Search

A defining feature of successful vaccination is the ability to induce long-lived antigen-specific memory cells. T follicular helper (Tfh) cells specialize in providing help to B cells in mounting protective humoral immunity in infection and after vaccination. Memory Tfh cells that retain the CXCR5 expression can confer protection through enhancing humoral response upon antigen re-exposure but how they are maintained is poorly understood. CXCR5+ memory Tfh cells in human blood are divided into Tfh1, Tfh2, and Tfh17 cells by the expression of chemokine receptors CXCR3 and CCR6 associated with Th1 and Th17, respectively. Here, we developed a new method to induce Tfh1, Tfh2, and Tfh17-like (iTfh1, iTfh2, and iTfh17) mouse cells in vitro. Although all three iTfh subsets efficiently support antibody responses in recipient mice with immediate immunization, iTfh17 cells are superior to iTfh1 and iTfh2 cells in supporting antibody response to a later immunization after extended resting in vivo to mimic memory maintenance. Notably, the counterpart human Tfh17 cells are selectively enriched in CCR7+ central memory Tfh cells with survival and proliferative advantages. Furthermore, the analysis of multiple human cohorts that received different vaccines for HBV, influenza virus, tetanus toxin or measles revealed that vaccine-specific Tfh17 cells outcompete Tfh1 or Tfh2 cells for the persistence in memory phase. Therefore, the complementary mouse and human results showing the advantage of Tfh17 cells in maintenance and memory function supports the notion that Tfh17-induced immunization might be preferable in vaccine development to confer long-term protection.

Estimates of seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies have been hampered by inadequate assay sensitivity and specificity. Using an enzyme-linked immunosorbent assay-based approach that combines data about immunoglobulin G responses to both the nucleocapsid and spike receptor binding domain antigens, we show that excellent sensitivity and specificity can be achieved. We used this assay to assess the frequency of virus-specific antibodies in a cohort of elective surgery patients in Australia and estimated seroprevalence in Australia to be 0.28% (95% Confidence Interval, 0-1.15%). These data confirm the low level of transmission of SARS-CoV-2 in Australia before July 2020 and validate the specificity of our assay.

Estimates of seroprevalence of SARS-CoV-2 antibodies have been hampered by inadequate assay sensitivity and specificity. Using an ELISA-based approach to that combines data about IgG responses to both the Nucleocapsid and Spike-receptor binding domain antigens, we show that near-optimal sensitivity and specificity can be achieved. We used this assay to assess the frequency of virus-specific antibodies in a cohort of elective surgery patients in Australia and estimated seroprevalence in Australia to be 0.28% (0 to 0.72%). These data confirm the low level of transmission of SARS-CoV-2 in Australia before July 2020 and validate the specificity of our assay.

Predictive business process monitoring methods exploit logs of completed cases of a process in order to make predictions about running cases thereof. Existing methods in this space are tailor-made for specific prediction tasks. Moreover, their relative accuracy is highly sensitive to the dataset at hand, thus requiring users to engage in trial-and-error and tuning when applying them in a specific setting. This paper investigates Long Short-Term Memory (LSTM) neural networks as an approach to build consistently accurate models for a wide range of predictive process monitoring tasks. First, we show that LSTMs outperform existing techniques to predict the next event of a running case and its timestamp. Next, we show how to use models for predicting the next task in order to predict the full continuation of a running case. Finally, we apply the same approach to predict the remaining time, and show that this approach outperforms existing tailor-made methods.

Process mining is a family of techniques to analyze business processes based on event logs recorded by their supporting information systems. Two recurrent bottlenecks of existing process mining techniques when confronted with real-life event logs are scalability and interpretability of the outputs. A common approach to tackle these limitations is to decompose the process under analysis into a set of stages, such that each stage can be mined separately. However, existing techniques for automated discovery of stages from event logs produce decompositions that are very different from those that domain experts would produce manually. This paper proposes a technique that, given an event log, discovers a stage decomposition that maximizes a measure of modularity borrowed from the field of social network analysis. An empirical evaluation on real-life event logs shows that the produced decompositions more closely approximate manual decompositions than existing techniques.

Business process performance may be affected by a range of factors, such as the volume and characteristics of ongoing cases or the performance and availability of individual resources. Event logs collected by modern information systems provide a wealth of data about the execution of business processes. However, extracting root causes for performance issues from these event logs is a major challenge. Processes may change continuously due to internal and external factors. Moreover, there may be many resources and case attributes influencing performance. This paper introduces a novel approach based on time series analysis to detect cause-effect relations between a range of business process characteristics and process performance indicators. The scalability and practical relevance of the approach has been validated by a case study involving a real-life insurance claims handling process.

Early detection of business process drifts from event logs enables analysts to identify changes that may negatively affect process performance. However, detecting a process drift without characterizing its nature is not enough to support analysts in understanding and rectifying process performance issues. We propose a method to characterize process drifts from event streams, in terms of the behavioral relations that are modified by the drift. The method builds upon a technique for online drift detection, and relies on a statistical test to select the behavioral relations extracted from the stream that have the highest explanatory power. The selected relations are then mapped to typical change patterns to explain the detected drifts. An extensive evaluation on synthetic and real-life logs shows that our method is fast and accurate in characterizing process drifts, and performs significantly better than alternative techniques.

Opsoclonus-myoclonus syndrome is a rare but serious neurological condition resulting in loss of control of eye movements, often accompanied by difficulties in posture and movement control with reports of sensory sensitivities potentially impacting on behaviour. This pilot study characterises the presence of atypical sensory behaviours in opsoclonus-myoclonus syndrome through questionnaire survey of a cohort of families. The Short Sensory Profile, Vineland Adaptive Behaviour Scale and Developmental Behaviour Checklist were distributed to 30 families; 16 were returned anonymously. Atypical sensory behaviours were identified in a large proportion (62.5%). Children reported as being more anxious showed greater sensitivity to auditory stimuli (U (14) 11, p = .026). This is consistent with recent recognition of more extensive disease neuro-cognitive effects in Opsoclonus-myoclonus syndrome. Further research is needed to increase understanding of the complex pathology of this disease and to provide indicators for sensory and behavioural as well as pharmacological interventions.

Opsoclonus-myoclonus syndrome is a rare but serious neurological condition resulting in loss of control of eye movements, often accompanied by difficulties in posture and movement control with reports of sensory sensitivities potentially impacting on behavior. This pilot study characterizes the presence of atypical sensory behaviors in opsoclonus-myoclonus syndrome through questionnaire survey of a cohort of families. The Short Sensory Profile, Vineland Adaptive Behavior Scale, and Developmental Behaviour Checklist were distributed to 30 families; 16 were returned anonymously. Atypical sensory behaviors were identified in a large proportion (62.5%). Children reported as being more anxious showed greater sensitivity to auditory stimuli, U(14) 11, P =.026. This is consistent with recent recognition of more extensive disease neurocognitive effects in Opsoclonus-myoclonus syndrome. Further research is needed to increase understanding of the complex pathology of this disease and to provide indicators for sensory and behavioral as well as pharmacological interventions.

Item does not contain fulltext

We present a technique to make applications resilient to failures. This technique is intended to maintain a faulty application functional in the field while the developers work on permanent and radical fixes. We target field failures in applications built on reusable components. In particular, the technique exploits the intrinsic redundancy of those components by identifying workarounds consisting of alternative uses of the faulty components that avoid the failure. The technique is currently implemented for Java applications but makes little or no assumptions about the nature of the application, and works without interrupting the execution flow of the application and without restarting its components. We demonstrate and evaluate this technique on four mid-size applications and two popular libraries of reusable components affected by real and seeded faults. In these cases the technique is effective, maintaining the application fully functional with between 19% and 48% of the failure-causing faults, depending on the application. The experiments also show that the technique incurs an acceptable runtime overhead in all cases

We present a combined experimental and theoretical study of the surface structure of single crystal Bi(100) via scanning tunneling microscopy (STM), low-energy electron diffraction intensity versus energy (LEED-IV) analysis and density functional theory (DFT). We find that the surface is unreconstructed and shows an unusually large oscillatory multilayer relaxation down to the sixth layer. This unexpected behavior will be explained by a novel mechanism related to the deeply penetrating electronic surface states. STM reveals wide (100) terraces, which are separated by two-layer high steps in which the shorter of the two interlayer spacings is terminating this surface, consistent with the LEED structural analysis and DFT.