Multi-step Approach Research Articles

Enhancing trust in global supply chains: Conceptualizing Digital Product Passports for a low-carbon hydrogen market

Industries and energy markets around the world are facing mounting pressure to decarbonize, prompting them to transform processes and supply chains towards sustainability. However, a lack of credible sustainability data proves to be a considerable barrier for emerging markets for sustainable products: Against the background of complex and globalized supply chains, it is necessary to verify the sustainability claim of products in order to demand price premiums for sustainable products in the long run. To enable this, it is necessary that stakeholders in globalized supply chains are willing to share relevant data along the entire supply chain for increasing traceability and reducing information asymmetries. Using the example of international hydrogen supply chains, we study how data can be shared between different stakeholders using Digital Product Passports while addressing stakeholders’ concerns about data privacy and disclosure. In our work, we develop design principles that provide insight into how a Digital Product Passport should be designed to verify the hydrogen’s carbon footprint in a reliable way and to ensure the willingness of stakeholders to share their data. We follow a multi-step approach with a structured literature review followed by expert interviews and qualitative content analysis for a synthesis of design principles. Our research illustrates that a Digital Product Passport must collect data comprehensively and automatically, process it in a decentralized and tamper-proof manner, protect privacy and sovereignty of stakeholders, and ensure interoperability.

A multistep approach for exploring quality markers of Shengjiang Xiexin decoction by integrating plasma pharmacochemistry-pharmacokinetics-pharmacology

Shengjiang Xiexin decoction (SXD), a well-known traditional Chinese medicine (TCM), was used to alleviate delayed-onset diarrhea induced by the chemotherapeutic agent irinotecan (CPT-11). Our previous study showed that SXD regulated multidrug resistance-associated protein 2 (Mrp-2) to alter the pharmacokinetics of CPT-11 and its metabolites. However, the pharmacodynamic constituents and the related quality markers of SXD are unclear. In this study, ultra-high performance liquid chromatography coupled with quadrupole orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS) was utilized to identify the prototypes and metabolites in rat plasma after oral administration of SXD. The pharmacokinetic markers (PK markers) were screened through quantification and semiquantification of SXD-related xenobiotics in plasma using liquid chromatography-mass spectrometry (LC-MS) combined with statistical analysis. Computational molecular docking was performed to assess the potential binding ability of the PK markers with the target Mrp-2. The results were verified by evaluating the impact on Mrp-2 function using Caco-2 cells. The quality markers were chosen from these PK markers based on the binding affinities with Mrp-2, the specificity and the traceability. As a result, a total of 142 SXD-related exogenous components, including 77 prototypes and 65 metabolites, were detected in rat plasma. Among these, 83 xenobiotics were selected as PK markers due to their satisfactory pharmacokinetic behaviors. Based on the characteristics of quality markers, the prototype-based PK markers were considered the indices of quality control for SXD, including baicalin, baicalein, wogonoside, wogonin, liquiritigenin, isoliquiritigenin, norwogonin, oroxylin A, dihydrobaicalin, chrysin, glycyrrhizic acid, glycyrrhetinic acid, oroxylin A 7-O-glucuronide, liquiritin and isoliquiritin. This study provided an interesting strategy for screening the quality markers involved in the pharmacokinetics of SXD and its action target, which offered important information for the modernization of SXD and other TCM formulae.

P455 Deploying machine learning algorithms to stratify patients with inflammatory bowel disease using routinely collected electronic health records

Abstract Background Personalised medicine approaches are eagerly awaited to facilitate individualisation of medical care for patients with inflammatory bowel disease (IBD). Multiple approaches have already been explored in attempts to stratify patients into different prognostic trajectories. In this study we aimed to use machine learning algorithms to cluster patients based on their routinely collected electronic health records in an unsupervised approach. Methods We interrogated the rich electronic health records (EHR) of 619 patients with a diagnosis of IBD requiring an advanced therapy and attending the IBD service at King’s College Hospital (KCH), London, UK. Data for a seven year period (June 2016-May 2023) were included with variables that captured diagnosis, treatments, service utilisation and investigations. Ethical approval was provided following application to the KERRI committee (KCH committee for accessing anonymised HER). In a preliminary analysis, we employed a multi-step approach to analyse patient data, identify significant features through k-prototypes clustering and a combination of parametric and non-parametric statistical tests. We examined different cluster solutions up to six clusters. Results We included 38 quantitative and 32 qualitative variables for 350 patients with a diagnosis of CD and 269 with a diagnosis of UC. Missing data were addressed by imputation by k-NN, mode value and zero. In the context of the unsupervised learning approach, when k=3, key differentiating features included faecal calprotectin levels, body mass index, mean platelet volume, marital status, taking advanced therapy for less than 3 years, and service access (telephone or clinic appointments). Conclusion This first pass analysis suggests that patients who are relatively early in their journey with at least moderately active IBD take up more recourses of the IBD service. This associates also with their social circumstances. Further work will explore these interactions in more detail.

Development of a multi-step screening procedure for redox active molecules in organic radical polymer anodes and as redox flow anolytes.

Benzo[d]-X-zolyl-pyridinyl (XO, S, NH) radicals represent a promising class of redox-active molecules for organic batteries. We present a multistep screening procedure to identify the most promising radical candidates. Experimental investigations and highly correlated wave function-based calculations are performed to determine benchmark redox potentials. Based on these, the accuracies of different methods (semi-empirical, density functional theory, wave function-based), solvent models, dispersion corrections, and basis sets are evaluated. The developed screening procedure consists of three steps: First, a conformer search is performed with CREST. The molecules are selected based on the redox potentials calculated using GFN2-xTB. Second, HOMO energies calculated with reparametrized B3LYP-D3(BJ) and the def2-SVP basis set are used as selection criteria. The final molecules are selected based on the redox potentials calculated from Gibbs energies using BP86-D3(BJ)/def2-TZVP. With this multistep screening approach, promising molecules can be suggested for synthesis, and structure-property relationships can be derived.

Computational exploration of FOXM1 inhibitors for glioblastoma: an integrated virtual screening and molecular dynamics simulation study

In this study, a comprehensive investigation of a set of phytochemicals to identify potential inhibitors for the Forkhead box protein M1 (FOXM1) was conducted. FOXM1 is overexpressed in glioblastoma (GBM) cells and plays a crucial role in cell cycle progression, proliferation, and invasion. FOXM1 inhibitors have shown promising results in preclinical studies, and ongoing clinical trials are assessing their efficacy in GBM patients. However, there are limited studies on the identification of novel compounds against this attractive therapeutic target. To address this, the NPACT database containing 1,574 phytochemicals was used, employing a hierarchical multistep docking approach, followed by an estimation of relative binding free energy. By fixing user-defined XP-dock and MM-GBSA cut-off scores of −6.096 and −37.881 kcal/mol, the chemical space was further narrowed. Through exhaustive analysis of molecular binding interactions and various pharmacokinetics profiles, we identified four compounds, namely NPACT00002, NPACT01454, NPACT00856, and NPACT01417, as potential FOXM1 inhibitors. To assess the stability of protein-ligand binding in dynamic conditions, 100 ns Molecular dynamics (MD) simulations studies were performed. Furthermore, Molecular mechanics with generalized Born and surface area solvation (MM-GBSA) based binding free energy estimations of the entire simulation trajectories revealed a strong binding affinity of all identified compounds towards FOXM1, surpassing that of the control drug Troglitazone. Based on extensively studied multistep docking approaches, we propose that these molecules hold promise as FOXM1 inhibitors for potential therapeutic applications in GBM. However, experimental validation will be necessary to confirm their efficacy as targeted therapies. Communicated by Ramaswamy H. Sarma

Urban Traffic Congestion Prediction: A Multi-Step Approach Utilizing Sensor Data and Weather Information

The continuous growth of urban populations has led to the persistent problem of traffic congestion, which imposes adverse effects on quality of life, such as commute times, road safety, and the local air quality. Advancements in Internet of Things (IoT) sensor technology have contributed to a plethora of new data streams regarding traffic conditions. Therefore, the recognition and prediction of traffic congestion patterns utilizing such data have become crucial. To that end, the integration of Machine Learning (ML) algorithms can further enhance Intelligent Transportation Systems (ITS), contributing to the smart management of transportation systems and effectively tackling traffic congestion in cities. This study seeks to assess a wide range of models as potential solutions for an ML-based multi-step forecasting approach intended to improve traffic congestion prediction, particularly in areas with limited historical data. Various interpretable predictive algorithms, suitable for handling the complexity and spatiotemporal characteristics of urban traffic flow, were tested and eventually shortlisted based on their predictive performance. The forecasting approach selects the optimal model in each step to maximize the accuracy. The findings demonstrate that, in a 24 h step prediction, variating Ensemble Tree-Based (ETB) regressors like the Light Gradient Boosting Machine (LGBM) exhibit superior performances compared to traditional Deep Learning (DL) methods. Our work provides a valuable contribution to short-term traffic congestion predictions and can enable more efficient scheduling of daily urban transportation.

Multi-step semi-synthesis, structural characterization and growth factor interaction study of regiochemically sulfated diabolican polysaccharides

Diabolican is an exopolysaccharide (EPS) produced by Vibrio diabolicus HE800, a mesophilic bacterium firstly isolated from a deep-sea hydrothermal field. Its glycosaminoglycan (GAG)-like structure, consisting of a tetrasaccharide repeating unit composed of two aminosugars (N-acetyl-glucosamine and N-acetyl-galactosamine) and two glucuronic acid units, suggested to subject it to regioselective sulfation processes, in order to obtain some sulfated derivatives potentially acting as GAG mimics. To this aim, a multi-step semi-synthetic approach, relying upon tailored sequence of regioselective protection, sulfation and deprotection steps, was employed in this work. The chemical structure of the obtained sulfated diabolican derivatives was characterized by a multi-technique analytic approach, in order to define both degree of sulfation (DS) and sulfation pattern within the polysaccharide repeating unit, above all. Finally, binding affinity for some growth factors relevant for biomedical applications was measured for both starting diabolican and sulfated derivatives thereof. Collected data suggested that sulfation pattern could be a key structural element for the selective interaction with signaling proteins not only in the case of native GAGs, as already known, but also for GAG-like structures obtained by regioselective sulfation of naturally unsulfated polysaccharides.

Full resolution reconstruction of whole-mount sections from digitized individual tissue fragments

Whole-mount sectioning is a technique in histopathology where a full slice of tissue, such as a transversal cross-section of a prostate specimen, is prepared on a large microscope slide without further sectioning into smaller fragments. Although this technique can offer improved correlation with pre-operative imaging and is paramount for multimodal research, it is not commonly employed due to its technical difficulty, associated cost and cumbersome integration in (digital) pathology workflows. In this work, we present a computational tool named PythoStitcher which reconstructs artificial whole-mount sections from digitized tissue fragments, thereby bringing the benefits of whole-mount sections to pathology labs currently unable to employ this technique. Our proposed algorithm consists of a multi-step approach where it (i) automatically determines how fragments need to be reassembled, (ii) iteratively optimizes the stitch using a genetic algorithm and (iii) efficiently reconstructs the final artificial whole-mount section on full resolution (0.25 µm/pixel). PythoStitcher was validated on a total of 198 cases spanning five datasets with a varying number of tissue fragments originating from different organs from multiple centers. PythoStitcher successfully reconstructed the whole-mount section in 86–100% of cases for a given dataset with a residual registration mismatch of 0.65–2.76 mm on automatically selected landmarks. It is expected that our algorithm can aid pathology labs unable to employ whole-mount sectioning through faster clinical case evaluation and improved radiology-pathology correlation workflows.

Microscale Investigation of Urban Heat Island (UHI) in Annaba City: Unveiling Factors and Mitigation Strategies

Cities are facing significant challenges related to climate change, particularly due to the increasing impact of the Urban Heat Island (UHI) phenomenon. The present study investigated the UHI phenomenon at the microscale in Annaba, Algeria. The research involved a multi-step approach, starting with on-site measurements of urban microclimate parameters, performed in downtown Annaba on 6 July 2023. The UHI intensity was quantified by comparing city-measured temperatures with rural surroundings. Thermal imaging is then used to empirically identify the contributing factors to UHI initiation at the microscale. The study employed the ENVI-met model to analyse mitigation strategies, manipulating parameters for six scenarios including the current design of the study area. Outputs were used to assess the impact of these strategies on air temperature, mean radiant temperature, relative humidity, and wind speed. The findings revealed an intense UHI effect in Annaba city with a peak difference of 6.9 °C, with practical implications for buildings, ground and roads, vehicles, air conditioners, and specific facade materials. Introducing urban vegetation, particularly urban trees and green roofs, proved highly effectiveness in mitigating the UHI in downtown Annaba. Urban trees demonstrated the most substantial impact, reducing temperatures by 1.9 °C at 1 p.m., while green roof temperature reductions ranged from 0.1 °C to 2 °C.

Prognostic analysis and risk assessment based on RNA editing in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, and prognosis assessment is crucial for guiding treatment decisions. In this study, we aimed to develop a personalized prognostic model for HCC based on RNA editing. RNA editing is a post-transcriptional process that can affect gene expression and, in some cases, play a role in cancer development. By analyzing RNA editing sites in HCC, we sought to identify a set of sites associated with patient prognosis and use them to create a prognostic model. We gathered RNA editing data from the Synapse database, comprising 9990 RNA editing sites and 250 HCC samples. Additionally, we collected clinical data for 377 HCC patients from the Cancer Genome Atlas (TCGA) database. We employed a multi-step approach to identify prognosis-related RNA editing sites (PR-RNA-ESs). We assessed how patients in the high-risk and low-risk groups, as defined by the model, fared in terms of survival. A nomogram was developed to predict the precise survival prognosis of HCC patients and assessed the prognostic model's utility through a receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Our analysis identified 33 prognosis-related RNA editing sites (PR-RNA-ESs) associated with HCC patient prognosis. Using a combination of LASSO regression and cross-validation, we constructed a prognostic model based on 13 PR-RNA-ESs. Survival analysis demonstrated significant differences in the survival outcomes of patients in the high-risk and low-risk groups defined by this model. Additionally, the differential expression of the 13 PR-RNA-ESs played a role in shaping patient survival. Risk-prognostic investigations further distinguished patients based on their risk levels. The nomogram enabled precise survival prognosis prediction. Our study has successfully developed a highly personalized and accurate prognostic model for individuals with HCC, leveraging RNA editing data. This model has the potential to revolutionize clinical evaluation and medical management by providing individualized prognostic information. The identification of specific RNA editing sites associated with HCC prognosis and their incorporation into a predictive model holds promise for improving the precision of treatment strategies and ultimately enhancing patient outcomes in HCC.

3D-QSAR Studies on High-affinity Phosphodiestera.

Recent studies have found that Phosphodiesterase-4 (PDE4) is closely related to the pathogenesis of depression, cognitive impairment and neurological impairment. Our objective is to develop potent inhibitors of the high-affinity phosphodiesterase 4D isoform (PDE4D) that can serve as radioligands for Positron Emission Tomography (PET) imaging, thereby advancing research in the field of neurological diseases. We employed a multi-step approach combining three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling, molecular docking, classification techniques, and CoMSIA analysis to investigate the conformational relationship of highaffinity PDE4D inhibitors as PET ligands. ADMET and Drug-likeness predictions were also conducted. By utilizing these methods, our aim was to identify more potent PDE4D inhibitors. The results showed that the CoMSIA model with the best principal component scores (n=7) had a cross-validated Q2 value of 0.602 and a non-cross-validated R2 value of 0.976. These results affirmed the excellent predictive capability of the established CoMSIA model. Analysis of the generated 3D-QSAR contour plots highlighted specific regions in the molecular structure of the compounds that can be further optimized and modified. Guided by the contour plots, we designed 100 novel PDE4D inhibitors, and molecular docking was performed for the top 4 compounds with high activity. The molecular docking scores were promising, and ADMET and drug similarity predictions yielded satisfactory results. Taking into consideration these factors, compound 51c was determined to be the optimal compound, laying a solid foundation for further research. For the continued development of PDE4D PET radioligand, these models and new compounds' developing methodology offer a theoretical foundation and crucial references.

Implementing the multistep adams-bashforth numerical approach on magnetohydrodynamic radiated casson fluid with darcy forchheimer, dissipation and activation energy

The main goal behind the conduction of this work is to use the Adams-Bashforth method in the field of fluid dynamics to solve the Navier–Stokes equations based on the boundary layer flow of magnetohydrodynamic Casson fluid passing on the upper horizontal parabolic surface with variable fluid characteristics and Darcy-Forchheimer effect. The temperature flow rate and mass rate are also investigated with the help of thermal radiation, viscous dissipation, heat generation and activation energy. The Adams-Bashforth technique is widely used in numerical analysis, scientific computing, fluid dynamics, chemical kinetics, astrophysics and is particularly useful in solving the stiff ODEs, where other methods may fail to converge. It is particularly useful for simulating the flow of fluids around complex geometries, such as aircraft wings or turbine blade. The equations of basic governing laws (Momentum, temperature and concentration) are achieved in the form of PDE’s. These PDEs are then transmuted into ODEs via similarity variables and the numerical behaviors of these ODEs are obtained with Adam-Bashforth (Predictor-Corrector) along with RK-4 technique on the Matlab software. The initial guesses are modified by using secant method. Graphical analyses are used to explain how different parameters affect the temperature, velocity and mass transport rate. The finding of results revealed that the inertia coefficient, Hartmann number and fluid parameter make the decrement in the velocity field. The amplification in temperature of fluid is noted for varying the radiation coefficient, dissipation and thermal generation coefficient. The solutal field drops due to activation energy and reaction coefficient. The findings are useful to observe the flow attributes, behaviors and helpful in the designing of thermal equipment in the thermal industries.

Dynamic prediction of survival using multivariate functional principal component analysis: A strict landmarking approach.

Dynamically predicting patient survival probabilities using longitudinal measurements has become of great importance with routine data collection becoming more common. Many existing models utilize a multi-step landmarking approach for this problem, mostly due to its ease of use and versatility but unfortunately most fail to do so appropriately. In this article we make use of multivariate functional principal component analysis to summarize the available longitudinal information, and employ a Cox proportional hazards model for prediction. Additionally, we consider a centred functional principal component analysis procedure in an attempt to remove the natural variation incurred by the difference in age of the considered subjects. We formalize the difference between a 'relaxed' landmarking approach where only validation data is landmarked and a 'strict' landmarking approach where both the training and validation data are landmarked. We show that a relaxed landmarking approach fails to effectively use the information contained in the longitudinal outcomes, thereby producing substantially worse prediction accuracy than a strict landmarking approach.

Abstract B013: A quest for deeper understanding of BRCA2 Variants of Uncertain Significance (VUS)

Abstract Deleterious mutations in homology-directed repair pathway genes impact both future cancer risk and therapeutic intervention options for cancer diagnosis and prognosis. Homology-directed repair pathway is a high-fidelity DNA repair pathway for resolving double-strand breaks (DSBs) throughout the human genome. BRCA2, as a homology-directed repair factor, is essential to ensure genome integrity through an error-free process. Deleterious BRCA2 mutations increase the lifetime risk of breast, ovarian, melanoma, prostate, and pancreatic cancers. Deleterious BRCA2 mutations cover only 25% of the total BRCA2 mutations identified in the population by genetic testing whereas 60% are mutations with unknown connection to the disease called Variants of Uncertain Significance (VUS). The majority of BRCA2 VUS are missense mutations with one point mutation which could potentially alter BRCA2 cellular localization and/or nuclear functions. Thus, evaluating BRCA2 cellular localization and function in BRCA2 missense carriers and tumors is urgently needed to provide clinical guidance regarding cancer risk and treatment options. We have identified a paradoxical relationship between BRCA2 nuclear import and treatment. Deleterious missense mutations in the DNA binding domain of BRCA2 direct the protein to the cytosol which increase sensitivity to PARP inhibitors (PARPi) and platinum drugs. Therefore, although BRCA2 mislocalization might be a cause of cancer, keeping it out of the nucleus allows for much better treatment outcomes. Here, we present how BRCA2 cancer-driver missense mutations lead to cytosolic mislocalization and how BRCA2 localization can be used as a diagnostic tool to stratify patients. Our robust multi-step molecular approach will guide future clinical decision rules surrounding BRCA2 VUS. By providing reliable and accurate functional assays to correctly evaluate the pathogenicity of BRCA2 VUS, our goal is to improve personalized medicine by providing actionable information to patients and their physicians. Citation Format: Cole Niedmann, Judit Jimenez Sainz. A quest for deeper understanding of BRCA2 Variants of Uncertain Significance (VUS) [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: DNA Damage Repair: From Basic Science to Future Clinical Application; 2024 Jan 9-11; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2024;84(1 Suppl):Abstract nr B013.

Forecasting greenhouse air and soil temperatures: A multi-step time series approach employing attention-based LSTM network

Greenhouses stand as key infrastructural components in contemporary agriculture, facilitating the perennial availability of vegetables. Harnessing accurate, real-time environmental data, particularly air and soil temperature, is pivotal to promoting the facility’s efficiency in pest and disease alarming and vegetable production. Nevertheless, despite consistent long-term temperature trends, short-term fluctuations prove to be considerably nonlinear, time-lagged, and multivariate-coupled due to the unique microclimate of the greenhouse. Addressing this, we propose a novel short-term multi-step prediction model that assimilates a range of environmental data including air temperature, humidity, soil temperature, and soil moisture content at diverse greenhouse heights. This model utilizes an Attention-LSTM based time series approach to accurately predict multi-step short-term temperature variations within the greenhouse. By employing a modest amount of historical data (approximately 48 h), the model provides future temperature forecasts within a range of 30 to 480 min with high accuracy. When benchmarked against renowned models such as RNN, GRU, and LSTM networks, our proposed models display impressive performance. It achieved R2 values of 0.93, 0.94, 0.95, and 0.86 for 1, 4, 8, and 16-step predictions, respectively, in the domain of air temperature forecasting. The corresponding MSE values stood at 0.42, 0.32, 0.27, and 0.81. In parallel, for soil temperature forecasts, our model recorded R2 values of 0.96, 0.94, 0.92, and 0.89 for 1, 4, 8, and 16-step predictions, respectively, alongside MSE values of 0.36, 0.48, 0.73, and 0.99 for the equivalent steps. To conclude, these results reveal the exceptional predictive aptitude of our model for both air and soil temperature forecasts within greenhouse environments, validating its standing as an effective tool for the optimization of vegetable farming practices. Consequently, our model demonstrates its ability to yield precise predictions of environmental variables within greenhouses, offering crucial early warning mechanisms for the enhancement of vegetable cultivation processes.

Isolation and Characterization of Cellulose from Pomegranate (Punica granatum) Peel

ABSTRACT A commonly wasted agricultural waste, pomegranate peel has attracted attention as a useful biomaterial with a wide range of potential uses. Fruit and vegetable wastes create environmental difficulties, as well as a loss of valuable biomass and a financial cost to enterprises, due to its high biodegradability. This study focuses on the extraction and characterization of cellulose from both the outer and inner layers of pomegranate peel. The cellulose extraction process involves a multi-step approach, encompassing delignification and mercerization. Fourier transform infrared spectroscopy (FTIR) revealed characteristic peaks associated with cellulose, confirming its presence in the extracted material. Field emission scanning electron microscopy (FESEM) depicted the morphological transformation of cellulose as non-cellulosic components were removed during the extraction process. Particle size analysis demonstrated that alkaline treatment reduced the cellulose particle size. X-ray diffraction (XRD) analysis indicated a significant increase in cellulose crystallinity due to the elimination of hemicellulose and lignin following the extraction process. The chemical composition analysis revealed that treated pomegranate peels contained higher cellulose content compared to their untreated counterparts. This study offers insightful information about the possible uses of pomegranate peel cellulose and its adaptability to a different application.

Targeted elimination of molybdenum ions from a leaching solution with the ability of radiated grafting GMA-PAN nanofibers

In this study, electrospun polyacrylonitrile nanofibers were effectively functionalized for enhanced molybdenum ion adsorption through a multi-step approach. Initially, glycidyl methacrylate was grafted onto the nanofibers via irradiation-induced grafting polymerization, followed by chemical modification with various amino groups, with triethylamine identified as the optimal modifier. The impacts of key synthesis parameters and reaction conditions on grafting level and adsorption capacity were thoroughly investigated, with a focus on achieving maximum efficiency. The resulting nanofibers were characterized using FTIR, SEM, and BET techniques, confirming the successful modification and structural features conducive to adsorption. Furthermore, a comprehensive experimental design, incorporating a central composite design, yielded optimal conditions for molybdenum adsorption, with key parameters including monomer concentration, irradiation dose, adsorbent mass, initial concentration, time, pH, temperature, and amine concentration. The adsorption kinetics were effectively described by the pseudo-second-order model, while the Langmuir isotherm model provided valuable insight into the adsorption behavior. Impressively, the adsorbent exhibited exceptional adsorption efficiency, surpassing 98% even after six adsorption–desorption cycles using 0.5 M HCl. Thermodynamic analysis revealed the exothermic nature of the adsorption process, along with decreased entropy and overall spontaneity, underlining the favorable conditions for molybdenum adsorption. Notably, the synthesized adsorbent demonstrated notable selectivity for molybdenum and achieved an impressive adsorption capacity of 109.79 mg/g, highlighting its potential for practical applications in molybdenum removal from aqueous solutions.

Multimodal treatment for extensive recurrent lentigo maligna melanoma

Lentigo maligna melanoma (LMM) is a type of invasive melanoma. When LMM comprises periocular and ocular tissues, sequential multimodality treatments may be needed to avoid wide surgical excisions. We report the case of a recurrent LMM after multiple excisions elsewhere that involved the entire lower eyelid and lateral part of the upper eyelid skin, extending to the lower tarsal conjunctiva and conjunctival fornix. It was successfully controlled by a multistep approach using orthovoltage radiotherapy, topical chemotherapy, and cryotherapy, saving the patient from exenteration. The patient is still in disease-free status in the fourth year after multistep approach.

In-Vivo Electrical Properties Estimation of Biological Tissues by Means of a Multi-Step Microwave Tomography Approach.

The accurate quantitative estimation of the electromagnetic properties of tissues can serve important diagnostic and therapeutic medical purposes. Quantitative microwave tomography is an imaging modality that can provide maps of the in-vivo electromagnetic properties of the imaged tissues, i.e. both the permittivity and the electric conductivity. A multi-step microwave tomography approach is proposed for the accurate retrieval of such spatial maps of biological tissues. The underlying idea behind the new imaging approach is to progressively add details to the maps in a step-wise fashion starting from single-frequency qualitative reconstructions. Multi-frequency microwave data is utilized strategically in the final stage. The approach results in improved accuracy of the reconstructions compared to inversion of the data in a single step. As a case study, the proposed workflow was tested on an experimental microwave data set collected for the imaging of the human forearm. The human forearm is a good test case as it contains several soft tissues as well as bone, exhibiting a wide range of values for the electrical properties.

Erratum to “Comparing pre- and post-pandemic greenhouse gas and noise emissions from road traffic in Rome (Italy): a multi-step approach”

Erratum to “Comparing pre- and post-pandemic greenhouse gas and noise emissions from road traffic in Rome (Italy): a multi-step approach”