bluuebunny/crossref_metadata_2025_split huggingface dataset:
Whole-body simulation of realistic fruit fly locomotion with deep reinforcement learning
Authors: Vaxenburg Roman , Siwanowicz Igor , Merel Josh , Robie Alice A. , Morrow Carmen , Novati Guido , Stefanidi Zinovia , Both Gert-Jan , Card Gwyneth M. , Reiser Michael B. , Botvinick Matthew M. , Branson Kristin M. , Tassa Yuval , Turaga Srinivas C. 
Published: March 2024

AbstractThe body of an animal influences how the nervous system produces behavior. Therefore, detailed modeling of the neural control of sensorimotor behavior requires a detailed model of the body. Here we contribute an anatomically-detailed biomechanical whole-body model of the fruit flyDrosophila melanogasterin the MuJoCo physics engine. Our model is general-purpose, enabling the simulation of diverse fly behaviors, both on land and in the air. We demonstrate the generality of our model by simulating realistic locomotion, both flight and walking. To support these behaviors, we have extended MuJoCo with phenomenological models of fluid forces and adhesion forces. Through data-driven end-to-end reinforcement learning, we demonstrate that these advances enable the training of neural network controllers capable of realistic locomotion along complex trajectories based on high-level steering control signals. We demonstrate the use of visual sensors and the re-use of a pre-trained general-purpose flight controller by training the model to perform visually guided flight tasks. Our project is an open-source platform for modeling neural control of sensorimotor behavior in an embodied context.
Elimination of human papillomavirus 16-induced tumors by a mucosal rAd5 therapeutic vaccination in a pre-clinical study
Authors: Braun Molly R. , Lindbloom Jonathan D. , Moore Anne C. , Hodgson Katherine A. , Dora Emery G. , Tucker Sean N. 
Published: March 2024

AbstractTherapeutic vaccination can harness the body’s cellular immune system to target and destroy cancerous cells. Several invasive treatments are currently used to eliminate cancerous lesions caused by human papillomaviruses (HPV), however therapeutic vaccination may offer and effective and minimally intrusive alternative. We have developed recombinant, non-replicating human adenovirus type 5 (rAd5) vaccines that encode the HPV16 oncogenic proteins E6 and E7 alongside a molecular dsRNA adjuvant. The potency of these vaccines were examined in a mouse model of HPV tumorigenesis where E6E7-expressing and transformed cells were implanted subcutaneously into C57BL/6 mice. After tumor growth, mice were treated via intranasal administration with E6E7-encoding rAd5 vaccines expressing either a mutant form of E6E7 (rAd5-16/E6E7m), or predicted T cell epitopes of E6E7 (rAd5-16/E6E7epitopes). Animals receiving therapeutic treatments of rAd5-16/E6E7mand rAd5-16/E6E7epitopeshad significant reductions in tumor volume and increased survival compared to animals treated with an empty rAd5 or left untreated. Further, antigen-specific CD8+ T effector memory cells (TEM) were observed in the animals treated with E6E7-encoding rAd5, but not in rAd5-empty group. The work described here demonstrates that mucosal rAd5 can be used in a therapeutic capacity to elicit antigen-specific cellular immunity and further identifies a clinical candidate with immense potential for the treatment and prevention of human cervical cancer.
AUTO-TUNE: SELECTING THE DISTANCE THRESHOLD FOR INFERRING HIV TRANSMISSION CLUSTERS
Authors: Weaver Steven , Dávila-Conn Vanessa , Ji Daniel , Verdonk Hannah , Ávila-Ríos Santiago , Brown Andrew J. Leigh , Wertheim Joel O. , Pond Sergei L. Kosakovsky 
Published: March 2024

Molecular surveillance of viral pathogens and inference of transmission networks from genomic data play an increasingly important role in public health efforts, especially for HIV-1. For many methods, the genetic distance threshold used to connect sequences in the transmission network is a key parameter informing the properties of inferred networks. Using a distance threshold that is too high can result in a network with many spurious links, making it difficult to interpret. Conversely, a distance threshold that is too low can result in a network with too few links, which may not capture key insights into clusters of public health concern. Published research using the HIV-TRACE software package frequently uses the default threshold of 0.015 substitutions/site for HIV pol gene sequences, but in many cases, investigators heuristically select other threshold parameters to better capture the underlying dynamics of the epidemic they are studying.Here, we present a general heuristic scoring approach for tuning a distance threshold adaptively, which seeks to prevent the formation of giant clusters. We prioritize the ratio of the sizes of the largest and the second largest cluster, maximizing the number of clusters present in the network.We apply our scoring heuristic to outbreaks with different characteristics, such as regional or temporal variability, and demonstrate the utility of using the scoring mechanism’s suggested distance threshold to identify clusters exhibiting risk factors that would have otherwise been more difficult to identify. For example, while we found that a 0.015 substitutions/site distance threshold is typical for US-like epidemics, recent outbreaks like the CRF07_BC subtype among men who have sex with men (MSM) in China have been found to have a lower optimal threshold of 0.005 to better capture the transition from injected drug use (IDU) to MSM as the primary risk factor. Alternatively, in communities surrounding Lake Victoria in Uganda, where there has been sustained hetero-sexual transmission for many years, we found that a larger distance threshold is necessary to capture a more risk factor-diverse population with sparse sampling over a longer period of time. Such identification may allow for more informed intervention action by respective public health officials.
SLRfinder: a method to detect candidate sex-linked regions with linkage disequilibrium clustering
Authors: Yi Xueling , Kemppainen Petri , Merilä Juha 
Published: March 2024

AbstractDespite their critical roles in genetic sex determination, sex chromosomes remain unknown in many non-model organisms. In contrast to conserved sex chromosomes in mammals and birds, studies of fish, amphibians, and reptiles have found highly labile sex chromosomes with newly evolved sex-linked regions (SLRs). These labile sex chromosomes are important for understanding early sex chromosome evolution but are difficult to identify due to the lack of Y/W degeneration and SLRs limited to small genomic regions. Here we present SLRfinder, a method to identify candidate SLRs and labile sex chromosomes using linkage disequilibrium (LD) clustering, patterns of heterozygosity, and genetic divergence. SLRfinder does not rely on specific sequencing methods or reference genomes and does not require phenotypic sexes which may be unknown from population sampling, although sex information can be incorporated to provide additional inference on candidate SLRs. We tested SLRfinder using various published datasets and compared it to SATC, a method that identifies sex chromosomes based on the depth of coverage and also does not require phenotypic sex. Results show that SATC works better on conserved sex chromosomes (e.g., in African leopards), whereas SLRfinder outperforms SATC in analyzing labile sex chromosomes (e.g., in nine-spined sticklebacks and chum salmon). Since SLRfinder primarily relies on LD clusters, it is expected to be most sensitive to the SLRs harboring structural variants (e.g., inversion) due to strongly reduced recombination rates in heterozygotes. SLRfinder provides a novel and complementary approach for identifying SLRs and uncovering additional sex chromosome diversity in nature.
The association between delirium and falls in older adults in the community: a systematic review
Authors: Eost-Telling Charlotte , McNally Lucy , Yang Yang , Shi Chunhu , Norman Gill , Ahmed Saima , Poku Brenda , Money Annemarie , Hawley-Hague Helen , Todd Chris , Shenkin Susan D. , Vardy Emma R.L.C. 
Published: March 2024

ABSTRACTObjectiveSystematically review and critically appraise evidence for the association between delirium and falls in community-dwelling adults aged 60 years and aboveMethodsWe searched EMBASE, MEDLINE, PsycINFO, Cochrane Database of Systematic Reviews, CINAHL and Evidence-Based Medicine Reviews (EBMR) databases in April 2023. Standard methods were used to screen, extract data, assess risk of bias (using Newcastle Ottawa scale), provide a narrative synthesis and where appropriate conduct meta-analysis.ResultsWe included eight studies, with at least 3505 unique participants. Five found limited evidence for an association between delirium and subsequent falls: one adjusted study showed an increase in falls (RR 6.66;95% CI 2.16-20.53) but the evidence was low certainty. Four non-adjusted studies found no clear effect. Three studies (one with two subgroups treated separately) found some evidence for an association between falls and subsequent delirium: meta-analysis of three adjusted studies showed an increase in delirium (pooled OR 2.01; 95%CI 1.52-2.66), one subgroup of non-adjusted data found no clear effect. Number of falls and fallers were reported in the studies. Four studies and one subgroup were at high risk of bias and one study had some concerns.ConclusionsWe found limited evidence for the association between delirium and falls. More methodologically rigorous research is needed to understand the complex relationship, establish how and why this operates bi-directionally and identify potential modifying factors involved. We recommend the use of standardised assessment measures for delirium and falls. Clinicians should be aware of the potential relationship between these common presentations.Key pointsThis is the first systematic review of the association between delirium and falls in the wider community population.There is relatively limited but consistent evidence on the direction of effect for both delirium preceding falls and falls preceding delirium.More high-quality longitudinal work is needed to explore the nature of this potentially complex and bidirectional relationship.History of falls and delirium should be considered when assessing patients with incidence/suspected incidence of falls or delirium.
Unlocking the Treatment of Facioscapulohumeral Muscular Dystrophy Type 2: The Bisphenol Connection
Authors: Sayad Saed , Hiatt Mark , Mustafa Hazem 
Published: March 2024

AbstractBackgroundFacioscapulohumeral muscular dystrophy type 2 (FSHD2) poses a significant challenge within the domain of neuromuscular disorders, marked by a progressive decline in muscle strength accompanied by tissue wasting. FSHD2 results from chromosomal deletions triggering the activation of a dormant gene known as DUX4. While DUX4 typically regulates early embryonic development, its activation in adult muscle cells leads to premature cell death. Despite this understanding, the exact pathology of FSHD2 remains unclear. To date, no effective treatment for FSHD2 exists.MethodWe acquired single-cell RNA sequencing (RNA-Seq) data (GSE143452) from primary myoblasts for FSHD2 from the United States National Institutes of Health (NIH) portal website. Our analysis encompassed a comprehensive examination of differentially expressed genes, alongside associated compounds sourced from the Chemical Entities of Biological Interest (ChEBI) database. Employing rigorous statistical methods, we pinpointed the most prominently upregulated and downregulated genes. Subsequently, we determined the compounds capable of modulating the expression of these top genes, either enhancing or reducing their activity.ResultsBisphenol S (BPS) can upregulate 52 of 100 top downregulated genes in FSHD2 without downregulating any other genes and Bisphenol F (BPF) can upregulate 45 of 100 downregulated genes with downregulating only one other gene. The enrichment analysis of both sets of 52 genes related to BPS and 45 genes corresponding to BPF highlights their significant involvement in various aspects of muscle biology, particularly as pertaining to the function and dysfunction of cardiac and skeletal muscle.ConclusionsLeveraging single-cell RNA-Seq data and computational analysis, we identified key dysregulated genes in FSHD2 and elucidated their modulation by compounds such as BPS and BPF. While effective treatments for FSHD2 remain elusive, our study provides valuable insights into potential therapeutic targets and pathways for further investigation in the pursuit of effective interventions for this debilitating condition. However, more research is needed to understand whether the roles of BPS and F are constructive or destructive.
Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and Australia
Authors: Finney Caitlin A. , Brown David A. , Shvetcov Artur ,   the Alzheimer’s Disease Neuroimaging Initiative,   the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing
Published: March 2024

ABSTRACTINTRODUCTIONExisting dementia prediction models using non-neuroimaging clinical measures have been limited in their ability to identify disease. This study used machine learning to re-examine the diagnostic potential of clinical measures for dementia.METHODSData was sourced from the Australian Imaging, Biomarkers, and Lifestyle Flagship Study of Ageing (AIBL) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Clinical variables included 21 measures across medical history, hematological and other blood tests, and APOE genotype. Tree-based machine learning algorithms and artificial neural networks were used.RESULTSAPOE genotype was the best predictor of dementia cases and healthy controls. Our results, however, demonstrated that there are limitations when using publicly accessible cohort data that may limit the generalizability and interpretability of such predictive models.DISCUSSIONFuture research should examine the use of routine APOE genetic testing for dementia diagnostics. It should also focus on clearly unifying data across clinical cohorts.
Neural Networks Estimate Muscle Force in Dynamic Conditions Better than Hill-type Muscle Models
Authors: Athanasiadou Maria Eleni , Daley Monica A. , Koelewijn Anne D. 
Published: March 2024

AbstractHill-type muscle models are widely used, even though they do not accurately represent certain muscle mechanics. We explored neural networks to develop new muscle models. We trained neural networks to estimate muscle force from activation, muscle length, and muscle velocity. Training data was recorded using sonomicrometry, electromyography, and a tendon buckle on two muscles of guinea fowl. First, we compared the neural network to a Hill-type muscle model, using the same data for network training and model optimization. Second, we trained neural networks on large datasets, in a more realistic machine learning scenario. We found that neural networks generally yielded higher coefficients of determination and lower errors than Hill-type muscle models. Our neural networks performed better when estimating forces on the muscle used for training, but on another bird, than on a different muscle of the same bird, which could be explained by inaccuracies in activation and force scaling. We extracted forcelength and force-velocity relationships from the trained neural networks and found that both effects were underestimated and that both relationships were not replicated well outside of the training data distribution. We discuss suggested experimental designs to collect suitable training data and conclude that neural networks could provide an accurate alternative to Hill-type muscle models, particularly for modeling dynamic muscle behavior that is prevalent in faster movements, given a suitable training dataset, while scaling of the training data should be comparable between muscles and animals.SummaryNeural networks predict muscle forces more accurately than Hill-type muscle models, particularly under dynamic conditions. However, they struggle to replicate the force-length and force-velocity relationships well.
Circular RNA circASH1L(4,5) protects microRNA-129-5p from target-directed microRNA degradation in human skin wound healing
Authors: Wang Qizhang , Niu Guanglin , Liu Zhuang , Toma Maria A. , Geara Jennifer , Bian Xiaowei , Zhang Letian , Piipponen Minna , Li Dongqing , Sommar Pehr , Landén Ning Xu 
Published: March 2024

SummaryBoth circular RNAs (circRNA) and microRNAs (miRNA) have emerged to play important roles in health and disease. To understand their function in tissue repair, we profiled circRNA, linear RNA, and miRNA expression dynamics in human wound-edge keratinocytes across the wound healing process. Our investigation spotlighted circASH1L(4,5) and its engagement with miR-129-5p, both of which levels were increased with wound repair. Unlike conventional miRNA sponging, circASH1L enhanced miR-129 stability and silencing activity by protecting this miRNA from target-directed miRNA degradation (TDMD) triggered by NR6A1 mRNA. TGF-β signaling, pivotal in wound healing, fostered circASH1L expression while suppressing NR6A1, thus enhancing the miR-129 abundance at the post-transcriptional level. Functionally, circASH1L and miR-129 enhanced keratinocyte migration and proliferation, crucial for re-epithelialization of human wounds. Collectively, our study uncovers circRNAs’ novel role as shields for miRNAs and sheds light on the physiological importance of regulated miRNA degradation in human skin wound healing.
Frontal engagement in perceptual integration under low subjective visibility
Authors: Bae Jisub , Jung Koeun , James Oliver , Suzuki Satoru , Kim Yee Joon 
Published: March 2024

AbstractWe investigated how spatiotemporal neural dynamics underlying perceptual integration changed with the degree of conscious access to a set of backward-masked pacman-shaped inducers that generated the percept of an illusory triangle. We kept the stimulus parameters at a fixed near-threshold level throughout the experiment and recorded electroencephalography from participants who reported the orientation and subjective visibility of the illusory triangle on each trial. Our multivariate pattern analysis revealed that posterior and central regions initially used dynamic neural code and later switched to stable neural code. The transition from dynamic to stable neural code in posterior region occurred increasingly later and eventually disappeared with decreasing conscious access. Anterior region primarily used stable neural code which waned with decreasing conscious access, but increased at below-median visibility and remained even when stimulus awareness was minimal. These results demonstrate differential spatiotemporal neural dynamics underlying perceptual integration depending on conscious access and emphasize a unique role of anterior region in processing integrated shape information especially under low subjective visibility.
Deciphering molecular mechanisms of synergistic growth reduction in kinase inhibitor combinations
Authors: Tsirvouli Eirini , Martinez del Val Ana , Thommesen Liv , Lægreid Astrid , Kuiper Martin , Olsen Jesper , Flobak Åsmund 
Published: March 2024

AbstractIn cancer treatment, the persistent challenge of unresponsiveness of certain patients to drugs or the development of resistance post-treatment remains a significant concern. Drug combinations that synergistically reduce tumor growth emerge as a promising avenue to address this issue. Here, we aimed to characterize the mechanism of action of two synergistic drug combinations that target PI3K together with MEK1 or with TAK1 and used time course measurements of phosphoproteomics and transcriptomics in response to single inhibitors and their combinations. Our analysis untangled those responses driven by single drugs and responses that were unique to the combinations. We observed a high overlap between single-drug responses and their combinations, suggesting that single-drug mechanisms dominate the mechanism of action of the combinations of the kinase inhibitors. Despite a high overlap, both drug combinations exhibited a synergistic modulation of several cell fate regulators found at the convergence points of the targeted pathways, including the key regulator of intrinsic apoptosis BCL2L11. Interestingly, the responses in both combinations were largely limited to the targeted pathways, namely PI3K/AKT and MAPKs, with very limited change of any other additional cell fate decision pathways. In addition, we observed a strong downregulation of nucleotide metabolism and tRNA biosynthesis uniquely in the combinations, which could be attributed to the reduced activity of mTOR and ATF4. Our approach provides insights into the molecular mechanisms affected by the PI3Ki-TAK1i and PI3Ki-MEKi combinations and can serve as a flexible framework for dissecting drug combination responses based on multi-omics measurements.
What makes clocks tick? Characterizing developmental dynamics of adult epigenetic clock sites
Authors: Mulder Rosa H. , Neumann Alexander , Felix Janine F. , Suderman Matthew , Cecil Charlotte A. M. 
Published: March 2024

AbstractDNA methylation (DNAm) at specific sites can be used to calculate ‘epigenetic clocks’, which in adulthood are used as indicators of age(ing). However, little is known about how these clock sites ‘behave’ during development and what factors influence their variability in early life. This knowledge could be used to optimize healthy aging well before the onset of age-related conditions. Here, we leveraged results from two longitudinal population-based cohorts (N=5,019 samples from 2,348 individuals) to characterize trajectories of adult clock sites from birth to early adulthood. We find that clock sites (i) diverge widely in their developmental trajectories, often showing non-linear change over time; (ii) are substantially more likely than non-clock sites to vary between individuals already from birth, differences that are predictive of DNAm variation at later ages; and (iii) show enrichment for genetic and prenatal environmental exposures, supporting an early-origins perspective to epigenetic aging.
Harnessing Genome Representation Learning for Decoding Phage-Host Interactions
Authors: Badam Sumanth , Rao Shrisha 
Published: March 2024

Accurate prediction of the phages that target a bacterial host plays an important role in combating anti-microbial resistance. Our work explores the power of deep neural networks, convolutional neural networks, and pre-trained large DNA/protein language models to predict the host for a given phage. This work mainly uses the data provided by Gonzales et al. that contains receptor-binding protein sequences of the phages and the target host genus. We used pre-trained language models to obtain the dense representations of protein/nucleotide sequences to train a deep neural network to predict the target host genus. Additionally, convolutional neural networks were trained on one-hot encoding of nucleotide sequences to predict the target host genus. We achieved a weighted F1-score of 73.76% outperforming state-of-the-art models with an improvement of around 11% by using the protein language model ESM-1b.The data and the source code are available athttps://github.com/sumanth2002629/Bacteriophage-Research.