bluuebunny/crossref_metadata_2025_split huggingface dataset:
FriendlyClearMap: an optimized toolkit for mouse brain mapping and analysis
Authors: Negwer Moritz , Bosch Bram , Bormann Maren , Hesen Rick , Lütje Lukas , Aarts Lynn , Rossing Carleen , Nadif Kasri Nael , Schubert Dirk 
Published: May 2023

Abstract

Background
Tissue clearing is currently revolutionizing neuroanatomy by enabling organ-level imaging with cellular resolution. However, currently available tools for data analysis require a significant time investment for training and adaptation to each laboratory’s use case, which limits productivity. Here, we present FriendlyClearMap, an integrated toolset that makes ClearMap1 and ClearMap2’s CellMap pipeline easier to use, extends its functions, and provides Docker Images from which it can be run with minimal time investment. We also provide detailed tutorials for each step of the pipeline.

Findings
For more precise alignment, we add a landmark-based atlas registration to ClearMap’s functions as well as include young mouse reference atlases for developmental studies. We provide an alternative cell segmentation method besides ClearMap’s threshold-based approach: Ilastik’s Pixel Classification, importing segmentations from commercial image analysis packages and even manual annotations. Finally, we integrate BrainRender, a recently released visualization tool for advanced 3-dimensional visualization of the annotated cells.

Conclusions
As a proof of principle, we use FriendlyClearMap to quantify the distribution of the 3 main GABAergic interneuron subclasses (parvalbumin+ [PV+], somatostatin+, and vasoactive intestinal peptide+) in the mouse forebrain and midbrain. For PV+ neurons, we provide an additional dataset with adolescent vs. adult PV+ neuron density, showcasing the use for developmental studies. When combined with the analysis pipeline outlined above, our toolkit improves on the state-of-the-art packages by extending their function and making them easier to deploy at scale.
Double-source frequency–wavenumber transform for accurate extraction of 2-D media dispersion
Authors: Liu Yuxiang , Wei Xueting , Liu Qi , Chen Xiaofei 
Published: May 2023

SUMMARY
Extracting the dispersion energy image is one of the key steps of surface wave analysis, which is considered an effective and important way to obtain the vertical velocity structure. The quality of the dispersion energy image directly affects the accuracy of selected dispersion curves and the subsequent inversion. However, if we can theoretically obtain an accurate 2-D dispersion energy diagram, we will skip the step of extracting the dispersion curves. Here, we present the double-source frequency–wavenumber (DSFK) transform to extract theoretical 2-D dispersion energy images from multichannel data. Unlike 1-D dispersion energy images, which are based on the assumption of a horizontally layered medium structure, 2-D dispersion energy images also reflect information about the irregular interfaces of media. We adopt a geometry in which two reciprocal sources are on both ends of a receiver array. Then, the multichannel data obtained by this geometry are transformed into the frequency domain, and a 2-D dispersion energy image that corresponds to the 2-D structure is obtained by the DSFK transform. By producing a variety of synthetic data and processing active source data from the Salton Seismic Imaging Project, we confirm the accuracy and applicability of the DSFK transform. As demonstrated by the results, the DSFK transform can obtain the theoretical 2-D dispersion energy distribution and shows significant potential in the field of seismic tomography.
Deep pre-trained FWI: where supervised learning meets the physics-informed neural networks
Authors: Muller Ana P O , Costa Jessé C , Bom Clecio R , Klatt Matheus , Faria Elisangela L , de Albuquerque Marcelo P , de Albuquerque Marcio P 
Published: May 2023

SUMMARYFull-waveform inversion (FWI) is the current standard method to determine final and detailed model parameters to be used in the seismic imaging process. However, FWI is an ill-posed problem that easily achieves a local minimum, leading the model solution in the wrong direction. Recently, some works proposed integrating FWI with Convolutional Neural Networks (CNN). In this case, the CNN weights are updated following the FWI gradient, defining the process as a Physics-Informed Neural Network (PINN). FWI integrated with CNN has an important advantage. The CNN stabilizes the inversion, acting like a regularizer, avoiding local minima-related problems and sparing an initial velocity model in some cases. However, such a process, especially when not requiring an initial model, is computationally expensive due to the high number of iterations required until the convergence. In this work, we propose an approach which relies on combining supervised learning and physics-informed by using a previously trained CNN to start the DL-FWI inversion. Loading the pre-trained weights configures transfer learning. The pre-trained CNN is obtained using a supervised approach based on training with a reduced and simple data set to capture the main velocity trend at the initial FWI iterations. The proposed training process is different from the initial works on the area which obtained the velocity model from the shots in supervised learning tasks and that required a large amount of labelled data to ensure reasonable model predictions. We investigated in our approach two CNN architectures, obtaining more robust results and a reduced number of parameters when using a modified U-Net. The method was probed over three benchmark models, showing consistently that the pre-training phase reduces the process’s uncertainties and accelerates the model convergence using minimal prior information. Besides, the final scores of the iterative process are better than the examples without transfer learning. Thus, transfer learning solved one main limitation of the previous PINN approaches: the unfeasible number of iterations when not using an initial model. Moreover, we tested the method using data with low-frequency band limitations, since the lack of low frequencies is a common issue within real seismic data. The inversion converges to reasonable results probing the method’s robustness with restricted frequency content.
Rare protective variants and glaucoma-relevant cell stressors modulate Angiopoietin-like 7 expression
Authors: Aboobakar Inas F , Collantes Edward Ryan A , Hauser Michael A , Stamer W Daniel , Wiggs Janey L 
Published: May 2023

Abstract
Rare missense and nonsense variants in the Angiopoietin-like 7 (ANGPTL7) gene confer protection from primary open-angle glaucoma (POAG), though the functional mechanism remains uncharacterized. Interestingly, a larger variant effect size strongly correlates with in silico predictions of increased protein instability (r = −0.98), suggesting that protective variants lower ANGPTL7 protein levels. Here, we show that missense and nonsense variants cause aggregation of mutant ANGPTL7 protein in the endoplasmic reticulum (ER) and decreased levels of secreted protein in human trabecular meshwork (TM) cells; a lower secreted:intracellular protein ratio strongly correlates with variant effects on intraocular pressure (r = 0.81). Importantly, accumulation of mutant protein in the ER does not increase expression of ER stress proteins in TM cells (P &gt; 0.05 for all variants tested). Cyclic mechanical stress, a glaucoma-relevant physiologic stressor, also significantly lowers ANGPTL7 expression in primary cultures of human Schlemm’s canal (SC) cells (−2.4-fold-change, P = 0.01). Collectively, these data suggest that the protective effects of ANGPTL7 variants in POAG stem from lower levels of secreted protein, which may modulate responses to physiologic and pathologic ocular cell stressors. Downregulation of ANGPTL7 expression may therefore serve as a viable preventative and therapeutic strategy for this common, blinding disease.
The Microbiome as a Maternal Effect: A Systematic Review on Vertical Transmission of Microbiota
Authors: Murphy Kaitlyn M , Le Samantha M , Wilson Alan E , Warner Daniel A 
Published: May 2023

SynopsisThe microbiome is an interactive and fluctuating community of microbes that colonize and develop across surfaces, including those associated with organismal hosts. A growing number of studies exploring how microbiomes vary in ecologically relevant contexts have recognized the importance of microbiomes in affecting organismal evolution. Thus, identifying the source and mechanism for microbial colonization in a host will provide insight into adaptation and other evolutionary processes. Vertical transmission of microbiota is hypothesized to be a source of variation in offspring phenotypes with important ecological and evolutionary implications. However, the life-history traits that govern vertical transmission are largely unexplored in the ecological literature. To increase research attention to this knowledge gap, we conducted a systematic review to address the following questions: (1) How often is vertical transmission assessed as a contributor to offspring microbiome colonization and development? (2) Do studies have the capacity to address how maternal transmission of microbes affects the offspring phenotype? (3) How do studies vary based on taxonomy and life history of the study organism, as well as the experimental, molecular, and statistical methods employed? Extensive literature searches reveal that many studies examining vertical transmission of microbiomes fail to collect whole microbiome samples from both maternal and offspring sources, particularly for oviparous vertebrates. Additionally, studies should sample functional diversity of microbes to provide a better understanding of mechanisms that influence host phenotypes rather than solely taxonomic variation. An ideal microbiome study incorporates host factors, microbe–microbe interactions, and environmental factors. As evolutionary biologists continue to merge microbiome science and ecology, examining vertical transmission of microbes across taxa can provide inferences on causal links between microbiome variation and phenotypic evolution.
Half a century of high-latitude fisheries oceanography research on the “recruitment problem” in Northeast Arctic cod (Gadus morhua)
Authors: Kjesbu O S , Tiedemann I , Alix M , Thorsen A , Sundby S 
Published: May 2023

AbstractPredicting recruitment in a reliable fashion is a great challenge within operational fisheries advice. Here, we consulted the unique but undercommunicated IMR Cod Larva Project (1975–1990), its spin-offs, placed in an international era of advancements over the last 50 years to glance into the future. Few initiatives of this kind have applied such extensive research approaches, spanning from laboratory, mesocosm, tank, and field studies to process modelling. The “critical period” concept appeared misleading, covering months rather than days of the early life history stages (ELHS) of Northeast Arctic cod. Larval feeding success was strongly modified by improved encounter rates from wind-induced turbulence. In addition, the following maternal effect studies evidenced that the dynamics of stock demography prior to spawning should be upheld to promote recruitment success. Although we now have lower-trophic level models as well as ELHS individual-based models, such models are still insufficiently reflecting the needed spatiotemporal resolution. The same problem applies to climate/circulation models. Nevertheless, this long-lasting research has significantly improved the mechanistic understanding of ELHS dynamics but also of the more predictable adult reproductive parameters. Based on a “to-list list,” we suggest research avenues that should be pursued to further improve our ability predicting recruitment strength in marine fish stocks.
De-black-boxing health AI: demonstrating reproducible machine learning computable phenotypes using the N3C-RECOVER Long COVID model in the All of Us data repository
Authors: Pfaff Emily R , Girvin Andrew T , Crosskey Miles , Gangireddy Srushti , Master Hiral , Wei Wei-Qi , Kerchberger V Eric , Weiner Mark , Harris Paul A , Basford Melissa , Lunt Chris , Chute Christopher G , Moffitt Richard A , Haendel Melissa ,   N3C and RECOVER Consortia
Published: May 2023

Abstract
Machine learning (ML)-driven computable phenotypes are among the most challenging to share and reproduce. Despite this difficulty, the urgent public health considerations around Long COVID make it especially important to ensure the rigor and reproducibility of Long COVID phenotyping algorithms such that they can be made available to a broad audience of researchers. As part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, researchers with the National COVID Cohort Collaborative (N3C) devised and trained an ML-based phenotype to identify patients highly probable to have Long COVID. Supported by RECOVER, N3C and NIH’s All of Us study partnered to reproduce the output of N3C’s trained model in the All of Us data enclave, demonstrating model extensibility in multiple environments. This case study in ML-based phenotype reuse illustrates how open-source software best practices and cross-site collaboration can de-black-box phenotyping algorithms, prevent unnecessary rework, and promote open science in informatics.
Are interactive and tailored data visualizations effective in promoting flu vaccination among the elderly? Evidence from a randomized experiment
Authors: Cotter Lynne M , Yang Sijia 
Published: May 2023

Abstract

Objective
Although interactive data visualizations are increasingly popular for health communication, it remains to be seen what design features improve psychological and behavioral targets. This study experimentally tested how interactivity and descriptive titles may influence perceived susceptibility to the flu, intention to vaccinate, and information recall, particularly among older adults.

Materials and Methods
We created data visualization dashboards on flu vaccinations, tested in a 2 (explanatory text vs none) × 3 (interactive + tailored, static + tailored, static + nontailored) + questionnaire-only control randomized between-participant online experiment (N   = 1378).

Results
The flu dashboards significantly increased perceived susceptibility to the flu compared to the control: static+nontailored dashboard, b = 0.14, P = .049; static-tailored, b = 0.16, P = .028; and interactive+tailored, b = 0.15, P = .039. Interactive dashboards potentially decreased recall particularly among the elderly (moderation by age: b = −0.03, P = .073). The benefits of descriptive text on recall were larger among the elderly (interaction effects: b = 0.03, P = .025).

Discussion
Interactive dashboards with complex statistics and limited textual information are widely used in health and public health but may be suboptimal for older individuals. We experimentally showed that adding explanatory text on visualizations can increase information recall particularly for older populations.

Conclusion
We did not find evidence to support the effectiveness of interactivity in data visualizations on flu vaccination intentions or on information recall. Future research should examine what types of explanatory text can best support improved health outcomes and intentions in other contexts. Practitioners should consider whether interactivity is optimal in data visualization dashboards for their populations.
The Revised Baux Score as a Predictor of Burn Mortality: A Systematic Review and Meta-Analysis
Authors: Edgar Michael C , Bond Stephanie M , Jiang Sam H , Scharf Isabel M , Bejarano Geronimo , Vrouwe Sebastian Q 
Published: May 2023

Abstract
Mortality following a severe burn is influenced by both patient- and injury-factors, and a number of predictive models have been developed or applied. As there is no consensus on the optimal formula to use, we aimed to investigate the predictive value of the revised Baux score in comparison to other models when determining mortality risk in patients with burn injuries. A systematic review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. The review yielded 21 relevant studies. The Prediction model Risk Of Bias ASsessment Tool quality appraisal checklist was used with many studies classified as “high” quality. All studies assessed the utility of the revised Baux score in comparison to other scoring systems such as the original Baux, Belgian Outcome in Burn Injury, Abbreviated Burn Severity Index, Acute Physiology and Chronic Health Evaluation II, Sequential Organ Failure Assessment, Boston Group/Ryan scores, the Fatality by Longevity, APACHE II score, Measured Extent of burn, and Sex model, and the Prognostic Burn Index. There was a range of 48 to 15975 participants per study, with a mean age range of 16 to 52 years old. The area under the curve (AUC) values of the rBaux score ranged from 0.682 to 0.99, with a summary AUC of 0.93 for all included studies (CI 0.91–0.95). This summary value demonstrates that the rBaux equation is a reliable predictor for mortality risk in heterogeneous populations. However, this study also identified that the rBaux equation has a diminished ability to predict mortality risk when applied to patients at both extremes of age, highlighting an important area for future research. Overall, the rBaux equation offers a relatively easy means to quickly assess the mortality risk from burn injury in a broad range of patient populations.
Mechanism of elastic energy storage of honey bee abdominal muscles under stress relaxation
Authors: Deng Zhizhong , Zhang Yuling , Yan Shaoze 
Published: May 2023

Abstract
Energy storage of passive muscles plays an important part in frequent activities of honey bee abdomens due to the muscle distribution and open circulatory system. However, the elastic energy and mechanical properties of structure in passive muscles remain unclear. In this article, stress relaxation tests on passive muscles from the terga of the honey bee abdomens were performed under different concentrations of blebbistatin and motion parameters. In stress relaxation, the load drop with the rapid and slow stages depending on stretching velocity and stretching length reflects the features of myosin–titin series structure and cross-bridge–actin cyclic connections in muscles. Then a model with 2 parallel modules based on the 2 feature structures in muscles was thus developed. The model described the stress relaxation and stretching of passive muscles from honey bee abdomen well for a good fitting in stress relaxation and verification in loading process. In addition, the stiffness change of cross-bridge under different concentrations of blebbistatin is obtained from the model. We derived the elastic deformation of cross-bridge and the partial derivatives of energy expressions on motion parameters from this model, which accorded the experimental results. This model reveals the mechanism of passive muscles from honey bee abdomens suggesting that the temporary energy storage of cross-bridge in terga muscles under abdomen bending provides potential energy for springback during the periodic abdomen bending of honey bee or other arthropod insects. The finding also provides an experimental and theoretical basis for the novel microstructure and material design of bionic muscle.
Multilognormal density structure in Cygnus-X molecular clouds: a fitting for N-PDF without power law
Authors: Murase Takeru , Handa Toshihiro , Matsusaka Ren , Shimajiri Yoshito , Kobayashi Masato I N , Kohno Mikito , Nishi Junya , Takeba Norimi , Shibata Yosuke 
Published: May 2023

ABSTRACT
We studied the H2 column density probability distribution function (N-PDF) based on molecular emission lines using the Nobeyama 45-m Cygnus X CO survey data. Using the DENDROGRAM and SCIMES algorithms, we identified 124 molecular clouds in the 13CO data. From these identified molecular clouds, an N-PDF was constructed for 11 molecular clouds with an extent of more than 0.4 deg2. From the fitting of the N-PDF, we found that the N-PDF could be well fitted with one or two lognormal distributions. These fitting results provided an alternative density structure for molecular clouds from a conventional picture. We investigated the column density, dense molecular cloud cores, and radio continuum source distributions in each cloud and found that the N-PDF shape was less correlated with the star-forming activity over a whole cloud. Furthermore, we found that the lognormal N-PDF parameters obtained from the fitting showed two impressive features. First, the lognormal distribution at the low-density part had the same mean column density (∼1021.5 cm−2) for almost all the molecular clouds. Second, the width of the lognormal distribution tended to decrease with an increasing mean density of the structures. These correlations suggest that the shape of the N-PDF reflects the relationship between the density and turbulent structure of the whole molecular cloud but is less affected by star-forming activities.
Prospects of detecting soft X-ray emission from typical WHIM filaments around massive clusters and the coma cluster soft excess
Authors: Churazov E , Khabibullin I I , Dolag K , Lyskova N , Sunyaev R A 
Published: May 2023

ABSTRACT
While hot ICM in galaxy clusters makes these objects powerful X-ray sources, the cluster’s outskirts and overdense gaseous filaments might give rise to much fainter sub-keV emission. Cosmological simulations show a prominent ‘focusing’ effect of rich clusters on the space density of the warm-hot intergalactic medium (WHIM) filaments up to a distance of $\sim 10\, {\rm Mpc}$ (∼ turnaround radius, rta) and beyond. Here, we use Magneticum simulations to characterize their properties in terms of integrated emission measure for a given temperature and overdensity cut and the level of contamination by the more dense gas. We suggest that the annuli $(\sim 0.5-1)\times \, r_{ta}$ around massive clusters might be the most promising sites for the search of the gas with overdensity ≲ 50. We model spectral signatures of the WHIM in the X-ray band and identify two distinct regimes for the gas at temperatures below and above $\sim 10^6\, {\rm K}$. Using this model, we estimate the sensitivity of X-ray telescopes to the WHIM spectral signatures. We found that the WHIM structures are within reach of future high spectral resolution missions, provided that the low-density gas is not extremely metal-poor. We then consider the Coma cluster observed by SRG/eROSITA during the CalPV phase as an example of a nearby massive object. We found that beyond the central r ∼ 40 arcmin ($\sim 1100\, {\rm kpc}$) circle, where calibration uncertainties preclude clean separation of the extremely bright cluster emission from a possible softer component, the conservative upper limits are about an order of magnitude larger than the levels expected from simulations.