A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units

The management of invasive mechanical ventilation, and the regulation of sedation and analgesia during ventilation, constitutes a major part of the care of patients admitted to intensive care units. Both prolonged dependence on mechanical ventilation and premature extubation are associated with increased risk of complications and higher hospital costs, but clinical opinion on the best protocol for weaning patients off of a ventilator varies. This work aims to develop a decision support tool that uses available patient information to predict time-to-extubation readiness and to recommend a personalized regime of sedation dosage and ventilator support. To this end, we use off-policy reinforcement learning algorithms to determine the best action at a given patient state from sub-optimal historical ICU data. We compare treatment policies from fitted Q-iteration with extremely randomized trees and with feedforward neural networks, and demonstrate that the policies learnt show promise in recommending weaning protocols with improved outcomes, in terms of minimizing rates of reintubation and regulating physiological stability

ALFALFA HI Data Stacking III. Comparison of environmental trends in HI gas mass fraction and specific star formation rate

It is well known that both the star formation rate and the cold gas content of a galaxy depend on the local density out to distances of a few Megaparsecs. In this paper, we compare the environmental density dependence of the atomic gas mass fractions of nearby galaxies with the density dependence of their central and global specific star formation rates. We stack HI line spectra extracted from the Arecibo Legacy Fast ALFA survey centered on galaxies with UV imaging from GALEX and optical imaging/spectroscopy from SDSS. We use these stacked spectra to evaluate the mean atomic gas mass fraction of galaxies in bins of stellar mass and local density. For galaxies with stellar masses less than 10^10.5 M_sun, the decline in mean atomic gas mass fraction with density is stronger than the decline in mean global and central specific star formation rate. The same conclusion does not hold for more massive galaxies. We interpret our results as evidence for ram-pressure stripping of atomic gas from the outer disks of low mass satellite galaxies. We compare our results with the semi-analytic recipes of Guo et al. (2011) implemented on the Millennium II simulation. These models assume that only the diffuse gas surrounding satellite galaxies is stripped, a process that is often termed "strangulation". We show that these models predict relative trends in atomic gas and star formation that are in disagreement with observations. We use mock catalogues generated from the simulation to predict the halo masses of the HI-deficient galaxies in our sample. We conclude that ram-pressure stripping is likely to become effective in dark matter halos with masses greater than 10^13 M_sun.Comment: 12 pages, 10 figures. Accepted for publication in MNRA

Synapse elimination and learning rules co-regulated by MHC class I H2-Db.

The formation of precise connections between retina and lateral geniculate nucleus (LGN) involves the activity-dependent elimination of some synapses, with strengthening and retention of others. Here we show that the major histocompatibility complex (MHC) class I molecule H2-D(b) is necessary and sufficient for synapse elimination in the retinogeniculate system. In mice lacking both H2-K(b) and H2-D(b) (K(b)D(b)(-/-)), despite intact retinal activity and basal synaptic transmission, the developmentally regulated decrease in functional convergence of retinal ganglion cell synaptic inputs to LGN neurons fails and eye-specific layers do not form. Neuronal expression of just H2-D(b) in K(b)D(b)(-/-) mice rescues both synapse elimination and eye-specific segregation despite a compromised immune system. When patterns of stimulation mimicking endogenous retinal waves are used to probe synaptic learning rules at retinogeniculate synapses, long-term potentiation (LTP) is intact but long-term depression (LTD) is impaired in K(b)D(b)(-/-) mice. This change is due to an increase in Ca(2+)-permeable AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid) receptors. Restoring H2-D(b) to K(b)D(b)(-/-) neurons renders AMPA receptors Ca(2+) impermeable and rescues LTD. These observations reveal an MHC-class-I-mediated link between developmental synapse pruning and balanced synaptic learning rules enabling both LTD and LTP, and demonstrate a direct requirement for H2-D(b) in functional and structural synapse pruning in CNS neurons

COLD GASS, an IRAM Legacy Survey of Molecular Gas in Massive Galaxies: III. Comparison with semi-analytic models of galaxy formation

We compare the semi-analytic models of galaxy formation of Fu et al. (2010), which track the evolution of the radial profiles of atomic and molecular gas in galaxies, with gas fraction scaling relations derived from the COLD GASS survey (Saintonge et al 2011). The models provide a good description of how condensed baryons in galaxies with gas are partitioned into stars, atomic and molecular gas as a function of galaxy stellar mass and surface density. The models do not reproduce the tight observed relation between stellar surface density and bulge-to-disk ratio for this population. We then turn to an analysis of the"quenched" population of galaxies without detectable cold gas. The current implementation of radio-mode feedback in the models disagrees strongly with the data. In the models, gas cooling shuts down in nearly all galaxies in dark matter halos above a mass of 10**12 M_sun. As a result, stellar mass is the observable that best predicts whether a galaxy has little or no neutral gas. In contrast, our data show that quenching is largely independent of stellar mass. Instead, there are clear thresholds in bulge-to-disk ratio and in stellar surface density that demarcate the location of quenched galaxies. We propose that processes associated with bulge formation play a key role in depleting the neutral gas in galaxies and that further gas accretion is suppressed following the formation of the bulge, even in dark matter halos of low mass.Comment: 12 figures, accepted for publication in MNRAS, the COLD GASS data is available at http://www.mpa-garching.mpg.de/COLD_GASS/data.shtm

Uncertainty as a Key Influence in the Decision To Admit Patients with Transient Ischemic Attack

Background Patients with transient ischemic attacks (TIA) are at high risk of subsequent vascular events. Hospitalization improves quality of care, yet admission rates for TIA patients vary considerably. Objectives We sought to identify factors associated with the decision to admit patents with TIA. Design We conducted a secondary analysis of a prior study’s data including semi-structured interviews, administrative data, and chart review. Participants We interviewed multidisciplinary clinical staff involved with TIA care. Administrative data included information for TIA patients in emergency departments or inpatient settings at VA medical centers (VAMCs) for fiscal years (FY) 2011 and 2014. Chart reviews were conducted on a subset of patients from 12 VAMCs in FY 2011. Approach For the qualitative data, we focused on interviewees’ responses to the prompt: “Tell me what influences you in the decision to or not to admit TIA patients.” We used administrative data to identify admission rates and chart review data to identify ABCD2 scores (a tool to classify stroke risk after TIA). Key Results Providers’ decisions to admit TIA patients were related to uncertainty in several domains: lack of a facility TIA-specific policy, inconsistent use of ABCD2 score, and concerns about facilities’ ability to complete a timely workup. There was a disconnect between staff perceptions about TIA admission and facility admission rates. According to chart review data, staff at facilities with higher admission rates in FY 2011 reported consistent reliance on ABCD2 scores and related guidelines in admission decision-making. Conclusions Many factors contributed to decisions regarding admitting a patient with TIA; however, clinicians’ uncertainty appeared to be a key driver. Further quality improvement interventions for TIA care should focus on facility adoption of TIA protocols to address uncertainty in TIA admission decision-making and to standardize timely evaluation of TIA patients and delivery of secondary prevention strategies

Batch and median neural gas

Neural Gas (NG) constitutes a very robust clustering algorithm given euclidian data which does not suffer from the problem of local minima like simple vector quantization, or topological restrictions like the self-organizing map. Based on the cost function of NG, we introduce a batch variant of NG which shows much faster convergence and which can be interpreted as an optimization of the cost function by the Newton method. This formulation has the additional benefit that, based on the notion of the generalized median in analogy to Median SOM, a variant for non-vectorial proximity data can be introduced. We prove convergence of batch and median versions of NG, SOM, and k-means in a unified formulation, and we investigate the behavior of the algorithms in several experiments.Comment: In Special Issue after WSOM 05 Conference, 5-8 september, 2005, Pari

Myeloid DAP12-associating lectin (MDL)-1 regulates synovial inflammation and bone erosion associated with autoimmune arthritis.

DNAX adaptor protein 12 (DAP12) is a trans-membrane adaptor molecule that transduces activating signals in NK and myeloid cells. Absence of functional Dap12 results in osteoclast defects and bone abnormalities. Because DAP12 has no extracelluar binding domains, it must pair with cell surface receptors for signal transduction. There are at least 15 known DAP12-associating cell surface receptors with distinct temporal and cell type-specific expression patterns. Our aim was to determine which receptors may be important in DAP12-associated bone pathologies. Here, we identify myeloid DAP12-associating lectin (MDL)-1 receptor (also known as CLEC5A) as a key regulator of synovial injury and bone erosion during autoimmune joint inflammation. Activation of MDL-1 leads to enhanced recruitment of inflammatory macrophages and neutrophils to the joint and promotes bone erosion. Functional blockade of MDL-1 receptor via Mdl1 deletion or treatment with MDL-1-Ig fusion protein reduces the clinical signs of autoimmune joint inflammation. These findings suggest that MDL-1 receptor may be a therapeutic target for treatment of immune-mediated skeletal disorders