Perfectly Correlated Research Articles

Navigated intraoperative ultrasound in neuro-oncology: volumetric accuracy and correlation with high-field MRI.

The use of intraoperative techniques to detect residual tumors has recently become increasingly important. Intraoperative MRI has long been considered the gold standard; however, it is not widely used because of high equipment costs and long acquisition times. Consequently, real-time intraoperative ultrasound (ioUS), which is much less expensive than MRI, has gained popularity. The aim of the present study was to evaluate the capacity of ioUS to accurately determine the primary tumor volume and detect residual tumors. A prospective study of adult patients who underwent surgery for intra-axial brain tumors between November 2017 and October 2020 was performed. Navigated intraoperative ultrasound (nioUS) of the brain was used to guide tumor resection and to detect the presence of residual disease. Both convex (5-8 MHz) and linear array (6-13 MHz) probes were used. Tumor volume and residual disease were measured with nioUS and compared with MR images. A linear regression model based on a machine learning pipeline and a Bland-Altman analysis were used to assess the accuracy of nioUS versus MRI. Eighty patients (35 females and 45 males) were included. The mean age was 58 years (range 25-80 years). A total of 88 lesions were evaluated; there were 64 (73%) gliomas, 19 (21.6%) metastases, and 5 (5.7%) other tumors, mostly located in the frontal (41%) and temporal (27%) lobes. Most of the tumors (75%) were perfectly visible on ioUS (grade 3, Mair grading system), except for those located in the insular lobe (grade 2). The regression model showed a nearly perfect correlation (R2 = 0.97, p < 0.001) between preoperative tumor volumes from both MRI and nioUS. Ultrasonographic visibility significantly influenced this correlation, which was stronger for highly visible (grade 3) tumors (p = 0.01). For residual tumors, the correlation between postoperative MRI and nioUS was weaker (R2 = 0.78, p < 0.001) but statistically significant. The Bland-Altman analysis showed minimal bias between the two techniques for pre- and postoperative scenarios, with statistically significant results for the preoperative concordance. The authors' findings show that most brain tumors are well delineated by nioUS and almost perfectly correlated with MRI-based measurements both pre- and postoperatively. These data support the hypothesis that nioUS is a reliable intraoperative technique that can be used for real-time monitoring of brain tumor resections and to perform volumetric analysis of residual disease.

The relationships between distances covered above generic and relative speed thresholds by male soccer players in English Premier League matches across two competitive seasons. The effects of positional demands and possession.

The aims of this study were to: a) examine the relationships between high-intensity distances covered above generic and relative speed thresholds in English Premier League (EPL) matches across two consecutive seasons and b) analyze the effects of playing position and team possession. Sixteen elite male soccer players (seven defenders, six midfielders and three forwards) participated in this study (age 27.8 ± 3.5 years, height 183.7 ± 5.4 cm, body mass 83.9 ± 7.1 kg). An Optical Tracking System was used to collect the following variables: total distance covered; high-speed running distance (HSRD) (> 5.5 m/s); high-intensity running distance (HIRD) (5.5-7 m/s); sprint distance (> 7 m/s); total distance covered above Maximal Aerobic Speed (MAS); distance covered > 85% peak speed (PS); and distance > 30% Anaerobic Speed Reserve (ASR). All measures were analyzed as whole match totals and as distances covered in the periods of the team in possession (TIP), opponent team in possession (OTIP), and ball out of play (BOP). Analysis by position based on defenders, midfielders and forwards was also performed. Distance > 30% ASR was almost perfectly correlated with HSRD (r = 0.98), while distances > MAS were highly correlated with both HIRD (r = 0.91) and HSRD (r = 0.91), and distance > 85% PS were highly correlated with SD (r = 0.70). Although the generic and relative speed thresholds show almost perfect correlation, the differences between HSRD, HIRD and distance > MAS indicate that players may be exposed to more HIRD when using relative thresholds.

Testing the concurrent validity and reliability of a lipowise digital skinfold caliper to assess muscle mass in healthy young adults

The aim of this study was to assess the validity and reliability of a novel tool to assess skinfolds and to compare the muscle mass measured through dual-x-ray-absorptiometry (DXA) and estimated using the Lee equation from the values of the skinfolds and girths in a healthy young adult population. Methods: The present study followed a cross-sectional design, including 38 participants, with 27 males (22.04 ± 5.20 years) and 11 females (21.55 ± 2.39 years). The measurement protocol included a DXA evaluation, basic measurements of body mass and stature, eight skinfolds with two skinfold calipers of different brands (Harpenden and Lipowise), and three girths. The order in which the skinfold calipers were used was randomized. The muscle mass was then calculated using the formula established by Lee et al. Results: No significant differences were found between the two skinfold calipers considering all the outcomes (p > 0.05). The correlation coefficients were between 0.724 and 0.991, which suggest very-large to nearly perfect correlations. The correlations performed revealed that muscle mass estimated from DXA is nearly perfectly correlated with both muscle mass estimated from the data obtained with the Harpenden skinfold caliper (r = 0.955) and muscle mass estimated from the data obtained with the Lipowise skinfold caliper (r = 0.954). From the results, we conclude that Lipowise caliper is an accurate skinfold caliper and it can be an alternative tool for the technician that need to assess body fat or muscle mass in precise, valid and time efficient evaluation. It should be noted that the caution to use skinfold calipers interchangeable with each other when evaluating skinfolds remains a necessity and is advisable to perform the measurements with the same brand and model of skinfold caliper when the purpose is to perform follow-up assessments.

Structural stress prediction based on deformations using artificial neural networks trained with artificial noise

AbstractArtificial Neural Networks (ANN) have found application for multiple problems in structural mechanics and civil engineering. In the new approach developed here, ANNs are used for the determination of the maximum stress resultants in a structure on the basis of monitored displacements. Initially, a simple supported beam subjected up to two vertical forces is considered. The beam is solved analytically for different combinations of load positions and magnitudes defined by Monte‐Carlo sampling. The resulting beam deflections and maximum stress resultants are employed for the training of feedforward neural networks in order to generate metamodels able to predict the maximum bending moment knowing only a few beam input deflections. Subsequently, the concept is applied to a real beam experiment, where a reinforced concrete beam has been tested. It is shown, that the perfect correlation of the beam displacements used as inputs for the ANN training leads to extrapolations when the metamodel is applied to the measured displacements of the real experiment, which are not perfectly correlated. This is source of wrong predictions when the network is applied to these data. The introduction of artificial noise in the synthetic input data in a controlled way before training helps to overcome extrapolation and to increase repeatability and robustness of the metamodels. Various noise levels are applied to the synthetic data to monitor how the ANN performances change over increasing noise and the results along with the limitations of the approach are illustrated.

Pre-Surgery Hip-Knee-Ankle Angle and Weightbearing Line Percentage Are Nearly Perfectly Correlated to the Miniaci Angle When Planning Open Wedge High Tibial Osteotomies

To investigate the conversion formulas between the Miniaci angle, presurgery parameters, and changes in presurgery parameters in open-wedge high tibial osteotomy (OWHTO), including hip-knee-ankle (HKA) angle, weightbearing line (WBL) percentage, mechanical medial proximal tibial angle (mMPTA), ΔHKA angle, ΔWBL percentage, ΔmMPTA, and other parameters. From January 2012 to December 2019, 247 lower limbs of 144 patients with medial unicompartmental knee osteoarthritis combined with proximal tibia vara were enrolled. Inclusion criteria were adults, medial unicompartmental knee osteoarthritis, Kellgren-Lawrence classification grade ≤ III, mMPTA ≤ 85°, normal mechanical lateral distal femoral angle (85°-90°), and patella facing anterior in the bipedal standing position. Exclusion criteria were history of fracture, trauma, or orthopaedic surgery; developmental dysplasia of the hip or femoral head necrosis; femoral bowing deformity; deformity of the tibial shaft; and leg length discrepancy. Using standing whole-leg radiographs, an OWHTO simulation was performed to determine the Miniaci angle by delivering the WBL to the Fujisawa point. The relationship of the Miniaci angle, the presurgery parameters, and the changes in presurgery parameters were analyzed by Spearman's correlation and linear regression analyses. The relationship between the postsurgery HKA angle and presurgery parameters was analyzed by multiple linear regression model. The Miniaci angle showed a near-perfect correlation with the presurgery HKA angle (y=-1.05x+ 192.10, r2= 0.99), presurgery WBL percentage (y=-0.25x+ 15.14, r2= 0.97), ΔHKA angle (y= 1.04x- 0.03, r2= 1.00), ΔWBL percentage (y= 0.25x- 0.52, r2= 0.97), and ΔmMPTA (y= 1.04x- 0.03, r2= 1.00). The ΔHKA angle showed nearly perfect correlation with the ΔmMPTA (y= 1.00x, r2= 1.00), and ΔWBL percentage (y= 0.24x- 0.47, r2= 0.97). The presurgery HKA angle, presurgery WBL percentage, ΔHKA angle, ΔWBL, and ΔmMPTA percentage are nearly perfectly correlated to the Miniaci angle, whereas the ΔmMPTA and ΔWBL percentage are nearly perfectly correlated to the ΔHKA angle. With the conversion formulas determined in the current study, surgeons can calculate the Miniaci angle based on the presurgery parameters without the assistance of digital software for complex surgical simulation. The Miniaci angle is closely related to the gap of the medial opening wedge. Based on the Miniaci angle and the depth of the osteotomy, surgeons can calculate the gap required before surgery using trigonometric functions and then simply measure the gap during surgery.

Analytical composition of flours through thermogravimetric and rheological combined methods

The suitability of thermogravimetric analysis (TGA) and rheology for predicting analytical composition of flour was verified. The percentage weight loss of flour samples in different temperature ranges and their analytical composition determined following the American Association of Cereal Chemists (AACC) approved methods were correlated. The moisture content determined by AACC method showed a strong correlation with the weight loss of dried samples in the temperature range 30 – 125°C. Similarly, the weight loss of fully swollen samples, determined by TGA in the temperature range of 30 – 125°C, perfectly correlated with the water-Solvent Retention Capacity (w-SRC) determined by standard method (AACC). The weight loss in the range 200 – 400°C revealed a good correlation with the starch content, while the weight loss in the range 400 – 600°C correlated with the gluten content. Finally, the so-called residue at 600°C (usually attributable to mineral content) correlated with the ash content (AACC standard method). The rheological parameters (G', G'' and tan δ) also showed a strong correlation with the analytical composition of analyzed flours. A perfect correlation was observed between the mineral content and the elastic modulus, as well as between both G' and G'' and the starch content. As the resilience of dough samples strongly depends on the relative amount of gliadins and glutenins, a strong correlation between the Gli/Glu ratio and tan δ was also found.

Modified Pearson correlation coefficient for two-color imaging in spherocylindrical cells

The revolution in fluorescence microscopy enables sub-diffraction-limit (“superresolution”) localization of hundreds or thousands of copies of two differently labeled proteins in the same live cell. In typical experiments, fluorescence from the entire three-dimensional (3D) cell body is projected along the z-axis of the microscope to form a 2D image at the camera plane. For imaging of two different species, here denoted “red” and “green”, a significant biological question is the extent to which the red and green spatial distributions are positively correlated, anti-correlated, or uncorrelated. A commonly used statistic for assessing the degree of linear correlation between two image matrices R and G is the Pearson Correlation Coefficient (PCC). PCC should vary from − 1 (perfect anti-correlation) to 0 (no linear correlation) to + 1 (perfect positive correlation). However, in the special case of spherocylindrical bacterial cells such as E. coli or B. subtilis, we show that the PCC fails both qualitatively and quantitatively. PCC returns the same + 1 value for 2D projections of distributions that are either perfectly correlated in 3D or completely uncorrelated in 3D. The PCC also systematically underestimates the degree of anti-correlation between the projections of two perfectly anti-correlated 3D distributions. The problem is that the projection of a random spatial distribution within the 3D spherocylinder is non-random in 2D, whereas PCC compares every matrix element of R or G with the constant mean value overline{R} or overline{G} . We propose a modified Pearson Correlation Coefficient (MPCC) that corrects this problem for spherocylindrical cell geometry by using the proper reference matrix for comparison with R and G. Correct behavior of MPCC is confirmed for a variety of numerical simulations and on experimental distributions of HU and RNA polymerase in live E. coli cells. The MPCC concept should be generalizable to other cell shapes.

Developing a questionnaire to assess the health effects of bioaerosols.

Bioaerosol exposure occurs in several industries including green waste recycling, poultry farming, grain, animal feed and cotton production. Whilst several studies have investigated exposure and health effects in compost workers, the best questions to ask about health are uncertain. This study aimed to develop a questionnaire to assess health symptoms in compost workers. A 46-item questionnaire to ascertain possible symptoms of occupational illness related to bioaerosol exposure in a cohort of UK industrial compost workers and delivery in an interviewer-led format. The reliability of the questionnaire was assessed using Cronbach's α. A principal component analysis (PCA) was conducted to condense the questionnaire for practical purposes. One hundred and eleven (89%) workers completed the questionnaire. All items showed very good reliability (Cronbach's α = 0.83). After removing perfectly correlated questions from the data set, the PCA was conducted on a reduced data set of 28 items to explore underlying themes. Nine components were identified that explained 77% of the total variation. Nine of the questions removed prior to PCA due to perfect correlation were reintroduced because they added clinical value. The final questionnaire therefore consisted of 37 items and retained very adequate reliability (Cronbach's α = 0.76). Our health questionnaire has demonstrated adequate reliability when used within this industrial composting workforce. Further applications may include health surveillance, investigating outbreaks of occupational disease or research. Future work should examine the predictive validity of the questionnaire in these settings.

Is Imperfection Better? Evidence from Predicting Stock and Bond Returns*

The standard predictive regression assumes expected returns to be perfectly correlated with predictors. In the recently introduced predictive system, imperfect predictors account only for a partial variance in expected returns. However, the out-of-sample benefits of relaxing the assumption of perfect correlation are unclear. We compare the performance of the two models from an investor’s perspective. In the Bayesian setup, we allow for various distributions of R2 to account for different degrees of optimism about predictability. We find that relaxing the assumption of perfect predictors does not pay off out-of-sample. Furthermore, extreme optimism or pessimism reduces the performance of both models.

Human brain functional areas of unitary pooled activity discovered with fMRI

We report the discovery of human brain functional areas of unitary pooled activity (FAUPAs) using fMRI. A FAUPA is defined as an area in which the temporal variation of the activity is the same across the entire area. This dynamically unitary activity implies a perfect temporal correlation everywhere within the FAUPA for the activity-induced BOLD response, i.e., the corresponding Pearson correlation coefficient (R) is 1 for the BOLD responses of any two locations within the FAUPA. A novel method was developed to identify the FAUPA. In this study, nine subjects had a resting-state (rs) fMRI and a task-fMRI. FAUPAs have been identified for both rs- and task-fMRI, and their determination was objective and automatic, with no a-priori knowledge. The group mean of R was 0.952 ± 0.004 for the rs-fMRI and 0.950 ± 0.002 for the task-fMRI, showing the dynamically unitary activity within each FAUPA. For the finger-tapping (FT) task, the group-mean BOLD signal time course of the FT-associated FAUPAs in the primary motor cortex was almost perfectly correlated with the FT-induced ideal response (R = 0.9330, P = 1.8 × 10−56), confirming the association of the FAUPA with the task. A task-associated FAUPA may play the role of a functional unit for a particular neural computation.

Linking parasite populations in hosts to parasite populations in space through Taylor's law and the negative binomial distribution

The spatial distribution of individuals of any species is a basic concern of ecology. The spatial distribution of parasites matters to control and conservation of parasites that affect human and nonhuman populations. This paper develops a quantitative theory to predict the spatial distribution of parasites based on the distribution of parasites in hosts and the spatial distribution of hosts. Four models are tested against observations of metazoan hosts and their parasites in littoral zones of four lakes in Otago, New Zealand. These models differ in two dichotomous assumptions, constituting a 2 × 2 theoretical design. One assumption specifies whether the variance function of the number of parasites per host individual is described by Taylor's law (TL) or the negative binomial distribution (NBD). The other assumption specifies whether the numbers of parasite individuals within each host in a square meter of habitat are independent or perfectly correlated among host individuals. We find empirically that the variance-mean relationship of the numbers of parasites per square meter is very well described by TL but is not well described by NBD. Two models that posit perfect correlation of the parasite loads of hosts in a square meter of habitat approximate observations much better than two models that posit independence of parasite loads of hosts in a square meter, regardless of whether the variance-mean relationship of parasites per host individual obeys TL or NBD. We infer that high local interhost correlations in parasite load strongly influence the spatial distribution of parasites. Local hotspots could influence control and conservation of parasites.

Is Imperfection Better? Evidence from Predicting Stock and Bond Returns

The standard predictive regression assumes expected returns to be perfectly correlated with predictors. In the recently-introduced predictive system, imperfect predictors account only for a partial variance in expected returns. However, the out-of-sample benefits of relaxing the assumption of perfect correlation are not clear. We compare the performance of the two models from an investor's perspective. In the Bayesian setup, we allow for various distributions of the coefficient of determination to account for different degrees of optimism about predictability. We find that relaxing the assumption of perfect predictors does not pay off out-of-sample. Furthermore, extreme optimism or pessimism reduces performance of both models.

Are we underestimating the genetic variances of dimorphic traits?

Populations often contain discrete classes or morphs (e.g., sexual dimorphisms, wing dimorphisms, trophic dimorphisms) characterized by distinct patterns of trait expression. In quantitative genetic analyses, the different morphs can be considered as different environments within which traits are expressed. Genetic variances and covariances can then be estimated independently for each morph or in a combined analysis. In the latter case, morphs can be considered as separate environments in a bivariate analysis or entered as fixed effects in a univariate analysis. Although a common approach, we demonstrate that the latter produces downwardly biased estimates of additive genetic variance and heritability unless the quantitative genetic architecture of the traits concerned is perfectly correlated between the morphs. This result is derived for four widely used quantitative genetic variance partitioning methods. Given that theory predicts the evolution of genotype-by-environment (morph) interactions as a consequence of selection favoring different trait combinations in each morph, we argue that perfect correlations between the genetic architectures of the different morphs are unlikely. A sampling of the recent literature indicates that the majority of researchers studying traits expressed in different morphs recognize this and do estimate morph-specific quantitative genetic architecture. However, ca. 16% of the studies in our sample utilized only univariate, fixed-effects models. We caution against this approach and recommend that it be used only if supported by evidence that the genetic architectures of the different morphs do not differ.

Dividing attention between two segregated tone streams

Listeners were presented with pure-tone sequences which had a high speed (25–50 tones/s) and consisted of two interleaved melodies, M1 and M2, spanning separate frequency ranges (624–786 Hz and 1483–1869 Hz). M1 and M2 were renewed from sequence to sequence and could be either (1) perfectly correlated, or (2) perfectly anticorrelated, or (3) independent of each other. The main task, performed in a 2I-2AFC paradigm, was to discriminate sequences of type 1 (perfect correlation) from sequences of type 2 or 3. This appeared to be relatively easy when, in the type-1 sequences, each note of M2 immediately preceded or followed the corresponding note of M1. In that case, however, listeners were unable to tell whether M2 preceded or followed M1, which shows that the two melodies were perceptually segregated. In another series of experimental sessions, listeners knew that M2 would always follow M1 after a fixed delay, corresponding to one tone in some sessions and three tones in other sessions. The main discrimination task was performed better for the one-tone delay, but performance was still well above chance for the three-tone delay. Overall, the data suggest that two segregated tone streams can be attended to simultaneously.

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Loss-of-function mutations affecting a specific Glycine max R2R3 MYB transcription factor result in brown hilum and brown seed coats

BackgroundAlthough modern soybean cultivars feature yellow seed coats, with the only color variation found at the hila, the ancestral condition is black seed coats. Both seed coat and hila coloration are due to the presence of phenylpropanoid pathway derivatives, principally anthocyanins. The genetics of soybean seed coat and hilum coloration were first investigated during the resurgence of genetics during the 1920s, following the rediscovery of Mendel's work. Despite the inclusion of this phenotypic marker into the extensive genetic maps developed for soybean over the last twenty years, the genetic basis behind the phenomenon of brown seed coats (the R locus) has remained undetermined until now.ResultsIn order to identify the gene responsible for the r gene effect (brown hilum or seed coat color), we utilized bulk segregant analysis and identified recombinant lines derived from a population segregating for two phenotypically distinct alleles of the R locus. Fine mapping was accelerated through use of a novel, bioinformatically determined set of Simple Sequence Repeat (SSR) markers which allowed us to delimit the genomic region containing the r gene to less than 200 kbp, despite the use of a mapping population of only 100 F6 lines. Candidate gene analysis identified a loss of function mutation affecting a seed coat-specific expressed R2R3 MYB transcription factor gene (Glyma09g36990) as a strong candidate for the brown hilum phenotype. We observed a near perfect correlation between the mRNA expression levels of the functional R gene candidate and an UDP-glucose:flavonoid 3-O-glucosyltransferase (UF3GT) gene, which is responsible for the final step in anthocyanin biosynthesis. In contrast, when a null allele of Glyma09g36990 is expressed no upregulation of the UF3GT gene was found.ConclusionsWe discovered an allelic series of four loss of function mutations affecting our R locus gene candidate. The presence of any one of these mutations was perfectly correlated with the brown seed coat/hilum phenotype in a broadly distributed survey of soybean cultivars, barring the presence of the epistatic dominant I allele or gray pubescence, both of which can mask the effect of the r allele, resulting in yellow or buff hila. These findings strongly suggest that loss of function for one particular seed coat-expressed R2R3 MYB gene is responsible for the brown seed coat/hilum phenotype in soybean.

A Model for Teacher Effects From Longitudinal Data Without Assuming Vertical Scaling

There is an increasing interest in using longitudinal measures of student achievement to estimate individual teacher effects. Current multivariate models assume each teacher has a single effect on student outcomes that persists undiminished to all future test administrations (complete persistence [CP]) or can diminish with time but remains perfectly correlated (variable persistence [VP]). However, when state assessments do not use a vertical scale or the evolution of the mix of topics present across a sequence of vertically aligned assessments changes as students advance in school, these assumptions of persistence may not be consistent with the achievement data. We develop the “generalized persistence” (GP) model, a Bayesian multivariate model for estimating teacher effects that accommodates longitudinal data that are not vertically scaled by allowing less than perfect correlation of a teacher’s effects across test administrations. We illustrate the model using mathematics assessment data.

The issue of power in the identification of “g” with lower-order factors

In higher order factor models, general intelligence (g) is often found to correlate perfectly with lower-order common factors, suggesting that g and some well-defined cognitive ability, such as working memory, may be identical. However, the results of studies that addressed the equivalence of g and lower-order factors are inconsistent. We suggest that this inconsistency may partly be attributable to the lack of statistical power to detect the distinctiveness of the two factors. The present study therefore investigated the power to reject the hypothesis that g and a lower-order factor are perfectly correlated using artificial datasets, based on realistic parameter values and on the results of selected publications. The results of the power analyses indicated that power was substantially influenced by the effect size and the number and the reliability of the indicators. The examination of published studies revealed that most case studies that reported a perfect correlation between g and a lower-order factor were underpowered, with power coefficients rarely exceeding 0.30. We conclude the paper by emphasizing the importance of considering power in the context of identifying g with lower-order factors.

A Note on Testing Perfect Correlations in SEM

This article is concerned with the seemingly simple problem of testing whether latent factors are perfectly correlated (i.e., statistically indistinct). In recent literature, researchers have used different approaches, which are not always correct or complete. We discuss the parameter constraints required to obtain such perfectly correlated latent factors in the context of 4 commonly used models: (a) the oblique factor model, (b) the hierarchical factor model, (c) models in which the factors are predicted by a covariate, and (d) models in which the factors are predictors of a dependent variable. It is shown that the necessary constraints depend on the choice of scaling. We illustrate testing the indistinctiveness of factors with 2 real data examples.

Quantum error correction against correlated noise

We consider quantum error correction against correlated noise using simple and concatenated Calderbank-Shor-Steane codes as well as $n$-qubit repetition codes. We characterize the performance of various codes by means of the fidelity following the error correction of a single logical qubit in a quantum register. For concatenated codes we find a threshold in the single-qubit error rate below which the encoded qubit is perfectly protected. The threshold depends on the correlation strength of the noise and goes to zero for perfect correlation. Finally, we concatenate the traditional error correcting codes with a decoherence free subspace and evaluate the performance over the whole range from uncorrelated noise to perfectly correlated noise.