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Concept: Pearson product-moment correlation coefficient


Human umbilical tissue-derived cells (hUTC) represent an attractive cell source and a potential technology for neurorestoration and improvement of functional outcomes following stroke. Male Wistar rats were subjected to a transient middle cerebral artery occlusion (tMCAo) and were intravenously administered hUTC (N = 11) or vehicle (N = 10) 48 hrs after stroke. White matter and vascular reorganization was monitored over a 12-week period using MRI and histopathology. MRI results were correlated with neurological functional and histology outcomes to demonstrate that MRI can be a useful tool to measure structural recovery after stroke. MRI revealed a significant reduction in the ventricular volume expansion and improvement in cerebral blood flow (CBF) in the hUTC treated group compared to vehicle treated group. Treatment with hUTC resulted in histological and functional improvements as evidenced by enhanced expression of vWF and synaptophysin, and improved outcomes on behavioral tests. Significant correlations were detected between MRI ventricular volumes and histological lesion volume as well as number of apoptotic cells. A positive correlation was also observed between MRI CBF or cerebral blood volume (CBV) and histological synaptic density. Neurological functional tests were also significantly correlated with MRI ventricular volume and CBV. Our data demonstrated that MRI measurements can detect the effect of hUTC therapy on the brain reorganization and exhibited positive correlation with histological measurements of brain structural changes and functional behavioral tests after stroke. MRI ventricular volumes provided the most sensitive index in monitoring brain remodeling and treatment effects and highly correlated with histological and functional measurements.

Concepts: Medicine, Brain, Blood, Spearman's rank correlation coefficient, Traumatic brain injury, Cerebrum, Correlation and dependence, Pearson product-moment correlation coefficient


BACKGROUND: Dengue, a mosquito-borne febrile viral disease, is found in tropical and sub-tropical regions and is now extending its range to temperate regions. The spread of the dengue viruses mainly depends on vector population (Aedes aegypti and Aedes albopictus), which is influenced by changing climatic conditions and various land-use/land-cover types. Spatial display of the relationship between dengue vector density and land-cover types is required to describe a near-future viral outbreak scenario. This study is aimed at exploring how land-cover types are linked to the behavior of dengue-transmitting mosquitoes. METHODS: Surveys were conducted in 92 villages of Phitsanulok Province Thailand. The sampling was conducted on three separate occasions in the months of March, May and July. Dengue indices, i.e. container index (C.I.), house index (H.I.) and Breteau index (B.I.) were used to map habitats conducible to dengue vector growth. Spatial epidemiological analysis using Bivariate Pearson’s correlation was conducted to evaluate the level of interdependence between larval density and land-use types. Factor analysis using principal component analysis (PCA) with varimax rotation was performed to ascertain the variance among land-use types. Furthermore, spatial ring method was used as to visualize spatially referenced, multivariate and temporal data in single information graphic. RESULTS: Results of dengue indices showed that the settlements around gasoline stations/workshops, in the vicinity of marsh/swamp and rice paddy appeared to be favorable habitat for dengue vector propagation at highly significant and positive correlation (p = 0.001) in the month of May. Settlements around the institutional areas were highly significant and positively correlated (p = 0.01) with H.I. in the month of March. Moreover, dengue indices in the month of March showed a significant and positive correlation (p <= 0.05) with deciduous forest. The H.I. of people living around horticulture land were significantly and positively correlated (p = 0.05) during the month ofMay, and perennial vegetation showed a highly significant and positive correlation (p = 0.001) in the month of March with C.I. and significant and positive correlation (p <= 0.05) with B.I., respectively. CONCLUSIONS: The study concluded that gasoline stations/workshops, rice paddy, marsh/swamp and deciduous forests played highly significant role in dengue vector growth. Thus, the spatio-temporal relationships of dengue vector larval density and land-use types may help to predict favorable dengue habitat, and thereby enables public healthcare managers to take precautionary measures to prevent impending dengue outbreak.

Concepts: Mosquito, Factor analysis, Principal component analysis, Correlation and dependence, Pearson product-moment correlation coefficient, Aedes aegypti, Aedes, Dengue fever


BACKGROUND: The treatment planning of spine pathologies requires information on the rigidity and permeability of the intervertebral discs (IVDs). Magnetic resonance imaging (MRI) offers great potential as a sensitive and non-invasive technique for describing the mechanical properties of IVDs. However, the literature reported small correlation coefficients between mechanical properties and MRI parameters. Our hypothesis is that the compressive modulus and the permeability of the IVD can be predicted by a linear combination of MRI parameters. METHODS: Sixty IVDs were harvested from bovine tails, and randomly separated in four groups (in-situ, digested-6h, digested-18h, digested-24h). Multi-parametric MRI acquisitions were used to quantify the relaxation times T1 and T2, the magnetization transfer ratio MTR, the apparent diffusion coefficient ADC and the fractional anisotropy FA. Unconfined compression, confined compression and direct permeability measurements were performed to quantify the compressive moduli and the hydraulic permeabilities. Differences between groups were evaluated from a one way ANOVA. Multi linear regressions were performed between dependent mechanical properties and independent MRI parameters to verify our hypothesis. A principal component analysis was used to convert the set of possibly correlated variables into a set of linearly uncorrelated variables. Agglomerative Hierarchical Clustering was performed on the 3 principal components. RESULTS: Multilinear regressions showed that 45 to 80% of the Young’s modulus E, the aggregate modulus in absence of deformation HA0, the radial permeability kr and the axial permeability in absence of deformation k0 can be explained by the MRI parameters within both the nucleus pulposus and the annulus pulposus. The principal component analysis reduced our variables to two principal components with a cumulative variability of 52-65%, which increased to 70-82% when considering the third principal component. The dendograms showed a natural division into four clusters for the nucleus pulposus and into three or four clusters for the annulus fibrosus. CONCLUSIONS: The compressive moduli and the permeabilities of isolated IVDs can be assessed mostly by MT and diffusion sequences. However, the relationships have to be improved with the inclusion of MRI parameters more sensitive to IVD degeneration. Before the use of this technique to quantify the mechanical properties of IVDs in vivo on patients suffering from various diseases, the relationships have to be defined for each degeneration state of the tissue that mimics the pathology. Our MRI protocol associated to principal component analysis and agglomerative hierarchical clustering are promising tools to classify the degenerated intervertebral discs and further find biomarkers and predictive factors of the evolution of the pathologies.

Concepts: Nuclear magnetic resonance, Magnetic resonance imaging, Principal component analysis, Diffusion MRI, Pearson product-moment correlation coefficient, Spin echo, Young's modulus, Helium


An important problem in systems biology is to reconstruct gene regulatory networks (GRNs) from experimental data and other a priori information. The DREAM project offers some types of experimental data, such as knockout data, knockdown data, time series data, etc. Among them, multifactorial perturbation data are easier and less expensive to obtain than other types of experimental data and are thus more common in practice. In this article, a new algorithm is presented for the inference of GRNs using the DREAM4 multifactorial perturbation data. The GRN inference problem among [Formula: see text] genes is decomposed into [Formula: see text] different regression problems. In each of the regression problems, the expression level of a target gene is predicted solely from the expression level of a potential regulation gene. For different potential regulation genes, different weights for a specific target gene are constructed by using the sum of squared residuals and the Pearson correlation coefficient. Then these weights are normalized to reflect effort differences of regulating distinct genes. By appropriately choosing the parameters of the power law, we constructe a 0-1 integer programming problem. By solving this problem, direct regulation genes for an arbitrary gene can be estimated. And, the normalized weight of a gene is modified, on the basis of the estimation results about the existence of direct regulations to it. These normalized and modified weights are used in queuing the possibility of the existence of a corresponding direct regulation. Computation results with the DREAM4 In Silico Size 100 Multifactorial subchallenge show that estimation performances of the suggested algorithm can even outperform the best team. Using the real data provided by the DREAM5 Network Inference Challenge, estimation performances can be ranked third. Furthermore, the high precision of the obtained most reliable predictions shows the suggested algorithm may be helpful in guiding biological experiment designs.

Concepts: DNA, Gene expression, Statistics, Spearman's rank correlation coefficient, Correlation and dependence, Pearson product-moment correlation coefficient, Regulation, Gene regulatory network


The aim of this study was to quantify anteroposterior facial soft tissue changes with respect to underlying skeletal movements after Le Fort I maxillary advancement and mandibular setback surgery with sagittal split osteotomy in Class III skeletal deformity by using lateral cephalograms taken before and after the operation. The material consisted of 31 patient (15 female, 16 male cases, mean age was 26.7 ± 2.5 years) with Class III skeletal deformity. All patients were treated by Le Fort I maxillary advancement and mandibular setback surgery with sagittal split osteotomy. Lateral cephalograms were taken before and 1.4 ± 0.3 years after surgery. Wilcoxon test was used to compare the pre- and post-surgical measurements. Pearson correlation test was used to compare the relationships between the skeletal, dental and facial soft tissue changes. In the maxilla, the APOINTAP (the anteroposterior position of A point) and ITIPAP (the anteroposterior position of upper incisor) showed significant protractions (-3.19 ± 3.63, and -3.19 ± 4.52, p < 0.01). In the mandible, the L1TIPAP (the anteroposterior position of lower incisor, -3.20 ± 5.83, p < 0.01), L1TIPSI (the superoinferior position of lower incisor, -2.43 ± 10.31, p < 0.05), BPOINTSP (the superoinferior position of B point, -2.28 ± 12.51, p < 0.05) and BPOINTAP (the anteroposterior position of B point, -3.19 ± 9.31, p < 0.01) showed significant retractions and upper positions after bimaxillary surgery. The insignificant decrease in soft tissue Pog-Vert distance was correlated the significant upper position of B point and lower incisor (r: 0.851, p < 0.001 and r: 0.842, p < 0.001).

Concepts: Spearman's rank correlation coefficient, Mandible, Tissues, Correlation and dependence, Pearson product-moment correlation coefficient, Soft tissue, Karl Pearson, Maxilla


BACKGROUND: Better knowledge of the suprascapular notch anatomy may help to prevent and to assess more accurately suprascapular nerve entrapment syndrome. Our purposes were to verify the reliability of the existing data, to assess the differences between the two genders, to verify the correlation between the dimensions of the scapula and the suprascapular notch, and to investigate the relationship between the suprascapular notch and the postero-superior limit of the safe zone for the suprascapular nerve. METHODS: We examined 500 dried scapulae, measuring seven distances related to the scapular body and suprascapular notch; they were also catalogued according to gender, age and side. Suprascapular notch was classified in accordance with Rengachary’s method. For each class, we also took into consideration the width/depth ratio. Furthermore, Pearson’s correlation was calculated. RESULTS: The frequencies were: Type I 12.4%, Type II 19.8%, Type III 22.8%, Type IV 31.1%, Type V 10.2%, Type VI 3.6%. Width and depth did not demonstrate a statistical significant difference when analyzed according to gender and side; however, a significant difference was found between the depth means elaborated according to median age (73 y.o.). Correlation indexes were weak or not statistically significant. The differences among the postero-superior limits of the safe zone in the six types of notches was not statistically significant. CONCLUSIONS: Patient’s characteristics (gender, age and scapular dimensions) are not related to the characteristics of the suprascapular notch (dimensions and Type); our data suggest that the entrapment syndrome is more likely to be associated with a Type III notch because of its specific features.

Concepts: Statistics, Statistical significance, Type I and type II errors, Ronald Fisher, Statistical hypothesis testing, Pearson product-moment correlation coefficient, Suprascapular nerve, Suprascapular notch


PURPOSE: Existing patient self-reported shoulder scoring systems fail to express physicians' points of view, and understanding the wording can sometimes lead to confusion in Easterners. We sought to develop a valid, reliable, and responsive shoulder scoring system that combines the points of view of physicians and patients and is easily understood for worldwide applicability. METHODS: Six steps were followed to develop the scale: (1) investigation, identification of a specific population, and patient and physician interviews; (2) item generation, according to existing shoulder scales, a literature review, and patient and physician interviews; (3) item reduction, by combining and adjusting items; (4) formatting of the questionnaire, designed using both subjective and objective scales, with a 100-point score range; (5) pretesting, to eliminate confusion and misunderstanding of items, and (6) preliminary evaluation. Pearson correlation coefficients were calculated to assess validity (compared with American Shoulder and Elbow Surgeons, Constant-Murley, and University of California, Los Angeles scores), intraclass correlation coefficients were calculated to assess reliability (with a 2-week test-retest interval), and the standardized response mean was calculated to assess responsiveness (comparing preoperative and postoperative scores in patients). RESULTS: The final scoring system was designed to have a 100-point score range, with higher scores indicating better function. It consisted of self-report assessment by patients (61 points in total) and objective assessment by physicians (39 points in total). Updated scales, including a night pain subscale, patient-physician satisfaction, and 2-dimensional visual analog scale tool, were incorporated in our system. Compared with the other 3 scoring systems (American Shoulder and Elbow Surgeons, Constant-Murley, and University of California, Los Angeles scores), the new scoring system has shown favorable validity, with a Pearson correlation coefficient greater than 0.7. In addition, the intraclass correlation coefficient was greater than 0.9 during a 2-week test-retest interval, indicating high reliability, and the standardized response mean of the new system was greater than that of the other 3 scoring systems, indicating sensitive responsiveness. CONCLUSIONS: A new shoulder scoring system has been developed based on patients' and physicians' points of view and worldwide applicability and was verified to be valid, reliable, and responsive. The new scoring system includes a 2-dimensional visual analog scale, night pain subscale, and patient-physician satisfaction scale, which are not included in the existing scoring systems. LEVEL OF EVIDENCE: Level III, development of diagnostic criteria.

Concepts: Spearman's rank correlation coefficient, Physician, Assessment, Psychometrics, Correlation and dependence, Reliability, Pearson product-moment correlation coefficient, Covariance and correlation


The purpose of this prospective study was to establish the ultrasonographic characteristics of the dimension of the right pancreatic lobe with that of the associated anatomic landmarks in healthy dogs. Ultrasonographic examinations were performed on 25 dogs. The thickness of the right pancreatic lobe was compared with that of mural thickness of duodenum, diameters of duodenum, pancreatic duct, abdominal aorta, portal vein, caudal vena cava, and length and width of the right kidney and right adrenal gland. The correlation between each pancreatic parameter and the dimensions of the anatomical landmarks were assessed using linear regression analysis and Pearson’s correlation coefficient ® test. Significant, but weak linear correlations were observed between thickness of right pancreatic lobe with that of duodenum mural thickness (r=0.605, R(2)=0.339, P=0.001); duodenum diameter (r=0.573, R(2)=0.299, P=0.003); and right adrenal gland length (r=0.508, R(2)=0.052, P=0.01). There was no significant dimensional relationship with other selected anatomic landmarks. The ratio between the thickness of right pancreatic lobe and the mural thickness of duodenum, diameter of duodenum and length of right adrenal gland were 2.88 ± 0.53, 1.27 ± 0.27, and 0.81 ± 0.15, respectively. Calculating the ratio of thickness of the right pancreatic lobe with the dimension of significantly correlated anatomic landmarks is a useful and simple method for evaluating the size of the right pancreatic lobe in dogs in clinical practice.

Concepts: Kidney, Pancreas, Spearman's rank correlation coefficient, Abdominal aorta, Correlation and dependence, Pearson product-moment correlation coefficient, Covariance and correlation, Adrenal gland


A striking contrast runs through the last 60 years of biopharmaceutical discovery, research, and development. Huge scientific and technological gains should have increased the quality of academic science and raised industrial R&D efficiency. However, academia faces a “reproducibility crisis”; inflation-adjusted industrial R&D costs per novel drug increased nearly 100 fold between 1950 and 2010; and drugs are more likely to fail in clinical development today than in the 1970s. The contrast is explicable only if powerful headwinds reversed the gains and/or if many “gains” have proved illusory. However, discussions of reproducibility and R&D productivity rarely address this point explicitly. The main objectives of the primary research in this paper are: (a) to provide quantitatively and historically plausible explanations of the contrast; and (b) identify factors to which R&D efficiency is sensitive. We present a quantitative decision-theoretic model of the R&D process. The model represents therapeutic candidates (e.g., putative drug targets, molecules in a screening library, etc.) within a “measurement space”, with candidates' positions determined by their performance on a variety of assays (e.g., binding affinity, toxicity, in vivo efficacy, etc.) whose results correlate to a greater or lesser degree. We apply decision rules to segment the space, and assess the probability of correct R&D decisions. We find that when searching for rare positives (e.g., candidates that will successfully complete clinical development), changes in the predictive validity of screening and disease models that many people working in drug discovery would regard as small and/or unknowable (i.e., an 0.1 absolute change in correlation coefficient between model output and clinical outcomes in man) can offset large (e.g., 10 fold, even 100 fold) changes in models' brute-force efficiency. We also show how validity and reproducibility correlate across a population of simulated screening and disease models. We hypothesize that screening and disease models with high predictive validity are more likely to yield good answers and good treatments, so tend to render themselves and their diseases academically and commercially redundant. Perhaps there has also been too much enthusiasm for reductionist molecular models which have insufficient predictive validity. Thus we hypothesize that the average predictive validity of the stock of academically and industrially “interesting” screening and disease models has declined over time, with even small falls able to offset large gains in scientific knowledge and brute-force efficiency. The rate of creation of valid screening and disease models may be the major constraint on R&D productivity.

Concepts: Scientific method, Pharmacology, Clinical trial, Spearman's rank correlation coefficient, Psychometrics, Correlation and dependence, Pearson product-moment correlation coefficient, Admissible decision rule


This study examines the distributional equity of urban tree canopy (UTC) cover for Baltimore, MD, Los Angeles, CA, New York, NY, Philadelphia, PA, Raleigh, NC, Sacramento, CA, and Washington, D.C. using high spatial resolution land cover data and census data. Data are analyzed at the Census Block Group levels using Spearman’s correlation, ordinary least squares regression (OLS), and a spatial autoregressive model (SAR). Across all cities there is a strong positive correlation between UTC cover and median household income. Negative correlations between race and UTC cover exist in bivariate models for some cities, but they are generally not observed using multivariate regressions that include additional variables on income, education, and housing age. SAR models result in higher r-square values compared to the OLS models across all cities, suggesting that spatial autocorrelation is an important feature of our data. Similarities among cities can be found based on shared characteristics of climate, race/ethnicity, and size. Our findings suggest that a suite of variables, including income, contribute to the distribution of UTC cover. These findings can help target simultaneous strategies for UTC goals and environmental justice concerns.

Concepts: Regression analysis, Spearman's rank correlation coefficient, Correlation and dependence, Pearson product-moment correlation coefficient, Least squares, Covariance and correlation, Statistical dependence, Household income in the United States