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Darkfield microscopy is an effective technique and well suited for use involving live and unstained biological samples, where a bulky condenser should be precisely aligned in the optical path to produce a cone of illumination light. Here, a compact darkfield illuminator is invented that has a planar shape and can work at broadband wavelengths. It can be easily incorporated into a conventional microscope as a substrate to produce hollow cones of light for large‐area darkfield illumination, resulting in a high‐contrast high‐sensitivity widefield microscopy images. Taking advantages of its planar and compact structure, this darkfield illuminator is successfully integrated with a microfluidic chip; thus, the actions and reactions of biological and chemical specimens flowing inside micrometer‐sized channels, can be real‐time imaged with high contrast and sensitivity in a large field‐of‐view (FoV). For the first time, the work integrates a compact darkfield illuminator with microfluidic chips, which produces a new platform for applications in biology, chemistry, and biomedical engineering. [ABSTRACT FROM AUTHOR]

Here, we report a rapid and accurate optical method for detecting cells from liquid samples in a label-free manner. The working principle of the method is based on the interference of parts of a conical laser beam, coming from a single-mode optical fiber directly, and reflected from a flat glass surface. The glass is functionalized by antibodies against the cells to be detected from the liquid sample. Cells bound to that surface modify the reflected beam, and hence, change the resulting interference pattern, too. By registering and interpreting the variation in the image, the presence of cells from the sample can be detected. As for a demonstration, cell suspensions from a U937 cell line were used in glass chambers functionalized by antibodies (TMG6-5 (mIgG1)) to which the cells specifically bind. The limit of detection (LOD) of the method was also estimated. This proof-of-concept setup offers a cost-effective and easy-to-use way of rapid and specific detection of any type of cells (including pathogens) from suspensions (e.g., body fluids). The possible portability of the device predicts its applicability as a rapid test in clinical diagnostics. [ABSTRACT FROM AUTHOR]

X-rays can penetrate deeply into biological cells and thus allow for examination of their internal structures with high spatial resolution. In this study, X-ray phase-contrast imaging and tomography is combined with an X-ray-compatible optical stretcher and microfluidic sample delivery. Using this setup, individual cells can be kept in suspension while they are examined with the X-ray beam at a synchrotron. From the recorded holograms, 2D phase shift images that are proportional to the projected local electron density of the investigated cell can be calculated. From the tomographic reconstruction of multiple such projections the 3D electron density can be obtained. The cells can thus be studied in a hydrated or even living state, thus avoiding artifacts from freezing, drying or embedding, and can in principle also be subjected to different sample environments or mechanical strains. This combination of techniques is applied to living as well as fixed and stained NIH3T3 mouse fibroblasts and the effect of the beam energy on the phase shifts is investigated. Furthermore, a 3D algebraic reconstruction scheme and a dedicated mathematical description is used to follow the motion of the trapped cells in the optical stretcher for multiple rotations.

X‐rays can penetrate deeply into biological cells and thus allow for examination of their internal structures with high spatial resolution. In this study, X‐ray phase‐contrast imaging and tomography is combined with an X‐ray‐compatible optical stretcher and microfluidic sample delivery. Using this setup, individual cells can be kept in suspension while they are examined with the X‐ray beam at a synchrotron. From the recorded holograms, 2D phase shift images that are proportional to the projected local electron density of the investigated cell can be calculated. From the tomographic reconstruction of multiple such projections the 3D electron density can be obtained. The cells can thus be studied in a hydrated or even living state, thus avoiding artifacts from freezing, drying or embedding, and can in principle also be subjected to different sample environments or mechanical strains. This combination of techniques is applied to living as well as fixed and stained NIH3T3 mouse fibroblasts and the effect of the beam energy on the phase shifts is investigated. Furthermore, a 3D algebraic reconstruction scheme and a dedicated mathematical description is used to follow the motion of the trapped cells in the optical stretcher for multiple rotations. [ABSTRACT FROM AUTHOR]

The relationship between the contrast to noise ratio and intensity based cross-correlation coefficients for both protein crystallography and X-ray imaging are compared. It is concluded that, for protein crystallography at near atomic resolution, the intensity based cross-correlation coefficients give a reasonable indication of the quality of the corresponding electron density. For X-ray imaging of biological materials such as cells and soft tissue, the wide range of contrast of the features means that intensity based correlation coefficients can give a poor indication of the interpretability of an image. Rather than the term resolution, it is the contrast to noise ratio for a feature of interest at the relevant spatial frequency that is more relevant. Additional metrics are required to describe the quality of an image, and these are discussed.

Here, we report a rapid and accurate optical method for detecting cells from liquid samples in a label-free manner. The working principle of the method is based on the interference of parts of a conical laser beam, coming from a single-mode optical fiber directly, and reflected from a flat glass surface. The glass is functionalized by antibodies against the cells to be detected from the liquid sample. Cells bound to that surface modify the reflected beam, and hence, change the resulting interference pattern, too. By registering and interpreting the variation in the image, the presence of cells from the sample can be detected. As for a demonstration, cell suspensions from a U937 cell line were used in glass chambers functionalized by antibodies (TMG6-5 (mIgG1)) to which the cells specifically bind. The limit of detection (LOD) of the method was also estimated. This proof-of-concept setup offers a cost-effective and easy-to-use way of rapid and specific detection of any type of cells (including pathogens) from suspensions (e.g., body fluids). The possible portability of the device predicts its applicability as a rapid test in clinical diagnostics.

Controlled transport of biological cells in biomedical applications such as sorting, cell sequencing, and assembly of multicellular structures is a technological challenge. Research areas such as drug delivery or tissue engineering can benefit from precise cell location resulting in faster response rates or more complex tissue structures. Using computational methods, different soft magnetic elements with curved edges are designed to form a transport network, enabling transport and all functionalities for the manipulation of microbeads and cells on surfaces by rotational magnetic fields. Building blocks with bimodal functionalities due to segments of differently curved edges permit breakpoints as well as switchable transport via splitting and combining elements. Connecting the elements, networked paths are realized which allow variable movement patterns of magnetic carriers and cells. The direction of magnetic field rotation is altered to direct the beads and cells into different transport lines, and the exact timing is not critical. The networks are used to achieve deterministic movement of microbeads and cells with minimal intervention. Programmed transport over one millimeter with cell transport velocities of several micrometers per s is demonstrated. Based on scalable microchip technology, the networks can be integrated with CMOS‐compatible materials and straightforwardly combined with sensing and diagnostic structures. [ABSTRACT FROM AUTHOR]

Abstract Background The cultivation, analysis, and isolation of single cells or cell cultures are fundamental to modern biological and medical processes. The novel LIFTOSCOPE technology aims to integrate analysis and isolation into one versatile, fully automated device. Methods LIFTOSCOPE’s three core technologies are high-speed microscopy for rapid full-surface imaging of cell culture vessels, AI-based semantic segmentation of microscope images for localization and evaluation of cells, and laser-induced forward transfer (LIFT) for contact-free isolation of cells and cell clusters. LIFT transfers cells from a standard microtiter plate (MTP) across an air gap to a receiver plate, from where they can be further cultivated. The LIFT laser is integrated into the optical path of an inverse microscope, allowing to switch quickly between microscopic observation and cell transfer. Results Tests of the individual process steps prove the feasibility of the concept. A prototype setup shows the compatibility of the microscope stage with the LIFT laser. A specifically designed MTP adapter to hold a receiver plate has been designed and successfully used for material transfers. A suitable AI algorithm has been found for cell selection. Conclusion LIFTOSCOPE speeds up cell cultivation and analysis with a target process time of 10 minutes, which can be achieved if the cell transfer is sped up using a more efficient path-finding algorithm. Some challenges remain, like finding a suitable cell transfer medium. Significance The LIFTOSCOPE system can be used to extend existing cell cultivation systems and microscopes for fully automated biotechnological applications.

Benefiting from the developments of advanced optical microscopy techniques, the mysteries of biological functions at the cellular and subcellular levels have been continuously revealed. Stimulated Raman scattering (SRS) microscopy is a rapidly growing technique that has attracted broad attentions and become a powerful tool for biology and biomedicine, largely thanks to its chemical specificity, high sensitivity and fast image speed. This review paper introduces the principles of SRS, discusses the technical developments and implementations of SRS microscopy, then highlights and summarizes its applications on biological cellular machinery and finally shares our visions of potential breakthroughs in the future. [ABSTRACT FROM AUTHOR]

Benefiting from the developments of advanced optical microscopy techniques, the mysteries of biological functions at the cellular and subcellular levels have been continuously revealed. Stimulated Raman scattering (SRS) microscopy is a rapidly growing technique that has attracted broad attentions and become a powerful tool for biology and biomedicine, largely thanks to its chemical specificity, high sensitivity and fast image speed. This review paper introduces the principles of SRS, discusses the technical developments and implementations of SRS microscopy, then highlights and summarizes its applications on biological cellular machinery and finally shares our visions of potential breakthroughs in the future.

Background: The cultivation, analysis, and isolation of single cells or cell cultures are fundamental to modern biological and medical processes. The novel LIFTOSCOPE technology aims to integrate analysis and isolation into one versatile, fully automated device. Methods: LIFTOSCOPE's three core technologies are high-speed microscopy for rapid full-surface imaging of cell culture vessels, AI-based semantic segmentation of microscope images for localization and evaluation of cells, and laser-induced forward transfer (LIFT) for contact-free isolation of cells and cell clusters. LIFT transfers cells from a standard microtiter plate (MTP) across an air gap to a receiver plate, from where they can be further cultivated. The LIFT laser is integrated into the optical path of an inverse microscope, allowing to switch quickly between microscopic observation and cell transfer. Results: Tests of the individual process steps prove the feasibility of the concept. A prototype setup shows the compatibility of the microscope stage with the LIFT laser. A specifically designed MTP adapter to hold a receiver plate has been designed and successfully used for material transfers. A suitable AI algorithm has been found for cell selection. Conclusion: LIFTOSCOPE speeds up cell cultivation and analysis with a target process time of 10 minutes, which can be achieved if the cell transfer is sped up using a more efficient path-finding algorithm. Some challenges remain, like finding a suitable cell transfer medium. Significance: The LIFTOSCOPE system can be used to extend existing cell cultivation systems and microscopes for fully automated biotechnological applications. [ABSTRACT FROM AUTHOR]

This paper presents a model that describes a possible mechanism for electromagnetic (EM) signal transmission and reception by bacterial cells within their biofilm communities. Bacterial cells in biofilms are embedded into a complex extracellular matrix containing, among other components, charged helical nanofibrils from amyloid-forming peptides. Based on the current knowledge about the nanoscale structure and dynamics of the amyloids, we explore a hypothetical model that the mechanical vibration of these nanofibrils allows the cells to transmit EM signals to their neighboring cells and the surrounding environment. For the reception, the induced electric field can either exert force on the charges of adjacent nanofibrils associated with the neighboring cells or affect the placement/conformation of a certain charged messenger protein within the cell. The proposed model is based on a coupled system of electrical and mechanical nanoscale structures, which predicts signal transmission and reception within kHz-GHz frequency ranges. Different mechanisms for generating EM signals at various frequency bands related to the structure of the cell and their biofilm constituents are discussed.

Matrix operation is easy to be paralleled by hardware, and the memristor network can realize a parallel matrix computing model with in-memory computing. This article proposes a matrix-friendly genetic algorithm (MGA), in which the population is represented by a matrix and the evolution of population is realized by matrix operations. Compared with the performance of a baseline genetic algorithm (GA) on solving the maximum value of the binary function, MGA can converge better and faster. In addition, MGA is more efficient because of its parallelism on matrix operations, and MGA runs 2.5 times faster than the baseline GA when using the NumPy library. Considering the advantages of the memristor in matrix operations, memristor circuits are designed for the deployment of MGA. This deployment method realizes the parallelization and in-memory computing (memristor is both memory and computing unit) of MGA. In order to verify the effectiveness of this deployment, a feature selection experiment of logistic regression (LR) on Sonar datasets is completed. LR with MGA-based feature selection uses 46 fewer features and achieves 11.9% higher accuracy. [ABSTRACT FROM AUTHOR]

[Display omitted] Generation of amplified stimulated emission inside mammalian cells has paved the way for a novel bioimaging and cell sensing approach. Single cells carrying gain media (e.g., fluorescent molecules) are placed inside an optical cavity, allowing the production of intracellular laser emission upon sufficient optical pumping. Here, we investigate the possibility to trigger another amplified emission phenomenon (i.e., amplified spontaneous emission or ASE) inside two different cell types, namely macrophage and epithelial cells from different species and tissues, in the presence of a poorly reflecting cavity. Furthermore, the resulting ASE properties can be enhanced by introducing plasmonic nanoparticles. The presence of gold nanoparticles (AuNPs) in rhodamine 6G-labeled A549 epithelial cells results in higher intensity and lowered ASE threshold in comparison to cells without nanoparticles, due to the effect of plasmonic field enhancement. An increase in intracellular concentration of AuNPs in rhodamine 6G-labeled macrophages is, however, responsible for the twofold increase in the ASE threshold and a reduction in the ASE intensity, dominantly due to a suppressed in and out-coupling of light at high nanoparticle concentrations. [ABSTRACT FROM AUTHOR]

In-situ tool condition monitoring (in-situ TCM) is vital for metal removal manufacturing which realizes on-machine diagnosis in a real-time manner. The limitation of in-situ TCM based on traditional deep learning lies in several aspects: the requirement of sufficient labeled data of health conditions, the empirically manual designed architecture, and the labor-intensive tuning of hyperparameters. Network-based deep transfer learning (NDTL) partially solves the problem of limited labeled data. However, the time-consuming architecture design and the tuning of hyperparameters also pose a great impact on the schedule of real in-situ TCM projects. In this article, a new NDTL method is proposed, which is automatically designed by the genetic algorithm. A degradation monitoring experiment is conducted employing edge computing devices under multiple working conditions, where the texture of the machined surface is collected during the whole life cycle of milling cutters. The experimental results suggest that the proposed method possesses competitive performance evaluated by both the robustness metrics (e.g., the area under the curve of precision-recall) and the efficiency metrics (e.g., multiply–accumulates), where the trial-and-errors are reduced significantly by the automatic architecture design and the selection of hyperparameters. [ABSTRACT FROM AUTHOR]

A coloring traveling salesman problem (CTSP) generalizes the well-known multiple traveling salesman problem, where colors are used to differentiate salesmen’s the accessibility to individual cities to be visited. As a useful model for a variety of complex scheduling problems, CTSP is computationally challenging. In this paper, we propose a Multi-neighborhood Simulated Annealing-based Iterated Local Search (MSAILS) to solve it. Starting from an initial solution, it iterates through three sequential search procedures: a multi-neighborhood simulated annealing search to find a local optimum, a local search-enhanced edge assembly crossover to find nearby high-quality solutions around a local optimum, and a solution reconstruction procedure to move away from the current search region. Experimental results on two groups of 45 medium and large benchmark instances show that it significantly outperforms state-of-the-art algorithms. In particular, it is able to discover new upper bounds for 29 instances while matching 8 previous best-known upper bounds. Hence, this work greatly advances the field of CTSP. [ABSTRACT FROM AUTHOR]

A deep genetic algorithm (GA) is proposed to optimize the high-speed channel for signal integrity. In the traditional genetic algorithm-based high-speed channel optimization method, the eye height and eye width of the eye diagram are obtained by eye diagram simulation based on the full-wave algorithm, which is computationally expensive. In this letter, a deep neural network (DNN) is trained to predict the eye diagram information corresponding to a set of given design parameters of the high-speed channel. This DNN is embedded into the genetic algorithm to carry out the evaluation operation, which can greatly accelerate the evaluation process. A high-speed channel model is constructed to demonstrate the optimization capability and the benefit of the proposed method. [ABSTRACT FROM AUTHOR]

In this article, we propose an efficient approach of 2-D materials and biological cells modeling, which is defined by redefining their electromagnetic boundary conditions in terms of waves. It is a 2-D approach, so it was carried out without involving volumetric discretization, which gives a short simulation time. Then, we propose and analyze a tunable THz sensor which consists of a patch and ring resonator, in which a sample carrier is located in the middle of it to put the biological cells. This resonator is composed by one or two layers of 2-D materials stacked in the $z$ -direction. The sensing performance of this sensor can be readily tuned by controlling the black phosphorus (BP) electron doping or the graphene chemical potential. In the experiment, the performances of the proposed sensor are tested by analyzing different configurations based on 2-D materials. The final results indicated that when we combined the advantages of graphene and BP (G-BP), the hybrid structure (G-BP) gives more efficient results compared to pure BP or graphene-based structure. In our electromagnetic simulations, the sensitivity reaches 23.866 and 28.149 THz/RIU in healthy and cancer cells. The proposed G-BP sensor in this article is a promising candidate for the THz biomedical sensors due to its high sensitivity, tunability, and simple structure. [ABSTRACT FROM AUTHOR]

Genetic algorithms (GAs) have been widely applied in Steiner tree optimization problems. However, as the core operation, existing crossover operators for tree-based GAs suffer from producing illegal offspring trees. Therefore, some global link information must be adopted to ensure the connectivity of the offspring, which incurs heavy computation. To address this problem, this article proposes a new crossover mechanism, called leaf crossover (LC), which generates legal offspring by just exchanging partial parent chromosomes, requiring neither the global network link information, encoding/decoding nor repair operations. Our simulation study indicates that GAs with LC outperform GAs with existing crossover mechanisms in terms of not only producing better solutions but also converging faster in networks of varying sizes. [ABSTRACT FROM AUTHOR]

Parameter estimation of layered media is an important application of ground penetrating radar (GPR). However, in the case of thin-layer detection, the limitation of GPR vertical resolution can lead to overlapping reflected signals, which seriously deteriorates the detection results. To solve this problem, a modified reflected signal reconstruction method based on reflected signal reconstruction is proposed in this letter. First, a forward model of the GPR reflected signal is constructed using the generalized reflection coefficient definition. Then, the parameter inversion is transformed into an optimization problem. The residue cost function is defined as the error square between the model and the actual reflected signal, which is minimized by using genetic algorithm. Furthermore, the generalized reflection coefficient spectrum is feasible to estimate the parameters of each layer as the initial values of the parameters required by the algorithm, thus improving the accuracy and convergence speed of the algorithm. Numerical, experimental, and field tests demonstrate that the method has a high time resolution and antinoise capability in the inversion of layered media parameters. [ABSTRACT FROM AUTHOR]

Intellectual biochemical detection is an essential part of a smart society, which is important for future personal health management. The judgment process of health level should be carried out by comprehensive consideration of multiple indicators. To achieve that, multi-substance sensors are necessary. Due to the high-throughput and rapid detection characteristics, the multi-material testing microfluidic devices are promising bio-monitoring devices. To ensure the detection accuracy of the multi-substance-detection microfluidic devices, the topological structure of the channels must be designed carefully. Not only the flow rate but also the substance residue is decided by the topological structure of the channels. Thus, in this paper, we report a genetic algorithm-based topological optimization method for microfluidic devices. The material residue is modeled as the energy dissipation caused by friction. The topology and the position of the inlet and outlets are jointly optimized to minimize the energy dissipation with different desired flow rates at the outlets. The proposed genetic algorithm-based two-tier method enhanced global search ability. The simulation and experiment results show that our algorithm can decrease the energy dissipation on the premise of ensuring the outlet flow rate with comparable computational complexity. [ABSTRACT FROM AUTHOR]

The inference of Boolean networks is crucial for analyzing the topology and dynamics of gene regulatory networks. Many data-driven approaches using evolutionary algorithms have been proposed based on time-series data. However, the ability to infer both network topology and dynamics is restricted by their inflexible encoding schemes. To address this problem, we propose a novel Boolean network inference algorithm for inferring both network topology and dynamics simultaneously. The main idea is that, we use a marker-based genetic algorithm to encode both regulatory nodes and logical operators in a chromosome. By using the markers and introducing more logical operators, the proposed algorithm can infer more diverse candidate Boolean functions. The proposed algorithm is applied to five networks, including two artificial Boolean networks and three real-world gene regulatory networks. Compared with other algorithms, the experimental results demonstrate that our proposed algorithm infers more accurate topology and dynamics. [ABSTRACT FROM AUTHOR]

The development of DNA sequencing technologies makes it possible to obtain reads originated from both copies of a chromosome (two parental chromosomes, or haplotypes) of a single individual. Reconstruction of both haplotypes (i.e., haplotype phasing) plays a crucial role in genetic analysis and provides relationship information between genetic variation and disease susceptibility. With the emerging third-generation sequencing technologies, most existing approaches for haplotype phasing suffer from performance issues to handle long and error-prone reads. We develop a divide-and-conquer algorithm, DCHap, to phase haplotypes using third-generation reads. We benchmark DCHap against three state-of-the-art phasing tools on both PacBio SMRT data and ONT Nanopore data. The experimental results show that DCHap generates more accurate or comparable results (measured by the switch errors) while being scalable for higher coverage and longer reads. DCHap is a fast and accurate algorithm for haplotype phasing using third-generation sequencing data. As the third-generation sequencing platforms continue improving on their throughput and read lengths, accurate and scalable tools like DCHap are important to improve haplotype phasing from the advances of sequencing technologies. The source code is freely available at https://github.com/yanboANU/Haplotype-phasing. [ABSTRACT FROM AUTHOR]

Background: In medicine, karyotyping chromosomes is important for medical diagnostics, drug development, and biomedical research. Unfortunately, chromosome karyotyping is usually done by skilled cytologists manually, which requires experience, domain expertise, and considerable manual efforts. Therefore, automating the karyotyping process is a significant and meaningful task. Method: This paper focuses on chromosome classification because it is critical for chromosome karyotyping. In recent years, deep learning-based methods are the most promising methods for solving the tasks of chromosome classification. Although the deep learning-based Inception architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge, it has not been used in chromosome classification tasks so far. Therefore, we develop an automatic chromosome classification approach named CIR-Net based on Inception-ResNet which is an optimized version of Inception. However, the classification performance of origin Inception-ResNet on the insufficient chromosome dataset still has a lot of capacity for improvement. Further, we propose a simple but effective augmentation method called CDA for improving the performance of CIR-Net. Results: The experimental results show that our proposed method achieves 95.98 percent classification accuracy on the clinical G-band chromosome dataset whose training dataset is insufficient. Moreover, the proposed augmentation method CDA improves more than 8.5 percent (from 87.46 to 95.98 percent) classification accuracy comparing to other methods. In this paper, the experimental results demonstrate that our proposed method is recent the most effective solution for solving clinical chromosome classification problems in chromosome auto-karyotyping on the condition of the insufficient training dataset. Code and Dataset are available at https://github.com/CloudDataLab/CIR-Net. [ABSTRACT FROM AUTHOR]

Deep convolutional neural network (CNN) shows excellent effectiveness on hyperspectral image (HSI) classification. However, the architecture design of CNN requires abundant expert knowledge and experience, which poses great prohibition to its wide application in real-world engineering. To alleviate the issue, this article proposes an evolving block-based CNN (EB-CNN) to search the optimal architecture based on the genetic algorithm (GA) automatically. Specifically, two kinds of basic blocks with totally six different configurations are first designed to construct the search space. Then, a flexible encoding strategy is devised for the GA to allow different chromosomes to evolve with different lengths. In this manner, the width of each layer and the depth of the architecture can be simultaneously optimized. Furthermore, a novel swapping mutation operator is proposed for the GA to speed up the search efficiency and save computing resources. With the abovementioned techniques, the proposed algorithm automatically seeks the optimal CNN architecture for HSI classification, leading to its better usability than handcrafted CNNs. At last, extensive experiments conducted on five commonly used HSI datasets demonstrate that the proposed EB-CNN achieves highly competitive or even better performance, as compared with the state-of-the-art peer algorithms. [ABSTRACT FROM AUTHOR]

An adaptive-network-based fuzzy inference system (ANFIS) offers a popular and powerful fuzzy inference mechanism. As with many other advanced data-driven techniques, developing an effective ANFIS typically requires sufficient training data. However, in many real-world applications, it is not always straightforward to obtain a large amount of representative data that cover the entire problem space to accomplish the required training, seriously restricting the performance of a learned ANFIS. This article introduces a new ANFIS learning approach through an evolutionary process, which is able to generate an ANFIS with only a small amount of training data in a certain problem region, by interpolating well-trained ANFISs in the neighboring regions. Such a process works by first producing an initial population of candidate fuzzy rules in the region of data shortage, through interpolating a rule dictionary constructed from trained ANFISs in the neighborhood regions. The crossover and mutation operations over these candidate rules are then executed in an effort to attain candidates of improved performance. When this genetic learning process terminates, the chromosomes in the final population either collectively form or each individually represents a learned ANFIS, depending on whether a single fuzzy rule or a set of fuzzy rules representing an entire ANFIS is implemented with a chromosome within the evolving population. Comparative experimental evaluations on both synthetic and real-world datasets are carried out, demonstrating that in spite of data shortage, the proposed interpolation approach is able to produce ANFIS models that significantly outperform those trained using existing learning mechanisms. [ABSTRACT FROM AUTHOR]

Quantum-inspired differential evolution (QDE) is an evolutionary algorithm, which can effectively solve complex optimization problems. However, sometimes, it easily leads to premature convergence and low search ability and falls to local optima. To overcome these problems, based on the MSIQDE (improved QDE with multistrategies) algorithm, an enhanced MSIQDE algorithm based on mixing multiple strategies, namely, EMMSIQDE is proposed in this article. In the EMMSIQDE, a new differential mutation strategy of a difference vector is proposed to enhance the search ability and descent ability. Then, a new multipopulation mutation evolution mechanism is designed to ensure the relative independence of each subpopulation and the population diversity. The feasible solution space transformation strategy is used to achieve the optimal solution by mapping the quantum chromosome from a unit space to solution space. Finally, some multidimensional unimodal and multimodal functions are selected to demonstrate the optimization performance of EMMSIQDE. The results demonstrate that the EMMSIQDE is significantly better than the DE, QDE, QGA, and MSIQDE, and has better optimization ability, scalability, efficiency, and stability. [ABSTRACT FROM AUTHOR]

In vitro platelet activation is established in various clinical settings for wound healing and tissue regeneration. After separating platelet rich plasma (PRP) from whole blood, a biochemical activator, typically bovine thrombin (BT), is applied to activate PRP, which clots the PRP and releases growth factors beneficial for wound healing. BT's drawbacks, particularly cost, availability, workflow challenges, and potential immune responses, motivated the development of ex vivo electrical activation of PRP using pulsed electric fields (PEFs) with durations on the order of hundreds of nanoseconds and electric field strengths from ~10-100 kV/cm. PEFs permeabilize platelets to facilitate Ca2+ transport into the cells to induce platelet activation; however, membrane permeabilization does not require PEFs of such short duration and high intensity. This study demonstrates that 5-100 μs duration PEFs effectively activate platelets. Treating PRP with ten different single pulse waveforms with durations between 5 and 100 μs induces similar or higher levels of platelet derived, vascular endothelial, and epidermal growth factor release compared to BT. These results indicate the potential clinical relevance of microsecond PEFs for platelet stimulation, which may reduce the expense and device footprint for PEF mediated platelet stimulation compared to nanosecond pulse generators, facilitating technology transition for clinical and trauma applications.

The Indoor positioning based on the ZigBee received signal strength index has attracted more and more researchers’ attention and, due to its low cost, low hardware power consumption and easy implementation. However, because of multipath effects and shadow effects, traditional indoor positioning algorithms cannot obtain good positioning effects. In order to improve the accuracy of ZigBee indoor positioning, this paper proposes an indoor positioning algorithm of annealing algorithm (SA) and genetic algorithm (GA) optimized neural network (SAGA-BP), and the superiority of this algorithm is proved through simulation and experiment. First, establish the position relationship between the received signal strength indicator(RSSI) and the target position, and arrange the node network structure model to collect signals to establish a fingerprint database. Then use the mechanism of the annealing algorithm combined with the genetic algorithm to optimize the initial weight and initial threshold of the neural network algorithm, so that it can quickly jump out of the local optimal solution and achieve high-precision positioning. Experiments have proved the effectiveness of the positioning algorithm. Compared with BP and GA-BP algorithms, SAGA-BP positioning algorithm has an average error of 0.75m for RSSI signals after acquisition and processing, and an average error of BP positioning algorithm is 1.24m. The average error of GA-BP algorithm is 0.98m. Thus, the SAGA-BP algorithm has higher positioning accuracy. [ABSTRACT FROM AUTHOR]

We propose a method for human embryo grading with its images. This grading has been achieved by positive-negative classification (i.e., live birth or non-live birth). However, negative (non-live birth) labels collected in clinical practice are unreliable because the visual features of negative images are equal to those of positive (live birth) images if these non-live birth embryos have chromosome abnormalities. For alleviating an adverse effect of these unreliable labels, our method employs Positive-Unlabeled (PU) learning so that live birth and non-live birth are labeled as positive and unlabeled, respectively, where unlabeled samples contain both positive and negative samples. In our method, this PU learning on a deep CNN is improved by a learning-to-rank scheme. While the original learning-to-rank scheme is designed for positive-negative learning, it is extended to PU learning. Furthermore, overfitting in this PU learning is alleviated by regularization with mutual information. Experimental results with 643 time-lapse image sequences demonstrate the effectiveness of our framework in terms of the recognition accuracy and the interpretability. In quantitative comparison, the full version of our proposed method outperforms positive-negative classification in recall and F-measure by a wide margin (0.22 vs. 0.69 in recall and 0.27 vs. 0.42 in F-measure). In qualitative evaluation, visual attentions estimated by our method are interpretable in comparison with morphological assessments in clinical practice. [ABSTRACT FROM AUTHOR]

In 2020, a research group made the stunning announcement that it had built programmable organisms—living robots they called xenobots—out of biological cells, and these xenobots could work together to perform simple tasks. Now, less than two years later, the same group has an even more astonishing announcement: The xenobots can autonomously self-replicate in a way that is completely different from any other living organisms, and their progeny are functional and similarly able to self-replicate. [ABSTRACT FROM AUTHOR]

Background: In medicine, chromosome karyotyping analysis plays a crucial role in prenatal diagnosis for diagnosing whether a fetus has severe defects or genetic diseases. However, chromosome instance segmentation is the most critical obstacle to automatic chromosome karyotyping analysis due to the complicated morphological characteristics of chromosome clusters, restricting chromosome karyotyping analysis to highly depend on skilled clinical analysts. Method: In this paper, we build a clinical dataset and propose multiple segmentation baselines to tackle the chromosome instance segmentation problem of various overlapping and touching chromosome clusters. First, we construct a clinical dataset for deep learning-based chromosome instance segmentation models by collecting and annotating 1,655 privacy-removal chromosome clusters. After that, we design a chromosome instance labeled dataset augmentation (CILA) algorithm for the clinical dataset to improve the generalization performance of deep learning-based models. Last, we propose a chromosome instance segmentation framework and implement multiple baselines for the proposed framework based on various instance segmentation models. Results and Conclusions:Experiments evaluated on the clinical dataset show that the best baseline of the proposed framework based on the Mask-RCNN model yields an outstanding result with $77\%$ 77 % $mAP$ m A P , $97.5\%$ 97. 5 % $AP^{50}$ A P 50 , and $95.5\%\ AP^{75}$ 95. 5 % A P 75 segmentation precision, and $95.38\%$ 95. 38 % accuracy, which exceeds results reported in current chromosome instance segmentation methods. The quantitative evaluation results demonstrate the effectiveness and advancement of the proposed method for the chromosome instance segmentation problem. The experimental code and privacy-removal clinical dataset can be found at Github. [ABSTRACT FROM AUTHOR]

Plasmids are extra-chromosomal genetic materials with important markers that affect the function and behaviour of the microorganisms supporting their environmental adaptations. Hence the identification and recovery of such plasmid sequences from assemblies is a crucial task in metagenomics analysis. In the past, machine learning approaches have been developed to separate chromosomes and plasmids. However, there is always a compromise between precision and recall in the existing classification approaches. The similarity of compositions between chromosomes and their plasmids makes it difficult to separate plasmids and chromosomes with high accuracy. However, high confidence classifications are accurate with a significant compromise of recall, and vice versa. Hence, the requirement exists to have more sophisticated approaches to separate plasmids and chromosomes accurately while retaining an acceptable trade-off between precision and recall. We present GraphPlas, a novel approach for plasmid recovery using coverage, composition and assembly graph topology. We evaluated GraphPlas on simulated and real short read assemblies with varying compositions of plasmids and chromosomes. Our experiments show that GraphPlas is able to significantly improve accuracy in detecting plasmid and chromosomal contigs on top of popular state-of-the-art plasmid detection tools. [ABSTRACT FROM AUTHOR]

The variability in the size of red blood cells (RBCs) is an important additional diagnostic parameter for diseases, which has been established as a part of the complete blood count (CBC). The CBC can be performed using an automated flow cytometer, but it is too bulky and expensive for point‐of‐care testing. A miniaturized lensless imaging system is a competitive modality for a CBC, that is small and inexpensive. There are two challenges in developing a lensless imaging system for taking the CBC, which make the measurement of the RBC size very difficult: the diffraction effect and the low resolution. In this paper, the RBC radius measurement is replaced with a diffraction ring radius measurement. The diffraction ring radius is much larger than the RBC radius. This feature can improve the imaging resolution. Based on Fresnel diffraction, the relationship between the radius of RBCs and the diffraction fringes is analyzed. Finally, a complete measurement algorithm for determining the RBC size based on the lensless imaging system is given, which can be used to measure the variability in the size of RBCs. In our experiment, the maximum error is less than 6.74%. [ABSTRACT FROM AUTHOR]

X-ray imaging of soft materials is often difficult because of the low contrast of the components. This particularly applies to frozen hydrated biological cells where the feature of interest can have a similar density to the surroundings. As a consequence, a high dose is often required to achieve the desired resolution. However, the maximum dose that a specimen can tolerate is limited by radiation damage. Results from 3D coherent diffraction imaging (CDI) of frozen hydrated specimens have given resolutions of ∼80 nm compared with the expected resolution of 10 nm predicted from theoretical considerations for identifying a protein embedded in water. Possible explanations for this include the inapplicability of the dose-fractionation theorem, the difficulty of phase determination, an overall object-size dependence on the required fluence and dose, a low contrast within the biological cell, insufficient exposure, and a variety of practical difficulties such as scattering from surrounding material. A recent article [Villaneuva-Perez et al. (2018), Optica, 5, 450–457] concluded that imaging by Compton scattering gave a large dose advantage compared with CDI because of the object-size dependence for CDI. An object-size dependence would severely limit the applicability of CDI and perhaps related coherence-based methods for structural studies. This article specifically includes the overall object size in the analysis of the fluence and dose requirements for coherent imaging in order to investigate whether there is a dependence on object size. The applicability of the dose-fractionation theorem is also discussed. The analysis is extended to absorption-based imaging and imaging by incoherent scattering (Compton) and fluorescence. This article includes analysis of the dose required for imaging specific low-contrast cellular organelles as well as for protein against water. This article concludes that for both absorption-based and coherent diffraction imaging, the dose-fractionation theorem applies and the required dose is independent of the overall size of the object. For incoherent-imaging methods such as Compton scattering, the required dose depends on the X-ray path length through the specimen. For all three types of imaging, the dependence of fluence and dose on a resolution d goes as 1/d4 when imaging uniform-density voxels. The independence of CDI on object size means that there is no advantage for Compton scattering over coherent-based imaging methods. The most optimistic estimate of achievable resolution is 3 nm for imaging protein molecules in water/ice using lensless imaging methods in the water window. However, the attainable resolution depends on a variety of assumptions including the model for radiation damage as a function of resolution, the efficiency of any phase-retrieval process, the actual contrast of the feature of interest within the cell and the definition of resolution itself. There is insufficient observational information available regarding the most appropriate model for radiation damage in frozen hydrated biological material. It is advocated that, in order to compare theory with experiment, standard methods of reporting results covering parameters such as the feature examined (e.g. which cellular organelle), resolution, contrast, depth of the material (for 2D), estimate of noise and dose should be adopted.

As an important part of the perception layer of the Internet of things, wireless sensor networks will provide sensing data for the application of the Internet of things, so it is necessary to optimize the routing path of wireless sensor nodes for the application of the Internet of things. Aiming at the problem of optimal selection of wireless sensor nodes for the Internet of things, this paper describes the mathematical model and abstracts it as a multi-objective optimization problem. Secondly, through the analysis of the deployment optimization process, this paper abstracts the optimization selection method of wireless sensor nodes facing the Internet of things and the guarantee method to avoid coverage holes. Then a node selection method based on a genetic algorithm is proposed to solve the problems of high redundancy and high energy consumption in the internet of things. In the application process of genetic algorithm, aiming at the possible problems of standard genetic algorithm, the similarity judgment is introduced into the crossover operation, and the operation of introducing new individuals is added into the genetic process. Finally, simulation experiments are carried out to verify the feasibility, efficiency, and parameter setting of the algorithm. The simulation results show that the proposed method can guarantee the coverage of the area to be monitored, reduce the network energy consumption and keep the energy consumption balanced. [ABSTRACT FROM AUTHOR]

Band selection (BS) can mitigate the “curse of dimensionality” problem and improve the performance of hyperspectral image (HSI) classification. Genetic algorithms (GAs) have been applied to the task of hyperspectral BS showing significant advantages compared with other literature methods. However, the traditional GAs-based methods often select sets of bands having residual redundancy due to the large search space related to hyperspectral BS and the limitation of premature convergence in GAs. Moreover, existing GAs-based methods often are supervised, and that needs a large number of labeled samples to compute the fitness value for assessing the quality of selected bands. In this article, an unsupervised BS approach based on an improved GA is proposed. A fitness function based on the fisher score combined with superpixel is designed for evaluating the discriminability of band subsets considering both spectral and spatial information. Then, modified genetic operations are constructed to restrain the search space and reduce the redundancy of selected bands. The performance of the proposed spectral-spatial GA-based BS method is evaluated on three HSIs. The experimental results demonstrate that the proposed method is superior to the traditional GA-based method and seven state-of-the-art unsupervised methods. [ABSTRACT FROM AUTHOR]

This article proposes a fuzzy logic-based energy-management system (FEMS) for a grid-connected microgrid with renewable energy sources (RESs) and energy storage system (ESS). The objectives of the FEMS are reducing the average peak load (APL) and operating cost through arbitrage operation of the ESS. These objectives are achieved by controlling the charge and discharge rate of the ESS based on the state of charge of ESS, the power difference between load and RES, and electricity market price. The effectiveness of the fuzzy logic greatly depends on the membership functions (MFs). The fuzzy MFs of the FEMS are optimized offline using a Pareto-based multiobjective evolutionary algorithm, nondominated sorting genetic algorithm (NSGA-II). The best compromise solution is selected as the final solution and implemented in the fuzzy-logic controller. A comparison with other control strategies with similar objectives is carried out at a simulation level. The proposed FEMS is experimentally validated on a real microgrid in the energy storage test bed at Newcastle University, U.K. [ABSTRACT FROM AUTHOR]

The inversion distance, that is the distance between two unichromosomal genomes with the same content allowing only inversions of DNA segments, can be exactly computed thanks to a pioneering approach of Hannenhalli and Pevzner from 1995. In 2000, El-Mabrouk extended the inversion model to perform the comparison of unichromosomal genomes with unequal contents, combining inversions with insertions and deletions (indels) of DNA segments, giving rise to the inversion-indel distance. However, only a heuristic was provided for its computation. In 2005, Yancopoulos, Attie and Friedberg started a new branch of research by introducing the generic double cut and join (DCJ) operation, that can represent several genome rearrangements (including inversions). In 2006, Bergeron, Mixtacki and Stoye showed that the DCJ distance can be computed in linear time with a very simple procedure. As a consequence, in 2010 we gave a linear-time algorithm to compute the DCJ-indel distance. This result allowed the inversion-indel model to be revisited from another angle. In 2013, we could show that, when the diagram that represents the relation between the two compared genomes has no bad components, the inversion-indel distance is equal to the DCJ-indel distance. In the present work we complete the study of the inversion-indel distance by giving the first algorithm to compute it exactly even in the presence of bad components. [ABSTRACT FROM AUTHOR]

Recognition of the functional sites of genes, such as translation initiation sites, donor and acceptor splice sites and stop codons, is a relevant part of many current problems in bioinformatics. The best approaches use sophisticated classifiers, such as support vector machines. However, with the rapid accumulation of sequence data, methods for combining many sources of evidence are necessary as it is unlikely that a single classifier can solve this problem with the best possible performance. A major issue is that the number of possible models to combine is large and the use of all of these models is impractical. In this paper we present a methodology for combining many sources of information to recognize any functional site using “floating search”, a powerful heuristics applicable when the cost of evaluating each solution is high. We present experiments on four functional sites in the human genome, which is used as the target genome, and use another 20 species as sources of evidence. The proposed methodology shows significant improvement over state-of-the-art methods. The results show an advantage of the proposed method and also challenge the standard assumption of using only genomes not very close and not very far from the human to improve the recognition of functional sites. [ABSTRACT FROM AUTHOR]

Gene order evolution of unichromosomal genomes, for example mitochondrial genomes, has been modelled mostly by four major types of genome rearrangements: inversions, transpositions, inverse transpositions, and tandem duplication random losses. Generalizing models that include all those rearrangements while admitting computational tractability are rare. In this paper, we study such a rearrangement model, namely the inverse tandem duplication random loss (iTDRL) model, where an iTDRL duplicates and inverts a continuous segment of a gene order followed by the random loss of one of the redundant copies of each gene. The iTDRL rearrangement has currently been proposed by several authors suggesting it to be a possible mechanisms of mitochondrial gene order evolution. We initiate the algorithmic study of this new model of genome rearrangement by proving that a shortest rearrangement scenario that transforms one given gene order into another given gene order can be obtained in quasilinear time. Furthermore, we show that the length of such a scenario, i.e., the minimum number of iTDRLs in the transformation, can be computed in linear time. [ABSTRACT FROM AUTHOR]

To solve the problem that maintenance tools are difficult to retrieve once being lost in maintenance of an electric section of the railway system, this study proposed a new method for estimating the object location optimized by a fuzzy neural network based on received signal strength indicator. Through a radio frequency identification reader and antennae, the received signal strength indicator values feed back by the dropped tools were collected. The environmental attenuation factor was fitted with field data. In the pre-processing stage, K-means algorithm optimized by the genetic algorithm, thus improving accuracy of data. The processed data were trained and tested by the fuzzy neural network, so that they could more accurately estimate the actual location of dropped tools. Data were collected on the railway lines of National Local Joint Engineering Research Center of Safety Guarantee Technology for Operation and Maintenance of Rail Transport Infrastructure. The collected data were used for subsequent processing and locating tools. The experimental results show that the proposed location method offers advantages in improving the location accuracy of dropped tools, which can improve the retrieval efficiency of tools in the field. [ABSTRACT FROM AUTHOR]

Many biological cells appear quasi-spherical, such as red blood cells, white blood cells, egg cells, cancer cells, etc. Cell size is an important basis for medical diagnosis. The traditional method is to use a microscope or flow cytometer to obtain the cell size. Either it depends on professionals and cannot be automated, or it is expensive and bulky, which are not suitable for point-of-care test. Lab-on-a-chip technology using a lensless imaging system gives a better solution for obtaining the cell size. In order to deal with the diffraction in the lensless imaging system, the distance between the light source and the cell, the distance between the cell and the CMOS image sensor and optical wavelength need to be accurately measured or controlled, which will greatly increase the complexity of the system, making it difficult to truly apply to point-of-care test. In this paper, an adaptive parameter model for quasi-spherical cell size measurement based on lensless imaging system is given. First, the diffraction theory used in the model is explained. Then, the adaptive algorithm of the system parameter is given. To illustrate the practicality of the algorithm, a quasi-spherical cell size measurement method and a super-resolution algorithm are given. Finally, the experiment proves that the adaptive parameter model is effective can meet the needs of quasi-spherical cell size measurement. [ABSTRACT FROM AUTHOR]

During the past two decades, the supramolecular conjugates of calixarenes have turned intriguing to the researchers due to their versatility in functionalization and applications spanning across various fields, such as chemical, biological and materials sciences. Though several review articles discuss host-guest and other functional aspects of the supramolecular conjugates of calixarenes, there is no such article that consolidates the role of the conjugates in the context of cell science. In this article, we focus on the utility of such conjugates in the biological cells as studied primarily using microscopy techniques that include endeavours emerging from our research group. Therefore, this will assist researchers working in this area in order to understand the microscopy aspects of calixarene based supramolecular conjugates inside the biological cells and thereby support the design of newer conjugates planned to target a specific purpose. Thus, this article focuses on the aspects, such as drug loading and bactericidal activity, gene transfer, selective ion sensing, and cell imaging by localizing in mitochondria, cytosol or Golgi apparatus, i.e., in any one of the subcellular components. Synopsis Supramolecular conjugates of calixarenes turned intriguing to the researchers due to their versatility in functionalization and in exhibiting diverse applications. This article focusses on the microscopy data for drug loading, bactericidal activity, gene transfer, ion sensing and cell imaging and thereby support the design of newer conjugates to target specific purposes. [ABSTRACT FROM AUTHOR]

Due to the complementarity of the process planning and shop scheduling, their integration can greatly facilitate the development of the intelligent manufacturing system. In the last decade, the integrated process planning and scheduling (IPPS) problem has become a research hotspot in the manufacturing system area. It is an NP-hard problem and is more complicated than the job shop scheduling problem. Although some progress has been obtained in the IPPS field, there are still many unsolved open problems. In this article, the novel integrated encoding and decoding methods are proposed by considering the OR-node of the process network graph. Moreover, a modified genetic algorithm (MGA) is designed based on the proposed coding methods. The process planning and the scheduling parts can be represented simultaneously in one individual. As for the precedence constraints between operations, the specifically designed operators are able to guarantee the feasibility of the operation sequence during the searching procedure. Then, the superiority of MGA is verified by updating nine new records on 37 well-known open problems, four of them reach their lower bounds. In addition, the proposed algorithm is also tested on a real-world case from a nonstandard equipment workshop in a Chinese machine tool company, which produces a common module of a packaging machine. The results show that the proposed MGA can solve the real-world case better than the comparative algorithms. [ABSTRACT FROM AUTHOR]