DRPT: Disentangled and Recurrent Prompt Tuning for Compositional Zero-Shot Learning

Compositional Zero-shot Learning (CZSL) aims to recognize novel concepts composed of known knowledge without training samples. Standard CZSL either identifies visual primitives or enhances unseen composed entities, and as a result, entanglement between state and object primitives cannot be fully utilized. Admittedly, vision-language models (VLMs) could naturally cope with CZSL through tuning prompts, while uneven entanglement leads prompts to be dragged into local optimum. In this paper, we take a further step to introduce a novel Disentangled and Recurrent Prompt Tuning framework termed DRPT to better tap the potential of VLMs in CZSL. Specifically, the state and object primitives are deemed as learnable tokens of vocabulary embedded in prompts and tuned on seen compositions. Instead of jointly tuning state and object, we devise a disentangled and recurrent tuning strategy to suppress the traction force caused by entanglement and gradually optimize the token parameters, leading to a better prompt space. Notably, we develop a progressive fine-tuning procedure that allows for incremental updates to the prompts, optimizing the object first, then the state, and vice versa. Meanwhile, the optimization of state and object is independent, thus clearer features can be learned to further alleviate the issue of entangling misleading optimization. Moreover, we quantify and analyze the entanglement in CZSL and supplement entanglement rebalancing optimization schemes. DRPT surpasses representative state-of-the-art methods on extensive benchmark datasets, demonstrating superiority in both accuracy and efficiency

Use of GoFundMe® to crowdfund complementary and alternative medicine treatments for cancer.

PurposeComplementary and alternative medicine (CAM) use is common amongst cancer patients. However, there is growing concern about its safety and efficacy. Online crowdfunding campaigns represent a unique avenue to understand the cancer patient's perspective for using CAM or declining conventional cancer therapy (CCT).MethodsFive hundred GoFundMe campaigns from 2012 to 2019 detailing financial need for cancer treatment were randomly selected and reviewed for endorsement of CAM use, reasons for using CAM, and reasons for declining CCT. Descriptive statistics were used to compare patient and campaign characteristics between 250 CAM users and 250 non-CAM users.ResultsCompared to non-CAM users, CAM users were more likely to be female (70% vs. 54%, p < 0.01), to report more stage IV cancer (54% vs. 12%, p < 0.01), and to have a history of delayed, missed, or misdiagnosis (10% vs. 4%, p < 0.01). Reasons for using CAM include endorsing curative/therapeutic effects 212 (85%), pain/stress reduction 137 (55%), and dissatisfaction with current or past medical treatment options 105 (42%). 87 (35%) CAM users that declined CCT reported that they wanted to try to fight off cancer using CAM first 57 (61%), that CCT was too "toxic" to the body 39 (42%), and cancer was already too advanced, so that CCT would be futile or too aggressive 25 (27%).ConclusionCancer patients on GoFundMe using CAM highly value quality of life, comfort, and autonomy. Physicians should educate themselves on CAM to set realistic expectations and provide comprehensive counseling of the risks and benefits of CAM usage to patients who choose to use CAM to either augment or completely replace CCT

Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators

Federated learning has become a popular method to learn from decentralized heterogeneous data. Federated semi-supervised learning (FSSL) emerges to train models from a small fraction of labeled data due to label scarcity on decentralized clients. Existing FSSL methods assume independent and identically distributed (IID) labeled data across clients and consistent class distribution between labeled and unlabeled data within a client. This work studies a more practical and challenging scenario of FSSL, where data distribution is different not only across clients but also within a client between labeled and unlabeled data. To address this challenge, we propose a novel FSSL framework with dual regulators, FedDure.} FedDure lifts the previous assumption with a coarse-grained regulator (C-reg) and a fine-grained regulator (F-reg): C-reg regularizes the updating of the local model by tracking the learning effect on labeled data distribution; F-reg learns an adaptive weighting scheme tailored for unlabeled instances in each client. We further formulate the client model training as bi-level optimization that adaptively optimizes the model in the client with two regulators. Theoretically, we show the convergence guarantee of the dual regulators. Empirically, we demonstrate that FedDure is superior to the existing methods across a wide range of settings, notably by more than 11% on CIFAR-10 and CINIC-10 datasets

Metagenomic analysis reveals gut plasmids as diagnosis markers for colorectal cancer

BackgroundColorectal cancer (CRC) is linked to distinct gut microbiome patterns. The efficacy of gut bacteria as diagnostic biomarkers for CRC has been confirmed. Despite the potential to influence microbiome physiology and evolution, the set of plasmids in the gut microbiome remains understudied.MethodsWe investigated the essential features of gut plasmid using metagenomic data of 1,242 samples from eight distinct geographic cohorts. We identified 198 plasmid-related sequences that differed in abundance between CRC patients and controls and screened 21 markers for the CRC diagnosis model. We utilize these plasmid markers combined with bacteria to construct a random forest classifier model to diagnose CRC.ResultsThe plasmid markers were able to distinguish between the CRC patients and controls [mean area under the receiver operating characteristic curve (AUC = 0.70)] and maintained accuracy in two independent cohorts. In comparison to the bacteria-only model, the performance of the composite panel created by combining plasmid and bacteria features was significantly improved in all training cohorts (mean AUCcomposite = 0.804 and mean AUCbacteria = 0.787) and maintained high accuracy in all independent cohorts (mean AUCcomposite = 0.839 and mean AUCbacteria = 0.821). In comparison to controls, we found that the bacteria-plasmid correlation strength was weaker in CRC patients. Additionally, the KEGG orthology (KO) genes in plasmids that are independent of bacteria or plasmids significantly correlated with CRC.ConclusionWe identified plasmid features associated with CRC and showed how plasmid and bacterial markers could be combined to further enhance CRC diagnosis accuracy

Macrophage polarization states in atherosclerosis

Atherosclerosis, a chronic inflammatory condition primarily affecting large and medium arteries, is the main cause of cardiovascular diseases. Macrophages are key mediators of inflammatory responses. They are involved in all stages of atherosclerosis development and progression, from plaque formation to transition into vulnerable plaques, and are considered important therapeutic targets. Increasing evidence suggests that the modulation of macrophage polarization can effectively control the progression of atherosclerosis. Herein, we explore the role of macrophage polarization in the progression of atherosclerosis and summarize emerging therapies for the regulation of macrophage polarization. Thus, the aim is to inspire new avenues of research in disease mechanisms and clinical prevention and treatment of atherosclerosis

Breast cancer subtype and survival among Indigenous American women in Peru.

Latina women in the U.S. have relatively low breast cancer incidence compared to Non-Latina White (NLW) or African American women but are more likely to be diagnosed with the more aggressive "triple negative" breast cancer (TNBC). Latinos in the U.S. are a heterogeneous group originating from different countries with different cultural and ancestral backgrounds. Little is known about the distribution of tumor subtypes in Latin American regions. Clinical records of 303 female Peruvian patients, from the Peruvian National Cancer Institute, were analyzed. Participants were diagnosed with invasive breast cancer between 2010 and 2015 and were identified as residing in either the Selva or Sierra region. We used Fisher's exact test for proportions and multivariable Cox Proportional Hazards Models to compare overall survival between regions. Women from the Selva region were more likely to be diagnosed with TNBC than women from the Sierra region (31% vs. 14%, p = 0.01). In the unadjusted Cox model, the hazard of mortality was 1.7 times higher in women from the Selva than the Sierra (p = 0.025); this survival difference appeared to be largely explained by differences in the prevalence of TNBC. Our results suggest that the distribution of breast cancer subtypes differs between highly Indigenous American women from two regions of Peru. Disentangling the factors that contribute to this difference will add valuable information to better target prevention and treatment efforts in Peru and improve our understanding of TNBC among all women. This study demonstrates the need for larger datasets of Latin American patients to address differences between Latino subpopulations and optimize targeted prevention and treatment

Use of gofundme (r) to crowdfund complementary and alternative medicine treatments for cancer

Purpose Complementary and alternative medicine (CAM) use is common amongst cancer patients. However, there is growing concern about its safety and efficacy. Online crowdfunding campaigns represent a unique avenue to understand the cancer patient's perspecQ2Q2Revista Internacional - IndexadaA1S