A simple, sensitive and selective quantum-dot-based western blot method for the simultaneous detection of multiple targets from cell lysates

Quantum dots (Qdots) are fluorescent nanoparticles that have great potential as detection agents in biological applications. Their optical properties, including photostability and narrow, symmetrical emission bands with large Stokes shifts, and the potential for multiplexing of many different colours, give them significant advantages over traditionally used fluorescent dyes. Here, we report the straightforward generation of stable, covalent quantum dot-protein A/G bioconjugates that will be able to bind to almost any IgG antibody, and therefore can be used in many applications. An additional advantage is that the requirement for a secondary antibody is removed, simplifying experimental design. To demonstrate their use, we show their application in multiplexed western blotting. The sensitivity of Qdot conjugates is found to be superior to fluorescent dyes, and comparable to, or potentially better than, enhanced chemiluminescence. We show a true biological validation using a four-colour multiplexed western blot against a complex cell lysate background, and have significantly improved previously reported non-specific binding of the Qdots to cellular proteins.

Protein oxidation:role in signalling and detection by mass spectrometry

Proteins can undergo a wide variety of oxidative post-translational modifications (oxPTM); while reversible modifications are thought to be relevant in physiological processes, non-reversible oxPTM may contribute to pathological situations and disease. The oxidant is also important in determining the type of oxPTM, such as oxidation, chlorination or nitration. The best characterized oxPTMs involved in signalling modulation are partial oxidations of cysteine to disulfide, glutathionylated or sulfenic acid forms that can be reversed by thiol reductants. Proline hydroxylation in HIF signalling is also quite well characterized, and there is increasing evidence that specific oxidations of methionine and tyrosine may have some biological roles. For some proteins regulated by cysteine oxidation, the residues and molecular mechanism involved have been extensively studied and are well understood, such as the protein tyrosine phosphatase PTP1B and MAP3 kinase ASK1, as well as transcription factor complex Keap1-Nrf2. The advances in understanding of the role oxPTMs in signalling have been facilitated by advances in analytical technology, in particular tandem mass spectrometry techniques. Combinations of peptide sequencing by collisionally induced dissociation and precursor ion scanning or neutral loss to select for specific oxPTMs have proved very useful for identifying oxidatively modified proteins and mapping the sites of oxidation. The development of specific labelling and enrichment procedures for S-nitrosylation or disulfide formation has proved invaluable, and there is ongoing work to establish analogous methods for detection of nitrotyrosine and other modifications.

Antibody detection of Burkholderia pseudommallei and Burkholderia mallei

Monoclonal and polyclonaI antibodies have been produced for use in immunological assays for the detection of Burkholderia pseudomallei and Burkholderia mallei. Monoclonal antibodies recognising a high molecular weight polysaccharide material found in some strains of both species have been shown to be effective in recognising B. pseudomallei and B. mallei and distinguishing them from other organisms. The high molecular weight polysaccharide material is thought to be the capsule of B. pseudomallei and B. mallei and may have important links with virulence. B. pseudomallei and B. mallei are known to be closely related, sharing many epitopes, but antigenic variation has been demonstrated within both the species. The lipopolysaccharide from strains of B. pseudomal/ei and B. mallei has been isolated and the silver stain profiles found to be visually very similar. A monoclonal antibody raised to B. mallei LPS has been found to recognise both B. mallei and B. pseudomallei strains. However, in a small number of B. pseudomallei strains a visually atypical LPS profile has been demonstrated. A monoclonal ant ibody rai sed against this atypical LPS showed no recognition of the typical LPS profile of either B. mallei or B. pseudomallei. This atypical LPS structure has not been reported and may be immunologically distinct from the typical LPS. Molecular biology and antibody engineering techniques have been used in an attempt to produce single-chain antibody fragments reactive to B. pseudomallei. Sequencing of one of the single-chain antibody fragments produced showed high homology with murine immunoglobulin genes, but none of the single-chain antibody fragments were found to be specific to B. pselldomallei.

Redox signalling and detection of protein oxidation by mass spectrometry

It is now recognised that redox control of proteins plays an important role in many signalling pathways both in health and disease. Proteins can undergo a wide variety of oxidative post-translational modifications (oxPTM); while the reversible modifications are thought to be most important in physiological processes, non-reversible oxPTM may contribute to pathological situations and disease. The oxidant is also important in determining the type of oxPTM (chlorination, nitration, etc.), and the susceptibilities of residues vary depending on their structural location. The best characterized oxPTMs involved in signalling modulation are partial oxidations of cysteine to the disulfide, glutathionylated or sulfenic acid forms, but there is increasing evidence that specific oxidations of methionine and tyrosine may have some biological roles. Well understood examples of oxidative regulation include protein tyrosine phosphatases, e.g. PTP1B/C, and members of the MAPK pathways such as MEKK1 and ASK1. Transcription factors such as NFkB and Nrf-2 are also regulated by redox-active cysteines. Improved methods for analysing specific oxPTMs in biological samples are critical for understanding the physiological and pathological roles of these changes, and tandem or MS3 mass spectrometry techniques interfaced with nano-LC separation are being now used. MS3 fragmentation markers for a variety of oxidized residues including tyrosine, tryptophan and proline have been identified, and a precursor ion scanning method that allows the selective identification of these oxPTMs in complex samples has been developed. Such advances in technology offer potential for biomarker development, disease diagnosis and understanding pathology.

Protein oxidative modifications:new mass spectrometry approaches to detection in biological samples

In inflammatory diseases, release of oxidants leads to oxidative damage to proteins. The precise nature of oxidative damage to individual proteins depends on the oxidant involved. Chlorination and nitration are markers of modification by the myeloperoxidase-H2O2-Cl- system and nitric oxide-derived oxidants, respectively. Although these modifications can be detected by western blotting, currently no reliable method exists to identify the specific sites damage to individual proteins in complex mixtures such as clinical samples. We are developing novel LCMS2 and precursor ion scanning methods to address this. LC-MS2 allows separation of peptides and detection of mass changes in oxidized residues on fragmentation of the peptides. We have identified indicative fragment ions for chlorotyrosine, nitrotyrosine, hydroxytyrosine and hydroxytryptophan. A nano-LC/MS3 method involving the dissociation of immonium ions to give specific fragments for the oxidized residues has been developed to overcome the problem of false positives from ions isobaric to these immonium ions that exist in unmodified peptides. The approach has proved able to identify precise protein modifications in individual proteins and mixtures of proteins. An alternative methodology involves multiple reaction monitoring for precursors and fragment ions are specific to oxidized and chlorinated proteins, and this has been tested with human serum albumin. Our ultimate aim is to apply this methodology to the detection of oxidative post-translational modifications in clinical samples for disease diagnosis, monitoring the outcomes of therapy, and improved understanding of disease biochemistry.

Increased prevalence of sex chromosome aneuploidies in specific language impairment and dyslexia

Aim: Sex chromosome aneuploidies increase the risk of spoken or written language disorders but individuals with specific language impairment (SLI) or dyslexia do not routinely undergo cytogenetic analysis. We assess the frequency of sex chromosome aneuploidies in individuals with language impairment or dyslexia. Method: Genome-wide single nucleotide polymorphism genotyping was performed in three sample sets: a clinical cohort of individuals with speech and language deficits (87 probands: 61 males, 26 females; age range 4 to 23 years), a replication cohort of individuals with SLI, from both clinical and epidemiological samples (209 probands: 139 males, 70 females; age range 4 to 17 years), and a set of individuals with dyslexia (314 probands: 224 males, 90 females; age range 7 to 18 years). Results: In the clinical language-impaired cohort, three abnormal karyotypic results were identified in probands (proband yield 3.4%). In the SLI replication cohort, six abnormalities were identified providing a consistent proband yield (2.9%). In the sample of individuals with dyslexia, two sex chromosome aneuploidies were found giving a lower proband yield of 0.6%. In total, two XYY, four XXY (Klinefelter syndrome), three XXX, one XO (Turner syndrome), and one unresolved karyotype were identified. Interpretation: The frequency of sex chromosome aneuploidies within each of the three cohorts was increased over the expected population frequency (approximately 0.25%) suggesting that genetic testing may prove worthwhile for individuals with language and literacy problems and normal non-verbal IQ. Early detection of these aneuploidies can provide information and direct the appropriate management for individuals. © 2013 The Authors. Developmental Medicine & Child Neurology published by John Wiley & Sons Ltd on behalf of Mac Keith Press.

Influential DMUs and outlier detection in data envelopment analysis with an application to health care

This paper explains some drawbacks on previous approaches for detecting influential observations in deterministic nonparametric data envelopment analysis models as developed by Yang et al. (Annals of Operations Research 173:89-103, 2010). For example efficiency scores and relative entropies obtained in this model are unimportant to outlier detection and the empirical distribution of all estimated relative entropies is not a Monte-Carlo approximation. In this paper we developed a new method to detect whether a specific DMU is truly influential and a statistical test has been applied to determine the significance level. An application for measuring efficiency of hospitals is used to show the superiority of this method that leads to significant advancements in outlier detection. © 2014 Springer Science+Business Media New York.

Protein lipoxidation:detection strategies and challenges

Enzymatic and non-enzymatic lipid metabolism can give rise to reactive species that may covalently modify cellular or plasma proteins through a process known as lipoxidation. Under basal conditions, protein lipoxidation can contribute to normal cell homeostasis and participate in signaling or adaptive mechanisms, as exemplified by lipoxidation of Ras proteins or of the cytoskeletal protein vimentin, both of which behave as sensors of electrophilic species. Nevertheless, increased lipoxidation under pathological conditions may lead to deleterious effects on protein structure or aggregation. This can result in impaired degradation and accumulation of abnormally folded proteins contributing to pathophysiology, as may occur in neurodegenerative diseases. Identification of the protein targets of lipoxidation and its functional consequences under pathophysiological situations can unveil the modification patterns associated with the various outcomes, as well as preventive strategies or potential therapeutic targets. Given the wide structural variability of lipid moieties involved in lipoxidation, highly sensitive and specific methods for its detection are required. Derivatization of reactive carbonyl species is instrumental in the detection of adducts retaining carbonyl groups. In addition, use of tagged derivatives of electrophilic lipids enables enrichment of lipoxidized proteins or peptides. Ultimate confirmation of lipoxidation requires high resolution mass spectrometry approaches to unequivocally identify the adduct and the targeted residue. Moreover, rigorous validation of the targets identified and assessment of the functional consequences of these modifications are essential. Here we present an update on methods to approach the complex field of lipoxidation along with validation strategies and functional assays illustrated with well-studied lipoxidation targets.

A sensing mechanism for the detection of carbon nanotubes using selective photoluminescent probes based on ionic complexes with organic dyes

The multifunctional properties of carbon nanotubes (CNTs) make them a powerful platform for unprecedented innovations in a variety of practical applications. As a result of the surging growth of nanotechnology, nanotubes present a potential problem as an environmental pollutant, and as such, an efficient method for their rapid detection must be established. Here, we propose a novel type of ionic sensor complex for detecting CNTs – an organic dye that responds sensitively and selectively to CNTs with a photoluminescent signal. The complexes are formed through Coulomb attractions between dye molecules with uncompensated charges and CNTs covered with an ionic surfactant in water. We demonstrate that the photoluminescent excitation of the dye can be transferred to the nanotubes, resulting in selective and strong amplification (up to a factor of 6) of the light emission from the excitonic levels of CNTs in the near-infrared spectral range, as experimentally observed via excitation-emission photoluminescence (PL) mapping. The chirality of the nanotubes and the type of ionic surfactant used to disperse the nanotubes both strongly affect the amplification; thus, the complexation provides sensing selectivity towards specific CNTs. Additionally, neither similar uncharged dyes nor CNTs covered with neutral surfactant form such complexes. As model organic molecules, we use a family of polymethine dyes with an easily tailorable molecular structure and, consequently, tunable absorbance and PL characteristics. This provides us with a versatile tool for the controllable photonic and electronic engineering of an efficient probe for CNT detection.

Residue-specific investigation of the relationship between oxidation and activity of a dual specificity phosphatase by mass spectrometry

Redox regulation of signalling pathways is critical in proliferation and apoptosis; redox imbalance can lead to pathologies such as inflammation and cancer. Vaccinia H1-related protein (VHR; DUSP3) is a dual-specificity phosphatase important in controlling MAP kinase activity during cell cycle. the active-site motif contains a cysteine that acts as a nucleophile during catalysis. We used VHR to investigate the effect of oxidation in vitro on phosphatase activity, with the aim of determining how the profile of site-specific modification related to catalytic activity. Recombinant human VHR was expressed in E. coli and purified using a GST-tag. Protein was subjected to oxidation with various concentrations of SIN-1 or tetranitromethane (TNM) as nitrating agents, or HOCl. the activity was assayed using either 3-O-methylfluorescein phosphate with fluorescence detection or PIP3 by phosphate release with malachite green. the sites of oxidation were mapped using HPLC coupled to tandem mass spectrometry on an ABSciex 5600TripleTOF following in-gel digestion. More than 25 different concentration-dependent oxidative modifications to the protein were detected, including oxidations of methionine, cysteine, histidine, lysine, proline and tyrosine, and the % oxidized peptide (versus unmodified peptide) was determined from the extracted ion chromatograms. Unsurprisingly, methionine residues were very susceptible to oxidation, but there was a significant different in the extent of their oxidation. Similarly, tyrosine residues varied greatly in their modifications: Y85 and Y138 were readily nitrated, whereas Y38, Y78 and Y101 showed little modification. Y138 must be phosphorylated for MAPK phosphatase activity, so this susceptibility impacts on signalling pathways. Di- and tri- oxidations of cysteine residues were observed, but did not correlate directly with loss of activity. Overall, the catalytic activity did not correlate with redox state of any individual residue, but the total oxidative load correlated with treatment concentration and activity. This study provides the first comprehensive analysis of oxidation modifications of VHR, and demonstrates both heterogenous oxidant effects and differential residue susceptibility in a signalling phosphatase.

A new unsupervised convolutional neural network model for Chinese scene text detection

As one of the most popular deep learning models, convolution neural network (CNN) has achieved huge success in image information extraction. Traditionally CNN is trained by supervised learning method with labeled data and used as a classifier by adding a classification layer in the end. Its capability of extracting image features is largely limited due to the difficulty of setting up a large training dataset. In this paper, we propose a new unsupervised learning CNN model, which uses a so-called convolutional sparse auto-encoder (CSAE) algorithm pre-Train the CNN. Instead of using labeled natural images for CNN training, the CSAE algorithm can be used to train the CNN with unlabeled artificial images, which enables easy expansion of training data and unsupervised learning. The CSAE algorithm is especially designed for extracting complex features from specific objects such as Chinese characters. After the features of articficial images are extracted by the CSAE algorithm, the learned parameters are used to initialize the first CNN convolutional layer, and then the CNN model is fine-Trained by scene image patches with a linear classifier. The new CNN model is applied to Chinese scene text detection and is evaluated with a multilingual image dataset, which labels Chinese, English and numerals texts separately. More than 10% detection precision gain is observed over two CNN models.

Automated detection of hematological abnormalities through classification of flow cytometric data patterns

Flow Cytometry analyzers have become trusted companions due to their ability to perform fast and accurate analyses of human blood. The aim of these analyses is to determine the possible existence of abnormalities in the blood that have been correlated with serious disease states, such as infectious mononucleosis, leukemia, and various cancers. Though these analyzers provide important feedback, it is always desired to improve the accuracy of the results. This is evidenced by the occurrences of misclassifications reported by some users of these devices. It is advantageous to provide a pattern interpretation framework that is able to provide better classification ability than is currently available. Toward this end, the purpose of this dissertation was to establish a feature extraction and pattern classification framework capable of providing improved accuracy for detecting specific hematological abnormalities in flow cytometric blood data. ^ This involved extracting a unique and powerful set of shift-invariant statistical features from the multi-dimensional flow cytometry data and then using these features as inputs to a pattern classification engine composed of an artificial neural network (ANN). The contribution of this method consisted of developing a descriptor matrix that can be used to reliably assess if a donor’s blood pattern exhibits a clinically abnormal level of variant lymphocytes, which are blood cells that are potentially indicative of disorders such as leukemia and infectious mononucleosis. ^ This study showed that the set of shift-and-rotation-invariant statistical features extracted from the eigensystem of the flow cytometric data pattern performs better than other commonly-used features in this type of disease detection, exhibiting an accuracy of 80.7%, a sensitivity of 72.3%, and a specificity of 89.2%. This performance represents a major improvement for this type of hematological classifier, which has historically been plagued by poor performance, with accuracies as low as 60% in some cases. This research ultimately shows that an improved feature space was developed that can deliver improved performance for the detection of variant lymphocytes in human blood, thus providing significant utility in the realm of suspect flagging algorithms for the detection of blood-related diseases.^

Site specific growth of metal catalyzed silica nanowires for biological and chemical sensing

In this research the integration of nanostructures and micro-scale devices was investigated using silica nanowires to develop a simple yet robust nanomanufacturing technique for improving the detection parameters of chemical and biological sensors. This has been achieved with the use of a dielectric barrier layer, to restrict nanowire growth to site-specific locations which has removed the need for post growth processing, by making it possible to place nanostructures on pre-pattern substrates. Nanowires were synthesized using the Vapor-Liquid-Solid growth method. Process parameters (temperature and time) and manufacturing aspects (structural integrity and biocompatibility) were investigated. Silica nanowires were observed experimentally to determine how their physical and chemical properties could be tuned for integration into existing sensing structures. Growth kinetic experiments performed using gold and palladium catalysts at 1050°C for 60 minutes in an open-tube furnace yielded dense and consistent silica nanowire growth. This consistent growth led to the development of growth model fitting, through use of the Maximum Likelihood Estimation (MLE) and Bayesian hierarchical modeling. Transmission electron microscopy studies revealed the nanowires to be amorphous and X-ray diffraction confirmed the composition to be SiO2 . Silica nanowires were monitored in epithelial breast cancer media using Impedance spectroscopy, to test biocompatibility, due to potential in vivo use as a diagnostic aid. It was found that palladium catalyzed silica nanowires were toxic to breast cancer cells, however, nanowires were inert at 1μg/mL concentrations. Additionally a method for direct nanowire integration was developed that allowed for silica nanowires to be grown directly into interdigitated sensing structures. This technique eliminates the need for physical nanowire transfer thus preserving nanowire structure and performance integrity and further reduces fabrication cost. Successful nanowire integration was physically verified using Scanning electron microscopy and confirmed electrically using Electrochemical Impedance Spectroscopy of immobilized Prostate Specific Antigens (PSA). The experiments performed above serve as a guideline to addressing the metallurgic challenges in nanoscale integration of materials with varying composition and to understanding the effects of nanomaterials on biological structures that come in contact with the human body.

Turning Passive Detection Systems into Field Experiments: An Application Using Wetland Fishes and Enclosures to Track Fine-Scale Movement and Habitat Choice

Understanding habitat selection and movement remains a key question in behavioral ecology. Yet, obtaining a sufficiently high spatiotemporal resolution of the movement paths of organisms remains a major challenge, despite recent technological advances. Observing fine-scale movement and habitat choice decisions in the field can prove to be difficult and expensive, particularly in expansive habitats such as wetlands. We describe the application of passive integrated transponder (PIT) systems to field enclosures for tracking detailed fish behaviors in an experimental setting. PIT systems have been applied to habitats with clear passageways, at fixed locations or in controlled laboratory and mesocosm settings, but their use in unconfined habitats and field-based experimental setups remains limited. In an Everglades enclosure, we continuously tracked the movement and habitat use of PIT-tagged centrarchids across three habitats of varying depth and complexity using multiple flatbed antennas for 14 days. Fish used all three habitats, with marked species-specific diel movement patterns across habitats, and short-lived movements that would be likely missed by other tracking techniques. Findings suggest that the application of PIT systems to field enclosures can be an insightful approach for gaining continuous, undisturbed and detailed movement data in unconfined habitats, and for experimentally manipulating both internal and external drivers of these behaviors.

Integrity-based kernel malware detection

Kernel-level malware is one of the most dangerous threats to the security of users on the Internet, so there is an urgent need for its detection. The most popular detection approach is misuse-based detection. However, it cannot catch up with today's advanced malware that increasingly apply polymorphism and obfuscation. In this thesis, we present our integrity-based detection for kernel-level malware, which does not rely on the specific features of malware. ^ We have developed an integrity analysis system that can derive and monitor integrity properties for commodity operating systems kernels. In our system, we focus on two classes of integrity properties: data invariants and integrity of Kernel Queue (KQ) requests. ^ We adopt static analysis for data invariant detection and overcome several technical challenges: field-sensitivity, array-sensitivity, and pointer analysis. We identify data invariants that are critical to system runtime integrity from Linux kernel 2.4.32 and Windows Research Kernel (WRK) with very low false positive rate and very low false negative rate. We then develop an Invariant Monitor to guard these data invariants against real-world malware. In our experiment, we are able to use Invariant Monitor to detect ten real-world Linux rootkits and nine real-world Windows malware and one synthetic Windows malware. ^ We leverage static and dynamic analysis of kernel and device drivers to learn the legitimate KQ requests. Based on the learned KQ requests, we build KQguard to protect KQs. At runtime, KQguard rejects all the unknown KQ requests that cannot be validated. We apply KQguard on WRK and Linux kernel, and extensive experimental evaluation shows that KQguard is efficient (up to 5.6% overhead) and effective (capable of achieving zero false positives against representative benign workloads after appropriate training and very low false negatives against 125 real-world malware and nine synthetic attacks). ^ In our system, Invariant Monitor and KQguard cooperate together to protect data invariants and KQs in the target kernel. By monitoring these integrity properties, we can detect malware by its violation of these integrity properties during execution.^