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The production of chimeric animals is frequently necessary for the constructing genetically modified animals, and has gained popularity in regenerative medicine in the recent years for the reconstruction of xenogeneic organs. The aggregation method and the injection method are generally used for producing chimeric mice. In the aggregation method, the chimeras are produced by co-culturing embryos and stem cells, and keeping them physically adhered. In the injection method, the chimeras are produced by injecting stem cells into the zona pellucida using microcapillaries. These methods only focus on the generation of chimeric animals, and are not expected to produce reproducible results or allow quantitative evaluation.This study aimed to establish a novel method for producing chimeric embryos via droplets for improving on the conventional methods that are used for producing chimeric embryos. In this study, the embryonic stem cells and embryos were successfully isolated in the droplets, and the emergence of chimeric embryos was confirmed by co-culture for 6 hours. By this method, the control and operability of stem cell numbers can be regulated, and the method allows better reproducibility and quantification during the production of chimeric embryos. In addition to the conventional methods for producing chimeric embryo, the novel method described herein could be employed for the efficient production of chimeric animals.

Current computers are limited by the von Neumann bottleneck, which constrains the throughput between the processing unit and the memory. Chemical processes have the potential to scale beyond current computing architectures as the processing unit and memory reside in the same space, performing computations through chemical reactions, yet their lack of programmability limits them. Herein, we present a programmable chemical processor comprising of a 5 by 5 array of cells filled with a switchable oscillating chemical (Belousov–Zhabotinsky) reaction. Each cell can be individually addressed in the ‘on’ or ‘off’ state, yielding more than 2.9 × 1017 chemical states which arise from the ability to detect distinct amplitudes of oscillations via image processing. By programming the array of interconnected BZ reactions we demonstrate chemically encoded and addressable memory, and we create a chemical Autoencoder for pattern recognition able to perform the equivalent of one million operations per second., Unconventional computing architectures might outperform current ones, but their realization has been limited to solving simple specific problems. Here, a network of interconnected Belousov-Zhabotinski reactions, operated by independent magnetic stirrers, performs encoding/decoding operations and data storage.

The need for protein production has been growing over the years in various industries. We here present a high-throughput screening strategy to isolate high producer budding yeast clones from a mutagenized cell population using gel microdrop (GMD) technology. We use a microfluidic water-in-oil (W/O) emulsion method to produce monodisperse GMDs and a microfluidic cell sorter for damage-free sorting of GMDs by fluorescently quantifying secreted proteins. As a result, this high-throughput GMD screening method effectively selects high producer clones and improves protein production up to five-fold. We speculate that this screening strategy can be applied, in principle, to select any types of high producer cells (bacterial, fungal, mammalian, etc.) which produce arbitrary target protein as it does not depend on enzymes to be produced.

Here, we integrate 3D-printing technology with low-cost open source electronics to develop a portable diagnostic platform suitable for a wide variety of diagnostic and sensing assays. We demonstrate two different clinical applications in the diagnosis of Clostridium difficile infection and malaria.

Crustacean aquaculture is expected to be a major source of fishery commodities in the near future. An immune priming system of shrimp is crucial for a sustainable supply, as shrimp do not have an adaptive immune system; however, little is known about their immunity. Hemocytes are known as key agents of the crustacean immune system; nevertheless, we have yet to identify the different cell types, functions, and differentiation and maturation processes associated with it. To date, only discrete and inconsistent information on the classification of shrimp hemocytes has been reported, showing that the morphological characteristics are not sufficient to resolve their actual roles. Therefore, we employed a single-cell transcriptome approach for shrimp hemocytes. Thousands of hemocytes from shrimp Marsupenaeus japonicus were subjected to single-cell mRNA sequencing (scRNA-seq). From the classification of cells based on their transcriptional profiles, we discovered nine different hemocyte subpopulations corresponding to different stages of the differentiation process that can be traced back to the first subpopulation. Using our classification, we also identified molecular markers for each subpopulation, and mapped their differentiation and maturation pathways. Interestingly, we also discovered growth factors that may play crucial roles during the differentiation process and provide key information for hemocyte cell culture. Among these subpopulations, different immune roles were suggested from the analysis of the differentially expressed immune-related genes. The present characterization results, based on the scRNA-seq, should set the fundamental ground for understanding shrimp immunity for the future development of shrimp aquaculture. Significance Statement Hemocytes are key players of the immune system in shrimps; however, their classification, maturation, and differentiation are still under debate. Our present study using single-cell RNA sequencing, revealed nine types of hemocytes based on their transcriptional profiles. We identified markers of each subpopulation and the differentiation pathways involved in their maturation. We also discovered cell growth factors that might play crucial roles in hemocyte differentiation. Different immune roles among these subpopulations were suggested from the analysis of differentially expressed immune-related genes. These results provide a unified classification of shrimp hemocytes, which improves the understanding of its immune system.

Understanding ecosystem dynamics is crucial as contemporary human societies face ecosystem degradation. One of the challenges that needs to be recognized is the complex hierarchical dynamics. Conventional dynamic models in ecology often represent only the population level and have yet to include the dynamics of the sub-organism level, which makes an ecosystem a complex adaptive system that shows characteristic behaviors such as resilience and regime shifts. The neglect of the sub-organism level in the conventional dynamic models would be because integrating multiple hierarchical levels makes the models unnecessarily complex unless supporting experimental data are present. Now that large amounts of molecular and ecological data are increasingly accessible in microbial experimental ecosystems, it is worthwhile to tackle the questions of their complex hierarchical dynamics. Here, we propose an approach that combines microbial experimental ecosystems and a hierarchical dynamic model named population–reaction model. We present a simple microbial experimental ecosystem as an example and show how the system can be analyzed by a population–reaction model. We also show that population–reaction models can be applied to various ecological concepts, such as predator–prey interactions, climate change, evolution, and stability of diversity. Our approach will reveal a path to the general understanding of various ecosystems and organisms.

Summary We present a proof-of-concept methodology for efficiently optimizing a chemical trait by using an artificial evolutionary workflow. We demonstrate this by optimizing the efficacy of antimicrobial peptides (AMPs). In particular, we used a closed-loop approach that combines a genetic algorithm, machine learning, and in vitro evaluation to improve the antimicrobial activity of peptides against Escherichia coli . Starting with a 13-mer natural AMP, we identified 44 highly potent peptides, achieving up to a ca. 160-fold increase in antimicrobial activity within just three rounds of experiments. During these experiments, the conformation of the peptides selected was changed from a random coil to an α-helical form. This strategy not only establishes the potential of in vitro molecule evolution using an algorithmic genetic system but also accelerates the discovery of antimicrobial peptides and other functional molecules within a relatively small number of experiments, allowing the exploration of broad sequence and structural space.

The number of effective antibiotics is rapidly decreasing due to the emergence of resistant bacteria, which necessitates the development of not only new, but novel strategies for developing antibiotics. Antimicrobial peptides (AMPs) are considered to be a promising new class of antibiotics, and effective strategies to predict potential candidates are still actively sought after. Here, we present a closed-loop artificial intelligence-based approach that combines an in silico genetic algorithm, machine learning prediction, and in vitro evaluation to improve the antimicrobial activity of peptides. Starting with a 13-mer natural AMP, 44 highly potent peptides were identified achieving up to a ca. 160-fold increase in antimicrobial activity within just three rounds of experiments. During these experiments, the conformation of the peptides selected was observed to alter from a random coil to α-helical form through the optimization process, and this is thought to significantly contribute to the improvement of antimicrobial activity. This strategy therefore shows it is possible to accelerate the discovery of antimicrobial peptides within a relatively small number of experiments, and to explore broad sequence and structural space.

Cell-free protein expression systems have been widely used for synthetic biology and metabolic engineering applications in recent years. Yet little is known about protein expression in the cell-free systems. Here we take a systems approach to uncover underlying dynamics of cell-free protein expression. We construct a set of T7 promoter variants to express proteins at different transcription rates in a reconstituted andE. coliextract-based cell-free systems. We find that the maximum expression level and the rate of protein synthesis as responses to the transcription rate change are different in the two cell-free systems, suggesting they are driven by different expression dynamics. We confirm this by constructing a simple mathematical model for each cell-free system, which well reproduce the experimental results and also identify different limiting factors for better protein expression in the two cell-free systems. In particular, they revealed there is a negative feedback effect in the mRNA-protein translation by the PURE system and also identified different limiting factors for better protein expression in the two cell-free systems.

The process of converting structural models derived from single-crystal X-ray diffraction experiments into physical models for the purposes of visualization/communication and collaboration by the use of three-dimensional (3D) printing techniques is described. Digital information regarding the relative positioning of atoms in a crystal structure is translated, using a suite of computer programs, into a 3D computer model of a solid form, corresponding to that information which can then be saved in a file format for 3D printing. These files are then used to produce to-scale physical models of the structural information using two different 3D printing methodologies.

We overview families of Boolean logical gates and circuits implemented in computer models and experimental laboratory prototypes of computing devices made of living slime mould Physarum polycephalum. These include attraction gates, based on chemo-tactic behaviour of slime mould; ballistic gates, employing inertial movement of the slime mould’s active zones and a repulsion between growing zones; repellent gates, exploited photo avoidance of P. polycephalum; frequency gates, based on modification of electrical potential oscillations frequency in protoplasmic tubes; fluidic gates, where a tactical response of the protoplasmic tubes is used for the actuation of two- and four-input logical gates and memory devices; and circuits based on quantitative transformations which completely avoids spatial propagation, branching and crossings in the design of circuits.

Whole-cell biosensors are a type of biosensor that incorporates living cells as a biorecognition element [1]. Conventionally, chemical or physical sensors, such as high-performance liquid chromatography (HPLC) or gas chromatography coupled with mass spectrometry (GC-MS), have been used for analyzing samples of interest (e.g., the composition and concentration of chemicals). Although they are highly accurate and results are reliable, such instrumentation is expensive and often requires special skills and facilities for analysis. An alternative approach in sensor technologies is sensors using biological components, such as enzymes and living cells. They are simpler and often singlepurpose devices, but can be easily produced (hence inexpensive). There are two different objectives in biosensor research: One objective is to develop biological counterparts of conventional chemical or physical sensors. Biological molecules (enzymes, antibodies, and nucleic acid fragments) are commonly used to develop such biosensors due to their substrate specificities [2]. Another objective is the toxicity assessment. While biomolecule-based biosensors are more accurate and give more detailed information about analytes, it is difficult to know the effect of the analyte on living organisms. Thus, a main application domain of biosensor using living cells or tissues is to obtain information about functional consequences of substrates being analyzed or the measurement of the total “bioavailability.” The bioavailable (or bioaccessible) compound is defined as “(a molecule that) is freely available to cross an organism’s cellular membrane from the medium the organism inhabits at a given time” [3], which would be useful for the understanding of, for example, the environmental effect of the compound. Potential applications of whole-cell biosensors could be the assessment of physiological effect of a pharmaceutical compound, checking if a substance is an agonist or an antagonist for a receptor, the cellular toxicity test, and the environmental pollution test [4]. As these types of information involve vital reactions

The slime mouldPhysarumpolycephalumis a suitable candidate organism for soft-matter robotics because it exhibits controllable transport, movement and guidance behaviour.Physarummay be considered as a smart computing and actuating material since both its motor and control systems are distributed within its undifferentiated tissue and can survive trauma such as excision, fission and fusion of plasmodia. Thus it may be suitable for exploring the generation and distribution of micro-actuation in individual units or planar arrays. We experimentally show how the plasmodium ofPhysarumis shaped to execute controllable oscillatory transport behaviour applicable in small hybrid engines. We measure the lifting force of the plasmodium and demonstrate how protoplasmic transport can be influenced by externally applied illumination stimuli. We provide an exemplar vehicle mechanism by coupling the oscillations of the plasmodium to drive the wheels of a Braitenberg vehicle and use light stimuli to effect a steering mechanism. Using a particle model ofPhysarumwe show how emergent travelling wave patterns produced by competing oscillatory domains may be used to to generate spatially represented actuation patterns. We demonstrate different patterns of controllable motion, including linear, reciprocal, rotational and helical, and demonstrate in simulation how dynamic oscillatory patterns may be translated into motive forces for simple transport of substances within a patterned environment.

The regeneration process of contractile oscillation in the plasmodium of Physarum polycephalum is investigated experimentally and modelled computationally. When placed in a well, the Physarum cell restructures the body (fusion of small granule-like cells) and shows various complex oscillation patterns. After it completed the restructuring and regained synchronized oscillation within the body, the cell shows bilateral oscillation or rotating wave pattern. This regeneration process did not depend on the well size and all the cases tested here showed similar time course. Phase synchronization analysis based on Hilbert Transform also suggested that the cell can develop a fully synchronized oscillation within a fixed time no matter what the cell size is. A particle-based computational model was developed in order to model the emergence of oscillation patterns. Particles employing very simple and identical sensory and motor behaviors interacted with each other via the sensing and deposition of chemoattractants in a diffusive environment. From a random and almost homogeneous distribution, emergent domains of oscillatory activity emerged. By increasing the sensory radius the model simulated the regeneration process of the real plasmodium. In addition, the model replicated the rotating wave and bilateral oscillation pattern when the sensory radius was increased. The results suggest that complex emergent oscillatory behaviors (and thus the high-level systems which may utilize them, such as pumping and transport mechanisms) may be developed from simple materials inspired by Physarum slime mold.

Three dimensional (3D) printing is actively sought after in recent years as a promising novel technology to construct complex objects, which scope spans from nano- to over millimeter scale. Previously we utilized Fused deposition modeling (FDM)-based 3D printer to construct complex 3D chemical fluidic systems, and here we demonstrate the construction of 3D milli-fluidic structures for programmable liquid handling and control of biological samples. Basic fluidic operation devices, such as water-in-oil (W/O) droplet generators for producing compartmentalized mono-disperse droplets, sensor-integrated chamber for online monitoring of cellular growth, are presented. In addition, chemical surface treatment techniques are used to construct valve-based flow selector for liquid flow control and inter-connectable modular devices for networking fluidic parts. As such this work paves the way for complex operations, such as mixing, flow control, and monitoring of reaction / cell culture progress can be carried out by constructing both passive and active components in 3D printed structures, which designs can be shared online so that anyone with 3D printers can reproduce them by themselves.

The control and prediction of complex chemical systems is a difficult problem due to the nature of the interactions, transformations and processes occurring. From self-assembly to catalysis and self-organization, complex chemical systems are often heterogeneous mixtures that at the most extreme exhibit system-level functions, such as those that could be observed in a living cell. In this paper, we outline an approach to understand and explore complex chemical systems using an automated droplet maker to control the composition, size and position of the droplets in a predefined chemical environment. By investigating the spatio-temporal dynamics of the droplets, the aim is to understand how to control system-level emergence of complex chemical behaviour and even view the system-level behaviour as a programmable entity capable of information processing. Herein, we explore how our automated droplet-maker platform could be viewed as a prototype chemical heterotic computer with some initial data and example problems that may be viewed as potential chemically embodied computations.

We here present a method to reconstruct effective free energy landscapes (FELs) of lipid vesicles from the statistical analysis of a large number of microscope images. This method, not only allows us to define possible energy landscapes, but also highlights minority vesicle shapes that were otherwise hidden in the majority. When compared with temporal evolution of deforming lipid vesicles, it was found that the trajectory of deforming vesicles was in accordance with the reconstructed landscape, in which the minority shapes play a key role. When compared with theoretical models, it revealed that the vesicle shapes characterised in the reconstructed FELs were consistent with the theoretically predicted shapes. These results suggest that the FEL analysis can be a useful tool to investigate the morphological dynamics of lipid vesicles, in conjunction with other analytical methods.

High-dose melphalan has been gaining recognition as a highly emetogenic agent used in hematopoietic stem cell transplantation (HSCT). The aim of this retrospective study was to elucidate the efficacy of aprepitant in preventing high-dose melphalan-induced emesis. Sixty patients who received melphalan (70 mg/m2/day, 2 days) and fludarabine (125 mg/m2/day, 5 days) as conditioning for allogeneic HSCT for hematological malignancies, and who received ondansetron and methylprednisolone as an antiemetic prophylaxis, were eligible. Twenty of these 60 patients also received aprepitant for 5 days (aprepitant group); the remaining 40 patients served as a control. The rates of complete response (CR), defined as no emesis without rescue medications, and complete protection (CP), defined as no emesis with or without rescue medications, were assessed between the two groups. The observation period was 12 days from the first day of melphalan administration. The CR and CP rates were significantly higher in the aprepitant group than in the control group during the observation period (35 % versus 10 %, P < 0.05; 85 % versus 33 %, P < 0.001; respectively). These results suggest that aprepitant in combination with ondansetron and steroid effectively ameliorates nausea and vomiting caused by the high-dose melphalan-based conditioning for allogeneic HSCT.

Plasmodium stage of Physarum polycephalum behaves as a distributed dynamical pattern formation mechanism who's foraging and migration is influenced by local stimuli from a wide range of attractants and repellents. Complex protoplasmic tube network structures are formed as a result, which serve as efficient `circuits' by which nutrients are distributed to all parts of the organism. We investigate whether this `bottom-up' circuit routing method may be harnessed in a controllable manner as a possible alternative to conventional template-based circuit design. We interfaced the plasmodium of Physarum polycephalum to the planar surface of the spatially represented computing device, (Mills' Extended Analog Computer, or EAC), implemented as a sheet of analog computing material whose behaviour is input and read by a regular 5x5 array of electrodes. We presented a pattern of current distribution to the array and found that we were able to select the directional migration of the plasmodium growth front by exploiting plasmodium electro-taxis towards current sinks. We utilised this directional guidance phenomenon to route the plasmodium across its habitat and were able to guide the migration around obstacles represented by repellent current sources. We replicated these findings in a collective particle model of Physarum polycephalum which suggests further methods to orient, route, confine and release the plasmodium using spatial patterns of current sources and sinks. These findings demonstrate proof of concept in the low-level dynamical routing for biologically implemented circuit design.