Dynamic identification method for rockburst hazard areas based on multivariate geophysical indicators and its application

It is of great significance to accurately identify the rockburst hazard areas and give the hazard level and its evolution law for rockburst prevention and control. In this study, the method combining deformation localization with multivariate geophysical indicators spatial scanning is used to explore the precursor characteristics of microseismic in the area of high-energy microseismic events and track the dynamic evolution process of rockburst hazard areas. Based on the principle of deformation localization, the gradient significance indicator is used to identify the deformation localization areas and delineate the hazard area. The sliding window scanning method is used to study the spatial distribution characteristics of physical indicators such as b value, A(b) value and S value in the deformation localization areas. The b value, A(b) value, S value, ∆F and A(t) value corresponding to the high-energy microseismic events identified by the gradient significance index during excavation are used as the threshold values for classifying the rockbrust hazard level during the mining operation. The Bayesian network method is used to analyze the effectiveness of each physical indicator in predicting the hazard areas, and a comprehensive predicting hazard areas model is constructed to calculate the weight of physical indicators and obtain the comprehensive predicting indicators. The 513 working face is analyzed as an example. The results show that the geophysical indicators can identify the microseismic gathering signal and assess the hazard areas. Three microseismic events gathering areas are determined according to the measured data of 513 working face. The spatial scanning results of physical indicators and the gathering areas of microseismic data have the synchronization characteristics. When some high-energy microseismic events occur, the physical indicator value of the area is higher than the rockburst hazard threshold, and the hazard areas identified by the physical indicator spatial scanning is basically consistent with the gathering areas of microseismic data. The integrated prediction model is used to predict the hazard area during the mining period of the working face. The results show that the rockburst hazard events mostly occur in the strong high hazard areas predicted by the integrated prediction indicator. With the superposition of microseismic data during the mining period, the high rockburst hazard areas is further concentrated, and the overlap degree with the high hazard event location is higher. The prediction efficiency of the integrated prediction indicator is generally higher than that of single physical indicator, which significantly enhances the ability to accurately predict the rockburst hazard areas

Real-world Effectiveness and Tolerability of Interferon-free Direct-acting Antiviral for 15,849 Patients with Chronic Hepatitis C: A Multinational Cohort Study

BACKGROUND AND AIMS: As practice patterns and hepatitis C virus (HCV) genotypes (GT) vary geographically, a global real-world study from both East and West covering all GTs can help inform practice policy toward the 2030 HCV elimination goal. This study aimed to assess the effectiveness and tolerability of DAA treatment in routine clinical practice in a multinational cohort for patients infected with all HCV GTs, focusing on GT3 and GT6. METHODS: We analyzed the sustained virological response (SVR12) of 15,849 chronic hepatitis C patients from 39 Real-World Evidence from the Asia Liver Consortium for HCV clinical sites in Asia Pacific, North America, and Europe between 07/01/2014-07/01/2021. RESULTS: The mean age was 62±13 years, with 49.6% male. The demographic breakdown was 91.1% Asian (52.9% Japanese, 25.7% Chinese/Taiwanese, 5.4% Korean, 3.3% Malaysian, and 2.9% Vietnamese), 6.4% White, 1.3% Hispanic/Latino, and 1% Black/African-American. Additionally, 34.8% had cirrhosis, 8.6% had hepatocellular carcinoma (HCC), and 24.9% were treatment-experienced (20.7% with interferon, 4.3% with direct-acting antivirals). The largest group was GT1 (10,246 [64.6%]), followed by GT2 (3,686 [23.2%]), GT3 (1,151 [7.2%]), GT6 (457 [2.8%]), GT4 (47 [0.3%]), GT5 (1 [0.006%]), and untyped GTs (261 [1.6%]). The overall SVR12 was 96.9%, with rates over 95% for GT1/2/3/6 but 91.5% for GT4. SVR12 for GT3 was 95.1% overall, 98.2% for GT3a, and 94.0% for GT3b. SVR12 was 98.3% overall for GT6, lower for patients with cirrhosis and treatment-experienced (TE) (93.8%) but ≥97.5% for treatment-naive patients regardless of cirrhosis status. On multivariable analysis, advanced age, prior treatment failure, cirrhosis, active HCC, and GT3/4 were independent predictors of lower SVR12, while being Asian was a significant predictor of achieving SVR12. CONCLUSIONS: In this diverse multinational real-world cohort of patients with various GTs, the overall cure rate was 96.9%, despite large numbers of patients with cirrhosis, HCC, TE, and GT3/6. SVR12 for GT3/6 with cirrhosis and TE was lower but still excellent (\u3e91%)

Improvement in Mechanical Properties of Al2024 Alloy Using Mechanical Working and Heat Treatment

Extrusion speed has a significant influence on the extrusion temperature, microstructure and mechanical properties of the material in the repetitive continuous extrusion forming (RCEF) process. In this work, the mechanical properties of Al2024 were improved by adjusting the speed (with a general range of 2–10 rpm) of repetitive continuous extrusion and applying subsequent heat treatment. During the RCEF process, an increase in the extrusion speed from 4 to 8 rpm was found to increase the extrusion temperature and then enhance the solid solution function. The grain size was affected by the combined effect of deformation speed and its induced temperature. A high-strength Al2024 (ultimate tensile strength of 497.6 MPa) with good elongation (12.93%) was obtained by increasing the extrusion speed and conducting solid solution and artificial aging treatments. The main strengthening mechanisms could be attributed to finer grain size and a larger amount of S (Al2CuMg) precipitates

Spatial and Functional Heterogeneities Shape Collective Behavior of Tumor-Immune Networks

<div><p>Tumor growth involves a dynamic interplay between cancer cells and host cells, which collectively form a tumor microenvironmental network that either suppresses or promotes tumor growth under different conditions. The transition from tumor suppression to tumor promotion is mediated by a tumor-induced shift in the local immune state, and despite the clinical challenge this shift poses, little is known about how such dysfunctional immune states are initiated. Clinical and experimental observations have indicated that differences in both the composition and spatial distribution of different cell types and/or signaling molecules within the tumor microenvironment can strongly impact tumor pathogenesis and ultimately patient prognosis. How such “functional” and “spatial” heterogeneities confer such effects, however, is not known. To investigate these phenomena at a level currently inaccessible by direct observation, we developed a computational model of a nascent metastatic tumor capturing salient features of known tumor-immune interactions that faithfully recapitulates key features of existing experimental observations. Surprisingly, over a wide range of model formulations, we observed that heterogeneity in both spatial organization and cell phenotype drove the emergence of immunosuppressive network states. We determined that this observation is general and robust to parameter choice by developing a systems-level sensitivity analysis technique, and we extended this analysis to generate other parameter-independent, experimentally testable hypotheses. Lastly, we leveraged this model as an in silico test bed to evaluate potential strategies for engineering cell-based therapies to overcome tumor associated immune dysfunction and thereby identified modes of immune modulation predicted to be most effective. Collectively, this work establishes a new integrated framework for investigating and modulating tumor-immune networks and provides insights into how such interactions may shape early stages of tumor formation.</p></div

Functional and spatial predictors of tumor clearance.

<p>The model was run 200 times at base parameter values, and runs were grouped based upon simulation outcome (tumor survival or tumor death). (A,B) Time evolution of cell counts (by type) for runs in which the tumor survived (A) or died (B). <i>Insets</i>: Expanded views of early time points (same axes). (C) Macrophage polarization index (MPI) at 0.5 d post tumor-initiation, classified by simulation outcome. Error bars indicate standard error. (D) Distributions of MPI at t = 0.5 d observed across multiple simulations, classified by outcome. (E) Spatial metrics of the TME at t = 0.5 d: domain-wide maximum M2S value and mean local coefficient of variation (CV) of M2S, classified by outcome. Local CV of M2S was calculated within each 10 x 10 lattice site (LS) array, and mean of all 100 such arrays across the domain is shown. (F) Tumor survival probability evaluated across variations in average initial M2S level (<i>p11</i>) and M2 polarization threshold (<i>p13</i>). (G) MPI at t = 0.5 d evaluated across the same parameter variations used in F. (H) Tumor cell counts at t = 0.5 d evaluated across the same parameter values used in F. (I) Normalized MPI (blue) and normalized tumor cell count (green), both at t = 0.5 d, calculated across the same parameter values used in F and plotted against tumor survival probability. Linear regression was performed on data points for which tumor survival probability was less than 1.</p

Qualitative recapitulation of tumor physiology.

<p>Qualitative behavior of the model is illustrated through snapshots in time from a single run of the HDC model using base parameter values. At each time point post-tumor initiation (in days), spatial distributions of cells (by type), M2S, oxygen, and vasculature are depicted. In this particular run, shortly after the final time point shown, all tumor cells were dead. Numerical ranges spanned by colored scale bars are: M2S (0–4 [pg/LS] x 10^–6), oxygen (0–1 [pg/LS]), vasculature (0–40 au).</p

Core features of the early tumor-immune network model.

<p>This schematic illustrates key processes captured in the HDC agent-based model. (A) <i>Macrophage polarization and tumor killing</i>. Naïve macrophages (MP) polarize to either an M1 state, in the presence of high levels of Activator signal, or an M2 state, with concomitant exposure to M2 signal (M2S). M1 cells secrete tumor lethality signal (TLS), high levels of which kills tumor cells. (B) <i>Macrophage chemotaxis</i>. All macrophages chemotax along gradients of M2S, which is secreted by the tumor and M2 cells. (C) <i>Vascularization</i>, <i>tumor proliferation</i>, <i>and the effects of oxygen</i>. Oxygen and naïve MP enter at sites of vascularization, which increases as a function of local levels of M2S. All cells die in anoxic conditions, and dead cells are retained (e.g., forming a necrotic tumor core, as depicted). Individual tumor cells divide at a fixed rate, expanding to “invade” neighboring lattice sites.</p

Systematic multi-parameter sensitivity analysis (MPSA).

<p>Parameter sensitivity was evaluated by running simulations using 5 values of each parameter, spanning a single order of magnitude around the base value (see text), and this was performed for each combination of values for each parameter pair. 50 simulation runs were performed for each set of parameter values, and the tally of simulation outcomes is indicated by the color. The change in each parameter magnitude across its range is indicated by its corresponding purple ramps on the boundaries of the plot.</p

Evaluation of potential engineered cell-based therapy strategies.

<p>Three potential strategies to treat cancer with engineered macrophages (EMP) were evaluated for efficacy in promoting tumor clearance in this model of the early TME. Enhanced immunostimulation (left column): when polarized to the M1 state, EMP released four times the base case level of TLS. Decreased immunosuppression (center column): EMP could not polarize to the M2 state. Active conversion (right column): EMP could not polarize to the M2 state and constitutively secreted a diffusible signal that blocked the M2 receptor on normal (unmodified) macrophages. For each strategy, tumor survival probability was calculated over simulations varying the time at which the first EMP were introduced and the fraction of incoming MP that were MP (vs. unmodified); 50 simulations were performed for each case.</p

Systematic evaluation of parameter contributions to model behaviors.

<p>(A) Using the data from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004181#pcbi.1004181.g004" target="_blank">Fig. 4</a>, sensitivities of four different metrics of model behavior were calculated over changes in each of the 18 model parameters. The range of each metric was different (indicated in key at top of panel). For each metric, normalized sensitivities were ranked by parameter (right column). (B) Illustration of M2 polarization probability “dose-response” curves (normalized) for different values of polarization stochasticity. (C) Illustrative spatial distributions of M2S for low and high values of the M2S heterogeneity parameter, <i>p</i>17, depicted at 0.1 d after tumor initiation, after which point initial diffusion had occurred but cellular contributions to the distribution were negligible. (D) Global correlation of tumor survival probability with MPI at t = 0.5 d across all parameter combinations evaluated in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004181#pcbi.1004181.g004" target="_blank">Fig. 4</a>. Red lines depict linear regressions, with coefficients shown. (E) Pairwise contributions of polarization stochasticity and all other parameter values to tumor survival probability.</p