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Journal articles on the topic 'Multiple UV detection points'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

Oukacine, Farid, Bernard Romestand, David M. Goodall, Gladys Massiera, Laurent Garrelly, and Hervé Cottet. "Study of Antibacterial Activity by Capillary Electrophoresis Using Multiple UV Detection Points." Analytical Chemistry 84, no. 7 (March 20, 2012): 3302–10. http://dx.doi.org/10.1021/ac300004t.

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2

Anderson, S. "Detection of multiple species through isosbestic points in UV-photoemission spectra: Application to sulfide adsorption on copper." Applied Surface Science 22-23 (May 1985): 325–37. http://dx.doi.org/10.1016/0169-4332(85)90065-0.

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3

Anderson, S. E., and G. L. Nyberg. "Detection of multiple species through isosbestic points in UV-photoemission spectra: Application to sulfide adsorption on copper." Applications of Surface Science 22-23 (May 1985): 325–37. http://dx.doi.org/10.1016/0378-5963(85)90065-0.

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4

Wempe, Michael F., Alan Oldland, Nancy Stolpman, and Tyree H. Kiser. "Stability of dronabinol capsules when stored frozen, refrigerated, or at room temperature." American Journal of Health-System Pharmacy 73, no. 14 (July 15, 2016): 1088–92. http://dx.doi.org/10.2146/ajhp150501.

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Abstract Purpose Results of a study to determine the 90-day stability of dronabinol capsules stored under various temperature conditions are reported. Methods High-performance liquid chromatography (HPLC) with ultraviolet (UV) detection was used to assess the stability of dronabinol capsules (synthetic delta-9-tetrahydrocannabinol [Δ9-THC] mixed with high-grade sesame oil and other inactive ingredients and encapsulated as soft gelatin capsules) that were frozen, refrigerated, or kept at room temperature for three months. The dronabinol capsules remained in the original foil-sealed blister packs until preparation for HPLC–UV assessment. The primary endpoint was the percentage of the initial Δ9-THC concentration remaining at multiple designated time points. The secondary aim was to perform forced-degradation studies under acidic conditions to demonstrate that the HPLC–UV method used was stability indicating. Results The appearance of the dronabinol capsules remained unaltered during frozen, cold, or room-temperature storage. Regardless of storage condition, the percentage of the initial Δ9-THC content remaining was greater than 97% for all evaluated samples at all time points over the three-month study. These experimental data indicate that the product packaging and the sesame oil used to formulate dronabinol capsules efficiently protect Δ9-THC from oxidative degradation to cannabinol; this suggests that pharmacies can store dronabinol capsules in nonrefrigerated automated dispensing systems, with a capsule expiration date of 90 days after removal from the refrigerator. Conclusion Dronabinol capsules may be stored at room temperature in their original packaging for up to three months without compromising capsule appearance and with minimal reduction in Δ9-THC concentration.

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Bardet, Jean-Marc, and Charlotte Dion. "Robust semi-parametric multiple change-points detection." Signal Processing 156 (March 2019): 145–55. http://dx.doi.org/10.1016/j.sigpro.2018.10.022.

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Wang, Yunlong, Changliang Zou, Zhaojun Wang, and Guosheng Yin. "Multiple change-points detection in high dimension." Random Matrices: Theory and Applications 08, no. 04 (October 2019): 1950014. http://dx.doi.org/10.1142/s201032631950014x.

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Change-point detection is an integral component of statistical modeling and estimation. For high-dimensional data, classical methods based on the Mahalanobis distance are typically inapplicable. We propose a novel testing statistic by combining a modified Euclidean distance and an extreme statistic, and its null distribution is asymptotically normal. The new method naturally strikes a balance between the detection abilities for both dense and sparse changes, which gives itself an edge to potentially outperform existing methods. Furthermore, the number of change-points is determined by a new Schwarz’s information criterion together with a pre-screening procedure, and the locations of the change-points can be estimated via the dynamic programming algorithm in conjunction with the intrinsic order structure of the objective function. Under some mild conditions, we show that the new method provides consistent estimation with an almost optimal rate. Simulation studies show that the proposed method has satisfactory performance of identifying multiple change-points in terms of power and estimation accuracy, and two real data examples are used for illustration.

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Maboudou-Tchao, Edgard M., and Douglas M. Hawkins. "Detection of multiple change-points in multivariate data." Journal of Applied Statistics 40, no. 9 (September 2013): 1979–95. http://dx.doi.org/10.1080/02664763.2013.800471.

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吴, 楠. "Multiple Change-Points Detection of Piecewise Stationary Time Series." Pure Mathematics 08, no. 02 (2018): 136–48. http://dx.doi.org/10.12677/pm.2018.82018.

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Pitschi, Florian, Claudine Devauchelle, and Eduardo Corel. "Automatic detection of anchor points for multiple sequence alignment." BMC Bioinformatics 11, no. 1 (2010): 445. http://dx.doi.org/10.1186/1471-2105-11-445.

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10

Sullivan, Joe H. "Detection of Multiple Change Points from Clustering Individual Observations." Journal of Quality Technology 34, no. 4 (October 2002): 371–83. http://dx.doi.org/10.1080/00224065.2002.11980170.

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Kittlitz, Rudy. "Detection of Multiple Change Points from Clustering Individual Observations." Journal of Quality Technology 35, no. 3 (July 2003): 237–38. http://dx.doi.org/10.1080/00224065.2003.11980216.

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Shen, Jie, Colin M. Gallagher, and QiQi Lu. "Detection of multiple undocumented change-points using adaptive Lasso." Journal of Applied Statistics 41, no. 6 (November 25, 2013): 1161–73. http://dx.doi.org/10.1080/02664763.2013.862220.

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13

Corneli, Marco, Pierre Latouche, and Fabrice Rossi. "Multiple change points detection and clustering in dynamic networks." Statistics and Computing 28, no. 5 (September 26, 2017): 989–1007. http://dx.doi.org/10.1007/s11222-017-9775-1.

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Lavielle, M., and G. Teyssière. "Detection of multiple change-points in multivariate time series." Lithuanian Mathematical Journal 46, no. 3 (July 2006): 287–306. http://dx.doi.org/10.1007/s10986-006-0028-9.

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15

Cheng, Dan, Zhibing He, and Armin Schwartzman. "Multiple testing of local extrema for detection of change points." Electronic Journal of Statistics 14, no. 2 (2020): 3705–29. http://dx.doi.org/10.1214/20-ejs1751.

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Deng, Dianliang, Hong-Bin Fang, Kian Razeghi Jahromi, Jiuzhou Song, and Ming Tan. "Detection of threshold points for gene expressions under multiple biological conditions." Statistics and Its Interface 10, no. 4 (2017): 643–55. http://dx.doi.org/10.4310/sii.2017.v10.n4.a9.

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Kim, Joungyoun. "Bayesian Detection of Multiple Change Points in a Piecewise Linear Function." Korean Journal of Applied Statistics 27, no. 4 (August 31, 2014): 589–603. http://dx.doi.org/10.5351/kjas.2014.27.4.589.

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18

Harle, Flore, Florent Chatelain, Cedric Gouy-Pailler, and Sophie Achard. "Bayesian Model for Multiple Change-Points Detection in Multivariate Time Series." IEEE Transactions on Signal Processing 64, no. 16 (August 15, 2016): 4351–62. http://dx.doi.org/10.1109/tsp.2016.2566609.

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Meyer, Eileen T., Maria Petropoulou, Markos Georganopoulos, Marco Chiaberge, Peter Breiding, and William B. Sparks. "Detection of an Optical/UV Jet/Counterjet and Multiple Spectral Components in M84." Astrophysical Journal 860, no. 1 (June 6, 2018): 9. http://dx.doi.org/10.3847/1538-4357/aabf39.

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Boscarino, Filice, Sciuto, Libertino, Scuderi, Galati, and Scalese. "Investigation of ZnO-decorated CNTs for UV Light Detection Applications." Nanomaterials 9, no. 8 (July 31, 2019): 1099. http://dx.doi.org/10.3390/nano9081099.

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Multi-walled carbon nanotubes (CNTs) decorated with zinc oxide nanoparticles (ZnO NPs) were prepared in isopropanol solution by a simple, room-temperature process and characterized from structural, morphological, electronic, and optical points of view. A strong interaction between ZnO and CNTs is fully confirmed by all the characterization techniques. ZnO-CNTs nanocomposites, with different weight ratios, were deposited as a dense layer between two electrodes, in order to investigate the electrical behaviour. In particular, the electrical response of the nanocomposite layers to UV light irradiation was recorded for a fixed voltage: As the device is exposed to the UV lamp, a sharp current drop takes place and then an increase is observed as the irradiation is stopped. The effect can be explained by adsorption and desorption phenomena taking place on the ZnO nanoparticle surface under irradiation and by charge transfer between ZnO and CNTs, thanks to the strong interaction between the two nanomaterials. The nanocomposite material shows good sensitivity and fast response to UV irradiation. Room temperature and low-cost processes used for the device preparation combined with room temperature and low voltage operational conditions make this methodology very promising for large scale UV detectors applications.

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Neubauer, Jirí, and Vítezslav Veselý. "Detection of multiple changes in mean by sparse parameter estimation." Nonlinear Analysis: Modelling and Control 18, no. 2 (April 25, 2013): 177–90. http://dx.doi.org/10.15388/na.18.2.14021.

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The contribution is focused on detection of multiple changes in the mean in a onedimensional stochastic process by sparse parameter estimation from an overparametrized model. The authors’ approach to change point detection differs entirely from standard statistical techniques. A stochastic process residing in a bounded interval with changes in the mean is estimated using dictionary (a family of functions, the so-called atoms, which are overcomplete in the sense of being nearly linearly dependent) and consisting of Heaviside functions. Among all possible representations of the process we want to find a sparse one utilizing a significantly reduced number of atoms. This problem can be solved by ℓ1-minimization. The basis pursuit algorithm is used to get sparse parameter estimates. In this contribution the authors calculate empirical probability of successful change point detection as a function depending on the number of change points and the level of standard deviation of additive white noise of the stochastic process. The empirical probability was computed by simulations where locations of change points were chosen randomly from uniform distribution. The authors’ approach is compared with LASSO algorithm, ℓ1 trend filtering and selected statistical methods. Such probability decreases with increasing number of change points and/or standard deviation of white noise. The proposed method was applied on the time series of nuclear magnetic response during the drilling of a well.

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Lam, Chi Kin, Huaqing Jin, Fei Jiang, and Guosheng Yin. "A variable selection approach to multiple change-points detection with ordinal data." Statistics and Its Interface 13, no. 2 (2020): 251–60. http://dx.doi.org/10.4310/sii.2020.v13.n2.a9.

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Amini, Arash Ali, and XuanLong Nguyen. "Sequential Detection of Multiple Change Points in Networks: A Graphical Model Approach." IEEE Transactions on Information Theory 59, no. 9 (September 2013): 5824–41. http://dx.doi.org/10.1109/tit.2013.2264716.

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Song, Chi, Xiaoyi Min, and Heping Zhang. "The screening and ranking algorithm for change-points detection in multiple samples." Annals of Applied Statistics 10, no. 4 (December 2016): 2102–29. http://dx.doi.org/10.1214/16-aoas966.

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Shankaran, Seetha, Abbot Laptook, Beena Sood, Barbara Do, Barbara Stoll, Krisa Van Meurs, Edward Bell, Abhik Das, John Barks, and Subrata Sarkar. "Screening Cranial Imaging at Multiple Time Points Improves Cystic Periventricular Leukomalacia Detection." American Journal of Perinatology 32, no. 10 (March 2, 2015): 973–79. http://dx.doi.org/10.1055/s-0035-1545666.

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Lai, Yinglei. "On the Adaptive Partition Approach to the Detection of Multiple Change-Points." PLoS ONE 6, no. 5 (May 24, 2011): e19754. http://dx.doi.org/10.1371/journal.pone.0019754.

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Kang, Sang Gil, Woo Dong Lee, and Yongku Kim. "Bayesian Multiple Change-Points Detection in a Normal Model with Heterogeneous Variances." Computational Statistics 36, no. 2 (January 12, 2021): 1365–90. http://dx.doi.org/10.1007/s00180-020-01054-3.

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Carreira-Casais, Anxo, Verónica Montes-García, Isabel Pastoriza-Santos, Miguel Ángel Prieto, Jesus Simal-Gandara, and Jorge Pérez-Juste. "Multiple SERS Detection of Phenol Derivatives in Tap Water." Proceedings 70, no. 1 (November 10, 2020): 88. http://dx.doi.org/10.3390/foods_2020-07755.

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Phenol and some of its derivatives are products of the petrochemical industry. These compounds are characterized by their exceptional ability to persist in media and reach both food and water used by human beings. The consumption of these compounds has harmful effects on health, producing both acute and chronic effects—among the most prominent damages are teratogenicity, mutagenicity and carcinogenicity. Compounds such as phenol, ortho-cresol, and 1-napthol are listed by agencies such as the EPA. The aim of this work was the development of a rapid method for the simultaneous detection of these compounds in water samples. The method was based on the colorimetric reaction between phenol derivatives and Gibbs reagent, which forms indophenolates; using increased surface Raman spectroscopy together with statistical methods, SERS spectra were acquired, which were then analyzed. The developed method allows one to at least equalize the detection limits of the colorimetric method through UV–VIS spectroscopy and to discriminate among the three mixed phenols in at least binary aqueous samples. The major advantage of the method is the possibility of discriminating between phenol spectra quickly and easily.

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Samad, Rosdiyana, Law Wen Yan, Mahfuzah Mustafa, Nor Rul Hasma Abdullah, and Dwi Pebrianti. "Multiple Human Body Postures Detection using Kinect." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 2 (May 1, 2018): 528. http://dx.doi.org/10.11591/ijeecs.v10.i2.pp528-536.

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<span lang="EN-US">This paper presents a method to detect multiple human body postures using Kinect sensor. In this study, a combination of shape features and body joint points are used as input features. The Kinect sensor which used infrared camera to produce a depth image is suitable to be used in an environment that has varying lighting conditions. The method for human detection is done by processing the depth image and joint data (skeleton) which able to overcome several problems such as cluttered background, various articulated poses, and change in color and illumination. Then, the body joint coordinates found on the object are used to calculate the body proportion ratio. In the experiment, the average body proportions from three body parts are obtained to verify the suitableness of golden ratio usage in this work. Finally, the measured body proportion is compared with Golden Ratio to determine whether the found object is a real human body or not. This method is tested for various scenarios, where true positive human detection is high for various postures. This method able to detect a human body in low lighting and dark room. The average body proportions obtained from the experiment show that the value is close to the golden ratio value.</span>

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Baranowski, Rafal, Yining Chen, and Piotr Fryzlewicz. "Narrowest-over-threshold detection of multiple change points and change-point-like features." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 81, no. 3 (May 30, 2019): 649–72. http://dx.doi.org/10.1111/rssb.12322.

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Priyadarshana, W. J. R. M., and Georgy Sofronov. "Multiple Break-Points Detection in Array CGH Data via the Cross-Entropy Method." IEEE/ACM Transactions on Computational Biology and Bioinformatics 12, no. 2 (March 2015): 487–98. http://dx.doi.org/10.1109/tcbb.2014.2361639.

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Bayram, Bülent, G. Çiğdem Çavdaroğlu, Dursun Zafer Şeker, and Sıtkı Külür. "A novel approach to automatic detection of interest points in multiple facial images." International Journal of Environment and Geoinformatics 4, no. 2 (May 15, 2017): 116–27. http://dx.doi.org/10.30897/ijegeo.312635.

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Qi, Minfang, Zhongguang Fu, and Fei Chen. "Outliers detection method of multiple measuring points of parameters in power plant units." Applied Thermal Engineering 85 (June 2015): 297–303. http://dx.doi.org/10.1016/j.applthermaleng.2015.04.008.

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Liu, Xinfang, Liyong Du, Rongfang Li, Ningning Ma, Mengdi You, and Xun Feng. "Different effects in the selective detection of aniline and Fe3+ by lanthanide-based coordination polymers containing multiple reactive sites." CrystEngComm 22, no. 16 (2020): 2837–44. http://dx.doi.org/10.1039/d0ce00238k.

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Isostructural Ln-CPs (1-Eu and 2-Tb) show almost the same high detection ability for Fe³⁺ and different detection abilities for aniline. The detection difference was studied through PXRD, UV-vis, luminescence lifetimes and Hirshfeld surface analysis.

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Li, Yan, Naisen Yu, Haiou Li, Dedi Liu, and Yunfeng Wu. "Solution-Processed Individual Multiple-Junction Structure Self-Assembled with ZnO Nanowires for UV/Blue Detection." Electronic Materials Letters 16, no. 6 (August 11, 2020): 564–72. http://dx.doi.org/10.1007/s13391-020-00240-1.

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36

Zhuang, Dan, and Youbo Liu. "A Fast Screen and Shape Recognition Algorithm for Multiple Change-Point Detection." Mathematical Problems in Engineering 2018 (October 11, 2018): 1–10. http://dx.doi.org/10.1155/2018/8371085.

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A Fast Screen and Shape Recognition (FSSR) algorithm is proposed with complexity down to O(n) for the multiple change-point detection problems. The proposed FSSR algorithm includes two steps. First, by dividing the data into several subsegments, FSSR algorithm can quickly lock some small subsegments that are likely to contain change-points. Second, through a point by point search in each selected subsegment, FSSR algorithm determines the precise location of the change-point. The simulation study shows that FSSR has obvious speed and stability advantages. Particularly, the sparser the change-points is, the better result will be achieved from FRRS. Finally, we apply FSSR to two real applications to demonstrate its feasibility and robustness. One is the problem of DNA copy number variations identifying; another is the problem of operation scenarios reduction for renewable integrated electrical distribution network.

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Jiang, Zhenxiang, and Jinping He. "Detection Model for Seepage Behavior of Earth Dams Based on Data Mining." Mathematical Problems in Engineering 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/8191802.

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Seepage behavior detecting is an important tool for ensuring the safety of earth dams. However, traditional seepage behavior detection methods have used insufficient monitoring data and have mainly focused on single-point measures and local seepage behavior. The seepage behavior of dams is not quantitatively detected based on the monitoring data with multiple measuring points. Therefore, this study uses data mining techniques to analyze the monitoring data and overcome the above-mentioned shortcomings. The massive seepage monitoring data with multiple points are used as the research object. The key information on seepage behavior is extracted using principal component analysis. The correlation between seepage behavior and upstream water level is described as mutual information. A detection model for overall seepage behavior is established. Result shows that the model can completely extract the seepage monitoring data with multiple points and quantitatively detect the overall seepage behavior of earth dams. The proposed method can provide a new and reasonable means of quantitatively detecting the overall seepage behavior of earth dams.

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Tourneret, Jean-Yves, Michel Doisy, and Marc Lavielle. "Bayesian off-line detection of multiple change-points corrupted by multiplicative noise: application to SAR image edge detection." Signal Processing 83, no. 9 (September 2003): 1871–87. http://dx.doi.org/10.1016/s0165-1684(03)00106-3.

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Barbosa, Wesley Oliveira, and Antonio Wilson Vieira. "On the Improvement of Multiple Circles Detection from Images Using Hough Transform." TEMA - Tendências em Matemática Aplicada e Computacional 20, no. 2 (July 29, 2019): 331. http://dx.doi.org/10.5540/tema.2019.020.02.331.

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The automatic detection of lines and curves from color images is a very important task in many applications, such as object recognition and scene reconstruction. Although there are closed formulation for curve fitting to a set of points, if the point set describes more than one instance of the object, as two circles for example, there is no closed formulation for obtaining the individual set of parameters without a priori information of which points belong to each object. However, it is usual the presence of multiple instances of objects such as lines and circles on an image. The well known Hough Transform is an efficient tool for recovering multiple objects from images using a voting process where the usual presence of false positives is an issue. In our work, we present an improvement on the voting process to detect multiple circles using Hough Transform in order to avoid false positives. Our experiments show that our voting process leads to a more robust detection, reducing the number of false positive and providing a more accurate detection even with large number of circles.

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Yang, Shi Yuan, Kiyotada Matsura, and Serikawa Seiichi. "Detection of Multiple Moving Points with a Position Sensitive Device Using Modulated Light Targets." Applied Mechanics and Materials 103 (September 2011): 683–86. http://dx.doi.org/10.4028/www.scientific.net/amm.103.683.

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Remote sensing of position detection and motion detection plays an important part in the welfare filed of human social life. Privacy is a serious problem when detecting human position and motion. A position sensitive device (PSD) only detects the position of a light target without detailed image of human. For detecting multiple light targets, there are various problems such as synchronized light source and noise in daily life space. In this study, we suggest a method using modulated light targets to separate different light targets and eliminate the noise in daily life space for motion detection of a PSD. The target light intensity is modulated with different high frequency source for different targets, so that the noise in daily life space can be eliminated and the positions of different targets can be separated by using band-pass filters. Experimental results showed this method is effective.

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Fang, QianSheng, JiXin Zhang, ChenLei Xie, and YaLong Yang. "Detection of multiple leakage points in water distribution networks based on convolutional neural networks." Water Supply 19, no. 8 (July 29, 2019): 2231–39. http://dx.doi.org/10.2166/ws.2019.105.

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Abstract Currently, a total of 3.6 billion people live in water-deficient areas, and the population living in water-deficient areas may reach from 4.8 to 5.7 billion by 2050. Despite that, the water distribution system (WDS) loses an average of 35% of its water resources, and the leakage rates may reach even higher values in some regions. The dual pressures of the lack of water resources and severe WDS leakage become even more problematic considering that commonly used leakage detection methods are time-consuming, labour-intensive, and can only detect single-point leakages. For multiple leakage point detection, these methods often perform poorly. To solve the problem of multiple leakage point detection, this paper presents a method for multiple leakage point detection based on a convolutional neural network (CNN). A CNN can forecast the leakages from a macro-perspective. It extracts the features of the collected historical leakage data by constructing a CNN model and predicts whether the real-time data are leakage data or not based on the learning of the features that are extracted from the historical data. The experimental results show that the detection accuracies based on 21 sensors of one, two, and three leakage points are 99.63%, 98.58% and 95.25%, respectively. After the number of sensors is reduced to eight, the leakage detection accuracies of one, two, and three leakage points are 96.43%, 94.88% and 91.56%, respectively.

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Jiang, Fei, Guosheng Yin, and Francesca Dominici. "Bayesian Model Selection Approach to Multiple Change-Points Detection with Non-Local Prior Distributions." ACM Transactions on Knowledge Discovery from Data 13, no. 5 (October 12, 2019): 1–17. http://dx.doi.org/10.1145/3340804.

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Cheung, Rex C. Y., Alexander Aue, Seungyong Hwang, and Thomas C. M. Lee. "Simultaneous Detection of Multiple Change Points and Community Structures in Time Series of Networks." IEEE Transactions on Signal and Information Processing over Networks 6 (2020): 580–91. http://dx.doi.org/10.1109/tsipn.2020.3012286.

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Bai, Peiliang, Abolfazl Safikhani, and George Michailidis. "Multiple Change Points Detection in Low Rank and Sparse High Dimensional Vector Autoregressive Models." IEEE Transactions on Signal Processing 68 (2020): 3074–89. http://dx.doi.org/10.1109/tsp.2020.2993145.

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Bertrand, Pierre Raphael, Mehdi Fhima, and Arnaud Guillin. "Off-Line Detection of Multiple Change Points by the Filtered Derivative withp-Value Method." Sequential Analysis 30, no. 2 (April 2011): 172–207. http://dx.doi.org/10.1080/07474946.2011.563710.

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Tominaga, Daisuke. "Periodicity Detection Method for Small-Sample Time Series Datasets." Bioinformatics and Biology Insights 4 (January 2010): BBI.S5983. http://dx.doi.org/10.4137/bbi.s5983.

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Time series of gene expression often exhibit periodic behavior under the influence of multiple signal pathways, and are represented by a model that incorporates multiple harmonics and noise. Most of these data, which are observed using DNA microarrays, consist of few sampling points in time, but most periodicity detection methods require a relatively large number of sampling points. We have previously developed a detection algorithm based on the discrete Fourier transform and Akaike's information criterion. Here we demonstrate the performance of the algorithm for small-sample time series data through a comparison with conventional and newly proposed periodicity detection methods based on a statistical analysis of the power of harmonics. We show that this method has higher sensitivity for data consisting of multiple harmonics, and is more robust against noise than other methods. Although “combinatorial explosion” occurs for large datasets, the computational time is not a problem for small-sample datasets. The MATLAB/GNU Octave script of the algorithm is available on the author's web site: http://www.cbrc.jp/%7Etominaga/piccolo/ .

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Kelly P, Kearse. "Environmental influence on blood serum detection using ultraviolet 365." Journal of Forensic Science and Research 5, no. 1 (March 26, 2021): 030–36. http://dx.doi.org/10.29328/journal.jfsr.1001024.

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The major use of alternative light sources (ALS) in the evaluation of bloodstains has been primarily focused on detection of whole blood, with relatively little attention to visualization of blood serum. Serum may become separated from blood pools during clotting, and because it is relatively invisible on certain backgrounds, go undetected by a perpetrator attempting to clean up a crime scene. Recently, Ultraviolet 365 (UV 365) was shown to be an effective tool in blood evaluation, useful for detection of even minute quantities of blood serum. Here the effects of environmental conditions on blood serum stain appearance were evaluated, including temperature, pH, protease sensitivity, solubility, and aging. Interestingly, it was found that the UV fluorescence of serum increases upon exposure to heat, which was accompanied by color changes under visible light and decreased solubility in multiple solvents. The efficiency of visualization of serum stains was somewhat variable, depending on the type of material on which it was dried. Finally, the current study documents the effect of heating on formation of fluorescent serum halo rings in dried bloodstains. Taken together, these data demonstrate that blood serum detection may be affected by certain conditions that influence its visualization under both visible and UV light.

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Piendl, Sebastian K., David Geissler, Laura Weigelt, and Detlev Belder. "Multiple Heart-Cutting Two-Dimensional Chip-HPLC Combined with Deep-UV Fluorescence and Mass Spectrometric Detection." Analytical Chemistry 92, no. 5 (February 6, 2020): 3795–803. http://dx.doi.org/10.1021/acs.analchem.9b05206.

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49

Martinez-Lopez, Joaquin, Juan J. Lahuerta, François Pepin, Marcos González, Santiago Barrio, Rosa Ayala, Noemí Puig, et al. "Prognostic value of deep sequencing method for minimal residual disease detection in multiple myeloma." Blood 123, no. 20 (May 15, 2014): 3073–79. http://dx.doi.org/10.1182/blood-2014-01-550020.

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Key Points MRD assessment by sequencing is prognostic of TTP and OS in multiple myeloma patients. Among patients in complete response, MRD assessment by sequencing enables identification of 2 distinct subgroups with different TTP.

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50

van Eggermond, Anna M., Michael Schaapveld, Pieternella J. Lugtenburg, Augustinus D. G. Krol, Jan Paul de Boer, Josée M. Zijlstra, John M. M. Raemaekers, et al. "Risk of multiple primary malignancies following treatment of Hodgkin lymphoma." Blood 124, no. 3 (July 17, 2014): 319–27. http://dx.doi.org/10.1182/blood-2013-10-532184.

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Key Points Hodgkin lymphoma survivors who developed a second malignancy remain at high risk of developing subsequent malignancies. Treatment options for these malignancies may be more restricted making early detection especially important to improving outcome.

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