Electron Donor Systems to Facilitate Development of Assays for Two Flavoproteins Involved in Tetrahydromethanopterin Biosynthesis

Methane production by archaea depends on tetrahydromethanopterin (H4MPT), a pterin-containing cofactor that carries one-carbon units. Two redox reactions within the nine steps of H4MPT side chain biosynthesis have been hypothesized. Biochemical assays have demonstrated that the archaeal iron-sulfur flavoprotein dihydromethanopterin reductase X (DmrX or MM1854) catalyzes the final reaction of the pathway, the reduction of dihydromethanopterin to H4MPT, using dithiothreitol (DTT) as an artificial electron donor. The crystal structure of DmrB, a bacterial DmrX homolog that lacks iron-sulfur clusters, has led to a proposed ping-pong mechanism of electron transfer between FMNH2 and the FMN prosthetic group of DmrB. However, an enzymatic assay to test the hypothetical DmrB mechanism is lacking because a suitable electron donor has not previously been identified. Furthermore, a second uncharacterized archaeal flavoprotein (MM1853) has been hypothesized to function in H4MPT side chain biosynthesis. In this work, to facilitate the development of assays to elucidate the functions of DmrB and MM1853, we tested a variety of electron donors, including dithiothreitol, ferredoxin, and a system consisting of NADH and an NADH-dependent flavin-reducing enzyme (Fre). Reduction of the DmrB prosthetic group (FMN) was measured as a decrease in absorbance at 460 nm. NADPH, NADH, and DTT were unable to reduce DmrB. However, NADH/Fre was able to reduce DmrB within 70 min (initial rate of 1.3 μM/min), providing the basis for a future DmrB activity assay. Carbon monoxide (CO)/CO dehydrogenase/ferredoxin reduced DmrB more rapidly within 6 min. Both electron transfer systems reduced a second flavin-containing archaeal protein MM1853, which is predicted to catalyze the third step of H4MPT biosynthesis. While NADH and NADPH were incapable of directly reducing the FMN cofactor of MM1853, DTT or NADH/Fre could eliminate the FMN peaks. These results establish the basis for new oxidoreductase assays that will facilitate testing several proposed DmrB mechanisms and defining the specific function of MM1853 in methanogen cofactor biosynthesis.

Vertical Schottky ultraviolet photodetector based on graphene and top–down fabricated GaN nanorod arrays

GaN has been widely used in the fabrication of ultraviolet photodetectors because of its outstanding properties.In this paper,we report a graphene–GaN nanorod heterostructure photodetector with fast photoresponse in the UV range.GaN nanorods were fabricated by a combination mode of dry etching and wet etching.Furthermore,a graphene–GaN nanorod heterostructure ultraviolet detector was fabricated and its photoelectric properties were measured.The device exhibits a fast photoresponse in the UV range.The rising time and falling time of the transient response were 13 and 8 ms,respectively.A high photovoltaic responsivity up to 13.9 A/W and external quantum efficiency up to 479%were realized at the UV range.The specific detectivity D*=1.44×10^(10) Jones was obtained at–1 V bias in ambient conditions.The spectral response was measured and the highest response was observed at the 360 nm band.

Single-shot BOTDA based on an optical chirp chain probe wave for distributed ultrafast measurement

Brillouin optical time-domain analysis(BOTDA)requires frequency mapping of the Brillouin spectrum to obtain environmental information(e.g.,temperature or strain)over the length of the sensing fiber,with the finite frequencysweeping time-limiting applications to only static or slowly varying strain or temperature environments.To solve this problem,we propose the use of an optical chirp chain probe wave to remove the requirement of frequency sweeping for the Brillouin spectrum,which enables distributed ultrafast strain measurement with a single pump pulse.The optical chirp chain is generated using a frequency-agile technique via a fast-frequency-changing microwave,which covers a larger frequency range around the Stokes frequency relative to the pump wave,so that a distributed Brillouin gain spectrum along the fiber is realized.Dynamic strain measurements for periodic mechanical vibration,mechanical shock,and a switch event are demonstrated at sampling rates of 25 kHz,2.5 MHz and 6.25 MHz,respectively.To the best of our knowledge,this is the first demonstration of distributed Brillouin strain sensing with a wide-dynamic range at a sampling rate of up to the MHz level.

Unstable unsteady aerodynamic modeling based on least squares support vector machines with general excitation

Common,unsteady aerodynamic modeling methods usually use wind tunnel test data from forced vibration tests to predict stable hysteresis loop.However,these methods ignore the initial unstable process of entering the hysteresis loop that exists in the actual maneuvering process of the aircraft.Here,an excitation input suitable for nonlinear system identification is introduced to model unsteady aerodynamic forces with any motion in the amplitude and frequency ranges based on the Least Squares Support Vector Machines(LS-SVMs).In the selection of the input form,avoiding the use of reduced frequency as a parameter makes the model more universal.After model training is completed,the method is applied to predict the lift coefficient,drag coefficient and pitching moment coefficient of the RAE2822 airfoil,in sine and sweep motions under the conditions of plunging and pitching at Mach number 0.8.The predicted results of the initial unstable process and the final stable process are in close agreement with the Computational Fluid Dynamics(CFD)data,demonstrating the feasibility of the model for nonlinear unsteady aerodynamics modeling and the effectiveness of the input design approach.

High-performance optical chirp chain BOTDA by using a pattern recognition algorithm and the differential pulse-width pair technique

Optical chirp chain Brillouin optical time-domain analysis(OCC-BOTDA) has the capabilities of fast measurement, high Brillouin threshold, and freedom from the nonlocal effect;at the same time, however, it also has problems introduced by transient stimulated Brillouin scattering. The influence of the transient interaction is reflected as the broadened asymmetric Brillouin spectrum, the ghost peak, and the frequency shift of the main peak. This introduces difficulty in computing the fiber Brillouin frequency shift with good measurement accuracy. Besides, the OCC modulation causes additional noise due to the uneven amplitude response for different frequency components. In this work, we propose a high-performance OCC-BOTDA using the principal component analysis(PCA) based pattern recognition algorithm and differential pulse-width pair(DPP) technique.After building the Brillouin spectrum database(i.e., all patterns), the fiber intrinsic Brillouin frequency shift can be recognized by the PCA algorithm from a nonstandard Brillouin spectrum profile, resulting in good measurement accuracy. Meanwhile, the DPP technique, subtracting between two Brillouin signals generated by two wide-width pump pulses, is utilized to reduce the OCC modulation noise and avoid the pulse self-phase modulation effect in long-range BOTDA sensing. In the experiment, a temperature measurement with 1.3 MHz measurement precision, 4 m spatial resolution, and 5 s measurement time is achieved over a 100 km single-mode fiber.

A Pilot Protection Scheme of DC Lines for MMC-HVDC Grid Using Random Matrix

The over-current capacity of half-bridge modular multi-level converter(MMC)is quite weak,which requests protections to detect faults accurately and reliably in several milliseconds after DC faults.The sensitivity and reliability of the existing schemes are vulnerable to high resistance and data errors.To improve the insufficiencies,this paper proposes a pilot protection scheme by using the random matrix for DC lines in the symmetrical bipolar MMC high-voltage direct current(HVDC)grid.Firstly,the 1-mode voltage time-domain characteristics of the line end,DC bus,and adjacent line end are analyzed by the inverse Laplace transform to find indicators of fault direction.To combine the actual model with the data-driven method,the methods to construct the data expansion matrix and to calculate additional noise are proposed.Then,the mean spectral radiuses of two random matrices are used to detect fault directions,and a novel pilot protection criterion is proposed.The protection scheme only needs to transmit logic signals,decreasing the communication burden.It performs well in high-resistance faults,abnormal data errors,measurement errors,parameters errors,and different topology conditions.Numerous simulations in PSCAD/EMTDC confirm the effectiveness and reliability of the proposed protection scheme.

Large-scale programmable assembly of functional microcomponents for advanced electronics via light-regulated adhesion and polymer growth

Large-area,programmable assembly of diverse micro-objects onto arbitrary substrates is a fundamental yet challenging task.Herein a simple wafer-level micro-assembly technique based on the light-triggered change in both surface topography and interfacial adhesion of a soft photo-sensitive polymer is proposed.In particular,the light-regulated polymer growth creates locally indented and elevated zones on the stamp surface.The light-mediated adhesion reduction,on the other hand,facilitates the inks to be released from the polymer.The interplay of these two effects makes it feasible for the programmable assembly of ultra-small components onto various substrates coated with supplementary adhesive layers.The fidelity of this technique is validated by assembling diverse materials and functional devices,with the printing size up to 4-inch.This work provides a rational strategy for large-scale and programmable assembly of diverse delicate micro-objects,bypassing the common issues of some existing techniques such as poor transfer uniformity,small printing area,and high cost.

Aggravation of membrane fouling and methane leakage by a three-phase separator in an external anaerobic ceramic membrane bioreactor

The three-phase separator is a critical component of high-rate anaerobic bioreactors due to its significant contribution in separation of biomass, wastewater, and biogas. However, its role in an anaerobic membrane bioreactor is still not clear. In this study, the distinction between an external anaerobic ceramic membrane bioreactor (EAnCMBR) unequipped (R1) and equipped (R2) with a three-phase separator was investigated in terms of treatment performance, membrane fouling, extracellular polymers of sludge, and microbial community structure. The results indicate that the COD removal efficiencies of Rl and R2 were 98.2%±0.4% and 98.1%±0.4%, respectively, but the start-up period of R2 was slightly delayed. Moreover, the membrane fouling rate of R2 (0.4 kPa/d) was higher than that of Rl (0.2 kPa/d). Interestingly, the methane leakage from R2 (0.1 L/d) was 20 times higher than that from Rl (0.005 L/d). The results demonstrate that the three-phase separator aggravated the membrane fouling rate and methane leakage in the EAnCMBR. Therefore, this study provides a novel perspective on the effects of a three-phase separator in an EAnCMBR.

Advanced deep learning methods for molecular property prediction

The prediction of molecular properties is a crucial task in the field of drug discovery.Computational methods that can accurately predict molecular properties can significantly accelerate the drug discovery process and reduce the cost of drug discovery.In recent years,iterative updates in computing hardware and the rise of deep learning have created a new and effective path for molecular property prediction.Deep learning methods can leverage the vast amount of data accumulated over the years in drug discovery and do not require complex feature engineering.in this review,we summarize molecular representations and commonly used datasets in molecular property prediction models and present advanced deep learning methods for molecular property prediction,including state-of-the-art deep learning networks such as graph neural networks and Transformer-based models,as well as state-of-the-art deep learning strategies such as 3D pre-train,contrastive learning,multi-task learning,transfer learning,and meta-learning.We also point out some critical issues such as lack of datasets,low information utilization,and lack of specificity for diseases.