Molecular control of sucrose utilization in Escherichia coli W, an efficient sucrose-utilizing strain

Sucrose is an industrially important carbon source for microbial fermentation. Sucrose utilization in Escherichia coli, however, is poorly understood, and most industrial strains cannot utilize sucrose. The roles of the chromosomally encoded sucrose catabolism (csc) genes in E. coli W were examined by knockout and overexpression experiments. At low sucrose concentrations, the csc genes are repressed and cells cannot grow. Removal of either the repressor protein (cscR) or the fructokinase (cscK) gene facilitated derepression. Furthermore, combinatorial knockout of cscR and cscK conferred an improved growth rate on low sucrose. The invertase (cscA) and sucrose transporter (cscB) genes are essential for sucrose catabolism in E. coli W, demonstrating that no other genes can provide sucrose transport or inversion activities. However, cscK is not essential for sucrose utilization. Fructose is excreted into the medium by the cscK-knockout strain in the presence of high sucrose, whereas at low sucrose (when carbon availability is limiting), fructose is utilized by the cell. Overexpression of cscA, cscAK, or cscAB could complement the W Delta cscRKAB knockout mutant or confer growth on a K-12 strain which could not naturally utilize sucrose. However, phenotypic stability and relatively good growth rates were observed in the K-12 strain only when overexpressing cscAB, and full growth rate complementation in W Delta cscRKA Balso required cscAB. Our understanding of sucrose utilization can be used to improve E. coli Wand engineer sucrose utilization in strains which do not naturally utilize sucrose, allowing substitution of sucrose for other, less desirable carbon sources in industrial fermentations

Metal oxide-based gas sensor array for VOCs determination in complex mixtures using machine learning.

Detection of volatile organic compounds (VOCs) from the breath is becoming a viable route for the early detection of diseases non-invasively. This paper presents a sensor array of 3 component metal oxides that give maximal cross-sensitivity and can successfully use machine learning methods to identify four distinct VOCs in a mixture. The metal oxide sensor array comprises NiO-Au (ohmic), CuO-Au (Schottky), and ZnO-Au (Schottky) sensors made by the DC reactive sputtering method and having a film thickness of 80-100 nm. The NiO and CuO films have ultrafine particle sizes of < 50 nm and rough surface texture, while ZnO films consist of nanoscale platelets. This array was subjected to various VOC concentrations, including ethanol, acetone, toluene, and chloroform, one by one and in a pair/mix of gases. Thus, the response values show severe interference and departure from commonly observed power law behavior. The dataset obtained from individual gases and their mixtures were analyzed using multiple machine learning algorithms, such as Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree, Linear Regression, Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Artificial Neural Network, and Support Vector Machine. KNN and RF have shown more than 99% accuracy in classifying different varying chemicals in the gas mixtures. In regression analysis, KNN has delivered the best results with an R2 value of more than 0.99 and LOD of 0.012 ppm, 0.015 ppm, 0.014 ppm, and 0.025 ppm for predicting the concentrations of acetone, toluene, ethanol, and chloroform, respectively, in complex mixtures. Therefore, it is demonstrated that the array utilizing the provided algorithms can classify and predict the concentrations of the four gases simultaneously for disease diagnosis and treatment monitoring

The genome sequence of E. coli W (ATCC 9637): comparative genome analysis and an improved genome-scale reconstruction of E. coli

Background: Escherichia coli is a model prokaryote, an important pathogen, and a key organism for industrial biotechnology. E. coli W (ATCC 9637), one of four strains designated as safe for laboratory purposes, has not been sequenced. E. coli W is a fast-growing strain and is the only safe strain that can utilize sucrose as a carbon source. Lifecycle analysis has demonstrated that sucrose from sugarcane is a preferred carbon source for industrial bioprocesses

Erratum: "Searches for Gravitational Waves from Known Pulsars at Two Harmonics in 2015–2017 LIGO Data" (2019, ApJ, 879, 10)

This is a correction for 2019 ApJ 879 1

All-sky search for continuous gravitational waves from isolated neutron stars in the early O3 LIGO data

We report on an all-sky search for continuous gravitational waves in the frequency band 20-2000 Hz and with a frequency time derivative in the range of [-1.0,+0.1]×10-8 Hz/s. Such a signal could be produced by a nearby, spinning and slightly nonaxisymmetric isolated neutron star in our Galaxy. This search uses the LIGO data from the first six months of Advanced LIGO's and Advanced Virgo's third observational run, O3. No periodic gravitational wave signals are observed, and 95% confidence-level (C.L.) frequentist upper limits are placed on their strengths. The lowest upper limits on worst-case (linearly polarized) strain amplitude h0 are ∼1.7×10-25 near 200 Hz. For a circularly polarized source (most favorable orientation), the lowest upper limits are ∼6.3×10-26. These strict frequentist upper limits refer to all sky locations and the entire range of frequency derivative values. For a population-averaged ensemble of sky locations and stellar orientations, the lowest 95% C.L. upper limits on the strain amplitude are ∼1.4×10-25. These upper limits improve upon our previously published all-sky results, with the greatest improvement (factor of ∼2) seen at higher frequencies, in part because quantum squeezing has dramatically improved the detector noise level relative to the second observational run, O2. These limits are the most constraining to date over most of the parameter space searched

Search for gravitational waves from Scorpius X-1 in the second Advanced LIGO observing run with an improved hidden Markov model

We present results from a semicoherent search for continuous gravitational waves from the low-mass x-ray binary Scorpius X-1, using a hidden Markov model (HMM) to track spin wandering. This search improves on previous HMM-based searches of LIGO data by using an improved frequency domain matched filter, the J-statistic, and by analyzing data from Advanced LIGO’s second observing run. In the frequency range searched, from 60 to 650 Hz, we find no evidence of gravitational radiation. At 194.6 Hz, the most sensitive search frequency, we report an upper limit on gravitational wave strain (at 95% confidence) of h95%0=3.47×10−25 when marginalizing over source inclination angle. This is the most sensitive search for Scorpius X-1, to date, that is specifically designed to be robust in the presence of spin wandering

Search for gravitational waves from Scorpius X-1 in the second Advanced LIGO observing run with an improved hidden Markov model

We present results from a semicoherent search for continuous gravitational waves from the low-mass x-ray binary Scorpius X-1, using a hidden Markov model (HMM) to track spin wandering. This search improves on previous HMM-based searches of LIGO data by using an improved frequency domain matched filter, the J-statistic, and by analyzing data from Advanced LIGO's second observing run. In the frequency range searched, from 60 to 650 Hz, we find no evidence of gravitational radiation. At 194.6 Hz, the most sensitive search frequency, we report an upper limit on gravitational wave strain (at 95% confidence) of h095%=3.47×10-25 when marginalizing over source inclination angle. This is the most sensitive search for Scorpius X-1, to date, that is specifically designed to be robust in the presence of spin wandering. © 2019 American Physical Society