Three-dimensional Structural Variations and Functional Implications in α-Amylases

Abstract Three-dimensional (3D) genome organization is tightly coupled with gene regulation in various biological processes and diseases. In cancer, various types of large-scale genomic rearrangements can disrupt the 3D genome, leading to oncogenic gene expression. However, unraveling the pathogenicity of the 3D cancer genome remains a challenge since closer examinations have been greatly limited due to the lack of appropriate tools specialized for disorganized higher-order chromatin structure. Here, we updated a 3D-genome Interaction Viewer and database named 3DIV by uniformly processing ∼230 billion raw Hi-C reads to expand our contents to the 3D cancer genome. The updates of 3DIV are listed as follows: (i) the collection of 401 samples including 220 cancer cell line/tumor Hi-C data, 153 normal cell line/tissue Hi-C data, and 28 promoter capture Hi-C data, (ii) the live interactive manipulation of the 3D cancer genome to simulate the impact of structural variations and (iii) the reconstruction of Hi-C contact maps by user-defined chromosome order to investigate the 3D genome of the complex genomic rearrangement. In summary, the updated 3DIV will be the most comprehensive resource to explore the gene regulatory effects of both the normal and cancer 3D genome. ‘3DIV’ is freely available at http://3div.kr.

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SVFX: a machine-learning framework to quantify the pathogenicity of structural variants

10.1101/739474 ◽

2019 ◽

Cited By ~ 2

Author(s):

Sushant Kumar ◽

Arif Harmanci ◽

Jagath Vytheeswaran ◽

Mark B. Gerstein

Keyword(s):

Machine Learning ◽

Large Scale ◽

Three Dimensional ◽

Point Mutations ◽

Dimensional Structure ◽

Rapid Decline ◽

Ras Signaling ◽

Cancer Genes ◽

Structural Variations ◽

Pathogenic Variants

AbstractA rapid decline in sequencing cost has made large-scale genome sequencing studies feasible. One of the fundamental goals of these studies is to catalog all pathogenic variants. Numerous methods and tools have been developed to interpret point mutations and small insertions and deletions. However, there is a lack of approaches for identifying pathogenic genomic structural variations (SVs). That said, SVs are known to play a crucial role in many diseases by altering the sequence and three-dimensional structure of the genome. Previous studies have suggested a complex interplay of genomic and epigenomic features in the emergence and distribution of SVs. However, the exact mechanism of pathogenesis for SVs in different diseases is not straightforward to decipher. Thus, we built an agnostic machine-learning-based workflow, called SVFX, to assign a “pathogenicity score” to somatic and germline SVs in various diseases. In particular, we generated somatic and germline training models, which included genomic, epigenomic, and conservation-based features for SV call sets in diseased and healthy individuals. We then applied SVFX to SVs in six different cancer cohorts and a cardiovascular disease (CVD) cohort. Overall, SVFX achieved high accuracy in identifying pathogenic SVs. Moreover, we found that predicted pathogenic SVs in cancer cohorts were enriched among known cancer genes and many cancer-related pathways (including Wnt signaling, Ras signaling, DNA repair, and ubiquitin-mediated proteolysis). Finally, we note that SVFX is flexible and can be easily extended to identify pathogenic SVs in additional disease cohorts.

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The three-dimensional structure of an eukaryotic glutamine synthetase: Functional implications of its oligomeric structure

Journal of Structural Biology ◽

10.1016/j.jsb.2006.06.003 ◽

2006 ◽

Vol 156 (3) ◽

pp. 469-479 ◽

Cited By ~ 37

Author(s):

Oscar Llorca ◽

Marco Betti ◽

José M. González ◽

Alfonso Valencia ◽

Antonio J. Márquez ◽

...

Keyword(s):

Glutamine Synthetase ◽

Three Dimensional ◽

Dimensional Structure ◽

Oligomeric Structure ◽

Three Dimensional Structure ◽

Functional Implications

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Automatic Diagnosis of Alzheimer’s Disease Using Deep Learning Techniques

NeuroQuantology ◽

10.14704/nq.2021.19.11.nq21183 ◽

2021 ◽

Vol 19 (11) ◽

pp. 126-140

Author(s):

Zahraa S. Aaraji ◽

Hawraa H. Abbas

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Data Analysis ◽

Binary Classification ◽

Brain Mri ◽

Three Dimensional ◽

Structural Variations ◽

Automatic Feature Extraction ◽

Multi Class Classification

Neuroimaging data analysis has attracted a great deal of attention with respect to the accurate diagnosis of Alzheimer’s disease (AD). Magnetic Resonance Imaging (MRI) scanners have thus been commonly used to study AD-related brain structural variations, providing images that demonstrate both morphometric and anatomical changes in the human brain. Deep learning algorithms have already been effectively exploited in other medical image processing applications to identify features and recognise patterns for many diseases that affect the brain and other organs; this paper extends on this to describe a novel computer aided software pipeline for the classification and early diagnosis of AD. The proposed method uses two types of three-dimensional Convolutional Neural Networks (3D CNN) to facilitate brain MRI data analysis and automatic feature extraction and classification, so that pre-processing and post-processing are utilised to normalise the MRI data and facilitate pattern recognition. The experimental results show that the proposed approach achieves 97.5%, 82.5%, and 83.75% accuracy in terms of binary classification AD vs. cognitively normal (CN), CN vs. mild cognitive impairment (MCI) and MCI vs. AD, respectively, as well as 85% accuracy for multi class-classification, based on publicly available data sets from the Alzheimer’s disease Neuroimaging Initiative (ADNI).

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Microribbons composed of directionally self-assembled nanoflakes as highly stretchable ionic neural electrodes

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2003079117 ◽

2020 ◽

Vol 117 (26) ◽

pp. 14667-14675 ◽

Cited By ~ 2

Author(s):

Mingchao Zhang ◽

Rui Guo ◽

Ke Chen ◽

Yiliang Wang ◽

Jiali Niu ◽

...

Keyword(s):

Self Assembly ◽

Structural Variation ◽

Three Dimensional ◽

Biological Tissues ◽

Superior Performance ◽

Ionic Conductors ◽

Structural Variations ◽

Neural Electrodes ◽

Self Assembled

Many natural materials possess built-in structural variation, endowing them with superior performance. However, it is challenging to realize programmable structural variation in self-assembled synthetic materials since self-assembly processes usually generate uniform and ordered structures. Here, we report the formation of asymmetric microribbons composed of directionally self-assembled two-dimensional nanoflakes in a polymeric matrix during three-dimensional direct-ink printing. The printed ribbons with embedded structural variations show site-specific variance in their mechanical properties. Remarkably, the ribbons can spontaneously transform into ultrastretchable springs with controllable helical architecture upon stimulation. Such springs also exhibit superior nanoscale transport behavior as nanofluidic ionic conductors under even ultralarge tensile strains (>1,000%). Furthermore, to show possible real-world uses of such materials, we demonstrate in vivo neural recording and stimulation using such springs in a bullfrog animal model. Thus, such springs can be used as neural electrodes compatible with soft and dynamic biological tissues.

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Different Benzendicarboxylate-Directed Structural Variations and Properties of Four New Porous Cd(II)-Pyridyl-Triazole Coordination Polymers

Frontiers in Chemistry ◽

10.3389/fchem.2020.616468 ◽

2020 ◽

Vol 8 ◽

Author(s):

Ying Zhao ◽

Jin Jing ◽

Ning Yan ◽

Min-Le Han ◽

Guo-Ping Yang ◽

...

Keyword(s):

Solid State ◽

Coordination Polymers ◽

Three Dimensional ◽

Two Dimensional ◽

Gas Sorption ◽

Structural Variations ◽

Benzenedicarboxylic Acid ◽

Topological Framework ◽

The Stability

Four new different porous crystalline Cd(II)-based coordination polymers (CPs), i. e., [Cd(mdpt)2]·2H2O (1), [Cd2(mdpt)2(m-bdc)(H2O)2] (2), [Cd(Hmdpt)(p-bdc)]·2H2O (3), and [Cd3(mdpt)2(bpdc)2]·2.5NMP (4), were obtained successfully by the assembly of Cd(II) ions and bitopic 3-(3-methyl-2-pyridyl)-5-(4-pyridyl)-1,2,4-triazole (Hmdpt) in the presence of various benzendicarboxylate ligands, i.e., 1,3/1,4-benzenedicarboxylic acid (m-H2bdc, p-H2bdc) and biphenyl-4,4′-bicarboxylate (H2bpdc). Herein, complex 1 is a porous 2-fold interpenetrated four-connected 3D NbO topological framework based on the mdpt− ligand; 2 reveals a two-dimensional (2D) hcb network. Interestingly, 3 presents a three-dimensional (3D) rare interpenetrated double-insertion supramolecular net via 2D ···ABAB··· layers and can be viewed as an fsh topological net, while complex 4 displays a 3D sqc117 framework. Then, the different gas sorption performances were carried out carefully for complexes 1 and 4, the results of which showed 4 has preferable sorption than that of 1 and can be the potential CO2 storage and separation material. Furthermore, the stability and luminescence of four complexes were performed carefully in the solid state.

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A Novel Method for Electrospinning Nanofibrous 3-D Structures

Fibers ◽

10.3390/fib8050027 ◽

2020 ◽

Vol 8 (5) ◽

pp. 27 ◽

Cited By ~ 3

Author(s):

Daniela Lubasova ◽

Anil N. Netravali

Keyword(s):

Three Dimensional ◽

Development Trend ◽

Proof Of Concept ◽

Structural Variations ◽

Fiber Volume ◽

Electrospinning Technique ◽

Web Structure ◽

Fiber Spacing ◽

Novel Method ◽

Volume Unit

The fast and precise fabrication of three-dimensional (3-D) structures made of nanofibers is an important development trend in the electrospinning technique. This paper describes a new and facile method of electrospinning to fabricate nanofibrous 3-D structures. The nanofibrous 3-D structures can be engineered to have the desired layer thicknesses, where the fiber spacing, density (i.e., fiber volume/unit volume), as well as shape of the structure may be controlled. While innumerable structural variations are possible with this method, this paper discusses, as proof-of-concept, a few cases that illustrate how 3-D nanofiber webs can be made for filtration application. Computerized automation of the method will make it possible to build almost any 3-D web structure suitable for a myriad of applications including ultra-light-weight insulation and scaffolds for hydrogel preparation and tissue.

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On Monitoring Structural Changes of Anisotropic Tissues by Means of Statistical Analysis of Ultrasonic Images

Ultrasonic Imaging ◽

10.1177/016173469301500404 ◽

1993 ◽

Vol 15 (4) ◽

pp. 324-334

Author(s):

C. Pecorari

Keyword(s):

Structural Changes ◽

Early Stage ◽

Three Dimensional ◽

Biological Tissues ◽

Small Time ◽

Image Texture ◽

Structural Variations ◽

Geometrical Properties ◽

The Time Domain ◽

Ultrasonic Images

An investigation into statistical properties of ultrasonic image texture from three-dimensional clusters of anisotropic scatterers is carried out. The structural properties of the clusters are modeled after those of soft biological tissues, such as skeletal muscle tissues, both in their healthy condition and at the early stage of degenerative diseases. The average axial autocorrelation function of the intensity of the image texture is used to characterize and monitor changes of the geometrical properties of the tissue components. A distinct local increase of the autocorrelation is observed within a range of small time shifts, and it is explained in terms of the structure of the time-domain backscattered signal from each individual scatterer. It is shown that such an increase is sensitive to structural variations of the cluster similar to those occurring at the early stage of several muscular diseases.

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