PHP12 THE PUBLIC'S PREFERENCE ON THE PRIORITIES IN HEALTH CARE

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Magnitude of the Difference Between Clinic and Ambulatory Blood Pressures and Risk of Adverse Outcomes in Patients With Chronic Kidney Disease.

Background Obtaining 24-hour ambulatory blood pressure ( BP ) is recommended for the detection of masked or white-coat hypertension. Our objective was to determine whether the magnitude of the difference between ambulatory and clinic BP s has prognostic implications. Methods and Results We included 610 participants of the AASK (African American Study of Kidney Disease and Hypertension) Cohort Study who had clinic and ambulatory BPs performed in close proximity in time. We used Cox models to determine the association between the absolute systolic BP ( SBP ) difference between clinic and awake ambulatory BPs (primary predictor) and death and end-stage renal disease. Of 610 AASK Cohort Study participants, 200 (32.8%) died during a median follow-up of 9.9 years; 178 (29.2%) developed end-stage renal disease. There was a U-shaped association between the clinic and ambulatory SBP difference with risk of death, but not end-stage renal disease. A 5- to <10-mm Hg higher clinic versus awake SBP (white-coat effect) was associated with a trend toward higher (adjusted) mortality risk (adjusted hazard ratio, 1.84; 95% CI, 0.94-3.56) compared with a 0- to <5-mm Hg clinic-awake SBP difference (reference group). A ≥10-mm Hg clinic-awake SBP difference was associated with even higher mortality risk (adjusted hazard ratio, 2.31; 95% CI, 1.27-4.22). A ≥-5-mm Hg clinic-awake SBP difference was also associated with higher mortality (adjusted hazard ratio, 1.82; 95% CI, 1.05-3.15) compared with the reference group. Conclusions A U-shaped association exists between the magnitude of the difference between clinic and ambulatory SBP and mortality. Higher clinic versus ambulatory BPs (as in white-coat effect) may be associated with higher risk of death in black patients with chronic kidney disease

On Exact Inversion of DPM-Solvers

Diffusion probabilistic models (DPMs) are a key component in modern generative models. DPM-solvers have achieved reduced latency and enhanced quality significantly, but have posed challenges to find the exact inverse (i.e., finding the initial noise from the given image). Here we investigate the exact inversions for DPM-solvers and propose algorithms to perform them when samples are generated by the first-order as well as higher-order DPM-solvers. For each explicit denoising step in DPM-solvers, we formulated the inversions using implicit methods such as gradient descent or forward step method to ensure the robustness to large classifier-free guidance unlike the prior approach using fixed-point iteration. Experimental results demonstrated that our proposed exact inversion methods significantly reduced the error of both image and noise reconstructions, greatly enhanced the ability to distinguish invisible watermarks and well prevented unintended background changes consistently during image editing. Project page: \url{https://smhongok.github.io/inv-dpm.html}.Comment: 16 page

Self-Contrastive Learning: Single-viewed Supervised Contrastive Framework using Sub-network

Contrastive loss has significantly improved performance in supervised classification tasks by using a multi-viewed framework that leverages augmentation and label information. The augmentation enables contrast with another view of a single image but enlarges training time and memory usage. To exploit the strength of multi-views while avoiding the high computation cost, we introduce a multi-exit architecture that outputs multiple features of a single image in a single-viewed framework. To this end, we propose Self-Contrastive (SelfCon) learning, which self-contrasts within multiple outputs from the different levels of a single network. The multi-exit architecture efficiently replaces multi-augmented images and leverages various information from different layers of a network. We demonstrate that SelfCon learning improves the classification performance of the encoder network, and empirically analyze its advantages in terms of the single-view and the sub-network. Furthermore, we provide theoretical evidence of the performance increase based on the mutual information bound. For ImageNet classification on ResNet-50, SelfCon improves accuracy by +0.6% with 59% memory and 48% time of Supervised Contrastive learning, and a simple ensemble of multi-exit outputs boosts performance up to +1.5%. Our code is available at https://github.com/raymin0223/self-contrastive-learning.Comment: AAAI 202

Output Effects of Unanticipated Money Growth in Asia

Money

Fine-Tuning the Retrieval Mechanism for Tabular Deep Learning

While interests in tabular deep learning has significantly grown, conventional tree-based models still outperform deep learning methods. To narrow this performance gap, we explore the innovative retrieval mechanism, a methodology that allows neural networks to refer to other data points while making predictions. Our experiments reveal that retrieval-based training, especially when fine-tuning the pretrained TabPFN model, notably surpasses existing methods. Moreover, the extensive pretraining plays a crucial role to enhance the performance of the model. These insights imply that blending the retrieval mechanism with pretraining and transfer learning schemes offers considerable potential for advancing the field of tabular deep learning.Comment: Table Representation Learning Workshop at NeurIPS 202

Biosynthesis of phenylpropanoids and their protective effect against heavy metals in nitrogen-fixing black locust (Robinia pseudoacacia)

Purpose: To examine the effect of various heavy metals (HMs) on phenylpropanoid pathway compounds in Robinia pseudoacacia.Methods: A series of pot culture experiments were performed to understand how the metabolic profile of phenylpropanoid compounds were affected by various HMs, such as redox-active HMs (AgNO3 and CuCl2), and non-redox-active HMs (HgCl2). Phenylpropanoid compound level was evaluated by high performance liquid chromatography.Results: The total phenylpropanoid level in leaves increased significantly in all the treated groups when compared to that in the untreated group (p < 0.05). However, a significant effect on the total phenylpropanoid levels was only found for redox-active HMs (p < 0.05), whereas non-redox-active HMs showed less accumulation. Chlorogenic acid and rutin were the two major phenylpropanoid compounds found after the plants were subjected to redox and non-redox-active HMs stress. However, when compared to these two compounds, the levels of catechin hydrate, epicatechin, p-coumaric acid, kaempferol, and quercetin were lower. Caffeic acid level was significantly decreased in both redox and non-redox-active HMs when compared to that in the control (p < 0.05). In addition, trans-cinnamic acid accumulation was altered based on the types and concentration of HMs.Conclusion: Phenylpropanoid metabolic pathway participated in the HM tolerance process for the protection of R. pseudoacacia from oxidative damage caused by HMs, thus allowing the species to grow in highly HMs-contaminated areas. Keywords: Heavy metals, Non-redox-active metals, Phenylpropanoid compounds, Redox-active metals, Robinia pseudoacaci