Rhodophytes (red algae) are a diverse group of algae with great ecological and economic importance. However, tools for post-genomic research on red algae are still largely lacking. Here, we report ...the development of an efficient genetic transformation system for the model rhodophyte Porphyridium purpureum. We show that transgenes can be expressed to unprecedented levels of up to 5% of the total soluble protein. Surprisingly, the transgenic DNA is maintained episomally, as extrachromosomal high-copy number plasmid. The bacterial replication origin confers replication in the algal nucleus, thus providing an intriguing example of a prokaryotic replication origin functioning in a eukaryotic system. The extended presence of bacterial episomal elements may provide an evolutionary explanation for the frequent natural occurrence of extrachromosomal plasmids in red algae, and may also have contributed to the high rate of horizontal gene transfer from bacteria to the nuclear genome of Porphyridium purpureum and other rhodophytes.
Giant magnetoresistance (GMR) magnetic field sensors are compact, low power, high sensitivity devices that are low cost and have very simple supporting electronics. One of the disadvantages of GMR ...sensors can be their nonlinearity, hysteresis, and temperature-dependent output, which can reduce measurement accuracy. This paper presents an approach to improve the measurement accuracy of GMR sensors using a closed-loop circuit, which includes the sensor, a biasing coil, and a feedback circuit. The current in the biasing coil is actively changed to ensure that the component of magnetic field along the sensitive axis of the device is held constant, so that as the external magnetic field or orientation of the GMR sensor changes, the output of GMR sensor remains stable. In this way, the external magnetic field component along the sensitive axis of the device can be calculated by measuring the current in the biasing coil surrounding the GMR sensor, regardless of the hysteresis and nonlinearly of GMR sensor. The linearity and the accuracy of magnetic field measurements using a GMR sensor are significantly improved and a hardware prototype has been constructed and tested under a reference magnetic field.
Convolutional neural network (CNN)-based remote sensing (RS) image segmentation has become a widely used method for building footprint mapping. Recently, DeeplabV3+, an advanced CNN architecture, has ...shown satisfactory performance for building extraction in different urban landscapes. However, it faces challenges due to the large amount of labeled data required for model training and the extremely high costs associated with the annotation of unlabelled data. These challenges encouraged us to design a framework for building footprint mapping with fewer labeled data. In this context, the published studies on RS image segmentation are reviewed first, with a particular emphasis on the use of active learning (AL), incremental learning (IL), transfer learning (TL), and their integration for reducing the cost of data annotation. Based on the literature review, we defined three candidate frameworks by integrating AL strategies (i.e., margin sampling, entropy, and vote entropy), IL, TL, and DeeplabV3+. They examine the efficacy of AL, the efficacy of IL in accelerating AL performance, and the efficacy of both IL and TL in accelerating AL performance, respectively. Additionally, these frameworks enable the iterative selection of image tiles to be annotated, training and evaluation of DeeplabV3+, and quantification of the landscape features of selected image tiles. Then, all candidate frameworks were examined using WHU aerial building dataset as it has sufficient (i.e., 8188) labeled image tiles with representative buildings (i.e., various densities, areas, roof colors, and shapes of the building). The results support our theoretical analysis: (1) all three AL strategies reduced the number of image tiles by selecting the most informative image tiles, and no significant differences were observed in their performance; (2) image tiles with more buildings and larger building area were proven to be informative for the three AL strategies, which were prioritized during the data selection process; (3) IL can expedite model training by accumulating knowledge from chosen labeled tiles; (4) TL provides a better initial learner by incorporating knowledge from a pre-trained model; (5) DeeplabV3+ incorporated with IL, TL, and AL has the best performance in reducing the cost of data annotation. It achieved good performance (i.e., mIoU of 0.90) using only 10–15% of the sample dataset; DeeplabV3+ needs 50% of the sample dataset to realize the equivalent performance. The proposed frameworks concerning DeeplabV3+ and the results imply that integrating TL, AL, and IL in human-in-the-loop building extraction could be considered in real-world applications, especially for building footprint mapping.
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Recently, the Healthcare Internet of Things (H-IoT) has been widely applied to alleviate the global challenge of the coronavirus disease 2019 (COVID-19) pandemic. However, security and limited energy ...capacity issues remain the two main factors that prevent the large-scale application of the H-IoT. Therefore, a permissioned blockchain and deep reinforcement learning (DRL)-empowered H-IoT system is presented in this research to address these two issues. The proposed H-IoT system can provide real-time security and energy-efficient healthcare services to control the propagation of the COVID-19 pandemic. To address the security issue, a permissioned blockchain method is adopted to guarantee the security of the proposed H-IoT system. As for handling the limited energy constraint, we employ the mobile edge computing (MEC) method to offload the computing tasks to alleviate the computational burden and energy consumption of the proposed H-IoT system. We also adopt an energy harvesting method to improve performance. In addition, a DRL method is employed to jointly optimize both the security and energy efficiency performance of the proposed system. The simulation results demonstrate that the proposed solution can balance the requirements of security and energy efficiency issues and hence can better respond to the COVID-19 pandemic.
Based on recent advances in organoid research as well as the need to find more accurate models for drug screening in cancer research, patient-derived organoids have emerged as an effective in vitro ...model system to study cancer. Showing numerous advantages over 2D cell lines, 3D cell lines, and primary cell culture, organoids have been applied in drug screening to demonstrate the correlation between genetic mutations and sensitivity to targeted therapy. Organoids have also been used in co-clinical trials to compare drug responses in organoids to clinical responses in the corresponding patients. Numerous studies have reported the successful use of organoids to predict therapy response in cancer patients. Recently, organoids have been adopted to predict treatment response to radiotherapy and immunotherapy. The development of high throughput drug screening and organoids-on-a-chip technology can advance the use of patient-derived organoids in clinical practice and facilitate therapeutic decision-making.
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With advancements in big geospatial data and artificial intelligence, multi-source data and diverse data-driven methods have become common in dengue risk prediction. Understanding the current state ...of data and models in dengue risk prediction enables the implementation of efficient and accurate prediction in the future. Focusing on predictors, data sources, spatial and temporal scales, data-driven methods, and model evaluation, we performed a literature review based on 53 journal and conference papers published from 2018 to the present and concluded the following. (1) The predominant predictors include local climate conditions, historical dengue cases, vegetation indices, human mobility, population, internet search indices, social media indices, landscape, time index, and extreme weather events. (2) They are mainly derived from the official meteorological agency satellite-based datasets, public websites, department of health services and national electronic diseases surveillance systems, official statistics, and public transport datasets. (3) Country-level, province/state-level, city-level, district-level, and neighborhood-level are used as spatial scales, and the city-level scale received the most attention. The temporal scales include yearly, monthly, weekly, and daily, and both monthly and weekly are the most popular options. (4) Most studies define dengue risk forecasting as a regression task, and a few studies define it as a classification task. Data-driven methods can be categorized into single models, ensemble learning, and hybrid learning, with single models being further subdivided into time series, machine learning, and deep learning models. (5) Model evaluation concentrates primarily on the quantification of the difference/correlation between time-series observations and predicted values, the ability of models to determine whether a dengue outbreak occurs or not, and model uncertainty. Finally, we highlighted the importance of big geospatial data, data cloud computing, and other deep learning models in future dengue risk forecasting.
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Abstract
Horizontal gene transfer has occurred between organisms of all domains of life and contributed substantially to genome evolution in both prokaryotes and eukaryotes. Phylogenetic evidence ...suggests that eukaryotic genes horizontally transferred to bacteria provided useful new gene functions that improved metabolic plasticity and facilitated adaptation to new environments. How these eukaryotic genes evolved into functional bacterial genes is not known. Here, we have conducted a genetic screen to identify the mechanisms involved in functional activation of a eukaryotic gene after its transfer into a bacterial genome. We integrated a eukaryotic selectable marker gene cassette driven by expression elements from the red alga Porphyridium purpureum into the genome of Escherichia coli. Following growth under non-selective conditions, gene activation events were indentified by antibiotic selection. We show that gene activation in the bacterial recipient occurs at high frequency and involves two major types of spontaneous mutations: deletion and gene amplification. We further show that both mechanisms result in promoter capture and are frequently triggered by microhomology-mediated recombination. Our data suggest that horizontally transferred genes have a high probability of acquiring functionality, resulting in their maintenance if they confer a selective advantage.
Postoperative cognitive dysfunction (POCD) is a common neurological complication following surgery and general anesthesia, especially in elderly patients. Severe cases delay patient discharge, affect ...the patient's quality of life after surgery, and are heavy burdens to society. In addition, as the population ages, surgery is increasingly used for older patients and those with higher prevalences of complications. This trend presents a huge challenge to the current healthcare system. Although studies on POCD are ongoing, the underlying pathogenesis is still unclear due to conflicting results and lack of evidence. According to existing studies, the occurrence and development of POCD are related to multiple factors. Among them, the pathogenesis of neuroinflammation in POCD has become a focus of research in recent years, and many clinical and preclinical studies have confirmed the correlation between neuroinflammation and POCD. In this article, we reviewed how central nervous system inflammation occurred, and how it could lead to POCD with changes in peripheral circulation and the pathological pathways between peripheral circulation and the central nervous system (CNS). Furthermore, we proposed some potential therapeutic targets, diagnosis and treatment strategies at the cellular and molecular levels, and clinical applications. The goal of this article was to provide a better perspective for understanding the occurrence of POCD, its development, and preventive strategies to help manage these vulnerable geriatric patients.
The main disadvantage of the electromagnetic acoustic transducer (EMAT) is low energy-conversion efficiency and low signal-to-noise ratio (SNR). This problem can be improved by pulse compression ...technology in the time domain. In this paper, a new coil structure with unequal spacing was proposed for a Rayleigh wave EMAT (RW-EMAT) to replace the conventional meander line coil with equal spacing, which allows the signal to be compressed in the spatial domain. Linear and nonlinear wavelength modulations were analyzed to design the unequal spacing coil. Based on this, the performance of the new coil structure was analyzed by the autocorrelation function. Finite element simulation and experiments proved the feasibility of the spatial pulse compression coil. The experimental results show that the received signal amplitude is increased by 2.3~2.6 times, the signal with a width of 20 μs could be compressed into a δ-like pulse of less than 0.25 μs and the SNR is increased by 7.1-10.1 dB. These indicate that the proposed new RW-EMAT can effectively enhance the strength, time resolution and SNR of the received signal.
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•Urban land use mapping through integrating remote sensing and geospatial big data.•Crucial features from remote sensing and geospatial big data are identified.•Decision-level and feature-level ...integration approaches are compared.
Remote Sensing (RS) has been used in urban mapping for a long time; however, the complexity and diversity of urban functional patterns are difficult to be captured by RS only. Emerging Geospatial Big Data (GBD) are considered as the supplement to RS data, and help to contribute to our understanding of urban lands from physical aspects (i.e., urban land cover) to socioeconomic aspects (i.e., urban land use). Integrating RS and GBD could be an effective way to combine physical and socioeconomic aspects with great potential for high-quality urban land use classification. In this study, we reviewed the existing literature and focused on the state-of-the-art and perspective of the urban land use categorization by integrating RS and GBD. Specifically, the commonly used RS features (e.g., spectral, textural, temporal, and spatial features) and GBD features (e.g., spatial, temporal, semantic, and sequence features) were identified and analyzed in urban land use classification. The integration strategies for RS and GBD features were categorized into feature-level integration (FI) and decision-level integration (DI). To be more specific, the FI method integrates the RS and GBD features and classifies urban land use types using the integrated feature sets; the DI method processes RS and GBD independently and then merges the classification results based on decision rules. We also discussed other critical issues, including analysis unit setting, parcel segmentation, parcel labeling of land use types, and data integration. Our findings provide a retrospect of different features from RS and GBD, strategies of RS and GBD integration, and their pros and cons, which could help to define the framework for future urban land use mapping and better support urban planning, urban environment assessment, urban disaster monitoring and urban traffic analysis.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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