Systemic Actions of Breast Cancer Facilitate Functional Limitations

Breast cancer is a disease of a specific organ, but its effects are felt throughout the body. The systemic effects of breast cancer can lead to functional limitations in patients who suffer from muscle weakness, fatigue, pain, fibromyalgia, or many other dysfunctions, which hasten cancer-associated death. Mechanistic studies have identified quite a few molecular defects in skeletal muscles that are associated with functional limitations in breast cancer. These include circulating cytokines such as TNF-α, IL-1, IL-6, and TGF-β altering the levels or function of myogenic molecules including PAX7, MyoD, and microRNAs through transcriptional regulators such as NF-κB, STAT3, and SMADs. Molecular defects in breast cancer may also include reduced muscle mitochondrial content and increased extracellular matrix deposition leading to energy imbalance and skeletal muscle fibrosis. This review highlights recent evidence that breast cancer-associated molecular defects mechanistically contribute to functional limitations and further provides insights into therapeutic interventions in managing functional limitations, which in turn may help to improve quality of life in breast cancer patients

Existence of cyclic (3,λ)-GDD of type gv having prescribed number of short orbits

AbstractIn this paper, the necessary and sufficient conditions for the existence of a cyclic (3,λ)-GDD of type gv with exactly α short block orbits are determined for all possible parameters λ,g,v and α

Historical Trends in Air Temperature, Precipitation, and Runoff of a Plateau Inland River Watershed in North China

Understanding historical trends in temperature, precipitation, and runoff is important but incomplete for developing adaptive measures to climate change to sustain fragile ecosystems in cold and arid regions, including the Balagaer River watershed on the Mongolian Plateau of northeast China. The objective of this study was to detect such trends in this watershed from 1959 to 2017. The detection was accomplished using a Mann-Kendall sudden change approach at annual and seasonal time scales. The results indicated that the abrupt changes in temperature preceded that in either runoff or precipitation; these abrupt changes occurred between 1970 and 2004. Significant (α = 0.05) warming trends were found at the minimum temperatures in spring (0.041 °C a−1), summer (0.037 °C a−1), fall (0.027 °C a−1), and winter (0.031 °C a−1). In contrast, significant decreasing trends were found in the precipitation (−1.27 mm a−1) and runoff (−0.069 mm a−1) in the summer. Marginally increasing trends were found in the precipitation in spring (0.18 mm a−1) and fall (0.032 mm a−1), whereas an insignificant decreasing trend was found in the runoffs in these two seasons. Both precipitation and runoff in the wet season exhibited a significant decreasing trend, whereas in the dry season, they exhibited a marginally increasing trend. Sudden changes in spring runoff and sudden rises in temperature are the main causes of sudden changes in basin rainfall

Characteristics of transient pressure performance of horizontal wells in fractured-vuggy tight fractal reservoirs considering nonlinear seepage

International audienceSince the classical seepage theory has limitations in characterizing the heterogeneity of fractured-vuggy tight reservoirs, well test interpretation results are not consistent with actual production by far. Based on the nonlinear percolation theory, a new nonlinear seepage equation considering the boundary layer and yield stress was derived to describe the seepage characteristics of dense matrix blocks and the stress sensitivity and fractal features of fracture systems were characterized by applying the fractal theory. Thus, the nonlinear model of a horizontal well in a fractured-vuggy tight fractal reservoir was established naturally. Then the finite element method was applied to solve the bottom hole pressure based on the processing of internal boundary conditions. After solving the model, the seepage characteristics of different models were summarized by analyzing the bottom hole pressure dynamic curves and the sensitivity analysis of multiple parameters such the nonlinear parameter and fractal index were conducted. Finally, the practicality of the model was proved through a field application. The results show that the pressure dynamic curves can be divided into nine flow stages and the increase of the nonlinear parameter will cause the intensity of the cross flow from matrix blocks to the fracture system to decrease. The fractal index is irrelevant to the intensity of the cross flow while it decides the upwarping degree of the curve at the middle and late flow stages. On the basis of the results of the field application, it can be concluded that the model fits well with actual production and the application of this model can improve the accuracy of well test interpretation

Reconstructing Graph Diffusion History from a Single Snapshot

Diffusion on graphs is ubiquitous with numerous high-impact applications. In these applications, complete diffusion histories play an essential role in terms of identifying dynamical patterns, reflecting on precaution actions, and forecasting intervention effects. Despite their importance, complete diffusion histories are rarely available and are highly challenging to reconstruct due to ill-posedness, explosive search space, and scarcity of training data. To date, few methods exist for diffusion history reconstruction. They are exclusively based on the maximum likelihood estimation (MLE) formulation and require to know true diffusion parameters. In this paper, we study an even harder problem, namely reconstructing Diffusion history from A single SnapsHot} (DASH), where we seek to reconstruct the history from only the final snapshot without knowing true diffusion parameters. We start with theoretical analyses that reveal a fundamental limitation of the MLE formulation. We prove: (a) estimation error of diffusion parameters is unavoidable due to NP-hardness of diffusion parameter estimation, and (b) the MLE formulation is sensitive to estimation error of diffusion parameters. To overcome the inherent limitation of the MLE formulation, we propose a novel barycenter formulation: finding the barycenter of the posterior distribution of histories, which is provably stable against the estimation error of diffusion parameters. We further develop an effective solver named DIffusion hiTting Times with Optimal proposal (DITTO) by reducing the problem to estimating posterior expected hitting times via the Metropolis--Hastings Markov chain Monte Carlo method (M--H MCMC) and employing an unsupervised graph neural network to learn an optimal proposal to accelerate the convergence of M--H MCMC. We conduct extensive experiments to demonstrate the efficacy of the proposed method.Comment: Full version of the KDD 2023 paper. Our code is available at https://github.com/q-rz/KDD23-DITT

Estimated Grass Grazing Removal Rate in a Semiarid Eurasian Steppe Watershed as Influenced by Climate

Grazing removal rate of grasses needs to be determined for various climate conditions to address eco-environmental concerns (e.g., desertification) related to steppe grassland degradation. The conventional approach, which requires survey data on animal species and heads as well as grass consumption per individual animal, is too costly and time-consuming to be applied at a watershed scale. The objective of this study was to present a new approach that can be used to estimate grazing removal rate with no requirement of animal-related data. The application of this new approach was demonstrated in a Eurasian semiarid typical-steppe watershed for an analysis period of 2000 to 2010. The results indicate that the removal rate tended to become larger, but its temporal variation tended to become smaller, from the upstream to downstream. Averaged across the watershed, the removal rate ranged from 63.9 to 401.0 g DM m-2 (or 22.4 to 60.9%) during the analysis period. As expected, the removal rate in an atmospherically wetter year was higher than that in an atmospherically drier year. Nevertheless, none of the eleven analysis years had a removal rate higher than the threshold value of 65%, above which the risk of grassland degradation would become much greater

Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders

Multivariate time series (MTS) imputation is a widely studied problem in recent years. Existing methods can be divided into two main groups, including (1) deep recurrent or generative models that primarily focus on time series features, and (2) graph neural networks (GNNs) based models that utilize the topological information from the inherent graph structure of MTS as relational inductive bias for imputation. Nevertheless, these methods either neglect topological information or assume the graph structure is fixed and accurately known. Thus, they fail to fully utilize the graph dynamics for precise imputation in more challenging MTS data such as networked time series (NTS), where the underlying graph is constantly changing and might have missing edges. In this paper, we propose a novel approach to overcome these limitations. First, we define the problem of imputation over NTS which contains missing values in both node time series features and graph structures. Then, we design a new model named PoGeVon which leverages variational autoencoder (VAE) to predict missing values over both node time series features and graph structures. In particular, we propose a new node position embedding based on random walk with restart (RWR) in the encoder with provable higher expressive power compared with message-passing based graph neural networks (GNNs). We further design a decoder with 3-stage predictions from the perspective of multi-task learning to impute missing values in both time series and graph structures reciprocally. Experiment results demonstrate the effectiveness of our model over baselines.Comment: KDD 202

Aromatase inhibitors augment nociceptive behaviors in rats and enhance the excitability of sensory neurons

Although aromatase inhibitors (AIs) are commonly used therapies for breast cancer, their use is limited because they produce arthralgia in a large number of patients. To determine whether AIs produce hypersensitivity in animal models of pain, we examined the effects of the AI, letrozole, on mechanical, thermal, and chemical sensitivity in rats. In ovariectomized (OVX) rats, administering a single dose of 1 or 5mg/kg letrozole significantly reduced mechanical paw withdrawal thresholds, without altering thermal sensitivity. Repeated injection of 5mg/kg letrozole in male rats produced mechanical, but not thermal, hypersensitivity that extinguished when drug dosing was stopped. A single dose of 5mg/kg letrozole or daily dosing of letrozole or exemestane in male rats also augmented flinching behavior induced by intraplantar injection of 1000nmol of adenosine 5'-triphosphate (ATP). To determine whether sensitization of sensory neurons contributed to AI-induced hypersensitivity, we evaluated the excitability of neurons isolated from dorsal root ganglia of male rats chronically treated with letrozole. Both small and medium-diameter sensory neurons isolated from letrozole-treated rats were more excitable, as reflected by increased action potential firing in response to a ramp of depolarizing current, a lower resting membrane potential, and a lower rheobase. However, systemic letrozole treatment did not augment the stimulus-evoked release of the neuropeptide calcitonin gene-related peptide (CGRP) from spinal cord slices, suggesting that the enhanced nociceptive responses were not secondary to an increase in peptide release from sensory endings in the spinal cord. These results provide the first evidence that AIs modulate the excitability of sensory neurons, which may be a primary mechanism for the effect of these drugs to augment pain behaviors in rats

Benzosiloles with Crystallization-induced Emission Enhancement of Electrochemiluminescence: Synthesis, Electrochemistry, and Crystallography

Crystallization-induced emission enhancement (CIEE) was demonstrated for the first time for electrochemilunimescence (ECL) with two new benzosiloles. Compared with their solution, the films of the two benzosiloles gave CIEE of 24 times and 16 times. The mechanism of the CIEE-ECL was examined by spooling ECL spectroscopy, X-ray crystal structure analysis, photoluminescence, and DFT calculation. This CIEE-ECL system is a complement to the well-established aggregation-induced emission enhancement (AIEE) systems. Unique intermolecular interactions are noted in the crystalline chromophore. The first heterogeneous ECL system is established for organic compounds with highly hydrophobic properties