PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image Classification

Multi-label image classification is a prediction task that aims to identify more than one label from a given image. This paper considers the semantic consistency of the latent space between the visual patch and linguistic label domains and introduces the conditional transport (CT) theory to bridge the acknowledged gap. While recent cross-modal attention-based studies have attempted to align such two representations and achieved impressive performance, they required carefully-designed alignment modules and extra complex operations in the attention computation. We find that by formulating the multi-label classification as a CT problem, we can exploit the interactions between the image and label efficiently by minimizing the bidirectional CT cost. Specifically, after feeding the images and textual labels into the modality-specific encoders, we view each image as a mixture of patch embeddings and a mixture of label embeddings, which capture the local region features and the class prototypes, respectively. CT is then employed to learn and align those two semantic sets by defining the forward and backward navigators. Importantly, the defined navigators in CT distance model the similarities between patches and labels, which provides an interpretable tool to visualize the learned prototypes. Extensive experiments on three public image benchmarks show that the proposed model consistently outperforms the previous methods. Our code is available at https://github.com/keepgoingjkg/PatchCT.Comment: accepted by ICCV2

Moon Imaging Technique and Experiments Based on Sanya Incoherent Scatter Radar

This article introduces the experiment design for Moon imaging based on Sanya incoherent scatter radar (SYISR) and algorithm research in data processing. The peak power of SYISR is 2 MW. The transmitted frequency used for Moon imaging experiments is 430 MHz. We conducted Moon imaging experiments using two types of waveforms, 13-bit Barker code, and linear frequency modulation (LFM) chirp. Considering both resolution and signal-to-noise ratio (SNR), the use of an LFM chirp with a bandwidth of 0.3 MHz and a pulsewidth of 2 ms can give higher SNR and resolution for Moon imaging using SYISR. Several key techniques were applied in the experiment design and data processing: 1) for the reliability of the imaging algorithm, the range-Doppler imaging algorithm commonly used in synthetic aperture imaging was applied; 2) to avoid the sidelobe effect of the 13-bit Barker code matched filter, a sidelobe-free filter was used; and 3) to mitigate the problem of “north–south ambiguity,” mosaic imaging of the Doppler northern and southern hemispheres of the nearside of the Moon was adopted. Two types of imaging results are obtained: mosaic images of the northern and southern hemispheres of the Moon and local regional images. The results demonstrate the feasibility and reliability of Moon imaging based on SYISR, which enables potential further lunar geology investigations in the future

Evaluation of the laser-induced thermotherapy treatment effect of breast cancer based on tissue viscoelastic properties

Photothermal therapy (PTT) has been emerging as an effective, minimally invasive approach to treat cancers. However, a method to quantitatively evaluate the treatment effect after laser-induced thermotherapy (LITT) is needed. In this study, we used 808 nm laser radiation with three different power densities to treat the breast cancer tissue from 4T1 cell lines in a mouse model. The viscoelastic properties of the treated cancer tissues were characterized by a two-term Prony series using a ramp-hold indentation method. We observed that instantaneous shear modulus G0 was significantly higher for the treated cancer tissues than that of the untreated tissue when treated with a power density of 1.5 W/cm2, but significantly lower with a power density of 2.5 W/cm2. The long-term shear modulus G∞ was also significantly higher for the cancer tissue at 1.5 W/cm2, compared to the untreated tissue. The treatment effects were verified by estimating the cell apoptosis rate using terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL). Our results indicate that the viscoelastic properties of the tissue could potentially be used as biomarkers for evaluating the LITT treatment effect. In addition, we also observed a strain-independent behavior of the treated cancer tissue, which provided useful information for applying in vivo imaging method such as magnetic resonance elastography (MRE) for treatment evaluation based on biomechanical properties

SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation

Expressive human pose and shape estimation (EHPS) unifies body, hands, and face motion capture with numerous applications. Despite encouraging progress, current state-of-the-art methods still depend largely on a confined set of training datasets. In this work, we investigate scaling up EHPS towards the first generalist foundation model (dubbed SMPLer-X), with up to ViT-Huge as the backbone and training with up to 4.5M instances from diverse data sources. With big data and the large model, SMPLer-X exhibits strong performance across diverse test benchmarks and excellent transferability to even unseen environments. 1) For the data scaling, we perform a systematic investigation on 32 EHPS datasets, including a wide range of scenarios that a model trained on any single dataset cannot handle. More importantly, capitalizing on insights obtained from the extensive benchmarking process, we optimize our training scheme and select datasets that lead to a significant leap in EHPS capabilities. 2) For the model scaling, we take advantage of vision transformers to study the scaling law of model sizes in EHPS. Moreover, our finetuning strategy turn SMPLer-X into specialist models, allowing them to achieve further performance boosts. Notably, our foundation model SMPLer-X consistently delivers state-of-the-art results on seven benchmarks such as AGORA (107.2 mm NMVE), UBody (57.4 mm PVE), EgoBody (63.6 mm PVE), and EHF (62.3 mm PVE without finetuning). Homepage: https://caizhongang.github.io/projects/SMPLer-X/Comment: Homepage: https://caizhongang.github.io/projects/SMPLer-X

The anti-resection activity of the X protein encoded by Hepatitis Virus B

Chronic infection of hepatitis virus B (HBV) is associated with an increased incidence of hepatocellular carcinoma (HCC). HBV encodes an oncoprotein (HBx) that is crucial for viral replication and interferes with multiple cellular activities including gene expression, histone modifications and genomic stability. To date, it remains unclear how disruption of these activities contributes to hepatocarcinogenesis. Here, we report that HBV exhibits a novel anti‐resection activity by disrupting DNA end resection, thus impairing the initial steps of homologous recombination (HR). This anti‐resection activity occurs in primary human hepatocytes (PHHs) undergoing a natural viral infection‐replication cycle, as well as in cells with integrated HBV genomes. Among the seven HBV‐encoded proteins, we identified HBx as the sole viral factor that inhibits resection. By disrupting an evolutionarily conserved Cullin4A‐DDB1‐RING type of E3 ligase, CRL4WDR70, via its H‐box, we show that HBx inhibits H2B monoubiquitylation at lysine 120 (uH2B) at double strand breaks, thus reducing the efficiency of long‐range resection. We further show that directly impairing H2B monoubiquitylation elicited tumorigenesis upon engraftment of deficient cells in athymic mice, confirming that the impairment of CRL4WDR70 function by HBx is sufficient to promote carcinogenesis. Finally, we demonstrated that lack of H2B monoubiquitylation is manifest in human HBV‐associated HCC (HBVHCC) when compared with HBV‐free HCC, implying corresponding defects of epigenetic regulation and end resection. We conclude that the anti‐resection activity of HBx induces an HR defect and genome instability and contributes to tumorigenesis of host hepatocytes

Exploring atherosclerosis imaging with contrast-enhanced MRI using PEGylated ultrasmall iron oxide nanoparticles

Plaque rupture is a critical concern due to its potential for severe outcomes such as cerebral infarction and myocardial infarction, underscoring the urgency of noninvasive early diagnosis. Magnetic resonance imaging (MRI) has gained prominence in plaque imaging, leveraging its noninvasiveness, high spatial resolution, and lack of ionizing radiation. Ultrasmall iron oxides, when modified with polyethylene glycol, exhibit prolonged blood circulation and passive targeting toward plaque sites, rendering them conducive for MRI. In this study, we synthesized ultrasmall iron oxide nanoparticles of approximately 3 nm via high-temperature thermal decomposition. Subsequent surface modification facilitated the creation of a dual-modality magnetic resonance/fluorescence probe. Upon intravenous administration of the probes, MRI assessment of atherosclerotic plaques and diagnostic evaluation were conducted. The application of Flash-3D sequence imaging revealed vascular constriction at lesion sites, accompanied by a gradual signal amplification postprobe injection. T1-weighted imaging of the carotid artery unveiled a progressive signal ratio increase between plaques and controls within 72 h post-administration. Fluorescence imaging of isolated carotid arteries exhibited incremental lesion-to-control signal ratios. Additionally, T1 imaging of the aorta demonstrated an evolving signal enhancement over 48 h. Therefore, the ultrasmall iron oxide nanoparticles hold immense promise for early and noninvasive diagnosis of plaques, providing an avenue for dynamic evaluation over an extended time frame

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages