bluuebunny/crossref_metadata_2025 huggingface dataset:
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

Significance

The 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.

Aim

We 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.

Approach

For 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.

Results

Our 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.

Conclusion

Our 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

Protein 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

Objective

This 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.

Methods

Ultrasound 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.

Results

Expert 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.

Conclusion

This 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

Introduction

Under 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 Analysis

This 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 Dissemination

Ethical 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

Biophysical 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.
Cellular evolution of the hypothalamic preoptic area of behaviorally divergent deer mice
Authors: Chen Jenny , Richardson Phoebe R , Kirby Christopher , Eddy Sean R , Hoekstra Hopi
Published: August 2024

Genetic variation is known to contribute to the variation of animal social behavior, but the molecular mechanisms that lead to behavioral differences are still not fully understood. Here, we investigate the cellular evolution of the hypothalamic preoptic area (POA), a brain region that plays a critical role in social behavior, across two sister species of deer mice (Peromyscus maniculatus and P. polionotus) with divergent social systems. These two species exhibit large differences in mating and parental care behavior across species and sex. Using single-nucleus RNA-sequencing, we build a cellular atlas of the POA for males and females of both Peromyscus species. We identify four cell types that are differentially abundant across species, two of which may account for species differences in parental care behavior based on known functions of these cell types. Our data further implicate two sex-biased cell types to be important for the evolution of sex-specific behavior. Finally, we show a remarkable reduction of sex-biased gene expression in P. polionotus, a monogamous species that also exhibits reduced sexual dimorphism in parental care behavior. Our POA atlas is a powerful resource to investigate how molecular neuronal traits may be evolving to give rise to innate differences in social behavior across animal species.
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

Objective

Photoplethysmography 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).

Approach

Five 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 results

MSPTDfast (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.

Significance

MSPTDfast (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

Introduction

Western 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.

Methods

The 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.

Results

Total 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).

Discussion

The 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 abstract

The 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

Molecular 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.

HIGHLIGHTS

NMDAR 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

High-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

Cell 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.
The three-dimensional anatomy and dorsoventral asymmetry of the matureMarchantia polymorphameristem develops from a symmetrical gemma meristem
Authors: Spencer Victoria , Wallner Eva-Sophie , Jandrasits Katharina , Edelbacher Natalie , Mosiolek Magdalena , Dolan Liam
Published: August 2024

Meristems are three-dimensional generative structures that maintain a population of stem cells whilst producing new organs and tissues. Meristems develop in all land plants, however we know relatively little about the spatial and temporal regulation of meristem structure in lineages such as the bryophytes. Here we describe the three-dimensional anatomy of the meristem during the development of the liverwort,Marchantia polymorpha.Using optical reconstructions of the frontal, sagittal and transverse planes through the mature meristem, we show that the apical stem cell is sub-apical, ventral, and located in the outer cell layer. The anatomy of the mature meristem is therefore asymmetrical in the dorsoventral axis, which is reflected by the domain specific protein localisation of Marchantia Class III and Class IV Homeodomain-Leucine-Zippers (MpC3HDZ and MpC4HDZ) and promoter activity of MpYUCCA2. The dorsoventral asymmetry that defines the mature meristem is absent in the juvenile meristems of the asexual propagules known as gemmae. We discovered that anatomical dorsoventral asymmetry of the meristem forms after two days of gemmaling growth and is accompanied by expression of the dorsal identity reporter, MpC3HDZ. We conclude that the gemma meristem is in a state of arrested development and undergoes anatomical rearrangement to develop the three-dimensional meristem structure of the mature plant.
A Comprehensive Evaluation of Self Attention for Detecting Feature Interactions
Authors: Jabeen Saira , Ben-Hur Asa
Published: August 2024

Using deep learning in computational biology requires methods that are able to extract meaningful biological information from the trained models. Although deep learning models excel in their prediction performance, interpreting them presents a challenge. Recent work has suggested that self-attention layers can be interpreted to predict cooperativity between binding of transcription factors. We extend this earlier work and demonstrate that the addition of an entropy term to the loss function yields sparser attention values that are both easier to interpret and provide higher precision interpretations. Furthermore, we performed a comprehensive evaluation of the relative performance of different flavors of attention-based transcription factor cooperativity discovery methods, and compared methods that use raw attention scores to the use of attribution over the attention scores, and the earlier DFIM model. We found that the entropy-enhanced attention-based models performed similarly to each other, and exhibited improved accuracy and reduced computation time compared to DFIM.
Listening to the bats of Carajás: Applied bioacoustics for species inventory and environment use in a mosaic of forests, savannas, and industrial mining in the Brazilian Amazonia
Authors: Gomes Lidiane , Bernard Enrico
Published: August 2024

Bats emit echolocation calls for orientation, foraging, and social interactions. These calls are mostly species-specific, reliable for inventories and to assess habitat use, characteristics useful for large, species-rich but poorly sampled areas. This is the case of Carajás, in Brazilian Amazonia, a mosaic of cave-rich dense forests and unique metalophilous savannas (known ascanga), harboring a rich bat fauna but also industrial iron ore mining, stressing the need to preserve biodiversity. We used bioacoustics (142,000 minutes of recording) to inventory bats at 61 points in Carajás and identified 43 sonotypes of seven bat families, including species rarely recorded with capture nets. Eleven species were recorded for the first time in Carajás. Species richness varied among environments – forests being the richest – butcangashad greater richness stability and a more distinct species composition. All areas with imminent mining had high bat richness. Richness in a post-mined area increased, possibly indicating resilience of some species. By providing a reference sound library for bats in Carajás, we proved the usefulness of biacoustics to improve the environmental licensing processes involving mining in biodiversity-rich areas, useful not only for Amazonia but also for other tropical environments with high bat species richness.
SQANTI-reads: a tool for the quality assessment of long read data in multi-sample lrRNA-seq experiments
Authors: Keil Netanya , Monzó Carolina , McIntyre Lauren , Conesa Ana
Published: August 2024

SQANTI-reads leverages SQANTI3, a tool for the analysis of the quality of transcript models, to develop a read-level quality control framework for replicated long-read RNA-seq experiments. The number and distribution of reads, as well as the number and distribution of unique junction chains (transcript splicing patterns), in SQANTI3 structural categories are informative of raw data quality. Multi-sample visualizations of QC metrics are presented by experimental design factors to identify outliers. We introduce new metrics for 1) the identification of potentially under-annotated genes and putative novel transcripts and for 2) quantifying variation in junction donors and acceptors. We applied SQANTI-reads to two different datasets, aDrosophiladevelopmental experiment and a multi-platform dataset from the LRGASP project and demonstrate that the tool effectively reveals the impact of read coverage on data quality, and readily identifies strong and weak splicing sites. SQANTI-reads is open source and available for download at GitHub.
A latent outcome variable approach for Mendelian randomization using the expectation maximization algorithm
Authors: Amente Lamessa Dube , Mills Natalie T , Le Thuc Duy , Hyppönen Elina , Lee S Hong
Published: August 2024

Mendelian randomization (MR) is a widely used tool to uncover causal relationships between exposures and outcomes. However, existing MR methods can suffer from inflated type I error rates and biased causal effects in the presence of invalid instruments. Our proposed method enhances MR analysis by augmenting latent phenotypes of the outcome, explicitly disentangling horizontal and vertical pleiotropy effects. This allows for explicit assessment of the exclusion restriction assumption and iteratively refines causal estimates through the expectation-maximization algorithm. This approach offers a unique and potentially more precise framework compared to existing MR methods. We rigorously evaluate our method against established MR approaches across diverse simulation scenarios, including balanced and directional pleiotropy, as well as violations of the Instrument Strength Independent of Direct Effect (InSIDE) assumption. Our findings consistently demonstrate superior performance of our method in terms of controlling type I error rates, bias, and robustness to genetic confounding. Additionally, our method facilitates testing for directional horizontal pleiotropy and outperforms MR-Egger in this regard, while also effectively testing for violations of the InSIDE assumption. We apply our method to real data, demonstrating its effectiveness compared to traditional MR methods. This analysis reveals the causal effects of body mass index (BMI) on metabolic syndrome (MetS) and a composite MetS score calculated by the weighted sum of its component factors. While the causal relationship is consistent across most methods, our proposed method shows fewer violations of the exclusion restriction assumption, especially for MetS scores where horizontal pleiotropy persists and other methods suffer from inflation.
Adaptation to yeast restriction through the evolution of resource acquisition, macronutrient reserve, and ovarian function
Authors: Dasgupta Purbasha , Koner Anish , Pal Rabi Sankar , Pradhan Pranav Nandan , Roychoudhury Kingkini , Nandy Bodhisatta
Published: August 2024

Optimization of reproduction under dietary adversity is an important aspect of diet-dependent adaptation. Yet, little is known about the mechanism of such adaptive evolution. Here, we investigated a set of experimentally evolved populations ofDrosophila melanogasterwhere early-life fecundity evolved as an adaptation to chronic protein restriction. We tested the role of resource acquisition and macronutrient storage, and changes in ovarian function that could have allowed such reproductive adaptation. We show that higher early-life fecundity was associated with the increased larval feeding rate, aiding in accumulation of higher protein content at the time of eclosion. Further evidence also suggests increase in reproductively allocated lipid content. Evolved females were found to have larger ovaries that also had a higher number of mature, post-vitellogenic oocytes that seem to readily allow the attainment of higher fecundity. Our results further show the evolution of plasticity in ovariole count (depending on mating status) and total egg-chamber count in females. These results are important in understanding the putative role of genetic variance and covariances that aid or limit the evolution of reproductive optimization, especially under nutritional adversity.
Revisiting acyclovir dosing for viral encephalitis using a Bayesian PBPK modeling approach
Authors: Sun Ming , Manson Martijn L. , Märtson Anne-Grete , Bodilsen Jacob , de Lange Elizabeth C.M. , Guo Tingjie
Published: August 2024

Acyclovir is a primary treatment for central nervous system (CNS) infections caused by herpes simplex virus (HSV) and varicella-zoster virus (VZV). However, patient outcomes remain suboptimal with high mortality and morbidity, following current dosing guidelines. Given the lack of alternative therapies, there is a pressing need to optimize acyclovir dosing, especially since initial regimens were developed in the 1980s with incomplete pharmacokinetic data in the CNS. This study aimed to evaluate both current and alternative acyclovir dosing regimens using a full Bayesian physiologically-based pharmacokinetic (PBPK) model tailored for viral encephalitis. We developed a CNS PBPK model to simulate acyclovir concentrations in plasma, brain extracellular fluid (ECF), and subarachnoid space (SAS). Drug efficacy was assessed using two pharmacokinetic targets, 50%fT>IC50and Cmin>IC50, with a safety threshold set at 25 mg/L in plasma. The standard dosing regimen (10 mg/kg TID) yielded sufficient acyclovir exposure in plasma, brain extracellular fluid (ECF), and subarachnoid space (SAS) compartments based on the 50%fT>IC50target. However, it did not consistently meet the Cmin>IC50target, indicating potential suboptimal exposure in these compartments when evaluated against this criterion. Notably, a higher probability of target attainment (PTA) was generally observed in the brain ECF and SAS compared to plasma. Increasing the dosing frequency to QID improved target attainment but exceeded the toxicity threshold at 20 mg/kg. Our findings suggest that a dosing regimen of 10 mg/kg or 15 mg/kg QID may offer a more effective and safer approach for managing CNS infections compared to the other tested alternative dosing regimens.
Hypertension, Intracranial Arteriosclerosis, and Structural Brain Changes in Patients with TIA or Ischemic Stroke
Authors: Li Xi , Berghout Bernhard P. , van Rooijen Gijs , Ikram M. Kamran , Roozenbeek Bob , Bos Daniel
Published: August 2024

Background

Hypertension is a major risk factor of structural brain changes, including atrophy and cerebral small vessel disease. Intracranial arteriosclerosis could be an underlying mechanism between hypertension and structural brain changes. This study investigated whether intracranial carotid artery calcification (ICAC), as a proxy for intracranial arteriosclerosis, explains the association between hypertension and structural brain changes in patients with TIA or ischemic stroke.

Methods

968 patients (mean age 62.7 years) with TIA or ischemic stroke from a registry study who underwent non-contrast CT (NCCT) and CT-angiography (CTA) were included in this cross-sectional study. Presence and volume (mm3) of ICAC were assessed on CTA. Subtypes of ICAC were assessed on NCCT, where ICAC was categorized into intimal and internal elastic lamina (IEL) type calcification. Structural brain changes, indicated by atrophy, periventricular and deep white matter lesions (WML), and lacunes were assessed on NCCT. Causal mediation analysis was performed using ICAC, ICAC volume, and ICAC subtypes as mediators.

Results

ICAC was prevalent in 67.8% of patients, with 52.6% of them exhibiting intimal calcification, and 26.5% exhibiting IEL calcification. Atrophy, periventricular WML, deep WML, and lacunes were present in 48.1%, 56.4%, 43.0% and 17.1% of patients respectively. The presence of ICAC explained 7.1% of the association of hypertension with periventricular WML, 3.6% with deep WML, and 17.6% with lacunes. Hypertension was associated with increased atrophy through ICAC (OR: 1.02, 95% CI: 1.00-1.05). In subgroup analyses, IEL calcification partly explained the association between hypertension and periventricular WML (16.8%), and atrophy (OR: 1.12, 95% CI: 1.02-1.27). Intimal calcification did not explain any association.

Conclusion

ICAC partially explained the association between hypertension and atrophy, periventricular and deep WML, and lacunes. Although intimal calcification was more prevalent in ischemic stroke patients, IEL calcification takes the leading role in explaining the association between hypertension and structural brain changes.
Cellular and molecular networks of intercellular signaling in bone marrow hematopoiesis
Authors: Thomas Zachary V. , Wang Bowen , Boohar Wade R. , Vergel Mary , Eerdeng Jiya , Shon Dayeon J. , Elowitz Michael B. , Lu Rong
Published: August 2024

Hematopoietic stem and progenitor cells (HSPCs) rely on intercellular signaling to maintain and adjust their production of blood and immune cells. This process occurs in the semi-fluidic bone marrow, hosting dozens of cell types that constantly migrate and interact. To elucidate the dynamic network of cell-cell interaction and signaling transduction underlying hematopoiesis, we developed an algorithm to measure Cell-cell Spatial Interaction Probability (CellSIP) by integrating data on ligand and receptor expression, cell type abundance, and cellular spatial positioning. Using new and published mouse datasets, we validated CellSIP and uncovered signaling transductions indicating feedback mechanisms underlying hematopoiesis. Moreover, we identified significant correlations between signaling pathways across individual HSPCs at the same hematopoiesis stage. These pathway correlations illuminate the organization of cellular and signaling networks underlying hematopoiesis, revealing new regulators through their associations with established ones. The signaling quantification and correlation data are available through the Hematopoiesis Intercellular Signaling Explorer (HISE).
Exploitation of phylum-spanning omics resources reveals complexity in the nematode FLP signalling system and provides insights intoflp-gene evolution
Authors: McCoy Ciaran J. , Wray Christopher , Freeman Laura , Crooks Bethany A. , Golinelli Luca , Marks Nikki J. , Temmerman Liesbet , Beets Isabel , Atkinson Louise E. , Mousley Angela
Published: August 2024

Background

Parasitic nematodes significantly undermine global human and animal health and productivity. Parasite control is reliant on anthelmintic administration however over-use of a limited number of drugs has resulted in escalating parasitic nematode resistance, threatening the sustainability of parasite control and underscoring an urgent need for the development of novel therapeutics. FMRFamide-like peptides (FLPs), the largest family of nematode neuropeptides, modulate nematode behaviours including those important for parasite survival, highlighting FLP receptors (FLP-GPCRs) as appealing putative novel anthelmintic targets. Advances in omics resources have enabled the identification of FLPs and neuropeptide-GPCRs in some parasitic nematodes, but remaining gaps in FLP-ligand libraries hinder the characterisation of receptor-ligand interactions, which are required to drive the development of novel control approaches.

Results

In this study we exploited recent expansions in nematode genome data to identify 2143flp-genes in >100 nematode species across free-living, entomopathogenic, plant, animal and human lifestyles and representing 7 of the 12 major nematode clades (1). Our data reveal that: (i) the phylum-spanningflps, flp-1, −8, −14, and −18, may be representative of theflpprofile of the last common ancestor of nematodes; (ii) the majority of parasitic nematodes have a reducedflpcomplement relative to free-living species; (iii) FLP prepropeptide architecture is variable within and betweenflp-genes and across nematode species; (iv) FLP prepropeptide signatures facilitateflp-gene discrimination; (v) FLP motifs display variable length, amino acid sequence, and conservation; (vi) CLANS analysis provides insight into the evolutionary history offlp-gene sequelogues and reveals putativeflp-gene paralogues and, (viii)flpexpression is upregulated in the infective larval stage of several nematode parasites.

Conclusions

These data provide the foundation required for phylum-spanning FLP-GPCR deorphanisation screens in nematodes to seed the discovery and development of novel parasite control approaches.
Divergent macrophage induced lung pathology occurs between infants and adults during respiratory viral infection
Authors: Verhoeven David , Verhoeven Davis
Published: August 2024

Young children, especially those aged 4 months to 2 years of age, frequently exhibit severe morbidity during respiratory viral infections. For influenza infections, macrophages/monocytes serve as front line defenses against early viral replication in the lungs until the adaptive immune system arrives to clear virus. However, infiltrating inflammatory monocytes are a significant cause of influenza induced lung pathology/morbidity. We utilized a young murine model of respiratory viral infections using 21-day-old mice to investigate the mechanisms driving the heightened influenza induced morbidity observed in human young children. We hypothesized that macrophages/monocytes responses to influenza would diverge between young and older mice despite our evidence that macrophages from both groups appear to control viral replication at similar rates. While inflammatory monocyte infiltration contributed to influenza induced morbidity/lung inflammation in adults, they did not appear to contribute to morbidity in young mice. Instead, young mice appeared to develop lung inflammation through a lack of interferon gamma (IFNψ) and infection of macrophage populations. In contrast, adult mice controlled early viral replication through macrophage populations (alveolar, interstitial, and inflammatory) and inflammatory monocytes. While intrinsic limitations in anti-viral cytokine responses, especially IFNψ, characterized the macrophage response to viral infection in young mice, the innate immune response to infection appears diminished compared to adults. This study highlights the intrinsic limitations in macrophage effector functions that may arise in young children but that also contribute to disease pathology.
mvh: an R tool to assemble and organize virtual herbaria from openly available specimen images
Authors: Vasconcelos Thais , Boyko James D.
Published: August 2024

Premise

Recent efforts in digitizing and imaging herbarium specimens have enhanced their use in systematics, ecology, and evolutionary studies. However, there is a lack of user-friendly tools that facilitate the assembly and organizing of customized sets of herbarium specimen images on personal devices, i.e. a personal virtual herbarium.

Methods

Here we present the R packagemvh(stands for “my virtual herbarium”), a software that includes functions designed to search and download metadata and openly available images associated with herbarium specimens based on taxon or geography. The download function also includes an argument to resize images according to a user-imputed quality preference.

Results

We tested the functionalities ofmvhby searching metadata associated with five randomly sampled sets of ten vascular plant species (taxon-based search) and five sets of ten terrestrial coordinates (geography-based search). The main download function had a success rate of 99%, downloading 291 out of the 293 images found in the search. Possible reasons for download failure are also reported as part of the functions’ output.

Conclusions

As long as stable internet connection is available, the R packagemvhmakes the assembly and organizing of personal virtual herbaria an easy task that can help botanists to investigate novel empirical questions as well as trends in digitization efforts.
Challenges in Detecting Somatic Recombination of Repeat Elements: Insights from Short and Long Read Datasets
Authors: Pascarella Giovanni , Frith Martin , Carninci Piero
Published: August 2024

Non-allelic copies of the two major families of repeat elements in the human genome, Alu and L1, recombine somatically at high frequency. Tissue-specific recombination profiles are dynamic in cell differentiation and are altered in neurodegeneration, suggesting that somatic recombination of repeat elements can contribute to functional heterogeneity of cells in health and disease. The study of these genomic variants, however, presents several technical challenges related to their extremely low copy number and their sequence content. Here, we address key issues regarding detecting and annotating structural variants derived from recombining repeat elements in NGS data. We show that PCR introduces significant changes of recombination profiles in sequencing libraries and that recombination profiles are affected by the choice of sequencing platform. We refine previous estimates of recombination in single cells by analyzing recombination profiles in PCR-free HG002 datasets sequenced by Oxford Nanopore Technologies and PacBio sequencers while describing several platform-specific differences. We additionally provide evidence that recombination events annotated in state-of-the-art single-cell HG002 whole-genome sequencing datasets are likely molecular artifacts generated by PCR. By exploring the limits of current technologies, this work establishes essential requirements for future developments to enhance the reliability of detecting somatic recombination of repeat elements in genomic datasets.
Machine-learning approach for operating electron beam at KEK electron/positron injector linac
Authors: Mitsuka Gaku , Kato Shinnosuke , Iida Naoko , Natsui Takuya , Satoh Masanori
Published: August 2024

In current accelerators, numerous parameters and monitored values are to be adjusted and evaluated, respectively. In addition, fine adjustments are required to achieve the target performance. Therefore, the conventional accelerator-operation method, in which experts manually adjust the parameters, is reaching its limits. We are currently investigating the use of machine learning for accelerator tuning as an alternative to expert-based tuning. In recent years, machine-learning algorithms have progressed significantly in terms of speed, sensitivity, and application range. In addition, various libraries are available from different vendors and are relatively easy to use. Herein, we report the results of electron-beam tuning experiments using Bayesian optimization, a tree-structured Parzen estimator, and a covariance matrix-adaptation evolution strategy. Beam-tuning experiments are performed at the KEK e−/e+ injector Linac to maximize the electron-beam charge and reduce the energy-dispersion function. In each case, the performance achieved is comparable to that of a skilled expert.

Published by the American Physical Society
2024
Tc
and the elastocaloric effect of
Sr2RuO4
under
〈110〉
uniaxial stress: No indications of transition splitting
Authors: Jerzembeck Fabian , Li You-Sheng , Palle Grgur , Hu Zhenhai , Biderang Mehdi , Kikugawa Naoki , Sokolov Dmitry A. , Ghosh Sayak , Ramshaw Brad J. , Scaffidi Thomas , Nicklas Michael , Schmalian Jörg , Mackenzie Andrew P. , Hicks Clifford W.
Published: August 2024

There is considerable evidence that the superconductivity of Sr2RuO4 has two components. Among this evidence is a jump in the shear elastic modulus c66 at the critical temperature Tc, observed in ultrasound measurements. Such a jump is forbidden for homogeneous single-component order parameters, and it implies that Tc should develop as a cusp under the application of shear strain with 〈110〉 principal axes. This shear strain should split the onset temperatures of the two components, if they coexist, or select one component if they do not. Here, we report measurements of Tc and the elastocaloric effect of Sr2RuO4 under uniaxial stress applied along the [110] lattice direction. Within experimental resolution, we resolve neither a cusp in the stress dependence of Tc, nor any second transition in the elastocaloric effect data. We show that reconciling these null results with the observed jumps in c66 requires extraordinarily fine tuning to a triple point of the Ginzburg-Landau parameter space. In addition, our results are inconsistent with homogeneous time-reversal symmetry breaking at a temperature T2≤Tc as identified in muon spin relaxation experiments.

Published by the American Physical Society
2024