multiple targets
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2022 ◽  
Vol 7 ◽  
pp. 3
Author(s):  
Christina W. Obiero ◽  
Wilson Gumbi ◽  
Stella Mwakio ◽  
Hope Mwangudzah ◽  
Anna C. Seale ◽  
...  

Background: Early onset neonatal sepsis (EONS) typically begins prior to, during or soon after birth and may be rapidly fatal. There is paucity of data on the aetiology of EONS in sub-Saharan Africa due to limited diagnostic capacity in this region, despite the associated significant mortality and long-term neurological impairment. Methods: We compared pathogens detected in cord blood samples between neonates admitted to hospital with possible serious bacterial infection (pSBI) in the first 48 hours of life (cases) and neonates remaining well (controls). Cord blood was systematically collected at Kilifi County Hospital (KCH) from 2011-2016, and later tested for 21 bacterial, viral and protozoal targets using multiplex PCR via TaqMan Array Cards (TAC). Results: Among 603 cases (101 [17%] of whom died), 179 (30%) tested positive for ≥1 target and 37 (6.1%) tested positive for multiple targets. Klebsiella oxytoca, Escherichia coli/Shigella spp., Pseudomonas aeruginosa, and Streptococcus pyogenes were commonest. Among 300 controls, 79 (26%) tested positive for ≥1 target, 11 (3.7%) were positive for multiple targets, and K. oxytoca and P. aeruginosa were most common. Cumulative odds ratios across controls: cases (survived): cases (died) were E. coli/Shigella spp. 2.6 (95%CI 1.6-4.4); E. faecalis 4.0 (95%CI 1.1-15); S. agalactiae 4.5 (95%CI 1.6-13); Ureaplasma spp. 2.9 (95%CI 1.3-6.4); Enterovirus 9.1 (95%CI 2.3-37); and Plasmodium spp. 2.9 (95%CI 1.4-6.2). Excluding K. oxytoca and P. aeruginosa as likely contaminants, aetiology was attributed in 9.4% (95%CI 5.1-13) cases using TAC. Leading pathogen attributions by TAC were E. coli/Shigella spp. (3.5% (95%CI 1.7-5.3)) and Ureaplasma spp. (1.7% (95%CI 0.5-3.0)). Conclusions: Cord blood sample may be useful in describing EONS pathogens at birth, but more specific tests are needed for individual diagnosis. Careful sampling of cord blood using aseptic techniques is crucial to minimize contamination. In addition to culturable bacteria, Ureaplasma and Enterovirus were causes of EONS.


PLoS Genetics ◽  
2021 ◽  
Vol 17 (12) ◽  
pp. e1009960
Author(s):  
Fang Zheng ◽  
Yi-Ji Liao ◽  
Mu-Yan Cai ◽  
Tian-Hao Liu ◽  
Shu-Peng Chen ◽  
...  

Automatica ◽  
2021 ◽  
Vol 134 ◽  
pp. 109934
Author(s):  
Shida Cao ◽  
Rui Li ◽  
Yingjing Shi ◽  
Yongduan Song
Keyword(s):  

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yasir Munir ◽  
Muhammad Umar Aftab ◽  
Danish Shehzad ◽  
Ali M. Aseere ◽  
Habib Shah

Localization of multiple targets is a challenging task due to immense complexity regarding data fusion received at the sensors. In this context, we propose an algorithm to solve the problem for an unknown number of emitters without prior knowledge to address the data fusion problem. The proposed technique combines the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurement data fusion which further uses the maximum likelihood of the measurements received at each sensor of the surveillance region. The measurement grids of the sensors are used to perform data association. The simulation results show that the proposed algorithm outperforms the multipass grid search and further effectively eliminated the ghost targets created due to the fusion of measurements received at each sensor. Moreover, the proposed algorithm reduces the computational complexity compared to other existing algorithms as it does not use repeated steps for convergence or any biological evolutions. Furthermore, the experimental testing of the proposed technique was executed successfully for tracking multiple targets in different scenarios passively.


2021 ◽  
Vol 15 (04) ◽  
Author(s):  
Wenchao Li ◽  
Wentao Zhang ◽  
Shirui Yang ◽  
Yulin Huang ◽  
Jianyu Yang

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Xuhan Liu ◽  
Kai Ye ◽  
Herman W. T. van Vlijmen ◽  
Michael T. M. Emmerich ◽  
Adriaan P. IJzerman ◽  
...  

AbstractIn polypharmacology drugs are required to bind to multiple specific targets, for example to enhance efficacy or to reduce resistance formation. Although deep learning has achieved a breakthrough in de novo design in drug discovery, most of its applications only focus on a single drug target to generate drug-like active molecules. However, in reality drug molecules often interact with more than one target which can have desired (polypharmacology) or undesired (toxicity) effects. In a previous study we proposed a new method named DrugEx that integrates an exploration strategy into RNN-based reinforcement learning to improve the diversity of the generated molecules. Here, we extended our DrugEx algorithm with multi-objective optimization to generate drug-like molecules towards multiple targets or one specific target while avoiding off-targets (the two adenosine receptors, A1AR and A2AAR, and the potassium ion channel hERG in this study). In our model, we applied an RNN as the agent and machine learning predictors as the environment. Both the agent and the environment were pre-trained in advance and then interplayed under a reinforcement learning framework. The concept of evolutionary algorithms was merged into our method such that crossover and mutation operations were implemented by the same deep learning model as the agent. During the training loop, the agent generates a batch of SMILES-based molecules. Subsequently scores for all objectives provided by the environment are used to construct Pareto ranks of the generated molecules. For this ranking a non-dominated sorting algorithm and a Tanimoto-based crowding distance algorithm using chemical fingerprints are applied. Here, we adopted GPU acceleration to speed up the process of Pareto optimization. The final reward of each molecule is calculated based on the Pareto ranking with the ranking selection algorithm. The agent is trained under the guidance of the reward to make sure it can generate desired molecules after convergence of the training process. All in all we demonstrate generation of compounds with a diverse predicted selectivity profile towards multiple targets, offering the potential of high efficacy and low toxicity.


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