Cost-effective novel flexible cell-level systolic architecture for high throughput implementation of 2-D FIR filters

1996 ◽  
Vol 143 (6) ◽  
pp. 436 ◽  
Author(s):  
B.K. Mohanty ◽  
P.K. Meher
Keyword(s):  
High Throughput ◽  
Cost Effective ◽  
Fir Filters ◽  
Cell Level ◽  
Systolic Architecture

scholarly journals Mito Hacker: a set of tools to enable high-throughput analysis of mitochondrial network morphology

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ali Rohani ◽  
Jennifer A. Kashatus ◽  
Dane T. Sessions ◽  
Salma Sharmin ◽  
David F. Kashatus
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Data Science ◽  
Structural Information ◽  
Morphological Changes ◽  
Mitochondrial Morphology ◽  
Single Cell Level ◽  
Mitochondrial Network ◽  
Cell Level ◽  
Wide Range

Abstract Mitochondria are highly dynamic organelles that can exhibit a wide range of morphologies. Mitochondrial morphology can differ significantly across cell types, reflecting different physiological needs, but can also change rapidly in response to stress or the activation of signaling pathways. Understanding both the cause and consequences of these morphological changes is critical to fully understanding how mitochondrial function contributes to both normal and pathological physiology. However, while robust and quantitative analysis of mitochondrial morphology has become increasingly accessible, there is a need for new tools to generate and analyze large data sets of mitochondrial images in high throughput. The generation of such datasets is critical to fully benefit from rapidly evolving methods in data science, such as neural networks, that have shown tremendous value in extracting novel biological insights and generating new hypotheses. Here we describe a set of three computational tools, Cell Catcher, Mito Catcher and MiA, that we have developed to extract extensive mitochondrial network data on a single-cell level from multi-cell fluorescence images. Cell Catcher automatically separates and isolates individual cells from multi-cell images; Mito Catcher uses the statistical distribution of pixel intensities across the mitochondrial network to detect and remove background noise from the cell and segment the mitochondrial network; MiA uses the binarized mitochondrial network to perform more than 100 mitochondria-level and cell-level morphometric measurements. To validate the utility of this set of tools, we generated a database of morphological features for 630 individual cells that encode 0, 1 or 2 alleles of the mitochondrial fission GTPase Drp1 and demonstrate that these mitochondrial data could be used to predict Drp1 genotype with 87% accuracy. Together, this suite of tools enables the high-throughput and automated collection of detailed and quantitative mitochondrial structural information at a single-cell level. Furthermore, the data generated with these tools, when combined with advanced data science approaches, can be used to generate novel biological insights.

scholarly journals HiSpike Method for High-Throughput Cost Effective Sequencing of the SARS-CoV-2 Spike Gene

2022 ◽  
Vol 8 ◽  
Author(s):  
Ephraim Fass ◽  
Gal Zizelski Valenci ◽  
Mor Rubinstein ◽  
Paul J. Freidlin ◽  
Shira Rosencwaig ◽  
...  
Keyword(s):  
Real Time ◽  
High Throughput ◽  
Cost Effective ◽  
Developed Countries ◽  
Spike Protein ◽  
Medical Community ◽  
Effective Vaccine ◽  
Gene Variants ◽  
Spike Gene ◽  
Field Samples

The changing nature of the SARS-CoV-2 pandemic poses unprecedented challenges to the world's health systems. Emerging spike gene variants jeopardize global efforts to produce immunity and reduce morbidity and mortality. These challenges require effective real-time genomic surveillance solutions that the medical community can quickly adopt. The SARS-CoV-2 spike protein mediates host receptor recognition and entry into the cell and is susceptible to generation of variants with increased transmissibility and pathogenicity. The spike protein is the primary target of neutralizing antibodies in COVID-19 patients and the most common antigen for induction of effective vaccine immunity. Tight monitoring of spike protein gene variants is key to mitigating COVID-19 spread and generation of vaccine escape mutants. Currently, SARS-CoV-2 sequencing methods are labor intensive and expensive. When sequence demands are high sequencing resources are quickly exhausted. Consequently, most SARS-CoV-2 strains are sequenced in only a few developed countries and rarely in developing regions. This poses the risk that undetected, dangerous variants will emerge. In this work, we present HiSpike, a method for high-throughput cost effective targeted next generation sequencing of the spike gene. This simple three-step method can be completed in < 30 h, can sequence 10-fold more samples compared to conventional methods and at a fraction of their cost. HiSpike has been validated in Israel, and has identified multiple spike variants from real-time field samples including Alpha, Beta, Delta and the emerging Omicron variants. HiSpike provides affordable sequencing options to help laboratories conserve resources for widespread high-throughput, near real-time monitoring of spike gene variants.

Cost effective microsatellite isolation and genotyping by high throughput sequencing

2019 ◽  
Vol 47 (2) ◽  
pp. 190 ◽  
Author(s):  
Henrik Krehenwinkel ◽  
Susanne Meese ◽  
Christoph Mayer ◽  
Jasmin Ruch ◽  
Jutta Schneider ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Cost Effective ◽  
Microsatellite Isolation

scholarly journals Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application

2020 ◽  
Vol 12 (6) ◽  
pp. 998 ◽  
Author(s):  
GyuJin Jang ◽  
Jaeyoung Kim ◽  
Ju-Kyung Yu ◽  
Hak-Jin Kim ◽  
Yoonha Kim ◽  
...  
Keyword(s):  
Plant Breeding ◽  
Unmanned Aerial Vehicle ◽  
High Throughput ◽  
New Technologies ◽  
Cost Effective ◽  
New Wave ◽  
Breeding Programs ◽  
Modern Agriculture ◽  
Aerial Vehicle ◽  
High Throughput Phenotyping

Utilization of remote sensing is a new wave of modern agriculture that accelerates plant breeding and research, and the performance of farming practices and farm management. High-throughput phenotyping is a key advanced agricultural technology and has been rapidly adopted in plant research. However, technology adoption is not easy due to cost limitations in academia. This article reviews various commercial unmanned aerial vehicle (UAV) platforms as a high-throughput phenotyping technology for plant breeding. It compares known commercial UAV platforms that are cost-effective and manageable in field settings and demonstrates a general workflow for high-throughput phenotyping, including data analysis. The authors expect this article to create opportunities for academics to access new technologies and utilize the information for their research and breeding programs in more workable ways.

scholarly journals 230 Genome editing applications in plants: high-throughput CRISPR/Cas editing for crop improvement

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 56-56
Author(s):  
Michael Thomson
Keyword(s):  
Genome Editing ◽  
High Throughput ◽  
Positional Cloning ◽  
Gene Editing ◽  
Crop Improvement ◽  
Cost Effective ◽  
Gene Families ◽  
Ease Of Use ◽  
Plant Genome ◽  
Wide Range

Abstract The precision and ease of use of CRISPR nucleases, such as Cas9 and Cpf1, for plant genome editing has the potential to accelerate a wide range of applications for crop improvement. For upstream research on gene discovery and validation, rapid gene knock-outs can enable testing of single genes and multi-gene families for functional effects. Large chromosomal deletions can test the function of tandem gene arrays and assist with positional cloning of QTLs by helping to narrow down the target region. Nuclease-deactivated Cas9 fusion proteins with transcriptional activators and repressors can be used to up and down-regulate gene expression. Even more promising, gene insertions and allele replacements can provide the opportunity to rapidly test the effects of different alleles at key loci in the same genetic background, providing a more precise alternative to marker-assisted backcrossing. Recently, Texas A&M AgriLife Research has supported the development of a Crop Genome Editing Lab at Texas A&M working towards optimizing a high-throughput gene editing pipeline and providing an efficient and cost-effective gene editing service for research and breeding groups. The lab is using rice as a model to test and optimize new approaches aimed towards overcoming current bottlenecks. For example, a wealth of genomics data from the rice community enables the development of novel approaches to predict which genes and target modifications may be most beneficial for crop improvement, taking advantage of known major genes, high-resolution GWAS data, multiple high-quality reference genomes, transcriptomics data, and resequencing data from the 3,000 Rice Genomes Project. Current projects have now expanded to work across multiple crops to provide breeding and research groups with a rapid gene editing pipeline to test candidate genes in their programs, with the ultimate goal of developing nutritious, high-yielding, stress-tolerant crops for the future.

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