Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods rely on regions of interest (ROIs) which require great efforts to annotate. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning (MIL) for labeling a set of instances/patches, we propose end-to-end trained deep multiinstance networks for mass classification based on whole mammogram without the aforementioned ROIs. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed networks compared to previous work using segmentation and detection annotations. 1

Three-dimensional (3D) cell culture provides an effective way over conventional two-dimensional (2D) monolayer culture to more closely imitate the complex cellular organization, heterogeneity, and interactions as well as tissue microenvironments in vivo. Here we present a novel droplet-based 3D cell culture method by using droplet array attached on the sidewall of a PDMS piece. Such an arrangement not only avoids cells from adhering on the chip surface for achieving 3D cell culture in nanoliter-scale droplets, but also facilitates performing multiple operations to cells in droplets, including cell suspension droplet generation, drug treatment, and cell staining with a capillary-based liquid handling system, as well as in situ observation and direct scanning with a confocal laser scanning microscope. We optimized the system by studying the effects of various conditions to cell culture including droplet volume, cell density and fabrication methods of the PDMS pieces. We have applied this system in the 3D culture of HepG2 cells and the stimulation testing of an anticancer drug, doxorubicin, to 3D cell spheroids.

Establishing cell migration assays in multiple different microenvironments is important in the study of tissue repair and regeneration, cancer progression, atherosclerosis, and arthritis. In this work, we developed a miniaturized and massive parallel microfluidic platform for multiple cell migration assays combining the traditional membrane-based cell migration technique and the droplet-based microfluidic technique. Nanoliter-scale droplets are flexibly assembled as building blocks based on a porous membrane to form microdroplet chains with diverse configurations for different assay modes. Multiple operations including in-droplet 2D/3D cell culture, cell co-culture and cell migration induced by a chemoattractant concentration gradient in droplet chains could be flexibly performed with reagent consumption in the nanoliter range for each assay and an assay scale-up to 81 assays in parallel in one microchip. We have applied the present platform to multiple modes of cell migration assays including the accurate cell migration assay, competitive cell migration assay, biomimetic chemotaxis assay, and multifactor cell migration assay based on the organ-on-a-chip concept, for demonstrating its versatility, applicability, and potential in cell migration-related research.

Abstract. Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods requires great effort to annotate the training data by costly manual labeling and specialized computational models to detect these annotations during test. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning for labeling a set of instances/patches, we propose end-to-end trained deep multiinstance networks for mass classification based on whole mammogram without the aforementioned costly need to annotate the training data. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed deep networks compared to previous work using segmentation and detection annotations in the training.

Herein, we developed an automated and flexible system for performing miniaturized liquid-liquid reactions and assays in the femtoliter to picoliter range, by combining the contact printing and the droplet-based microfluidics techniques. The system mainly consisted of solid pins and an oil-covered hydrophilic micropillar array chip fixed on an automated x- y- z translation stage. A novel droplet manipulation mode called "dipping-depositing-moving" (DDM) was proposed, which was based on the programmable combination of three basic operations, dipping liquids and depositing liquids with the solid pins and moving the two-dimensional oil-covered hydrophilic pillar microchip. With the DDM mode, flexible generation and manipulation of small droplets with volumes down to 179 fL could be achieved. For overcoming the scale phenomenon specially appeared in picoliter-scale droplets, we used a design of water moat to protect the femtoliter to picoliter droplets from volume loss through the cover oil during the droplet generation, manipulation, reaction and assay processes. Moreover, we also developed a precise quantitative method, quantitative droplet dilution method, to accurately measure the volumes of femtoliter to picoliter droplets. To demonstrate its feasibility and adaptability, we applied the present system in the determination of kinetics parameter for matrix metalloproteinases (MMP-9) in 1.81 pL reactors and the measurement the activity of β-galactosidase in single cells (HepG2 cells) in picoliter droplet array. The ultrasmall volumes of the droplet reactors avoided the excessive dilution to the reaction solutions and enabled the highly sensitive measurement of enzyme activity in the single cell level.

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The heterogeneity in biofilms is a major challenge in biofilm therapies due to different susceptibility of bacteria and extracellular polymeric substances (EPS) to antibacterial agents. Here, we describe a therapeutic strategy that overcame biofilm heterogeneity, where antibacterial agent (NO) and EPS dispersant (reactive oxygen species (ROS)‐inducing Fe3+) were separately loaded in the yolk and shell compartment of a yolk–shell nanoplatform. Compared with traditional combinational chemotherapies which suffer from inconsistent pharmacokinetics profiles, this strategy drew on the pharmacokinetic complementarity of ROS and NO, where ROS with a short diffusion distance and a high redox potential corrupted the EPS, facilitating NO, which has a long diffusion distance and a broad antimicrobial spectrum, to penetrate the biofilm and eliminate the resident bacteria. Additionally, the construction of a three‐dimensional spherical biofilm model is novel and clinically relevant.

Seru production can be used to enhance productivity, such as makespan reduction and manpower saving. Seru system operation includes two NP-hard problems, i.e., seru formation and seru scheduling. The exact solution cannot be obtained by solving only one of seru formation and seru scheduling. We develop a cooperative coevolution algorithm for the Seru production with minimizing makespan by solving the seru formation and seru scheduling simultaneously. The cooperative coevolution algorithm includes two evolution algorithms, i.e., the algorithm combining generic algorithm and local search, and the ant colony optimization algorithm. The former algorithm is used to deal with the evolution of seru formation. The latter is used to find a better seru scheduling. In the cooperative mechanism, the two algorithms cooperate to seek a better solution of seru system operation. Finally, extensive-tested experiments show that the proposed cooperative coevolution algorithm can obtain a better solution than all the existing algorithms and, even, can obtain the exact solution for some medium-and-small instances.INDEX TERMS Cooperative coevolution, manufacturing, makespan, Seru production. I. INTRODUCTIONTo minimize makespan is a widely-used objective in the problems of flow shop, open shop and job shop [1]- [6]. Seru Production can be used to greatly reduce the makespan. Seru Production has been successfully applied by many leading Japanese companies such as Sony, Canon, Panasonic, NEC, Fujitsu, Sharp and Sanyo [4]. Seru Production is based on at least a seru system. A seru system contains one or more serus. Seru, conceived by Sony and Canon in Japan, is lean and agile. Seru is an assembly unit including some simple equipment and one or more multi-skilled workers. Workers in a seru are cross-trained [5] and implement most or all of tasks in the seru. Seru system can obtain a better performance by the reconfiguration of workers than the traditional convey assembly line. Moreover, Seru Production can reduce makespan, setup time, required workers, cost, and shop floor space. A detailed introduction of the seru system and its managerial mechanism can be found in Yin et al. [7], Tekin et al. [8], and Stecke et al. [9]. Some previous researches focused on how seru system to reduce makespan [10]-[13], manpower [14], and training cost [15]. Kaku et al. [10] established a model to investigate