Body area networks, including smart sensors, are widely reshaping health applications in the new era of smart cities. To meet increasing security and privacy requirements, physiological signalbased ...biometric human identification is gaining tremendous attention. This paper focuses on two major impediments: the signal processing technique is usually both complicated and data-dependent and the feature engineering is time-consuming and can fit only specific datasets . To enable a data-independent and highly generalizable signal processing and feature learning process, a novel wavelet domain multiresolution convolutional neural network is proposed. Specifically, it allows for blindly selecting a physiological signal segment for identification purpose, avoiding the complicated signal fiducial characteristics extraction process. To enrich the data representation, the random chosen signal segment is then transformed to the wavelet domain, where multiresolution time-frequency representation is achieved. An auto-correlation operation is applied to the transformed data to remove the phase difference as the result of the blind segmentation operation. Afterward, a multiresolution 1-D-convolutional neural network (1-D-CNN) is introduced to automatically learn the intrinsic hierarchical features from the wavelet domain raw data without datadependent and heavy feature engineering, and perform the user identification task. The effectiveness of the proposed algorithm is thoroughly evaluated on eight electrocardiogram datasets with diverse behaviors, such as with or without severe heart diseases, and with different sensor placement methods. Our evaluation is much more extensive than the state-of-the-art works, and an average identification rate of 93.5% is achieved. The proposed multiresolution 1-D-CNN algorithm can effectively identify human subjects, even from randomly selected signal segments and without heavy feature engineering. This paper is expected to demonstrate the feasibility and effectiveness of applying the blind signal processing and deep learning techniques to biometric human identification, to enable a low algorithm engineering effort and also a high generalization ability.
The unsatisfactory response rate of immune checkpoint blockade (ICB) immunotherapy severely limits its clinical application as a tumor therapy. Here, we generate a vaccine-based nanosystem by ...integrating siRNA for Cd274 into the commercial human papillomavirus (HPV) L1 (HPV16 L1) protein. This nanosystem has good biosafety and enhances the therapeutic response rate of anti-tumor immunotherapy. The HPV16 L1 protein activates innate immunity through the type I interferon pathway and exhibits an efficient anti-cancer effect when cooperating with ICB therapy. For both resectable and unresectable breast tumors, the nanosystem decreases 71% tumor recurrence and extends progression-free survival by 67%. Most importantly, the nanosystem successfully induces high response rates in various genetically modified breast cancer models with different antigen loads. The strong immune stimulation elicited by this vaccine-based nanosystem might constitute an approach to significantly improve current ICB immunotherapy.
The computation-intensive circuit simulation makes the analog circuit sizing challenging for large-scale/complicated analog/RF circuits. A Bayesian optimization approach has been proposed recently ...for the optimization problems involving the evaluations of black-box functions with high computational cost in either objective functions or constraints. In this paper, we propose a weighted expected improvement-based Bayesian optimization approach for automated analog circuit sizing. Gaussian processes (GP) are used as the online surrogate models for circuit performances. Expected improvement is selected as the acquisition function to balance the exploration and exploitation during the optimization procedure. The expected improvement is weighted by the probability of satisfying the constraints. In this paper, we propose a complete Bayesian optimization framework for the optimization of analog circuits with constraints for the first time. The existing GP model-based optimization methods for analog circuits take the GP models as either offline models or as assistance for the evolutionary algorithms. We also extend the Bayesian optimization algorithm to handle multi-objective optimization problems. Compared with the state-of-the-art approaches listed in this paper, the proposed Bayesian optimization method achieves better optimization results with significantly less number of simulations.
Summary
Phytohormone, particularly jasmonate (JA) and salicylate (SA) signaling, plays a central role in plant responses to herbivore and pathogen attack. Generally, SA mediates resistance responses ...against biotrophic pathogens and phloem‐feeding insects, while JA mediates responses against necrotrophic pathogens and chewing insects. The phytohormonal responses mediating rice resistance to a piercing‐sucking herbivore, the brown planthopper (BPH), remains unknown.
Here, we combined transcriptome analysis, hormone measurements, genetic analysis and a field study to address this issue.
Infestation by BPH adult females resulted in significant transcriptional reprograming. The upregulated genes were enriched in the JA signaling pathway. Consistently, the concentrations of JAs, but not SA, were dramatically increased in response to BPH attack. Two JA‐deficient lines (AOC and MYC2 knockout) and two SA‐deficient lines (nahG overexpression and NPR1 knockout) were constructed. BPH performed better on JA‐deficient lines than on wild‐type (WT) plants, but similarly on SA‐deficient and WT plants. During BPH attack, the accumulation of defensive secondary metabolites was attenuated in JA‐deficient lines compared with WT plants. Moreover, MYC2 mutants were more susceptible to planthoppers than WT plants in nature.
This study reveals that JA signaling functions in rice defense against BPH.
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Natural products produced by microorganisms and plants are a major resource of antibacterial and anticancer drugs as well as industrially useful compounds. However, the native producers often suffer ...from low productivity and titers. Here we summarize the recent applications of heterologous biosynthesis for the production of several important classes of natural products such as terpenoids, flavonoids, alkaloids, and polyketides. In addition, we will discuss the new tools and strategies at multi-scale levels including gene, pathway, genome and community levels for highly efficient heterologous biosynthesis of natural products.
Biosynthesis of natural products in heterologous hosts is improved significantly with new tools and strategies in synthetic biology.
Mounting evidence suggests that the gut microbiota contribute to colorectal cancer (CRC) tumorigenesis, in which the symbiotic
(
) selectively increases immunosuppressive myeloid-derived suppressor ...cells (MDSCs) to hamper the host's anticancer immune response. Here, a specifically
-binding M13 phage was screened by phage display technology. Then, silver nanoparticles (AgNP) were assembled electrostatically on its surface capsid protein (M13@Ag) to achieve specific clearance of
and remodel the tumor-immune microenvironment. Both in vitro and in vivo studies showed that of M13@Ag treatment could scavenge
in gut and lead to reduction in MDSC amplification in the tumor site. In addition, antigen-presenting cells (APCs) were activated by M13 phages to further awaken the host immune system for CRC suppression. M13@Ag combined with immune checkpoint inhibitors (α-PD1) or chemotherapeutics (FOLFIRI) significantly prolonged overall mouse survival in the orthotopic CRC model.
Polycyclic aromatic derivatives can trap 1O2 to form endoperoxides (EPOs) for O2 storage and as sources of reactive oxygen species. However, these materials suffer from structural amorphism, which ...limit both practical applications and fundamental studies on their structural optimization for O2 capture and release. Metal–organic frameworks (MOFs) offer advantages in O2 binding, such as clear structure–performance relationships and precise controllability. Herein, we report the reversible binding of O2 is realized via the chemical transformation between anthracene‐based and the corresponding EPO‐based MOF. It is shown that anthracene‐based MOF, the framework featuring linkers with polycyclic aromatic structure, can rapidly trap 1O2 to form EPOs and can be restored upon UV irradiation or heating to release O2. Furthermore, we confirm that photosensitizer‐incorporated anthracene‐based MOF are promising candidates for reversible O2 carriers controlled by switching Vis/UV irradiation.
Capture and release: Controlled reversible binding of oxygen can be obtained through the chemical transformation between an anthracene‐based metal–organic framework (MOF) and the corresponding endoperoxide‐based MOF. With UV/Vis irradiation, the MOFs can be switched between trapping and releasing oxygen.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
MicroRNA‐142‐3p (miR‐142‐3p) was previously investigated in various cancers, whereas, it's role in breast cancer (BC) remains far from understood. In this study, we found that miR‐142‐3p was markedly ...decreased both in cell lines and BC tumor tissues. Elevated miR‐142‐3p expression suppressed growth and metastasis of BC cell lines via gain‐of‐function assay in vitro and in vivo. Mechanistically, miR‐142‐3p could regulate the ras‐related C3 botulinum toxin substrate 1 (RAC1) expression in protein level, which simultaneously suppressed the epithelial‐to‐mesenchymal transition related protein levels and the activity of PAK1 phosphorylation, respectively. In addition, rescue experiments revealed RAC1 overexpression could reverse tumor‐suppressive role of miR‐142‐3p. Our results showed miR‐142‐3p could function as a tumor suppressor via targeting RAC1/PAK1 pathway in BC, suggesting a potent therapeutic target for BC treatment.
MiR‐142‐3p is downregulated in breast cancer (BC) tissues and cell lines. MiR‐142‐3p may act as a tumor suppressor by regulating cell proliferation, migration, invasion, angiogenesis, and epithelial‐to‐mesenchymal transition process through directly downregulation of ras‐related C3 botulinum toxin substrate 1 expression and subsequently inhibiting the activity of PAK1 phosphorylation in BC.
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In this paper, we propose a fully ear-worn long-term blood pressure (BP) and heart rate (HR) monitor to achieve a higher wearability. Moreover, to enable practical application scenarios, we present a ...machine learning framework to deal with severe motion artifacts induced by head movements. We suggest situating all electrocardiogram (ECG) and photoplethysmography (PPG) sensors behind two ears to achieve a super wearability, and successfully acquire weak ear-ECG/PPG signals using a semi-customized platform. After introducing head motions toward real-world application scenarios, we apply a support vector machine classifier to learn and identify raw heartbeats from motion artifacts-impacted signals. Furthermore, we propose an unsupervised learning algorithm to automatically filter out residual distorted/faking heartbeats, for ECG-to-PPG pulse transit time (PTT) and HR estimation. Specifically, we introduce a dynamic time warping-based learning approach to quantify distortion conditions of raw heartbeats referring to a high-quality heartbeat pattern, which are then compared with a threshold to perform purification. The heartbeat pattern and the distortion threshold are learned by a K-medoids clustering approach and a histogram triangle method, respectively. Afterward, we perform a comparative analysis on ten PTT or PTT&HR-based BP learning models. Based on an acquired data set, the BP and HR estimation using the proposed algorithm has an error of -1.4±5.2 mmHg and 0.8±2.7 beats/min, respectively, both much lower than the state-of-the-art approaches. These results demonstrate the capability of the proposed machine learning-empowered system in ear-ECG/PPG acquisition and motion-tolerant BP/HR estimation. This proof-of-concept system is expected to illustrate the feasibility of ear-ECG/PPG-based motion-tolerant BP/HR monitoring.
AbstractTypical engineering design of rock weirs rely on simplified one-dimensional equations dependent on empirical coefficients. However, most simplified methods fail to accurately predict the ...hydraulics through rock weirs because they do not consider flow through interstitial spaces between rocks and the way interstitial flow alters the head-discharge relationship. To improve the design methodology and better capture the complex hydraulics past rock weirs, a three-dimensional high-resolution computational fluid dynamics model was utilized to study the problem. The simulation results demonstrate that the flow phenomena and head-discharge relationship are significantly different between broad-crested weirs and rock weirs. The interstitial spaces between rocks not only drain a portion of total discharge, but also accelerate the weir overflow. Based on the results, a flow decomposition approach is proposed to quantify the discharge through a rock weir. The decomposition includes contributing flows from (1) weir flow over the individual rocks, and (2) interstitial flow between rocks. Discharge coefficients for both contributing flows were found to be approximately linearly proportional to the porosity. The applicability of the proposed decomposition was demonstrated with an independent case. Despite the success, future improvement is needed with more rock weir variations.
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