scholarly journals Comparative Analysis and Classification of Cassette Exons and Constitutive Exons

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Ying Cui ◽  
Meng Cai ◽  
H. Eugene Stanley
Keyword(s):  
Genetic Diseases ◽  
Gc Content ◽  
High Accuracy ◽  
Common Type ◽  
Hereditary Diseases ◽  
Cassette Exon ◽  
Mammalian Genomes ◽  
Alternatively Spliced ◽  
Underlying Mechanisms

Alternative splicing (AS) is a major engine that drives proteome diversity in mammalian genomes and is a widespread cause of human hereditary diseases. More than 95% of genes in the human genome are alternatively spliced, and the most common type of AS is the cassette exon. Recent discoveries have demonstrated that the cassette exon plays an important role in genetic diseases. To discover the formation mechanism of cassette exon events, we statistically analyze cassette exons and find that cassette exon events are strongly influenced by individual exons that are smaller in size and that have a lower GC content, more codon terminations, and weaker splice sites. We propose an improved random-forest-based hybrid method of distinguishing cassette exons from constitutive exons. Our method achieves a high accuracy in classifying cassette exons and constitutive exons and is verified to outperform previous approaches. It is anticipated that this study will facilitate a better understanding of the underlying mechanisms in cassette exons.

Optimization of K Value in KNN Algorithm for Spam and Ham Email Classification

2020 ◽  
Vol 4 (2) ◽  
pp. 377-383
Author(s):  
Eko Laksono ◽  
Achmad Basuki ◽  
Fitra Bachtiar
Keyword(s):  
Frequency Distribution ◽  
Confusion Matrix ◽  
High Accuracy ◽  
K Value ◽  
A Value ◽  
Optimal Value ◽  
Classification Evaluation ◽  
Email Spam ◽  
Email Classification

There are many cases of email abuse that have the potential to harm others. This email abuse is commonly known as spam, which contains advertisements, phishing scams, and even malware. This study purpose to know the classification of email spam with ham using the KNN method as an effort to reduce the amount of spam. KNN can classify spam or ham in an email by checking it using a different K value approach. The results of the classification evaluation using confusion matrix resulted in the KNN method with a value of K = 1 having the highest accuracy value of 91.4%. From the results of the study, it is known that the optimization of the K value in KNN using frequency distribution clustering can produce high accuracy of 100%, while k-means clustering produces an accuracy of 99%. So based on the results of the existing accuracy values, the frequency distribution clustering and k-means clustering can be used to optimize the K-optimal value of the KNN in the classification of existing spam emails.

scholarly journals Towards a contemporary approach for understanding personality pathology in developmental context: An integrative model

2021 ◽  
pp. 1-10
Author(s):  
Melody R. Altschuler ◽  
Robert F. Krueger
Keyword(s):  
Personality Disorders ◽  
Personality Pathology ◽  
Integrative Model ◽  
Research Domain Criteria ◽  
Developmental Context ◽  
Dimensional Classification ◽  
Developmental Theories ◽  
Underlying Mechanisms ◽  
Research Domain

Abstract Traditional categorical approaches to classifying personality disorders are limited in important ways, leading to a shift in the field to dimensional approaches to conceptualizing personality pathology. Different areas of psychology – personality, developmental, and psychopathology – can be leveraged to understand personality pathology by examining its structure, development, and underlying mechanisms. However, an integrative model that encompasses these distinct lines of inquiry has not yet been proposed. In order to address this gap, we review the latest evidence for dimensional classification of personality disorders based on structural models of maladaptive personality traits, provide an overview of developmental theories of pathological personality, and summarize the Research Domain Criteria (RDoC) initiative, which seeks to understand underlying mechanisms of psychopathology. We conclude by proposing an integrative model of personality pathology development that aims to elucidate the developmental pathways of personality pathology and its underlying mechanisms.

scholarly journals Vanishing GC-Rich Isochores in Mammalian Genomes

Genetics ◽  
2002 ◽  
Vol 162 (4) ◽  
pp. 1837-1847 ◽  
Author(s):  
Laurent Duret ◽  
Marie Semon ◽  
Gwenaël Piganeau ◽  
Dominique Mouchiroud ◽  
Nicolas Galtier
Keyword(s):  
Gc Content ◽  
Synonymous Substitution ◽  
Equilibrium Analysis ◽  
Large Excess ◽  
Substitution Pattern ◽  
Origin And Evolution ◽  
Evolutionary Force ◽  
Mammalian Genomes ◽  
Human Polymorphism ◽  
Probability Of Fixation

AbstractTo understand the origin and evolution of isochores—the peculiar spatial distribution of GC content within mammalian genomes—we analyzed the synonymous substitution pattern in coding sequences from closely related species in different mammalian orders. In primate and cetartiodactyls, GC-rich genes are undergoing a large excess of GC → AT substitutions over AT → GC substitutions: GC-rich isochores are slowly disappearing from the genome of these two mammalian orders. In rodents, our analyses suggest both a decrease in GC content of GC-rich isochores and an increase in GC-poor isochores, but more data will be necessary to assess the significance of this pattern. These observations question the conclusions of previous works that assumed that base composition was at equilibrium. Analysis of allele frequency in human polymorphism data, however, confirmed that in the GC-rich parts of the genome, GC alleles have a higher probability of fixation than AT alleles. This fixation bias appears not strong enough to overcome the large excess of GC → AT mutations. Thus, whatever the evolutionary force (neutral or selective) at the origin of GC-rich isochores, this force is no longer effective in mammals. We propose a model based on the biased gene conversion hypothesis that accounts for the origin of GC-rich isochores in the ancestral amniote genome and for their decline in present-day mammals.

scholarly journals A Machine Vision Approach for Bioreactor Foam Sensing

2021 ◽  
pp. 247263032110088
Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
State Of The Art ◽  
Low Cost ◽  
High Accuracy ◽  
Consumer Electronics ◽  
Learning System ◽  
Automotive Applications ◽  
Fine Grained

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

scholarly journals Deep Learning for the Classification of Non-Hodgkin Lymphoma on Histopathological Images

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2419
Author(s):  
Georg Steinbuss ◽  
Mark Kriegsmann ◽  
Christiane Zgorzelski ◽  
Alexander Brobeil ◽  
Benjamin Goeppert ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hodgkin Lymphoma ◽  
Expert Knowledge ◽  
B Cell Lymphoma ◽  
High Accuracy ◽  
Lymphocytic Leukemia ◽  
Non Hodgkin Lymphoma ◽  
Image Patches ◽  
Histopathological Images

The diagnosis and the subtyping of non-Hodgkin lymphoma (NHL) are challenging and require expert knowledge, great experience, thorough morphological analysis, and often additional expensive immunohistological and molecular methods. As these requirements are not always available, supplemental methods supporting morphological-based decision making and potentially entity subtyping are required. Deep learning methods have been shown to classify histopathological images with high accuracy, but data on NHL subtyping are limited. After annotation of histopathological whole-slide images and image patch extraction, we trained and optimized an EfficientNet convolutional neuronal network algorithm on 84,139 image patches from 629 patients and evaluated its potential to classify tumor-free reference lymph nodes, nodal small lymphocytic lymphoma/chronic lymphocytic leukemia, and nodal diffuse large B-cell lymphoma. The optimized algorithm achieved an accuracy of 95.56% on an independent test set including 16,960 image patches from 125 patients after the application of quality controls. Automatic classification of NHL is possible with high accuracy using deep learning on histopathological images and routine diagnostic applications should be pursued.

Color naming, unique hues, and hue cancellation predicted from singularities in reflection properties

2006 ◽  
Vol 23 (3-4) ◽  
pp. 331-339 ◽  
Author(s):  
DAVID L. PHILIPONA ◽  
J. KEVIN O'REGAN
Keyword(s):  
Color Naming ◽  
Surface Reflection ◽  
Unique Hues ◽  
Neuronal Representation ◽  
Underlying Mechanisms ◽  
The One ◽  
Unique Green ◽  
Psychophysical Studies ◽  
Hue Cancellation

Psychophysical studies suggest that different colors have different perceptual status: red and blue for example are thought of as elementary sensations whereas yellowish green is not. The dominant account for such perceptual asymmetries attributes them to specificities of the neuronal representation of colors. Alternative accounts involve cultural or linguistic arguments. What these accounts have in common is the idea that there are no asymmetries in the physics of light and surfaces that could underlie the perceptual structure of colors, and this is why neuronal or cultural processes must be invoked as the essential underlying mechanisms that structure color perception. Here, we suggest a biological approach for surface reflection properties that takes into account only the information about light that is accessible to an organism given the photopigments it possesses, and we show that now asymmetries appear in the behavior of surfaces with respect to light. These asymmetries provide a classification of surface properties that turns out to be identical to the one observed in linguistic color categorization across numerous cultures, as pinned down by cross cultural studies. Further, we show that data from psychophysical studies about unique hues and hue cancellation are consistent with the viewpoint that stimuli reported by observers as special are those associated with this singularity-based categorization of surfaces under a standard illuminant. The approach predicts that unique blue and unique yellow should be aligned in chromatic space while unique red and unique green should not, a fact usually conjectured to result from nonlinearities in chromatic pathways.

Lowering The Burden of Hereditary Diseases in a Traditional, Inbred Community: Ethical Aspects of Genetic Research and Its Application

1998 ◽  
Vol 11 (3-4) ◽  
pp. 391-395 ◽  
Author(s):  
Rivka Carmi ◽  
Khalil Elbedour ◽  
Dahlia Wietzman ◽  
Val Sheffield ◽  
Ilana Shoham-Vardi
Keyword(s):  
Genetic Diseases ◽  
Genetic Research ◽  
Hereditary Diseases ◽  
Successful Implementation ◽  
Culturally Appropriate ◽  
Traditional Values ◽  
Ethical Aspects ◽  
Societal Values ◽  
Traditional Societies ◽  
Remarkable Progress

The ArgumentThe remarkable progress in modern genetic technology enables the identification of genes causing devastating diseases and thereby the development of tools for prenatal diagnosis and carrier detection. To implement the results of genetic research in traditional societies, where genetic diseases are more prevalent due to inbreeding, necessitates a culturally appropriate approach that also promotes traditional and societal values important to the relevant community. This paper presents our experience with implementing the results of modern genetic research among the traditional community of the Negev Bedouin of Israel. Although the benefit of using those results for the prevention of genetic diseases seems obvious, successful implementation relies on a carefully designed educational program aimed at changing culturally related attitudes and perceptions. Such a program should attend to the needs of the community and be sensitive to its traditional values.

scholarly journals Genomic Basis of Aromatase Excess Syndrome: Recombination- and Replication-Mediated Rearrangements Leading to CYP19A1 Overexpression

2013 ◽  
Vol 98 (12) ◽  
pp. E2013-E2021 ◽  
Author(s):  
Maki Fukami ◽  
Takayoshi Tsuchiya ◽  
Heike Vollbach ◽  
Kristy A. Brown ◽  
Shuji Abe ◽  
...  
Keyword(s):  
Gc Content ◽  
Replication Timing ◽  
Nonhomologous End Joining ◽  
Coding Region ◽  
End Joining ◽  
Clinical Consequences ◽  
Underlying Mechanisms ◽  
Nonallelic Homologous Recombination ◽  
Genomic Disorders ◽  
Flanking Regions

Context: Genomic rearrangements at 15q21 have been shown to cause overexpression of CYP19A1 and resultant aromatase excess syndrome (AEXS). However, mutation spectrum, clinical consequences, and underlying mechanisms of these rearrangements remain to be elucidated. Objective: The aim of the study was to clarify such unsolved matters. Design, Setting, and Methods: We characterized six new rearrangements and investigated clinical outcome and local genomic environments of these rearrangements and of three previously reported duplications/deletions. Results: Novel rearrangements included simple duplication involving exons 1–10 of CYP19A1 and simple and complex rearrangements that presumably generated chimeric genes consisting of the coding region of CYP19A1 and promoter-associated exons of neighboring genes. Clinical severities were primarily determined by the copy number of CYP19A1 and the property of the fused promoters. Sequences at the fusion junctions suggested nonallelic homologous recombination, nonhomologous end-joining, and replication-based errors as the underlying mechanisms. The breakpoint-flanking regions were not enriched with GC content, palindromes, noncanonical DNA structures, or known rearrangement-associated motifs. The rearrangements resided in early-replicating segments. Conclusions: These results indicate that AEXS is caused by duplications involving CYP19A1 and simple and complex rearrangements that presumably lead to the usage of cryptic promoters of several neighboring genes. Our data support the notion that phenotypes depend on the dosage of CYP19A1 and the characteristics of the fused promoters. Furthermore, we show that the rearrangements in AEXS are generated by both recombination- and replication-mediated mechanisms, independent of the known rearrangement-inducing DNA features or late-replication timing. Thus, AEXS represents a unique model for human genomic disorders.

In situ classification of rocks using stand-off laser-induced breakdown spectroscopy with a compact spectrometer

2018 ◽  
Vol 33 (3) ◽  
pp. 461-467 ◽  
Author(s):  
W. T. Li ◽  
Y. N. Zhu ◽  
X. Li ◽  
Z. Q. Hao ◽  
L. B. Guo ◽  
...  
Keyword(s):  
High Accuracy ◽  
Remote Detection ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

The ASPI-LDA algorithm combined with a compact spectrometer to achieve high accuracy classification, which has a great potential for field in situ remote detection.

scholarly journals Automatic Prediction of Meningioma Grade Image Based on Data Amplification and Improved Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Hong Zhu ◽  
Qianhao Fang ◽  
Hanzhi He ◽  
Junfeng Hu ◽  
Daihong Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Accuracy ◽  
World Health ◽  
Tumor Type ◽  
Grade Prediction ◽  
Health Organization ◽  
Deep Learning Model ◽  
The Brain

Meningioma is the second most commonly encountered tumor type in the brain. There are three grades of meningioma by the standards of the World Health Organization. Preoperative grade prediction of meningioma is extraordinarily important for clinical treatment planning and prognosis evaluation. In this paper, we present a new deep learning model for assisting automatic prediction of meningioma grades to reduce the recurrence of meningioma. Our model is based on an improved LeNet-5 model of convolutional neural network (CNN) and does not require the extraction of the diseased tissue, which can greatly enhance the efficiency. To address the issue of insufficient and unbalanced clinical data of meningioma images, we use an oversampling technique which allows us to considerably improve the accuracy of classification. Experiments on large clinical datasets show that our model can achieve quite high accuracy (i.e., as high as 83.33%) for the classification of meningioma images.

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