Search Results (58)

To address the ill-posedness of the Hessian matrix in monocular visual-inertial SLAM (Simultaneous Localization and Mapping) caused by unobservable depth of feature points, which leads to convergence difficulties and reduced robustness, this paper proposes a Pre-Dog-Leg feature optimization method based on an adaptive preconditioner. First, we propose a multi-candidate initialization method with robust characteristics. This method effectively circumvents erroneous depth initialization by introducing multiple depth assumptions and geometric consistency constraints. Second, we address the pathology of the Hessian matrix of the feature points by constructing a hybrid SPAI-Jacobi adaptive preconditioner. This preconditioner is capable of identifying matrix pathology and dynamically enabling preconditioning as a strategy. Finally, we construct a hybrid adaptive preconditioner for the traditional Dog-Leg numerical optimization method. To address the issue of degraded convergence performance when solving pathological problems, we map the pathological optimization problem from the original parameter space to a well-conditioned preconditioned space. The optimization equivalence is maintained by variable recovery. The experiments on the EuRoC dataset show that the method reduces the number of Hessian matrix conditionals by a factor of 7.9, effectively suppresses outliers, and significantly improves the overall convergence time. From the analysis of trajectory error, the absolute trajectory error is reduced by up to 16.48% relative to RVIO2 on the MH_01 sequence, 20.83% relative to VINS-mono on the MH_02 sequence, and up to 14.73% relative to VINS-mono and 34.0% relative to OpenVINS on the highly dynamic MH_05 sequence, indicating that the algorithm achieves higher localization accuracy and stronger system robustness. Full article

Copaifera langsdorffii is a neotropical tree widely distributed in the Brazilian Atlantic Forest and Brazilian Savanna. Population genetic analyses can identify the scale at which tree species are impacted by human activities and provide useful demographic information for management and conservation. Using a Restriction site Associated DNA Sequencing approach, we assessed the genomic variability of six C. langsdorffii population relicts in a transition zone between the Seasonal Atlantic Forest and Savanna biomes in Southeastern Brazil. We identified 2797 high-confidence SNP markers from six remnant populations, with 10 to 29 individuals perpopulation, in a transition zone between the Seasonal Atlantic Forest and Savanna biomes in Southeastern Brazil. Observed heterozygosity values (0.197) were lower than expected heterozygosity (0.264) in all populations, indicating an excess of homozygotes. Differentiation among populations (F_ST) was low (0.023), but significant (0.007–0.044, c.i. 95%). A clear correlation was observed between geographic versus genetic distances, suggesting a pattern of isolation by distance. Bayesian inferences of population structure detected partial structuring due to the transition between the Atlantic Forest and the Brazilian Savanna, also suggested by spatial interpolation of ancestry coefficients. Through the analysis of F_ST outliers, 28 candidates for selection have been identified and may be associated with adaptation to these different phytophysiognomies. We conclude that the genetic variation found in these populations can be exploited in programs for the genetic conservation of the species. Full article

Scanned data often contain substantial outliers due to environmental interference, which drastically decreases the performance of traditional registration algorithms. To address this issue, this article proposes an improved principal component analysis (PCA) and Jacobian-enhanced whale optimization collaborative method for point cloud registration. First, an improved PCA point cloud initial registration algorithm is proposed by introducing the normal vector local information to set the screening conditions. This algorithm can streamline the original set of 48 candidate rotation matrices down to 4, achieving rapid point cloud registration at the data level between the scanned and model point clouds. Second, a Jacobian whale optimization algorithm for fine registration (JWOA-FR) is proposed by incorporating local gradient information. The algorithm employs gradient descent on optimal whale individuals to dynamically guide global search updates, thereby enhancing both registration accuracy and efficiency. Finally, a threshold is set to remove the outliers contained in the workpieces based on the information of the matched point pairs. The iterative closest point (ICP) algorithm is further used to improve registration accuracy for data without outliers. The experimental results showed that registration errors of large workpieces 1, 2, and 3 were 2.0755 mm, 2.3955 mm, and 2.5823 mm, respectively, after outlier removal, which indicates that the proposed method is applicable to data with outliers, and the registration accuracy meets the requirements. Full article

Fabry disease is a rare genetic disorder caused by deficient activity of the lysosomal enzyme alpha-galactosidase A (AGAL), resulting in the accumulation of globotriaosylceramides (Gb3) in tissues and organs. This buildup leads to progressive, multi-systemic complications that severely impact quality of life and can be life-threatening. Interpreting the functional consequences of missense variants in the GLA gene remains a significant challenge, especially in rare diseases where experimental evidence is scarce. In this study, we present an integrative computational framework that combines structural, interaction, pathogenicity, and stability data from both in silico tools and experimental sources, enriched through expert curation and structural analysis. Given the clinical relevance of pharmacological chaperones in Fabry disease, we focus in particular on the structural characteristics of variants classified as “amenable” to such treatments. Our multidimensional analysis—using tools such as AlphaMissense, EVE, FoldX, and ChimeraX—identifies key molecular features that distinguish amenable from non-amenable variants. We find that amenable mutations tend to preserve protein stability, while non-amenable ones are associated with structural destabilisation. By comparing AlphaMissense with alternative predictors rooted in evolutionary (EVE) and thermodynamic (FoldX) models, we explore the relative contribution of different biological paradigms to variant classification. Additionally, the investigation of outlier variants—where AlphaMissense predictions diverge from clinical annotations—highlights candidates for further experimental validation. These findings demonstrate how combining structural bioinformatics with machine learning–based predictions can improve missense variant interpretation and support precision medicine in rare genetic disorders. Full article

Outlier mining constitutes an essential aspect of modern data analytics, focusing on the identification and interpretation of anomalous observations. Conventional density-based local outlier detection methodologies frequently exhibit limitations due to their inherent lack of data preprocessing capabilities, consequently demonstrating degraded performance when applied to novel or heterogeneous datasets. Moreover, the computation of the outlier factor for each sample in these algorithms results in considerably higher computational cost, especially in the case of large datasets. This paper introduces a local outlier detection method named FOLOF (FCM Objective Function-based LOF) through an examination of existing algorithms. The approach starts by applying the elbow rule to determine the optimal number of clusters in the dataset. Subsequently, the FCM objective function is employed to prune the dataset to extract a candidate set of outliers. Finally, a weighted local outlier factor detection algorithm computes the degree of anomaly for each sample in the candidate set. For the analysis, the Golden Section method was used to classify the outliers. The underlying causes of these outliers can be revealed by exploring the anomalous properties of each outlier data point through the outlier factors of each dimension property. This approach has been validated on artificial datasets, the UCI dataset, and an NBA player dataset to demonstrate its effectiveness. Full article

Optimal litter size on goat farms is an important trait for production and economic efficiency. The ovary and uterus, key components of the reproductive system, play essential roles in reproductive performance. In recent years, numerous genes linked to goat reproductive performance have been identified. However, reliable marker genes that are specifically associated with litter size require further exploration. In this study, eight Jining Grey goats were divided into high-yield (n = 4) and low-yield (n = 4) groups on the basis of their kidding records to identify key regulatory genes associated with litter size. Ovarian and uterine tissues were collected during oestrus for RNA sequencing (RNA-seq). After two outlier uterine tissue samples were excluded, the remaining 14 samples were subjected to WGCNA and differential expression gene (DEG) analysis. A total of 1224 DEGs were identified (|log2(fold change) ≥ 1|, p ≤ 0.05), including 912 in ovarian tissues (monozygotic vs. polyzygotic, MO vs. PO) and 312 in uterine tissues (MU vs. PU). Through WGCNA, we identified 15 coexpression modules, among which four key modules were significantly correlated with litter size. Our analysis focused on the magenta and green modules, as they contained 11 and 3 candidate genes overlapping with the DEGs, respectively. Notably, three genes—FOXC1, FOSB, and FGL2—were found to play important roles in both ovarian and uterine tissues. These genes mainly participate in regulatory processes such as RNA polymerase II transcription factor activity, calcium ion binding, and extracellular space organization, highlighting their potential as key candidates for future research. Overall, we identified several gene modules associated with litter size in goats, providing potential molecular markers for investigating litter size traits in Jining Grey goats. Full article

Triazolopyrimidine and its analogs represent an important scaffold in medicinal chemistry research. The heterocycle of 1,2,4-triazolo[1,5-a] pyrimidine (1,2,4-TAP) serves as a bioisostere candidate for purine scaffolds, N-acetylated lysine, and carboxylic acid. This study modeled the quantitative structure–activity relationship (QSAR) of 125 congeners of 1,2,4-TAP from the ChEMBL database in the inhibition of Plasmodium falciparum using six machine learning algorithms. The most significant features among 306 molecular descriptors, including one molecular outlier, were selected using recursive feature elimination. A ratio of 20% was used to split the x- and y-matrices into 99 training and 24 test compounds. The regression models were built using machine learning sci-kit-learn algorithms (multiple linear regression (MLR), k-nearest neighbours (kNN), support vector regressor (SVR), random forest regressor (RFR) RIDGE regression, and LASSO). Model performance was evaluated using the coefficient of determination (R²), mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), p-values, F-statistic, and variance inflation factor (VIF). Five significant variables were considered in constructing the model (p < 0.05) with the following regression equation: pIC₅₀ = 5.90 − 0.71npr1 − 1.52pmi3 + 0.88slogP − 0.57vsurf-CW2 + 1.11vsurf-W2. On five-fold cross-validation, three algorithms—kNN (MSE = 0.46, R² = 0.54, MAE = 0.54, RMSE = 0.68), SVR (MSE = 0.33, R² = 0.67, MAE = 0.46, RMSE = 0.57), and RFR (MSE = 0.43, R² = 0.58, MAE = 0.51, RMSE = 0.66)—showed strong robustness, efficiency, and reliability in predicting the pIC₅₀ of 1,2,4-triazolo[1,5-a]pyrimidine. The models provided useful data on the functionalities necessary for developing more potent 1,2,4-TAP analogs as anti-malarial agents. Full article

Target detection in synthetic aperture radar (SAR) imagery remains a significant technical challenge, particularly in scenarios involving multi-target interference and clutter edge effects that cannot be disregarded, notably in high-resolution imaging applications. To tackle this issue, a novel two-stage superpixel-level constant false-alarm rate (CFAR) detection method based on a truncated kernel density estimation (KDE) model is proposed in this article. The contribution mainly lies in three aspects. First, a truncated KDE model is used to fit the statistical distribution of clutter in the detection window, and adaptive thresholding is used for clutter truncation to remove outliers from the clutter samples while preserving the real clutter. Second, based on the clutter statistics, the KDE model is accurately constructed using the quartile based on the truncated clutter statistics. Third, target superpixel detection is performed using a two-stage CFAR detection scheme enhanced with local contrast measure (LCM), consisting of a global stage followed by a local stage. In the global detection phase, we identify candidate target superpixels (CTSs) based on the superpixel segmentation results. In the local detection phase, a local CFAR detector using a truncated KDE model is employed to improve the detection process, and further screening is performed on the global detection results combined with local contrast. Experimental results show that the proposed method achieves excellent detection performance, while significantly reducing detection time compared to current popular methods. Full article

A comprehensive understanding of the pattern of genetic variation among populations and adaptations to environmental heterogeneity is very important for conservation and genetic improvement. Forest tree species are ideal resources for understanding population genetic differentiation and detecting signatures of selection due to their adaptations to heterogeneous landscapes. Ulmus lamellosa is a tree species that is endemic to northern China. In this study, using restriction-site-associated DNA sequencing (RAD-seq) data, 12,179 single-nucleotide polymorphisms were identified across 51 individuals from seven populations. There was a high level of genetic diversity and population differentiation in U. lamellosa. Population genetic structure analyses revealed a significant genetic structure related to the configuration of the mountains. Additionally, we found that the isolation-by-distance pattern explained the genetic differentiation best, and environmental heterogeneity also played a role in shaping the landscape genetics of this species inhabiting mountain ecosystems. The F_ST-based outlier and genotype–environment association approaches were used to explore the genomic signatures of selection and local adaptation and detected 317 candidate outlier loci. Precipitation seasonality (coefficient of variation), precipitation in the driest month, and enhanced vegetation index were important determinants of adaptive genetic variation. This study provides abundant genetic resources for U. lamellosa and insights into the genetic variation patterns among populations. Full article

Background/Objectives: Host cell protein (HCP) content is a major attribute for biological and vaccine products that must be extensively characterized prior to product licensure. Enzyme Linked Immunosorbent Assay (ELISA) and Mass Spectrometry (MS) are conventional methods for quantitative host cell protein analysis in biologic and vaccine products. Both techniques are usually very tedious, labor-intensive, and challenging to transfer to other laboratories. In addition, the ELISA methodology requires 2D SDS PAGE and 2D western blot antibody reagent validation to establish reagent coverage. This reagent coverage provides a rather weak link that is currently accepted, as the western blot is run under denaturing conditions and the ELISA is run under native conditions. Simple Western™ is a relatively new, automated, capillary western blot-based technology that allows for the separation, blotting, and detection of proteins. But, unlike traditional western blots, Simple Western™ is quantitative, allowing for the quantification of HCP content in biologic and vaccine samples. Antibody reagent validation is much more straightforward, as the reagent coverage can be directly linked between the 2D methodology and Simple Western™, as they are both run under denatured and reduced conditions. Methods: Herein we describe the development of a capillary western blot method to quantify the HCP content in samples generated using a Vero cell line for the production of an investigational live virus vaccine candidate (V590) for Coronavirus Disease-2019 (COVID-19). The HCP content in COVID-19 vaccine samples was evaluated using three methods: the new capillary western, the gold standard ELISA, and SDS-PAGE. Results/Conclusions: Strong agreement was observed in the HCP content data between the capillary western and SDS PAGE methods, whereas the ELISA HCP data were outliers, suggesting that the capillary western is generating HCP concentrations closer to the true concentration. This is the first report of using capillary western technology in analyzing HCP in vaccine samples. Full article

In the production of high-definition maps, it is necessary to achieve the three-dimensional instantiation of road furniture that is difficult to depict on traditional maps. The development of mobile laser measurement technology provides a new means for acquiring road furniture data. To address the issue of traffic marking extraction accuracy in practical production, which is affected by degradation, occlusion, and non-standard variations, this paper proposes a 3D reconstruction method based on energy functions and template matching, using zebra crossings in vehicle-mounted LiDAR point clouds as an example. First, regions of interest (RoIs) containing zebra crossings are obtained through manual selection. Candidate point sets are then obtained at fixed distances, and their neighborhood intensity features are calculated to determine the number of zebra stripes using non-maximum suppression. Next, the slice intensity feature of each zebra stripe is calculated, followed by outlier filtering to determine the optimized length. Finally, a matching template is selected, and an energy function composed of the average intensity of the point cloud within the template, the intensity information entropy, and the intensity gradient at the template boundary is constructed. The 3D reconstruction result is obtained by solving the energy function, performing mode statistics, and normalization. This method enables the complete 3D reconstruction of zebra stripes within the RoI, maintaining an average planar corner accuracy within 0.05 m and an elevation accuracy within 0.02 m. The matching and reconstruction time does not exceed 1 s, and it has been applied in practical production. Full article

Background: Adjacent segment degeneration (ASD) is a significant complication following lumbar spinal fusion, often necessitating further surgical interventions and impairing patient outcomes. Interspinous process devices were introduced as an alternative treatment for spinal stenosis and degenerative spondylolisthesis and can potentially reduce the incidence of ASDd. This systematic review and meta-analysis aims to evaluate the effectiveness of interspinous process devices or IPDs in managing ASD following a previous spinal fusion compared to traditional fusion techniques. Methods: Electronic databases, including PubMed, Embase, and the Cochrane Library, were queried for studies assessing IPDs against traditional lumbar fusion methods for managing ASD after previous lumbar fusion, which had been published between January 2014 and the present. Statistical analysis was conducted using Review Manager 5.4. Results: Seven retrospective cohort studies involving 546 patients met the inclusion criteria. The analysis revealed that IPDs were associated with a statistically significant reduction in the incidence of ASD (OR = 0.28, 95% CI: 0.16 to 0.51, p < 0.0001, and I² = 0% after excluding outliers). The ODI demonstrated a non-significant trend towards improved outcomes with IPDs at the 2-year follow-up (SMD = −3.94; 95% CI: −11.72 to 3.85). Range of motion (ROM) was better preserved with IPDs compared to fusion (SMD = 0.00, 95% CI: −0.41 to 0.41, p = 1.00, I² = 60%). The visual analogue scale or VAS lower back pain scores were significantly reduced at the 2-year follow-up (SMD = −0.69, 95% CI: −1.18 to −0.19, p = 0.006, and I² = 74%). VAS leg pain showed consistent improvements (SMD = −0.29; 95% CI: −0.63 to 0.04). Intraoperative blood loss was significantly lower with IPDs (SMD = −2.07; 95% CI: −3.27 to −0.87, p = 0.0007, and I² = 95%), and operation times were shorter (SMD = −2.22, 95% CI: −3.31 to −1.12, p < 0.0001, and I² = 94%). Conclusions: The judicious use of IPDs might benefit a subset of patients, particularly those who are not suitable candidates for major corrective surgery. Full article

Monitoring human activities, such as walking, falling, and jumping, provides valuable information for personalized health assistants. Existing solutions require the user to carry/wear certain smart devices to capture motion/audio data, use a high-definition camera to record video data, or deploy dedicated devices to collect wireless data. However, none of these solutions are widely adopted for reasons such as discomfort, privacy, and overheads. Therefore, an effective solution to provide non-intrusive, secure, and low-cost human activity monitoring is needed. In this study, we developed a contactless human activity monitoring system that utilizes channel state information (CSI) of the existing ubiquitous WiFi signals. Specifically, we deployed a low-cost commercial off-the-shelf (COTS) router as a transmitter and reused a desktop equipped with an Intel WiFi Link 5300 NIC as a receiver, allowing us to obtain CSI data that recorded human activities. To remove the outliers and ambient noise existing in raw CSI signals, an integrated filter consisting of Hampel, wavelet, and moving average filters was designed. Then, a new metric based on kurtosis and standard deviation was designed to obtain an optimal set of subcarriers that is sensitive to all target activities from the candidate 30 subcarriers. Finally, we selected a group of features, including time- and frequency-domain features, and trained a classification model to recognize different indoor human activities. Our experimental results demonstrate that the proposed system can achieve a mean accuracy of above 93%, even in the face of a long sensing distance. Full article

With the development of information technology, tremendous vulnerabilities and backdoors have evolved, causing inevitable and severe security problems in cyberspace. To fix them, the endogenous safety and security (ESS) theory and one of its practices, the Dynamic Heterogeneous Redundant (DHR) architecture, are proposed. In the DHR architecture, as an instance of the multi-heterogeneous system, a decision module is designed to obtain intermediate results from heterogeneous equivalent functional executors. However, privacy-preserving is not paid attention to in the architecture, which may cause privacy breaches without compromising the ESS theory. In this paper, based on differential privacy (DP), a theoretically rigorous privacy tool, we propose a privacy-preserving DHR framework called DP-DHR. Gaussian random noise is injected into each (online) executor output in DP-DHR to guarantee DP, but it also makes the decision module unable to choose the final result because each executor output is potentially correct even if it is compromised by adversaries. To weaken this disadvantage, we propose the advanced decision strategy and the hypersphere clustering algorithm to classify the perturbed intermediate results into two categories, candidates and outliers, where the former is closer to the correct value than the latter. Finally, the DP-DHR is proven to guarantee DP, and the experimental results also show that the utility is not sacrificed for the enhancement of privacy by much (a ratio of 4–7% on average), even in the condition of some executors (less than one-half) being controlled by adversaries. Full article

E. coli nitroreductase A (NfsA) is a candidate for gene-directed prodrug cancer therapy using bioreductively activated nitroaromatic compounds (ArNO₂). In this work, we determined the standard redox potential of FMN of NfsA to be −215 ± 5 mV at pH 7.0. FMN semiquinone was not formed during 5-deazaflavin-sensitized NfsA photoreduction. This determines the two-electron character of the reduction of ArNO₂ and quinones (Q). In parallel, we characterized the oxidant specificity of NfsA with an emphasis on its structure. Except for negative outliers nitracrine and SN-36506, the reactivity of ArNO₂ increases with their electron affinity (single-electron reduction potential, E¹₇) and is unaffected by their lipophilicity and Van der Waals volume up to 386 Å. The reactivity of quinoidal oxidants is not clearly dependent on E¹₇, but 2-hydroxy-1,4-naphthoquinones were identified as positive outliers and a number of compounds with diverse structures as negative outliers. 2-Hydroxy-1,4-naphthoquinones are characterized by the most positive reaction activation entropy and the negative outlier tetramethyl-1,4-benzoquinone by the most negative. Computer modelling data showed that the formation of H bonds with Arg15, Arg133, and Ser40, plays a major role in the binding of oxidants to reduced NfsA, while the role of the π–π interaction of their aromatic structures is less significant. Typically, the calculated hydride-transfer distances during ArNO₂ reduction are smallwer than for Q. This explains the lower reactivity of quinones. Another factor that slows down the reduction is the presence of positively charged aliphatic substituents. Full article