Inactivation and Survival of Bacteriophage Φ6 on Tvyek Suits

Healthcare providers encounter a wide range of hazards on the job, including exposure to infectious diseases. Protecting them from occupational infectious disease is very important. Healthcare workers use personal protective equipment (PPE) as a measure to decrease the risk of getting infected during patient care. For high-risk diseases like Ebola, Tyvek suits are coverall suits that protect the body and reduce the risk of body fluid exposure. However, a person removing a contaminated suit may also be exposed to virus. Previous studies have shown that enveloped viruses can survive on different types of surfaces, so the objective of this study is to determine the inactivation of bacteriophage Φ6, a surrogate for enveloped human virus, on the surface of Tyvek suits at two different relative humidity levels, 40% and 60% at 22°C. The results showed the inactivation rate of virus was higher at 60% RH than 40% RH. There was ~3log10 (99.9%) reduction of virus inactivation after 6 hours at 40% but ~3log10 (99.9%) inactivation took 9 hours at 60%. This suggests that enveloped viruses can survive on the surface of Tyvek suits for more than 6 hours, and should be considered a potential risk for contamination when they are taken off after use

Efficient Pareto Manifold Learning with Low-Rank Structure

Multi-task learning, which optimizes performance across multiple tasks, is inherently a multi-objective optimization problem. Various algorithms are developed to provide discrete trade-off solutions on the Pareto front. Recently, continuous Pareto front approximations using a linear combination of base networks have emerged as a compelling strategy. However, it suffers from scalability issues when the number of tasks is large. To address this issue, we propose a novel approach that integrates a main network with several low-rank matrices to efficiently learn the Pareto manifold. It significantly reduces the number of parameters and facilitates the extraction of shared features. We also introduce orthogonal regularization to further bolster performance. Extensive experimental results demonstrate that the proposed approach outperforms state-of-the-art baselines, especially on datasets with a large number of tasks.Comment: ICML 2024 (Spotlight

Tackling the Non-IID Issue in Heterogeneous Federated Learning by Gradient Harmonization

Federated learning (FL) is a privacy-preserving paradigm for collaboratively training a global model from decentralized clients. However, the performance of FL is hindered by non-independent and identically distributed (non-IID) data and device heterogeneity. In this work, we revisit this key challenge through the lens of gradient conflicts on the server side. Specifically, we first investigate the gradient conflict phenomenon among multiple clients and reveal that stronger heterogeneity leads to more severe gradient conflicts. To tackle this issue, we propose FedGH, a simple yet effective method that mitigates local drifts through Gradient Harmonization. This technique projects one gradient vector onto the orthogonal plane of the other within conflicting client pairs. Extensive experiments demonstrate that FedGH consistently enhances multiple state-of-the-art FL baselines across diverse benchmarks and non-IID scenarios. Notably, FedGH yields more significant improvements in scenarios with stronger heterogeneity. As a plug-and-play module, FedGH can be seamlessly integrated into any FL framework without requiring hyperparameter tuning

Patch-based 3D Natural Scene Generation from a Single Example

We target a 3D generative model for general natural scenes that are typically unique and intricate. Lacking the necessary volumes of training data, along with the difficulties of having ad hoc designs in presence of varying scene characteristics, renders existing setups intractable. Inspired by classical patch-based image models, we advocate for synthesizing 3D scenes at the patch level, given a single example. At the core of this work lies important algorithmic designs w.r.t the scene representation and generative patch nearest-neighbor module, that address unique challenges arising from lifting classical 2D patch-based framework to 3D generation. These design choices, on a collective level, contribute to a robust, effective, and efficient model that can generate high-quality general natural scenes with both realistic geometric structure and visual appearance, in large quantities and varieties, as demonstrated upon a variety of exemplar scenes.Comment: 23 pages, 26 figures, accepted by CVPR 2023. Project page: http://weiyuli.xyz/Sin3DGen

Pliocene to Recent deep-ocean temperature and seawater δ18O from clumped isotopes for the Southern Ocean

Understanding how deep ocean circulation and water masses respond to long-term climate change is critical for predicting future climate trajectories. The Late Pliocene and Pleistocene offer valuable analogs for near-future climate states, motivating studies of how ocean circulation, ice volume, and climate feedbacks respond to elevated greenhouse gases and orbital forcing. This study focuses on the Pliocene–Quaternary interval (~3.8 Ma), capturing the transition from the warm Late Pliocene through the intensification of Northern Hemisphere Glaciation (NHG) and the Mid-Pleistocene Transition (MPT) to recent glacial–interglacial cycles. Previous deep ocean temperature reconstructions using Mg/Ca show evidence of a decrease in temperature and changes in ocean stratification. But these proxies can be subject to limitations due to their systematics, such as diagenesis and non-thermal effects like carbon ion saturation state and changing seawater chemistry. Here we apply clumped isotope (Δ47) thermometry on mixed-benthic foraminifera from Site 1088, located at ~2082 m water depth in the Atlantic sector of the Southern Ocean. By combining it with benthic foraminiferal δ18O measurements from the same samples, we derive deepwater bottom water temperatures (BWT) and seawater δ18O (δ18Osw, reflecting ice volume and salinity) records for the last 3.8 Ma. Our results reveal a long-term cooling trend in BWT at Site 1088. This cooling is accompanied by a general increase in δ18Osw, consistent with the growth of global ice volume during this period. We observe shifts in BWT, δ18Osw, δ13C, and water mass properties across key climate transitions, including the Late-Pliocene Transition (LPT), NHG, and MPT. Our temperature and δ18Osw records, combined with other published δ13C records from other sites (Site 607 and Site 1208), reflect shifts in deepwater ventilation and circulation, potentially linked to the steps observed in δ13C gradients at ~2.75 Ma and 1.55 Ma which have been interpreted as changes in SCW ventilation or NCW influence. These Southern Ocean findings complement other deep ocean studies showing significant temperature and δ18Osw gradients across the Pliocene-Quaternary. Our paired Δ47 and δ18Osw reconstruction from the Site 1088 provide a more robust view of deepwater thermal evolution and ice volume contributions than single-proxy methods

Co-delivery of siRNAs and anti-cancer drugs using layered double hydroxide nanoparticles

In this research we employed layered double hydroxide nanoparticles (LDHs) to simultaneously deliver an anticancer drug 5-fluorouracil (5-FU) and Allstars Cell Death siRNA (CD-siRNA) for effective cancer treatment. The strategy takes advantage of the LDH anion exchange capacity to intercalate 5-FU into its interlayer spacing and load siRNA on the surface of LDH nanoparticles. LDH nanoparticles have been previously demonstrated as an effective cellular delivery system for 5-FU and siRNA separately in various investigations. More excitedly, the combination of CD-siRNA and anticancer drug 5-FU with the same LDH particles significantly enhanced cytotoxicity to three cancer cell lines, e.g. MCF-7, U2OS and HCT-116, compared to the single treatment with either CD-siRNA or 5-FU. This enhancement is probably a result of coordinate mitochondrial damage process. Thus, the strategy to co-deliver siRNA and an anticancer drug by LDHs has great potential to overcome the drug resistance and enhance cancer treatment