Applications of Direct Injection Soft Chemical Ionisation-Mass Spectrometry for the Detection of Pre-blast Smokeless Powder Organic Additives

Analysis of smokeless powders is of interest from forensics and security perspectives. This article reports the detection of smokeless powder organic additives (in their pre-detonation condition), namely the stabiliser diphenylamine and its derivatives 2-nitrodiphenylamine and 4-nitrodiphenylamine, and the additives (used both as stabilisers and plasticisers) methyl centralite and ethyl centralite, by means of swab sampling followed by thermal desorption and direct injection soft chemical ionisation-mass spectrometry. Investigations on the product ions resulting from the reactions of the reagent ions H3O+ and O2+ with additives as a function of reduced electric field are reported. The method was comprehensively evaluated in terms of linearity, sensitivity and precision. For H3O+, the limits of detection (LoD) are in the range of 41-88 pg of additive, for which the accuracy varied between 1.5 and 3.2%, precision varied between 3.7 and 7.3% and linearity showed R20.9991. For O2+, LoD are in the range of 72 to 1.4 ng, with an accuracy of between 2.8 and 4.9% and a precision between 4.5 and 8.6% and R20.9914. The validated methodology was applied to the analysis of commercial pre-blast gun powders from different manufacturers.(VLID)4826148Accepted versio

Artificial Neural Network Inference (ANNI): A Study on Gene-Gene Interaction for Biomarkers in Childhood Sarcomas

Objective: To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI). Method: To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs) dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model. Results: Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS); FCGRT and OLFM1 in Ewing’s sarcoma (EWS)) suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen. Conclusions: The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas

Machine-learning of atomic-scale properties based on physical principles

We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train from linear functionals of the potential energy, such as the total energy and atomic forces. We then give a detailed account of the Smooth Overlap of Atomic Positions (SOAP) representation and kernel, showing how it arises from an abstract representation of smooth atomic densities, and how it is related to several popular density-based representations of atomic structure. We also discuss recent generalisations that allow fine control of correlations between different atomic species, prediction and fitting of tensorial properties, and also how to construct structural kernels---applicable to comparing entire molecules or periodic systems---that go beyond an additive combination of local environments

Cerebellar Integrity in the Amyotrophic Lateral Sclerosis - Frontotemporal Dementia Continuum

Amyotrophic lateral sclerosis (ALS) and behavioural variant frontotemporal dementia (bvFTD) are multisystem neurodegenerative disorders that manifest overlapping cognitive, neuropsychiatric and motor features. The cerebellum has long been known to be crucial for intact motor function although emerging evidence over the past decade has attributed cognitive and neuropsychiatric processes to this structure. The current study set out i) to establish the integrity of cerebellar subregions in the amyotrophic lateral sclerosis-behavioural variant frontotemporal dementia spectrum (ALS-bvFTD) and ii) determine whether specific cerebellar atrophy regions are associated with cognitive, neuropsychiatric and motor symptoms in the patients. Seventy-eight patients diagnosed with ALS, ALS-bvFTD, behavioural variant frontotemporal dementia (bvFTD), most without C9ORF72 gene abnormalities, and healthy controls were investigated. Participants underwent cognitive, neuropsychiatric and functional evaluation as well as structural imaging using voxel-based morphometry (VBM) to examine the grey matter subregions of the cerebellar lobules, vermis and crus. VBM analyses revealed: i) significant grey matter atrophy in the cerebellum across the whole ALS-bvFTD continuum; ii) atrophy predominantly of the superior cerebellum and crus in bvFTD patients, atrophy of the inferior cerebellum and vermis in ALS patients, while ALS-bvFTD patients had both patterns of atrophy. Post-hoc covariance analyses revealed that cognitive and neuropsychiatric symptoms were particularly associated with atrophy of the crus and superior lobule, while motor symptoms were more associated with atrophy of the inferior lobules. Taken together, these findings indicate an important role of the cerebellum in the ALS-bvFTD disease spectrum, with all three clinical phenotypes demonstrating specific patterns of subregional atrophy that associated with different symptomology

The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning

Positive testing is characteristic of exploratory behavior, yet it seems to be at odds with the aim of information seeking. After all, repeated demonstrations of one’s current hypothesis often produce the same evidence and fail to distinguish it from potential alternatives. Research on the development of scientific reasoning and adult rule learning have both documented and attempted to explain this behavior. The current chapter reviews this prior work and introduces a novel theoretical account—the Search for Invariance (SI) hypothesis—which suggests that producing multiple positive examples serves the goals of causal learning. This hypothesis draws on the interventionist framework of causal reasoning, which suggests that causal learners are concerned with the invariance of candidate hypotheses. In a probabilistic and interdependent causal world, our primary goal is to determine whether, and in what contexts, our causal hypotheses provide accurate foundations for inference and intervention—not to disconfirm their alternatives. By recognizing the central role of invariance in causal learning, the phenomenon of positive testing may be reinterpreted as a rational information-seeking strategy

Cloning of cDNA and chromosomal location of genes encoding the three types of subunits of the wheat tetrameric inhibitor of insect a-amylase

We have characterized three cDNA clones corresponding to proteins CM1, CM3 and CM16, which represent the three types of subunits of the wheat tetrameric inhibitor of insect -amylases. The deduced amino acid sequences of the mature polypeptides are homologous to those of the dimeric and monomeric -amylase inhibitors and of the trypsin inhibitors. The mature polypeptides are preceded by typical signal peptides. Southern blot analysis of appropriate aneuploids, using the cloned cDNAs as probes, has revealed the location of genes for subunits of the CM3 and of the CM16 type within a few kb of each other in chromosomes 4A, 4B and 4D, and those for the CM1 type of subunit in chromosomes 7A, 7B and 7D. Known subunits of the tetrameric inhibitor corresponding to genes from the B and D genomes have been previously characterized. No proteins of this class have been found to be encoded by the A genome in hexaploid wheat (genomes AA, BB, DD) or in diploid wheats (AA) and no anti -amylase activity has been detected in the latter, so that the A-genome genes must be either silent (pseudogenes) or expressed at a much lower level

Co-authorship Network Analysis: A Powerful Tool for Strategic Planning of Research, Development and Capacity Building Programs on Neglected Diseases

The selection and prioritization of research proposals is always a challenge, particularly when addressing neglected tropical diseases, as the scientific communities are relatively small, funding is usually limited and the disparity between the science and technology capacity of different countries and regions is enormous. When the Ministry of Health and the Ministry of Science and Technology of Brazil decided to launch an R&D program on neglected diseases for which at least 30% of the Program's resources were supposed to be invested in institutions and authors from the poorest regions of Brazil, it became clear to us that new strategies and approaches would be required. Social network analysis of co-authorship networks is one of the new approaches we are exploring to develop new tools to help policy-/decision-makers and academia jointly plan, implement, monitor and evaluate investments in this area. Publications retrieved from international databases provide the starting material. After standardization of names and addresses of authors and institutions with text mining tools, networks are assembled and visualized using social network analysis software. This study enabled the development of innovative criteria and parameters, allowing better strategic planning, smooth implementation and strong support and endorsement of the Program by key stakeholders

Lessons and implications from a mass immunization campaign in squatter settlements of Karachi, Pakistan: an experience from a cluster-randomized double-blinded vaccine trial [NCT00125047]

OBJECTIVE: To determine the safety and logistic feasibility of a mass immunization strategy outside the local immunization program in the pediatric population of urban squatter settlements in Karachi, Pakistan. METHODS: A cluster-randomized double blind preventive trial was launched in August 2003 in 60 geographic clusters covering 21,059 children ages 2 to 16 years. After consent was obtained from parents or guardians, eligible children were immunized parenterally at vaccination posts in each cluster with Vi polysaccharide or hepatitis A vaccine. Safety, logistics, and standards were monitored and documented. RESULTS: The vaccine coverage of the population was 74% and was higher in those under age 10 years. No life-threatening serious adverse events were reported. Adverse events occurred in less than 1% of all vaccine recipients and the main reactions reported were fever and local pain. The proportion of adverse events in Vi polysaccharide and hepatitis A recipients will not be known until the end of the trial when the code is broken. Throughout the vaccination campaign safe injection practices were maintained and the cold chain was not interrupted. Mass vaccination in slums had good acceptance. Because populations in such areas are highly mobile, settlement conditions could affect coverage. Systemic reactions were uncommon and local reactions were mild and transient. Close community involvement was pivotal for information dissemination and immunization coverage. CONCLUSION: This vaccine strategy described together with other information that will soon be available in the area (cost/effectiveness, vaccine delivery costs, etc) will make typhoid fever control become a reality in the near future

To servitize is to (re)position : utilizing a Porterian view to understand servitization and value systems

Drawing on the case of a global servitizing company in the ship power industry, we use a Porterian toolkit for analyzing the implications of industry power and its consequences on firm vertical (re)positioning within the value system. Whereas repositioning has been seen as a way of moving closer to customers and obtaining new competencies, strategic moves aimed at increasing companies’ sphere of influence were neglected. This chapter illustrates how the power approach to repositioning, through different alternative mechanisms, complements the widespread capability view and contributes to value system analysis in servitization.fi=vertaisarvioitu|en=peerReviewed

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

<p>Abstract</p> <p>Background</p> <p>Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2).</p> <p>Methods</p> <p>A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances.</p> <p>Results</p> <p>The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity.</p> <p>Conclusions</p> <p>Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting.</p