DCPT: Darkness Clue-Prompted Tracking in Nighttime UAVs

Existing nighttime unmanned aerial vehicle (UAV) trackers follow an "Enhance-then-Track" architecture - first using a light enhancer to brighten the nighttime video, then employing a daytime tracker to locate the object. This separate enhancement and tracking fails to build an end-to-end trainable vision system. To address this, we propose a novel architecture called Darkness Clue-Prompted Tracking (DCPT) that achieves robust UAV tracking at night by efficiently learning to generate darkness clue prompts. Without a separate enhancer, DCPT directly encodes anti-dark capabilities into prompts using a darkness clue prompter (DCP). Specifically, DCP iteratively learns emphasizing and undermining projections for darkness clues. It then injects these learned visual prompts into a daytime tracker with fixed parameters across transformer layers. Moreover, a gated feature aggregation mechanism enables adaptive fusion between prompts and between prompts and the base model. Extensive experiments show state-of-the-art performance for DCPT on multiple dark scenario benchmarks. The unified end-to-end learning of enhancement and tracking in DCPT enables a more trainable system. The darkness clue prompting efficiently injects anti-dark knowledge without extra modules. Code and models will be released.Comment: Under revie

Perioperative management and prognosis in over aged patients undergoing non-cardiac surgery： experience with 828 cases in a single center

Objective To investigate perioperative management and prognosis in patients aged≥80 years old undergoing non-cardiac surgery. Methods Clinical data of 828 patients aged≥80 years old undergoing non-cardiac surgery were retrospectively analyzed. Intraoperative hypotension， accumulated time of hypotension， operation time， intraoperative blood loss， postoperative ICU admission， length of ICU stay， length of hospital stay， cost of care， perioperative complications need to be treated， and perioperative death were recorded and analyzed. Results The mean age of 828 patients was （84±4） years old. All cases were classified as American Society of Anesthesiologist （ASA） gradeⅡ-Ⅴ. Postoperative complications occurred in 111 patients （13.4%）， and postoperative death occurred in 24 patients （2.9%）. The incidence of postoperative complications and death in thoracic， neuro-，and vascular surgery was 29% and 17%， the highest among various types of operations （all P < 0.05）. Among patients with different ASA grades， the incidence of postoperative complications and death in patients with ASA gradeⅣand V was significantly higher than that in their counterparts with ASA gradeⅡ（both P < 0.001）. The selection of anesthesia approach did not affect the incidence of postoperative complications and death in different operations except thoracic， neuro-， and vascular surgery under general anesthesia （P > 0.05）. Compared with patients with intraoperative systolic pressure of <120 mmHg， the incidence of postoperative complications was significantly higher in those with systolic pressure of < 90 mmHg（P < 0.05）. The proportion of ICU admission， length of hospital stay， and incidence of postoperative complications were significantly increased over age （all P < 0.01）， whereas postoperative death rate did not differ among patients of different ages （P > 0.05）. Conclusions Patients aged≥80 years old have high incidence of postoperative complications and death rates， which is probably associated with high-risk operation and intraoperative hypotension， especially systolic hypotension of < 90 mmHg enduring for≥10 min

An Adaptive Vehicle Clustering Algorithm Based on Power Minimization in Vehicular Ad-Hoc Networks

In this paper, we propose an adaptive vehicle clustering algorithm based on fuzzy C-means algorithm, which aims at minimizing power consumption of the vehicles. Specifically, the proposed algorithm firstly dynamically allocates the computing resources of each virtual machine in the vehicle, according to the popularity of different virtualized network functions. The optimal clustering number to minimize the total energy consumption of vehicles is determined using the fuzzy C-means algorithm and the clustering head is selected based on vehicles moving direction, weighted mobility, and entropy. Simulation results are provided to confirm that the proposed algorithm can decrease the power consumption of vehicles while satisfying the vehicle delay requirement

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

CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans

Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice, while most medical AI systems are built to focus on single organs with a narrow list of a few diseases. This might severely limit AI's clinical adoption. A certain number of AI models need to be assembled non-trivially to match the diagnostic process of a human reading a CT scan. In this paper, we construct a Unified Tumor Transformer (CancerUniT) model to jointly detect tumor existence & location and diagnose tumor characteristics for eight major cancers in CT scans. CancerUniT is a query-based Mask Transformer model with the output of multi-tumor prediction. We decouple the object queries into organ queries, tumor detection queries and tumor diagnosis queries, and further establish hierarchical relationships among the three groups. This clinically-inspired architecture effectively assists inter- and intra-organ representation learning of tumors and facilitates the resolution of these complex, anatomically related multi-organ cancer image reading tasks. CancerUniT is trained end-to-end using a curated large-scale CT images of 10,042 patients including eight major types of cancers and occurring non-cancer tumors (all are pathology-confirmed with 3D tumor masks annotated by radiologists). On the test set of 631 patients, CancerUniT has demonstrated strong performance under a set of clinically relevant evaluation metrics, substantially outperforming both multi-disease methods and an assembly of eight single-organ expert models in tumor detection, segmentation, and diagnosis. This moves one step closer towards a universal high performance cancer screening tool.Comment: ICCV 2023 Camera Ready Versio

Bilirubin Restrains the Anticancer Effect of Vemurafenib on BRAF-Mutant Melanoma Cells Through ERK-MNK1 Signaling

Melanoma, the most threatening cancer in the skin, has been considered to be driven by the carcinogenic RAF-MEK1/2-ERK1/2 signaling pathway. This signaling pathway is usually mainly dysregulated by mutations in BRAF or RAS in skin melanomas. Although inhibitors targeting mutant BRAF, such as vemurafenib, have improved the clinical outcome of melanoma patients with BRAF mutations, the efficiency of vemurafenib is limited in many patients. Here, we show that blood bilirubin in patients with BRAF-mutant melanoma treated with vemurafenib is negatively correlated with clinical outcomes. In vitro and animal experiments show that bilirubin can abrogate vemurafenib-induced growth suppression of BRAF-mutant melanoma cells. Moreover, bilirubin can remarkably rescue vemurafenib-induced apoptosis. Mechanically, the activation of ERK-MNK1 axis is required for bilirubin-induced reversal effects post vemurafenib treatment. Our findings not only demonstrate that bilirubin is an unfavorable for patients with BRAF-mutant melanoma who received vemurafenib treatment, but also uncover the underlying mechanism by which bilirubin restrains the anticancer effect of vemurafenib on BRAF-mutant melanoma cells

Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purposeunderground liquid scintillator detector, was proposed with the determinationof the neutrino mass hierarchy as a primary physics goal. It is also capable ofobserving neutrinos from terrestrial and extra-terrestrial sources, includingsupernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos,atmospheric neutrinos, solar neutrinos, as well as exotic searches such asnucleon decays, dark matter, sterile neutrinos, etc. We present the physicsmotivations and the anticipated performance of the JUNO detector for variousproposed measurements. By detecting reactor antineutrinos from two power plantsat 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4sigma significance with six years of running. The measurement of antineutrinospectrum will also lead to the precise determination of three out of the sixoscillation parameters to an accuracy of better than 1\%. Neutrino burst from atypical core-collapse supernova at 10 kpc would lead to ~5000inverse-beta-decay events and ~2000 all-flavor neutrino-proton elasticscattering events in JUNO. Detection of DSNB would provide valuable informationon the cosmic star-formation rate and the average core-collapsed neutrinoenergy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400events per year, significantly improving the statistics of existing geoneutrinosamples. The JUNO detector is sensitive to several exotic searches, e.g. protondecay via the $p\to K^++\bar\nu$ decay channel. The JUNO detector will providea unique facility to address many outstanding crucial questions in particle andastrophysics. It holds the great potential for further advancing our quest tounderstanding the fundamental properties of neutrinos, one of the buildingblocks of our Universe