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Dysregulation of histone deacetylases (HDACs) is closely related to tumor development and progression. As promising anticancer targets, HDACs have gained a great deal of research interests and two decades of effort has led to the approval of five HDAC inhibitors (HDACis). However, currently traditional HDACis, although effective in approved indications, exhibit severe off-target toxicities and low sensitivities against solid tumors, which have urged the development of next-generation of HDACi. This review investigates the biological functions of HDACs, the roles of HDACs in oncogenesis, the structural features of different HDAC isoforms, isoform-selective inhibitors, combination therapies, multitarget agents and HDAC PROTACs. We hope these data could inspire readers with new ideas to develop novel HDACi with good isoform selectivity, efficient anticancer effect, attenuated adverse effect and reduced drug resistance.

In response to asynchronous and delayed sensors within multi-sensor integrated navigation systems, the computational complexity of joint optimization navigation solutions persistently rises. This paper introduces an adaptive fast integrated navigation algorithm for INS/GPS/VO based on factor graph. The factor graph model for INS/GPS/VO is developed subsequent to individual modeling of the Inertial Navigation System (INS), Global Positioning System (GPS), and Visual Odometer (VO) using the factor graph model approach. Additionally, an Adaptive Fast Incremental Smoothing (AFIS) factor graph optimization algorithm is proposed. The simulation results demonstrate that the factor-graph-based integrated navigation algorithm consistently yields high-precision navigation outcomes even amidst dynamic changes in sensor validity and the presence of asynchronous and delayed sensor measurements. Notably, the AFIS factor graph optimization algorithm significantly enhances real-time performance compared to traditional Incremental Smoothing (IF) algorithms, while maintaining comparable real-time accuracy.

In this paper, we present an innovative inertial navigation system (INS)/celestial navigation system (CNS)/scene-matching navigation (SMN) adaptive integrated navigation algorithm designed to achieve prolonged and highly precise navigation in sea areas. The algorithm establishes the structure of the INS/CNS/SMN integrated navigation system. To ensure the availability of CNS in the Nanhai Sea (South China Sea) area, a cloud and fog model is meticulously constructed. Three distinct types of sea area landmarks are defined, and an automated classification model for sea area landmarks, employing support vector machines (SVM), is developed. Corresponding matching methods and strategies for these landmarks are also delineated. Concurrently, the observable probability of each landmark is computed to generate a probability cloud, representing the usability of sea area landmarks. The proposed INS/CNS/SMN adaptive integrated navigation algorithm is simulated and validated across varied altitudes and trajectories in the sea area. The results show that CNS and SMN can dynamically assist INS in achieving prolonged and highly precise navigation.

Abstract The false alarm of a fault detection module in aircraft will interfere with flight. If there is a time‐offset between the inertial navigation system (INS) and air data system (ADS), the probability of a false alarm (PFA) of the fault detection module will increase, which is ignored in existing approaches. To address the problem, a fault detection approach is proposed applied to INS/ADS with a time‐offset. An INS/ADS fault detection model based on kinematic equations is developed, and we combine an unscented Kalman filter (UKF) with Runge‐Kutta to deal with the non‐linear and discretisation problem. A time‐offset estimator is designed and the observability of time‐offset is analysed, showing that time‐offset is observable only in the manoeuvre phase. A fault detection architecture applied to INS/ADS with a time‐offset is designed, which solves the problem of the high PFA of INS/ADS fault detection under a time‐offset. Simulation results show that under multiple time‐offset scenarios, the root mean square error of the proposed approach can reach 0.0134 s minimally, and after time alignment, the PFA of the fault detection can be reduced to 1.8%.

Approaches that use more than two consecutive video frames in the optical flow estimation have a long research history. However, almost all such methods utilize extra information for a pre-processing flow prediction or for a post-processing flow correction and filtering. In contrast, this paper differs from previously developed techniques. We propose a new algorithm for the likelihood function calculation (alternatively the matching cost volume) that is used in the maximum a posteriori estimation. We exploit the fact that in general, optical flow is locally constant in the sense of time and the likelihood function depends on both the previous and the future frame. Implementation of our idea increases the robustness of optical flow estimation. As a result, our method outperforms 9% over the DCFlow technique, which we use as prototype for our CNN based computation architecture, on the most challenging MPI-Sintel dataset for the non-occluded mask metric. Furthermore, our approach considerably increases the accuracy of the flow estimation for the matching cost processing, consequently outperforming the original DCFlow algorithm results up to 50% in occluded regions and up to 9% in non-occluded regions on the MPI-Sintel dataset. The experimental section shows that the proposed method achieves state-of-the-arts results especially on the MPI-Sintel dataset.

Differences in acquisition time, light conditions, and viewing angle create significant differences among the airborne remote sensing images from Unmanned Aerial Vehicles (UAVs). Real-time scene matching navigation applications based on fixed reference maps are error-prone and have poor robustness. This paper presents a novel shadow-based matching method for the localization of low-altitude flight UAVs. A reference shadow map is generated from an accurate (0.5 m spatial resolution) Digital Surface Model (DSM) with the known date and time information; a robust shadow detection algorithm is employed to detect shadows in aerial images; the shadows can then be used as a stable feature for scene matching navigation. Combining the conventional intensity-based matching method, a fusion scene navigation scheme that is more robust to illumination variations is proposed. Experiments were performed with Google satellite maps, DSM data, and real aerial images of the Zurich region. The radial localization error of the Shadow-based Matching (SbM) is less than 7.3 m at flight height below 1200 m. The fusion navigation approach also achieves an optimal combination of shadow-based matching and intensity-based matching. This study shows the solution to the inconsistencies caused by changes in light, viewing angle, and acquisition time for accurate and effective scene matching navigation.

Satellite selection is an effective way to overcome the challenges for the processing capability and channel limitation of the receivers due to superabundant satellites in view. The satellite selection strategies have been widely investigated to construct the subset with high accuracy but deserve further studies when applied to safety-critical applications such as the receiver autonomous integrity monitoring (RAIM) technique. In this study, the impacts of subset size on the accuracy and integrity of the subset and computation load are analyzed at first to confirm the importance of the satellite selection strategy for the RAIM process. Then the integrated performance impact of a single satellite on the current subset is evaluated according to the performance requirement of the flight phase. Subsequently, a performance-requirement-driven fast satellite selection algorithm is proposed based on the impact evaluation to construct a relatively small subset that satisfies the accuracy and integrity requirements. Comparison simulations show that the proposed algorithm can keep similar accuracy and better integrity performances than the geometric algorithm and the downdate algorithm when the subset size is fixed to 12, and can achieve an average 1.0 to 2.0 satellites smaller subset in the Lateral Navigation (LNAV) and approach procedures with vertical guidance (APV-I) horizontal requirement trial. Thus, it is suitable for real-time RAIM applications and low-cost navigation devices.

Recent studies of 16S rRNA sequences through next-generation sequencing have revolutionized our understanding of the microbial community composition and structure. One common approach in using these data to explore the genetic diversity in a microbial community is to cluster the 16S rRNA sequences into Operational Taxonomic Units (OTUs) based on sequence similarities. The inferred OTUs can then be used to estimate species, diversity, composition, and richness. Although a number of methods have been developed and commonly used to cluster the sequences into OTUs, relatively little guidance is available on their relative performance and the choice of key parameters for each method. In this study, we conducted a comprehensive evaluation of ten existing OTU inference methods. We found that the appropriate dissimilarity value for defining distinct OTUs is not only related with a specific method but also related with the sample complexity. For data sets with low complexity, all the algorithms need a higher dissimilarity threshold to define OTUs. Some methods, such as, CROP and SLP, are more robust to the specific choice of the threshold than other methods, especially for shorter reads. For high-complexity data sets, hierarchical cluster methods need a more strict dissimilarity threshold to define OTUs because the commonly used dissimilarity threshold of 3% often leads to an under-estimation of the number of OTUs. In general, hierarchical clustering methods perform better at lower dissimilarity thresholds. Our results show that sequence abundance plays an important role in OTU inference. We conclude that care is needed to choose both a threshold for dissimilarity and abundance for OTU inference.