumour annotation and analysis for molecular pathology using TissueMark (R)

Objective: Molecular pathology relies on identifying anomalies using PCR or analysis of DNA/RNA. This is important in solid tumours where molecular stratification of patients define targeted treatment. These molecular biomarkers rely on examination of tumour, annotation for possible macro dissection/tumour cell enrichment and the estimation of % tumour. Manually marking up tumour is error prone. Method: We have developed a method for automated tumour mark-up and % cell calculations using image analysis called TissueMark® based on texture analysis for lung, colorectal and breast (cases=245, 100, 100 respectively). Pathologists marked slides for tumour and reviewed the automated analysis. A subset of slides was manually counted for tumour cells to provide a benchmark for automated image analysis. Results: There was a strong concordance between pathological and automated mark-up (100 % acceptance rate for macro-dissection). We also showed a strong concordance between manually/automatic drawn boundaries (median exclusion/inclusion error of 91.70 %/89 %). EGFR mutation analysis was precisely the same for manual and automated annotation-based macrodissection. The annotation accuracy rates in breast and colorectal cancer were 83 and 80 % respectively. Finally, region-based estimations of tumour percentage using image analysis showed significant correlation with actual cell counts. Conclusion: Image analysis can be used for macro-dissection to (i) annotate tissue for tumour and (ii) estimate the % tumour cells and represents an approach to standardising/improving molecular diagnostics.

Rapid, automated detection of stem canker symptoms in woody perennials using artificial neural network analysis

Background Pseudomonas syringae can cause stem necrosis and canker in a wide range of woody species including cherry, plum, peach, horse chestnut and ash. The detection and quantification of lesion progression over time in woody tissues is a key trait for breeders to select upon for resistance. Results In this study a general, rapid and reliable approach to lesion quantification using image recognition and an artificial neural network model was developed. This was applied to screen both the virulence of a range of P. syringae pathovars and the resistance of a set of cherry and plum accessions to bacterial canker. The method developed was more objective than scoring by eye and allowed the detection of putatively resistant plant material for further study. Conclusions Automated image analysis will facilitate rapid screening of material for resistance to bacterial and other phytopathogens, allowing more efficient selection and quantification of resistance responses.

An automated failure mode and effect analysis based on high-level design specificication with behavior trees

Formal methods have significant benefits for developing safety critical systems, in that they allow for correctness proofs, model checking safety and liveness properties, deadlock checking, etc. However, formal methods do not scale very well and demand specialist skills, when developing real-world systems. For these reasons, development and analysis of large-scale safety critical systems will require effective integration of formal and informal methods. In this paper, we use such an integrative approach to automate Failure Modes and Effects Analysis (FMEA), a widely used system safety analysis technique, using a high-level graphical modelling notation (Behavior Trees) and model checking. We inject component failure modes into the Behavior Trees and translate the resulting Behavior Trees to SAL code. This enables us to model check if the system in the presence of these faults satisfies its safety properties, specified by temporal logic formulas. The benefit of this process is tool support that automates the tedious and error-prone aspects of FMEA.

Tool-supported dataflow analysis of a security-critical embedded device

Defence organisations perform information security evaluations to confirm that electronic communications devices are safe to use in security-critical situations. Such evaluations include tracing all possible dataflow paths through the device, but this process is tedious and error-prone, so automated reachability analysis tools are needed to make security evaluations faster and more accurate. Previous research has produced a tool, SIFA, for dataflow analysis of basic digital circuitry, but it cannot analyse dataflow through microprocessors embedded within the circuit since this depends on the software they run. We have developed a static analysis tool that produces SIFA compatible dataflow graphs from embedded microcontroller programs written in C. In this paper we present a case study which shows how this new capability supports combined hardware and software dataflow analyses of a security critical communications device.

Improved ultra-performance liquid chromatography with electrospray ionization quadrupole-time-of-flight high-definition mass spectrometry method for the rapid analysis of the chemical constituents of a typical medical formula: Liuwei Dihuang Wan

Liuwei Dihuang Wan (LWD), a classic Chinese medicinal formulae, has been used to improve or restore declined functions related to aging and geriatric diseases, such as impaired mobility, vision, hearing, cognition and memory. It has attracted increasingly much attention as one of the most popular and valuable herbal medicines. However, the systematic analysis of the chemical constituents of LDW is difficult and thus has not been well established. In this paper, a rapid, sensitive and reliable ultra-performance liquid chromatography with electrospray ionization quadrupole time-of-flight high-definition mass spectrometry (UPLC-ESI-Q-TOF-MS) method with automated MetaboLynx analysis in positive and negative ion mode was established to characterize the chemical constituents of LDW. The analysis was performed on a Waters UPLCTM HSS T3 using a gradient elution system. MS/MS fragmentation behavior was proposed for aiding the structural identification of the components. Under the optimized conditions, a total of 50 peaks were tentatively characterized by comparing the retention time and MS data. It is concluded that a rapid and robust platform based on UPLC-ESI-Q-TOF-MS has been successfully developed for globally identifying multiple-constituents of traditional Chinese medicine prescriptions. This is the first report on systematic analysis of the chemical constituents of LDW. This article is protected by copyright. All rights reserved.

Monte Carlo analysis of a puzzle game

When a puzzle game is created, its design parameters must be chosen to allow solvable and interesting challenges to be created for the player. We investigate the use of random sampling as a computationally inexpensive means of automated game analysis, to evaluate the BoxOff family of puzzle games. This analysis reveals useful insights into the game, such as the surprising fact that almost 100% of randomly generated challenges have a solution, but less than 10% will be solved using strictly random play, validating the inventor’s design choices. We show the 1D game to be trivial and the 3D game to be viable.

Rhythm Analysis during Cardiopulmonary Resuscitation: Past, Present, and Future

Survival from out-of-hospital cardiac arrest depends largely on two factors: early cardiopulmonary resuscitation (CPR) and early defibrillation. CPR must be interrupted for a reliable automated rhythm analysis because chest compressions induce artifacts in the ECG. Unfortunately, interrupting CPR adversely affects survival. In the last twenty years, research has been focused on designing methods for analysis of ECG during chest compressions. Most approaches are based either on adaptive filters to remove the CPR artifact or on robust algorithms which directly diagnose the corrupted ECG. In general, all the methods report low specificity values when tested on short ECG segments, but how to evaluate the real impact on CPR delivery of continuous rhythm analysis during CPR is still unknown. Recently, researchers have proposed a new methodology to measure this impact. Moreover, new strategies for fast rhythm analysis during ventilation pauses or high-specificity algorithms have been reported. Our objective is to present a thorough review of the field as the starting point for these late developments and to underline the open questions and future lines of research to be explored in the following years.