bluuebunny/crossref_metadata_2025_split huggingface dataset:
CryptoBench: Cryptic protein-ligand binding sites dataset and benchmark
Authors: Škrhák Vít , Novotný Marian , Feidakis Christos P. , Krivák Radoslav , Hoksza David 
Published: August 2024

AbstractStructure-based methods for detecting protein-ligand binding sites play a crucial role in various domains, from fundamental research to biomedical applications. However, current prediction methodologies often rely on holo (ligand-bound) protein conformations for training and evaluation, overlooking the significance of the apo (ligand-free) states. This oversight is particularly problematic in the case of cryptic binding sites (CBSs) where holo-based assessment yields unrealistic performance expectations. To advance the development in this domain, we introduce CryptoBench, a benchmark dataset tailored for training and evaluating novel CBS prediction methodologies. CryptoBench is constructed upon a large collection of apo-holo protein pairs, grouped by UniProtID, clustered by sequence identity, and filtered to contain only structures with substantial structural change in the binding site. CryptoBench comprises 1,107 structures with predefined cross-validation splits, making it the most extensive CBS dataset to date. To establish a performance baseline, we measured the predictive power of sequence- and structure-based CBS residue prediction methods using the benchmark. We selected PocketMiner as the state-of-the-art representative of the structure-based methods for CBS detection, and P2Rank, a widely-used structure-based method for general binding site prediction that is not specifically tailored for cryptic sites. For sequence-based approaches, we trained a neural network to classify binding residues using protein language model embeddings. Our sequence-based approach outperformed PocketMiner and P2Rank across key metrics, including AUC, AUPRC, MCC, and F1 scores. These results provide baseline benchmark results for future CBS and potentially also non-CBS prediction endeavors, leveraging CryptoBench as the foundational platform for further advancements in the field.
Exogenous ECM in an environmentally-mediatedin vitromodel for cardiac fibrosis
Authors: Pachter Natalie , Allen Kristen , Hookway Tracy A 
Published: August 2024

AbstractFew clinical solutions exist for cardiac fibrosis, creating the need for a tunablein vitromodel to better understand fibrotic disease mechanisms and screen potential therapeutic compounds. Here, we combined cardiomyocytes, cardiac fibroblasts, and exogenous extracellular matrix (ECM) proteins to create an environmentally-mediatedin vitrocardiac fibrosis model. Cells and ECM were combined into 2 types of cardiac tissues-aggregates and tissue rings. The addition of collagen I had a drastic negative impact on aggregate formation, but ring formation was not as drastically affected. In both tissue types, collagen and other ECM did not severely affect contractile function. Histological analysis showed direct incorporation of collagen into tissues, indicating that we can directly modulate the cells’ ECM environment. This modulation affects tissue formation and distribution of cells, indicating that this model provides a useful platform for understanding how cells respond to changes in their extracellular environment and for potential therapeutic screening.
Revisiting equivalent optical properties for cerebrospinal fluid to improve diffusion-based modeling accuracy in the brain
Authors: Lewis Aiden Vincent , Fang Qianqian 
Published: August 2024

AbstractSignificanceThe diffusion approximation (DA) is used in functional near-infrared spectroscopy (fNIRS) studies despite its known limitations due to the presence of cerebrospinal fluid (CSF). Many of these studies rely on a set of empirical CSF optical properties, recommended by a previous simulation study, that were not selected for the purpose of minimizing DA modeling errors.AimWe aim to directly quantify the accuracy of DA solutions in brain models by comparing those with the gold-standard solutions produced by the mesh-based Monte Carlo (MMC), based on which we derive updated recommen-dations.ApproachFor both a 5-layer head and Colin27 atlas models, we obtain DA solutions by independently sweeping the CSF absorption (µa) and reduced scatteringcoefficients. Using an MMC solution with literature CSF optical properties as reference, we compute the errors for surface fluence, total brain sensitivity and brain energy-deposition, and identify the optimized settings where the such error is minimized.ResultsOur results suggest that previously recommended CSF properties can cause significant errors (8.7% to 52%) in multiple tested metrics. By simultaneously sweepingµaand,we can identify infinite numbers of solutions that can exactly match DA with MMC solutions for any single tested metric. Furthermore, it is also possible to simul-taneously minimize multiple metrics at multiple source/detector separations, leading to our new recommendation of settingwhile maintaining physiologicalµafor CSF in DA simulations.ConclusionOur new recommendation of CSF equivalent optical properties can greatly reduce the model mismatches between DA and MMC solutions at multiple metrics without sacrificing computational speed. We also show that it is possible to eliminate such a mismatch for a single or a pair of metrics of interest.
OPUS-Design: Designing Protein Sequence from Backbone Structure with 3DCNN and Protein Language Model
Authors: Xu Gang , Yang Yulu , Zhang Yiqiu , Wang Qinghua , Ma Jianpeng 
Published: August 2024

AbstractProtein sequence design, also known as protein inverse folding, is a crucial task in protein engineering and design. Despite the recent advancements in this field, which have facilitated the identification of amino acid sequences based on backbone structures, achieving higher levels of accuracy in sequence recovery rates remains challenging. It this study, we introduce a two-stage protein sequence design method named OPUS-Design. Our evaluation on recently released targets from CAMEO and CASP15 shows that OPUS-Design significantly surpasses several other leading methods on both monomer and oligomer targets in terms of sequence recovery rate. Furthermore, by utilizing its finetune version OPUS-Design-ft and our previous work OPUS-Mut, we have successfully designed a thermal-tolerant double-point mutant of T4 lysozyme that demonstrates a residual enzyme activity exceeding that of the wild-type T4 by more than twofold when both are subjected to extreme heat treatment at 70°C. Importantly, this accomplishment is achieved through the experimental verification of less than 10 mutant candidates, thus significantly alleviating the burden of experimental verification process.
Machine Learning-Based Pixel-Level Quantification of Intramuscular Connective Tissue using Ultrasound Texture Analysis
Authors: Pincheira Patricio A. , Kim Jong H. , Hodges Paul W. 
Published: August 2024

AbstractObjectiveThis study aimed to develop a machine learning method for characterizing muscle composition on ultrasound imaging, focusing on pixel-level quantification of connective tissue using texture analysis.MethodsUltrasound images of the multifidus muscle from 20 healthy young adults were included in the analysis. Texture features including Local Binary Patterns, Histograms of Oriented Gradients, Grey Level Co-occurrence Matrix, and Discrete Wavelet Transforms, were extracted from the images across multiple scales. Within a positive-unlabeled machine learning framework, two competing models, Bagging Support Vector Machine and Random Forests with Recursive Greedy Risk Minimization were trained for each texture and scale. The outputs of the texture-based pixel-level classification were compared to traditional echo intensity-based methods. Metrics such as the F-measure were employed to evaluate the models’ performance. Expert consensus was utilised to evaluate the accuracy of the classified images and identify the best-performing combination of model, texture, and scale.ResultsExpert evaluation identified the Bagging Support Vector Machine model trained with Local Binary Pattern histograms extracted at a scale of 9×9 pixel region of interest as the best combination for accurately classifying connective tissue-like pixels (F-measure= 0.88). The proposed method demonstrated high repeatability (intraclass correlation coefficient= 0.92) and robustness to echo intensity variations, outperforming traditional echo intensity-based methods.ConclusionThis approach offers a valid method for pixel-level quantification of intramuscular connective tissue from ultrasound images. It overcomes the limitations of traditional analyses relying on echo intensity and demonstrates robustness against variations in echo intensity, representing an operator-independent advancement in ultrasound-based muscle composition analysis.
COVER-ME: Developing and Evaluating community-based interventions to promote vaccine uptake in East London minority ethnicity (ME) populations; underserved migrants and persons with low income: protocol for a pilot randomised controlled trial
Authors: Chaudhry T. , Tum P. , Tam Z H. , Brentnall A. , Smethurst H. , Kielmann K , Kunst H. , Hargreaves S. , Campbell N J C. , Griffiths C , Zenner D 
Published: August 2024

AbstractIntroductionUnder vaccination amongst underserved groups remains low due to existing disparities. This is particularly the case with post-pandemic COVID-19 vaccinations, and other vaccine-preventable diseases including measles, Mumps and Rubella (MMR) or influenza. Therefore, we aim to 1) to determine the feasibility and practicality of implementing a patient engagement tool (PET) and gain vital insights to plan a subsequent definitive randomised controlled trial (RCT) to evaluate the effectiveness of this tool for increasing uptake of COVID-19 and Flu vaccination; 2) and to define the appropriate level of support needed for health care providers at site-level to ensure successful implementation of the PET and to identify supporting activities needed to implement interventions for COVID-19 and Flu vaccinations.Methods and AnalysisThis is a randomised controlled feasibility study evaluating a co-designed PET, involving randomisation at individual and cluster level. For individual randomisation, patients will be individually randomised 1:1 to receive the intervention (PET) or routine care; whereas for cluster randomisation six GP practices will be randomised 1:1, and divided into two tranches at two separate time points. Both groups will receive training and activation of the software. Data will be analysed using statistical software R (4.0 or greater) or STATA (17 or greater). Baseline characteristics will be summarised and presented in groups based on an intention to treat (ITT) basis with categorical data; including demographics, socioeconomic variables, co-morbidities, and vaccination status.Ethics and DisseminationEthical approval was granted Westminster Ethics Committee (ref: 316860). Our dissemination strategy targets three audiences: (1) Policy makers, public and health service managers and clinicians responsible for delivering vaccines and infection prevention services; (2) patients and public from underserved population groups (3) academics.
Differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics
Authors: Deistler Michael , Kadhim Kyra L. , Pals Matthijs , Beck Jonas , Huang Ziwei , Gloeckler Manuel , Lappalainen Janne K. , Schröder Cornelius , Berens Philipp , Gonçalves Pedro J. , Macke Jakob H. 
Published: August 2024

AbstractBiophysical neuron models provide insights into cellular mechanisms underlying neural computations. However, a central challenge has been the question of how to identify the parameters of detailed biophysical models such that they match physiological measurements at scale or such that they perform computational tasks. Here, we describe a framework for simulation of detailed biophysical models in neuroscience—Jaxley—which addresses this challenge. By making use of automatic differentiation and GPU acceleration, Jaxleyopens up the possibility to efficiently optimize large-scale biophysical models with gradient descent. We show that Jaxleycan learn parameters of biophysical neuron models with several hundreds of parameters to match voltage or two photon calcium recordings, sometimes orders of magnitude more efficiently than previous methods. We then demonstrate that Jaxleymakes it possible to train biophysical neuron models to perform computational tasks. We train a recurrent neural network to perform working memory tasks, and a feedforward network of morphologically detailed neurons with 100,000 parameters to solve a computer vision task. Our analyses show that Jaxleydramatically improves the ability to build large-scale data- or task-constrained biophysical models, creating unprecedented opportunities for investigating the mechanisms underlying neural computations across multiple scales.
The MSPTDfast photoplethysmography beat detection algorithm: Design, benchmarking, and open-source distribution
Authors: Charlton Peter H , Javier Argüello-Prada Erick , Mant Jonathan , Kyriacou Panicos A 
Published: August 2024

AbstractObjectivePhotoplethysmography is widely used for physiological monitoring, whether in clinical devices such as pulse oximeters, or consumer devices such as smart-watches. A key step in the analysis of photoplethysmogram (PPG) signals is detecting heartbeats. The MSPTD algorithm has been found to be one of the most accurate PPG beat detection algorithms, but is less computationally efficient than other algorithms. Therefore, the aim of this study was to develop a more efficient, open-source implementation of the MSPTD algorithm for PPG beat detection, named MSPTDfast (v.2).ApproachFive potential improvements to MSPTD were identified and evaluated on four datasets. MSPTDfast (v.2) was designed by incorporating each improvement which on its own reduced execution time whilst maintaining a high F1-score. After internal validation, MSPTDfast (v.2) was benchmarked against state-of-the-art beat detection algorithms on four additional datasets.Main resultsMSPTDfast (v.2) incorporated two key improvements: pre-processing PPG signals to reduce the sampling frequency to 20 Hz; and only calculating scalogram scales corresponding to heart rates >30 bpm. During internal validation MSPTDfast (v.2) was found to have an execution time of between approximately one-third and one-twentieth of MSPTD, and a comparable F1-score. During benchmarking MSPTDfast (v.2) was found to have the highest F1-score alongside MSPTD, and amongst one of the lowest execution times with only MSPTDfast (v.1), qppgfast and MMPD (v.2) achieving shorter execution times.SignificanceMSPTDfast (v.2) is an accurate and efficient PPG beat detection algorithm, available in an open-source Matlab toolbox.
Possible impact of national responses to the COVID pandemic on medal tallies at the Paris 2024 Olympics
Authors: Orchard John W. , Luies Nathan , Buckley Robert J. , Castricum Adam 
Published: August 2024

AbstractIntroductionWestern Pacific nations have experienced lower excess mortality compared to rest of the world since 2020 and recently performed exceptionally well on the medal tally at the 2024 Paris Olympics. This study aimed to analyse any possible connection between these factors.MethodsThe top performing 18 nations from 2012, 2016 and 2020 Olympics (after Russia and Ukraine were excluded) had their relative Gold medals, total medals and medal points (Gold =3, Silver=2, Bronze=1) for Paris 2024 analysed using a backward stepwise linear regression model. Initial input factors included previous medal tallies, home city advantage, time zone effects, national excess deaths 2020-2023, average GDP growth 2020-2023 and number of country signatories to the Great Barrington Declaration (GBD), with factors >P=0.10 removed sequentially.ResultsTotal medals were best predicted by previous total medals (t=21.0, P<0.001) and home city advantage (t=4.1, P<0.001). Gold medals were best predicted by previous Gold medals (t=10.3, P<0.001), low national excess deaths (t=-3.2, P<0.007) and low signatories to the GBD (t=-2.2, P<0.05). Medal points were best predicted by previous medal points (t=18.1, P<0.001), home city advantage (t=3.2, P<0.007) and low national excess deaths (t=-1.8, P<0.09).DiscussionThe Western Pacific countries with a COVID-cautious national perspective (Australia, China, Japan, New Zealand, South Korea) tended to win more Gold medals than expected in Paris, compared to countries with a COVID-stoical national perspective (e.g. Great Britain, United States) which won fewer Golds than expected. This suggests that a COVID-cautious mentality may have contributed to better performance than a COVID-stoical approach. It is unclear whether any mechanism was physiological (less infectious disease impact before or during the Olympics) or psychological. If this effect existed for Golds, it did not appear to have any effect on Silver and Bronze medals.Graphical abstractThe four countries with higher than 10% Excess mortality over the time period 2020-2023 all won fewer Gold medals than expected (based on Gold medals won in the three previous Olympiads). The five countries with lower than 6% Excess mortality over the time period 2020-2023 all won more Gold medals than expected in Paris. The relationship between Excess mortality and change in Gold medals was negative and moderately strong. In the linear regression for prediction of Gold medals in Paris, Excess mortality was a highly significant predictor (t=-3.2, P<0.007). The exact mechanism of this relationship (physiological via reduced infection or psychological or via unassessed confounders) is unclear, but it can be confidently stated that in the countries which had tighter pandemic restrictions in 2020-21 and lower excess mortality, athletes have not suffered any “immunity debt” relative to the rest of the world.
Input-specific localization of NMDA receptor GluN2 subunits in thalamocortical neurons
Authors: Topolski Mackenzie A. , Gilmore Brian L. , Khondaker Rabeya , Michniak Juliana A. , Studtmann Carleigh , Chen Yang , Wagner Gwen N. , Pozo-Aranda Aaron E. , Farris Shannon , Swanger Sharon A. 
Published: August 2024

ABSTRACTMolecular and functional diversity among synapses is generated, in part, by differential expression of neurotransmitter receptors and their associated protein complexes.N-methyl-D-aspartate receptors (NMDARs) are tetrameric ionotropic glutamate receptors that most often comprise two GluN1 and two GluN2 subunits. NMDARs generate functionally diverse synapses across neuron populations through cell-type-specific expression patterns of GluN2 subunits (GluN2A – 2D), which have vastly different functional properties and distinct downstream signaling. Diverse NMDAR function has also been observed at anatomically distinct inputs to a single neuron population. However, the mechanisms that generate input-specific NMDAR function remain unknown as few studies have investigated subcellular GluN2 subunit localization in native brain tissue. We investigated NMDAR synaptic localization in thalamocortical (TC) neurons expressing all four GluN2 subunits. Utilizing super resolution imaging and knockout-validated antibodies, we revealed subtype- and input-specific GluN2 localization at corticothalamic (CT) versus sensory inputs to TC neurons in 4-week-old male and female C57Bl/6J mice. GluN2B was the most abundant postsynaptic subunit across all glutamatergic synapses followed by GluN2A and GluN2C, and GluN2D was localized to the fewest synapses. GluN2B was preferentially localized to CT synapses over sensory synapses, while GluN2A and GluN2C were more abundant at sensory inputs compared to CT inputs. Furthermore, postsynaptic scaffolding proteins PSD95 and SAP102 were preferentially localized with specific GluN2 subunits, and SAP102 was more abundant at sensory synapses than PSD95. This work indicates that TC neurons exhibit subtype- and input-specific localization of diverse NMDARs and associated scaffolding proteins that likely contribute to functional differences between CT and sensory synapses.HIGHLIGHTSNMDAR subtypes and synaptic scaffolding proteins show preferential localization at specific inputs to thalamocortical neurons.GluN2B was preferentially localized to corticothalamic synapses, while GluN2A and GluN2C were more abundant at sensory inputs to thalamocortical neurons.Colocalization between synaptic scaffolding proteins with NMDARs was GluN2 subtype-dependent.NMDAR subsynaptic organization in thalamocortical neurons is input- and GluN2-subtype specific.
Population coding under the scale-invariance of high-dimensional noise
Authors: Moosavi S. Amin , Hindupur Sai Sumedh R. , Shimazaki Hideaki 
Published: August 2024

AbstractHigh-dimensional neural activities exhibiting scale-invariant, power-law noise spectra are ubiquitously observed across various brain regions and species. However, their impact on information coding remains unclear. We provide the scaling conditions for noise covariance that clarify the boundedness of information and establish a quantitative relation between information capacity and population size, based on the properties of scale-invariant noise covariance observed in stimulus-evoked activities of mouse V1 neurons. Our analysis reveals that sublinearly scaling small noise components align sufficiently with the signal direction, enabling neurons to convey stimulus information unboundedly as population size increases. These findings demonstrate that the quasi-universal scaling of neural noise covariance lays the foundation for understanding the scaling and boundedness of population codes, highlighting the critical need to consider the full spectrum of high-dimensional noise.
The Regulatory Logic of Planarian Stem Cell Differentiation
Authors: Pérez-Posada Alberto , García-Castro Helena , Emili Elena , Vanni Virginia , Arias-Baldrich Cirenia , Frölich Siebren , van Heeringen Simon J. , Kenny Nathan , Solana Jordi 
Published: August 2024

AbstractCell type identity is determined by gene regulatory networks (GRNs), comprising the expression of specific transcription factors (TFs) regulating target genes (TGs) via binding to open chromatin regions (OCRs). The regulatory logic of differentiation includes factors specific to one or multiple cell types, functioning in a combinatorial fashion. Classic approaches of GRN discovery used perturbational data to elucidate TF-TG links, but are laborious and not scalable across the tree of life. Single cell transcriptomics has emerged as a revolutionary approach to study gene expression with cell type resolution, but incorporating perturbational data is challenging. Planarians, with their pluripotent neoblast stem cells continuously giving rise to all cell types, offer an ideal model to attempt this integration. Despite extensive single cell transcriptomic studies, the transcriptional and chromatin regulation at the cell type level remains unexplored. Here, we investigate the regulatory logic of planarian stem cell differentiation by obtaining an organism-level integration of single cell transcriptomics and single cell accessibility data. We identify specific open chromatin profiles for major differentiated cell types and analyse their transcriptomic landscape, revealing distinct gene modules expressed in individual types and combinations of them. Integrated analysis unveils gene networks reflecting known TF interactions in each type and identifies TFs potentially driving differentiation across multiple cell types. To validate our predictions, we combined TF knockdown RNAi experiments with single cell transcriptomics. We focus onhnf4, a TF known to be expressed in gut phagocytes, and confirm its influence on other types, including parenchymal cells. Our results demonstrate high overlap between predicted targets and experimentally-validated differentially-regulated genes. Overall, our study integrates TFs, TGs and OCRs to reveal the regulatory logic of planarian stem cell differentiation, showcasing that the combination of single cell methods and perturbational studies will be key for characterising GRNs widely.