The transition zone (TZ) of primary cilia serves as a diffusion barrier to regulate ciliogenesis and receptor localization for key signaling events such as sonic hedgehog signaling. Its gating ...mechanism is poorly understood due to the tiny volume accommodating a large number of ciliopathy-associated molecules. Here we performed stimulated emission depletion (STED) imaging of collective samples and recreated superresolved relative localizations of eight representative species of ciliary proteins using position averages and overlapped with representative electron microscopy (EM) images, defining an architectural foundation at the ciliary base. Upon this framework, transmembrane proteins TMEM67 and TCTN2 were accumulated at the same axial level as MKS1 and RPGRIP1L, suggesting that their regulation roles for tissue-specific ciliogenesis occur at a specific level of the TZ. CEP290 is surprisingly localized at a different axial level bridging the basal body (BB) and other TZ proteins. Upon this molecular architecture, two reservoirs of intraflagellar transport (IFT) particles, correlating with phases of ciliary growth, are present: one colocalized with the transition fibers (TFs) while the other situated beyond the distal edge of the TZ. Together, our results reveal an unprecedented structural framework of the TZ, facilitating our understanding in molecular screening and assembly at the ciliary base.
Structural bolts are critical components used in different structural elements, such as beam‐column connections and friction damping devices. The clamping force in structural bolts is highly ...influenced by the bolt rotation. Much of the existing vision‐based research about bolt rotation estimation relies on traditional computer vision algorithms such as Hough transform to assess static images of bolts. This requires careful image preprocessing, and it may not perform well in the situation of complicated bolt assemblies, or in the presence of surrounding objects and background noise, thus hindering their real‐world applications. In this study, an integrated real‐time detect‐track method, namely, RTDT‐bolt, is proposed to monitor the bolt rotation angle. First, a real‐time convolutional‐neural‐networks‐based object detector, named YOLOv3‐tiny, is established and trained to localize structural bolts. Then, the target‐free object tracking algorithm based on optical flow is implemented to continuously monitor and quantify the rotation of structural bolts. In order to enhance the tracking performance against background noise and potential illumination changes during tracking, the YOLOv3‐tiny is integrated with the optical flow tracking algorithm to re‐detect the bolts when the tracking gets lost. Extensive parameter studies were conducted to identify optimal tracking performance and examine the potential limitations. The results indicate that the RTDT‐bolt method can greatly enhance the tracking performance of bolt rotation, which can achieve over 90% accuracy using the recommended range for the parameters.
To improve the efficiency of big data feature learning, the paper proposes a privacy preserving deep computation model by offloading the expensive operations to the cloud. Privacy concerns become ...evident because there are a large number of private data by various applications in the smart city, such as sensitive data of governments or proprietary information of enterprises. To protect the private data, the proposed model uses the BGV encryption scheme to encrypt the private data and employs cloud servers to perform the high-order back-propagation algorithm on the encrypted data efficiently for deep computation model training. Furthermore, the proposed scheme approximates the Sigmoid function as a polynomial function to support the secure computation of the activation function with the BGV encryption. In our scheme, only the encryption operations and the decryption operations are performed by the client while all the computation tasks are performed on the cloud. Experimental results show that our scheme is improved by approximately 2.5 times in the training efficiency compared to the conventional deep computation model without disclosing the private data using the cloud computing including ten nodes. More importantly, our scheme is highly scalable by employing more cloud servers, which is particularly suitable for big data.
When Internet of Things (IoT) applications become a part of people’s daily life, security issues in IoT have caught significant attention in both academia and industry. Compared to traditional ...computing systems, IoT systems have more inherent vulnerabilities, and meanwhile, could have higher security requirements. However, the current design of IoT does not effectively address the higher security requirements posed by those vulnerabilities. Many recent attacks on IoT systems have shown that novel security solutions are needed to protect this emerging system. This paper aims to analyze security challenges resulted from the special characteristics of the IoT systems and the new features of the IoT applications. This could help pave the road to better security solution design. In addition, three architectural security designs are proposed and analyzed. Examples of how to implement these designs are discussed. Finally, for each layer in IoT architecture, open issues are also identified.
•Analysis of security related characteristics of IoT systems and IoT applications.•The insufficiency of the existing security solutions.•Comparison of three architectural security designs.•Example implementations of the three architectural security designs.•Identifying a set of open security issues in the context of IoT architecture.
Superdirective beamforming (SDB) has received a lot of interest recently since by this technique a small circular array, i.e., small ka, where a is the radius and k is the wave number, can achieve ...small beamwidth, and high directivity factor (DF) as offered by a large circular array using conventional beamforming (CBF). However, the nice features derived theoretically are difficult to achieve in practice since SDB is highly sensitive to signal mismatch, such as errors in array configuration, incorrect signal model, etc. Many methods have been proposed to balance the performance (beamwidth and DF) against the sensitivity. The tradeoff analyses are application and data dependent and computationally intensive. We show in this paper, by deconvolving the CBF beam output using the known beam pattern, that the deconvolved beam output can achieve a narrower beamwidth, higher DF, and lower sidelode level than the SDB, and offers the same robustness as CBF. The performance of the various beamforming methods are quantitatively analyzed and systematically compared in this paper for a wide range of ka and input signal level. Deconvolution takes negligible processing time and can be applied to existing systems where the beam data are often the only data available.
Follicular lymphoma (FL) is the most common indolent non-Hodgkin lymphoma in the Western hemisphere. After decades of stagnation, the natural history of FL appears to have been favorably impacted by ...the introduction of rituximab. Randomized clinical trials have demonstrated that the addition of rituximab to standard chemotherapy induction has improved the overall survival. Maintenance rituximab strategies can improve progression-free survival. Even chemotherapy platforms have changed in the past 5 years, as bendamustine combined with rituximab has rapidly become a standard frontline strategy in North America and parts of Europe. Recent discoveries have identified patients at high risk for poor outcomes to first-line therapy (m7–Follicular Lymphoma International Prognostic Index m7-FLIPI) and for poor outcomes after frontline therapy (National LymphoCare Study). However, several unmet needs remain, including a better ability to identify high-risk patients at diagnosis, the development of predictive biomarkers for targeted agents, and strategies to reduce the risk of transformation. The development of targeted agents, exploiting our current understanding of FL biology, is a high research priority. A multitude of novel therapies are under investigation in both the frontline and relapsed/refractory settings. It will be critical to identify the most appropriate populations for new agents and to develop validated surrogate end points, so that novel agents can be tested (and adopted, if appropriate) efficiently.
Alloys with ultra-high strength and sufficient ductility are highly desired for modern engineering applications but difficult to develop. Here we report that, by a careful controlling alloy ...composition, thermomechanical process, and microstructural feature, a Co-Cr-Ni-based medium-entropy alloy (MEA) with a dual heterogeneous structure of both matrix and precipitates can be designed to provide an ultra-high tensile strength of 2.2 GPa and uniform elongation of 13% at ambient temperature, properties that are much improved over their counterparts without the heterogeneous structure. Electron microscopy characterizations reveal that the dual heterogeneous structures are composed of a heterogeneous matrix with both coarse grains (10∼30 μm) and ultra-fine grains (0.5∼2 μm), together with heterogeneous L1
-structured nanoprecipitates ranging from several to hundreds of nanometers. The heterogeneous L1
nanoprecipitates are fully coherent with the matrix, minimizing the elastic misfit strain of interfaces, relieving the stress concentration during deformation, and playing an active role in enhanced ductility.
Over the last few years, we have witnessed an exponential increase in the computing and storage capabilities of smart devices that has led to the popularity of an emerging technology called edge ...computing. Compared to the traditional cloud-computing- based infrastructure, computing and storage facilities are available near end users in edge computing. Moreover, with the widespread popularity of unmanned aerial vehicles (UAVs), huge amounts of information will be shared between edge devices and UAVs in the coming years. In this scenario, traffic surveillance using UAVs and edge computing devices is expected to become an integral part of the next generation intelligent transportation systems. However, surveillance in ITS requires uninterrupted data sharing, cooperative decision making, and stabilized network formation. Edge computing supports data processing and analysis closer to the deployed machines (i.e., the sources of the data). Instead of simply storing data and missing the opportunity to capitalize on it, edge devices can analyze data to gain insights before acting on them. Transferring data from the vehicle to the edge for real-time analysis can be facilitated by the use of UAVs, which can act as intermediate aerial nodes between the vehicles and edge nodes. However, as the communication between UAVs and edge devices is generally done using an open channel, there is a high risk of information leakage in this environment. Keeping our focus on all these issues, in this article, we propose a data-driven transportation optimization model where cyber-threat detection in smart vehicles is done using a probabilistic data structure (PDS)- based approach. A triple Bloom filter PDS- based scheduling technique for load balancing is initially used to host the real-time data coming from different vehicles, and then to distribute/collect the data to/from edges in a manner that minimizes the computational effort. The results obtained show that the proposed system requires comparatively less computational time and storage for load sharing, authentication, encryption, and decryption of data in the considered edge-computing-based smart transportation framework.
Two template removal methods were employed to create porosity in mesoporous silica SBA-15: ethanol extraction versus conventional high-temperature calcination. The resulting silicas were subjected to ...amine (3-aminopropyl) grafting and studied for their CO2 adsorption properties. The goal was to significantly increase the surface silanol density, and hence the grafted amine loading, leading directly to increased CO2 adsorption capacity and CO2/N2 selectivity. Thus, the silanol density was increased from 3.4 OH/nm2 for the calcined SBA-15 to 8.5 OH/nm2 for the SBA-15 by solvent extraction. Correspondingly, for these two samples, the grafted amine loading was increased from 2.2 to 3.2 mmol/g, and the CO2 adsorption capacity was increased from 1.05 to 1.6 mmol/g at conditions relevant to CO2 capture (0.15 bar and 25 °C), or a 52% increase. The CO2/N2 selectivity was increased from 46 to 131. The isosteric heats of adsorption, the sorbent stability during cyclic adsorption–desorption, and the (positive) effects of moisture on CO2 adsorption were also investigated and compared.
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