Typhoon‐induced, highly nonlinear internal solitary waves off the east coast of Korea

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94585/1/grl22467.pd

Mathematical vibration modeling for an electrostatic precipitator system

The rapping acceleration of collecting plates in electrostatic precipitator system determines the dust- rapping performance of electromagnetic vibration exciter. To maximize the acceleration, the resonance phenomena needs to be driven by matching the mechanical natural frequency of the electrostatic precipitator system and the input frequency of electric current which energizes the electromagnetic vibrator. In this paper, the dust collecting plates and the electromagnetic vibration exciter in electrostatic precipitator system are vibration-modeled mathematically to characterize the resonance frequency. The effective mass and stiffness for each mode of the collecting plates are calculated using finite elements analysis and the natural frequency are computed by the method of least error square. In addition, the effective mass and stiffness of the exciter are computed. Then, the whole electrostatic precipitator system is analyzed. A frequency response analysis based on a sine sweep signal experiment is performed on a prototype for verification of calculated theoretical resonance frequency

Precision genome engineering with programmable DNA-nicking enzymes

Zinc finger nucleases (ZFNs) are powerful tools of genome engineering but are limited by their inevitable reliance on error-prone nonhomologous end-joining (NHEJ) repair of DNA double-strand breaks (DSBs), which gives rise to randomly generated, unwanted small insertions or deletions (indels) at both on-target and off-target sites. Here, we present programmable DNA-nicking enzymes (nickases) that produce single-strand breaks (SSBs) or nicks, instead of DSBs, which are repaired by error-free homologous recombination (HR) rather than mutagenic NHEJ. Unlike their corresponding nucleases, zinc finger nickases allow site-specific genome modifications only at the on-target site, without the induction of unwanted indels. We propose that programmable nickases will be of broad utility in research, medicine, and biotechnology, enabling precision genome engineering in any cell or organism.

DNA-free two-gene knockout in Chlamydomonas reinhardtii via CRISPR-Cas9 ribonucleoproteins

Microalgae are versatile organisms capable of converting CO2, H2O, and sunlight into fuel and chemicals for domestic and industrial consumption. Thus, genetic modifications of microalgae for enhancing photosynthetic productivity, and biomass and bio-products generation are crucial for both academic and industrial applications. However, targeted mutagenesis in microalgae with CRISPR-Cas9 is limited. Here we report, a one-step transformation of Chlamydomonas reinhardtii by the DNA-free CRISPR-Cas9 method rather than plasmids that encode Cas9 and guide RNAs. Outcome was the sequential CpFTSY and ZEP two-gene knockout and the generation of a strain constitutively producing zeaxanthin and showing improved photosynthetic productivity.

Risk Factors of Emergence Agitation in Adults Undergoing General Anesthesia for Nasal Surgery

ObjectivesTo identify the incidence and the risk factors of emergence agitation in adults undergoing general anesthesia for nasal surgery.MethodsWe retrospectively examined 792 patients aged ≥18 years who underwent general anesthesia for elective nasal surgery between July 2012 and August 2013. Patients in the postanesthesia care unit with a Richmond Agitation Sedation Scale≥+1 at any time were considered to have emergence agitation.ResultsThe overall incidence of emergence agitation is 22.2%. From multivariate regression analysis, the following six variables were found to be significantly associated with emergence agitation (P<0.05): younger age, recent smoking, sevoflurane anesthesia, postoperative pain on the numerical rating scale (NRS)≥5, presence of a tracheal tube, and presence of a urinary catheter. Presence of a tracheal tube was the greatest risk factor, increasing the risk of developing emergence agitation by approximately fivefold (odds ratio, 5.448; 95% confidence interval, 2.973 to 9.982). Younger age was also a strong risk factor (odds ratio, 0.975 for each 1-year increase; 95% confidence interval, 0.964 to 0.987). Current smoking, sevoflurane anesthesia, postoperative pain of NRS≥5, and the presence of a urinary catheter nearly doubled the risk of emergence agitation.ConclusionEmergence agitation following general anesthesia is a common complication in adult nasal surgery patients. To reduce the occurrence and consequences of agitation episodes, elimination of the associated risk factors is necessary, especially in at-risk patients

Potential benefit of a screening tool in selecting head-injured children aged 36 months or younger who can cooperate in computed tomography

Purpose We aimed to investigate the benefit of a screening tool in selecting head-injured children who can potentially cooperate with computed tomography (CT). Methods The study population consisted of head-injured children aged 36 months or younger who visited the emergency department (ED) and underwent CT from January 2013 through December 2020. Procedural sedation and analgesia (PSA) using per os chloral hydrate or per rectal thiopental was implemented to children presumed less cooperative for CT as per a clinical screening tool for cooperative candidates for CT (e.g., ability to lie still on bed for 10 seconds without a guardian). According to the PSA and the first attempt success of CT, we compared baseline characteristics, CT findings, clinically important traumatic brain injury, ED length of stay (EDLOS), and ED disposition. Results Among the 247 children, PSA was used in 102 (41.3%). The PSA group showed a higher proportion of 3-36 months of age (PSA, 96.1% vs. non-PSA, 82.8%; P = 0.001) and longer median EDLOS (PSA, 127.0 [interquartile range, 101.0-172.0] vs. non-PSA, 85.0 minutes [63.0-130.0]; P < 0.001). The number of children undergoing the first attempt success was 213 (86.2%) without a difference in both groups (PSA, 84.3% vs. non-PSA, 87.6%; P = 0.645). In the 213 children, the implementation of PSA was associated with longer median EDLOS (PSA, 121.0 [99.0-156.0] vs. non-PSA, 77.0 minutes [60.0-122.0]; P < 0.001). In the non-PSA group (n = 145), the first attempt failure was associated with the presence of skull fracture (success, 7.1% vs. failure, 27.8%; P = 0.009). Conclusion This study suggests a potential benefit of the screening tool in selecting cooperative candidates for CT, i.e., those who could forgo PSA using oral chloral hydrate or per rectal thiopental, in young children with head injury

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As remote sensing and GIS have been considered to be essential technologies for disasters information production, researches on developing methods for analyzing spatial data, and developing new technologies for such purposes, have been actively conducted. Especially, it is assumed that the use of remote sensing and GIS for disaster management will continue to develop thanks to the launch of recent satellite constellations, the use of various remote sensing platforms, the improvement of acquired data processing and storage capacity, and the advancement of artificial intelligence technology. This spatial issue presents 10 research papers regarding ship detection, building information extraction, ocean environment monitoring, flood monitoring, forest fire detection, and decision making using remote sensing and GIS technologies, which can be applied at the disaster prediction, monitoring and response stages. It is anticipated that the papers published in this special issue could be a valuable reference for developing technologies for disaster management and academic advancement of related fields. ??????????????? GIS??? ????????? ???????????? ????????? ?????? ????????? ???????????? ????????? ?????? ????????? ???????????? ??????????????? ?????? ?????? ??? ?????? ????????? ?????? ????????? ????????? ???????????? ??????. ?????? ??????????????? ????????? ????????? ???????????????????????? ??????, ????????? ????????? ?????? ??? ?????? ????????? ??????, ???????????? ????????? ?????? ????????? ?????? ?????? ????????? ????????????????????? GIS ????????? ????????? ?????? ????????? ????????? ????????? ??????. ?????? ??????????????? ????????? ??????, ?????? ????????? ?????? ???????????? ????????????, ????????? ??????, ???????????? ??????, ????????????, ????????????, ????????? ?????? ????????? ??????????????????????????? ????????? ??????????????? GIS ????????? ????????? ????????? ????????? 10?????? ????????? ???????????????. ?????? ???????????? ????????????????????? ?????? ?????? ????????? ????????? ?????? ?????? ????????? ????????? ????????? ???????????? ??? ????????? ????????????