Optimal Results and Numerical Simulations for Flow Shop Scheduling Problems

This paper considers the m-machine flow shop problem with two objectives: makespan with release dates and total quadratic completion time, respectively. For Fm|rj|Cmax, we prove the asymptotic optimality for any dense scheduling when the problem scale is large enough. For Fm‖ΣCj2, improvement strategy with local search is presented to promote the performance of the classical SPT heuristic. At the end of the paper, simulations show the effectiveness of the improvement strategy

Robust Spontaneous Raman Flow Cytometry for Single‐Cell Metabolic Phenome Profiling via pDEP‐DLD‐RFC

Abstract A full‐spectrum spontaneous single‐cell Raman spectrum (fs‐SCRS) captures the metabolic phenome for a given cellular state of the cell in a label‐free, landscape‐like manner. Herein a positive dielectrophoresis induced deterministic lateral displacement‐based Raman flow cytometry (pDEP‐DLD‐RFC) is established. This robust flow cytometry platform utilizes a periodical positive dielectrophoresis induced deterministic lateral displacement (pDEP‐DLD) force that is exerted to focus and trap fast‐moving single cells in a wide channel, which enables efficient fs‐SCRS acquisition and extended stable running time. It automatically produces deeply sampled, heterogeneity‐resolved, and highly reproducible ramanomes for isogenic cell populations of yeast, microalgae, bacteria, and human cancers, which support biosynthetic process dissection, antimicrobial susceptibility profiling, and cell‐type classification. Moreover, when coupled with intra‐ramanome correlation analysis, it reveals state‐ and cell‐type‐specific metabolic heterogeneity and metabolite‐conversion networks. The throughput of ≈30–2700 events min−1 for profiling both nonresonance and resonance marker bands in a fs‐SCRS, plus the >5 h stable running time, represent the highest performance among reported spontaneous Raman flow cytometry (RFC) systems. Therefore, pDEP‐DLD‐RFC is a valuable new tool for label‐free, noninvasive, and high‐throughput profiling of single‐cell metabolic phenomes

Artificial intelligence‐assisted automatic and index‐based microbial single‐cell sorting system for One‐Cell‐One‐Tube

Abstract Identification, sorting, and sequencing of individual cells directly from in situ samples have great potential for in‐depth analysis of the structure and function of microbiomes. In this work, based on an artificial intelligence (AI)‐assisted object detection model for cell phenotype screening and a cross‐interface contact method for single‐cell exporting, we developed an automatic and index‐based system called EasySort AUTO, where individual microbial cells are sorted and then packaged in a microdroplet and automatically exported in a precisely indexed, “One‐Cell‐One‐Tube” manner. The target cell is automatically identified based on an AI‐assisted object detection model and then mobilized via an optical tweezer for sorting. Then, a cross‐interface contact microfluidic printing method that we developed enables the automated transfer of cells from the chip to the tube, which leads to coupling with subsequent single‐cell culture or sequencing. The efficiency of the system for single‐cell printing is >93%. The throughput of the system for single‐cell printing is ~120 cells/h. Moreover, >80% of single cells of both yeast and Escherichia coli are culturable, suggesting the superior preservation of cell viability during sorting. Finally, AI‐assisted object detection supports automated sorting of target cells with high accuracy from mixed yeast samples, which was validated by downstream single‐cell proliferation assays. The automation, index maintenance, and vitality preservation of EasySort AUTO suggest its excellent application potential for single‐cell sorting