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Finding top-k frequent items has been a hot issue in databases. Finding top-k persistent items is a new issue, and has attracted increasing attention in recent years. In practice, users often want to know which items are significant, i.e., not only frequent but also persistent. No prior art can address both of the above two issues at the same time. Also, for high-speed data streams, prior art cannot achieve high accuracy when the memory is tight. In this paper, we define a new issue, named finding significant items, and propose a novel algorithm namely LTC to address this issue. It includes two key techniques, Long-tail Restoring and CLOCK, as well as three optimizations. In addition, LTC is extended to support finding significant items with thresholds. We theoretically derive the correct rate and error bound, and conduct extensive experiments on three real datasets to test the performance of LTC. Our experimental results show that LTC can achieve ${10^{5}}$ times higher accuracy in terms of average relative error than other related algorithms. Lastly, LTC is applied to a DDoS detection task and it shows that finding significant items is more powerful than finding frequent items.

We present a process of producing antibacterial AuNPs on a large scale, and the AuNP-based medical dressing prepared using continuous dyeing and finishing machines is promising to be used in clinical antibacterial applications.

Cells cope with and adapt to ever-changing environmental conditions. Sophisticated regulatory networks allow cells to adjust to these fluctuating environments. One such archetypal system is the

Photoionization mass spectrometry (PI-MS) has become a versatile tool in the real-time analysis of volatile organic compounds (VOCs) from the atmosphere or exhaled breath. However, some key species

Deep reinforcement learning (DRL) has been widely used in autonomous control due to its superior performance. DRL-based autonomous control model (ACM) aims to train an agent to achieve self-control and learn optimal policy through pre-defined rewards. Despite the super-human performance, ACM is regarded as a black box, and the interpretation of its internal working mechanism remains a challenge to domain experts. In addition, adjusting the reward settings of ACM is also challenging due to the uncertain relationship between rewards setting and strategies. In this paper, we propose ACMViz, a visual analytics system to explore control strategies at different stages and reveal the relationship between rewards and action patterns. Focusing on controlling a lunar lander, ACMViz investigates different landing trajectories and action sequences to interpret the model and control the training. From our visual analytics of the action patterns, we diagnose and improve reward settings for different control targets. Through our case studies with deep learning experts, we validate the effectiveness of ACMViz.

Peroxydisulfate (PDS) can be activated by electrochemistry, for which using atom H* as an activator is feasibly favorable in theoretical and experimental applications. Studies have shown that atomic H* can cleave the peroxide bond as a single-electron reducing agent in Na2S2O8 to generate SO4•−, thus achieving the degradation of pollutants. Herein, Pd nanoparticles synthesized by in an in situ solution were dispersed in carbon black and then loaded on carbon felt, called Pd/C@CF, as the cathode for peroxydisulfate activation. This showed an ideal degradation effect on a small electrode (10 mm × 10 mm). Cyclic voltammetry (CV) and linear sweep voltammetry (LSV) tests were taken to verify the significant increase in the yield of the reduction of Na2S2O8 by H*. The degradation experiments and free-radical scavenging experiment confirmed that the atomic H* was the dominant component triggering the activation of PDS to generate SO4•−. A Pd/C@CF composite electrodes have low pH dependence, high stability and recyclability, etc., which has many potential practical applications in wastewater treatment. In addition, H* can also reduce H2O2 to •OH by breaking the peroxide bond, so the removal of pollutants by the same amount of H2O2 and Na2S2O8 under the same conditions is compared, and their application prospects are analyzed and compared.

Satellites detect the distribution of meteorological data worldwide. However, due to the orbital constraints, the satellite can only reach the same area again after one orbiting cycle. The interval between two detections in the same area is long, and the variation of meteorological data between the two detections is unknown. Moreover, meteorological satellite data are only located near the orbit in one cycle, while the global distribution of meteorological data is unknown. Our method allows to train a regression model with only few meteorological satellite data by taking advantage of the recent advances in deep learning. In detail, we train a model-agnostic meta-learning (MAML) model with data from ground stations instead of meteorological satellites and get the initial network parameters. Based on the initial network parameters trained by MAML, we train the regression models again for different areas. We sample the regression curves of all areas by time and get a time series of global meteorological data distribution. Through case studies conducted together with domain experts, we validate the effectiveness of our method.

The demand for leaf area index (LAI) retrieval from spaceborne full-waveform LiDAR increases due to its direct sampling of the three-dimensional forest structure at a near-global scale. However, the nonrandomness (i.e., clumping effect) of canopy composition limits the reliability of LAI derived from two common methods. They either assume a homogeneous scene in the footprint or just correct for the large gaps-induced between-crown clumping. The clumping in the crown is still an unaddressed issue. We proposed a method to compensate occlusion (i.e., lower canopy layers are occluded by the upper canopy in the process of LiDAR measurement), through which the vertical canopy profile can be resolved from the waveform. Further, we developed a method of deriving relative path length distribution that can reflect the heterogeneity of the canopy from the occlusion-corrected waveform. In addition to correcting the between-crown clumping, we corrected the within-crown clumping further using the derived relative path length distribution, based on path length distribution (PATH) theory. We used simulated waveform data with known LAI and GLAS data with corresponding field-measured LAI to test the performance of our and the other two common LAI retrieval methods. Results show that the errors of our approach are the lowest (with an error generally below 10% and the maximum error below 20%, compared with up to 69% and 47% for the other two methods), and it is relatively stable in various scenes. This study demonstrated the potential of improving LAI retrieval through full utilization of full-waveform data.

Cells cope with and adapt to ever-changing environmental conditions. Sophisticated regulatory networks allow cells to adjust to these fluctuating environments. One such archetypal system is the Saccharomyces cerevisiae Pho regulon. When external inorganic phosphate ( P i ) concentration is low, the Pho regulon activates, expressing genes that scavenge external and internal P i . However, the precise mechanism controlling this regulon remains elusive. We conducted a systems analysis of the Pho regulon on the single-cell level under well-controlled environmental conditions. This analysis identified a robust, perfectly adapted Pho regulon state in intermediate P i conditions, and we identified an intermediate nuclear localization state of the transcriptional master regulator Pho4p. The existence of an intermediate nuclear Pho4p state unifies and resolves outstanding incongruities associated with the Pho regulon, explains the observed programmatic states of the Pho regulon, and improves our general understanding of how nature evolves and controls sophisticated gene regulatory networks. We further propose that robustness and perfect adaptation are not achieved through complex network-centric control but by simple transport biophysics. The ubiquity of multitransporter systems suggests that similar mechanisms could govern the function of other regulatory networks as well.

Summary Advances in bioelectronics have great potential to address unsolved biomedical problems in the cardiovascular system. By using poly(L-lactide-co-ϵ-caprolactone), which encapsulates liquid metal to make flexible and biodegradable electrical circuitry, we develop an electronic blood vessel that can integrate flexible electronics with three layers of blood-vessel cells, to mimic and go beyond the natural blood vessel. It can improve the endothelialization process through electrical stimulation and can enable controlled gene delivery into specific parts of the blood vessel via electroporation. The electronic blood vessel has excellent biocompatibility in the vascular system and shows great patency 3 months post-implantation in a rabbit model. The electronic blood vessel would be an ideal platform to enable diagnostics and treatments in the cardiovascular system and can greatly empower personalized medicine by creating a direct link of the vascular tissue-machine interface.

Intimal hyperplasia (IH) in vein grafts (VGs) is a major issue in coronary artery bypass grafting (CABG) surgery. Although external stents can attenuate IH of VGs to some extent, none of the existing external stents have shown satisfactory clinical outcomes. Here we develop a flexible, biodegradable, and conductive external metal-polymer conductor stent (MPCS) that can electroporate the vessel wall and produce a protein that prevents IH. We designed the plasmid DNA encoding the tissue inhibitor of metalloproteinases-3 (TIMP-3) and lyophilized it on the inner surface of the MPCS to deliver into the adventitia and the middle layer of VGs for gene therapy. Coupled with its continuous mechanical support to prevent dilation after implanting, the MPCS can inhibit the IH of VGs significantly in the rabbit model. This proof-of-concept demonstration may aid the development of other implantable bioelectronics for electroporation gene therapy.

The automatic segmentation of the left ventricle in magnetic resonance (MR) images is the basis of computer-aided diagnosis systems. To accurately extract the endocardium and epicardium of the left ventricle from MR images, a method based on a dilated dense convolutional network (DDCN) has been proposed in this article. First, to reduce memory consumption, computing time and the class imbalance between the target and background, a clustering algorithm that combines the prior knowledge of the spatial relationship between the slices has been proposed to crop the region of interest (ROI). Then, the DDCN model with 8 dilated convolutional layers and dense connections, which is efficient with respect to its memory consumption and training time, has been proposed to delineate the endocardium and epicardium. To compare the DDCN model with other algorithms, 30 sequences of the MICCAI 2009 left ventricle segmentation challenge database are used to train the proposed model and the other 15 sequences are used for testing. The performance of the proposed method is evaluated by the percentage of “good” contours (PGC), average Dice metric (ADM) and average perpendicular distance (APD). Our results show that for the endocardial and epicardial contours, the PGCs are 99.49%±1.99% and 100%±0%, the APDs are 1.50±0.34 mm and 1.31±0.22 mm, and the ADMs are 0.93±0.03 and 0.96±0.01, respectively, which indicates that our method provides contours with great agreement with the ground truth. In addition, the comparison results show that our method exhibits outstanding performance and possesses promising potential to be used in computer-aided diagnosis systems for cardiovascular disease.

Transcription factor binding to a single binding site and its functional consequence in a promoter context are beginning to be relatively well understood. However, binding to clusters of sites has yet to be characterized in depth, and the functional relevance of binding site clusters remains uncertain. We employed a high-throughput biochemical method to characterize transcription factor binding to clusters varying across a range of affinities and configurations. We found that transcription factors can bind concurrently to overlapping sites, challenging the notion of binding exclusivity. Furthermore, compared to an individual high-affinity binding site, small clusters with binding sites an order of magnitude lower in affinity give rise to higher mean occupancies at physiologically-relevant transcription factor concentrations in vitro. To assess whether the observed in vitro occupancies translate to transcriptional activation in vivo, we tested low-affinity binding site clusters by inserting them into a synthetic minimal CYC1 and the native PHO5 S. cerevisiae promoter. In the minCYC1 promoter, clusters of low-affinity binding sites can generate transcriptional output comparable to a promoter containing three consensus binding sites. In the PHO5 promoter, replacing the native Pho4 binding sites with clusters of low-affinity binding sites recovered activation of these promoters as well. This systematic characterization demonstrates that clusters of low-affinity binding sites achieve substantial occupancies, and that this occupancy can drive expression in eukaryotic promoters.

The billion tons of synthetic-polymer-based materials (i.e. plastics) produced yearly are a great challenge for humanity. Nature produces even more natural polymers, yet they are sustainable. Proteins are sequence-defined natural polymers that are constantly recycled when living systems feed. Digestion is the protein depolymerization into amino acids (the monomers) followed by their re-assembly into new proteins of arbitrarily different sequence and function. This breaks a common recycling paradigm where a material is recycled into itself. Organisms feed off of random protein mixtures that are "recycled" into new proteins whose identity depends on the cell's specific needs. In this study, mixtures of several peptides and/or proteins are depolymerized into their amino acid constituents, and these amino acids are used to synthesize new fluorescent, and bioactive proteins extracellularly by using an amino-acid-free, cell-free transcription-translation (TX-TL) system. Specifically, three peptides (magainin II, glucagon, and somatostatin 28) are digested using thermolysin first and then using leucine aminopeptidase. The amino acids so produced are added to a commercial TX-TL system to produce fluorescent proteins. Furthermore, proteins with high relevance in materials engineering (beta-lactoglobulin films, used for water filtration, or silk fibroin solutions) are successfully recycled into biotechnologically relevant proteins (fluorescent proteins, catechol 2,3-dioxygenase)., Advanced Materials, 33 (44), ISSN:0935-9648, ISSN:1521-4095

The billion tons of synthetic polymer-based materials (i.e. plastics) produced every year are one of the greatest challenges that humanity has to face. Nature produces even more natural polymers, yet they are sustainable. For example, proteins are sequence-defined natural polymers, that are constantly recycled when living systems feed. Indeed, digestion is the protein depolymerization into amino acids (i.e. the monomers) followed by their re-assembly into new proteins of arbitrarily different sequence and function. This process breaks a common recycling paradigm where a material is recycled into itself. Organisms feed of random protein mixtures that are ‘recycled’ into new proteins whose identity depends on the cell’s needs at the time of protein synthesis. Currently, advanced materials are increasingly made of proteins, but the abovementioned ideal recyclability of such materials has yet to be recognized and established. In this study mixtures of several (up to >30) peptides and/or proteins were depolymerized into their amino acid constituents, and these amino acids were used to synthesize new fluorescent, and bio-active proteins extra-cellularly by using an amino acid-free cell-free transcription-translation system. Proteins with high relevance in materials engineering (β-lactoglobulin films, used for water filtration, or silk fibroin solutions) were successfully recycled into biotechnologically relevant proteins (green, and red fluorescent proteins, catechol 2,3-dioxygenase). The potential long-term impact of this approach to recycling lies in its compatibility with circular-economy models where raw materials remain in use as long as possible, thus reducing the burden on the planet.

Leaf inclination angle distribution plays an important role in indirect leaf area measurement methods. Path length distribution method (PATH) is an indirect leaf area measurement method based on Beer's Law, which has been applied to isolated trees with Terrestrial Laser Scanning (TLS). However, it can only set the leaf projection G to a constant value. In this paper, the PATH method considering leaf inclination models is introduced, which allows the G function to be a variable corresponding to leaf inclination. On the basis of this method, this paper explores the effect of different leaf inclination angle distribution assumptions on the inversion of the leaf area of isolated trees based on TLS. The results show that compared with the measured leaf inclination, the relative errors of the inversion results based on the six typical leaf inclination assumptions are between −14.3 % to +41.2%, which indicates that leaf inclination has a significant effect on the inversion of the leaf area. Further, experiments in this paper show that the mean value of the G function is a relatively accurate representation of it.

Small-diameter vascular grafts (inner diameter < 6 mm) are useful in treating cardiovascular diseases. The off-the-shelf small-diameter vascular grafts for clinical applications remain a great limitation owing to their thrombogenicity or intimal hyperplasia. Herein, bilayer anticoagulant hydrogel tubes with poly(e-caprolactone) (PCL) sheaths are prepared by freeze-thawing and electrospinning, which contain nanofibrillated cellulose (NFC)/poly(vinyl alcohol) (PVA)-heparin/poly-L-lysine nanoparticles tube as an inner layer and PCL sheath as an outer layer. The structure, anticoagulant property, and biocompatibility of the inner layer are studied. The effects of thickness of the outer layer on perfusion performance and mechanical property of hydrogel tubes with PCL sheaths (PCL-NFC/PVA-NPs tubes) are investigated. The effect of compliance of PCL-NFC/PVA-NPs tubes on their blood flow is studied by numerical simulation. The tissue compatibility and the patency of PCL-NFC/PVA-NPs tubes are evaluated by implantation in subcutaneous tissue of rats and carotid artery of rabbits. PCL-NFC/PVA-NPs tubes have prominent anticoagulation, sufficient burst pressure and good compliance similar to native arteries. PCL-NFC/PVA-NPs tubes facilitate infiltration of host cells and achieve active proliferation of recruited cells, which will be a promising candidate for small-diameter vascular grafts.

The FY-3C satellite returns more than 250,000 occultation data each year, each of which records high vertical resolution profiles of various climatic parameters in the range of 0–1000 km above the Earth. These long-term, stable, and globally distributed observations can be used to improve climate modeling and analyze trends in spatial climate. However, traditional methods based on small samples meet challenges in analyzing the multi-dimensional occultation data in large volume. In this paper, we developed OccVis-a visual analytics system for multi-dimensional and multi-scale comparative analysis of occultation data. With a novel workflow, a series of data processing methods are proposed to support data correcting, patching, and clustering. Moreover, a matrix view with two modes is presented for overview, and a detail view along with time series view is provided for further analysis. Domain scientists can easily utilize our system to visually and interactively explore multivariable occultation data at different time and space scales. Finally, we conduct case studies in climate modeling of ionosphere and obtain several preliminary results to demonstrate the usage and effectiveness of our system.

Blattisocius Keegan is a genus of predatory mites in the family Blattisociidae, with potential utility as biological control agents. In the present study, Illumina HiSeq was used to sequence the co...

Leaf angle distribution (LAD) is an important attribute of forest canopy architecture and affects the solar radiation regime within the canopy. Terrestrial laser scanning (TLS) has been increasingly used in LAD estimation. The point clouds data suffer from the occlusion effect, which leads to incomplete scanning and depends on measurement strategies such as the number of scans and scanner location. Evaluating these factors is important to understand how to improve LAD, which is still lacking. Here, we introduce an easy way of estimating the LAD using open source software. Importantly, the influence of the occlusion effect on the LAD was evaluated by combining the proposed complete point clouds (CPCs) with the simulated data of 3D tree models of Aspen, Pin Oak and White Oak. We analyzed the effects of the point density, the number of scans and the scanner height on the LAD and G-function. Results show that: (1) the CPC can be used to evaluate the TLS-based normal vector reconstruction accuracy without an occlusion effect; (2) the accuracy is slightly affected by the normal vector reconstruction method and is greatly affected by the point density and the occlusion effect. The higher the point density (with a number of points per unit leaf area of 0.2 cm−2 to 27 cm−2 tested), the better the result is; (3) the performance is more sensitive to the scanner location than the number of scans. Increasing the scanner height improves LAD estimation, which has not been seriously considered in previous studies. It is worth noting that relatively tall trees suffer from a more severe occlusion effect, which deserves further attention in further study.

Heavy metals (HMs) in farmland soils lead to adverse influences on ecosystem and human health. Despite that, data on quantitative risk from different sources are still scarce. In this study, 100 farmland soil samples in Jiuyuan District were collected and analyzed for selected HMs (As, Cd, Co, Cr, Cu, Mn, Ni, Pb, V and Zn) content characteristics and pollution statuses. The positive matrix factorization (PMF) model combined with the Nemerow integrated risk index (NIRI) and human health risk assessment (HHRA) was used to quantitatively identify the primary risk sources. The results indicated that the mean contents or median values (mg/kg) of 10 HMs were all higher than the background values. The contamination factor (CF) and pollution load index (PLI) revealed that the soil was severely polluted. Based on PMF, the main source of HM pollution was anthropogenic activities, accounting for 78.91%. Sewage irrigation represented the biggest input but was not associated with the highest risk. The results of PMF-based NIRI and PMF-based HHRA showed that the chemical fertilizers and pesticides were the largest and priority risk sources with contribution rates of 38.10% to ecological risk and 34.61 and 32.82% to non-carcinogenic and carcinogenic risk, respectively. In addition, non-carcinogenic risk of children was higher than that of adults, while the carcinogenic risk was the opposite. The integrated approaches were beneficial for priority risk quantification from different sources and can provide direct risk information and effective policy recommendations for management and control of key risk sources.

Narrowing the mechanical mismatch between biological tissues (typically soft) and neural interfaces (hard) is essential for maintaining signal quality for the electrical recording of neural activity. However, only a few materials can satisfy all requirements for such electronics, which need to be both biocompatible and sufficiently soft. Here, a highly stretchable electrode array (SEA) is introduced, based on the liquid metal-polymer conductor (MPC), achieving high mechanical flexibility and good cytocompatability for neural interfaces. By utilizing the MPC, the SEA exhibits high stretchability (≈100%) and excellent cycling stability (>400 cycles). The cytocompatability of the SEA can allow for long-term culturing of primary neurons and enable signal recording of primary hippocampal neurons. In the future, the SEA could serve as a reliable and robust platform for diagnostics in neuronal tissues and greatly advance brain-machine interfaces.

Existing temporary epicardial pacing wires (TPWs) are rigid and non-absorbable, such that they can cause severe complications after cardiac surgery. Here, a soft and absorbable temporary epicardial pacing wire (saTPW) for effectively correcting abnormal heart rates in a rabbit model, such as bradycardia and ventricular premature beat, is developed. The saTPW exhibits excellent conductivity, flexibility, cycling stability (>100 000 cycles), and less inflammatory response during two-month subcutaneous implantation in a rat model. The saTPW which consists of poly(l-lactide-co-e-caprolactone) and liquid metal, can degrade about 13% (mass loss) in the rats over a two-month subcutaneous implantation. It can be absorbed over time in the body. The cytocompatibility and absorbability avoid secondary injuries caused by remaining wires which are permanently left in the body. The saTPW will provide a great platform for diagnosis and treatments in cardiovascular diseases by delivering the physiological signal and applying electrical stimulation for therapy.