Data fusion and 3D visualization for optimized representation of neurovascular relationships in the posterior fossa

Background Reliable 3D visualization of neurovascular relationships in the posterior fossa at the surface of the brainstem is still critical due to artifacts of imaging. To assess neurovascular compression syndromes more reliably, a new approach of 3D visualization based on registration and fusion of high-resolution MR data is presented. Methods A total of 80 patients received MRI data with 3D-CISS and 3D-TOF at 3.0 Tesla. After registration and subsequent segmentation, the vascular information of the TOF data was fused into the CISS data. Two 3D visualizations were created for each patient, one before and one after fusion, which were verified with the intraoperative situation during microvascular decompression (MVD). The reproduction quality of vessels was evaluated with a rating system. Results In all cases, the presented approach compensated for typical limitations in the 3D visualization of neurovascular compression such as the partial or complete suppression of larger vessels, suppression of smaller vessels at the CSF margin, and artifacts from heart pulsation. In more than 95% of the cases of hemifacial spasm and glossopharyngeal neuralgia, accurate assessment of the compression was only possible after registration and fusion. In more than 50% of the cases with trigeminal neuralgia, the presented approach was crucial to finding the actually offending vessel. Conclusions 3D visualization of fused image data allows for a more complete representation of the vessel-nerve situation. The results from this approach are reproducible and the assessment of neurovascular compression is safer. It is a powerful tool for planning MVD.

The application of probabilistic linguistic CODAS method based on new score function in multi-criteria decision-making

Probabilistic linguistic term sets (PLTSs) play an important role in multi-criteria decision-making(MCDM) problems because it can not only describe objects with several possible linguistic terms, but also represent the proportion of each linguistic term, which can effectively avoid the distortion of decision information to a greater extent and ensure the credibility of decision results. First, to compare PLTS more simply and reasonably, we define a new score function that takes into account partial deviations. Then considering the superiority of the classic combinative distance-based assessment (CODAS) method in the complete representation of information, it is extended to the probabilistic linguistic environment. Subsequently, we improved the classic CODAS method and proposed the PL-CODAS method. Finally, we apply the PL-CODAS method to a cases of venture investors choosing emerging companies, and we compare the proposed method with PL-TOPSIS method, PL-TODIM method and PL-MABAC method to verify its applicability and effectiveness.

A high-resolution inland surface water body dataset for the tundra and boreal forests of North America

Inland surface waters are abundant in the tundra and boreal forests in North America, essential to environments and human societies but vulnerable to climate changes. These high-latitude water bodies differ greatly in their morphological and topological characteristics related to the formation, type, and vulnerability. In this paper we present an inland surface water body inventory (SWBI) dataset for the tundra and boreal forests of North America. Nearly 6.7 million water bodies were identified, with approximately 6 million (~90 %) of them smaller than 0.1 km2. The dataset provides geometry coverage and morphological attributes for every water body. During this study we developed an automated approach for detecting surface water extent and identifying water bodies in the 10 m resolution Sentinel-2 multispectral satellite data to enhance the capability for delineating small water bodies and their morphological attributes. The approach was applied to the Sentinel-2 data acquired in 2019 to produce the water body dataset for the entire tundra and boreal forests in North America, providing a more complete representation of the region than existing regional datasets, e.g., Permafrost Region Pond and Lake (PeRL). Total accuracy of the detected water extent by SWBI dataset was 96.36 % by comparing to interpreted data for locations randomly sampled across the region. Compared to the 30 m or coarser resolution water datasets, e.g., JRC GSW yearly water history, HydroLakes, and Global Lakes and Wetlands Database (GLWD), the SWBI provided an improved ability on delineating water bodies, and reported higher accuracies in the size, number, and perimeter attributes of water body by comparing to PeRL and interpreted regional dataset. This dataset is available on the National Tibetan Plateau/Third Pole Environment Data Center (TPDC, http://data.tpdc.ac.cn): DOI: 10.11888/Hydro.tpdc.271021 (Feng et al., 2020).

Experimental Methods for the Evaluation of the Efficiency of an Innovative Sulfur-Dissolving Product in HP-HT Conditions

During production from sour gas reservoirs, precipitation of elemental sulfur can take place in production tubing, resulting in plugging of the well and stop of production. Injection in tubing of products devoted to dissolving sulfur can be an efficient solution for plug removal and production restoring. Traditionally, organic solvents (like toluene) are employed for solid sulfur dissolution. In the present work, experimental investigations have been performed on a particular innovative liquid product designed as active phase for wellbore injection or near wellbore applications. The analyses about the behavior of the considered product were conducted at HP-HT conditions. For this purpose, PVT laboratory equipment was employed, being able to reproduce the conditions of interest for the formation of elemental sulfur plug in well. An important preliminary optimization phase on the experimental setup was necessary to assure the correct management of studied liquid substance and solid sulfur. Integration of main outcomes with other kind of analyses allowed to depict a complete representation of the behavior: microscopy analysis of the liquid phase and high-resolution tomography of solid sulfur before and after the interaction were employed. A key point of the experimental characterization is the reproduction of significant involved phenomena. A preliminary effort was necessary for reproducing the realistic crystal form expected during the precipitation of solid sulfur in well. The dissolution efficiency of the liquid product is evaluated by observing its physical interaction with sulfur in a HP-HT cell. Particular attention was paid to correctly handling employed substances at the considered pressure and temperature conditions. A detailed description of the optimized equipment used in laboratory is provided. Several dissolution tests have been conducted at different temperature and pressure conditions, aiming to observe the dependence of the dissolution efficiency on the thermodynamic parameters. A visual qualitative analysis was performed on both the liquid product and the solid plug, before and after the interaction in cell. This allowed to deepen the comprehension of the dynamics of sulfur dissolution, which takes place not only from the top face of the plug, but also from preferential paths (fractures) present inside the plug itself. The presence of sulfur crystals dispersed in the liquid product after sampling from the cell is also evident at the end of the tests. The studied novel sulfur-dissolving liquid active phase is a candidate for remedial job injection at well in case of plugging due to solid elemental sulfur precipitation. The analyses here presented allowed to characterize the dissolution potential of this product. An optimized workflow was designed, including different kind of experimental disciplines.

ODGI: understanding pangenome graphs

Motivation: Pangenome graphs provide a complete representation of the mutual alignment of collections of genomes. These models offer the opportunity to study the entire genomic diversity of a population, including structurally complex regions. Nevertheless, analyzing hundreds of gigabase-scale genomes using pangenome graphs is difficult as it is not well-supported by existing tools. Hence, fast and versatile software is required to ask advanced questions to such data in an efficient way. Results: We wrote ODGI, a novel suite of tools that implements scalable algorithms and has an efficient in-memory representation of DNA variation graphs. ODGI includes tools for detecting complex regions, extracting loci, removing artifacts, exploratory analysis, manipulation, validation, and visualization. Its fast parallel execution facilitates routine pangenomic tasks, as well as pipelines that can quickly answer complex biological questions of gigabase-scale pangenome graphs. Availability: ODGI is published as free software under the MIT open source license. Source code can be downloaded from https://github.com/pangenome/odgi and documentation is available at https://odgi.readthedocs.io. ODGI can be installed via Bioconda https: //bioconda.github.io/recipes/odgi/README.html or GNU Guix https://github.com/ ekg/guix-genomics/blob/master/odgi.scm.

Colombia’s GHG Emissions Reduction Scenario: Complete Representation of the Energy and Non-Energy Sectors in LEAP

The signatory countries of the Paris Agreement must submit their updated Intended National Determined Contributions (INDCs) to the UNFCCC secretariat every five years. In Colombia, this activity was historically carried out with a wide set of diverse non-interconnected sector-specific models. Given the complexity of GHG emissions reporting and the evaluation of mitigation actions on a national scale, the need for a centralized platform was evident. Such approach would allow the integration and analysis of potential interactions among sectors, as well as to guarantee the homogeneity of assumptions and input parameters. In this paper, we describe the construction of an integrated bottom-up LEAP model tailored to the Colombian case, which covers all IPCC sectors. An integrated model facilitates capturing synergies and intersectoral interactions within the national GHG emissions system. Hence, policies addressing one sector and influencing others are identified and correctly assessed. Thus, 44 mitigation policies and mitigation actions were included in the model, in this way, identifying the sectors directly and being indirectly affected by them. The mitigation scenario developed in this paper reaches a reduction of 28% of GHG emissions compared with the reference scenario. The importance of including non-energy sectors is evident in the Colombian case, as GHG emission reductions are mainly driven by AFOLU. The first section describes the GHG emissions context in Colombia. Next, we describe the model structure, main input parameters, assumptions, considerations, and used LEAP functionalities. Results are presented from a GHG emissions accounting and energy demand perspective. The model allows for the correct estimate of the scope and potential of mitigation actions by considering indirect, unintended emissions reductions in all IPCC categories, as well as synergies with all mitigation actions included in the mitigation scenario. Moreover, the structure of the model is suitable for testing potential emission trajectories, facilitating its adoption by official entities and its application in climate policymaking.

Cyclostationary Approach to the Analysis of the Power in Electric Circuits under Periodic Excitations

This paper proposes a cyclostationary based approach to power analysis carried out for electric circuits under arbitrary periodic excitation. Instantaneous power is considered to be a particular case of the two-dimensional cross correlation function (CCF) of the voltage across, and current through, an element in the electric circuit. The cyclostationary notation is used for deriving the frequency domain counterpart of CCF—voltage–current cross spectrum correlation function (CSCF). Not only does the latter exhibit the complete representation of voltage–current interaction in the element, but it can be systematically exploited for evaluating all commonly used power measures, including instantaneous power, in the form of Fourier series expansion. Simulation examples, which are given for the parallel resonant circuit excited by the periodic currents expressed as a finite sum of sinusoids and periodic train of pulses with distorted edges, numerically illustrate the components of voltage–current CSCF and the characteristics derived from it. In addition, the generalization of Tellegen’s theorem, suggested in the paper, leads to the immediate formulation of the power conservation law for each CSCF component separately.

IBI-CCS: a regional high-resolution model to evaluate western Europe sea level changes

Projections of coastal sea level (SL) changes are of great interest for coastal risk assessment and decision-making. SL projections are typically produced using global climate models (GCMs) which cannot fully resolve SL changes at the coast due to their coarse resolution and lack of representation of some relevant processes. To overcome these limitations and refine projections at regional scales, GCMs can be dynamically downscaled through the implementation of a high-resolution regional climate model (RCM). In this study, we developed the IBI-CCS regional ocean model based on a 1/12 ° north-eastern Atlantic NEMO ocean model configuration to dynamically downscale CNRM-CM6-1-HR, a GCM with a ¼ ° resolution ocean model component developed for the Coupled Model Intercomparison Project 6th Phase (CMIP6) by the Centre National de Recherches Météorologiques (CNRM). For a more complete representation of processes driving coastal SL changes, tides and atmospheric surface pressure forcing are explicitly resolved in IBI-CCS in addition to the ocean general circulation. To limit the propagation of climate drifts and biases from the GCM into the regional simulations, several corrections are applied to the GCM fields used to force the RCM. The regional simulations are performed over the 1950 to 2100 period for two climate change scenarios (SSP1-2.6 and SSP5-8.5). To validate the dynamical downscaling method, the RCM and GCM simulations are compared to reanalyses and observations over the 1993–2014 period for a selection of ocean variables including SL. Results indicate that large-scale performances of IBI-CCS are better than those of the GCM thanks to the corrections applied to the RCM. Extreme SLs are also satisfactorily represented in the IBI-CCS historical simulation. Comparison of the RCM and GCM 21st century projections show a limited impact of increased resolution (1/4° to 1/12°) on SL changes. Overall, bias corrections have a moderate impact on projected coastal SL changes projections, except in the Mediterranean Sea where GCM biases were substantial.

Online private self-disclosure’s potential for experiential value co-creation

Purpose This study aims to integrate and extend existing approaches from self-identity literature by examining the underexplored aspects of online private self-disclosure. The study first explores the experiential value co-created when consumers voluntarily self-disclose on public platforms. Second, it sheds light on what motivates such consumers to disclose private self-images and experiences, thus giving up some degree of privacy on an unrestricted platform. Design/methodology/approach This study conducted 65 laddering interviews and observed the profiles of ten consumers, who actively posted self-images on Instagram, through a netnographic study. Then, this study implemented a means-ends chain analysis on interview data. Findings This study found that online private self-disclosure can involve a co-created experiential value that consists of consumers’ self-affirmation, affective belief and emotional connection. These value components derive from three higher-order psychological consequences – empowerment, buffering offline inadequacy of self-worth and engagement – and four functional consequences – opportunity to learn, online control, self-brand authenticity and impression management. Implications Operationally, this study proposes that Instagram could be configured and synched with other social networking sites to provide a more complete representation of the online self. Using algorithms that simultaneously pull from other social networking sites can emotionally connect consumers to a more relevant and gratifying personalized experience. Additionally, managers could leverage the findings to tailor supporting tools to transfer consumers’ private self-disclosure skills learned during online communication into their offline settings. Originality This research contributes to the extant marketing literature by providing insights into how consumers can use private self-disclosure to co-create experiential value, an emerging concept in modern marketing that is key to attaining satisfied and loyal consumers. This study shows that, even in anonymous online settings, consumers are willing to self-disclose and progress to stable intimate exchanges of disclosure by breaking their inner repression and becoming more comfortable with releasing their desires in an emotional exchange.

DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding

Long non-coding RNAs (lncRNAs) are a class of RNA molecules with more than 200 nucleotides. A growing amount of evidence reveals that subcellular localization of lncRNAs can provide valuable insights into their biological functions. Existing computational methods for predicting lncRNA subcellular localization use k-mer features to encode lncRNA sequences. However, the sequence order information is lost by using only k-mer features. We proposed a deep learning framework, DeepLncLoc, to predict lncRNA subcellular localization. In DeepLncLoc, we introduced a new subsequence embedding method that keeps the order information of lncRNA sequences. The subsequence embedding method first divides a sequence into some consecutive subsequences and then extracts the patterns of each subsequence, last combines these patterns to obtain a complete representation of the lncRNA sequence. After that, a text convolutional neural network is employed to learn high-level features and perform the prediction task. Compared with traditional machine learning models, popular representation methods and existing predictors, DeepLncLoc achieved better performance, which shows that DeepLncLoc could effectively predict lncRNA subcellular localization. Our study not only presented a novel computational model for predicting lncRNA subcellular localization but also introduced a new subsequence embedding method which is expected to be applied in other sequence-based prediction tasks. The DeepLncLoc web server is freely accessible at http://bioinformatics.csu.edu.cn/DeepLncLoc/, and source code and datasets can be downloaded from https://github.com/CSUBioGroup/DeepLncLoc.