Collision-Free Robot Navigation in Crowded Environments using Learning based Convex Model Predictive Control

Navigating robots safely and efficiently in crowded and complex environments remains a significant challenge. However, due to the dynamic and intricate nature of these settings, planning efficient and collision-free paths for robots to track is particularly difficult. In this paper, we uniquely bridge the robot's perception, decision-making and control processes by utilizing the convex obstacle-free region computed from 2D LiDAR data. The overall pipeline is threefold: (1) We proposes a robot navigation framework that utilizes deep reinforcement learning (DRL), conceptualizing the observation as the convex obstacle-free region, a departure from general reliance on raw sensor inputs. (2) We design the action space, derived from the intersection of the robot's kinematic limits and the convex region, to enable efficient sampling of inherently collision-free reference points. These actions assists in guiding the robot to move towards the goal and interact with other obstacles during navigation. (3) We employ model predictive control (MPC) to track the trajectory formed by the reference points while satisfying constraints imposed by the convex obstacle-free region and the robot's kinodynamic limits. The effectiveness of proposed improvements has been validated through two sets of ablation studies and a comparative experiment against the Timed Elastic Band (TEB), demonstrating improved navigation performance in crowded and complex environments

AICAttack: Adversarial Image Captioning Attack with Attention-Based Optimization

Recent advances in deep learning research have shown remarkable achievements across many tasks in computer vision (CV) and natural language processing (NLP). At the intersection of CV and NLP is the problem of image captioning, where the related models' robustness against adversarial attacks has not been well studied. In this paper, we present a novel adversarial attack strategy, which we call AICAttack (Attention-based Image Captioning Attack), designed to attack image captioning models through subtle perturbations on images. Operating within a black-box attack scenario, our algorithm requires no access to the target model's architecture, parameters, or gradient information. We introduce an attention-based candidate selection mechanism that identifies the optimal pixels to attack, followed by Differential Evolution (DE) for perturbing pixels' RGB values. We demonstrate AICAttack's effectiveness through extensive experiments on benchmark datasets with multiple victim models. The experimental results demonstrate that our method surpasses current leading-edge techniques by effectively distributing the alignment and semantics of words in the output

Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans

Gastric cancer is the third leading cause of cancer-related mortality worldwide, but no guideline-recommended screening test exists. Existing methods can be invasive, expensive, and lack sensitivity to identify early-stage gastric cancer. In this study, we explore the feasibility of using a deep learning approach on non-contrast CT scans for gastric cancer detection. We propose a novel cluster-induced Mask Transformer that jointly segments the tumor and classifies abnormality in a multi-task manner. Our model incorporates learnable clusters that encode the texture and shape prototypes of gastric cancer, utilizing self- and cross-attention to interact with convolutional features. In our experiments, the proposed method achieves a sensitivity of 85.0% and specificity of 92.6% for detecting gastric tumors on a hold-out test set consisting of 100 patients with cancer and 148 normal. In comparison, two radiologists have an average sensitivity of 73.5% and specificity of 84.3%. We also obtain a specificity of 97.7% on an external test set with 903 normal cases. Our approach performs comparably to established state-of-the-art gastric cancer screening tools like blood testing and endoscopy, while also being more sensitive in detecting early-stage cancer. This demonstrates the potential of our approach as a novel, non-invasive, low-cost, and accurate method for opportunistic gastric cancer screening.Comment: MICCAI 202

Calcium Modulates the Tethering of Bcor-PRC1.1 Enzymatic Core to KDM2B via Liquid-Liquid Phase Separation

Recruitment of non-canonical BCOR-PRC1.1 to non-methylated CpG islands via KDM2B plays a fundamental role in transcription control during developmental processes and cancer progression. However, the mechanism is still largely unknown on how this recruitment is regulated. Here, we unveiled the importance of the Poly-D/E regions within the linker of BCOR for its binding to KDM2B. Interestingly, we also demonstrated that these negatively charged Poly-D/E regions on BCOR play autoinhibitory roles in liquid-liquid phase separation (LLPS) of BCORANK-linker-PUFD/PCGF1RAWUL. Through neutralizing negative charges of these Poly-D/E regions, Ca2+ not only weakens the interaction between BCOR/PCGF1 and KDM2B, but also promotes co-condensation of the enzymatic core of BCOR-PRC1.1 with KDM2B into liquid-like droplet. Accordingly, we propose that Ca2+ could modulate the compartmentation and recruitment of the enzymatic core of BCOR-PRC1.1 on KDM2B target loci. Thus, our finding advances the mechanistic understanding on how the tethering of BCOR-PRC1.1 enzymatic core to KDM2B is regulated

Improved Prognostic Prediction of Pancreatic Cancer Using Multi-Phase CT by Integrating Neural Distance and Texture-Aware Transformer

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer in which the tumor-vascular involvement greatly affects the resectability and, thus, overall survival of patients. However, current prognostic prediction methods fail to explicitly and accurately investigate relationships between the tumor and nearby important vessels. This paper proposes a novel learnable neural distance that describes the precise relationship between the tumor and vessels in CT images of different patients, adopting it as a major feature for prognosis prediction. Besides, different from existing models that used CNNs or LSTMs to exploit tumor enhancement patterns on dynamic contrast-enhanced CT imaging, we improved the extraction of dynamic tumor-related texture features in multi-phase contrast-enhanced CT by fusing local and global features using CNN and transformer modules, further enhancing the features extracted across multi-phase CT images. We extensively evaluated and compared the proposed method with existing methods in the multi-center (n=4) dataset with 1,070 patients with PDAC, and statistical analysis confirmed its clinical effectiveness in the external test set consisting of three centers. The developed risk marker was the strongest predictor of overall survival among preoperative factors and it has the potential to be combined with established clinical factors to select patients at higher risk who might benefit from neoadjuvant therapy.Comment: MICCAI 202

Cas-OPRAD: a one-pot RPA/PCR CRISPR/Cas12 assay for on-site Phytophthora root rot detection

Phytophthora sojae is a devastating plant pathogen that causes soybean Phytophthora root rot worldwide. Early on-site and accurate detection of the causal pathogen is critical for successful management. In this study, we have developed a novel and specific one-pot RPA/PCR-CRISPR/Cas12 assay for on-site detection (Cas-OPRAD) of Phytophthora root rot (P. sojae). Compared to the traditional RPA/PCR detection methods, the Cas-OPRAD assay has significant detection performance. The Cas-OPRAD platform has excellent specificity to distinguish 33 P. sojae from closely related oomycetes or fungal species. The PCR-Cas12a assay had a consistent detection limit of 100 pg. μL−1, while the RPA-Cas12a assay achieved a detection limit of 10 pg. μL−1. Furthermore, the Cas-OPRAD assay was equipped with a lateral flow assay for on-site diagnosis and enabled the visual detection of P. sojae on the infected field soybean samples. This assay provides a simple, efficient, rapid (<1 h), and visual detection platform for diagnosing Phytophthora root rot based on the one-pot CRISPR/Cas12a assay. Our work provides important methods for early and accurate on-site detection of Phytophthora root rot in the field or customs fields

Chinese Cerebrovascular Neurosurgery Society and Chinese Interventional & Hybrid Operation Society, of Chinese Stroke Association Clinical Practice Guidelines for Management of Brain Arteriovenous Malformations in Eloquent Areas

Aim: The aim of this guideline is to present current and comprehensive recommendations for the management of brain arteriovenous malformations (bAVMs) located in eloquent areas.Methods: An extended literature search on MEDLINE was performed between Jan 1970 and May 2020. Eloquence-related literature was further screened and interpreted in different subcategories of this guideline. The writing group discussed narrative text and recommendations through group meetings and online video conferences. Recommendations followed the Applying Classification of Recommendations and Level of Evidence proposed by the American Heart Association/American Stroke Association. Prerelease review of the draft guideline was performed by four expert peer reviewers and by the members of Chinese Stroke Association.Results: In total, 809 out of 2,493 publications were identified to be related to eloquent structure or neurological functions of bAVMs. Three-hundred and forty-one publications were comprehensively interpreted and cited by this guideline. Evidence-based guidelines were presented for the clinical evaluation and treatment of bAVMs with eloquence involved. Topics focused on neuroanatomy of activated eloquent structure, functional neuroimaging, neurological assessment, indication, and recommendations of different therapeutic managements. Fifty-nine recommendations were summarized, including 20 in Class I, 30 in Class IIa, 9 in Class IIb, and 2 in Class III.Conclusions: The management of eloquent bAVMs remains challenging. With the evolutionary understanding of eloquent areas, the guideline highlights the assessment of eloquent bAVMs, and a strategy for decision-making in the management of eloquent bAVMs

Comparative Evaluation of PCR-Based, LAMP and RPA-CRISPR/Cas12a Assays for the Rapid Detection of <i>Diaporthe aspalathi</i>

Southern stem canker (SSC) of soybean, attributable to the fungal pathogen Diaporthe aspalathi, results in considerable losses of soybean in the field and has damaged production in several of the main soybean-producing countries worldwide. Early and precise identification of the causal pathogen is imperative for effective disease management. In this study, we performed an RPA-CRISPR/Cas12a, as well as LAMP, PCR and real-time PCR assays to verify and compare their sensitivity, specificity and simplicity and the practicality of the reactions. We screened crRNAs targeting a specific single-copy gene, and optimized the reagent concentrations, incubation temperatures and times for the conventional PCR, real-time PCR, LAMP, RPA and Cas12a cleavage stages for the detection of D. aspalathi. In comparison with the PCR-based assays, two thermostatic detection technologies, LAMP and RPA-CRISPR/Cas12a, led to higher specificity and sensitivity. The sensitivity of the LAMP assay could reach 0.01 ng μL−1 genomic DNA, and was 10 times more sensitive than real-time PCR (0.1 ng μL−1) and 100 times more sensitive than conventional PCR assay (1.0 ng μL−1); the reaction was completed within 1 h. The sensitivity of the RPA-CRISPR/Cas12a assay reached 0.1 ng μL−1 genomic DNA, and was 10 times more sensitive than conventional PCR (1.0 ng μL−1), with a 30 min reaction time. Furthermore, the feasibility of the two thermostatic methods was validated using infected soybean leaf and seeding samples. The rapid, visual one-pot detection assay developed could be operated by non-expert personnel without specialized equipment. This study provides a valuable diagnostic platform for the on-site detection of SSC or for use in resource-limited areas

Phase-controlled synthesis of α-NiS nanoparticles confined in carbon nanorods for high performance supercapacitors

A facile and phase-controlled synthesis of α-NiS nanoparticles (NPs) embedded in carbon nanorods (CRs) is reported by in-situ sulfurating the preformed Ni/CRs. The nanopore confinement by the carbon matrix is essential for the formation of α-NiS and preventing its transition to β-phase, which is in strong contrast to large aggregated β-NiS particles grown freely without the confinement of CRs. When used as electrochemical electrode, the hybrid electrochemical charge storage of the ultrasmall α-NiS nanoparticels dispersed in CRs is benefit for the high capacitor (1092, 946, 835, 740 F g−1 at current densities of 1, 2, 5, 10 A g−1, respectively.). While the high electrochemical stability (approximately 100% retention of specific capacitance after 2000 charge/discharge cycles) is attributed to the supercapacitor-battery electrode, which makes synergistic effect of capacitor (CRs) and battery (NiS NPs) components rather than a merely additive composite. This work not only suggests a general approach for phase-controlled synthesis of nickel sulfide but also opens the door to the rational design and fabrication of novel nickel-based/carbon hybrid supercapacitor-battery electrode materials.ASTAR (Agency for Sci., Tech. and Research, S’pore)Published versio

Structural basis for the immunomodulatory function of cysteine protease inhibitor from human roundworm Ascaris lumbricoides.

Immunosuppression associated with infections of nematode parasites has been documented. Cysteine protease inhibitor (CPI) released by the nematode parasites is identified as one of the major modulators of host immune response. In this report, we demonstrated that the recombinant CPI protein of Ascaris lumbricoides (Al-CPI) strongly inhibited the activities of cathepsin L, C, S, and showed weaker effect to cathepsin B. Crystal structure of Al-CPI was determined to 2.1 Å resolution. Two segments of Al-CPI, loop 1 and loop 2, were proposed as the key structure motifs responsible for Al-CPI binding with proteases and its inhibitory activity. Mutations at loop 1 and loop 2 abrogated the protease inhibition activity to various extents. These results provide the molecular insight into the interaction between the nematode parasite and its host and will facilitate the development of anthelmintic agents or design of anti-autoimmune disease drugs