Soil Magnetism Research: State of the Art and Future Directions

Magnetic properties of soils have been highlighted as a primary detrimental environmental effect on the performance of geophysical systems for detection of unexploded ordnance (UXO) and mine targets. A recent workshop at Cranfield University, U.K., aimed to identify knowledge gaps related to soil magnetism. Eight invited speakers from multidisciplinary areas provided briefings on state‐of‐the‐art research linked to soil magnetism and geophysical sensing. Contributions from other participants provided additional insights from a range of disciplines through case studies and applications. The workshop included break‐out sessions to identify current gaps in knowledge and to determine priority areas for investment in research to further developments in UXO and mine detection in magnetic soil environments. Key recommendations for future research investments have been grouped in categories including soils, theory and modeling, instrumentation, and communication.

Indoor air: Contemporary sources, exposures and global implications

Recent 'Global Burden of Disease' studies have provided quantitative evidence of the significant role air pollution plays as a human health risk factor (Lim et al., The Lancet, 380: 2224–2260, 2012). Tobacco smoke, including second hand smoke, household air pollution from solid fuels and ambient particulate matter are among the top risks, leading to lower life expectancy around the world. Indoor air constitutes an environment particularly rich in different types of pollutants, originating from indoor sources, as well as penetrating from outdoors, mixing, interacting or growing (when considering microbes) under the protective enclosure of the building envelope. Therefore, it is not a simple task to follow the dynamics of the processes occurring there, or to quantify the outcomes of the processes in terms of pollutant concentrations and other characteristics. This is further complicated by limitations such as building access for the purpose of air quality monitoring, or the instrumentation which can be used indoors, because of their possible interference with the occupants comfort (due to their large size, noise generated or amount of air drawn). European studies apportioned contributions of indoor versus outdoor sources of indoor air contaminants in 26 European countries and quantified IAQ associated DALYs (Disability-Adjusted Life Years) in those countries (Jantunen et al., Promoting actions for healthy indoor air (IAIAQ), European Commission Directorate General for Health and Consumers, Luxembourg, 2011). At the same time, there has been an increase in research efforts around the world to better understand the sources, composition, dynamics and impacts of indoor air pollution. Particular focus has been directed towards the contemporary sources, novel pollutants and new detection methods. The importance of exposure assessment and personal exposure, the majority of which occurs in various indoor micro¬environments, has also been realized. Overall, this emerging knowledge has been providing input for global assessments of indoor environments, the impact of indoor pollutants and their science based management and control. It was a major outcome of recent international conferences that interdisciplinarity and especially a better colla¬boration between exposure and indoor sciences would be of high benefit for the health related evaluation of environmental stress factors and pollutants. A very good example is the combination of biomonitoring and indoor air, particle and dust analysis to study the exposure routes of semi volatile organic compounds (SVOCs). We have adopted the idea of combining the forces of exposure and indoor sciences for this Special Issue, identified new and challenging topics and have attracted colleagues who are top researchers in their field to provide their inputs. The Special Issue includes papers, which collectively present advances in current research topics and in our view, build the bridge between indoor and exposure sciences.

Molecular recognition of 2,4,6-trinitrotoluene by 6-aminohexanethiol and surface-enhanced Raman scattering sensor

2,4,6-trinitrotoluene (TNT) is one of the most commonly used nitro aromatic explosives in landmine, military and mining industry. This article demonstrates rapid and selective identification of TNT by surface-enhanced Raman spectroscopy (SERS) using 6-aminohexanethiol (AHT) as a new recognition molecule. First, Meisenheimer complex formation between AHT and TNT is confirmed by the development of pink colour and appearance of new band around 500 nm in UV-visible spectrum. Solution Raman spectroscopy study also supported the AHT:TNT complex formation by demonstrating changes in the vibrational stretching of AHT molecule between 2800-3000 cm−1. For surface enhanced Raman spectroscopy analysis, a self-assembled monolayer (SAM) of AHT is formed over the gold nanostructure (AuNS) SERS substrate in order to selectively capture TNT onto the surface. Electrochemical desorption and X-ray photoelectron studies are performed over AHT SAM modified surface to examine the presence of free amine groups with appropriate orientation for complex formation. Further, AHT and butanethiol (BT) mixed monolayer system is explored to improve the AHT:TNT complex formation efficiency. Using a 9:1 AHT:BT mixed monolayer, a very low detection limit (LOD) of 100 fM TNT was realized. The new method delivers high selectivity towards TNT over 2,4 DNT and picric acid. Finally, real sample analysis is demonstrated by the extraction and SERS detection of 302 pM of TNT from spiked.

Frequency response function based damage identification using principal component analysis and pattern recognition technique

Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.

Cloning and development of pathotype-specific SCAR marker associated with Sclerospora graminicola isolates from pearl millet

Downy mildew pathogen of pearl millet in India is associated with the spread of the highly virulent Sclerospora graminicola pathotype-1. Twenty-seven S. graminicola isolates were screened using 20 intersimple sequence repeats (ISSR). Dinucleotide repeat primer [17898A-(CA)(6) AC] amplified a similar to 600 bp fragment specific to five isolates of pathotype-1 (Sg 048, Sg 153, Sg 212, DM-11 and DM-90). The ISSR fragment linked with pathotype-1 was cloned successfully and sequenced. To convert ISSR fragments into pathotype-specific sequence characterised amplified region (SCAR) markers, PCR primers were designed using a sequence of the cloned DNA fragment. PCR amplification using SCAR primer pair (UOM3-Sg-Path1-F/R) amplified a single 284 bp band only in isolates of S. graminicola pathotype-1. This SCAR primer pair did not amplify the 284 bp product from the other five S. graminicola pathotypes or a negative control, which demonstrates primer specificity for pathotype-1. The SCAR primer pair (UOM3-Sg-Path1-F/R) obtained in this study will provide a valuable tool for rapid identification and specific detection of S. graminicola pathotype-1.

Trapping to monitor tephritid movement: Results, best practice, and assessment of alternatives

Movement of tephritid flies underpins their survival, reproduction, and ability to establish in new areas and is thus of importance when designing effective management strategies. Much of the knowledge currently available on tephritid movement throughout landscapes comes from the use of direct or indirect methods that rely on the trapping of individuals. Here, we review published experimental designs and methods from mark-release-recapture (MRR) studies, as well as other methods, that have been used to estimate movement of the four major tephritid pest genera (Bactrocera, Ceratitis, Anastrepha, and Rhagoletis). In doing so, we aim to illustrate the theoretical and practical considerations needed to study tephritid movement. MRR studies make use of traps to directly estimate the distance that tephritid species can move within a generation and to evaluate the ecological and physiological factors that influence dispersal patterns. MRR studies, however, require careful planning to ensure that the results obtained are not biased by the methods employed, including marking methods, trap properties, trap spacing, and spatial extent of the trapping array. Despite these obstacles, MRR remains a powerful tool for determining tephritid movement, with data particularly required for understudied species that affect developing countries. To ensure that future MRR studies are successful, we suggest that site selection be carefully considered and sufficient resources be allocated to achieve optimal spacing and placement of traps in line with the stated aims of each study. An alternative to MRR is to make use of indirect methods for determining movement, or more correctly, gene flow, which have become widely available with the development of molecular tools. Key to these methods is the trapping and sequencing of a suitable number of individuals to represent the genetic diversity of the sampled population and investigate population structuring using nuclear genomic markers or non-recombinant mitochondrial DNA markers. Microsatellites are currently the preferred marker for detecting recent population displacement and provide genetic information that may be used in assignment tests for the direct determination of contemporary movement. Neither MRR nor molecular methods, however, are able to monitor fine-scale movements of individual flies. Recent developments in the miniaturization of electronics offer the tantalising possibility to track individual movements of insects using harmonic radar. Computer vision and radio frequency identification tags may also permit the tracking of fine-scale movements by tephritid flies by automated resampling, although these methods come with the same problems as traditional traps used in MRR studies. Although all methods described in this chapter have limitations, a better understanding of tephritid movement far outweighs the drawbacks of the individual methods because of the need for this information to manage tephritid populations.

Progress towards the eradication of mikania vine (Mikania micrantha) and limnocharis (Limnocharis flava) in northern Australia.

To eradicate a weed invasion, its extent must be delimited and each infestation must be extirpated. Measures for both of these criteria are utilized to assess the progress of current eradication programs targeting mikania vine and limnocharis in northern Australia. The known infested area for each species is less than 5 ha and has remained largely static for the last 3 or more years against a backdrop of refined and enhanced detection methods. This suggests that delimitation has been approached, if not achieved. Different methods of detection have their places, relative to the stage of the program and the spatial distribution of infestations. Although all known infestations of both species are effectively monitored and controlled, ongoing emergence from persistent seed banks limits progress towards the extirpation of infestations to a slow, but measurable, rate. Nomenclature: Glyphosate. N-phosphonomethyl)glycine; fluroxypyr, [(4-amino-3,5-dichloro-6-fluoro-2-pyridinyl)oxy]acetic acid; limnocharis, Limnocharis flava (L.) Buchenau LIFL5; mikania vine (mile-a-minute), Mikania micrantha Kunth MIKMI.

Molecular genetics and evolution of Puumala hantavirus

Puumala virus (PUUV) is the causative agent of nephropathia epidemica (NE), a mild form of hemorrhagic fever with renal syndrome. Finland has the highest documented incidence of NE with around 1000 cases diagnosed annually. PUUV is also found in other Scandinavian countries, Central Europe and the European part of Russia. PUUV belongs to the genus Hantavirus in the family Bunyaviridae. Hantaviruses are rodent-borne viruses each carried by a specific host that is persistently and asymptomatically infected by the virus. PUUV is carried by the bank voles (Myodes glareolus, previously known as Clethrionomys glareolus). Hantaviruses have co-evolved with their carrier rodents for millions of years and these host animals are the evolutionary scene of hantaviruses. In this study, PUUV sequences were recovered from bank voles captured in Denmark and Russian Karelia to study the evolution of PUUV in Scandinavia. Phylogenetic analysis of these strains showed a geographical clustering of genetic variants following the presumable migration pattern of bank voles during the recolonization of Scandinavia after the last ice age approximately 10 000 years ago. The currently known PUUV genome sequences were subjected to in-depth phylogenetic analyses and the results showed that genetic drift seems to be the major mechanism of PUUV evolution. In general, PUUV seems to evolve quite slowly following a molecular clock. We also found evidence for recombination in the evolution of some genetic lineages of PUUV. Viral microevolution was studied in controlled virus transmission in colonized bank voles and changes in quasispecies dynamics were recorded as the virus was transmitted from one animal to another. We witnessed PUUV evolution in vivo, as one synonymous mutation became repeatedly fixed in the viral genome during the experiment. The detailed knowledge on the PUUV diversity was used to establish new sensitive and specific detection methods for this virus. Direct viral invasion of the hypophysis was demonstrated for the first time in a lethal case of NE. PUUV detection was done by immunohistochemistry, in situ hybridization and RT-nested-PCR of the autopsy tissue samples.

Effect of phenolic-rich plant materials on protein and lipid oxidation reactions

The antioxidant activity of natural plant materials rich in phenolic compounds is being widely investigated for protection of food products sensitive to oxidative reactions. In this thesis plant materials rich in phenolic compounds were studied as possible antioxidants to prevent protein and lipid oxidation reactions in different food matrixes such as pork meat patties and corn oil-in water emulsions. Loss of anthocyanins was also measured during oxidation in corn oil-in-water emulsions. In addition, the impact of plant phenolics on amino acid level was studied using tryptophan as a model compound to elucidate their role in preventing the formation of tryptophan oxidation products. A high-performance liquid chromatography (HPLC) method with ultraviolet and fluorescence detection (UV-FL) was developed that enabled fast investigation of formation of tryptophan derived oxidation products. Byproducts of oilseed processes such as rapeseed (Brassica rapa L.), camelina (Camelina sativa) and soy meal (Glycine max L.) as well as Scots pine bark (Pinus sylvestris) and several reference compounds were shown to act as antioxidants toward both protein and lipid oxidation in cooked pork meat patties. In meat, the antioxidant activity of camelina, rapeseed and soy meal were more pronounced when used in combination with a commercial rosemary extract (Rosmarinus officinalis). Berry phenolics such as black currant (Ribes nigrum) anthocyanins and raspberry (Rubus idaeus) ellagitannins showed potent antioxidant activity in corn oil-in-water emulsions toward lipid oxidation with and without β-lactoglobulin. The antioxidant effect was more pronounced in the presence of β-lactoglobulin. The berry phenolics also inhibited the oxidation of tryptophan and cysteine side chains of β-lactoglobulin. The results show that the amino acid side chains were oxidized prior the propagation of lipid oxidation, thereby inhibiting fatty acid scission. In addition, the concentration and color of black currant anthocyanins decreased during the oxidation. Oxidation of tryptophan was investigated in two different oxidation models with hydrogen peroxide (H2O2) and hexanal/FeCl2. Oxidation of tryptophan in both models resulted in oxidation products such as 3a-hydroxypyrroloindole-2-carboxylic acid, dioxindolylalanine, 5-hydroxy-tryptophan, kynurenine, N-formylkynurenine and β-oxindolylalanine. However, formation of tryptamine was only observed in tryptophan oxidized in the presence of H2O2. Pine bark phenolics, black currant anthocyanins, camelina meal phenolics as well as cranberry proanthocyanidins (Vaccinium oxycoccus) provided the best antioxidant effect toward tryptophan and its oxidation products when oxidized with H2O2. The tryptophan modifications formed upon hexanal/FeCl2 treatment were efficiently inhibited by camelina meal followed by rapeseed and soy meal. In contrast, phenolics from raspberry, black currant, and rowanberry (Sorbus aucuparia) acted as weak prooxidants. This thesis contributes to elucidating the effects of natural phenolic compounds as potential antioxidants in order to control and prevent protein and lipid oxidation reactions. Understanding the relationship between phenolic compounds and proteins as well as lipids could lead to the development of new, effective, and multifunctional antioxidant strategies that could be used in food, cosmetic and pharmaceutical applications.

Problems and Algorithms for Sequence Segmentations

The analysis of sequential data is required in many diverse areas such as telecommunications, stock market analysis, and bioinformatics. A basic problem related to the analysis of sequential data is the sequence segmentation problem. A sequence segmentation is a partition of the sequence into a number of non-overlapping segments that cover all data points, such that each segment is as homogeneous as possible. This problem can be solved optimally using a standard dynamic programming algorithm. In the first part of the thesis, we present a new approximation algorithm for the sequence segmentation problem. This algorithm has smaller running time than the optimal dynamic programming algorithm, while it has bounded approximation ratio. The basic idea is to divide the input sequence into subsequences, solve the problem optimally in each subsequence, and then appropriately combine the solutions to the subproblems into one final solution. In the second part of the thesis, we study alternative segmentation models that are devised to better fit the data. More specifically, we focus on clustered segmentations and segmentations with rearrangements. While in the standard segmentation of a multidimensional sequence all dimensions share the same segment boundaries, in a clustered segmentation the multidimensional sequence is segmented in such a way that dimensions are allowed to form clusters. Each cluster of dimensions is then segmented separately. We formally define the problem of clustered segmentations and we experimentally show that segmenting sequences using this segmentation model, leads to solutions with smaller error for the same model cost. Segmentation with rearrangements is a novel variation to the segmentation problem: in addition to partitioning the sequence we also seek to apply a limited amount of reordering, so that the overall representation error is minimized. We formulate the problem of segmentation with rearrangements and we show that it is an NP-hard problem to solve or even to approximate. We devise effective algorithms for the proposed problem, combining ideas from dynamic programming and outlier detection algorithms in sequences. In the final part of the thesis, we discuss the problem of aggregating results of segmentation algorithms on the same set of data points. In this case, we are interested in producing a partitioning of the data that agrees as much as possible with the input partitions. We show that this problem can be solved optimally in polynomial time using dynamic programming. Furthermore, we show that not all data points are candidates for segment boundaries in the optimal solution.

Deploying speaker verification techniques to vision-based RPA detect and avoid

Detect and Avoid (DAA) technology is widely acknowledged as a critical enabler for unsegregated Remote Piloted Aircraft (RPA) operations, particularly Beyond Visual Line of Sight (BVLOS). Image-based DAA, in the visible spectrum, is a promising technological option for addressing the challenges DAA presents. Two impediments to progress for this approach are the scarcity of available video footage to train and test algorithms, in conjunction with testing regimes and specifications which facilitate repeatable, statistically valid, performance assessment. This paper includes three key contributions undertaken to address these impediments. In the first instance, we detail our progress towards the creation of a large hybrid collision and near-collision encounter database. Second, we explore the suitability of techniques employed by the biometric research community (Speaker Verification and Language Identification), for DAA performance optimisation and assessment. These techniques include Detection Error Trade-off (DET) curves, Equal Error Rates (EER), and the Detection Cost Function (DCF). Finally, the hybrid database and the speech-based techniques are combined and employed in the assessment of a contemporary, image based DAA system. This system includes stabilisation, morphological filtering and a Hidden Markov Model (HMM) temporal filter.

Detection of microorganisms using biosensors-a smarter way towards detection techniques.

Along with useful microorganisms, there are some that cause potential damage to the animals and plants. Detection and identification of these harmful organisms in a cost and time effective way is a challenge for the researchers. The future of detection methods for microorganisms shall be guided by biosensor, which has already contributed enormously in sensing and detection technology. Here, we aim to review the use of various biosensors, developed by integrating the biological and physicochemical/mechanical properties (of tranducers), which can have enormous implication in healthcare, food, agriculture and biodefence. We have also highlighted the ways to improve the functioning of the biosensor.

A method of fraud & intrusion detection for e-payment systems in mobile

The need for paying with mobile devices has urged the development of payment systems for mobile electronic commerce. In this paper we have considered two important abuses in electronic payments systems for detection. The fraud, which is an intentional deception accomplished to secure an unfair gain, and an intrusion which are any set of actions that attempt to compromise the integrity, confidentiality or availability of a resource. Most of the available fraud and intrusion detection systems for e-payments are specific to the systems where they have been incorporated. This paper proposes a generic model called as Activity-Event-Symptoms(AES) model for detecting fraud and intrusion attacks which appears during payment process in the mobile commerce environment. The AES model is designed to identify the symptoms of fraud and intrusions by observing various events/transactions occurs during mobile commerce activity. The symptoms identification is followed by computing the suspicion factors for event attributes, and the certainty factor for a fraud and intrusion is generated using these suspicion factors. We have tested the proposed system by conducting various case studies, on the in-house established mobile commerce environment over wired and wire-less networks test bed.

Examination of EXOSC4 as a new prognostic marker and a novel therapeutic avenue in lung adenocarcinoma

Although prevention and early detection of the disease greatly improved over the past few years, lung cancer remains the leading cause of cancer deaths. In order to be able to treat a larger population, we are in urgent need for novel treatments. While it is known that DNA repair genes play a major role in the oncogenic transformation, they also represent a weakness of cancers that constitute a therapeutic opportunity. To identify novel DNA repair genes implicated in Lung cancers, we conducted an in silico investigation to identify genes co-regulated with two DNA repair factors, BRCA2 and hSSB1. This approach allowed for the identification of EXOSC4, a component of the RNA Exosome machinery, as a potential factor involved in the maintenance of genome stability and that is deregulated in lung cancer.

Development of a probe-based blotting technique for the detection of Tobacco streak virus

Digoxigenin (DIG)-labeled DNA probe was developed for a sensitive and rapid detection of the Tobacco streak virus (TSV) isolates in India by dot-blot and tissue print hybridization techniques. DIG-labeled DNA probe complementary to the coat protein (CP) region of TSV sunflower isolate was designed and used to detect the TSV presence at field levels. Dot-blot hybridization was used to check a large number of TSV isolates with a single probe. In addition, a sensitivity of the technique was examined with the different sample extraction methods. Another technique, the tissue blot hybridization offered a simple, reliable procedure and did not require a sample processing. Thus, both non-radioactively labeled probe techniques could facilitate the sample screening during TSV outbreaks and offer an advantage in quarantine services.