Output Graphs Research Articles

Islet Gene View-a tool to facilitate islet research.

Characterization of gene expression in pancreatic islets and its alteration in type 2 diabetes (T2D) are vital in understanding islet function and T2D pathogenesis. We leveraged RNA sequencing and genome-wide genotyping in islets from 188 donors to create the Islet Gene View (IGW) platform to make this information easily accessible to the scientific community. Expression data were related to islet phenotypes, diabetes status, other islet-expressed genes, islet hormone-encoding genes and for expression in insulin target tissues. The IGW web application produces output graphs for a particular gene of interest. In IGW, 284 differentially expressed genes (DEGs) were identified in T2D donor islets compared with controls. Forty percent of DEGs showed cell-type enrichment and a large proportion significantly co-expressed with islet hormone-encoding genes; glucagon (<i>GCG</i>, 56%), amylin (<i>IAPP</i>, 52%), insulin (<i>INS</i>, 44%), and somatostatin (<i>SST</i>, 24%). Inhibition of two DEGs, <i>UNC5D</i> and <i>SERPINE2</i>, impaired glucose-stimulated insulin secretion and impacted cell survival in a human β-cell model. The exploratory use of IGW could help designing more comprehensive functional follow-up studies and serve to identify therapeutic targets in T2D.

MLNe: Simulating and estimating effective size and migration rate from temporal changes in allele frequencies.

In studies of molecular ecology, conservation biology, and evolutionary biology, the current or recent effective size (Ne) of a population is frequently estimated from the marker genotype data of two or more temporally spaced samples of individuals taken from the population. Despite the developments of numerous Bayesian, likelihood, and moment estimators, only a couple of them can use both temporally and spatially spaced samples of individuals to estimate jointly the effective size (Ne) of and the migration rate (m) into a population. In this note, I describe new implementations of these joint estimators of Ne and m in software MLNe which runs on multiple platforms (Windows, Mac, Linux) with or without a graphical user interface (GUI), and has an integrated simulation module to simulate genotype data for investigating the impacts of various factors (such as sample size and sampling interval) on estimation precision and accuracy, exploits both message passing interface (MPI) and openMP for parallel computations using multiple cores and nodes to speed up analysis. The program does not require data pre-processing and accepts multiple formats of a file of original genotype data and a file of parameters as input. The GUI facilitates data and parameter inputs and produces publication-quality output graphs, while the non-GUI version of software is convenient for batch analysis of multiple datasets as in simulations. MLNe will help advance the analysis of temporal genetic marker data for estimating Ne of and m between populations, which are important parameters that will help biologists for the conservation management of natural and managed populations. MLNe can be downloaded free from the website http://www.zsl.org/science/research/software/.

Prediction and optimizing the multiple responses of the overall energy efficiency (OEE) of a tractor-implement system using response surface methodology

The aim of this paper was to predict and optimize the overall energy efficiency (OEE) of a tractor-implement system in semi-deep tillage via response surface methodology (RSM) approach. The OEE was affected by two tillage tines (i.e., subsoiler and paraplow), four forward speeds (i.e., 1.8, 2.3, 2.9 and 3.5 km/h), three operating depths (i.e., 30, 40 and 50 cm) and vertical load imposed on tractor rear wheels at two levels (i.e., 225 kg and no-weight). All tests were replicated four times, forming 192 data points. Field test results revealed all variables were influential on the OEE except vertical load. An increment of speed and depth increased OEE. The RSM approach displayed 3D views with higher accuracy for the OEE change due to changing tine, speed, depth and vertical load relative to regression prediction models. Another feature of the RSM approach was the output graphs with many small pixels. Accordingly, input variables changes and their influences on the OEE are more locational and visible. Moreover, the RSM model accurately predicted the OEE as 35.216% with the desirability of 0.843 at the optimum state as paraplow tine, 30 cm depth, 2.07 km/h forward speed, and 0.01 kg vertical load.

Sediment-hosted disseminated gold deposits in orogenic belts: An example from the giant Jiagantan gold deposit in the West Qinling orogen, China

Sediment-hosted disseminated gold deposits in orogenic belts share many similarities to both epizonal orogenic and Carlin-type gold deposits, leading to a lot of difficulties in their genetic classification. The giant Jiagantan gold deposit was newly discovered in the West Qinling orogen, central China, and was selected to provide a better understanding of this issue. Gold mineralization at Jiagantan is hosted within sandstone and silty slate, and the gold is intimately associated to disseminated pyrite and arsenopyrite. Three generations of pyrite (Py0, Py1, and Py2) and two generations of arsenopyrite (Apy1 and Apy2) are recognized and used to perform in situ textural, chemical, and sulfur isotopic analyses.The diagenetic Py0 is framboidal and porous, and contains elevated concentrations of As, Sb, Co, Ni, Cu, Ti, and V. The early mineralization stage Py1 and Apy1 are disseminated within sandstone and silty slate, and have the highest average Au concentrations of 7.7 ppm and 12.6 ppm, respectively. The intermediate mineralization stage Py2 and Apy2 are hosted by quartz veins and veinlets, coexisting with stibnite, chalcopyrite, and minor amounts of other sulfides. Gold mineralization mainly took place during the early mineralization stage. Microscopic observations and laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) output graphs confirm the majority of gold may reside in the crystallographic structures of Py1 and Apy1.The diagenetic Py0 grains show negative δ34S values of −25.2 to −20.1‰, implying the derivation of sulfur by bacterial reduction of marine sulfate. The hydrothermal Py1, Py2, Apy1, and Apy2 have higher but still negative δ34S values ranging from −16.4 to −5.0‰, suggesting an origin of sulfur from metamorphism of the underlying Devonian sedimentary rocks.The Jiagantan gold deposit can best be classified as an epizonal orogenic gold deposit owing to the possible metamorphic origin of Au, As, S, and other associated elements. The source of ore-forming materials is the key to discriminating orogenic gold deposits from Carlin-type gold deposits, while the type of host rocks, the style of mineralization, and the occurrence state of gold may not be the effective indicators.

Panakeia - a universal tool for bacterial pangenome analysis

BackgroundDevelopment of new pan-genome analysis tools is important, as the pangenome of a microbial species has become an important method to define the diversity of a selected taxon, most commonly a species, in the last years. This enables comparison of strains from different ecological niches and can be used to define the functional potential in a bacterial population. It gives us a much better view of microbial genomics than can be gained from singular genomes which after all are just single representatives of a much more varied population.ResultsWe present Panakeia, a tool which strives to be easy to use and providing a detailed view of the pangenome structure which can efficiently be utilised for discovery, or further in-depth analysis, of features of interest. It analyses synteny and multiple structural patterns of the pangenome, giving insights into the biological diversity and evolution of the studied taxon. Panakeia hence provides both broad and detailed information on the structure of a pangenome, for diverse and highly clonal populations of bacteria.ConclusionsPreviously published pangenome tools often reduce the information to a presence/absence matrix of unconnected genes or generate massive hard to interpret output graphs. However, Panakeia includes synteny and structural information and presents it in a way that can readily be used for further analysis. Panakeia can be downloaded at https://github.com/BioSina/Panakeiatogether with a detailed User Guide.

Emergency Blower-Based Ventilator with Novel-Designed Ventilation Sensor and Actuator

The ventilator, a life-saving device for COVID-19-infected patients, especially for pneumonia patients whose lungs are infected, has overwhelmingly skyrocketed since the pandemic of COVID-19 diseases started in December 2019. As a result, many biomedical engineers have rushed to design and construct emergency ventilators, using the Ambu-bag squeezing ventilator to compensate for the insufficient ventilators supply. The Ambu-bag squeezing ventilator, however, suffers from the limitation of delivered tidal volume to the patient, the setting respiration rate and the noisy operational sound due to the movement of mechanic parts. The Ambu-bag based ventilator is, hence, not suitable for prolonged treatment of the patient. This paper presents a design and construction of a blower-based pressure-controlled ventilator for home-treatment COVID-19 patients featured with our novel-designed flow and pressure sensor, electronic peep valve and proportional controlled valve. Our proposed ventilator can be programmed with the suitable parameter setting depending upon the weight, height, gender, and blood oxygen saturation (SpO2) of the patients. This is useful in the current situation of COVID-19 pandemics, where trained medical staff is limited. The designed ventilator is also equipped with a safety mechanism, including an excessive-pressure-release valve, excessive flow rate, overpressure, and over-temperature blower to prevent any hazardous event. A home ventilator server is also set where all ventilator parameters will be acquired and broadcasted for remote access of the health provider. The designed blower-based ventilator has been calibrated and evaluated with a lung simulator and standard ventilator tester, including alarmed functions, safety mechanism, sound level, and regulated pressure. The respiration output graph is complied with the simulation. The blower-based ventilator for home-treatment COVID-19 patients is suitable for life support, commensurate with the strict requirements of the FDA for life-support ventilators, and ready to be tested with animal subjects in the next phase.

Effective toxicity assessment of synthetic dye in microbial fuel cell biosensor with spinel nanofiber anode

Textile dyes are one of the most commonly found contaminants in wastewater requiring their insitu real-time monitoring and biotreatment. In the present study, two different microbial fuel cells with plain and magnesium ferrite coated stainless steel mesh (PBS/SS and PBS/MF-SS) bioanodes were tested for the simultaneous biosensing and biotreatment of three different concentrations of Ponceau BS dye with mixed microbial community as biological sensing elements. The study revealed the direct correlation of added toxicant and the performance of microbial fuel cell biosensor and observed an almost perfect linear fit for concentration vs. voltage output graph corroborating the potential of microbial fuel cell as a toxicity detector. The cyclic voltammetry confirmed the bioactivity of microbial fuel cell reactor with reference to the unacclimated system. In addition, the cyclic voltammetry analysis of microbial fuel cells at different dye concentration suggested its promising toxicity sensing potential. Electrochemical Impedance Spectroscopy data confirmed that biofouling affected the internal resistances of microbial fuel cells. In addition, the effect of extended run-time (of a year) on the performance of microbial fuel cell was established by comparing the voltage outputs of this study with our previous work. As compared to the previous study, the voltage output for microbial fuel cells with plain and magnesium ferrite coated stainless steel mesh showed a decline by nearly 53% and 41% respectively suggesting the fouling of system. The lesser drop in the performance for microbial fuel cell with modified electrode showed the potential of modified electrode in more efficient electron transfer in the long run. The results conclude that the microbial fuel cell biosensor provides a quick and effective approach for real-time monitoring of hazardous and recalcitrant contaminants such as dyes in environmental samples; nevertheless, fouling of the system is a significant impediment to the long-term practical implementation of microbial fuel cells.

Kernel Regression for Matrix-Variate Gaussian Distributed Signals Over Sample Graphs

Recent advances of kernel regression assume that target signals lie over a feature graph such that their values can be predicted with the assistance of the graph learned from training data. In this paper, we propose a novel kernel regression framework whose outputs follow a matrix-variate Gaussian distribution (MVGD) such that the kernel matrix can be viewed as the column covariance matrix of outputs, and the hyperparameters of a chosen kernel can be optimized using gradient methods. Furthermore, in contrast to the state-of-the-art kernel regression algorithms over graph (KRG), a sample graph of target outputs is introduced to work with regression coefficients and hyperparameters of a chosen kernel in our algorithms. The proposed KRG framework is decomposed into two stages, including the estimation of row and column covariance matrices of MVGD and graph learning along with the estimation of regression coefficients. Numerical approaches are developed to tackle the corresponding optimization problems. Experimental results over synthetic and real-world datasets demonstrate that the performance of the proposed algorithms is superior to that of the state-of-the-art methods.

Analyzing Oscillations on a Bridge

When people cross bridges, they create vibrations on these bridges because of the impulses they exert. In some cases, vertical oscillations have caused bridges to collapse due to a phenomenon called resonance. This paper utilizes simple harmonic motion to analyze the oscillations of bridges to create a mathematical model determining when certain bridges collapse. The paper first utilizes Hooke’s Law and Newton’s Second Law to create a second order differential equation of the motion of the bridge whose solution is a sine wave. Substituting in values for momentum for a singular impulse on the bridge, the paper then solves for the amplitude after the impulse. Adapting this model for impulses at different times and plotting the output graphs using Python, the bridge is shown to exhibit resonance and suggestions for damping are made.

Predicting the natural history of metabolic syndrome with a Markov-system dynamic model: a novel approach

BackgroundMarkov system dynamic (MSD) model has rarely been used in medical studies. The aim of this study was to evaluate the performance of MSD model in prediction of metabolic syndrome (MetS) natural history.MethodsData gathered by Tehran Lipid & Glucose Study (TLGS) over a 16-year period from a cohort of 12,882 people was used to conduct the analyses. First, transition probabilities (TPs) between 12 components of MetS by Markov as well as control and failure rates of relevant interventions were calculated. Then, the risk of developing each component by 2036 was predicted once by a Markov model and then by a MSD model. Finally, the two models were validated and compared to assess their performance and advantages by using mean differences, mean SE of matrices, fit of the graphs, and Kolmogorov-Smirnov two-sample test as well as R2 index as model fitting index.ResultsBoth Markov and MSD models were shown to be adequate for prediction of MetS trends. But the MSD model predictions were closer to the real trends when comparing the output graphs. The MSD model was also, comparatively speaking, more successful in the assessment of mean differences (less overestimation) and SE of the general matrix. Moreover, the Kolmogorov-Smirnov two-sample showed that the MSD model produced equal distributions of real and predicted samples (p = 0.808 for MSD model and p = 0.023 for Markov model). Finally, R2 for the MSD model was higher than Markov model (73% for the Markov model and 85% for the MSD model).ConclusionThe MSD model showed a more realistic natural history than the Markov model which highlights the importance of paying attention to this method in therapeutic and preventive procedures.

Swept-source OCT reduces the risk of axial length measurement errors in eyes with cataract and epiretinal membranes.

AimsTo compare the biometric data from partial coherence interferometry (PCI) and swept-source OCT (SS-OCT) in patients with age-related cataract and epiretinal membrane (ERM): ERM, ERM with foveoschisis and macular pseudohole.Methods49 eyes of 49 subjects including 36 ERM, 9 ERM foveoschisis and 4 macular pseudohole were analysed to evaluate the axial length (AL) measurements and the presence of AL measurement errors, defined basing on the shape of the biometric output graphs and on the concordance of AL values between instruments. Eyes with ERM were divided in four stages according to OCT features (i.e. presence/absence of the foveal pit, presence of ectopic inner foveal layers, disrupted retinal layers).ResultsThe devices provided similar mean AL measurements in all subgroups, with differences <0.1 mm in 41/49 cases (83.6%). AL measurement errors were observed in ERM stages 3 and 4, characterized by ectopic inner foveal layers, and were significantly more frequent with the PCI (8/17, 47%) as compared with the SS-OCT device (2/17, 12%), p = 0.02. The refractive prediction error in cases with AL measurement errors was significantly greater using the PCI compared to the SS-OCT device (p<0.05).ConclusionBoth devices provide reliable biometric data in the majority of patients and can be used in the preoperative assessment of patients with age-related cataract and ERM. In eyes with ectopic inner foveal layers, attention should be paid as AL measurement and refractive prediction errors may occur, more frequently with the PCI device.

Nature Inspired Bargain Optimization Algorithm for Effective Interpretation of Geoelectrical Data

Geoelectrical resistivity data collected from the ground contain lot of noises and errors. It requires efficient algorithm to reduce the errors to make an actual inversion models. Though different algorithm can be applied, nature inspired algorithm is more potential in inverting geoelectrical data in an elegant and comprehensive way. Bargain Optimization (BO) algorithm is framed on the concept of bargaining things to purchase for needs. In general, effective bargaining results in more profit and leads to loss when it fails. In this research work, Bargain Optimization algorithm is applied to invert geoelectrical data and the effective bargaining will take time to process and to obtain the required model. The input data is AB/2, apparent resistivity data and the inverted model through BO algorithm is successfully matched with the available litholog section of the study area. The output graphs have profit/loss bar graph, which reveals the status of bargaining during a particular number of epochs.

Design and fabrication of an online inductive sensor for identification of ferrous wear particles in engine oil

PurposeThe presence of ferrous wear debris in lubricating oil may cause progressive damage in the internal combustion engines. Online monitoring of the size and concentration of these particles in the oil is a way to optimize the engine performance and its life cycle.Design/methodology/approachIn this study, an online sensor was designed and fabricated to identify ferrous wear particles in the engine oil based on the induction method. The diameter of the sensor outlet duct was designed as small as possible to generate a high-intensity magnetic induction and achieve a proper sensitivity in the sensor. The experiments were designed and performed in offline mode. Furthermore, to evaluate the actual performance of the sensor in presence of iron particles in the oil, online tests were performed at different sizes and concentrations.FindingsIt was concluded from offline tests that the highest sensitivity of the sensor occurs at the frequency and voltage of 2.5 kHz and 120 V, respectively. According to the results of the online tests, the larger the particle size, the higher the peaks at the sensor output. Also, a high density of the peaks was observed in the sensor output graphs as the concentration of particles was increased.Originality/valueThe proposed sensor was able to identify ferrous wear particles larger than 125 µm separately, which is the failure limit in the internal combustion engines.

Graph-to-Graph: Towards Accurate and Interpretable Online Handwritten Mathematical Expression Recognition

Recent handwritten mathematical expression recognition (HMER) approaches treat the problem as an image-to-markup generation task where the handwritten formula is translated into a sequence (e.g. LaTeX). The encoder-decoder framework is widely used to solve this image-to-sequence problem. However, (i) for structured mathematical formula, the hierarchical structure neither in the formula nor in the markup has been explored adequately. In addition, (ii) existing image-to-markup methods could not explicitly segment mathematical symbols in the formula corresponding to each target markup token. In this paper, we address the above issues by formulating the HMER as a graph-to-graph (G2G) learning problem. Graph is more flexible and general for structure representation and learning compared with image or sequence. At the core of our method lies the embedding of input formula and output markup into graphs on primitives, with Graph Neural Networks (GNN) to explore the structural information, and a novel sub-graph attention mechanism to match primitives in the input and output graphs. We conduct extensive experiments on CROHME datasets to demonstrate the benefits of the proposed G2G model. Our method yields significant improvements over previous SOTA image-to-markup systems. Moreover, it explicitly resolves the symbol segmentation problem while still being trained end-to-end, making the whole system much more accurate and interpretable.

Design and optimization of digitalization device of temperature control system using PID Ziegler-Nichols control in chemical engineering laboratory

The Temperature Control Module is a tool that is useful for adjusting the temperature of a liquid or solution so that the temperature of a liquid is as determined. A device that uses a heat exchanger as a place to combine temperature, so that the temperature of a liquid with another liquid will meet there so that the temperature of the liquid in the heat exchanger can be changed. From the temperature control practicum module prototype in the chemical laboratory, it is known that in the open loop test with hot water input temperature above 90 °C and cold water input temperature 28 °C an output value of 69 °C is obtained. This value is stable but slowly decreases. By providing feedback and the controller in the form of sensors and data will be processed by the PLC, then a value that corresponds to a set of values is obtained, namely 60 °C, but the output temperature is unstable because the output graph is still fluctuating and unstable. Based on experimental calculations and PID calculations using the Ziegler Nichols method, the Kp = 30, Ti = 77.95, and Td = 19.489 were obtained. By providing the PID algorithm calculated by the Ziegler Nichols method, it can increase the output temperature to 58 °C. This is due to the addition of the PID gain to be more substantial so that the system becomes unstable.

Adaptive Estimation of Play Radii for a Prandtl–Ishlinskii Hysteresis Operator

The Prandtl–Ishlinskii (PI) operator is a mathematical model for hysteresis, and it is comprised of weighted superposition of multiple play (backlash) operators. The PI operator has been widely used in modeling and compensation of hysteresis in smart material actuators, robots, and mechanical systems. The behavior of a PI operator is determined by both the weights and the radii of individual play operators. However, existing modeling work has mostly been focused on the identification of the weight parameters by assuming the play radii to be known. While the latter approach is convenient due to the linear relationship between the operator output and the weight parameters, it often requires a large number of plays with preassigned radii in order to adequately capture the hysteresis in a given application. In this work, for the first time, we propose an adaptive estimation algorithm to identify the play radii of a PI operator, to enable accurate modeling of hysteresis with a small number of plays and, thus, reduce the complexity in control. The major challenge lies in the nonlinear, complex, time-varying relationship between the PI operator output and the play radii. The proposed algorithm utilizes available measurement and information, including the instantaneous slope of the hysteretic input–output graph, to derive a modified estimation error function that is proportional to the parameter error. With a mild condition on the input, we establish the persistent excitation of the resulting regressor vector and the parameter convergence under a gradient algorithm with parameter projection. Both simulation and experimental results are presented to illustrate the proposed approach. In particular, comparison results based on experimental data from a piezoelectric nanopositioner show that, with the proposed method, the resulting PI operator outperforms identified PI operators of larger numbers of plays with preassigned radius values.

Distributed Nonlinear Polynomial Graph Filter and Its Output Graph Spectrum: Filter Analysis and Design

While frequency-domain algorithms have been demonstrated to be powerful for conventional nonlinear signal processing, there is still not much progress in literature dedicated to nonlinear graph signal processing in frequency domain. The difficulties come from the irregular structure of graph signals and the lack of a straightforward description for the nonlinear relationship between input and output graph signals. In this paper, a distributed nonlinear polynomial graph filter (NPGF) is proposed to characterize the nonlinear input-output relationship by employing polynomial-type nonlinearity based on Hadamard product. Then, the nonlinear output graph signal is reformulated based on the multi-dimensional inverse graph Fourier transform (GFT), where the m-dimensional GFT coefficients are defined as explicit functions of input graph spectrum and filter structural factors (filter parameters, graph shift matrix, nonlinear degree, and graph shift order). The effects of filter structural factors on the output graph spectrum are further evaluated by proposing graph frequency response (GFR) coefficients, which inherently characterize the irregular structural behaviors of a distributed NPGF in frequency domain. The nonlinear output graph spectrum is then developed based on GFR vectors by mapping the GFR coefficients in the tensor formulation to the output GFT coefficients in the vector expression. This can greatly improve the efficiency in calculating the output graph signal if the input signal only involves a few frequency components. Finally, algorithms are proposed to design the filter parameters for the desired output signal. Numerical studies are presented to illustrate and validate the proposed frequency-domain framework for nonlinear graph signal processing.

Discovering Causal Models with Optimization: Confounders, Cycles, and Feature Selection

We propose a new method for learning causal structures from observational data, a process known as causal discovery. Our method takes as input observational data over a set of variables and returns a graph in which causal relations are specified by directed edges. We consider a highly general search space that accommodates latent confounders and feedback cycles, which few extant methods do. We formulate the discovery problem as an integer program, and propose a solution technique that leverages the conditional independence structure in the data to identify promising edges for inclusion in the output graph. In the large-sample limit, our method recovers a graph that is equivalent to the true data-generating graph. Computationally, our method is competitive with the state-of-the-art, and can solve in minutes instances that are intractable for alternative causal discovery methods. We demonstrate our approach by showing how it can be used to examine the validity of instrumental variables, which are widely used for causal inference. In particular, we analyze US Census data from the seminal paper on the returns to education by Angrist and Krueger (1991), and find that the causal structures uncovered by our method are consistent with the literature.

An Auction-Based Incentivized Solution Against DDoS Attacks

There has been a giant growth in complexity and severity of DDoS attacks from the last past decade. Major research has focused solely on technical solutions. Economic solutions based on incentives have not been given attention. Every DDoS attack is driven by some strong incentive of an attacker. DDoS attacks can only be tackled by providing incentives to legitimate users and at the same time by making attacker deprived of the same. In this paper, VCG-based bidding mechanism has been presented to handle DDoS attacks. VCG is an economic approach used to allocate public good fairly among users. Proposed approach is inherently incentive compatible where attackers do not have any incentive to announce false bid value. Along with making attacker incentive-less, proposed approach is satisfying desirable properties like truthfulness, incentive compatibility, allocative efficiency, and individual rationality. Experimentations have been conducted for proposed method using Java. Output graphs have shown satisfying above mentioned properties.

Evaluation of Bernstein Polynomial as a Machine Learning technique

Regression algorithms are used to device a function to predict the output based on that function for new unseen inputs. We have used Bernstein polynomial as a type of approximation curve to fit a polynomial between all features of a dataset so as to perform regression on it. As approximation curves do not generally meet most of the input points, which are used to create it, hence the chances of it over-fitting to a model are low. In this paper, we have used Bernstein polynomial separately for each feature versus output graph, assuming that each feature in the dataset is independent of each other and combines the outcome of each curve to predict the final result.