Miracle-dz/UniFAR-FTUs huggingface dataset:
Inhibition of Monocyte Adhesion to Endothelial Cells and Attenuation of Atherosclerotic Lesion by a Glucagon-like Peptide-1 Receptor Agonist, Exendin-4

OBJECTIVE Exogenous administration of glucagon-like peptide-1 (GLP-1) or GLP-1 receptor agonists such as an exendin-4 has direct beneficial effects on the cardiovascular system. However, their effects on atherosclerogenesis have not been elucidated. The aim of this study was to investigate the effects of GLP-1 on accumulation of monocytes/macrophages on the vascular wall, one of the earliest steps in atherosclerogenesis. RESEARCH DESIGN AND METHODS After continuous infusion of low (300 pmol · kg−1 · day−1) or high (24 nmol · kg−1 · day−1) dose of exendin-4 in C57BL/6 or apolipoprotein E–deficient mice (apoE−/−), we evaluated monocyte adhesion to the endothelia of thoracic aorta and arteriosclerotic lesions around the aortic valve. The effects of exendin-4 were investigated in mouse macrophages and human monocytes. RESULTS Treatment with exendin-4 significantly inhibited monocytic adhesion in the aortas of C57BL/6 mice without affecting metabolic parameters. In apoE−/− mice, the same treatment reduced monocyte adhesion to the endothelium and suppressed atherosclerogenesis. In vitro treatment of mouse macrophages with exendin-4 suppressed lipopolysaccharide-induced mRNA expression of tumor necrosis factor-α and monocyte chemoattractant protein-1, and suppressed nuclear translocation of p65, a component of nuclear factor-κB. This effect was reversed by either MDL-12330A, a cAMP inhibitor or PKI14-22, a protein kinase A–specific inhibitor. In human monocytes, exendin-4 reduced the expression of CD11b. CONCLUSIONS Our data suggested that GLP-1 receptor agonists reduced monocyte/macrophage accumulation in the arterial wall by inhibiting the inflammatory response in macrophages, and that this effect may contribute to the attenuation of atherosclerotic lesion by exendin-4.
Incorporating Higher-order Structural Information for Graph Clustering

Clustering holds profound significance in data mining. In recent years, graph convolutional network (GCN) has emerged as a powerful tool for deep clustering, integrating both graph structural information and node attributes. However, most existing methods ignore the higher-order structural information of the graph. Evidently, nodes within the same cluster can establish distant connections. Besides, recent deep clustering methods usually apply a self-supervised module to monitor the training process of their model, focusing solely on node attributes without paying attention to graph structure. In this paper, we propose a novel graph clustering network to make full use of graph structural information. To capture the higher-order structural information, we design a graph mutual infomax module, effectively maximizing mutual information between graph-level and node-level representations, and employ a trinary self-supervised module that includes modularity as a structural constraint. Our proposed model outperforms many state-of-the-art methods on various datasets, demonstrating its superiority.
2nd Australia–China Biomedical Research Conference (ACBRC2009) MicroRNAs in common diseases and potential therapeutic applications

1. Evidence gathered in recent years has revealed microRNAs (miRNAs) fine-tune gene expression and play an important role in various cellular processes, including cell growth, differentiation, proliferation and apoptosis. 2. The present review summarizes current knowledge of miRNA pathways in the pathogenesis of cancer, cardiac diseases, neurodegenerative diseases, diabetes, autoimmune/inflammatory diseases and infection. 3. There is considerable potential to target miRNAs as a novel approach in the treatment of human diseases. Currently, miRNA-based therapies are being examined in both animal models and human clinical trials.
COLEP: Certifiably Robust Learning-Reasoning Conformal Prediction via Probabilistic Circuits

Conformal prediction has shown spurring performance in constructing statistically rigorous prediction sets for arbitrary black-box machine learning models, assuming the data is exchangeable. However, even small adversarial perturbations during the inference can violate the exchangeability assumption, challenge the coverage guarantees, and result in a subsequent decline in empirical coverage. In this work, we propose a certifiably robust learning-reasoning conformal prediction framework (COLEP) via probabilistic circuits, which comprise a data-driven learning component that trains statistical models to learn different semantic concepts, and a reasoning component that encodes knowledge and characterizes the relationships among the trained models for logic reasoning. To achieve exact and efficient reasoning, we employ probabilistic circuits (PCs) within the reasoning component. Theoretically, we provide end-to-end certification of prediction coverage for COLEP in the presence of bounded adversarial perturbations. We also provide certified coverage considering the finite size of the calibration set. Furthermore, we prove that COLEP achieves higher prediction coverage and accuracy over a single model as long as the utilities of knowledge models are non-trivial. Empirically, we show the validity and tightness of our certified coverage, demonstrating the robust conformal prediction of COLEP on various datasets, including GTSRB, CIFAR10, and AwA2. We show that COLEP achieves up to 12% improvement in certified coverage on GTSRB, 9% on CIFAR-10, and 14% on AwA2.
Time series analysis of vegetation-cover response to environmental factors and residential development in a dryland region

Land-use changes as a result of residential development often lead to degradation and alter vegetation cover (VC). Although these are worldwide phenomena, sufficient knowledge about anthropogenic effects caused by various populated areas in dryland ecosystems is lacking. This study explored anthropogenic development in rural areas and its effects on the conservation of protected areas in drylands, focusing on the change in VC, the reasons, extent, and the drivers of change. We propose a novel framework for exploring VC change (VCC) as a function of environmental and human-driven factors including different types of populated areas in drylands. As a case study, we used a 30-year time series of Landsat satellite images over the arid region of Israel to analyze spatiotemporal VCC. The temporal analysis involved the Contextual Mann-Kendall significance test and spatial analysis to model clustering of VCC. A Gradient Boosted Regression machine learning algorithm was applied to study the relative influence of environmental and human-driven factors on VCC. In addition, we used ANOVA to examine differences between the effects of three types of populated areas on the spatiotemporal trends of VC. The results show that the most influential environmental variable on VCC was elevation (relative contribution of 17%), followed by slope (14.8%) and distance from populated areas (14.6%). Moreover, different types of populated areas affected VC differently with varying distances from residential centroids. The nature reserves increased VC positively and significantly, while livestock settlements had a negative effect. Change in vegetation was mostly confined to the stream network and occurred in lower elevations. The study demonstrates how different land-use practices alter the landscape in terms of VC and differ in their extents, patterns, and effects. With the expected growth in population and residential development worldwide, the proposed framework may assist conservation managements and policy makers in minimizing environmental degradation in drylands.
Boosting Flow-based Generative Super-Resolution Models via Learned Prior

Flow-based super-resolution (SR) models have demonstrated astonishing capabilities in generating high-quality images. However, these methods encounter several challenges during image generation, such as grid artifacts, exploding inverses, and suboptimal results due to a fixed sampling temperature. To overcome these issues, this work introduces a conditional learned prior to the inference phase of a flow-based SR model. This prior is a latent code predicted by our proposed latent module conditioned on the low-resolution image, which is then transformed by the flow model into an SR image. Our framework is designed to seamlessly integrate with any contemporary flow-based SR model without modifying its architecture or pre-trained weights. We evaluate the effectiveness of our proposed framework through extensive experiments and ablation analyses. The proposed framework successfully addresses all the inherent issues in flow-based SR models and enhances their performance in various SR scenarios. Our code is available at: https://github.com/liyuantsao/FlowSR-LP
Quantifying the Sim2real Gap for GPS and IMU Sensors

Simulation can and should play a critical role in the development and testing of algorithms for autonomous agents. What might reduce its impact is the ``sim2real'' gap -- the algorithm response differs between operation in simulated versus real-world environments. This paper introduces an approach to evaluate this gap, focusing on the accuracy of sensor simulation -- specifically IMU and GPS -- in velocity estimation tasks for autonomous agents. Using a scaled autonomous vehicle, we conduct 40 real-world experiments across diverse environments then replicate the experiments in simulation with five distinct sensor noise models. We note that direct comparison of raw simulation and real sensor data fails to quantify the sim2real gap for robotics applications. We demonstrate that by using a state of the art state-estimation package as a ``judge'', and by evaluating the performance of this state-estimator in both real and simulated scenarios, we can isolate the sim2real discrepancies stemming from sensor simulations alone. The dataset generated is open-source and publicly available for unfettered use.
NetTrack: Tracking Highly Dynamic Objects with a Net

The complex dynamicity of open-world objects presents non-negligible challenges for multi-object tracking (MOT), often manifested as severe deformations, fast motion, and occlusions. Most methods that solely depend on coarse-grained object cues, such as boxes and the overall appearance of the object, are susceptible to degradation due to distorted internal relationships of dynamic objects. To address this problem, this work proposes NetTrack, an efficient, generic, and affordable tracking framework to introduce fine-grained learning that is robust to dynamicity. Specifically, NetTrack constructs a dynamicity-aware association with a fine-grained Net, leveraging point-level visual cues. Correspondingly, a fine-grained sampler and matching method have been incorporated. Furthermore, NetTrack learns object-text correspondence for fine-grained localization. To evaluate MOT in extremely dynamic open-world scenarios, a bird flock tracking (BFT) dataset is constructed, which exhibits high dynamicity with diverse species and open-world scenarios. Comprehensive evaluation on BFT validates the effectiveness of fine-grained learning on object dynamicity, and thorough transfer experiments on challenging open-world benchmarks, i.e., TAO, TAO-OW, AnimalTrack, and GMOT-40, validate the strong generalization ability of NetTrack even without finetuning. Project page: https://george-zhuang.github. io/nettrack/.
Strict Partitioning for Sporadic Rigid Gang Tasks

The rigid gang task model is based on the idea of executing multiple threads simultaneously on a fixed number of processors to increase efficiency and performance. Although there is extensive literature on global rigid gang scheduling, partitioned approaches have several practical advantages (e.g., task isolation and reduced scheduling overheads). In this paper, we propose a new partitioned scheduling strategy for rigid gang tasks, named strict partitioning. The method creates disjoint partitions of tasks and processors to avoid inter-partition interference. Moreover, it tries to assign tasks with similar volumes (i.e., parallelisms) to the same partition so that the intra-partition interference can be reduced. Within each partition, the tasks can be scheduled using any type of scheduler, which allows the use of a less pessimistic schedulability test. Extensive synthetic experiments and a case study based on Edge TPU benchmarks show that strict partitioning achieves better schedulability performance than state-of-the-art global gang schedulability analyses for both preemptive and non-preemptive rigid gang task sets.
Applying multi-level topology control to satellite formations - a mobile sensor network in space

Several currently planned space missions consist of a set of satellites flying in a formation. This enables a much higher functionality of the mission compared to missions consisting of only a single satellite. The sensors present in each of the satellites in the formation enable sensing of photometric data from outer space. Using optical interferometry, the data from the individual satellite sensors is collected at a designated satellite which requires communication between the satellites, giving rise to a mobile sensor network in space. In order to gain most accurate data, the satellites must be kept in deterministic close relative positions, otherwise, the aggregated data is invalidated. Current control laws to establish reliable formations of satellites face many challenges especially for larger number of satellites. In this paper we present a distributed algorithm to scale current control laws to arbitrarily large satellite formations. This is achieved by a multi-level topology which is based on multiple weighted metrics and can cope even with the case of unpredictable metric values. We substantiate the performance of our algorithm by extensive simulations.
Reactive Oxygen Species and the Central Nervous System

Abstract: Radicals are species containing one or more unpaired electrons, such as nitric oxide (NO•−). The oxygen radical superoxide (O2•−) and the nonradical hydrogen peroxide (H2O2) are produced during normal metabolism and perform several useful functions. Excessive production of O2•‐ and H2O2 can result in tissue damage, which often involves generation of highly reactive hydroxyl radical (•−OH) and other oxidants in the presence of “catalytic” iron or copper ions. An important form of antioxidant defense is the storage and transport of iron and copper ions in forms that will not catalyze formation of reactive radicals. Tissue injury, e.g., by ischemia or trauma, can cause increased metal ion availability and accelerate free radical reactions. This may be especially important in the brain because areas of this organ are rich in iron and CSF cannot bind released iron ions. Oxidative stress on nervous tissue can produce damage by several interacting mechanisms, including increases in intracellular free Ca2+ and, possibly, release of excitatory amino acids. Recent suggestions that free radical reactions are involved in the neurotoxicity of aluminum and in damage to the substantia nigra in patients with Parkinson's disease are reviewed. Finally, the nature of antioxidants is discussed, it being suggested that antioxidant enzymes and chelators of transition metal ions may be more generally useful protective agents than chain‐breaking antioxidants. Careful precautions must be used in the design of antioxidants for therapeutic use.
KARINA: An Efficient Deep Learning Model for Global Weather Forecast

Deep learning-based, data-driven models are gaining prevalence in climate research, particularly for global weather prediction. However, training the global weather data at high resolution requires massive computational resources. Therefore, we present a new model named KARINA to overcome the substantial computational demands typical of this field. This model achieves forecasting accuracy comparable to higher-resolution counterparts with significantly less computational resources, requiring only 4 NVIDIA A100 GPUs and less than 12 hours of training. KARINA combines ConvNext, SENet, and Geocyclic Padding to enhance weather forecasting at a 2.5{\deg} resolution, which could filter out high-frequency noise. Geocyclic Padding preserves pixels at the lateral boundary of the input image, thereby maintaining atmospheric flow continuity in the spherical Earth. SENet dynamically improves feature response, advancing atmospheric process modeling, particularly in the vertical column process as numerous channels. In this vein, KARINA sets new benchmarks in weather forecasting accuracy, surpassing existing models like the ECMWF S2S reforecasts at a lead time of up to 7 days. Remarkably, KARINA achieved competitive performance even when compared to the recently developed models (Pangu-Weather, GraphCast, ClimaX, and FourCastNet) trained with high-resolution data having 100 times larger pixels. Conclusively, KARINA significantly advances global weather forecasting by efficiently modeling Earth's atmosphere with improved accuracy and resource efficiency.
Nonlinearity-induced Scattering Zero Degeneracies for Spectral Management of Coherent Perfect Absorption in Complex Systems

We develop a Coherent Perfect Absorption (CPA) protocol for cases where scale invariance is violated due to the presence of nonlinear mechanisms. We demonstrate, using a microwave setting that lacks geometrical symmetries, that the nonlinearity offers new reconfigurable modalities: the destruction or formation of nonlinear CPAs (NL-CPAs), and their frequency positioning and bandwidth management using the incident power as a control knob. The latter occurs via the formation of exceptional point degeneracies of the zeroes of nonlinear scattering processes. Our results establish NL-CPA protocols as a versatile scheme for the creation of reconfigurable hot/cold-spots in complicated enclosures (e.g. buildings or vessels) with applications to next-generation telecommunications, long-range wireless power transfer, and electromagnetic warfare.
GAgent: An Adaptive Rigid-Soft Gripping Agent with Vision Language Models for Complex Lighting Environments

This paper introduces GAgent: an Gripping Agent designed for open-world environments that provides advanced cognitive abilities via VLM agents and flexible grasping abilities with variable stiffness soft grippers. GAgent comprises three primary components - Prompt Engineer module, Visual-Language Model (VLM) core and Workflow module. These three modules enhance gripper success rates by recognizing objects and materials and accurately estimating grasp area even under challenging lighting conditions. As part of creativity, researchers also created a bionic hybrid soft gripper with variable stiffness capable of gripping heavy loads while still gently engaging objects. This intelligent agent, featuring VLM-based cognitive processing with bionic design, shows promise as it could potentially benefit UAVs in various scenarios.
Insight into the Genome of Staphylococcus xylosus, a Ubiquitous Species Well Adapted to Meat Products

Staphylococcus xylosus belongs to the vast group of coagulase-negative staphylococci. It is frequently isolated from meat products, either fermented or salted and dried, and is commonly used as starter cultures in sausage manufacturing. Analysis of the S. xylosus genome together with expression in situ in a meat model revealed that this bacterium is well adapted to meat substrates, being able to use diverse substrates as sources of carbon and energy and different sources of nitrogen. It is well-equipped with genes involved in osmotic, oxidative/nitrosative, and acidic stress responses. It is responsible for the development of the typical colour of cured meat products via its nitrate reductase activity. It contributes to sensorial properties, mainly by the the catabolism of pyruvate and amino acids resulting in odorous compounds and by the limiting of the oxidation of fatty acids, thereby avoiding rancidity.
Interactive $360^{\circ}$ Video Streaming Using FoV-Adaptive Coding with Temporal Prediction

For $360^{\circ}$ video streaming, FoV-adaptive coding that allocates more bits for the predicted user's field of view (FoV) is an effective way to maximize the rendered video quality under the limited bandwidth. We develop a low-latency FoV-adaptive coding and streaming system for interactive applications that is robust to bandwidth variations and FoV prediction errors. To minimize the end-to-end delay and yet maximize the coding efficiency, we propose a frame-level FoV-adaptive inter-coding structure. In each frame, regions that are in or near the predicted FoV are coded using temporal and spatial prediction, while a small rotating region is coded with spatial prediction only. This rotating intra region periodically refreshes the entire frame, thereby providing robustness to both FoV prediction errors and frame losses due to transmission errors. The system adapts the sizes and rates of different regions for each video segment to maximize the rendered video quality under the predicted bandwidth constraint. Integrating such frame-level FoV adaptation with temporal prediction is challenging due to the temporal variations of the FoV. We propose novel ways for modeling the influence of FoV dynamics on the quality-rate performance of temporal predictive coding.We further develop LSTM-based machine learning models to predict the user's FoV and network bandwidth.The proposed system is compared with three benchmark systems, using real-world network bandwidth traces and FoV traces, and is shown to significantly improve the rendered video quality, while achieving very low end-to-end delay and low frame-freeze probability.
Towards Practical Fabrication Stage Attacks Using Interrupt-Resilient Hardware Trojans

We introduce a new class of hardware trojans called interrupt-resilient trojans (IRTs). Our work is motivated by the observation that hardware trojan attacks on CPUs, even under favorable attack scenarios (e.g., an attacker with local system access), are affected by unpredictability due to non-deterministic context switching events. As we confirm experimentally, these events can lead to race conditions between trigger signals and the CPU events targeted by the trojan payloads (e.g., a CPU memory access), thus affecting the reliability of the attacks. Our work shows that interrupt-resilient trojans can successfully address the problem of non-deterministic triggering in CPUs, thereby providing high reliability guarantees in the implementation of sophisticated hardware trojan attacks. Specifically, we successfully utilize IRTs in different attack scenarios against a Linux-capable CPU design and showcase its resilience against context-switching events. More importantly, we show that our design allows for seamless integration during fabrication stage attacks.We evaluate different strategies for the implementation of our attacks on a tape-out ready high-speed RISC-V microarchitecture in a 28nm commercial technology process and successfully implement them with an average overhead delay of only 20 picoseconds, while leaving the sign-off characteristics of the layout intact. In doing so, we challenge the common wisdom regarding the low flexibility of late supply chain stages (e.g., fabrication) for the insertion of powerful trojans. To promote further research on microprocessor trojans, we open-source our designs and provide the accompanying supporting software logic.