Zebrafish have several advantages compared to other vertebrate models used in modeling human diseases, particularly for large-scale genetic mutant and therapeutic compound screenings, and other ...biomedical research applications. With the impactful developments of CRISPR and next-generation sequencing technology, disease modeling in zebrafish is accelerating the understanding of the molecular mechanisms of human genetic diseases. These efforts are fundamental for the future of precision medicine because they provide new diagnostic and therapeutic solutions. This review focuses on zebrafish disease models for biomedical research, mainly in developmental disorders, mental disorders, and metabolic diseases.
Recent advances in soft materials and system integration technologies have provided a unique opportunity to design various types of wearable flexible hybrid electronics (WFHE) for advanced human ...healthcare and human–machine interfaces. The hybrid integration of soft and biocompatible materials with miniaturized wireless wearable systems is undoubtedly an attractive prospect in the sense that the successful device performance requires high degrees of mechanical flexibility, sensing capability, and user‐friendly simplicity. Here, the most up‐to‐date materials, sensors, and system‐packaging technologies to develop advanced WFHE are provided. Details of mechanical, electrical, physicochemical, and biocompatible properties are discussed with integrated sensor applications in healthcare, energy, and environment. In addition, limitations of the current materials are discussed, as well as key challenges and the future direction of WFHE. Collectively, an all‐inclusive review of the newly developed WFHE along with a summary of imperative requirements of material properties, sensor capabilities, electronics performance, and skin integrations is provided.
Recent advances in soft materials and system integration technologies have provided a unique opportunity to design various types of wearable flexible hybrid electronics (WFHE) for advanced human healthcare and human–machine interfaces. The most up‐to‐date materials, sensors, and system‐packaging technologies to develop advanced WFHE are provided.
Iron-nitrogen on carbon (Fe-N/C) catalysts have emerged as promising nonprecious metal catalysts (NPMCs) for oxygen reduction reaction (ORR) in energy conversion and storage devices. It has been ...widely suggested that an active site structure for Fe-N/C catalysts contains Fe-Nx coordination. However, the preparation of high-performance Fe-N/C catalysts mostly involves a high-temperature pyrolysis step, which generates not only catalytically active Fe-Nx sites, but also less active large iron-based particles. Herein, we report a general "silica-protective-layer-assisted" approach that can preferentially generate the catalytically active Fe-Nx sites in Fe-N/C catalysts while suppressing the formation of large Fe-based particles. The catalyst preparation consisted of an adsorption of iron porphyrin precursor on carbon nanotube (CNT), silica layer overcoating, high-temperature pyrolysis, and silica layer etching, which yielded CNTs coated with thin layer of porphyrinic carbon (CNT/PC) catalysts. Temperature-controlled in situ X-ray absorption spectroscopy during the preparation of CNT/PC catalyst revealed the coordination of silica layer to stabilize the Fe-N4 sites. The CNT/PC catalyst contained higher density of active Fe-Nx sites compared to the CNT/PC prepared without silica coating. The CNT/PC showed very high ORR activity and excellent stability in alkaline media. Importantly, an alkaline anion exchange membrane fuel cell (AEMFC) with a CNT/PC-based cathode exhibited record high current and power densities among NPMC-based AEMFCs. In addition, a CNT/PC-based cathode exhibited a high volumetric current density of 320 A cm-3 in acidic proton exchange membrane fuel cell. We further demonstrated the generality of this synthetic strategy to other carbon supports.
Colloidal metal–halide perovskite quantum dots (QDs) with a dimension less than the exciton Bohr diameter D B (quantum size regime) emerged as promising light emitters due to their spectrally narrow ...light, facile color tuning, and high photoluminescence quantum efficiency (PLQE). However, their size-sensitive emission wavelength and color purity and low electroluminescence efficiency are still challenging aspects. Here, we demonstrate highly efficient light-emitting diodes (LEDs) based on the colloidal perovskite nanocrystals (NCs) in a dimension > D B (regime beyond quantum size) by using a multifunctional buffer hole injection layer (Buf-HIL). The perovskite NCs with a dimension greater than D B show a size-irrespective high color purity and PLQE by managing the recombination of excitons occurring at surface traps and inside the NCs. The Buf-HIL composed of poly(3,4-ethylenedioxythiophene)/poly(styrenesulfonate) (PEDOT:PSS) and perfluorinated ionomer induces uniform perovskite particle films with complete film coverage and prevents exciton quenching at the PEDOT:PSS/perovskite particle film interface. With these strategies, we achieved a very high PLQE (∼60.5%) in compact perovskite particle films without any complex post-treatments and multilayers and a high current efficiency of 15.5 cd/A in the LEDs of colloidal perovskite NCs, even in a simplified structure, which is the highest efficiency to date in green LEDs that use colloidal organic–inorganic metal–halide perovskite nanoparticles including perovskite QDs and NCs. These results can help to guide development of various light-emitting optoelectronic applications based on perovskite NCs.
•We propose a C-LSTM neural network for effectively detecting anomalies in web traffic data.•CNN extracts spatial features and LSTM models temporal characteristics.•It outperforms the machine ...learning methods for Yahoo's Webscope S5 dataset.•We reveal the internal operation of anomaly detection process by t-SNE algorithm.
Web traffic refers to the amount of data that is sent and received by people visiting online websites. Web traffic anomalies represent abnormal changes in time series traffic, and it is important to perform detection quickly and accurately for the efficient operation of complex computer networks systems. In this paper, we propose a C-LSTM neural network for effectively modeling the spatial and temporal information contained in traffic data, which is a one-dimensional time series signal. We also provide a method for automatically extracting robust features of spatial-temporal information from raw data. Experiments demonstrate that our C-LSTM method can extract more complex features by combining a convolutional neural network (CNN), long short-term memory (LSTM), and deep neural network (DNN). The CNN layer is used to reduce the frequency variation in spatial information; the LSTM layer is suitable for modeling time information; and the DNN layer is used to map data into a more separable space. Our C-LSTM method also achieves nearly perfect anomaly detection performance for web traffic data, even for very similar signals that were previously considered to be very difficult to classify. Finally, the C-LSTM method outperforms other state-of-the-art machine learning techniques on Yahoo's well-known Webscope S5 dataset, achieving an overall accuracy of 98.6% and recall of 89.7% on the test dataset.
The rapid increase in human population and development in technology have sharply raised power consumption in today's world. Since electricity is consumed simultaneously as it is generated at the ...power plant, it is important to accurately predict the energy consumption in advance for stable power supply. In this paper, we propose a CNN-LSTM neural network that can extract spatial and temporal features to effectively predict the housing energy consumption. Experiments have shown that the CNN-LSTM neural network, which combines convolutional neural network (CNN) and long short-term memory (LSTM), can extract complex features of energy consumption. The CNN layer can extract the features between several variables affecting energy consumption, and the LSTM layer is appropriate for modeling temporal information of irregular trends in time series components. The proposed CNN-LSTM method achieves almost perfect prediction performance for electric energy consumption that was previously difficult to predict. Also, it records the smallest value of root mean square error compared to the conventional forecasting methods for the dataset on individual household power consumption. The empirical analysis of the variables confirms what affects to forecast the power consumption most.
•We propose a novel deep learning model to stably predict electric energy consumption.•We analyze the model with the large data collected in an actual residential house.•We achieve the highest performance in high resolution compared with the previous works.•We explain the variables of appliances that influence the prediction performance.
The advancement in virtual reality/augmented reality (VR/AR) has been achieved by breakthroughs in the realistic perception of virtual elements. Although VR/AR technology is advancing fast, enhanced ...sensor functions, long‐term wearability, and seamless integration with other electronic components are still required for more natural interactions with the virtual world. Here, this report reviews the recent advances in multifunctional wearable sensors and integrated functional devices for VR/AR applications. Specified device designs, packaging strategies, and interactive physiological sensors are summarized based on their methodological approaches for sensory inputs and virtual feedback. In addition, limitations of the existing systems, key challenges, and future directions are discussed. It is envisioned that this progress report's outcomes will expand the insights on wearable functional sensors and device interfaces toward next‐generation VR/AR technologies.
This progress report delivers technological summaries on recent advances in VR/AR systems whose key functionalities and performances are achieved by wearable sensory and feedback systems. Sections have been categorized to describe the individual progress in various wearable‐enabled VR/AR systems based on their associated human sensory systems and physiology (e.g., vision, motion, physiological signals, and haptic).
The rapid and sensitive classification of bacteria is the first step of bacterial community research and the treatment of infection. Herein, a fluorescent probe BacGO is presented, which shows the ...best universal selectivity for Gram‐positive bacteria among known probes with a minimum staining procedure for sample detection and enrichment of the live bacteria. BacGO could also be used to assess of the Gram status in the bacterial community from wastewater sludge. Furthermore, BacGO could sensitively and selectively detect a Gram‐positive bacterial infection, not only in vitro but also using an in vivo keratitis mouse model. BacGO provides an unprecedented research tool for the study of dynamic bacterial communities and for clinical application.
BacGO, a novel Gram‐positive bacterial probe, was developed from a library of fluorescent molecules with a boronic‐acid motif that binds to peptidoglycan on the Gram‐positive bacterial cell wall. BacGO can be used to identify Gram‐positive bacteria in diverse, highly complex samples, and is an attractive alternative to Gram staining.
Activated macrophages have the potential to be ideal targets for imaging inflammation. However, probe selectivity over non-activated macrophages and probe delivery to target tissue have been ...challenging. Here, we report a small molecule probe specific for activated macrophages, called CDg16, and demonstrate its application to visualizing inflammatory atherosclerotic plaques in vivo. Through a systematic transporter screen using a CRISPR activation library, we identify the orphan transporter Slc18b1/SLC18B1 as the gating target of CDg16.
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