Antimicrobial resistance is a silent pandemic exacerbated by the uncontrolled use of antibiotics. Since the discovery of penicillin, we have been largely dependent on microbe-derived small molecules ...to treat bacterial infections. However, the golden era of antibiotics is coming to an end, as the emergence and spread of antimicrobial resistance against these antibacterial compounds are outpacing the discovery and development of new antibiotics. The current antibiotic market suffers from various shortcomings, including the absence of profitability and investment. The most important underlying issue of traditional antibiotics arises from the inherent properties of these small molecules being mostly broad-spectrum and non-programmable. As the scientific knowledge of microbes progresses, the scientific community is starting to explore entirely novel approaches to tackling antimicrobial resistance. One of the most prominent approaches is to develop next-generation antibiotics. In this review, we discuss three innovations of next-generation antibiotics compared to traditional antibiotics as specificity, evolvability, and non-immunogenicity. We present a number of potential antimicrobial agents, including bacteriophage-based therapy, CRISPR-Cas-based antimicrobials, and microbiome-derived antimicrobial agents. These alternative antimicrobial agents possess innovative properties that may overcome the inherent shortcomings of traditional antibiotics, and some of these next-generation antibiotics are not merely far-fetched ideas but are currently in clinical development. We further discuss some related issues and challenges such as infection diagnostics and regulatory frameworks that still need to be addressed to bring these next-generation antibiotics to the antibiotic market as viable products to combat antimicrobial resistance using a diversified set of strategies.
CRISPR-Cas systems are an adaptive immunity that protects prokaryotes against foreign genetic elements. Genetic templates acquired during past infection events enable DNA-interacting enzymes to ...recognize foreign DNA for destruction. Due to the programmability and specificity of these genetic templates, CRISPR-Cas systems are potential alternative antibiotics that can be engineered to self-target antimicrobial resistance genes on the chromosome or plasmid. However, several fundamental questions remain to repurpose these tools against drug-resistant bacteria. For endogenous CRISPR-Cas self-targeting, antimicrobial resistance genes and functional CRISPR-Cas systems have to co-occur in the target cell. Furthermore, these tools have to outplay DNA repair pathways that respond to the nuclease activities of Cas proteins, even for exogenous CRISPR-Cas delivery. Here, we conduct a comprehensive survey of CRISPR-Cas genomes. First, we address the co-occurrence of CRISPR-Cas systems and antimicrobial resistance genes in the CRISPR-Cas genomes. We show that the average number of these genes varies greatly by the CRISPR-Cas type, and some CRISPR-Cas types (IE and IIIA) have over 20 genes per genome. Next, we investigate the DNA repair pathways of these CRISPR-Cas genomes, revealing that the diversity and frequency of these pathways differ by the CRISPR-Cas type. The interplay between CRISPR-Cas systems and DNA repair pathways is essential for the acquisition of new spacers in CRISPR arrays. We conduct simulation studies to demonstrate that the efficiency of these DNA repair pathways may be inferred from the time-series patterns in the RNA structure of CRISPR repeats. This bioinformatic survey of CRISPR-Cas genomes elucidates the necessity to consider multifaceted interactions between different genes and systems, to design effective CRISPR-based antimicrobials that can specifically target drug-resistant bacteria in natural microbial communities.
Recent studies reveal that even the smallest genomes such as viruses evolve through complex and stochastic processes, and the assumption of independent alleles is not valid in most applications. ...Advances in sequencing technologies produce multiple time-point whole-genome data, which enable potential interactions between these alleles to be investigated empirically. To investigate these interactions, we represent alleles as distributed vectors that encode for relationships with other alleles in the course of evolution and apply artificial neural networks to time-sampled whole-genome datasets for feature learning. We build this platform using methods and algorithms derived from natural language processing (NLP), and we denote it as the nucleotide skip-gram neural network. We learn distributed vectors of alleles using the changes in allele frequency of echovirus 11 in the presence or absence of the disinfectant (ClO2) from the experimental evolution data. Results from the training using a new open-source software TensorFlow show that the learned distributed vectors can be clustered using principal component analysis and hierarchical clustering to reveal a list of non-synonymous mutations that arise on the structural protein VP1 in connection to the candidate mutation for ClO2 adaptation. Furthermore, this method can account for recombination rates by setting the extent of interactions as a biological hyper-parameter, and the results show that the most realistic scenario of mid-range interactions across the genome is most consistent with the previous studies.
This study is an investigation into the factors affecting Metro demand at a station level. These factors were examined in the Seoul metropolitan area, which is one of the most densely populated ...regions in the world. A regression analysis was conducted with weekly average of station boardings as the dependent variable. Twenty-four independent variables were chosen based on insights and findings from previous studies. They were categorized into three groups: built environment, external connectivity, and intermodal connection. Seven variables proved to be significantly associated with station boardings: employment, commercial floor area, office floor area, net population density, number of transfers, number of feeder bus lines, and a dummy variable indicating transfer stations. Furthermore, in order to identify indirect or cyclic relationships among variables in generating station boardings, which cannot be accounted for in regression analysis, a structural equation model (SEM) was adopted. Prior to the model identification, a conceptual framework was hypothesized. After testing a number of candidate SEMs within the framework, the final SEM was established. All links in the final model were statistically significant. Several links represented cyclic relationships between variables. As a result, some variables that were insignificant in the regression analysis were included in the final SEM. Variables in the external connectivity group were found to be associated with station boardings, and variables for land use and walkability proved to be indirectly related to station boardings through employment. In particular, the number of feeder bus lines was found to have a reciprocal relationship with station boardings. Several separate links represented a cyclic relationship between external connectivity and intermodal connection.
With novel developments in sequencing technologies, time‐sampled data are becoming more available and accessible. Naturally, there have been efforts in parallel to infer population genetic parameters ...from these data sets. Here, we compare and analyse four recent approaches based on the Wright–Fisher model for inferring selection coefficients (s) given effective population size (Ne), with simulated temporal data sets. Furthermore, we demonstrate the advantage of a recently proposed approximate Bayesian computation (ABC)‐based method that is able to correctly infer genomewide average Ne from time‐serial data, which is then set as a prior for inferring per‐site selection coefficients accurately and precisely. We implement this ABC method in a new software and apply it to a classical time‐serial data set of the medionigra genotype in the moth Panaxia dominula. We show that a recessive lethal model is the best explanation for the observed variation in allele frequency by implementing an estimator of the dominance ratio (h).
Abstract
We present bolometric luminosity (
L
bol
) and black hole (BH) mass (
M
BH
) estimators based on mid-infrared (MIR) continuum luminosity (hereafter,
L
MIR
) that are measured from infrared ...(IR) photometric data. The
L
MIR
-based estimators are relatively immune from dust extinction effects, hence they can be used for dust-obscured quasars. To derive the
L
bol
and
M
BH
estimators, we use unobscured quasars selected from the Sloan Digital Sky Survey (SDSS) quasar catalog, which have wide ranges of
L
bol
(10
44.62
–10
46.16
erg s
−1
) and
M
BH
(10
7.14
–10
9.69
M
⊙
). We find empirical relations between (i) continuum luminosity at 5100 Å (hereafter, L5100) and
L
MIR
; (ii)
L
bol
and
L
MIR
. Using these relations, we derive the
L
MIR
-based
L
bol
and
M
BH
estimators. We find that our estimators allow the determination of
L
bol
and
M
BH
at an accuracy of ∼0.2 dex against the fiducial estimates based on the optical properties of the unobscured quasars. We apply the
L
MIR
-based estimators to SDSS quasars at
z
≲ 0.5 including obscured ones. The ratios of
L
bol
from the
L
MIR
-based estimators to those from the optical luminosity-based estimators become larger with the amount of the dust extinction, and a non-negligible fraction (∼15%) of the SDSS quasars exhibits ratios greater than 1.5. This result suggests that dust extinction can significantly affect physical parameter derivations even for SDSS quasars, and that dust extinction needs to be carefully taken into account when deriving quasar properties.
Thermogravimetry does not give specific information on residual organic solvents in polymeric matrices unless it is hyphenated with the so-called evolved gas analysis. The purpose of this study was ...to apply, for the first time, derivative thermogravimetry (DTG) to characterize a residual solvent and a drug in poly-d,l-lactide-co-glycolide (PLGA) microspheres. Ethyl formate, an ICH class 3 solvent, was used to encapsulate progesterone into microspheres. DTG provided a distinct peak, displaying the onset and end temperatures at which ethyl formate started to evolve from to where it completely escaped out of the microspheres. DTG also gave the area and height of the solvent peak, as well as the temperature of the highest mass change rate of the microspheres. These derivative parameters allowed for the measurement of the amount of residual ethyl formate in the microspheres. Interestingly, progesterone affected not only the residual solvent amount but also these derivative parameters. Another intriguing finding was that there was a linear relationship between progesterone content and the peak height of ethyl formate. The residual solvent data calculated by DTG were quite comparable to those measured by gas chromatography. In summary, DTG could be an efficient and practical quality control tool to evaluate residual solvents and drugs in various polymeric matrices.
Recently, the optical counterpart of the gravitational-wave source GW170817 has been identified in the NGC 4993 galaxy. Together with evidence from observations in electromagnetic waves, the event ...has been suggested as a result of a merger of two neutron stars (NSs). We analyze the multi-wavelength data to characterize the host galaxy property and its distance to examine if the properties of NGC 4993 are consistent with this picture. Our analysis shows that NGC 4993 is a bulge-dominated galaxy with and a Sérsic index of for the bulge component. The spectral energy distribution from 0.15 to 24 m indicates that this galaxy has no significant ongoing star formation, a mean stellar mass of , a mean stellar age greater than ∼3 Gyr, and a metallicity of about 20%-100% of solar abundance. Optical images reveal dust lanes and extended features that suggest a past merging activity. Overall, NGC 4993 has characteristics of normal, but slightly disturbed elliptical galaxies. Furthermore, we derive the distance to NGC 4993 with the fundamental plane relation using 17 parameter sets of 7 different filters and the central stellar velocity dispersion from the literature, finding an angular diameter distance of 37.7 8.7 Mpc. NGC 4993 is similar to some host galaxies of short gamma-ray bursts (GRBs) but much different from those of long GRBs, supporting the picture of GW170817 as a result of the merger of two NSs.
The aim of this research study was to explore learning, i.e., changes in the knowledge structures of three in-service science teachers for the subject of active galaxies, through group activity ...conducted as part of a teacher training program. Qualitative methods were used in this study consisting of creating visual representations of the teachers’ knowledge structures by analyzing texts, drawings, and data obtained through observations of and interviews with the teachers. The results show that new information acquired through conversations was incorporated into the teachers’ preexisting knowledge structures through elaboration and organization. In the case of a teacher with a cognitive conflict, acceptance of the new information depended on the teacher’s level of satisfaction with the explanations given. The main influence factor for the modification of knowledge structures was the teachers’ orientation to science teaching. These findings suggest that in-service teacher training based on group activities can effectively enhance teachers’ confidence in content knowledge (CK) of recent updates in scientific research. However, some format modifications are needed to guarantee efficient learning, such as the addition of a CK representation stage and discussion items based on the preactivity knowledge structure selected through expert review.
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