Background: Many studies have investigated the devastating health effects of heat waves, but less is known about health risks related to cold spells, despite evidence that extreme cold may contribute ...to a larger proportion of deaths. Objectives: We aimed to systematically investigate the association between cold spells and mortality in Japan. Methods: Daily data for weather conditions and 12 common causes of death during the 1972-2015 cold seasons (November- March) were obtained from 47 Japanese prefectures. Cold spells were defined as greater than or equal to2 consecutive days with daily mean temperatures less than or equal to5th percentile for the cold season in each prefecture. Quasi-Poisson regression was combined with a distributed lag model to estimate prefecture-specific associations, and pooled associations at the national level were obtained through random-effects meta-analysis. The potential influence of cold spell characteristics (intensity, duration, and timing in season) on associations between cold spells and mortality was examined using a similar two-stage approach. Temporal trends were investigated using a meta-regression model. Results: A total of 18,139,498 deaths were recorded during study period. Mortality was significantly higher during cold spell days vs. other days for all selected causes of death. Mortality due to age-related physical debilitation was more strongly associated with cold spells than with other causes of death. Associations between cold spells and mortality from all causes and several more specific outcomes were stronger for longer and more intense cold spells and for cold spells earlier in the cold season. However, although all outcomes were positively associated with cold spell duration, findings for cold spell intensity and seasonal timing were heterogeneous across the outcomes. Associations between cold spells and mortality due to cerebrovascular disease, cerebral infarction, and age-related physical debility decreased in magnitude over time, whereas temporal trends were relatively flat for all-cause mortality and other outcomes. Discussion: Our findings may have implications for establishing tailored public health strategies to prevent avoidable cold spell-related health consequences.
SARS-CoV-2 has been spreading around the world for the past year. Recently, several variants such as B.1.1.7 (alpha), B.1.351 (beta), and P.1 (gamma), which share a key mutation N501Y on the ...receptor-binding domain (RBD), appear to be more infectious to humans. To understand the underlying mechanism, we used a cell surface-binding assay, a kinetics study, a single-molecule technique, and a computational method to investigate the interaction between these RBD (mutations) and ACE2. Remarkably, RBD with the N501Y mutation exhibited a considerably stronger interaction, with a faster association rate and a slower dissociation rate. Atomic force microscopy (AFM)-based single-molecule force microscopy (SMFS) consistently quantified the interaction strength of RBD with the mutation as having increased binding probability and requiring increased unbinding force. Molecular dynamics simulations of RBD-ACE2 complexes indicated that the N501Y mutation introduced additional π-π and π-cation interactions that could explain the changes observed by force microscopy. Taken together, these results suggest that the reinforced RBD-ACE2 interaction that results from the N501Y mutation in the RBD should play an essential role in the higher rate of transmission of SARS-CoV-2 variants, and that future mutations in the RBD of the virus should be under surveillance.
Canonical Correlation Analysis (CCA) is a well-known technique for finding the correlations between two sets of multidimensional variables. It projects both sets of variables onto a lower-dimensional ...space in which they are maximally correlated. CCA is commonly applied for supervised dimensionality reduction in which the two sets of variables are derived from the data and the class labels, respectively. It is well-known that CCA can be formulated as a least-squares problem in the binary class case. However, the extension to the more general setting remains unclear. In this paper, we show that under a mild condition which tends to hold for high-dimensional data, CCA in the multilabel case can be formulated as a least-squares problem. Based on this equivalence relationship, efficient algorithms for solving least-squares problems can be applied to scale CCA to very large data sets. In addition, we propose several CCA extensions, including the sparse CCA formulation based on the 1-norm regularization. We further extend the least-squares formulation to partial least squares. In addition, we show that the CCA projection for one set of variables is independent of the regularization on the other set of multidimensional variables, providing new insights on the effect of regularization on CCA. We have conducted experiments using benchmark data sets. Experiments on multilabel data sets confirm the established equivalence relationships. Results also demonstrate the effectiveness and efficiency of the proposed CCA extensions.
Objective
Recent evidence suggests that functional deficiency in regulatory T cells (Tregs), an innate immunomodulator, exacerbates brain damage after cerebral ischemia. We therefore evaluated the ...effect of Treg transfer in rodent models of ischemic stroke and further investigated the mechanism underlying Treg‐afforded neuroprotection.
Methods
We examined the therapeutic potential of Tregs and the mechanisms of neuroprotection in vivo in 2 rodent models of ischemic stroke and in vitro in Treg–neutrophil cocultures using a combined approach including cell‐specific depletion, gene knockout mice, and bone marrow chimeras.
Results
Systemic administration of purified Tregs at 2, 6, or even 24 hours after middle cerebral artery occlusion resulted in a marked reduction of brain infarct and prolonged improvement of neurological functions lasting out to 4 weeks. Treg‐afforded neuroprotection was accompanied by attenuated blood–brain barrier (BBB) disruption during early stages of ischemia, decreased cerebral inflammation, and reduced infiltration of peripheral inflammatory cells into the lesioned brain. Surprisingly, Tregs exerted early neuroprotection without penetrating into the brain parenchyma or inhibiting the activation of residential microglia. Rather, both in vivo and in vitro studies demonstrated that Tregs suppressed peripheral neutrophil‐derived matrix metallopeptidase‐9 production, thus preventing proteolytic damage of the BBB. In addition to its potent central neuroprotection, Treg treatment was shown to ameliorate poststroke lymphopenia, suggesting a beneficial effect on immune status.
Interpretation
Our study suggests that Treg adoptive therapy is a novel and potent cell‐based therapy targeting poststroke inflammatory dysregulation and neurovascular disruption. Ann Neurol 2013;74:458–471
The belief that the vertebrate brain functions normally without classical lymphatic drainage vessels has been held for many decades. On the contrary, new findings show that functional lymphatic ...drainage does exist in the brain. The brain lymphatic drainage system is composed of basement membrane-based perivascular pathway, a brain-wide glymphatic pathway, and cerebrospinal fluid (CSF) drainage routes including sinus-associated meningeal lymphatic vessels and olfactory/cervical lymphatic routes. The brain lymphatic systems function physiological as a route of drainage for interstitial fluid (ISF) from brain parenchyma to nearby lymph nodes. Brain lymphatic drainage helps maintain water and ion balance of the ISF, waste clearance, and reabsorption of macromolecular solutes. A second physiological function includes communication with the immune system modulating immune surveillance and responses of the brain. These physiological functions are influenced by aging, genetic phenotypes, sleep-wake cycle, and body posture. The impairment and dysfunction of the brain lymphatic system has crucial roles in age-related changes of brain function and the pathogenesis of neurovascular, neurodegenerative, and neuroinflammatory diseases, as well as brain injury and tumors. In this review, we summarize the key component elements (regions, cells, and water transporters) of the brain lymphatic system and their regulators as potential therapeutic targets in the treatment of neurologic diseases and their resulting complications. Finally, we highlight the clinical importance of ependymal route-based targeted gene therapy and intranasal drug administration in the brain by taking advantage of the unique role played by brain lymphatic pathways in the regulation of CSF flow and ISF/CSF exchange.
The direct functionalization of C-H bonds has drawn the attention of chemists for almost a century. C-H activation has mainly been achieved through four metal-mediated pathways: oxidative addition, ...electrophilic substitution, σ-bond metathesis and metal-associated carbene/nitrene/oxo insertion. However, the identification of methods that do not require transition-metal catalysts is important because methods involving such catalysts are often expensive. Another advantage would be that the requirement to remove metallic impurities from products could be avoided, an important issue in the synthesis of pharmaceutical compounds. Here, we describe the identification of a cross-coupling between aryl iodides/bromides and the C-H bonds of arenes that is mediated solely by the presence of 1,10-phenanthroline as catalyst in the presence of KOt-Bu as a base. This apparently transition-metal-free process provides a new strategy with which to achieve direct C-H functionalization.
We present an approach to estimate the operational distinguishability between an entangled state and any separable state directly from measuring an entanglement witness. We show that this estimation ...also implies bounds on a variety of other well-known entanglement quantifiers. This approach for entanglement estimation is then extended to both the measurement-device-independent scenario and the fully device-independent scenario, where we obtain nontrivial but suboptimal bounds. The procedure requires no numerical optimization and is easy to compute. It offers ways for experimenters to not only detect, but also quantify, entanglement from the standard entanglement witness procedure.
Accurate and reliable monthly runoff forecasting plays an important role in making full use of water resources. In recent years, long short-term memory neural networks (LSTM), as a deep learning ...technology, has been successfully applied in forecasting monthly runoff. However, the hyperparameters of LSTM are predetermined, which has a significant influence on model performance. In this study, given that the decomposition of monthly runoff series may provide a more accurate prediction, as revealed by many previous studies, a hybrid model, namely, VMD-GWO-LSTM, is proposed for monthly runoff forecasting. The proposed hybrid model comprises two main components, namely, variational mode decomposition (VMD) coupled with the gray wolf optimizer (GWO)-based LSTM. First, VMD is utilized to decompose raw monthly runoff series into several subsequences. Second, GWO is implemented to optimize the hyperparameters of the LSTM for each subsequence on the condition that the inputs are determined. Finally, the total output of all subsequences is aggregated as the final forecast result. Four quantitative indices are employed to evaluate the model performance. The proposed model is demonstrated using 73 and 62 years of monthly runoff series data derived from the Xinfengjiang and Guangzhao Reservoirs in China's Pearl River system, respectively. To identify the feasibility and superiority of the proposed model, backpropagation neural networks (BPNN), support vector machine (SVM), LSTM, EMD-LSTM, VMD-LSTM and GWO-LSTM are also utilized for comparison. The results indicate that the proposed hybrid model can yield best forecast accuracy among these models, making it a promising new method for monthly runoff forecasting.
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