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Abstract Neoadjuvant radiotherapy is the standard care of locally advanced rectal cancer. Although a majority of patients received the same dose, the curative efficacy varies among individuals. In recent years, cancer treatment has entered the era of precise medical care, and how to identify patients for proper treatment by molecular signature is an important path of individualized therapy. This study aimed to establish and validate a genome-based model for adjusting radiation dose (GARD) for Chinese locally advanced rectal cancer through gene expression microarrays, and to evaluate the response of the GARD model in predicting the efficacy of neoadjuvant radiotherapy. Fresh-frozen primary tumor from 64 patients with locally advanced rectal cancer undergoing neoadjuvant radiotherapy from 2015 to 2018 were included. The gene expression profile was analyzed using Affymetrix 3000Dx gene-chip scanner. The radiosensitivity index (RSI) and GARD were calculated using the pGRT™ algorithm. Neoadjuvant rectal cancer score (NAR) was selected as efficacy evaluation indicators. Patients were divided into high and low NAR scoring groups, and two-sample t-test was used to analyze the differences in GARD values between different NAR subgroups. ROC curves were used to calculate the cut-off values and the area under the curve (AUC) for assessing the validity of the GARD models. The personalized radiation dose ( pGRT dose )can be computed using the formula nd = GARD / (α + βd). Among patients, 1.5% T2, 46.3% T3, and 52.2% T4. Wherein pCR (n = 10; 15.6%) and no pCR (n = 54; 84.4%). The median NAR is 8.43 (rang from 0 to 50.34, IQR 3.75–14.98). NAR > 8.43 (n = 27; 42.2%) and NAR ≤ 8.43 (n = 37; 57.8%), suggesting that there are significant individual differences in clinical efficacy of patients with similar tumor stages and under the same treatment conditions. The median RSI is 0.48 (rang from 0.22 to 0.92, IQR 0.41–0.55). Median GARD was 18.40 rang from (rang from 2.26 to 37.52, IQR 14.94–22.28) within tumor tissue, suggesting individual differences in the efficacy of radiation therapy. The RSI value was significantly lower in the NAR low group (NAR ≤ 8.43) than in NAR high group (NAR > 8.43) (0.44 vs. 0.54, p = 0.0003). The GARD value was significantly higher in the NAR low group (NAR ≤ 8.43) than in NAR high group (NAR > 8.43) (21.01 vs. 15.88, p = 0.0004). Using the Receiver Operating Characteristic (ROC) curve analysis, a GARD threshold of 17 was identified as optimal, covering 37.5% of the 64-patient sample, with an area under the curve (AUC) of 0.75. In the external validation cohort, the high GARD score group demonstrated superior DFS compared to the low GARD score group(p

In the context of China’s vigorous promotion of inter-provincial electricity market construction， there exists a competitive dynamic among multiple purchasing provinces participating in the inter-provincial electricity spot market. To devise a rational bidding strategy for these purchasing provinces， a game bidding model for electricity purchasing tailored to the inter-provincial electricity spot market is proposed. Firstly， the marginal inter-provincial electricity purchase quantity-price curve is calculated based on the operational conditions of the electricity market within each province， serving as the bidding curve for its participation in the inter-provincial electricity spot market. Subsequently， a model for market clearance and pricing in the inter-provincial electricity spot market is proposed under the scenario of variable flow directions of tie-lines. A non-cooperative game bidding model for the inter-provincial market， involving multiple purchasing provinces， is established， where the bidding curve obtained under Nash equilibrium represents the optimal electricity purchase bid for the purchasing provinces. Finally， a simulation calculation is conducted on a case study system comprising two selling provinces and three purchasing provinces to validate the effectiveness of the proposed model and obtain the Nash equilibrium solution of the bidding model for electricity purchase.

The chemical reactions between certain interstellar molecules are exothermic in nature and barrierless in the entrance channel, allowing these reactions to occur rapidly even at low astronomical temperatures, e.g., C and O2 interaction. Obtaining detailed rovibrational transition parameters for the reaction between C and O2, such as state-selected rate coefficients, is crucial for studying the associated atmospheric and astronomical environments. Hence, this work presents an approach that combines quasi-classical trajectory calculations with machine learning techniques based on Neural Network (NN) and Gaussian Process Regression (GPR) to determine state-selected rate coefficients. Employing this approach, we significantly reduced the computational requirements while simultaneously obtaining the accurate state-selected reaction cross sections and rate coefficients for the collision of C and O2. Both the NN-based and GPR-based models established in this work accurately predict the results calculated from explicit numerical calculations in the explored temperature range of 50–1500 K, achieving a coefficient of determination R2 > 0.96. Most importantly, the current work provides the most comprehensive dataset of rovibrational rate coefficients of v = 0–4, j = 0–70 → v′ = 0–15 for the astrophysical modeling of the C–O2 collision system. [ABSTRACT FROM AUTHOR]

We present a near-real-time global gridded daily CO2 emissions dataset (GRACED) throughout 2021. GRACED provides gridded CO2 emissions at a 0.1° × 0.1° spatial resolution and 1-day temporal resolution from cement production and fossil fuel combustion over seven sectors, including industry, power, residential consumption, ground transportation, international aviation, domestic aviation, and international shipping. GRACED is prepared from the near-real-time daily national CO2 emissions estimates (Carbon Monitor), multi-source spatial activity data emissions and satellite NO2 data for time variations of those spatial activity data. GRACED provides the most timely overview of emissions distribution changes, which enables more accurate and timely identification of when and where fossil CO2 emissions have rebounded and decreased. Uncertainty analysis of GRACED gives a grid-level two-sigma uncertainty of value of ±19.9% in 2021, indicating the reliability of GRACED was not sacrificed for the sake of higher spatiotemporal resolution that GRACED provides. Continuing to update GRACED in a timely manner could help policymakers monitor energy and climate policies' effectiveness and make adjustments quickly.