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Tissue engineered vascular grafts (TEVGs) represent a promising therapeutic option for emergency vascular intervention. Although the application of small-diameter TEVGs using patient-specific primary endothelial cells (ECs) to prevent thrombosis and occlusion prior to implantation could be hindered by the long time course required for in vitro endothelialization, human induced pluripotent stem cells (hiPSCs) provide a robust source to derive immunocompatible ECs (hiPSC-ECs) for immediate TEVG endothelialization. To achieve clinical application, hiPSC-ECs should be derived under culture conditions without the use of animal-derived reagents (xenogeneic-free conditions), to avoid unwanted host immune responses from xenogeneic reagents. However, a completely xenogeneic-free method of hiPSC-EC generation has not previously been established. Herein, we substituted animal-derived reagents used in a standard method of xenogeneic hiPSC-EC differentiation with functional counterparts of human origin. As a result, we generated xenogeneic-free hiPSC-ECs (XF-hiPSC-ECs) with similar marker expression and function to those of human primary ECs. Furthermore, XF-hiPSC-ECs functionally responded to shear stress with typical cell alignment and gene expression. Finally, we successfully endothelialized decellularized human vessels with XF-hiPSC-ECs in a dynamic bioreactor system. In conclusion, we developed xenogeneic-free conditions for generating functional hiPSC-ECs suitable for vascular tissue engineering, which will further move TEVG therapy toward clinical application.

Metallic stents cause vascular wall damage with subsequent smooth muscle cell (SMC) proliferation, neointimal hyperplasia, and treatment failure. To combat in-stent restenosis, drug-eluting stents (DES) delivering mTOR inhibitors such as sirolimus or everolimus have become standard for coronary stenting. However, the relatively non-specific action of mTOR inhibitors prevents efficient endothelium recovery and mandates dual antiplatelet therapy to prevent thrombosis. Unfortunately, long-term dual antiplatelet therapy leads to increased risk of bleeding/stroke and, paradoxically, myocardial infarction. Here, we took advantage of the fact that nitric oxide (NO) increases Fas receptors on the SMC surface. Fas forms a death-inducing complex upon binding to Fas ligand (FasL), while endothelial cells (ECs) are relatively resistant to this pathway. Selected doses of FasL and NO donor synergistically increased SMC apoptosis and inhibited SMC growth more potently than did everolimus or sirolimus, while having no significant effect on EC viability and proliferation. This differential effect was corroborated in ex vivo pig coronaries, where the neointimal formation was inhibited by the drug combination, but endothelial viability was retained. We also deployed FasL-NO donor-releasing ethylene-vinyl acetate copolymer (EVAc)-coated stents into pig coronary arteries, and cultured them in perfusion bioreactors for one week. FasL and NO donor, released from the stent coating, killed SMCs close to the stent struts, even in the presence of flow rates mimicking those of native arteries. Thus, the FasL-NO donor-combination has a potential to prevent intimal hyperplasia and in-stent restenosis, without harming endothelial restoration, and hence may be a superior drug delivery strategy for DES.

Whole organ engineering paradigms typically involve repopulating acellular organ scaffolds with recipient-compatible cells, to generate a neo-organ that may provide key physiological functions. In the case of whole lung engineering, functionally endothelialized pulmonary vasculature is critical for establishing a fluid-tight barrier at the level of the alveolus, so that oxygen and carbon dioxide can be exchanged in the organ. We have previously developed a protocol to efficiently seed endothelial cells into the microvascular channels of decellularized lung scaffolds, but fully functional endothelial coverage, in terms of barrier function and resistance to thrombosis, was not achieved. In this study, we investigated whether various small molecules could favorably impact endothelial functionality after seeding into decellularized lung scaffolds. We demonstrated that the Epac-selective cAMP analog 8CPT-2Me-cAMP improves endothelial barrier function in repopulated lung scaffolds. When treated with the Epac agonist, barrier function of human umbilical vein endothelial cells (HUVECs) improved, and was maintained for at least three days, whereas the effect of other tested molecules lasted for only 5 h. Treatment with the Epac agonist re-organized actin structure, and appeared to increase the continuity of junction proteins such as VE-cadherin and ZO1. Blockade of actin polymerization abolished the effect of the Epac agonist on barrier function and actin reorganization, confirming a strong actin-mediated effect. Similarly, after treatment with Epac agonist, the barrier function in iPSC-derived endothelial colony forming cells (ECFCs) was increased and the enhanced barrier was maintained for at least 60 h. After culture in lung scaffolds for 5 days, iPSC-ECFCs maintained their phenotype by expressing CD31 , eNOS, vWF, and VE-Cadherin. Treatment with the Epac agonist significantly improved the barrier function of iPSC-ECFC-repopulated lung for at least 6 h. Taken together, these findings demonstrated that Epac-selective 8CPT-2Me-cAMP activation enhanced vascular barrier in iPSC-ECFC-engineered lungs, and may be useful to improve endothelial functionality for whole organ tissue engineering.

Growth of organic thin films using physical vapor deposition typically follows a three-dimensional mode, resulting in a rough surface, which undermines their application potential. To address this issue, we have studied the effect of electric field and temperature on the growth dynamics, especially the heterogeneous nucleation process, of croconic acid (CA) films, taking advantage of the large dipole moment of the molecules and the ferroelectric polarization of the molecular crystals. We found that the nucleation rate reaches the maximum at intermediate temperature, and the electric field shifts the maximum nucleation rate towards lower temperature. An analysis using the classical nucleation theory suggests that the electric field decreases the sublimation temperature, increases the wetting angle, and decreases the surface diffusion barrier. These results provide important insight into the growth of molecular crystal films under electric fields and pave the way to fabricating films with better surface characteristics for molecular ferroelectric films.

Data query operations of different data structures are ubiquitous and critical in today’s data center infrastructures and applications. However, query operations are not always performance-optimal to be executed on general-purpose CPU cores. These operations exhibit insufficient memory-level parallelism and frontend bottlenecks due to unstructured control flow. Furthermore, the data access patterns are not cache- or prefetch-friendly. Based on our performance analysis on a commodity server, query operations can consume a large percentage of the CPU cycles in various modern cloud workloads. Existing accelerator solutions for query operations do not strike a balance between their generality, scalability, latency, and hardware complexity. In this paper, we propose QEI, a generic, integrated, and efficient acceleration solution for various data structure queries. We first abstract the query operations to a few regular steps and map them to a simple and hardware-friendly configurable finite automaton model. Based on this model, we develop the QEI architecture that allows multiple query operations to execute in parallel to maximize throughput. We also propose a novel way to integrate the accelerator into the CPU that balances performance, latency, and hardware cost. QEI keeps the main control logic near the L2 cache to leverage existing hardware resources in the core while distributing the data-intensive comparison logic to each last-level cache slice for higher parallelism. Our results with five representative data center workloads show that QEI can achieve $6. 5 \times \sim 11. 2 \times $ performance improvement in various scenarios with low overhead.

In Underwater Acoustic Sensor Networks (UASN), certain key nodes in the network will cause network partition, communication failure, packet loss once they failed to work. Also, because the key nodes are key positions of the UASN, once the malicious behavior of the key nodes occurs, the network performance will drop sharply. This paper first puts forward the key node recognition method for UASN. Also, we propose node usage and hop distance factors to evaluate node’s importance. Five factors at the topological level are combined, and using Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method to evaluate the importance of each node in the UASN. In the experiment, the key nodes are replaced by attack nodes to verify the performance of the algorithm. Experiment results show that the algorithm has a good and effective value.

Barrier coverage is one of the research hotspots in underwater wireless sensor networks (UWSNs). However, the barrier coverage in underwater directional sensor networks(UDSNs) has not been paid much attention. In the past works, barrier coverage algorithms in UWSNs are based on directional barrier graph(DBG). Judging the position relationship between two directional sensors is the core of establishing DBG. In this paper, the geographical relations is exploited to build DBG. Based on the DBG, the adjacency matrix-based algorithm in UDSNs is discussed. The following experimental studies demonstrate the effectiveness of the proposed algorithm.

Coverage of blood contacting surfaces by a functional endothelial layer is likely required to induce and maintain homeostasis. Blood outgrowth endothelial cells (BOECs), cultured from human peripheral blood monocytes, are readily available and functional autologous endothelial source that may represent a reasonable alternative to vascular derived cells. Endothelial nitric oxide synthase (eNOS) produces NO, an important factor that regulates homeostasis at the blood-contacting surface. We found that BOECs express markedly lower levels of eNOS protein (34% ± 13%, Western blot) and mRNA (29% ± 17%, qRT-PCR), as well as exhibiting reduced activity (49% ± 18%, Nitrite analysis) when compared to human umbilical vein endothelial cells (HUVECs) and human aortic endothelial cells. HUVECs grown on fibronectin, type I collagen, or laminin -coated surfaces exhibited significant reduction of eNOS mRNA and protein expression. However, no decrease in eNOS levels was observed in BOECs. Interestingly BOECs expressed significantly higher Collagen (Col) I compared to HUVECs, and blocking Col I synthesis significantly enhanced eNOS expression in BOECs. Inhibition of β1 integrin, focal adhesion kinase (FAK), or actin polymerization increased eNOS in both BOECs and HUVECs suggesting involvement of a signaling pathway culminating in stabilization of the cytoskeleton. Finally, we demonstrated that a Rho-associated protein kinases (ROCK) inhibitor, as a disruptor of actin stabilization, enhanced both eNOS expression and bioactivity. Taken together, our findings demonstrate that cell-ECM interactions are fundamental to the regulation of eNOS in BOECs and suggest that disruption of key intracellular pathways (such as ROCK) may be necessary to enhance functional activity of an endothelialized surface. STATEMENT OF SIGNIFICANCE: Development of biocompatible blood-contacting biomaterial surfaces has not been possible to date, leading many investigators to believe that a complete autologous endothelial layer will be necessary. Blood outgrowth endothelial cells (BOECs), cultured from human peripheral blood monocytes, are readily available and functional autologous endothelial source. Endothelial nitric oxide synthase (eNOS) produces NO, an important factor that regulates homeostasis at the blood-contacting surface. In this study, we show that eNOS displays limited expression in cultured BOECs. We further demonstrate that a strong negative regulation of eNOS is mediated by collagen substrates and that treatment with ROCK inhibitor could enhance both eNOS expression and activity in BOECs and help to rapidly establish a functional autologous endothelial layer on cardiovascular biomaterials.

Reinforcement learning (RL) has attracted much attention recently, as new and emerging AI-based applications are demanding the capabilities to intelligently react to environment changes. Unlike distributed deep neural network (DNN) training, the distributed RL training has its unique workload characteristics - it generates orders of magnitude more iterations with much smaller sized but more frequent gradient aggregations. More specifically, our study with typical RL algorithms shows that their distributed training is latency critical and that the network communication for gradient aggregation occupies up to 83.2% of the execution time of each training iteration. In this paper, we present iSwitch, an in-switch acceleration solution that moves the gradient aggregation from server nodes into the network switches, thus we can reduce the number of network hops for gradient aggregation. This not only reduces the end-to-end network latency for synchronous training, but also improves the convergence with faster weight updates for asynchronous training. Upon the in-switch accelerator, we further reduce the synchronization overhead by conducting on-the-fly gradient aggregation at the granularity of network packets rather than gradient vectors. Moreover, we rethink the distributed RL training algorithms and also propose a hierarchical aggregation mechanism to further increase the parallelism and scalability of the distributed RL training at rack scale. We implement iSwitch using a real-world programmable switch NetFPGA board. We extend the control and data plane of the programmable switch to support iSwitch without affecting its regular network functions. Compared with state-of-the-art distributed training approaches, iSwitch offers a system-level speedup of up to 3.66× for synchronous distributed training and 3.71× for asynchronous distributed training, while achieving better scalability.

Preserving the history of storage states is critical to ensuring system reliability and security. It facilitates system functions such as debugging, data recovery, and forensics. Existing software-based approaches like data journaling, logging, and backups not only introduce performance and storage cost, but also are vulnerable to malware attacks, as adversaries can obtain kernel privileges to terminate or destroy them. In this paper, we present Project Almanac, which includes (1) a time-travel solid-state drive (SSD) named TimeSSD that retains a history of storage states in hardware for a window of time, and (2) a toolkit named TimeKits that provides storage-state query and rollback functions. TimeSSD tracks the history of storage states in the hardware device, without relying on explicit backups, by exploiting the property that the flash retains old copies of data when they are updated or deleted. We implement TimeSSD with a programmable SSD and develop TimeKits for several typical system applications. Experiments, with a variety of real-world case studies, demonstrate that TimeSSD can retain all the storage states for eight weeks, with negligible performance overhead, while providing the device-level time-travel property.

Microvascular leak is a phenomenon witnessed in multiple disease states. In organ engineering, regaining a functional barrier is the most crucial step towards creating an implantable organ. All previous methods of measuring microvascular permeability were either invasive, lengthy, introduced exogenous macromolecules, or relied on extrapolations from cultured cells. We present here a system that enables real-time measurement of microvascular permeability in intact rat lungs. Our unique system design allows direct, non-invasive measurement of average alveolar and capillary pressures, tracks flow paths within the organ, and enables calculation of lumped internal resistances including microvascular barrier. We first describe the physiology of native and decellularized lungs and the inherent properties of the extracellular matrix as functions of perfusion rate. We next track changing internal resistances and flows in injured native rat lungs, resolving the onset of microvascular leak, quantifying changing vascular resistances, and identifying distinct phases of organ failure. Finally, we measure changes in permeability within engineered lungs seeded with microvascular endothelial cells, quantifying cellular effects on internal vascular and barrier resistances over time. This system marks considerable progress in bioreactor design for intact organs and may be used to monitor and garner physiological insights into native, decellularized, and engineered tissues.

Electro-deposition is a smart, safe and efficient method for biomaterial manufacturing. Collagen, a functional protein with excellent biocompatibility and biosafety, is a promising candidate for tissue engineering and biomedical applications. However, there are few reports on electro-deposition of biomaterials using collagen without electrically or magnetically active nanoparticles. In this study, electro-deposition was employed to swiftly fabricate tube-like collagen–chitosan hydrogels in a mild environment. Fourier transform infrared spectroscopy was employed to analyze the ingredients of the tube-like hydrogels. The result showed that the hydrogels contained both collagen and chitosan. The distribution and content of collagen in the hydrogels was further measured by hematoxylin–eosin staining and hydroxyproline titration. Collagen was distributed homogeneously and its content was related to the initial collagen:chitosan ratio. The tension resistance of the composite gels and the thermal stability of collagen in the composites were obviously enhanced by the chitosan doping. Meanwhile, the tube-like hydrogels retained a good ability to promote cell proliferation of collagen. This method offers a convenient approach to the design and fabrication of collagen-based materials, which could effectively retain the bioactivity and biosafety of collagen and furnish a new way to enhance the stability of collagen and the tensile strength of collagen-based materials.

In recent years, the global climate governance is not just for a global environmental problem of climate change, more involved in international politics, economy, investment, technology and other fields, and involves the transformation of production and life style. Accompanied by global energy and technology revolution, the development of low carbon is our inevitable requirement. Recently, China submitted to UNFCCC (United Nations Framework Convention on Climate Change) the Secretariat of the national independent contribution plan and set emission reduction targets: carbon dioxide emissions reach the peak in 2030 and strive to reach the peak as early as possible; carbon emission intensity decreased by 60% - 65% compared with that in 2005. This means that our government will intensify its efforts to reduce carbon emissions. In the past, our target was to reduce carbon emissions intensity, but now our country is to realize the reduction of total carbon emissions, or “absolute reduction”. Carbon finance is an important breakthrough in the development of low carbon economy and the promotion of carbon emission reduction. This paper focuses on the analysis of the development status of China’s carbon financial market, the problems and the opportunities and challenges faced, and finally, the relevant countermeasures and suggestions are given from the perspectives of policy, talent, mechanism, international cooperation and so on, in order to promote the development of carbon financial market innovation, to seize the commanding heights of the era of low-carbon economy.

To investigate fine structure in biological samples, over recent decades, super-resolution methods in light microscopy have been honed. Here, we report a technique integrating nonlinearity arising from fluorophore saturation with a new version of I5S to produce quasi-isotropic three-dimensional (3D) wide-field (WF) super-resolution imaging. This extended technique—saturated I5S (SI5S)—promises lateral and axial resolutions below 60 nm when the illuminating light intensity approaches the saturation threshold in simulation. A linear processing algorithm is used to acquire SI5S image reconstructions, and comparisons with 3D structured illumination microscopy (3D-SIM) and I5S show that a substantial improvement in resolution can be realized using SI5S. The notable improvement in 3D resolution means that SI5S could facilitate volumetric visualization of intracellular fine structure within biological samples.

Being the established imaging tool for cell membrane-associated studies, total internal reflection fluorescence microscopy (TIRFM) still has some limitations. The most important one is the inhomogeneous evanescent excitation field mainly caused by the large-angle and fixed-azimuth illumination scheme, which can be eliminated by using ring-shaped illumination (ring TIRFM). However, it is challenging in assembling a ring TIRFM system with precise parameter control that works well. Here we emphasize the quantification of the ring TIRFM system and introduce a robust calibration routine to simultaneously rectify the asymmetry of the spinning light beam and determine the crucial experimental parameter, i.e., the incident angle. The calibration routine requires no specific sample preparation and is entirely based on the automatic back focal plane manipulation, avoiding possible errors caused by the sample difference and manual measurement. Its effectiveness is experimentally demonstrated by both the qualitative and quantitative comparisons of the images acquired using different samples, illumination schemes, and calibration approaches. These characteristics should enable our approach to greatly improve the practicability of TIRFM in life sciences.

Training real-world Deep Neural Networks (DNNs) can take an eon (i.e., weeks or months) without leveraging distributed systems. Even distributed training takes inordinate time, of which a large fraction is spent in communicating weights and gradients over the network. State-of-the-art distributed training algorithms use a hierarchy of worker-aggregator nodes. The aggregators repeatedly receive gradient updates from their allocated group of the workers, and send back the updated weights. This paper sets out to reduce this significant communication cost by embedding data compression accelerators in the Network Interface Cards (NICs). To maximize the benefits of in-network acceleration, the proposed solution, named INCEPTIONN (In-Network Computing to Exchange and Process Training Information Of Neural Networks), uniquely combines hardware and algorithmic innovations by exploiting the following three observations. (1) Gradients are significantly more tolerant to precision loss than weights and as such lend themselves better to aggressive compression without the need for the complex mechanisms to avert any loss. (2) The existing training algorithms only communicate gradients in one leg of the communication, which reduces the opportunities for in-network acceleration of compression. (3) The aggregators can become a bottleneck with compression as they need to compress/decompress multiple streams from their allocated worker group. To this end, we first propose a lightweight and hardware-friendly lossy-compression algorithm for floating-point gradients, which exploits their unique value characteristics. This compression not only enables significantly reducing the gradient communication with practically no loss of accuracy, but also comes with low complexity for direct implementation as a hardware block in the NIC. To maximize the opportunities for compression and avoid the bottleneck at aggregators, we also propose an aggregator-free training algorithm that exchanges gradients in both legs of communication in the group, while the workers collectively perform the aggregation in a distributed manner. Without changing the mathematics of training, this algorithm leverages the associative property of the aggregation operator and enables our in-network accelerators to (1) apply compression for all communications, and (2) prevent the aggregator nodes from becoming bottlenecks. Our experiments demonstrate that INCEPTIONN reduces the communication time by 70.9~80.7% and offers 2.2~3.1x speedup over the conventional training system, while achieving the same level of accuracy.

LDA (Latent Dirichlet Allocation) model is a kind of unsupervised learning model which can extract the hidden topic from text in recent years. In this paper, we proposed a novel LDA model based on the traditional LDA model, which is integrated into the information of text category (Activity-topic LDA). In this paper, the Activity-topic LDA is proposed to improve the original latent Dirichlet allocation (LDA) model. On the basis of the LDA, the proposed method adds the tourism activity information, and obtains the probability distribution model of the tourism activities. Based on this model, we can identify and discover the theme of tourism activities.

As the fast growth of users, matching a given fingerprint with the ones in a massive database precisely and efficiently becomes more and more difficult. To fight against this challenging issue in "big data" era, we have designed in this paper a novel large-scale distributed Redis-based fingerprint recognition system called DFRS that introduces an innovative framework for fingerprint processing while incorporating many key technologies for data compression and computing acceleration. By using Base64 compressive encoding method together with key-value pair storage structure, the space reduction can be achieved upi¾?to 40i¾?% in our experiments --- which is particularly important as Redis is an in memory read-write NoSQL data storage system. To compensate the cost introduced by compressive encoding, the parallel decoding is adopted with the help of OpenMP, saving the time by above one third. Furthermore, the granularity-based division RM$$+$$AM architecture and the Quick-Return strategy bring significant improvement in matching time, making the whole system --- DFRS feasible and efficient in large scale for massive data volume.