Generating motions for robots interacting with objects of various shapes is a complex challenge, further complicated by the robot’s geometry and multiple desired behaviors. While current robot programming tools (such as inverse kinematics, collision avoidance, and manipulation planning) often treat these problems as constrained optimization, many existing solvers focus on specific problem domains or do not exploit geometric constraints effectively. We propose an efficient first-order method, Augmented Lagrangian Spectral Projected Gradient Descent (ALSPG), which leverages geometric projections via Euclidean projections, Minkowski sums, and basis functions. We show that by using geometric constraints rather than full constraints and gradients, ALSPG significantly improves real-time performance. Compared to second-order methods like iLQR, ALSPG remains competitive in the unconstrained case. We validate our method through toy examples and extensive simulations, and demonstrate its effectiveness on a 7-axis Franka robot, a 6-axis P-Rob robot and a 1:10 scale car in real-world experiments. Source codes, experimental data and videos are available on the project webpage: https://sites.google.com/view/alspg-oc

BACKGROUND: Alimentary tract malignancies (ATM) caused nearly one-third of all tumor-related death. Cuproptosis is a newly identified cell death pattern. The role of cuproptosis-associated lncRNAs in ATM is unknown. METHOD: Data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to identify prognostic lncRNAs by Cox regression and LASSO. Then a predictive nomogram was constructed based on seven prognostic lncRNAs. In addition, the prognostic potential of the seven-lncRNA signature was verified via survival analysis, the receiver operating characteristic (ROC) curve, calibration curve, and clinicopathologic characteristics correlation analysis. Furthermore, we explored the associations between the signature risk score and immune landscape, and somatic gene mutation. RESULTS: We identified 1211 cuproptosis-related lncRNAs and seven survival-related lncRNAs. Patients were categorized into high-risk and low-risk groups with significantly different prognoses. ROC and calibration curve confirmed the good prediction capability of the risk model and nomogram. Somatic mutations between the two groups were compared. We also found that patients in the two groups responded differently to immune checkpoint inhibitors and immunotherapy. CONCLUSION: The proposed novel seven lncRNAs nomogram could predict prognosis and guide treatment of ATM. Further research was required to validate the nomogram.

MOTIVATION: Genome-scale CRISPR/Cas9 system has been a democratized gene editing technique and widely used to investigate gene functions in some biological processes and diseases especially cancers. Aiming to characterize gene aberrations and assess their effects on cancer, we designed a pipeline to identify the essential genes for pan-cancer. METHODS: CRISPR screening data were used to identify the essential genes that were collected from published data and integrated by Robust Rank Aggregation algorithm. Then, hypergeometrics test and random walks with restart (RWR) were used to predict additional essential genes on broader scale. Finally, the expression status and potential roles of these genes were explored based on TCGA portal and regulatory network analysis. RESULTS: We collected 926 samples from 10 CRISPR-based screening studies involving 33 different types of cancer to identify cancer-essential genes, which consists of 799 protein-coding genes (PCGs) and 97 long non-coding RNAs (lncRNAs). Then, we constructed a 'bi-colored' network with both PCGs and lncRNAs and applied it to predict additional essential genes including 495 PCGs and 280 lncRNAs on a broader scale using hypergeometrics test and RWR. After obtaining all essential genes, we further investigated their potential roles in cancer and found that essential genes have higher and more stable expression levels, and are associated with multiple cancer-associated biological processes and survival time. The regulatory network analysis detected two intriguing modules of essential genes participating in the regulation of cell cycle and ribosome biogenesis in cancer. AVAILABILITY AND IMPLEMENTATION: . SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.