bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modeling metastatic progression from cross-sectional cancer genomics data
Authors: Rupp Kevin , Lösch Andreas , Hu Yanren Linda , Nie Chenxi , Schill Rudolf , Klever Maren , Pfahler Simon , Grasedyck Lars , Wettig Tilo , Beerenwinkel Niko , Spang Rainer 
Published: June 2024

Abstract

Motivation
Metastasis formation is a hallmark of cancer lethality. Yet, metastases are generally unobservable during their early stages of dissemination and spread to distant organs. Genomic datasets of matched primary tumors and metastases may offer insights into the underpinnings and the dynamics of metastasis formation.

Results
We present metMHN, a cancer progression model designed to deduce the joint progression of primary tumors and metastases using cross-sectional cancer genomics data. The model elucidates the statistical dependencies among genomic events, the formation of metastasis, and the clinical emergence of both primary tumors and their metastatic counterparts. metMHN enables the chronological reconstruction of mutational sequences and facilitates estimation of the timing of metastatic seeding. In a study of nearly 5000 lung adenocarcinomas, metMHN pinpointed TP53 and EGFR as mediators of metastasis formation. Furthermore, the study revealed that post-seeding adaptation is predominantly influenced by frequent copy number alterations.

Availability and implementation
All datasets and code are available on GitHub at https://github.com/cbg-ethz/metMHN.
Floria: fast and accurate strain haplotyping in metagenomes
Authors: Shaw Jim , Gounot Jean-Sebastien , Chen Hanrong , Nagarajan Niranjan , Yu Yun William 
Published: June 2024

Abstract

Summary
Shotgun metagenomics allows for direct analysis of microbial community genetics, but scalable computational methods for the recovery of bacterial strain genomes from microbiomes remains a key challenge. We introduce Floria, a novel method designed for rapid and accurate recovery of strain haplotypes from short and long-read metagenome sequencing data, based on minimum error correction (MEC) read clustering and a strain-preserving network flow model. Floria can function as a standalone haplotyping method, outputting alleles and reads that co-occur on the same strain, as well as an end-to-end read-to-assembly pipeline (Floria-PL) for strain-level assembly. Benchmarking evaluations on synthetic metagenomes show that Floria is  &gt; 3× faster and recovers 21% more strain content than base-level assembly methods (Strainberry) while being over an order of magnitude faster when only phasing is required. Applying Floria to a set of 109 deeply sequenced nanopore metagenomes took &lt;20 min on average per sample and identified several species that have consistent strain heterogeneity. Applying Floria’s short-read haplotyping to a longitudinal gut metagenomics dataset revealed a dynamic multi-strain Anaerostipes hadrus community with frequent strain loss and emergence events over 636 days. With Floria, accurate haplotyping of metagenomic datasets takes mere minutes on standard workstations, paving the way for extensive strain-level metagenomic analyses.

Availability and implementation
Floria is available at https://github.com/bluenote-1577/floria, and the Floria-PL pipeline is available at https://github.com/jsgounot/Floria_analysis_workflow along with code for reproducing the benchmarks.
SPRITE: improving spatial gene expression imputation with gene and cell networks
Authors: Sun Eric D , Ma Rong , Zou James 
Published: June 2024

Abstract

Motivation
Spatially resolved single-cell transcriptomics have provided unprecedented insights into gene expression in situ, particularly in the context of cell interactions or organization of tissues. However, current technologies for profiling spatial gene expression at single-cell resolution are generally limited to the measurement of a small number of genes. To address this limitation, several algorithms have been developed to impute or predict the expression of additional genes that were not present in the measured gene panel. Current algorithms do not leverage the rich spatial and gene relational information in spatial transcriptomics. To improve spatial gene expression predictions, we introduce Spatial Propagation and Reinforcement of Imputed Transcript Expression (SPRITE) as a meta-algorithm that processes predictions obtained from existing methods by propagating information across gene correlation networks and spatial neighborhood graphs.

Results
SPRITE improves spatial gene expression predictions across multiple spatial transcriptomics datasets. Furthermore, SPRITE predicted spatial gene expression leads to improved clustering, visualization, and classification of cells. SPRITE can be used in spatial transcriptomics data analysis to improve inferences based on predicted gene expression.

Availability and implementation
The SPRITE software package is available at https://github.com/sunericd/SPRITE. Code for generating experiments and analyses in the manuscript is available at https://github.com/sunericd/sprite-figures-and-analyses.
A machine-learning-based alternative to phylogenetic bootstrap
Authors: Ecker Noa , Huchon Dorothée , Mansour Yishay , Mayrose Itay , Pupko Tal 
Published: June 2024

Abstract

Motivation
Currently used methods for estimating branch support in phylogenetic analyses often rely on the classic Felsenstein’s bootstrap, parametric tests, or their approximations. As these branch support scores are widely used in phylogenetic analyses, having accurate, fast, and interpretable scores is of high importance.

Results
Here, we employed a data-driven approach to estimate branch support values with a probabilistic interpretation. To this end, we simulated thousands of realistic phylogenetic trees and the corresponding multiple sequence alignments. Each of the obtained alignments was used to infer the phylogeny using state-of-the-art phylogenetic inference software, which was then compared to the true tree. Using these extensive data, we trained machine-learning algorithms to estimate branch support values for each bipartition within the maximum-likelihood trees obtained by each software. Our results demonstrate that our model provides fast and more accurate probability-based branch support values than commonly used procedures. We demonstrate the applicability of our approach on empirical datasets.

Availability and implementation
The data supporting this work are available in the Figshare repository at https://doi.org/10.6084/m9.figshare.25050554.v1, and the underlying code is accessible via GitHub at https://github.com/noaeker/bootstrap_repo.
MolPLA: a molecular pretraining framework for learning cores, R-groups and their linker joints
Authors: Gim Mogan , Park Jueon , Park Soyon , Lee Sanghoon , Baek Seungheun , Lee Junhyun , Nguyen Ngoc-Quang , Kang Jaewoo 
Published: June 2024

Abstract

Motivation
Molecular core structures and R-groups are essential concepts in drug development. Integration of these concepts with conventional graph pre-training approaches can promote deeper understanding in molecules. We propose MolPLA, a novel pre-training framework that employs masked graph contrastive learning in understanding the underlying decomposable parts in molecules that implicate their core structure and peripheral R-groups. Furthermore, we formulate an additional framework that grants MolPLA the ability to help chemists find replaceable R-groups in lead optimization scenarios.

Results
Experimental results on molecular property prediction show that MolPLA exhibits predictability comparable to current state-of-the-art models. Qualitative analysis implicate that MolPLA is capable of distinguishing core and R-group sub-structures, identifying decomposable regions in molecules and contributing to lead optimization scenarios by rationally suggesting R-group replacements given various query core templates.

Availability and implementation
The code implementation for MolPLA and its pre-trained model checkpoint is available at https://github.com/dmis-lab/MolPLA.
Optimal sequencing budget allocation for trajectory reconstruction of single cells
Authors: Moriel Noa , Memet Edvin , Nitzan Mor 
Published: June 2024

Abstract

Background
Charting cellular trajectories over gene expression is key to understanding dynamic cellular processes and their underlying mechanisms. While advances in single-cell RNA-sequencing technologies and computational methods have pushed forward the recovery of such trajectories, trajectory inference remains a challenge due to the noisy, sparse, and high-dimensional nature of single-cell data. This challenge can be alleviated by increasing either the number of cells sampled along the trajectory (breadth) or the sequencing depth, i.e. the number of reads captured per cell (depth). Generally, these two factors are coupled due to an inherent breadth-depth tradeoff that arises when the sequencing budget is constrained due to financial or technical limitations.

Results
Here we study the optimal allocation of a fixed sequencing budget to optimize the recovery of trajectory attributes. Empirical results reveal that reconstruction accuracy of internal cell structure in expression space scales with the logarithm of either the breadth or depth of sequencing. We additionally observe a power law relationship between the optimal number of sampled cells and the corresponding sequencing budget. For linear trajectories, non-monotonicity in trajectory reconstruction across the breadth-depth tradeoff can impact downstream inference, such as expression pattern analysis along the trajectory. We demonstrate these results for five single-cell RNA-sequencing datasets encompassing differentiation of embryonic stem cells, pancreatic beta cells, hepatoblast and multipotent hematopoietic cells, as well as induced reprogramming of embryonic fibroblasts into neurons. By addressing the challenges of single-cell data, our study offers insights into maximizing the efficiency of cellular trajectory analysis through strategic allocation of sequencing resources.
MolLM: a unified language model for integrating biomedical text with 2D and 3D molecular representations
Authors: Tang Xiangru , Tran Andrew , Tan Jeffrey , Gerstein Mark B 
Published: June 2024

Abstract

Motivation
The current paradigm of deep learning models for the joint representation of molecules and text primarily relies on 1D or 2D molecular formats, neglecting significant 3D structural information that offers valuable physical insight. This narrow focus inhibits the models’ versatility and adaptability across a wide range of modalities. Conversely, the limited research focusing on explicit 3D representation tends to overlook textual data within the biomedical domain.

Results
We present a unified pre-trained language model, MolLM, that concurrently captures 2D and 3D molecular information alongside biomedical text. MolLM consists of a text Transformer encoder and a molecular Transformer encoder, designed to encode both 2D and 3D molecular structures. To support MolLM’s self-supervised pre-training, we constructed 160K molecule-text pairings. Employing contrastive learning as a supervisory signal for learning, MolLM demonstrates robust molecular representation capabilities across four downstream tasks, including cross-modal molecule and text matching, property prediction, captioning, and text-prompted molecular editing. Through ablation, we demonstrate that the inclusion of explicit 3D representations improves performance in these downstream tasks.

Availability and implementation
Our code, data, pre-trained model weights, and examples of using our model are all available at https://github.com/gersteinlab/MolLM. In particular, we provide Jupyter Notebooks offering step-by-step guidance on how to use MolLM to extract embeddings for both molecules and text.
Adaptive digital tissue deconvolution
Authors: Görtler Franziska , Mensching-Buhr Malte , Skaar Ørjan , Schrod Stefan , Sterr Thomas , Schäfer Andreas , Beißbarth Tim , Joshi Anagha , Zacharias Helena U , Grellscheid Sushma Nagaraja , Altenbuchinger Michael 
Published: June 2024

Abstract

Motivation
The inference of cellular compositions from bulk and spatial transcriptomics data increasingly complements data analyses. Multiple computational approaches were suggested and recently, machine learning techniques were developed to systematically improve estimates. Such approaches allow to infer additional, less abundant cell types. However, they rely on training data which do not capture the full biological diversity encountered in transcriptomics analyses; data can contain cellular contributions not seen in the training data and as such, analyses can be biased or blurred. Thus, computational approaches have to deal with unknown, hidden contributions. Moreover, most methods are based on cellular archetypes which serve as a reference; e.g. a generic T-cell profile is used to infer the proportion of T-cells. It is well known that cells adapt their molecular phenotype to the environment and that pre-specified cell archetypes can distort the inference of cellular compositions.

Results
We propose Adaptive Digital Tissue Deconvolution (ADTD) to estimate cellular proportions of pre-selected cell types together with possibly unknown and hidden background contributions. Moreover, ADTD adapts prototypic reference profiles to the molecular environment of the cells, which further resolves cell-type specific gene regulation from bulk transcriptomics data. We verify this in simulation studies and demonstrate that ADTD improves existing approaches in estimating cellular compositions. In an application to bulk transcriptomics data from breast cancer patients, we demonstrate that ADTD provides insights into cell-type specific molecular differences between breast cancer subtypes.

Availability and implementation
A python implementation of ADTD and a tutorial are available at Gitlab and zenodo (doi:10.5281/zenodo.7548362).
CPC07 Occupational cutaneous larva migrans presenting as diffuse folliculitis with concurrent helminthiasis
Authors: Beck-Sander Kellen , Coulton Alice , Cooke Joanna , Cooper Hywel 
Published: June 2024

Abstract
Cutaneous larva migrans (CLM) occurs following human skin contact with the larvae of animal hookworms, including the dog hookworm Ancylostoma caninum. To date, these hookworms cannot complete their life cycle in humans. The condition is mostly seen in the tropics or subtropics or in returning travellers; however, cases following local exposure to animal hookworms have been identified. Less than 5% of cases of CLM reported in the literature present as folliculitis. Strongyloidiasis is caused by infection with the soil-transmitted roundworm Strongyloides stercoralis and is seen in areas of poor sanitation. It can complete its life cycle within the human host, and infection can persist asymptomatically for decades. Toxocariasis is infection caused by the larvae of the dog or cat Toxocara spp. roundworm. Human infection occurs through ingestion of eggs, but these roundworms do not complete their life cycle in humans. Infection can be subclinical or present as visceral or ocular larva migrans. A 62-year-old man was admitted to hospital with an un­usual rash and significant metabolic derangement. He had a 4-week history of a diffuse, pruritic, erythematous, papulonodular rash extending from his flanks to his thighs with florid pustular areas on examination. He had no pre-existing medical conditions and denied sexual contact or foreign travel for over a decade. He worked as a cleaner in a large governmental organization with regular exposure to human and animal excrement, although he reported adequate personal protective equipment. Laboratory results revealed a new diagnosis of type 2 diabetes and eosinophils of 0.1 × 109 L−1, and blood culture grew multiple gastrointestinal bacteria. A skin biopsy demonstrated purulent folliculitis, abundant eosinophil infiltration, and multiple rhabditiform larvae within dilated and ruptured follicles. Slides were sent for specialist opinion, and filiform larvae with the characteristic double lateral alae of Ancylostoma spp. were identified. Given the patient’s occupational exposure, the findings were deemed to be compatible with CLM caused by the dog hookworm, A. caninum. Serology also revealed weak-positive Strongyloides, and positive Toxocara antibodies, although two faecal samples were negative for helminths. The patient was treated with a one-off dose of ivermectin and a 7-day course of albendazole. This is a highly unusual case of CLM presenting as fulminant folliculitis secondary to A. caninum, in the absence of foreign travel, and likely contracted through occupational exposure. The possible coinfection with S. stercoralis and Toxocara canis prompts discussion of the transmission, life cycle, diagnostic testing and management of all three helminths.
P010 Eosinophilic dermatosis of haematological malignancy mimicking insect bite-like reaction in a patient with chronic lymphocytic leukaemia: a case report
Authors: Nallathamby Kasthuri , Moss Kat , Azurdia Richard , Khirwadkar Nitin 
Published: June 2024

Abstract
We present a case of a patient presenting with severe insect bite-like reactions in her skin with underlying chronic lymphocytic leukaemia (CLL). A 71-year-old woman was diagnosed with CLL in 2017 after presenting with a low-level lymphocytosis. She has never required treatment for CLL and remains under active observation in the haematology clinic. She was initially referred to dermatology 5 years ago with a nonresolving insect bite-like rash on her chest. The biopsy of the lesion was thought to be reactive with CLL infiltration. Over the years she has had a number of flare-ups of differing severity; the rash has always been itchy, and she has often presented with tense vesiculobullous lesions, occasionally haemorrhagic in appearance. Multiple biopsies were taken over a period of 5 years due to the diagnostic challenge, with the main differentials being urticarial vasculitis, insect bite-like reactions, Wells syndrome/dermal hypersensitivity reaction and Churg–Strauss syndrome. One of the common trends with the patient’s rash and blistering lesions is that they have often flared up during the summer, during gardening or travelling, and she has also reported them after receiving the COVID-19 vaccine. There was no reaction following the flu vaccine. Biopsies were always negative for direct immunofluorescence, ruling out immunobullous reactions. There was often the presence of a heavy neutrophilic infiltrate, vascular inflammation, and vague granulomata with absence of frank vasculitis. Following multidisciplinary team discussion and close clinicopathological correlation, we have concluded that this could be eosinophilic dermatosis of haematological malignancy mimicking insect bites, of which only a few reported cases have been documented. This case report reviews the association between CLL and insect bite-like reaction and highlights the challenges associated with diagnosing and managing such dermatological manifestations. Previous literature showed no clear association with regards to treatment of CLL causing regression of the dermatological manifestation. The treatment is difficult and recurrences are common (Rodríguez-Lojo R, Almagro M, Piñeyro F et al. Eosinophilic panniculitis and insect bite-like eruption in a patient with chronic lymphocytic leukaemia: a spectrum of the same entity. Dermatol Res Pract 2010; 2010: 263827). In our case, the plan is to actively treat the underlying haematological malignancy and to assess the possibility of bringing the cutaneous manifestation under control. This case also highlights the lack of tailored therapeutic strategies for patients with CLL, in the context of its diverse and complex dermatological presentation.
P043 Readability assessment of British Journal of Dermatology plain language summaries
Authors: Barry Rory , O’Gorman Susan 
Published: June 2024

Abstract
Plain language summaries (PLSs) are being increasingly implemented and required by regulatory bodies to improve accessibility of scientific research for the general population. In 2014, the British Journal of Dermatology (BJD) introduced freely available PLSs to improve patient access to high-quality dermatology research. Over 40% of adults in England do not have adequate literacy skills for health information. As such, when developing PLSs, the NHS recommend targeting a reading age of 9–11 years, up to 14 years in particularly complex medical information. While social media may remain more accessible, the misinformation risk underlines the important role of scientific journals in engaging the lay community in high-quality published research. Based on NHS guidelines, we assessed the readability level of published PLSs in the last 12 issues of the BJD, from 1 December 2022 to 16 November 2023. Multiple established readability metrics were used, including Flesch–Kincaid (FK), Simple Measure of Gobbledygook (SMOG), Gunning Fog Index (GFI), FORCAST, and Flesch Reading Ease (FRE). Only English-language PLSs were included for analysis. Ninety-nine English-language PLSs were published in those 12 BJD issues. According to FK, the mean readability US grade level was 13.0 [95% confidence interval (CI) 12.7–13.4]. A US grade level is one integer less than the UK year system (e.g. US grade 6 = UK year 7). As such, a US grade level of 13 corresponds to an age range of 18–20 years, or a college-level student. SMOG, an NHS-recommended readability instrument, evaluates text for 100% comprehensibility. Evaluating BJD PLSs using SMOG, the mean US grade level was 11.9 (95% CI 11.5–12.2), indicative of a 17–18-year reading age in the UK. The GFI analysis gave a mean US grade level of 16.2 (95% CI 15.7–16.6) for the PLSs, equating to a reading age of 22 years. The average grade level from FORCAST assessment was 11.5 (95% CI 11.4–11.7), giving a reading age of 17 years. Finally, using FRE testing, the mean output was 41.5 (95% CI 39.4–43.6), corresponding to the ‘difficult’ category in the Flesch read­ability chart, a reading age of 18 years or above. This research suggests that the BJD PLSs are not appropriately written for dermatology patients, according to the England population literacy level and NHS guidelines. Readability formulas, while potentially limited through lack of comprehension and reading ease assessment, provide authors guidance of appropriate literacy level PLS. By encouraging authors to use readability resources and PLS guidelines the BJD may improve patient engagement with the journal.