Multiple Combinations Research Articles

Gaze-Contingent Layered Optical See-Through Displays with a Confidence-Driven View Volume.

The vergence-accommodation conflict (VAC) presents a major perceptual challenge for head-mounted displays with a fixed image plane. Varifocal and layered display designs can mitigate the VAC. However, the image quality of varifocal displays is affected by imprecise eye tracking, whereas layered displays suffer from reduced image contrast as the distance between layers increases. Combined designs support a larger workspace and tolerate some eye-tracking error. However, any layered design with a fixed layer spacing restricts the amount of error compensation and limits the in-focus contrast. We extend previous hybrid designs by introducing confidence-driven volume control, which adjusts the size of the view volume at runtime. We use the eye tracker's confidence to control the spacing of display layers and optimize the trade-off between the display's view volume and the amount of eye tracking error the display can compensate. In the case of high-quality focus point estimation, our approach provides high in-focus contrast, whereas low-quality eye tracking increases the view volume to tolerate the error. We describe our design, present its implementation as an optical-see head-mounted display using a multiplicative layer combination, and present an evaluation comparing our design with previous approaches.

Was Oleksandr Dovzhenko a Judophobe (According to the materials of the Soviet special services)

The submitted article is aimed at the revealing and representing previously unknown archival documents from the funds of the Soviet special services, concerning the non-conformist attitudes of Oleksandr Dovzhenko, his resentment towards the Jews in particular. The author’s task is directed at stimulating further studies and discussions in order to find means of depicting a realistic image of Dovzhenko. Methodological principles are based on the fundamentals of historicism and critical analysis of sources with a complex combination of the entire multiplicity of newly discovered historical facts and events in accordance with specific historical circumstances. The scientific novelty consists in the comprehension of the documents of the communist special services as a source component of unprejudiced research strategies of modern Dovzhenko Studies. Conclusions. Newly discovered archival documents prove that Dovzhenko’s critical attitude towards Jews is the result of complex and situationally determined attitudes and life experiences. This is why these cannot be simplistic and straightforward narratives. Communist special services have monitored closely Dovzhenko’s anti-Soviet sentiments, suspected him of «counter-revolutionary activities», and also recorded his statements directed at Jewish cinematographers. However, they haven’t given unequivocal assessments to such statements. Dovzhenko has collaborated and made friends creatively with many Jewish artists, and his critical attitudes have never turned into denunciation and other practical forms of revenge.

Abstract 2053: Spatiotemporal tumor immune modulation by localized delivery of cancer therapeutics using an implantable microdevice

Abstract Mechanism of action studies are imperative to translating oncology therapeutics into the clinic informing testable hypotheses and potential combination partners. This often involves large and time-consuming mouse studies to test a small number of compounds in an attempt to show statistically significant benefit. Likewise, testing multiple compounds and combination partners in these systems is limited by time, cost, and a desire to reduce the number of animals used in biological research. To overcome these limitations, we utilized an implantable microdevice (IMD) developed by KiburMed to measure intra-tumoral drug responses and to differentiate the mechanism of action of multiple cytokine agents in parallel in the MC38 mouse model. After sizable tumor growth in mice and implantation of the IMD device with 9 different therapeutic candidates, MC38 mice were taken down at different time points (24 hour, 4 days,7 days) to assess response. Local tumor response in the study was measured by cyclical immunofluorescence for deep cellular response phenotyping with a panel of 32 markers for comprehensive immune cell phenotyping. Among the candidate murine surrogate compounds in the device, SAR445877 (anti-PD-1-IL-15 mutein) was tested as a monotherapy, as a combination partner with anti-PDL1 and compared against anti-PD1 monotherapy. With further validation of this novel platform, results from this work are aligned with previous studies (1) showing a murine surrogate for SAR445877 in combination with anti-PDL1 increases immune cell populations more than either single agent alone. These analyses show CD3, CD4, and CD8 were significantly increased in SAR’877 murine surrogate + anti-PDL1 compared to SAR’877 alone or anti-PD1 alone. In addition, T cells could infiltrate at a distance away from treatment release region, well into tumor core. Other significant changes in immune cell markers include increases in CD45+, CD11b, CD11c, CD20 and CD27. In conclusion, we have shown that multiple oncology therapeutics can be screened simultaneously in a small number of mice using an implantable microdevice and data obtained with this technology is consistent with that obtained from traditional mouse tumor experiments. (1) Preclinical characterization of SAR445877, an anti-PD-1 antibody-IL-15 mutein fusion protein with robust anti-tumor efficacy as monotherapy and in combination with PD-L1 blockade. Marie Bernardo; Yu-an Zhang; Dan Lu; Stella Martomo; Fatima Menas; Chen Zhu; Raymond Perez; Jeegar Patel; Donald Shaffer; Xiangming Li, Cancer Res (2023) 83 (7_Supplement): 2972. *This work was supported and funded by Sanofi. Citation Format: Xiangming Li, Julien Tessier, Joon Sang Lee, Seth Garren, Virna Cortez-Retamozo, Meenu Sharma, Angela Jankowski, Erik Zarazinski, Ann Fiore, Oliver Jonas, Colin Brenan, Donald Shaffer, Angela Hadjipanayis. Spatiotemporal tumor immune modulation by localized delivery of cancer therapeutics using an implantable microdevice [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2053.

Improvement of differential diagnosis of lung cancer by use of multiple protein tumor marker combinations.

Differential diagnosis of non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) in hospitalized patients is crucial for appropriate treatment choice. To investigate the relevance of serum tumor markers (STMs) and their combinations for the differentiation of NSCLC and SCLC subtypes. Between 2000 and 2003, 10 established STMs were assessed retrospectively in 311 patients with NSCLC, 128 with SCLC prior systemic first-line therapy and 51 controls with benign lung diseases (BLD), by automatized electrochemiluminescence immunoassay technology. Receiver operating characteristic (ROC) curves and logistic regression analyses were used to evaluate the diagnostic efficacy of both individual and multiple STMs with corresponding sensitivities at 90% specificity. Standards for Reporting of Diagnostic Accuracy (STARD guidelines) were followed. CYFRA 21-1 (cytokeratin-19 fragment), CEA (carcinoembryonic antigen) and NSE (neuron specific enolase) were significantly higher in all lung cancers vs BLD, reaching AUCs of 0.81 (95% CI 0.76-0.87), 0.78 (0.73-0.84), and 0.88 (0.84-0.93), respectively. By the three marker combination, the discrimination between benign and all malignant cases was improved resulting in an AUC of 0.93 (95% CI 0.90-0.96). In NSCLC vs. BLD, CYFRA 21-1, CEA and NSE were best discriminative STMs, with AUCs of 0.86 (95% CI 0.81-0.91), 0.80 (0.74-0.85), and 0.85 (0.79-0.91). The three marker combination also improved the AUC: 0.92; 95% CI 0.89-0.96). In SCLC vs. BLD, ProGRP (pro-gastrin-releasing peptide) and NSE were best discriminative STMs, with AUCs of 0.89 (95% CI 0.84-0.94) and 0.96 (0.93-0.98), respectively, and slightly improved AUC of 0.97 (95% CI 0.95-0.99) when in combination. Finally, discrimination between SCLC and NSCLC was possible by ProGRP (AUC 0.86; 95% CI 0.81-0.91), NSE (AUC 0.83; 0.78-0.88) and CYFRA 21-1 (AUC 0.69; 0.64-0.75) and by the combination of the 3 STMs (AUC 0.93; 0.91-0.96), with a sensitivity of 88% at 90% specificity. The results confirm the power of STM combinations for the differential diagnosis of lung cancer from benign lesions and between histological lung cancer subtypes.

The utility of layering systemic therapies in connective tissue diseases

AbstractBackgroundCutaneous manifestations in connective tissue diseases (CTDs) are often chronic and difficult to control.ObjectivesThis study aims to review the use of combination systemic medications in CTDs and assess disease response.MethodsAn IRB‐approved, retrospective chart review was conducted at a single large academic institution in New Orleans, Louisiana. Inclusion criteria were adult patients with CTDs on two or more systemic therapies.ResultsOut of 101 patients, 37.62% were taking combination systemic agents. Of these patients, 44% were diagnosed with lupus, 18% with dermatomyositis (DM), 8% with systemic scleroderma (SSc), 10% with morphea/limited scleroderma (M), 12% with mixed connective tissue disease (MCTD), and 8% with rheumatoid arthritis (RA). The most common regimen for lupus, MCTD, and SSc patients was hydroxychloroquine (HCQ) + prednisone (PRED) + mycophenolate mofetil (MMF). DM patients were most commonly taking combinations of the above regimen with intravenous immunoglobulin (IVIG). Morphea and RA patients were on multiple combinations of steroid and nonsteroidal agents including MMF, IVIG, azathioprine, methotrexate, HCQ, sulfasalazine, upadacitinib, apremilast, and tofacitinib.ConclusionsThis study aims to assess the efficacy of combination systemic medications and disease response in CTDs. Lupus was the most common CTD in our study, with patients taking 2–4 combination medications. HCQ, MMF, and PRED were the most common medications used within a combination regimen with minimal adverse effects. Though our study has several limitations including its retrospective nature and small sample size, it shows that CTDs can be resistant to monotherapy and that combination therapy might be required. More research in layering systemic agents is needed since this topic is currently limited to minimal case series and clinical trials.

An Analytical Solution for the Bending of Anisotropic Rectangular Thin Plates with Elastic Rotation Supports

The mechanical analysis of thin-plate structures is a major challenge in the field of structural engineering, especially when they have nonclassical boundary conditions, such as those encountered in cement concrete road slabs connected by transfer bars. Conventional analytical solutions are usually limited to classical boundary conditions—clamped support, simple support, and free edges—and cannot adequately describe many engineering scenarios. In this study, an analytical solution to the bending problem of an anisotropic thin plate subjected to a pair of edges with free opposing elastic rotational constraints is found using a two-dimensional augmented Fourier series solution method. In the derivation process, the thin-plate problem can be transformed into a problem of solving a system of linear algebraic equations by applying Stoke’s transform method, which greatly reduces the mathematical difficulty of solving the problem. Complex boundary conditions can be optimally handled without the need for large computational resources. The paper addresses the exact analytical solutions for bending problems with multiple combinations of boundary conditions, such as contralateral free–contralateral simple support (SFSF), contralateral free–contralateral solid support–simple support (CFSF), and contralateral free–contralateral clamped support (CFCF). These solutions are realized by employing the Stoke transformation and adjusting the spring parameters in the analyzed solutions. The results of this method are also compared with the finite element method and analytical solutions from the literature, and good agreement is obtained, demonstrating the effectiveness of the method. The significance of the study findings lies in the simplification of complex nonclassical boundary condition problems using a simple and reliable analytical method applicable to a wide range of engineering thin-plate structures.

Component design procedure for LCC‐S wireless power transfer systems based on genetic algorithms and sensitivity analysis

AbstractThis paper introduces a novel approach for designing a Wireless Power Transfer (WPT) system with LCC‐S compensation. Since WPT systems operate under resonant conditions, even small deviations of the components from the nominal values can result in a significant reduction of the power transferred to the load, and in an increment of the circulating currents, reducing the system efficiency. The design techniques available today in the literature provide a unique combination of passive components capable of transferring a certain power to the load. This is a limitation, because, in practice, there are several combinations that allow reaching the desired output power, but they are usually neglected because they are extremely difficult to compute analytically. For this reason, in this paper, the authors present an innovative design procedure that enables, through a Genetic Algorithm, the identification of multiple feasible combinations of the LCC‐S components capable of achieving the desired output power. Moreover, the authors evaluate the effects of the component tolerances on the output power to determine which combinations are more robust to component variations. This task is performed by calculating the probability that a particular combination yields the desired output power, once the tolerances have been considered, following a Monte Carlo approach. This information is utilized to decide whether it is possible to reduce the component quality (worsening the tolerance) without affecting the performance. Finally, an optimal solution granting both low‐cost and robustness against component tolerances can be individuated. The proposed design procedure is applied to a case study and validated experimentally.

Relational Graph Convolution Network with Multi Features for AntiCOVID-19 Drugs Discovery using 3CLpro Potential Target

Background: The potential of graph neural networks (GNNs) to revolutionize the analysis of non-Euclidean data has gained attention recently, making them attractive models for deep machine learning. However, insufficient compound or moleculargraphs and feature representations might significantly impair and jeopardize their full potential. Despite the devastating impacts of ongoing COVID-19 across the globe, for which there is no drug with proven efficacy that has been shown tobe effective. As various stages of drug discovery and repositioning require the accurate prediction of drugtarget interactions(DTI), here, we propose a relational graph convolution network using multi-features based on the developed drug chemicalcompound-coronavirus target graph representation and combination of features. During the implementation of the model, we further introduced the use of not only the feature module to understand the topological structure of drugs but also the structure of the proven drug target (i.e., 3CLpro) for SARS-Cov-2 that shares a genome sequence similar to that of other members of the beta-coronavirus group such as SARS-Cov, MERS-CoV, bat coronavirus. Our feature comprises topologicalinformation in molecular SMILES and local chemical context in the SMILES sequence for the drug chemical compound and drug target. Our proposed method prevailed with high and compelling performance accuracy of 97.30% which could beprioritized as the potential and promising prediction route for the development of novel oral antiviral medicine for COVID-19 drugs. Objective: Forecasting DTI stands as a pivotal aspect of drug discovery. The focus on computational methods in DTI prediction has intensified due to the considerable expense and time investment associated with conducting extensive in vitro and in vivo experiments. Machine learning techniques, particularly deep learning, have found broad applications in DTI prediction. We are convinced that this study could be prioritized and utilized as the promising predictive route for the development of novel oral antiviral treatments for COVID-19 and other variants of coronaviruses. Methods: This study addressed the problem of COVID-19 drugs using proposed RGCN with multifeatures as an attractive and potential route. This study focused mainly on the prediction of novel antiviral drugs against coronaviruses using graph-based methodology, namely RGCN. This research further utilized the features of both drugs and common potential drug targets found in betacoronaviruses group to deepen understanding of their underlying relation. Results: Our suggested approach prevailed with a high and convincing performance accuracy of 97.30%, which may be utilizedas a top priority to support and advance this field in the prediction and development of novel antiviral treatments against coronaviruses and their variants. Conclusion: We recursively performed experiments using the proposed method on our constructed DCCCvT graph dataset from our collected dataset with various single and multiple combinations of features and found that our model had achieved comparable best-averaged accuracy performance on T7 features followed by a combination of T7, R6, and L8. The proposed model implemented in this investigation turns out to outperform the previous related works.

Abstract A040: Improving specificity for ovarian cancer screening using a novel extracellular vesicle-based blood test

Abstract Introduction: The low incidence of ovarian cancer (OC) in average risk individuals dictates any OC screening method needs to be both highly sensitive and highly specific. Even using the multimodal OC screening strategy combining longitudinal serum CA125 and transvaginal ultrasound, 16% of OCs were not detected and false positives due to benign ovarian tumors, which are 5-10 times more prevalent than OC, remained high. Due to lack of mortality benefit, population screening is still not recommended. We hypothesized that detecting multiple colocalized protein or glycosylation epitopes (PGEs) on single tumor-derived extracellular vesicles (EVs) would increase specificity and sensitivity for detection of OC. Methods: The Mercy Halo™ OC test employs immunoaffinity capture and proximity-ligation qPCR to detect combinations of up to 3 PGE biomarkers to maximize specificity and measures multiple combinations to maximize sensitivity. We selected 3 PGE combinations to form a panel test to distinguish high-grade serous ovarian cancer (HGSC) from benign ovarian tumors and normal patients and compared performance of our test to CA125. We used a case-control training set from university-associated Canadian biobanks comprised of EDTA plasma from 124 healthy controls, 89 HGSC cases (17 Stage I, 35 Stage II, 37 Stage III)(10 BRCA+), and 192 benign adnexal masses to lock down the test, the data interpretation algorithm, and the cut-off between cancer and non-cancer. Performance was verified in an independent blinded diagnostic case-control set of 397 serum samples from 138 healthy controls, 111 benign adnexal masses, 20 borderlines, 66 HGSC and 62 non-HGSC from the UK Ovarian Cancer Population Study. For comparison, a CA125 ELISA was run for all samples in both studies. Results: Using the locked assay, algorithm, and cut-off established in the training cohort, our test showed a specificity in healthy controls of 92.8% (128/138; 95% CI: 0.87-0.96), a sensitivity in HGSC of 97.0% (64/66; 95% CI: 0.90-0.99), and an AUC of 0.92 (95% CI 0.89-0.95) in the verification cohort. We correctly identified 11/13 Stage I and all 54 Stage II/III/IV HGSC cases. Our test also detected 76.8% (63/82) of the borderline and non-HGSC histotypes. CA125 detected 92.4% (61/66) of the HGSC cases and 84.1% (69/82) of the borderlines and non-HGSC. The greatest improvement in test performance relative to CA125 was seen with benign adnexal masses. In the verification cohort 35.1% (39/111) of the benign samples were CA125 positive compared to 21.6% (24/111) with our test. This reduction in false positives was seen across 10 benign histotypes tested. Conclusions: These results demonstrate that our test using colocalized PGEs on EVs can detect OC, especially HGSC, in both plasma and serum with high sensitivity and specificity. The combinations of 5 biomarkers rather than CA125 alone also decreased benign false positives. The test performance suggests that our test may be useful in average and/or high-risk ovarian cancer screening. Future studies will explore this in asymptomatic populations. Citation Format: Emily S. Winn-Deen, Sanchari Banerjee, Daniel Gusenleitner, Laura T. Bortolin, Jonian Grosha, Kelly M. Biette, Karen Copeland, Christopher R. Sedlak, Bilal Hamzeh, Anthony D. Couvillon, Delaney M. Byrne, Peter A. Duff, Lauren T. Cuoco, MacKenzie S. King, Aleksandra Gentry-Maharaj, Sophia Apostolidou, Dawn R. Mattoon, Christine D. Berg, David F. Ransohoff, Steven J. Skates, Usha Menon. Improving specificity for ovarian cancer screening using a novel extracellular vesicle-based blood test [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr A040.

Impact of rift history on the structural style of intracontinental rift-inversion orogens

Abstract Although many collisional orogens form after subduction of oceanic lithosphere between two continents, some orogens result from strain localization within a continent via inversion of structures inherited from continental rifting. Intracontinental rift-inversion orogens exhibit a range of structural styles, but the underlying causes of such variability have not been extensively explored. We use numerical models of intracontinental rift inversion to investigate the impact of parameters including rift structure, rift duration, post-rift cooling, and convergence velocity on orogen structure. Our models reproduce the natural variability of rift-inversion orogens and can be categorized using three endmember styles: asymmetric underthrusting (AU), distributed thickening (DT), and localized polarity flip (PF). Inversion of narrow rifts tends to produce orogens with more localized deformation (styles AU and PF) than those resulting from wide rifts. However, multiple combinations of the parameters we investigated can produce the same structural style. Thus, our models indicate no unique relationship between orogenic structure and the conditions prior to and during inversion. Because the style of rift-inversion orogenesis is highly contingent upon the rift history prior to inversion, knowing the geologic history that preceded rift inversion is essential for translating orogenic structure into the processes that produced that structure.

Formal Analysis of DTLS-SRTP Combined Protocol Based on Logic of Events

Security analysis of composite protocols is a critical issue in the field of network security. In complex network environments, the traditional approach of analyzing a single protocol becomes inadequate when dealing with scenarios involving multiple interactions and combinations of protocols. To address this challenge, this paper extends the Logic of Events Theory (LoET) and proposes a method for proving the security of composite protocols. Building upon the Logic of Events Theory, we introduce sequential composition rules, ordering rules, and relevant axioms. We incorporate the concept of invariants and formally abstract the DTLS-SRTP protocol, thereby verifying the mutual authentication and confidentiality of the two sub-protocols. In conclusion, our study demonstrates that the extended Logic of Events Theory offers an effective means of verifying the security of composite protocols.

Cumulative risk assessment of dietary exposure of the adult population in Serbia to pesticides that have chronic effects on the thyroid gland through fresh fruits and vegetables

In contrast to the traditional approach to risk assessment, which focuses on a single chemical, cumulative exposure and risk assessment considers the consequences of exposure to multiple chemical combinations. A cumulative risk assessment of dietary exposure of adult females and adult males to pesticides with chronic effects on the thyroid was conducted by estimation of the Total Margin of Exposure (MOET). Exposure to each active substance was estimated using a second-order Monte Carlo simulation. Input values for the simulation were based on over 2300 conventionally produced fruit and vegetable samples analysed from 2021 to 2023 and consumption data collected using the Food Frequency Questionnaire (FFQ) and 24h recall method. MOET values, for both populations assessed, did not exceed thresholds for regulatory consideration established by risk managers. Considering that MOETs values from consumption of fresh fruits and vegetables were relatively close to the threshold value, total exposure to the pesticides could likely reach risk-associated MOET levels, through the consumption of other food products that may be contaminated.

Exploring the topic structure and abuse trends of new psychoactive Substance since the 21st century from a bibliometric perspective

Nowadays, NPS abuse are continuing to expand in terms of harm and scope, due to its cheap and easy to manufacture anywhere in the world. This study reviewed articles related to seven heavily abused NPS to analyze the structure and trends of NPS abuse. A total of 2476 articles were retrieved based on the search strategy for bibliometric analysis. A significant trend of research in recent years was the increasing number of research on synthetic opioids and designer benzodiazepines, but synthetic cannabinoid and synthetic cathinone still dominate, which were mainly concerned with the development of metabolic models and determining methods as well as their abuse characteristics and reasons. However, with the introduction of class-wide ban on synthetic cannabinoid in China and a series of enhancements in other countries, the abuse of it might decrease to some extent, but more than 20 kinds of synthetic cannabinoid beyond the scope of ban in China still reminded researchers of their potential threats. As for synthetic cathinone, an important phenomenon was some of the drugs first identified during certain period might be more widely distributed in the future. Besides, several problems such as the regulation and prevention mode of emerging NPS, development of testing technologies as well as the interpretation and identification of multiple NPS combinations were also worth paying attention to. This study could help entrants better understand the structure of NPS abuse and provided direction for future research in forensic toxicology.

Enhancing amphibian biomonitoring through eDNA metabarcoding.

Surveying biodiversity has taken a quantum leap with environmental DNA (eDNA) metabarcoding, an immensely powerful approach lauded for its efficiency, sensitivity, and non-invasiveness. This approach emerges as a game-changer for the elusive realm of endangered and rare species-think nocturnal, environmentally elusive amphibians. Here, we have established a framework for constructing a reliable metabarcoding pipeline for amphibians, covering primer design, performance evaluation, laboratory validation, and field validation processes. The Am250 primer, located on the mitochondrial 16S gene, was optimal for the eDNA monitoring of amphibians, which demonstrated higher taxonomic resolution, smaller species amplification bias, and more extraordinary detection ability compared to the other primers tested. Am250 primer exhibit an 83.8% species amplification rate and 75.4% accurate species identification rate for Chinese amphibians in the in silico PCR and successfully amplified all tested species of the standard samples in the invitro assay. Furthermore, the field-based mesocosm experiment showed that DNA can still be detected by metabarcoding even days to weeks after organisms have been removed from the mesocosm. Moreover, field mesocosm findings indicate that eDNA metabarcoding primers exhibit different read abundances, which can affect the relative biomass of species. Thus, appropriate primers should be screened and evaluated by three experimental approaches: in silico PCR simulation, target DNA amplification, and mesocosm eDNA validation. The selection of a single primer set or multiple primers' combination should be based on the monitoring groups to improve the species detection rate and the credibility of results.

Location-Aware Deep Interaction Forest for Web Service QoS Prediction

With the rapid development of the web service market, the number of web services shows explosive growth. QoS is an important factor in the recommendation scene; how to accurately recommend a high-quality service for users among the massive number of web services has become a tough problem. Previous methods usually acquired feature interaction information by network structures like DNN to improve the QoS prediction accuracy, but this generates unnecessary computations. Aiming at addressing the above problem, inspired by the multigrained scanning mechanism in a deep forest, we propose a location-aware deep interaction forest approach for web service QoS prediction (LDIF). This approach offers the following innovations: The model fuses the location similarity of users and services as a latent feature representation of them. In addition, we designed a scanning interaction structure (SIS), which obtains multiple local feature combinations from the interaction between user and service features, uses interactive computing to extract feature interaction information, and concatenates the feature interaction information with original features, which aims to enhance the dimension of the features. Equipped with these, we compose a layer-by-layer cascade by using SIS to fuse low- and high-order feature interaction information, and the early-stop mechanism controls the cascade depth to avoid unnecessary computation. The experiments demonstrate that our model outperforms eight other state-of-the-art methods on MAE and RMSE common metrics on real public datasets.

Multi-Modelling Approach to Support Decision Making in Crop Protection

Abstract A more sustainable control of plant diseases is needed for the development of less intensive farming systems with reduced inputs of chemicals, including pesticides, and reduced use of natural resources. Integrated pest management (IPM) is currently the most effective method for sustainable disease control. IPM has higher complexity than conventional disease management and involves decision making at strategic, tactic, and operational levels, which is often challenging for farmers. Mathematical models are simplified representations of a crop system, which provide dynamic information about the crop status and can support decision making. In an IPM approach, plant disease models must be complemented with other models, such as models for crop growth and development, and models for fungicide dynamics. This case study describes the integration of mathematical models for plant disease, crop development, and plant protection products in a multi-modelling approach able to support multiple decision making in IPM. Downy mildew ( Plasmopara viticola) of grapevines ( Vitis ) is used as a successful story of multiple model combinations through a web-based decision support system. Information © The Author 2024

Perspectives on 3D printed personalized medicines for pediatrics

In recent years, the rapid advancement of three-dimensional (3D) printing technology has yielded distinct benefits across various sectors, including pharmaceuticals. The pharmaceutical industry has particularly experienced advantages from the utilization of 3D-printed medications, which have invigorated the development of tailored drug formulations. The approval of 3D-printed drugs by the U.S. Food and Drug Administration (FDA) has significantly propelled personalized drug delivery. Additionally, 3D printing technology can accommodate the precise requirements of pediatric drug dosages and the complexities of multiple drug combinations. This review specifically concentrates on the application of 3D printing technology in pediatric preparations, encompassing a broad spectrum of uses and refined pediatric formulations. It compiles and evaluates the fundamental principles associated with the application of 3D printing technology in pediatric preparations, including its merits and demerits, and anticipates its future progression. The objective is to furnish theoretical underpinning for 3D printing technology to facilitate personalized drug delivery in pediatrics and to advocate for its implementation in clinical settings.

Early Diagenetic Controls on Sedimentary Iodine Release and Iodine‐To‐Organic Carbon Ratios in the Paleo‐Record

AbstractIodine cycling in the ocean is closely linked to productivity, organic carbon export, and oxygenation. However, iodine sources and sinks at the seafloor are poorly constrained, which limits the applicability of iodine as a biogeochemical tracer. We present pore water and solid phase iodine data for sediment cores from the Peruvian continental margin, which cover a range of bottom water oxygen concentrations, organic carbon rain rates and sedimentation rates. By applying a numerical reaction‐transport model, we evaluate how these parameters determine benthic iodine fluxes and sedimentary iodine‐to‐organic carbon ratios (I:Corg) in the paleo‐record. Iodine is delivered to the sediment with organic material and released into the pore water as iodide (I−) during early diagenesis. Under anoxic conditions in the bottom water, most of the iodine delivered is recycled, which can explain the presence of excess dissolved iodine in near‐shore anoxic seawater. According to our model, the benthic I− efflux in anoxic areas is mainly determined by the organic carbon rain rate. Under oxic conditions, pore water dissolved I− is oxidized and precipitated at the sediment surface. Much of the precipitated iodine re‐dissolves during early diagenesis and only a fraction is buried. Particulate iodine burial efficiency and I:Corg burial ratios do increase with bottom water oxygen. However, multiple combinations of bottom water oxygen, organic carbon rain rate and sedimentation rate can lead to identical I:Corg, which limits the utility of I:Corg as a quantitative oxygenation proxy. Our findings may help to better constrain the ocean's iodine mass balance, both today and in the geological past.

Accelerations of large inertial particles in turbulence

Understanding the dynamics of material objects advected by turbulent flows is a long-standing question in fluid dynamics. In this perspective article we focus on the characterization of the statistical properties of non-interacting finite-sized massive spherical particles advected by a vigorous turbulent flow. We study the fluctuations and temporal correlations of particle accelerations and explore their behaviours with respect to the particle size and the particle mass density by means of fully resolved numerical simulations. We observe that the measured trends cannot be interpreted as the simple multiplicative combination of the two dominant effects: the spatial filtering of fluid accelerations and the added-mass–adjusted fluid-to-particle density ratio. We argue that other hydrodynamical forces or effects, e.g., preferential flow sampling, have still a significant role even at the largest particle sizes, which reach here the integral scale of turbulence.

System identifiability in a time-evolving agent-based model.

Mathematical models are a valuable tool for studying and predicting the spread of infectious agents. The accuracy of model simulations and predictions invariably depends on the specification of model parameters. Estimation of these parameters is therefore extremely important; however, while some parameters can be derived from observational studies, the values of others are difficult to measure. Instead, models can be coupled with inference algorithms (i.e., data assimilation methods, or statistical filters), which fit model simulations to existing observations and estimate unobserved model state variables and parameters. Ideally, these inference algorithms should find the best fitting solution for a given model and set of observations; however, as those estimated quantities are unobserved, it is typically uncertain whether the correct parameters have been identified. Further, it is unclear what 'correct' really means for abstract parameters defined based on specific model forms. In this work, we explored the problem of non-identifiability in a stochastic system which, when overlooked, can significantly impede model prediction. We used a network, agent-based model to simulate the transmission of Methicillin-resistant staphylococcus aureus (MRSA) within hospital settings and attempted to infer key model parameters using the Ensemble Adjustment Kalman Filter, an efficient Bayesian inference algorithm. We show that even though the inference method converged and that simulations using the estimated parameters produced an agreement with observations, the true parameters are not fully identifiable. While the model-inference system can exclude a substantial area of parameter space that is unlikely to contain the true parameters, the estimated parameter range still included multiple parameter combinations that can fit observations equally well. We show that analyzing synthetic trajectories can support or contradict claims of identifiability. While we perform this on a specific model system, this approach can be generalized for a variety of stochastic representations of partially observable systems. We also suggest data manipulations intended to improve identifiability that might be applicable in many systems of interest.