Determining the Projected Crystal Structure from Four-dimensional Scanning Transmission Electron Microscopy via the Scattering Matrix.

We present a gradient-descent-based approach to determining the projected electrostatic potential from four-dimensional scanning transmission electron microscopy measurements of a periodic, crystalline material even when dynamical scattering occurs. The method solves for the scattering matrix as an intermediate step, but overcomes the so-called truncation problem that limited previous scattering-matrix-based projected structure determination methods. Gradient descent is made efficient by using analytic expressions for the gradients. Through simulated case studies, we show that iteratively improving the scattering matrix determination can significantly improve the accuracy of the projected structure determination.

Alternative Mechanism for White Adipose Tissue Lipolysis after Thermal Injury.

Extensively burned patients often suffer from sepsis, a complication that enhances postburn hypermetabolism and contributes to increased incidence of multiple organ failure, morbidity and mortality. Despite the clinical importance of burn sepsis, the molecular and cellular mechanisms of such infection-related metabolic derangements and organ dysfunction are still largely unknown. We recently found that upon endoplasmic reticulum (ER) stress, the white adipose tissue (WAT) interacts with the liver via inflammatory and metabolic signals leading to profound hepatic alterations, including hepatocyte apoptosis and hepatic fatty infiltration. We therefore hypothesized that burn plus infection causes an increase in lipolysis of WAT after major burn, partially through induction of ER stress, contributing to hyperlipidemia and profound hepatic lipid infiltration. We used a two-hit rat model of 60% total body surface area scald burn, followed by intraperitoneal (IP) injection of Pseudomonas Aeruginosa-derived lipopolysaccharide (LPS) 3 d postburn. One day later, animals were euthanized and liver and epididymal WAT (EWAT) samples were collected for gene expression, protein analysis and histological study of inflammasome activation, ER stress, apoptosis and lipid metabolism. Our results showed that burn plus LPS profoundly increased lipolysis in WAT associated with significantly increased hepatic lipid infiltration. Burn plus LPS augmented ER stress by upregulating CHOP and activating ATF6, inducing NLRP3 inflammasome activation and leading to increased apoptosis and lipolysis in WAT with a distinct enzymatic mechanism related to inhibition of AMPK signaling. In conclusion, burn sepsis causes profound alterations in WAT and liver that are associated with changes in organ function and structure.

A Python package based on robust statistical analysis for serial crystallography data processing.

The term robustness in statistics refers to methods that are generally insensitive to deviations from model assumptions. In other words, robust methods are able to preserve their accuracy even when the data do not perfectly fit the statistical models. Robust statistical analyses are particularly effective when analysing mixtures of probability distributions. Therefore, these methods enable the discretization of X-ray serial crystallography data into two probability distributions: a group comprising true data points (for example the background intensities) and another group comprising outliers (for example Bragg peaks or bad pixels on an X-ray detector). These characteristics of robust statistical analysis are beneficial for the ever-increasing volume of serial crystallography (SX) data sets produced at synchrotron and X-ray free-electron laser (XFEL) sources. The key advantage of the use of robust statistics for some applications in SX data analysis is that it requires minimal parameter tuning because of its insensitivity to the input parameters. In this paper, a software package called Robust Gaussian Fitting library (RGFlib) is introduced that is based on the concept of robust statistics. Two methods are presented based on the concept of robust statistics and RGFlib for two SX data-analysis tasks: (i) a robust peak-finding algorithm and (ii) an automated robust method to detect bad pixels on X-ray pixel detectors.

Automatic bad-pixel mask maker for X-ray pixel detectors with application to serial crystallography.

X-ray crystallography has witnessed a massive development over the past decade, driven by large increases in the intensity and brightness of X-ray sources and enabled by employing high-frame-rate X-ray detectors. The analysis of large data sets is done via automatic algorithms that are vulnerable to imperfections in the detector and noise inherent with the detection process. By improving the model of the behaviour of the detector, data can be analysed more reliably and data storage costs can be significantly reduced. One major requirement is a software mask that identifies defective pixels in diffraction frames. This paper introduces a methodology and program based upon concepts of machine learning, called robust mask maker (RMM), for the generation of bad-pixel masks for large-area X-ray pixel detectors based on modern robust statistics. It is proposed to discriminate normally behaving pixels from abnormal pixels by analysing routine measurements made with and without X-ray illumination. Analysis software typically uses a Bragg peak finder to detect Bragg peaks and an indexing method to detect crystal lattices among those peaks. Without proper masking of the bad pixels, peak finding methods often confuse the abnormal values of bad pixels in a pattern with true Bragg peaks and flag such patterns as useful regardless, leading to storage of enormous uninformative data sets. Also, it is computationally very expensive for indexing methods to search for crystal lattices among false peaks and the solution may be biased. This paper shows how RMM vastly improves peak finders and prevents them from labelling bad pixels as Bragg peaks, by demonstrating its effectiveness on several serial crystallography data sets.

Data reduction for serial crystallography using a robust peak finder.

A peak-finding algorithm for serial crystallography (SX) data analysis based on the principle of 'robust statistics' has been developed. Methods which are statistically robust are generally more insensitive to any departures from model assumptions and are particularly effective when analysing mixtures of probability distributions. For example, these methods enable the discretization of data into a group comprising inliers (i.e. the background noise) and another group comprising outliers (i.e. Bragg peaks). Our robust statistics algorithm has two key advantages, which are demonstrated through testing using multiple SX data sets. First, it is relatively insensitive to the exact value of the input parameters and hence requires minimal optimization. This is critical for the algorithm to be able to run unsupervised, allowing for automated selection or 'vetoing' of SX diffraction data. Secondly, the processing of individual diffraction patterns can be easily parallelized. This means that it can analyse data from multiple detector modules simultaneously, making it ideally suited to real-time data processing. These characteristics mean that the robust peak finder (RPF) algorithm will be particularly beneficial for the new class of MHz X-ray free-electron laser sources, which generate large amounts of data in a short period of time.

Observation of substrate diffusion and ligand binding in enzyme crystals using high-repetition-rate mix-and-inject serial crystallography.

Here, we illustrate what happens inside the catalytic cleft of an enzyme when substrate or ligand binds on single-millisecond timescales. The initial phase of the enzymatic cycle is observed with near-atomic resolution using the most advanced X-ray source currently available: the European XFEL (EuXFEL). The high repetition rate of the EuXFEL combined with our mix-and-inject technology enables the initial phase of ceftriaxone binding to the Mycobacterium tuberculosis ß-lactamase to be followed using time-resolved crystallography in real time. It is shown how a diffusion coefficient in enzyme crystals can be derived directly from the X-ray data, enabling the determination of ligand and enzyme-ligand concentrations at any position in the crystal volume as a function of time. In addition, the structure of the irreversible inhibitor sulbactam bound to the enzyme at a 66 ms time delay after mixing is described. This demonstrates that the EuXFEL can be used as an important tool for biomedically relevant research.

Efficient Robust Model Fitting for Multistructure Data Using Global Greedy Search.

In this paper, a new robust model fitting method is proposed to efficiently segment multistructure data even when they are heavily contaminated by outliers. The proposed method is composed of three steps: first, a conventional greedy search strategy is employed to generate (initial) model hypotheses based on the sequential "fit-and-remove" procedure because of its computational efficiency. Second, to efficiently generate accurate model hypotheses close to the true models, a novel global greedy search strategy initially samples from the inliers of the obtained model hypotheses and samples subsequent data subsets from the whole input data. Third, mutual information theory is applied to fuse the model hypotheses of the same model instance. The conventional greedy search strategy is used to generate model hypotheses for the remaining model instances, if the number of retained model hypotheses is less than that of the true model instances after fusion. The second and the third steps are performed iteratively until an adequate solution is obtained. Experimental results demonstrate the effectiveness and efficiency of the proposed method for model fitting.

Effective Sampling: Fast Segmentation Using Robust Geometric Model Fitting.

Identifying the underlying models in a set of data points that is contaminated by noise and outliers leads to a highly complex multi-model fitting problem. This problem can be posed as a clustering problem by the projection of higher-order affinities between data points into a graph, which can be clustered using spectral clustering. Calculating all possible higher-order affinities is computationally expensive. Hence, in most cases, only a subset is used. In this paper, we propose an effective sampling method for obtaining a highly accurate approximation of the full graph, which is required to solve multi-structural model fitting problems in computer vision. The proposed method is based on the observation that the usefulness of a graph for segmentation improves as the distribution of the hypotheses that are used to build the graph approaches the distribution of the actual parameters for the given data. In this paper, we approximate this actual parameter distribution by using a th-order statistics-based cost function, and the samples are generated using a greedy algorithm that is coupled with a data sub-sampling strategy. The experimental analysis shows that the proposed method is both accurate and computationally efficient compared with the state-of-the-art robust multi-model fitting techniques. The implementation of the method is publicly available from https://github.com/RuwanT/model-fitting-cbs.

Accumulation of myeloid lineage cells is mapping out liver fibrosis post injury: a targetable lesion using Ketanserin.

Liver fibrosis is problematic after persistent injury. However, little is known about its response to an acute insult. Accumulation of myeloid lineage cells contributes into the promotion and resolution of inflammation and fibrosis. Using Cre-transgenic mice that specifically mark myeloid lineage cells with EYFP and burn as a model of acute systemic injury, we investigated the role of myeloid lineage cells in the liver after acute injury. Our data show that thermal injury in mice (30% total body surface area) induces fibrosis predominantly around portal venules whereas myeloid cells are enriched throughout the liver. The fibrosis peaks around 1-2 weeks post injury and resolves by week 3. Ablating myeloid cells led to lower fibrosis. Through FACS sorting, we isolated myeloid lineage cells (EYFP +ve cells) from injured animals and from the control uninjured animals and subjected the extracted RNA from these cells to microarray analysis. Microarray analysis revealed an inflammatory signature for EYFP +ve cells isolated from injured animals in comparison with control cells. Moreover, it showed modulation of components of the serotonin (5-HT) pathway in myeloid cells. Antagonizing the 5HT2A/2C receptor decreased fibrosis in thermally injured mice by skewing macrophages away from their pro-fibrotic phenotype. Macrophages conditioned with Ketanserin showed a lower pro-fibrotic phenotype in a co-culture system with mesenchymal cells. There is a spatiotemporal pattern in liver fibrosis post-thermal injury, which is associated with the influx of myeloid cells. Treating mice with a 5HT2A/2C receptor antagonist promotes an anti-fibrotic effect, through modulating the phenotype of macrophages.

Hepatic steatosis associated with decreased ß-oxidation and mitochondrial function contributes to cell damage in obese mice after thermal injury.

Severely burned patients who are morbidly obese have poor clinical outcomes with aggravated metabolic consequences, a higher incidence of multiple organ dysfunction/failure, and significantly increased morbidity and mortality. The underlying mechanisms of these adverse outcomes are essentially unknown. Since the liver is one of the central metabolic organs, we hypothesized that thermal injury in obese patients leads to substantially increased lipolysis, hepatic fat infiltration, resulting in profound hepatic cellular and organellar alterations, consequently causing liver damage and severely augmented metabolic dysfunction. We tested this hypothesis using an obese mouse model subjected to a 20% total body surface area burn injury. C57BL/6 mice were randomly divided into low-fat diet (LFD) and high-fat diet (HFD) sham and burn groups (n = 6 per group) and fed for 16 weeks. 7 days after the thermal injury portal and cardiac blood were taken separately and liver tissue was collected for western blotting and immunohistochemical analysis. Gross examination of the liver showed apparent lipid infiltration in HFD fed and burned mice. We confirmed that augmented ER stress and inhibition of Akt-mTOR signaling dysregulated calcium homeostasis, contributed to the decrease of ER-mitochondria contact, and reduced mitochondrial ß-oxidation in HFD fed and burned mice, leading to profound hepatic fat infiltration and substantial liver damage, hence increased morbidity and mortality. We conclude that obesity contributes to hepatic fat infiltration by suppressing ß-oxidation, inducing cell damage and subsequent organ dysfunction after injury.

Clustering with Hypergraphs: The Case for Large Hyperedges.

The extension of conventional clustering to hypergraph clustering, which involves higher order similarities instead of pairwise similarities, is increasingly gaining attention in computer vision. This is due to the fact that many clustering problems require an affinity measure that must involve a subset of data of size more than two. In the context of hypergraph clustering, the calculation of such higher order similarities on data subsets gives rise to hyperedges. Almost all previous work on hypergraph clustering in computer vision, however, has considered the smallest possible hyperedge size, due to a lack of study into the potential benefits of large hyperedges and effective algorithms to generate them. In this paper, we show that large hyperedges are better from both a theoretical and an empirical standpoint. We then propose a novel guided sampling strategy for large hyperedges, based on the concept of random cluster models. Our method can generate large pure hyperedges that significantly improve grouping accuracy without exponential increases in sampling costs. We demonstrate the efficacy of our technique on various higher-order grouping problems. In particular, we show that our approach improves the accuracy and efficiency of motion segmentation from dense, long-term, trajectories.

A Surgical Device to Study the Efficacy of Bioengineered Skin Substitutes in Mice Wound Healing Models.

Due to the poor regenerative capacity of adult mammalian skin, there is a need to develop effective skin substitutes for promoting skin regeneration after a severe wound. However, the complexity of skin biology has made it difficult to enable perfect regeneration of skin. Thus, animal models are being used to test potential skin substitutes. Murine models are valuable but their healing process involves dermal contraction. We have developed a device called a dome that is able to eliminate the contraction effect of rodent skin while simultaneously housing a bioengineered skin graft. The dome comes in two models, which enables researchers to evaluate the cells that contribute in wound healing from neighboring intact tissue during skin healing/regeneration. This protocol simplifies grafting of skin substitutes, eliminates the contraction effect of surrounding skin, and summarizes a simple method for animal surgery for wound healing and skin regeneration studies.

The response of muscle progenitor cells to cutaneous thermal injury.

BACKGROUND: Severe burn results in a systemic response that leads to significant muscle wasting. It is believed that this rapid loss in muscle mass occurs due to increased protein degradation combined with reduced protein synthesis. Alterations in the microenvironment of muscle progenitor cells may partially account for this pathology. The aim of this study was to ascertain the response of muscle progenitor cells following thermal injury in mice and to enlighten the cellular cascades that contribute to the muscle wasting. METHODS: C57BL/6 mice received a 20% total body surface area (TBSA) thermal injury. Gastrocnemius muscle was harvested at days 2, 7, and 14 following injury for protein and histological analysis. RESULTS: We observed a decrease in myofiber cross-sectional area at 2 days post-burn. This muscle atrophy was compensated for by an increase in myofiber cross-sectional area at 7 and 14 days post-burn. Myeloperoxidase (MPO)-positive cells (neutrophils) increased significantly at 2 days. Moreover, through Western blot analysis of two key mediators of the proteolytic pathway, we show there is an increase in Murf1 and NF-κB 2 days post-burn. MPO-positive cells were also positive for NF-κB, suggesting that neutrophils attain NF-κB activity in the muscle. Unlike inflammatory and proteolytic pathways, the number of Pax7-positive muscle progenitor cells decreased significantly 2 days post-burn. This was followed by a recovery in the number of Pax7-positive cells at 7 and 14 days, suggesting proliferation of muscle progenitors that accompanied regrowth. CONCLUSION: Our data show a biphasic response in the muscles of mice exposed to burn injury, with phenotypic characteristics of muscle atrophy at 2 days while compensation was observed later with a change in Pax7-positive muscle progenitor cells. Targeting muscle progenitors may be of therapeutic benefit in muscle wasting observed after burn injury.

IL-6 Signal From the Bone Marrow is Required for the Browning of White Adipose Tissue Post Burn Injury.

The hypermetabolic stress response after burn contributes to multi-organ failure, sepsis, morbidity, and mortality. The cytokine interleukin 6 (IL-6) has been hypothesized to mediate not only white adipose tissue (WAT) browning in burns, but also other hypermetabolic conditions. In addition to its inflammatory effects, IL-6 also acts as a metabolic mediator that affects metabolic tissues. Therefore, we sought to uncover the origin of circulating IL-6 post burn injury that regulates WAT browning. WAT and sera samples were collected from both adult burn patients admitted to the Ross Tilley Burn Centre at Sunnybrook Hospital and mice subjected to a burn injury. Collected tissues were analyzed for browning markers and metabolic state via histology, gene expression, and resting energy expenditure. Increased WAT browning was observed in burn patients as well as mice subjected to burn injury. Circulating IL-6 levels were significantly elevated post burn injury in mice (<0.05) and in burn patients (<0.05), the latter of which was positively correlated with elevated REE. Genetic loss of whole body IL-6 in mice prevented burn-induced WAT browning. Transplanting IL-6 knockout (KO) mice with bone marrow (BM) from wild-type (WT) mice, recovered the browning phenotype in these mice, as evaluated by increased uncoupling protein 1 (UCP1) expression (<0.05). Conversely, transplanting irradiated WT mice with BM from IL-6 KO mice impaired burn induced browning with no significant expression of UCP1. Together, our findings implicate BM derived IL-6 as the source controlling browning of WAT post burn injury. Thus, targeting IL-6 is a promising target for hypermetabolism in burns.

Advances in Liver Regeneration: Revisiting Hepatic Stem/Progenitor Cells and Their Origin.

The liver has evolved to become a highly plastic organ with extraordinary regenerative capabilities. What drives liver regeneration is still being debated. Adult liver stem/progenitor cells have been characterized and used to produce functional hepatocytes and biliary cells in vitro. However, in vivo, numerous studies have questioned whether hepatic progenitor cells have a significant role in liver regeneration. Mature hepatocytes have recently been shown to be more plastic than previously believed and give rise to new hepatocytes after acute and chronic injury. In this review, we discuss current knowledge in the field of liver regeneration and the importance of the serotonin pathway as a clinical target for patients with liver dysfunction.

IDH1 regulates phospholipid metabolism in developing astrocytes.

Isocitrate dehydrogenase 1 (IDH1) is an evolutionarily conserved enzyme that catalyzes the interconversion of isocitrate to α-ketoglutarate with the concomitant reduction of NADP(+) to NADPH. IDH1 has previously been shown to participate in lipid biosynthesis in various tissues such as the liver and adipose tissue. We examined the potential role of IDH1 in phospholipid metabolism in the brain. Here we show that IDH1 is highly expressed in the brain and astrocytes during embryonic development and the postnatal period and subsequently declines in adulthood. Silencing of IDH1 expression using siRNA in astrocytes isolated from E18.5 mouse cortices led to increased incorporation of [(3)H]-palmitate into the phosphatidylcholines (PCs) and decreased the incorporation of [(3)H]-palmitate into sphingomyelin and the phosphatidylethanolamines (PEs). In pulse-chase experiments, knock-down of IDH1 expression impaired the turnover of PCs and decreased the synthesis of PEs. The decrease in [(3)H]-palmitate incorporation into PEs when IDH1 was knocked-down in astrocytes was not due to impairments within the CDP-ethanolamine pathway or in the rate of decarboxylation of phosphatidylserine (PS). In conclusion, our results reveal a role for IDH1 in the synthesis/turnover of phospholipids in developing astrocytes and highlight the lipid alterations resulting from the loss of wild-type IDH1 activity.

Designing the Vocal Alarm and improving medical ventilator.

BACKGROUND: Today, as many as 1.5 million Americans use ventilators once in a year. Response to mechanical ventilation alarms remains to be one of the most challenging tasks facing physicians, nurses or other medical personnel in the ICU. In the present study we aimed to compare the response times to "vocal alarms" and "visual or audible ones". METHODS: In the present study we developed a system to evaluate the "Vocal Alarm" and improve the medical ventilator "Benet 7200 Alarms" with it. Ventilator generates the alarms when patient has any problem. The time of Activation & Deactivation is recorded. The survey was done in central ICU for six days, 3 days with vocal alarm, and 3days with "Beep" alarms and detected Alarm events then recorded seventy-two hours of data for each type of alarms. All of events information saved in the memory and SPSS was used to determine difference between two types of alarms. RESULTS: On the average, the duration of the ventilator alarms activation were 33 ± 21 seconds for vocal alarms and 60 ± 46 for audible "Beep" alarms. The response times for vocal alarms were significantly lower (P = 0.001). CONCLUSIONS: The response times for normal "Beep" alarms were longer than vocal alarms.