bluuebunny/crossref_metadata_2025_split huggingface dataset:
Learning meaningful representation of single-neuron morphology via large-scale pre-training
Authors: Fan Yimin , Li Yaxuan , Zhong Yunhua , Hong Liang , Li Lei , Li Yu 
Published: September 2024

Abstract

Summary
Single-neuron morphology, the study of the structure, form, and shape of a group of specialized cells in the nervous system, is of vital importance to define the type of neurons, assess changes in neuronal development and aging and determine the effects of brain disorders and treatments. Despite the recent surge in the amount of available neuron morphology reconstructions due to advancements in microscopy imaging, existing computational and deep learning methods for modeling neuron morphology have been limited in both scale and accuracy. In this paper, we propose MorphRep, a model for learning meaningful representation of neuron morphology pre-trained with over 250 000 existing neuron morphology data. By encoding the neuron morphology into graph-structured data, using graph transformers for feature encoding and enforcing the consistency between multiple augmented views of neuron morphology, MorphRep achieves the state of the art performance on widely used benchmarking datasets. Meanwhile, MorphRep can accurately characterize the neuron morphology space across neuron morphometrics, fine-grained cell types, brain regions and ages. Furthermore, MorphRep can be applied to distinguish neurons under a wide range of conditions, including genetic perturbation, drug injection, environment change and disease. In summary, MorphRep provides an effective strategy to embed and represent neuron morphology and can be a valuable tool in integrating cell morphology into single-cell multiomics analysis.

Availability and implementation
The codebase has been deposited in https://github.com/YaxuanLi-cn/MorphRep.
CLigOpt: controllable ligand design through target-specific optimization
Authors: Li Yutong , Avelar Pedro Henrique da Costa , Chen Xinyue , Zhang Li , Wu Min , Tsoka Sophia 
Published: September 2024

Abstract

Motivation
A key challenge in deep generative models for molecular design is to navigate random sampling of the vast molecular space, and produce promising molecules that strike a balance across multiple chemical criteria. Fragment-based drug design (FBDD), using fragments as starting points, is an effective way to constrain chemical space and improve generation of biologically active molecules. Furthermore, optimization approaches are often implemented with generative models to search through chemical space, and identify promising samples which satisfy specific properties. Controllable FBDD has promising potential in efficient target-specific ligand design.

Results
We propose a controllable FBDD model, CLigOpt, which can generate molecules with desired properties from a given fragment pair. CLigOpt is a variational autoencoder-based model which utilizes co-embeddings of node and edge features to fully mine information from molecular graphs, as well as a multi-objective Controllable Generation Module to generate molecules under property controls. CLigOpt achieves consistently strong performance in generating structurally and chemically valid molecules, as evaluated across six metrics. Applicability is illustrated through ligand candidates for hDHFR and it is shown that the proportion of feasible active molecules from the generated set is increased by 10%. Molecular docking and synthesizability prediction tasks are conducted to prioritize generated molecules to derive potential lead compounds.

Availability and implementation
The source code is available via https://github.com/yutongLi1997/CLigOpt-Controllable-Ligand-Design-through-Target-Specific-Optimisation.
skandiver: a divergence-based analysis tool for identifying intercellular mobile genetic elements
Authors: Zhang Xiaolei Brian , Oualline Grace , Shaw Jim , Yu Yun William 
Published: September 2024

Abstract

Motivation: Mobile genetic elements (MGEs) are as ubiquitous in nature as they are varied in type, ranging from viral insertions to transposons to incorporated plasmids. Horizontal transfer of MGEs across bacterial species may also pose a significant threat to global health due to their capability to harbor antibiotic resistance genes. However, despite cheap and rapid whole-genome sequencing, the varied nature of MGEs makes it difficult to fully characterize them, and existing methods for detecting MGEs often do not agree on what should count. In this manuscript, we first define and argue in favor of a divergence-based characterization of mobile-genetic elements.
Results: Using that paradigm, we present skandiver, a tool designed to efficiently detect MGEs from whole-genome assemblies without the need for gene annotation or markers. skandiver determines mobile elements via genome fragmentation, average nucleotide identity (ANI), and divergence time. By building on the scalable skani software for ANI computation, skandiver can query hundreds of complete assemblies against &gt;65 000 representative genomes in a few minutes and 19 GB memory, providing scalable and efficient method for elucidating mobile element profiles in incomplete, uncharacterized genomic sequences. For isolated and integrated large plasmids (&gt;10 kb), skandiver’s recall was 48% and 47%, MobileElementFinder was 59% and 17%, and geNomad was 86% and 32%, respectively. For isolated large plasmids, skandiver’s recall (48%) is lower than state-of-the-art reference-based methods geNomad (86%) and MobileElementFinder (59%). However, skandiver achieves higher recall on integrated plasmids and, unlike other methods, without comparing against a curated database, making skandiver suitable for discovery of novel MGEs.

Availability and implementation
https://github.com/YoukaiFromAccounting/skandiver
Ouroboros: cross-linking protein expression perturbations and cancer histology imaging with generative-predictive modeling
Authors: Deshpande Srijay , Georgaka Sokratia , Haley Michael , Sellers Robert , Minshull James , Nallala Jayakrupakar , Fergie Martin , Stone Nicholas , Rajpoot Nasir , Baker Syed Murtuza , Iqbal Mudassar , Couper Kevin , Roncaroli Federico , Minhas Fayyaz 
Published: September 2024

Abstract

Summary
Imagine if we could simultaneously predict spatial protein expression in tissues from their routine Hematoxylin and Eosin (H&E) stained images, and create tissue images given protein expression profiles thus enabling virtual simulations of how protein expression alterations impact histology in complex diseases like cancer. Such an approach could lead to more informed diagnostic and therapeutic decisions for precision medicine at lower costs and shorter turnaround times, more detailed insights into underlying disease pathology as well as improvement in predictive and generative performance. In this study, we investigate the intricate correlation between protein expressions obtained from Hyperion mass cytometry and histopathological microstructures in conventional H&E stained glioblastoma (GBM) samples, unveiling morphological patterns and cellular-level spatial alterations associated with protein expression changes. To model these complex relationships, we propose a novel generative-predictive framework called Ouroboros for producing H&E images from protein expressions and simultaneously predicting protein expressions from H&E images. Our comprehensive sample-independent validation over 9920 tissue spots from 4 GBM samples encompassing visual image analysis, quantitative analysis, subspace alignment and perturbation experiments shows that the proposed generative-predictive approach offers significant improvements in predicting protein expression from images in comparison to baseline methods as well as accurate generation of virtual GBM sample images. This proof of concept study can contribute to advancing our understanding of histological responses to protein expression perturbations and lays the foundations for further developments in this area.

Availability and implementation
Implementation and associated data for the proposed approach are available at the URL: https://github.com/Srijay/Ouroboros.
Improving dictionary-based named entity recognition with deep learning
Authors: Nastou Katerina , Koutrouli Mikaela , Pyysalo Sampo , Jensen Lars Juhl 
Published: September 2024

Abstract

Motivation
Dictionary-based named entity recognition (NER) allows terms to be detected in a corpus and normalized to biomedical databases and ontologies. However, adaptation to different entity types requires new high-quality dictionaries and associated lists of blocked names for each type. The latter are so far created by identifying cases that cause many false positives through manual inspection of individual names, a process that scales poorly.

Results
In this work, we aim to improve block list s by automatically identifying names to block, based on the context in which they appear. By comparing results of three well-established biomedical NER methods, we generated a dataset of over 12.5 million text spans where the methods agree on the boundaries and type of entity tagged. These were used to generate positive and negative examples of contexts for four entity types (genes, diseases, species, and chemicals), which were used to train a Transformer-based model (BioBERT) to perform entity type classification. Application of the best model (F1-score = 96.7%) allowed us to generate a list of problematic names that should be blocked. Introducing this into our system doubled the size of the previous list of corpus-wide blocked names. In addition, we generated a document-specific list that allows ambiguous names to be blocked in specific documents. These changes boosted text mining precision by ∼5.5% on average, and over 8.5% for chemical and 7.5% for gene names, positively affecting several biological databases utilizing this NER system, like the STRING database, with only a minor drop in recall (0.6%).

Availability and implementation
All resources are available through Zenodo https://doi.org/10.5281/zenodo.11243139 and GitHub https://doi.org/10.5281/zenodo.10289360.
Metadata-guided feature disentanglement for functional genomics
Authors: Rakowski Alexander , Monti Remo , Huryn Viktoriia , Lemanczyk Marta , Ohler Uwe , Lippert Christoph 
Published: September 2024

Abstract
Summary: With the development of high-throughput technologies, genomics datasets rapidly grow in size, including functional genomics data. This has allowed the training of large Deep Learning (DL) models to predict epigenetic readouts, such as protein binding or histone modifications, from genome sequences. However, large dataset sizes come at a price of data consistency, often aggregating results from a large number of studies, conducted under varying experimental conditions. While data from large-scale consortia are useful as they allow studying the effects of different biological conditions, they can also contain unwanted biases from confounding experimental factors. Here, we introduce Metadata-guided Feature Disentanglement (MFD)—an approach that allows disentangling biologically relevant features from potential technical biases. MFD incorporates target metadata into model training, by conditioning weights of the model output layer on different experimental factors. It then separates the factors into disjoint groups and enforces independence of the corresponding feature subspaces with an adversarially learned penalty. We show that the metadata-driven disentanglement approach allows for better model introspection, by connecting latent features to experimental factors, without compromising, or even improving performance in downstream tasks, such as enhancer prediction, or genetic variant discovery. The code will be made available at https://github.com/HealthML/MFD.
Guide assignment in single-cell CRISPR screens using crispat
Authors: Braunger Jana M , Velten Britta 
Published: September 2024

Abstract

Motivation
Pooled single-cell CRISPR screens have emerged as a powerful tool in functional genomics to probe the effect of genetic interventions at scale. A crucial step in the analysis of the resulting data is the assignment of cells to gRNAs corresponding to a specific genetic intervention. However, this step is challenging due to a lack of systematic benchmarks and accessible software to apply and compare different guide assignment strategies. To address this, we here propose crispat (CRISPR guide assignment tool), a Python package to facilitate the choice of a suitable guide assignment strategy for single-cell CRISPR screens.

Results
We demonstrate the package on four single-cell CRISPR interference screens at low multiplicity of infection from two studies, where crispat identifies strong differences in the number of assigned cells, downregulation of the target genes and number of discoveries across different guide assignment strategies, highlighting the need for a suitable guide assignment strategy to obtain optimal power in single-cell CRISPR screens.

Availability and implementation
crispat is implemented in python, the source code, installation instructions and tutorials can be found at https://github.com/velten-group/crispat and it can be installed from PyPI (https://pypi.org/project/crispat/). Code to reproduce all findings in this paper is available at https://github.com/velten-group/crispat_analysis, as well as at https://zenodo.org/records/13373265.
Development and characterization of human T-cell receptor (TCR) alpha and beta clones’ library as biological standards and resources for TCR sequencing and engineering
Authors: Wei Yu-Chun , Pospiech Mateusz , Meng Yiting , Alachkar Houda 
Published: September 2024

Abstract
Characterization of T-cell receptors (TCRs) repertoire was revolutionized by next-generation sequencing technologies; however, standardization using biological controls to facilitate precision of current alignment and assembly tools remains a challenge. Additionally, availability of TCR libraries for off-the-shelf cloning and engineering TCR-specific T cells is a valuable resource for TCR-based immunotherapies. We established nine human TCR α and β clones that were evaluated using the 5′-rapid amplification of cDNA ends-like RNA-based TCR sequencing on the Illumina platform. TCR sequences were extracted and aligned using MiXCR, TRUST4, and CATT to validate their sensitivity and specificity and to validate library preparation methods. The correlation between actual and expected TCR ratios within libraries confirmed accuracy of the approach. Our findings established the development of biological standards and library of TCR clones to be leveraged in TCR sequencing and engineering. The remaining human TCR clones’ libraries for a more diverse biological control will be generated.
Patterns of metabolic response in patients receiving commercial CAR T-cells for relapsing/refractory aggressive B cells lymphoma
Authors: Vercellino Laetitia , Betterki Siham , Blanc Estelle , de Kerviler Eric , Cristinelli Caterina , Merlet Pascal , Thieblemont Catherine , Meignin Véronique , Di Blasi Roberta 
Published: September 2024

Abstract
CAR T-cells is an innovative treatment for relapsed/refractory aggressive B cell lymphomas, initially proposed as third-line therapy and beyond, now allowed as soon as second-line treatment for patients with early relapse after first-line treatment. FDG PET/CT remains the modality of choice to evaluate response to this therapeutic strategy, to detect or confirm treatment failure, and allow for salvage therapy if needed. Correct classification of patients regarding response is thus of the utmost importance. In many cases, metabolic response follows classical known patterns, and Deauville score and Lugano criteria yield accurate characterization of patient status. However, given its specific mode of action, it can result in delayed response or atypical patterns of response. We report here a few examples of response from our experience to illustrate the existence of tricky cases. These atypical cases require multidisciplinary management, with clinical, biological, imaging, and pathological work-up.
Short-term outcomes after primary total mesorectal excision (TME) versus local excision followed by completion TME for early rectal cancer: population-based propensity-matched study
Authors: van Lieshout Annabel S , Smits Lisanne J H , Sijmons Julie M L , van Dieren Susan , van Oostendorp Stefan E , Tanis Pieter J , Tuynman Jurriaan B 
Published: September 2024

Abstract

Background
Colorectal cancer screening programmes have led to a shift towards early-stage colorectal cancer, which, in selected cases, can be treated using local excision. However, local excision followed by completion total mesorectal excision (two-stage approach) may be associated with less favourable outcomes than primary total mesorectal excision (one-stage approach). The aim of this population study was to determine the distribution of treatment strategies for early rectal cancer in the Netherlands and to compare the short-term outcomes of primary total mesorectal excision with those of local excision followed by completion total mesorectal excision.

Methods
Short-term data for patients with cT1–2 N0xM0 rectal cancer who underwent local excision only, primary total mesorectal excision, or local excision followed by completion total mesorectal excision between 2012 and 2020 in the Netherlands were collected from the Dutch Colorectal Audit. Patients were categorized according to treatment groups and logistic regressions were performed after multiple imputation and propensity score matching. The primary outcome was the end-ostomy rate.

Results
From 2015 to 2020, the proportion for the two-stage approach increased from 22.3% to 43.9%. After matching, 1062 patients were included. The end-ostomy rate was 16.8% for the primary total mesorectal excision group versus 29.6% for the local excision followed by completion total mesorectal excision group (P &lt; 0.001). The primary total mesorectal excision group had a higher re-intervention rate than the local excision followed by completion total mesorectal excision group (16.7% versus 11.8%; P = 0.048). No differences were observed with regard to complications, conversion, diverting ostomies, radical resections, readmissions, and death.

Conclusion
This study shows that, over time, cT1–2 rectal cancer has increasingly been treated using the two-stage approach. However, local excision followed by completion total mesorectal excision seems to be associated with an elevated end-ostomy rate. It is important that clinicians and patients are aware of this risk during shared decision-making.
Role of the serosa in intestinal anastomotic healing: insights from in-depth histological analysis of human and murine anastomoses
Authors: Weber Marie-Christin , Clees Zoé , Buck Annalisa , Fischer Adrian , Steffani Marcella , Wilhelm Dirk , Martignoni Marc , Friess Helmut , Rinkevich Yuval , Neumann Philipp-Alexander 
Published: September 2024

Abstract

Background
Anastomotic leakage following colorectal surgery remains a significant complication despite advances in surgical techniques. Recent findings on serosal injury repair in coelomic cavities, such as the peritoneum, challenge the current understanding of the cellular origins and mechanisms underlying intestinal anastomotic healing. Understanding the contribution of each layer of the intestinal wall during anastomotic healing is needed to find new therapeutic strategies to prevent anastomotic leakage. The aim of this experimental study was to investigate the role of the serosal layer of the intestinal wall in anastomotic healing.

Materials and methods
Comprehensive histologic analysis of human and murine anastomoses was performed to elucidate histologic changes in the different intestinal layers during anastomotic healing. In vivo staining of the extracellular matrix (ECM) in the serosal layer was performed using a fluorophore-conjugated N-hydroxysuccinimide-ester before anastomosis surgery in a murine model.

Results
Histological examination of both human and murine anastomoses revealed that closure of the serosal layer occurred first during the healing process. In vivo serosal ECM staining demonstrated that a significant portion of the newly formed ECM within the anastomosis was indeed deposited onto the serosal layer. Furthermore, mesenchymal cells within the anastomotic scar were positive for mesothelial cell markers, podoplanin and Wilms tumour protein.

Conclusions
In this experimental study, the results suggest that serosal scar formation is an important mechanism for anastomotic integrity in intestinal anastomoses. Mesothelial cells may significantly contribute to scar formation during anastomotic healing through epithelial-to-mesenchymal transition, potentially suggesting a novel therapeutic target to prevent anastomotic leakage by enhancing physiological healing processes.
The Ripple Effect: Unpacking the Impact of Early Educational Disruptions on Rural Migrant Children’s Learning in China
Authors: Wu Aihui , Wang Cuntong , Zhu Ruoyuan 
Published: September 2024

Abstract
This study investigates the effects of early educational disruptions on the academic achievements of rural migrant children in China. Using a life course theory framework, it examines whether early educational disruptions negatively affect children’s future cognitive abilities, academic performance and ability to cope with academic challenges, drawing on data from the 2013 to 2014 China Education Panel Survey. The analysis shows that early educational disruptions lead to a decline in the academic achievements of rural migrant children during their middle school years, affirming the assertions of life course theory. Notably, gender does not significantly modulate the influence of educational disruptions. Parental educational expectations and the management practices of schools emerge as critical moderators, with high parental expectations potentially exacerbating the adverse effects of frequent disruptions. Conversely, the experience of teachers and strict school discipline play protective roles, highlighting the importance of supportive learning environments and effective educational strategies. These findings are pivotal for social workers intervening with migrant children. They advocate for the development of comprehensive social work strategies—encompassing both curative and preventive measures—that proactively address the unique challenges faced by this population, fostering resilience and enhancing life trajectories in a context characterised by significant societal and educational transitions.
Ubiquitin-specific peptidase 49 promotes adenocarcinoma of the esophagogastric junction malignant progression via activating SHCBP1–β-catenin–GPX4 axis
Authors: Ding Yun , Liu Zhen , Dai Xiaofeng , Ruan Ruiwen , Zhong Hongguang , Wu Zhipeng , Yao Yangyang , Chen Jun , Deng Jun , Xiong Jianping 
Published: September 2024

Abstract
Adenocarcinoma of the esophagogastric junction (AEG) has received widespread attention because of its increasing incidence. However, the molecular mechanism underlying tumor progression remains unclear. Here, we report that the downregulation of ubiquitin-specific peptidase 49 (USP49) promotes ferroptosis in OE33 and OE19 cells, thereby inhibiting cell proliferation in vitro and in vivo, whereas the overexpression of USP49 had the opposite effect. In addition, USP49 downregulation promoted AEG cell radiotherapy sensitivity. Moreover, overexpression of Glutathione PeroXidase 4 reversed the ferroptosis and proliferation inhibition induced by USP49 knockdown. Mechanistically, USP49 deubiquitinates and stabilizes Shc SH2-domain-binding protein 1, subsequently facilitating the entry of β-catenin into the nucleus to enhance Glutathione PeroXidase 4 transcriptional expression. Finally, high USP49 expression was correlated with shorter overall survival in patients with AEG. In summary, our findings identify USP49 as a novel regulator of ferroptosis in AEG cells, indicating that USP49 may be a potential therapeutic target in AEG.