Pre-defined Set Research Articles

651: Maternal smoking during pregnancy and the risk of pediatric cardiovascular diseases of the offspring: A population-based cohort study with up to 18-years follow up

Cigarette smoke is a well-known reproductive toxicant, and maternal cigarette smoke exposure was shown to be associated with maternal cardiovascular morbidity. The understanding of the effect of in utero exposure to cigarette smoke on long-term childhood diseases and specifically cardiovascular morbidity is limited. We aimed to study the long-term effect of cigarette smoking during pregnancy on the risk for childhood cardiovascular morbidity of the offspring. A population based cohort analysis was performed comparing total and different subtypes of cardiovascular related pediatric hospitalizations among offspring of smoking mothers versus offspring of non-smoking mothers. The analysis included all singletons born between the years 1999-2014 at a single tertiary regional medical center. Cardiovascular related morbidities included hospitalizations involving a pre-defined set of ICD-9 codes, as recorded in the hospital computerized files. Infants with congenital malformations, multiple gestations, and all perinatal deaths were excluded from the analysis. A Kaplan-Meier survival curve was used to compare the cumulative cardiovascular morbidity, and a Cox proportional hazards model was constructed to adjust for confounders. The study population included 242,342 newborns which met inclusion criteria; among them 2861 were born to mothers who smoke during pregnancy. Offspring of smoking mothers had significantly higher rates of cardiovascular-related hospitalizations in comparison to offspring of non-smoking mothers (1.3% vs. 0.6%, OR 2.1, 95% CI 1.5-2.9; p<0.001; Kaplan-meier survival curve presented in graph). The association remained significant and independent while adjusting for gestational age, maternal age, hypertension and diabetes (Adjusted HR=2.3, 95% CI 1.6-3.1; p<0.001). Smoking exposure during pregnancy is associated with an increased risk for long-term pediatric cardiovascular morbidity of the offspring. Patients should be informed about the long-term consequences of maternal smoking on offspring health, which may facilitate smoking cessation in this population.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Auditing local methods for quality assurance in radiotherapy using the same set of predefined treatment plans

Background and purposeLocal implementation of plan-specific quality assurance (QA) methods for intensity-modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) treatment plans may vary because of dissimilarities in procedures, equipment and software. The purpose of this work is detecting possible differences between local QA findings and those of an audit, using the same set of treatment plans.MethodsA pre-defined set of clinical plans was devised and imported in the participating institute’s treatment planning system for dose computation. The dose distribution was measured using an ionisation chamber, radiochromic film and an ionisation chamber array. The centres performed their own QA, which was compared to the audit findings. The agreement/disagreement between the audit and the institute QA results were assessed along with the differences between the dose distributions measured by the audit team and computed by the institute.ResultsFor the majority of the cases the results of the audit were in agreement with the institute QA findings: ionisation chamber: 92%, array: 88%, film: 76% of the total measurements. In only a few of these cases the evaluated measurements failed for both: ionisation chamber: 2%, array: 4%, film: 0% of the total measurements.ConclusionUsing predefined treatment plans, we found that in approximately 80% of the evaluated measurements the results of local QA of IMRT and VMAT plans were in line with the findings of the audit. However, the percentage of agreement/disagreement depended on the characteristics of the measurement equipment used and on the analysis metric.

All for one or some for all? Evaluating informative hypotheses using multiple N =\u20091 studies

Analyses are mostly executed at the population level, whereas in many applications the interest is on the individual level instead of the population level. In this paper, multiple N = 1 experiments are considered, where participants perform multiple trials with a dichotomous outcome in various conditions. Expectations with respect to the performance of participants can be translated into so-called informative hypotheses. These hypotheses can be evaluated for each participant separately using Bayes factors. A Bayes factor expresses the relative evidence for two hypotheses based on the data of one individual. This paper proposes to “average” these individual Bayes factors in the gP-BF, the average relative evidence. The gP-BF can be used to determine whether one hypothesis is preferred over another for all individuals under investigation. This measure provides insight into whether the relative preference of a hypothesis from a pre-defined set is homogeneous over individuals. Two additional measures are proposed to support the interpretation of the gP-BF: the evidence rate (ER), the proportion of individual Bayes factors that support the same hypothesis as the gP-BF, and the stability rate (SR), the proportion of individual Bayes factors that express a stronger support than the gP-BF. These three statistics can be used to determine the relative support in the data for the informative hypotheses entertained. Software is available that can be used to execute the approach proposed in this paper and to determine the sensitivity of the outcomes with respect to the number of participants and within condition replications.

Maternal history of recurrent pregnancy loss is associated with increased risk for long-term pediatric gastrointestinal morbidity in the offspring.

Recurrent pregnancy loss (RPL) potentially involves an abnormal maternal inflammatory response. We investigated whether children of mothers with a history of RPL are at an increased risk for childhood gastrointestinal (GI) morbidity, with a specific focus on inflammatory bowel diseases (IBD). A population-based cohort analysis comparing the risk for long-term GI morbidity in children born to mothers with and without a history of RPL. Gastrointestinal (GI) morbidity included hospitalizations involving a pre-defined set of ICD-9 codes. During the study period, 242 186 newborns met the inclusion criteria; 5% of which were offspring to mothers with a history of RPL. Gastrointestinal morbidity was significantly more common in the RPL group (6.6% vs 5.3%). Specifically, offspring to mothers with a history of RPL had significantly higher rates of IBD (2.1% vs 1.7%). Maternal history of RPL is associated with an increased risk for pediatric GI morbidity in the offspring.

Cortical computations via transient attractors.

The ability of sensory networks to transiently store information on the scale of seconds can confer many advantages in processing time-varying stimuli. How a network could store information on such intermediate time scales, between typical neurophysiological time scales and those of long-term memory, is typically attributed to persistent neural activity. An alternative mechanism which might allow for such information storage is through temporary modifications to the neural connectivity which decay on the same second-long time scale as the underlying memories. Earlier work that has explored this method has done so by emphasizing one attractor from a limited, pre-defined set. Here, we describe an alternative, a Transient Attractor network, which can learn any pattern presented to it, store several simultaneously, and robustly recall them on demand using targeted probes in a manner reminiscent of Hopfield networks. We hypothesize that such functionality could be usefully embedded within sensory cortex, and allow for a flexibly-gated short-term memory, as well as conferring the ability of the network to perform automatic de-noising, and separation of input signals into distinct perceptual objects. We demonstrate that the stored information can be refreshed to extend storage time, is not sensitive to noise in the system, and can be turned on or off by simple neuromodulation. The diverse capabilities of transient attractors, as well as their resemblance to many features observed in sensory cortex, suggest the possibility that their actions might underlie neural processing in many sensory areas.

Heart Rate Fragmentation: A Symbolic Dynamical Approach.

Background: We recently introduced the concept of heart rate fragmentation along with a set of metrics for its quantification. The term was coined to refer to an increase in the percentage of changes in heart rate acceleration sign, a dynamical marker of a type of anomalous variability. The effort was motivated by the observation that fragmentation, which is consistent with the breakdown of the neuroautonomic-electrophysiologic control system of the sino-atrial node, could confound traditional short-term analysis of heart rate variability.Objective: The objectives of this study were to: (1) introduce a symbolic dynamical approach to the problem of quantifying heart rate fragmentation; (2) evaluate how the distribution of the different dynamical patterns (“words”) varied with the participants' age in a group of healthy subjects and patients with coronary artery disease (CAD); and (3) quantify the differences in the fragmentation patterns between the two sample populations.Methods: The symbolic dynamical method employed here was based on a ternary map of the increment NN interval time series and on the analysis of the relative frequency of symbolic sequences (words) with a pre-defined set of features. We analyzed annotated, open-access Holter databases of healthy subjects and patients with CAD, provided by the University of Rochester Telemetric and Holter ECG Warehouse (THEW).Results: The degree of fragmentation was significantly higher in older individuals than in their younger counterparts. However, the fragmentation patterns were different in the two sample populations. In healthy subjects, older age was significantly associated with a higher percentage of transitions from acceleration/deceleration to zero acceleration and vice versa (termed “soft” inflection points). In patients with CAD, older age was also significantly associated with higher percentages of frank reversals in heart rate acceleration (transitions from acceleration to deceleration and vice versa, termed “hard” inflection points). Compared to healthy subjects, patients with CAD had significantly higher percentages of soft and hard inflection points, an increased percentage of words with a high degree of fragmentation and a decreased percentage of words with a lower degree of fragmentation.Conclusion: The symbolic dynamical method employed here was useful to probe the newly recognized property of heart rate fragmentation. The findings from these cross-sectional studies confirm that CAD and older age are associated with higher levels of heart rate fragmentation. Furthermore, fragmentation with healthy aging appears to be phenotypically different from fragmentation in the context of CAD.

Bridging the Gap between the Social and Semantic Web: Extracting domain-specific ontology from folksonomy

Folksonomies have become very popular as means to organize large sets of resources shared over the Social Web. The bottom-up nature of folksonomies has proved to be an interesting alternative to the current effort at semantic web ontologies since folksonomies provide a rich terminology generated by large user-communities. Besides, ontologies extracted from folksonomies can represent the intelligence collective of social communities. Such ontologies also represent a core element of a new feature of the Web, the Internet of Things. Many research studies have captured semantics in folksonomies, some of which have developed ontologies from folksonomy. However, the formal specific-domain ontology consisting of domain-dependent relations has not been researched yet. This paper introduces an algorithm for deriving a domain-specific ontology from folksonomy tags. The proposed algorithm starts by collecting a domain-specific terminology; next, discovering a pre-defined set of conceptual relationships among the domain terminologies. The evaluation of the algorithm, using a dataset extracted from BibSonomy, demonstrated that the algorithm could effectively learn domain ontologies consisting of domain concepts linked by meaningful and high accurate relationships. Furthermore, the proposed algorithm can help reduce common issues related to tag ambiguity and synonymous tags.

Designing Randomized DNA Sequences Free of Restriction Enzyme Recognition Sites.

DNA libraries containing random "barcodes" complicate synthetic biology workflows that utilize restriction enzymes since restriction sites can appear inside some barcodes. By removing bases at particular sites in the barcodes, it is possible to create semi-random pools of barcodes that do not contain any restriction sites. The challenge is to remove as few bases as possible to maximize the number of sequences in the pool while ensuring all sequences are free of restriction sites. The authors present CutFree, a computational approach to create pools of random DNA barcodes that lack a pre-defined set of restriction sites. The resulting pools can be inexpensively produced en masse with standard DNA synthesis techniques. CutFree is experimentally validated by blocking digestion of pools of barcodes designed to frequently contain restriction sites. Using CutFree, a pool of 1.3 billion barcodes that are free from recognition sites for 182 commercially available restriction enzymes is designed. CutFree is available as a software package and an online tool (http://jensenlab.net/tools).

SAIC: an iterative clustering approach for analysis of single cell RNA-seq data

BackgroundResearch interests toward single cell analysis have greatly increased in basic, translational and clinical research areas recently, as advances in whole-transcriptome amplification technique allow scientists to get accurate sequencing result at single cell level. An important step in the single-cell transcriptome analysis is to identify distinct cell groups that have different gene expression patterns. Currently there are limited bioinformatics approaches available for single-cell RNA-seq analysis. Many studies rely on principal component analysis (PCA) with arbitrary parameters to identify the genes that will be used to cluster the single cells.ResultsWe have developed a novel algorithm, called SAIC (Single cell Analysis via Iterative Clustering), that identifies the optimal set of signature genes to separate single cells into distinct groups. Our method utilizes an iterative clustering approach to perform an exhaustive search for the best parameters within the search space, which is defined by a number of initial centers and P values. The end point is identification of a signature gene set that gives the best separation of the cell clusters. Using a simulated data set, we showed that SAIC can successfully identify the pre-defined signature gene sets that can correctly separated the cells into predefined clusters. We applied SAIC to two published single cell RNA-seq datasets. For both datasets, SAIC was able to identify a subset of signature genes that can cluster the single cells into groups that are consistent with the published results. The signature genes identified by SAIC resulted in better clusters of cells based on DB index score, and many genes also showed tissue specific expression.ConclusionsIn summary, we have developed an efficient algorithm to identify the optimal subset of genes that separate single cells into distinct clusters based on their expression patterns. We have shown that it performs better than PCA method using published single cell RNA-seq datasets.

Point Cloud-Based Automatic Assessment of 3D Computer Animation Courseworks

Computer-supported assessment tools can bring significant benefits to both students and teachers. When integrated in traditional education workflows, they may help to reduce the time required to perform the evaluation and consolidate the perception of fairness of the overall process. When integrated within on-line intelligent tutoring systems, they could provide students with a timely feedback and support self-assessment activities. The current work presents an alternative approach (and not just a “yet-another-implementation”) to the problem of automatically evaluating technical skills needed to create 3D computer animations. Although some solutions have been reported already in the literature, their applicability is partially constrained, as they require the teaching staff to define evaluation criteria that are strictly linked to the particular animation technique being assessed. Students are forced to operate in environments where they can only perform a part of the required animation steps, by using a pre-defined set of techniques and tools. To address such limitations, the proposed system exploits shape- and time-based features extracted from the 3D point clouds (i.e., the set of data points) describing animated geometries, which are independent of the particular animation techniques used. Experimental observations collected in the evaluation of course assignments in which students were asked to recreate 3D animations of deformable meshes prepared by the teaching staff showed a good correlation between automatic and manual evaluations. Obtained results confirmed the ability of the proposed approach to cope with heterogeneous evaluation tasks in which the relevant learning outcomes can be properly considered.

A hypothesis-driven approach to assessing significance of differences in RNA expression levels among specific groups of genes

Genome-wide molecular gene expression studies generally compare expression values for each gene across multiple conditions followed by cluster and gene set enrichment analysis to determine whether differentially expressed genes are enriched in specific biochemical pathways, cellular components, biological processes, and/or molecular functions, etc. This approach to analyzing differences in gene expression enables discovery of gene function, but is not useful to determine whether pre-defined groups of genes share or diverge in their expression patterns in response to treatments nor to assess the correctness of pre-defined gene set groupings. Here we present a simple method that changes the dimension of comparison by treating genes as variable traits to directly assess significance of differences in expression levels among pre-defined gene groups. Because expression distributions are typically skewed (thus unfit for direct assessment using Gaussian statistical methods) our method involves transforming expression data to approximate a normal distribution followed by dividing the genes into groups, then applying Gaussian parametric methods to assess significance of observed differences. This method enables the assessment of differences in gene expression distributions within and across samples, enabling hypothesis-based comparison among groups of genes. We demonstrate this method by assessing the significance of specific gene groups’ differential response to heat stress conditions in maize.

Fuzzy Logic-Based Energy Management System Design for Residential Grid-Connected Microgrids

This paper presents the design of a low complexity fuzzy logic controller of only 25-rules to be embedded in an energy management system for a residential grid-connected microgrid including renewable energy sources and storage capability. The system assumes that neither the renewable generation nor the load demand is controllable. The main goal of the design is to minimize the grid power profile fluctuations while keeping the battery state of charge within secure limits. Instead of using forecasting-based methods, the proposed approach use both the microgrid energy rate-of-change and the battery state of charge to increase, decrease, or maintain the power delivered/absorbed by the mains. The controller design parameters (membership functions and rule-base) are adjusted to optimize a pre-defined set of quality criteria of the microgrid behavior. A comparison with other proposals seeking the same goal is presented at simulation level, whereas the features of the proposed design are experimentally tested on a real residential microgrid implemented at the Public University of Navarre.

Do third-year mental health nursing students feel prepared to assess physical health?

Background The life expectancy for people with mental health issues is significantly lower than the general population, however, their physical health needs are often unrecognised by health professionals.Aim To investigate whether third-year mental health nursing students are clinically prepared to undertake a pre-defined set of physical health checks.Method A 34-item questionnaire was completed by two cohorts of mental health nursing students in their third and final year. Participants self-reported on their competence to assess a range of physical health checks. 37 questionnaires were completed and analysed.Findings Three groups emerged: group 1 – 100% of students self-declared competence in assessments including temperature and pulse, group 2 – more than 50% of students self-declared competence in assessments including urinalysis and pulse oximetry, and group 3 – less than 50% of students self-declared competence in taking electrocardiograms and using the hydration assessment tool.Conclusion The student participants of this study were not adequately prepared to undertake a complete range of physical health assessments for people with mental health issues.

Incidence, temporal trend and factors associated with ventilator-associated pneumonia in mainland China: a systematic review and meta-analysis

BackgroundData to date is far from sufficient to describe the recent epidemiology of ventilator-associated pneumonia (VAP) in mainland China. This study aimed to estimate the overall incidence of VAP, with a special focus on its temporal trend and associated factors.MethodsMeta-analyses of 195 studies published from 2010 to 2015 were conducted, followed by subgroup analyses by methodological quality, pre-defined setting characteristics and attributes of populations.ResultsThe overall cumulative VAP incidence in mainland China was 23.8% (95% confidence interval (CI) 20.6–27.2%), with the results showing high heterogeneity. The pooled incidence densities were 24.14 (95% CI 21.19–27.51) episodes and 22.83 (95% CI 19.88–26.23) patients per 1000 ventilator-days. A decline in the cumulative incidence was observed from 2006 (49.5%, 95% CI 40.0–59.0%) to 2014 (19.6%, 95% CI 10.4–31.0%); differences in the incidence rates were also documented according to Chinese provinces and diagnostic criteria (p < 0.001). Older age (≥60 years), coma, re-intubation, tracheotomy and prolonged ventilation were the factors significantly associated with the occurrence of VAP.ConclusionsThe incidence of VAP remains high in mainland China but has decreased since 2006. The reported rates vary considerably across individual studies, probably due to variations in diagnosis and geographical region. More studies using standard definitions and cut-off points are needed to better clarify the epidemiology of VAP across the country.

Security-Constrained Unit Commitment With Flexible Uncertainty Set for Variable Wind Power

Power system operation has recently witnessed major challenges, which are often due to large-scale integrations of wind power generation. In this paper, a two-stage robust security-constrained unit commitment (SCUC) model is proposed for managing the wind power uncertainty in the hourly scheduling of power system generation. Different from previous studies on robust SCUC, which considered a predefined uncertainty set, the proposed method applies a flexible uncertainty set for managing the variable wind power generation. The proposed method seeks a feasible and economic dispatch in the flexible uncertainty set, takes into account wind spillage and load curtailment risks, and makes a tradeoff between the optimal wind power absorption and the economic grid operation. Several case studies are applied to the proposed method and the corresponding solutions are analyzed in the paper. The impacts of major factors, including flexible generation resources and power transmission capacity, on the proposed solution are also discussed. The numerical results demonstrate the merits of the proposed method for managing large variations in the hourly wind power generation and lowering the power system operation cost in uncertain conditions.

On Operating Envelope Protection Design for Nonlinear Discrete-time Systems

This paper is concerned with the operating envelope protection design for a broad class of nonlinear discrete-time systems based on a Robust Control Invariant (RCI) set framework. Existing techniques for a direct design of a RCI set for a general system suffer from the computational intractability connected with the complexity of a system model and model dimension. For a nonlinear system with mild assumptions on its model here we use a suitably defined linear model with additive uncertainty confined in a finite set and its corresponding maximum RCI set. Robust constraints satisfaction for the original system is ensured for a pre-defined set of disturbance trajectories. The approach is based on a superposition of a nominal and perturbed dynamics, whereas the coupling effects between respective dynamics are encompassed with the additive uncertainty. The additive uncertainty set is estimated by employing the trajectories of disturbance and a system description. The presented approach is scalable with respect to the system dimension. A simple example from the wind energy field is used to illustrate the proposed method.

SpaceScanner: COPASI wrapper for automated management of global stochastic optimization experiments.

Due to their universal applicability, global stochastic optimization methods are popular for designing improvements of biochemical networks. The drawbacks of global stochastic optimization methods are: (i) no guarantee of finding global optima, (ii) no clear optimization run termination criteria and (iii) no criteria to detect stagnation of an optimization run. The impact of these drawbacks can be partly compensated by manual work that becomes inefficient when the solution space is large due to combinatorial explosion of adjustable parameters or for other reasons. SpaceScanner uses parallel optimization runs for automatic termination of optimization tasks in case of consensus and consecutively applies a pre-defined set of global stochastic optimization methods in case of stagnation in the currently used method. Automatic scan of adjustable parameter combination subsets for best objective function values is possible with a summary file of ranked solutions. https://github.com/atiselsts/spacescanner . egils.stalidzans@lu.lv. Supplementary data are available at Bioinformatics online.

A molecular lump based model for simulation of industrial naphtha hydro treators

A detailed, molecule-based kinetic model has been developed to simulate industrial naphtha hydro treators. A feed characterization module has been developed to calculate the composition of pre-defined pure molecules representing the feed quality without any detailed experimental component analysis. Feed naphtha is represented by a pre-defined set of 95 pure molecules, from carbon numbers C5 to C11, and the proportion of these components is calculated using an optimization algorithm so as to match the physico-chemical properties of the mixture with those of naphtha, as measured in the laboratory. The output from this optimization model is used as a direct input to a model of a hydro treating reactor. The main reactions occurring in the hydro treator, such as hydro desulfurization reactions and olefin saturation reactions, are modeled. The kinetic parameters for these reactions have been estimated from a comprehensive set of experimental pilot plant data spanning a wide range of process conditions. The kinetic model quantitatively predicts the exit composition, temperature rise and pressure drop in the catalyst bed for a range of conditions for two industrial refinery units.

Local search methods for the solution of implicit inverse problems

In this paper, we propose two local search algorithms based on the Tabu Search and the Simulated Annealing methods, respectively, for the solution of implicit inverse problems. In general, the proposed methods work as follows: given a noisy observation and a right-hand side function of a partial differential equation, for each model component a sub-domain is built about it and the corresponding part of observed components in such sub-domain is projected onto a space generated by a pre-defined set of local basis functions. For the projection, the singular value decomposition is applied to the data set in order to discard singular vectors corresponding to small singular values in pursuance of reducing the impact of noise on the projected data. The right-hand side function is then utilized in order to estimate the quality of the projection onto such sub-space. Since different basis functions provide different spaces onto which the data can be projected, the well-known Tabu Search and Simulated Annealing methods are utilized in order to enrich the search space of the optimal set of basis functions. This is the optimal combination of basis functions which minimizes the error during the projection step. After this, the local solutions are mapped back onto the global domain from which the global solution of the inverse problem is approximated. A strength of our proposed method is that no assumption is needed over the measurements to be assimilated. Experimental tests are performed making use of a parabolic partial differential equation and different noise levels for the data error. The results reveal that the use of the proposed implementations can provide accurate estimates in a root-mean-square error sense of the reference solution with even large data errors.

Characterization of anti-CD19 chimeric antigen receptor (CAR) T cell-mediated tumor microenvironment immune gene profile in a multicenter trial (ZUMA-1) with axicabtagene ciloleucel (axi-cel, KTE-C19).

3025 Background: Axi-cel is an autologous anti-CD19 CAR T cell therapy. ZUMA-1 is a multicenter, registrational trial of axi-cel in patients (pts) with refractory/aggressive B-cell non-Hodgkin lymphoma (NHL). In a pre-specified interim analysis, ZUMA-1 met its primary endpoint with 76% objective response rate and 47% complete response (Blood 2016;128:LBA-6). We describe, for the first time, a tumor microenvironment immune gene signature associated with CAR T cell treatment (tx) of NHL pts. Methods: Paired biopsies, pre- and within 3 weeks post-axi-cel tx, were analyzed by digital gene expression followed by a pre-specified bioinformatics algorithm applied to IGES15 and IGES21 genes involved in immune-mediated tumor regression (Immunosign; Galon Immunity 2013). Immunosign profiles expression of a pre-defined set of effector T cell, Th1, chemokine, and cytokine genes. Expression analysis and hierarchical clustering were used to define an axi-cel-related tumor immune gene signature. Wilcoxon signed rank test with multiple test correction by FDR (Benjamini-Yekutieli) was used. Results: Gene expression profile comparisons of pre- and post-axi-cel tx biopsies from 14 pts showed profound changes in gene expression within the tumor environment after infusion. The most upregulated genes post-axi-cel tx were CCL5 (RANTES), CTLA4, and GZMA (log2 fold change &gt; 2, P&lt; 0.05, FDR &lt; 0.050). Immune checkpoints PD-L1 and LAG3 were also upregulated post-axi-cel (log2 fold change &gt; 1.6, P&lt; 0.05, FDR &lt; 0.055). Other genes associated with T cell proliferation, homing, and effector function were also upregulated: IL-15, GZMK, CXC3CL1 (Fractalkine), CD8A, and STAT4 (log2 fold change &gt; 1.6; P&lt; 0.05, FDR &lt; 0.074). Additional baseline tumor characteristics and associative analysis will be presented. Conclusions: We define a mechanistic tumor immune gene signature in NHL pts associated with axi-cel tx. This signature comprises upregulation of T cell activation, effector, chemokine, and immune checkpoint genes. These data will potentially lead to rational optimization of T cell interventions in cancer Clinical trial information: NCT02348216.