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Abstract Non-small cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) mutation is brain metastasis (BM)-prone. We determined the impact of this hallmark, along with EGFR subtype and generation of tyrosine kinase inhibitor (TKI) treatment, on patients’ outcome. 553 metastatic EGFR-mutant NSCLC patients received front-line EGFR-TKI treatment. Progression-free survival (PFS), overall survival (OS) and secondary T790M rate were analysed. BM was observed in 211 (38.2%) patients. BM (HR 1.20 [95% CI 0.99–1.48]; p = 0.053), ECOG PS 0–1 (HR 0.71 [95% CI 0.54–0.93]; p = 0.014) and afatinib treatment (HR 0.81 [95% CI 0.66–0.99]; p = 0.045) were associated with PFS. Afatinib-treated patients without BM demonstrated a significantly longer PFS (16.3 months) compared to afatinib-treated patients with BM (13.7 months) and to gefitinib/erlotinib-treated patients with (11.1 months) or without BM (14.2 months; p

Background: Substitution of methionine for threonine at codon 790 (T790M) of epidermal growth factor receptor (EGFR) represents the major mechanism of resistance to EGFR tyrosine kinase inhibitors (TKIs) in EGFR -mutant non-small-cell lung cancer. We determined the prognostic impact and association of secondary T790M mutations with the outcomes of osimertinib and chemotherapy. Methods: Patients (n = 460) progressing from first-line EGFR-TKI treatment were assessed. Tissue and/or liquid biopsies were used to determine T790M status; post-progression overall survival (OS) was analyzed. Results: Overall, 143 (31.1%) patients were T790M positive, 95 (20.7%) were T790M negative, and 222 (48.2%) had unknown T790M status. T790M status [T790M positive versus T790M negative: hazard ratio (HR) 0.48 (95% confidence interval (CI), 0.32–0.70); p < 0.001, T790M unknown versus T790M negative: HR 1.97 (95% CI, 1.47–2.64); p < 0.001] was significantly associated with post-progression OS. T790M positivity rates were similar for tissue (90/168, 53.6%) and liquid (53/90, 58.9%) biopsies (Fisher’s exact test, p = 0.433). Tumor T790M-positive patients had significantly longer post-progression OS than tumor T790M-negative patients (34.1 versus 17.1 months; log-rank test, p = 8 × 10 −5 ). Post-progression OS was similar between plasma T790M-positive and -negative patients (17.4 versus not reached; log-rank test, p = 0.600). In tumor T790M-positive patients, post-progression OS was similar after osimertinib and chemotherapy [34.1 versus 29.1 months; log-rank test, p = 0.900; HR 1.06 (95% CI, 0.44–2.57); p = 0.897]. Conclusion: T790M positivity predicts better post-progression OS than T790M negativity; tumor T790M positivity has a stronger prognostic impact than plasma T790M positivity. Osimertinib and chemotherapy provide similar OS benefits in patients with T790M-positive tumors.

Abstract Comparison of epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) monotherapy or with bevacizumab in real-world non-small cell lung cancer (NSCLC) patients was lacking. 310 patients of advanced NSCLC with common EGFR mutation receiving first-generation EGFR-TKI monotherapy or with bevacizumab were included and propensity-score matched. Progression-free survival (PFS), overall survival (OS) and secondary T790M mutation were analysed. Patients receiving EGFR-TKI and bevacizumab were significantly younger, had better performance status and with high incidence of brain metastasis (55.8%). In the propensity-score matched cohort, PFS (13.5 vs. 13.7 months; log-rank p = 0.700) was similar between the two groups. The OS (61.3 vs. 34.2 months; log-rank p = 0.010) and risk reduction of death (HR 0.42 [95% CI 0.20–0.85]; p = 0.017) were significantly improved in EGFR-TKI plus bevacizumab group. Analysis of treatment by brain metastasis status demonstrated EGFR-TKI plus bevacizumab in patients with brain metastasis was associated with significant OS benefit compared to other groups (log-rank p = 0.030) and these patients had lower early-CNS and early-systemic progressions. The secondary T790M did not significantly differ between EGFR-TKI plus bevacizumab and EGFR-TKI monotherapy groups (66.7% vs. 75.0%, p = 0.460). Forty-one (31.1%) and 31 (23.5%) patients received subsequent osimertinib and chemotherapy, respectively. The post-progression OS of osimertinib and chemotherapy were 22.1 and 44.9 months in EGFR-TKI plus bevacizumab group and were 10.0 and 14.1 months in EGFR-TKI monotherpay group, respectively. First-generation EGFR-TKI with bevacizumab improved treatment efficacy in real-world patients of NSCLC with EGFR mutation. Patients with brain metastasis received additional OS benefit from this treatment.

Abstract Transposable elements (TEs) are widespread across eukaryotic genomes, yet their content varies widely between different species. Factors shaping the diversity of TEs are poorly understood. Understanding the evolution of TEs is difficult because their sequences diversify rapidly and TEs are often transferred through non‐conventional means such as horizontal gene transfer. We developed a method to track TE evolution using network analysis to visualise TE sequence and TE content across different genomes. We illustrate our method by first using a monopartite network to study the sequence evolution of Tc1/mariner elements across focal species. We identify a connection between two subfamilies associated with convergent acquisition of a domain from a protein‐coding gene. Second, we use a bipartite network to study how TE content across species is shaped by epigenetic silencing mechanisms. We show that the presence of Piwi‐interacting RNAs is associated with differences in network topology after controlling for phylogenetic effects. Together, our method demonstrates how a network‐based approach can identify hitherto unknown properties of TE evolution across species.

Abstract Background Crizotinib is the approved treatment for advanced non-small cell lung cancers (NSCLCs) of anaplastic lymphoma kinase (ALK) fusion. Failure of crizotinib treatment frequently involves drug intolerance or resistance. Comparison of using second-generation ALK inhibitors in this setting remains lacking. Methods Sixty-five ALK-positive advanced NSCLC patients receiving second-generation ALK inhibitors following treatment failure of crizotinib were retrospectively analyzed for the therapeutic efficacy. Results Forty-three (66.2%) and 22 (33.8%) patients received alectinib and ceritinib, respectively. Comparing alectinib to ceritinib treatment: the 12-month progression-free survival (PFS) rate (61.0% [95% confidence interval, 47.1 to 78.9%] vs. 54.5% [95% CI, 37.3 to 79.9%]); the hazard ratio (HR) for disease progression or death, 0.61 (95% CI, 0.31–1.17; p = 0.135). Multivariate Cox regression showed ECOG PS (0–1 vs. 2–3 HR 0.09 [95% CI, 0.02–0.33]; p

Temporal knowledge graphs can be used to represent the current state of the world and, as daily events happen, the need to update the temporal knowledge graph, in order to stay consistent with the state of the world, becomes very important. However, there is currently no reliable method to accurately validate the update and evolution of knowledge graphs. There has been a recent development in text summarisation, whereby question answering is used to both guide and fact-check summarisation quality. The exact process can be applied to the temporal knowledge graph update process. To the best of our knowledge, there is currently no dataset that connects temporal knowledge graphs with documents with question–answer pairs. In this paper, we proposed the TKGQA dataset, consisting of over 5000 financial news documents related to M&A. Each document has extracted facts, question–answer pairs, and before and after temporal knowledge graphs, to highlight the state of temporal knowledge and any changes caused by the facts extracted from the document. As we parse through each document, we use question–answering to check and guide the update process of the temporal knowledge graph.

Background Treatment for stage III non‐small cell lung cancer (NSCLC) of unresectable disease mainly involves concurrent chemoradiation (CRT). Post‐CRT consolidation treatment with durvalumab is a major therapeutic advance that provides survival benefit in this group of patients. However, the performance of this treatment strategy remains to be studied in a real‐world setting. Methods A total of 31 patients who had disease control post‐CRT were included in the durvalumab early access program (EAP) as an intent‐to‐treat cohort and retrospectively reviewed for post‐CRT progression‐free survival (PFS) and time to metastatic disease or death (TMDD). The neutrophil‐to‐lymphocyte ratio (NLR) at the initiation of durvalumab was analyzed in 29 patients. Results The median time from the completion of concurrent CRT to the initiation of durvalumb was 2.8 months. The objective response was 25.8% and the 12 month PFS and TMDD‐free rate were 56.4% and 66.9%, respectively. The low NLR patients showed a significantly longer post‐CRT PFS (not reach vs. 12.0 months [95% CI: 5.5–not estimable]; P = 0.040; the hazard ratio for disease progression or death, 0.23 [95% CI: 0.05–1.00]; P = 0.048) and the 12 month post‐CRT PFS rate (82.5 vs. 42.6%). The post‐CRT TMDD (not reach vs. 12.6 months, [95% CI: 10.8–not estimable]; P = 0.010; the hazard ratio for distant metastasis or death, 0.11 [95% CI: 0.01–0.88]; P = 0.037) and 12 month post‐CRT TMDD‐free rate (90.9 vs. 57.1%) were also significantly higher in the low NLR patients. Conclusions Durvalumab consolidation treatment in real‐world patients showed substantial efficacy and the correlation with the NLR level warrants further investigation.

Background Approximately 3%–5% of lung adenocarcinoma is driven by anaplastic lymphoma kinase (ALK) fusion oncogene, whose activity can be suppressed by multiple ALK inhibitors. Crizotinib and ceritinib have demonstrated superior efficacy to platinum‐based chemotherapy as front‐line treatment for patients with ALK‐positive advanced non‐small cell lung cancer (NSCLC). However, the direct comparison between them in the front‐line setting remains lacking. Methods A total of 48 patients with ALK‐positive, previously untreated advanced NSCLC, who received crizotinib and ceritinib as front‐line treatment were retrospectively investigated. The efficacy and pattern of disease progression were analyzed. Results Patients receiving ceritinib treatment were significantly younger than those receiving crizotinib treatment (52.0 vs. 63.0, P = 0.016). The median progression‐free survival (PFS) was significantly longer with ceritinib than with crizotinib treatment (32.3 vs. 12.9 months; log‐rank P = 0.020); the hazard ratio for disease progression or death, 0.27 (95% CI, 0.08–0.90; P = 0.033). An objective response was noted in all patients in the ceritinib group and in 23 patients in the crizotinib group (74.2%; 95% CI, 59.0 to 88.5). The rate of systemic progression was significantly lower over time with ceritinib treatment compared to crizotinib treatment (cause‐specific hazard ratio, 0.21; 95% CI 0.06–0.73; P = 0.014). Serious adverse events were noted in one (2.9%) patient showing elevated liver function in the crizotinib group and three (23.1%) patients showing diarrhea in the ceritinib group. Dose reduction was needed in five out of 13 (38.5%) patients receiving ceritinib treatment. Conclusion Ceritinib showed higher efficacy associated with a better control of systemic progression compared to crizotinib for the front‐line treatment of ALK‐positive advanced NSCLCs.

Background: There are limited comparisons of first- and second-generation EGFR tyrosine kinase inhibitors (TKIs) in large, real-world cohorts of non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor ( EGFR ) mutations. Methods: Patients with advanced NSCLC ( N = 612) with common EGFR mutations receiving first-line gefitinib/erlotinib and afatinib were grouped and propensity-score matched. Progression-free survival (PFS), overall survival (OS) and secondary T790M mutations were analyzed. Results: The gefitinib/erlotinib and afatinib groups each contained 206 patients after matching. Compared with gefitinib/erlotinib, patients receiving afatinib achieved longer median PFS (16.3 versus 14.2 months; log-rank test p = 0.020) and had a lower risk of progression [hazard ratio (HR) 0.73 (95% confidence interval (CI), 0.57–0.94); p = 0.017]. Median OS (37.3 versus 34.2 months; log-rank test p = 0.500) and reduction in risk of death [HR 0.89 (95% CI, 0.65–1.23); p = 0.476] did not differ significantly between groups. T790M positivity was significantly higher in the gefitinib/erlotinib than afatinib group (70.9% versus 44.6%, p

In this paper, we suggest a new methodology which combines Neural Networks (NN) into Data Assimilation (DA). Focusing on the structural model uncertainty, we propose a framework for integration NN with the physical models by DA algorithms, to improve both the assimilation process and the forecasting results. The NNs are iteratively trained as observational data is updated. The main DA models used here are the Kalman filter and the variational approaches. The effectiveness of the proposed algorithm is validated by examples and by a sensitivity study.

We investigated whether and how political misinformation is engineered using a dataset of four months worth of tweets related to the 2016 presidential election in the United States. The data contained tweets that achieved a significant level of exposure and was manually labelled into misinformation and regular information. We found that misinformation was produced by accounts that exhibit different characteristics and behaviour from regular accounts. Moreover, the content of misinformation is more novel, polarised and appears to change through coordination. Our findings suggest that engineering of political misinformation seems to exploit human traits such as reciprocity and confirmation bias. We argue that investigating how misinformation is created is essential to understand human biases, diffusion and ultimately better produce public policy.

Parameter identification for wildfire forecasting models often relies on case-by-case tuning or posterior diagnosis/analysis, which can be computationally expensive due to the complexity of the forward prediction model. In this paper, we introduce an efficient parameter flexible fire prediction algorithm based on machine learning and reduced order modelling techniques. Using a training dataset generated by physics-based fire simulations, the method forecasts burned area at different time steps with a low computational cost. We then address the bottleneck of efficient parameter estimation by developing a novel inverse approach relying on data assimilation techniques (latent assimilation) in the reduced order space. The forward and the inverse modellings are tested on two recent large wildfire events in California. Satellite observations are used to validate the forward prediction approach and identify the model parameters. By combining these forward and inverse approaches, the system manages to integrate real-time observations for parameter adjustment, leading to more accurate future predictions.

Numerical simulations are widely used as a predictive tool to better understand complex air flows and pollution transport on the scale of individual buildings, city blocks, and entire cities. To improve prediction for air flows and pollution transport, we propose a Variational Data Assimilation (VarDA) model which assimilates data from sensors into the open-source, finite-element, fluid dynamics model Fluidity. VarDA is based on the minimization of a function which estimates the discrepancy between numerical results and observations assuming that the two sources of information, forecast and observations, have errors that are adequately described by error covariance matrices. The conditioning of the numerical problem is dominated by the condition number of the background error covariance matrix which is ill-conditioned. In this paper, a preconditioned VarDA model is presented, it is based on a reduced background error covariance matrix. The Empirical Orthogonal Functions (EOFs) method is used to alleviate the computational cost and reduce the space dimension. Experimental results are provided assuming observed values provided by sensors from positions mainly located on roofs of buildings.

For decades, traditional correlation analysis and regression models have been used in social science research. However, the development of machine learning algorithms makes it possible to apply machine learning techniques for social science research and social issues, which may outperform standard regression methods in some cases. Under the circumstances, this article proposes a methodological workflow for data analysis by machine learning techniques that have the possibility to be widely applied in social issues. Specifically, the workflow tries to uncover the natural mechanisms behind the social issues through a data-driven perspective from feature selection to model building. The advantage of data-driven techniques in feature selection is that the workflow can be built without so much restriction of related knowledge and theory in social science. The advantage of using machine learning techniques in modelling is to uncover non-linear and complex relationships behind social issues. The main purpose of our methodological workflow is to find important fields relevant to the target and provide appropriate predictions. However, to explain the result still needs theory and knowledge from social science. In this paper, we trained a methodological workflow with left-behind children as the social issue case, and all steps and full results are included.

In this paper, we propose Deep Data Assimilation (DDA), an integration of Data Assimilation (DA) with Machine Learning (ML). DA is the Bayesian approximation of the true state of some physical system at a given time by combining time-distributed observations with a dynamic model in an optimal way. We use a ML model in order to learn the assimilation process. In particular, a recurrent neural network, trained with the state of the dynamical system and the results of the DA process, is applied for this purpose. At each iteration, we learn a function that accumulates the misfit between the results of the forecasting model and the results of the DA. Subsequently, we compose this function with the dynamic model. This resulting composition is a dynamic model that includes the features of the DA process and that can be used for future prediction without the necessity of the DA. In fact, we prove that the DDA approach implies a reduction of the model error, which decreases at each iteration; this is achieved thanks to the use of DA in the training process. DDA is very useful in that cases when observations are not available for some time steps and DA cannot be applied to reduce the model error. The effectiveness of this method is validated by examples and a sensitivity study. In this paper, the DDA technology is applied to two different applications: the Double integral mass dot system and the Lorenz system. However, the algorithm and numerical methods that are proposed in this work can be applied to other physics problems that involve other equations and/or state variables.

BackgroundThe immune cells in the local environments surrounding non-small cell lung cancer (NSCLC) implicate the balance of pro- and antitumor immunity; however, their transcriptomic profiles remain poorly understood.MethodsA transcriptomic microarray study of bronchoalveolar lavage (BAL) cells harvested from tumor-bearing lung segments was performed in a discovery group. The findings were validated (1) in published microarray datasets, (2) in an independent group by RT-qPCR, and (3) in non-diseased and tumor adjacent non-neoplastic lung tissue by immunohistochemistry and in BAL cell lysates by immunoblotting.ResultThe differential expression of 129 genes was identified in the discovery group. These genes revealed functional enrichment in Fc gamma receptor-dependent phagocytosis and circulating immunoglobulin complex among others. Microarray datasets analysis (n = 607) showed that gene expression of BAL cells of tumor-bearing lung segment was also the unique transcriptomic profile of tumor adjacent non-neoplastic lung of early stage NSCLC and a significantly gradient increase of immunoglobulin genes’ expression for non-diseased lungs, tumor adjacent non-neoplastic lungs, and tumors was identified (ANOVA, p

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) belongs to a subgroup of coronaviruses rampant in bats for centuries. It caused the coronavirus disease 2019 (COVID-19) pandemic. Most patients recover, but a minority of severe cases experience acute respiratory distress or an inflammatory storm devastating many organs that can lead to patient death. The spread of SARS-CoV-2 was facilitated by the increasing intensity of air travel, urban congestion and human contact during the past decades. Until therapies and vaccines are available, tests for virus exposure, confinement and distancing measures have helped curb the pandemic. Vision: The COVID-19 pandemic calls for safeguards and remediation measures through a systemic response. Self-organizing initiatives by scientists and citizens are developing an advanced collective intelligence response to the coronavirus crisis. Their integration forms Olympiads of Solidarity and Health. Their ability to optimize our response to COVID-19 could serve as a model to trigger a global metamorphosis of our societies with far-reaching consequences for attacking fundamental challenges facing humanity in the 21 st century. Mission: For COVID-19 and these other challenges, there is no alternative but action. Meeting in Paris in 2003, we set out to 'rethink research to understand life and improve health.' We have formed an international coalition of academia and industry ecosystems taking a systems medicine approach to understanding COVID-19 by thoroughly characterizing viruses, patients and populations during the pandemic, using openly shared tools. All results will be publicly available with no initial claims for intellectual property rights. This World Alliance for Health and Wellbeing will catalyze the creation of medical and health products such as diagnostic tests, drugs and vaccines that become common goods accessible to all, while seeking further alliances with civil society to bridge with socio-ecological and technological approaches that characterise urban systems, for a collective response to future health emergencies.

Background: Severe neutrophilic asthma is resistant to treatment with glucocorticoids. The immunomodulatory protein macrophage migration inhibitory factor (MIF) promotes neutrophil recruitment to the lung and antagonises responses to glucocorticoids. We hypothesised that MIF promotes glucocorticoid resistance of neutrophilic inflammation in severe asthma.Methods: We examined whether sputum MIF protein correlated with clinical and molecular characteristics of severe neutrophilic asthma in the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED) cohort. We also investigated whether MIF regulates neutrophilic inflammation and glucocorticoid responsiveness in a murine model of severe asthma in vivo.Results: MIF protein levels positively correlated with the number of exacerbations in the previous year, sputum neutrophils and oral corticosteroid use across all U-BIOPRED subjects. Further analysis of MIF protein expression according to U-BIOPRED-defined transcriptomic-associated clusters (TACs) revealed increased MIF protein and a corresponding decrease in annexin-A1 protein in TAC2, which is most closely associated with airway neutrophilia and NLRP3 inflammasome activation. In a murine model of severe asthma, treatment with the MIF antagonist ISO-1 significantly inhibited neutrophilic inflammation and increased glucocorticoid responsiveness. Coimmunoprecipitation studies using lung tissue lysates demonstrated that MIF directly interacts with and cleaves annexin-A1, potentially reducing its biological activity.Conclusion: Our data suggest that MIF promotes glucocorticoid-resistance of neutrophilic inflammation by reducing the biological activity of annexin-A1, a potent glucocorticoid-regulated protein that inhibits neutrophil accumulation at sites of inflammation. This represents a previously unrecognised role for MIF in the regulation of inflammation and points to MIF as a potential therapeutic target for the management of severe neutrophilic asthma. [ABSTRACT FROM AUTHOR]

Motivation: High-throughput molecular profiling has greatly improved patient stratification and mechanistic understanding of diseases. With the increasing amount of data used in translational medicine studies in recent years, there is a need to improve the performance of data warehouses in terms of data retrieval and statistical processing. Both relational and Key Value models have been used for managing molecular profiling data. Key Value models such as SeqWare have been shown to be particularly advantageous in terms of query processing speed for large datasets. However, more improvement can be achieved, particularly through better indexing techniques of the Key Value models, taking advantage of the types of queries which are specific for the highthroughput molecular profiling data. Results: In this article, we introduce a Collaborative Genomic Data Model (CGDM), aimed at significantly increasing the query processing speed for the main classes of queries on genomic databases. CGDM creates three Collaborative Global Clustering Index Tables (CGCITs) to solve the velocity and variety issues at the cost of limited extra volume. Several benchmarking experiments were carried out, comparing CGDM implemented on HBase to the traditional SQL data model (TDM) implemented on both HBase and MySQL Cluster, using large publicly available molecular profiling datasets taken from NCBI and HapMap. In the microarray case, CGDM on HBase performed up to 246 times faster than TDM on HBase and 7 times faster than TDM on MySQL Cluster. In single nucleotide polymorphism case, CGDM on HBase outperformed TDM on HBase by up to 351 times and TDM on MySQL Cluster by up to 9 times. [ABSTRACT FROM AUTHOR]