Selection of optimal oligonucleotide probes for microarrays using multiple criteria, global alignment and parameter estimation

The oligonucleotide specificity for microarray hybridization can be predicted by its sequence identity to non-targets, continuous stretch to non-targets, and/or binding free energy to non-targets. Most currently available programs only use one or two of these criteria, which may choose ‘false’ specific oligonucleotides or miss ‘true’ optimal probes in a considerable proportion. We have developed a software tool, called CommOligo using new algorithms and all three criteria for selection of optimal oligonucleotide probes. A series of filters, including sequence identity, free energy, continuous stretch, GC content, self-annealing, distance to the 3′-untranslated region (3′-UTR) and melting temperature (T(m)), are used to check each possible oligonucleotide. A sequence identity is calculated based on gapped global alignments. A traversal algorithm is used to generate alignments for free energy calculation. The optimal T(m) interval is determined based on probe candidates that have passed all other filters. Final probes are picked using a combination of user-configurable piece-wise linear functions and an iterative process. The thresholds for identity, stretch and free energy filters are automatically determined from experimental data by an accessory software tool, CommOligo_PE (CommOligo Parameter Estimator). The program was used to design probes for both whole-genome and highly homologous sequence data. CommOligo and CommOligo_PE are freely available to academic users upon request

Study on the correlation between river network patterns and topography in the Haihe River basin

In recent decades, the river network patterns (RNPs) in China’s Haihe River basin have changed dramatically, and the topology of the river network has become increasingly complex. It is important to quantitatively study the correlation between river network patterns and topography (CRNPT) and the changes in the correlation. In this paper, the Haihe River basin was spatially gridded (4 km × 4 km), and different geomorphological areas were extracted for a multiarea study. We selected topographic and river network indicators and proposed new indicators to characterize regional topographic ‘stressfulness’ and then used redundancy analysis for correlation studies. The results showed that the variance of RNP explained by topography was 53.39%. The combined contribution of the topographic wetness index (TWI) and topographic wetness stress index (TSI) ranged from 35.66% to 78.29% in multiple areas, and the TSI showed stronger explanatory power. The regional effect of the CRNPT was significant, with mountains and transition areas having higher effects than plain areas. Compared to the natural river network, the CRNPT of the current river network was significantly lower. Among the RNP indicators, the artificial channel proportion (Pac) had the highest proportion of variance, and the CRNPT was strongly influenced by artificial channels. Artificial channels changed the consistency of topography with the RNP and reduced the topographic interpretation of the RNP, which may weaken the stability and hydrological connectivity of the river network. The variation in interpretation was related to the distribution of artificial channels, which showed a logarithmic function relationship between them

AdvMono3D: Advanced Monocular 3D Object Detection with Depth-Aware Robust Adversarial Training

Monocular 3D object detection plays a pivotal role in the field of autonomous driving and numerous deep learning-based methods have made significant breakthroughs in this area. Despite the advancements in detection accuracy and efficiency, these models tend to fail when faced with such attacks, rendering them ineffective. Therefore, bolstering the adversarial robustness of 3D detection models has become a crucial issue that demands immediate attention and innovative solutions. To mitigate this issue, we propose a depth-aware robust adversarial training method for monocular 3D object detection, dubbed DART3D. Specifically, we first design an adversarial attack that iteratively degrades the 2D and 3D perception capabilities of 3D object detection models(IDP), serves as the foundation for our subsequent defense mechanism. In response to this attack, we propose an uncertainty-based residual learning method for adversarial training. Our adversarial training approach capitalizes on the inherent uncertainty, enabling the model to significantly improve its robustness against adversarial attacks. We conducted extensive experiments on the KITTI 3D datasets, demonstrating that DART3D surpasses direct adversarial training (the most popular approach) under attacks in 3D object detection $AP_{R40}$ of car category for the Easy, Moderate, and Hard settings, with improvements of 4.415%, 4.112%, and 3.195%, respectively