A new model to downscale urban and rural surface and air temperatures evaluated in Shanghai, China

A simple model, TsT2m (Surface Temperature and near surface air Temperature (at 2 m) model), is developed to downscale numerical model output (such as from ECMWF) to obtain higher temporal and spatial resolution surface and near surface air temperature. It is evaluated in Shanghai, China. Surface temperature (TS) and near surface air temperature (Ta) sub-models account for variations in land covers and their different thermal properties, resulting in spatial variations of surface and air temperature. The Net All Wave Radiation Parameterization (NARP) scheme is used to compute net wave radiation for the surface temperature sub-model, the Objective Hysteresis Model (OHM) is used to calculate the net storage heat fluxes, and the surface temperature is obtained by the force-restore method. The near surface air temperature sub-model considers the horizontal and vertical energy changes for a column of well mixed air above the surface. Modeled surface temperatures reproduce the general pattern of MODIS images well, while providing more detailed patterns of the surface urban heat island. However, the simulated surface temperatures capture the warmer urban land cover and are 10.3°C warmer on average than those derived from the coarser MODIS data. For other land cover types values are more similar. Downscaled, higher temporal and spatial resolution air temperatures are compared to observations at 110 Automatic Weather Stations across Shanghai. After downscaling with the TsT2m model, the average forecast accuracy of near surface air temperature is improved by about 20%. The scheme developed has considerable potential for prediction and mitigation of urban climate conditions, particularly for weather and climate services related to heat stres

Identification and validation of a novel cuproptosis-related gene signature in multiple myeloma

Background: Cuproptosis is a newly identified unique copper-triggered modality of mitochondrial cell death, distinct from known death mechanisms such as necroptosis, pyroptosis, and ferroptosis. Multiple myeloma (MM) is a hematologic neoplasm characterized by the malignant proliferation of plasma cells. In the development of MM, almost all patients undergo a relatively benign course from monoclonal gammopathy of undetermined significance (MGUS) to smoldering myeloma (SMM), which further progresses to active myeloma. However, the prognostic value of cuproptosis in MM remains unknown.Method: In this study, we systematically investigated the genetic variants, expression patterns, and prognostic value of cuproptosis-related genes (CRGs) in MM. CRG scores derived from the prognostic model were used to perform the risk stratification of MM patients. We then explored their differences in clinical characteristics and immune patterns and assessed their value in prognosis prediction and treatment response. Nomograms were also developed to improve predictive accuracy and clinical applicability. Finally, we collected MM cell lines and patient samples to validate marker gene expression by quantitative real-time PCR (qRT-PCR).Results: The evolution from MGUS and SMM to MM was also accompanied by differences in the CRG expression profile. Then, a well-performing cuproptosis-related risk model was developed to predict prognosis in MM and was validated in two external cohorts. The high-risk group exhibited higher clinical risk indicators. Cox regression analyses showed that the model was an independent prognostic predictor in MM. Patients in the high-risk group had significantly lower survival rates than those in the low-risk group (p < 0.001). Meanwhile, CRG scores were significantly correlated with immune infiltration, stemness index and immunotherapy sensitivity. We further revealed the close association between CRG scores and mitochondrial metabolism. Subsequently, the prediction nomogram showed good predictive power and calibration. Finally, the prognostic CRGs were further validated by qRT-PCR in vitro.Conclusion: CRGs were closely related to the immune pattern and self-renewal biology of cancer cells in MM. This prognostic model provided a new perspective for the risk stratification and treatment response prediction of MM patients

A novel glycolysis-related gene signature for predicting the prognosis of multiple myeloma

Background: Metabolic reprogramming is an important hallmark of cancer. Glycolysis provides the conditions on which multiple myeloma (MM) thrives. Due to MM’s great heterogeneity and incurability, risk assessment and treatment choices are still difficult.Method: We constructed a glycolysis-related prognostic model by Least absolute shrinkage and selection operator (LASSO) Cox regression analysis. It was validated in two independent external cohorts, cell lines, and our clinical specimens. The model was also explored for its biological properties, immune microenvironment, and therapeutic response including immunotherapy. Finally, multiple metrics were combined to construct a nomogram to assist in personalized prediction of survival outcomes.Results: A wide range of variants and heterogeneous expression profiles of glycolysis-related genes were observed in MM. The prognostic model behaved well in differentiating between populations with various prognoses and proved to be an independent prognostic factor. This prognostic signature closely coordinated with multiple malignant features such as high-risk clinical features, immune dysfunction, stem cell-like features, cancer-related pathways, which was associated with the survival outcomes of MM. In terms of treatment, the high-risk group showed resistance to conventional drugs such as bortezomib, doxorubicin and immunotherapy. The joint scores generated by the nomogram showed higher clinical benefit than other clinical indicators. The in vitro experiments with cell lines and clinical subjects further provided convincing evidence for our study.Conclusion: We developed and validated the utility of the MM glycolysis-related prognostic model, which provides a new direction for prognosis assessment, treatment options for MM patients

Unleashing the Power of ChatGPT in Finance Research: Opportunities and Challenges

Natural language processing (NLP) technologies, such as ChatGPT, are revolutionizing various fields, including finance research. This article explores the multifaceted potential of ChatGPT as a transformative tool for finance researchers, highlighting the benefits, challenges, and novel insights it can offer to facilitate the research. We demonstrate applications in coding support, theoretical derivation, research idea assistance, and professional editing. A comparison of ChatGPT-3.5, ChatGPT-4, and Microsoft Bing reveals unique features and applicability. By discussing pitfalls and ethical concerns, we encourage responsible AI adoption and a comprehensive understanding of advanced NLP’s impact on finance research and practice

The Synthesis Process and Thermal Stability of V₂C MXene

The effect of etching solution on the synthesis process of two-dimensional vanadium carbide (V2C MXene) was researched. Three etching solutions were used to etch ternary carbide V2AlC at 90 °C. The three solutions were: lithium fluoride + hydrochloric acid (LiF + HCl), sodium fluoride + hydrochloric acid (LiF + HCl), and potassium fluoride + hydrochloric acid (KF + HCl). It was found that only NaF + HCl solution was effective for synthesizing highly pure V2C MXene. The existence of sodium (Na+) and chloridion (Cl−) in etching solution was essential for the synthesis. The thermal stability of the as-prepared V2C MXene in argon or air was studied. From thermogravimetry and differential thermal analysis, V2C MXene was found to be stable in argon atmosphere at a temperature of up to 375 °C. As the temperature increased, V2C MXene was gradually oxidized to form nanoparticles composed of vanadium trioxide (V2O3) and a part of V2C MXene was broken and transformed to vanadium carbide (V8C7) at 1000 °C. In air atmosphere, V2C MXene was stable at 150 °C. At 1000 °C, V2C MXene was oxidized to form vanadium pentoxide (V2O5)

An Efficient Approximate Algorithm for Disjoint QoS Routing

Disjoint routing is used to find the disjoint paths between a source and a destination subject to QoS requirements. Disjoint QoS routing is an effective strategy to achieve robustness, load balancing, congestion reduction, and an increased throughput in computer networks. For multiple additive constraints, disjoint QoS routing is an NP-complete class that cannot be exactly solved in polynomial time. In the paper, the disjoint QoS routing problem was formulated as a 0-1 integer linear programming. The complicating constraints were included in the objective function using an adaptive penalty function. The special model with a totally unimodular constraint coefficient matrix was constructed and could be solved rapidly as a linear programming. An efficient algorithm using an adaptive penalty function and 0-1 integer linear programming for the disjoint QoS routing problems was designed. The proposed algorithm could obtain the optimal solution, considerably reducing the computational time consumption and improving the computational efficiency. Theoretical analysis and simulation experiments were performed to evaluate the proposed algorithm performance. Through the establishment of random network topologies using Matlab, the average running time, the optimal objective value, and the success rate were evaluated based on the optimal values obtained in Cplex. The simulation experiments validated the effectiveness of the proposed heuristic algorithm

Carbon dioxide adsorption of two-dimensional carbide MXenes

Abstract Two-dimensional carbide MXenes (Ti3C2T x and V2CT x ) were prepared by exfoliating MAX phases (Ti3AlC2 and V2AlC) powders in the solution of sodium fluoride (NaF) and hydrochloric acid (HCl). The specific surface area (SSA) of as-prepared Ti3C2T x was 21 m2/g, and that of V2CT x was 9 m2/g. After intercalation with dimethylsulfoxide, the SSA of Ti3C2T x was increased to 66 m2/g; that of V2CT x was increased to 19 m2/g. Their adsorption properties on carbon dioxide (CO2) were investigated under 0–4 MPa at room temperature (298 K). Intercalated Ti3C2T x had the adsorption capacity of 5.79 mmol/g, which is close to the capacity of many common sorbents. The theoretical capacity of Ti3C2T x with the SSA of 496 m2/g was up to 44.2 mmol/g. Additionally, due to high pack density, MXenes had very high volume-uptake capacity. The capacity of intercalated Ti3C2T x measured in this paper was 502 V·v–1. This value is already higher than volume capacity of most known sorbents. These results suggest that MXenes have some advantage features to be researched as novel CO2 capture materials

Mechanical and Electrochemical Properties of Cubic and Tetragonal LixLa0.557TiO3 Perovskite Oxide Electrolytes

Solid oxide electrolytes with high Li ion conductivity and mechanical stability are vital for all solid-state lithium ion batteries. The perovskite material LixLa0.557TiO3 with various initial Li (0.303 ≤ x ≤ 0.370) is synthesized by traditional solid-state reaction. The cubic and tetragonal structures are prepared with fast and slow cooling, respectively. The results reveal that the Li ion conductivity of the cubic structure is higher. In fact, the bulk conductivity of 1.65 × 10−3 S cm−1 is obtained at room temperature for x = 0.350. The crystal structure is not affected by the Li2O quantity. In addition, Young's modulus, hardness, and fracture toughness are determined with indentation method for both structures. The Young's modulus increases with increasing Li2O. However, hardness and fracture toughness keep a relatively stable value independent of Li2O quantit

Pretreatment 18F‐FDG uptake heterogeneity may predict treatment outcome of combined Trastuzumab and Pertuzumab therapy in patients with metastatic HER2 positive breast cancer

Abstract Objective Intra-tumoral heterogeneity of 18F‐fluorodeoxyglucose (18F‐FDG) uptake has been proven to be a surrogate marker for predicting treatment outcome in various tumors. However, the value of intra-tumoral heterogeneity in metastatic Human epidermal growth factor receptor 2(HER2) positive breast cancer (MHBC) remains unknown. The aim of this study was to evaluate 18F‐FDG uptake heterogeneity to predict the treatment outcome of the dual target therapy with Trastuzumab and Pertuzumab(TP) in MHBC. Methods Thirty-two patients with MHBC who underwent 18F-FDG positron emission tomography/computed tomography (PET/CT) scan before TP were enrolled retrospectively. The region of interesting (ROI) of the lesions were drawn, and maximum standard uptake value (SUVmax), mean standard uptake value (SUVmean), total lesion glycolysis (TLG), metabolic tumor volume (MTV) and heterogeneity index (HI) were recorded. Correlation between PET/CT parameters and the treatment outcome was analyzed by Spearman Rank Test. The ability to predict prognosis were determined by time‐dependent survival receiver operating characteristic (ROC) analysis. And the survival analyses were then estimated by Kaplan‐Meier method and compared by log‐rank test. Results The survival analysis showed that HI50% calculated by delineating the lesion with 50%SUVmax as threshold was a significant predictor of patients with MHBC treated by the treatment with TP. Patients with HI50% (≥ 1.571) had a significantly worse prognosis of progression free survival (PFS) (6.87 vs. Not Reach, p = 0.001). The area under curve (AUC), the sensitivity and the specificity were 0.88, 100% and 63.6% for PFS, respectively. Conclusion 18F-FDG uptake heterogeneity may be useful for predicting the prognosis of MHBC patients treated by TP