bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimization study of a new β
+-γ coincidence positron annihilation lifetime spectrometer using Geant4
Authors: Xu W. , Li Y.H. , Pan Z.W. , Liu J.D. , Zhang H.J. , Ye B.J.
Published: June 2024

Positron annihilation lifetime (PAL) spectroscopy is a
unique method for characterizing atomic-scale defects and
ultramicropores in materials. The conventional PAL spectrometer
adopts the γ-γ coincidence principle, and its
performance, especially the coincidence counting rate (CCR), can
hardly be further increased. Another coincidence principle,
 β
+-γ coincidence, has the potential to
simultaneously improve the CCR and coincidence time resolution (CTR)
of PAL spectrometers. However, early β
+-γ 
coincidence PAL spectrometers have not been widely applied due to
the considerable room for improvement in their performance. In this
work, we proposed a new β
+-γ coincidence PAL
spectrometer utilizing silicon photomultiplier (SiPM) array as the
positron detector and conducted a comprehensive optimization of its
structure with the aim of achieving a breakthrough in
performance. The effects of start signal threshold and structure
parameters on its CTR, CCR, and proportion of source contribution
(P
SC) were studied using Geant4. The simulation
results show that, with a 68Ge positron source of 30 μCi,
the optimized β
+-γ coincidence PAL spectrometer
can achieve an extremely high CCR exceeding 10000 counts per second
(cps) and an outstanding CTR below 160 picoseconds (ps) while
maintaining a low P
SC below 12%. This study
provides valuable guidance for constructing high-performance
 β
+-γ coincidence PAL spectrometers.
Assessment of 180 nm double SOI technology for analog front-end design with back-gate voltage
Authors: Li Y. , Liu F. , Lu B. , Li Z. , Chen S. , Zhang C. , Zhu H. , Yao X. , Bu J. , Wan J. , Xu Y. , Cristoloveanu S. , Li B. , Luo J. , Ye T.
Published: June 2024

This paper provides an assessment of the electrical and noise performance in the 180 nm double silicon-on-insulator (DSOI) technology, which shows advantages for analog front-end radiation detectors. For the first time, the impact of the back-gate voltage on the electrical and noise performance of DSOI MOSFETs is investigated. The transconductance-to-current (gm
/ID
) ratio and low-frequency (1/f) noise were measured as a function of the MOS device types (NMOS/PMOS), gate length, and bias condition of front- and back-gates. Experimental results show that positive back-gate voltage deteriorates the gm
/ID
ratio of the MOSFETs in weak inversion region. The DSOI NMOS devices overwhelm the PMOS with better gm
/ID
and 1/f performance. The DSOI devices have a comparable 1/f noise with the 180 nm SOI counterparts. With negative back-gate voltage applied, the low frequency noise performance of NMOS is improved. This assessment of DSOI technology gives a guideline for the readout circuit design in detector front-end systems.
Climate and air quality impact of using ammonia as an alternative shipping fuel
Authors: Wong Anthony Y H , Selin Noelle E , Eastham Sebastian D , Mounaïm-Rousselle Christine , Zhang Yiqi , Allroggen Florian
Published: June 2024

As carbon-free fuel, ammonia has been proposed as an alternative fuel to facilitate maritime decarbonization. Deployment of ammonia-powered ships is proposed as soon as 2024. However, NO
x
, NH3 and N2O from ammonia combustion could impact air quality and climate. In this study, we assess whether and under what conditions switching to ammonia fuel might affect climate and air quality. We use a bottom–up approach combining ammonia engine experiment results and ship track data to estimate global tailpipe NO
x
, NH3 and N2O emissions from ammonia-powered ships with two possible engine technologies (NH3–H2 (high NO
x
, low NH3 emissions) vs pure NH3 (low NO
x
, very high NH3 emissions) combustion) under three emission regulation scenarios (with corresponding assumptions in emission control technologies), and simulate their air quality impacts using GEOS–Chem high performance global chemical transport model. We find that the tailpipe N2O emissions from ammonia-powered ships have climate impacts equivalent to 5.8% of current shipping CO2 emissions. Globally, switching to NH3–H2 engines avoids 16 900 mortalities from PM2.5 and 16 200 mortalities from O3 annually, while the unburnt NH3 emissions (82.0 Tg NH3 yr−1) from pure NH3 engines could lead to 668 100 additional mortalities from PM2.5 annually under current legislation. Requiring NH3 scrubbing within current emission control areas leads to smaller improvements in PM2.5-related mortalities (22 100 avoided mortalities for NH3–H2 and 623 900 additional mortalities for pure NH3 annually), while extending both Tier III NO
x
standard and NH3 scrubbing requirements globally leads to larger improvement in PM2.5-related mortalities associated with a switch to ammonia-powered ships (66 500 avoided mortalities for NH3–H2 and 1200 additional mortalities for pure NH3 annually). Our findings suggest that while switching to ammonia fuel would reduce tailpipe greenhouse gas emissions from shipping, stringent ammonia emission control is required to mitigate the potential adverse effects on air quality.
A health economics perspective on behavioural responses to climate change across geographic, socio-economic and demographic strata
Authors: Borghi Josephine , Kuhn Michael
Published: June 2024

This perspective examines the relationship between climate change, health outcomes, and behavioural responses across the life course. It identifies three primary channels through which climate change impacts behaviours which in turn affect health: increased morbidity driving healthcare demand and accessibility, reduced productivity and income affecting health care investments, and combined health and economic risks shaping migration patterns, dietary choices and human capital investment across the life course and generations. Climate-induced changes in behaviours exacerbate existing health-related and socio-economic vulnerabilities. While climate-related shocks elevate demand for healthcare services, disruptions in infrastructure hinder access, especially for the poorest, widening health inequities. Loss of income and disrupted employment further compound health and economic risks, pushing vulnerable communities towards informal care options and impoverishment tied to health expenditures. Increased health and economic risks are associated with migration affecting healthcare access and health outcomes. They also influence dietary choices, with health consequences. Finally, deteriorating prospects of leading a long, prosperous and healthy life may induce individuals to reduce their time horizon and assign lower values to long-term survival, impacting human capital investments across the life course and generations. Again, these impacts are prone to exhibit a social gradient with vulnerable individuals being more likely to give up on striving for a healthier life. Effective policies must integrate climate, health, and socioeconomic factors, considering long-term behavioural responses and their health and socio-economic implications. Adapting health financing mechanisms to account for climate risks and incentivise resilience-building behaviours within health and social care systems is essential for protecting health across the life course, and avoiding widening inequities.
Topological analysis of the complex SSH model using the quantum geometric tensor
Authors: Cheng Eve , Batchelor Murray T , Cocks Danny
Published: June 2024

This paper presents two methods for topological analysis of the complex Hermitian Su–Schrieffer–Heeger (SSH) model using the quantum geometric tensor: Berry phase and topological data analysis. We demonstrate how both methods can effectively generate topological phase diagrams for the model, revealing two distinct regions based on the relative magnitudes of the parameters

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, it exhibits topologically non-trivial behavior. Our results contribute to building the groundwork for topological analysis of more complicated SSH-type models.
Study of bifacial photovoltaics with fluorinated ethylene propylene as an anti-reflective layer
Authors: Sivasubramanian Ramsundar , Aravind Vaithilingam Chockalingam , Tan Sin Jin
Published: June 2024

Bifacial photovoltaics is a type of solar photovoltaics technology that is fast growing in popularity owing to the several advantages it offers. In this study, the inclusion of fluorinated ethylene propylene polymer as one of the constituent materials in bifacial photovoltaic modules under various configurations and its effect on the module’s optical performance was investigated and compared with a commercial bifacial module. Monte Carlo ray tracing was used to conduct the study and the system was analyzed under both non tracking and uniaxial tracking conditions for varying surface albedo values corresponding to an ideal scattering surface, white concrete and sand. The analyses performed under normal incidence condition revelated that the net irradiance on the PV layers varied by as much as 96.0314 W m−2 between the best and worst performing bifacial configurations. Under uniaxial tracking, the top and rear surfaces of the PV modules could cumulatively be subjected to 21.799 kWh of solar irradiation flux per day over a generation window of eleven hours. The proposed changes could offer cost savings of USD 0.0118 per panel per day and up to an additional 5.802 kg of CO2 equivalent offsets per panel per year.
A simple physical model for simulation and design magneto-plethysmograph in application non-invasive hemoglobin measurement
Authors: Wardoyo Sony , Djamal Mitra , Budiman Maman
Published: June 2024

The magneto-plethysmograph method is a combination of magnetic field and sensors used to detect changes in blood flow pulsation. However, to detect the magnetic properties of blood related to hemoglobin concentration, physical modeling and simulation are required. This approach involves designing simulations using magnetic field equations and magnetic susceptibility, where a permanent magnet is placed on the surface of blood vessels, and sensors based on giant magnetoresistance are placed at a distance r. The design originates from a simple approach involving the magnetization and detection of Fe atoms in hemoglobin. Parameters involved include the magnetic susceptibility of oxyhemoglobin and deoxyhemoglobin, with an external magnetic field exceeding 1 Tesla. From the physical modeling and simulation, graphs are obtained depicting the influence of hemoglobin concentration on the number of Fe atoms and its magnetization. This enables the design of non-invasive hemoglobin measurement sensor devices. The uniqueness of this simple physical model and simulation lies in its ability to produce specially designed device models for measuring hemoglobin concentration. This differs from other research focusing on blood flow pulse measurements; the results of this study provide new insights into the benefits of simple physics equations that can be developed for medical diagnostic research and device development.
Reading of gafchromic EBT-3 film using an overhead scanner
Authors: Bantan H , Yasuda H
Published: June 2024

Gafchromic film, a commercially available radiochromic film, has been developed and widely used as an effective tool for radiation dose verification and quality assurance in radiotherapy. However, the orientation effect in scanning a film remains a concern for practical application in beam profile monitoring. To resolve this issue, the authors introduced a novel method using an overhead scanner (OHS) coupled with a tracing light board instead of a conventional flatbed scanner (FBS) to read Gafchromic EBT3 films. We investigated the orientation effect of the EBT3 film with a regular hexagonal shape after irradiation with 5 Gy x-rays (160 kV, 6.3 mA) and compared the digitized images acquired using a commercially available OHS (CZUR Aura) and a conventional FBS (EPSON GT-X980). As a result, RGB color intensities acquired from the OHS showed significantly lower orientation effect of the color intensities of RGB components than those from FBS. This finding indicates the high potential of the proposed method for achieving more precise two-dimensional dosimetry. Further studies are required to confirm the effectiveness of this method under different irradiation conditions over a wider dose range.
A chip-integrated homodyne detection system with enhanced bandwidth performance for quantum applications
Authors: Qi Ng Si , Zhang Gong , Lim Charles , Wang Chao
Published: June 2024

The rapid development of quantum technology has driven the need for high-performance quantum signal processing modules. Balanced homodyne detector (BHD) is one of the most promising options for practical quantum state measurement, providing substantial advantages of cost-effectiveness, no cooling requirement, and system compactness. However, due to the stringent requirements in BHD design, it typically suffers from a relatively small operating bandwidth which limits the overall speed of a quantum system. In this study, we propose comprehensive modelling for the BHD in quantum applications and enhance the performance of BHDs based on our modelling. Specifically, we utilise a photonic chip approach and optimise the electronic design to create the integrated BHD, which significantly boosts the 3 dB bandwidth to 4.75 GHz and achieves a shot-noise-limited bandwidth of 23 GHz. We demonstrate the capability of this setup to generate quantum random numbers at a rate of 240 Gbit s−1, highlighting its potential for ultra-high-speed quantum communication and quantum cryptography applications.
A comparative study of heterogeneous machine learning algorithms for arrhythmia classification using feature selection technique and multi-dimensional datasets
Authors: Sharma Abhinav , Dhanka Sanjay , Kumar Ankur , Maini Surita
Published: June 2024

Arrhythmia, a common cardiovascular disorder, refers to the abnormal electrical activity within the heart, leading to irregular heart rhythms. This condition affects millions of people worldwide, with severe implications on cardiac function and overall health. Arrhythmias can strike anyone at any age which is a significant cause of morbidity and mortality on a global scale. About 80% of deaths related to heart disease are caused by ventricular arrhythmias. This research investigated the application of an optimized multi-objectives supervised Machine Learning (ML) models for early arrhythmia diagnosis. The authors evaluated the model’s performance on the arrhythmia dataset from the UCI ML repository with varying train-test splits (70:30, 80:20, and 90:10). Standard preprocessing techniques such as handling missing values, formatting, balancing, and directory analysis were applied along with Pearson correlation for feature selection, all aimed at enhancing model performance. The proposed optimized RF model achieved impressive performance metrics, including accuracy (95.24%), precision (100%), sensitivity (89.47%), and specificity (100%). Furthermore, the study compared the proposed approach to existing models, demonstrating significant improvements across various performance measures.
Multi-resolution short-time Fourier transform providing deep features for 3D CNN to classify rolling bearing fault vibration signals
Authors: Zhang Meng
Published: June 2024

The time-frequency domain features of vibration signals provide valuable information for deep learning-based rolling bearing fault diagnosis methods, where fault signal classification aiding in the identification of nominal fault types during diagnosis. The Short-Time Fourier Transform (STFT) is a widely used time-frequency transformation method, and its window length is the key parameter that determines the trade-off between time and frequency resolution. The primary motivation of this study is to address the limitation in traditional STFT-based 2D CNN methods: the inability to adapt the window length to different types of signals. To achieve accurate classification of bearing fault types, this study proposes a method based on three-dimensional convolutional neural networks (3D CNNs) to deeply explore the time-frequency domain information of one-dimensional vibration signals from faulty bearings. This method first applies STFT with multiple window sizes to perform multi-resolution time-frequency transformations on the time-domain vibration signals, yielding three-dimensional data. Subsequently, a classifier is trained based on the proposed 3D CNN. Experimental results on public datasets show that, without any sophisticated techniques, the proposed method achieves an average classification accuracy of 99.2% for six types of bearing faults using a relatively simple CNN structure. Compared to 1D CNN and 2D CNN methods that use fixed window sizes for STFT, the proposed method significantly enhances classification performance. Furthermore, it demonstrates robust classification results even on small-scaled bearing datasets.
Design of bionic active folding flapping wing vehicle
Authors: Zhang Pingxia , Yu Shiyuan , Dong Qirui , Wu Wenjin , Zhu Yongqiang
Published: June 2024

Flapping wing vehicles mimic the wing flapping of flying creatures such as birds, bats and insects, and are characterized by simplicity, lightness, good concealment, high maneuverability and diversified flight attitudes. Currently, the development of wing-fluttering vehicles mainly focuses on wing-fluttering configurations, while there is little research on mimicking the large-scale active folding of wings of birds and bats. Here, we developed two types of large-scale folding wing vehicles with light mass and actively folded wings respectively, based on the property that the wings of flying organisms can be actively folded and contracted in a large scale, and tested their flight capabilities. The test results show that the latter vehicle, which combines the flapping wing mechanism and the active folding mechanism, has good flight performance and is able to accomplish the airborne folding of the wings on the basis of the flapping wing flight.
A comprehensive review of hydrophobic concrete: surface and bulk modifications for enhancing corrosion resistance
Authors: Gnanaraj S Joseph , K Vasugi
Published: June 2024

Corrosion in concrete structures constitutes significant challenges, threatening integrity and requiring high maintenance costs. Hydrophobic concrete presents an emerging solution for addressing corrosion in concrete structures, especially in marine environments. This review article investigates advanced techniques for surface and bulk modifications of hydrophobic concrete, focusing on their efficacy in enhancing corrosion resistance. Surface modifications using silane-siloxane coatings and polyurethane treatments significantly reduce water absorption by up to 30% and chloride ion permeability by up to 50%. Bulk modifications incorporating nano-silica and calcium stearate demonstrate marked improvements in mechanical properties and corrosion resistance, with calcium stearate reducing corrosion rates by 40%. These treatments also contribute to an average increase in compressive strength of 15% and a decrease in corrosion current densities, indicating practical utility in environments such as marine conditions. Additionally, innovative techniques like fluorosilane treatments and steel fibres further fortify impermeability and structural integrity, demonstrating the versatility of hydrophobic concrete in addressing corrosion challenges. Moreover, using eco-friendly ingredients underscores its potential for sustainable infrastructure development. In summary, hydrophobic concrete offers a comprehensive strategy for combating corrosion, providing surface and bulk modifications that enhance durability, reduce maintenance costs, and ensure the long-term sustainability of modern infrastructure.
Forseti: a mechanistic and predictive model of the splicing status of scRNA-seq reads
Authors: He Dongze , Gao Yuan , Chan Spencer Skylar , Quintana-Parrilla Natalia , Patro Rob
Published: June 2024

Motivation

Short-read single-cell RNA-sequencing (scRNA-seq) has been used to study cellular heterogeneity, cellular fate, and transcriptional dynamics. Modeling splicing dynamics in scRNA-seq data is challenging, with inherent difficulty in even the seemingly straightforward task of elucidating the splicing status of the molecules from which sequenced fragments are drawn. This difficulty arises, in part, from the limited read length and positional biases, which substantially reduce the specificity of the sequenced fragments. As a result, the splicing status of many reads in scRNA-seq is ambiguous because of a lack of definitive evidence. We are therefore in need of methods that can recover the splicing status of ambiguous reads which, in turn, can lead to more accuracy and confidence in downstream analyses.

Results

We develop Forseti, a predictive model to probabilistically assign a splicing status to scRNA-seq reads. Our model has two key components. First, we train a binding affinity model to assign a probability that a given transcriptomic site is used in fragment generation. Second, we fit a robust fragment length distribution model that generalizes well across datasets deriving from different species and tissue types. Forseti combines these two trained models to predict the splicing status of the molecule of origin of reads by scoring putative fragments that associate each alignment of sequenced reads with proximate potential priming sites. Using both simulated and experimental data, we show that our model can precisely predict the splicing status of many reads and identify the true gene origin of multi-gene mapped reads.

Availability and implementation

Forseti and the code used for producing the results are available at https://github.com/COMBINE-lab/forseti under a BSD 3-clause license.
Enhancing Hi-C contact matrices for loop detection with Capricorn: a multiview diffusion model
Authors: Fang Tangqi , Liu Yifeng , Woicik Addie , Lu Minsi , Jha Anupama , Wang Xiao , Li Gang , Hristov Borislav , Liu Zixuan , Xu Hanwen , Noble William S , Wang Sheng
Published: June 2024

Motivation

High-resolution Hi-C contact matrices reveal the detailed three-dimensional architecture of the genome, but high-coverage experimental Hi-C data are expensive to generate. Simultaneously, chromatin structure analyses struggle with extremely sparse contact matrices. To address this problem, computational methods to enhance low-coverage contact matrices have been developed, but existing methods are largely based on resolution enhancement methods for natural images and hence often employ models that do not distinguish between biologically meaningful contacts, such as loops and other stochastic contacts.

Results

We present Capricorn, a machine learning model for Hi-C resolution enhancement that incorporates small-scale chromatin features as additional views of the input Hi-C contact matrix and leverages a diffusion probability model backbone to generate a high-coverage matrix. We show that Capricorn outperforms the state of the art in a cross-cell-line setting, improving on existing methods by 17% in mean squared error and 26% in F1 score for chromatin loop identification from the generated high-coverage data. We also demonstrate that Capricorn performs well in the cross-chromosome setting and cross-chromosome, cross-cell-line setting, improving the downstream loop F1 score by 14% relative to existing methods. We further show that our multiview idea can also be used to improve several existing methods, HiCARN and HiCNN, indicating the wide applicability of this approach. Finally, we use DNA sequence to validate discovered loops and find that the fraction of CTCF-supported loops from Capricorn is similar to those identified from the high-coverage data. Capricorn is a powerful Hi-C resolution enhancement method that enables scientists to find chromatin features that cannot be identified in the low-coverage contact matrix.

Availability and implementation

Implementation of Capricorn and source code for reproducing all figures in this paper are available at https://github.com/CHNFTQ/Capricorn.
An empirical study on KDIGO-defined acute kidney injury prediction in the intensive care unit
Authors: Lyu Xinrui , Fan Bowen , Hüser Matthias , Hartout Philip , Gumbsch Thomas , Faltys Martin , Merz Tobias M , Rätsch Gunnar , Borgwardt Karsten
Published: June 2024

Motivation

Acute kidney injury (AKI) is a syndrome that affects a large fraction of all critically ill patients, and early diagnosis to receive adequate treatment is as imperative as it is challenging to make early. Consequently, machine learning approaches have been developed to predict AKI ahead of time. However, the prevalence of AKI is often underestimated in state-of-the-art approaches, as they rely on an AKI event annotation solely based on creatinine, ignoring urine output.

 

We construct and evaluate early warning systems for AKI in a multi-disciplinary ICU setting, using the complete KDIGO definition of AKI. We propose several variants of gradient-boosted decision tree (GBDT)-based models, including a novel time-stacking based approach. A state-of-the-art LSTM-based model previously proposed for AKI prediction is used as a comparison, which was not specifically evaluated in ICU settings yet.

Results

We find that optimal performance is achieved by using GBDT with the time-based stacking technique (AUPRC = 65.7%, compared with the LSTM-based model’s AUPRC = 62.6%), which is motivated by the high relevance of time since ICU admission for this task. Both models show mildly reduced performance in the limited training data setting, perform fairly across different subcohorts, and exhibit no issues in gender transfer.

 

Following the official KDIGO definition substantially increases the number of annotated AKI events. In our study GBDTs outperform LSTM models for AKI prediction. Generally, we find that both model types are robust in a variety of challenging settings arising for ICU data.

Availability and implementation

The code to reproduce the findings of our manuscript can be found at: https://github.com/ratschlab/AKI-EWS
Accurate assembly of multiple RNA-seq samples with Aletsch
Authors: Shi Qian , Zhang Qimin , Shao Mingfu
Published: June 2024

Motivation

High-throughput RNA sequencing has become indispensable for decoding gene activities, yet the challenge of reconstructing full-length transcripts persists. Traditional single-sample assemblers frequently produce fragmented transcripts, especially in single-cell RNA-seq data. While algorithms designed for assembling multiple samples exist, they encounter various limitations.

Results

We present Aletsch, a new assembler for multiple bulk or single-cell RNA-seq samples. Aletsch incorporates several algorithmic innovations, including a “bridging” system that can effectively integrate multiple samples to restore missed junctions in individual samples, and a new graph-decomposition algorithm that leverages “supporting” information across multiple samples to guide the decomposition of complex vertices. A standout feature of Aletsch is its application of a random forest model with 50 well-designed features for scoring transcripts. We demonstrate its robust adaptability across different chromosomes, datasets, and species. Our experiments, conducted on RNA-seq data from several protocols, firmly demonstrate Aletsch’s significant outperformance over existing meta-assemblers. As an example, when measured with the partial area under the precision-recall curve (pAUC, constrained by precision), Aletsch surpasses the leading assemblers TransMeta by 22.9%–62.1% and PsiCLASS by 23.0%–175.5% on human datasets.

Availability and implementation

Aletsch is freely available at https://github.com/Shao-Group/aletsch. Scripts that reproduce the experimental results of this manuscript is available at https://github.com/Shao-Group/aletsch-test.
A count-based model for delineating cell–cell interactions in spatial transcriptomics data
Authors: Sarkar Hirak , Chitra Uthsav , Gold Julian , Raphael Benjamin J
Published: June 2024

Motivation

Cell–cell interactions (CCIs) consist of cells exchanging signals with themselves and neighboring cells by expressing ligand and receptor molecules and play a key role in cellular development, tissue homeostasis, and other critical biological functions. Since direct measurement of CCIs is challenging, multiple methods have been developed to infer CCIs by quantifying correlations between the gene expression of the ligands and receptors that mediate CCIs, originally from bulk RNA-sequencing data and more recently from single-cell or spatially resolved transcriptomics (SRT) data. SRT has a particular advantage over single-cell approaches, since ligand–receptor correlations can be computed between cells or spots that are physically close in the tissue. However, the transcript counts of individual ligands and receptors in SRT data are generally low, complicating the inference of CCIs from expression correlations.

Results

We introduce Copulacci, a count-based model for inferring CCIs from SRT data. Copulacci uses a Gaussian copula to model dependencies between the expression of ligands and receptors from nearby spatial locations even when the transcript counts are low. On simulated data, Copulacci outperforms existing CCI inference methods based on the standard Spearman and Pearson correlation coefficients. Using several real SRT datasets, we show that Copulacci discovers biologically meaningful ligand–receptor interactions that are lowly expressed and undiscoverable by existing CCI inference methods.

Availability and implementation

Copulacci is implemented in Python and available at https://github.com/raphael-group/copulacci.
scGrapHiC: deep learning-based graph deconvolution for Hi-C using single cell gene expression
Authors: Murtaza Ghulam , Butaney Byron , Wagner Justin , Singh Ritambhara
Published: June 2024

Single-cell Hi-C (scHi-C) protocol helps identify cell-type-specific chromatin interactions and sheds light on cell differentiation and disease progression. Despite providing crucial insights, scHi-C data is often underutilized due to the high cost and the complexity of the experimental protocol. We present a deep learning framework, scGrapHiC, that predicts pseudo-bulk scHi-C contact maps using pseudo-bulk scRNA-seq data. Specifically, scGrapHiC performs graph deconvolution to extract genome-wide single-cell interactions from a bulk Hi-C contact map using scRNA-seq as a guiding signal. Our evaluations show that scGrapHiC, trained on seven cell-type co-assay datasets, outperforms typical sequence encoder approaches. For example, scGrapHiC achieves a substantial improvement of 23.2% in recovering cell-type-specific Topologically Associating Domains over the baselines. It also generalizes to unseen embryo and brain tissue samples. scGrapHiC is a novel method to generate cell-type-specific scHi-C contact maps using widely available genomic signals that enables the study of cell-type-specific chromatin interactions.

Availability and implementation

The GitHub link: https://github.com/rsinghlab/scGrapHiC contains the source code of scGrapHiC and associated scripts to preprocess publicly available datasets to produce the results and visualizations we have discuss in this manuscript.
Median and small parsimony problems on RNA trees
Authors: Marchand Bertrand , Anselmetti Yoann , Lafond Manuel , Ouangraoua Aïda
Published: June 2024

Motivation

Noncoding RNAs (ncRNAs) express their functions by adopting molecular structures. Specifically, RNA secondary structures serve as a relatively stable intermediate step before tertiary structures, offering a reliable signature of molecular function. Consequently, within an RNA functional family, secondary structures are generally more evolutionarily conserved than sequences. Conversely, homologous RNA families grouped within an RNA clan share ancestors but typically exhibit structural differences. Inferring the evolution of RNA structures within RNA families and clans is crucial for gaining insights into functional adaptations over time and providing clues about the Ancient RNA World Hypothesis.

Results

We introduce the median problem and the small parsimony problem for ncRNA families, where secondary structures are represented as leaf-labeled trees. We utilize the Robinson-Foulds (RF) tree distance, which corresponds to a specific edit distance between RNA trees, and a new metric called the Internal-Leafset (IL) distance. While the RF tree distance compares sets of leaves descending from internal nodes of two RNA trees, the IL distance compares the collection of leaf-children of internal nodes. The latter is better at capturing differences in structural elements of RNAs than the RF distance, which is more focused on base pairs. We also consider a more general tree edit distance that allows the mapping of base pairs that are not perfectly aligned. We study the theoretical complexity of the median problem and the small parsimony problem under the three distance metrics and various biologically relevant constraints, and we present polynomial-time maximum parsimony algorithms for solving some versions of the problems. Our algorithms are applied to ncRNA families from the RFAM database, illustrating their practical utility.

Availability and implementation

https://github.com/bmarchand/rna\_small\_parsimony.
DDAffinity: predicting the changes in binding affinity of multiple point mutations using protein 3D structure
Authors: Yu Guanglei , Zhao Qichang , Bi Xuehua , Wang Jianxin
Published: June 2024

Motivation

Mutations are the crucial driving force for biological evolution as they can disrupt protein stability and protein–protein interactions which have notable impacts on protein structure, function, and expression. However, existing computational methods for protein mutation effects prediction are generally limited to single point mutations with global dependencies, and do not systematically take into account the local and global synergistic epistasis inherent in multiple point mutations.

Results

To this end, we propose a novel spatial and sequential message passing neural network, named DDAffinity, to predict the changes in binding affinity caused by multiple point mutations based on protein 3D structures. Specifically, instead of being on the whole protein, we perform message passing on the k-nearest neighbor residue graphs to extract pocket features of the protein 3D structures. Furthermore, to learn global topological features, a two-step additive Gaussian noising strategy during training is applied to blur out local details of protein geometry. We evaluate DDAffinity on benchmark datasets and external validation datasets. Overall, the predictive performance of DDAffinity is significantly improved compared with state-of-the-art baselines on multiple point mutations, including end-to-end and pre-training based methods. The ablation studies indicate the reasonable design of all components of DDAffinity. In addition, applications in nonredundant blind testing, predicting mutation effects of SARS-CoV-2 RBD variants, and optimizing human antibody against SARS-CoV-2 illustrate the effectiveness of DDAffinity.

Availability and implementation

DDAffinity is available at https://github.com/ak422/DDAffinity.
REUNION: transcription factor binding prediction and regulatory association inference from single-cell multi-omics data
Authors: Yang Yang , Pe’er Dana
Published: June 2024

Motivation

Profiling of gene expression and chromatin accessibility by single-cell multi-omics approaches can help to systematically decipher how transcription factors (TFs) regulate target gene expression via cis-region interactions. However, integrating information from different modalities to discover regulatory associations is challenging, in part because motif scanning approaches miss many likely TF binding sites.

Results

We develop REUNION, a framework for predicting genome-wide TF binding and cis-region-TF-gene “triplet” regulatory associations using single-cell multi-omics data. The first component of REUNION, Unify, utilizes information theory-inspired complementary score functions that incorporate TF expression, chromatin accessibility, and target gene expression to identify regulatory associations. The second component, Rediscover, takes Unify estimates as input for pseudo semi-supervised learning to predict TF binding in accessible genomic regions that may or may not include detected TF motifs. Rediscover leverages latent chromatin accessibility and sequence feature spaces of the genomic regions, without requiring chromatin immunoprecipitation data for model training. Applied to peripheral blood mononuclear cell data, REUNION outperforms alternative methods in TF binding prediction on average performance. In particular, it recovers missing region-TF associations from regions lacking detected motifs, which circumvents the reliance on motif scanning and facilitates discovery of novel associations involving potential co-binding transcriptional regulators. Newly identified region-TF associations, even in regions lacking a detected motif, improve the prediction of target gene expression in regulatory triplets, and are thus likely to genuinely participate in the regulation.

Availability and implementation

All source code is available at https://github.com/yangymargaret/REUNION.
Predicting protein functions using positive-unlabeled ranking with ontology-based priors
Authors: Zhapa-Camacho Fernando , Tang Zhenwei , Kulmanov Maxat , Hoehndorf Robert
Published: June 2024

Automated protein function prediction is a crucial and widely studied problem in bioinformatics. Computationally, protein function is a multilabel classification problem where only positive samples are defined and there is a large number of unlabeled annotations. Most existing methods rely on the assumption that the unlabeled set of protein function annotations are negatives, inducing the false negative issue, where potential positive samples are trained as negatives. We introduce a novel approach named PU-GO, wherein we address function prediction as a positive-unlabeled ranking problem. We apply empirical risk minimization, i.e. we minimize the classification risk of a classifier where class priors are obtained from the Gene Ontology hierarchical structure. We show that our approach is more robust than other state-of-the-art methods on similarity-based and time-based benchmark datasets.

Availability and implementation

Data and code are available at https://github.com/bio-ontology-research-group/PU-GO.
Enhancing generalizability and performance in drug–target interaction identification by integrating pharmacophore and pre-trained models
Authors: Zhang Zuolong , He Xin , Long Dazhi , Luo Gang , Chen Shengbo
Published: June 2024

Motivation

In drug discovery, it is crucial to assess the drug–target binding affinity (DTA). Although molecular docking is widely used, computational efficiency limits its application in large-scale virtual screening. Deep learning-based methods learn virtual scoring functions from labeled datasets and can quickly predict affinity. However, there are three limitations. First, existing methods only consider the atom-bond graph or one-dimensional sequence representations of compounds, ignoring the information about functional groups (pharmacophores) with specific biological activities. Second, relying on limited labeled datasets fails to learn comprehensive embedding representations of compounds and proteins, resulting in poor generalization performance in complex scenarios. Third, existing feature fusion methods cannot adequately capture contextual interaction information.

Results

Therefore, we propose a novel DTA prediction method named HeteroDTA. Specifically, a multi-view compound feature extraction module is constructed to model the atom–bond graph and pharmacophore graph. The residue concat graph and protein sequence are also utilized to model protein structure and function. Moreover, to enhance the generalization capability and reduce the dependence on task-specific labeled data, pre-trained models are utilized to initialize the atomic features of the compounds and the embedding representations of the protein sequence. A context-aware nonlinear feature fusion method is also proposed to learn interaction patterns between compounds and proteins. Experimental results on public benchmark datasets show that HeteroDTA significantly outperforms existing methods. In addition, HeteroDTA shows excellent generalization performance in cold-start experiments and superiority in the representation learning ability of drug–target pairs. Finally, the effectiveness of HeteroDTA is demonstrated in a real-world drug discovery study.

Availability and implementation

The source code and data are available at https://github.com/daydayupzzl/HeteroDTA.
GraphCompass: spatial metrics for differential analyses of cell organization across conditions
Authors: Ali Mayar , Kuijs Merel , Hediyeh-zadeh Soroor , Treis Tim , Hrovatin Karin , Palla Giovanni , Schaar Anna C , Theis Fabian J
Published: June 2024

Spatial omics technologies are increasingly leveraged to characterize how disease disrupts tissue organization and cellular niches. While multiple methods to analyze spatial variation within a sample have been published, statistical and computational approaches to compare cell spatial organization across samples or conditions are mostly lacking. We present GraphCompass, a comprehensive set of omics-adapted graph analysis methods to quantitatively evaluate and compare the spatial arrangement of cells in samples representing diverse biological conditions. GraphCompass builds upon the Squidpy spatial omics toolbox and encompasses various statistical approaches to perform cross-condition analyses at the level of individual cell types, niches, and samples. Additionally, GraphCompass provides custom visualization functions that enable effective communication of results. We demonstrate how GraphCompass can be used to address key biological questions, such as how cellular organization and tissue architecture differ across various disease states and which spatial patterns correlate with a given pathological condition. GraphCompass can be applied to various popular omics techniques, including, but not limited to, spatial proteomics (e.g. MIBI-TOF), spot-based transcriptomics (e.g. 10× Genomics Visium), and single-cell resolved transcriptomics (e.g. Stereo-seq). In this work, we showcase the capabilities of GraphCompass through its application to three different studies that may also serve as benchmark datasets for further method development. With its easy-to-use implementation, extensive documentation, and comprehensive tutorials, GraphCompass is accessible to biologists with varying levels of computational expertise. By facilitating comparative analyses of cell spatial organization, GraphCompass promises to be a valuable asset in advancing our understanding of tissue function in health and disease.
AttentionPert: accurately modeling multiplexed genetic perturbations with multi-scale effects
Authors: Bai Ding , Ellington Caleb N , Mo Shentong , Song Le , Xing Eric P
Published: June 2024

Motivation

Genetic perturbations (e.g. knockouts, variants) have laid the foundation for our understanding of many diseases, implicating pathogenic mechanisms and indicating therapeutic targets. However, experimental assays are fundamentally limited by the number of measurable perturbations. Computational methods can fill this gap by predicting perturbation effects under novel conditions, but accurately predicting the transcriptional responses of cells to unseen perturbations remains a significant challenge.

Results

We address this by developing a novel attention-based neural network, AttentionPert, which accurately predicts gene expression under multiplexed perturbations and generalizes to unseen conditions. AttentionPert integrates global and local effects in a multi-scale model, representing both the nonuniform system-wide impact of the genetic perturbation and the localized disturbance in a network of gene–gene similarities, enhancing its ability to predict nuanced transcriptional responses to both single and multi-gene perturbations. In comprehensive experiments, AttentionPert demonstrates superior performance across multiple datasets outperforming the state-of-the-art method in predicting differential gene expressions and revealing novel gene regulations. AttentionPert marks a significant improvement over current methods, particularly in handling the diversity of gene perturbations and in predicting out-of-distribution scenarios.

Availability and implementation

Code is available at https://github.com/BaiDing1234/AttentionPert.
Biomarker identification by interpretable maximum mean discrepancy
Authors: Adamer Michael F , Brüningk Sarah C , Chen Dexiong , Borgwardt Karsten
Published: June 2024

Motivation

In many biomedical applications, we are confronted with paired groups of samples, such as treated versus control. The aim is to detect discriminating features, i.e. biomarkers, based on high-dimensional (omics-) data. This problem can be phrased more generally as a two-sample problem requiring statistical significance testing to establish differences, and interpretations to identify distinguishing features. The multivariate maximum mean discrepancy (MMD) test quantifies group-level differences, whereas statistically significantly associated features are usually found by univariate feature selection. Currently, few general-purpose methods simultaneously perform multivariate feature selection and two-sample testing.

Results

We introduce a sparse, interpretable, and optimized MMD test (SpInOpt-MMD) that enables two-sample testing and feature selection in the same experiment. SpInOpt-MMD is a versatile method and we demonstrate its application to a variety of synthetic and real-world data types including images, gene expression measurements, and text data. SpInOpt-MMD is effective in identifying relevant features in small sample sizes and outperforms other feature selection methods such as SHapley Additive exPlanations and univariate association analysis in several experiments.

Availability and implementation

The code and links to our public data are available at https://github.com/BorgwardtLab/spinoptmmd.
Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks
Authors: Deng Chao , Li Hong-Dong , Zhang Li-Shen , Liu Yiwei , Li Yaohang , Wang Jianxin
Published: June 2024

Motivation

Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and biological processes. The knowledge of annotated gene sets is critical for discovering cancer genes but remains to be fully exploited.

Results

Here, we present the DIsease-Specific Hypergraph neural network (DISHyper), a hypergraph-based computational method that integrates the knowledge from multiple types of annotated gene sets to predict cancer genes. First, our benchmark results demonstrate that DISHyper outperforms the existing state-of-the-art methods and highlight the advantages of employing hypergraphs for representing annotated gene sets. Second, we validate the accuracy of DISHyper-predicted cancer genes using functional validation results and multiple independent functional genomics data. Third, our model predicts 44 novel cancer genes, and subsequent analysis shows their significant associations with multiple types of cancers. Overall, our study provides a new perspective for discovering cancer genes and reveals previously undiscovered cancer genes.

Availability and implementation

DISHyper is freely available for download at https://github.com/genemine/DISHyper.