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Why “fractional” anisotropy?

Why “fractional” anisotropy?


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Why is Fractional Anisotropy (FA), a measure in diffusion tensor imaging, called "fractional"? I'd have thought "local", i.e. voxel-wise, would have been the correct way to qualify the anisotropy that is calculated from the tensor model, so why fractional?


Like lea's comment indicated, FA is called "fractional" anisotropy simply because it's the degree of anisotropic diffusion, i.e., a ratio (Soares, Marques, Alves and Sousa, 2013).

Fractional Anisotropy is a normalized measure of the fraction of the tensor's magnitude due to anisotropic diffusion, corresponding to the degree of anisotropic diffusion or directionality and ranges from 0 (isotropic diffusion) to 1 (anisotropic diffusion).

References


Peak Broadening Anisotropy and the Contrast Factor in Metal Alloys

0.15%, whereas those for nickel are close to zero and often negative indicating the expected changes given by planar faults is not met. These results are consistent with the expectation that the quantity of planar faults is higher in stainless steel than in nickel. In addition, although not quantified the asymmetry changes shown in Figure 10 which show a greater difference in asymmetry between 111 and 222 peaks, or 200 and 400, in steel than in nickel is also consistent with these results. The change in intergranular strains of 200 and 400 peaks of stainless steel are unusual as they change in the opposite way than expected, i.e., g/g is higher for 200 the opposite of what is expected from the values in Table 3). This leads to a negative value for the fault percentage, although the absolute magnitude of the value is close to that found from the 111/222 peaks.

3.1.3. Plasticity Approach

20% and the 220 peak increases by

40% from the values at 0°. Whereas for nickel after 10% strain, the full-width of the 111 peak falls by

30% and the 220 peak increases by

25% from the values at 0° These variations exist at all strains measured, and for both the fatigue and compression samples shown in the figure. Furthermore, the same broad changes in the diffraction peak broadening with angle is observed for all samples. Since, the magnitude of the full-width values at 2% strain are lower the relative error in the full-widths will be higher, which may explain the slight difference to the higher applied strains. The broadening anisotropy with angle are observed for both the low stacking fault energy stainless-steel and the high stacking fault energy nickel including a fall in the full-width of most peaks from 0° to 90°, minima for 200 and 111 peaks near 45°, and maxima for the 220 peak near 45°.

10% in different orientations, which is not found for steel samples. These differences are consistent with differences that may be expected between the alloys. However, the approach does not fully address the cause of the broadening anisotropy because of its simplicity. A more comprehensive approach would instead incorporate more detailed models of work-hardening, along with more detailed descriptions of how different dislocation arrangements contribute to peak profiles, such as is being developed by Bertin and Cai [55].

50%, for both 111/22 and 200/400 analyses, from the values found when the diffraction vector and compression direction were parallel to each other to those found when they were perpendicular. The magnitude of these changes in dislocation density, and the overall trends, were consistent with the predicted changes in the contrast factor calculated by the plasticity approach, due only to slip anisotropy. In addition, the crystal size increased by

40% with the fall in dislocation density, which may be due to the difficulty in separating size and strain components in the Warren-Averbach approach [5]. Therefore, the possible errors to the results of DPPA by incorrect calculation of the contrast factor are significant. The problem is that in many cases the active slip systems are not known. Even when the imposed deformation is known, limitations in crystal plasticity models and difficulties in relating the model to dislocations, means there will always be an uncertainty in knowing the actual average contrast factor.

3.2. Hexagonal Close Packed Alloys

3.2.1. Homogeneous Approach

0 for samples up to 3.5%, and at higher strains q1 <0, but lower than q1 at lower strains, and q2 >0. Which is what is found from fits to a modified Williamson-Hall equation, as shown in Figure 18b.

3.2.2. Plasticity Approach

2 in the transverse plot), and less likely when the normal of the basal plane is parallel to the tensile direction ( x


Difference Between Isotropic and Anisotropic

“Isotropic” and “anisotropic” are two contrasting adjectives and nouns used to describe the properties of materials and minerals. Both “isotropic” and “anisotropic” also contain the element of direction in their descriptions.

“Anisotropic” refers to the properties of a material that is dependent on the direction. Another condition that can fit the anisotropic definition is the presence of different properties in different directions. A different chemical bonding in all directions is also a condition for anisotropy.

A mineral can be considered as anisotropic if it allows some light to pass through it. The mineral’s upper polar system allows light to pass through in truth, it affects the polarization of light. The velocity of light is also different, and there is double refraction (which means that light is split in two directions).

In anisotropic minerals, double refraction can lead to either of its two possible types – uniaxial (meaning one optic axis) or biaxial (two axes).

Anisotropic materials are often found in different fields like computer graphics, chemistry, real-world imagery, physics, geography and geophysics, medical acoustics, material science and engineering, microfabrication, and neuroscience.

On the other hand, isotropic materials or minerals have the uniform properties in all directions isotropic materials are said to be independent in direction or manner. An implication of a material or mineral being isotropic is that the chemical bonds within it are all identical in all directions.

An isotropic mineral can appear or remain dark when light passes through it the uniform structure of the mineral blocks the light from all directions. In addition, light doesn’t affect the mineral’s polarization or the direction of light. The velocity of light is in all directions, and the index of refraction is everywhere.

Isotropic materials are found in many industries like mathematics, physics, materials science, geography, economics, and biology. In terms of word structure, “anisotropic” is derived from “isotropic.” The Greek prefix “an” indicates a contrast in meaning and use from the attached base or root word. In this case, the root word is “isotropic,” which literally means “equal direction.” “Iso” is the Greek word for “equal,” while “tropic” means “direction” in the Greek language.

Both anisotropic and isotropic can be used as nouns and adjectives. They can also form other parts of speech, such as adverbs or other adjectives.

Summary:

1.“Isotropic” and “anisotropic” are related words that are opposites of each other. “Isotropic” is a noun and adjective that describes something with identical properties in all directions.
2.As its opposite, anisotropic also serves the same purpose (as a noun and adjective) for materials with different properties in all directions.
3.“Isotropic” is independent of direction, while “anisotropic” materials are highly dependent on it.
4.Anisotropic minerals can be penetrated by light due to their inconsistent properties in all directions. The opposite is true for isotropic minerals light cannot penetrate the mineral because the mineral’s properties block the light in any direction.
5.Chemical bonding is another point of difference. Anisotropic minerals have different and inconsistent chemical bonding. Isotropic minerals, on the other hand, exhibit consistent and uniform chemical bonding within the mineral.
6.Anisotropic minerals have the characteristic of double refraction, which can be classified as uniaxial or biaxial. Meanwhile, isotropic minerals don’t have this characteristic.
7.In terms of structure, “anisotropic” is a derived term. It is a word that came from “isotropic,” which means “equal direction.” The addition of the Greek prefix “an” makes the word’s meaning the complete opposite of its root or base word. This is also true for other words with this prefix.


The effects of bullying in depression on white matter integrity

Individuals with elevated symptoms of depression exhibit alterations in white matter integrity, including lower fractional anisotropy (FA) evident on diffusion tensor imaging (DTI). Similarly, individuals with a history of early life stress (ELS) exhibit lower FA in the white matter independent of concurrent depression. Prior studies have not determined whether the neuroimaging signature of comorbid ELS and adult depression differs from the pattern of brain white matter changes associated with depression in the absence of self-reported ELS. The current study examined FA in multiple white matter tracts in 186 adults (93 males 93 females) with a current diagnosis of major depressive disorder, including 88 who reported a history of bullying before the age of 18 (43 males 45 females). All patients were antidepressant medication free at the time of testing. After adjusting for demographics and other ELS subtypes, participants with a history of bullying exhibited increased FA in the right medial lemniscus (p =.039) and left posterior corona radiata (p =.008) compared to participants with depression but no self-reported history of bullying. Both groups endorsed similar levels of depression. Group differences were most pronounced among individuals who endorsed bullying in late adolescence (14-17 years of age). Results suggest bullying in late adolescence is uniquely related to abnormal brain microstructure among individuals with current diagnoses of depression, possibly due to an overactive fear response. Further work is needed to differentiate why ELS within bullying is associated with higher FA.

Trial registration: ClinicalTrials.gov NCT00693849.

Keywords: Depression Diffusion tensor imaging Stress.


Method

Sample

We recruited 24 subjects at the University of Mainz aged from 18 to 28 years (M = 21.66, SD = 6.92, 6 male), with an average body height of 170.96 cm (SD = 7.25 cm). Prior to testing, they gave written consent in accordance with the declaration of Helsinki and filled out a demographic questionnaire. Prior to the study, the Institutional Review Board (IRB) of the Institute of Psychology at the University of Mainz had informed us that in accordance with the department's ethics guidelines no explicit ethics vote of the IRB was necessary for our study, because we designed the experiments to test healthy adult volunteers, to present only harmless visual stimuli, to rule out physical or psychological stress, and to refrain from measuring physiological parameters. We did not intend to collect sensitive data like personality or clinical scales, or to provide misleading or wrong information to participants. All subjects reported their acquaintance with the confederate (good friend–mere acquaintance—stranger). All participants rated the confederates to be strangers. They had normal or corrected-to-normal visual acuity (Snellen fraction 1.0 or larger) as measured by the Freiburg Acuity Test [31] and they received partial course credit for participation.

Design and stimuli

Subjects were placed at 15 frontal IPDs to a confederate varying from 40 cm to 250 cm in steps of 15 cm, which corresponds to the mean minimum and maximum distance for conversation obtained by Williams [11]. These distances were marked–but not labelled–with tape on the floor. On a given trial, both subject and confederate were positioned on a random pairing of these marks aligned to their body-center. The body-center was estimated to be the middle of the foot, marked by dots on the shoes. Subjects as well as the confederate were instructed to look straight at each other’s face throughout the whole experiment. The two confederates taking part in this study were both young females. One of the confederates was 165 cm in height and had blond hair, the other was 167 cm tall and had brown hair. The two confederates took turns between sessions in order to counteract potential confounding variables, i. e. fatigue, poor concentration, etc. Both confederates wore a white shirt and blue jeans, see Fig 1. The individuals in this Figure have given written informed consent (as outlined in PLOS consent form) to publish this photograph.

Tape on the floor marked the 15 IPDs.

Procedure

For all testing blocks, we standardized the social situation to minimize situational effects on IPD [3]. Subjects had to imagine a scenario in which they were in an open space in an unfamiliar city asking a stranger for directions. Subjects were placed at 15 different IPDs in a fixed-distance task and were asked to rate subjective discomfort verbally on a rating scale ranging from -100 (maximum discomfort, too close) to 0 (ideal distance) to +100 (maximum discomfort, too far). In Block 1, the subject was directed by the experimenter and the confederate remained stationary. In Block 2, the subject remained stationary and the confederate moved to the respective positions between trials. Subjects were blindfolded during the positioning. After the positioning, the blindfold was lifted and he/she rated subjective discomfort.

Block 3 followed the procedure of Block 2, but subjects rated discomfort by positioning a joystick. This was done to control for social desirability, the confederate was unable to see the exact tilt of the joystick. Subjects were instructed to tilt the joystick away from themselves as a function of experienced discomfort when IPD was deemed too close, or to tilt the joystick towards themselves when the distance was not close enough. All possible orders of Blocks 1, 2 and 3 were used and counterbalanced between subjects. Within each block, the order of distances was randomized.

Next, subjects completed two repetitions of an active and a passive stop-distance task to estimate the preferred IPD. In the active stop-distance task, the subject approached the confederate until comfortable IPD had been reached. In the passive stop-distance task, the subject was slowly approached by the confederate until the subject signaled the confederate to stop. Subjects were allowed to fine-tune this distance by instructing the confederate to adjust forward or backward. Preferred IPD was measured via a tape measure on the floor and recorded as the distance between the subject’s and the confederate’s body center. Order of the passive and active stop-distance task was counterbalanced within the sample. Subjects were tested in individual sessions of approximately 60 minutes. No time constraints were imposed in any of the trials [24]. After the procedure, the subjects were thanked and debriefed. We report all measures and scale manipulations in this study. We did not exclude any of the experimental trials from data analysis and sample size was not increased after data analysis.

Statistical analysis

To enable the examination of the Null-hypothesis, we have opted for a Bayesian approach to data analysis. The Bayes Factor (BF) is used for statistical inference and is computed using the BayesFactor-package [32, 33] in R [34]. Here, the BF quantifies the relative likelihood of the Null-model as compared to the alternative-model given the observed data. We either provide the likelihood for the Null-model relative to the alternative model (BF01) or the reverse fraction (BF10). Note that we have compared different weakly informative priors in a prior-sensitivity analysis. The choice of priors did not influence statistical inference in this study as the data obtained clearly overwhelmed the priors when computing the Bayes Factors. Thus, we stuck with the default priors of the BayesFactor-package in t-tests, regressions and analyses of variance. We report median estimates of parameters with high density intervals at 95% from the posterior distribution. To model the relation of IPD and discomfort, we calculated a Bayesian linear mixed model (BLMM) using brms [35], a wrapper for the STAN-sampler [36] for R [34]. We applied normally distributed priors (M = 0, SD = 1) on all beta-coefficients, with Cholesky priors on the residual correlation (η = 1) and a t-distributed prior to allow for thicker tails (df = 3, M = 0, SD = 10) on the centered intercept, the variance parameters and sigma. These priors are only very weakly informative and mostly help in the regularization of the posterior distributions. We computed 4 Hamilton-Monte-Carlo chains with 10000 iterations each and 20% warm-up samples. Trace plots of the Markov-chain-Monte-Carlo permutations were inspected for divergent transitions. All Rubin-Gelman statistics were well below 1.1. The experimental data and the R code can be found in the Supplementary Material S1 Data and S1 Code. The files provided comprise the minimal underlying data that an independent researcher would need in order to replicate all of our results, conclusions, figures and summary statistics. The files do not contain any personally identifying information.


ANISOTROPIC EXPANSION OF THE PLANT CELL WALL

AbstractPlants shape their organs with a precision demanded by optimal function organ shaping requires control over cell wall expansion anisotropy. Focusing on multicellular organs, I survey the occurrence of expansion anisotropy and discuss its causes and proposed controls. Expansion anisotropy of a unit area of cell wall is characterized by the direction and degree of anisotropy. The direction of maximal expansion rate is usually regulated by the direction of net alignment among cellulose microfibrils, which overcomes the prevailing stress anisotropy. In some stems, the directionality of expansion of epidermal cells is controlled by that of the inner tissue. The degree of anisotropy can vary widely as a function of position and of treatment. The degree of anisotropy is probably controlled by factors in addition to the direction of microfibril alignment. I hypothesize that rates of expansion in maximal and minimal directions are regulated by distinct molecular mechanisms that regulate interactions between matrix and microfibrils.


Incompressible Fluid Dynamics: Some Fundamental Formulation Issues

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning (ML) offers a wealth of techniques to extract . Read More

Figure 1: Machine learning algorithms may be categorized into supervised, unsupervised, and semisupervised, depending on the extent and type of information available for the learning process. Abbrevia.

Figure 2: First example of learning and automation in experimental fluid mechanics: Rechenberg's (1964) experiments for optimally corrugated plates for drag reduction using the Galtonbrett (Galton boa.

Figure 3: The learning problem. A learning machine uses inputs from a sample generator and observations from a system to generate an approximation of its output. Figure based on an idea from Cherkassk.

Figure 4: Recurrent neural networks (RNNs) for time series predictions and the long short-term memory (LSTM) regularization. Abbreviations: , previous cell memory , current cell memory , previous ce.

Figure 5: PCA/POD (left) versus shallow autoencoders (center) and deep autoencoders (right). If the node activation functions in the shallow autoencoder are linear, then u and are matrices that minim.

Figure 6: Unsupervised learning example: merging of two vortices (top), proper orthogonal decomposition (POD) modes (middle), and respective modes from a linear autoencoder (bottom). Note that unlike .

Figure 7: Comparison of standard neural network architecture (a) with modified neural network for identifying Galilean invariant Reynolds stress models (b). Abbreviations: , anisotropy tensor , scala.

Figure 8: Deep reinforcement learning schematic (a) and application to the study of the collective motion of fish via the Navier–Stokes equations (b). Panel b adapted from Verma et al. (2018).


After studying personal space in the lab, you became affected in an intensely personal way as well. Tell us about your son’s dyspraxia—and how it reinforced what you had discovered as a scientist.

My son has dyspraxia, which is surprisingly common. About one in 20 children have it. It’s an inability to move in a fully coordinated way with respect to your environment. Some people have described it as knowing what you want to do but having difficulty getting it out in a coordinated way.

There are lots of flavors of dyspraxia but, in my son’s case, it seemed more to have to do with his personal space—understanding objects in the space immediately around the body and how to interact with them. He had a host of difficulties. He tended to bump into things. It was hard for him to learn how to hold a pencil.

It was shocking to us, how much of our ordinary everyday life is built out of personal space. When you don’t have a clear sense of the space around you, it isn’t just that you bump into things, or have trouble learning tool use. You have trouble learning math because you can’t point accurately. The first lesson you learn in school is to point and count for math. You also can’t read well because you have trouble understanding where the book is—where the words are—in relationship to you.

But the social impact was most shocking to us. He would lean and bump against people, stand too close, or barge through, squeezing between people where there wasn’t a space. All the things that, at some unconscious level, bother people socially. People are very attuned to this special social dance. When that goes wrong, people don’t know why, they just don’t like it.

His whole social world came crashing down. His school didn’t know what was wrong but they didn’t like it and expelled him from school. He was six at the time, in first grade, and they thought he was sexually assaulting the other students! Of course, he had no idea what he was doing. We had expert after expert saying, “He needs physical therapy, he doesn’t understand the space around his body.”

We went through a whole court case on that. It taught me that this is probably way more common than we think. Personal space is so under the surface, it’s so unconscious most of the time we don’t notice it. But, boy, when it goes wrong, you notice!


Conclusion

In this study, we found no compelling evidence for disrupted spatial processing of tactile input on the hand dorsum in CRPS: tactile anisotropy and anisotropic perception bias were both comparable between CRPS patients, pain-controls and pain-free participants. The lack of a difference between groups in tactile anisotropy (and bias) supports the idea that S1 hand representation is differently disrupted than previously thought. Furthermore, the present findings suggest that the presumed relationship between tactile dysfunction, S1 abnormalities and distorted hand perception should be revisited. Further research and clinical studies are required to interrogate the possible mechanisms that underpin hand perception distortion in CRPS.


Peak Broadening Anisotropy and the Contrast Factor in Metal Alloys

0.15%, whereas those for nickel are close to zero and often negative indicating the expected changes given by planar faults is not met. These results are consistent with the expectation that the quantity of planar faults is higher in stainless steel than in nickel. In addition, although not quantified the asymmetry changes shown in Figure 10 which show a greater difference in asymmetry between 111 and 222 peaks, or 200 and 400, in steel than in nickel is also consistent with these results. The change in intergranular strains of 200 and 400 peaks of stainless steel are unusual as they change in the opposite way than expected, i.e., g/g is higher for 200 the opposite of what is expected from the values in Table 3). This leads to a negative value for the fault percentage, although the absolute magnitude of the value is close to that found from the 111/222 peaks.

3.1.3. Plasticity Approach

20% and the 220 peak increases by

40% from the values at 0°. Whereas for nickel after 10% strain, the full-width of the 111 peak falls by

30% and the 220 peak increases by

25% from the values at 0° These variations exist at all strains measured, and for both the fatigue and compression samples shown in the figure. Furthermore, the same broad changes in the diffraction peak broadening with angle is observed for all samples. Since, the magnitude of the full-width values at 2% strain are lower the relative error in the full-widths will be higher, which may explain the slight difference to the higher applied strains. The broadening anisotropy with angle are observed for both the low stacking fault energy stainless-steel and the high stacking fault energy nickel including a fall in the full-width of most peaks from 0° to 90°, minima for 200 and 111 peaks near 45°, and maxima for the 220 peak near 45°.

10% in different orientations, which is not found for steel samples. These differences are consistent with differences that may be expected between the alloys. However, the approach does not fully address the cause of the broadening anisotropy because of its simplicity. A more comprehensive approach would instead incorporate more detailed models of work-hardening, along with more detailed descriptions of how different dislocation arrangements contribute to peak profiles, such as is being developed by Bertin and Cai [55].

50%, for both 111/22 and 200/400 analyses, from the values found when the diffraction vector and compression direction were parallel to each other to those found when they were perpendicular. The magnitude of these changes in dislocation density, and the overall trends, were consistent with the predicted changes in the contrast factor calculated by the plasticity approach, due only to slip anisotropy. In addition, the crystal size increased by

40% with the fall in dislocation density, which may be due to the difficulty in separating size and strain components in the Warren-Averbach approach [5]. Therefore, the possible errors to the results of DPPA by incorrect calculation of the contrast factor are significant. The problem is that in many cases the active slip systems are not known. Even when the imposed deformation is known, limitations in crystal plasticity models and difficulties in relating the model to dislocations, means there will always be an uncertainty in knowing the actual average contrast factor.

3.2. Hexagonal Close Packed Alloys

3.2.1. Homogeneous Approach

0 for samples up to 3.5%, and at higher strains q1 <0, but lower than q1 at lower strains, and q2 >0. Which is what is found from fits to a modified Williamson-Hall equation, as shown in Figure 18b.

3.2.2. Plasticity Approach

2 in the transverse plot), and less likely when the normal of the basal plane is parallel to the tensile direction ( x


Incompressible Fluid Dynamics: Some Fundamental Formulation Issues

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning (ML) offers a wealth of techniques to extract . Read More

Figure 1: Machine learning algorithms may be categorized into supervised, unsupervised, and semisupervised, depending on the extent and type of information available for the learning process. Abbrevia.

Figure 2: First example of learning and automation in experimental fluid mechanics: Rechenberg's (1964) experiments for optimally corrugated plates for drag reduction using the Galtonbrett (Galton boa.

Figure 3: The learning problem. A learning machine uses inputs from a sample generator and observations from a system to generate an approximation of its output. Figure based on an idea from Cherkassk.

Figure 4: Recurrent neural networks (RNNs) for time series predictions and the long short-term memory (LSTM) regularization. Abbreviations: , previous cell memory , current cell memory , previous ce.

Figure 5: PCA/POD (left) versus shallow autoencoders (center) and deep autoencoders (right). If the node activation functions in the shallow autoencoder are linear, then u and are matrices that minim.

Figure 6: Unsupervised learning example: merging of two vortices (top), proper orthogonal decomposition (POD) modes (middle), and respective modes from a linear autoencoder (bottom). Note that unlike .

Figure 7: Comparison of standard neural network architecture (a) with modified neural network for identifying Galilean invariant Reynolds stress models (b). Abbreviations: , anisotropy tensor , scala.

Figure 8: Deep reinforcement learning schematic (a) and application to the study of the collective motion of fish via the Navier–Stokes equations (b). Panel b adapted from Verma et al. (2018).


ANISOTROPIC EXPANSION OF THE PLANT CELL WALL

AbstractPlants shape their organs with a precision demanded by optimal function organ shaping requires control over cell wall expansion anisotropy. Focusing on multicellular organs, I survey the occurrence of expansion anisotropy and discuss its causes and proposed controls. Expansion anisotropy of a unit area of cell wall is characterized by the direction and degree of anisotropy. The direction of maximal expansion rate is usually regulated by the direction of net alignment among cellulose microfibrils, which overcomes the prevailing stress anisotropy. In some stems, the directionality of expansion of epidermal cells is controlled by that of the inner tissue. The degree of anisotropy can vary widely as a function of position and of treatment. The degree of anisotropy is probably controlled by factors in addition to the direction of microfibril alignment. I hypothesize that rates of expansion in maximal and minimal directions are regulated by distinct molecular mechanisms that regulate interactions between matrix and microfibrils.


Conclusion

In this study, we found no compelling evidence for disrupted spatial processing of tactile input on the hand dorsum in CRPS: tactile anisotropy and anisotropic perception bias were both comparable between CRPS patients, pain-controls and pain-free participants. The lack of a difference between groups in tactile anisotropy (and bias) supports the idea that S1 hand representation is differently disrupted than previously thought. Furthermore, the present findings suggest that the presumed relationship between tactile dysfunction, S1 abnormalities and distorted hand perception should be revisited. Further research and clinical studies are required to interrogate the possible mechanisms that underpin hand perception distortion in CRPS.


Method

Sample

We recruited 24 subjects at the University of Mainz aged from 18 to 28 years (M = 21.66, SD = 6.92, 6 male), with an average body height of 170.96 cm (SD = 7.25 cm). Prior to testing, they gave written consent in accordance with the declaration of Helsinki and filled out a demographic questionnaire. Prior to the study, the Institutional Review Board (IRB) of the Institute of Psychology at the University of Mainz had informed us that in accordance with the department's ethics guidelines no explicit ethics vote of the IRB was necessary for our study, because we designed the experiments to test healthy adult volunteers, to present only harmless visual stimuli, to rule out physical or psychological stress, and to refrain from measuring physiological parameters. We did not intend to collect sensitive data like personality or clinical scales, or to provide misleading or wrong information to participants. All subjects reported their acquaintance with the confederate (good friend–mere acquaintance—stranger). All participants rated the confederates to be strangers. They had normal or corrected-to-normal visual acuity (Snellen fraction 1.0 or larger) as measured by the Freiburg Acuity Test [31] and they received partial course credit for participation.

Design and stimuli

Subjects were placed at 15 frontal IPDs to a confederate varying from 40 cm to 250 cm in steps of 15 cm, which corresponds to the mean minimum and maximum distance for conversation obtained by Williams [11]. These distances were marked–but not labelled–with tape on the floor. On a given trial, both subject and confederate were positioned on a random pairing of these marks aligned to their body-center. The body-center was estimated to be the middle of the foot, marked by dots on the shoes. Subjects as well as the confederate were instructed to look straight at each other’s face throughout the whole experiment. The two confederates taking part in this study were both young females. One of the confederates was 165 cm in height and had blond hair, the other was 167 cm tall and had brown hair. The two confederates took turns between sessions in order to counteract potential confounding variables, i. e. fatigue, poor concentration, etc. Both confederates wore a white shirt and blue jeans, see Fig 1. The individuals in this Figure have given written informed consent (as outlined in PLOS consent form) to publish this photograph.

Tape on the floor marked the 15 IPDs.

Procedure

For all testing blocks, we standardized the social situation to minimize situational effects on IPD [3]. Subjects had to imagine a scenario in which they were in an open space in an unfamiliar city asking a stranger for directions. Subjects were placed at 15 different IPDs in a fixed-distance task and were asked to rate subjective discomfort verbally on a rating scale ranging from -100 (maximum discomfort, too close) to 0 (ideal distance) to +100 (maximum discomfort, too far). In Block 1, the subject was directed by the experimenter and the confederate remained stationary. In Block 2, the subject remained stationary and the confederate moved to the respective positions between trials. Subjects were blindfolded during the positioning. After the positioning, the blindfold was lifted and he/she rated subjective discomfort.

Block 3 followed the procedure of Block 2, but subjects rated discomfort by positioning a joystick. This was done to control for social desirability, the confederate was unable to see the exact tilt of the joystick. Subjects were instructed to tilt the joystick away from themselves as a function of experienced discomfort when IPD was deemed too close, or to tilt the joystick towards themselves when the distance was not close enough. All possible orders of Blocks 1, 2 and 3 were used and counterbalanced between subjects. Within each block, the order of distances was randomized.

Next, subjects completed two repetitions of an active and a passive stop-distance task to estimate the preferred IPD. In the active stop-distance task, the subject approached the confederate until comfortable IPD had been reached. In the passive stop-distance task, the subject was slowly approached by the confederate until the subject signaled the confederate to stop. Subjects were allowed to fine-tune this distance by instructing the confederate to adjust forward or backward. Preferred IPD was measured via a tape measure on the floor and recorded as the distance between the subject’s and the confederate’s body center. Order of the passive and active stop-distance task was counterbalanced within the sample. Subjects were tested in individual sessions of approximately 60 minutes. No time constraints were imposed in any of the trials [24]. After the procedure, the subjects were thanked and debriefed. We report all measures and scale manipulations in this study. We did not exclude any of the experimental trials from data analysis and sample size was not increased after data analysis.

Statistical analysis

To enable the examination of the Null-hypothesis, we have opted for a Bayesian approach to data analysis. The Bayes Factor (BF) is used for statistical inference and is computed using the BayesFactor-package [32, 33] in R [34]. Here, the BF quantifies the relative likelihood of the Null-model as compared to the alternative-model given the observed data. We either provide the likelihood for the Null-model relative to the alternative model (BF01) or the reverse fraction (BF10). Note that we have compared different weakly informative priors in a prior-sensitivity analysis. The choice of priors did not influence statistical inference in this study as the data obtained clearly overwhelmed the priors when computing the Bayes Factors. Thus, we stuck with the default priors of the BayesFactor-package in t-tests, regressions and analyses of variance. We report median estimates of parameters with high density intervals at 95% from the posterior distribution. To model the relation of IPD and discomfort, we calculated a Bayesian linear mixed model (BLMM) using brms [35], a wrapper for the STAN-sampler [36] for R [34]. We applied normally distributed priors (M = 0, SD = 1) on all beta-coefficients, with Cholesky priors on the residual correlation (η = 1) and a t-distributed prior to allow for thicker tails (df = 3, M = 0, SD = 10) on the centered intercept, the variance parameters and sigma. These priors are only very weakly informative and mostly help in the regularization of the posterior distributions. We computed 4 Hamilton-Monte-Carlo chains with 10000 iterations each and 20% warm-up samples. Trace plots of the Markov-chain-Monte-Carlo permutations were inspected for divergent transitions. All Rubin-Gelman statistics were well below 1.1. The experimental data and the R code can be found in the Supplementary Material S1 Data and S1 Code. The files provided comprise the minimal underlying data that an independent researcher would need in order to replicate all of our results, conclusions, figures and summary statistics. The files do not contain any personally identifying information.


The effects of bullying in depression on white matter integrity

Individuals with elevated symptoms of depression exhibit alterations in white matter integrity, including lower fractional anisotropy (FA) evident on diffusion tensor imaging (DTI). Similarly, individuals with a history of early life stress (ELS) exhibit lower FA in the white matter independent of concurrent depression. Prior studies have not determined whether the neuroimaging signature of comorbid ELS and adult depression differs from the pattern of brain white matter changes associated with depression in the absence of self-reported ELS. The current study examined FA in multiple white matter tracts in 186 adults (93 males 93 females) with a current diagnosis of major depressive disorder, including 88 who reported a history of bullying before the age of 18 (43 males 45 females). All patients were antidepressant medication free at the time of testing. After adjusting for demographics and other ELS subtypes, participants with a history of bullying exhibited increased FA in the right medial lemniscus (p =.039) and left posterior corona radiata (p =.008) compared to participants with depression but no self-reported history of bullying. Both groups endorsed similar levels of depression. Group differences were most pronounced among individuals who endorsed bullying in late adolescence (14-17 years of age). Results suggest bullying in late adolescence is uniquely related to abnormal brain microstructure among individuals with current diagnoses of depression, possibly due to an overactive fear response. Further work is needed to differentiate why ELS within bullying is associated with higher FA.

Trial registration: ClinicalTrials.gov NCT00693849.

Keywords: Depression Diffusion tensor imaging Stress.


Difference Between Isotropic and Anisotropic

“Isotropic” and “anisotropic” are two contrasting adjectives and nouns used to describe the properties of materials and minerals. Both “isotropic” and “anisotropic” also contain the element of direction in their descriptions.

“Anisotropic” refers to the properties of a material that is dependent on the direction. Another condition that can fit the anisotropic definition is the presence of different properties in different directions. A different chemical bonding in all directions is also a condition for anisotropy.

A mineral can be considered as anisotropic if it allows some light to pass through it. The mineral’s upper polar system allows light to pass through in truth, it affects the polarization of light. The velocity of light is also different, and there is double refraction (which means that light is split in two directions).

In anisotropic minerals, double refraction can lead to either of its two possible types – uniaxial (meaning one optic axis) or biaxial (two axes).

Anisotropic materials are often found in different fields like computer graphics, chemistry, real-world imagery, physics, geography and geophysics, medical acoustics, material science and engineering, microfabrication, and neuroscience.

On the other hand, isotropic materials or minerals have the uniform properties in all directions isotropic materials are said to be independent in direction or manner. An implication of a material or mineral being isotropic is that the chemical bonds within it are all identical in all directions.

An isotropic mineral can appear or remain dark when light passes through it the uniform structure of the mineral blocks the light from all directions. In addition, light doesn’t affect the mineral’s polarization or the direction of light. The velocity of light is in all directions, and the index of refraction is everywhere.

Isotropic materials are found in many industries like mathematics, physics, materials science, geography, economics, and biology. In terms of word structure, “anisotropic” is derived from “isotropic.” The Greek prefix “an” indicates a contrast in meaning and use from the attached base or root word. In this case, the root word is “isotropic,” which literally means “equal direction.” “Iso” is the Greek word for “equal,” while “tropic” means “direction” in the Greek language.

Both anisotropic and isotropic can be used as nouns and adjectives. They can also form other parts of speech, such as adverbs or other adjectives.

Summary:

1.“Isotropic” and “anisotropic” are related words that are opposites of each other. “Isotropic” is a noun and adjective that describes something with identical properties in all directions.
2.As its opposite, anisotropic also serves the same purpose (as a noun and adjective) for materials with different properties in all directions.
3.“Isotropic” is independent of direction, while “anisotropic” materials are highly dependent on it.
4.Anisotropic minerals can be penetrated by light due to their inconsistent properties in all directions. The opposite is true for isotropic minerals light cannot penetrate the mineral because the mineral’s properties block the light in any direction.
5.Chemical bonding is another point of difference. Anisotropic minerals have different and inconsistent chemical bonding. Isotropic minerals, on the other hand, exhibit consistent and uniform chemical bonding within the mineral.
6.Anisotropic minerals have the characteristic of double refraction, which can be classified as uniaxial or biaxial. Meanwhile, isotropic minerals don’t have this characteristic.
7.In terms of structure, “anisotropic” is a derived term. It is a word that came from “isotropic,” which means “equal direction.” The addition of the Greek prefix “an” makes the word’s meaning the complete opposite of its root or base word. This is also true for other words with this prefix.


After studying personal space in the lab, you became affected in an intensely personal way as well. Tell us about your son’s dyspraxia—and how it reinforced what you had discovered as a scientist.

My son has dyspraxia, which is surprisingly common. About one in 20 children have it. It’s an inability to move in a fully coordinated way with respect to your environment. Some people have described it as knowing what you want to do but having difficulty getting it out in a coordinated way.

There are lots of flavors of dyspraxia but, in my son’s case, it seemed more to have to do with his personal space—understanding objects in the space immediately around the body and how to interact with them. He had a host of difficulties. He tended to bump into things. It was hard for him to learn how to hold a pencil.

It was shocking to us, how much of our ordinary everyday life is built out of personal space. When you don’t have a clear sense of the space around you, it isn’t just that you bump into things, or have trouble learning tool use. You have trouble learning math because you can’t point accurately. The first lesson you learn in school is to point and count for math. You also can’t read well because you have trouble understanding where the book is—where the words are—in relationship to you.

But the social impact was most shocking to us. He would lean and bump against people, stand too close, or barge through, squeezing between people where there wasn’t a space. All the things that, at some unconscious level, bother people socially. People are very attuned to this special social dance. When that goes wrong, people don’t know why, they just don’t like it.

His whole social world came crashing down. His school didn’t know what was wrong but they didn’t like it and expelled him from school. He was six at the time, in first grade, and they thought he was sexually assaulting the other students! Of course, he had no idea what he was doing. We had expert after expert saying, “He needs physical therapy, he doesn’t understand the space around his body.”

We went through a whole court case on that. It taught me that this is probably way more common than we think. Personal space is so under the surface, it’s so unconscious most of the time we don’t notice it. But, boy, when it goes wrong, you notice!



Comments:

  1. Waren

    I want and take

  2. Haroutyoun

    How should I know?

  3. Fekora

    Almost the same.

  4. Yozshuzshura

    This is not at all what is necessary for me.

  5. Terence

    This is not the joke!



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