Some results on the existence of weak periodic solutions for quasilinear parabolic systems with L1 data

The aim of this paper is to prove the existence of weak periodic solution and super solution for M×M reaction diffusion system with L1 data and nonlinearity on the gradient. The existence is proved by the technique of sub and super solution and Schauder fixed point theorem.

Machine Learning Using Real-World and Translational Data to Improve Treatment Selection for NSCLC Patients Treated with Immunotherapy

(1) Background: In advanced non-small cell lung cancer (aNSCLC), programmed death ligand 1 (PD-L1) remains the only biomarker for candidate patients to immunotherapy (IO). This study aimed at using artificial intelligence (AI) and machine learning (ML) tools to improve response and efficacy predictions in aNSCLC patients treated with IO. (2) Methods: Real world data and the blood microRNA signature classifier (MSC) were used. Patients were divided into responders (R) and non-responders (NR) to determine if the overall survival of the patients was likely to be shorter or longer than 24 months from baseline IO. (3) Results: One-hundred sixty-four out of 200 patients (i.e., only those ones with PD-L1 data available) were considered in the model, 73 (44.5%) were R and 91 (55.5%) NR. Overall, the best model was the linear regression (RL) and included 5 features. The model predicting R/NR of patients achieved accuracy ACC = 0.756, F1 score F1 = 0.722, and area under the ROC curve AUC = 0.82. LR was also the best-performing model in predicting patients with long survival (24 months OS), achieving ACC = 0.839, F1 = 0.908, and AUC = 0.87. (4) Conclusions: The results suggest that the integration of multifactorial data provided by ML techniques is a useful tool to select NSCLC patients as candidates for IO.

Prognostic Potential of METTL7B in Glioma

<b><i>Objective:</i></b> Methyltransferase-like 7B (METTL7B) is a member of methyltransferase-like family. Little is known about the exact role of METTL7B in cancer. This study aims to investigate the role of METTL7B in gliomas. <b><i>Methods:</i></b> The expression of METTL7B in glioma and adjacent normal tissues were examined by using TCGA, Chinese Glioma Genome Atlas (CGGA) database, and clinical tissues. <b><i>Results:</i></b> The results showed that METTL7B was highly expressed in glioma. Patients with high levels of METTL7B usually had poor survival in glioma, especially in low-grade glioma (LGG). Data from CGGA showed that METTL7B was an independent risk factor of glioma and can be used to evaluate the survival time of glioma patients. Hypomethylation in the METTL7B CpG islands was lower in LGG, and all the hypomethylated METTL7B islands were correlated with poor LGG survival. Furthermore, METTL7B levels were correlated with high numbers of tumor infiltrated immune cells in glioma, especially in LGG. ). Gene Set Enrichment Analysis found METTL7B was correlated with leukocyte proliferation, T-cell proliferation, peptidase activity, lymphocyte activation, etc. TCGA and CGGA database analysis showed that there were 1,546 and 1,117 genes that had a synergistic effect with METTL7B in glioma, respectively, and there were 372 genes overlapped between the 2 groups, including PD-L1. Data from clinical tissues also showed PD-L1 was highly expressed in glioma tissues and was positively correlated with METTL7B. <b><i>Conclusion:</i></b> Our study suggested that METTL7B was a potential prognostic biomarker for glioma and other cancers, and it may act as an oncogenic driver and may be a potential therapeutic target in human cancer, especially in LGG.

Using CYGNSS Data to Map Flood Inundation during the 2021 Extreme Precipitation in Henan Province, China

On 20 July 2021, parts of China’s Henan Province received the highest precipitation levels ever recorded in the region. Floods caused by heavy rainfall resulted in hundreds of casualties and tens of billions of dollars’ worth of property loss. Due to the highly dynamic nature of flood disasters, rapid and timely spatial monitoring is conducive for early disaster prevention, mid-term disaster relief, and post-disaster reconstruction. However, existing remote sensing satellites cannot provide high-resolution flood monitoring results. Seeing as spaceborne global navigation satellite system-reflectometry (GNSS-R) can observe the Earth’s surface with high temporal and spatial resolutions, it is expected to provide a new solution to the problem of flood hazards. Here, using the Cyclone Global Navigation Satellite System (CYGNSS) L1 data, we first counted various signal-to-noise ratios and the corresponding reflectivity to surface features in Henan Province. Subsequently, we analyzed changes in the delay-Doppler map of CYGNSS when the observed area was submerged and not submerged. Finally, we determined the submerged area affected by extreme precipitation using the threshold detection method. The results demonstrated that the flood range retrieved by CYGNSS agreed with that retrieved by the Soil Moisture Active Passive (SMAP) mission and the precipitation data retrieved and measured by the Global Precipitation Measurement mission and meteorological stations. Compared with the SMAP results, those obtained by CYGNSS have a higher spatial resolution and can monitor changes in the areas affected by the floods over a shorter period.

The first global 883 GHz cloud ice survey: IceCube Level 1 data calibration, processing and analysis

Sub-millimeter (200–1000 GHz) wavelengths contribute a unique capability to fill in the sensitivity gap between operational visible–infrared (VIS–IR) and microwave (MW) remote sensing for atmospheric cloud ice and snow. Being able to penetrate clouds to measure cloud ice mass and microphysical properties in the middle to upper troposphere, a critical spectrum range, is necessary for us to understand the connection between cloud ice and precipitation processes. As the first spaceborne 883 GHz radiometer, the IceCube mission was NASA's latest spaceflight demonstration of commercial sub-millimeter radiometer technology. Successfully launched from the International Space Station, IceCube is essentially a free-running radiometer and collected valuable 15-month measurements of atmosphere and cloud ice. This paper describes the detailed procedures for Level 1 (L1) data calibration, processing and validation. The scientific quality and value of IceCube data are then discussed, including radiative transfer model validation and evaluation, as well as the unique spatial distribution and diurnal cycle of cloud ice that are revealed for the first time on a quasi-global scale at this frequency. IceCube Level 1 dataset is publicly available at Gong and Wu (2021) (https://doi.org/10.25966/3d2p-f515).

Machine Learning to Improve Treatment Selection for NSCLC Patients Treated with Immunotherapy using Real World and Translational Data

Introduction: In advanced Non-Small Cell Lung Cancer (NSCLC), Programmed Death Ligand 1 (PD-L1) remains the only used biomarker to candidate patients to immunotherapy (IO) with many limits. Given the complex dynamics of the immune system it is improbable that a single biomarker could be able to profile prediction with high accuracy. A promising solution cope with this complexity is provided by Artificial Intelligence (AI) and Machine Learning (ML), which are techniques able to analyse and interpret big multifactorial data. The present study aims at using AI tools to improve response and efficacy prediction in NSCLC patients treated with IO.MethodsReal world data (clinical data, PD-L1, histology, molecular, lab tests) and the blood microRNA signature classifier (MSC), which include 24 different microRNAs, were used. Patients were divided into responders (R), who obtained a complete or partial response or stable disease as best response, and non-responders (NR), who experienced progressive or hyperprogressive disease and those who died before the first radiologic evaluation. Moreover, we used the same data to determine if the overall survival of the patients was likely to be shorter or longer than 24 months from baseline IO. For A literature review and forward feature selection technique was used to extract a specific subset of the patients’ data. To develop the final predictive model, different ML methods have been tested, i.e., Feedforward Neural Network (FFNN), Logistic Regression (LR), K-nearest neighbours (K-NN), Support Vector Machines (SVM), and Random Forest (RF).Results 200 patients were included. 164 out of 200 (i.e., only those patients with PD-L1 data available) were considered in the model, 73 (44.5%) were R and 91 (55.5%) NR. Overall, the best model was the LR and included 5 features: 2 clinical features including the ECOG performance status and IO-line of therapy; 1 tissue feature such as PD-L1 tumour expression; and 2 blood features including the MSC test and the neutrophil-to-lymphocyte ratio (NLR). The model predicting R/NR of the patient achieves accuracy ACC= 0.756, F1 score F1=0.722, and Area Under the ROC Curve AUC=0.82. The use of the PD-L1 alone has an ACC=0.655. The accuracy of the ML models excluding some of the features from the model were as follow: without PD-L1 value (ACC=0.726), MSC (ACC=0.750), and both PD-L1 and MSC (ACC=0.707), i.e., considering only clinical features. At data cut-off (Nov 2020), median Overall Survival (mOS) for R was 38.5 months (m) (95%IC 23.9 - 53.1) vs 3.8 m (95%IC 2.8 - 4.7) for NR, with p<0.001. LR was the most performing model in predicting patients with long survival (24-months OS), achieving ACC=0.839, F1=0.908, and AUC=0.87.ConclusionsThe results suggest that the integration of multifactorial data provided by ML techniques is a useful tool to improve personalized selection of NSCLC patients candidates to IO. In particular, compared to PD-L1 alone the expected improvement was around 10%. In particular, the model shows that the higher the ECOG, NLR value, IO-line, and MSC test level the lower the response, and the higher PD-L1 the higher the response. Considering the difference in survival among R and NR groups, these results suggest that the model can also be used to indirectly predict survival. Moreover, a second model was able to predict long survival patients with good accuracy.

Locality-Based Cache Management and Warp Scheduling for Reducing Cache Contention in GPU

GPGPUs has gradually become a mainstream acceleration component in high-performance computing. The long latency of memory operations is the bottleneck of GPU performance. In the GPU, multiple threads are divided into one warp for scheduling and execution. The L1 data caches have little capacity, while multiple warps share one small cache. That makes the cache suffer a large amount of cache contention and pipeline stall. We propose Locality-Based Cache Management (LCM), combined with the Locality-Based Warp Scheduling (LWS), to reduce cache contention and improve GPU performance. Each load instruction can be divided into three types according to locality: only used once as streaming data locality, accessed multiple times in the same warp as intra-warp locality, and accessed in different warps as inter-warp data locality. According to the locality of the load instruction, LWS applies cache bypass to the streaming locality request to improve the cache utilization rate, extend inter-warp memory request coalescing to make full use of the inter-warp locality, and combine with the LWS to alleviate cache contention. LCM and LWS can effectively improve cache performance, thereby improving overall GPU performance. Through experimental evaluation, our LCM and LWS can obtain an average performance improvement of 26% over baseline GPU.

Mutations of PI3K-AKT-mTOR Pathway as Predictors for Immune Cell Infiltration and Immunotherapy Efficacy in dMMR/MSI-H Gastric Adenocarcinoma

Background: A significant subset of mismatch repair-deficient (dMMR)/microsatellite instability-high (MSI-H) gastric adenocarcinomas (GAC) are resistant to immune checkpoint inhibitors (ICIs), yet the underlying mechanism remains largely unknown. We sought to investigate the genomic correlates of the density of tumor-infiltrating immune cells (DTICs) and primary resistance to ICI treatment.Methods: Four independent cohorts of MSI-H GAC were included: (i) the surgery cohort (n=175) with genomic and DTIC data, (ii) the 3DMed cohort (n=32) with genomic and PD-L1 data, (iii) the Cancer Genome Atlas (TCGA) cohort (n=73) with genomic, transcriptomic, and survival data and (iv) the ICI treatment cohort (n=36) with pre-treatment genomic profile and ICI efficacy data.Results: In the dMMR/MSI-H GAC, the number of mutated genes in the PI3K-AKT-mTOR pathway (NMP) was positively correlated with tumor mutational burden (P<0.001) and sensitivity to PI3K-AKT-mTOR inhibitors, and negatively correlated with CD3+ (P<0.001), CD4+ (P=0.065), CD8+ (P=0.004), and FOXP3+ cells (P=0.033) in the central-tumor rather than invasive-margin area, and the transcription of immune-related genes. Compared to the NMP-low (NMP=0/1) patients, the NMP-high (NMP≥2) patients exhibited a poorer objective response rate (29.4% vs. 85.7%, P<0.001), progression-free survival (HR=3.40, P=0.019), and overall survival (HR=3.59, P=0.048) upon ICI treatment.Conclusion: Higher NMP was identified as a potential predictor of lower DTICs and primary resistance to ICIs in the dMMR/MSI-H GAC. Our results highlight the possibility of using mutational data to estimate DTICs and administering the PI3K-AKT-mTOR inhibitor as an immunotherapeutic adjuvant in NMP-high subpopulation to overcome the resistance to ICIs.