Evaluation of reagents for the chemical enhancement of fingermarks on porous surfaces : optimisation and characterisation of the 1,2-indanedione technique

University of Technology, Sydney. Faculty of Science.There is a continual search for new and improved reagents to detect fingermarks on a variety of surfaces. With increased technology and resources the possibilities are continually expanding. 1,2-Indanedione is a relatively new reagent for the development of fingermarks on porous surfaces. Its boundaries have not been completely explored nor has the method of reaction with fingermarks been determined. The initial aim of this project was to investigate the fingermark reagent 1,2-indanedione and determine if it was a viable reagent for routine use in Australia. The secondary aim was to study the reaction that occurs between 1,2-indanedione and amino acids and the subsequent reaction with metal salts to gain further insight into the reaction than has been previously published. Additionally the fingermark reagent 5-methylthioninhydrin, which although had shown good results in detecting fingermarks in the early 1990’s, did not seem to be widely used or studied since its commercial manufacture began. A new reagent for the problematic and increasingly encountered thermal paper, ThermaNin, was also evaluated and compared to other proposed methods for the development of fin germ arks on thermal paper. The investigation of 1,2-indanedione as a fingermark reagent for use in Australia was performed by comparing a number of formulations and development procedures, encompassing all published recommendations as well as some novel approaches. 1,2-indanedione formulations were compared with respect to initial colour, fluorescence, concentration of the reagent, acetic acid concentration and the effect of different carrier solvents. Numerous development conditions were also investigated, including a conventional oven, a heat press and humidity. Further enhancement using metal salts and liquid nitrogen was also evaluated. The heat press set at 165°C for 10 s proved to give the best initial colour and most intense luminescence. Secondary metal salt treatment improved initial colour and luminescence and was found to provide consistent results despite different environmental conditions. It is for this reason that it is recommended that metal salt treatment consistently be performed after treatment with 1,2-indanedione or included in the formulation of 1,2-indanedione. The Polilight, the VSC 2000, and the Condor Chemical Imaging macroscope have been used to detect fingermarks developed with 1,2-indanedione on a variety of high- and low- quality porous and semi-porous surfaces with impressive results overall. Laboratory and field tests were conducted to compare 1,2-indanedione with DFO and ninhydrin as well as to investigate the position of 1,2-indanedione in the sequence of reagents for fingermark detection on porous surfaces. Overall 1,2-indanedione proved to be a viable alternative to tradition methods for the detection of fingermarks on porous surfaces, with more fingermarks being developed using this reagent on real samples than both DFO and ninhydrin and a combination of the two reagents. The isolation of a single pure product from the reaction of 1,2-indanedione with several different amino acids was not achieved. The study was able to establish that 1,2-indanedione reacts differently with different amino acids with some reactions, such as those with alanine and cysteine, following a similar pathway. A study performed by nuclear magnetic resonance spectroscopy and colour reactions showed that increasing the content of water in the reaction retarded the kinetics of the reaction and thus it is possible that the concentration of water in the reaction may influence the path the reaction takes. Solid state nuclear magnetic resonance spectroscopy indicated that the product of the reaction is ionic, which may help explain the problems encountered on separation and isolation of the product. Thermal and elemental analysis provided some information on the by-products released by the reaction, whilst mass spectroscopy provided information on the possible pathway of the reaction. The results of this study support the proposal made by Petrovskaia (1999) that the main reaction product of 1,2-indanedione and amino acids is a Ruhemann’s purple type product with a molecular mass of 275. A study of the reaction between metal salts and the 1,2-indanedione/amino acid product was also performed on a crude reaction mixture. This was due to the inability to provide a pure starting materials as well as the unsuccessful separation of the complex by thin layer chromatography. The information gained; however, from a study via nuclear magnetic resonance spectroscopy, mass spectroscopy, UV-visible spectroscopy and infrared spectroscopy indicates that two 1,2-indanedione molecules react with the nitrogen atom in the amino acid forming a tridentate ligand which then complexes with the metal ion. The evaluation of 5-methylthioninhydrin found that the reagent is superior to ninhydrin; however, 1,2-indanedione exhibits much stronger luminescence when used to treat latent fingermarks. The high cost of the reagent accompanied by the fact that 1,2-indanedione was found to be a superior reagent and is already in use in many laboratories precludes a recommendation for its routine use. ThermaNin was evaluated against other recommended reagents for the development of fingermarks on thermal paper. ThermaNin itself was found to be extremely sensitive to water and humidity and must be made fresh before its use due to poor stability. Once again a 1,2-indanedione formulation, albeit without acetic acid, was found to be the optimal method to detect fingermarks on this particular surface

A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data: SPSS TwoStep Cluster analysis, Latent Gold and SNOB

Background: There are various methodological approaches to identifying clinically important subgroups and one method is to identify clusters of characteristics that differentiate people in cross-sectional and/or longitudinal data using Cluster Analysis (CA) or Latent Class Analysis (LCA). There is a scarcity of head-to-head comparisons that can inform the choice of which clustering method might be suitable for particular clinical datasets and research questions. Therefore, the aim of this study was to perform a head-to-head comparison of three commonly available methods (SPSS TwoStep CA, Latent Gold LCA and SNOB LCA). Methods. The performance of these three methods was compared: (i) quantitatively using the number of subgroups detected, the classification probability of individuals into subgroups, the reproducibility of results, and (ii) qualitatively using subjective judgments about each program's ease of use and interpretability of the presentation of results.We analysed five real datasets of varying complexity in a secondary analysis of data from other research projects. Three datasets contained only MRI findings (n = 2,060 to 20,810 vertebral disc levels), one dataset contained only pain intensity data collected for 52 weeks by text (SMS) messaging (n = 1,121 people), and the last dataset contained a range of clinical variables measured in low back pain patients (n = 543 people). Four artificial datasets (n = 1,000 each) containing subgroups of varying complexity were also analysed testing the ability of these clustering methods to detect subgroups and correctly classify individuals when subgroup membership was known. Results: The results from the real clinical datasets indicated that the number of subgroups detected varied, the certainty of classifying individuals into those subgroups varied, the findings had perfect reproducibility, some programs were easier to use and the interpretability of the presentation of their findings also varied. The results from the artificial datasets indicated that all three clustering methods showed a near-perfect ability to detect known subgroups and correctly classify individuals into those subgroups. Conclusions: Our subjective judgement was that Latent Gold offered the best balance of sensitivity to subgroups, ease of use and presentation of results with these datasets but we recognise that different clustering methods may suit other types of data and clinical research questions

Share of afghanistan populace in hepatitis B and hepatitis C infection's pool: is it worthwhile?

There is a notable dearth of data about Hepatitis B Virus (HBV) and Hepatitis C Virus(HCV) prevalence in Afghanistan. Awareness program and research capacity in the field of hepatitis are very limited in Afghanistan. Number of vulnerabilities and patterns of risk behaviors signal the need to take action now

Not all coping strategies are created equal: a mixed methods study exploring physicians' self reported coping strategies

<p>Abstract</p> <p>Background</p> <p>Physicians experience workplace stress and draw on different coping strategies. The primary goal of this paper is to use interview data to explore physicians' self reported coping strategies. In addition, questionnaire data is utilized to explore the degree to which the coping strategies are used and are associated with feelings of emotional exhaustion, a key symptom of burnout.</p> <p>Methods</p> <p>This mixed methods study explores factors related to physician wellness within a large health region in Western Canada. This paper focuses on the coping strategies that physicians use in response to work-related stress. The qualitative component explores physicians' self reported coping strategies through open ended interviews of 42 physicians representing diverse medical specialties and settings (91% response rate). The major themes extracted from the qualitative interviews were used to construct 12 survey items that were included in the comprehensive quantitative questionnaire. Questionnaires were sent to all eligible physicians in the health region with 1178 completed surveys (40% response rate.) Questionnaire items were used to measure how often physicians draw on the various coping strategies. Feelings of burnout were also measured in the survey by 5 items from the Emotional Exhaustion subscale of the revised Maslach Burnout Inventory.</p> <p>Results</p> <p>Major themes identified from the interviews include coping strategies used at work (e.g., working through stress, talking with co-workers, taking a time out, using humor) and after work (e.g., exercise, quiet time, spending time with family). Analysis of the questionnaire data showed three often used workplace coping strategies were positively correlated with feeling emotionally exhausted (i.e., keeping stress to oneself (r = .23), concentrating on what to do next (r = .16), and going on as if nothing happened (r = .07)). Some less often used workplace coping strategies (e.g., taking a time out) and all those used after work were negatively correlated with frequency of emotional exhaustion.</p> <p>Conclusions</p> <p>Physicians' self reported coping strategies are not all created equal in terms of frequency of use and correlation with feeling emotionally exhausted from one's work. This knowledge may be integrated into practical physician stress reduction interventions.</p

Algorithmic statistics: forty years later

Algorithmic statistics has two different (and almost orthogonal) motivations. From the philosophical point of view, it tries to formalize how the statistics works and why some statistical models are better than others. After this notion of a "good model" is introduced, a natural question arises: it is possible that for some piece of data there is no good model? If yes, how often these bad ("non-stochastic") data appear "in real life"? Another, more technical motivation comes from algorithmic information theory. In this theory a notion of complexity of a finite object (=amount of information in this object) is introduced; it assigns to every object some number, called its algorithmic complexity (or Kolmogorov complexity). Algorithmic statistic provides a more fine-grained classification: for each finite object some curve is defined that characterizes its behavior. It turns out that several different definitions give (approximately) the same curve. In this survey we try to provide an exposition of the main results in the field (including full proofs for the most important ones), as well as some historical comments. We assume that the reader is familiar with the main notions of algorithmic information (Kolmogorov complexity) theory.Comment: Missing proofs adde

SMART: Unique splitting-while-merging framework for gene clustering

Copyright @ 2014 Fa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Successful clustering algorithms are highly dependent on parameter settings. The clustering performance degrades significantly unless parameters are properly set, and yet, it is difficult to set these parameters a priori. To address this issue, in this paper, we propose a unique splitting-while-merging clustering framework, named “splitting merging awareness tactics” (SMART), which does not require any a priori knowledge of either the number of clusters or even the possible range of this number. Unlike existing self-splitting algorithms, which over-cluster the dataset to a large number of clusters and then merge some similar clusters, our framework has the ability to split and merge clusters automatically during the process and produces the the most reliable clustering results, by intrinsically integrating many clustering techniques and tasks. The SMART framework is implemented with two distinct clustering paradigms in two algorithms: competitive learning and finite mixture model. Nevertheless, within the proposed SMART framework, many other algorithms can be derived for different clustering paradigms. The minimum message length algorithm is integrated into the framework as the clustering selection criterion. The usefulness of the SMART framework and its algorithms is tested in demonstration datasets and simulated gene expression datasets. Moreover, two real microarray gene expression datasets are studied using this approach. Based on the performance of many metrics, all numerical results show that SMART is superior to compared existing self-splitting algorithms and traditional algorithms. Three main properties of the proposed SMART framework are summarized as: (1) needing no parameters dependent on the respective dataset or a priori knowledge about the datasets, (2) extendible to many different applications, (3) offering superior performance compared with counterpart algorithms.National Institute for Health Researc

Improvements in survival of the uncemented Nottingham Total Shoulder prosthesis: a prospective comparative study

<p>Abstract</p> <p>Background</p> <p>The uncemented Nottingham Total Shoulder Replacement prosthesis system (Nottingham TSR) was developed from the previous BioModular^®shoulder prosthesis taking into consideration the causes of the initial implant's failure.</p> <p>We investigated the impact of changes in the design of Nottingham TSR prosthesis on its survivorship rate.</p> <p>Methods</p> <p>Survivorship analyses of three types of uncemented total shoulder arthroplasty prostheses (BioModular^®, initial Nottingham TSR and current Nottingham TSR systems with 11, 8 and 4 year survivorship data respectively) were compared. All these prostheses were implanted for the treatment of disabling pain in the shoulder due to primary and secondary osteoarthritis or rheumatoid arthritis. Each type of the prosthesis studied was implanted in consecutive group of patients – 90 patients with BioModular^®system, 103 with the initial Nottingham TSR and 34 patients with the current Nottingham TSR system.</p> <p>The comparison of the annual cumulative survivorship values in the compatible time range between the three groups was done according to the paired <it>t </it>test.</p> <p>Results</p> <p>The 8-year and 11-year survivorship rates for the initially used modified BioModular^®uncemented prosthesis were relatively low (75.6% and 71.7% respectively) comparing to the reported survivorship of the conventional cemented implants. The 8-year survivorship for the uncemented Nottingham TSR prosthesis was significantly higher (81.8%), but still not in the desired range of above 90%, that is found in other cemented designs. Glenoid component loosening was the main factor of prosthesis failure in both prostheses and mainly occurred in the first 4 postoperative years. The 4-year survivorship of the currently re-designed Nottingham TSR prosthesis, with hydroxylapatite coating of the glenoid baseplate, was significantly higher, 93.1% as compared to 85.1% of the previous Nottingham TSR.</p> <p>Conclusion</p> <p>The initial Nottingham shoulder prosthesis showed significantly higher survivorship than the BioModular^®uncemented prosthesis, but lower than expected. Subsequently re-designed Nottingham TSR system presented a high short term survivorship rate that encourages its ongoing use</p

Search Engine Similarity Analysis: A Combined Content and Rankings Approach

How different are search engines? The search engine wars are a favorite topic of on-line analysts, as two of the biggest companies in the world, Google and Microsoft, battle for prevalence of the web search space. Differences in search engine popularity can be explained by their effectiveness or other factors, such as familiarity with the most popular first engine, peer imitation, or force of habit. In this work we present a thorough analysis of the affinity of the two major search engines, Google and Bing, along with DuckDuckGo, which goes to great lengths to emphasize its privacy-friendly credentials. To do so, we collected search results using a comprehensive set of 300 unique queries for two time periods in 2016 and 2019, and developed a new similarity metric that leverages both the content and the ranking of search responses. We evaluated the characteristics of the metric against other metrics and approaches that have been proposed in the literature, and used it to (1) investigate the similarities of search engine results, (2) the evolution of their affinity over time, (3) what aspects of the results influence similarity, and (4) how the metric differs over different kinds of search services. We found that Google stands apart, but Bing and DuckDuckGo are largely indistinguishable from each other.Comment: Shorter version of this paper was accepted in the 21st International Conference on Web Information Systems Engineering (WISE 2020). The final authenticated version is available online at https://doi.org/10.1007/978-3-030-62008-0_

Identifying the Machine Learning Family from Black-Box Models

[EN] We address the novel question of determining which kind of machine learning model is behind the predictions when we interact with a black-box model. This may allow us to identify families of techniques whose models exhibit similar vulnerabilities and strengths. In our method, we first consider how an adversary can systematically query a given black-box model (oracle) to label an artificially-generated dataset. This labelled dataset is then used for training different surrogate models (each one trying to imitate the oracle¿s behaviour). The method has two different approaches. First, we assume that the family of the surrogate model that achieves the maximum Kappa metric against the oracle labels corresponds to the family of the oracle model. The other approach, based on machine learning, consists in learning a meta-model that is able to predict the model family of a new black-box model. We compare these two approaches experimentally, giving us insight about how explanatory and predictable our concept of family is.This material is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-17-1-0287, the EU (FEDER), and the Spanish MINECO under grant TIN 2015-69175-C4-1-R, the Generalitat Valenciana PROMETEOII/2015/013. F. Martinez-Plumed was also supported by INCIBE under grant INCIBEI-2015-27345 (Ayudas para la excelencia de los equipos de investigacion avanzada en ciberseguridad). J. H-Orallo also received a Salvador de Madariaga grant (PRX17/00467) from the Spanish MECD for a research stay at the CFI, Cambridge, and a BEST grant (BEST/2017/045) from the GVA for another research stay at the CFI.Fabra-Boluda, R.; Ferri Ramírez, C.; Hernández-Orallo, J.; Martínez-Plumed, F.; Ramírez Quintana, MJ. (2018). Identifying the Machine Learning Family from Black-Box Models. 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