High-stability time-domain balanced homodyne detector for ultrafast optical pulse applications

Abstract

Low-noise, efficient, phase-sensitive time-domain optical detection is essential for foundational tests of quantum physics based on optical quantum states and the realization of numerous applications ranging from quantum key distribution to coherent classical telecommunications. Stability, bandwidth, efficiency, and signal-to-noise ratio are crucial performance parameters for effective detector operation. Here we present a high-bandwidth, low-noise, ultra-stable time-domain coherent measurement scheme based on balanced homodyne detection ideally suited to characterization of quantum and classical light fields in well-defined ultrashort optical pulse modes.     

Persistence and dissemination of the multiple-antibiotic-resistance plasmid pB10 in the microbial communities of wastewater sludge microcosms

Plasmid-mediated dissemination of antibiotic resistance genes is widely recognized to take place in many environmental compartments but remains difficult to study in a global perspective because of the complexity of the environmental matrices considered and the lack of exhaustive tools. In this report, we used a molecular approach based on quantitative PCR to monitor the fate of the antibiotic resistance plasmid pB10 and its donor host in microbial communities collected from various wastewater treatment plant (WWTP) sludges and maintained in microcosms under different conditions. In aerated activated sludge microcosms, pB10 did not persist because of an apparent loss of the donor bacteria. The persistence of the donor bacteria noticeably increased in non-aerated activated sludge microcosms or after amending antibiotics (sulfamethoxazole or amoxicillin) at sub-inhibitory concentrations, but the persistence of the donor bacteria did not stimulate the dissemination of pB10. The dissemination of the plasmid appeared as an increasing plasmid to donor ratio in microcosm setups with microbial communities collected in anaerobic digesters or the spatially organized communities from fixed biofilm reactors. As a whole, the data collected suggest that some WWTP processes, more than others, may sustain microbial communities that efficiently support the dissemination of the multiple-antibiotic-resistance plasmid pB10.     

Study of pyrene biodegradation capacity in two types of solid media

Removal of pyrene, a representative PAH, was studied using laboratory tests in two different types of solid media: an organic matter collected on the surface of a vertical flow constructed wetland (VFCW) and a formulated clay silicate sand (inorganic matter). The aim of this study was to evaluate the capacity of pyrene biodegradation in these media in order to use them for treating run-off water. The sorption process, the kinetics of pyrene biodegradation and the influence of selected bacteria were also investigated. The sorption process was evaluated by adsorption isotherms and desorption kinetics using a batch equilibration method. The adsorption coefficient values of 28.8 and 2.1 for the organic and the inorganic matter respectively, confirmed the relationship of adsorption with organic carbon content. A small proportion of the sorbed pyrene was available for desorption (8% and 15% for the organic and the inorganic matter, respectively), indicating that sorption was partially irreversible, with the presence of hysteresis. For the formulated clay silicate sand inoculated with a specific bacteria (Mycobacterium sp.6PY1), selected for its ability to degrade PAHs, pyrene removal was complete in 32 days. With the organic matter, these values ranged from 40% to 95% for the different experiments, following a lag time of 3 weeks before observation of a significant degradation. Indigenous bacterial species in the organic medium had the metabolic capacity to degrade pyrene, and microbial populations pre-exposed to the PAH degraded pyrene faster than similar unexposed populations. Three metabolites of pyrene degradation by Mycobacterium were found. They accumulated in both organic and inorganic matter, indicating that the enzymes catalyzing them have slow kinetics.     

miR-23b-3p,miR-124-3p and miR-218-5p Synergistic or Additive Effects on Cellular Processes That Modulate Cervical Cancer Progression? A Molecular Balance That Needs Attention

In cervical cancer (CC), miR-23b-3p, miR-124-3p, and miR-218-5p have been found to act as tumor suppressors by regulating cellular processes related to progression and metastasis. The objective of the present review is to provide an update on the experimental evidence about the role of miR-23b-3p, miR-124-3p, and miR-218-5p in the regulation of CC progression. Additionally, we present the results of a bioinformatic analysis that suggest that these miRNAs have a somewhat redundant role in the same cellular processes that may result in a synergistic effect to promote CC progression. The results indicate that specific and common target genes for miR-23b-3p, miR-124-3p, and miR-218-5p regulate proliferation, migration, apoptosis, and angiogenesis, all processes that are related to CC maintenance and progression. Furthermore, several target genes may regulate cancer-related signaling pathways. We found that a total of 271 proteins encoded by the target mRNAs of miR-23b-3p, miR-124-3p, or miR-218-5p interact to regulate the cellular processes previously mentioned, and some of these proteins are regulated by HPV-16 E7. Taken together, information analysis indicates that miR-23b-3p, miR-124-3p, and miR-218-5p may potentiate their effects to modulate the cellular processes related to the progression and maintenance of CC with and without HPV-16 involvement.     

The Genesis distributed-memory benchmarks. Part 1: Methodology and general relativity benchmark with results for the SUPRENUM computer

This is the first of a series of papers on the Genesis distributed-memory benchmarks, which were developed under the European ESPRIT research program. The benchmarks provide a standard reference Fortran77 uniprocessor version, a distributed memory. MIMD version, and in some cases a Fortran90 version suitable for SIMD computers. The problems selected all have a scientific origin (mostly from physics or theoretical chemistry), and range from synthetic code fragments designed to measure the basic hardware properties of the computer (especially communication and synchronisation overheads), through commonly used library subroutines, to full application codes. This first paper defines the methodology to be used to analyse the benchmark results, and gives an example of a fully analysed application benchmark from General Relativity (GR1). First, suitable absolute performance metrics are carefully defined, then the performance analysis treats the execution time and absolute performance as functions of at least two variables, namely the problem size and the number of proecssors. The theoretical predictions are compared with, or fitted to, the measured results, and then used to predict (with due caution) how the performance might scale for larger problems and more processors than were actually available during the benchmarking. Benchmark measurements are given primarily for the German SUPRENUM computer, but also for the IBM 3083J, Convex C210 and a Parsys Supernode with 32 T800-20 transputers.     

Adaptation of Dynamic Data-Driven Models for Real-Time Applications: From Simulated to Real Batch Distillation Trajectories by Transfer Learning

In the absence of knowledge about challenging dynamic phenomena involved in batch distillation processes, e.g., complex flow regimes or appearing and vanishing phases, generation of accurate mechanistic models is limited. Real plant data containing this missing information is scarce, also limiting the use of data-driven models. To exploit the information contained in measurement data and a related but inaccurate first-principles model, transfer learning from simulated to real plant data is analyzed. For the use case of a batch distillation column, the adapted model provides more accurate predictions than a data-driven model trained exclusively on scarce real plant data or simulated data. Its enhanced convergence and lower computational cost make it suitable for optimization in real-time.