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Forecasting of multivariate time series data, for instance the prediction of electricity consumption, solar power production, and polyphonic piano pieces, has numerous valuable applications. However, complex and non-linear interdependencies between time steps and series complicate this task. To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved by recurrent neural networks (RNNs) with an attention mechanism. The typical attention mechanism reviews the information at each previous time step and selects relevant information to help generate the outputs; however, it fails to capture temporal patterns across multiple time steps. In this paper, we propose using a set of filters to extract time-invariant temporal patterns, similar to transforming time series data into its “frequency domain”. Then we propose a novel attention mechanism to select relevant time series, and use its frequency domain information for multivariate forecasting. We apply the proposed model on several real-world tasks and achieve state-of-the-art performance in almost all of cases. Our source code is available at https://github.com/gantheory/TPA-LSTM.

Hypoxic tumors present a significant challenge in cancer therapy due to their ability to adaptation in low-oxygen environments, which supports tumor survival and resistance to treatment. Enhanced mitophagy, the selective degradation of mitochondria by autophagy, is a crucial mechanism that helps sustain cellular homeostasis in hypoxic tumors. In this study, we develop an azocalix[4]arene-modified supramolecular albumin nanoparticle, that co-delivers hydroxychloroquine and a mitochondria-targeting photosensitizer, designed to induce cascaded oxidative stress by regulating mitophagy for the treatment of hypoxic tumors. These nanoparticles are hypoxia-responsive and release loaded guest molecules in hypoxic tumor cells. The released hydroxychloroquine disrupts the mitophagy process, thereby increasing oxidative stress and further weakening the tumor cells. Additionally, upon laser irradiation, the photosensitizer generates reactive oxygen species independent of oxygen, inducing mitochondria damage and mitophagy activation. The dual action of simultaneous spatiotemporal mitophagy activation and mitophagy flux blockade results in enhanced autophagic and oxidative stress, ultimately driving tumor cell death. Our work highlights the effectiveness of hydroxychloroquine-mediated mitophagy blockade combined with mitochondria-targeted photosensitizer for cascade-amplified oxidative stress against hypoxic tumors. Enhanced mitophagy has been recognized as crucial mechanism to sustain cellular homeostasis in hypoxic tumors. Here, this group fabricates an azocalix[4]arene-modified supramolecular albumin nanoparticle codelivering hydroxychloroquine (HCQ) and sulfur-substituted methylated nile blue analog, capable of inducing cascaded oxidative stress via regulating mitophagy for hypoxic tumors treatment.

In this paper, we study distance covariance, Hilbert–Schmidt covariance (aka Hilbert–Schmidt independence criterion [In Advances in Neural Information Processing Systems (2008) 585–592]) and related independence tests under the high dimensional scenario. We show that the sample distance/Hilbert–Schmidt covariance between two random vectors can be approximated by the sum of squared componentwise sample cross-covariances up to an asymptotically constant factor, which indicates that the standard distance/Hilbert–Schmidt covariance based test can only capture linear dependence in high dimension. Under the assumption that the components within each high dimensional vector are weakly dependent, the distance correlation based t test developed by Székely and Rizzo (J. Multivariate Anal. 117 (2013) 193–213) for independence is shown to have trivial limiting power when the two random vectors are nonlinearly dependent but component-wisely uncorrelated. This new and surprising phenomenon, which seems to be discovered and carefully studied for the first time, is further confirmed in our simulation study. As a remedy, we propose tests based on an aggregation of marginal sample distance/Hilbert–Schmidt covariances and show their superior power behavior against their joint counterparts in simulations. We further extend the distance correlation based t test to those based on Hilbert–Schmidt covariance and marginal distance/Hilbert–Schmidt covariance. A novel unified approach is developed to analyze the studentized sample distance/Hilbert–Schmidt covariance as well as the studentized sample marginal distance covariance under both null and alternative hypothesis. Our theoretical and simulation results shed light on the limitation of distance/Hilbert–Schmidt covariance when used jointly in the high dimensional setting and suggest the aggregation of marginal distance/Hilbert–Schmidt covariance as a useful alternative.

We introduce an 𝓛₂-type test for testing mutual independence and banded dependence structure for high dimensional data. The test is constructed on the basis of the pairwise distance covariance and it accounts for the non-linear and non-monotone dependences among the data, which cannot be fully captured by the existing tests based on either Pearson correlation or rank correlation. Our test can be conveniently implemented in practice as the limiting null distribution of the test statistic is shown to be standard normal. It exhibits excellent finite sample performance in our simulation studies even when the sample size is small albeit the dimension is high and is shown to identify non-linear dependence in empirical data analysis successfully. On the theory side, asymptotic normality of our test statistic is shown under quite mild moment assumptions and with little restriction on the growth rate of the dimension as a function of sample size. As a demonstration of good power properties for our distance-covariance-based test, we further show that an infeasible version of our test statistic has the rate optimality in the class of Gaussian distributions with equal correlation.

For sharing privacy-sensitive data, de-identification is commonly regarded as adequate for safeguarding privacy. Synthetic data is also being considered as a privacy-preserving alternative. Recent successes with numerical and tabular data generative models and the breakthroughs in large generative language models raise the question of whether synthetically generated clinical notes could be a viable alternative to real notes for research purposes. In this work, we demonstrated that (i) de-identification of real clinical notes does not protect records against a membership inference attack, (ii) proposed a novel approach to generate synthetic clinical notes using the current state-of-the-art large language models, (iii) evaluated the performance of the synthetically generated notes in a clinical domain task, and (iv) proposed a way to mount a membership inference attack where the target model is trained with synthetic data. We observed that when synthetically generated notes closely match the performance of real data, they also exhibit similar privacy concerns to the real data. Whether other approaches to synthetically generated clinical notes could offer better trade-offs and become a better alternative to sensitive real notes warrants further investigation.

Alzheimer’s disease (AD) is the most common chronic neurodegenerative disease worldwide. It causes cognitive dysfunction, such as aphasia and agnosia, and mental symptoms, such as behavioral abnormalities; all of which place a significant psychological and economic burden on the patients’ families. No specific drugs are currently available for the treatment of AD, and the current drugs for AD only delay disease onset and progression. The pathophysiological basis of AD involves abnormal deposition of beta-amyloid protein (Aβ), abnormal tau protein phosphorylation, decreased activity of acetylcholine content, glutamate toxicity, autophagy, inflammatory reactions, mitochondria-targeting, and multi-targets. The US Food and Drug Administration (FDA) has approved five drugs for clinical use: tacrine, donepezil, carbalatine, galantamine, memantine, and lecanemab. We have focused on the newer drugs that have undergone clinical trials, most of which have not been successful as a result of excessive clinical side effects or poor efficacy. Although aducanumab received rapid approval from the FDA on 7 June 2021, its long-term safety and tolerability require further monitoring and confirmation. In this literature review, we aimed to explore the possible pathophysiological mechanisms underlying the occurrence and development of AD. We focused on anti-Aβ and anti-tau drugs, mitochondria-targeting and multi-targets, commercially available drugs, bottlenecks encountered in drug development, and the possible targets and therapeutic strategies for future drug development. We hope to present new concepts and methods for future drug therapies for AD.

Patches with the capacity of controllable delivering active molecules toward the wound bed to promote wound healing are expectant all along. Herein, a novel porous metal‐organic framework (MOF) microneedle (MN) patch enabling photothermal‐responsive nitric oxide (NO) delivery for promoting diabetic wound healing is presented. As the NO‐loadable copper‐benzene‐1,3,5‐tricarboxylate (HKUST‐1) MOF is encapsulated with graphene oxide (GO), the resultant NO@HKUST‐1@GO microparticles (NHGs) are imparted with the feature of near‐infrared ray (NIR) photothermal response, which facilitate the controlled release of NO molecules. When these NHGs are embedded in a porous PEGDA‐MN, the porous structure, larger specific surface area, and sufficient mechanical strength of the integrated MN could promote a more accurate and deeper delivery of NO molecules into the wound site. By applying the resultant NHG‐MN to the wound of a type I diabetic rat model, the authors demonstrate that it is capable of accelerating vascularization, tissue regeneration, and collagen deposition, indicating its bright prospect applied in wound healing and other therapeutic scenarios. A porous metal‐organic framework (MOF) microneedle (MN) patch is presented for wound healing. Due to the photothermal response of NO@HKUST‐1@GO microparticles (NHGs), the NHGs embedded MN patch could promote a more accurate delivery of NO molecules into the wound site through near‐infrared ray (NIR) irradiation. Thus, such MOF MN patch offers a promising approach in diabetic wound healing.

Guo Moruo’s “Rebirth of the Goddesses” is among the landmark works of modern Chinese poetry. Its myth-rewriting amounts to an act of “disenchantment” carried out amid the ruins of “enchantment”. Yet this heroic undertaking is caught in a triple dialectical vortex: in order to disenchant, it must appeal to the primordial “energies” of myth (nature, life, imagination); in order for disenchantment to be effective, it strategically “uses enchantment” (by requisitioning textual canons and ritual authority); and in the end—because of the intensity of rewriting and the depth of political and spiritual investment—it becomes itself a new layer of enchantment (a cycle of re-enchantment). This exposes the core dilemma of China’s modernity project: to build a new order on the ruins of tradition is necessarily a tragic enterprise of rupture and continuity, of disenchantment and re-enchantment at once.

Monitoring and evaluating the biofouling status of a ship’s hull and its effects on the vessel’s performance attracts the attention of both researchers and industry. In this study, two types of monitoring equipment were used to observe organism growth on two fishing vessels for approximately six months. Combining underwater photography technology with periodic cleaning methods can effectively prevent the occurrence of problems including hull biofouling. The monitoring system developed in this study is cheap and easy to operate, and can be stored on board and regularly operated by the crew to eliminate various issues below the waterline, which in turn enhances sustainable shipping.

Laser-induced breakdown spectroscopy (LIBS) has been widely studied due to its unique advantages such as remote sensing, real-time multi-elemental detection and none-to-little damage. With the efforts of researchers around the world, LIBS has been developed by leaps and bounds. Moreover, in recent years, more and more Chinese LIBS researchers have put tremendous energy in promoting LIBS applications. It is worth mentioning that the application of LIBS in a specific field has its special application background and technical difficulties, therefore it may develop in different stages. A review summarizing the current development status of LIBS in various fields would be helpful for the development of LIBS technology as well as its applications especially for Chinese LIBS community since most of the researchers in this field work in application. In the present work, we summarized the research status and latest progress of main research groups in coal, metallurgy, and water, etc. Based on the current research status, the challenges and opportunities of LIBS were evaluated, and suggestions were made to further promote LIBS applications.