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The guanosine analog AT-527 represents a promising candidate against Severe Acute Respiratory Syndrome coronavirus type 2 (SARS-CoV-2). AT-527 recently entered phase III clinical trials for the treatment of COVID-19. Once in cells, AT-527 is converted into its triphosphate form, AT-9010, that presumably targets the viral RNA-dependent RNA polymerase (RdRp, nsp12), for incorporation into viral RNA. Here we report a 2.98 Å cryo-EM structure of the SARS-CoV-2 nsp12-nsp7-nsp8 2-RNA complex, showing AT-9010 bound at three sites of nsp12. In the RdRp active-site, one AT-9010 is incorporated at the 3′ end of the RNA product strand. Its modified ribose group (2′-fluoro, 2′-methyl) prevents correct alignment of the incoming NTP, in this case a second AT-9010, causing immediate termination of RNA synthesis. The third AT-9010 is bound to the N-terminal domain of nsp12known as the NiRAN. In contrast to native NTPs, AT-9010 is in a flipped orientation in the active-site, with its guanine base unexpectedly occupying a previously unnoticed cavity. AT-9010 outcompetes all native nucleotides for NiRAN binding, inhibiting its nucleotidyltransferase activity. The dual mechanism of action of AT-527 at both RdRp and NiRAN active sites represents a promising research avenue against COVID-19.

Recently, land vehicle navigation, and especially by the use of low-cost sensors, has been the object of a huge level of research interest. Consumer Portable Devices (CPDs) such as tablets and smartphones are being widely used by many consumers all over the world. CPDs contain sensors (accelerometers, gyroscopes, magnetometer, etc.) that can be used for many land vehicle applications such as navigation. This paper presents a novel approach for estimating steering wheel angles using CPD accelerometers by attaching CPDs to the steering wheel. The land vehicle change of heading is then computed from the estimated steering wheel angle. The calculated change of heading is used to update the navigation filter to aid the onboard Inertial Measurement Unit (IMU) through the use of an Extended Kalman Filter (EKF) in GNSS-denied environments. Four main factors that may affect the steering wheel angle accuracy are considered and modeled during steering angle estimations: static onboard IMU leveling, inclination angle of the steering wheel, vehicle acceleration, and vehicle inclination. In addition, these factors are assessed for their effects on the final result. Therefore, three methods are proposed for steering angle estimation: non-compensated, partially-compensated, and fully-compensated methods. A road experimental test was carried out using a Pixhawk (PX4) navigation system, iPad Air, and the OBD-II interface. The average Root Mean Square Error (RMSE) of the change of heading estimated by the proposed method was 0.033 rad/s. A navigation solution was estimated while changes of heading and forward velocity updates were used to aid the IMU during different GNSS signal outages. The estimated navigation solution is enhanced when applying the proposed updates to the navigation filter by 91% and 97% for 60 s and 120 s of GNSS signal outage, respectively, compared to the IMU standalone solution.

Drones are becoming increasingly significant for vast applications, such as firefighting, and rescue. While flying in challenging environments, reliable Global Navigation Satellite System (GNSS) measurements cannot be guaranteed all the time, and the Inertial Navigation System (INS) navigation solution will deteriorate dramatically. Although different aiding sensors, such as cameras, are proposed to reduce the effect of these drift errors, the positioning accuracy by using these techniques is still affected by some challenges, such as the lack of the observed features, inconsistent matches, illumination, and environmental conditions. This paper presents an integrated navigation system for Unmanned Aerial Vehicles (UAVs) in GNSS denied environments based on a Radar Odometry (RO) and an enhanced Visual Odometry (VO) to handle such challenges since the radar is immune against these issues. The estimated forward velocities of a vehicle from both the RO and the enhanced VO are fused with the Inertial Measurement Unit (IMU), barometer, and magnetometer measurements via an Extended Kalman Filter (EKF) to enhance the navigation accuracy during GNSS signal outages. The RO and VO are integrated into one integrated system to help overcome their limitations, since the RO measurements are affected while flying over non-flat terrain. Therefore, the integration of the VO is important in such scenarios. The experimental results demonstrate the proposed system’s ability to significantly enhance the 3D positioning accuracy during the GNSS signal outage.

Landslides often cause economic losses, property damage, and loss of lives. Monitoring landslides using high spatial and temporal resolution imagery and the ability to quickly identify landslide regions are the basis for emergency disaster management. This study presents a comprehensive system that uses unmanned aerial vehicles (UAVs) and Semi-Global dense Matching (SGM) techniques to identify and extract landslide scarp data. The selected study area is located along a major highway in a mountainous region in Jordan, and contains creeping landslides induced by heavy rainfall. Field observations across the slope body and a deformation analysis along the highway and existing gabions indicate that the slope is active and that scarp features across the slope will continue to open and develop new tension crack features, leading to the downward movement of rocks. The identification of landslide scarps in this study was performed via a dense 3D point cloud of topographic information generated from high-resolution images captured using a low-cost UAV and a target-based camera calibration procedure for a low-cost large-field-of-view camera. An automated approach was used to accurately detect and extract the landslide head scarps based on geomorphological factors: the ratio of normalized Eigenvalues (i.e., λ1/λ2 ≥ λ3) derived using principal component analysis, topographic surface roughness index values, and local-neighborhood slope measurements from the 3D image-based point cloud. Validation of the results was performed using root mean square error analysis and a confusion (error) matrix between manually digitized landslide scarps and the automated approaches. The experimental results using the fully automated 3D point-based analysis algorithms show that these approaches can effectively distinguish landslide scarps. The proposed algorithms can accurately identify and extract landslide scarps with centimeter-scale accuracy. In addition, the combination of UAV-based imagery, 3D scene reconstruction, and landslide scarp recognition/extraction algorithms can provide flexible and effective tool for monitoring landslide scarps and is acceptable for landslide mapping purposes.

Despite the availability of highly effective direct-acting antiviral (DAA) regimens for the treatment of hepatitis C virus (HCV) infections, sustained viral response (SVR) rates remain suboptimal for difficult-to-treat patient populations such as those with HCV genotype 3, cirrhosis or prior treatment experience, warranting development of more potent HCV replication antivirals. AT-527 is the hemi-sulfate salt of AT-511, a novel phosphoramidate prodrug of 2'-fluoro-2'-C-methylguanosine-5'-monophosphate that has potent in vitro activity against HCV. The EC50 of AT-511, determined using HCV laboratory strains and clinical isolates with genotypes 1-5, ranged from 5-28 nM. The active 5'-triphosphate metabolite, AT-9010, specifically inhibited the HCV RNA-dependent RNA polymerase. AT-511 did not inhibit the replication of other selected RNA or DNA viruses in vitro. AT-511 was approximately 10-fold more active than sofosbuvir (SOF) against a panel of laboratory strains and clinical isolates of HCV genotypes 1-5 and remained fully active against S282T resistance-associated variants, with up to 58-fold more potency than SOF. In vitro, AT-511 did not inhibit human DNA polymerases or elicit cytotoxicity or mitochondrial toxicity at concentrations up to 100 μM. Unlike the other potent guanosine analogs PSI-938 and PSI-661, no mutagenic O6-alkylguanine bases were formed when incubated with cytochrome P450 (CYP) 3A4, and AT-511 had IC50 values ≥25 μM against a panel of CYP enzymes. In hepatocytes from multiple species, the active triphosphate was the predominant metabolite produced from the prodrug, with a half-life of 10 h in human hepatocytes. When given orally to rats and monkeys, AT-527 preferentially delivered high levels of AT-9010 in the liver in vivo. These favorable preclinical attributes support the ongoing clinical development of AT-527 and suggest that, when used in combination with an HCV DAA from a different class, AT-527 may increase SVR rates, especially for difficult-to-treat patient populations, and could potentially shorten treatment duration for all patients.

Context-awareness is an interesting topic in mobile navigation scenarios where the context of the application is highly dynamic. Using context-aware computing, navigation services consider the situation of user, not only in the design process, but in real time while the device is in use. The basic idea is that mobile navigation services can provide different services based on different contexts—where contexts are related to the user’s activity and the device placement. Context-aware systems are concerned with the following challenges which are addressed in this paper: context acquisition, context understanding, and context-aware application adaptation. The proposed approach in this paper is using low-cost sensors in a multi-level fusion scheme to improve the accuracy and robustness of context-aware navigation system. The experimental results demonstrate the capabilities of the context-aware Personal Navigation Systems (PNS) for outdoor personal navigation using a smartphone.

In the remote sensing field, object detection holds immense value for applications such as land use classification, disaster monitoring, and infrastructure planning, where accurate and efficient identification of objects within images is essential for informed decision making. However, achieving object localization with high precision can be challenging even if minor errors exist at the pixel level, which can significantly impact the ground distance measurements. To address this critical challenge, our research introduces an innovative hybrid approach that combines the capabilities of the You Only Look Once version 7 (YOLOv7) and DEtection TRansformer (DETR) algorithms. By bridging the gap between local receptive field and global context, our approach not only enhances overall object detection accuracy, but also promotes precise object localization, a key requirement in the field of remote sensing. Furthermore, a key advantage of our approach is the introduction of an automatic selection module which serves as an intelligent decision-making component. This module optimizes the selection process between YOLOv7 and DETR, and further improves object detection accuracy. Finally, we validate the improved performance of our new hybrid approach through empirical experimentation, and thus confirm its contribution to the field of target recognition and detection in remote sensing images.

Nowadays, autonomous vehicles have achieved a lot of research interest regarding the navigation, the surrounding environmental perception, and control. Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) is one of the significant components of any vehicle navigation system. However, GNSS has limitations in some operating scenarios such as urban regions and indoor environments where the GNSS signal suffers from multipath or outage. On the other hand, INS standalone navigation solution degrades over time due to the INS errors. Therefore, a modern vehicle navigation system depends on integration between different sensors to aid INS for mitigating its drift during GNSS signal outage. However, there are some challenges for the aiding sensors related to their high price, high computational costs, and environmental and weather effects. This paper proposes an integrated aiding navigation system for vehicles in an indoor environment (e.g., underground parking). This proposed system is based on optical flow and multiple mass flow sensors integrations to aid the low-cost INS by providing the navigation extended Kalman filter (EKF) with forward velocity and change of heading updates to enhance the vehicle navigation. The optical flow is computed for frames taken using a consumer portable device (CPD) camera mounted in the upward-looking direction to avoid moving objects in front of the camera and to exploit the typical features of the underground parking or tunnels such as ducts and pipes. On the other hand, the multiple mass flow sensors measurements are modeled to provide forward velocity information. Moreover, a mass flow differential odometry is proposed where the vehicle change of heading is estimated from the multiple mass flow sensors measurements. This integrated aiding system can be used for unmanned aerial vehicles (UAV) and land vehicle navigations. However, the experimental results are implemented for land vehicles through the integration of CPD with mass flow sensors to aid the navigation system.

Landslides are major and constantly changing threats to urban landscapes and infrastructure. It is essential to detect and capture landslide changes regularly. Traditional methods for monitoring landslides are time-consuming, costly, dangerous, and the quality and quantity of the data is sometimes unable to meet the necessary requirements of geotechnical projects. This motivates the development of more automatic and efficient remote sensing approaches for landslide progression evaluation. Automatic change detection involving low-altitude unmanned aerial vehicle image-based point clouds, although proven, is relatively unexplored, and little research has been done in terms of accounting for volumetric changes. In this study, a methodology for automatically deriving change displacement rates, in a horizontal direction based on comparisons between extracted landslide scarps from multiple time periods, has been developed. Compared with the iterative closest projected point (ICPP) registration method, the developed method takes full advantage of automated geometric measuring, leading to fast processing. The proposed approach easily processes a large number of images from different epochs and enables the creation of registered image-based point clouds without the use of extensive ground control point information or further processing such as interpretation and image correlation. The produced results are promising for use in the field of landslide research.

Bemnifosbuvir (AT-527) and AT-752 are guanosine analogues currently in clinical trials against several RNA viruses. Here, we show that these drugs require a minimal set of 5 cellular enzymes for activation to their common 5′-triphosphate AT-9010, with an obligate order of reactions. AT-9010 selectively inhibits essential viral enzymes, accounting for antiviral potency. Functional and structural data at atomic resolution decipher N 6 -purine deamination compatible with its metabolic activation. Crystal structures of human histidine triad nucleotide binding protein 1, adenosine deaminase-like protein 1, guanylate kinase 1, and nucleoside diphosphate kinase at 2.09, 2.44, 1.76, and 1.9 Å resolution, respectively, with cognate precursors of AT-9010 illuminate the activation pathway from the orally available bemnifosbuvir to AT-9010, pointing to key drug–protein contacts along the activation pathway. Our work provides a framework to integrate the design of antiviral nucleotide analogues, confronting requirements and constraints associated with activation enzymes along the 5′-triphosphate assembly line.