bluuebunny/crossref_metadata_2025_split huggingface dataset:
Neural differential equations enable early-stage prediction of preterm birth using vaginal microbiota
Authors: Karambelkar Kaushik , Baranwal Mayank 
Published: September 2023

AbstractPreterm births (PTBs), i.e., births before 37 weeks of gestation are completed, are one of the leading issues concerning infant health, and is a problem that plagues all parts of the world. Millions of infants are born preterm globally each year, resulting in developmental disorders in infants and increase in neonatal mortality. Although there are known risk factors for PTB, the current procedures used to assess PTB risk are effective only at the later stages of pregnancy, which reduces the impact of currently possible interventions administered to prevent PTB or mitigate its ill-effects. Vaginal microbial communities have recently garnered attention in the context of PTB, with the notion that a highly diverse microbiome is detrimental as far as PTB is concerned. Increased abundance or scarcity of certain microbial species belonging to specific genera has also been linked to PTB risk. Consequently, attempts have been made towards establishing a correlation between alpha-diversity indices associated with vaginal microbial communities, and PTB. However, the vaginal microbiome varies greatly from individual to individual, and this variation is more pronounced in racially, ethnically and geographically diverse populations, which diversity indices may not be able to overcome. Machine learning (ML)-based approaches have also previously been explored, however, the success of these approaches reported thus far has been limited. Additionally, microbial communities have been reported to evolve during the duration of the pregnancy, and capturing such a signature may require higher, more complex modeling paradigms. Thus, alternative approaches are necessary to identify signatures in these microbial communities that are capable of distinguishing PTB from a full-term pregnancy. In this study, we have highlighted the limitations of diversity indices for prediction of PTB in racially diverse cohorts. We applied Deep Learning (DL)-based methods to vaginal microbial abundance profiles obtained at various stages of pregnancy, and Neural Controlled Differential Equations (CDEs) are able to identify a signature in the temporally-evolving vaginal microbiome during trimester 2 and can predict incidences of PTB (mean test set ROC-AUC = 0.81, accuracy = 75%, F1-score = 0.71) significantly better than traditional ML classifiers such as Random Forests (mean test set ROC-AUC = 0.65, accuracy = 66%, F1-score = 0.42) and Decision Trees (mean test set ROC-AUC = 0.48, accuracy = 46%, F1-score = 0.40), thus enabling effective early-stage PTB risk assessment.GraphicalAbstract
Single-cell mechanical analysis reveals viscoelastic similarities between normal and neoplastic brain cells
Authors: Onwudiwe Killian , Najera Julian , Holen Luke , Burchett Alice A. , Rodriguez Dorielis , Zarodniuk Maksym , Siri Saeed , Datta Meenal 
Published: September 2023

AbstractUnderstanding cancer cell mechanics allows for the identification of novel disease mechanisms, diagnostic biomarkers, and targeted therapies. In this study, we utilized our previously established fluid shear stress assay to investigate and compare the viscoelastic properties of normal immortalized human astrocytes (IHAs) and invasive human glioblastoma (GBM) cells when subjected to physiological levels of shear stress that are present in the brain microenvironment. We used a parallel-flow microfluidic shear system and a camera-coupled optical microscope to expose single cells to fluid shear stress and monitor the resulting deformation in real-time, respectively. From the video-rate imaging, we fed cell deformation information from digital image correlation into a three-parameter generalized Maxwell model to quantify the nuclear and cytoplasmic viscoelastic properties of single cells. We further quantified actin cytoskeleton density and alignment in IHAs and GBM cells via immunofluorescence microscopy and image analysis techniques. Results from our study show that contrary to the behavior of many extracranial cells, normal and cancerous brain cells do not exhibit significant differences in their viscoelastic behavior. Moreover, we also found that the viscoelastic properties of the nucleus and cytoplasm as well as the actin cytoskeletal densities of both brain cell types are similar. Our work suggests that malignant GBM cells exhibit unique mechanical behaviors not seen in other cancer cell types. These results warrant future study to elucidate the distinct biophysical characteristics of the brain and reveal novel mechanical attributes of GBM and other primary brain tumors.
Directional integration and pathway enrichment analysis for multi-omics data
Authors: Slobodyanyuk Mykhaylo , Bahcheli Alexander T. , Klein Zoe P. , Bayati Masroor , Strug Lisa J. , Reimand Jüri 
Published: September 2023

ABSTRACTOmics techniques generate comprehensive profiles of biomolecules in cells and tissues. However, a holistic understanding of the data requires joint multi-omics analyses that are challenging. Here we present DPM, a data fusion method for combining multiple omics datasets using directionality and significance estimates of genes, transcripts, or proteins. DPM allows users to define how the input datasets are expected to interact directionally, reflecting the initial experimental design or regulatory relationships between the datasets. DPM statistically prioritises genes and pathways that change consistently across the datasets, while penalising those violating the constraints. Joint analyses of transcriptomic, proteomic, DNA methylation, and clinical datasets of cancer samples demonstrate how directional integration identifies genes and pathways modulated across omics datasets, highlights those with inconsistent evidence, and reveals candidate biomarkers with prognostic signals in multiple datasets. DPM is implemented in the ActivePathways method and provides a general framework for testing detailed hypotheses in multi-omics data.
Sex differences in avoidance behavior and cued threat memory dynamics in mice: Interactions between estrous cycle and genetic background
Authors: Ryherd Garret L. , Bunce Averie L. , Edwards Haley A. , Baumgartner Nina E. , Lucas Elizabeth K. 
Published: September 2023

ABSTRACTAnxiety disorders are the most prevalent mental illnesses worldwide, exhibit high heritability, and affect twice as many women as men. To evaluate potential interactions between genetic background and cycling ovarian hormones on sex differences in susceptibility to negative valence behaviors relevant to anxiety disorders, we assayed avoidance behavior and cued threat memory dynamics in gonadally-intact adult male and female mice across four common inbred mouse strains: C57Bl/6J, 129S1/SVlmJ, DBA/2J, and BALB/cJ. Independent of sex, C57Bl/6J mice exhibited low avoidance but high threat memory, 129S1/SvlmJ mice high avoidance and high threat memory, DBA/2J mice low avoidance and low threat memory, and BALB/cJ mice high avoidance but low threat memory. Within-strain comparisons revealed reduced avoidance behavior in the high hormone phase of the estrous cycle (proestrus) compared to all other estrous phases in all strains except DBA/2J, which did not exhibit cycle-dependent behavioral fluctuations. Robust and opposing sex differences in threat conditioning and extinction training were found in the C57Bl/6J and 129S1/SvlmJ lines, whereas no sex differences were observed in the DBA/2J or BALB/cJ lines. C57Bl/6J males exhibited enhanced acute threat memory, whereas 129S1/SvlmJ females exhibited enhanced sustained threat memory, compared to their sex-matched littermates. These effects were not mediated by estrous cycle stage or sex differences in active versus passive defensive behavioral responses. Our data demonstrate that core features of behavioral endophenotypes relevant to anxiety disorders, such as avoidance and threat memory, are genetically driven yet dissociable and can be influenced further by cycling ovarian hormones.
The invasive brown seaweedRugulopteryx okamurae(Dictyotales, Ochrophyta) continues to expand: first record in Italy
Authors: Bellissimo Giancarlo , Altamirano María , Muñoz Antonio Román , De la Rosa Julio , Hung Tin Hang , Rizzuto Gabriele , Vizzini Salvatrice , Tomasello Agostino 
Published: September 2023

AbstractThe brown seaweedRugulopteryx okamurae(Dictyotales, Ochrophyta), native to the Pacific Ocean and widely distributed in Asia, has been recently recognized as an emblematic case of biological invasion by marine macroalgae in European waters. Since 2015 and from the Strait of Gibraltar,R. okamuraehas rapidly spread towards Atlantic and Mediterranean coastal areas exhibiting an invasive behaviour with significant ecological and economic impacts. Here, we report by morphology and genetics the first observation of this species in Italy along the north-western coast of Sicily (Gulf of Palermo), as drifted material and an established population onPosidonia oceanica, representing its new eastern distribution limit in the Mediterranean Sea, previously established in Marseilles (France). Furthermore, we have performed with the current introduced distribution of the species a favorability distribution model for the Mediterranean, which shows most of the western Mediterranean, including the Balearic archipelago, Corsica and Sardinia, central Mediterranean, including Sicily, and the northern coast of Africa together with eastern Mediterranean basin, as highly favorable forR. okamurae. Arrival of the species into this new area is suggested by means of sea currents and maritime traffic, including fishing activities, hypothesis supported by some of the ranked variables that entered the favorability model, i.e, current velocity, and proximity of fishing ports. These results are a warning that the species can cover large sea distances favored by sea currents, thus also threatening the ecosystems and marine resources of the central and eastern Mediterranean, highly favorable regions for the species. We suggest coordinated actions at the European level regarding prevention, among which those that have the complicity of the fishing sector should be considered, both because it is a highly affected sector and because it potentially has a very important role in the dispersion of the species.
Visual Image Reconstructed Without Semantics from Human Brain Activity Using Linear Image Decoders and Nonlinear Noise Suppression
Authors: Li Qiang 
Published: September 2023

AbstractIn recent years, substantial strides have been made in the field of visual image reconstruction, particularly in its capacity to generate high-quality visual representations from human brain activity while considering semantic information. This advancement not only enables the recreation of visual content but also provides valuable insights into the intricate processes occurring within high-order functional brain regions, contributing to a deeper understanding of brain function. However, considering fusion semantics in reconstructing visual images from brain activity involves semantic-to-image guide reconstruction and may ignore underlying neural computational mechanisms, which does not represent true reconstruction from brain activity. In response to this limitation, our study introduces a novel approach that combines linear mapping with nonlinear noise suppression to reconstruct visual images perceived by subjects based on their brain activity patterns. The primary challenge associated with linear mapping lies in its susceptibility to noise interference. To address this issue, we leverage a flexible denoised deep convolutional neural network, which can suppress noise from linear mapping. Our investigation encompasses linear mapping as well as the training of shallow and deep autoencoder denoised neural networks, including a pre-trained, state-of-the-art denoised neural network. The outcome of our study reveals that combining linear image decoding with nonlinear noise reduction significantly enhances the quality of reconstructed images from human brain activity. This suggests that our methodology holds promise for decoding intricate perceptual experiences directly from brain activity patterns without semantic information. Moreover, the model has strong neural explanatory power because it shares structural and functional similarities with the visual brain.
Single cell analysis uncovers striking cellular heterogeneity of lung infiltrating Tregs during eosinophilic vs. neutrophilic allergic airway inflammation
Authors: Iamsawat Supinya , Yu Rongzhen , Kim Sohee , Qiu Kevin , Choi Jaehyuk , Min Booki 
Published: September 2023

AbstractAllergic airway inflammation is a chronic inflammatory condition resulting from uncontrolled immune responses to environmental antigens. While it is well-established that allergic immune responses exhibit a high degree of diversity, driven by primary effector cell types like eosinophils, neutrophils, or CD4 T cells with distinct effector signatures, the exact mechanisms responsible for such pathogenesis remain largely elusive. Foxp3+regulatory T cells (Treg) is an essential immune regulator during chronic inflammation including allergic airway inflammation. Emerging evidence suggests that Tregs infiltrating inflamed tissues exhibit distinct phenotypes dependent on the specific tissue sites and can display significant heterogeneity and tissue residency. Whether diverse allergic inflammatory responses in the lung influences infiltrating Treg heterogeneity or Treg lung residency has not previously been explored. We employed an unbiased single-cell RNAseq approach to investigate lung-infiltrating Tregs in models of eosinophilic and neutrophilic airway inflammation models, in which Tregs are critical regulators of inflammation. We found that lung-infiltrating Tregs are highly heterogeneous and that Tregs displaying lung resident phenotypes are significantly different depending on the types of inflammation. Tregs expression of ST2, a receptor for alarmin cytokine IL-33, was predominantly induced by eosinophilic inflammation and by tissue residency. However, Treg-specific ST2 deficiency did not affect the development of eosinophilic allergic inflammation nor the generation of lung resident Tregs. These results uncover a striking heterogeneity among Tregs infiltrating the lungs during allergic airway inflammation. The results also indicate that varying types of inflammation may give rise to phenotypically distinct lung resident Tregs, underscoring a novel mechanism by which inflammatory cues may shape the composition of infiltrating Tregs, allowing them to regulate inflammatory responses through tissue-adapted mechanisms.
Analyzing Pain Patterns in the Emergency Department: Leveraging Clinical Text Deep Learning Models for Real-World Insights
Authors: Hughes James A , Wu Yutong , Jones Lee , Douglas Clint , Brown Nathan , Hazelwood Sarah , Lyrstedt Anna-Lisa , Jarugula Rajeev , Chu Kevin , Nguyen Anthony 
Published: September 2023

ABSTRACTObjectiveTo estimate the prevalence of patients presenting in pain to an inner-city emergency department (ED), describing this population, their treatment, and the effect of the COVID-19 pandemic.Materials and MethodsWe applied a clinical text deep learning model to the free text nursing assessments to identify the prevalence of pain on arrival to the ED. Using interrupted time series analysis, we examined the prevalence over three years. We describe this population pre- and post-pandemic in terms of their demographics, arrival patterns and treatment.Results55.16% (95%CI 54.95% - 55.36%) of all patients presenting to this ED had pain on arrival. There were significant differences in demographics, arrival and departure patterns between those patients with and without pain. The COVID-19 pandemic initially precipitated a decrease followed by a sharp, sustained rise in the prevalence of pain on arrival, altering the population arriving in pain and their treatment.DiscussionThe application of a clinical text deep learning model has successfully identified the prevalence of pain on arrival. The description of this population and their treatment forms the basis of intervention to improve care for patients presenting with pain. The combination of the clinical text deep learning model and interrupted time series analysis has identified the effects of the COVID-19 pandemic on pain care in the ED.ConclusionA clinical text deep learning model has led to identifying the prevalence of pain on arrival and was able to identify the effect a major pandemic had on pain care in this ED.
Coronavirus and Post-COVID-19 Syndrome: A Systematic Review
Authors: Albalushi Sarvinoz , Zarif Azmaeen , Karaduman Suheyla , Talpeka Alesia , Tran Khoa 
Published: September 2023

AbstractCoronavirus infectious Disease 2019 (COVID-19) was first reported in Wuhan, China, and with its rapidly mutating variants, it soon became a global concern. In response to the pandemic, intensive research and development efforts led to the development of six vaccines approved by the World Health Organization (WHO). Coronavirus is divided into four genera: alpha, beta, gamma and delta. Its unstable ssRNA resulted in multiple strains in a short period, which acted as a selection pressure for transmissibility. Sequelae of COVID-19 infection include multiple syndromes which have been reported at high incidence globally. Using the Cochrane guidelines and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we present a systematic review of the most common syndromes reported. A total of 12 eligible studies were included in this review. Syndromes reported in the literature include immune thrombocytopenic purpura (ITP), viral encephalomyelitis, hemophagocytic lymphohistiocytosis, thrombotic thrombocytopenic purpura (TTP), Guillain-Barrè syndrome (GBS) and postural orthostatic tachycardia syndrome (POTS). We cover the hypothesized pathophysiology, presenting symptoms and treatment for each respective syndrome. We aim to discuss coronavirus and its variants to provide a foundation on which to examine the syndromes manifested after COVID-19 infection (post-COVID-19 syndrome).
The use and outcomes of non-pharmacological analgesia in the adult emergency department
Authors: Assiry Aisha Amzaidy M , Brown Nathan J , Hazelwood Sarah , Lyrstedt Anna-Lisa , Jarugula Rajeev , Jones Lee , Chu Kevin , Hughes James A 
Published: September 2023

AbstractBackgroundThe treatment of pain in the emergency department (ED) has historically relied on pharmacological analgesia. However, little is known about the concurrent use of non-pharmacological analgesia.Aims/ObjectivesThis research explores the self-reported use and outcomes of non-pharmacological analgesia in adult ED patients with moderate to severe pain.MethodsThis is a cross-sectional study in an adult ED of self-reported use of non-pharmacological pain management via a patient-reported outcome measure. The results are presented with descriptive statistics and multivariable modelling.Results45.8% (n=296) of all respondents used non-pharmacological interventions. The most used non-pharmacological interventions are hot packs (34.9%, n=103), distraction (22.3%, n=66), and cold packs (12.9%, n=38). In multivariate modelling, females, patients who did not receive pre-hospital analgesia, and daily average access block time all affected the use of non-pharmacological analgesia. Within a multivariable model, non-pharmacological analgesia increased the amount of pain relief achieved.ConclusionNon-pharmacological analgesia use is affected by gender, treatment before the ED and ED workload. Nevertheless, clear benefits to the use of non-pharmacological analgesia were seen. Further work must be undertaken to encourage providers’ use of this modality and capture any additional benefits to the patient.
Fecal Virome ofCoendou spinosus(Paraguaian Hairy Dwarf Porcupine)
Authors: Miranda Thamiris dos Santos , de Matos Isabelle Gomes , D’arc Mirela , Moreira Filipe Romero Rebello , Schiffler Francine Bittencourt , Cosentino Matheus Augusto Calvano , Coimbra Amanda , de Oliveira Thiago Henrique , Mouta Ricardo , Medeiros Gabriel , Girardi Déa Luiza , Lima Monique , Wanderkoke Victor , Francisco Talitha Mayumi , Soffiati Flávio Landim , Ferreira Suelen Sanches , Ruiz-Miranda Carlos Ramon , Soares Marcelo Alves , dos Santos André Felipe Andrade 
Published: September 2023

ABSTRACTThe porcupine (Coendou spinosus) is a rodent species widely distributed in the Brazilian Atlantic forest. Like other rodent species, porcupines can be potential reservoirs of zoonotic agents. However, little is known about the viral diversity in these animals. Therefore, the objective of the present study was to evaluate, through massive sequencing, the virome of the feces of seven adult healthy free-living porcupines living in forest fragments from Silva Jardim, State of Rio de Janeiro, Brazil. A total of 41 viral families were classified, of which only seven were validated by both Kraken2 and Diamond taxonomic analysis tools, including bacteriophages (Siphoviridae, Myoviridae, Podoviridae), vertebrate viruses, such asPapillomaviridae, and unclassified RNA viruses. In addition, we also observed the presence of pathogenic bacteria and fungi already described in porcupines. The present study describes for the first time the microbiome in fecal samples from Brazilian porcupines, contributing to the global metagenomic characterization.
Analyzing the link between RNA secondary structures and R-loop formation with tree polynomials
Authors: Liu Pengyu , Lusk Jacob , Jonoska Nataša , Vázquez Mariel 
Published: September 2023

AbstractR-loops are a class of non-canonical nucleic acid structures that typically form during transcription when the nascent RNA hybridizes the DNA template strand, leaving the DNA coding strand unpaired. Co-transcriptional R-loops are abundant in nature and biologically relevant. Recent research shows that DNA sequence and topology affect R-loops, yet it remains unclear how these and other factors drive R-loop formation. In this work, we investigate a link between the secondary structure of the nascent RNA and the probability of R-loop formation. We introduce tree-polynomial representations, a class of mathematical objects that enable accurate and efficient data analysis of RNA secondary structures. With tree-polynomials, we establish a strong correlation between the secondary structure of the RNA transcript and the probability of R-loop formation. We identify that branches with short stems separated by multiple ‘bubbles’ in the RNA secondary structure are associated with the strong correlation and are predictive of R-loop formation.