Computational Framework Research Articles

Identification and validation of microbial biomarkers from cross-cohort datasets using xMarkerFinder.

Microbial signatures have emerged as promising biomarkers for disease diagnostics and prognostics, yet their variability across different studies calls for a standardized approach to biomarker research. Therefore, we introduce xMarkerFinder, a four-stage computational framework for microbial biomarker identification with comprehensive validations from cross-cohort datasets, including differential signature identification, model construction, model validation and biomarker interpretation. xMarkerFinder enables the identification and validation of reproducible biomarkers for cross-cohort studies, along with the establishment of classification models and potential microbiome-induced mechanisms. Originally developed for gut microbiome research, xMarkerFinder's adaptable design makes it applicable to various microbial habitats and data types. Distinct from existing biomarker research tools that typically concentrate on a singular aspect, xMarkerFinder uniquely incorporates a sophisticated feature selection process, specifically designed to address the heterogeneity between different cohorts, extensive internal and external validations, and detailed specificity assessments. Execution time varies depending on the sample size, selected algorithm and computational resource. Accessible via GitHub ( https://github.com/tjcadd2020/xMarkerFinder ), xMarkerFinder supports users with diverse expertise levels through different execution options, including step-to-step scripts with detailed tutorials and frequently asked questions, a single-command execution script, a ready-to-use Docker image and a user-friendly web server ( https://www.biosino.org/xmarkerfinder ).

DPMGCDA: Deciphering circRNA-Drug Sensitivity Associations with Dual Perspective Learning and Path-Masked Graph Autoencoder.

Accumulating evidence has indicated that the expression of circular RNAs (circRNAs) can affect the cellular sensitivity to drugs and significantly influence drug efficacy. However, traditional experimental approaches for validating these associations are resource-intensive and time-consuming. To address this challenge, we propose a computational framework termed DPMGCDA leveraging dual perspective learning and path-masked graph autoencoder to predict circRNA-drug sensitivity associations. Initially, we construct circRNA-circRNA fusion similarity networks and drug-drug fusion similarity networks using similarity network fusion, ensuring a comprehensive integration of information. Based on the above, we built the circRNA homogeneous graph, the drug homogeneous graph, and the circRNA-drug heterogeneous graph. Next, we form the initial node features in the circRNA-drug heterogeneous graph from the homogeneous graph-level perspective and the combined feature-level perspective and complete the prediction of potential associations using the path-masked graph autoencoder in both perspectives. The predictions under both perspectives are finally combined to obtain the final prediction score. Transductive setting experiments and inductive setting experiments all demonstrate that our method, DPMGCDA, outperforms state-of-the-art approaches. Additionally, we verify the necessity of employing dual perspective learning through ablation tests and analyze the effective encoding capability of the path-masked graph autoencoder for features through embedding visualization. Moreover, case studies on four drugs corroborate DPMGCDA's ability to identify potential circRNAs associated with new drugs.

Machine learning and multi-omics data reveal driver gene-based molecular subtypes in hepatocellular carcinoma for precision treatment.

The heterogeneity of Hepatocellular Carcinoma (HCC) poses a barrier to effective treatment. Stratifying highly heterogeneous HCC into molecular subtypes with similar features is crucial for personalized anti-tumor therapies. Although driver genes play pivotal roles in cancer progression, their potential in HCC subtyping has been largely overlooked. This study aims to utilize driver genes to construct HCC subtype models and unravel their molecular mechanisms. Utilizing a novel computational framework, we expanded the initially identified 96 driver genes to 1192 based on mutational aspects and an additional 233 considering driver dysregulation. These genes were subsequently employed as stratification markers for further analyses. A novel multi-omics subtype classification algorithm was developed, leveraging mutation and expression data of the identified stratification genes. This algorithm successfully categorized HCC into two distinct subtypes, CLASS A and CLASS B, demonstrating significant differences in survival outcomes. Integrating multi-omics and single-cell data unveiled substantial distinctions between these subtypes regarding transcriptomics, mutations, copy number variations, and epigenomics. Moreover, our prognostic model exhibited excellent predictive performance in training and external validation cohorts. Finally, a 10-gene classification model for these subtypes identified TTK as a promising therapeutic target with robust classification capabilities. This comprehensive study provides a novel perspective on HCC stratification, offering crucial insights for a deeper understanding of its pathogenesis and the development of promising treatment strategies.

Ab Initio Modeling of Mixed-Dimensional Heterostructures: A Path Forward.

Understanding the electronic structure of mixed-dimensional heterostructures is essential for maximizing their application potential. However, accurately modeling such interfaces is challenging due to the complex interplay between the subsystems. We employ a computational framework integrating first-principles methods, including GW, density functional theory (DFT), and the polarizable continuum model, to elucidate the electronic structure of mixed-dimensional heterojunctions formed by free-base phthalocyanines and monolayer molybdenum disulfide. We assess the impact of dielectric screening across various scenarios, from isolated molecules to organic films on a substrate-supported monolayer. Our findings show that while polarization effects cause significant renormalization of molecular energy levels, band energies and alignments in the most relevant setup can be accurately predicted through DFT simulations of the individual subsystems. Additionally, we analyze orbital hybridization, revealing potential pathways for interfacial charge transfer. This study offers new insights into hybrid inorganic/organic interfaces and provides a practical computational protocol suitable for scaled-up studies.

Incorporation of adaptive compression into a GPU parallel computing framework for analyzing large-scale vessel trajectories

Automatic Identification System (AIS) offers a wealth of vessel navigation data, which underpins research in maritime data mining, situational awareness, and knowledge discovery within the realm of intelligent transportation systems. The flourishing marine industry has prompted AIS satellites and base stations to generate massive amounts of vessel trajectory data, escalating both data storage and calculation costs. The conventional Douglas-Peucker (DP) algorithm used for trajectory compression sets a uniform threshold, which hampers effective compression. Additionally, compressing and accelerating the computation of large datasets poses a significant challenge in real-world applications. To address these limitations, this paper aims to develop a new Graphics Processing Unit (GPU) parallel computing and compression framework that enables the acceleration of the optimal threshold calculation for each trajectory automatically in maritime big data mining. It achieves this by incorporating a new Adaptive DP with Speed and Course (ADPSC) algorithm, which utilizes the dynamic navigation characteristics of different vessels. It can effectively solve the associated computational time cost concern when using the ADPSC algorithm to compress vast trajectory datasets in the real world. Additionally, this paper proposes a novel evaluation metric for assessing compression efficacy based on the Dynamic Time Warping (DTW) method. Comprehensive experiments encompass vessel trajectory datasets from three representative research areas: Tianjin Port, Chengshan Jiao Promontory, and Caofeidian Port. The experimental results demonstrate that 1) the newly developed ADPSC method outperforms in terms of compression, and 2) the designed GPU parallel computing framework can significantly shorten the compression time for extensive datasets. The GPU-accelerated compression methodology not only minimizes storage and transmission costs for data from both manned and unmanned vessels but also enhances data processing speed, supporting real-time decision-making. From a theoretical perspective, it provides the key to the puzzle of realizing the real-time anti-collision of manned and unmanned ships, particularly in complex waters. It hence makes significant contributions to maritime safety in the autonomous shipping era.

AMPActiPred: A three‐stage framework for predicting antibacterial peptides and activity levels with deep forest

AbstractThe emergence and spread of antibiotic‐resistant bacteria pose a significant public health threat, necessitating the exploration of alternative antibacterial strategies. Antibacterial peptide (ABP) is a kind of antimicrobial peptide (AMP) that has the potential ability to fight against bacteria infection, offering a promising avenue for developing novel therapeutic interventions. This study introduces AMPActiPred, a three‐stage computational framework designed to identify ABPs, characterize their activity against diverse bacterial species, and predict their activity levels. AMPActiPred employed multiple effective peptide descriptors to effectively capture the compositional features and physicochemical properties of peptides. AMPActiPred utilized deep forest architecture, a cascading architecture similar to deep neural networks, capable of effectively processing and exploring original features to enhance predictive performance. In the first stage, AMPActiPred focuses on ABP identification, achieving an Accuracy of 87.6% and an MCC of 0.742 on an elaborate dataset, demonstrating state‐of‐the‐art performance. In the second stage, AMPActiPred achieved an average GMean at 82.8% in identifying ABPs targeting 10 bacterial species, indicating AMPActiPred can achieve balanced predictions regarding the functional activity of ABP across this set of species. In the third stage, AMPActiPred demonstrates robust predictive capabilities for ABP activity levels with an average PCC of 0.722. Furthermore, AMPActiPred exhibits excellent interpretability, elucidating crucial features associated with antibacterial activity. AMPActiPred is the first computational framework capable of predicting targets and activity levels of ABPs. Finally, to facilitate the utilization of AMPActiPred, we have established a user‐friendly web interface deployed at https://awi.cuhk.edu.cn/∼AMPActiPred/.

PlantMine: A Machine-Learning Framework to Detect Core SNPs in Rice Genomics

As a fundamental global staple crop, rice plays a pivotal role in human nutrition and agricultural production systems. However, its complex genetic architecture and extensive trait variability pose challenges for breeders and researchers in optimizing yield and quality. Particularly to expedite breeding methods like genomic selection, isolating core SNPs related to target traits from genome-wide data reduces irrelevant mutation noise, enhancing computational precision and efficiency. Thus, exploring efficient computational approaches to mine core SNPs is of great importance. This study introduces PlantMine, an innovative computational framework that integrates feature selection and machine learning techniques to effectively identify core SNPs critical for the improvement of rice traits. Utilizing the dataset from the 3000 Rice Genomes Project, we applied different algorithms for analysis. The findings underscore the effectiveness of combining feature selection with machine learning in accurately identifying core SNPs, offering a promising avenue to expedite rice breeding efforts and improve crop productivity and resilience to stress.

Enhancing ECG signal classification through pre-trained stacked-CNN embeddings: a transfer learning approach

Rapid and accurate electrocardiogram (ECG) signal classification is crucial in high-stakes healthcare settings. However, existing computational models often struggle to balance high performance with computational efficiency. This study introduces an innovative computational framework that combines transfer learning with traditional machine learning to optimize ECG classification. We use a pre-trained Stacked Convolutional Neural Network (SCNN) to generate high-dimensional feature embeddings, which are then evaluated by an array of machine learning classifiers. Our models demonstrate exceptional performance, particularly when utilizing embeddings from SCNNs trained on diverse datasets. This underscores the importance of data diversity in improving classifier discrimination. Notably, Multilayer Perceptrons (MLPs) stand out for their ability to balance computational efficiency with strong performance, achieving test F1-scores of 0.94 and 1.00 in multi-class and binary tasks on the CinC2017 dataset, and 0.85 and 0.99 on the CPSC2018 dataset. Our approach consistently outperforms existing methods, setting new benchmarks in ECG classification. The synergy between deep learning-based feature extraction and traditional machine learning through transfer learning offers a robust, efficient, and adaptable strategy for ECG classification, addressing a critical research gap and laying the groundwork for future advancements in this crucial healthcare field.

Neutron scattering and neural-network quantum molecular dynamics investigation of the vibrations of ammonia along the solid-to-liquid transition

Vibrational spectroscopy allows us to understand complex physical and chemical interactions of molecular crystals and liquids such as ammonia, which has recently emerged as a strong hydrogen fuel candidate to support a sustainable society. We report inelastic neutron scattering measurement of vibrational properties of ammonia along the solid-to-liquid phase transition with high enough resolution for direct comparisons to ab-initio simulations. Theoretical analysis reveals the essential role of nuclear quantum effects (NQEs) for correctly describing the intermolecular spectrum as well as high energy intramolecular N-H stretching modes. This is achieved by training neural network models using ab-initio path-integral molecular dynamics (PIMD) simulations, thereby encompassing large spatiotemporal trajectories required to resolve low energy dynamics while retaining NQEs. Our results not only establish the role of NQEs in ammonia but also provide general computational frameworks to study complex molecular systems with NQEs.

Examination of the role of mutualism in immune evasion

Though the earliest stages of oncogenesis, post initiation, are not well understood, it is generally appreciated that a successful transition from a collection of dysregulated cells to an aggressive tumour requires complex ecological interactions between cancer cells and their environment. One key component of tumorigenesis is immune evasion. To investigate the interplay amongst the ecological behaviour of mutualism and immune evasion, we used a computational simulation framework. Sensitivity analyses of the growth of a virtual tumour implemented as a 2D-hexagonal lattice model suggests tumour survival depends on the interplay between growth rates, mutualism and immune evasion. In 60% of simulations, cancer clones with low growth rates, but exhibiting mutualism were able to evade the immune system and continue progressing suggesting that tumours with equivalent growth rates and no mutualism are more likely to be eliminated than tumours with mutualism. Tumours with faster growth rates showed a lower dependence upon mutualism for progression. Geostatistical analysis showed decreased spatial heterogeneity over time for polyclonal tumours with a high division rate. Overall, these results suggest that in slow growing tumours, mutualism is critical for early tumorigenesis.

Modeling and analysis for landing airbag–lunar soil interaction using a CPU–GPU-based FMBD-DEM computational framework

To investigate the landing performance of airbag cushion systems on the discrete lunar soil, this study proposes a novel modeling approach that couples multi-physics fields, including the flexible multibody systems (FMBS), the granular systems, and the gas effects. In this model, the GPU parallelism technique is utilized to address challenges arising from the large number of particles and complex contact detection involving large deformation and overall motions, and an efficient contact implementation between finite elements and particles is introduced. Additionally, contact modeling between airbags is realized using master–slave techniques. Then, a modified conventional serial staggered procedure is employed to couple flexible multibody dynamics (FMBD) and discrete element method (DEM) modules. Finally, a series of numerical examples is presented to validate the proposed dynamic model. The validated model successfully simulates the interaction between landing airbags and lunar soil. Subsequent parametric analysis contributes to the design optimization of the landing system. Moreover, discussions regarding computational efficiency illustrate that the proposed CPU–GPU-based FMBD-DEM is advantageous in engineering scenarios involving flexible components and large-scale granular systems.

Multi-objective reinforcement learning based on nonlinear scalarization and long-short-term optimization

Purpose Many practical control problems require achieving multiple objectives, and these objectives often conflict with each other. The existing multi-objective evolutionary reinforcement learning algorithms cannot achieve good search results when solving such problems. It is necessary to design a new multi-objective evolutionary reinforcement learning algorithm with a stronger searchability. Design/methodology/approach The multi-objective reinforcement learning algorithm proposed in this paper is based on the evolutionary computation framework. In each generation, this study uses the long-short-term selection method to select parent policies. The long-term selection is based on the improvement of policy along the predefined optimization direction in the previous generation. The short-term selection uses a prediction model to predict the optimization direction that may have the greatest improvement on overall population performance. In the evolutionary stage, the penalty-based nonlinear scalarization method is used to scalarize the multi-dimensional advantage functions, and the nonlinear multi-objective policy gradient is designed to optimize the parent policies along the predefined directions. Findings The penalty-based nonlinear scalarization method can force policies to improve along the predefined optimization directions. The long-short-term optimization method can alleviate the exploration-exploitation problem, enabling the algorithm to explore unknown regions while ensuring that potential policies are fully optimized. The combination of these designs can effectively improve the performance of the final population. Originality/value A multi-objective evolutionary reinforcement learning algorithm with stronger searchability has been proposed. This algorithm can find a Pareto policy set with better convergence, diversity and density.

Integrated Communication, Sensing, and Computation Framework for 6G Networks

In the sixth generation (6G) era, intelligent machine network (IMN) applications, such as intelligent transportation, require collaborative machines with communication, sensing, and computation (CSC) capabilities. This article proposes an integrated communication, sensing, and computation (ICSAC) framework for 6G to achieve the reciprocity among CSC functions to enhance the reliability and latency of communication, accuracy and timeliness of sensing information acquisition, and privacy and security of computing to realize the IMN applications. Specifically, the sensing and communication functions can merge into unified platforms using the same transmit signals, and the acquired real-time sensing information can be exploited as prior information for intelligent algorithms to enhance the performance of communication networks. This is called the computing-empowered integrated sensing and communications (ISAC) reciprocity. Such reciprocity can further improve the performance of distributed computation with the assistance of networked sensing capability, which is named the sensing-empowered integrated communications and computation (ICAC) reciprocity. The above ISAC and ICAC reciprocities can enhance each other iteratively and finally lead to the ICSAC reciprocity. To achieve these reciprocities, we explore the potential enabling technologies for the ICSAC framework. Finally, we present the evaluation results of crucial enabling technologies to show the feasibility of the ICSAC framework.

Advanced Computational Framework to Analyze the Stability of Non-Newtonian Fluid Flow through a Wedge with Non-Linear Thermal Radiation and Chemical Reactions

The main idea of this investigation is to introduce an integrated intelligence approach that investigates the chemically reacting flow of non-Newtonian fluid with a backpropagation neural network (LMS-BPNN). The AI-based LMS-BPNN approach is utilized to obtain the optimal solution of an MHD flow of Eyring–Powell over a porous shrinking wedge with a heat source and nonlinear thermal radiation (Rd). The partial differential equations (PDEs) that define flow problems are transformed into a system of ordinary differential equations (ODEs) through efficient similarity variables. The reference solution is obtained with the bvp4c function by changing parameters as displayed in Scenarios 1–7. The label data are divided into three portions, i.e., 80% for training, 10% for testing, and 10% for validation. The label data are used to obtain the approximate solution using the activation function in LMS-BPNN within the MATLAB built-in command ‘nftool’. The consistency and uniformity of LMS-BPNN are supported by fitness curves based on the MSE, correlation index (R), regression analysis, and function fit. The best validation performance of LMS-BPNN is obtained at 462, 369, 642, 542, 215, 209, and 286 epochs with MSE values of 8.67 × 10−10, 1.64 × 10−9, 1.03 × 10−9, 302 9.35 × 10−10, 8.56 × 10−10, 1.08 × 10−9, and 6.97 × 10−10, respectively. It is noted that f′(η), θ(η), and ϕ(η) satisfy the boundary conditions asymptotically for Scenarios 1–7 with LMS-BPNN. The dual solutions for flow performance outcomes (Cfx, Nux, and Shx) are investigated with LMS-BPNN. It is concluded that when the magnetohydrodynamics increase (M=0.01, 0.05, 0.1), then the solution bifurcates at different critical values, i.e., λc=−1.06329,−1.097,−1.17694. The stability analysis is conducted using an LMS-BPNN approximation, involving the computation of eigenvalues for the flow problem. The deduction drawn is that the upper (first) branch solution remains stable, while the lower branch solution causes a disturbance in the flow and leads to instability. It is observed that the boundary layer thickness for the lower branch (second) solution is greater than the first solution. A comparison of numerical results and predicted solutions with LMS-BPNN is provided and they are found to be in good agreement.

Preparation, physicochemical analyses, and comparative evaluation studies of deep eutectic solvents (DES) from non-conventional precursor materials applied as catalytic curing agents for epoxy resin

Curing agents for epoxy resins play a significant role in initiating crosslinking reactions resulting in the hardened and durable material with enhanced properties as per end-user applications. The industrial world still lacks efficient, environmentally friendly, and cheap curing agents for epoxy resins. Here, we report the development of deep eutectic solvents (DESs) based on N, N’-Bis(2-aminoethyl)ethane-1,2-diamine (commonly known as triethylenetetramine, TETA) as hydrogen bond donor (HBD), and salicylic acid (SA) and choline chloride (CC) as hydrogen bond acceptor (HBA). The formulated DESs are suggested to be relatively safe, affordable, and eco-friendly and the active groups in the DESs are capable of initiating crosslinking reactions. The physical stability of the formulated deep eutectic solvents was examined by polarizing optical microscopic (POM) analysis, which confirmed the absence of any crystals or residues. The purity was ensured by 13C nmr spectroscopy. Additionally, supportive evidence for the hydrogen bond formation between the constituents was gained from the outcomes of FTIR, 1H nmr, and mass spectrometry. Deep depression in melting points was confirmed from DSC studies, and TGA suggested better thermal stability for DESs than its constituents. Density functional theory (DFT) at B3LYP6-31G* of Gaussian computational framework was applied to energetically optimize the isolated structure and determine the bond lengths in angstroms. The structural characteristics and nonbonding interactions of DESs were investigated by the same. In addition, the density and rheological characteristics were assessed for the ideal utilization of the formulated DESs as epoxy resin curing agents. The detailed comparative studies demonstrated the advantages of deep eutectic solvents over conventional curing agents. The efficacy of the formulations as a curing agent was primarily confirmed from FTIR analysis and the curing behavior was investigated by DSC studies.

Structural Reconstruction of E. coli Ubi Metabolon Using an AlphaFold2-Based Computational Framework.

Ubiquinone (UQ) is a redox polyisoprenoid lipid found in the membranes of bacteria and eukaryotes that has important roles, notably one in respiratory metabolism, which sustains cellular bioenergetics. In Escherichia coli, several steps of the UQ biosynthesis take place in the cytosol. To perform these reactions, a supramolecular assembly called Ubi metabolon is involved. This latter is composed of seven proteins (UbiE, UbiG, UbiF, UbiH, UbiI, UbiJ, and UbiK), and its structural organization is unknown as well as its protein stoichiometry. In this study, a computational framework has been designed to predict the structure of this macromolecular assembly. In several successive steps, we explored the possible protein interactions as well as the protein stoichiometry, to finally obtain a structural organization of the complex. The use of AlphaFold2-based methods combined with evolutionary information enabled us to predict several models whose quality and confidence were further analyzed using different metrics and scores. Our work led to the identification of a "core assembly" that will guide functional and structural characterization of the Ubi metabolon.

Smart traffic management of vehicles using faster R-CNN based deep learning method

With constant growth of civilization and modernization of cities all across the world since past few centuries smart traffic management of vehicles is one of the most sorted after problem by research community. Smart traffic management basically involves segmentation of vehicles, estimation of traffic density and tracking of vehicles. The vehicle segmentation from videos helps realization of niche applications such as monitoring of speed and estimation of traffic. When occlusions, background with clutters and traffic with density variations, this problem becomes more intractable in nature. Keeping this motivation in this research work, we investigate Faster R-CNN based deep learning method towards segmentation of vehicles. This problem is addressed in four steps viz minimization with adaptive background model, Faster R-CNN based subnet operation, Faster R-CNN initial refinement and result optimization with extended topological active nets. The computational framework uses adaptive background modeling. It also addresses shadow and illumination issues. Higher segmentation accuracy is achieved through topological active net deformable models. The topological and extended topological active nets help to achieve stated deformations. Mesh deformation is achieved with minimization of energy. The segmentation accuracy is improved with modified version of extended topological active net. The experimental results demonstrate superiority of this framework with respect to other methods.

Quaternary blended eco high performance concrete utilizing high volumes of siliceous additives

This research examines the optimal feasibility of incorporating siliceous additives as an adjunct material for cement in the formulation of environmentally sustainable high-performance concrete (Eco-HPC). Siliceous materials, including Nano silica (NS), Rice Husk Ash (RHA), and Fly Ash (FA), were fine-tuned within the cementitious system in order to achieve the amplified performance of Eco-HPC by reducing the required cement content from 370 kg/m3 to 273 kg/m3. This is achieved by employing the Modified Andreasen and Andreasen (MA&A) particle packing model, with ‘q’ (distribution factor) as ‘0.3’. The Levenberg Marquardt algorithm (curve fitting) was utilized to validate the packing density of each concrete blends. Initially, the dry NS agglomerates are synthesized into stabilized aqueous dispersion through High shear mixing (10 min/1750 rpm) followed by probe sonication (15 min/(1/3) s/s) with anionic surfactant dosage of 0.1 g/10 g. Incorporating secondary siliceous binders into the cement matrix led to an enhancement in the packing density, increasing from 0.94 (nullary) to 0.96 (quaternary) which in turn alters the reactivity and cohesiveness of the matrix. Compressive strength values for Binary (NS2), Ternary (RN15, FN20), and quaternary mix (FRN25) were observed as 57.42 MPa, 56.83 MPa, 55.34 MPa, and 52.58 MPa, respectively, for primary binder contents of 362 kg/m3, 314 kg/m3, 296 kg/m3, and 273 kg/m3 without compromising durability parameters. An assessment on reduced carbon dioxide emissions indicated that increasing the cement replacement percentages in Eco-HPC by up to 30% for binder contents of 358 kg/m3, 296 kg/m3, 259 kg/m3, and 273 kg/m3 resulted in a reduction of CO2 emissions by 2.98%, 18%, 27%, and 23.33%, respectively. Among the twelve formulated mixes, FRN25, FRN20, FRN15 and FN30 are identified as Eco-HPC mix through a computational framework which integrates the packing density, carbon emission, strength and diffusion coefficient.

MGSurvE: A framework to optimize trap placement for genetic surveillance of mosquito populations.

Genetic surveillance of mosquito populations is becoming increasingly relevant as genetics-based mosquito control strategies advance from laboratory to field testing. Especially applicable are mosquito gene drive projects, the potential scale of which leads monitoring to be a significant cost driver. For these projects, monitoring will be required to detect unintended spread of gene drive mosquitoes beyond field sites, and the emergence of alternative alleles, such as drive-resistant alleles or non-functional effector genes, within intervention sites. This entails the need to distribute mosquito traps efficiently such that an allele of interest is detected as quickly as possible-ideally when remediation is still viable. Additionally, insecticide-based tools such as bednets are compromised by insecticide-resistance alleles for which there is also a need to detect as quickly as possible. To this end, we present MGSurvE (Mosquito Gene SurveillancE): a computational framework that optimizes trap placement for genetic surveillance of mosquito populations such that the time to detection of an allele of interest is minimized. A key strength of MGSurvE is that it allows important biological features of mosquitoes and the landscapes they inhabit to be accounted for, namely: i) resources required by mosquitoes (e.g., food sources and aquatic breeding sites) can be explicitly distributed through a landscape, ii) movement of mosquitoes may depend on their sex, the current state of their gonotrophic cycle (if female) and resource attractiveness, and iii) traps may differ in their attractiveness profile. Example MGSurvE analyses are presented to demonstrate optimal trap placement for: i) an Aedes aegypti population in a suburban landscape in Queensland, Australia, and ii) an Anopheles gambiae population on the island of São Tomé, São Tomé and Príncipe. Further documentation and use examples are provided in project's documentation. MGSurvE is intended as a resource for both field and computational researchers interested in mosquito gene surveillance.

Binary black holes and quantum off-shell recursion

The quantum off-shell recursion provides an efficient and universal computational tool for loop-level scattering amplitudes. In this work, we present a new comprehensive computational framework based on the quantum off-shell recursion for binary black hole systems. Using the quantum perturbiner method, we derive the recursions and solve them explicitly up to two-loop order. We introduce an indicator that enables straightforward distinguishing diagrams and deriving integral families in the context of off-shell recursions. By employing the soft expansion technique, we remove irrelevant terms from the loop integrands and express them in terms of master integrals. We classify the one-loop and the two-loop classical diagrams, and their loop integrands are represented by linear combinations of the master integrals. Finally, we explicitly calculate the classical scalar 2 → 2 amplitudes in the potential region up to the 3PM order and reproduce the known results.