A C ValueThe C Value method is based on noticing distribution of the words in a text and word clustering [14]. To quantify the clustering of a word the parameter (the standard deviation of the normalized distance between consecutive occurrence of a word) is defined by pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ??s ?< s2 > ? s >2 fpsyg.2017.00209 ; Where s is the normalized distance between consecutive occurrences, s = d/ < d >, and < d > is the average distance between occurrences. can be normalized with respect to standard deviation of the distance between consecutive occurrences of words in a random text, which has a pffiffiffiffiffiffiffiffiffiffiffi geometrical spatial distribution of word types, sgeo fpsyg.2014.00726 ?1 ?P. Where p = M/N is the probability of occurrence of a word type with get Cyclopamine frequency equal to M in a text with total N words, snor ?s ; sgeo ?0?C nor ; M??snor ?< snor ?> ; sd nor M??1?PLOS ONE | DOI:10.1371/journal.pone.0130617 June 19,16 /The Fractal Patterns of Words in a Text1 Where < snor >?2M? and sd nor ??pffiffiffi??:8M?:865 ?are the mean value of the normalized 2M? Mstandard deviation and standard deviation of the distribution of nor in a random text, respectively. C = 0 means the word is distributed randomly in a text and C > 0 means the word forms cluster.B EntropyEntropy is another parameter used to rank the words of a text [17]. For this purpose a text with P P N words is devided into P parts. The ith part contains Ni words which Ni ?N. So the ralai?tive frequency of occurrence of the word type in the part i is fi ??Mi ? where Mi() and M ?M() are the frequency of word type in the ith part and in the whole text, respectively, where P P Mi ?M. With this explanation the probability measure over the partitions can be definedi?as pi ??fi ?: X fj ?P j??2?The following relation is the Shannon’s information entropy for a discrete distribution pi() S ??P ? X p n i : Ln ?i? i?3?There is a problem with this relation; it is zero for words with frequency equal to 1. To take into account the IRC-022493 chemical information effect of frequency, the following relation seems to be a better choice Enor ??M 1 ?S ?Eran ??4?P? where Eran ??2ln ?is the entropy of the word type in a random text.AcknowledgmentsWe acknowledge valuable comments from referees which substantially improved the paper.Author ContributionsConceived and designed the experiments: EN AHD. Performed the experiments: EN AHD. Analyzed the data: EN AHD. Contributed reagents/materials/analysis tools: EN AHD. Wrote the paper: EN AHD.
RESEARCH ARTICLEAltered Structural and Functional Connectivity in Late Preterm Preadolescence: An Anatomic Seed-Based Study of Resting State Networks Related to the Posteromedial and Lateral Parietal CortexAndrew J. Degnan1,2, Jessica L. Wisnowski1,3,5, SoYoung Choi3, Rafael Ceschin1,6, Chitresh Bhushan4, Richard M. Leahy4, Patricia Corby7,8, Vincent J. Schmithorst1, Ashok Panigrahy1,3,5,6*1 Department of Pediatric Radiology, Children’s Hospital of Pittsburgh of UPMC, 4401 Penn Avenue, Floor 2, Pittsburgh, PA, 15224, United States of America, 2 Department of Radiology, University of Pittsburgh Medical Center (UPMC), 3950 Presby South Tower, 200 Lothrop Street, Pittsburgh, PA 15213, United States of America, 3 Brain and Creativity Institute, University of Southern California, 3620A McClintock Avenue, Los Angeles, CA 90089, United States of America, 4 Signal and Image Processing Institute, University of Southern California, Los.A C ValueThe C Value method is based on noticing distribution of the words in a text and word clustering [14]. To quantify the clustering of a word the parameter (the standard deviation of the normalized distance between consecutive occurrence of a word) is defined by pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ??s ?< s2 > ? s >2 fpsyg.2017.00209 ; Where s is the normalized distance between consecutive occurrences, s = d/ < d >, and < d > is the average distance between occurrences. can be normalized with respect to standard deviation of the distance between consecutive occurrences of words in a random text, which has a pffiffiffiffiffiffiffiffiffiffiffi geometrical spatial distribution of word types, sgeo fpsyg.2014.00726 ?1 ?P. Where p = M/N is the probability of occurrence of a word type with frequency equal to M in a text with total N words, snor ?s ; sgeo ?0?C nor ; M??snor ?< snor ?> ; sd nor M??1?PLOS ONE | DOI:10.1371/journal.pone.0130617 June 19,16 /The Fractal Patterns of Words in a Text1 Where < snor >?2M? and sd nor ??pffiffiffi??:8M?:865 ?are the mean value of the normalized 2M? Mstandard deviation and standard deviation of the distribution of nor in a random text, respectively. C = 0 means the word is distributed randomly in a text and C > 0 means the word forms cluster.B EntropyEntropy is another parameter used to rank the words of a text [17]. For this purpose a text with P P N words is devided into P parts. The ith part contains Ni words which Ni ?N. So the ralai?tive frequency of occurrence of the word type in the part i is fi ??Mi ? where Mi() and M ?M() are the frequency of word type in the ith part and in the whole text, respectively, where P P Mi ?M. With this explanation the probability measure over the partitions can be definedi?as pi ??fi ?: X fj ?P j??2?The following relation is the Shannon’s information entropy for a discrete distribution pi() S ??P ? X p n i : Ln ?i? i?3?There is a problem with this relation; it is zero for words with frequency equal to 1. To take into account the effect of frequency, the following relation seems to be a better choice Enor ??M 1 ?S ?Eran ??4?P? where Eran ??2ln ?is the entropy of the word type in a random text.AcknowledgmentsWe acknowledge valuable comments from referees which substantially improved the paper.Author ContributionsConceived and designed the experiments: EN AHD. Performed the experiments: EN AHD. Analyzed the data: EN AHD. Contributed reagents/materials/analysis tools: EN AHD. Wrote the paper: EN AHD.
RESEARCH ARTICLEAltered Structural and Functional Connectivity in Late Preterm Preadolescence: An Anatomic Seed-Based Study of Resting State Networks Related to the Posteromedial and Lateral Parietal CortexAndrew J. Degnan1,2, Jessica L. Wisnowski1,3,5, SoYoung Choi3, Rafael Ceschin1,6, Chitresh Bhushan4, Richard M. Leahy4, Patricia Corby7,8, Vincent J. Schmithorst1, Ashok Panigrahy1,3,5,6*1 Department of Pediatric Radiology, Children’s Hospital of Pittsburgh of UPMC, 4401 Penn Avenue, Floor 2, Pittsburgh, PA, 15224, United States of America, 2 Department of Radiology, University of Pittsburgh Medical Center (UPMC), 3950 Presby South Tower, 200 Lothrop Street, Pittsburgh, PA 15213, United States of America, 3 Brain and Creativity Institute, University of Southern California, 3620A McClintock Avenue, Los Angeles, CA 90089, United States of America, 4 Signal and Image Processing Institute, University of Southern California, Los.
