Moderate Number Research Articles

A comparison of Oxford nanopore library strategies for bacterial genomics

BackgroundOxford nanopore Technologies (ONT) provides three main library preparation strategies to sequence bacterial genomes. These include tagmentation (TAG), ligation (LIG) and amplification (PCR). Despite ONT’s recommendations, making an informed decision for preparation choice remains difficult without a side-by-side comparison. Here, we sequenced 12 bacterial strains to examine the overall output of these strategies, including sequencing noise, barcoding efficiency and assembly quality based on mapping to curated genomes established herein.ResultsAverage read length ranged closely for TAG and LIG (> 5,000 bp), while being drastically smaller for PCR (< 1,100 bp). LIG produced the largest output with 33.62 Gbp vs. 11.72 Gbp for TAG and 4.79 Gbp for PCR. PCR produced the most sequencing noise with only 22.7% of reads mappable to the curated genomes, vs. 92.9% for LIG and 87.3% for TAG. Output per channel was most homogenous in LIG and most variable in PCR, while intermediate in TAG. Artifactual tandem content was most abundant in PCR (22.5%) and least in LIG and TAG (0.9% and 2.2%). Basecalling and demultiplexing of barcoded libraries resulted in ~ 20% data loss as unclassified reads and 1.5% read leakage.ConclusionThe output of LIG was best (low noise, high read numbers of long lengths), intermediate in TAG (some noise, moderate read numbers of long lengths) and less desirable in PCR (high noise, high read numbers of short lengths). Overall, users should not accept assembly results at face value without careful replicon verification, including the detection of plasmids assembled from leaked reads.

Differential responses to maternal diabetes in embryo and visceral yolk sac.

Introduction: Maternal diabetes during pregnancy is well known to be associated with a higher risk for structural birth defects in the offspring. Recent searches for underlying mechanisms have largely focused on aberrant processes in the embryo itself, although prior research in rodent models implicated dysfunction also of the visceral yolk sac. The objective of our research was to investigate both tissues within the conceptus simultaneously. Methods: We conducted unbiased transcriptome profiling by RNA sequencing on pairs of individual yolk sacs and their cognate embryos, using the non-obese diabetic (NOD) mouse model. The analysis was performed at gestational day 8.5 on morphologically normal specimen to circumvent confounding by defective development. Results: Even with large sample numbers (n = 33 in each group), we observed considerable variability of gene expression, primarily driven by exposure to maternal diabetes, and secondarily by developmental stage of the embryo. Only a moderate number of genes changed expression in the yolk sac, while in the embryo, the exposure distinctly influenced the relationship of gene expression levels to developmental progression, revealing a possible role for altered cell cycle regulation in the response. Also affected in embryos under diabetic conditions were genes involved in cholesterol biosynthesis and NAD metabolism pathways. Discussion: Exposure to maternal diabetes during gastrulation changes transcriptomic profiles in embryos to a substantially greater effect than in the corresponding yolk sacs, indicating that despite yolk sac being of embryonic origin, different mechanisms control transcriptional activity in these tissues. The effects of maternal diabetes on expression of many genes that are correlated with developmental progression (i.e. somite stage) highlight the importance of considering developmental maturity in the interpretation of transcriptomic data. Our analyses identified cholesterol biosynthesis and NAD metabolism as novel pathways not previously implicated in diabetic pregnancies. Both NAD and cholesterol availability affect a wide variety of cellular signaling processes, and can be modulated by diet, implying that prevention of adverse outcomes from diabetic pregnancies may require broad interventions, particularly in the early stages of pregnancy.

Packet Classification Using TCAM of Narrow Entries

Packet classification based on rules of packet header fields is the key technology for enabling software-defined networking (SDN). Ternary content addressable memory (TCAM) is a widely used hardware for packet classification; however, commercially available TCAM chips have only limited storage. As the number of supported header fields in SDN increases, the number of supported rules in a TCAM chip is reduced. In this work, we present a novel scheme to enable packet classification using TCAM with entries that are narrower than rules by storing the most representative field of a ruleset in TCAM. Due to the fact that not all rules can be distinguished using one field, our scheme employs a TCAM-based multimatch packet classification technique to ensure correctness. We further develop approaches to reduce redundant TCAM accesses for multimatch packet classification. Although our scheme requires additional TCAM accesses, it supports packet classification upon long rules with narrow TCAM entries, and drastically reduces the required TCAM storage. Our experimental results show that our scheme requires a moderate number of additional TCAM accesses and consumes much less storage compared to the basic TCAM-based packet classification. Thus, it can provide the required scalability for long rules required by potential applications of SDN.

Exclude with Impunity: Personalized Indexing and Stock Restrictions

Using simulated historical backtests, we study the impact of stock exclusions on the performance of passive and active portfolios. We find that at low to moderate numbers, stock exclusions have very little influence on passive portfolios. Their effects on active portfolios vary by the factor in consideration and the portfolio construction method, but the magnitudes are much smaller than suggested by the percentage of stocks being excluded. We find similar patterns with industry-concentrated exclusions. Overall, our results suggest that investors should feel comfortable excluding a fairly large number of stocks before experiencing any significant deterioration in their investment performance.

Microbiologist in the clinic: coitally related symptoms with negative urine cultures.

In this first episode of the Microbiologist in the Clinic series, clinicians and laboratory scientists share their perspectives about a 30 y/o woman, who is seeking specialty consultation for frequent episodes of urinary urgency, frequency, and dysuria, which respond to short courses of antibiotics. Although her home dipsticks suggest that she has a UTI, and her urinalysis typically has a moderate number of white blood cells, her urine cultures are always negative. The challenges of this clinical presentation are discussed with evidence for evaluation and treatment.

Decision avoidance and post-decision regret: A systematic review and meta-analysis.

Decision Avoidance (DA) strategies allow people to forego or abandon effortful deliberation by postponing, bypassing, or delegating a decision. DA is thought to reduce regret, primarily by allowing decision makers to evade personal responsibility for potential negative outcomes. We tested this relation between DA and post-decision regret in a multilevel meta-analysis of 59 effect estimates coming from 13 papers. Five DA strategies were considered: status quo preservation, action omission, inaction inertia, choice delegation and choice deferral. Across all effects and DA strategies, there was a non-significant trend toward DA reducing regret (Hedges' g = -0.23, p = 0.063). When assessing individual strategies, we found that only status quo preservation reduced regret reliably (Hedges' g = -0.45, p = 0.006). The relationship between DA and regret was unclear for the other DA strategies. We tested a number of moderators for the effect. Only 'previous experience' (i.e., the outcome of a previous decision) influenced the relation between DA and regret reliably. That is, if participants choose the DA option when the same choice previously led to a negative outcome, regret is actually enhanced. Overall, there is clear evidence that status quo preservation can reduce regret, but it is currently unclear whether the same holds for other DA strategies.

Reproducibility of biophysical in silico neuron states and spikes from event-based partial histories.

Biophysically detailed simulations of neuronal activity often rely on solving large systems of differential equations; in some models, these systems have tens of thousands of states per cell. Numerically solving these equations is computationally intensive and requires making assumptions about the initial cell states. Additional realism from incorporating more biological detail is achieved at the cost of increasingly more states, more computational resources, and more modeling assumptions. We show that for both a point and morphologically-detailed cell model, the presence and timing of future action potentials is probabilistically well-characterized by the relative timings of a moderate number of recent events alone. Knowledge of initial conditions or full synaptic input history is not required. While model time constants, etc. impact the specifics, we demonstrate that for both individual spikes and sustained cellular activity, the uncertainty in spike response decreases as the number of known input events increases, to the point of approximate determinism. Further, we show cellular model states are reconstructable from ongoing synaptic events, despite unknown initial conditions. We propose that a strictly event-based modeling framework is capable of representing the complexity of cellular dynamics of the differential-equations models with significantly less per-cell state variables, thus offering a pathway toward utilizing modern data-driven modeling to scale up to larger network models while preserving individual cellular biophysics.

Computed tomography findings in poxvirus pneumonia in a cat

AbstractA 2‐year‐old, domestic shorthair cat was presented to a referral hospital with a 4‐day history of inappetence and lethargy, which had progressed to dyspnoea and tachypnoea. Investigations performed included full haematology and biochemistry and computed tomography of the thorax. A large, heterogeneously contrast enhancing and poorly marginated mass lesion was noted in the left caudal lung lobe, along with areas of ground‐glass attenuation throughout the left lung field. Fine‐needle aspirates of the pulmonary mass revealed neutrophilic and macrophagic inflammation, with moderate numbers of mildly atypical epithelial cells. The cat was treated with broad‐spectrum antibiotics and returned with multifocal crusted cutaneous lesions 5 days later. Poxvirus polymerase chain reaction was performed on the skin lesion smears and subsequently on the pulmonary mass aspirates, both proving positive for poxvirus. Repeat computed tomography was performed 5 weeks later, revealing complete resolution of the pulmonary changes.

Manymome: An R package for computing the indirect effects, conditional effects, and conditional indirect effects, standardized or unstandardized, and their bootstrap confidence intervals, in many (though not all) models

Mediation, moderation, and moderated mediation are common in behavioral research models. Several tools are available for estimating indirect effects, conditional effects, and conditional indirect effects and forming their confidence intervals. However, there are no simple-to-use tools that can appropriately form the bootstrapping confidence interval for standardized conditional indirect effects. Moreover, some tools are restricted to a limited type of models. We developed an R package, manymome, which can be used to estimate and form confidence intervals for indirect effects, conditional effects, and conditional indirect effects, standardized or not, using a two-step approach: model parameters are estimated either by structural equation modeling using lavaan or by a set of linear regression models using lm, and then the coefficients are used to compute the requested effects and form confidence intervals. It can be used when there are missing data if the model is fitted by structural equation modeling. There are only a few limitations on some aspects of a model, and no inherent limitations on the number of predictors, the number of independent variables, or the number of moderators and mediators. The goal is to have a tool that allows researchers to focus on model fitting first and worry about estimating the effects later. The use of the model is illustrated using a few numerical examples, and the limitations of the package are discussed.

Detailed numerical investigation of the drop aerobreakup in the bag breakup regime

Aerobreakup of drops is a fundamental two-phase flow problem that is essential to many spray applications. A parametric numerical study was performed by varying the gas stream velocity, focusing on the regime of moderate Weber numbers, in which the drop deforms to a forward bag. When the bag is unstable, it inflates and disintegrates into small droplets. Detailed numerical simulations were conducted using the volume-of-fluid method on an adaptive octree mesh to investigate the aerobreakup dynamics. Grid-refinement studies show that converged three-dimensional simulation results for drop deformation and bag formation are achieved by the refinement level equivalent to 512 cells across the initial drop diameter. To resolve the thin liquid sheet when the bag inflates, the mesh is refined further to 2048 cells across the initial drop diameter. The simulation results for the drop length and radius were validated against previous experiments, and good agreement was achieved. The high-resolution results of drop morphological evolution were used to identify the different phases in the aerobreakup process, and to characterize the distinct flow features and dominant mechanisms in each phase. In the early time, the drop deformation and velocity are independent of the Weber number, and a new internal-flow deformation model, which respects this asymptotic limit, has been developed. The pressure and velocity fields around the drop were shown to better understand the internal flow and interfacial instability that dictate the drop deformation. Finally, the impact of drop deformation on the drop dynamics was discussed.

A shallow physics-informed neural network for solving partial differential equations on static and evolving surfaces

In this paper, we introduce a shallow (one-hidden-layer) physics-informed neural network (PINN) for solving partial differential equations on static and evolving surfaces. For the static surface case, with the aid of a level set function, the surface normal and mean curvature used in the surface differential expressions can be computed easily. So, instead of imposing the normal extension constraints used in literature, we write the surface differential operators in the form of traditional Cartesian differential operators and use them in the loss function directly. We demonstrate a series of performance study for the present methodology by solving Laplace–Beltrami equations and surface diffusion equations on complex static surfaces. With just a moderate number of neurons used in the hidden layer, we are able to attain satisfactory prediction results. We then extend the present methodology to solve the advection–diffusion equation on an evolving surface with a given velocity. To track the deforming surface, we additionally introduce a network, in which a prescribed hidden layer is employed to enforce the topological structure of the surface and learn the homeomorphism between the surface and the prescribed topology. The proposed network structure is designed to track the surface and solve the equation simultaneously. Again, the numerical results show comparable accuracy as the static cases. As an application, we simulate surfactant transportation on a droplet surface under shear flow and obtain some physically plausible results.

Effects of cigarette smoke inhalation on the immune-inflammatory profile of experimental apical periodontitis in rats.

To evaluate the effects of cigarette smoke inhalation on the immune-inflammatory profile of experimental apical periodontitis in rats. In total, 32 male Wistar rats were divided into four groups (n = 8): AP-induced apical periodontitis; S-cigarette smoke inhalation; APS-induced AP and cigarette smoke inhalation; and C (control)-neither AP nor cigarette smoke inhalation. To induce cigarette smoke inhalation, the animals were kept in a chamber filled with tobacco smoke for 8 min thrice a day for 50 days. AP was induced 20 days after inhalation initiation by exposing their coronary pulp to their oral environment for 30 days. After animals were euthanized, their right hemimaxillae were removed for histopathological, semi-quantitative and immunohistochemical (F4/80, CD206 and iNOS) analyses. Quantitative data showed a moderate number of inflammatory infiltrates in AP and an intense number in APS (p < .05). Comparing F4/80+ cells showed no statistically significant differences among groups, but we found more CD206+ cells in AP than in C and S (p > .05). INOS+ immunostaining showed a significant increase in AP and APS, when compared with C and S (p < .05). APS had more iNOS+ cells than AP (p < .05). Cigarette smoke inhalation worsened AP, leading to a predominantly pro- inflammatory profile in our experimental model.

Overview of criteria to estimate aerodynamic limits of the flight envelope of a transonic aircraft based on RANS simulations

The aerodynamic limits of the flight envelope of an aircraft play an important role for flight characteristics and loads, so they must be estimated already in the early stages of aircraft design. However, without wind tunnel tests or costly scale-resolving simulations, it is difficult to make an accurate prediction. From a computational perspective, this problem can be split into two parts: (1) generating a valid flow solution for edge-of-the-envelope conditions and (2) extracting the exact lift limit from these computations. Not only the first point can be very challenging, but also the second point. At low Mach numbers, the lift limit may be detected relatively easily, but already at moderate Mach numbers, maximum lift and the lift limit do not necessarily coincide any more, as the actual achievable lift may be limited by the onset of transonic buffet before maximum lift is achieved. Since buffet itself can only be predicted accurately with high effort using time-resolving CFD methods, indirect criteria are required for estimating the lift limit based on steady RANS simulations. This is the focus of the present paper. For this purpose, various criteria proposed in the literature are presented, applied to a test case and comparatively evaluated. For the test case, steady RANS simulations of the generic wing-fuselage configuration LEISA at different Mach numbers and angles of attack are carried out. Finally, the advantages and disadvantages of the different criteria are identified and discussed.

Extensive fatal Pyoderma gangrenosum in a dog after drug exposure.

A 4-year-old, spayed female mixed breed dog was presented with large crater-like, well-demarcated, erosive and ulcerative necrotic lesions of the skin, elevated body temperature and lethargy, that began 14 days after vaccination and treatment with fluralaner and milbemycin/praziquantel. Cytology revealed severe pyogranulomatous inflammation with moderate numbers of extracellular microorganisms. Histopathologic examination showed severe multifocal pyogranulomatous dermatitis and panniculitis with severe dermal edema and severe neutrophilic exocytosis with band-like infiltration of the lower portion of the epidermis consistent with pyoderma gangrenosum. Despite intensive immunosuppressive and antimicrobial therapy and intensive inpatient care, the dog was euthanized 16 days after admission due to complications with clinical signs of sepsis, acute dyspnea and thoracic effusion.

Fast and robust Bayesian inference using Gaussian processes with GPry

We present the GPry algorithm for fast Bayesian inference of general (non-Gaussian) posteriors with a moderate number of parameters. GPry does not need any pre-training, special hardware such as GPUs, and is intended as a drop-in replacement for traditional Monte Carlo methods for Bayesian inference. Our algorithm is based on generating a Gaussian Process surrogate model of the log-posterior, aided by a Support Vector Machine classifier that excludes extreme or non-finite values. An active learning scheme allows us to reduce the number of required posterior evaluations by two orders of magnitude compared to traditional Monte Carlo inference. Our algorithm allows for parallel evaluations of the posterior at optimal locations, further reducing wall-clock times. We significantly improve performance using properties of the posterior in our active learning scheme and for the definition of the GP prior. In particular we account for the expected dynamical range of the posterior in different dimensionalities. We test our model against a number of synthetic and cosmological examples. GPry outperforms traditional Monte Carlo methods when the evaluation time of the likelihood (or the calculation of theoretical observables) is of the order of seconds; for evaluation times of over a minute it can perform inference in days that would take months using traditional methods. GPry is distributed as an open source Python package (pip install gpry) and can also be found at https://github.com/jonaselgammal/GPry.

Simulation and modeling of the vaporization of a freely moving and deforming drop at low to moderate Weber numbers

The vaporization of a freely moving drop in a uniform, high-temperature gas stream is investigated through direct numerical simulation. The incompressible Navier-Stokes equations with surface tension and phase change are solved in conjunction with the energy equations of each phase. The sharp liquid-gas interface is tracked using the geometric Volume-of-Fluid (VOF) method and an immersed Dirichlet boundary condition for temperature is imposed at the interface. The simulation approach is validated by simulating water and acetone drops at nearly zero Weber numbers, and the simulation results agree very well with the empirical relation for spherical drops. Parametric simulations were conducted to investigate the aerodynamic breakup of vaporizing drops at low to moderate Weber and Reynolds numbers. The range of Weber numbers considered has covered the vibrational and bag breakup regimes. Through the simulation results, we have characterized the impact of drop deformation and breakup on the drop vaporization rate. When the drop Weber number increases, the windward surface area increases more rapidly over time. As a result, the rate of drop volume reduction also increases. The correlation between the vaporization rate and the windward surface area is examined for different Weber and Reynolds numbers. Using the approximate correlation between the drop vaporization rate and the windward surface area and the TAB model for drop deformation, a new time-dependent drop vaporization model is proposed. The present model agrees well with the simulation results and shows a significant improvement over the conventional model for spherical drops.

Damage evolution and crystalline orientation effects in ultrafast laser micro/nano processing of single-crystal diamond

Single-crystal diamond is a typical difficult-to-machine material because of its extreme hardness and high brittleness. Compared with traditional machining techniques, ultrafast laser is believed to enable materials processing with high efficiency, high quality and high spatial resolution. To understand the formation mechanism and crystalline orientation effects of damage in ultrafast laser processing diamond, single-path scans were performed in different crystal directions, and groove shape, surface morphology, subsurface damage, phase transformation, and laser-induced periodic surface structure (LIPSS) were systematically analyzed. The experimental results illustrate that the damage evolution can be divided into three different stages according to the pulse number, named weak ablation stage, severe cracking stage, and severe phase transformation stage. Crystal orientation significantly affects the groove shapes, cracks, and phase transformation, and these differences are related to the cleavage energy and atomic rearrangement energy of the crystal plane deposited by the laser energy during processing. At low pulse numbers, the surface graphitization is initiated easier along the <110> direction, which makes the absorption rate of laser energy locally enhanced, further driving the anisotropy of the processing damage at moderate and high pulse numbers. This work provides a new perspective on ultrafast laser processing of single-crystal diamond, which is crucial for ultra-precision and low-damage fabrication of diamond-based functional micro/nano devices.

Study of heat transfer in MHD viscoelastic fluid of second grade over a stretching porous sheet with electromagnetic effects and nonuniform source/sink

This work is devoted to the study of non-Newtonian fluid of second grade. This model assumes that the flow and heat transfer in MHD viscoelastic fluid is subject to convective boundary conditions in the presence of electromagnetic field and nonuniform source/sink. The flow is considered over a porous sheet. The governing partial differential equations are converted as ordinary differential equations using suitable similarity transformations. The resulting coupled of nonlinear differential equations are successfully solved by means of Optimal Homotopy Asymptotic Method (OHAM). The effects of different physical parameters such as: the second-grade viscoelastic parameter, thermal radiation parameter or Eckert number are analyzed. Several closed form analytical approximate solutions for dimensionless stream function and temperature are presented for some particular cases. Our procedure is based upon an original construction of the solutions using a moderate number of the convergence-control parameters. These parameters lead to very high precision, comparing our approximate solutions with numerical solutions. Let us note that every nonlinear differential equation is reduced to two linear differential equations. The values of the convergence-control parameters are determined using rigorous mathematical procedures. OHAM is an iterative approach which rapidly converges after only one approximation, is effective, explicit, simple and easy to apply. These remarks are in fact the true power of our procedure to solving nonlinear problems without the presence of small or large parameters in the governing equations or boundary/initial conditions.

Maximum Augmented Empirical Likelihood Estimation of Categorical Marginal Models for Large Sparse Contingency Tables.

Categorical marginal models (CMMs) are flexible tools for modelling dependent or clustered categorical data, when the dependencies themselves are not of interest. A major limitation of maximum likelihood (ML) estimation of CMMs is that the size of the contingency table increases exponentially with the number of variables, so even for a moderate number of variables, say between 10 and 20, ML estimation can become computationally infeasible. An alternative method, which retains the optimal asymptotic efficiency of ML, is maximum empirical likelihood (MEL) estimation. However, we show that MEL tends to break down for large, sparse contingency tables. As a solution, we propose a new method, which we call maximum augmented empirical likelihood (MAEL) estimation and which involves augmentation of the empirical likelihood support with a number of well-chosen cells. Simulation results show good finite sample performance for very large contingency tables.

A siloxane interlayer approach to enhance surface metallization on polyamide fabrics via electroless copper deposition

Synthetic polymers, such as polyamide (PA), inherently possess a moderate number of surface functionalities compared to natural polymers, which negatively impacts the uniformity of metallic coatings obtained through wet-chemical methods like electroless plating. The paper presents the use of a siloxane interlayer formed from the condensation of the hydrolyzed 3-triethoxysilylpropyl succinic anhydride (TESPSA) precursor as a strategy to modify the surface properties of polyamide 6.6 (PA66) fabrics and improve the uniformity of the copper surface coating. The application of the siloxane intermediate coating demonstrates a significant improvement in electrical conductivity, up to 20 times higher than fabrics without the interlayer. The morphology of the coatings was investigated using scanning electron (SEM) and laser confocal scanning microscopy (LSM). In addition, dye adsorption, flexural rigidity, air permeability and contact angle measurements were conducted to monitor the change in the PA66 properties after the siloxane functionalization.