Capsule Architecture Research Articles

Climatic Media: Transpacific Experiments in Atmospheric Control. By Yuriko Furuhata.

Yuriko Furuhata’s Climatic Media is a fascinating study of climate control technologies in twentieth-century Japan. The book turns on the concept of “atmospheric control,” which Furuhata describes as “a double process of condition: air conditioning and social conditioning” (8, emphasis in original). She introduces the term “climatic media” to name the techniques and technologies associated with climate control, from scientific research on weather prediction to architectural structures designed for cold climates. Furuhata carefully unfolds genealogies of climatic media that attend to the material, epistemological, aesthetic, and geopolitical connections among artificial fog, weather control, and digital computing; air conditioning, meteorology, and futurology; greenhouses, architectural bubbles, and space colonies; spaceships, capsule architecture, and plastic; and tear gas, networked surveillance, and cybernetics. Throughout, she attends to the role of Japan’s colonial history and its alliance with the United States in the Cold War in shaping these geological connections. Furuhata’s study of climatic media’s transpacific connections between Japan and the United States is a rich and insightful demonstration of a comparative and historically, culturally, and geopolitically situated approach to elemental media studies. Despite her attention to transpacific geopolitics, Furuhata employs a “mobile analysis” whose movement is modeled on the “borderless” circulation of weather events rather than on the borders of geopolitical territories (18). This mobile analysis moves dynamically across art, science, architecture, and digital computing, touching down on key examples and highlighting junctures where these fields intersect to trace climatic media’s complex genealogies. Chapter 1, “Outdoor Weather: Artificial Fog and Weather Control,” aptly demonstrates Furuhata’s method. Its examination of Nakaya Fujiko’s fog sculpture opens up to artworks and architectural forms that employ artificial fog technologies, as well as scientific research on weather modification; as the chapter moves across these domains, Furuhata identifies key personal and institutional transpacific connections between Japan and the United States across shifting geopolitical contexts.

An Antifibrotic Breast Implant Surface Coating Significantly Reduces Periprosthetic Capsule Formation.

The body responds to prosthetic materials with an inflammatory foreign body response and deposition of a fibrous capsule, which may be deleterious to the function of the device and cause significant discomfort for the patient. Capsular contracture (CC) is the most common complication of aesthetic and reconstructive breast surgery. The source of significant patient morbidity, it can result in pain, suboptimal aesthetic outcomes, implant failure, and increased costs. The underlying mechanism remains unknown. Treatment is limited to reoperation and capsule excision, but recurrence rates remain high. In this study, the authors altered the surface chemistry of silicone implants with a proprietary anti-inflammatory coating to reduce capsule formation. Silicone implants were coated with Met-Z2-Y12, a biocompatible, anti-inflammatory surface modification. Uncoated and Met-Z2-Y12-coated implants were implanted in C57BL/6 mice. After 21, 90, or 180 days, periprosthetic tissue was removed for histologic analysis. The authors compared mean capsule thickness at three time points. At 21, 90, and 180 days, there was a statistically significant reduction in capsule thickness of Met-Z2-Y12-coated implants compared with uncoated implants ( P < 0.05). Coating the surface of silicone implants with Met-Z2-Y12 significantly reduced acute and chronic capsule formation in a mouse model for implant-based breast augmentation and reconstruction. As capsule formation obligatorily precedes CC, these results suggest contracture itself may be significantly attenuated. Furthermore, as periprosthetic capsule formation is a complication without anatomical boundaries, this chemistry may have additional applications beyond breast implants, to a myriad of other implantable medical devices. Coating of the silicone implant surface with Met-Z2-Y12 alters the periprosthetic capsule architecture and significantly reduces capsule thickness for at least 6 months postoperatively in a murine model. This is a promising step forward in the development of a therapy to prevent capsular contracture.

Aspect-Based Sentiment Classification for Detecting the Cognitive Triad Mechanism of Depression

The cognitive triad mechanism of Beck’s cognitive theory is critical for early diagnosis and prognosis of depression. According to the cognitive triad mechanism, negative views about the self, the future, and the world appear to be routine in depressed people, occurring spontaneously. The challenge in the clinical interview is that the individuals may have trouble explaining their past and current symptoms for a variety of factors, such as the existence of concurrent mental health or medical problems and memory difficulties. The aspect-based sentiment classification technique can help psychologists overcome this difficulty by identifying the cognitive triad pairs {(self, negative), (future, negative), (world, negative)} from the individual's clinical chatbot and social media messages. The proposed multilayer RNN-capsule architecture on Cognitive Triad Dataset (CTD) consists of two layers, i.e., sentiment recognition and aspect identification layers. Through experiments on the CTD, the multilayer RNN-capsule model outperforms the single-layer RNN-capsule model for aspect-based sentiment classification. The accuracy and F1 score of a single-layer RNN-capsule model for aspect-based sentiment classification are 0.857 and 0.858, respectively. The accuracy and F1 score of the sentiment recognition capsule in the multilayer RNN-capsule model are 0.89 and 0.892, while the accuracy and F1 score of the aspect detection capsule are 0.957 and 0.956. Also, the multilayer RNN-capsule model outperforms most of its counterparts like GNN, LSTM, and BiLSTM. More importantly, capsule architecture is capable of producing words containing sentiment and aspect inclinations that reflect the attributes of sentiment and aspect capsules, respectively, without the use of any linguistic knowledge.

PointCaps: Raw point cloud processing using capsule networks with Euclidean distance routing

Raw point cloud processing using capsule networks is widely adopted in classification, reconstruction, and segmentation due to its ability to preserve spatial agreement of the input data. However, most of the existing capsule based network approaches are computationally heavy and fail at representing the entire point cloud as a single capsule. We address these limitations in existing capsule network based approaches by proposing PointCaps, a novel convolutional capsule architecture with parameter sharing. Along with PointCaps, we propose a novel Euclidean distance routing algorithm and a class-independent latent representation. The latent representation captures physically interpretable geometric parameters of the point cloud, with dynamic Euclidean routing, PointCaps well-represents the spatial (point-to-part) relationships of points. PointCaps has a significantly lower number of parameters and requires a significantly lower number of FLOPs while achieving better reconstruction with comparable classification and segmentation accuracy for raw point clouds compared to state-of-the-art capsule networks.

An improved capsule network for glioma segmentation on MRI images: A curriculum learning approach

Glioma segmentation is an essential step in tumor identification and treatment planning. Glioma segmentation is a challenging task because it appears with blurred and irregular boundaries in a variety of shapes. In this paper, we propose an efficient and novel model for automatic glioma segmentation based on capsule neural networks. We improved the architecture and training of the SegCaps model, the first capsule-based segmentation network. The proposed architecture is improved by introducing dilation blocks in the primary capsule block to get deeper features while avoiding resolution reduction. The prediction layer of the network is also modified using one-dimensional convolution filters, enabling the network to not only maximize tumor existence likelihood but also regularize the capsule orientations within the tumor. Our main contribution, however, is to introduce an enhanced curriculum-based training algorithm into the proposed SegCaps model. We adapt the curriculum learning for the model by suggesting a new pacing mechanism based on a roulette-wheel selection algorithm that enriches randomness in the network and prevents bias. A hybrid dice loss function is also employed, which is better adapted to the introduced curriculum-based training procedure. We evaluated the performance of improved SegCaps on the BraTS2020, a multimodal benchmark dataset for brain tumor segmentation. The experimental results confirmed that the improvements yield a top-performing yet memory-efficient deep capsule architecture. The proposed model outperformed the best-reported accuracies on the BraTS2020, achieving improved dice scores of 85.16% and 81.88% for tumor core and enhancing tumor segmentation, respectively. Using 90%, fewer parameters than the popular U-Net also confirmed the high memory efficiency of our proposed model.

Cryptococcus neoformans capsule regrowth experiments reveal dynamics of enlargement and architecture

The polysaccharide capsule of fungal pathogen Cryptococcus neoformans is a critical virulence factor that has historically evaded complete characterization. Cryptococcal polysaccharides are known to either remain attached to the cell as capsular polysaccharides (CPSs) or to be shed into the extracellular space as exopolysaccharides (EPSs). While many studies have examined the properties of EPS, far less is known about CPS. In this work, we detail the development of new physical and enzymatic methods for the isolation of CPS which can be used to explore the architecture of the capsule and isolated capsular material. We show that sonication or Glucanex enzyme cocktail digestion yields soluble CPS preparations, while use of a French pressure cell press or Glucanex digestion followed by cell disruption removed the capsule and produced cell wall–associated polysaccharide aggregates that we call “capsule ghosts”, implying an inherent organization that allows the CPS to exist independent of the cell wall surface. Since sonication and Glucanex digestion were noncytotoxic, it was also possible to observe the cryptococcal cells rebuilding their capsule, revealing the presence of reducing end glycans throughout the capsule. Finally, analysis of dimethyl sulfoxide-extracted and sonicated CPS preparations revealed the conservation of previously identified glucuronoxylomannan motifs only in the sonicated CPS. Together, these observations provide new insights into capsule architecture and synthesis, consistent with a model in which the capsule is assembled from the cell wall outward using smaller polymers, which are then compiled into larger ones.

Improved search space shrinking for medical image retrieval using capsule architecture and decision fusion

Medical diagnosis is a challenging procedure that involves issues such as data imbalance, insufficient labels, obscure images, redundancy and lack of effective model training directions to shrink the semantic gap between human knowledge and computer algorithms. Due to privacy norms, sometimes medical images are difficult to access and therefore, retrieval of identical existing images from an existing repository is quite useful. This paper proposes a search space to narrow down the identical images in an archive by using (i) Capsule Networks, followed by a (ii) decision fusion with Wavelet-Discrete Cosine Transform (W-DCT) and Radon Barcodes (RBC). Empirical case study has been applied on IRMA (Image Retrieval in Medical Applications) dataset, ImageCLEFMed-2009, containing 14,410 X-ray images, but the proposed method is generic, reproducible and scalable. Subjective and quantitative performance has been compared with the state-of-art and it has been found superior to yield accuracy of 92.83% and IRMA error of 124.25 for 193 class-code category. Thus, the proof-of-concept helps to improves diagnosis efficiency for automatic image retrieval and annotation by clustering similar images from the underlying repository.

An EM Algorithm for Capsule Regression.

We investigate a latent variable model for multinomial classification inspired by recent capsule architectures for visual object recognition (Sabour, Frosst, & Hinton, 2017). Capsule architectures use vectors of hidden unit activities to encode the pose of visual objects in an image, and they use the lengths of these vectors to encode the probabilities that objects are present. Probabilities from different capsules can also be propagated through deep multilayer networks to model the part-whole relationships of more complex objects. Notwithstanding the promise of these networks, there still remains much to understand about capsules as primitive computing elements in their own right. In this letter, we study the problem of capsule regression-a higher-dimensional analog of logistic, probit, and softmax regression in which class probabilities are derived from vectors of competing magnitude. To start, we propose a simple capsule architecture for multinomial classification: the architecture has one capsule per class, and each capsule uses a weight matrix to compute the vector of hidden unit activities for patterns it seeks to recognize. Next, we show how to model these hidden unit activities as latent variables, and we use a squashing nonlinearity to convert their magnitudes as vectors into normalized probabilities for multinomial classification. When different capsules compete to recognize the same pattern, the squashing nonlinearity induces nongaussian terms in the posterior distribution over their latent variables. Nevertheless, we show that exact inference remains tractable and use an expectation-maximization procedure to derive least-squares updates for each capsule's weight matrix. We also present experimental results to demonstrate how these ideas work in practice.

Electrochemical Dissolved Oxygen Sensor-Integrated Platform for Wireless In Situ Bioprocess Monitoring

This work presents a bio-processing online device (bPod) platform, capable of real-time in situ monitoring of bioreactor cell culture parameters, such as dissolved oxygen (DO). The bPod is an integrated system comprised of a potentiostat analog-front-end (AFE), a Bluetooth Low Energy (BLE) microcontroller, and a chemical sensor, exemplified here by a Clark-type DO sensor, which enables monitoring of dissolved oxygen content. The Clark-type electrochemical sensor performs chronoamperometric measurement of DO percent saturation, and the BLE microcontroller wirelessly transmits data to a smartphone while submerged in aqueous media. After several electrode design modifications, the bPod showed a linear electrochemical current response corresponding to DO percent saturation levels with a sensitivity of 37.5 nA/DO% and limit of detection of 8.26 DO%, covering concentration ranges relevant for mammalian culture processes within bioreactors. The wireless bPod provides a free-floating capsule architecture for monitoring DO and can be adapted for an array of electrochemical sensors, targeting different process parameters for diverse bioprocess monitoring applications.

With Over 60 Independent Losses, Stomata Are Expendable in Mosses.

Because stomata in bryophytes are uniquely located on sporangia, the physiological and evolutionary constraints placed on bryophyte stomata are fundamentally different from those on leaves of tracheophytes. Although losses of stomata have been documented in mosses, the extent to which this evolutionary process occurred remains relatively unexplored. We initiated this study by plotting the known occurrences of stomata loss and numbers per capsule on the most recent moss phylogeny. From this, we identified 40 families and 74 genera that lack stomata, of which at least 63 are independent losses. No trends in stomata losses or numbers are evident in any direction across moss diversity. Extant taxa in early divergent moss lineages either lack stomata or produce pseudostomata that do not form pores. The earliest land plant macrofossils from 400 ma exhibit similar sporangial morphologies and stomatal distribution to extant mosses, suggesting that the earliest mosses may have possessed and lost stomata as is common in the group. To understand why stomata are expendable in mosses, we conducted comparative anatomical studies on a range of mosses with and without stomata. We compared the anatomy of stomate and astomate taxa and the development of intercellular spaces, including substomatal cavities, across mosses. Two types of intercellular spaces that develop differently are seen in peristomate mosses, those associated with stomata and those that surround the spore sac. Capsule architecture in astomate mosses ranges from solid in the taxa in early divergent lineages to containing an internal space that is directly connected to the conducing tissue and is involved in capsule expansion and the nourishment, hydration and development of spores. This anatomy reveals there are different architectural arrangements of tissues within moss capsules that are equally effective in accomplishing the essential processes of sporogenesis and spore dispersal. Stomata are not foundational to these processes.

Deep Q-CapsNet Reinforcement Learning Framework for Intrauterine Cavity Segmentation in TTTS Fetal Surgery Planning.

Fetoscopic laser photocoagulation is the most effective treatment for Twin-to-Twin Transfusion Syndrome, a condition affecting twin pregnancies in which there is a deregulation of blood circulation through the placenta, that can be fatal to both babies. For the purposes of surgical planning, we design the first automatic approach to detect and segment the intrauterine cavity from axial, sagittal and coronal MRI stacks. Our methodology relies on the ability of capsule networks to successfully capture the part-whole interdependency of objects in the scene, particularly for unique class instances (i.e., intrauterine cavity). The presented deep Q-CapsNet reinforcement learning framework is built upon a context-adaptive detection policy to generate a bounding box of the womb. A capsule architecture is subsequently designed to segment (or refine) the whole intrauterine cavity. This network is coupled with a strided nnU-Net feature extractor, which encodes discriminative feature maps to construct strong primary capsules. The method is robustly evaluated with and without the localization stage using 13 performance measures, and directly compared with 15 state-of-the-art deep neural networks trained on 71 singleton and monochorionic twin pregnancies. An average Dice score above 0.91 is achieved for all ablations, revealing the potential of our approach to be used in clinical practice.

Comprehensive NILM Framework: Device Type Classification and Device Activity Status Monitoring Using Capsule Network

Non-intrusive load monitoring (NILM) discerns the individual electrical appliances of a residential or commercial building by disaggregating the accumulated energy consumption data without accessing to the individual components applying a single-point sensor. The fundamental concept is to decompose the aggregate load into a family of appliances that can explain its characteristics. In the age of smart grid networks and sophisticated energy management infrastructures, NILM can be considered as a significant tool pertaining to smart and inexpensive energy metering technique. In this article, a novel NILM solution based on capsule network is proposed, where convolutional neural network (CNN) is employed to extract potential features from a set of non-overlapping energy measurement data segments and the capsule architecture is designed to predict class probabilities of the individual segments. Then, a decision making algorithm is proposed to compute the final classification based on the predicted class probabilities of the segments. The presented research design comprises two unique NILM applications – device type classification from individual sensor recordings stored in COOLL and PLAID public databases, and device activity status monitoring at any particular time instant from aggregated energy consumption data recorded in UK-DALE database. Additionally, substantial experimental investigations have been carried out for device type classification accounting on various types of train and test set distributions as well as individual instrument and house classifications. The presented framework analyzes different parameters and metrics in depth to corroborate the efficacious performance evaluations for real-time applications. Relevant performance comparisons with existing works in literature validate the sustainability of the proposed solution.

Interfacial Engineering of Thermoresponsive Microgel Capsules: Polymeric Wetting vs Colloidal Adhesion

Control of the microstructure of microgels adsorbed on solid surfaces plays an essential role in various fields, including lithography, optical sensing, and biocatalysis. Here, we adopt an experimentally validated molecular dynamics simulation approach to investigate the structural properties and deswelling dynamics of thermoresponsive microgel capsules on solid substrates. Specifically, by examining the interfacial elastocapillarity of the adsorbed microgel capsules, we find that the poorly cross-linked microgel capsules (i.e., cross-link density ψ ≤ 0.0217) display a crossover adsorption regime between polymeric wetting and colloidal adhesion, whereas the highly cross-linked microgel capsules present only colloidal adhesion adsorption. As the system temperature increases, the microgel capsules progressively transform from the fully swollen state to the fully collapsed state on the solid substrates, whereas the capsule architecture remains. The adsorption regime of the microgel capsule is mutually determined by the elastic deformation and surface attraction strength. In addition, the elastic deformation is attributed to the internal structure of the microgel capsule, which varies with the system temperature and cross-link density. Aiming to identify the adsorption regime over wider ranges of the control variables, a machine learning study on an artificial neural network is further carried out and a three-dimensional phase diagram of the adsorption regime and multiple control variables is constructed, unraveling the comprehensive relationship among the adsorption regime and the operational parameters.

A Deep Learning Iris Recognition Method Based on Capsule Network Architecture

Iris recognition is one of the most representative identification technologies in biometric recognition, which is widely used in various fields. Recently, many deep learning methods have been used in biometric recognition, owing to their advantages such as automatic learning, high accuracy, and strong generalization ability. The deep convolutional neural network (CNN) is the mainstream method of image processing widely used in many domains, but it has poor anti-noise capacity in image classification and is easily affected by slight disturbances. CNN also needs a large number of samples for training. The recent capsule network not only has high recognition accuracy in classification tasks but can also learn part-whole relationships, increasing the robustness of the model. Furthermore, it can be trained using a small number of samples. In this paper, we propose a deep learning method based on the capsule network architecture in iris recognition. The structure detail of the network is adjusted, and we provide a modified routing algorithm based on the dynamic routing between two capsule layers to make this technique adapt to iris recognition. Migration learning makes the deep learning method available even when the number of samples is limited. Therefore, three state-of-the-art pretrained models, VGG16, InceptionV3, and ResNet50, are introduced. We divide the three networks into a series of subnetwork structures according to the number of their major constituent blocks. They are used as the convolutional part to extract primary features, instead of a single convolutional layer in the capsule network. Our experiments are conducted on three iris datasets, JluIrisV3.1, JluIrisV4, and CASIA-V4 Lamp, to analyze the performance of different network structures. We also test the proposed networks in simulated strong and weak light environments, showing that the networks with capsule architecture are more stable than those without.

Estetika održivosti - arhitektura kapsule u gradu i u prirodi

Architecture of minimum dwellings has been a hot topic recently. When minimum dwellings are compact, well-equipped, connected to the network, structurally, functionally and visually recognized as one thing, temporary and mobile or transportable, they may be designated as capsule architecture. Temporary by nature, these small dwellings, shelters, redesigned container units, special technological structures, parasites and other manifestations of the capsules concept encompass the logic of technological facilities with a distinct architectural expression. At the same time, it is a manifestation of the rule of sustainable design, sustainable architecture and sustainability in general. In this context, the case of small dwellings shows its difference as opposed to other sustainable architecture approaches and aesthetics. It subverts the generally sustainable approaches with exposed importance of locality within the global forces, usually relying on context - location, local culture and environmental characteristics, etc. The aesthetic regime of temporary, changeable, a-contextual and autonomous architectural structures can be regarded as an aesthetics of otherness, which relates them to the legacy of the Modern movement's existenzminimum experiments, the New Brutalism, radical experiments of the 1960s and other avantgarde and NEO-avantgarde practices of the twentieth century, but firmly placed in the context of individualized, indeterminate, dispersed and ambiguous contemporaneity.

Deswelling Dynamics of Thermoresponsive Microgel Capsules and Their Ultrasensitive Sensing Applications: A Mesoscopic Simulation Study

Thermoresponsive microgels with a hollow capsule architecture have been widely used in drug delivery and molecular encapsulation, and their efficacy is contingent on the internal structure in the deswelling dynamics process. Despite a large number of experimental studies on microgels, proper theoretical methods based on an individual microgel capsule are still a few because of the complexity of the microgels. Herein, we first propose a novel methodology to investigate the structural properties and deswelling dynamics of microgel capsules by integrating a temperature-dependent Morse potential with Langevin dynamics simulation. Different properties, including volume phase transition temperature, temperature-dependent diameter, and structural morphologies of individual microgels, are assessed to rationalize our simulation method, and a good agreement between simulation predictions and experimental observations has been obtained. Depending on the system temperature, the morphological transition of three regimes in the shell structure is identified: scattered nanogels, progressively porous sponge gels, and dense ribbonlike gels. The temperature-switchable sensors composed of microgel capsules on the substrates are devised, which exhibit tunable reflectivity or thickness by simply varying the system temperature. Our mesoscale results provide helpful insights into the transient structure within the networked microgels and the design of smart polymeric nanomaterials, such as biosensors, drug delivery systems, and actuators.

Capsules Based Chinese Word Segmentation for Ancient Chinese Medical Books

Neural network models are popularly used in Chinese word segmentation task. The capsule architecture is proposed recently which has solved some defects of convolutional neural network. In this paper, we first introduce the capsule architecture to Chinese word segmentation. We utilize capsules as neural units. Before doing routing algorithm, we make a sliding capsule window to select the features which are extracted from the primary capsule layer. The sliding capsule window is proposed to adapt the capsule architecture to the sequence labeling task. The experiment results show that our proposed capsules based Chinese word segmentation model achieves competitive performances with the previous state-of-the-art methods. Ancient Chinese medical books record a lot of valuable experiences from the ancient medical workers. However, the research about the automatic text analysis on ancient Chinese medical documents is just a beginning. Due to the lack of the annotated data for Chinese medicine, we develop the word segmentation guideline for the ancient Chinese medical documents and select 10 genres, 30 ancient Chinese medical books to set up the annotation dataset. And with the annotated data, we develop the segmenter for the ancient Chinese medical text. Experiments show that the $F_{1}$ measures of our model on the two datasets are 94.9% and 81.4% on Chinese Treebank6.0 and Ancient Chinese Medical Books, respectively.

A Histological Comparison of Two Human Acellular Dermal Matrix Products in Prosthetic-Based Breast Reconstruction.

Background:Acellular dermal matrices (ADMs) are an integral component of breast reconstruction. The ideal matrix would be relatively immuno-inert, allow rapid vascularization, and be affordable. The purpose of this study was to histologically compare 2 commonly used ADM products.Methods:This is a prospective histological study of 17 patients (20 breasts) following prosthetic-based breast reconstruction with ADM: Alloderm (LifeCell Corp, Branchburg, N.J.) or Cortiva (RTI Surgical, Alachua Fla.). Biopsies were taken from the dermal matrix and natural capsules surrounding the expander/implant during secondary surgery [Range, 72—694 days (mean, 217 days)]. Biopsy specimens were prepared via hematoxylin and eosin, Masson's trichrome, elastin, and transforming growth factor (TGF)-1 stains. Quantitative analysis of staining was performed with ImageJ software. The clinical outcome of each patient is analyzed in relation to capsule architecture and ADM performance.Results:There were 7 breasts in the Alloderm group and 13 in the Cortiva group. Both groups had similar demographic, aesthetic results, and complication profiles. The TGF-1 staining demonstrated significantly lower levels in the Cortiva capsules (P = 0.0139). The percentage of elastin and collagen are similar in the Cortiva, Alloderm, and natural peri-implant capsules. The native capsules show a significantly greater number of blood vessels when compared with Cortiva and Alloderm (P = 0.0371 and P = 0.0347, respectively); however, there is no difference in vascular pattern between the 2 dermal matrices.Discussion:Postoperatively, Cortiva demonstrates equal vascularity with less TGF-1 activation compared with Alloderm. The clinical success and complication profile were similar between the Alloderm and Cortiva patients.

Bioinspired Spiky Micromotors Based on Sporopollenin Exine Capsules

The development of a micromotor with unique spiky morphology based on sporopollenin exine capsules (SECs) is reported here. A widely abundant natural material extracted from sunflower pollen grains, the SECs are physically robust, highly monodisperse microcapsules that are ornamented with spiky appendages, opening the door to exploring bubble generation on this unique biomaterial surface. Partial platinum coating on the SEC surface enables catalytic decomposition of hydrogen peroxide that leads to bubble‐propelled motion of individual SECs. Moreover, the hollow capsule architecture provides a large internal cavity for macromolecular encapsulation, as demonstrated here by the loading and transport of bovine serum albumin. Taking advantage of the sporopollenin biopolymer's capacity for heavy metal binding, it is further demonstrated that fluid mixing induced by the motion and bubble generation of the SEC micromotors dramatically improves heavy metal binding and removal. The bioinspired micromotors combine the advantageous properties of SECs with autonomous motion ability, resulting in a multifunctional platform for drug delivery and water purification applications.

The effect of a bacterial contamination on the formation of capsular contracture with polyurethane breast implants in comparison with textured silicone implants: An animal study

One of the most common complications following breast augmentation is capsular contracture. The subclinical infection of the implant is often considered to be one of the main risk factors. It is believed that polyurethane (PU) implants, because of their larger foam-like surface, have lower capsular contracture rates due to better tissue integration. It remains unclear if bacterial contamination and biofilm formation result in higher capsular contracture rates under the condition of the increased surface of PU implants compared to textured silicone-gel implants. The effect of this bacterial contamination was examined in an animal-based study. A total of 80 mini implants (40 textured silicone-gel implants and 40 PU implants) were implanted in the dorsum of female Wistar rats. In each group, 20 implants were inoculated before implantation with a standard amount of Staphylococcus epidermidis. Capsules and implants were explanted after 60 days, followed by double-blind histological, immunohistochemical, and microbiological examinations. Macroscopic separation of the total capsule in the textured implant group was possible whereas the growth of surrounding tissue into the foam structure of PU implants made separation in that group difficult. After contamination, a thicker capsule could be observed in both groups without significant differences. Histologically, capsules around PU implants showed significantly lower expression of parallel myofibrils. We were able to describe a significant higher infiltration with inflammatory cells in capsules around PU implants both with and without contamination. Microbiological investigations revealed positive growth of S. epidermidis around one PU implant without related signs of capsular contracture. This study demonstrates that aside from the surface of silicone implants, bacterial contamination has major impact on the architecture of capsule formation. In our study, we were able to demonstrate that bacterial contamination leads to a thicker capsule and an increased tissue reaction with a higher amount of inflammatory cells. However, a resulting bacterial infection was only demonstrated in one case and had an insignificant influence on capsule architecture. The observed inflammatory reaction around PU implants was observed as a nonbacterial, granulomatose foreign body reaction. Level I: Evidence obtained from at least one properly designed randomized controlled trial.