Critical care patients are monitored closely through the course of their illness. As a result of this monitoring, large amounts of data are routinely collected for these patients. Philips Healthcare ...has developed a telehealth system, the eICU Program, which leverages these data to support management of critically ill patients. Here we describe the eICU Collaborative Research Database, a multi-center intensive care unit (ICU)database with high granularity data for over 200,000 admissions to ICUs monitored by eICU Programs across the United States. The database is deidentified, and includes vital sign measurements, care plan documentation, severity of illness measures, diagnosis information, treatment information, and more. Data are publicly available after registration, including completion of a training course in research with human subjects and signing of a data use agreement mandating responsible handling of the data and adhering to the principle of collaborative research. The freely available nature of the data will support a number of applications including the development of machine learning algorithms, decision support tools, and clinical research.
A large body of evidence, including longitudinal analyses of personality change, suggests that core personality traits are predominantly stable after age 30. To our knowledge, no study has ...demonstrated changes in personality in healthy adults after an experimentally manipulated discrete event. Intriguingly, double-blind controlled studies have shown that the classic hallucinogen psilocybin occasions personally and spiritually significant mystical experiences that predict long-term changes in behaviors, attitudes and values. In the present report we assessed the effect of psilocybin on changes in the five broad domains of personality – Neuroticism, Extroversion, Openness, Agreeableness, and Conscientiousness. Consistent with participant claims of hallucinogen-occasioned increases in aesthetic appreciation, imagination, and creativity, we found significant increases in Openness following a high-dose psilocybin session. In participants who had mystical experiences during their psilocybin session, Openness remained significantly higher than baseline more than 1 year after the session. The findings suggest a specific role for psilocybin and mystical-type experiences in adult personality change.
Psilocybin has been used for centuries for religious purposes; however, little is known scientifically about its long-term effects. We previously reported the effects of a double-blind study ...evaluating the psychological effects of a high psilocybin dose. This report presents the 14-month follow-up and examines the relationship of the follow-up results to data obtained at screening and on drug session days. Participants were 36 hallucinogen-naïve adults reporting regular participation in religious/ spiritual activities. Oral psilocybin (30 mg/70 kg) was administered on one of two or three sessions, with methylphenidate (40 mg/70 kg) administered on the other session(s). During sessions, volunteers were encouraged to close their eyes and direct their attention inward. At the 14-month follow-up, 58% and 67%, respectively, of volunteers rated the psilocybin-occasioned experience as being among the five most personally meaningful and among the five most spiritually significant experiences of their lives; 64% indicated that the experience increased well-being or life satisfaction; 58% met criteria for having had a 'complete' mystical experience. Correlation and regression analyses indicated a central role of the mystical experience assessed on the session day in the high ratings of personal meaning and spiritual significance at follow-up. Of the measures of personality, affect, quality of life and spirituality assessed across the study, only a scale measuring mystical experience showed a difference from screening. When administered under supportive conditions, psilocybin occasioned experiences similar to spontaneously occurring mystical experiences that, at 14-month follow-up, were considered by volunteers to be among the most personally meaningful and spiritually significant of their lives.
Bacterial cell-wall recycling Johnson, Jarrod W.; Fisher, Jed F.; Mobashery, Shahriar
Annals of the New York Academy of Sciences,
January 2013, Letnik:
1277, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Many Gram‐negative and Gram‐positive bacteria recycle a significant proportion of the peptidoglycan components of their cell walls during their growth and septation. In many—and quite possibly ...all—bacteria, the peptidoglycan fragments are recovered and recycled. Although cell‐wall recycling is beneficial for the recovery of resources, it also serves as a mechanism to detect cell‐wall–targeting antibiotics and to regulate resistance mechanisms. In several Gram‐negative pathogens, anhydro‐MurNAc‐peptide cell‐wall fragments regulate AmpC β‐lactamase induction. In some Gram‐positive organisms, short peptides derived from the cell wall regulate the induction of both β‐lactamase and β‐lactam–resistant penicillin‐binding proteins. The involvement of peptidoglycan recycling with resistance regulation suggests that inhibitors of the enzymes involved in the recycling might synergize with cell‐wall–targeted antibiotics. Indeed, such inhibitors improve the potency of β‐lactams in vitro against inducible AmpC β‐lactamase–producing bacteria. We describe the key steps of cell‐wall remodeling and recycling, the regulation of resistance mechanisms by cell‐wall recycling, and recent advances toward the discovery of cell‐wall–recycling inhibitors.
Goal: Current methods for estimating respiratory rate (RR) from the photoplethysmogram (PPG) typically fail to distinguish between periods of high- and low-quality input data, and fail to perform ...well on independent "validation" datasets. The lack of robustness of existing methods directly results in a lack of penetration of such systems into clinical practice. The present work proposes an alternative method to improve the robustness of the estimation of RR from the PPG. Methods: The proposed algorithm is based on the use of multiple autoregressive models of different orders for determining the dominant respiratory frequency in the three respiratory-induced variations (frequency, amplitude, and intensity) derived from the PPG. The algorithm was tested on two different datasets comprising 95 eight-minute PPG recordings (in total) acquired from both children and adults in different clinical settings, and its performance using two window sizes (32 and 64 seconds) was compared with that of existing methods in the literature. Results: The proposed method achieved comparable accuracy to existing methods in the literature, with mean absolute errors (median, 25th-75th percentiles for a window size of 32 seconds) of 1.5 (0.3-3.3) and 4.0 (1.8-5.5) breaths per minute (for each dataset respectively), whilst providing RR estimates for a greater proportion of windows (over 90% of the input data are kept). Conclusion: Increased robustness of RR estimation by the proposed method was demonstrated. Significance: This work demonstrates that the use of large publicly available datasets is essential for improving the robustness of wearable-monitoring algorithms for use in clinical practice.
Microglia play a vital role maintaining brain homeostasis but can also cause persistent neuroinflammation. Short-chain fatty acids (SCFAs) produced by the intestinal microbiota have been suggested to ...regulate microglia inflammation indirectly by signaling through the gut-brain axis or directly by reaching the brain. The present work evaluated the anti-inflammatory effects of SCFAs on lipopolysaccharide (LPS)-stimulated microglia from mice fed inulin, a soluble fiber that is fermented by intestinal microbiota to produce SCFAs in vivo, and SCFAs applied to primary microglia in vitro. Feeding mice inulin increased SCFAs in the cecum and in plasma collected from the hepatic portal vein. Microglia isolated from mice fed inulin and stimulated with LPS in vitro secreted less tumor necrosis factor α (TNF-α) compared to microglia from mice not given inulin. Additionally, when mice were fed inulin and injected i.p with LPS, the ex vivo secretion of TNF-α by isolated microglia was lower than that secreted by microglia from mice not fed inulin and injected with LPS. Similarly, in vitro treatment of primary microglia with acetate and butyrate either alone or in combination downregulated microglia cytokine production with the effects being additive. SCFAs reduced histone deacetylase activity and nuclear factor-κB nuclear translocation after LPS treatment in vitro. Whereas microglia expression of SCFA receptors Ffar2 or Ffar3 was not detected by single-cell RNA sequencing analysis, the SCFA transporters Mct1 and Mct4 were. Nevertheless, inhibiting monocarboxylate transporters on primary microglia did not interfere with the anti-inflammatory effects of SCFAs, suggesting that if SCFAs produced in the gut regulate microglia directly it is likely through an epigenetic mechanism following diffusion.
Temporal dataset shift associated with changes in healthcare over time is a barrier to deploying machine learning-based clinical decision support systems. Algorithms that learn robust models by ...estimating invariant properties across time periods for domain generalization (DG) and unsupervised domain adaptation (UDA) might be suitable to proactively mitigate dataset shift. The objective was to characterize the impact of temporal dataset shift on clinical prediction models and benchmark DG and UDA algorithms on improving model robustness. In this cohort study, intensive care unit patients from the MIMIC-IV database were categorized by year groups (2008-2010, 2011-2013, 2014-2016 and 2017-2019). Tasks were predicting mortality, long length of stay, sepsis and invasive ventilation. Feedforward neural networks were used as prediction models. The baseline experiment trained models using empirical risk minimization (ERM) on 2008-2010 (ERM08-10) and evaluated them on subsequent year groups. DG experiment trained models using algorithms that estimated invariant properties using 2008-2016 and evaluated them on 2017-2019. UDA experiment leveraged unlabelled samples from 2017 to 2019 for unsupervised distribution matching. DG and UDA models were compared to ERM08-16 models trained using 2008-2016. Main performance measures were area-under-the-receiver-operating-characteristic curve (AUROC), area-under-the-precision-recall curve and absolute calibration error. Threshold-based metrics including false-positives and false-negatives were used to assess the clinical impact of temporal dataset shift and its mitigation strategies. In the baseline experiments, dataset shift was most evident for sepsis prediction (maximum AUROC drop, 0.090; 95% confidence interval (CI), 0.080-0.101). Considering a scenario of 100 consecutively admitted patients showed that ERM08-10 applied to 2017-2019 was associated with one additional false-negative among 11 patients with sepsis, when compared to the model applied to 2008-2010. When compared with ERM08-16, DG and UDA experiments failed to produce more robust models (range of AUROC difference, - 0.003 to 0.050). In conclusion, DG and UDA failed to produce more robust models compared to ERM in the setting of temporal dataset shift. Alternate approaches are required to preserve model performance over time in clinical medicine.
Aim: Identifying and protecting refugia is a priority for conservation under projected anthropogenic climate change, because of their demonstrated ability to facilitate the survival of biota under ...adverse conditions. Refugia are habitats that components of biodiversity retreat to, persist in and can potentially expand from under changing environmental conditions. However, the study and discussion of refugia has often been ad hoc and descriptive in nature. We therefore: (1) provide a habitat-based concept of refugia, and (2) evaluate methods for the identification of refugia. Location: Global. Methods: We present a simple conceptual framework for refugia and examine the factors that describe them. We then demonstrate how different disciplines are contributing to our understanding of refugia, and the tools that they provide for identifying and quantifying refugia. Results: Current understanding of refugia is largely based on Quaternary phylogeographic studies on organisms in North America and Europe during significant temperature fluctuations. This has resulted in gaps in our understanding of refugia, particularly when attempting to apply current theory to forecast anthropogenic climate change. Refugia are environmental habitats with space and time dimensions that operate on evolutionary time-scales and have facilitated the survival of biota under changing environmental conditions for millennia. Therefore, they offer the best chances for survival under climate change for many taxa, making their identification important for conservation under anthropogenic climate change. Several methods from various disciplines provide viable options for achieving this goal. Main conclusions: The framework developed for refugia allows the identification and description of refugia in any environment. Various methods provide important contributions but each is limited in scope; urging a more integrated approach to identify, define and conserve refugia. Such an approach will facilitate better understanding of refugia and their capacity to act as safe havens under projected anthropogenic climate change.
Back in the 1990s, we started work on pedagogical agents — a novel paradigm for interactive learning. Pedagogical agents are autonomous characters that inhabit learning environments to engage with ...learners in rich, face‐to‐face interactions. Building on this work, in 2000, together with our colleague Jeff Rickel, we published an article on pedagogical agents (Johnson, Rickel, and Lester 2000) that surveyed and discussed the potential of this new paradigm. We made the case that pedagogical agents that interact with learners in natural, lifelike ways can help learning environments achieve improved learning outcomes. This article has been widely cited, and was a winner of the 2017 IFAAMAS Award for Influential Papers in Autonomous Agents and Multiagent Systems.1
On the occasion of receiving the IFAAMAS award, and after 20 years of work on pedagogical agents, we take another look at the future of the field. We start by revisiting the predictions we made in 2000 for pedagogical agents, and examine which predictions panned out. Then, informed by what we have learned since then, we take another look at emerging trends and reexamine the future of pedagogical agents. Advances in natural language dialogue, affective computing, machine learning, virtual environments, and robotics are making possible even more lifelike and effective pedagogical agents, with potentially profound effects on the way people learn.
PDF
