Mechanical Framework for Geopolymer Gels Construction: An Optimized LSTM Technique to Predict Compressive Strength of Fly Ash-Based Geopolymer Gels Concrete.

As an environmentally responsible alternative to conventional concrete, geopolymer concrete recycles previously used resources to prepare the cementitious component of the product. The challenging issue with employing geopolymer concrete in the building business is the absence of a standard mix design. According to the chemical composition of its components, this work proposes a thorough system or framework for estimating the compressive strength of fly ash-based geopolymer concrete (FAGC). It could be possible to construct a system for predicting the compressive strength of FAGC using soft computing methods, thereby avoiding the requirement for time-consuming and expensive experimental tests. A complete database of 162 compressive strength datasets was gathered from the research papers that were published between the years 2000 and 2020 and prepared to develop proposed models. To address the relationships between inputs and output variables, long short-term memory networks were deployed. Notably, the proposed model was examined using several soft computing methods. The modeling process incorporated 17 variables that affect the CSFAG, such as percentage of SiO2 (SiO2), percentage of Na2O (Na2O), percentage of CaO (CaO), percentage of Al2O3 (Al2O3), percentage of Fe2O3 (Fe2O3), fly ash (FA), coarse aggregate (CAgg), fine aggregate (FAgg), Sodium Hydroxide solution (SH), Sodium Silicate solution (SS), extra water (EW), superplasticizer (SP), SH concentration, percentage of SiO2 in SS, percentage of Na2O in SS, curing time, curing temperature that the proposed model was examined to several soft computing methods such as multi-layer perception neural network (MLPNN), Bayesian regularized neural network (BRNN), generalized feed-forward neural networks (GFNN), support vector regression (SVR), decision tree (DT), random forest (RF), and LSTM. Three main innovations of this study are using the LSTM model for predicting FAGC, optimizing the LSTM model by a new evolutionary algorithm called the marine predators algorithm (MPA), and considering the six new inputs in the modeling process, such as aggregate to total mass ratio, fine aggregate to total aggregate mass ratio, FASiO2:Al2O3 molar ratio, FA SiO2:Fe2O3 molar ratio, AA Na2O:SiO2 molar ratio, and the sum of SiO2, Al2O3, and Fe2O3 percent in FA. The performance capacity of LSTM-MPA was evaluated with other artificial intelligence models. The results indicate that the R2 and RMSE values for the proposed LSTM-MPA model were as follows: MLPNN (R2 = 0.896, RMSE = 3.745), BRNN (R2 = 0.931, RMSE = 2.785), GFFNN (R2 = 0.926, RMSE = 2.926), SVR-L (R2 = 0.921, RMSE = 3.017), SVR-P (R2 = 0.920, RMSE = 3.291), SVR-S (R2 = 0.934, RMSE = 2.823), SVR-RBF (R2 = 0.916, RMSE = 3.114), DT (R2 = 0.934, RMSE = 2.711), RF (R2 = 0.938, RMSE = 2.892), LSTM (R2 = 0.9725, RMSE = 1.7816), LSTM-MPA (R2 = 0.9940, RMSE = 0.8332), and LSTM-PSO (R2 = 0.9804, RMSE = 1.5221). Therefore, the proposed LSTM-MPA model can be employed as a reliable and accurate model for predicting CSFAG. Noteworthy, the results demonstrated the significance and influence of fly ash and sodium silicate solution chemical compositions on the compressive strength of FAGC. These variables could adequately present variations in the best mix designs discovered in earlier investigations. The suggested approach may also save time and money by accurately estimating the compressive strength of FAGC with low calcium content.

Inflammatory response signature score model for predicting immunotherapy response and pan-cancer prognosis.

Background: Inflammatory responses influence the outcome of immunotherapy and tumorigenesis by modulating host immunity. However, systematic inflammatory response assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers remain unexplored. Here, we investigated an inflammatory response score model to predict CIT responses and patient survival in a pan-cancer analysis. Methods: We retrieved 12 CIT response gene expression datasets from the Gene Expression Omnibus database (GSE78220, GSE19423, GSE100797, GSE126044, GSE35640, GSE67501, GSE115821 and GSE168204), Tumor Immune Dysfunction and Exclusion database (PRJEB23709, PRJEB25780 and phs000452.v2.p1), European Genome-phenome Archive database (EGAD00001005738), and IMvigor210 cohort. The tumor samples from six cancers types: metastatic urothelial cancer, metastatic melanoma, gastric cancer, primary bladder cancer, renal cell carcinoma, and non-small cell lung cancer.We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm. Findings: The model had high predictive accuracy in both the training and validation cohorts. During sub-group analysis, area under the curve (AUC) values of 0.82, 0.80, 0.71, 0.7, 0.67, and 0.64 were obtained for the non-small cell lung cancer, gastric cancer, metastatic urothelial cancer, primary bladder cancer, metastatic melanoma, and renal cell carcinoma cohorts, respectively. CIT response rates were higher in the high-scoring training cohort subjects (51%) than the low-scoring subjects (27%). The five-year survival rates in the high- and low score groups of the training cohorts were 62% and 21%, respectively, while those of the validation cohorts were 54% and 22%, respectively (P < 0·001 in all cases). Inflammatory response signature score derived from on-treatment tumor specimens are highly predictive of response to CIT in patients with metastatic melanoma. A significant correlation was observed between the inflammatory response scores and tumor purity. Regardless of the tumor purity, patients in the low score group had a significantly poorer prognosis than those in the high score group. Immune cell infiltration analysis indicated that in the high score cohort, tumor-infiltrating lymphocytes were significantly enriched, particularly effector and natural killer cells. Inflammatory response scores were positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may have benefited patients with high scores. Analysis of signature scores across different cancer types from The Cancer Genome Atlas revealed that the prognostic performance of inflammatory response scores for survival in patients who have not undergone immunotherapy can be affected by tumor purity. Interleukin 21 (IL21) had the highest weight in the inflammatory response model, suggesting its vital role in the prediction mode. Since the number of metastatic melanoma patients (n = 429) was relatively large among CIT cohorts, we further performed a co-culture experiment using a melanoma cell line and CD8 + T cell populations generated from peripheral blood monocytes. The results showed that IL21 therapy combined with anti-PD1 (programmed cell death 1) antibodies (trepril monoclonal antibodies) significantly enhanced the cytotoxic activity of CD8 + T cells against the melanoma cell line. Conclusion: In this study, we developed an inflammatory response gene signature model that predicts patient survival and immunotherapy response in multiple malignancies. We further found that the predictive performance in the non-small cell lung cancer and gastric cancer group had the highest value among the six different malignancy subgroups. When compared with existing signatures, the inflammatory response gene signature scores for on-treatment samples were more robust predictors of the response to CIT in metastatic melanoma.

A Comprehensive Analysis of NRP1 in Malignancies Provide Therapeutic Implication for Treating Cancer Patients Infected with SARS-CoV-2.

COVID-19 (Coronavirus disease 2019) is caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus-2), which can lead to pneumonia, cytokine storms, and lymphopenia. Patients with cancer are more susceptible to SARS-CoV-2 infection and severe COVID-19 due to immunosuppression. Recent studies have indicated that NRP1 (Neuropilin 1) may act as a novel mediator of SARS-CoV-2 entry into the host cell. As no systematic review has been performed investigating the characteristics of NRP1 in pan-carcinoma, we comprehensively analyzed NRP1 in patients with pan-cancer. Using a bioinformatics approach, we aimed to systematically examine NRP1 expression profiles in both pan-carcinoma and healthy tissues. We found that lung and genitourinary cancers have a relatively higher NRP-1 expression than other cancer patients, suggesting that these patients may be more susceptible to SARS-CoV-2. Our analysis further revealed that NRP1 expression was downregulated in Vero E6 cells, whole blood, lung organoids, testis tissue, and alveolospheres infected with SARS-CoV-2. Notably, NRP1 was associated with immune cell infiltration, immune checkpoint genes, and immune-related genes in most patients with cancer. These findings suggest that, in patients with specific types of cancer, especially lung and genitourinary, high expression of NRP1 contributes to greater susceptibility to SARS-CoV-2 infection and an increased risk of damage due to cytokine storms. Overall, NRP1 appears to play a critical role in regulating immunological properties and metabolism in many tumor types. Specific inhibitors of the NRP1 antigen (pegaptanib, EG00229, or MNRP1685A) combined with other anti-SARS-CoV-2 strategies may aid in treating patients with lung and genitourinary cancers following SARS-CoV-2 infection.

A tumor-associated endothelial signature score model in immunotherapy and prognosis across pan-cancers.

Background: The tumor-associated endothelial cell (TAE) component plays a vital role in tumor immunity. However, systematic tumor-associated endothelial-related gene assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers have not been explored. Herein, we investigated a TAE gene risk model to predict CIT responses and patient survival in a pan-cancer analysis. Methods: We analyzed publicly available datasets of tumor samples with gene expression and clinical information, including gastric cancer, metastatic urothelial cancer, metastatic melanoma, non-small cell lung cancer, primary bladder cancer, and renal cell carcinoma. We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm. Results: The model demonstrated a high predictive accuracy in both training and validation cohorts. The response rate of the high score group to immunotherapy in the training cohort was significantly higher than that of the low score group, with CIT response rates of 51% and 27%, respectively. The survival analysis showed that the prognosis of the high score group was significantly better than that of the low score group (all p < 0·001). Tumor-associated endothelial gene signature scores positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may benefit patients in the high score group. The analysis of TAE scores across 33 human cancers revealed that the TAE model could reflect immune cell infiltration and predict the survival of cancer patients. Conclusion: The TAE signature model could represent a CIT response prediction model with a prognostic value in multiple cancer types.

TSC22D3 as an immune-related prognostic biomarker for acute myeloid leukemia.

Acute myeloid leukemia (AML) is the type of hematologic neoplasm most common in adults. Glucocorticoid-induced gene TSC22D3 regulates cell proliferation through its function as a transcription factor. However, there is no consensus on the prognostic and immunoregulatory significance of TSC22D3 in AML. In the present study, we evaluated the correlation between TSC22D3 expression, immunoinfiltration, and prognostic significance in AML. Knockdown of TSC22D3 significantly attenuated the proliferation of Hel cells and increased sensitivity to cytarabine (Ara-c) drugs. Furthermore, TSC22D3 reduced the release of interleukin-1ß (IL-1ß) by inhibiting the NF-κB/NLRP3 signaling pathway, thereby inhibiting macrophage polarization to M1 subtype, and attenuating the pro-inflammatory tumor microenvironment. In conclusion, this study identified TSC22D3 as an immune-related prognostic biomarker for AML patients and suggested that therapeutic targeting of TSC22D3 may be a potential treatment option for AML through tumor immune escape.

A Novel MBAS-RF Approach to Predict Mechanical Properties of Geopolymer-Based Compositions.

Using gels to replace a certain amount of cement in concrete is conducive to the green concrete industry, while testing the compressive strength (CS) of geopolymer concrete requires a substantial amount of substantial effort and expense. To solve the above issue, a hybrid machine learning model of a modified beetle antennae search (MBAS) algorithm and random forest (RF) algorithm was developed in this study to model the CS of geopolymer concrete, in which MBAS was employed to adjust the hyperparameters of the RF model. The performance of the MBAS was verified by the relationship between 10-fold cross-validation (10-fold CV) and root mean square error (RMSE) value, and the prediction performance of the MBAS and RF hybrid machine learning model was verified by evaluating the correlation coefficient (R) and RMSE values and comparing with other models. The results show that the MBAS can effectively tune the performance of the RF model; the hybrid machine learning model had high R values (training set R = 0.9162 and test set R = 0.9071) and low RMSE values (training set RMSE = 7.111 and test set RMSE = 7.4345) at the same time, which indicated that the prediction accuracy was high; NaOH molarity was confirmed as the most important parameter regarding the CS of geopolymer concrete, with the importance score of 3.7848, and grade 4/10 mm was confirmed as the least important parameter, with the importance score of 0.5667.

Comprehensive analysis of glycoprotein VI-mediated platelet activation signaling pathway for predicting pan-cancer survival and response to anti-PD-1 immunotherapy.

Platelets play a vital role in cancer and immunity. However, few comprehensive studies have been conducted on the role of platelet-related signaling pathways in various cancers and their responses to immune checkpoint blockade (ICB) therapy. In the present study, we focused on the glycoprotein VI-mediated platelet activation (GMPA) signaling pathway and comprehensively evaluated its roles in 19 types of cancers listed in The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Cox regression and meta-analyses showed that for all 19 types of cancers, patients with high GMPA scores tended to have a good prognosis. Furthermore, the GMPA signature score could serve as an independent prognostic factor for patients with skin cutaneous melanoma (SKCM). The GMPA signature was linked to tumor immunity in all 19 types of cancers, and was correlated with SKCM tumor histology. Compared to other signature scores, the GMPA signature scores for on-treatment samples were more robust predictors of the response to anti-PD-1 blockade in metastatic melanoma. Moreover, the GMPA signature scores were significantly negatively correlated with EMMPRIN (CD147) and positively correlated with CD40LG expression at the transcriptomic level in most cancer patient samples from the TCGA cohort and on-treatment samples from anti-PD1 therapy cohorts. The results of this study provide an important theoretical basis for the use of GMPA signatures, as well as GPVI-EMMPRIN and GPVI-CD40LG pathways, to predict the responses of cancer patients to various types of ICB therapy.

Chemo-free maintenance therapy in adult T-cell acute lymphoblastic leukemia: A case report and literature review.

Maintenance therapy in adult T-cell acute lymphoblastic leukemia (T-ALL) is the longest phase but with limited option. The classic drugs used in the maintenance phase such as 6-mercaptopurine, methotrexate, corticosteroid and vincristine have potentially serious toxicities. Optimizing therapy in the modern age, chemo-free maintenance therapy regimens for patients with T-ALL may dramatically improve the maintenance therapeutic landscape. We report here the combination of Anti-programmed cell death protein 1 antibody and histone deacetylase inhibitor as chemo-free maintenance treatment in a T-ALL patient with literature review, thus providing a unique perspective in addition to valuable information which may inform novel therapeutic approaches.

Inflammatory and Nutritional Scoring System for Predicting Prognosis in Patients with Newly Diagnosed Multiple Myeloma.

Purpose: We aimed to assess the prognostic value of pretreatment inflammatory and nutritional parameters for predicting overall survival (OS) in patients with newly diagnosed multiple myeloma (NDMM), and to build a new scoring system using the most important variables. Methods: We retrospectively analyzed baseline clinical and laboratory data for patients with NDMM, who were randomly grouped into training and validation cohorts at a ratio of 8:2. The Inflammatory Nutritional Score (INS) was developed based on the least absolute shrinkage and selection operator (LASSO) Cox regression. The INS and other independent prognostic factors were entered into a multivariate Cox model and merged to generate a nomogram model for predictive optimization. Performance and predictive accuracy were assessed using the concordance index (C-index), calibration plots, and time-dependent receiver operating characteristic (ROC) curves. Results: In total, 442 eligible patients were enrolled. Six inflammatory/nutritional variables, including the Nutritional Risk Index (NRI), body mass index (BMI), neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), platelet-lymphocyte ratio (PLR), and albumin-alkaline phosphatase ratio (AAPR), were integrated to construct the INS using the LASSO Cox model. The predictive nomogram constructed following the multivariate Cox analysis included INS, performance status, lactate dehydrogenase, age, and C-reactive protein. The model exhibited good predictive performance, with a C-index of 0.708 in the training cohort and 0.749 in the validation cohort. Moreover, the calibration curves also demonstrated excellent consistency between predicted and observed survival in both cohorts. In the time-dependent ROC analysis, our nomogram model exhibited better performance than other staging systems for multiple myeloma. Conclusion: The INS represents an independent prognostic signature in patients with NDMM. Our novel nomogram based on INS may aid in predicting survival probability and stratifying risk.

Prognostic value of the nutritional risk index in patients with newly diagnosed multiple myeloma.

The nutritional risk index (NRI), which is based on weight and albumin levels, is closely associated with the prognosis of many cancers. However, its prognostic value has not been investigated in patients with newly diagnosed multiple myeloma (NDMM). We aimed to assess the association between the NRI and survival outcomes in patients with NDMM. We retrospectively collected and analyzed clinical and laboratory data from patients with NDMM between 2005 and 2019 at our center. Patients were stratified into the high NRI (> 89) and low NRI (≤ 89) groups for prognostic analysis. The NRI and other variables were also explored to evaluate their prognostic value for overall survival (OS). A total of 638 patients diagnosed with NDMM were retrospectively included. Patients in the high NRI group had a significantly better median OS than those in the low NRI group (64 months vs 43 months, p < 0.001). In the multivariate analysis, a high NRI was shown to be an independent prognostic factor for OS (hazard ratio, 0.758; 95% confidence interval, 0.587-0.977; p = 0.033). Age, performance status, transplant status, and lactate dehydrogenase level were also independent prognostic factors for OS. In conclusion, our study demonstrates that the NRI is a simple and useful predictor of survival outcomes in patients with NDMM.

Establishment and validation of a prognostic immune-related lncRNA risk model for acute myeloid leukemia.

Background: Acute myeloid leukemia (AML) is a cancer arising in the bone marrow and is the most common type of adult leukemia. AML has a poor prognosis, and currently, its prognosis evaluation does not include immune status assessment. This study established an immune-related long non-coding RNA (lncRNA) prognostic risk model for AML based on immune lncRNAs screening. Methods: To construct training and validation cohorts, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) public databases were accessed to obtain gene expression profiles and clinical data. The correlation between lncRNAs and immunity genes was analyzed using the "limma" package, and the immune-related lncRNAs were obtained. Through least absolute shrinkage and selection operator regression, a prognostic model was established with immune-related lncRNAs. Using the median risk score, patients were divided into high- and low-risk groups. The Kaplan-Meier method was used for survival analysis, whereas the accuracy of the risk model was evaluated using time-dependent receiver operating characteristic curves, risk score distribution, survival status, and risk heat maps. We utilized univariate and multivariate Cox regression to examine the association between risk score and clinical variables and AML survival and prognosis. Results: In the immune-related lncRNA prognostic risk model, the prognosis was better for low-risk than for high-risk patients, indicating risk score of this model as an independent indicator of prognosis. The area under the curve value for 1-, 3-, and 5-year survival of TCGA patients was 0.817, 0.859, and 0.909, respectively, whereas that of GEO patients (of dataset GPL96-GSE37642) was 0.603, 0.652, and 0.624, respectively. Gene set enrichment analysis revealed the enrichment of multiple pathways, such as antigen processing, B-cell receptor signaling pathway, natural killer cell-mediated cytotoxicity, and chemokines, in high-risk patients. Conclusions: In this study, immune-related lncRNA prognostic risk models effectively predicted AML survival and provided potential treatment targets.

A prognostic model for patients with primary extramedullary multiple myeloma.

Background: Extramedullary disease is a manifestation of multiple myeloma, the prognosis of which remains poor even in the era of novel drugs. Therefore, we aimed to develop a predictive model for patients with primary extramedullary multiple myeloma (EMM). Methods: Clinical and laboratory data of patients diagnosed with primary EMM between July 2007 and July 2021 were collected and analyzed. Univariate and least absolute shrinkage and selection operation Cox regression analyses (LASSO) were used to select prognostic factors for overall survival (OS) to establish a nomogram prognostic model. The performance of the model was evaluated using concordance index which was internally validated by bootstraps with 1,000 resample, area under the curve (AUCs), and calibration curves. Results: 217 patients were included in this retrospective study. Patients with EMM had a higher rate of belonging to the male sex, age >50 years, advanced Durie-Salmon stage III, hypercalcemia, and low hemoglobin level. Compared with patients with bone-related extramedullary disease, those with extraosseous-related extramedullary disease had a higher frequency of advanced Durie-Salmon stage III, lower rate of hypercalcemia, and elevated prothrombin time. The OS and progression-free survival (PFS) of patients with bone-related extramedullary disease were significantly higher than those of patients with extraosseous-related extramedullary disease. After the univariate and LASSO analyses, six prognostic factors, including performance status, number of extramedullary involved sites, ß2-microglobulin, lactate dehydrogenase, monocyte-lymphocyte ratio, and prothrombin time, were integrated to establish a nomogram. The model showed robust discrimination with a concordance index (C-index) of 0.775 (95% confidence interval [CI], 0.713-0.836), internally validated with the corrected C-index of 0.756, and excellent performance in time-dependent AUCs compared with other staging systems. The AUCs for 1-, 3-, and 5-year OS were 0.814, 0.744, and 0.832, respectively. The calibration curves exhibited good consistency between the observed and nomogram-predicted OS. The 5-year OS of patients in the high-risk group (23.3%; 95% CI, 13.9%-39.3%) was much worse than that in the low-risk group (73.0%; 95% CI, 62.5%-85.4%; p < 0.001). Conclusion: The nomogram predictive model based on six clinical variables showed good prognostic performance and could better predict individual survival in patients with EMM.

Survival prediction optimization of acute myeloid leukaemia based on T-cell function-related genes and plasma proteins.

Interactions between acute myeloid leukaemia (AML) cells and immune cells are postulated to corelate with outcomes of AML patients. However, data on T-cell function-related signature are not included in current AML survival prognosis models. We examined data of RNA matrices from 1611 persons with AML extracted from public databases arrayed in a training and three validation cohorts. We developed an eight-gene T-cell function-related signature using the random survival forest variable hunting algorithm. Accuracy of gene identification was tested in a real-world cohort by quantifying cognate plasma protein concentrations. The model had robust prognostic accuracy in the training and validation cohorts with five-year areas under receiver-operator characteristic curve (AUROC) of 0.67-0.76. The signature was divided into high- and low-risk scores using an optimum cut-off value. Five-year survival in the high-risk groups was 6%-23% compared with 42%-58% in the low-risk groups in all the cohorts (all p values <0.001). In multivariable analyses, a high-risk score independently predicted briefer survival with hazard ratios of death in the range 1.28-2.59. Gene set enrichment analyses indicated significant enrichment for genes involved in immune suppression pathways in the high-risk groups. Accuracy of the gene signature was validated in a real-world cohort with 88 pretherapy plasma samples. In scRNA-seq analyses most genes in the signature were transcribed in leukaemia cells. Combining the gene expression signature with the 2017 European LeukemiaNet classification significantly increased survival prediction accuracy with a five-year AUROC of 0.82 compared with 0.76 (p < 0.001). Our T-cell function-related risk score complements current AML prognosis models.

Comprehensive Profiling Analysis of CD209 in Malignancies Reveals the Therapeutic Implication for Tumor Patients Infected With SARS-CoV-2.

Coronavirus disease 2019 (COVID-19), which is known to be caused by the virus severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is characterized by pneumonia, cytokine storms, and lymphopenia. Patients with malignant tumors may be particularly vulnerable to SARS-CoV-2 infection and possibly more susceptible to severe complications due to immunosuppression. Recent studies have found that CD209 (DC-SIGN) might be a potential binding receptor for SARS-CoV-2 in addition to the well-known receptor ACE2. However, pan-cancer studies of CD209 remain unclear. In this study, we first comprehensively investigated the expression profiles of CD209 in malignancies in both pan-carcinomas and healthy tissues based on bioinformatic techniques. The CD209 expression declined dramatically in various cancer types infected by SARS-CoV-2. Remarkably, CD209 was linked with diverse immune checkpoint genes and infiltrating immune cells. These findings indicate that the elevation of CD209 among specific cancer patients may delineate a mechanism accounting for a higher vulnerability to infection by SARS-CoV-2, as well as giving rise to cytokine storms. Taken together, CD209 plays critical roles in both immunology and metabolism in various cancer types. Pharmacological inhibition of CD209 antigen (D-mannose), together with other anti-SARS-CoV-2 strategies, might provide beneficial therapeutic effects in specific cancer patients.

Genetic Algorithm in Multimedia Dynamic Prediction of Groundwater in Open-Pit Mine.

This study is aiming at the nonlinear mapping relationship between the groundwater level and its influencing factors. Through the design and calculation process of matlab7 platform, taking the monitoring wells distributed in an open-pit mining area as an example, the short-term prediction of groundwater dynamics in the study area is carried out by using BP neural network model and BP neural network model based on genetic algorithm. Root mean squared error (RMSE), Mean absolute percent-age error (MAPE) and Nash-Sutcliffe efficiency (NSE) are used coefficients,, and the results were compared with BP neural network and stepwise regression model. From the results of the comparative analysis, the genetic algorithm optimized the BP neural network model in the training phase and the test phase, the RMSE was 0.25 and 0.36, the MAPE was 6.7 and 8.13%, and the NSE was 0.87 and 0.72, respectively. The BP neural network model optimized by genetic algorithm is obviously superior to the BP neural network model, which is an ideal prediction model for short-term groundwater level. This model can provide a prediction method for groundwater dynamic prediction and has a good application prospect.

Multimedia Tilt Photography-Assisted Remote Sensing Technology in Mine Ecological Restoration.

Ecological restoration with the assistance of certain artificial measures is to restore the original ecological function and productivity of the ecosystem or to make the ecosystem develop to a virtuous circle, which is a complex systematic project. Based on this, combined with the actual needs of a mine ecological restoration project, this paper puts forward the technical process and method of quickly acquiring the geographic information of abandoned mines by using the oblique photography technology of unmanned aerial vehicles. The verification results show that the maximum plane/elevation residual error of the checkpoints measured in the field is 0.221 m-0.181 m, and the median error is 0.030 m-0.112 m. According to the encryption requirement of 1∶500 scale topographic mapping in hilly land, the plane position error of the inspection point relative to the field control point should be less than 0.175 m. The elevation should be less than 0.280 m, and the experimental results in this paper can meet the requirements. Oblique photography can provide abundant digital results, and it can play an important role in mine restoration scheme design, restoration construction, and monitoring and management after restoration.

Changing causes of death in persons with haematological cancers 1975-2016.

Causes of death in persons with haematological cancers include the index cancer, a new cancer or a seemingly unrelated cause such as cardio-vascular disease. These causes are complex and sometimes confounded. We analyzed trends in cause of death in 683,333 persons with an index haematological cancer diagnosed in 1975-2016 reported in the Surveillance, Epidemiology and End Results dataset. Non-cancer deaths were described using standardized mortality ratios. The index cancer was the predominant cause of death amongst persons with plasma cell myeloma, acute lymphoblastic leukaemia and acute myeloid leukaemia. Non-cancer death was the major cause of death in persons with chronic lymphocytic leukaemia, Hodgkin lymphoma and chronic myeloid leukaemia, mostly from cardio-vascular diseases. The greatest relative decrease in index-cancer deaths was amongst persons with Hodgkin lymphoma, chronic myeloid leukaemia and chronic lymphocytic leukaemia, where the proportion of non-cancer deaths increased substantially. Changing distribution of causes of death across haematological cancers reflects substantial progress in some cancers and suggests strategies to improve the survival of persons with haematological cancers in the future.

Functional Gene Expression Signatures from On-Treatment Tumor Specimens Predict Anti-PD1 Blockade Response in Metastatic Melanoma.

Functional gene expression signatures (FGES) from pretreatment biopsy samples have been used to predict the responses of metastatic melanoma to immune checkpoint blockade (ICB) therapies. However, there are no predictive FGE signatures from patients receiving treatment. Here, using the Elastic Net Regression (ENLR) algorithm, we analyzed transcriptomic and matching clinical data from a dataset of patients with metastatic melanoma treated with ICB therapies and produced an FGE signature for pretreatment (FGES-PRE) and on-treatment (FGES-ON). Both the FGES-PRE and FGES-ON signatures are validated in three independent datasets of metastatic melanoma as the validation set, achieving area under the curve (AUC) values of 0.44-0.81 and 0.82-0.83, respectively. Then, we combined all test samples and obtained AUCs of 0.71 and 0.82 for the FGES-PRE and FGES-ON signatures, respectively. The FGES-ON signatures had a higher predictive value for prognosis than the FGES-PRE signatures. The FGES-PRE and FGES-ON signatures were divided into high- and low-risk scores using the signature score mean value. Patients with a high FGE signature score had better survival outcomes than those with low scores. Overall, we determined that the FGES-ON signature is an effective biomarker for metastatic melanoma patients receiving ICB therapy. This work would provide an important theoretical basis for applying FGE signatures derived from on-treatment tumor samples to predict patients' therapeutic response to ICB therapies.

Notch3 Knockout Suppresses Mouse Mammary Gland Development and Inhibits the Proliferation of 4T1 Murine Mammary Carcinoma Cells via CCL2/CCR4 Axis.

Our previous study found that Notch3 knockout mice exhibit defects in mammary gland development. To elucidate the underlying mechanism, tissue samples were subjected to RNA-seq, GO, and KEGG enrichment analyses and qRT-PCR validation. Of enriched pathways, chemokine signaling pathway and cytokine-cytokine receptor interaction were noticed in both Notch3wt/wt/Notch3wt/- and Notch3wt/wt/Notch3-/- mice, in which the expression of chemokine ligand 2 (CCL2) was sharply reduced in Notch3wt/- and Notch3-/- mammary gland tissues. The Mouse ENCODE transcriptome data reveal that the mammary gland fat pad exhibits a high CCL2, CCR2, and CCR4 expression, indicating that these molecules play important roles during mammary gland development. Specifically, defective mammary glands in Notch3 knockout mice could be partially rescued by CCL2 overexpression lentivirus through intraductal injection. An in vitro study showed that CCL2 overexpression promoted the proliferation, migration, and cancerous acinar formation of 4T1 cells, which could rescue the defective migration of 4T1 cells caused by Notch3 knockdown. We also found that Notch3 transcriptionally regulated the expression of CCL2 in a classical pattern. Our findings illustrated that Notch3-regulating CCL2/CCR4 axis should be an important signaling pathway for mammary gland development and should be a candidate target for breast cancer therapy.

Notch3 promotes 3T3-L1 pre-adipocytes differentiation by up-regulating the expression of LARS to activate the mTOR pathway.

Adipocytes constitute a major component of the tumour microenvironment. Numerous studies have shown that adipocytes promote aggressiveness and invasion by stimulating cancer cells proliferation and modulating their metabolism. Herein, we reported that Notch3 promotes mouse 3T3-L1 pre-adipocytes differentiation by performing the integrative transcriptome and TMT-based proteomic analyses. The results revealed that aminoacyl-tRNA_biosynthesis pathway was significantly influenced with Nocth3 change during 3T3-L1 pre-adipocytes differentiation, and the expression of LARS in this pathway was positively correlated with Notch3. Published studies have shown that LARS is a sensor of leucine that regulates the mTOR pathway activity, and the latter involves in adipogenesis. We therefore supposed that Notch3 might promote 3T3-L1 pre-adipocytes differentiation by up-regulating LARS expression and activating mTOR pathway. CHIP and luciferase activity assay uncovered that Notch3 could transcriptionally regulate the expression of LARS gene. Oil Red staining identified a positive correlation between Notch3 expression and adipocytic differentiation. The activation of mTOR pathway caused by Notch3 overexpression could be attenuated by knocking down LARS expression. Altogether, our study revealed that Notch3 promotes adipocytic differentiation of 3T3-L1 pre-adipocytes cells by up-regulating LARS expression and activating the mTOR pathway, which might be an emerging target for obesity treatment.