Development Of Model Research Articles

Development of a 2-D Motion Tracker Application Project Assisted By Computer Vision to Enhance Student’s Laboratory Skills

The lecture method that is often applied by the teacher in class can also cause the learning experience of students to not be optimal; as a result, students only understand the theory and concept of projectile motion. One method that can be used to learn physics is the experimental method. This study aims to produce a projectile motion tracker application project and describe the effect of applying this application project in the physics learning process, especially related to students' laboratory skills. The research method used is research and development (RD), using the 4-D development model (Define, Design, Develop, and Disseminate). Pre- and post-test results were obtained from 74 high school students to assess their laboratory skills before and after treatment. The instruments used were questionnaires and tests. The results show that the projectile motion tracker application project can be employed as a tool for physics education, based on expert test results with very good categories in language, visual appearance, and software engineering. In addition, based on the results of the practicality test for teachers and students, the test results were in the very practical category, respectively, from the aspects of ease of use, attractiveness, efficiency, and usefulness. The results of students' pre- and post-test on their laboratory skills show that the projectile motion tracker application project can enhance students' laboratory skills. The projectile motion tracker application project can be used as an alternative by teachers, lecturers, or education practitioners to support teaching and learning activities.

BadSAM: Exploring Security Vulnerabilities of SAM via Backdoor Attacks (Student Abstract)

Image segmentation is foundational to computer vision applications, and the Segment Anything Model (SAM) has become a leading base model for these tasks. However, SAM falters in specialized downstream challenges, leading to various customized SAM models. We introduce BadSAM, a backdoor attack tailored for SAM, revealing that customized models can harbor malicious behaviors. Using the CAMO dataset, we confirm BadSAM's efficacy and identify SAM vulnerabilities. This study paves the way for the development of more secure and customizable vision foundation models.

Rethinking the Development of Large Language Models from the Causal Perspective: A Legal Text Prediction Case Study

While large language models (LLMs) exhibit impressive performance on a wide range of NLP tasks, most of them fail to learn the causality from correlation, which disables them from learning rationales for predicting. Rethinking the whole developing process of LLMs is of great urgency as they are adopted in various critical tasks that need rationales, including legal text prediction (e.g., legal judgment prediction). In this paper, we first explain the underlying theoretical mechanism of their failure and argue that both the data imbalance and the omission of causality in model design and selection render the current training-testing paradigm failed to select the unique causality-based model from correlation-based models. Second, we take the legal text prediction task as the testbed and reconstruct the developing process of LLMs by simultaneously infusing causality into model architectures and organizing causality-based adversarial attacks for evaluation. Specifically, we base our reconstruction on our theoretical analysis and propose a causality-aware self-attention mechanism (CASAM), which prevents LLMs from entangling causal and non-causal information by restricting the interaction between causal and non-causal words. Meanwhile, we propose eight kinds of legal-specific attacks to form causality-based model selection. Our extensive experimental results demonstrate that our proposed CASAM achieves state-of-the-art (SOTA) performances and the strongest robustness on three commonly used legal text prediction benchmarks. We make our code publicly available at https://github.com/Carrot-Red/Rethink-LLM-development.

Enhancing Job Recommendation through LLM-Based Generative Adversarial Networks

Recommending suitable jobs to users is a critical task in online recruitment platforms. While existing job recommendation methods encounter challenges such as the low quality of users' resumes, which hampers their accuracy and practical effectiveness.With the rapid development of large language models (LLMs), utilizing the rich external knowledge encapsulated within them, as well as their powerful reasoning capabilities, is a promising way to complete users' resumes for more accurate recommendations. However, directly leveraging LLMs to enhance recommendation results is not a one-size-fits-all solution, as LLMs may suffer from fabricated generation and few-shot problems, which degrade the quality of resume completion. In this paper, we propose a novel LLM-based approach for job recommendation. To alleviate the limitation of fabricated generation for LLMs, we extract accurate and valuable information beyond users' self-description, which helps the LLMs better profile users for resume completion. Specifically, we not only extract users' explicit properties (e.g., skills, interests) from their self-description but also infer users' implicit characteristics from their behaviors for more accurate and meaningful resume completion. Nevertheless, some users still suffer from few-shot problems, which arise due to scarce interaction records, leading to limited guidance for high-quality resume generation. To address this issue, we propose aligning unpaired low-quality with high-quality generated resumes by Generative Adversarial Networks (GANs), which can refine the resume representations for better recommendation results. Extensive experiments on three large real-world recruitment datasets demonstrate the effectiveness of our proposed method.

Memory-Augmenting Decoder-Only Language Models through Encoders (Student Abstract)

The Transformer architecture has seen a lot of attention in recent years also thanks to its ability to scale well and allow massive parallelism during training. This has made possible the development of Language Models (LMs) of increasing size and the discovery of latent abilities that completely outclass traditional methods e.g. rule-based systems. However, they also introduced new issues, like their inability to retain the history of previous interactions due to their stateless nature or the difficulty in controlling their generation. Different attempts have been made to address these issues, e.g. a `brute force' approach to solving the memory issue is to include the full conversation history in the context window, a solution that is limited by the quadratic scalability of Transformers. In this work, we explore computationally practical solutions to the memory problem. We propose to augment the decoder-only architecture of (most) Large LMs with a (relatively small) memory encoder. Its output is prepended to the decoder's input in a similar fashion to recent works in Adapters and the original Transformer architecture. Initial experiments show promising results, however future work is needed to compare with State-of-the-Art methods.

RecWizard: A Toolkit for Conversational Recommendation with Modular, Portable Models and Interactive User Interface

We present a new Python toolkit called RecWizard for Conversational Recommender Systems (CRS). RecWizard offers support for development of models and interactive user interface, drawing from the best practices of the Huggingface ecosystems. CRS with RecWizard are modular, portable, interactive and Large Language Models (LLMs)-friendly, to streamline the learning process and reduce the additional effort for CRS research. For more comprehensive information about RecWizard, please check our GitHub https://github.com/McAuley-Lab/RecWizard.

Abstract 3127: Development of three-dimensional cancer cell line spheroid models for the in vitro functional characterization of cytotoxic antibody-drug conjugates

Abstract Background: Antibody-drug conjugates (ADCs) are an effective class of cancer therapeutics which have gained prominence for the treatment of several malignancies. A cytotoxic ADC consists of a linker-payload conjugated to a monoclonal antibody, which targets a distinct tumor-associated antigen (TAA) to enable the delivery of the cytotoxic payload to cancer cells. Presently, there is a need for in vitro models that better recapitulate in vivo tumor tissue complexity to aid in the screening and evaluation of ADCs during preclinical development. Hence, efforts have been made to develop in vitro three-dimensional (3D) models with improved translation to in vivo tumor models compared to traditional two-dimensional (2D) cancer cell line monolayer models. We aimed to generate monoculture cancer cell line spheroids in a high-throughput manner. Subsequently, we sought to evaluate the spheroid penetration capability of structurally distinct ADCs and to assess the 3D cytotoxic activity of a variety of microtubule inhibitor (MTI) and topoisomerase 1 inhibitor (TOPO1i)-bearing ADCs targeting multiple TAAs, comparing to activity in 2D models. Methods: Monoculture cancer cell spheroids were generated by seeding cells from a variety of tumor types, using an automated liquid-handling robot, into microtiter plates treated with ultra-low attachment coating, which allows for scaffold-free self-assembly of cancer cells into a 3D arrangement. Following spheroid formation by incubation for 2-3 days under standard culturing conditions, spheroids were treated with ADCs at a range of concentrations for functional evaluation: the spheroid penetration capability of ADCs of different antibody formats was assessed using high-content confocal imaging of spheroids treated with fluorescently labeled antibodies; the cytotoxic activity of ADCs was characterized using an ATP quantification luminescent reagent, live/dead cell fluorescent stains, and confocal imaging. Results: Monoculture spheroids of varying morphologies were successfully and reproducibly generated with a large panel of >50 cancer cell lines derived from >10 tumor types. Antibodies with different formats demonstrated target-mediated binding and a range of spheroid penetration depths. Additionally, MTI and TOPO1i ADCs evaluated in a panel of cancer cell spheroids demonstrated robust 3D cytotoxicity and differentiated activity when compared to the 2D monolayer assay. In a number of cases, the potency trends across multiple ADCs in 3D cytotoxicity assays were more predictive of in vivo efficacy compared to the 2D assay. Taken together, our high-throughput spheroid assays present important and straightforward in vitro tools to aid in the screening and selection of therapeutic cytotoxic ADC candidates for the treatment of solid tumors. Citation Format: Jodi Wong, Andrea Hernández Rojas, Allysha Bissessur, Lemlem T. Degefie, Araba P. Sagoe-Wagner, Samir Das, Vincent Fung, Kevin Yin, Renee Duan, Sam Lawn, Laurence Madera, Catrina Mi Jung Kim, Alex Wu, Mark E. Petersen, Raffaele Colombo, Jamie R. Rich, Stuart D. Barnscher. Development of three-dimensional cancer cell line spheroid models for the in vitro functional characterization of cytotoxic antibody-drug conjugates [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 3127.

Abstract 904: AI platform provides an EDGE and enables state-of-the-art identification of peptide-HLAs for the development of T cell inducing vaccines

Abstract T cell inducing vaccines are key for the development of effective therapies against cancer and infectious diseases. Peptides, presented by Human Leukocyte Antigens (HLAs), are the targets of T cells, and their identification is therefore critical to the development of such vaccines. Here, we present the latest improvement in our EDGETM (Epitope Discovery for GEnomes) platform to address this critical need. EDGE is comprised of AI models that can predict peptide presentation by HLA class I and class II. Although the models are trained primarily using immunopeptidomics data, EDGE scores are predictive of peptide-HLA immunogenicity. There are three class I presentation models in EDGE: an allele-specific model, a pan-specific model, and a model specific for infectious diseases. The allele-specific model is applicable to a large but pre-defined set of HLA alleles. On a large test dataset, the allele-specific model achieved an average precision (AP) of 63% (PPV40=79%) compared to the AP of a standard best-available public model of 21% (PPV40=28%). A Ph1/2 clinical study of personalized cancer vaccines encoding neoantigens predicted from the allele-specific model demonstrated a ~50% molecular response (defined as >=30% reduction in circulating tumor DNA relative to baseline) rate with associated extended overall survival (vs non-responders) in metastatic, microsatellite stable colorectal cancer patients. We observed that >50% of the mutations were able to elicit T cell responses. The pan-specific class I model uses HLA sequences as input feature when training and, therefore, is applicable to any HLA. On the same test dataset as above, it achieved an AP of 65% (PPV40=81%) and performed better on average for ~40 less-common HLA alleles. Prediction of viral peptide presentation by HLA class I is challenging due to the lack of immunopeptidomics data. The class I model for infectious diseases was specifically optimized to predict for viral peptides and, therefore, performed better than available class I models on published HIV and Influenza A datasets. Prediction of peptide presentation by HLA class II is challenging due to the flexibility in how the longer peptides interact with open HLA grooves as well as the lack of immunopeptidomics data as compared to the class I peptides. The class II model in EDGE, EDGE-II, uses the latest developments in protein large language models, a novel learned HLA allele-deconvolution strategy, and in-house immunopeptidomics data, resulting in improved prediction of peptide presentation by HLA class II and immunogenicity driven by CD4+ T cells. On a benchmark validation dataset, EDGE-II achieved an AP of 71% as compared to AP of 62% of a leading published model. In summary, EDGETM provides a comprehensive state-of-the-art platform for the development of vaccines that can induce both CD8+ and CD4+ T cell responses to provide durable benefit to patients. Citation Format: Joshua Klein, Daniel Sprague, Monica Lane, Meghan Hart, Olivia Petrillo, Italo Faria do Valle, Matthew Davis, Andrew Ferguson, Andrew Allen, Karin Jooss, Ankur Dhanik. AI platform provides an EDGE and enables state-of-the-art identification of peptide-HLAs for the development of T cell inducing vaccines [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 904.

Improving somatic exome sequencing performance by biological replicates

BackgroundNext-generation sequencing (NGS) technologies offer fast and inexpensive identification of DNA sequences. Somatic sequencing is among the primary applications of NGS, where acquired (non-inherited) variants are based on comparing diseased and healthy tissues from the same individual. Somatic mutations in genetic diseases such as cancer are tightly associated with genomic instability. Genomic instability increases heterogenity, complicating sequencing efforts further, a task already challenged by the presence of short reads and repetitions in human DNA. This leads to low concordance among studies and limits reproducibility. This limitation is a significant problem since identified mutations in somatic sequencing are major biomarkers for diagnosis and the primary input of targeted therapies. Benchmarking studies were conducted to assess the error rates and increase reproducibility. Unfortunately, the number of somatic benchmarking sets is very limited due to difficulties in validating true somatic variants. Moreover, most NGS benchmarking studies are based on relatively simpler germline (inherited) sequencing. Recently, a comprehensive somatic sequencing benchmarking set was published by Sequencing Quality Control Phase 2 (SEQC2). We chose this dataset for our experiments because it is a well-validated, cancer-focused dataset that includes many tumor/normal biological replicates. Our study has two primary goals. First goal is to determine how replicate-based consensus approaches can improve the accuracy of somatic variant detection systems. Second goal is to develop highly predictive machine learning (ML) models by employing replicate-based consensus variants as labels during the training phase.ResultsEnsemble approaches that combine alternative algorithms are relatively common; here, as an alternative, we study the performance enhancement potential of biological replicates. We first developed replicate-based consensus approaches that utilize the biological replicates available in this study to improve variant calling performance. Subsequently, we trained ML models using these biological replicates and achieved performance comparable to optimal ML models, those trained using high-confidence variants identified in advance.ConclusionsOur replicate-based consensus approach can be used to improve variant calling performance and develop efficient ML models. Given the relative ease of obtaining biological replicates, this strategy allows for the development of efficient ML models tailored to specific datasets or scenarios.

Abstract 6900: Patient-derived xenografts from circulating cancer stem cells and their potential utility in personalized medicine of pancreatic cancer

Abstract Background: Pancreatic cancer carries a poor prognosis in the majority of patients, with an overall 5-year survival rate of less than 10%. Therefore, the development of new therapies and preclinical models is crucial and urgently needed. Circulating cancer stem cells (cCSCs) are a subset of circulating cancer cells that have stem cell-like properties and play a significant role in tumor progression, metastasis, and treatment resistance. Patient-derived xenografts (PDX) reflect the genotypic and phenotypic landscape of patients' cancers and are useful for drug screening, biomarker development, and preclinical evaluation of personalized medicine strategies. In this study, we describe a fast, simple, and cost effective method to generate PDX on Chorioallantoic Membrane (CAM) from circulating cancer stem cells. Methods: In this study, primary cultures from circulating cancer stem cells were established using sphere-forming assays from 30 patients with newly diagnosed pancreatic cancer. Subsequently, tumorspheres were transplanted onto the CAM membrane of fertilized chicken eggs to form patient- derived xenografts. Results: For the identification of cCSCs from peripheral blood of pancreatic cancer patients, an innovative in vitro sphere-forming assay was established. The number of tumorspheres was correlated with clinicopathological parameters like tumor size, lymph node involvement and grading status of the primary tumor. Implantation of tumorspheres onto the CAM was successful in ~90 % of the applied samples. The histological analysis suggests that the PDX tumors were morphologically and pathologically identical to primary tumor tissue. Conclusions: The number of tumorspheres was associated with clinicopathological parameters. Further follow-up is needed to evaluate the prognostic significance of tumorsphere detection in pancreatic cancer. Generation of PDX from cCSCs on CAM has a high success rate and may become an integral part of the drug development arena, including drug screening and biomarker development. Citation Format: Monika Pizon, Dorothea Schott, Andreas Ettner-Sittner, Benedikt J. Wagner, Katharina Pachmann, Thiha Aung, Christina Hackl, Silke Haerteis. Patient-derived xenografts from circulating cancer stem cells and their potential utility in personalized medicine of pancreatic cancer [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 6900.

Abstract 6699: Delineating germline, tumor and extrinsic factors driving mucosal melanoma (MM) risk and response to immune checkpoint inhibitor (ICI) treatment

Abstract Introduction MM is a rare melanoma subtype with distinct biology, low immunogenicity and tumor mutational burden, and subsequently lower response rates to ICIs. The biology of MM is poorly understood. We sought to prospectively, uniformly, and comprehensively profile a cohort of MM patients to determine the interplay of molecular variation, germline predisposition, immunity, gut, mucosal and tumor microbiome and modifiable risk factors on ICI response and resistance in advanced MM. Methods Patients (pts) with MM treated with standard-of-care ICI as any treatment line presenting to MD Anderson are being prospectively enrolled for longitudinal collection for molecular, microbiome, and lifestyle factor profiling. Planned analyses include: whole genome sequencing of normal and tumor tissue, RNA sequencing, and T cell receptor sequencing from fresh tumor samples; germline sequencing and genotype analyses; NanoString Digital Spatial Profiling (DSP) from formalin-fixed paraffin embedded (FFPE) specimens; whole genome shotgun, microbiome profiling and 16S rRNA gene sequencing (16S) of fecal specimens and affected mucosal and tumor sites (swabs); participant lifestyle and patient reported outcomes (PROs) assessments. Fresh tissue is collected and used to develop PDX models and cell lines. Results 98 pts have been enrolled to date; 83 (85%) Caucasians, 8 (8%) Hispanic, 4 (4%) Asians. 67 (68%) females. Median age at MM diagnosis is 64 years (range 32-91 years). MM primary sites include 24 (24%) naso-oral, 29 (30%) urogenital, 26 (27%) anorectal, 7 (7%) conjunctival and 12 (12%) other. Clinical somatic mutation testing was performed in 76 (76%) pts and common mutations were KIT (n = 12, 16%), NRAS (n = 8, 11%) and BRAF V600 or non-V600 (n = 9, 12%). At data cut-off (November 2023), 18 (18%) pts deceased. Median follow-up from first diagnosis to last visit/death was 16 months (range 1-178 months). Sample collections at data cut-off include: blood samples (n = 95; 219 samples); fecal specimens (n = 50; 97 samples); mucosal swabs (tumor/tumor adjacent/oral cavity; n = 38; 101 samples); fresh frozen tumor/normal (n = 38; 55 samples), fresh tumor/normal (n = 28; 36 samples) and FFPE tumor (n = 21; 37 samples) tissue. PROs and dietary assessments have been collected from 47 pts. Fresh tissue for PDX model and cell line development has been collected from 11 pts. Conclusion This is an ongoing prospective study that is expected to drive insights into the tumor/microenvironment/host interactions and factors regulating immunogenicity to predict response and resistance to ICIs in a rare and understudied melanoma subtype. Interrogation of the role of the gut microbiome and its modifiable determinants will lead to the investigation of new therapeutic strategies to modulate the microbiome to improve treatment outcomes in MM. Data will be presented from initial cohort analyses. Citation Format: Florentia Dimitriou, Priyadharsini Nagarajan, Sabitha Prabhakaran, Neus Bota, Mark Knafl, Randy A. Chu, Ashish V. Damania, Pranoti V. Sahasrabhojane, Yasmine Hoballah, Jillian S. Losh, Nadim J. Ajami, Scott E. Woodman, Jennifer A. Wargo, Andrew Futreal, Jennifer L. McQuade. Delineating germline, tumor and extrinsic factors driving mucosal melanoma (MM) risk and response to immune checkpoint inhibitor (ICI) treatment [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 6699.

Development of Simulation Scenario for the Circulatory System in Humans Material for Grade XI High School

This research aims to produce simulation scenarios for biology materials in Grade XI of Senior High School focusing on the mechanism of human blood circulation. The type of research used is R&D research employing the 4D development model (Define, Design, Develop, and Disseminate). The validity and simulation scenarios were assessed by two validated biologist experts from the Faculty of Mathematics and Natural Sciences (FMIPA) at UNM, using simulators and simulation scenario questionnaires. Based on the questionnaire data obtained, the validity of the simulation scenario is Va = 4.12, which falls into the "valid" category (3.5 ≤ Va ≤ 4.5). The practicality of the simulation scenarios was assessed by collecting data through teacher and student assessments based on a practicality assessment questionnaire. The obtained data showed an average percentage of all aspects at 87.41%, indicating a very positive teacher response to the circulation mechanism simulation scenario, while data based on student responses showed an average percentage of all aspects at 82.62%, indicating a positive response from students to the circulatory mechanism simulation scenario. Based on the research conducted, it can be concluded that the simulation scenario for the human circulation mechanism is both valid and practical.Keywords: Simulation scenario, simulation method, 4D, validity, practicality

Modeling Heartland virus disease in mice and therapeutic intervention with 4'-fluorouridine.

More than 60 cases of HRTV disease spanning 14 states have been reported to the United States Centers for Disease Control and Prevention. The expanding range of the Lone Star tick that transmits HRTV, the growing population of at-risk persons living in geographic areas where the tick is abundant, and the lack of antiviral treatments or vaccines raise significant public health concerns. Here, we report the development of a new small-animal model of lethal HRTV disease to gain insight into HRTV pathogenesis and the application of this model for the preclinical development of a promising new antiviral drug candidate, EIDD-2749. Our findings shed light on how the virus causes disease and support the continued development of EIDD-2749 as a therapeutic for severe cases of HRTV infection.

Immunological parameters of maternal peripheral blood as predictors of future pregnancy outcomes in patients with unexplained recurrent pregnancy loss.

Unexplained recurrent pregnancy loss (URPL), affecting approximately 1%-5% of women, exhibits a strong association with various maternal factors, particularly immune disorders. However, accurately predicting pregnancy outcomes based on the complex interactions and synergistic effects of various immune parameters without an automated algorithm remains challenging. In this historical cohort study, we analyzed the medical records of URPL patients treated at Xiangya Hospital, Changsha, China, between January 2020 and October 2022. The primary outcomes included clinical pregnancy and miscarriage. Predictors included complement, autoantibodies, peripheral lymphocytes, immunoglobulins, thromboelastography findings, and serum lipids. Least absolute shrinkage and selection operator (LASSO) analysis and logistic regression analysis was performed for model development. The model's performance, discriminatory, and clinical applicability were assessed using area under the curve (AUC), calibration curve, and decision curve analysis, respectively. Additionally, models were visualized by constructing dynamic and static nomograms. In total, 502 patients with URPL were enrolled, of whom 291 (58%) achieved clinical pregnancy and 211 (42%) experienced miscarriage. Notable differences in complement, peripheral lymphocytes, and serum lipids were observed between the two outcome groups. Moreover, URPL patients with elevated peripheral NK cells (absolute counts and proportion), decreased complement levels, and dyslipidemia demonstrated a significantly increased risk of miscarriage. Four models were developed in this study, of which Model 2 demonstrated superior performance with only seven predictors, achieving an AUC of 0.96 (95% CI: 0.93-0.99) and an accuracy of 0.92. A web-based platform was established to visually present model 2 and to facilitate its utilization by clinicians in outpatient settings (available from: https://yingrongli.shinyapps.io/liyingrong/). Our findings suggest that the implementation of such prediction models could serve as valuable tools for providing comprehensive information and facilitating clinicians in their decision-making processes.

The Effect of Polymorphisms and Other Biomarkers on Infliximab Exposure in Paediatric Inflammatory Bowel Disease: Development of a Population Pharmacokinetic Model.

Therapeutic drug monitoring (TDM) of infliximab has been shown to be a effective strategy for inflammatory bowel disease (IBD). Population pharmacokinetic (PopPK) modeling can predict trough concentrations for individualized dosing. The aim of this study was to develop a PopPK model of infliximab in a paediatric population with IBD, assessing the effect of single nucleotide polymorphisms (SNPs) and other biomarkers on infliximab clearance. This observational and ambispective single-centre study was conducted in paediatric patients with IBD treated with infliximab between July 2016 and July 2022 in the Paediatric Gastroenterology Service of the Hospital Universitari Vall d'Hebron (HUVH) (Spain). Demographic, clinical, and analytical variables were collected. Twenty SNPs potentially associated with variations in the response to infliximab plasma concentrations were analysed. infliximab serum concentrations and antibodies to infliximab (ATI) were determined by ELISA. PopPK modelling was performed using nonlinear mixed-effects analysis (NONMEM). Thirty patients (21 males) were included. The median age (range) at the start of infliximab treatment was 13 years (16 months to 16 years). A total of 190 samples were obtained for model development (49 [25.8%] during the induction phase). The pharmacokinetics (PK) of infliximab were described using a two-compartment model. Weight, erythrocyte sedimentation rate (ESR), faecal calprotectin (FC), and the SNP rs1048610 (ADAM17) showed statistical significance for clearance (CL), and albumin for inter-compartmental clearance (Q). Estimates of CL1 (genotype 1-AA), CL2 (genotype 2-AG), CL3 (genotype 3-GG), Q, Vc, and Vp (central and peripheral distribution volumes) were 0.0066L/h/46.4kg, 0.0055L/h/46.4kg, 0.0081L/h/46.4kg, 0.0029L/h/46.4kg, 0.6750L/46.4kg, and 1.19L/46.4kg, respectively. The interindividual variability (IIV) estimates for clearance, Vc, and Vp were 19.33, 16.42, and 36.02%, respectively. A popPK model utilising weight, albumin, FC, ESR, and the SNP rs1048610 accurately predicted infliximab trough concentrations in children with IBD.

Statistical and machine learning analysis for the application of microbially induced carbonate precipitation as a physical barrier to control seawater intrusion

Seawater intrusion in coastal aquifers is a significant problem that can be addressed through the construction of subsurface dams or physical cut-off barriers. An alternative method is the use of microbially induced carbonate precipitation (MICP) to reduce the hydraulic conductivity of the porous medium and create a physical barrier. However, the effectiveness of this method depends on various factors, and the scientific literature presents conflicting results, making it challenging to generalise the findings. To overcome this challenge, a statistical and machine learning (ML) approach is employed to infer the causes for the reduction in hydraulic conductivity and identify the optimum MICP parameters for preventing seawater intrusion. The study involves data curation, exploratory analysis, and the development of various models to fit the input data (k-Nearest Neighbours – kNN, Support Vector Regression – SVR, Random Forests – RF, Gradient Boosting – XgBoost, Linear model with interaction terms, Ensemble learning algorithms with weighted averages – EnL-WA and stacking – EnL-Stack). The models performed reasonably well in the region where permeability reduction is sensitive to carbonate increase capturing the permeability reduction profile with respect to cementation level while demonstrating that they can be used in initial assessments of the specific conditions (e.g., soil properties). The best performing algorithms were the EnL-Stack and RF followed by XgBoost and SVR. The MICP method is effective in reducing hydraulic conductivity provided that the various biochemical parameters are optimised. Critical biochemical parameters for successful MICP formulations are the bacterial optical density, the urease activity, calcium chloride concentration and flow rate as well as the interaction terms across the properties of the porous media and the biochemical parameters. The models were used to identify the optimum MICP formulation for various porous media properties and the maximum permeability reduction profiles across cementation levels have been derived.

Prediction of silicon content of alloy in ferrochrome smelting using data-driven models

Ferrochrome (FeCr) is a vital ingredient in stainless steel production and is commonly produced by smelting chromite ores in submerged arc furnaces. Silicon (Si) is a componrnt of the FeCr alloy from the smelting process. Being both a contaminant and an indicator of the state of the process, its content needs to be kept within a narrow range. The complex chemistry of the smelting process and interactions between various factors make Si prediction by fundamental models infeasible. A data-driven approach offers an alternative by formulating the model based on historical data. This paper presents a systematic development of a data-driven model for predicting Si content. The model includes dimensionality reduction, regularized linear regression, and a boosting method to reduce the variability of the linear model residuals. It shows a good performance on testing data (R2 = 0.63). The most significant predictors, as determined by linear model analysis and permutation testing, are previous Si content, carbon and titanium in the alloy, calcium oxide in the slag, resistance between electrodes, and electrode slips. Further analysis using thermodynamic data and models, links these predictors to electrode control and slag

Role of virtual reality in advancing forensic medical examination in cases of occupational safety violations: a review

This article discusses the prospects for introducing innovative technologies in the form of virtual and augmented reality when conducting forensic examinations of certain categories of criminal cases, such as violations of labor protection rules. Problematic aspects are revealed when conducting inspections of crime scenes in hard-to-reach areas with possible risks to people.
 Based on a generalization of scientific works and practical experience of foreign countries, the application of innovations in scientific and technological progress in forensic examinations were analyzed.
 The authors proposed an algorithm for conducting forensic examination in the field of labor protection using virtual reality technology, as well as its further application in legal proceedings. The proposed algorithm can serve as basis for the development of more effective and safe methods for conducting forensic examinations in virtual reality.
 The development of detailed 3D injury models that simulate real-life accident scenarios will enable more accurate and objective injury assessments. Moreover, compared to traditional forensics, the use of virtual reality technology ensures high accuracy of measurements and coverage of the entire object under investigation, significantly reduces operating time, and stores all received information in its original state, which is an excellent opportunity to recreate the scene of an incident in real time.
 The introduction of virtual reality into forensic examination in cases of violation of labor protection rules will contribute to efficiency, objectivity, completeness, and ensuring the reliability of not only the conclusions of forensic examinations but also legal proceedings in general. Additionally, this direction of technology development represents significant benefits for the judicial system, contributing to the reduction of time and fair trials in the field of labor protection and in other categories of criminal cases.

Towards circular economy indicators: Evidence from the European Union.

The European Union (EU) is moving towards sustainable development, and a key role is played by circular economy (CE) models geared towards reducing pressure on natural resources, generating jobs and fostering economic opportunities. Indicators are able to aggregate a variety of information and their use, through the use of multi-criteria decision analysis (MCDA), allows the performance of alternatives to be monitored. This work aims to calculate the performance of the EU27 in the years 2019 and 2020 according to 15 CE indicators available on Eurostat. The results of the Analytic Hierarchy process show that the greatest impact on circularity is determined by the category 'competitiveness and innovation', which together with the category 'global sustainability and resilience' accounts for two thirds of the overall weight. The MCDA results show that Belgium prevails in both the baseline and alternative scenarios, ahead of Italy and the Netherlands respectively. In general, circular policies see western European countries excel, while the performance of eastern European countries is weaker. The implications of this work highlight the three main barriers to the development of CE models: (i) illegal waste management; (ii) lack of knowledge and low level of investment in circular technologies and (iii) low distribution of value among stakeholders. In this way, resource management based on circularity will enable Europe to meet the challenges of sustainability with less dependence on imported raw materials.

Development of a novel nonlinear model and control strategy for soft continuum robots featuring hard magnetoactive elastomers

Magnetoactive soft continuum robots (MSCRs), capable of controllable steering and navigation, hold substantial promise for healthcare applications. However, advancements in MSCRs have been hindered by a limited understanding of MSCR dynamics and a lack of effective control methods. Addressing these gaps, this study presents a novel, time-dependent, and computationally efficient analytical model of MSCR, alongside a new optimal closed-loop control strategy for precise high-frequency trajectory tracking. A finite element (FE) model of the MSCR is initially developed, with its validity confirmed through rigorous laboratory measurements. Using the formulated FE model, a new and computationally efficient analytical model is subsequently developed to accurately predict the highly nonlinear response of MSCR. This model operates as a system of switched linear models, each of which is a reduced-order version of its corresponding high-order linear model extracted from the FE analysis. This innovative approach not only maintains the predictive accuracy of the FE model but also significantly reduces computational demands, operating in just a few seconds. The results highlight that the developed model can accurately predict the dynamic responses of the MSCR while significantly reducing the computational load by almost 80 orders of magnitude compared with the FE model on the same simulation platform. The proposed model has been effectively utilized to develop a novel optimal control strategy using the feedforward interval type-2 fractional-order fuzzy-PID method. A hardware-in-the-loop experimental test has been finally designed to demonstrate the superior performance of the MSCR under the proposed controller.