Chemical-free transfer of patterned reduced graphene oxide thin films for large area flexible electronics and nanoelectromechanical systems.

In this paper, a wet-dry hybrid technique to transfer patterned reduced graphene oxide (rGO) thin film to arbitrary substrates at predetermined locations without using any chemicals is reported. The transfer process involves water-assisted delamination of rGO, followed by dry transfer to an acceptor substrate using viscoelastic stamp. Patterned reduced graphene oxide films are transferred to silicon dioxide (SiO2/Si) substrate to begin with. Subsequently, the method is deployed to transfer rGO to different polymer substrates such as poly(methyl methacrylate) (PMMA), and crosslinked poly(4-vinylphenol) (c-PVP), which are commonly used as gate dielectric in flexible electronic applications. The credibility of the transfer process with precise spatial positioning on the target substrate leads to fabrication of freely suspended reduced graphene oxide membrane towards nanoelectromechanical systems (NEMS) based devices such as nanomechanical drum resonators.

Aryldithiocarbamates as thiol alternatives in Cu-catalyzed C(aryl)-S coupling reactions using aryldiazonium tetrafluoroborate salts.

An efficient method for the synthesis of unsymmetrical diaryl sulfides has been developed by the C-S cross coupling of aryldithiocarbamates and aryldiazonium salts in the presence of CuI-2,2'-bipyridine and Zn. Aryldithiocarbamate compounds have been used here as thiol substitutes. The protocol shows wide substrate scope and good yields of the products.

Multivariate Pointwise Information-Driven Data Sampling and Visualization.

With increasing computing capabilities of modern supercomputers, the size of the data generated from the scientific simulations is growing rapidly. As a result, application scientists need effective data summarization techniques that can reduce large-scale multivariate spatiotemporal data sets while preserving the important data properties so that the reduced data can answer domain-specific queries involving multiple variables with sufficient accuracy. While analyzing complex scientific events, domain experts often analyze and visualize two or more variables together to obtain a better understanding of the characteristics of the data features. Therefore, data summarization techniques are required to analyze multi-variable relationships in detail and then perform data reduction such that the important features involving multiple variables are preserved in the reduced data. To achieve this, in this work, we propose a data sub-sampling algorithm for performing statistical data summarization that leverages pointwise information theoretic measures to quantify the statistical association of data points considering multiple variables and generates a sub-sampled data that preserves the statistical association among multi-variables. Using such reduced sampled data, we show that multivariate feature query and analysis can be done effectively. The efficacy of the proposed multivariate association driven sampling algorithm is presented by applying it on several scientific data sets.

Information Guided Exploration of Scalar Values and Isocontours in Ensemble Datasets.

Uncertainty of scalar values in an ensemble dataset is often represented by the collection of their corresponding isocontours. Various techniques such as contour-boxplot, contour variability plot, glyphs and probabilistic marching-cubes have been proposed to analyze and visualize ensemble isocontours. All these techniques assume that a scalar value of interest is already known to the user. Not much work has been done in guiding users to select the scalar values for such uncertainty analysis. Moreover, analyzing and visualizing a large collection of ensemble isocontours for a selected scalar value has its own challenges. Interpreting the visualizations of such large collections of isocontours is also a difficult task. In this work, we propose a new information-theoretic approach towards addressing these issues. Using specific information measures that estimate the predictability and surprise of specific scalar values, we evaluate the overall uncertainty associated with all the scalar values in an ensemble system. This helps the scientist to understand the effects of uncertainty on different data features. To understand in finer details the contribution of individual members towards the uncertainty of the ensemble isocontours of a selected scalar value, we propose a conditional entropy based algorithm to quantify the individual contributions. This can help simplify analysis and visualization for systems with more members by identifying the members contributing the most towards overall uncertainty. We demonstrate the efficacy of our method by applying it on real-world datasets from material sciences, weather forecasting and ocean simulation experiments.

Uncertainty-Aware Deep Neural Representations for Visual Analysis of Vector Field Data.

The widespread use of Deep Neural Networks (DNNs) has recently resulted in their application to challenging scientific visualization tasks. While advanced DNNs demonstrate impressive generalization abilities, understanding factors like prediction quality, confidence, robustness, and uncertainty is crucial. These insights aid application scientists in making informed decisions. However, DNNs lack inherent mechanisms to measure prediction uncertainty, prompting the creation of distinct frameworks for constructing robust uncertainty-aware models tailored to various visualization tasks. In this work, we develop uncertainty-aware implicit neural representations to model steady-state vector fields effectively. We comprehensively evaluate the efficacy of two principled deep uncertainty estimation techniques: (1) Deep Ensemble and (2) Monte Carlo Dropout, aimed at enabling uncertainty-informed visual analysis of features within steady vector field data. Our detailed exploration using several vector data sets indicate that uncertaintyaware models generate informative visualization results of vector field features. Furthermore, incorporating prediction uncertainty improves the resilience and interpretability of our DNN model, rendering it applicable for the analysis of non-trivial vector field data sets.

Visual Analysis of Prediction Uncertainty in Neural Networks for Deep Image Synthesis.

Ubiquitous applications of Deep neural networks (DNNs) in different artificial intelligence systems have led to their adoption in solving challenging visualization problems in recent years. While sophisticated DNNs offer an impressive generalization, it is imperative to comprehend the quality, confidence, robustness, and uncertainty associated with their prediction. A thorough understanding of these quantities produces actionable insights that help application scientists make informed decisions. Unfortunately, the intrinsic design principles of the DNNs cannot beget prediction uncertainty, necessitating separate formulations for robust uncertainty-aware models for diverse visualization applications. To that end, this contribution demonstrates how the prediction uncertainty and sensitivity of DNNs can be estimated efficiently using various methods and then interactively compared and contrasted for deep image synthesis tasks. Our inspection suggests that uncertainty-aware deep visualization models generate illustrations of informative and superior quality and diversity. Furthermore, prediction uncertainty improves the robustness and interpretability of deep visualization models, making them practical and convenient for various scientific domains that thrive on visual analyses.

Targeting the SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) with synthetic/designer unnatural nucleoside analogs: an in silico study.

CONTEXT: Since the outbreak of COVID-19 in December 2019, it developed into a pandemic affecting all the countries and millions of people around the globe. Until now, there is no medicine available to contain the spread of the virus. As an aid to drug discovery, the molecular docking and molecular dynamic tools were applied extensively. In silico studies made it possible for rapid screening of potential molecules as possible inhibitors/drugs against the targeted proteins. As a continuation of our drug discovery research, we have carried out molecular docking studies of our 12 reported unnatural nucleosides and 14 designer Avigan analogs with SARS-CoV-2, RNA-dependent RNA polymerase (RdRp), which we want to report herein. The same calculation was also carried out, taking 11 known/under trail/commercial nucleoside drug molecules for a comparison of the binding interactions in the catalytic site of RdRp. The docking results and binding efficiencies of our reported nucleosides and designer nucleosidic were compared with the binding energy of commercially available drugs such as remdesevir and favipiravir. Furthermore, we evaluated the protein-drug binding efficiency and stability of the best docked molecules by molecular dynamic studies (MD). From our study, we have found that few of our proposed drugs show promising binding efficiency at the catalytic pocket of SARS-CoV-2 RdRp and can be a promising RdRp inhibitor drug candidate. Hence, this study will be of importance to make progress toward developing successful nucleoside-based drugs and conduct the antiviral test in the wet lab to understand their efficacy against COVID-19. METHOD: All the docking studies were carried out with AutoDock 4.2, AutoDock Vina and Molegro Virtual Docker. Following the docking studies, the MD simulations were carried out following the standard protocol with the GROMACS ver. 2019.6. by applying the CHARMM36 all-atom biomolecular force field. The drug-protein interaction was studied using the Biovia Discovery Studio suite, Ligplot software, and Protein-Ligand Interaction Profiler (PLIP).

Geometry-Driven Detection, Tracking and Visual Analysis of Viscous and Gravitational Fingers.

Viscous and gravitational flow instabilities cause a displacement front to break up into finger-like fluids. The detection and evolutionary analysis of these fingering instabilities are critical in multiple scientific disciplines such as fluid mechanics and hydrogeology. However, previous detection methods of the viscous and gravitational fingers are based on density thresholding, which provides limited geometric information of the fingers. The geometric structures of fingers and their evolution are important yet little studied in the literature. In this article, we explore the geometric detection and evolution of the fingers in detail to elucidate the dynamics of the instability. We propose a ridge voxel detection method to guide the extraction of finger cores from three-dimensional (3D) scalar fields. After skeletonizing finger cores into skeletons, we design a spanning tree based approach to capture how fingers branch spatially from the finger skeletons. Finally, we devise a novel geometric-glyph augmented tracking graph to study how the fingers and their branches grow, merge, and split over time. Feedback from earth scientists demonstrates the usefulness of our approach to performing spatio-temporal geometric analyses of fingers.

Endovascular interventions in management of renal artery aneurysm.

Renal artery aneurysm (RAA) is a rare disease. With modern non-invasive imaging modalities, the disease is being increasingly diagnosed. It is a slow-growing aneurysm with high mortality in the event of rupture; especially in pregnant females for in which case patients were treated surgically. With advances in endovascular therapy, numerous techniques have been employed to manage complex RAA in artery bifurcation, branch and segmental arteries with excellent technical and clinical success. The various recent techniques include the use of flow diverter stents, remodelling with stent-assisted coil embolization (SACE), balloon-assisted coil embolization (BACE), selective embolization with coils-sac packing, inflow occlusion and coil trapping and selective embolization with liquid embolic agents-hystroacril and onyx. A combination of stent-graft with liquid embolization and liquid with microcoil embolization has been advocated with success. The most common complication encountered is renal infarction. This is mostly without impairment of renal function and secondary to embolization. Endovascular therapy has shorter operative time, less blood loss, shorter intensive care stay, done under conscious sedation and is associated with lesser postoperative morbidity compared to surgery. Reduction in hypertension, improvement of renal function and symptoms has been seen in most studies. Endovascular management of RAA has become the management of choice even with complex anatomy and technically challenging lesions.

Probabilistic Data-Driven Sampling via Multi-Criteria Importance Analysis.

Although supercomputers are becoming increasingly powerful, their components have thus far not scaled proportionately. Compute power is growing enormously and is enabling finely resolved simulations that produce never-before-seen features. However, I/O capabilities lag by orders of magnitude, which means only a fraction of the simulation data can be stored for post hoc analysis. Prespecified plans for saving features and quantities of interest do not work for features that have not been seen before. Data-driven intelligent sampling schemes are needed to detect and save important parts of the simulation while it is running. Here, we propose a novel sampling scheme that reduces the size of the data by orders-of-magnitude while still preserving important regions. The approach we develop selects points with unusual data values and high gradients. We demonstrate that our approach outperforms traditional sampling schemes on a number of tasks.

An inherent instability study using ab initio computational methods and experimental validation of Pb(SCN)2 based perovskites for solar cell applications.

Perovskite materials with ABX3 chemistries are promising candidates for photovoltaic applications, owing to their suitable optoelectronic properties. However, they are highly hydrophilic and unstable in nature, limiting the commercialization of perovskite photovoltaics. Mixed halide ion-doped perovskites are reported to be more stable compared to simple ABX3 chemistries. This paper describes ab initio modeling, synthesis, and characterization of thiocyanate doped lead iodide CH3NH3PbI(3-x)(SCN)x perovskites. Several perovskite chemistries with an increasing concentration of (SCN)- at x = 0, 0.25, 0.49, 1.0, 1.45 were evaluated. Subsequently, 'n-i-p' and 'p-i-n' perovskite solar device architectures, corresponding to x = 0, 0.25, 0.49, 1.0 thiocyanate doped lead halide perovskite chemistry were fabricated. The study shows that among all the devices fabricated for different compositions of perovskites, p-i-n perovskite solar cell fabricated using CH3NH3PbI(3-x)(SCN)x perovskite at x = 1.0 exhibited the highest stability and device efficiency was retained until 450 h. Finally, a solar panel was fabricated and its stability was monitored.

Pitch-rotational manipulation of single cells and particles using single-beam thermo-optical tweezers.

3D pitch rotation of microparticles and cells assumes importance in a wide variety of applications in biology, physics, chemistry and medicine. Applications such as cell imaging and injection benefit from pitch-rotational manipulation. Generation of such motion in single beam optical tweezers has remained elusive due to the complexities of generating high enough ellipticity perpendicular to the direction of propagation. Further, trapping a perfectly spherical object at two locations and subsequent pitch rotation hasn't yet been demonstrated to be possible. Here, we use hexagonal-shaped upconverting particles and single cells trapped close to a gold-coated glass cover slip in a sample chamber to generate complete 360 degree and continuous pitch motion even with a single optical tweezer beam. The tweezers beam passing through the gold surface is partially absorbed and generates a hot-spot to produce circulatory convective flows in the vicinity which rotates the objects. The rotation rate can be controlled by the intensity of the laser light. Thus such a simple configuration can turn the particle in the pitch sense. The circulatory flows in this technique have a diameter of about 5 µm which is smaller than those reported using acousto-fluidic techniques.

Dosimetric impact of variable bladder filling on IMRT planning for locally advanced carcinoma cervix.

BACKGROUND: To evaluate the dosimetric impact of variable bladder filling on target and organ at risk (OARs) in cervical cancer patients undergoing chemoradiation. Forty consecutive patients with cervical cancer underwent radiotherapy planning as per the departmental protocol. All patients were asked to empty their bowel and bladder before simulation and catheterization was done. Normal saline was instilled into the bladder through Foleys till the patient had a maximal urge to urinate. Pelvic cast fabrication and CT simulation was done. Then, 30%, 50%, and 100% of the instilled saline was removed and rescans taken. Planning was done on full bladder (X) and the same plan applied to the contours with bladder volumes 0.7X (PLAN70), 0.5X (PLAN50), and empty (PLAN0). A dose of 50 Gy/25# was prescribed to the PTV and plans evaluated. Intensity-modulated radiotherapy plans with full bladder were implemented for each patient. Shifts in the center of mass (COM) of the cervix/uterus with variable bladder filling identified were noted. Statistical analysis was performed using SPSS software. A p value < 0.05 was considered significant. RESULTS: Bladder volume in 70%, 50%, and empty bladder planning was 78.34% (388.35 + 117.44 ml), 64.44% (320.60 + 106.20 ml), and 13.63% (62.60 + 23.12 ml), respectively. The mean dose received by 95% PTV was 49.76 Gy + 1.30 Gy. Though the difference in target coverage was significant between PLAN100 and other plans, the mean difference was minimal. A decrease in bladder filling resulted in an increase in OAR dose. Variation in the increase in dose to OARs was not significant if bladder filling was > 78.34% and > 64.44% of a full bladder with respect to the bowel and rectal/bladder doses, respectively. Inconsistent bladder filling led to a maximal shift in COM (uterus/cervix) in the Y- and Z-axis. CONCLUSION: Bladder filling variations have an impact on cervico-uterine motion/shape, thereby impacting the dose to the target and OARs. It is recommended to have a threshold bladder volume of at least 70-75% of optimally filled bladder during daily treatment. TRIAL REGISTRATION: Institutional review board (IRB) registered by Drug Controller General (India) with registration number ECR/10/Ins/DC/2013. Trial Registration number - RGCIRC/IRB/44/2016, registered and approved on the 14th of May 2016.

Conductive sub-micrometric wires of platinum-carbonyl clusters fabricated by soft-lithography.

Conductive wires of sub-micrometer width made from platinum-carbonyl clusters have been fabricated by solution-infilling of microchannels as in microinject molding in capillaries (MIMIC). The process is driven by the liquid surface tension within the micrometric channels followed by the precipitation of the solute. Orientation of supramolecular crystalline domains is imparted by the solution confinement combined with unidirectional flow. The wires exhibit ohmic conductivity with a value of 0.2 S/cm that increases, after thermal decomposition of the platinum-carbonyl cluster precursor to Pt, to 35 S/cm.

CoDDA: A Flexible Copula-based Distribution Driven Analysis Framework for Large-Scale Multivariate Data.

CoDDA (Copula-based Distribution Driven Analysis) is a flexible framework for large-scale multivariate datasets. A common strategy to deal with large-scale scientific simulation data is to partition the simulation domain and create statistical data summaries. Instead of storing the high-resolution raw data from the simulation, storing the compact statistical data summaries results in reduced storage overhead and alleviated I/O bottleneck. Such summaries, often represented in the form of statistical probability distributions, can serve various post-hoc analysis and visualization tasks. However, for multivariate simulation data using standard multivariate distributions for creating data summaries is not feasible. They are either storage inefficient or are computationally expensive to be estimated in simulation time (in situ) for large number of variables. In this work, using copula functions, we propose a flexible multivariate distribution-based data modeling and analysis framework that offers significant data reduction and can be used in an in situ environment. The framework also facilitates in storing the associated spatial information along with the multivariate distributions in an efficient representation. Using the proposed multivariate data summaries, we perform various multivariate post-hoc analyses like query-driven visualization and sampling-based visualization. We evaluate our proposed method on multiple real-world multivariate scientific datasets. To demonstrate the efficacy of our framework in an in situ environment, we apply it on a large-scale flow simulation.

Vulvar carcinoma: dilemma, debates, and decisions.

Vulvar carcinoma is a rare and aggressive gynecological malignancy. It affects elderly females, with the mean age at diagnosis being 55-60 years. Regional metastasis to inguinal lymph nodes is common. There is a high incidence of pelvic node involvement, especially in those with pathologically positive inguinal nodes. Surgery appears to be the only curative treatment option in the early stages of the disease. But in most patients, surgery is associated with considerable morbidities and psychosexual issues. Hence, in the quest for a less morbid form of treatment, multimodality approaches with various combinations of surgery, chemotherapy, and radiation therapy have been suggested for advanced vulvar cancers. Due to the low incidence of the disease, the level of evidence for the success of these treatment modalities is poor. In countries like India, a heterogeneous incidence of vulvar carcinoma exists across the country, with patients presenting at advanced stages when the option of surgery is often supplemented or replaced by chemotherapy and radiotherapy. In this review, we attempt to study the available published literature and trials and discuss the treatment options in various stages of vulvar carcinoma.

In Situ Distribution Guided Analysis and Visualization of Transonic Jet Engine Simulations.

Study of flow instability in turbine engine compressors is crucial to understand the inception and evolution of engine stall. Aerodynamics experts have been working on detecting the early signs of stall in order to devise novel stall suppression technologies. A state-of-the-art Navier-Stokes based, time-accurate computational fluid dynamics simulator, TURBO, has been developed in NASA to enhance the understanding of flow phenomena undergoing rotating stall. Despite the proven high modeling accuracy of TURBO, the excessive simulation data prohibits post-hoc analysis in both storage and I/O time. To address these issues and allow the expert to perform scalable stall analysis, we have designed an in situ distribution guided stall analysis technique. Our method summarizes statistics of important properties of the simulation data in situ using a probabilistic data modeling scheme. This data summarization enables statistical anomaly detection for flow instability in post analysis, which reveals the spatiotemporal trends of rotating stall for the expert to conceive new hypotheses. Furthermore, the verification of the hypotheses and exploratory visualization using the summarized data are realized using probabilistic visualization techniques such as uncertain isocontouring. Positive feedback from the domain scientist has indicated the efficacy of our system in exploratory stall analysis.

Distribution Driven Extraction and Tracking of Features for Time-varying Data Analysis.

Effective analysis of features in time-varying data is essential in numerous scientific applications. Feature extraction and tracking are two important tasks scientists rely upon to get insights about the dynamic nature of the large scale time-varying data. However, often the complexity of the scientific phenomena only allows scientists to vaguely define their feature of interest. Furthermore, such features can have varying motion patterns and dynamic evolution over time. As a result, automatic extraction and tracking of features becomes a non-trivial task. In this work, we investigate these issues and propose a distribution driven approach which allows us to construct novel algorithms for reliable feature extraction and tracking with high confidence in the absence of accurate feature definition. We exploit two key properties of an object, motion and similarity to the target feature, and fuse the information gained from them to generate a robust feature-aware classification field at every time step. Tracking of features is done using such classified fields which enhances the accuracy and robustness of the proposed algorithm. The efficacy of our method is demonstrated by successfully applying it on several scientific data sets containing a wide range of dynamic time-varying features.

Visualization and Analysis of Rotating Stall for Transonic Jet Engine Simulation.

Identification of early signs of rotating stall is essential for the study of turbine engine stability. With recent advancements of high performance computing, high-resolution unsteady flow fields allow in depth exploration of rotating stall and its possible causes. Performing stall analysis, however, involves Significant effort to process large amounts of simulation data, especially when investigating abnormalities across many time steps. In order to assist scientists during the exploration process, we present a visual analytics framework to identify suspected spatiotemporal regions through a comparative visualization so that scientists are able to focus on relevant data in more detail. To achieve this, we propose efficient stall analysis algorithms derived from domain knowledge and convey the analysis results through juxtaposed interactive plots. Using our integrated visualization system, scientists can visually investigate the detected regions for potential stall initiation and further explore these regions to enhance the understanding of this phenomenon. Positive feedback from scientists demonstrate the efficacy of our system in analyzing rotating stall.

An information-aware framework for exploring multivariate data sets.

Information theory provides a theoretical framework for measuring information content for an observed variable, and has attracted much attention from visualization researchers for its ability to quantify saliency and similarity among variables. In this paper, we present a new approach towards building an exploration framework based on information theory to guide the users through the multivariate data exploration process. In our framework, we compute the total entropy of the multivariate data set and identify the contribution of individual variables to the total entropy. The variables are classified into groups based on a novel graph model where a node represents a variable and the links encode the mutual information shared between the variables. The variables inside the groups are analyzed for their representativeness and an information based importance is assigned. We exploit specific information metrics to analyze the relationship between the variables and use the metrics to choose isocontours of selected variables. For a chosen group of points, parallel coordinates plots (PCP) are used to show the states of the variables and provide an interface for the user to select values of interest. Experiments with different data sets reveal the effectiveness of our proposed framework in depicting the interesting regions of the data sets taking into account the interaction among the variables.