| 1. |
- Beal, Jacob, et al.
(författare)
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Robust estimation of bacterial cell count from optical density
- 2020
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Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
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| 2. |
- Liu, Haifeng, et al.
(författare)
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Laser diagnostics and chemical kinetic analysis of PAHs and soot in co-flow partially premixed flames using diesel surrogate and oxygenated additives of n-butanol and DMF
- 2018
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Ingår i: Combustion and Flame. - : Elsevier BV. - 0010-2180. ; 188, s. 129-141
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Tidskriftsartikel (refereegranskat)abstract
- Effects of oxygenated fuels on soot reduction strongly depend on the base fuel. Interesting candidates from oxygenated fuels in this respect include both n-butanol and 2,5-dimethylfuran (DMF), because they have already been used in diesel engines recently. However, information is rather limited on n-butanol and DMF added into a diesel fuel surrogate in fundamental flames to investigate the mechanism of soot reduction. In the current work, both n-butanol and DMF was successively added into diesel surrogate (80% n-heptane and 20% toluene in volume, named as T20) in co-flow partially premixed flames. The effects of different oxygenated structures on polycyclic aromatic hydrocarbons (PAHs) and soot were investigated at the same oxygen weight fractions of 4% and the same volume fractions of 20%. The diagnostics on PAHs, soot volume fractions and soot sizes were conducted by using both laser-induced fluorescence (LIF) and two-color laser-induced incandescence (2C-LII). A combined detailed kinetic model (n-heptane/toluene/butanols/DMF/PAHs) has been obtained in order to clarify the chemical effects of the different oxygenated fuels on PAHs formation. Results show that the reduced toluene content due to the addition of oxygenated fuels is the dominant factor for the reduction of soot, as compared with the base fuel of T20. The oxygenated structure of n-butanol has a higher ability to reduce PAHs and soot as compared with the addition of DMF. This is due to the fact that the consumption of DMF leads to much formation of C5H5 which enhances the formation of PAHs and subsequent soot. However, the formation of PAHs can be inhibited remarkably as blending n-butanol because only small hydrocarbons like C2H2 and C3H3 etc. are formed. The formation rate of A4 is more similar to that of soot in comparison with the smaller ring aromatics. For the size of soot particles, the distribution range is shrunk from 19–70 nm for T20 to 20–40 nm for the addition of oxygenated fuels. As compared to the effects of oxygenated structures, DMF20 presents a little wider distribution on soot sizes than that of B16.8. Some larger soot particles are detected in DMF20 flame but cannot be found in B20 flame.
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| 3. |
- Huang, Wei, et al.
(författare)
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Substrate Promiscuity, Crystal Structure, and Application of a Plant UDP-Glycosyltransferase UGT74AN3
- 2024
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Ingår i: ACS Catalysis. - : American Chemical Society (ACS). - 2155-5435. ; 14:1, s. 475-488
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Tidskriftsartikel (refereegranskat)abstract
- Glycosyltransferases are effective enzymes for glycosylating natural products (NPs), and some of them have the unusual property of being exceedingly promiscuous catalytically toward a range of substrates. UGT74AN3 is a plant glycosyltransferase identified from Catharanthus roseus in our previous work. In this study, we found that UGT74AN3 exhibits high substrate promiscuity toward 78 acceptors and 6 sugar donors and also exhibits N-/S-glycosylation activity toward simple aromatic compounds. The crystal structures of UGT74AN3 in the complex with various NPs were solved. Sugar donor recognition of UGT74AN3 was altered by structure-based mutagenesis, and the T145V mutant shifted its sugar donor preference from UDP-Glc to UDP-Xyl. Structural analysis reveals that a spacious U-shaped hydrophobic binding pocket accounts for the high substrate promiscuity of UGT74AN3. The residues E85 and F193 might serve as gatekeepers of UGT74AN3 to control substrate binding. In addition, a rare substrate binding mode was discovered in the structure of UGT74AN3, and the process of substrate flipping in the pocket was charted by molecular dynamics simulations. Moreover, a cost-effective one-pot system by coupling UGT74AN3 with AtSuSy, a sucrose synthase, was established for in situ generating and recycling UDP-Glc from sucrose and UDP to glycosylate NPs. Our study reveals the structural basis underlying the substrate promiscuity of UGT74AN3 and provides an efficient and economical enzymatic synthesis strategy for producing valuable glycosides for drug discovery.
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| 4. |
- Li, Xiuxian, et al.
(författare)
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Distributed Online Convex Optimization With an Aggregative Variable
- 2022
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Ingår i: IEEE Transactions on Control of Network Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2325-5870. ; 9:1, s. 438-449
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Tidskriftsartikel (refereegranskat)abstract
- This article investigates distributed online convex optimization in the presence of an aggregative variable without any global/central coordinators over a multiagent network. In this problem, each individual agent is only able to access partial information of time-varying global loss functions, thus requiring local information exchanges between neighboring agents. Motivated by many applications in reality, the considered local loss functions depend not only on their own decision variables, but also on an aggregative variable, such as the average of all decision variables. To handle this problem, an online distributed gradient tracking algorithm (O-DGT) is proposed with exact gradient information and it is shown that the dynamic regret is upper bounded by three terms: 1) a sublinear term; 2) a path variation term; and 3) a gradient variation term. Meanwhile, the O-DGT algorithm is also analyzed with stochastic/noisy gradients, showing that the expected dynamic regret has the same upper bound as the exact gradient case. To our best knowledge, this article is the first to study online convex optimization in the presence of an aggregative variable, which enjoys new characteristics in comparison with the conventional scenario without the aggregative variable. Finally, a numerical experiment is provided to corroborate the obtained theoretical results.
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| 5. |
- Li, Xiuxian, et al.
(författare)
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Distributed Online Optimization for Multi-Agent Networks With Coupled Inequality Constraints
- 2021
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Ingår i: IEEE Transactions on Automatic Control. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9286 .- 1558-2523. ; 66:8, s. 3575-3591
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Tidskriftsartikel (refereegranskat)abstract
- This article investigates the distributed online optimization problem over a multi-agent network subject to local set constraints and coupled inequality constraints, which has a lot of applications in many areas, such as wireless sensor networks, power systems, and plug-in electric vehicles. In this problem, the cost function at each time step is the sum of local cost functions with each of them being gradually revealed to its corresponding agent, and meanwhile only local functions in coupled inequality constraints are accessible to each agent. To address this problem, a modified primal-dual algorithm, called distributed online primal-dual push-sum algorithm, is developed in this article, which does not rest on any assumption on parameter boundedness and is applicable to unbalanced networks. It is shown that the proposed algorithm is sublinear for both the dynamic regret and the violation of coupled inequality constraints. Finally, the theoretical results are supported by a simulation example.
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| 6. |
- Li, Xinlei, et al.
(författare)
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Shaped by uneven Pleistocene climate: mitochondrial phylogeographic pattern and population history of White Wagtail Motacilla alba (Aves: Passeriformes).
- 2016
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Ingår i: Journal of Avian Biology. - : Wiley. - 0908-8857 .- 1600-048X. ; 47, s. 263-274
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Tidskriftsartikel (refereegranskat)abstract
- We studied the phylogeography and population history of the white wagtail Motacilla alba, which has a vast breeding range, covering areas with different Pleistocene climatic histories. The mitochondrial NADH dehydrogenase subunit II gene (ND2) and Control Region (CR) were analyzed for 273 individuals from 45 localities. Our data comprised all nine subspecies of white wagtail. Four primary clades were inferred (M, N, SW and SE), with indications of M. grandis being nested within M. alba. The oldest split was between two haplotypes from the endemic Moroccan M. a. subpersonata (clade M) and the others, at 0.63–0.96 Mya; other divergences were at 0.31–0.38 Mya. The entire differentiation falls within the part of the Pleistocene characterized by Milankovitch cycles of large amplitudes and durations. Clade N was distributed across the northern Palearctic; clade SW in southwestern Asia plus the British Isles and was predicted by Ecological niche models (ENMs) to occur also in central and south Europe; and clade SE was distributed in central and east Asia. e deep divergence within M. a. subpersonata may reflect retention of ancestral haplotypes. Regional differences in historical climates have had different impacts on different populations: clade N expanded after the last glacial maximum (LGM), whereas milder Pleistocene climate of east Asia allowed clade SE a longer expansion time (since MIS 5); clade SW expanded over a similarly long time as clade SE, which is untypical for European species. ENMs supported these conclusions in that the northern part of the Eurasian continent was unsuitable during the LGM, whereas southern parts remained suitable. e recent divergences and poor structure in the mitochondrial tree contrasts strongly with the pronounced, well defined phenotypical differentiation, indicating extremely fast plumage divergence.
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| 7. |
- Yang, T., et al.
(författare)
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Event-triggered Distributed Optimization Algorithms
- 2022
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Ingår i: Zidonghua Xuebao/Acta Automatica Sinica. - : Science Press. - 0254-4156. ; 48:1, s. 133-143
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Tidskriftsartikel (refereegranskat)abstract
- This paper studies a class of distributed optimization problems, whose objective is to minimize the global cost function formed by a sum of local cost functions through local information exchanges. For undirected connected graphs, we propose two distributed optimization algorithms based on the proportional-integral feedback mechanism. Under the condition that the local cost functions are differentiable and convex, it is proved that the proposed algorithms asymptotically converge to a global minimum. For the case that the local cost functions have local Lipschitz gradient and the global cost function is strongly convex with respect to the global minimum, the exponential convergences of the two distributed optimization algorithms are established. In addition, in order to avoid continuous communication between agents and reduce communication burden, by integrating the two proposed distributed optimization algorithms with event-triggered communications, two event-triggered based distributed optimization algorithms are developed. It is shown that the two proposed event-triggered optimization algorithms are free of Zeno behavior. Moreover, the two proposed event-triggered based distributed optimization algorithms maintain the same convergence properties as the distributed optimization algorithms with continuous communications under the corresponding conditions. Finally, the above theoretical results are verified by numerical simulations. Copyright
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| 8. |
- Yi, Xinlei, et al.
(författare)
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A Distributed Algorithm for Online Convex Optimization with Time-Varying Coupled Inequality Constraints
- 2019
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Ingår i: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728113982 ; , s. 555-560
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Konferensbidrag (refereegranskat)abstract
- This paper considers distributed online optimization with time-varying coupled inequality constraints. The global objective function is composed of local convex cost and regularization functions and the coupled constraint function is the sum of local convex constraint functions. A distributed online primal-dual mirror descent algorithm is proposed to solve this problem, where the local cost, regularization, and constraint functions are held privately and revealed only after each time slot. We first derive regret and constraint violation bounds for the algorithm and show how they depend on the stepsize sequences, the accumulated variation of the comparator sequence, the number of agents, and the network connectivity. As a result, we prove that the algorithm achieves sublinear dynamic regret and constraint violation if the accumulated variation of the optimal sequence also grows sublinearly. We also prove that the algorithm achieves sublinear static regret and constraint violation under mild conditions. In addition, smaller bounds on the static regret are achieved when the objective functions are strongly convex. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.
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| 9. |
- Yi, Xinlei, et al.
(författare)
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A Distributed Primal-Dual Algorithm for Bandit Online Convex Optimization with Time-Varying Coupled Inequality Constraints
- 2020
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Ingår i: Proceedings 2020 American Control Conference, ACC 2020. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 327-332
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Konferensbidrag (refereegranskat)abstract
- This paper considers distributed bandit online optimization with time-varying coupled inequality constraints. The global cost and the coupled constraint functions are the summations of local convex cost and constraint functions, respectively. The local cost and constraint functions are held privately and only at the end of each period the constraint functions are fully revealed, while only the values of cost functions at queried points are revealed, i.e., in a so called bandit manner. A distributed bandit online primal-dual algorithm with two queries for the cost functions per period is proposed. The performance of the algorithm is evaluated using its expected regret, i.e., the expected difference between the outcome of the algorithm and the optimal choice in hindsight, as well as its constraint violation. We show that O(T-c) expected regret and O(T1-c/2) constraint violation are achieved by the proposed algorithm, where T is the total number of iterations and c is an element of [0.5, 1) is a user-defined trade-off parameter. Assuming Slater's condition, we show that O(root T) expected regret and O(root T) constraint violation are achieved. The theoretical results are illustrated by numerical simulations.
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| 10. |
- Yi, Xinlei, et al.
(författare)
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Distributed Bandit Online Convex Optimization With Time-Varying Coupled Inequality Constraints
- 2021
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Ingår i: IEEE Transactions on Automatic Control. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9286 .- 1558-2523. ; 66:10, s. 4620-4635
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Tidskriftsartikel (refereegranskat)abstract
- Distributed bandit online convex optimization with time-varying coupled inequality constraints is considered, motivated by a repeated game between a group of learners and an adversary. The learners attempt tominimize a sequence of global loss functions and at the same time satisfy a sequence of coupled constraint functions, where the constraints are coupled across the distributed learners at each round. The global loss and the coupled constraint functions are the sum of local convex loss and constraint functions, respectively, which are adaptively generated by the adversary. The local loss and constraint functions are revealed in a bandit manner, i.e., only the values of loss and constraint functions are revealed to the learners at the sampling instance, and the revealed function values are held privately by each learner. Both one- and two-point bandit feedback are studied with the two corresponding distributed bandit online algorithms used by the learners. We show that sublinear expected regret and constraint violation are achieved by these two algorithms, if the accumulated variation of the comparator sequence also grows sublinearly. In particular, we show that O(T-theta) expected static regret and O(T7/4-theta) constraint violation are achieved in the one-point bandit feedback setting, and O((T max{kappa,1-kappa})) expected static regret and O(T1-kappa/2) constraint violation in the two-point bandit feedback setting, where theta is an element of(3/4, 5/6] and kappa is an element of(0, 1) are user-defined tradeoff parameters. Finally, the tightness of the theoretical results is illustrated by numerical simulations of a simple power grid example, which also compares the proposed algorithms to algorithms existing in the literature.
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| 11. |
- Yi, Xinlei, et al.
(författare)
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Distributed Online Convex Optimization With Time-Varying Coupled Inequality Constraints
- 2020
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Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 68, s. 731-746
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Tidskriftsartikel (refereegranskat)abstract
- This paper considers distributed online optimization with time-varying coupled inequality constraints. The global objective function is composed of local convex cost and regularization functions and the coupled constraint function is the sum of local convex functions. Adistributed online primal-dual dynamic mirror descent algorithm is proposed to solve this problem, where the local cost, regularization, and constraint functions are held privately and revealed only after each time slot. Without assuming Slater's condition, we first derive regret and constraint violation bounds for the algorithm and show how they depend on the stepsize sequences, the accumulated dynamic variation of the comparator sequence, the number of agents, and the network connectivity. As a result, under some natural decreasing stepsize sequences, we prove that the algorithm achieves sublinear dynamic regret and constraint violation if the accumulated dynamic variation of the optimal sequence also grows sublinearly. We also prove that the algorithm achieves sublinear static regret and constraint violation under mild conditions. Assuming Slater's condition, we show that the algorithm achieves smaller bounds on the constraint violation. In addition, smaller bounds on the static regret are achieved when the objective function is strongly convex. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.
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| 12. |
- Yi, Xinlei, et al.
(författare)
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Regret and Cumulative Constraint Violation Analysis for Distributed Online Constrained Convex Optimization
- 2023
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Ingår i: IEEE Transactions on Automatic Control. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9286 .- 1558-2523. ; 68:5, s. 2875-2890
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Tidskriftsartikel (refereegranskat)abstract
- This article considers the distributed online convex optimization problem with time-varying constraints over a network of agents. This is a sequential decision making problem with two sequences of arbitrarily varying convex loss and constraint functions. At each round, each agent selects a decision from the decision set, and then only a portion of the loss function and a coordinate block of the constraint function at this round are privately revealed to this agent. The goal of the network is to minimize the network-wide loss accumulated over time. Two distributed online algorithms with full-information and bandit feedback are proposed. Both dynamic and static network regret bounds are analyzed for the proposed algorithms, and network cumulative constraint violation is used to measure constraint violation, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. In particular, we show that the proposed algorithms achieve O(Tmax { \κ,1-\κ }) static network regret and O(T1-κ /2) network cumulative constraint violation, where T is the time horizon and κ \in (0,1) is a user-defined tradeoff parameter. Moreover, if the loss functions are strongly convex, then the static network regret bound can be reduced to O(Tκ ). Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.
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| 13. |
- Yi, Xinlei, et al.
(författare)
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Regret and Cumulative Constraint Violation Analysis for Online Convex Optimization with Long Term Constraints
- 2021
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Ingår i: Proceedings of the 38 th International Conference on Machine Learning, PMLR 139, 2021.. - : The Journal of Machine Learning Research (JMLR).
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Konferensbidrag (refereegranskat)abstract
- This paper considers online convex optimization with long term constraints, where constraints can be violated in intermediate rounds, but need to be satisfied in the long run. The cumulative constraint violation is used as the metric to measure constraint violations, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. A novel algorithm is first proposed and it achieves an O(T-max{c ,T-1-c}) bound for static regret and an O(T(1-c)/2) bound for cumulative constraint violation, where c is an element of (0, 1) is a user-defined trade-off parameter, and thus has improved performance compared with existing results. Both static regret and cumulative constraint violation bounds are reduced to O(log(T)) when the loss functions are strongly convex, which also improves existing results. In order to achieve the optimal regret with respect to any comparator sequence, another algorithm is then proposed and it achieves the optimal O(root T(1+ P-T)) regret and an O(root T) cumulative constraint violation, where P-T is the path-length of the comparator sequence. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.
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