A two-way regularization method for MEG source reconstruction

The MEG inverse problem refers to the reconstruction of the neural activity of the brain from magnetoencephalography (MEG) measurements. We propose a two-way regularization (TWR) method to solve the MEG inverse problem under the assumptions that only a small number of locations in space are responsible for the measured signals (focality), and each source time course is smooth in time (smoothness). The focality and smoothness of the reconstructed signals are ensured respectively by imposing a sparsity-inducing penalty and a roughness penalty in the data fitting criterion. A two-stage algorithm is developed for fast computation, where a raw estimate of the source time course is obtained in the first stage and then refined in the second stage by the two-way regularization. The proposed method is shown to be effective on both synthetic and real-world examples.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS531 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Coupled Reversible and Irreversible Bistable Switches Underlying TGF-\beta-induced Epithelial to Mesenchymal Transition

Epithelial to mesenchymal transition (EMT) plays important roles in embryonic development, tissue regeneration and cancer metastasis. While several feedback loops have been shown to regulate EMT, it remains elusive how they coordinately modulate EMT response to TGF-\beta treatment. We construct a mathematical model for the core regulatory network controlling TGF-\beta-induced EMT. Through deterministic analyses and stochastic simulations, we show that EMT is a sequential two-step program that an epithelial cell first transits to partial EMT then to the mesenchymal state, depending on the strength and duration of TGF-\beta stimulation. Mechanistically the system is governed by coupled reversible and irreversible bistable switches. The SNAIL1/miR-34 double negative feedback loop is responsible for the reversible switch and regulates the initiation of EMT, while the ZEB/miR-200 feedback loop is accountable for the irreversible switch and controls the establishment of the mesenchymal state. Furthermore, an autocrine TGF-\beta/miR-200 feedback loop makes the second switch irreversible, modulating the maintenance of EMT. Such coupled bistable switches are robust to parameter variation and molecular noise. We provide a mechanistic explanation on multiple experimental observations. The model makes several explicit predictions on hysteretic dynamic behaviors, system response to pulsed stimulation and various perturbations, which can be straightforwardly tested.Comment: 32 pages, 8 figures, accepted by Biophysical Journa

Syntheses and applications of soluble polyisobutylene (PIB)-supported transition metal catalysts

Soluble polymer supports facilitate the recovery and recycling of expensive transition metal complexes. Recently, polyisobutylene (PIB) oligomers have been found to be suitable polymer supports for the recovery of a variety of transition metal catalysts using liquid/liquid biphasic separations after a homogeneous reaction. Our work has shown that PIB-supported Ni(II) and Co(II) β-diketonates prepared from commercially available vinyl terminated PIB oligomers possess catalytic activity like that of their low molecular weight analogs in Mukaiyama epoxidation of olefins. Carboxylic acid terminated PIB derivatives can act as carboxylate ligands for Rh(II) cyclopropanation catalysts. An achiral PIB-supported Rh(II) carboxylate catalyst showed good activity in cyclopropanation of styrene in hydrocarbon solvents, and could be easily recycled nine times by a post reaction extraction. Further application of PIB supports in asymmetric cyclopropanation reactions were investigated using PIBsupported arenesulfonyl Rh(II) prolinates derived from L-proline as examples. The PIBsupported chiral Rh carboxylates demonstrated moderate activity and were recovered and reused for four to five cycles. The prolinate catalyst prepared from PIB-anisole also showed encouraging enantioselectivity and about 8% ee and 13% ee were observed on trans- and cis-cyclopropanation product respectively. Finally, PIB oligomers can be modified in a multi step sequence to prepare PIBsupported chiral bisoxazolines that can in turn be used to prepare active, recyclable PIBsupported Cu(I) bisoxazoline complexes for olefin cyclopropanation. These chiral copper catalysts showed moderate catalytic activity and good stereoselectivity in cyclopropanation of styrene. A chiral ligand prepared from D-phenylglycinol provided the most effective stereo control and gave the trans- and cis-cyclopropanation product in 94% ee and 68% ee respectively. All three PIB-supported chiral bisoxazoline-Cu(I) catalysts could be reused five to six times

A unit cost adjusting heuristic algorithm for the integrated planning and scheduling of a two-stage supply chain

Purpose: The stable relationship of one-supplier-one-customer is replaced by a dynamic relationship of multi-supplier-multi-customer in current market gradually, and efficient scheduling techniques are important tools of the dynamic supply chain relationship establishing process. This paper studies the optimization of the integrated planning and scheduling problem of a two-stage supply chain with multiple manufacturers and multiple retailers to obtain a minimum supply chain operating cost, whose manufacturers have different production capacities, holding and producing cost rates, transportation costs to retailers. Design/methodology/approach: As a complex task allocation and scheduling problem, this paper sets up an INLP model for it and designs a Unit Cost Adjusting (UCA) heuristic algorithm that adjust the suppliers’ supplying quantity according to their unit costs step by step to solve the model. Findings: Relying on the contrasting analysis between the UCA and the Lingo solvers for optimizing many numerical experiments, results show that the INLP model and the UCA algorithm can obtain its near optimal solution of the two-stage supply chain’s planning and scheduling problem within very short CPU time. Research limitations/implications: The proposed UCA heuristic can easily help managers to optimizing the two-stage supply chain scheduling problems which doesn’t include the delivery time and batch of orders. For two-stage supply chains are the most common form of actual commercial relationships, so to make some modification and study on the UCA heuristic should be able to optimize the integrated planning and scheduling problems of a supply chain with more reality constraints. Originality/value: This research proposes an innovative UCA heuristic for optimizing the integrated planning and scheduling problem of two-stage supply chains with the constraints of suppliers’ production capacity and the orders’ delivering time, and has a great practical significance to the dynamic relationship establishment of multi-supplier-multi-customer in current market.Peer Reviewe

A unit cost adjusting heuristic algorithm for the integrated planning and scheduling of a two-stage supply chain

Purpose: The stable relationship of one-supplier-one-customer is replaced by a dynamic relationship of multi-supplier-multi-customer in current market gradually, and efficient scheduling techniques are important tools of the dynamic supply chain relationship establishing process. This paper studies the optimization of the integrated planning and scheduling problem of a two-stage supply chain with multiple manufacturers and multiple retailers to obtain a minimum supply chain operating cost, whose manufacturers have different production capacities, holding and producing cost rates, transportation costs to retailers. Design/methodology/approach: As a complex task allocation and scheduling problem, this paper sets up an INLP model for it and designs a Unit Cost Adjusting (UCA) heuristic algorithm that adjust the suppliers’ supplying quantity according to their unit costs step by step to solve the model. Findings: Relying on the contrasting analysis between the UCA and the Lingo solvers for optimizing many numerical experiments, results show that the INLP model and the UCA algorithm can obtain its near optimal solution of the two-stage supply chain’s planning and scheduling problem within very short CPU time. Research limitations/implications: The proposed UCA heuristic can easily help managers to optimizing the two-stage supply chain scheduling problems which doesn’t include the delivery time and batch of orders. For two-stage supply chains are the most common form of actual commercial relationships, so to make some modification and study on the UCA heuristic should be able to optimize the integrated planning and scheduling problems of a supply chain with more reality constraints. Originality/value: This research proposes an innovative UCA heuristic for optimizing the integrated planning and scheduling problem of two-stage supply chains with the constraints of suppliers’ production capacity and the orders’ delivering time, and has a great practical significance to the dynamic relationship establishment of multi-supplier-multi-customer in current market.Peer Reviewe