LOW-COST, SOFTWARE DEFINED FMCW RADAR FOR OBSERVATIONS OF DRONES

This thesis focuses on implementing a small, low-cost, Frequency-Modulated-Continuous-Wave (FMCW) radar sensor using Software-Defined Radio (SDR) platforms. Extensive investigation and comparisons of existing SDRs are provided. Two of the sensor platforms are tested using both commercial FMCW radar kit and USRP-N210 radio. The main application of the effort will be detecting small unmanned aerial vehicles (UAVs) using low-cost, reconfigurable FMCW radar sensors. Simulation studies of various SDR system configurations are done in MATLAB, and hardware calibration using loop-back testing is performed. Detailed discussions on system performance predictions and enhancements, as well as design considerations, are provided. The implemented sensor systems are then brought to outdoor experiments with targets such as reflective plates, water-towers and small drones

ChatFace: Chat-Guided Real Face Editing via Diffusion Latent Space Manipulation

Editing real facial images is a crucial task in computer vision with significant demand in various real-world applications. While GAN-based methods have showed potential in manipulating images especially when combined with CLIP, these methods are limited in their ability to reconstruct real images due to challenging GAN inversion capability. Despite the successful image reconstruction achieved by diffusion-based methods, there are still challenges in effectively manipulating fine-gained facial attributes with textual instructions.To address these issues and facilitate convenient manipulation of real facial images, we propose a novel approach that conduct text-driven image editing in the semantic latent space of diffusion model. By aligning the temporal feature of the diffusion model with the semantic condition at generative process, we introduce a stable manipulation strategy, which perform precise zero-shot manipulation effectively. Furthermore, we develop an interactive system named ChatFace, which combines the zero-shot reasoning ability of large language models to perform efficient manipulations in diffusion semantic latent space. This system enables users to perform complex multi-attribute manipulations through dialogue, opening up new possibilities for interactive image editing. Extensive experiments confirmed that our approach outperforms previous methods and enables precise editing of real facial images, making it a promising candidate for real-world applications. Project page: https://dongxuyue.github.io/chatface

A Phase 1 Drug-Drug Interaction Study Between Brigatinib and the CYP3A Substrate Midazolam in Patients With ALK-Positive or ROS1-Positive Solid Tumors

Anaplastic lymphoma kinase; Brigatinib; Drug-drug interactionQuinasa del linfoma anaplásico; Brigatinib; Interacción fármaco-fármacoQuinasa del limfoma anaplàstic; Brigatinib; Interacció medicament-medicamentBrigatinib is a next-generation anaplastic lymphoma kinase (ALK) inhibitor approved for the treatment of patients with ALK-positive (ALK+) non–small cell lung cancer (NSCLC). A phase 1 drug-drug interaction study was conducted to evaluate the effect of multiple-dose administration of brigatinib on the single-dose pharmacokinetics of midazolam, a sensitive cytochrome P450 3A substrate. In cycle 1, patients with ALK+ or ROS1+ solid tumors, including NSCLC, received a single 3-mg dose of midazolam as an oral solution alone on day 1 and then coadministered with brigatinib on day 21 (brigatinib 90 mg once daily on days 2-8; 180 mg once daily on days 9-28). After cycle 1, patients could continue to receive brigatinib in 28-day treatment cycles. The primary study objective was to characterize the effect of brigatinib 180 mg once daily on midazolam pharmacokinetics. The secondary objective was to assess safety. Exploratory efficacy endpoints included objective response rate and progression-free survival. Brigatinib was generally well tolerated, and safety data were consistent with the known safety profile. Among the 10 patients with ALK+ NSCLC, the confirmed objective response rate was 30% and median progression-free survival was 7.2 months. Coadministration of brigatinib reduced midazolam maximum observed plasma concentration by ≈16% (geometric least-squares mean ratio, 0.836 [90%CI, 0.662-1.056]) and area under the plasma concentration–time curve from time 0 to infinity by ≈26% (geometric least-squares mean ratio, 0.741 [90%CI, 0.600-0.915]). Thus, brigatinib is a weak inducer of cytochrome P450 3A in vivo.This study was sponsored by Takeda Development Center Americas, Inc., Lexington, Massachusetts, USA

Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Models

Video-based large language models (Video-LLMs) have been recently introduced, targeting both fundamental improvements in perception and comprehension, and a diverse range of user inquiries. In pursuit of the ultimate goal of achieving artificial general intelligence, a truly intelligent Video-LLM model should not only see and understand the surroundings, but also possess human-level commonsense, and make well-informed decisions for the users. To guide the development of such a model, the establishment of a robust and comprehensive evaluation system becomes crucial. To this end, this paper proposes \textit{Video-Bench}, a new comprehensive benchmark along with a toolkit specifically designed for evaluating Video-LLMs. The benchmark comprises 10 meticulously crafted tasks, evaluating the capabilities of Video-LLMs across three distinct levels: Video-exclusive Understanding, Prior Knowledge-based Question-Answering, and Comprehension and Decision-making. In addition, we introduce an automatic toolkit tailored to process model outputs for various tasks, facilitating the calculation of metrics and generating convenient final scores. We evaluate 8 representative Video-LLMs using \textit{Video-Bench}. The findings reveal that current Video-LLMs still fall considerably short of achieving human-like comprehension and analysis of real-world videos, offering valuable insights for future research directions. The benchmark and toolkit are available at: \url{https://github.com/PKU-YuanGroup/Video-Bench}.Comment: Benchmark is available at https://github.com/PKU-YuanGroup/Video-Benc

Trained immunity: a cutting edge approach for designing novel vaccines against parasitic diseases?

The preventive situation of parasitosis, a global public health burden especially for developing countries, is not looking that good. Similar to other infections, vaccines would be the best choice for preventing and controlling parasitic infection. However, ideal antigenic molecules for vaccine development have not been identified so far, resulting from the complicated life history and enormous genomes of the parasites. Furthermore, the suppression or down-regulation of anti-infectious immunity mediated by the parasites or their derived molecules can compromise the effect of parasitic vaccines. Comparing the early immune profiles of several parasites in the permissive and non-permissive hosts, a robust innate immune response is proposed to be a critical event to eliminate the parasites. Therefore, enhancing innate immunity may be essential for designing novel and effective parasitic vaccines. The newly emerging trained immunity (also termed innate immune memory) has been increasingly recognized to provide a novel perspective for vaccine development targeting innate immunity. This article reviews the current status of parasitic vaccines and anti-infectious immunity, as well as the conception, characteristics, and mechanisms of trained immunity and its research progress in Parasitology, highlighting the possible consideration of trained immunity in designing novel vaccines against parasitic diseases

Cigarette Smoke (CS) and Nicotine Delay Neutrophil Spontaneous Death via Suppressing Production of Diphosphoinositol Pentakisphosphate

Diphosphoinositol pentakisphosphate (InsP7), a higher inositol phosphate containing energetic pyrophosphate bonds, is beginning to emerge as a key cellular signaling molecule. However, the various physiological and pathological processes that involve InsP7 are not completely understood. Here we report that cigarette smoke (CS) extract and nicotine reduce InsP7 levels in aging neutrophils. This subsequently leads to suppression of Akt deactivation, a causal mediator of neutrophil spontaneous death, and delayed neutrophil death. The effect of CS extract and nicotine on neutrophil death can be suppressed by either directly inhibiting the PtdIns(3,4,5)P3/Akt pathway, or increasing InsP7 levels via overexpression of InsP6K1, an inositol hexakisphosphate (InsP6) kinase responsible for InsP7 production in neutrophils. Delayed neutrophil death contributes to the pathogenesis of CS-induced chronic obstructive pulmonary disease. Therefore, disruption of InsP6K1 augments CS-induced neutrophil accumulation and lung damage. Taken together, these results suggest that CS and nicotine delay neutrophil spontaneous death by suppressing InsP7 production and consequently blocking Akt deactivation in aging neutrophils. Modifying neutrophil death via this pathway provides a strategy and therapeutic target for the treatment of tobacco-induced chronic obstructive pulmonary disease

CYP2C19 genotype and platelet aggregation test-guided dual antiplatelet therapy after off-pump coronary artery bypass grafting: A retrospective cohort study

BackgroundDual antiplatelet therapy (DAPT) is recommended in patients undergoing off-pump coronary artery bypass graft surgery (OPCAB). Clopidogrel is less effective among patients with loss-of-function (LoF) of CYP2C19 alleles, while ticagrelor has direct effects on P2Y12 receptor. Whether a CYP2C19 genotype plus platelet aggregation test (PAgT)-guided DAPT after CABG could improve clinical outcomes remain uncertain.Materials and methodsFrom August 2019 to December 2020, 1,134 consecutive patients who underwent OPCAB received DAPT for 1 year after surgery in Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. According to the actual treatment they received in real-world, 382 (33.7%) of them received a traditional DAPT: aspirin 100 mg qd + clopidogrel 75 mg qd, no matter the CYP2C19 genotype and response in platelet aggregation test (PAgT). The other 752 (66.3%) patients received an individual DAPT based on CYP2C19 genotype and PAgT: aspirin 100 mg qd + clopidogrel 75 mg qd if CYP2C19 was extensive metabolizer, or moderate metabolizer but normal response in PAgT; aspirin 100 mg qd + ticagrelor 90 mg bid if CYP2C19 was poor metabolizer, or moderate metabolizer but no or low response in PAgT. One-year follow-up was achieved for all patients. The primary outcome was major adverse cardiovascular events (MACE), a composite of cardiovascular death, myocardial infarction, and stroke. The safety outcome was thrombolysis in myocardial infarction (TIMI) criteria major bleeding.ResultsCompared with the traditional DAPT group, the risk of MACE in the individual DAPT group was significantly lower (5.5 vs. 9.2%, HR 0.583; 95% CI, 0.371–0.915; P = 0.019), mainly due to the decreased risk of MI (1.7 vs. 4.2%, HR 0.407; 95% CI, 0.196–0.846; P = 0.016). The risk of TIMI major bleeding events was similar between the two groups (5.3 vs. 6.0%, RR 0.883; 95% CI, 0.537–1.453; P = 0.626).ConclusionFor patients who underwent OPCAB, individual DAPT (CYP2C19 genotype plus PAgT-guided strategy) was associated with a lower risk of MACE and a similar risk of major bleeding

Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning

Advances in deep learning have greatly improved structure prediction of molecules. However, many macroscopic observations that are important for real-world applications are not functions of a single molecular structure, but rather determined from the equilibrium distribution of structures. Traditional methods for obtaining these distributions, such as molecular dynamics simulation, are computationally expensive and often intractable. In this paper, we introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems. Inspired by the annealing process in thermodynamics, DiG employs deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system, such as a chemical graph or a protein sequence. This framework enables efficient generation of diverse conformations and provides estimations of state densities. We demonstrate the performance of DiG on several molecular tasks, including protein conformation sampling, ligand structure sampling, catalyst-adsorbate sampling, and property-guided structure generation. DiG presents a significant advancement in methodology for statistically understanding molecular systems, opening up new research opportunities in molecular science.Comment: 80 pages, 11 figure

Computation and Data Driven Discovery of Topological Phononic Materials

© 2021, The Author(s). The discovery of topological quantum states marks a new chapter in both condensed matter physics and materials sciences. By analogy to spin electronic system, topological concepts have been extended into phonons, boosting the birth of topological phononics (TPs). Here, we present a high-throughput screening and data-driven approach to compute and evaluate TPs among over 10,000 real materials. We have discovered 5014 TP materials and grouped them into two main classes of Weyl and nodal-line (ring) TPs. We have clarified the physical mechanism for the occurrence of single Weyl, high degenerate Weyl, individual nodal-line (ring), nodal-link, nodal-chain, and nodal-net TPs in various materials and their mutual correlations. Among the phononic systems, we have predicted the hourglass nodal net TPs in TeO3, as well as the clean and single type-I Weyl TPs between the acoustic and optical branches in half-Heusler LiCaAs. In addition, we found that different types of TPs can coexist in many materials (such as ScZn). Their potential applications and experimental detections have been discussed. This work substantially increases the amount of TP materials, which enables an in-depth investigation of their structure-property relations and opens new avenues for future device design related to TPs