Advancing Bayesian Optimization via Learning Correlated Latent Space

Bayesian optimization is a powerful method for optimizing black-box functions with limited function evaluations. Recent works have shown that optimization in a latent space through deep generative models such as variational autoencoders leads to effective and efficient Bayesian optimization for structured or discrete data. However, as the optimization does not take place in the input space, it leads to an inherent gap that results in potentially suboptimal solutions. To alleviate the discrepancy, we propose Correlated latent space Bayesian Optimization (CoBO), which focuses on learning correlated latent spaces characterized by a strong correlation between the distances in the latent space and the distances within the objective function. Specifically, our method introduces Lipschitz regularization, loss weighting, and trust region recoordination to minimize the inherent gap around the promising areas. We demonstrate the effectiveness of our approach on several optimization tasks in discrete data, such as molecule design and arithmetic expression fitting, and achieve high performance within a small budget

Enterobacter cloacae sacroiliitis with actue respiratory distress syndrome in an adolescent

Enterobacter cloacae has emerged as an important nosocomial pathogen, but is rarely a cause of sacroiliitis. Herein, we present the first reported case of Enterobacter cloacae sacroiliitis associated with sepsis and acute respiratory distress syndrome (ARDS). A previously healthy 14-year-old boy presented with low-grade fever and pain in the left side of the hip that was aggravated by walking. Pelvic computed tomography (CT) showed normal findings, and the patient received supportive care for transient synovitis with no antibiotics. However, there was no clinical improvement. On the third day of hospitalization, magnetic resonance imaging of the hip revealed findings compatible with sacroiliitis, for which vancomycin and ceftriaxone were administered. The patient suddenly developed high fever with dyspnea. Chest radiography and CT findings and a PaO2/FiO2 ratio <200 mmHg were suggestive of ARDS; the patient subsequently received ventilatory support and low-dose methylprednisolone infusions. Within one week, defervescence occurred, and the patient was able to breathe on his own. Following the timely recognition of, and therapeutic challenge to, ARDS, and after 6 weeks of parenteral antimicrobial therapy, the patient was discharged in good health with no complications

Nanovesicle-Based Bioelectronic Nose for the Diagnosis of Lung Cancer from Human Blood

A human nose-mimetic diagnosis system that can distinguish the odor of a lung cancer biomarker, heptanal, from human blood is presented. Selective recognition of the biomarker is mimicked in the human olfactory system. A specific olfactory receptor recognizing the chemical biomarker is first selected through screening a library of human olfactory receptors (hORs). The selected hOR is expressed on the membrane of human embryonic kidney (HEK)-293 cells. Nanovesicles containing the hOR on the membrane are produced from these cells, and are then used for the functionalization of single-walled carbon nanotubes. This strategy allows the development of a sensitive and selective nanovesicle-based bioelectronic nose (NvBN). The NvBN is able to selectively detect heptanal at a concentration as low as 1 x 10(-14) m, a sufficient level to distinguish the blood of a lung cancer patient from the blood of a healthy person. In actual experiments, NvBN could detect an extremely small increase in the amount of heptanal from human blood plasma without any pretreatment processes. This result offers a rapid and easy method to analyze chemical biomarkers from human blood in real-time and to diagnose lung cancer.OAIID:oai:osos.snu.ac.kr:snu2014-01/102/0000002410/3SEQ:3PERF_CD:SNU2014-01EVAL_ITEM_CD:102USER_ID:0000002410ADJUST_YN:YEMP_ID:A002014DEPT_CD:458CITE_RATE:0FILENAME:3. (2014.3) nanovesicle-based bioelectronic nose for the diagnosis of.pdfDEPT_NM:화학생물공학부SCOPUS_YN:NCONFIRM:

Real-time monitoring of geosmin and 2-methylisoborneol, representative odor compounds in water pollution using bioelectronic nose with human-like performance

A bioelectronic nose for the real-time assessment of water quality was constructed with human olfactory receptor (hOR) and single-walled carbon nanotube field-effect transistor (swCNT-FET). Geosmin (GSM) and 2-methylisoborneol (MIB), mainly produced by bacteria, are representative odor compounds and also indicators of contamination in the water supply system. For the screening of hORs which respond to these compounds, we performed CRE-luciferase assays of the two odorants in heterologous cell system. Human OR51S1 for GSM and OR3A4 for MIB were selected, and nanovesicles expressing the hORs on surface were produced from HEK-293 cell. Carbon nanotube field-effect transistor was functionalized with the nanovesicles. The bioelectronic nose was able to selectively detect GSM and MIB at concentrations as low as a 10 ng L−1. Furthermore, detection of these compounds from the real samples such as tap water, bottled water and river water was available without any pretreatment processes.OAIID:oai:osos.snu.ac.kr:snu2015-01/102/0000002410/12ADJUST_YN:YEMP_ID:A002014DEPT_CD:458CITE_RATE:6.409DEPT_NM:화학생물공학부SCOPUS_YN:NCONFIRM:

Family-selective detection of antibiotics using antibody-functionalized carbon nanotube sensors

The development of a rapid and sensitive detection method for well-known, yet abused, antibiotics has been an important issue for food safety and environmental protection. This paper presents a simple and sensitive method for the specific or family-selective detection of antibiotics using carbon nanotube (CNT)-based sensors. Herein, CNT-based sensor transducers were functionalized with the single-chain variable-fragment (scFv) of antibodies that can selectively bind to a specific antibiotic or the certain family of antibiotics. Our CNT-based sensors functionalized with A2 scFv or F9 scFv exhibited the specific detection of enrofloxacin or the family-selective detection of fluoroquinolone-based antibiotics, respectively, in a real-time manner. This simple but efficient strategy can be utilized for various applications in the fields of food safety and environmental protection. (C) 2012 Elsevier B.V. All rights reserved

Bioelectronic Nose Using Odorant Binding Protein-Derived Peptide and Carbon Nanotube Field-Effect Transistor for the Assessment of <i>Salmonella</i> Contamination in Food

<i>Salmonella</i> infection is the one of the major causes of food borne illnesses including fever, abdominal pain, diarrhea, and nausea. Thus, early detection of <i>Salmonella</i> contamination is important for our healthy life. Conventional detection methods for the food contamination have limitations in sensitivity and rapidity; thus, the early detection has been difficult. Herein, we developed a bioelectronic nose using a carbon nanotube (CNT) field-effect transistor (FET) functionalized with <i>Drosophila</i> odorant binding protein (OBP)-derived peptide for easy and rapid detection of <i>Salmonella</i> contamination in ham. 3-Methyl-1-butanol is known as a specific volatile organic compound, generated from the ham contaminated with <i>Salmonella</i>. We designed and synthesized the peptide based on the sequence of the <i>Drosophila</i> OBP, LUSH, which specifically binds to alcohols. The C-terminus of the synthetic peptide was modified with three phenylalanine residues and directly immobilized onto CNT channels using the π–π interaction. The p-type properties of FET were clearly maintained after the functionalization using the peptide. The biosensor detected 1 fM of 3-methyl-1-butanol with high selectivity and successfully assessed <i>Salmonella</i> contamination in ham. These results indicate that the bioelectronic nose can be used for the rapid detection of <i>Salmonella</i> contamination in food