Improved detection of chemical substances from colorimetric sensor data using probabilistic machine learning

We present a data-driven machine learning approach to detect drug- and explosives-precursors using colorimetric sensor technology for air-sampling. The sensing technology has been developed in the context of the CRIM-TRACK project. At present a fullyintegrated portable prototype for air sampling with disposable sensing chips and automated data acquisition has been developed. The prototype allows for fast, user-friendly sampling, which has made it possible to produce large datasets of colorimetric data for different target analytes in laboratory and simulated real-world application scenarios. To make use of the highly multi-variate data produced from the colorimetric chip a number of machine learning techniques are employed to provide reliable classification of target analytes from confounders found in the air streams. We demonstrate that a data-driven machine learning method using dimensionality reduction in combination with a probabilistic classifier makes it possible to produce informative features and a high detection rate of analytes. Furthermore, the probabilistic machine learning approach provides a means of automatically identifying unreliable measurements that could produce false predictions. The robustness of the colorimetric sensor has been evaluated in a series of experiments focusing on the amphetamine pre-cursor phenylacetone as well as the improvised explosives pre-cursor hydrogen peroxide. The analysis demonstrates that the system is able to detect analytes in clean air and mixed with substances that occur naturally in real-world sampling scenarios. The technology under development in CRIM-TRACK has the potential as an effective tool to control traf- ficking of illegal drugs, explosive detection, or in other law enforcement applications.EU FP7 Grant Agreement Number 31320

Classification of colorimetric sensor data using time series

Colorimetric sensors are widely used as pH indicators, medical diagnostic devices and detection devices. The colorimetric sensor captures the color changes of a chromic chemical (dye) or array of chromic chemicals when exposed to a target substance (analyte). Sensing is typically carried out using the difference in dye color before and after exposure. This approach neglects the kinetic response, that is, the temporal evolution of the dye, which potentially contains additional information. We investigate the importance of the kinetic response by collecting a sequence of images over time. We applied end-to-end learning using three different convolution neural networks (CNN) and a recurrent network. We compared the performance to logistic regression, k-nearest-neighbor and random forest, where these methods only use the difference color from start to end as feature vector. We found that the CNNs were able to extract features from the kinetic response profiles that significantly improves the accuracy of the sensor. Thus, we conclude that the kinetic responses indeed improves the accuracy, which paves the way for new and better chemical sensors based on colorimetric responses

Bootstrapping Q Methodology to Improve the Understanding of Human Perspectives.

Q is a semi-qualitative methodology to identify typologies of perspectives. It is appropriate to address questions concerning diverse viewpoints, plurality of discourses, or participation processes across disciplines. Perspectives are interpreted based on rankings of a set of statements. These rankings are analysed using multivariate data reduction techniques in order to find similarities between respondents. Discussing the analytical process and looking for progress in Q methodology is becoming increasingly relevant. While its use is growing in social, health and environmental studies, the analytical process has received little attention in the last decades and it has not benefited from recent statistical and computational advances. Specifically, the standard procedure provides overall and arguably simplistic variability measures for perspectives and none of these measures are associated to individual statements, on which the interpretation is based. This paper presents an innovative approach of bootstrapping Q to obtain additional and more detailed measures of variability, which helps researchers understand better their data and the perspectives therein. This approach provides measures of variability that are specific to each statement and perspective, and additional measures that indicate the degree of certainty with which each respondent relates to each perspective. This supplementary information may add or subtract strength to particular arguments used to describe the perspectives. We illustrate and show the usefulness of this approach with an empirical example. The paper provides full details for other researchers to implement the bootstrap in Q studies with any data collection design

Isolation and antibiotic susceptibility of Shigella species from stool samples among hospitalized children in Abadan, Iran

AIM: The aim of this study was to determine the incidence of Shigella species and their antimicrobial susceptibility patterns in hospitalized children with Shigellosis in Abadan, Iran. BACKGROUND: Shigellosis is caused by different species of Shigella and one of the most common causes of diarrhea in children. This disease is endemic in many developing countries including Iran. PATIENTS AND METHODS: This prospective cross sectional study was conducted in a teaching hospital in Abadan, Iran during June 2011 to May 2013. Stool specimens were collected from pediatric age group. All isolates were confirmed as Shigella species by biochemical and serologic tests. Antibiotic sensitivity pattern of these isolates was studied by disk diffusion Method. RESULTS: Among all 705 stool samples, 36 (5.1) yielded Shigella. Of cases, 392 (55.6) were girl and 313 (44.4) were boy. The most common Shigella isolates were S. flexneri (n=19, 52.7) followed by S. sonnei (n=11, 30.5), S. boydii (n=4, 11.1) and S. dysenteriae 2(5.5). Of the Shigella isolates, 47.2 showed resistance to two or more antimicrobial agents. Resistance pattern against various antimicrobials were as follows: trimethoprim-sulphamethoxazole (80.5), ampicillin (63.8), tetracycline (58.3), chloramphenicol (33.3), nalidixic acid (27.7), and cefixime (16.6). There was no resistance against ciprofloxacin and ceftriaxone. CONCLUSION: The most common isolates were S. flexneri followed by S. Sonnei. There was no antibiotic resistance against ciprofloxacin and ceftriaxone. TMP-SMZ showed highest resistance pattern

Reciprocity in the Developmental Regulation of Aquaporins 1, 3 and 5 during Pregnancy and Lactation in the Rat

Milk secretion involves significant flux of water, driven largely by synthesis of lactose within the Golgi apparatus. It has not been determined whether this flux is simply a passive consequence of the osmotic potential between cytosol and Golgi, or whether it involves regulated flow. Aquaporins (AQPs) are membrane water channels that regulate water flux. AQP1, AQP3 and AQP5 have previously been detected in mammary tissue, but evidence of developmental regulation (altered expression according to the developmental and physiological state of the mammary gland) is lacking and their cellular/subcellular location is not well understood. In this paper we present evidence of developmental regulation of all three of these AQPs. Further, there was evidence of reciprocity since expression of the rather abundant AQP3 and less abundant AQP1 increased significantly from pregnancy into lactation, whereas expression of the least abundant AQP5 decreased. It would be tempting to suggest that AQP3 and AQP1 are involved in the secretion of water into milk. Paradoxically, however, it was AQP5 that demonstrated most evidence of expression located at the apical (secretory) membrane. The possibility is discussed that AQP5 is synthesized during pregnancy as a stable protein that functions to regulate water secretion during lactation. AQP3 was identified primarily at the basal and lateral membranes of the secretory cells, suggesting a possible involvement in regulated uptake of water and glycerol. AQP1 was identified primarily at the capillary and secretory cell cytoplasmic level and may again be more concerned with uptake and hence milk synthesis, rather than secretion. The fact that expression was developmentally regulated supports, but does not prove, a regulatory involvement of AQPs in water flux through the milk secretory cell

Immunostaining of rat mammary gland for AQP5 in three groups.

<p>Panel a) immuno staining of developing alveoli, pregnant group (20X). Panel b) immuno staining Early lactation group (20X) and staining towards (inset 40X indicated by the arrow), apical membrane. Panel c) Peak lactation group (20X), immune staining towards apical membrane (higher magnification 40X indicated by the arrow). Panel d) Positive control in submandibular gland of rat, staining at mucus acini (both in low magnification 20X and 40X). Panel e) Negative control during early lactation (20X). Panel f) Isotype control, IgG during early lactation (20X).</p

Protein expression of AQP3 (molecular weight of 31kDa: panel b) in pregnant, early lactation and peak lactation groups.

<p>Panel a) reports expression relative to GAPDH (molecular weight of 37 kDa: panel c) as mean ± SE. (0.86±0.15, 3.11±1.17 and 3.15±1.55 for pregnant, early lactation and peak lactation, respectively).</p

Expression of AQP1, AQP3 and AQP5 reported relative to expression of GAPDH.

<p>Results are mean ± SD and P-values from one-way ANOVA. Different letters within a row indicate significant difference between stages, where P = pregnant, E = early lactation, L = peak lactation.</p><p>Expression of AQP1, AQP3 and AQP5 reported relative to expression of GAPDH.</p