A multisensor approach for improved protein A load phase monitoring by conductivity-based background subtraction of UV spectra

Real‐time monitoring and control of protein A capture steps by process analytical technologies (PATs) promises significant economic benefits due to the improved usage of the column\u27s binding capacity, by eliminating time‐consuming off‐line analytics and costly resin lifetime studies, and enabling continuous production. The PAT method proposed in this study relies on ultraviolet (UV) spectroscopy with a dynamic background subtraction based on the leveling out of the conductivity signal. This point in time can be used to collect a reference spectrum for removing the majority of spectral contributions by process‐related contaminants. The removal of the background spectrum facilitates chemometric model building and model accuracy. To demonstrate the benefits of this method, five different feedstocks from our industry partner were used to mix the load material for a case study. To our knowledge, such a large design space, which covers possible variations in upstream condition besides the product concentration, has not been disclosed yet. By applying the conductivity‐based background subtraction, the root mean square error of prediction (RMSEP) of the partial least squares (PLS) model improved from 0.2080 to 0.0131 gL$^{-1}$. Finally, the potential of the background subtraction method was further evaluated for single wavelength‐based predictions to facilitate implementation in production processes. An RMSEP of 0.0890 gL$^{-1}$ with univariate linear regression was achieved, showing that by subtraction of the background better prediction accuracy is achieved then without subtraction and a PLS model. In summary, the developed background subtraction method is versatile, enables accurate prediction results, and is easily implemented into existing chromatography setups with typically already integrated sensors

An Improved Method for 21cm Foreground Removal

21 cm tomography is expected to be difficult in part because of serious foreground contamination. Previous studies have found that line-of-sight approaches are capable of cleaning foregrounds to an acceptable level on large spatial scales, but not on small spatial scales. In this paper, we introduce a Fourier-space formalism for describing the line-of-sight methods, and use it to introduce an improved new method for 21 cm foreground cleaning. Heuristically, this method involves fitting foregrounds in Fourier space using weighted polynomial fits, with each pixel weighted according to its information content. We show that the new method reproduces the old one on large angular scales, and gives marked improvements on small scales at essentially no extra computational cost.Comment: 6 pages, 5 figures, replaced to match accepted MNRAS versio

The Thermal Properties of Solar Flares Over Three Solar Cycles Using GOES X-ray Observations

Solar flare X-ray emission results from rapidly increasing temperatures and emission measures in flaring active region loops. To date, observations from the X-Ray Sensor (XRS) onboard the Geostationary Operational Environmental Satellite (GOES) have been used to derive these properties, but have been limited by a number of factors, including the lack of a consistent background subtraction method capable of being automatically applied to large numbers of flares. In this paper, we describe an automated temperature and emission measure-based background subtraction method (TEBBS), which builds on the methods of Bornmann (1990). Our algorithm ensures that the derived temperature is always greater than the instrumental limit and the pre-flare background temperature, and that the temperature and emission measure are increasing during the flare rise phase. Additionally, TEBBS utilizes the improved estimates of GOES temperatures and emission measures from White et al. (2005). TEBBS was successfully applied to over 50,000 solar flares occurring over nearly three solar cycles (1980-2007), and used to create an extensive catalog of the solar flare thermal properties. We confirm that the peak emission measure and total radiative losses scale with background subtracted GOES X-ray flux as power-laws, while the peak temperature scales logarithmically. As expected, the peak emission measure shows an increasing trend with peak temperature, although the total radiative losses do not. While these results are comparable to previous studies, we find that flares of a given GOES class have lower peak temperatures and higher peak emission measures than previously reported. The resulting TEBBS database of thermal flare plasma properties is publicly available on Solar Monitor (www.solarmonitor.org/TEBBS/) and will be available on Heliophysics Integrated Observatory (www.helio-vo.eu)

Improving Multiple Object Tracking with Optical Flow and Edge Preprocessing

In this paper, we present a new method for detecting road users in an urban environment which leads to an improvement in multiple object tracking. Our method takes as an input a foreground image and improves the object detection and segmentation. This new image can be used as an input to trackers that use foreground blobs from background subtraction. The first step is to create foreground images for all the frames in an urban video. Then, starting from the original blobs of the foreground image, we merge the blobs that are close to one another and that have similar optical flow. The next step is extracting the edges of the different objects to detect multiple objects that might be very close (and be merged in the same blob) and to adjust the size of the original blobs. At the same time, we use the optical flow to detect occlusion of objects that are moving in opposite directions. Finally, we make a decision on which information we keep in order to construct a new foreground image with blobs that can be used for tracking. The system is validated on four videos of an urban traffic dataset. Our method improves the recall and precision metrics for the object detection task compared to the vanilla background subtraction method and improves the CLEAR MOT metrics in the tracking tasks for most videos

Background Subtraction via Generalized Fused Lasso Foreground Modeling

Background Subtraction (BS) is one of the key steps in video analysis. Many background models have been proposed and achieved promising performance on public data sets. However, due to challenges such as illumination change, dynamic background etc. the resulted foreground segmentation often consists of holes as well as background noise. In this regard, we consider generalized fused lasso regularization to quest for intact structured foregrounds. Together with certain assumptions about the background, such as the low-rank assumption or the sparse-composition assumption (depending on whether pure background frames are provided), we formulate BS as a matrix decomposition problem using regularization terms for both the foreground and background matrices. Moreover, under the proposed formulation, the two generally distinctive background assumptions can be solved in a unified manner. The optimization was carried out via applying the augmented Lagrange multiplier (ALM) method in such a way that a fast parametric-flow algorithm is used for updating the foreground matrix. Experimental results on several popular BS data sets demonstrate the advantage of the proposed model compared to state-of-the-arts

The Infrared Database of Extragalactic Observables from Spitzer I: the redshift catalog

This is the first of a series of papers on the Infrared Database of Extragalactic Observables from Spitzer (IDEOS). In this work we describe the identification of optical counterparts of the infrared sources detected in Spitzer Infrared Spectrograph (IRS) observations, and the acquisition and validation of redshifts. The IDEOS sample includes all the spectra from the Cornell Atlas of Spitzer/IRS Sources (CASSIS) of galaxies beyond the Local Group. Optical counterparts were identified from correlation of the extraction coordinates with the NASA Extragalactic Database (NED). To confirm the optical association and validate NED redshifts, we measure redshifts with unprecedented accuracy on the IRS spectra ({\sigma}(dz/(1+z))=0.0011) by using an improved version of the maximum combined pseudo-likelihood method (MCPL). We perform a multi-stage verification of redshifts that considers alternate NED redshifts, the MCPL redshift, and visual inspection of the IRS spectrum. The statistics is as follows: the IDEOS sample contains 3361 galaxies at redshift 0<z<6.42 (mean: 0.48, median: 0.14). We confirm the default NED redshift for 2429 sources and identify 124 with incorrect NED redshifts. We obtain IRS-based redshifts for 568 IDEOS sources without optical spectroscopic redshifts, including 228 with no previous redshift measurements. We provide the entire IDEOS redshift catalog in machine-readable formats. The catalog condenses our compilation and verification effort, and includes our final evaluation on the most likely redshift for each source, its origin, and reliability estimates.Comment: 11 pages, 6 figures, 1 table. Accepted for publication in MNRAS. Full redshift table in machine-readable format available at http://ideos.astro.cornell.edu/redshifts.htm

Trans unsaturated fatty acids : a study of methodology and levels in New Zealand food fats including milkfat : a thesis presented in partial fulfilment of the requirements for the degree of Masters of Science in Biochemistry at Massey University, New Zealand

Trans fatty acids (TFAs) occur naturally in small amounts in foods such as milk, butter and tallow as a result of biohydrogenation by ruminant gut microflora. They are formed in much larger quantities during chemical hydrogenation of fats and oils. The relationship between dietary TFAs and blood cholesterol has been investigated over the last 30 years with equivocal results because of methodological limitations, including difficulties of quantifying the consumption of TFAs. The present study was conducted to investigate the methodologies used to quantify TFAs in fat samples. Two methodologies, based on infrared spectrophotometry and argentation-thin layer chromatography/gas chromatography (Ag-TLC/GC), were optimised for TFA quantification. Improvements in the infrared methods were made using a calibration standard made up with two non-trans components (stearin and olein) in order to mimic the fatty acid background in the samples. Further improvements were made using a spectral subtraction technique where the non-trans background spectrum was subtracted from the sample spectrum using Fourier-transform infrared spectrophotometry software. Results from the improved infrared methods were compared with TFA measurements by the more detailed Ag-TLC/GC method. The spectral subtraction technique for the methyl ester samples produced results that were closest to those of the Ag-TLC/GC method. This Ag-TLC/GC method gives information about the individual trans isomers (C18:1 trans positional isomers and C18:2 and C18:3 trans isomers) that is not available by infrared. The present study was also conducted to determine, as accurately as possible, the TFA content in 18 manufactured foods commonly available in New Zealand using the TFA methods mentioned above. The TFA contents in some of the foods determined by the Ag-TLC/GC method were, margarine (15.43-15.57%), butter (6.58%), milk (5.26-6.03%), meat patties (3.42%), plain sweet biscuits (3.65%) and white bread (4.41 %). Using these TFA data and the food consumption data from a Life in New Zealand (Horwarth et al., 1991), the estimated TFA intakes in the average New Zealand diet were approximately 3.99 and 5.75 g/person/day for females and males respectively. These figures were similar to or lower than those estimated for Northern Hemisphere countries. The predominant TFA isomer in the New Zealand diet was identified as the C18:1 Δ11t positional isomer (30-33%). Further studies were made on the total TFA content in New Zealand milkfat. These studies indicated that the total TFA levels in New Zealand milkfat were influenced by seasonal variations, with the highest TFA content recorded in spring (September, 6.7%) and the lowest in summer (January, 5.3%). The C18:1 Δ11t isomer was found to be the predominant isomer in milkfat, making up 49-60% of the total TFA. Similar ranges were observed for several overseas butter samples. However, major differences were observed with the distribution of the C18:1 trans positional isomers. These differences are currently suspected to be influenced by the feed and animal husbandry methods used in some Northern Hemisphere countries, where cows are mainly grain fed in the winter months. The seasonal variation of TFAs in New Zealand butter and possible effects of feed and animal husbandry methods on the C18:1 trans positional isomer distribution are important factors that the New Zealand dairy industry could exploit for the production of low trans milkfat and/or other dairy products in which the levels of specific trans isomers implicated to be "harmful" to humans could be minimised. Margarines display a trans isomer distribution that is quite distinct from that of butter. Unlike milkfat where the predominant trans isomer is C18:1 Δ11t, in margarines and hydrogenated fats and oils the positional isomers show a normal distribution around the C18:1 Δ10t-11t isomers. The predominant isomers for the margarines analysed in this study were Δ9t-Δ12t (90%) with the polyunsaturated C18:2 and C18:3 trans making up less than 2%. The distinct distribution of C18:1 trans positional isomers could serve as an additional tool for the identification of animal or hydrogenated vegetable oils used in food fats