scholarly journals Microscopy and ICT vs LAMP assay in the diagnosis of malaria: A real-world time and cost-effective analysis

Pathology ◽  
2020 ◽  
Vol 52 ◽  
pp. S110-S111
Author(s):  
Jeremy Er ◽  
Chris Barnes
2017 ◽  
Vol 26 (1) ◽  
pp. 53-62 ◽  
Author(s):  
Richard Bell ◽  
Braden Te Ao ◽  
Natasha Ironside ◽  
Adam Bartlett ◽  
John A. Windsor ◽  
...  

1999 ◽  
Vol 2 (3) ◽  
pp. 184 ◽  
Author(s):  
EA Alemao ◽  
PS Cady ◽  
HM Phatak ◽  
VL Culbertson

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alisa Alekseenko ◽  
Donal Barrett ◽  
Yerma Pareja-Sanchez ◽  
Rebecca J. Howard ◽  
Emilia Strandback ◽  
...  

AbstractRT-LAMP detection of SARS-CoV-2 has been shown to be a valuable approach to scale up COVID-19 diagnostics and thus contribute to limiting the spread of the disease. Here we present the optimization of highly cost-effective in-house produced enzymes, and we benchmark their performance against commercial alternatives. We explore the compatibility between multiple DNA polymerases with high strand-displacement activity and thermostable reverse transcriptases required for RT-LAMP. We optimize reaction conditions and demonstrate their applicability using both synthetic RNA and clinical patient samples. Finally, we validate the optimized RT-LAMP assay for the detection of SARS-CoV-2 in unextracted heat-inactivated nasopharyngeal samples from 184 patients. We anticipate that optimized and affordable reagents for RT-LAMP will facilitate the expansion of SARS-CoV-2 testing globally, especially in sites and settings where the need for large scale testing cannot be met by commercial alternatives.


Author(s):  
Claudia Flores-Saviaga ◽  
Ricardo Granados ◽  
Liliana Savage ◽  
Lizbeth Escobedo ◽  
Saiph Savage

Crowdsourced content creation like articles or slogans can be powered by crowds of volunteers or workers from paid task markets. Volunteers often have expertise and are intrinsically motivated, but are a limited resource, and are not always reliably available. On the other hand, paid crowd workers are reliably available, can be guided to produce high-quality content, but cost money. How can these different populations of crowd workers be leveraged together to power cost-effective yet high-quality crowd-powered content-creation systems? To answer this question, we need to understand the strengths and weaknesses of each. We conducted an online study where we hired paid crowd workers and recruited volunteers from social media to complete three content creation tasks for three real-world non-profit organizations that focus on empowering women. These tasks ranged in complexity from simply generating keywords or slogans to creating a draft biographical article. Our results show that paid crowds completed work and structured content following editorial guidelines more effectively. However, volunteer crowds provide content that is more original. Based on the findings, we suggest that crowd-powered content-creation systems could gain the best of both worlds by leveraging volunteers to scaffold the direction that original content should take; while having paid crowd workers structure content and prepare it for real world use.


2008 ◽  
Vol 41 (2) ◽  
pp. 126-134 ◽  
Author(s):  
A.M.E.C. Barreto ◽  
K. Takei ◽  
Sabino E.C. ◽  
M.A.O. Bellesa ◽  
N.A. Salles ◽  
...  

Author(s):  
Hao Zhang ◽  
Liangxiao Jiang ◽  
Wenqiang Xu

Crowdsourcing services provide a fast, efficient, and cost-effective means of obtaining large labeled data for supervised learning. Ground truth inference, also called label integration, designs proper aggregation strategies to infer the unknown true label of each instance from the multiple noisy label set provided by ordinary crowd workers. However, to the best of our knowledge, nearly all existing label integration methods focus solely on the multiple noisy label set itself of the individual instance while totally ignoring the intercorrelation among multiple noisy label sets of different instances. To solve this problem, a multiple noisy label distribution propagation (MNLDP) method is proposed in this study. MNLDP first transforms the multiple noisy label set of each instance into its multiple noisy label distribution and then propagates its multiple noisy label distribution to its nearest neighbors. Consequently, each instance absorbs a fraction of the multiple noisy label distributions from its nearest neighbors and yet simultaneously maintains a fraction of its own original multiple noisy label distribution. Promising experimental results on simulated and real-world datasets validate the effectiveness of our proposed method.


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