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Within the Computer Science community, many ethical issues have emerged as significant and critical concerns. Computer ethics is an academic field in its own right and there are unique ethical issues associated with information technology. It encompasses a range of issues and concerns including privacy and agency around personal information, Artificial Intelligence and pervasive technology, the Internet of Things and surveillance applications. As computing technology impacts society at an ever growing pace, there are growing calls for more computer ethics content to be included in Computer Science curricula. In this paper we present the results of a survey that polled faculty from Computer Science and related disciplines about teaching practices for computer ethics at their institutions. The survey was completed by respondents from 61 universities across 23 European countries. Participants were surveyed on whether or not computer ethics is taught to Computer Science students at each institution, the reasons why computer ethics is or is not taught, how computer ethics is taught, the background of staff who teach computer ethics and the scope of computer ethics curricula. This paper presents and discusses the results of the survey.

The increasing industrial demand for handling complex functionalities has influenced the design of hardware architectures for time critical embedded systems, during the past decade. Multicore systems facilitate the inclusion of many complex functionalities, while, at the same time, inducing cache related overheads, as well as adding partitioning complexity to the overall system schedulability. One of the efficient paradigms for controlling and reducing the cache related costs in real-time systems is limited preemptive scheduling (LPS), with its particular instance fixed preemption points scheduling (LP-FPPS), which has been shown to outperform other alternatives as well as has been supported by and investigated in the automotive domain. With respect to the partitioning constraints, partitioned scheduling has been widely used to preruntime allocate tasks to specific cores, resulting in predictable cache-related preemption delays estimations. In this paper, we propose to integrate the LP-FPPS and partitioned scheduling on fixed-priority multicore real-time systems in order to increase the overall system schedulability. We define a new joint approach for task partitioning and preemption point selection, which is based on the computation of the maximum blocking tolerance upon each allocation, thus being able to quantify the schedulability of the taskset on each processor. Furthermore, we investigate the partitioning strategies based on different heuristics, i.e., first fit decreasing and worst fit decreasing, and priority and density taskset orderings. The evaluation performed on randomly generated tasksets shows that in the general case, no single partitioning strategy fully dominates the others. However, the evaluation results reveal that the certain partitioning strategies perform significantly better with respect to the overall schedulability for specific taskset characteristics. The results also reveal that the proposed partitioning strategies outperform fully preemptive and nonpreemptive partitioned scheduling in terms of successful partitioning.

In recent years, regulatory bodies have increasingly recognized the utility of real-world evidence (RWE) for supplementing and supporting clinical trial data in new drug applications. Nevertheless, the integration of RWE into established regulatory processes is complex and the generation of ‘regulatory-grade’ real-world data faces operational, methodological, data-related and policy-related challenges. In parallel with this evolving role for RWE, immuno-oncology therapies have emerged as leading cancer treatments and are expected to continue to play a central role in the future. In this article, we review the current literature on the use of RWE for regulatory submissions, with a focus on novel anticancer immunotherapies, and discuss the utility and current limitations of RWE in the context of drug development and regulatory approvals.

ImportanceUnderstanding the mechanisms of primary resistance to immune checkpoint blockade therapy is of paramount importance for treatment selection. Somatic mutations in STK11 and KEAP1, frequently co-mutated in nonsquamous non–small cell lung cancer, have been associated with poor response to immune checkpoint blockade. However, previous reports lack non–immune checkpoint blockade controls needed to properly ascertain the predictive nature of those biomarkers.ObjectiveTo evaluate the predictive vs prognostic effect of STK11 or KEAP1 mutations across different treatment classes in nonsquamous non–small cell lung cancer.DesignA retrospective, real-world data cohort from the Flatiron Health network linked with genetic testing from Foundation Medicine, from January 1, 2011, through December 31, 2018.SettingMulticenter, including academic and community practices.ParticipantsPatients diagnosed with stage IIIB, IIIC, IVA, or IVB nonsquamous non–small cell lung cancer who initiated first-line treatment within 90 days after diagnosis.Main Outcomes and MeasuresReal-world, progression-free survival and overall survival calculated from time of initiation of first-line treatment.ResultsWe analyzed clinical and mutational data for 2276 patients with advanced, nonsquamous non–small cell lung cancer (mean age at advanced diagnosis, 66.3 years [SD 10.3], 54.4% female, 80.1% with a history of smoking), including patients treated with anti–programmed death-1/anti–programmed death ligand 1 inhibitors at first line (n = 574). Mutations in STK11 or KEAP1 were associated with poor outcomes across multiple therapeutic classes and were not specifically associated with poor outcomes in immune checkpoint blockade cohorts. There was no observable interaction between STK11 mutations and anti–programmed death-1/anti–programmed death ligand 1 treatment on real-world, progression-free survival (HR, 1.05; 95% CI, 0.76-1.44; P = .785) or overall survival (HR, 1.13; 95% CI, 0.76-1.67; P = .540). Similarly, there was no observable interaction between KEAP1 on real-world, progression-free survival (HR, 0.93; 95% CI, 0.67-1.28; P = .653) or overall survival (HR, 0.98; 95% CI, 0.66-1.45; P = .913). Results were consistent in KRAS-mutated patients.Conclusion and RelevanceOur results show that STK11-KEAP1 mutations are prognostic, not predictive, biomarkers for anti–programmed death-1/anti–programmed death ligand 1 therapy.Key PointsQuestionAre loss-of-function somatic mutations in STK11 and KEAP1 predictive of response to immune checkpoint blockade or simply prognostic?FindingsIn this observational real-world cohort totaling 2276 patients, including 574 treated with immune checkpoint blockade, we find that mutations in STK11 and KEAP1 are associated with poor prognosis across multiple first-line treatment classes.MeaningMutations in STK11 and KEAP1 are prognostic biomarkers of poor response to both immune checkpoint blockade and chemotherapy.

A major challenge in inflammatory bowel disease (IBD) is the integration of diverse IBD data sets to construct predictive models of IBD. We present a predictive model of the immune component of IBD that informs causal relationships among loci previously linked to IBD through genome-wide association studies (GWAS) using functional and regulatory annotations that relate to the cells, tissues, and pathophysiology of IBD. Our model consists of individual networks constructed using molecular data generated from intestinal samples isolated from three populations of patients with IBD at different stages of disease. We performed key driver analysis to identify genes predicted to modulate network regulatory states associated with IBD, prioritizing and prospectively validating 12 of the top key drivers experimentally. This validated key driver set not only introduces new regulators of processes central to IBD but also provides the integrated circuits of genetic, molecular, and clinical traits that can be directly queried to interrogate and refine the regulatory framework defining IBD.

Gene expression data are routinely used to identify genes that on average exhibit different expression levels between a case and a control group. Yet, very few of such differentially expressed genes are detectably perturbed in individual patients. Here, we develop a framework to construct personalized perturbation profiles for individual subjects, identifying the set of genes that are significantly perturbed in each individual. This allows us to characterize the heterogeneity of the molecular manifestations of complex diseases by quantifying the expression-level similarities and differences among patients with the same phenotype. We show that despite the high heterogeneity of the individual perturbation profiles, patients with asthma, Parkinson and Huntington’s disease share a broadpool of sporadically disease-associated genes, and that individuals with statistically significant overlap with this pool have a 80–100% chance of being diagnosed with the disease. The developed framework opens up the possibility to apply gene expression data in the context of precision medicine, with important implications for biomarker identification, drug development, diagnosis and treatment., Molecular Medicine: Personalized expression profiles elucidate disease heterogeneity A comparison of gene expression profiles of different patients with the same disease reveals a combinatorial model for disease heterogeneity. A framework is developed to generalize group-wise differential expression profiles to personalized perturbation profiles (PEEPs) for individual subjects. It is shown that similarities among patients with the same disease cannot be attributed to a few widely shared genes, but arise from more complex patterns of pairwise overlaps that often correspond to perturbations within the same biological pathway. This points to the existence of a disease module, i.e. a broader group of functionally related genes whose perturbations are associated with the specific disease. The overlap of an individual’s PEEP with the disease module accurately predicts disease status, suggesting their potential for developing combinatorial biomarker signatures or identifying most widely effective drug targets.

This paper proposes an efficient way to handle fault in controller area network (CAN)-based networked control system (NCS). A fault in a bus line of CAN will induce a data error which will result in data dropout or time delay, and subsequently may lead to performance degradation or system instability. A strategy to handle fault occurrence in CAN bus is proposed to properly analyze the effect of the fault to CAN-based NCS performance. The fault occurrences are modeled based on fault interarrival time, fault bursts’ duration, and Poisson law. Using fault and messages’ attributes, response time analysis (RTA) is performed and the probability of control message missing its deadline is calculated. Utilizing the new error handling algorithm to replace the native error handling of CAN, the probability of a control message missing its deadline can be translated into the probability of data dropout for control message. This methodology is evaluated using steer-by-wire system of vehicle to analyze the effect of fault occurrences in CAN. It is found that the proposed error handling mechanism has resulted in better NCS performance and the range of data dropout probability for control message also could be obtained, which serves as crucial input for NCS controller design.

Nowadays, industrial networks are often used for safety-critical applications with real-time requirements. Such applications usually have a time-triggered nature with message scheduling as a core property. Scheduling requires nodes to share the same notion of time, that is, to be synchronized. Therefore, clock synchronization is a fundamental asset in real-time networks. However, since typical standards for clock synchronization, for example, IEEE 1588, do not provide the required level of security, it raises the question of clock synchronization protection. In this paper, we identify a way to break synchronization based on the IEEE 1588 standard, by conducting a man-in-the-middle (MIM) attack followed by a delay attack. A MIM attack can be accomplished through, for example, Address Resolution Protocol (ARP) poisoning. Using the AVISPA tool, we evaluate the potential to perform a delay attack using ARP poisoning and analyze its consequences showing both that the attack can, indeed, break clock synchronization and that some design choices, such as a relaxed synchronization condition mode, delay bounding, and using knowledge of environmental conditions, can make the network more robust/resilient against these kinds of attacks. Lastly, a Configuration Agent is proposed to monitor and detect anomalies introduced by an adversary performing attacks targeting clock synchronization.

Background The molecular aetiology of inflammatory bowel disease [IBD] and its two subtypes, ulcerative colitis [UC] and Crohn’s disease [CD], have been carefully investigated at genome and transcriptome levels. Recent advances in high-throughput proteome quantification has enabled comprehensive large-scale plasma proteomics studies of IBD. Methods The study used two cohorts: [1] The CERTIFI-cohort: 42 samples from the CERTIFI trial of anti-TNFα–refractory CD patients; [2] the PROgECT-UNITI-HCs cohort: 46 UC samples of the PROgECT study, 84 CD samples of the UNITI I and UNITI II studies, and 72 healthy controls recruited in Mount Sinai Hospital, New York, USA. The plasma proteome for these two cohorts was quantified using high-throughput platforms. Results For the PROgECT-UNITI-HCs cohort, we measured a total of 1310 proteins. Of these, 493 proteins showed different plasma levels in IBD patients to the plasma levels in controls at 10% false discovery rate [FDR], among which 11 proteins had a fold change greater than 2. The proteins upregulated in IBD were associated with immunity functionality, whereas the proteins downregulated in IBD were associated with nutrition and metabolism. The proteomic profiles were very similar between UC and CD. In the CERTIFI cohort, 1014 proteins were measured, and it was found that the plasma protein level had little correlation with the blood or intestine transcriptomes. Conclusions We report the largest proteomics study to date on IBD and controls. A large proportion of plasma proteins are altered in IBD, which provides insights into the disease aetiology and indicates a potential for biomarker discovery.

The preemptive scheduling paradigm is known to strictly dominate the non-preemptive scheduling paradigm with respect to feasibility. On the other hand, preemptively scheduling real-time tasks on uniprocessors, unlike non-preemptive scheduling, may lead to unschedulability due to, e.g., preemption related overheads. The limited-preemptive scheduling paradigm, which is a generalization of preemptive and non-preemptive paradigms, has, however, the potential to reduce the preemption related overheads while enabling high processor utilization. In this paper, we focus on the characterization of the effects of increasing the computational resources on the limited-preemptive feasibility of real-time tasks in order to quantify the sub-optimality of limited-preemptive scheduling. Specifically, we first derive the required processor speed-up bound that guarantees limited-preemptive feasibility of any uniprocessor feasible taskset. Secondly, we demonstrate the applicability of the results in the context of controlling preemption related overheads while minimizing the required processor speed-up. In particular, we identify the preemptive behavior that minimizes preemption-related overheads, as well as derive the optimal processor speed associated with it. Finally, we examine the consequences of having more processors on limited-preemptive feasibility and derive the bound on the number of processors that guarantees a specified limited-preemptive behavior for any uniprocessor feasible real-time taskset. This paper essentially bridges the preemptive and non-preemptive real-time scheduling paradigms by providing significant theoretical results building on the limited-preemptive scheduling paradigm, as well as provides analytical inputs to developers in order to perform various trade-offs, e.g., code refactoring, to control the preemptive behavior of real-time tasks.

Fixed priority scheduling is used in many real-time systems; however, both preemptive and non-preemptive variants (FP-P and FP-NP) are known to be sub-optimal when compared to an optimal uniprocessor scheduling algorithm such as preemptive earliest deadline first (EDF-P). In this paper, we investigate the sub-optimality of fixed priority non-preemptive scheduling. Specifically, we derive the exact processor speed-up factor required to guarantee the feasibility under FP-NP (i.e. schedulability assuming an optimal priority assignment) of any task set that is feasible under EDF-P. As a consequence of this work, we also derive a lower bound on the sub-optimality of non-preemptive EDF (EDF-NP). As this lower bound matches a recently published upper bound for the same quantity, it closes the exact sub-optimality for EDF-NP. It is known that neither preemptive, nor non-preemptive fixed priority scheduling dominates the other, in other words, there are task sets that are feasible on a processor of unit speed under FP-P that are not feasible under FP-NP and vice-versa. Hence comparing these two algorithms, there are non-trivial speedup factors in both directions. We derive the exact speed-up factor required to guarantee the FP-NP feasibility of any FP-P feasible task set. Further, we derive the exact speed-up factor required to guarantee FP-P feasibility of any constrained-deadline FP-NP feasible task set.

Background: Immunomodulatory cancer drugs such as anti-programmed death 1 (PD-1) and anti-cytotoxic T-lymphocyte antigen 4 (CTLA4) antibodies have shown significant clinical benefits across multiple indications. However, not all patients benefit from checkpoint blockade (CB) treatment strategies, highlighting the need for predictive biomarkers of response. Features of the T-cell receptor (TCR) repertoire have been reported to correlate with CB treatment and response (1). Here, we performed a comprehensive, retrospective analysis of TCR repertoires from different tissues (blood and tumor) at different time points (baseline and on treatment). To the best of our knowledge, our study, comprising a total of 558 samples across 3 tumor types (non-small cell lung cancer [NSCLC], renal cell carcinoma [RCC], and melanoma), is the largest analysis of TCR-sequencing data in CB clinical trials to date. Methods: We performed TCR sequencing on formalin-fixed paraffin-embedded tissue samples or peripheral blood samples using immunoSEQ Assays (Adaptive Biotechnologies Corp.) to profile the immune repertoire of patients from 3 clinical trials of nivolumab (anti-PD-1) or nivolumab + ipilimumab (anti-CTLA4) (NCT02041533, NCT01358721, NCT01621490). Repertoire clonality and clonal expansion statistics were computed, compared, and tested for their association with clinical activity. Results: We characterized the TCR repertoire from peripheral blood at baseline for 263 patients (209 NSCLC, 54 RCC) with matched on-treatment profiles for 49 patients (49 RCC). In addition, we profiled tumor TCR repertoires at baseline and on treatment for 127 patients (54 RCC, 73 melanoma) at time points predefined in each study’s protocol. Our analyses show that clonal expansion was accompanied by a comparable contraction of other clones and thus was not directionally enriched upon CB treatment. Moreover, comparison of baseline and on-treatment repertoire clonality did not reveal a significant shift following CB treatment. Results were consistent in blood and tumor tissue and across response groups. Baseline peripheral blood TCR clonality was not associated with best overall response or progression-free survival. Conclusions: Our results highlight that TCR repertoires are highly dynamic and that the currently established metrics are difficult to interpret or implement clinically as pharmacodynamic or response biomarkers. The variability in TCR clonality modulation upon CB treatment underscores the challenge of selecting the adequate on-treatment time point. Thus, our comprehensive analysis highlights the need for a better understanding of the TCR repertoire dynamics and development of methods to identify and functionally annotate tumor-specific TCRs. Reference: 1. Riaz N et al. Cell 2017;171:934-49 Citation Format: Simon Papillon-Cavanagh, Alice M. Walsh, Zhenhao Qi, Megan Wind-Rotolo, Abdel Saci, Parul Doshi, Radu Dobrin, Joseph Szustakowski. T-cell receptor sequencing for pharmacodynamic and response biomarkers of checkpoint blockade [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3221.

Background & Aims Severe forms of inflammatory bowel disease (IBD) that develop in very young children can be caused by variants in a single gene. We performed whole-exome sequence (WES) analysis to identify genetic factors that might cause granulomatous colitis and severe perianal disease, with recurrent bacterial and viral infections, in an infant of consanguineous parents. Methods We performed targeted WES analysis of DNA collected from the patient and her parents. We validated our findings by a similar analysis of DNA from 150 patients with very-early-onset IBD not associated with known genetic factors analyzed in Toronto, Oxford, and Munich. We compared gene expression signatures in inflamed vs noninflamed intestinal and rectal tissues collected from patients with treatment-resistant Crohn's disease who participated in a trial of ustekinumab. We performed functional studies of identified variants in primary cells from patients and cell culture. Results We identified a homozygous variant in the tripartite motif containing 22 gene (TRIM22) of the patient, as well as in 2 patients with a disease similar phenotype. Functional studies showed that the variant disrupted the ability of TRIM22 to regulate nucleotide binding oligomerization domain containing 2 (NOD2)−dependent activation of interferon-beta signaling and nuclear factor−κB. Computational studies demonstrated a correlation between the TRIM22−NOD2 network and signaling pathways and genetic factors associated very early onset and adult-onset IBD. TRIM22 is also associated with antiviral and mycobacterial effectors and markers of inflammation, such as fecal calprotectin, C-reactive protein, and Crohn's disease activity index scores. Conclusions In WES and targeted exome sequence analyses of an infant with severe IBD characterized by granulomatous colitis and severe perianal disease, we identified a homozygous variant of TRIM22 that affects the ability of its product to regulate NOD2. Combined computational and functional studies showed that the TRIM22-NOD2 network regulates antiviral and antibacterial signaling pathways that contribute to inflammation. Further study of this network could lead to new disease markers and therapeutic targets for patients with very early and adult-onset IBD.

Background Although genome-wide association studies (GWAS) have identified over 100 genetic loci associated with rheumatoid arthritis (RA), our ability to translate these results into disease understanding and novel therapeutics is limited. Most RA GWAS loci reside outside of protein-coding regions and likely affect distal transcriptional enhancers. Furthermore, GWAS do not identify the cell types where the associated causal gene functions. Thus, mapping the transcriptional regulatory roles of GWAS hits and the relevant cell types will lead to better understanding of RA pathogenesis. Results We combine the whole-genome sequences and blood transcription profiles of 377 RA patients and identify over 6000 unique genes with expression quantitative trait loci (eQTLs). We demonstrate the quality of the identified eQTLs through comparison to non-RA individuals. We integrate the eQTLs with immune cell epigenome maps, RA GWAS risk loci, and adjustment for linkage disequilibrium to propose target genes of immune cell enhancers that overlap RA risk loci. We examine 20 immune cell epigenomes and perform a focused analysis on primary monocytes, B cells, and T cells. Conclusions We highlight cell-specific gene associations with relevance to RA pathogenesis including the identification of FCGR2B in B cells as possessing both intragenic and enhancer regulatory GWAS hits. We show that our RA patient cohort derived eQTL network is more informative for studying RA than that from a healthy cohort. While not experimentally validated here, the reported eQTLs and cell type-specific RA risk associations can prioritize future experiments with the goal of elucidating the regulatory mechanisms behind genetic risk associations. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0948-6) contains supplementary material, which is available to authorized users.

Global limited preemptive real-time scheduling in multiprocessor systems using Fixed Preemption Points FPP brings in an additional challenge with respect to the choice of the task to be preempted in order to maximize schedulability. Two principal choices with respect to the preemption approach exist 1 the scheduler waits for the lowest priority job to become preemptible, 2 the scheduler preempts the first job, among the lower priority ones, that becomes preemptible. We refer to the former as the Lazy Preemption Approach LPA and the latter as the Eager Preemption Approach EPA. Each of these choice has a different effect on the actual number of preemptions in the schedule, that in turn determine the runtime overheads. In this paper, we perform an empirical comparison of the run-time preemptive behavior of Global Preemptive Scheduling and Global Limited Preemptive Scheduling with EPA and LPA, under both Earliest Deadline First EDF and Fixed Priority Scheduling FPS paradigms. Our experiments reveal interesting observations some of which are counter-intuitive. We then analyse the counter-intuitive observations and identify the associated reasons. The observations presented facilitate the choice of appropriate strategies when using limited preemptive schedulers on multiprocessor systems.

International audience; Fixed priority scheduling is used in many real-time systems; however, both preemptive and non-preemptive variants (FP-P and FP-NP) are known to be sub-optimal when compared to an optimal uniprocessor scheduling algorithm such as preemptive Earliest Deadline First (EDF-P). In this paper, we investigate the sub-optimality of fixed priority non-preemptive scheduling. Specifically, we derive the exact processor speed-up factor required to guarantee the feasibility under FP-NP (i.e. schedulablability assuming an optimal priority assignment) of any task set that is feasible under EDF-P. As a consequence of this work, we also derive a lower bound on the sub-optimality of non-preemptive EDF (EDF-NP), which since it matches a recently published upper bound gives the exact sub-optimality for EDF-NP. It is known that neither preemptive, nor non-preemptive fixed priority scheduling dominates the other, i.e., there are task sets that are feasible on a processor of unit speed under FP-P that are not feasible under FP-NP and vice-versa. Hence comparing these two algorithms, there are non-trivial speedup factors in both directions. We derive the exact speed-up factor required to guarantee the FP-NP feasibility of any FP-P feasible task set. Further, we derive upper and lower bounds on the speed-up factor required to guarantee FP-P feasibility of any FP-NP feasible task set. Empirical evidence suggests that the lower bound may be tight, and hence equate to the exact speed-up factor in this case.

Challenges associated with allowing preemptions and migrations are compounded in multicore systems, particularly under global scheduling policies, because of the potentially high overheads. For example, multiple levels of cache greatly increase preemption and migration related overheads as well as the difficulty involved in accurately accounting for them, leading to substantially inflated worst-case execution times (WCETs). Preemption and migration related overheads can be significantly reduced, both in number and in size, by using fixed preemption points in the tasks' code; thus dividing each task into a series of non-preemptive regions (NPRs). This leads to an additional consideration in the scheduling policy. When a high priority task is released and all of the processors are executing non-preemptive regions of lower priority tasks, then there is a choice to be made in terms of how to manage the next preemption. With an eager approach the first lower priority task to reach a preemption point is preempted even if it is not the lowest priority running task. Alternatively, with a lazy approach, preemption is delayed until the lowest priority currently running task reaches its next preemption point.In this paper, we show that under global fixed priority scheduling with eager preemptions each task suffers from at most a single priority inversion each time it resumes execution. Building on this observation, we derive a new response time based schedulability test for tasks with fixed preemption points. Experimental evaluations show that global fixed priority scheduling with eager preemptions is significantly more effective than with lazy preemption using link based scheduling in terms of task set schedulability.

SummaryNetworks are a powerful and flexible methodology for expressing biological knowledge for computation and communication. Network-encoded information can include systematic screens for molecular interactions, biological relationships curated from literature, and outputs from analyses of Big Data. NDEx, the Network Data Exchange (www.ndexbio.org), is an online commons where scientists can upload, share, and publicly distribute networks. Networks in NDEx receive globally unique accession IDs and can be stored for private use, shared in pre-publication collaboration, or released for public access. Standard and novel data formats are accommodated in a flexible storage model. Organizations can use NDEx as a distribution channel for networks they generate or curate. Developers of bioinformatic applications can store and query NDEx networks via a common programmatic interface. NDEx helps expand the role of networks in scientific discourse and facilitates the integration of networks as data in publications. It is a step toward an ecosystem in which networks bearing data, hypotheses, and findings flow easily between scientists.