bluuebunny/crossref_metadata_2025_split huggingface dataset:
Statistical modelling of determinants of child stunting using secondary data and Bayesian networks: a UKRI Global Challenges Research Fund (GCRF) Action Against Stunting Hub protocol paper
Authors: Rosenstock Todd S , Yet Barbaros 
Published: March 2024

IntroductionSeveral factors have been implicated in child stunting, but the precise determinants, mechanisms of action and causal pathways remain poorly understood. The objective of this study is to explore causal relationships between the various determinants of child stunting.Methods and analysisThe study will use data compiled from national health surveys in India, Indonesia and Senegal, and reviews of published evidence on determinants of child stunting. The data will be analysed using a causal Bayesian network (BN)—an approach suitable for modelling interdependent networks of causal relationships. The model’s structure will be defined in a directed acyclic graph and illustrate causal relationship between the variables (determinants) and outcome (child stunting). Conditional probability distributions will be generated to show the strength of direct causality between variables and outcome. BN will provide evidence of the causal role of the various determinants of child stunning, identify evidence gaps and support in-depth interrogation of the evidence base. Furthermore, the method will support integration of expert opinion/assumptions, allowing for inclusion of the many factors implicated in child stunting. The development of the BN model and its outputs will represent an ideal opportunity for transdisciplinary research on the determinants of stunting.Ethics and disseminationNot applicable/no human participants included.
Digital spatial proteomic profiling reveals immune checkpoints as biomarkers in lymphoid aggregates and tumor microenvironment of desmoplastic melanoma
Authors: Su David G , Schoenfeld David A. , Ibrahim Wael , Cabrejo Raysa , Djureinovic Dijana , Baumann Raymond , Rimm David L , Khan Sajid A , Halaban Ruth , Kluger Harriet M , Olino Kelly , Galan Anjela , Clune James 
Published: March 2024

BackgroundDesmoplastic melanoma (DM) is a rare melanoma subtype characterized by dense fibrous stroma, a propensity for local recurrence, and a high response rate to programmed cell death protein 1 (PD-1) blockade. Occult sentinel lymph node positivity is significantly lower in both pure and mixed DM than in conventional melanoma, underscoring the need for better prognostic biomarkers to inform therapeutic strategies.MethodsWe assembled a tissue microarray comprising various cores of tumor, stroma, and lymphoid aggregates from 45 patients with histologically confirmed DM diagnosed between 1989 and 2018. Using a panel of 62 validated immune-oncology markers, we performed digital spatial profiling using the NanoString GeoMx platform and quantified expression in three tissue compartments defined by fluorescence colocalization (tumor (S100+/PMEL+/SYTO+), leukocytes (CD45+/SYTO+), and non-immune stroma (S100−/PMEL−/CD45−/SYTO+)).ResultsWe observed higher expression of immune checkpoints (lymphocyte-activation gene 3 [LAG-3] and cytotoxic T-lymphocyte associated protein-4 [CTLA-4]) and cancer-associated fibroblast (CAF) markers (smooth muscle actin (SMA)) in the tumor compartments of pure DMs than mixed DMs. When comparing lymphoid aggregates (LA) to non-LA tumor cores, LAs were more enriched with CD20+B cells, but non-LA intratumoral leukocytes were more enriched with macrophage/monocytic markers (CD163, CD68, CD14) and had higher LAG-3 and CTLA-4 expression levels. Higher intratumoral PD-1 and LA-based LAG-3 expression appear to be associated with worse survival.ConclusionsOur proteomic analysis reveals an intra-tumoral population of SMA+CAFs enriched in pure DM. Additionally, increased expressions of immune checkpoints (LAG-3 and PD-1) in LA and within tumor were associated with poorer prognosis. These findings might have therapeutic implications and help guide treatment selection in addition to informing potential prognostic significance.
Flavours and design features on tobacco product packaging near schools before and after plain packaging implementation in Montevideo, Uruguay
Authors: Minacapilli Manetti Mauricio Luis , Gonzalez Peluffo Valentina , Taglioretti Victoria , Barros Mary , Nunez Virginia , Melian Tamara , Grilo Graziele , Llambi Laura 
Published: March 2024

IntroductionMonitoring tobacco industry marketing strategies in countries that have introduced plain packaging helps with documenting variations in the market during the transition period. Uruguay implemented plain packaging in February 2020. We describe changes in the characteristics of tobacco packaging, content and sticks before and after plain packaging implementation.MethodsData were collected across 15 neighbourhoods in different socioeconomic areas in Montevideo, Uruguay, before and after implementation (2019 and 2021). A high school or college was selected in each neighbourhood as the walking protocol starting point. Two stores were visited per neighbourhood. Cigarettes and roll-your-own tobacco (RYO) were purchased and coded for the presence of taste or sensation lexical and imagery features.ResultsThe number of unique products increased between 2019 (n=23) and 2021 (n=40). Prior to implementation, all packs presented design features. After its implementation, 95.7% of cigarette packs complied with regulations. Overall, 34.7% of cigarettes and RYO were flavoured in 2019 versus 50.0% in 2021 (p=0.01). In 2019, all flavoured cigarette packs conveyed taste through language and/or imagery, while cigarettes had designs on the filter suggesting the potential for altering the flavour. In 2021, 44.0% of cigarette packs indicated flavour through lexicon; and 81.0% of cigarette sticks still included a flavour capsule.ConclusionsAfter implementation, we noticed an increase in the availability of unique flavoured cigarettes and RYO among surveyed retailers. However, this increase was less pronounced compared with what is reported in the Latin American region. Non-compliance was identified. Greater efforts should be made enforcing current policy.
Spectra of phosphorus ions for astrophysical modeling: P I–P XV
Authors: Nahar Sultana N. , Shafique Bilal 
Published: March 2024

Phosphorus (P), a basic element of life, has been a least studied element due to its poor presence in astrophysical spectra. However, search for the P lines has increased considerably with discoveries of exoplanets and are being detected by high resolution and sophisticated astronomical observatories, e.g., James Webb Space Telescope (JWST). JWST may provide a clue for life with detection of P in its infrared (IR) region. Identification of the element and analysis of the observed spectra will require high accuracy data for atomic processes that produces lines and their predicted features. The present study focuses on these needs and reports systematically regions of wavelengths, from X-ray to IR, that show prominent lines by the 15 individual ionization stages of phosphorus, P I–P XV for the first time. We present large amount of relevant atomic data for energies, transition parameters, and lifetimes obtained in relativistic Breit–Pauli approximation using the R-matrix method and atomic structure program SUPERSTRUCTURE. Our spectral features for the 15 ions, P I–P XV, predict strengths of lines in various wavelength regions. They show dominance of P I and P II in the IR region and other ions in the ultraviolet and optical regions often stretching to IR in the continuum. For determination of accuracy, we have made extensive comparisons of our atomic data with available experimental and theoretical values. Based on these, our results and features are expected to provide precise plasma diagnostics and astrophysical modeling.
Hand Gestures Recognition Model Using Adaptive Feature Extraction with Attention-Based Hybrid Deep Learning via Optimization Strategy
Authors: Sampath Gnanapriya , Basha Rahimunnisa Kamal , Muthu Mohana , Bhagyalakshmi L. 
Published: March 2024

Research on continuous hand gesture recognition is motivated by applications of deaf people to provide effective communication between one another. Numerous techniques are adapted for effectively identifying the hand gesture. Moreover, the collection of data becomes a challenging task in the existing models. In some instances, hand gesture in low-resolution images becomes a complicated task. In this research work, a deep learning-based hand gesture detection model is designed using hand data points to uncover patterns that are used to convey information. Initially, hand gesture images are obtained from public sources. The collected inputs are preprocessed through Contrast-Limited Adaptive Histogram Equalization and filtering to eliminate the unnecessary blurs in the images. The preprocessed images undergo hand segmentation using four methods color space transformation, skin color detection, active contour, and morphological operation. The segmented image is subjected to data point feature extraction, where the Adaptive Weighted Scale-Invariant Feature Transform is utilized for further enhancement. The hand data point-extracted features are given to the recognition of hand gestures with the support of an attention-based hybrid network, where the Attention-based Hybrid 1D Convolutional Neural Network with Recurrent Neural Network recognizes the hand gestures from the hand data points. In the hybridization network, the values are optimized with the help of the investigated Adaptive Dandelion White Shark Optimizer to enhance recognition effectiveness. The test results are validated with the existing hand gesture recognition models using diverse evaluation metrics. The findings of the recommended method show 97% and 99% in terms of accuracy and NPV. Thus, the developed model is validated to outperform significant performance rather than the existing models.
Inferring Transcriptional Bursting Kinetics Using Gene Expression Model with Memory and Crosstalk from scRNA-seq Data
Authors: Wang Mengyuan , Cao Wenjie , Guo Yanbing , Wang Guilin , Jiang Jian , Qiu Huahai , Zhang Ben-Gong 
Published: March 2024

Genetically identical cell populations growing in the same environment show a large degree of heterogeneity in gene expression profiles, resulting in significant phenotypic consequences. One of the important sources of this heterogeneity is transcriptional bursting. In recent years, single cell transcriptome sequencing (scRNA-seq) data have been widely used to infer the kinetics of transcriptional bursting. Moreover, the increasing evidence showed that genes are jointly regulated by several competitive pathways when activated by external signals, and there is molecular memory in the process of gene state switching. Therefore, a gene expression model considering both gene activation pathway and molecular memory can better reflect the nature of transcriptional bursting. In this paper, we used an approximate Bayesian computation algorithm called ABC-PRC combined with a gene expression model that considered crosstalk and memory, to infer the kinetics of transcriptional bursting. We analyze the internal relationship between transcriptional bursting and gene expression using scRNA-seq data obtained from mouse embryonic stem cells and mouse embryonic fibroblasts. Both model analysis and data simulations demonstrate that the bimodal coefficient increases with the increase of burst size, and the noise intensity decreases with the increase of burst frequency. Additionally, we show that some previously proposed models can be simplified as special cases of the gene expression model and inference algorithm proposed here under certain circumstances.
Xylem: An Energy-efficient, Globally Redistributive, Financial Infrastructure Using Proof-by-Location
Authors: Patterson Donald Jay , Tomlinson Bill 
Published: March 2024

The Proof-of-Work algorithm that underlies Bitcoin, Ethereum
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and many other cryptocurrencies is well known for its energy-intensive requirements. The Proof-of-Stake algorithm that underlies Ethereum and various other cryptocurrencies is less impactful environmentally, but it has a second, looming issue: the problem of wealth inequality. We have developed an alternative to Proof-of-Work and Proof-of-Stake, called Proof-by-Location, that has the potential to address both of these issues. This article describes Proof-by-Location and a financial platform called Xylem that is based on it. This platform seeks to distribute transaction fees to billions of cryptocurrency “Notaries” around the world (essentially, anyone with a smartphone), who work together to establish a distributed consensus about financial transactions. In this article, we demonstrate that this platform can scale to more than 3.9 trillion transactions per year (more than triple the number of digital payments per year currently occurring). We show a reduction of electricity usage per transaction of 99.9999914% compared to Bitcoin, 99.999905% compared to Ethereum
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, 99.83% compared to Ethereum, and 95.9% compared to the Visa financial services company. We demonstrate that this platform would have a redistributive rather than consolidatory effect on wealth compared to any of these platforms, leading to a source of income for more than 1 billion people around the world, including more than 110 million in the bottom 10th to 20th percentile by income, with income for that group equivalent to 8.8 million full-time jobs. Finally, this currency provides a positive, non-compulsory mechanism for shaping human habitation patterns in ways that can slow global biodiversity loss and enable ecological restoration. Using Xylem as a global financial infrastructure could lead to significantly better social and environmental outcomes than existing financial platforms.

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Abstract 1045: A freely expandable 20-plex immuno-oncology antibody core panel for spatial biology experiments on human FFPE cancer sections: REAplex IO Core for MICS technology
Authors: Femel Julia , DiBasio Eric C. , Schönborn Katrin , Büttner Maximilian , Düking Tim , Habich Markus , Wantenaar Tanya , Eulitz Stefan , Bosio Andreas 
Published: March 2024

Abstract
Spatial biology is integral to understanding the dynamics of immune cells, tumor microenvironment (TME), and therapeutic responses in cancer research. Ultra-high-plex immunofluorescence has transformed deep phenotyping and cell neighborhood analysis, providing unprecedented insight into complex and heterogeneous samples such as the TME. Many spatial biology platforms reduce operator hands-on time by automating image acquisition. However, determining the most appropriate markers to distinguish key cell types, finding application-compatible antibody clones and optimizing their dilution remains a tedious manual process. To streamline the panel-building process, we have developed a series of pre-defined, commercially available antibody panels for the MACSima™ Imaging Cyclic Staining (MICS) technology. So far, these have covered the spectrum from ultra-high-plex screening panels with up to 205 antibodies (REAscreen™ MAX) to the more focused REAscreen Immuno Oncology antibody panel with 61 cancer and immune-related antibodies for the comprehensive analysis of various different human FFPE samples. Here, we introduce a new, 20-plex, pre-defined antibody panel. Organized along essential marker groups, it can function as a stand-alone panel that allows the identification of the major tumor-infiltrating leukocyte populations with a focus on T-cells, and providing insight into TME architecture, tumor neovascularization, and cellular activity. Because it can be combined and expanded with any number of antibodies or even entire panel modules, it can be used as a standardized basis for highly multiplexed custom panels optimized for the research question at hand. We demonstrate its performance and broad applicability across several cancer types analyzed with the MICS technology. Finally, preset template examples for panel-specific data processing with MACS iQ View analysis software demonstrate the simplicity yet power of an integrated end-to-end workflow.
Citation Format: Julia Femel, Eric C. DiBasio, Katrin Schönborn, Maximilian Büttner, Tim Düking, Markus Habich, Tanya Wantenaar, Stefan Eulitz, Andreas Bosio. A freely expandable 20-plex immuno-oncology antibody core panel for spatial biology experiments on human FFPE cancer sections: REAplex IO Core for MICS technology [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 1045.
Abstract 1060: Circulating early myeloid derived suppressor cells as biomarkers for early detection of lung cancer
Authors: Fortunato Orazio , Bassani Barbara , Lecchi Mara , Verderio Paolo , Pastorino Ugo , Sozzi Gabriella , Colombo Mario Paolo , Sangaletti Sabina 
Published: March 2024

Abstract
Background. Lung cancer (LC) patients, despite innovative cancer treatments such as target and immune-therapies, still have the poorest five-year survival rate. In this context the possibility of an early detection can anticipate therapeutic intervention, avoiding aggressive therapies thus prolonging disease-free and overall survival. Therefore, the identification of new circulating early biomarkers still represents a relevant clinical need.
Methods. Lung cancer (LC) patients (n=63) and heavy smokers (HS) cancer free-subjects (n=52) were evaluated for the enrichment of specific subtypes of myeloid cells. Circulating early myeloid derived suppressor cells (eMDSCs) and low-density neutrophils (LDNs) were assessed by flow cytometry on peripheral blood mononuclear cells (PMBCs). Early MDSCs were defined as CD11b+CD33+CD15-CD14- cells among Lineage negative (Lin-) HLA-DR- PBMCs, whereas LDNs were defined as CD66b+CD15+ cells within the gate of CD11b+ PBMCs. Mature and immature subtypes were discriminated according to the expression of CD10.
Results. Performing a sex- and smoking -matched case-control analysis in 39 LC and 39 HS subjects we were able to compare the capacity of specific myeloid population to discriminate lung cancer patients from heavy smokers. The analysis showed significantly higher values of eMDSC (OR=1.12; 95% Confidence Interval (CI) 1.03-1.21), total LDN (CD66b+CD15+; OR=1.23; 95%CI 1.04-1.45) and of mature CD10+ LDN (OR=1.30; 95%CI 1.05-1.61). Moreover, within the overall cohort of 63 LC patients, we analyzed the distributions of the different myeloid populations according to stage (I-II vs III-IV). In this comparison, total LDN (CD66b+CD15+; Kruskal-Wallis (KW) Test p-value=0.014), and mature CD10+ LDN (KW test p-value=0.006) showed significant different distributions according to tumor stage, observing higher values in patients with advanced stage. On the contrary, no difference among stages was observed for the eMDSC population.
Conclusions. These findings point to eMDSCs as an early marker for lung cancer detection whereas LDN could be as well associated with disease progression.
Citation Format: Orazio Fortunato, Barbara Bassani, Mara Lecchi, Paolo Verderio, Ugo Pastorino, Gabriella Sozzi, Mario Paolo Colombo, Sabina Sangaletti. Circulating early myeloid derived suppressor cells as biomarkers for early detection of lung 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 1060.
Abstract 1115: Enhancing lung cancer sensitivity to chemotherapy, radiation, and chemo-radiation through inhibition of a mitotic checkpoint kinase BUB1
Authors: Thoidingjam Shivani , Sriramulu Sushmitha , Brown Stephen L. , Siddiqui Farzan , Movsas Benjamin , Gadgeel Shirish , Nyati Shyam 
Published: March 2024

Abstract
Non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) represent two major subtypes of lung cancer, marked by challenging treatment resistance. Our study explores the inhibition of BUB1, a critical mitotic checkpoint kinase, in conjunction with radiotherapy and chemotherapy. This approach has the potential to overcome resistance, thus enhancing the efficacy of treatment and improving treatment outcomes for lung cancer patients. We analyzed BUB1 expression in lung cancer using TCGA datasets and patient tissue microarrays (TMAs). In vitro experiments used four NSCLC and two SCLC cell lines with BUB1 inhibitor (BAY1816032), Paclitaxel, Cisplatin and Olaparib. Cells were irradiated (2, 4, or 6 Gy), and assays measured proliferation, long-term effects, DNA damage, and repair. Bioinformatic analysis in NSCLC subtype, lung adenocarcinoma (LUAD) demonstrated marked overexpression of BUB1, correlated with cancer stage, tumor grade and survival. Lung cancer TMAs revealed elevated expression of BUB1 protein in SCLC that correlated with poorer survival. BUB1 inhibition (BUB1i) induced cytotoxicity in LUAD cell lines at micromolar concentrations, while nanomolar concentrations enhanced cell death significantly when combined with radiation. Furthermore, BUB1i sensitized LUAD cells to different classes of chemotherapy (Paclitaxel, Cisplatin), and PARP inhibitor (Olaparib) alone (chemo-sensitization) or with radiation therapy (chemo-radiosensitization). H2030 and H1975 demonstrated higher chemo-radiosensitization efficacy compared to A549, likely due to genetic differences, potentially including a p53 mutation that improves sensitivity by affecting DNA repair and cell cycle regulation. Preliminary investigations revealed DNA double-strand breaks, indicated by prolonged γH2AX foci, and the inhibition of the NHEJ DNA damage repair pathway after BUB1 ablation. Among SCLC cell lines, NCI-H1876 demonstrated higher radiosensitivity upon BUB1 ablation compared to NCI-H2198, potentially due to genetic differences/mutations affecting radiation response. The study highlights the potential of BUB1i in enhancing the sensitivity of NSCLC and SCLC cells to radiotherapy and chemotherapy and provide rationale to explore BUB1i as a novel approach to enhance lung cancer therapy in clinical trials.
Citation Format: Shivani Thoidingjam, Sushmitha Sriramulu, Stephen L. Brown, Farzan Siddiqui, Benjamin Movsas, Shirish Gadgeel, Shyam Nyati. Enhancing lung cancer sensitivity to chemotherapy, radiation, and chemo-radiation through inhibition of a mitotic checkpoint kinase BUB1 [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 1115.