Shifted Excitation Raman Difference Spectroscopy Combined with Wide Area Illumination and Sample Rotation for Wood Species Classification

Raman spectroscopy has found its way into a wide range of applications and is successfully applied for qualitative and quantitative studies. Despite significant technical progress over the last few decades, there are still some challenges that limit its more widespread usage. This paper presents a holistic approach to addressing simultaneously the problems of fluorescence interference, sample heterogeneity, and laser-induced sample heating. Long wavelength shifted excitation Raman difference spectroscopy (SERDS) at 830 nm excitation combined with wide-area illumination and sample rotation is presented as a suitable approach for the investigation of selected wood species. Wood as a natural specimen represents a well-suited model system for our study as it is fluorescent, heterogeneous, and susceptible to laser-induced modifications. Two different subacquisition times (50 and 100 ms) and two sample rotation speeds (12 and 60 r/min) were exemplarily assessed. Results demonstrate that SERDS can effectively separate the Raman spectroscopic fingerprints of the wood species balsa, beech, birch, hickory, and pine from intense fluorescence interference. Sample rotation in conjunction with 1 mm-diameter wide-area illumination was suitable to obtain representative SERDS spectra of the wood species within 4.6 s. Using partial least squares discriminant analysis, a classification accuracy of 99.4% for the five investigated wood species was realized. This study highlights the large potential of SERDS combined with wide-area illumination and sample rotation for the effective analysis of fluorescent, heterogeneous, and thermally sensitive specimens in a wide range of application areas.

Problematic gaming risk among European adolescents: a cross-national evaluation of individual and socio-economic factors

BACKGROUND AND AIMS

Previous research has identified numerous risk and protective factors of adolescent problematic gaming (PG) at the individual and social levels; however, the influence of socio-economic indicators on PG is less known. This study aimed to measure the contribution of individual and socio-economic factors involved in PG risk among adolescents from 30 European countries.

DESIGN

Multi-level logistic regression analysis of survey data from the 2019 European School Survey Project on Alcohol and Other Drugs (ESPAD) cross-sectional study using self-administered anonymous questionnaires.

SETTING

Thirty European countries.

PARTICIPANTS

A representative cohort of 15-16-year-old students (n = 88 998 students; males = 49.2%).

MEASUREMENTS

The primary outcome measure was adolescents' (low and high) risk of PG. Individual key predictors included self-report assessments of socio-demographic characteristics, time spent gaming and family variables (parental regulation and monitoring, family support). Main country-level predictors comprised Gini coefficient for economic inequalities and benefits for families and children (% gross domestic product), retrieved from international public data sets and national thematic reports. The data analysis plan involved multi-level logistic regression.

FINDINGS

Participants who reported stronger parental regulation [odds ratio (OR) = 0.81, 95% confidence interval (CI) = 0.79-0.83] and higher family support (OR = 0.93, 95% CI = 0.91-0.95) reported lower risk of PG. At the country-level, economic inequalities (OR = 1.05, 95% CI = 1.03-1.07) were positively associated with the risk of PG, while benefits for families and children (OR = 0.78, 95% CI = 0.70-0.89) were negatively correlated with the risk of PG.

CONCLUSIONS

Supportive family environments, lower country-level economic inequalities and higher government expenditures on benefits for families and children appear to be associated with a lower risk of problematic gaming among European adolescents.

Feasibility of In-Line Raman Spectroscopy for Quality Assessment in Food Industry: How Fast Can We Go?

Raman spectroscopy is a viable tool within process analytical technologies due to recent technological advances. In this article, we evaluate the feasibility of Raman spectroscopy for in-line applications in the food industry by estimating the concentration of the fatty acids EPA + DHA in ground salmon samples (n = 63) and residual bone concentration in samples of mechanically recovered ground chicken (n = 66). The samples were measured under industry like conditions: They moved on a conveyor belt through a dark cabinet where they were scanned with a wide area illumination standoff Raman probe. Such a setup should be able to handle relevant industrial conveyor belt speeds, and it was studied how different speeds (i.e., exposure times) influenced the signal-to-noise ratio (SNR) of the Raman spectra as well as the corresponding model performance. For all samples we applied speeds that resulted in 1 s, 2 s, 4 s, and 10 s exposure times. Samples were scanned in both heterogenous and homogenous state. The slowest speed (10 s exposure) yielded prediction errors (RMSECV) of 0.41%EPA + DHA and 0.59% ash for the salmon and chicken data sets, respectively. The more in-line relevant exposure time of 1 s resulted in increased RMSECV values, 0.84% EPA + DHA and 0.84% ash, respectively. The increase in prediction error correlated closely with the decrease in SNR. Further improvements of model performance were possible through different noise reduction strategies. Model performance for homogenous and heterogenous samples was similar, suggesting that the presented Raman scanning approach has the potential to work well also on intact heterogenous foods. The estimation errors obtained at these high speeds are likely acceptable for industrial use, but successful strategies to increase SNR will be key for widespread in-line use in the food industry.

Assessment of Bulk Composition of Heterogeneous Food Matrices Using Raman Spectroscopy

Raman spectroscopy (RS) has for decades been considered a promising tool for food analysis, but widespread adoption has been held back by, e.g., high instrument costs and sampling limitations regarding heterogeneous samples. The aim of the present study was to use wide area RS in conjunction with surface scanning to overcome the obstacle of heterogeneity. Four different food matrices were scanned (intact and homogenized pork and by-products from salmon and poultry processing) and the bulk chemical parameters such as fat and protein content were estimated using partial least squares regression (PLSR). The performance of PLSR models from RS was compared with near-infrared spectroscopy (NIRS). Good to excellent results were obtained with PLSR models from RS for estimation of fat content in all food matrices (coefficient of determination for cross-validation (R²_CV) from 0.73 to 0.96 and root mean square error of cross-validation (RMSECV) from 0.43% to 2.06%). Poor to very good PLSR models were obtained for estimation of protein content in salmon and poultry by-product using RS (R²_CV from 0.56 to 0.92 and RMSECV from 0.85% to 0.94%). The performance of RS was similar to NIRS for all analyses. This work demonstrates the applicability of RS to analyze bulk composition in heterogeneous food matrices and paves way for future applications of RS in routine food analyses.

Improved Sample Selection and Preparation Methods for Sampling Plans Used to Facilitate Rapid and Reliable Estimation of Aflatoxin in Chicken Feed

Aflatoxin B1 (AFB1), a toxic fungal metabolite associated with human and animal diseases, is a natural contaminant encountered in agricultural commodities, food and feed. Heterogeneity of AFB1 makes risk estimation a challenge. To overcome this, novel sample selection, preparation and extraction steps were designed for representative sampling of chicken feed. Accuracy, precision, limits of detection and quantification, linearity, robustness and ruggedness were used as performance criteria to validate this modification and Horwitz function for evaluating precision. A modified sampling protocol that ensured representativeness is documented, including sample selection, sampling tools, random procedures, minimum size of field-collected aggregate samples (primary sampling), procedures for mass reduction to 2 kg laboratory (secondary sampling), 25 g test portion (tertiary sampling) and 1.3 g analytical samples (quaternary sampling). The improved coning and quartering procedure described herein (for secondary and tertiary sampling) has acceptable precision, with a Horwitz ratio (HorRat = 0.3) suitable for splitting of 25 g feed aliquots from laboratory samples (tertiary sampling). The water slurring innovation (quaternary sampling) increased aflatoxin extraction efficiency to 95.1% through reduction of both bias (-4.95) and variability of recovery (1.2-1.4) and improved both intra-laboratory precision (HorRat = 1.2-1.5) and within-laboratory reproducibility (HorRat = 0.9-1.3). Optimal extraction conditions are documented. The improved procedure showed satisfactory performance, good field applicability and reduced sample analysis turnaround time.

Representative Sequencing: Unbiased Sampling of Solid Tumor Tissue

Although thousands of solid tumors have been sequenced to date, a fundamental under-sampling bias is inherent in current methodologies. This is caused by a tissue sample input of fixed dimensions (e.g., 6 mm biopsy), which becomes grossly under-powered as tumor volume scales. Here, we demonstrate representative sequencing (Rep-Seq) as a new method to achieve unbiased tumor tissue sampling. Rep-Seq uses fixed residual tumor material, which is homogenized and subjected to next-generation sequencing. Analysis of intratumor tumor mutation burden (TMB) variability shows a high level of misclassification using current single-biopsy methods, with 20% of lung and 52% of bladder tumors having at least one biopsy with high TMB but low clonal TMB overall. Misclassification rates by contrast are reduced to 2% (lung) and 4% (bladder) when a more representative sampling methodology is used. Rep-Seq offers an improved sampling protocol for tumor profiling, with significant potential for improved clinical utility and more accurate deconvolution of clonal structure.

Moderation of classic social psychological effects by demographics in the U.S. adult population: New opportunities for theoretical advancement

For decades, social psychologists have collected data primarily from college undergraduates and, recently, from haphazard samples of adults. Yet researchers have routinely presumed that thus observed treatment effects characterize "people" in general. Tests of seven highly cited social psychological phenomena (two involving opinion change resulting from social influence and five involving the use of heuristics in social judgments) using data collected from randomly sampled, representative groups of American adults documented generalizability of the six phenomena that have been replicated previously with undergraduate samples. The 1 phenomenon (a cross-over interaction revealing an ease of retrieval effect) that has not been replicated successfully previously in undergraduate samples was also not observed here. However, the observed effect sizes for the replicated phenomena were notably smaller on average than the meta-analytic effect sizes documented by past studies of college students. Furthermore, the phenomena were strongest among participants with the demographic characteristics of the college students who typically provided data for past published studies, even after correcting for publication bias in past studies using a new method, called the behaviorally-informed file-drawer adjustment. The six successful replications suggest that phenomena identified in traditional laboratory research also appear as expected in representative samples but more weakly, so observed effect sizes should be generalized with caution. The evidence of demographic moderators suggests interesting opportunities for future research to better understand the mechanisms of the effects and their limiting conditions. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Pattern identification of lung cancer patients based on body constitution questionnaires (BCQ) and glycoproteomics for precision medicine

BACKGROUND

The patient's pattern identification has been used for personalized medicine in traditional Korean medicine (TKM) and aims for patient-specific therapy by Korean medical doctors. The pattern identification in this trial will be diagnosed from body constitution questionnaire (BCQ) with a more objective diagnosis of it but this method still needs a more concrete scientific basis. Glycoproteins are well-known to be associated with diseases (especially cancers) so glycoproteomics can be applied to differentiate pattern identification types of lung cancer patients. Thus, for the first time proteomics approach will be applied to the pattern identification by comparing BCQ assessment in order to establish a scientific basis with clinical proteomics for precision medicine.

METHODS

This observational trial will at first diagnose the pattern identification types of lung cancer patients with BCQ assessment and then elucidate their relationships with proteomics. Blood samples will be collected before surgery along with clinical information of participants. The patients' pattern identification in TKM will be diagnosed from BCQ assessment. Then, lung cancer patients will be divided and pooled into 3 lung cancer entire (LCE) groups according to their pattern identification types (Xu, Stasis, or Gentleness). Three lung cancer representative (LCR) groups will be selected and pooled from each LCE group by selecting those with the same control factors. The 3 LCE groups and the 3 LCR groups from lung cancer patients will be independently analyzed through the glycoproteomics approach based on the patients' pattern identification. Glycoproteins from the 6 groups will be identified through proteomics approach and then categorized for analysis.

DISCUSSION

This study intends to diagnose pattern identification of patients in TKM with BCQ assessment and proteomics approach. The identification of the glycoproteins in each group will lead to the scientific foundation of personalized medicine in TKM according to patients' pattern identification for lung cancer therapy. We intend to(1) diagnose the pattern identification types of lung cancer patients with BCQ under the framework of TKM;(2) evaluate BCQ assessment with glycoproteomics approach for precision medicine.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03384680. Registered 27 December 2017. Retrospectively registered.

Optimizing methods to estimate zooplankton concentration based on generalized patterns of patchiness inside ballast tanks and ballast water discharges

Zooplankton populations are spatially heterogeneous in nature and inside ship ballast tanks. Sampling methods should take heterogeneity into account, particularly when estimating quantitative variables such as abundance or concentration. It is particularly important to generate unbiased estimates of zooplankton concentration in ballast water when assessing compliance with new international ballast water discharge standards. We measured spatial heterogeneity of zooplankton within ballast water using three sampling methodologies. In-tank pump samples were collected at fixed depths within the vertical part of the ballast tank (side tank). Vertical net-haul samples were collected from the upper portion of the tank as a depth-integrated and historically relevant method. In-line, time-integrated samples were collected during ballast discharge by an isokinetic sample probe, likely representing the double bottom part of the ballast tank. The bias and precision associated with each sampling method were evaluated in reference to the estimated average abundance of the entire ballast tank, which was modeled from the data collected by all methods. In-tank pump samples provided robust evidence for vertical stratification of zooplankton concentration in the side tank. A consistent trend was also observed for in-line discharge samples, with zooplankton concentration decreasing through time as the ballast tank is being discharged. Sample representativeness, as compared to the tank average, varied depending on the depth or tank volume discharged. In-line discharge samples provided the least biased and most precise estimate of average tank abundance (having lowest mean squared error) when collected during the time frame of 20%-60% of the tank volume being discharged. Results were consistent across five trips despite differences in ballast water source, season, and age.

Solid recovered fuels in the cement industry--semi-automated sample preparation unit as a means for facilitated practical application

One of the challenges for the cement industry is the quality assurance of alternative fuel (e.g., solid recovered fuel, SRF) in co-incineration plants--especially for inhomogeneous alternative fuels with large particle sizes (d95⩾100 mm), which will gain even more importance in the substitution of conventional fuels due to low production costs. Existing standards for sampling and sample preparation do not cover the challenges resulting from these kinds of materials. A possible approach to ensure quality monitoring is shown in the present contribution. For this, a specially manufactured, automated comminution and sample divider device was installed at a cement plant in Rohožnik. In order to prove its practical suitability with methods according to current standards, the sampling and sample preparation process were validated for alternative fuel with a grain size >30 mm (i.e., d95=approximately 100 mm), so-called 'Hotdisc SRF'. Therefore, series of samples were taken and analysed. A comparison of the analysis results with the yearly average values obtained through a reference investigation route showed good accordance. Further investigations during the validation process also showed that segregation or enrichment of material throughout the comminution plant does not occur. The results also demonstrate that compliance with legal standards regarding the minimum sample amount is not sufficient for inhomogeneous and coarse particle size alternative fuels. Instead, higher sample amounts after the first particle size reduction step are strongly recommended in order to gain a representative laboratory sample.

On the Creation of Representative Samples of Random Quasi-Orders

Dependencies between educational test items can be represented as quasi-orders on the item set of a knowledge domain and used for an efficient adaptive assessment of knowledge. One approach to uncovering such dependencies is by exploratory algorithms of item tree analysis (ITA). There are several methods of ITA available. The basic tool to compare such algorithms concerning their quality are large-scale simulation studies that are crucially set up on a large collection of quasi-orders. A serious problem is that all known ITA algorithms are sensitive to the structure of the underlying quasi-order. Thus, it is crucial to base any simulation study that tries to compare the algorithms upon samples of quasi-orders that are representative, meaning each quasi-order is included in a sample with the same probability. Up to now, no method to create representative quasi-orders on larger item sets is known. Non-optimal algorithms for quasi-order generation were used in previous studies, which caused misinterpretations and erroneous conclusions. In this paper, we present a method for creating representative random samples of quasi-orders. The basic idea is to consider random extensions of quasi-orders from lower to higher dimension and to discard extensions that do not satisfy the transitivity property.

Water chemistry in 179 randomly selected Swedish headwater streams related to forest production, clear-felling and climate

From a policy perspective, it is important to understand forestry effects on surface waters from a landscape perspective. The EU Water Framework Directive demands remedial actions if not achieving good ecological status. In Sweden, 44 % of the surface water bodies have moderate ecological status or worse. Many of these drain catchments with a mosaic of managed forests. It is important for the forestry sector and water authorities to be able to identify where, in the forested landscape, special precautions are necessary. The aim of this study was to quantify the relations between forestry parameters and headwater stream concentrations of nutrients, organic matter and acid-base chemistry. The results are put into the context of regional climate, sulphur and nitrogen deposition, as well as marine influences. Water chemistry was measured in 179 randomly selected headwater streams from two regions in southwest and central Sweden, corresponding to 10 % of the Swedish land area. Forest status was determined from satellite images and Swedish National Forest Inventory data using the probabilistic classifier method, which was used to model stream water chemistry with Bayesian model averaging. The results indicate that concentrations of e.g. nitrogen, phosphorus and organic matter are related to factors associated with forest production but that it is not forestry per se that causes the excess losses. Instead, factors simultaneously affecting forest production and stream water chemistry, such as climate, extensive soil pools and nitrogen deposition, are the most likely candidates The relationships with clear-felled and wetland areas are likely to be direct effects.

Robustness in experimental design: A study on the reliability of selection approaches

The quality criteria for experimental design approaches in chemoinformatics are numerous. Not only the error performance of a model resulting from the selected compounds is of importance, but also reliability, consistency, stability and robustness against small variations in the dataset or structurally diverse compounds. We developed a new stepwise, adaptive approach, DescRep, combining an iteratively refined descriptor selection with a sampling based on the putatively most representative compounds. A comparison of the proposed strategy was based on statistical performance of models derived from such a selection to those derived by other popular and frequently used approaches, such as the Kennard-Stone algorithm or the most descriptive compound selection. We used three datasets to carry out a statistical evaluation of the performance, reliability and robustness of the resulting models. Our results indicate that stepwise and adaptive approaches have a better adaptability to changes within a dataset and that this adaptability results in a better error performance and stability of the resulting models.

Early experiences and predictors of recruitment success for the National Children's Study

OBJECTIVES

We aimed to describe 17 months of experience with household recruitment of live births for the National Children's Study in Queens, a highly urban, diverse borough of New York City (NYC), and to assess predictors of recruitment success.

METHODS

Recruitment data (enumeration, pregnancy screening of age-eligible women, identification of pregnancies, and consent) for the period of January 2009 through May 2010 were calculated. Geographic information systems were used to create 11 community-level variables for each of the 18 study segments where recruitment occurred, using US Census, NYC Office of Vital Statistics, NYC Department of City Planning, and NYC Police Department data. Recruitment yields were analyzed with respect to these variables at the segment level.

RESULTS

Enumeration identified 4889 eligible women, of whom 4333 (88.6%) completed the pregnancy screener. At least 115 births were lost because of an inability of the pregnancy screener to identify pregnant women, whereas another 115 could be expected to be lost because of missed enumerations and pregnancy screeners. The consent rate was 60.3%. Segments with higher percentages of low birth weight had higher enumeration, pregnancy screening, and consent rates.

CONCLUSIONS

In a highly immigrant, urban setting, households could be approached for recruitment of women to participate in the National Children's Study with consent rates equal to those experienced in clinical settings. Refinement of the pregnancy screener and other recruitment materials presents an opportunity to optimize recruitment, improve the representativeness of study participants, and improve the cost-effectiveness of study execution.