Understanding MOF nucleation from solution with Evolving Graphs

Molecular modeling is ordinarily employed to understand the synthesis of complex materials. In this work, we investigate the collective assembly of building units that have been experimentally observed to initiate Metal-Organic Framework (MOF) nucleation. MOFs exhibit attractive characteristics such as remarkable surface area and diverse porosities, however, a mechanistic understanding of their synthesis and scale-up remains underexplored due to the complicated nature of the building block interactions. Here, we tackle this problem with large-scale molecular dynamics simulations under a variety of synthesis conditions and mixture compositions. We observe that the connectivity of building units, as well as their level of crystalline order and fractal dimension, largely vary depending on the synthesis conditions. However, these properties naturally emerge when interpreting the self-assembly process of MOF nuclei as the time-evolution of an undirected graph. The results show that solution-induced conformational complexity and ionic concentration have a dramatic effect on the morphology of clusters emerging during assembly, such diversity is captured by key features of the graph representation. Principal Component Analysis (PCA) on graph properties successfully deconvolutes MOF self-assembly to be characterized by a small number of molecular descriptors, such as average coordination number between half-secondary building units (half-SBUs) and fractal dimension, which can be followed by time-resolved spectroscopy. We conclude that graph theory can be used to understand complex processes such as MOF nucleation by providing molecular descriptors accessible by both simulation and experiment.

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A Hybrid Bioprinting Approach for Scale-Up Tissue Fabrication

Journal of Manufacturing Science and Engineering ◽

10.1115/1.4028511 ◽

2014 ◽

Vol 136 (6) ◽

Cited By ~ 54

Author(s):

Yin Yu ◽

Yahui Zhang ◽

Ibrahim T. Ozbolat

Keyword(s):

Tissue Engineering ◽

Self Assembly ◽

Large Scale ◽

Vascular System ◽

Scale Up ◽

Organ Printing ◽

Promising Application ◽

Organ Models

Tissue engineering has been focused on the fabrication of vascularized 3D tissue for decades. Most recently, bioprinting, especially tissue and organ printing, has shown great potential to enable automated robotic-based fabrication of 3D vascularized tissues and organs that are readily available for in vitro studies or in vivo transplantation. Studies have demonstrated the feasibility of the tissue printing process through bioprinting of scaffold-free cellular constructs that are able to undergo self-assembly for tissue formation; however, they are still limited in size and thickness due to the lack of a vascular network. In this paper, we present a framework concept for bioprinting 3D large-scale tissues with a perfusable vascular system in vitro to preserve cell viability and tissue maturation. With the help of a customized Multi-Arm Bioprinter (MABP), we lay out a hybrid bioprinting system to fabricate scale-up tissues and organ models and demonstrated envision its promising application for in vitro tissue engineering and its potential for therapeutic purposes with our proof of concept study.

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Solid Lipid Nanoparticles: A Promising Drug Delivery Technology

International Journal of Pharmaceutical Sciences and Nanotechnology ◽

10.37285/ijpsn.2009.2.2.3 ◽

2009 ◽

Vol 2 (2) ◽

pp. 509-516

Author(s):

S. Pragati ◽

S. Kuldeep ◽

S. Ashok ◽

M. Satheesh

Keyword(s):

Drug Delivery ◽

Solid Lipid Nanoparticles ◽

Large Scale ◽

Colloidal Particles ◽

Scale Up ◽

Lipid Nanoparticles ◽

Scale Production ◽

Research Activity ◽

New Approach ◽

Solid Lipid

One of the situations in the treatment of disease is the delivery of efficacious medication of appropriate concentration to the site of action in a controlled and continual manner. Nanoparticle represents an important particulate carrier system, developed accordingly. Nanoparticles are solid colloidal particles ranging in size from 1 to 1000 nm and composed of macromolecular material. Nanoparticles could be polymeric or lipidic (SLNs). Industry estimates suggest that approximately 40% of lipophilic drug candidates fail due to solubility and formulation stability issues, prompting significant research activity in advanced lipophile delivery technologies. Solid lipid nanoparticle technology represents a promising new approach to lipophile drug delivery. Solid lipid nanoparticles (SLNs) are important advancement in this area. The bioacceptable and biodegradable nature of SLNs makes them less toxic as compared to polymeric nanoparticles. Supplemented with small size which prolongs the circulation time in blood, feasible scale up for large scale production and absence of burst effect makes them interesting candidates for study. In this present review this new approach is discussed in terms of their preparation, advantages, characterization and special features.

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Recombinant Protein Production in Microalgae: Emerging Trends

Protein and Peptide Letters ◽

10.2174/0929866526666191014124855 ◽

2020 ◽

Vol 27 (2) ◽

pp. 105-110 ◽

Cited By ~ 4

Author(s):

Niaz Ahmad ◽

Muhammad Aamer Mehmood ◽

Sana Malik

Keyword(s):

Chloroplast Genome ◽

Recombinant Proteins ◽

Large Scale ◽

Higher Plants ◽

Scale Up ◽

Chloroplast Transformation ◽

Point Of View ◽

Nuclear Transformation ◽

Scale Production ◽

Algal Chloroplast

: In recent years, microalgae have emerged as an alternative platform for large-scale production of recombinant proteins for different commercial applications. As a production platform, it has several advantages, including rapid growth, easily scale up and ability to grow with or without the external carbon source. Genetic transformation of several species has been established. Of these, Chlamydomonas reinhardtii has become significantly attractive for its potential to express foreign proteins inexpensively. All its three genomes – nuclear, mitochondrial and chloroplastic – have been sequenced. As a result, a wealth of information about its genetic machinery, protein expression mechanism (transcription, translation and post-translational modifications) is available. Over the years, various molecular tools have been developed for the manipulation of all these genomes. Various studies show that the transformation of the chloroplast genome has several advantages over nuclear transformation from the biopharming point of view. According to a recent survey, over 100 recombinant proteins have been expressed in algal chloroplasts. However, the expression levels achieved in the algal chloroplast genome are generally lower compared to the chloroplasts of higher plants. Work is therefore needed to make the algal chloroplast transformation commercially competitive. In this review, we discuss some examples from the algal research, which could play their role in making algal chloroplast commercially successful.

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Content-Based Estimation of Brain MRI Tilt in Three Orthogonal Directions

Journal of Digital Imaging ◽

10.1007/s10278-020-00400-7 ◽

2021 ◽

Author(s):

Pooja Prabhu ◽

A. K. Karunakar ◽

Sanjib Sinha ◽

N. Mariyappa ◽

G. K. Bhargava ◽

...

Keyword(s):

Large Scale ◽

Brain Mri ◽

Similarity Measures ◽

Principal Component ◽

Brain Anatomy ◽

Morphological Operations ◽

Mr Images ◽

Tilt Correction ◽

Brain Mr Images ◽

The Brain

AbstractIn a general scenario, the brain images acquired from magnetic resonance imaging (MRI) may experience tilt, distorting brain MR images. The tilt experienced by the brain MR images may result in misalignment during image registration for medical applications. Manually correcting (or estimating) the tilt on a large scale is time-consuming, expensive, and needs brain anatomy expertise. Thus, there is a need for an automatic way of performing tilt correction in three orthogonal directions (X, Y, Z). The proposed work aims to correct the tilt automatically by measuring the pitch angle, yaw angle, and roll angle in X-axis, Z-axis, and Y-axis, respectively. For correction of the tilt around the Z-axis (pointing to the superior direction), image processing techniques, principal component analysis, and similarity measures are used. Also, for correction of the tilt around the X-axis (pointing to the right direction), morphological operations, and tilt correction around the Y-axis (pointing to the anterior direction), orthogonal regression is used. The proposed approach was applied to adjust the tilt observed in the T1- and T2-weighted MR images. The simulation study with the proposed algorithm yielded an error of 0.40 ± 0.09°, and it outperformed the other existing studies. The tilt angle (in degrees) obtained is ranged from 6.2 ± 3.94, 2.35 ± 2.61, and 5 ± 4.36 in X-, Z-, and Y-directions, respectively, by using the proposed algorithm. The proposed work corrects the tilt more accurately and robustly when compared with existing studies.

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Principal component analysis tomography in near-infrared integral field spectroscopy of young stellar objects – I. Revisiting the high-mass protostar W33A

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stab358 ◽

2021 ◽

Vol 503 (1) ◽

pp. 270-291

Author(s):

F Navarete ◽

A Damineli ◽

J E Steiner ◽

R D Blum

Keyword(s):

Large Scale ◽

Near Infrared ◽

Rotation Axis ◽

Principal Component ◽

Young Stellar Objects ◽

High Mass ◽

Continuum Emission ◽

Rotating Disc ◽

Stellar Objects ◽

K Band

ABSTRACT W33A is a well-known example of a high-mass young stellar object showing evidence of a circumstellar disc. We revisited the K-band NIFS/Gemini North observations of the W33A protostar using principal components analysis tomography and additional post-processing routines. Our results indicate the presence of a compact rotating disc based on the kinematics of the CO absorption features. The position–velocity diagram shows that the disc exhibits a rotation curve with velocities that rapidly decrease for radii larger than 0.1 arcsec (∼250 au) from the central source, suggesting a structure about four times more compact than previously reported. We derived a dynamical mass of 10.0$^{+4.1}_{-2.2}$ $\rm {M}_\odot$ for the ‘disc + protostar’ system, about ∼33 per cent smaller than previously reported, but still compatible with high-mass protostar status. A relatively compact H2 wind was identified at the base of the large-scale outflow of W33A, with a mean visual extinction of ∼63 mag. By taking advantage of supplementary near-infrared maps, we identified at least two other point-like objects driving extended structures in the vicinity of W33A, suggesting that multiple active protostars are located within the cloud. The closest object (Source B) was also identified in the NIFS field of view as a faint point-like object at a projected distance of ∼7000 au from W33A, powering extended K-band continuum emission detected in the same field. Another source (Source C) is driving a bipolar $\rm {H}_2$ jet aligned perpendicular to the rotation axis of W33A.

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Hyperspectral Image Classification with Localized Graph Convolutional Filtering

Remote Sensing ◽

10.3390/rs13030526 ◽

2021 ◽

Vol 13 (3) ◽

pp. 526

Author(s):

Shengliang Pu ◽

Yuanfeng Wu ◽

Xu Sun ◽

Xiaotong Sun

Keyword(s):

Hyperspectral Image ◽

Principal Component ◽

Representation Learning ◽

Classification Performance ◽

Hyperspectral Data ◽

Spectral Graph Theory ◽

Feature Reduction ◽

Graph Representation ◽

Novel Method ◽

Local Graph

The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be how to convert hyperspectral data into irregular domains from regular grids. In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. First, we conducted principal component analysis (PCA) preprocessing to create localized hyperspectral data cubes with unsupervised feature reduction. These feature cubes combined with localized adjacent matrices were fed into the popular graph convolution network in a standard supervised learning paradigm. Finally, we succeeded in analyzing diversified land covers by considering local graph structure with graph convolutional filtering. Experiments on real hyperspectral datasets demonstrated that the presented method offers promising classification performance compared with other popular competitors.

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A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽

10.3390/cancers13092111 ◽

2021 ◽

Vol 13 (9) ◽

pp. 2111

Author(s):

Bo-Wei Zhao ◽

Zhu-Hong You ◽

Lun Hu ◽

Zhen-Hao Guo ◽

Lei Wang ◽

...

Keyword(s):

Drug Target ◽

Large Scale ◽

Computational Models ◽

Structural Information ◽

Characteristic Curve ◽

Representation Learning ◽

Graph Representation ◽

Convolutional Network ◽

Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

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Expanding the boundaries of learning

Phi Delta Kappan ◽

10.1177/00317217211013930 ◽

2021 ◽

Vol 102 (8) ◽

pp. 8-13

Author(s):

Thomas Hatch

Keyword(s):

Instructional Practices ◽

New Hampshire ◽

Large Scale ◽

Educational Experiences ◽

Scale Up ◽

Project Based Learning

Taking advantage of the possibilities for learning outside of school requires us to build on what we know about why it is so hard to sustain and scale up unconventional educational experiences within conventional schools. To illustrate the opportunities and challenges, Thomas Hatch describes a large-scale approach to project-based learning developed in a camp in New Hampshire and incorporated in a Brooklyn school, a trip-based program in Detroit, and Singapore’s systemic embrace of learning outside school. By understanding the conditions that can sustain alternative instructional practices, educators can find places to challenge the boundaries of schooling and create visions of the possible that exceed current constraints.

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Farmers’ Perceptions of Commercial Insect-Based Feed for Sustainable Livestock Production in Kenya

Sustainability ◽

10.3390/su13105359 ◽

2021 ◽

Vol 13 (10) ◽

pp. 5359

Author(s):

Afrika Onguko Okello ◽

Jonathan Makau Nzuma ◽

David Jakinda Otieno ◽

Michael Kidoido ◽

Chrysantus Mbi Tanga

Keyword(s):

Food Systems ◽

Large Scale ◽

Principal Component ◽

Farm Income ◽

Purchase Decisions ◽

Wealth Status ◽

Feed Formulation ◽

Alternative Protein ◽

Livestock Products ◽

The Impact

The utilization of insect-based feeds (IBF) as an alternative protein source is increasingly gaining momentum worldwide owing to recent concerns over the impact of food systems on the environment. However, its large-scale adoption will depend on farmers’ acceptance of its key qualities. This study evaluates farmer’s perceptions of commercial IBF products and assesses the factors that would influence its adoption. It employs principal component analysis (PCA) to develop perception indices that are subsequently used in multiple regression analysis of survey data collected from a sample of 310 farmers. Over 90% of the farmers were ready and willing to use IBF. The PCA identified feed performance, social acceptability of the use of insects in feed formulation, feed versatility and marketability of livestock products reared on IBF as the key attributes that would inform farmers’ purchase decisions. Awareness of IBF attributes, group membership, off-farm income, wealth status and education significantly influenced farmers’ perceptions of IBF. Interventions such as experimental demonstrations that increase farmers’ technical knowledge on the productivity of livestock fed on IBF are crucial to reducing farmers’ uncertainties towards acceptability of IBF. Public partnerships with resource-endowed farmers and farmer groups are recommended to improve knowledge sharing on IBF.

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