Learning to Segment Microscopy Images with Lazy Labels

The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for segmenting bioimages that can contain numerous object instances with thin separations. In this paper, we introduce a deep convolutional neural network for microscopy image segmentation. Annotation issues are circumvented by letting the network being trainable on coarse labels combined with only a very small number of images with pixel-wise annotations. We call this new labelling strategy `lazy' labels. Image segmentation is stratified into three connected tasks: rough inner region detection, object separation and pixel-wise segmentation. These tasks are learned in an end-to-end multi-task learning framework. The method is demonstrated on two microscopy datasets, where we show that the model gives accurate segmentation results even if exact boundary labels are missing for a majority of annotated data. It brings more flexibility and efficiency for training deep neural networks that are data hungry and is applicable to biomedical images with poor contrast at the object boundaries or with diverse textures and repeated patterns

The influence of anesthetics, neurotransmitters and antibiotics on the relaxation processes in lipid membranes

In the proximity of melting transitions of artificial and biological membranes fluctuations in enthalpy, area, volume and concentration are enhanced. This results in domain formation, changes of the elastic constants, changes in permeability and slowing down of relaxation processes. In this study we used pressure perturbation calorimetry to investigate the relaxation time scale after a jump into the melting transition regime of artificial lipid membranes. This time corresponds to the characteristic rate of domain growth. The studies were performed on single-component large unilamellar and multilamellar vesicle systems with and without the addition of small molecules such as general anesthetics, neurotransmitters and antibiotics. These drugs interact with membranes and affect melting points and profiles. In all systems we found that heat capacity and relaxation times are related to each other in a simple manner. The maximum relaxation time depends on the cooperativity of the heat capacity profile and decreases with a broadening of the transition. For this reason the influence of a drug on the time scale of domain formation processes can be understood on the basis of their influence on the heat capacity profile. This allows estimations of the time scale of domain formation processes in biological membranes.Comment: 12 pages, 6 figure

Challenges Predicting Ligand-Receptor Interactions of Promiscuous Proteins: The Nuclear Receptor PXR

Transcriptional regulation of some genes involved in xenobiotic detoxification and apoptosis is performed via the human pregnane X receptor (PXR) which in turn is activated by structurally diverse agonists including steroid hormones. Activation of PXR has the potential to initiate adverse effects, altering drug pharmacokinetics or perturbing physiological processes. Reliable computational prediction of PXR agonists would be valuable for pharmaceutical and toxicological research. There has been limited success with structure-based modeling approaches to predict human PXR activators. Slightly better success has been achieved with ligand-based modeling methods including quantitative structure-activity relationship (QSAR) analysis, pharmacophore modeling and machine learning. In this study, we present a comprehensive analysis focused on prediction of 115 steroids for ligand binding activity towards human PXR. Six crystal structures were used as templates for docking and ligand-based modeling approaches (two-, three-, four- and five-dimensional analyses). The best success at external prediction was achieved with 5D-QSAR. Bayesian models with FCFP_6 descriptors were validated after leaving a large percentage of the dataset out and using an external test set. Docking of ligands to the PXR structure co-crystallized with hyperforin had the best statistics for this method. Sulfated steroids (which are activators) were consistently predicted as non-activators while, poorly predicted steroids were docked in a reverse mode compared to 5α-androstan-3β-ol. Modeling of human PXR represents a complex challenge by virtue of the large, flexible ligand-binding cavity. This study emphasizes this aspect, illustrating modest success using the largest quantitative data set to date and multiple modeling approaches

Clinical decision support of therapeutic drug monitoring of phenytoin: measured versus adjusted phenytoin plasma concentrations

<p>Abstract</p> <p>Background</p> <p>Therapeutic drug monitoring of phenytoin by measurement of plasma concentrations is often employed to optimize clinical efficacy while avoiding adverse effects. This is most commonly accomplished by measurement of total phenytoin plasma concentrations. However, total phenytoin levels can be misleading in patients with factors such as low plasma albumin that alter the free (unbound) concentrations of phenytoin. Direct measurement of free phenytoin concentrations in plasma is more costly and time-consuming than determination of total phenytoin concentrations. An alternative to direct measurement of free phenytoin concentrations is use of the Sheiner-Tozer equation to calculate an adjusted phenytoin that corrects for the plasma albumin concentration. Innovative medical informatics tools to identify patients who would benefit from adjusted phenytoin calculations or from laboratory measurement of free phenytoin are needed to improve safety and efficacy of phenytoin pharmacotherapy. The electronic medical record for an academic medical center was searched for the time period from August 1, 1996 to November 30, 2010 for patients who had total phenytoin and free phenytoin determined on the same blood draw, and also a plasma albumin measurement within 7 days of the phenytoin measurements. The measured free phenytoin plasma concentration was used as the gold standard.</p> <p>Results</p> <p>In this study, the standard Sheiner-Tozer formula for calculating an estimated (adjusted) phenytoin level more frequently underestimates than overestimates the measured free phenytoin relative to the respective therapeutic ranges. Adjusted phenytoin concentrations provided superior classification of patients than total phenytoin measurements, particularly at low albumin concentrations. Albumin plasma concentrations up to 7 days prior to total phenytoin measurements can be used for adjusted phenytoin concentrations.</p> <p>Conclusions</p> <p>The results suggest that a measured free phenytoin should be obtained where possible to guide phenytoin dosing. If this is not feasible, then an adjusted phenytoin can supplement a total phenytoin concentration, particularly for patients with low plasma albumin.</p

The snow must go on: Ground ice encasement, snow compaction and absence of snow differently cause soil hypoxia, CO2 accumulation and tree seedling damage in boreal forest

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Coincident Activity of Converging Pathways Enables Simultaneous Long-Term Potentiation and Long-Term Depression in Hippocampal CA1 Network In Vivo

Memory is believed to depend on activity-dependent changes in the strength of synapses, e.g. long-term potentiation (LTP) and long-term depression (LTD), which can be determined by the sequence of coincident pre- and postsynaptic activity, respectively. It remains unclear, however, whether and how coincident activity of converging efferent pathways can enable LTP and LTD in the pathways simultaneously. Here, we report that, in pentobarbital-anesthetized rats, stimulation (600 pulses, 5 Hz) to Schaffer preceding to commissural pathway within a 40-ms timing window induced similar magnitudes of LTP in both pathways onto synapses of CA1 neurons, with varied LTP magnitudes after reversal of the stimulation sequence. In contrast, in urethane-anesthetized or freely-moving rats, the stimulation to Schaffer preceding to commissural pathway induced Schaffer LTP and commissural LTD simultaneously within a 40-ms timing window, without affecting synaptic efficacy in the reversed stimulation sequence. Coincident activity of Schaffer pathways confirmed the above findings under pentobarbital and urethane anesthesia. Thus, coincident activity of converging afferent pathways tends to switch the pathways to be LTP only or LTP/LTD depending on the activity states of the hippocampus. This network rule strengthens the view that activity-dependent synaptic plasticity may well contribute to memory process of the hippocampal network with flexibility or stability from one state to another

Active Nuclear Receptors Exhibit Highly Correlated AF-2 Domain Motions

Nuclear receptor ligand binding domains (LBDs) convert ligand binding events into changes in gene expression by recruiting transcriptional coregulators to a conserved activation function-2 (AF-2) surface. While most nuclear receptor LBDs form homo- or heterodimers, the human nuclear receptor pregnane X receptor (PXR) forms a unique and essential homodimer and is proposed to assemble into a functional heterotetramer with the retinoid X receptor (RXR). How the homodimer interface, which is located 30 Å from the AF-2, would affect function at this critical surface has remained unclear. By using 20- to 30-ns molecular dynamics simulations on PXR in various oligomerization states, we observed a remarkably high degree of correlated motion in the PXR–RXR heterotetramer, most notably in the four helices that create the AF-2 domain. The function of such correlation may be to create “active-capable” receptor complexes that are ready to bind to transcriptional coactivators. Indeed, we found in additional simulations that active-capable receptor complexes involving other orphan or steroid nuclear receptors also exhibit highly correlated AF-2 domain motions. We further propose a mechanism for the transmission of long-range motions through the nuclear receptor LBD to the AF-2 surface. Taken together, our findings indicate that long-range motions within the LBD scaffold are critical to nuclear receptor function by promoting a mobile AF-2 state ready to bind coactivators

Population based models of cortical drug response: insights from anaesthesia

A great explanatory gap lies between the molecular pharmacology of psychoactive agents and the neurophysiological changes they induce, as recorded by neuroimaging modalities. Causally relating the cellular actions of psychoactive compounds to their influence on population activity is experimentally challenging. Recent developments in the dynamical modelling of neural tissue have attempted to span this explanatory gap between microscopic targets and their macroscopic neurophysiological effects via a range of biologically plausible dynamical models of cortical tissue. Such theoretical models allow exploration of neural dynamics, in particular their modification by drug action. The ability to theoretically bridge scales is due to a biologically plausible averaging of cortical tissue properties. In the resulting macroscopic neural field, individual neurons need not be explicitly represented (as in neural networks). The following paper aims to provide a non-technical introduction to the mean field population modelling of drug action and its recent successes in modelling anaesthesia