Computational Analysis Suggests That Lyssavirus Glycoprotein Gene Plays a Minor Role in Viral Adaptation

The Lyssavirus glycoprotein (G) is a membrane protein responsible for virus entry and protective immune responses. To explore possible roles of the glycoprotein in host shift or adaptation of Lyssavirus, we retrieved 53 full-length glycoprotein gene sequences from NCBI GenBank. The sequences were from different host isolates over a period of 70 years in 21 countries. Computational analyses detected 1 recombinant (AY987478, a dog isolate of CHAND03, genotype 1 in India) with incongruent phylogenetic support. No recombination was detected when AY98748 was excluded in the analyses. We applied different selection models to identify selection pressure on the glycoprotein gene. One codon at amino acid residual 483 was found to be under weak positive selection with marginal probability of 95% by using the maximum likelihood method. We found no significant evidence of positive selection on any site of the glycoprotein gene when the putative recombinant AY987478 was excluded. The computational analyses suggest that the G gene has been under purifying selection and that the evolution of the G gene may not play a significant role in Lyssavirus adaptation

Enantiocomplementary synthesis of chiral alcohols combining photocatalysis and whole-cell biocatalysis in a one-pot cascade process

As a powerful tool in synthetic organic chemistry, photocatalysis has the features of green, better atom economy, and mild conditions [1-2]. Recently, some cascade reaction protocols have been properly designed by combining photocatalysis and biocatalysis[3-4]. For example, Zhao and Hartwig reported an asymmetric reaction which coupled photocatalysts for E/Z isomerization of alkenes with ene-reductases for the reduction of carbon–carbon double bonds, to generate valuable enantioenriched products [5], which achieved the dual-advantages of both photocatalysis and biocatalysis. We envisioned a photochemo-enzymatic one-pot whole-cell process to convert a series of carboxylic acids into corresponding chiral alcohols with good yields (up to 93%) and excellent stereoselectivity (up to 99% ee). The photocatalysis step was conducted in aqueous phase by using O2 as oxidant and the following whole cell bioreduction without the addition of the expensive cofactor NADPH was a much milder and more efficient approach to obtain chiral alcohols. All these advantages indicate that the photochemo-enzymatic one-pot transformation may have great potential in green synthetic chemistry. Please click Additional Files below to see the full abstract

Light-driven kinetic resolution of α-functionalized acids enabled by engineered Fatty Acid Photodecarboxylase

Multifunctional chiral molecules such as unnatural α-amino acids and α-hydroxy acids are valuable precursors to a variety of medicines and natural products.[1] The biocatalysis provides a greener and more sustainable process than transition metal catalysts and complex chiral ligands. For example, keto reductases (KRED) and imine reductases (IRED) have been successfully used to convert α-keto acids into α-hydroxy/amino acids.[2] Another widely used method was kinetic resolution (KR) or dynamic kinetic resolution (DKR) by employing lipases.[3] Herein, we described the variants of fatty acid photodecarboxylase (CvFAP), which was used to convert long-chain fatty acids into hydrocarbons,[4] catalyze kinetic resolution of α-amino acids and α-hydroxy acids with high conversion and excellent nonreacted (R)-configured substrate stereoselectivity (ee up to 99%). This efficient light-driven process does not require NADPH recycle nor prerequisite preparation of esters in contrast with other biocatalytic methods (Scheme 1). To our delight, although most biocatalysts are hardly to be universal, the best mutant G462Y displayed a satisfactory substrate scope (Figure 1). The structure-guided engineering strategy was introduced by large-size amino acid scanning at hot position to narrow the substrate binding tunnel. We believed that this research conformed to the conference topic of Enzyme promiscuity, evolution and dynamics. Please click Additional Files below to see the full abstract

Computation Offloading in Beyond 5G Networks: A Distributed Learning Framework and Applications

Facing the trend of merging wireless communications and multi-access edge computing (MEC), this article studies computation offloading in the beyond fifth-generation networks. To address the technical challenges originating from the uncertainties and the sharing of limited resource in an MEC system, we formulate the computation offloading problem as a multi-agent Markov decision process, for which a distributed learning framework is proposed. We present a case study on resource orchestration in computation offloading to showcase the potentials of an online distributed reinforcement learning algorithm developed under the proposed framework. Experimental results demonstrate that our learning algorithm outperforms the benchmark resource orchestration algorithms. Furthermore, we outline the research directions worth in-depth investigation to minimize the time cost, which is one of the main practical issues that prevent the implementation of the proposed distributed learning framework

Annual Report of the Commission of the Department of Public Utilities for the Year Ending November 30, 1937

Millimeter wave (mmWave) communications provide great potential for next-generation cellular networks to meet the demands of fast-growing mobile data traffic with plentiful spectrum available. However, in a mmWave cellular system, the shadowing and blockage effects lead to the intermittent connectivity, and the handovers are more frequent. This paper investigates an ``all-mmWave'' cloud radio access network (cloud-RAN), in which both the fronthaul and the radio access links operate at mmWave. To address the intermittent transmissions, we allow the mobile users (MUs) to establish multiple connections to the central unit over the remote radio heads (RRHs). Specifically, we propose a multipath transmission framework by leveraging the ``all-mmWave'' cloud-RAN architecture, which makes decisions of the RRH association and the packet transmission scheduling according to the time-varying network statistics, such that a MU experiences the minimum queueing delay and packet drops. The joint RRH association and transmission scheduling problem is formulated as a Markov decision process (MDP). Due to the problem size, a low-complexity online learning scheme is put forward, which requires no a priori statistic information of network dynamics. Simulations show that our proposed scheme outperforms the state-of-art baselines, in terms of average queue length and average packet dropping rate

Distributed Spectrum and Power Allocation for D2D-U Networks: A Scheme based on NN and Federated Learning

In this paper, a Device-to-Device communication on unlicensed bands (D2D-U) enabled network is studied. To improve the spectrum efficiency (SE) on the unlicensed bands and fit its distributed structure while ensuring the fairness among D2D-U links and the harmonious coexistence with WiFi networks, a distributed joint power and spectrum scheme is proposed. In particular, a parameter, named as price, is defined, which is updated at each D2D-U pair by a online trained Neural network (NN) according to the channel state and traffic load. In addition, the parameters used in the NN are updated by two ways, unsupervised self-iteration and federated learning, to guarantee the fairness and harmonious coexistence. Then, a non-convex optimization problem with respect to the spectrum and power is formulated and solved on each D2D-U link to maximize its own data rate. Numerical simulation results are demonstrated to verify the effectiveness of the proposed scheme

Applying Axiomatic Design Theory to the Multi-objective Optimization of Disk Brake

Abstract. The multi-objective optimization involves multiple, competing functionality requirements, which is mainly limited to downstream detailed design. Axiomatic design provides the theory to design a complex system top down and deals with multiple functional requirements (FR). It has demonstrated its strength in various types of design tasks. In fact, the objective function is a FR and those variables affecting the objective function are the design parameters (DPs). This paper presents an application of axiomatic design to multi-objective optimization. First, identify the relationship between FRs and DPs in terms of contribution of each DP to each FR by using orthogonal experiment and analysis of variance (ANOVA); then identify important design parameters to a FR and classify design variables into groups based on uncoupled design philosophy; and then establish the function dependence table, and sequentially optimize every objective function. An application in a disk brake design is used to demonstrate the use of the proposed method in dealing with real-world design problems. The results show that the proposed method provides a promising approach to optimize multiple, competing design objectives