Nested Active-Time Scheduling

The active-time scheduling problem considers the problem of scheduling preemptible jobs with windows (release times and deadlines) on a parallel machine that can schedule up to g jobs during each timestep. The goal in the active-time problem is to minimize the number of active steps, i.e., timesteps in which at least one job is scheduled. In this way, the active time models parallel scheduling when there is a fixed cost for turning the machine on at each discrete step. This paper presents a 9/5-approximation algorithm for a special case of the active-time scheduling problem in which job windows are laminar (nested). This result improves on the previous best 2-approximation for the general case

Self-supervised Representation Learning on Electronic Health Records with Graph Kernel Infomax

Learning Electronic Health Records (EHRs) representation is a preeminent yet under-discovered research topic. It benefits various clinical decision support applications, e.g., medication outcome prediction or patient similarity search. Current approaches focus on task-specific label supervision on vectorized sequential EHR, which is not applicable to large-scale unsupervised scenarios. Recently, contrastive learning shows great success on self-supervised representation learning problems. However, complex temporality often degrades the performance. We propose Graph Kernel Infomax, a self-supervised graph kernel learning approach on the graphical representation of EHR, to overcome the previous problems. Unlike the state-of-the-art, we do not change the graph structure to construct augmented views. Instead, we use Kernel Subspace Augmentation to embed nodes into two geometrically different manifold views. The entire framework is trained by contrasting nodes and graph representations on those two manifold views through the commonly used contrastive objectives. Empirically, using publicly available benchmark EHR datasets, our approach yields performance on clinical downstream tasks that exceeds the state-of-the-art. Theoretically, the variation on distance metrics naturally creates different views as data augmentation without changing graph structures

Immunostimulatory Effect of Flagellin on MDR-<i>Klebsiella</i>-Infected Human Airway Epithelial Cells

Pneumonia caused by multi-drug-resistant Klebsiella pneumoniae (MDR-Kpneu) poses a major public health threat, especially to immunocompromised or hospitalized patients. This study aimed to determine the immunostimulatory effect of the Toll-like receptor 5 ligand flagellin on primary human lung epithelial cells during infection with MDR-Kpneu. Human bronchial epithelial (HBE) cells, grown on an air–liquid interface, were inoculated with MDR-Kpneu on the apical side and treated during ongoing infection with antibiotics (meropenem) and/or flagellin on the basolateral and apical side, respectively; the antimicrobial and inflammatory effects of flagellin were determined in the presence or absence of meropenem. In the absence of meropenem, flagellin treatment of MDR-Kpneu-infected HBE cells increased the expression of antibacterial defense genes and the secretion of chemokines; moreover, supernatants of flagellin-exposed HBE cells activated blood neutrophils and monocytes. However, in the presence of meropenem, flagellin did not augment these responses compared to meropenem alone. Flagellin did not impact the outgrowth of MDR-Kpneu. Flagellin enhances antimicrobial gene expression and chemokine release by the MDR-Kpneu-infected primary human bronchial epithelium, which is associated with the release of mediators that activate neutrophils and monocytes. Topical flagellin therapy may have potential to boost immune responses in the lung during pneumonia.</p

Immunostimulatory Effect of Flagellin on MDR-<i>Klebsiella</i>-Infected Human Airway Epithelial Cells

Pneumonia caused by multi-drug-resistant Klebsiella pneumoniae (MDR-Kpneu) poses a major public health threat, especially to immunocompromised or hospitalized patients. This study aimed to determine the immunostimulatory effect of the Toll-like receptor 5 ligand flagellin on primary human lung epithelial cells during infection with MDR-Kpneu. Human bronchial epithelial (HBE) cells, grown on an air–liquid interface, were inoculated with MDR-Kpneu on the apical side and treated during ongoing infection with antibiotics (meropenem) and/or flagellin on the basolateral and apical side, respectively; the antimicrobial and inflammatory effects of flagellin were determined in the presence or absence of meropenem. In the absence of meropenem, flagellin treatment of MDR-Kpneu-infected HBE cells increased the expression of antibacterial defense genes and the secretion of chemokines; moreover, supernatants of flagellin-exposed HBE cells activated blood neutrophils and monocytes. However, in the presence of meropenem, flagellin did not augment these responses compared to meropenem alone. Flagellin did not impact the outgrowth of MDR-Kpneu. Flagellin enhances antimicrobial gene expression and chemokine release by the MDR-Kpneu-infected primary human bronchial epithelium, which is associated with the release of mediators that activate neutrophils and monocytes. Topical flagellin therapy may have potential to boost immune responses in the lung during pneumonia.</p

Long-term evolution of human seasonal influenza virus A(H3N2) is associated with an increase in polymerase complex activity

Since the influenza pandemic in 1968, influenza A(H3N2) viruses have become endemic. In this state, H3N2 viruses continuously evolve to overcome immune pressure as a result of prior infection or vaccination, as is evident from the accumulation of mutations in the surface glycoproteins hemagglutinin (HA) and neuraminidase (NA). However, phylogenetic studies have also demonstrated ongoing evolution in the influenza A(H3N2) virus RNA polymerase complex genes. The RNA polymerase complex of seasonal influenza A(H3N2) viruses produces mRNA for viral protein synthesis and replicates the negative sense viral RNA genome (vRNA) through a positive sense complementary RNA intermediate (cRNA). Presently, the consequences and selection pressures driving the evolution of the polymerase complex remain largely unknown. Here, we characterize the RNA polymerase complex of seasonal influenza A(H3N2) viruses representative of nearly 50 years of influenza A(H3N2) virus evolution. The H3N2 polymerase complex is a reassortment of human and avian influenza virus genes. We show that since 1968, influenza A(H3N2) viruses have increased the transcriptional activity of the polymerase complex while retaining a close balance between mRNA, vRNA, and cRNA levels. Interestingly, the increased polymerase complex activity did not result in increased replicative ability on differentiated human airway epithelial (HAE) cells. We hypothesize that the evolutionary increase in polymerase complex activity of influenza A(H3N2) viruses may compensate for the reduced HA receptor binding and avidity that is the result of the antigenic evolution of influenza A(H3N2) viruses

Determinants of epidemic size and the impacts of lulls in seasonal influenza virus circulation

During the COVID-19 pandemic, levels of seasonal influenza virus circulation were unprecedentedly low, leading to concerns that a lack of exposure to influenza viruses, combined with waning antibody titres, could result in larger and/or more severe post-pandemic seasonal influenza epidemics. However, in most countries the first post-pandemic influenza season was not unusually large and/or severe. Here, based on an analysis of historical influenza virus epidemic patterns from 2002 to 2019, we show that historic lulls in influenza virus circulation had relatively minor impacts on subsequent epidemic size and that epidemic size was more substantially impacted by season-specific effects unrelated to the magnitude of circulation in prior seasons. From measurements of antibody levels from serum samples collected each year from 2017 to 2021, we show that the rate of waning of antibody titres against influenza virus during the pandemic was smaller than assumed in predictive models. Taken together, these results partially explain why the re-emergence of seasonal influenza virus epidemics was less dramatic than anticipated and suggest that influenza virus epidemic dynamics are not currently amenable to multi-season prediction

Potential impacts of prolonged absence of influenza virus circulation on subsequent epidemics

BACKGROUND: During the first two years of the COVID-19 pandemic, the circulation of seasonal influenza viruses was unprecedentedly low. This led to concerns that the lack of immune stimulation to influenza viruses combined with waning antibody titres could lead to increased susceptibility to influenza in subsequent seasons, resulting in larger and more severe epidemics. METHODS: We analyzed historical influenza virus epidemiological data from 2003-2019 to assess the historical frequency of near-absence of seasonal influenza virus circulation and its impact on the size and severity of subsequent epidemics. Additionally, we measured haemagglutination inhibition-based antibody titres against seasonal influenza viruses using longitudinal serum samples from 165 healthy adults, collected before and during the COVID-19 pandemic, and estimated how antibody titres against seasonal influenza waned during the first two years of the pandemic. FINDINGS: Low country-level prevalence of influenza virus (sub)types over one or more years occurred frequently before the COVID-19 pandemic and had relatively small impacts on subsequent epidemic size and severity. Additionally, antibody titres against seasonal influenza viruses waned negligibly during the first two years of the pandemic. INTERPRETATION: The commonly held notion that lulls in influenza virus circulation, as observed during the COVID-19 pandemic, will lead to larger and/or more severe subsequent epidemics might not be fully warranted, and it is likely that post-lull seasons will be similar in size and severity to pre-lull seasons. FUNDING: European Research Council, Netherlands Organization for Scientific Research, Royal Dutch Academy of Sciences, Public Health Service of Amsterdam. RESEARCH IN CONTEXT: Evidence before this study: During the first years of the COVID-19 pandemic, the incidence of seasonal influenza was unusually low, leading to widespread concerns of exceptionally large and/or severe influenza epidemics in the coming years. We searched PubMed and Google Scholar using a combination of search terms (i.e., "seasonal influenza", "SARS-CoV-2", "COVID-19", "low incidence", "waning rates", "immune protection") and critically considered published articles and preprints that studied or reviewed the low incidence of seasonal influenza viruses since the start of the COVID-19 pandemic and its potential impact on future seasonal influenza epidemics. We found a substantial body of work describing how influenza virus circulation was reduced during the COVID-19 pandemic, and a number of studies projecting the size of future epidemics, each positing that post-pandemic epidemics are likely to be larger than those observed pre-pandemic. However, it remains unclear to what extent the assumed relationship between accumulated susceptibility and subsequent epidemic size holds, and it remains unknown to what extent antibody levels have waned during the COVID-19 pandemic. Both are potentially crucial for accurate prediction of post-pandemic epidemic sizes.Added value of this study: We find that the relationship between epidemic size and severity and the magnitude of circulation in the preceding season(s) is decidedly more complex than assumed, with the magnitude of influenza circulation in preceding seasons having only limited effects on subsequent epidemic size and severity. Rather, epidemic size and severity are dominated by season-specific effects unrelated to the magnitude of circulation in the preceding season(s). Similarly, we find that antibody levels waned only modestly during the COVID-19 pandemic.Implications of all the available evidence: The lack of changes observed in the patterns of measured antibody titres against seasonal influenza viruses in adults and nearly two decades of epidemiological data suggest that post-pandemic epidemic sizes will likely be similar to those observed pre-pandemic, and challenge the commonly held notion that the widespread concern that the near-absence of seasonal influenza virus circulation during the COVID-19 pandemic, or potential future lulls, are likely to result in larger influenza epidemics in subsequent years

Overqualified Human Resources, Career Development Experiences, and Work Outcomes: Leveraging an Underutilized Resource with Political Skill

We argue in this paper that overqualified employees represent an underutilized human resource that has the potential to be leveraged in impactful ways to enhance both personal and organizational effectiveness. Our proposed framework suggests that if organizations provide opportunities for employees to engage in career development experiences (i.e., job crafting, informal leadership, mentoring relationships), politically skilled overqualified employees will capitalize on these opportunities and utilize their additional knowledge, skills, abilities, and experience to make unique contributions, providing valued human resources to the organizations. Furthermore, the politically skilled overqualified employees\u27 capitalization on opportunities to undertake career development opportunities will results in positive outcomes for both the employees (i.e., increased job satisfaction and reputation) and the organization (i.e., increased organizational commitment). Implications and directions for future research are discussed

Hate speech detection: Challenges and solutions.

As online content continues to grow, so does the spread of hate speech. We identify and examine challenges faced by online automatic approaches for hate speech detection in text. Among these difficulties are subtleties in language, differing definitions on what constitutes hate speech, and limitations of data availability for training and testing of these systems. Furthermore, many recent approaches suffer from an interpretability problem-that is, it can be difficult to understand why the systems make the decisions that they do. We propose a multi-view SVM approach that achieves near state-of-the-art performance, while being simpler and producing more easily interpretable decisions than neural methods. We also discuss both technical and practical challenges that remain for this task