Number Of Complexes Research Articles

Complex-Valued 2D-3D Hybrid Convolutional Neural Network with Attention Mechanism for PolSAR Image Classification

The recently introduced complex-valued convolutional neural network (CV-CNN) has shown considerable advancements for polarimetric synthetic aperture radar (PolSAR) image classification by effectively incorporating both magnitude and phase information. However, a solitary 2D or 3D CNN encounters challenges such as insufficiently extracting scattering channel dimension features or excessive computational parameters. Moreover, these networks’ default is that all information is equally important, consuming vast resources for processing useless information. To address these issues, this study presents a new hybrid CV-CNN with the attention mechanism (CV-2D/3D-CNN-AM) to classify PolSAR ground objects, possessing both excellent computational efficiency and feature extraction capability. In the proposed framework, multi-level discriminative features are extracted from preprocessed data through hybrid networks in the complex domain, along with a special attention block to filter the feature importance from both spatial and channel dimensions. Experimental results performed on three PolSAR datasets demonstrate our present approach’s superiority over other existing ones. Furthermore, ablation experiments confirm the validity of each module, highlighting our model’s robustness and effectiveness.

Anticoagulation in Chronic Kidney Disease.

The nuanced landscape of anticoagulation therapy in patients with chronic kidney disease (CKD) presents a formidable challenge, intricately balancing the dual hazards of hemorrhage and thrombosis. These patients find themselves in a precarious position, teetering on the edge of these risks due to compromised platelet functionality and systemic disturbances within their coagulation frameworks. The management of such patients necessitates a meticulous approach to dosing adjustments and vigilant monitoring to navigate the perilous waters of anticoagulant therapy. This is especially critical considering the altered pharmacokinetics in CKD, where the clearance of drugs is significantly impeded, heightening the risk of accumulation and adverse effects. In the evolving narrative of anticoagulation therapy, the introduction of direct oral anticoagulants (DOACs) has heralded a new era, offering a glimmer of hope for those navigating the complexities of CKD. These agents, with their promise of easier management and a reduced need for monitoring, have begun to reshape the contours of care, particularly for patients not yet on dialysis. However, this is not without its caveats. The application of DOACs in the context of advanced CKD remains a largely uncharted territory, necessitating a cautious exploration to unearth their true potential and limitations. Moreover, the advent of innovative strategies such as left atrial appendage occlusion (LAAO) underscores the dynamic nature of anticoagulation therapy, potentially offering a tailored solution for those at the intersection of CKD and elevated stroke risk. Yet the journey toward integrating such advancements into standard practice is laden with unanswered questions, demanding rigorous investigation to illuminate their efficacy and safety across the spectrum of kidney disease. In summary, the management of anticoagulation in CKD is a delicate dance, requiring a harmonious blend of precision, caution, and innovation. As we venture further into this complex domain, we must build upon our current understanding, embracing both emerging therapies and the need for ongoing research. Only then can we hope to offer our patients a path that navigates the narrow strait between bleeding and clotting, toward safer and more effective care.

Superposition‐based concurrent multiscale approaches for porodynamics

AbstractThe current study presents superposition‐based concurrent multiscale approaches for porodynamics, capable of capturing related physical phenomena, such as soil liquefaction and dynamic hydraulic fracture branching, across different spatial length scales. Two scenarios are considered: superposition of finite element discretizations with varying mesh densities, and superposition of peridynamics (PD) and finite element method (FEM) to handle discontinuities like strain localization and cracks. The approach decomposes the acceleration and the rate of change in pore water pressure into subdomain solutions approximated by different models, allowing high‐fidelity models to be used locally in regions of interest, such as crack tips or shear bands, without neglecting the far‐field influence represented by low‐fidelity models. The coupled stiffness, mass, compressibility, permeability, and damping matrices were derived based on the superposition‐based current multiscale framework. The proposed FEM‐FEM porodynamic coupling approach was validated against analytical or numerical solutions for one‐ and two‐dimensional dynamic consolidation problems. The PD‐FEM porodynamic coupling model was applied to scenarios like soil liquefaction‐induced shear strain accumulation near a low‐permeability interlayer in a layered deposit and dynamic hydraulic fracturing branching. It has been shown that the coupled porodynamic model offers modeling flexibility and efficiency by taking advantage of FEM in modeling complex domains and the PD ability to resolve discontinuities.

Complex Residual Attention U-Net for Fast Ultrasound Imaging from a Single Plane-Wave Equivalent to Diverging Wave Imaging.

Plane wave imaging persists as a focal point of research due to its high frame rate and low complexity. However, in spite of these advantages, its performance can be compromised by several factors such as noise, speckle, and artifacts that affect the image quality and resolution. In this paper, we propose an attention-based complex convolutional residual U-Net to reconstruct improved in-phase/quadrature complex data from a single insonification acquisition that matches diverging wave imaging. Our approach introduces an attention mechanism to the complex domain in conjunction with complex convolution to incorporate phase information and improve the image quality matching images obtained using coherent compounding imaging. To validate the effectiveness of this method, we trained our network on a simulated phased array dataset and evaluated it using in vitro and in vivo data. The experimental results show that our approach improved the ultrasound image quality by focusing the network's attention on critical aspects of the complex data to identify and separate different regions of interest from background noise.

Redefining Neural Architecture Search of Heterogeneous Multinetwork Models by Characterizing Variation Operators and Model Components.

With neural architecture search (NAS) methods gaining ground on manually designed deep neural networks-even more rapidly as model sophistication escalates-the research trend is shifting toward arranging different and often increasingly complex NAS spaces. In this conjuncture, delineating algorithms which can efficiently explore these search spaces can result in a significant improvement over currently used methods, which, in general, randomly select the structural variation operator, hoping for a performance gain. In this article, we investigate the effect of different variation operators in a complex domain, that of multinetwork heterogeneous neural models. These models have an extensive and complex search space of structures as they require multiple subnetworks within the general model in order to answer different output types. From that investigation, we extract a set of general guidelines whose application is not limited to that particular type of model and are useful to determine the direction in which an architecture optimization method could find the largest improvement. To deduce the set of guidelines, we characterize both the variation operators, according to their effect on the complexity and performance of the model; and the models, relying on diverse metrics which estimate the quality of the different parts composing it.

AB067. New drug development for the use of PARP1-E2F1 transcriptional inhibitors in the treatment of glioblastoma.

Glioblastoma (GBM) is the most malignant brain tumor and ranks among the most lethal of all human cancers, without improvements in survival over the last 30 years. Data obtained in our group suggest that PARP1, a well-known DNA-repairing protein, could also play a key role in the regulation of cell cycle through its interaction with the transcription factor E2F1. Therefore, considering that most oncogenic processes are associated with cell cycle deregulation, we hypothesized that disruption of PARP1-E2F1 interaction would provide a novel therapeutic approach to different types of cancer. The identification of novel compounds disrupting PARP1-E2F1 interaction was carried out by combining in silico and in vitro screening, using a rational drug design. The virtual screen was performed using a molecular library of several million compounds at the selected target site, using AtomNet® (Atomwise, San Francisco, CA, USA), the first deep learning neural network for structure-based drug design and discovery. Since there is no complete structural information of the PARP1-E2F1 protein-protein interaction, a homologous structure of the BRCT domain of BRCA1 complex with the phospho-peptide (PDBID: 1T2V) was used to identify the potential binding interface of BRCT domain of PARP-1 (PDBID: 2COK) and the E2F1 protein. Top scoring compounds were clustered and filtered to obtain a final subset of 83 compounds that were incorporated to our in vitro screening, which included both transcriptional E2F1 activity and survival studies. Complete culture medium supplemented with the compounds selected in the in silico screening (10 μM) were added and incubated for 24 hours. E2F1 activity was observed by measuring luminescence. For the viability assay, the fluorescence reading was performed (excitation 544 nm and emission 590 nm). The in silico and in vitro screening resulted in 12 compounds that inhibited E2F1 transcriptional activity and significantly reduced cell number. The highest inhibition of both E2F1 transcriptional activity and cell growth was observed with compound 3797, which was selected for further studies. Both in silico and in vitro results indicate that inhibition of PARP1-E2F1 transcriptional activity may provide a new rationale for designing novel therapeutic approaches for the treatment of GBM.

Growth of ferroelectric domain nuclei: Insight from a sharp-interface model

We present an analytical framework to study the impact of electromechanical properties on the growth of a ferroelectric nucleus. Ferroelectric domain evolution is typically simulated by phase-field models, which have shown that nuclei evolve from needle-like structures into complex domain patterns. However, there has been limited in-depth analysis of the interplay between electrostatics, mechanics and piezoelectricity and their effect on nucleus growth because of the complexity involved in the phase-field description. In this study, we describe the ferroelectric domain wall as a sharp interface and solve for the fields inside an elliptic ferroelectric nucleus via Eshelby’s inclusion problem. We analytically determine the driving traction profile around the nucleus to gain insight into the movement of the domain wall with and without applied electromechanical loading. We analyze how the growth is affected by the permittivity, elasticity, and piezoelectricity as well as the nucleus’ eccentricity. We further demonstrate that applied loads do not significantly affect nucleus growth, which is primarily determined by the self-equilibrated mechanical and electric field, and that the anisotropy in material properties is essential in determining the growth of a ferroelectric nucleus.

Sparsity-aware complex-valued least mean kurtosis algorithms

Complex-valued least mean kurtosis (CLMK) algorithm and its augmented version (ACLMK) have recently become popular as workhorse tools in the processing of complex-valued signals due to their superior performances. Unfortunately, they are not yet suitable for sparse system identification problems. In this paper, combining the well-known sparsity-promoting strategies into the cost function based on the negated kurtosis of the error signal, we introduce a suit of sparsity-aware CLMK algorithms, named l0-norm CLMK (l0-CLMK), l0-ACLMK, zero-attraction CLMK (ZA-CLMK), ZA-ACLMK, reweighted ZA-CLMK (RZA-CLMK), and RZA-ACLMK. Simulation results on synthetic and real-world sparse system identification scenarios in the complex domain show that the proposed algorithms outperform the existing sparsity-aware algorithms in terms of convergence rate, tracking, and steady-state error.

On the Complementarity between CMMN and iStar in Complex Domain Modeling

Case Management Modeling and Notation (CMMN) and iStar are two distinct, multi-purposed modeling techniques that may be used to represent organizational challenges at separate levels of abstraction. CMMN, a flexible process-oriented technique, aims to extract knowledge that enhances the representational capacity of activity flows for a specific case. Conversely, the iStar framework adopts a goal-oriented modeling approach, effectively capturing the interplay of social actors and their influence on the attainment of organizational objectives. While prior studies have explored methods for integrating these techniques to attain a more comprehensive understanding of the organizational landscape, these remain primarily associated with a distinct level of the organizational matrix (i.e., CMMN for operations and tactics, and iStar for more strategic aspects). As such, any effort to evaluate their semantic proximity appears fragmented, as it deals only with the partial association of specific notations and elements. This article describes the conduct of a dual-purposed literature review to identify specific criteria that might accommodate a more holistic assessment of the two modeling techniques; these criteria are then employed as a guiding framework to construct a set of propositions that articulate the areas of complementarity and/or divergence between these two techniques, as identified in previous research. These propositions are subsequently subjected to validation by domain experts, leveraging a real-world case study in the educational domain. The results show that there can be areas of semantic convergence between the two techniques, suggesting their parallel use to effectively model complex domain problems. Overall, the present study aims to crystallize an approach for conducting complex modeling comparisons that transcends technical considerations.

SMN1 c.5C>G (p.Ala2Gly) missense variant, a challenging molecular SMA diagnosis associated with mild disease, preserves SMN nuclear gems in patient-specific fibroblasts.

Spinal muscular atrophy (SMA) is caused by homozygous loss of the SMN1 gene with SMN2 gene copy number correlating with disease severity. Rarely SMA is caused by a deletion on one allele and a pathogenic variant on the other. The pathogenic missense variant c.5C>G (p.Ala2Gly) correlates with a mild disease phenotype that does not correlate with SMN2 copy number. In a mouse model the c.5C>G transgene produces SMN that is thought to form partially functional SMN complexes, but levels in humans have not yet been investigated. We identified two patients with mild SMA caused by a heterozygous deletion of SMN1 and the heterozygous variant, c.5C>G. Molecular findings were confirmed with deletion/duplication analysis and Sanger sequencing. Skin fibroblasts were collected and cultured, and SMN expression was analyzed using immunofluorescence. Two patients with slowly progressing mild weakness were confirmed to have heterozygous pathogenic missense variant c.5C>G and a heterozygous deletion of SMN1. Their clinical presentation revealed much milder disease progression than patients with matched SMN2 copy number. Analysis of the patients' fibroblasts revealed much higher numbers of SMN nuclear complexes than a patient with a homozygous SMN1 deletion and matched SMN2 copy number. These case reports reinforce that the rare c.5C>G variant causes mild disease. Furthermore, the analysis of SMA nuclear gems in patient samples supports the theory that the p.Ala2Gly SMN can form partially functional SMN complexes that may carry out essential cellular functions and result in mild disease.

Analysis of different objective functions in petroleum field development optimization

Oilfield development optimization plays a vital role in maximizing the potential of hydrocarbon reservoirs. Decision-making in this complex domain can rely on various objective functions, including net present value (NPV), expected monetary value (EMV), cumulative oil production (COP), cumulative gas production (CGP), cumulative water production (CWP), project costs, and risks. However, EMV is often the main function when optimization is performed under uncertainty. The behavior and performance of different objective functions has been investigated in this paper, when EMV is the primary criterion for optimization under reservoir and economic uncertainty. One of the goals of this study is to provide insights into the advantages and limitations of employing EMV as the sole objective function in oil field development decision-making. The designed optimization problem included sequential optimization of design variables including well positions, well quantity, well type, platform capacity, and internal control valve placements. A comparative analysis is presented, contrasting the outcomes obtained from optimizing the EMV-based objective function against traditional objective functions. The study underscores the importance of incorporating multiple objective functions alongside EMV to guide decision-making in oilfield development. Potential benefits in minimizing CGP and CWP are revealed, aiding in the mitigation of environmental impact and optimization of resource utilization. A strong correlation between EMV and COP is identified, highlighting EMV’s role in improving COP and RF.

SonicParanoid2: fast, accurate, and comprehensive orthology inference with machine learning and language models

Accurate inference of orthologous genes constitutes a prerequisite for comparative and evolutionary genomics. SonicParanoid is one of the fastest tools for orthology inference; however, its scalability and accuracy have been hampered by time-consuming all-versus-all alignments and the existence of proteins with complex domain architectures. Here, we present a substantial update of SonicParanoid, where a gradient boosting predictor halves the execution time and a language model doubles the recall. Application to empirical large-scale and standardized benchmark datasets shows that SonicParanoid2 is much faster than comparable methods and also the most accurate. SonicParanoid2 is available at https://gitlab.com/salvo981/sonicparanoid2 and https://zenodo.org/doi/10.5281/zenodo.11371108.

A ligand discovery toolbox for the WWE domain family of human E3 ligases

The WWE domain is a relatively under-researched domain found in twelve human proteins and characterized by a conserved tryptophan-tryptophan-glutamate (WWE) sequence motif. Six of these WWE domain-containing proteins also contain domains with E3 ubiquitin ligase activity. The general recognition of poly-ADP-ribosylated substrates by WWE domains suggests a potential avenue for development of Proteolysis-Targeting Chimeras (PROTACs). Here, we present novel crystal structures of the HUWE1, TRIP12, and DTX1 WWE domains in complex with PAR building blocks and their analogs, thus enabling a comprehensive analysis of the PAR binding site structural diversity. Furthermore, we introduce a versatile toolbox of biophysical and biochemical assays for the discovery and characterization of novel WWE domain binders, including fluorescence polarization-based PAR binding and displacement assays, 15N-NMR-based binding affinity assays and 19F-NMR-based competition assays. Through these assays, we have characterized the binding of monomeric iso-ADP-ribose (iso-ADPr) and its nucleotide analogs with the aforementioned WWE proteins. Finally, we have utilized the assay toolbox to screen a small molecule fragment library leading to the successful discovery of novel ligands targeting the HUWE1 WWE domain.

Structure of biomolecular films through advanced imaging and statistical analysis

This manuscript is dedicated to advancing the understanding of biomolecular film on solid target formation, with a particular emphasis on the intricacies of structure nonuniformities in amino acid and protein-based dehydrated films. Experimental and theoretical studying the structure of films at the quantitative level has been created and tested. By integrating advanced imaging and statistical analysis, we dissect the dynamics and intricacies of domain structures, including cellular and microcrystalline formations, that significantly influence dehydrated biomolecular film properties. The research process entails preparing amino acid and protein solutions, capturing high-resolution images during dehydration, and implementing advanced digital image processing techniques like Laplacian Pyramid Restoration and Gaussian Differential Scale-Invariance for enhanced image clarity. Analytical approach includes domain segmentation and texture analysis using Haralick features, enabling precise quantification of structural complexities in films formed under various dehydration conditions and protein concentrations. The suggested physical model describes the transition from equilibrium solutions through nonequilibrium processes to complex bubble vaporization, dehydration-induced Dewetting and domain differentiation, thus explained observed spiral formations within protein domains. This model provids a detailed mechanistic understanding of how environmental conditions and molecular dynamics drive the formation of domains, paving the way for developing films with tailored properties for diverse applications. These insights hold significant implications across various fields, including material science, nanotechnology, biomedical applications, environmental monitoring, electronics, and photonics.

Identifying point defects and ordering in the high-entropy layered oxide Li1.5MO3-δ (M=Mn, Al, Fe, Co, Ni) for energy storage applications

High-entropy layered oxides (HELOs) represent a very promising class of next-generation battery cathodes, combining the well-studied properties of layered cathode materials such as LiCoO2 with the chemical tunability and stability of high-entropy materials. HELO materials often form particles with complex defects and domain structures, complicating accurate characterization of structure and cation mixing. Understanding disorder and order in HELO materials is necessary for understanding their performance and utility as cathodes. Here we demonstrate the characterization of the HELO Li1.5MO3-δ (M = Mn, Al, Fe, Co, Ni), wherein X-ray powder diffraction, transmission electron microscopy imaging, and electron diffraction patterns are analyzed to reveal the presence of ordering in the HELO, with imaging and diffraction simulation employed to compare experimental results to atomic modeling. Without rigorous characterization at the atomic scale, important features such as defect ordering can be easily overlooked and therefore remain unconsidered when interpreting experimental results.

Segment Anything Model-Based Building Footprint Extraction for Residential Complex Spatial Assessment Using LiDAR Data and Very High-Resolution Imagery

With rapid urbanization, retrieving information about residential complexes in a timely manner is essential for urban planning. To develop efficiency and accuracy of building extraction in residential complexes, a Segment Anything Model-based residential building instance segmentation method with an automated prompt generator was proposed combining LiDAR data and VHR remote sensing images in this study. Three key steps are included in this method: approximate footprint detection using LiDAR data, automatic prompt generation for the SAM, and residential building footprint extraction. By applying this method, residential building footprints were extracted in Pukou District, Nanjing, Jiangsu Province. Based on this, a comprehensive assessment model was constructed to systematically evaluate the spatial layout of urban complexes using six dimensions of assessment indicators. The results showed the following: (1) The proposed method was used to effectively extract residential building footprints. (2) The residential complexes in the study area were classified into four levels. The numbers of complexes classified as Excellent, Good, Average, and Poor were 10, 29, 16, and 1, respectively. Residential complexes of different levels exhibited varying spatial layouts and building distributions. The results provide a visual representation of the spatial distribution of residential complexes that belong to different levels within the study area, aiding in urban planning.

X-SLAM: Scalable Dense SLAM for Task-aware Optimization using CSFD

We present X-SLAM, a real-time dense differentiable SLAM system that leverages the complex-step finite difference (CSFD) method for efficient calculation of numerical derivatives, bypassing the need for a large-scale computational graph. The key to our approach is treating the SLAM process as a differentiable function, enabling the calculation of the derivatives of important SLAM parameters through Taylor series expansion within the complex domain. Our system allows for the real-time calculation of not just the gradient, but also higher-order differentiation. This facilitates the use of high-order optimizers to achieve better accuracy and faster convergence. Building on X-SLAM, we implemented end-to-end optimization frameworks for two important tasks: camera relocalization in wide outdoor scenes and active robotic scanning in complex indoor environments. Comprehensive evaluations on public benchmarks and intricate real scenes underscore the improvements in the accuracy of camera relocalization and the efficiency of robotic navigation achieved through our task-aware optimization. The code and data are available at https://gapszju.github.io/X-SLAM.

Complex-valued recurrent neural network equalizer with low complexity for a 120-Gbps 50-km optical PAM-4 IM/DD system

This paper introduces a novel complex-valued recurrent neural networks equalizer (RNNE) designed for a 120-Gbps, 50-km optical 4-level pulse-amplitude modulation (PAM-4) intensity modulation and direct detection (IM/DD) system. By mapping adjacent symbols of PAM-4 signals onto the complex domain, the correlation between two adjacent symbols of PAM-4 signals can be preserved. Based on experimental results, the proposed complex-valued RNNE outperforms the traditional real-valued RNNE with a 1.38-dB system power budget gain at the 7% overhead forward error correction BER threshold of 3.8 × 10−3. We believe that complex-valued RNNE has an advantage over real-valued RNNE in processing real-valued signals in IM/DD systems.

Analytical realization of complex thermal meta-devices

Fourier’s law dictates that heat flows from warm to cold. Nevertheless, devices can be tailored to cloak obstacles or even reverse the heat flow. Mathematical transformation yields closed-form equations for graded, highly anisotropic thermal metamaterial distributions needed for obtaining such functionalities. For simple geometries, devices can be realized by regular conductor distributions; however, for complex geometries, physical realizations have so far been challenging, and sub-optimal solutions have been obtained by expensive numerical approaches. Here we suggest a straightforward and highly efficient analytical de-homogenization approach that uses optimal multi-rank laminates to provide closed-form solutions for any imaginable thermal manipulation device. We create thermal cloaks, rotators, and concentrators in complex domains with close-to-optimal performance and esthetic elegance. The devices are fabricated using metal 3D printing, and their omnidirectional thermal functionalities are investigated numerically and validated experimentally. The analytical approach enables next-generation free-form thermal meta-devices with efficient synthesis, near-optimal performance, and concise patterns.

Investigating rheological characteristics of nonlinear fluid model in an asymmetric channel: A peristaltic pumping of blood with curvature and hydrodynamic impacts

Theoretical research is investigated for the steady rheology of incompressible blood from an asymmetric (penetrable) channel in the presence of curvature and non-linear hydrodynamic impacts. The motion takes place in an asymmetric channel due to a train of sinusoidal (complex nature) waves along the channel boundaries having different phases. In this study, the Casson fluid is used as blood. The governing equations are modeled in terms of Cartesian coordinates. The impacts of non-linear curvature become significant on the transportation of biological fluid in a complex domain such as the non-pregnant uterus at a higher Reynolds number. An approximate solution of flow models is obtained to the second order in the wavenumber (giving the curvature effect). A domain transformation is used to transform the variable cross-sectional of the flow regime into the uniform cross-section. This transformation helps to find the closed-form solution of transformed rheological equations in higher order. The physical impacts of the curvature parameter, Casson parameter, phase difference, flow rate, Reynolds number, and permeability parameter on the axial velocity, pressure gradient, stress tensor, trapping phenomena, and pressure rise are investigated. The velocity of blood is smaller as it is associated with the viscous fluid. The number of boluses was reduced by increasing the Casson parameter and Reynolds number in the trapping phenomenon. The stress tensor is an increasing function of the permeability parameter and the Reynolds number. The pressure gradient is affected by the permeability parameter. The comparison between viscous fluid and blood is also addressed in the current study. The outcomes of the current study arise in the medical domains, blood study, manufacturing of nano-blood pumps, bio-physical food processing, and chemical industry.