Computational Tools Research Articles

CyclicPepedia: a knowledge base of natural and synthetic cyclic peptides.

Cyclic peptides offer a range of notable advantages, including potent antibacterial properties, high binding affinity and specificity to target molecules, and minimal toxicity, making them highly promising candidates for drug development. However, a comprehensive database that consolidates both synthetically derived and naturally occurring cyclic peptides is conspicuously absent. To address this void, we introduce CyclicPepedia (https://www.biosino.org/iMAC/cyclicpepedia/), a pioneering database that encompasses 8744 known cyclic peptides. This repository, structured as a composite knowledge network, offers a wealth of information encompassing various aspects of cyclic peptides, such as cyclic peptides' sources, categorizations, structural characteristics, pharmacokinetic profiles, physicochemical properties, patented drug applications, and a collection of crucial publications. Supported by a user-friendly knowledge retrieval system and calculation tools specifically designed for cyclic peptides, CyclicPepedia will be able to facilitate advancements in cyclic peptide drug development.

Estimating welding deformation of ultra-thin mild steel bead-on-plate joints by means of inherent strain method

The inherent strain method as an efficient computational tool has been widely used to predict welding deformation for large and complex structures with thin and medium-thick plates. For ultra-thin plate welded structures, geometric non-linear phenomena often arise during welding because of the low stiffness. In this study, the welding deformation of 0.8 mm thick bead-on-plate mild steel joints was studied by both experimental method and thermal-elastic-plastic finite element method (T-E-P FEM), and the influences of weld lengths on Tendon force, transverse shrinkage and angular distortion were examined experimentally and numerically. Meanwhile, a non-linear elastic finite element method based on the inherent strain theory using ABAQUS software as a platform was developed to reproduce the welding deformation of the ultra-thin bead-on-plate joints with different weld lengths, and the adaptability of the inherent strain method to ultra-thin plate weldments was clarified.

Development of a three-stage hierarchical model for quick calculating stair evacuation time of high-rise building coupled with simulation analysis

Calculating the evacuation time during staircase evacuation is essential to evaluate the security conditions of high-rise buildings under emergency scenarios. A three-stage hierarchical model for calculating emergency evacuation time is constructed by combining discrete state update rules and continuous forms in computation to investigate the evacuation process of pedestrians in multi-floor buildings in emergency circumstances. Combined with simulation analysis, the model considers the capacity constraint of stairwell and congestion effects and calculates the evacuation time hierarchically. The model's validity is verified by the existing evacuation simulator for a hypothetical building case study, coupled with a comprehensive computational comparison. Calculations on entire and separate evacuation times are explored considering different stairway capacity restrictions and geometric features. Computational results demonstrated that this model is effective and may be regarded as a valid tool for staircase evacuation time calculation in the context of high-rise buildings.

Abstract 3761: A modified SELEX enables the efficient identification of DNA aptamers with potential for specific cancer targeting

Abstract Background: Aptamers have sparked significant interest in cell recognition due to their superior binding specificity and biocompatibility. Cell recognition can be mediated by targeting the major histocompatibility complex (MHC) that presents short peptides derived from intracellular antigens. Although numerous antibodies have demonstrated a specific affinity for the peptide-MHC complex, the number of aptamers that exhibit comparable characteristics is limited. However, the success rate of aptamer identification is low. This is possibly due to the presence of complementary sequences or sequences rich in quinine and cytosine that are less accessible for primers. Here, we use ovalbumin model antigen to explore whether aptamers with specificity for binding to a defined peptide-MHC complex can be identified. We developed DNA aptamers with modified SELEX by employing systemic consecutive selections with minimal PCR amplification. Furthermore, the other tumour antigen, claudin-6, was also used as a target to determine whether our modification for SELEX can be used with any selection technology. Methods: To determine whether our modified SELEX has potential for selecting aptamer capable of cancer-specific targeting, we used ovalbumin presented by H-2Kb (to represent the specific antigen presented by MHC) and claudin-6 (a cancer-associated adhesion molecule) as a target for the selection of aptamers. This modified SELEX consisted of consecutive positive and negative selections with minimal PCR amplification. Meanwhile, the selection of candidate aptamers is based on their appearance in multiple rounds rather than enrichment, and is facilitated by several computational tools. The specificity and affinity of the candidate aptamer are validated by flow cytometry, immunohistochemistry, and immunofluorescence analysis with cells with or without these molecules. Results: After high-throughput sequencing, a total of 211 candidate aptamers were selected for further analysis. The quadruplex forming G-rich sequences (QGRS) mapper, mFold modeling, and NUPACK web servers were used to predict the potential of G-quadruplex formation, resulting in only four potential aptamers. Using MTCQ1-OVA cells with FITC-conjugated aptamers, two aptamers showed selective binding to MTCQ1-OVA cells and had no binding specificity to MTCQ1-WT cells. The binding specificity was further validated by immunohistochemistry in tumour derived from mice inoculated with MTCQ1-WT or MTCQ1-OVA cells. In the pull-down assay, the binding affinity of these two candidate aptamers was 136.2 nM and 155.1 nM affinity. Conclusions: This preliminary study demonstrated that our modification for SELEX is effective in the selection of aptamers with promising potential for specific cancer targeting. The identification of aptamers may facilitate further application in diverse biomedical fields. Citation Format: Yang Lin, Cho-Yi Chen, Zhi-Qian Lin, Bing-Hong Chen, Yi-Chen Sun, Kai-Feng Hung. A modified SELEX enables the efficient identification of DNA aptamers with potential for specific cancer targeting [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 3761.

Abstract 868: A novel unsupervised machine learning model applied to neuroblastoma single-cell RNA-seq data reveals a drug-induced mesenchymal-like gene expression program

Abstract High-risk neuroblastoma is one of the most difficult to treat pediatric cancers, with a survival rate of only about 50% and significant long-term consequences from current chemotherapy. Preclinical models such as cell lines and mice are the backbone of drug development and experimental-mechanistic oncology, but the development of new treatments is hampered in part by a lack of understanding of the direct clinical relevance of such data collected in preclinical models. Despite this, few formal methods have been developed to determine how these various models represent/resemble primary patient tumors. Here, we present the first comprehensive single-cell RNA-seq analysis of neuroblastoma across an extensive cohort of patient tumors and a variety of preclinical model systems (n = 126 total samples assembled - the largest cohort of its kind). By building an innovative unsupervised machine learning method, which we term “automatic consensus nonnegative matrix factorization” (acNMF), we have integrated and contrasted the transcriptional landscapes of patient tumors with those of cell lines, patient-derived xenografts (PDX), and genetic mouse models (GEMM).Using these tools, we discovered the dominant adrenergic gene expression programs found in neuroblastoma patient tumors were preserved across all preclinical models. However, the presumptive chemo-resistant mesenchymal-like programs, while identifiable in cell lines, were primarily restricted to subpopulations of cancer-associated fibroblasts and Schwann-like cells in vivo. Surprisingly, a mesenchymal-like program could be acutely chemotherapy-induced in GEMM and was evident in pre-treated patient and PDX samples, suggesting a previously uncharacterized mechanism of therapy escape. In addition to these core findings, our computational tools were able to further delineate the classical neuroblastoma adrenergic and mesenchymal gene expression programs, discovering for example, novel subpopulations of cancer associated fibroblasts. These behaviors were conserved across tumors and most preclinical models, which we validated by RNA in situ hybridization, which is a high resolution ultra sensitive spatial transcriptomics technology. Our work cautions against overreliance on traditional preclinical models without recognizing their limitations and we offer a nuanced, high-resolution view of neuroblastoma pre-clinical systems for advancing therapeutic development. We have launched an open-source web resource, featuring this integrated map to aid the scientific community in further exploration of these data and hypothesis generation (available at http://pscb.stjude.org). Citation Format: Rich Chapple, Xueying Liu, Sivaraman Natarajan, Margaret I. Alexander, Yuna Kim, Anand Patel, Christy W. LaFlamme, Min Pan, William C. Wright, Hyeong-Min Lee, Yinwen Zhang, Meifen Lu, Selene C. Koo, Courtney Long, John Harper, Chandra Savage, Melissa D. Johnson, Thomas Confer, Walter J. Akers, Michael A. Dyer, Heather Sheppard, John Easton, Paul Geeleher. A novel unsupervised machine learning model applied to neuroblastoma single-cell RNA-seq data reveals a drug-induced mesenchymal-like gene expression program [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 868.

Abstract 2277: Understanding the impact of clonal hematopoiesis on multiple myeloma: Insights from exosomal RNA analysis and survival analysis

Abstract Clonal Hematopoiesis of Indeterminate Potential (CHIP) is a trait characterized by the accumulation of somatic mutations in hematopoietic stem cells in certain adults. It has been observed that the presence of CHIP can have significant implications for the development and prognosis of various blood cancers. Multiple myeloma (MM), a cancer involving the abnormal proliferation of malignant plasma cells in the bone marrow, is one such condition. Exosomes, a type of extracellular vesicles, play a role in cell communication by carrying information between cells. Analysis of UK Biobank data confirmed the poor prognosis of multiple myeloma patients with CHIP and identified a potential of paracrine effect on plasma cancer cells. To gain deeper insights into this paracrine effect on plasma cells, we attempted to analyze cell-to-cell interaction, such as the expression of miRNAs in exosomes, through exosome RNA sequencing. In our research, exosomes were extracted from bone marrow samples of 30 multiple myeloma patients, and exosome RNA sequencing was performed. Differential expression of miRNAs was identified. A total of 14 down-regulated miRNAs were identified based on adjusted p value < 0.05, log2 fold change < -1. We found that hsa-let-7f-5p, hsa-let-7a-5p, and hsa-miR-148a-3p, which are mainly miRNAs targeting MAP kinase, were significantly down-regulated in the CHIP cases. A computational tool was used to predict and integrate the target genes and pathways regulated by these DE miRNAs. The results suggested that certain inflammatory and oncogenic pathways including the MAPK signaling pathways, were not effectively suppressed in cases with CHIP (KEGG, p < 0.005). In addition, the chemokine signaling pathway and cytokine-cytokine receptor interaction (KEGG, p < 0.005) related to the paracrine effect, integrins in angiogenesis (PID, p < 0.005) related to poor prognosis and metastasis of cancer were also not adequately inhibited in the CHIP cases. Using UK Biobank data, we called somatic CHIP variants excluding those with VAF < 0.02 and analyzed them against ICD-10 codes. Hazard ratios (HR) were computed through Cox proportional regression with covariates. CHIP presence significantly correlated with MM diagnosis (HR: 1.64, CI: 1.35-2, p < 0.001), notably in TET2 mutation carriers (HR: 2.35, CI: 1.63-3.4, p < 0.001). TET2 mutations intensified MGUS to MM progression (HR: 5.0, CI: 2.27-11.0, p < 0.001). CHIP in MM impacted EFS (HR: 1.61, CI: 1.25-2.1, p < 0.001), with TET2 mutation carriers facing higher hazards (HR: 1.66, CI: 1.05-2.6, p = 0.031). CHIP, especially TET2 mutations, significantly contribute to adverse MM outcomes and MGUS to MM evolution. Our findings provide insight into how CHIP influences the tumorigenesis and cancer progression of MM. Citation Format: Jeongmin Park, GangPyo Ryu, Daniel Nachun, Maggie Maurer, Siddhartha Jaiswal, Dong-Yeop Shin, Ja Min Byun, Junshik Hong, Youngil Koh, Sung-Soo Yoon. Understanding the impact of clonal hematopoiesis on multiple myeloma: Insights from exosomal RNA analysis and survival analysis [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 2277.

Building orientation radiation analysis: An early design study using Ladybug

AbstractThe role of building orientation in decreasing energy consumption for cooling purposes was examined in this study. A radiation analysis was performed during the early stage of the architectural design process on a proposed four-story apartment building located in Cebu. The building's performance was evaluated using the Grasshopper Ladybug plugin on a 3D model developed in Rhinoceros. Results indicated that the optimal building orientation that produced the lowest total radiation was situated at 290° from the center of the building, with a recorded value of 731,356 kWh m−2. Conversely, the building orientation that resulted in the highest total radiation was recorded at 210°, with a value of 755,596 kWh m−2. The analysis results were utilized to make informed decisions about building orientation based on environmental factors and surrounding areas. The use of computational design tools in the early stages of architecture design improves the process by enabling designers to optimize building performance and identify potential design issues early, thereby avoiding costly problems.

Abstract 7418: LongFuse: Detecting gene fusion transcripts from high throughput long-read single cell RNA sequencing data

Abstract Gene fusion resulted from genomic translocation plays important oncogenic roles including but not limited to gene activation and changing protein coding sequences. Historically, fusion gene analysis has been limited to bulk levels resulting in mixed oncogene activation models. Single cell Nanopore RNA sequencing (scNanoRNAseq) has the unique strength in detecting full-length chimeric transcripts in thousands of single cells in parallel. However, robust computational tools for detecting fusion genes in single cells from scNanoRNAseq data are missing. Here, we developed LongFuse, a fast and accurate tool, to detect fusion genes and their splicing isoforms in single cells from scNanoRNAseq data. LongFuse implements an XOR logic gate algorithm to speed up fusion candidate searching and stringent criteria to eliminate false discoveries due to trans-splicing events and randomly ligated chimeras. We applied LongFUSE onto scNanoRNAseq data of both human prostate cancer cell line, VCaP, and tumor tissues with known TMPRSS2-ERG fusion. Our results showed that both cancer cell line and prostate tumors harbored a mixture of fusion positive and fusion negative tumor cells. Gene expression analysis revealed transcriptional differences between fusion positive and negative cells including genes involved in cancer cell proliferation and invasion programs such as ELL2, ZBTB16, IGF1R, ANK3, MYPOP and PLPP1 genes. LongFUSE also calculates the splicing isoforms of fusion gene transcripts, allowing prediction of protein sequences of fusion gene transcripts. Taken together, our data demonstrated that LongFUSE is a robust computational tool for detecting gene fusions and their splicing isoforms at single cell level from scNanoRNAseq data. Citation Format: Cheng-Kai Shiau, Yueying He, Hsiao-Yun Lin, Lina Lu, Xiao-Dong Lu, Jindan Yu, Ruli Gao. LongFuse: Detecting gene fusion transcripts from high throughput long-read single cell RNA sequencing data [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 7418.

Abstract 1151: Reconstruction of the spatial ecosystem of glioblastoma reveals relationships between tumor cell states and microenvironment

Abstract Glioblastoma multiforme (GBM) is the most aggressive form of primary brain tumor, with no curative treatment options. Multiple studies have characterized at single cell resolution the GBM as being composed of transcriptional cell states interconnected with components in the tumor immune microenvironment (TME). Our group proposed and validated the first single cell guided functional classification of GBM in four tumor-intrinsic cell states which informed clinical outcome and delivered therapeutic options. However, single cell technologies are unable to unravel the spatial relationships among the cell states of GBM and between GBM cell states and TME. Spatially resolved transcriptomic technologies are emerging as powerful tools to reconstruct the spatial architecture of a tissue. We performed spatial transcriptomics of multicellular regions of interest (ROI) in 6 primary IDH wild-type GBM and 2 recurrent GBM with both CosMx Spatial Molecular Imager, which analyzes 1,000 RNA probes and 64 proteins at single cell resolution, and GeoMx Digital Spatial Profiler which profiles the whole transcriptome (~18,000 genes) at ROI resolution. The development of computational tools aimed to integrate spatial proximity and CosMx derived single-cell transcriptomics revealed spatial segregation of the tumor cell clones and cellular states and highlighted recurrent patterns of cell states, distinct TME cell types associated with coherent histopathological features across multiple samples. The development of a spatial informed intercellular communication algorithm and the reconstruction of ligand-receptor-target networks will allow the discovery of tumor cell states-TME cross-talks and the biological signaling regulated by these interactions that are driving the heterogeneity of GBM and therefore potentially therapeutically targetable. Analysis of matched regions of interest profiled by GeoMx and spatial proteomics with CosMx further cross-validated the spatial ecosystem of glioblastoma as reconstructed at single-cell resolution. Our studies established a scalable approach to resolve the transcriptional heterogeneity of GBM and reconstruct the architecture of GBM cell states and tumor microenvironment. Citation Format: Bruno Adabbo, Simona Migliozzi, Luciano Garofano, Fulvio D'Angelo, Pedro Davila, Sakir H. Gultekin, Daniel Bilbao Cortes, Benjamin Currall, Sion L. Williams, Marc Sanson, Franck Bielle, Anna Luisa Di Stefano, Michele Ceccarelli, Anna Lasorella, Antonio Iavarone. Reconstruction of the spatial ecosystem of glioblastoma reveals relationships between tumor cell states and microenvironment [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 1151.

Hidden in plain sight: Payoffs, probability, space, and time in isomorphic tasks

Highest stoppage games in economics—first price sealed bid auction and Dutch auction, among others—have produced striking puzzles, including violation of revenue equivalence between auctions and adjustment of behavior across interfaces. The latter is also observed in risk elicitation. Here, we use existing puzzles to shed light on each other, by nesting auction and risk tasks in a common environment. We find behavior consistent with a spatial interface imparting the same relevant auction-theoretic surplus and probability information as does an interactive numerical calculation tool. Empirical revenue equivalence (respectively, risky choice) can thus be altered by design.

Investigation of different solvents for selective, safe and rapid extraction of curcumin from various food and herbal supplement products: Multivariate strategy and assessment of green profile

In the paper, different extraction solvents including magnetic ionic liquids (MILs), deep eutectic solvents, SUPRAS and surfactant were investigated for the fast, reliable and simple extraction of curcumin in the dispersive liquid–liquid microextraction (DLLME) method prior to UV–Vis spectrophotometric analysis. As a result of preliminary studies, the [Co(C8IM)42+]2[Cl−] MIL was selected as the extraction solvent for the extraction of curcumin from various matrix samples. Optimization of essential factors in the DLLME method was carried out with central composite design (CCD). Effective dispersion of the [Co(C8IM)42+]2[Cl−] MIL in the sample solution was achieved by orbital shaking (OS). After the CCD modelling, the OS-MIL-DLLME method provided linearity in the range of 0.5–225 ng mL−1 with a detection limit of 0.15 ng mL−1 for the model solution. The OS-MIL-DLLME method showed a matrix effect of 7.6 % with an enrichment factor of 135. Validation parameters of the OS-MIL-DLLME method such as selectivity, robustness, intraday/interday precision and recovery test were investigated in detail. The green analytical scale of the method was determined using the analytical greenness calculator tool. The OS-MIL-DLLME method was applied to the foods and herbal supplement products together with the reference method, and quantitative recoveries and statistically reliable results were obtained.

Integration of Febio as an Instructional Tool in the Undergraduate Biomechanics Curriculum.

Computer simulations play an important role in a range of biomedical engineering applications. Thus, it is important that biomedical engineering students engage with modeling in their undergraduate education and establish an understanding of its practice. In addition, computational tools enhance active learning and complement standard pedagogical approaches to promote student understanding of course content. Herein, we describe the development and implementation of learning modules for computational modeling and simulation (CM&S) within an undergraduate biomechanics course. We developed four CM&S learning modules that targeted predefined course goals and learning outcomes within the febio studio software. For each module, students were guided through CM&S tutorials and tasked to construct and analyze more advanced models to assess learning and competency and evaluate module effectiveness. Results showed that students demonstrated an increased interest in CM&S through module progression and that modules promoted the understanding of course content. In addition, students exhibited increased understanding and competency in finite element model development and simulation software use. Lastly, it was evident that students recognized the importance of coupling theory, experiments, and modeling and understood the importance of CM&S in biomedical engineering and its broad application. Our findings suggest that integrating well-designed CM&S modules into undergraduate biomedical engineering education holds much promise in supporting student learning experiences and introducing students to modern engineering tools relevant to professional development.

Mesosilicate materials for environmental applications: Adsorption separation analysis via hybrid computational and machine learning tools

In this extensive analysis, we delve into the utilization of advanced hybrid model for molecular separation description via mesoporous materials. The process considered here is adsorption which is utilized for molecular separation in liquid solution. Data for adsorption are obtained in terms of solute concentration by solving mass transfer equations numerically. Then, the data is used for training and validation of several machine learning techniques to establish the hybrid model. For machine learning model, several methods are utilized including three robust tree-based ensemble models, namely Gradient Boosting (GB), Random Forest (RF), and Extra Trees (ET). The methos were utilized to tackle the challenging task of predicting chemical concentrations in a dataset comprising an array of over 19,000 data. These data points are characterized by input variables representing coordinates (x and y), while the output variable encapsulates chemical concentration (C). By subjecting these models to a meticulous optimization process, we achieved substantial enhancements in their predictive capabilities. RF emerged as the standout performer, boasting a remarkable R2 score of 0.99902. ET and GB also demonstrated commendable performance improvements, underscoring the versatility of tree-based ensemble models in delivering precise and reliable predictions for chemical concentrations within intricate and multifaceted datasets.

Design of experiments and white analytical chemistry-driven green and sensitive spectrofluorimetric estimation of pregabalin in its pharmaceutical dosage forms and spiked human plasma

BackgroundPregabalin (PGB) is a medication with anticonvulsant, analgesic and anxiolytic properties, employed in the treatment of epilepsy, neuropathic pain, fibromyalgia, restless leg syndrome, opioid withdrawal syndrome and generalized anxiety disorder. Several spectrofluorimetric techniques have been documented for the determination of PGB in pharmaceutical dosage forms. However, these published methods typically involve the use of expensive and toxic organic solvents and reagents, as well as high reaction temperatures for PGB analysis. These components pose risks to aquatic life and the environment, making them less environmentally friendly and user-friendly. A recent advancement in analytical chemistry has introduced a white analytical approach, providing an economical, eco-friendly and user-friendly method for the development of analytical procedures.ObjectivesTherefore, a green and sensitive spectrofluorimetric determination of PGB, guided by white analytical chemistry principles, has been conducted utilizing distilled water as an environmentally friendly solvent.MethodsThe establishment of the spectrofluorimetric method involved employing the design of experiments approach to ensure a robust, precise and accurate estimation of PGB. Response surface analysis and optimization of critical procedural variables and responses were carried out using the central composite design. The validation of the developed method adhered to the guidelines outlined in ICH (International Council for Harmonization) Q2 (R1) and M10.ResultsThe established spectrofluorimetric method was utilized to determine the PGB content in commercially available formulations and human plasma samples spiked with PGB. The obtained results were in accordance with the labeled claim of PGB in the formulations. The recovery of PGB in the spiked human plasma samples ranged from 85 to 90% of the spiked amount.ConclusionsThe greenness profiles of the published and suggested spectrofluorimetric methods for PGB estimation were evaluated and compared using the AGREE calculator, GAPI software and ESA tool. The suggested method demonstrated sensitivity, robustness, environmental friendliness and user-friendliness.

Transformer-based spatial-temporal detection of apoptotic cell death in live-cell imaging.

Intravital microscopy has revolutionized live-cell imaging by allowing the study of spatial-temporal cell dynamics in living animals. However, the complexity of the data generated by this technology has limited the development of effective computational tools to identify and quantify cell processes. Amongst them, apoptosis is a crucial form of regulated cell death involved in tissue homeostasis and host defense. Live-cell imaging enabled the study of apoptosis at the cellular level, enhancing our understanding of its spatial-temporal regulation. However, at present, no computational method can deliver robust detection of apoptosis in microscopy timelapses. To overcome this limitation, we developed ADeS, a deep learning-based apoptosis detection system that employs the principle of activity recognition. We trained ADeS on extensive datasets containing more than 10,000 apoptotic instances collected both in vitro and in vivo, achieving a classification accuracy above 98% and outperforming state-of-the-art solutions. ADeS is the first method capable of detecting the location and duration of multiple apoptotic events in full microscopy timelapses, surpassing human performance in the same task. We demonstrated the effectiveness and robustness of ADeS across various imaging modalities, cell types, and staining techniques. Finally, we employed ADeS to quantify cell survival in vitro and tissue damage in mice, demonstrating its potential application in toxicity assays, treatment evaluation, and inflammatory dynamics. Our findings suggest that ADeS is a valuable tool for the accurate detection and quantification of apoptosis in live-cell imaging and, in particular, intravital microscopy data, providing insights into the complex spatial-temporal regulation of this process.

The Kantorovich-Wasserstein distance for spatial statistics: The Spatial-KWD library

In this paper we present Spatial-KWD, a free open-source tool for efficient computation of the Kantorovich-Wasserstein Distance (KWD), also known as Earth Mover Distance, between pairs of binned spatial distributions (histograms) of a non-negative variable. KWD can be used in spatial statistics as a measure of (dis)similarity between spatial distributions of physical or social quantities. KWD represents the minimum total cost of moving the “mass” from one distribution to the other when the “cost” of moving a unit of mass is proportional to the euclidean distance between the source and destination bins. As such, KWD captures the degree of “horizontal displacement” between the two input distributions. Despite its mathematical properties and intuitive physical interpretation, KWD has found little application in spatial statistics until now, mainly due to the high computational complexity of previous implementations that did not allow its application to large problem instances of practical interest. Building upon recent advances in Optimal Transport theory, the Spatial-KWD library allows to compute KWD values for very large instances with hundreds of thousands or even millions of bins. Furthermore, the tool offers a rich set of options and features to enable the flexible use of KWD in diverse practical applications.

What is the connection between Gen Y employees' loyalty, satisfaction, and workplace spirituality?

Maintaining and enhancing employee loyalty and satisfaction has always been a top priority for management in every firm. This study aims to investigate the relationship between workplace spirituality, loyalty, and Generation Y employees at private organisations in Indonesia. A quantitative approach with descriptive and verification analysis techniques is used to see the picture and examine the relationship between variables. Data and information were obtained from a sample of 132 people, through the process of distributing questionnaires and surveys. Data analysis using statistical software calculation tools. The results showed that each variable has a fairly high criterion. Employee loyalty is significantly influenced by workplace spirituality and job satisfaction. The results demonstrate that workers who uphold spiritual principles at work are more likely to desire to remain with the company. The theoretical contribution of this study is that Generation Y employee loyalty can be increased by increasing job satisfaction and workplace spirituality. The growth of spiritual values needs to be supported by creating a spiritual work environment, providing fair remuneration is expected to foster loyal attitudes and behavior of employees. Research is limited to Y-generation and to objects in private tertiary institutions in Garut Regency where there is a possibility of cultural similarities, therefore further research is suggested with a wider scope.

Valvular Heart Disease in Pregnancy.

Valvular heart disease is a common cause of peripartum cardiovascular morbidity and mortality. The hemodynamic changes of pregnancy and their impact on preexisting valvular lesions are described in this paper. Tools for calculation of maternal and fetal risk during pregnancy are also discussed. The pathophysiology and management of valvular lesions, both obstructive and regurgitant, are then described, followed by discussion of mechanical and bioprosthetic valve complications during pregnancy.

Calculation of Homogenized Mechanical Coefficients of Fiber-Reinforced Composite Using Finite Element Method.

Determining the mechanical properties of a composite material represents an important stage in its design and is generally a complicated operation. These values are influenced by the topology and geometry of the resulting composite and the values of the elastic constants of the components. Due to the importance of this subject and the increasing use of composite materials, different calculation methods have been developed over the last fifty years. Some of the methods are theoretical, with results that are difficult to apply in practice due to difficulties related to numerical calculation. In the current paper, using theoretical results offered by the homogenization theory, values of engineering elastic constants are obtained. The finite element method (FEM) is used to determine the stress and strain field required in these calculations; this is an extremely powerful and verified calculation tool for the case of a material with any type of structure and geometry. In order to minimize errors, the paper proposes the method of least squares, a mathematical method that provides the best estimate for the set of values obtained by calculating FEM. It is useful to consider as many load cases as possible to obtain the best estimates. The elastic constants for a transversely isotropic material (composite reinforced with cylindrical fibers) are thus determined for a real case.

LinChemIn: Route Arithmetic─Operations on Digital Synthetic Routes.

Computational tools are revolutionizing our understanding and prediction of chemical reactivity by combining traditional data analysis techniques with new predictive models. These tools extract additional value from the reaction data corpus, but to effectively convert this value into actionable knowledge, domain specialists need to interact easily with the computer-generated output. In this application note, we demonstrate the capabilities of the open-source Python toolkit LinChemIn, which simplifies the manipulation of reaction networks and provides advanced functionality for working with synthetic routes. LinChemIn ensures chemical consistency when merging, editing, mining, and analyzing reaction networks. Its flexible input interface can process routes from various sources, including predictive models and expert input. The toolkit also efficiently extracts individual routes from the combined synthetic tree, identifying alternative paths and reaction combinations. By reducing the operational barrier to accessing and analyzing synthetic routes from multiple sources, LinChemIn facilitates a constructive interplay between artificial intelligence and human expertise.