Samples Of Background Research Articles

Establishment and Application Prospect of Reverse Transcriptase Recombinase-Aided Amplification Assay for Subgroup C Avian Metapneumovirus.

Among broilers, the main pathogen that leads to swollen head syndrome (SHS) is the subgroup C avian metapneumovirus (aMPV-C). The aMPV-C infection can lead to an upsurge in the rate of soft-shell eggs, resulting in reduced egg production and seriously affecting the economy of the livestock industry. Therefore, a rapid method for aMPV-C detection needs to be invented. According to the N gene of aMPV-C, we designed the specific probe and primer and created a reverse transcription recombinase-aided amplification assay (RT-RAA) for the detection of aMPV-C. aMPV-C could be detected quickly and specifically by this method at 41 °C for 30 min. The sensitivity assay inferred that the minimum detection threshold of RT-RAA was 3.38 × 101 copies/μL. A specificity assay showed that the RT-RAA method did not cross-react with other subgroups (aMPV-A, aMPV-B, aMPV-D) or other viruses (H9N2, NDV, IBV, IBDV). Forty samples of known clinical background were tested by RT-RAA and RT-qPCR. The two approaches had a 100% correlation rate. In conclusion, this research successfully created an RT-RAA assay for aMPV-C.

Human epidermal growth factor receptor 2/3 (HER2/HER3) expression in higher-grade meningioma.

e14063 Background: Meningiomas are the most frequently diagnosed primary central nervous system (CNS) tumors in adults. Although most meningiomas can be cured by resection, higher-grade lesions (CNS WHO grades 2 & 3) tend to recur, with limited systemic treatment options after exhaustion of local therapies. Antibody drug conjugates (ADCs) targeting human epidermal growth factor receptors 2 and 3 HER2/HER3) have shown remarkable clinical activity in solid tumors including breast cancer brain metastases and are under investigation in various malignancies. Methods: 64 patients with WHO grade 2 (atypical) and 3 (anaplastic) meningioma have been included. DNA methylation analysis has been performed using Illumina EPIC methylation arrays. Panel sequencing for NF2, PIK3CA, SMO, ARID1A, ARID1B, KLF4, SUFU, TERT promotor, and TRAF7 has been executed applying the Illumina NextSeq 500 platform. HER2 and HER3 expression was evaluated by immunohistochemical analysis using PATHWAY HER2 (4B5, Roche) and HER3 #12708 (Cell Signaling) antibodies on a Ventana Benchmark Ultra staining system. Results: In total, 44/64 (68.8%) patients with WHO grade 2 and 20/64 (31.2%) patients with WHO grade 3 meningioma were included. Median age at surgery was 60 years (range: 19-83), and 37/64 (57.8%) patients were female. DNA methylation classes according to the Molecular Neuropathology classifier were benign-1 in 10 (15.6%), benign-2 in 10 (15.6%), benign-3 in 4 (6.3%), intermediate-A in 26 (40.6%), intermediate-B in 4 (6.3%), and malignant in 10 (15.6%) out of 64 tumors. Weak membranous HER2 expression was observed in 1/64 (1.6%) tumor, which was categorized as WHO grade 3/intermediate-B meningioma characterized by infiltration of adjacent bone and fat tissue as well as positive Ki-67 staining of up to 39% of cells. Similarly, only one sample (1.6%) was HER3-positive which was diagnosed as WHO grade 2 tumor of methylation class benign-2, showing meningothelial morphology with elevated mitotic activity and a mutation in TRAF7. The single HER2+ as well as the HER3+ sample were from two distinct female patients. Conclusions: In our cohort, HER2 and HER3 were only infrequently expressed in a small subset of samples of different WHO grade, methylation class, and genetic background. Exploration of further targets of ADCs which are approved or under investigation in other tumors is currently ongoing.

DbPepVar: A Novel Cancer Proteogenomics Database

Cancers arise from the acquisition of DNA mutations, such as substitutions, deletions, amplifications, and rearrangements. Understanding the distribution and correlation of such mutations in cancer may aid the characterization of the disease and subsequent identification of biomarkers for diagnosis and treatment. The proteogenomics database (dbPepVar) created here combines genetic variation information from dbSNP with protein sequences from NCBI’s RefSeq. Public mass spectrometry datasets (Ovarian, Colorectal, Breast, and Prostate) were used to perform a pan-cancer analysis, allowing the identification of unique genetic variations. As a result, 3,726 variant peptides were identified in samples from patients with ovarian cancer, 2,543 in prostate, 2,661 in breast and 2,411 in colon-rectal cancer patients. Data resulting from the proteogenomics approach employed and connected to other biological databases is now available in an intuitive and dynamic web portal where novice users can explore general aspects of the dataset in graph or table format, or dive in to filter the data with click and select options or using more advanced queries with regex. All data can be downloaded in csv or pdf format. In perspective, the web portal developed may direct studies to identify new therapeutic targets for different cancers, and one can also use our database for characterization of variants in samples of unknown genetic background, such as archived samples.

Online μSEC2-nRPLC-MS for Improved Sensitivity of Intact Protein Detection of IEF-Separated Nonhuman Primate Cerebrospinal Fluid Proteins.

Proteoform-resolved information, obtained by top-down (TD) "intact protein" proteomics, is expected to contribute substantially to the understanding of molecular pathogenic mechanisms and, in turn, identify novel therapeutic and diagnostic targets. However, the robustness of mass spectrometry (MS) analysis of intact proteins in complex biological samples is hindered by the high dynamic range in protein concentration and mass, protein instability, and buffer complexity. Here, we describe an evolutionary step for intact protein investigations through the online implementation of tandem microflow size-exclusion chromatography with nanoflow reversed-phase liquid chromatography and MS (μSEC2-nRPLC-MS). Online serial high-/low-pass SEC filtration overcomes the aforementioned hurdles to intact proteomic analysis through automated sample desalting/cleanup and enrichment of target mass ranges (5-155 kDa) prior to nRPLC-MS. The coupling of μSEC to nRPLC is achieved through a novel injection volume control (IVC) strategy of inserting protein trap columns, pre- and post-μSEC columns, to enable injection of dilute samples in high volumes without loss of sensitivity or resolution. Critical characteristics of the approach are tested via rigorous investigations on samples of varied complexity and chemical background. Application of the platform to cerebrospinal fluid (CSF) prefractionated by OFFGEL isoelectric focusing drastically increases the number of intact mass tags (IMTs) detected within the target mass range (5-30 kDa) in comparison to one-dimensional nRPLC-MS with approximately 100× less CSF than previous OFFGEL studies. Furthermore, the modular design of the μSEC2-nRPLC-MS platform is robust and promises significant flexibility for large-scale TDMS analysis of diverse samples either directly or in concert with other multidimensional fractionation steps.

Extract hidden patterns in students' academic information to improve the curriculum by using data mining

AbstractEducational data mining is an emerging exquisite field that has been successfully implemented in higher education. One of the best ways to increase the efficiency of this emerging phenomenon is to select efficient professors and effective teaching methods. This study is intended to show academic success factors to have better management in student curriculum, contextualizing the progress and to prevent unfavorable conditions for students. In this research, students of Shahrood University of Technology were studied. Initially, 3,765 samples of students' educational background were considered. Then, pre-processing was performed to make the data normalized. Next, several predictive models were developed using a supervised data mining approach. Next, five algorithms by the best result were selected. Comparing the results of algorithms applied to data, the two algorithms, radial basis function network and the Naïve Bayes with respectively value F-measure 0.929 and 0.942 showed more accurate results. Finally, the most effective feature was selected, the attributes ‘maximum semester’ and ‘the total number of units dropped’ were ranked an the most important, and the three attributes of ‘the basic courses average’, ‘the number of units of main courses’ and ‘the total average’, were placed in the next ranks.

Abstract 3418: A NGS-based SNP array for the identification of human xenograft tumors, mouse homograft tumors, human and mouse cell lines, organoids

Abstract Transplanted tumor models, including xenograft tumors (e.g. patient derived xenograft, or PDX, human cell line derived tumors, mouse homograft (e.g. syngeneic tumors), as well as many cancer cell lines of both human and mouse origins are the key preclinical tools used in cancer researches, including drug efficacy assessment, drug target/biomarker discovery, as well as drug mechanism of actions and resistances, etc. The broad applications of these models in all different global laboratories at different times creates great risk of cross-contaminations (intra-/inter-species) as well as unintended de-identification. It is prudent to be able to track and QC tumors to ensure their authenticity by a simple and routine assays. The most common assay to identify human xenografts are Short Tandem Repeat (STR) test using multiple independently assorted STR loci. However, STR testing may suffer from several limitations, e.g. inadequate accuracy, especially for samples of kinship or close genetic background, inability to distinguish tumor of mouse origins as well as assessing mouse components in human xenografts. In addition, STR is also labor intensive, costly, low through-put, and ineffective for samples of low amount/poor quality. Single Nucleotide Polymorphisms (SNPs) based assays are increasingly used recently to replace STR testing with higher accuracy1-4. SNPs are genetic markers at single-nucleotide resolution. It is also amicable to automation, suitable for low amount/degraded samples with fragment length as short as only 60-80bp. Multiple SNPs can be simultaneously assayed by Next Generation Sequencing (NGS) technology. We developed a NGS-based SNP array for the identification of our libraries of xenografts, homografts, cell lines, etc. The assay is consisted of ~300 SNPs, 200 of which are used to authenticate different human origins, which were first constructed as unique fingerprints for every PDX from Whole-Exome Sequencing (WES) and RNAseq data, followed by that a sample is tested by the NGS-based SNP array to obtain its SNP fingerprint which is compared against all known fingerprints. The remaining 100 SNPs are used to infer gender and ethnicity, common viral infection, mouse content, mouse strain, homograft tumors, etc., as well as possible mycoplasmas contamination. Validation studies on multiple PDX models show that the array achieves near perfect results. In summary, we have established a high-throughput multi-purpose NGS-based SNP array that can be applied to the quality assurance of all the common cancer models. Citation Format: Xiaobo Chen, Wubin Qian, Sheng Guo, Henry Li. A NGS-based SNP array for the identification of human xenograft tumors, mouse homograft tumors, human and mouse cell lines, organoids [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3418.

Adaptive Scale Compressive Tracking with Feature Integration

Compressive tracking (CT) is utilized to cope with real-time tracking, which use a very sparse measurement matrix to compressive samples of targets and background, then a classifier is trained to distinguish foreground and background. However, this algorithm suffers from the drifting problem, and used the fixed size tracking box to detect, recognize, and update the samples and classifier. In order to solve these problems, we adopt a different way to extracted positive samples, and employ powerful features to exploit the advantages of feature fusion to describe target, a scale pyramid is used to realize adaptive scale tracking. Experimental results on various benchmark video sequences demonstrate the superior performance of our algorithm.

Inter-laboratory evaluation of the performance parameters of a Lateral Flow Test device for the detection of Bluetongue virus-specific antibodies

Bluetongue (BT) is a viral vector-borne disease affecting domestic and wild ruminants worldwide. In this study, a commercial rapid immuno-chromatographic method or Lateral Flow Test (LFT) device, for the detection of BT virus-specific antibodies in animal serum, was evaluated in an international inter-laboratory proficiency test. The evaluation was done with sera samples of variable background (ruminant species, serotype, field samples, experimental infections, vaccinated animals). The diagnostic sensitivity was 100% (95% C.I. [90.5–100]) and the diagnostic specificity was 95.2% (95% C.I. [76.2–99.9]). The repeatability (accordance) and reproducibility (concordance) were 100% for seropositive samples but were lower for two of the seronegative samples (45% and 89% respectively). The analytical sensitivity, evaluated by testing positive sera at increasing dilutions was better for the BT LFT compared to some commercial ELISAs. Seroconversion of an infected sheep was detected at 4 days post infection. Analytical specificity was impaired by cross-reactions observed with some of the samples seropositive for Epizootic Haemorrhagic Disease Virus (EHDV). The agreement (Cohen's kappa) between the LFT and a commercial BT competitive ELISA was 0.79 (95% CI [0.62–0.95]). Based on these results, it can be concluded that the BT LFT device is a rapid and sensitive first-line serological test that can be used in the field, especially in areas endemic for the disease where there is a lack of diagnostic facilities.

An Approach for Routine Analytical Detection of Beeswax Adulteration Using FTIR-ATR Spectroscopy

Abstract Although beeswax adulteration represents one of the main beeswax quality issues, there are still no internationally standardised analytical methods for routine quality control. The objective of this study was to establish an analytical procedure suitable for routine detection of beeswax adulteration using FTIR-ATR spectroscopy. For the purpose of this study, reference IR spectra of virgin beeswax, paraffin, and their mixtures containing different proportions of paraffin (5 - 95%), were obtained. Mixtures were used for the establishment of calibration curves. To determine the prediction strength of IR spectral data for the share of paraffin in mixtures, the Partial Least Squares Regression method was used. The same procedure was conducted on beeswax-beef tallow mixtures. The model was validated using comb foundation samples of an unknown chemical background which had been collected from the international market (n = 56). Selected physico-chemical parameters were determined for comparison purposes. Results revealed a strong predictive power (R2 = 0.999) of IR spectra for the paraffin and beef tallow share in beeswax. The results also revealed that the majority of the analysed samples (89%) were adulterated with paraffin; only 6 out of 56 (11%) samples were identified as virgin beeswax, 28% of the samples exhibited a higher level of paraffin adulteration (>46% of paraffin), while the majority of the analysed samples (50%) were found to be adulterated with 5 - 20% of paraffin. These results indicate an urgent need for routine beeswax authenticity control. In this study, we demonstrated that the analytical approach defining the standard curves for particular adulteration levels in beeswax, based on chemometric modelling of specific IR spectral region indicative for adulteration, enables reliable determination of the adulterant proportions in beeswax.

Supervised geochemical anomaly detection by pattern recognition

Geochemical anomaly detection is an important issue in mineral exploration. The availability of a training dataset consisting of labeled geochemical samples of background and anomaly classes enables us to define a supervised pattern recognition framework for geochemical anomaly detection. Therefore, various classification and feature selection algorithms can be utilized to build a predictive model and classify the unseen geochemical samples into the pre-defined anomaly and background classes. In this study, some of the state-of-art feature selection and classification algorithms were utilized for supervised anomaly detection in the Kuh Panj porphyry-Cu district. Filter, wrapper and embedded mode feature selection algorithms were used to remove redundant and irrelevant elements from the classification procedure. Subsequently, AdaBoost (ADB), support vector machine (SVM) and Random Forest (RF) algorithms were trained with borehole and surface rock samples from the drilled parts of the study area to create a classified map depicting anomalous areas in the undrilled parts of the district. Results show that feature selection algorithms could play an important role in increasing the accuracy and generalization ability of the classifiers used. Wrapper mode subset selection method combined with a genetic algorithm (GA) search method resulted in the best performance in the study area. Applied classification algorithms outperform Gaussian linear discriminant analysis (GLDA) and provide more accurate, robust and reliable results. Among the applied classification methods, ADB achieved the best leave-one-out cross-validation (LOO) error rate of 0.06. Meanwhile, comparison of the resulted classified map using ADB with another one created via concentration–area fractal model indicated advantage of the former one in terms of detecting high-promising prospective target areas in the study region.

NEAR REAL-TIME AUTOMATIC MARINE VESSEL DETECTION ON OPTICAL SATELLITE IMAGES

Abstract. Vessel monitoring and surveillance is important for maritime safety and security, environment protection and border control. Ship monitoring systems based on Synthetic-aperture Radar (SAR) satellite images are operational. On SAR images the ships made of metal with sharp edges appear as bright dots and edges, therefore they can be well distinguished from the water. Since the radar is independent from the sun light and can acquire images also by cloudy weather and rain, it provides a reliable service. Vessel detection from spaceborne optical images (VDSOI) can extend the SAR based systems by providing more frequent revisit times and overcoming some drawbacks of the SAR images (e.g. lower spatial resolution, difficult human interpretation). Optical satellite images (OSI) can have a higher spatial resolution thus enabling the detection of smaller vessels and enhancing the vessel type classification. The human interpretation of an optical image is also easier than as of SAR image. In this paper I present a rapid automatic vessel detection method which uses pattern recognition methods, originally developed in the computer vision field. In the first step I train a binary classifier from image samples of vessels and background. The classifier uses simple features which can be calculated very fast. For the detection the classifier is slided along the image in various directions and scales. The detector has a cascade structure which rejects most of the background in the early stages which leads to faster execution. The detections are grouped together to avoid multiple detections. Finally the position, size(i.e. length and width) and heading of the vessels is extracted from the contours of the vessel. The presented method is parallelized, thus it runs fast (in minutes for 16000 × 16000 pixels image) on a multicore computer, enabling near real-time applications, e.g. one hour from image acquisition to end user.

Energy dispersive X-ray diffraction research using coherence function

A method based on energy dispersive X-ray diffraction (EDXRD) spectrum of illicit objects of complex background using coherence function was proposed. After theoretical analysis and calculation of the coherence function, we got the best extracting frequency and the numbers of segment, and the results can also ensure that the requirements of resolution and smoothness. Then, the spectra of EDXRD of several samples of complex background were identified by coherence function, which method has been proved effective and reasonable in identifying the presence of suspicious objects packaged with complex background.

Measured Elemental Carbon by Thermo-Optical Transmittance Analysis in Water-Soluble Extracts from Diesel Exhaust, Woodsmoke, and Ambient Particulate Samples

Elemental carbon has been proposed as a marker of diesel particulate matter. The objective of this study was to investigate if water-soluble carbonaceous compounds could be responsible for positive bias of elemental carbon using NIOSH Method 5040 with a thermo-optical carbon transmittance analyzer. Filter samples from eight different aerosol environments were used: pure diesel exhaust fume with a high content of elemental carbon, pure diesel exhaust fume with a low content of elemental carbon, pure biodiesel exhaust fume, pure woodsmoke, an urban road tunnel, an urban street canyon, an urban background site, and residential woodburning in an urban area. Part of each filter sample was analyzed directly with a thermo-optical carbon analyzer, and another part was extracted with water. This water-soluble extract was filtered to remove particles, spiked onto filter punches, and analyzed with a thermo-optical transmittance carbon analyzer. The ratio of elemental carbon in the water-soluble extract to the particulate sample measurement was 18, 12, and 7%, respectively, for the samples of pure woodsmoke, residential woodburning, and urban background. Samples with diesel particulate matter and ambient samples with motor exhaust detected no elemental carbon in the water-soluble extract. Since no particles were present in the filtered water-soluble extract, part of the water-soluble organic carbon species, existing or created during analysis, are misclassified as elemental carbon with this analysis. The conclusion is that in measuring elemental carbon in particulate aerosol samples with thermo-optical transmittance analysis, woodsmoke, and biomass combustion samples show a positive bias of elemental carbon. The water-soluble EC could be used as a simple method to indicate other sources, such as wood or other biomass combustion aerosol particles.

Imputation of missing genotypes: an empirical evaluation of IMPUTE

BackgroundImputation of missing genotypes is becoming a very popular solution for synchronizing genotype data collected with different microarray platforms but the effect of ethnic background, subject ascertainment, and amount of missing data on the accuracy of imputation are not well understood.ResultsWe evaluated the accuracy of the program IMPUTE to generate the genotype data of partially or fully untyped single nucleotide polymorphisms (SNPs). The program uses a model-based approach to imputation that reconstructs the genotype distribution given a set of referent haplotypes and the observed data, and uses this distribution to compute the marginal probability of each missing genotype for each individual subject that is used to impute the missing data. We assembled genome-wide data from five different studies and three different ethnic groups comprising Caucasians, African Americans and Asians. We randomly removed genotype data and then compared the observed genotypes with those generated by IMPUTE. Our analysis shows 97% median accuracy in Caucasian subjects when less than 10% of the SNPs are untyped and missing genotypes are accepted regardless of their posterior probability. The median accuracy increases to 99% when we require 0.95 minimum posterior probability for an imputed genotype to be acceptable. The accuracy decreases to 86% or 94% when subjects are African Americans or Asians. We propose a strategy to improve the accuracy by leveraging the level of admixture in African Americans.ConclusionOur analysis suggests that IMPUTE is very accurate in samples of Caucasians origin, it is slightly less accurate in samples of Asians background, but substantially less accurate in samples of admixed background such as African Americans. Sample size and ascertainment do not seem to affect the accuracy of imputation.

Visual sensitivity to a conspicuous male cue varies by reproductive state inPhysalaemus pustulosusfemales

The vocal sac is a visually conspicuous attribute of most male frogs, but its role in visual communication has only been demonstrated recently in diurnally displaying frogs. Here we characterized the spectral properties of the inflated vocal sac of male túngara frogs (Physalaemus pustulosus), a nocturnal species, and túngara visual sensitivity to this cue across reproductive state and sex. We measured the spectral and total reflectance of different male body regions, including inflated and non-inflated vocal sacs, along with samples of the visual background against which males are perceived. Inflated vocal sacs were the most reflective of all body parts, being one log unit more reflective than background materials. We utilized an optomotor drum with black stripes and stripes that mimicked the spectral reflectance of the inflated vocal sacs with various nocturnal light intensities to measure the visual sensitivity thresholds of males, non-reproductive females and reproductive females. All three groups exhibited visual sensitivities corresponding to intensities below moonless conditions in open habitats or at the edge of secondary tropical forests. Reproductive females exhibited the greatest visual sensitivity of all groups, and were significantly more sensitive than non-reproductive females. Though the mechanism for this physiological difference between reproductive and non-reproductive females is unknown, it is consistent with previously observed patterns of light-dependent phonotaxic behavior in túngaras. We suggest that the visual ecology of the vocal sac, especially in nocturnal frogs, offers a rich source for investigations of visual ecology and physiological regulation of vision.

Disruptive and cryptic coloration

Camouflage may be achieved in three ways: crypsis, disruptive coloration and masquerade ([Endler 1981][1]). Cryptic prey resemble random samples of the visual background ([Endler 1978][2], [1981][1], [1984][3]), minimizing their signal/noise ratio (S/N). Disruptively coloured prey contain some

Visual signal detection in structured backgrounds. II. Effects of contrast gain control, background variations, and white noise.

Studies of visual detection of a signal superimposed on one of two identical backgrounds show performance degradation when the background has high contrast and is similar in spatial frequency and/or orientation to the signal. To account for this finding, models include a contrast gain control mechanism that pools activity across spatial frequency, orientation and space to inhibit (divisively) the response of the receptor sensitive to the signal. In tasks in which the observer has to detect a known signal added to one of M different backgrounds grounds due to added visual noise, the main sources of degradation are the stochastic noise in the image and the suboptimal visual processing. We investigate how these two sources of degradation (contrast gain control and variations in the background) interact in a task in which the signal is embedded in one of M locations in a complex spatially varying background (structured background). We use backgrounds extracted from patient digital medical images. To isolate effects of the fixed deterministic background (the contrast gain control) from the effects of the background variations, we conduct detection experiments with three different background conditions: (1) uniform background, (2) a repeated sample of structured background, and (3) different samples of structured background. Results show that human visual detection degrades from the uniform background condition to the repeated background condition and degrades even further in the different backgrounds condition. These results suggest that both the contrast gain control mechanism and the background random variations degrade human performance in detection of a signal in a complex, spatially varying background. A filter model and added white noise are used to generate estimates of sampling efficiencies, an equivalent internal noise, an equivalent contrast-gain-control-induced noise, and an equivalent noise due to the variations in the structured background.

The ratio of aerosol optical absorption coefficients to sulfur concentrations, as an indicator of smoke from forest fires when sampling in polar regions

Analyses of aerosol composition data from seven National Park Service locations in Alaska were performed. All of the sampling sites in Alaska experience aerosol pollution from long distance and regional sources. One of the most important regional sources of particles in the area is wood smoke from forest fires. The Denali National Park aerosol data and the corresponding meteorological data made it possible to identify samples of background (long-range-transported) aerosol, which are likely to be without contamination from local and regional sources. Background values of optical absorption (BABS) and sulfur concentration for this site are well correlated and in ranges of 2 × 10 −7−50 × 10 −7 m −1 and 10–350 ng m −3, respectively. Positive matrix factorization method was used for obtaining information about possible sources of aerosol in the remote locations. Seasonal variations with maxima during the winter-spring season and minima in summer were observed for the sulfur concentration but not for BABS. Seasonal variations were always higher for the more northerly locations. The influence of emissions from regional sources on the BABS values was higher than for sulfur concentrations. The ratios of optical absorption coefficient to sulfur concentration were used to characterize the relative influence of regional pollution sources on the measured particle compositions.

Precursors of repeated short-term sick-leave

The aim of the study was to search for bivariate associations between on the one hand samples of background, job-related and well-being characteristics, and on the other hand longitudinally recorded sick-leave in individuals with repeated short-term sick-leave. Out of altogether 123 subjects included, 113 self-rated their well-being at the onset of, and 88 also after, a supportive programme. The well-being rating covered experience of treatment by other people, reservation, loneliness, inferiority, tension, vulnerability, guilt, security and indolence. Socio-demographic and job-related characteristics and attitudes were registered and declared at the initial contact. Correlations between these separate sets of independent variables, and sick-leave occasions and days in each of the two years following the contact were computed. There was an almost complete lack of covariation between background variables and sick-leave. Neither did job-related characteristics correlate. Only an expressed wish for a job change was vaguely associated with more absence days, but not with more spells. In striking contrast, a convincing consistent longitudinal pattern between self-rated well-being and sick-leave behaviour emerged. The poorer the well-being, the greater the subsequent sick-leave. Yet, a major part of the sick-leave variance remained unexplained. Accordingly, other etiological factors may still interact with well-being in the comprehensive setting behind the sick-leave behaviour.(ABSTRACT TRUNCATED AT 250 WORDS)

SOME QUANTITATIVE CHARACTERISTICS OF THE SCHUMANN-ELF NATURAL ELECTROMAGNETIC BACKGROUND

Samples of Schumann-ELF natural background recorded in three components at widely separated points have been resolved, both by analogue and digital methods, into their constituent modes for quantitative comparison. ELF bursts display a world-wide coherence and uniform average signal strength to a high degree. Corresponding X and Y component traces are virtually identical at sites separated by 1 000 km or less. In contrast, among stations set in differing geological environments the strength of the vertical magnetic components of the same bursts may vary by orders of magnitude. No evidence of solar, auroral, or other nonlightning sources for natural ELF activity has been found.